The Evolution of Low- and Medium-Voltage Distribution System Development Planning Procedures and Methods—A Review

Abstract

The increasing number of prosumers presents a significant challenge for power grid operators at low- and medium-voltage levels. This necessitates a fresh approach to the development of planning procedures and methods. In this review, we focus on four key areas regarding distribution system development planning: (1) the application of multi-criteria analysis methods, (2) the integration of distributed energy resources, (3) the impact of prosumer inverters on the design and planning of networks and protection systems, and (4) maintaining voltage levels and local power balancing under market rules. We analyzed the major contribution of the existing literature to the field and identified key trends. We also proposed future directions for scientific research in the area.

1. Introduction

1.1. Evolution of Power System Expansion Planning

Power system expansion planning (PSEP) is a complex, multi-criteria process that involves addressing numerous uncertainties. The long planning horizon, the influence of political decisions, and the integration of emerging technologies necessitate advanced PSEP methods that surpass traditional approaches. These methods must accurately model existing generation and network/grid infrastructures while incorporating anticipated new technologies. Achieving an “optimal” power system structure requires balancing long term energy policy objectives, a task complicated by the risk of strategic documents and energy plans being based on flawed assumptions, potentially leading to misguided investment signals. To address these challenges, robust tools for PSEP are essential. Such tools process large datasets, employ scientifically validated methodologies, and facilitate stakeholder engagement through visual representation of data and results [1].

Historically, PSEP began with subsystem-focused approaches in the late 1940s and 1950s, evolving with the advent of computer-aided methods in the 1950s and 1960s. Early models prioritized cost and reliability using simple algorithms based on economic calculations and probability methods. By the 1970s, optimization techniques such as linear programming were applied to address investment needs and enhance network reliability. Over time, models incorporated uncertainty related to long term demand changes and began integrating power flow optimization and mixed-integer programming (MIP) techniques. This evolution also marked the development of comprehensive models addressing both generation units and network infrastructure simultaneously [1].

Distribution system expansion planning (DSEP) aims to ensure the reliable, cost-effective, and sustainable delivery of electricity to meet growing demand while addressing modern challenges such as renewable energy integration, distributed energy resources (DERs, Figure 1), and evolving customer expectations. Key objectives include: (1) Reliability and Resilience: Ensuring that the distribution network can handle peak loads and contingency scenarios (e.g., N-1 criterion) without service interruptions; (2) Cost Optimization: Minimizing investment costs while balancing operational expenses, including upgrades to substations, feeders, and DER integration; (3) Environmental Sustainability: Incorporating renewable energy and reducing carbon emissions to align with climate goals; (4) Flexibility: Enhancing the network’s ability to adapt to uncertain load growth, DER penetration, and emerging technologies through advanced planning methods; (5) Customer-Centric Planning: Addressing customer preferences for reliability levels and integrating local electricity markets (LEMs) for greater participation in energy systems.

Despite the emergence of AI methods and blockchain technology, our focus is on classical analytic methods. However, we understand that DSEP must evolve from traditional infrastructure-centric approaches to integrated frameworks that address economic, environmental, and social priorities. By incorporating advanced optimization techniques, DERs, and LEMs into planning processes, utilities can achieve greater flexibility and sustainability while meeting reliability standards. However, overcoming regulatory barriers and uncertainty challenges remains critical for realizing the full potential of modern DSEP methodologies [2]. Therefore, in this review, we focus our attention on four key aspects that, in our opinion, require a closer look, i.e., (1) multi-criteria methods in DSEP, (2) DERs integration with distribution networks, (3) the impact of DERs and prosumer inverters in particular on the design and planning of networks and protection systems, and (4) maintaining voltage levels and local power balancing under market rules.

The four topics are closely interconnected in modern distribution network planning. Multi-criteria methods in DSEP provide structured frameworks to evaluate diverse technical, economic, environmental, and social criteria for network investments, especially under increasing complexity due to DERs integration. DERs, including prosumer inverters, significantly impact network design and protection by introducing bidirectional power flows and variability, which require adaptive planning and protection schemes to maintain reliability and safety. Maintaining voltage levels and local power balancing becomes more challenging with DER proliferation, necessitating advanced control and market mechanisms to ensure grid stability while accommodating prosumer participation and local energy markets. Together, these aspects demand integrated, multi-stage, and multi-objective planning approaches that consider DER characteristics, protection requirements, and regulatory frameworks to optimize network expansion and operation under evolving market rules.

1.2. DER Integration—Emerging Markets and New Agents

The integration of DERs has catalyzed the emergence of innovative market frameworks rooted in a multi-tiered hierarchical architecture wherein aggregating intermediaries represent DER portfolios at higher market levels. Four principal stakeholder classes are delineated within this paradigm [3]:

• Transmission system operator (TSO) that may also function as an Independent System Operator (ISO), emphasizing operational neutrality in market coordination.
• Distribution system operator (DSO), including flexibility market operators (FMOs) as DSO proxies in market administration.
• DER Facilities—heterogeneous assets—including distributed generation (DG) units, storage systems, demand-responsive loads, or clustered configurations (e.g., smart buildings)—capable of providing grid services or energy transactions.
• Aggregators—pivotal intermediaries that consolidate DER portfolios for upstream market participation.

This architecture facilitates decentralized coordination of DER flexibility while preserving grid stability across transmission and distribution layers.

Four principal categories of electricity markets can be distinguished [3]:

• Conventional wholesale markets—facilitate the participation of aggregators and are primarily designed to balance electricity supply and demand while provisioning frequency restoration reserves for the TSO.
• Flexibility markets—emergent market structures where DSOs procure services from distributed assets within their networks to maintain operational security and reliability.
• Local electricity markets (LEMs)—decentralized trading environments in which assets within an aggregator’s portfolio transact either with the aggregator or amongst themselves to determine dispatch schedules, often reflecting the collective position of the community or aggregator. Peer-to-peer (P2P) market frameworks are typically subsumed under this category.
• Hybrid LEMs and flexibility markets—integrate features of both LEMs and flexibility markets, enabling assets within a specific distribution system to participate collectively in conventional wholesale markets while simultaneously addressing DSO operational constraints and balance responsibilities. In this context, microgrids can be conceptualized as geographically bounded communities that are balance-responsible, with the microgrid operator functioning as an aggregator—sometimes referred to as a “Distribution Company (DISCO)”.

This taxonomy reflects the evolving landscape of electricity markets, accommodating increasing decentralization, the proliferation of distributed energy resources, and the need for enhanced operational flexibility.

1.3. DER Integration—Issues Regarding Voltage and Protection Systems

The changing structure of distribution grids, mainly low-voltage (LV) ones, expanding DG, especially based on photovoltaic (PV) sources, and the large share of electronic power converters have a significant impact on the voltage levels [4,5], grid load balance [6,7,8], and selection of protections in these grids, mainly from the point of view of power flow directions as well as protection against electric shock and fire [9,10,11,12]. It should be borne in mind that in modern grids there are circuits classified as special circuits/installations, such as PV installations [13] or charging systems for electric vehicles (EV) [14]. In the abovementioned special electrical circuits, there may be an increased risk of an electric shock, and it is also not possible to use the entire available spectrum of protective measures listed in the standard [15]. In low-voltage grids there is a tendency to widespread local DC distribution systems [16,17]. AC systems are no longer as advantageous when using PV sources, energy storage, and DC loads (e.g., LED lamps), because the DC bus allows for the integration of these sources/components, and multiple AC/DC and DC/AC transformations can be avoided. This reduces energy losses and has economic benefits [18,19]. The changing low-voltage grids and installations, the use of DC circuits, and the wide presence of power electronic converters make it necessary to use more advanced protection devices, in particular residual current devices (RCDs) [20], which are mandatory in some circuits [21]. The most common RCD protections of type AC and type A, as characterized by the standard [22], are no longer sufficient, and there is a need to use more advanced and more expensive RCDs of type F, type B [23], or type B+ [24]. The structure and properties of the PV inverters [25] and other converters mean that the earth current can be unidirectional [26,27] or contain a high level of higher harmonics [28,29,30]. This in turn has a negative effect on the operation of RCDs. Their incorrect operation can result in a lack of protection against electric shock. An important issue is electrical safety at EV charging points. There are more and more of such vehicles on the roads, the number of charging points is also increasing, and in consequence their availability and commonness are increasing. In the case of charging points using connectors in accordance with IEC 62196 standards [31,32,33], special care should be taken with the DC component. For this reason, it is required to use either RCDs of type B or dedicated residual direct current detection devices (RDC-DDs), in accordance with the standard [34]. These protections, such as in-cable control and protection devices (IC-CPDs) [35], are compatible with detecting the very high value of the earth fault DC component expected in an EV charging circuit. A fairly new protection, not yet seen on a large scale, is a residual current operated protective device for DC systems (DC-RCDs), compliant with [36]. This is a protection strictly dedicated to DC systems, and its widespread use is expected in internal DC distribution installations and DC final circuits. Selected detailed analyses relating to the most important issue in modern low-voltage systems—electrical safety and protections—are included in Section 4 (“Protections in Modern Power Systems”).

1.4. Past Work Review

There were previous review papers concerning power distribution grid expansion planning. The review paper by Aschidamini et al. [37] comprehensively examines the integration of reliability considerations into power distribution system (PDS) expansion planning. The authors emphasize the increasing consumer demand for high-quality and continuous power supply, making the incorporation of reliability indices crucial in PDS planning models. The review highlights the challenges of including reliability, particularly the estimation of failure rates for networks and devices, which vary based on feeder characteristics. It surveys relevant papers published between 1996 and 2022, categorizing them by optimization method, number of stages (single or multistage), objective type (single or multiobjective), and the reliability indices used. The paper identifies the decision variables, reliability indices, and test systems used in each study, offering a practical guide for researchers. A key finding is that, despite the positive influence of expansion plans on service quality, quantifying gains in reliability indices remains a complex task. The review notes that traditional PDS expansion planning models often overlook reliability criteria, even though service interruptions can lead to significant financial and social losses. To address this gap, the paper focuses on identifying and comparing methods that explicitly incorporate reliability indices into optimization models. Furthermore, the authors explore the computational complexity associated with these models.

The comprehensive review paper by Rezk et al. [38] explores the application of metaheuristic optimization algorithms (MOAs) in addressing various operational and management challenges within microgrids (MGs). The paper provides a detailed introduction to MOAs, outlining their fundamental principles, key implementation steps, and common underlying concepts such as parallelism, acceptance, elitism, selection, decay, immunity, self-adaptation, and topology. This serves as a valuable resource for researchers and practitioners seeking to understand and apply these techniques. The study offers a structured review of MOA applications in MGs, covering a wide range of problem areas, including unit commitment, economic dispatch, optimal power flow, distribution system reconfiguration, transmission network expansion and distribution system planning, load and generation forecasting, maintenance schedules, and renewable sources maximum power point tracking. The review identifies and analyzes the most significant MOAs that have been employed in MG research in recent years, providing insights into their strengths and weaknesses for specific applications. This helps researchers select appropriate algorithms for their MG optimization problems. The paper highlights the increasing importance of renewable energy sources (RESs) in MGs and the challenges associated with their integration, such as source intermittency and power quality issues. It emphasizes the role of MOAs in optimizing the operation and management of these renewable-based MGs to ensure their reliable and efficient performance. The review acknowledges the “no free lunch” theorem, which states that no single optimization algorithm is universally superior for all problems. It encourages the development of new and effective optimizers tailored to the specific characteristics of MG optimization problems.

1.5. Major Contributions and Paper Structure

While review papers [37,38] thoroughly examined selected topics and methodology applications in the field of DSEP, there is still a need for the extended analysis of emerging electricity markets, DER integration, and technical challenges regarding maintaining voltage levels as well as planning and ensuring the proper operation of protection systems in power grids. This stems from a rapid growth of a number of prosumers in the LV grid. To the best of the author’s knowledge, and based on the literature research, this review paper is the first attempt at incorporating these subjects in DSEP.

This review paper contributes to the scientific discourse in the following ways:

• It uniquely structures its review around four tightly interlinked themes, i.e., (A) multi-criteria analysis methods in DSEP; (B) integration of DERs; (C) impact of prosumer inverters on network design and protection; and (D) maintaining voltage levels and local power balancing under market rules. This integrated approach allows for a comprehensive understanding of how these areas interact, rather than treating them in isolation, which is common in earlier reviews.
• It highlights the growing influence of prosumer inverters and DERs on the need for advanced protection schemes in low- and medium-voltage networks.
• It provides a detailed discussion of how traditional protection devices (e.g., RCDs type AC/A) are becoming insufficient due to new grid characteristics, such as increased DC components and harmonics from PV systems and EVs.
• It discusses the necessity for advanced protection devices (e.g., RCDs type F, B, B+, DC-RCDs) and the challenges in ensuring electrical safety in evolving grid environments—an area often underrepresented in previous reviews.
• It does not only discuss technical advancements but also addresses regulatory, standardization, and practical implementation barriers, especially regarding protection systems and DER integration.
• It highlights the need for regulatory adaptation to accommodate new technologies and market models, a theme often overlooked in more technically focused reviews.
• It situates reliability, optimization, DER integration, and market mechanisms within a broader, interconnected framework, aiming to bridge gaps between technical, economic, and regulatory perspectives.
• It concludes by identifying key trends and proposing future research directions, particularly emphasizing the need for integrated, multi-stage, and multi-objective planning approaches that account for DER characteristics, protection requirements, and evolving market rules. This future-oriented perspective helps guide both researchers and practitioners toward addressing the most pressing and complex challenges in modern DSEP.

The remainder of this review paper is structured as follows: Section 2 provides a comprehensive analysis of the multicriteria methods applied in distribution system development planning. Section 3 is a comprehensive review of the work related to DER integration impact on planning methods and procedures in distribution systems for electricity. Section 4 analyzes the impact of prosumer inverters on protection systems in distribution networks. Section 5 reviews DSEP from the viewpoint of local energy balancing and voltage stability. Section 6 concludes the major findings of this review paper.

2. Multi-Criteria Methods in Distribution System Expansion Planning

2.1. Background

Distribution system expansion planning for LV and medium-voltage (MV) grids is an inherently complex task that requires balancing a diverse set of technical, economic, environmental, and social criteria. Historically, planning decisions focused primarily on economic and reliability metrics; however, the increasing penetration of DERs, evolving regulatory frameworks, and growing stakeholder expectations have necessitated more comprehensive and nuanced decision-making frameworks. Multi-criteria analysis (MCA) methods have thus become indispensable tools for DSOs to systematically evaluate and prioritize expansion options.

This section presents an overview of the state-of-the-art multi-criteria decision-making (MCDM) methodologies applied in DSEP, highlighting their ability to integrate heterogeneous criteria and stakeholder preferences. The major methodological categories include Data Envelopment Analysis (DEA), multi-criteria ranking methods, and the Analytic Hierarchy Process (AHP), often employed in hybrid frameworks to enhance robustness and objectivity. Recent studies demonstrate innovative applications of MCDM techniques, such as combining technical simulation tools with decision analysis, incorporating fuzzy logic to handle data uncertainty, and leveraging automated multi-attribute decision-making (MADM) to reduce bias. These approaches enable a more holistic assessment of grid expansion options, accounting for factors such as investment costs, power quality, voltage stability, environmental impacts, and social acceptance.

2.2. Review of Related Work

MCDM requires the definition of a set of criteria for evaluating investment alternatives within the framework of DSEP. Equally important in this process is the appropriate determination of the criteria weights, which has a crucial impact on the outcome of the analysis and, consequently, on the decisions made. The literature includes studies in which analyses have been conducted using MCDM methods in DSEP. Bucko et al. [39] proposed the following eight criteria for assessing options of load/micro-generation connection to LV and MV distribution grids, namely: (1) Capital expenditures on grid interconnection; (2) Average annual cost of electricity, including capital costs, fixed operation and maintenance costs, and variable costs of electricity losses; (3) Annual active energy losses in grid components; (4) Voltage levels in power grid nodes; (5) Permissible grid component loads; (6) Dynamic voltage change; (7) Electric shock protection; (8) The ratio of short-circuit capacity in connection node to output power of micro-generation.

Lenarczyk et al. [40] represent a significant contribution to the discourse on renewable energy policy and planning. By applying a hybrid multi-criteria decision-making framework to evaluate RES technologies in Poland, the authors provide critical insights into the misalignment between national energy strategies and techno-economic realities. The study introduces a novel hybrid methodology combining the AHP and numerical taxonomy (NT), addressing limitations in conventional single-method approaches. By employing AHP to weight 30 sub-criteria across five domains (technical, economic, environmental, social, and regulatory) and NT for linear ordering of technologies, the framework achieves enhanced objectivity in ranking RES options. This dual-layer approach mitigates biases inherent in subjective weighting systems while maintaining adaptability to Poland’s unique energy landscape.

Ismail et al. [41] present three key innovations: (1) A groundbreaking framework combining Hybrid Optimization of Multiple Energy Resources (HOMERs) and Electrical Transient Analysis Program (ETAP) software and Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) simulations to optimize renewable energy system selection using technical, environmental, economic, and socio-political criteria; (2) The first integration of power system stability assessments (via transient analysis) into technical evaluations and pioneering use of PAPRIKA for multi-criteria decision analysis in this context; (3) Successful validation through a complex industrial case study demonstrating practical applicability.

Celli et al. [42] present a structured approach for selecting smart grid development projects, enabling optimal distribution network planning that aligns with stakeholders’ expectations. The methodology uses an automated MADM technique to evaluate a large set of planning options, enhancing objectivity and reducing bias compared to traditional methods. By analyzing a Pareto front of Active Distribution Network (ADN) designs, the approach identifies the best alternative considering multiple stakeholders’ perspectives, particularly focusing on non-network solutions such as distributed energy storage. This automation simplifies the decision-making process, providing concise insights into the most robust planning options amidst complex evaluation criteria.

Khajouei et al. [43] contribute to the planning of passive harmonic filters by employing a multi-objective optimization framework combined with MCDM techniques such as Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and AHP. The approach optimizes filter parameters, placement, and quantity to improve voltage quality and power factor and reduce harmonic distortion and power loss. A novel metric, the Total Harmonic Distortion Severity Index (THDSI), is introduced to more accurately assess harmonic distortion levels, allowing for more effective filter planning under variable system conditions influenced by RES and load fluctuations. This methodology enhances the robustness of harmonic filter design in dynamic power systems.

Wu et al. [44] make a significant contribution by addressing the complex renewable energy and storage (R and S) power generation investment portfolio and planning problems in an uncertain environment, covering aspects of generation, transmission, and terminal applications. It introduces a comprehensive decision-making framework using the Interval Valued Fuzzy Neutrosophic–Preference Ranking Organization Method for Enrichment Evaluations (IVFN–PROMETHEE) model to evaluate and select promising energy storage technologies (ESTs) from various application sides. The paper provides targeted discussions on the planning results, offering reference values that can guide further research, government planning, and public investors. Additionally, it enhances robustness analysis through a Pearson correlation test, adding a new dimension to the study of R and S applications and expanding existing knowledge in this field.

Zhou et al. [45] propose a probabilistic multi-criteria evaluation (PMCE) framework to quantitatively assess energy supply systems for Integrated Energy System (IES) planning. The framework evaluates systems across four key dimensions: economy, efficiency, environment, and security. It uses a probability density-based model for criteria weights, incorporating a Dirichlet mixture model (DMM) to establish prior probability densities, with parameters identified via the expectation maximization (EM) algorithm. The effectiveness and robustness of this approach are validated through case studies on five traditional energy supply systems, including comparative experiments. The results show that the proposed method can effectively evaluate energy supply schemes while demonstrating strong robustness.

The study by Ullah et al. [46] makes several significant contributions to the field of hybrid energy systems, particularly in the context of the Pakistani energy sector. Firstly, it is the first paper to focus on the design optimization and assessment of solar/wind/hydro/biomass hybrid energy systems for both on-grid and off-grid rural electrification scenarios, analyzing six configurations in each scenario. Secondly, it introduces a comprehensive multi-dimensional decision-making model that evaluates systems based on five key sustainability pillars: economy, reliability, ecology, society, and topography. This approach differs from previous studies that often focus on only one or two aspects. Lastly, the study innovatively applies a hybrid MCDM model combining Fuzzy Analytic Hierarchy Process (F-AHP), Multi Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Evaluation Based on Distance from Average Solution (EDAS) methods to address the conflicting aspects of hybrid energy system design. This integrated approach allows for prioritizing criteria and selecting the optimal design by comparing and ranking different alternatives, marking a significant departure from existing methods.

The paper by Zubiria et al. [47] addresses the critical role of energy storage systems (ESS) in achieving carbon neutrality by 2050. The paper identifies ESS as key providers of the flexibility services needed to balance supply and demand, maintain power quality, and support distributed energy systems, especially as renewable generation becomes more decentralized. It reviews existing MCDM approaches used to select and rank EST, noting that these methods often do not account for the specific requirements of individual grid services, such as response time and cycle life. The main contribution of this study is the development of an intuitive MCDM methodology based on Triangular Fuzzy Numbers (TFN) and the TOPSIS method, explicitly considering the capability of different ESTs to meet the technical demands of various grid services. The paper also proposes a standardized approach to quantifying evaluation uncertainties, facilitating straightforward implementation in simulation tools. Additionally, it broadens the scope of EST assessment by including a wider range of battery storage technologies, thereby enhancing the practical relevance of its findings for future grid planning and energy community independence.

2.3. Section Summary

The analyzed studies demonstrate several consistent features in scientific works addressing energy system planning and renewable energy integration through MCDM approaches. These features reflect the field’s evolution toward integrated assessment frameworks that combine technical optimization with stakeholder value modeling, particularly in renewable energy transitions. The studies exemplify how modern MCDM applications address complex tradeoffs between grid reliability, decarbonization goals, and economic constraints through methodologically rigorous yet adaptable approaches. The summary of related work is presented in

Table 1. The summary of MCDM applications for distribution network development planning.

Ref.	Content Summary
[39]	Eight criteria for grid connection evaluation (cost, losses, voltage, safety, etc.); structured DSEP assessment.
[40]	Hybrid AHP + Numerical Taxonomy; 30 sub-criteria, five domains; RES ranking in Poland; addresses policy–reality gap.
[41]	HOMER + ETAP + PAPRIKA integrates stability analysis and multi-criteria industrial case validation.
[42]	Automated MADM; Pareto front of ADN designs; smart grid project selection; stakeholder alignment; storage focus.
[43]	NSGA-II + AHP; passive harmonic filter planning; introduces THDSI metric; robust design under dynamic conditions.
[44]	IVFN–PROMETHEE; energy storage tech evaluation; covers generation to terminal; robustness via correlation test.
[45]	Probabilistic MCDM; Dirichlet mixture model for weights; four-dimension IES planning; validated on five cases.
[46]	Hybrid MCDM (F-AHP, MOORA, TOPSIS, EDAS); five sustainability pillars; on/off-grid hybrid systems in Pakistan.
[47]	TFN + TOPSIS; ESS selection for grid services; uncertainty quantification; broad battery tech scope.

.

Despite the advances summarized in Table 1, several scientific gaps remain:

• Many hybrid MCDM frameworks involve computationally intensive simulations and optimization, limiting their applicability to large-scale, real-world networks without further algorithmic improvements.
• Current methods predominantly address planning at strategic or tactical timescales, with limited integration of real-time operational flexibility and adaptive re-planning capabilities.
• There is a lack of standardized frameworks and data formats to seamlessly integrate diverse MCDM tools, simulation platforms, and stakeholder inputs, which hampers broader adoption.
• Existing models often insufficiently capture the impacts of prosumer behavior, peer-to-peer energy trading, and multi-energy system coupling on distribution expansion decisions.
• While some studies include social criteria, comprehensive modeling of equity, community engagement, and regulatory acceptance remains underdeveloped.

Addressing these gaps requires interdisciplinary research combining power systems engineering, operations research, economics, and social sciences to develop scalable, adaptive, and stakeholder-inclusive multi-criteria planning methodologies. Such advancements will be crucial to enabling resilient, sustainable, and cost-effective distribution system expansion in the face of accelerating energy transition dynamics.

3. Distributed Energy Resources Integration

3.1. Background

The integration of DER is fundamentally transforming the landscape of distribution system planning. As DERs proliferate, the complexity of planning and operating distribution networks has increased dramatically. This complexity arises from the variability and uncertainty inherent in DER outputs, the need to accommodate new forms of flexible demand and supply, and the challenge of maintaining grid reliability and efficiency in the face of dynamic, decentralized generation patterns.

Flexibility has emerged as a critical enabler in this evolving environment. By leveraging flexible resources—ranging from demand response (DR) and controllable loads (CLs) to advanced storage solutions—DSOs can better manage congestion, integrate higher shares of renewables, and defer costly network reinforcements. Flexibility also supports the development of adaptive, resilient grid designs that can respond to real-time fluctuations and long term uncertainties in both supply and demand.

To address these challenges, there is a growing consensus on the need for integrated planning approaches. Modern frameworks combine advanced optimization techniques, holistic uncertainty modeling, dynamic time-series analysis, and the coordinated integration of market mechanisms and stakeholder interactions. These comprehensive strategies enable planners to balance investment, operational efficiency, and system reliability while accommodating the rapid evolution of DERs and market structures. As a result, DSEP is shifting from traditional, static methodologies to agile, data-driven processes that are better equipped to meet the demands of a decentralized, flexible, and sustainable energy future. In the following subsections, major contributions of related scientific papers were presented.

3.2. Foundational and Comprehensive Planning Frameworks

Modern DSEP methodologies have evolved significantly to address the complexities of decentralized energy systems, integrating advanced computational frameworks with multi-domain optimization strategies. Contemporary approaches now prioritize dynamic resource allocation, stochastic modeling of renewable generation, and granular operational constraints to balance grid reliability against escalating decarbonization targets. Cutting-edge techniques leverage hybrid optimization algorithms that combine metaheuristic search methods with physics-based power flow models, enabling simultaneous optimization of DERs and network infrastructure under uncertainty. These methodologies incorporate temporal resolution at sub-hourly intervals while maintaining computational tractability through decomposition strategies and machine learning-enhanced scenario reduction. A paradigm shift toward holistic cost-benefit analysis frameworks now systematically quantifies tradeoffs between capital investments, operational flexibility, and long term asset utilization, particularly for rural electrification and urban grid modernization projects. Emerging planning tools also address previously overlooked factors such as reactive power requirements, storage degradation patterns, and end-user tariff structures, providing utilities with adaptive decision-support systems for phased infrastructure deployment. This convergence of high-fidelity modeling, probabilistic constraint handling, and multi-stage investment optimization represents a fundamental advancement in creating resilient, cost-effective smart grids capable of managing energy transitions at scale.

Valencia et al. [48] present an innovative approach to solving the joint planning problem of MV/LV three-phase distribution systems with integrated DERs. The research specifically addresses the optimization of RESs and battery energy storage systems (BESSs) within these power networks. The authors develop a hybrid optimization methodology that combines an iterated local search (ILS) algorithm with a two-step decomposition stochastic optimal power flow (TDSOPF). The ILS algorithm employs neighborhood structures derived from the three-phase bus impedance matrix to generate high-quality solutions for the investment expansion problem.

The work by Gouin et al. [49] advances grid planning by addressing critical gaps in uncertainty handling, temporal resolution, and flexibility integration. It introduces a novel framework to integrate uncertainties from RES, EVs, and CLs, which are often excluded in conventional power grid planning studies. It also replaces static peak-consumption scenarios with dynamic time-series data to better reflect real-world grid behavior influenced by DG and EVs. Their work incorporates grid reconfiguration and DR strategies into long term planning, enabling adaptive and resilient grid designs. It provides year-by-year projections of network constraints and required investments, offering granular insights into planning outcomes over time. Finally, it quantifies the relationship between investment costs and the probability of avoiding grid constraints, with and without flexibility tools (e.g., reconfiguration, DR), to inform decision-making.

The work by Yi et al. [50] advances grid expansion strategies by balancing operational accuracy, stochastic modeling, and computational tractability. It introduces a scenario-based stochastic programming approach for distribution network expansion planning (DNEP). This method ensures sufficient hosting capacity and enhances dispatchability in ADNs amid rising stochastic renewable generation and demand. Energy storage systems and line investments using the modified augmented relaxed optimal power flow (MAR-OPF) model are optimized. This framework accurately evaluates ADN operations while accounting for dynamic network topology changes caused by asset investments.

The study by De la Cruz et al. [51] introduces an improved method for determining the location and capacity of DG in power systems by considering both active and reactive power loads, unlike many recent approaches that focus only on active power. Including reactive power is important because ignoring it can cause voltage imbalances, increased energy losses, and reduced system efficiency. The proposed approach offers a more comprehensive and practical framework, addressing real-world challenges faced by power grids. It provides power grid operators and planners with actionable tools to make better decisions about DG siting and sizing.

The study by Zhang et al. [52] introduces several key innovations to improve distribution network expansion planning. First, it proposes a multi-stage rolling planning method that allows for periodic updates to the planning scheme based on the latest information, enhancing the flexibility, economic efficiency, and rationality of the network expansion. Second, the study incorporates users’ electricity bills into the objective function, addressing the often-overlooked impact of network fees on operating costs, which leads to more accurate cost calculations and better-informed planning decisions. Third, it tackles the issue of energy storage capacity decay by applying the rainflow counting method to model this degradation precisely. An iterative solution framework is then developed to incorporate these capacity decay characteristics into the planning process. Together, these contributions enable a more realistic, cost-effective, and reliable expansion strategy that balances investment benefits, equipment utilization, and operational safety.

The study by Cortés-Caicedo et al. [53] addresses key methodological gaps in distribution network planning by proposing a novel, integrated approach that simultaneously optimizes feeder routing and conductor sizing for rural distribution networks. Unlike many existing methods that treat these as separate problems or rely on simplified linear models, the authors develop a comprehensive mixed-integer nonlinear programming (MINLP) formulation that accurately captures the nonlinear power flow equations while preserving the radial network topology. To solve the complex MINLP efficiently, the study employs an exact optimization strategy combining branch-and-bound with an interior-point solver, implemented in the Julia programming language using the Julia for Mathematical Programming (JuMP) environment. The problem is modeled considering a single distribution substation supplying all end users under peak load conditions, ensuring conductor sizing meets maximum current demands reliably. The set of feasible feeder routes is predefined based on geographical constraints, reflecting practical considerations from distribution companies.

3.3. Integration of Flexibility and Demand Response

DR enables electricity consumers to adjust their consumption patterns in response to signals from grid operators or utilities, such as price changes or requests during periods of grid stress. This approach is a key form of flexibility, allowing the power system to more efficiently balance supply and demand, especially as renewable energy sources introduce greater variability into the grid. By shifting or reducing demand during peak periods, DR helps avoid the need for costly infrastructure upgrades and supports grid reliability and stability.

The paper by Faia et al. [54] introduces a decision-support tool based on DC optimal power flow to assist DSOs in planning future investments in distribution networks. The model leverages demand-side flexibility to optimize investment decisions while reducing computational complexity and improving tractability. Simulation results demonstrate that flexibility contracts significantly influence line loss costs and expected undelivered energy costs, highlighting the economic benefits of incorporating demand flexibility into network planning.

Tomaselli et al. [55] present a novel approach to assess how behind-the-meter (BTM) prosumer flexibility can alleviate grid congestion in future power distribution grids. The authors developed a method to determine the future BTM asset mix for a specific region and allocated these assets geographically to households, using open-source data and existing tools to create a power flow-ready grid model. They introduced a modeling framework that utilizes a receding horizon (RH) optimal power flow (OPF) algorithm to manage flexible BTM assets over time without requiring perfect foresight while accounting for grid constraints. The approach was applied in a case study in Schutterwald, Germany, demonstrating its ability to quantify the impact of BTM asset penetration on grid congestion with minimal input data and evaluate effective control measures for flexible BTM assets.

Laribi and Rudion [56] present a novel planning method implemented within a time series-based framework to capture load and generation variability throughout the year with 15-min resolution. This method integrates traditional grid expansion techniques with the use of BESSs and Demand Peak Control (DPC) into a unified automated reinforcement algorithm for distribution grids. The algorithm identifies the least-cost combination of reinforcement measures that effectively prevent forecasted overloads and voltage violations over a full year of operation. A linearized load flow calculation, accounting for both active and reactive power, is employed to reduce errors compared to conventional Newton–Raphson methods. The grid planning is performed in a single optimization step, considering the total costs of reinforcement measures over a 40-year economic lifespan. To achieve cost optimization, a mixed integer linear programming (MILP) model is embedded within the algorithm, minimizing overall planning expenses.

3.4. Advanced Metering and Observability

Advanced Metering Infrastructure (AMI) is a foundational technology for enhancing observability and operational efficiency in modern PDS. AMI consists of smart meters, robust communication networks, and advanced data management systems that enable two-way, real-time data exchange between utilities and end-users. This infrastructure provides granular visibility into electricity consumption, power flows, and grid-edge conditions, allowing utilities to monitor and control the PDS down to individual service points. By delivering detailed, time-stamped usage data and immediate outage notifications, AMI supports accurate billing, rapid fault detection, and improved response to service interruptions. Furthermore, the integration of AMI data with distribution automation systems facilitates advanced applications such as load management, asset health monitoring, and the seamless incorporation of DERs. As a result, AMI is a critical enabler for data-driven DSEP, supporting the transition toward smarter, more resilient, and sustainable power grids.

Zhang et al. [57] propose three key advancements in AMI planning: (1) A redundancy-based robust model ensuring continuous medium-voltage distributed grid (MVDG) observability during single-node failures through fortified communication paths; (2) A mathematical reformulation converting complex observability constraints into solvable communication fortification problems using rank multiplication principles; (3) A heuristic decomposition method overcoming computational limitations of traditional integer linear programming (ILP) approaches for large-scale low-voltage grid implementations while maintaining solution accuracy.

3.5. Market Mechanisms, Stakeholder Interactions, and Prosumers

Market transformations and decentralization of generation in power systems have led to attempts to organize new local structures that allow for better integration of new generation sources with the system. Currently, one of the fundamental goals of creating local organizational structures in power systems is to facilitate their integration with the system by attempting to create locally balanced structures capable of local energy consumption and limiting energy exchange with distant areas of the system.

Creating structures integrating producers and consumers allows, at least partially, the realization of such goals. Examples of such local structures include virtual power plants or balancing groups. The idea of the functioning of both entities is similar and consists of using mutual balancing of power and energy between entities that are part of them. In relation to the system, these types of structures can offer both the possibility of maintaining energy exchange at a predictable level (declared in contracts) and the delivery of regulatory services in the scope of active power with shorter access times. A similar role can be played by separate distribution systems, prosumer groups, energy cooperatives, and energy clusters with varying degrees of integration with the system. The creation of such entities is mainly an organizational and legal challenge. The way they function and cooperate with DSOs should be organized on market principles. Taking into account market conditions and local balancing based on them is included in the methods of planning network development [58,59,60].

The scientific gap addressed by Huang et al. [58] involves the need for a comprehensive framework that captures the complex interactions between DSOs and multiple stakeholders in a deregulated retail market, particularly in terms of robust expansion planning and dynamic pricing mechanisms. A novel generalized double-nested game model is proposed to formulate a cost-effective, robust expansion planning scheme. This model simulates the bidirectional interactions between DSOs and various stakeholders, coordinating network solutions with worst-case non-network scenarios in a deregulated market. In addition, a closed-loop scheduling mechanism is introduced, incorporating dynamic pricing based on a multi-sided trading pattern. Consumers adjust their demand in response to dynamic retail prices reflecting operational status, while retailers rebid both price and quantity after observing updated demand. Finally, to solve the complex nested game model, a tri-layer decomposition method is developed. This method enables distributed optimization of coupled decision-making among entities, ultimately achieving a global optimal solution corresponding to the Nash equilibrium, which cannot be directly solved by conventional tools.

The paper by Dai et al. [59] introduces a novel model for distribution network expansion planning that incorporates prosumer participation in the peak-shaving ancillary services market. Prosumers, who both consume and produce electricity, are integrated into the grid planning process to optimize resource utilization and reduce costs. A mathematical optimization framework is proposed to balance grid expansion costs and operational efficiency. This framework considers multiple variables, including ESS, PV generation, and market transactions for peak shaving. The model accounts for constraints such as load scenarios, net generation, and planned grid capacity. Compared to traditional methods, the proposed approach reduces the number of lines requiring capacity expansion.

Petrou et al. [60] propose a framework to address the challenges of managing DER-rich MV-LV distribution networks with active prosumer participation. The framework separates the DSO’s responsibility for network management and integrity from prosumer decision-making, aligning with regulations that prohibit DSOs from controlling behind-the-meter devices. In addition, a scalable convex three-phase OPF method is introduced to manage large networks, incorporating voltage and thermal constraints and ensuring fairness by limiting prosumer exports or imports only when network integrity is at risk.

3.6. Electric Vehicles and Energy Storage Integration

Integrating EVs and ESS with the PDS presents both challenges and opportunities for grid management. Smart charging and storage integration enable flexible demand response, helping to balance supply and demand, reduce peak loads, and prevent local network congestion, especially in LV grids where most EV charging occurs. Advanced coordination, such as cloud-based EV charging control and dynamic tariffs allows utilities and service providers to optimize charging schedules, minimize power losses, and enhance overall grid reliability while supporting the widespread adoption of EVs and distributed storage.

The study by Yao et al. [61] presents an integrated framework for jointly planning distribution networks and electric vehicle charging stations (EVCS) through PV-grid-EV market transactions, enhancing both operational flexibility and economic efficiency. The framework incorporates PV-grid-EV transactions into joint infrastructure planning, enabling short-term operational adjustments (e.g., energy trading) alongside long term investments in substations, lines, PV generation, and storage. This dual approach defers grid expansion costs while improving EVCS profitability and reducing user expenses. In addition, a novel algorithm synchronizes power flow and EV charging patterns by incentivizing optimal charging behavior through dynamic pricing. This reduces charging distances, increases EVCS revenue, and facilitates PV integration without requiring direct control over EV charging schedules. Finally, the proposed EV charging price reflects both planning and operational costs, balancing grid security, user satisfaction, and profitability. It quantifies infrastructure impacts to guide economically optimal EVCS placement and capacity decisions.

The study by Hu et al. [62] introduces a robust virtual battery (VB) formulation that leverages EVs as a key flexible prosumer resource. This formulation incorporates bidirectional power capabilities and accounts for uncertainties in end-user driving behavior. By doing so, it reduces the number of decision variables in the optimization problem, enhancing computational efficiency for prosumer energy management. The work employs an optimization framework with the iterative distribution locational marginal price (iDLMP) method. The research integrates both day-ahead optimal scheduling and spinning reserve provisions into the optimization framework. It utilizes the iDLMP method to align with distribution grid interests. The iDLMP method is innovative in that it employs two types of price signals to prevent both excessive power consumption and injection by prosumers, offering a more balanced and efficient grid operation compared to previous approaches.

Ghofrani and Majidi [63] developed an innovative optimization framework for coordinating EVs and RESs in distribution systems, addressing both market economics and operational reliability simultaneously. The authors propose a bilateral contract between EV aggregators and renewable distributed generators to maximize revenues through optimal EV charging/discharging control while also developing an optimal capacitor sizing and placement method to resolve potential negative impacts on distribution networks. The methodology employs Autoregressive-Moving-Average (ARMA) models to characterize renewable generation uncertainties and Monte Carlo Simulation to simulate system states based on developed models. Genetic Algorithm optimization coordinates EV charging/discharging with scheduled renewable generation to minimize penalties for renewable power mismatches while maximizing Vehicle-to-Grid (V2G) service revenues. The approach validates its effectiveness using a real distribution network with three-phase unbalanced load flow calculations, demonstrating significant cost savings alongside reductions in system losses and voltage deviations that would otherwise occur from uncoordinated EV scheduling.

The accelerating electrification of mobility and heating sectors imposes critical challenges on LV grid infrastructure, necessitating fundamental revisions to traditional Planning and Operation Guidelines (POGs). The study by Wintzek et al. [64] addresses these challenges by developing updated POGs through systematic grid planning methodologies that account for modern load dynamics from EVs and heat pumps (HPs). The authors employ a scenario-based approach to evaluate diverse power development pathways for representative urban LV grids, incorporating both public charging points (PuCPs) and private charging points (PrCPs)—a combinatorial analysis absent in prior research. A novel economic sensitivity analysis further quantifies the cost-benefit trade-offs of implementing dynamic load management (DLM) systems, extending beyond the technical scope of earlier studies.

The study by Ghofrani [65] addresses the complex challenges arising from the rapid integration of EVs and renewable DG into modern power distribution networks. The main issues stem from the intermittent nature of renewables and the unpredictable charging patterns of EVs, both of which can cause grid overloading, voltage deviations, increased energy losses, and potential equipment failures if not properly managed. To tackle these problems, the author proposes using distribution network reconfiguration (DNR), an approach that dynamically adjusts the grid topology by controlling network switches to optimize power flows, reduce losses, and maintain voltage stability. A key innovation of the study is its detailed sensitivity analysis, which examines how varying penetration levels of EVs and renewable DGs affect network performance, such as system losses and voltage stability—an aspect often neglected in previous research. This sensitivity analysis provides actionable insights for grid operators, revealing the optimal network configurations and DG placements under different integration scenarios and potentially deferring costly infrastructure upgrades. The method also incorporates techniques to handle real-time uncertainties from renewable generation and EV demand, making the optimization adaptive and robust to dynamic grid conditions.

Another study by Ghofrani [66] addresses the integration challenges of renewable DGs and EVs in distribution systems by proposing a novel optimization framework that combines coordination and system reconfiguration methods. Existing approaches typically focus on one of three categories: EV charging coordination to manage renewable fluctuations, capacitor optimization to improve voltage profiles, or system reconfiguration to optimize power flow by adjusting network topology. However, these methods often operate independently and fail to capture the interactions among multiple energy entities or the interconnected nature of distribution microgrids. The proposed framework overcomes these limitations by enabling a global optimization that considers the synergies between EVs, renewable DGs, and interconnected grids, rather than optimizing each system in isolation. It integrates technical, economic, and market objectives to provide collective benefits and incentives for all participants, including utilities, operators, and consumers, rather than focusing solely on individual gains.

3.7. Forecasting and Scenario Generation

Accurate forecasting and realistic scenario generation are critical components in effective DSEP, especially given the increasing complexity of modern PDS. Advanced forecasting techniques, such as machine learning models, enable more precise predictions of load and generation patterns over mid- to long term horizons. Coupled with robust scenario generation methods, these forecasts provide planners with comprehensive insights into future uncertainties, supporting adaptive and cost-effective grid development strategies.

Saldaña-González et al. [67] identified the scientific gap in the literature involving the lack of planning methods that utilize long term load forecasting with recurrent neural networks (RNNs) to generate expansion scenarios for distribution networks. This study addresses this gap by proposing a framework that integrates an RNN model, specifically a Long-Short Term Memory (LSTM) model, with a comprehensive planning method. The planning method incorporates an LSTM model with confidence intervals for mid and long term predictions, enhancing the accuracy of time series forecasting for distribution networks. A five-phase planning approach is proposed, which includes time series power flow analysis, asset congestion assessment, strategic planning actions, and techno-economic evaluation.

The paper by Lu et al. [68] introduces an innovative Q-learning-based Distributionally Robust Optimization (DRO) framework for distribution network expansion planning under multiple uncertainties, addressing limitations of existing methods. Unlike traditional approaches that often overlook the combined effects of substation planning, renewable DG, and ESS, this model simultaneously optimizes the configuration of these components while minimizing total costs. A key strength lies in integrating Q-learning with DRO, enabling the system to adaptively learn and make robust decisions even against worst-case uncertainty distributions related to renewable generation, load fluctuations, and contingency events. The model utilizes Latin Hypercube Sampling (LHS) to generate multi-scenario data representing these uncertainties and incorporates piecewise linearization to manage the computational complexity of AC power flow. By employing Q-learning, the framework achieves adaptive and data-efficient decision-making, reducing reliance on extensive historical data and allowing for more flexible network planning. The approach demonstrates improved reliability and economic performance compared to traditional methods, particularly in volatile grid conditions.

3.8. Hybrid and Multi-Domain Energy Systems

Hybrid and multi-domain energy systems represent a transformative approach to modern energy planning by integrating multiple energy carriers, such as electricity, heating, and cooling, within unified operational frameworks. These systems combine various renewable and conventional resources, leveraging their complementary characteristics to enhance reliability, efficiency, and flexibility across diverse demand profiles. By coordinating electric, thermal, and even mobility sectors, hybrid and multi-domain solutions address the challenges of renewable intermittency, enable advanced Demand-Side Management (DSM), and support resilient, cost-effective energy infrastructure development for both grid-connected and isolated applications.

The methodology presented by Jelić et al. [69] advances Hybrid Renewable Energy System (HRES) design by unifying multi-domain demand modeling, grid interaction dynamics, DSM considerations, and systematic multi-criteria evaluation. Each design alternative (or HRES configuration) defines discrete sizes for renewable energy technology (RET) components, combining topology and asset sizing into a unified planning process. The authors propose a holistic planning framework that simultaneously addresses both electric and thermal energy demands, overcoming the traditional siloed approach. This is critical for modern systems using devices such as heat pumps, which impact both energy domains. Their work also extends conventional isolated-system planning to grid-connected scenarios, incorporating dynamic energy pricing and unrestricted grid exchange. This enables cost-benefit analysis of energy import/export strategies. It also evaluates the impact of demand response schemes on appliance-level operation, informing HRES component sizing and topology design to align with real-world flexibility needs. They apply MCDMA (Multi-Criteria Decision-Making Analysis) to assess HRES configurations across technical, economic, environmental, and societal criteria, ensuring balanced and stakeholder-aligned solutions.

The study by Liu et al. [70] introduces a three-stage MILP model for optimizing the expansion planning and operation of isolated microgrids serving combined electricity, heating, and cooling demands. The framework simultaneously minimizes annualized investment/operational costs and improves technical performance by reducing voltage deviations and power losses. The method integrates electrical, thermal, and cooling loads with dispatchable distributed generators, ESS, and legacy diesel generators in a unified optimization structure. The model represents DG and ESS capacities as discrete variables rather than idealized continuous values, aligning with real-world equipment availability and sizing standards.

The study by Jia et al. [71] proposes an advanced expansion planning method for ADNs that incorporates the spatiotemporal regulation features of renewable power forecasting control (RPFC) and energy router (ER). It develops a two-stage ADN expansion planning model aimed at minimizing the annualized comprehensive cost while integrating RPFC and ER. To solve the resulting nonlinear programming problem, the authors introduce a hybrid algorithm combining improved particle swarm optimization (IPSO) with second-order cone programming (SOCP). The paper’s key innovations include the first-ever grid planning model that explicitly incorporates the RPFC output and its constraints, providing a foundational framework for future RPFC planning. Additionally, it offers a novel application scenario by integrating RPFC and ER siting and planning within distribution networks. The hybrid IPSO-SOCP solution method effectively addresses the dual-layer model and is validated through simulations on a 33-bus test system, confirming the model’s feasibility and efficiency. This work advances distribution network planning by combining innovative modeling and solution techniques tailored to renewable integration challenges.

The study by Gou et al. [72] presents a novel expansion planning method for AC/DC hybrid distribution networks (HDNs) that integrates both spatial and temporal information to improve network design. The approach models the network’s spatial topology using a graph-based framework and applies time-series analysis to capture dynamic variations in load demand and renewable energy generation. The main objectives are to increase renewable energy accommodation and reduce power losses within the distribution system. To solve this complex problem, the authors develop a hybrid optimization algorithm combining a modified graph attention network (MGAT) with deep reinforcement learning (DRL), enabling intelligent and adaptive decision-making for resource allocation and network expansion. The paper introduces three types of expansion lines—bidirectional AC, bidirectional DC, and unidirectional DC lines—each tailored to different power flow needs, improving efficiency and reducing losses. Simulation results on standard test systems show notable improvements in renewable energy utilization and demonstrate the hybrid algorithm’s superior convergence and decision accuracy.

3.9. DERs, Non-Utility Resources, and Resilience

The growing integration of DERs and non-utility distributed energy resources (NDERs) is fundamentally reshaping the resilience and reliability of modern power distribution systems. DERs—including renewables, ESS, and flexible demand assets—offer utilities and communities new tools to withstand and recover from grid disruptions, providing localized generation, redundancy, and adaptive control capabilities. NDERs, such as customer-owned generation and storage, further enhance system flexibility and can play a pivotal role in both routine operations and post-contingency restoration scenarios. As the frequency and severity of external shocks increase, leveraging DERs and non-utility assets becomes essential for resilient DSEP, enabling targeted reliability improvements, faster recovery, and cost-effective expansion strategies.

Zakernezhad et al. [73] introduce an integrated framework for assessing how NDERs influence optimal resource distribution system expansion planning (ORDSEP) under external shock conditions, addressing a gap in existing literature. Its primary contributions include a three-stage algorithmic approach that incorporates NDERs’ strategic bidding behaviors into planning models while accounting for five uncertainty dimensions: electricity price fluctuations, NDER installation parameters, generation patterns, utility-owned resource outputs, and shock characteristics (location, duration, and magnitude). Additionally, the work evaluates NDERs’ market power in post-contingency restoration scenarios, providing novel insights into resilience planning under disrupted grid conditions.

Aschidamini et al. [74] present a comprehensive framework for evaluating the reliability of power distribution systems and assessing how expansion projects affect key reliability indices. A major contribution is the method to calculate failure rates for each network zone by combining the zone’s length with its historical fault data, enabling precise identification of the most critical areas. Unlike previous studies, the framework evaluates a broad set of reliability indices, including system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS), as well as load node indices, i.e., customer interruption frequency (CIF) and customer interruption duration (CID). The approach also analyzes the impact of various expansion measures, such as installing sectionalizing switches, feeder interconnections, automation, and reconductoring, on system reliability. By identifying zones that most influence reliability improvements, the method supports targeted prioritization of reinforcement projects.

The paper by Mubarak et al. [75] presents a novel methodology for short-term DSEP that uniquely incorporates N-1 contingency analysis for all branches. Unlike prior work, it considers fluctuating load profiles and optimizes the size and location of DG units while ensuring network reliability during outages. The N-1 contingency constraint is applied independently to each branch, allowing the model to find cost-effective expansion plans that minimize investment and power loss. To solve this complex problem, the authors employ a hybrid metaheuristic algorithm combining firefly algorithm (FA) and particle swarm optimization (PSO), which effectively avoids local optima that traditional mathematical models might encounter. The study integrates costs related to circuit and switchgear construction, power losses, and DG installation into a single-stage planning framework. Through this approach, the paper investigates how DG integration impacts network performance and sustainability under contingency conditions.

The paper by Ahmad and Asar [76] focuses on enhancing the reliability of electric distribution networks through the optimal placement of DG using an artificial neural network (ANN) technique. The paper addresses the increasing complexity and reliability challenges in power systems, particularly in distribution networks, which contribute significantly to overall reliability issues. The authors propose using DG to improve reliability by locating generation closer to load centers. They employ an ANN to determine the optimal location for DG SAIFI placement. The ANN’s performance is validated by installing DG at the locations it prescribes. The Roy Billinton Test System (RBTS) is used as a test case. Reliability indices are calculated using the analytical method. ETAP software is used to validate the results. The study demonstrates the effectiveness of DG in enhancing distribution system reliability by reducing SAIFI, SAIDI, and EENS values.

Hoang et al. [77] address the problem of protecting distribution networks with high penetration of electronically coupled distributed energy resources (EC-DERs) while ensuring fault ride-through (FRT) and dynamic voltage support (DVS). A scientific gap exists because current protection schemes often neglect the interface protection of EC-DERs, focus solely on FRT, or rely on expensive communication-based approaches unsuitable for the large number of LV networks. Furthermore, many studies treat MV and LV networks independently, failing to address their coordination and the impact of external disturbances on LV microgrids (LV-MGs). The paper’s main contribution is developing a cost-effective protection scheme that ensures selective operation between MV and LV protection elements, fulfills EC-DER FRT requirements, and enables smooth islanding of LV-MGs.

3.10. Summary, Trends, and Challenges in DER Integration in DSEP

The integration of DERs is driving transformative shifts in DSEP, with emerging trends and persistent challenges shaping the field, namely:

• Advanced stochastic modeling leverages time-series analysis and scenario-based frameworks to address renewable generation variability, as seen in MAR-OPF-driven investment optimization and probabilistic cost-benefit frameworks. Machine learning enhances demand forecasting and route optimization, reducing operational costs while improving grid dispatchability.
• Market mechanisms integrate prosumers into ancillary services (e.g., peak-shaving markets), optimizing grid investments and operational efficiency. Decentralized structures such as energy clusters and virtual power plants enable local balancing, reducing reliance on centralized systems.
• Holistic cross-sector planning combines electricity, gas, and heating networks, supported by georeferenced modeling and sector-coupling units. DC technology and flexibility tools improve grid adaptability, enabling efficient integration of renewables and storage.
• Redundancy-based AMI systems ensure grid reliability through fortified communication paths and linearized load-flow calculations. Real-time coordination frameworks manage behind-the-meter assets dynamically, mitigating congestion without perfect foresight.

There are the following research gaps that require further studies:

• Limited methods exist for real-time grid-device coordination across heterogeneous DERs, particularly in large-scale networks. Scalability challenges persist in optimization algorithms for high-resolution, long term planning.
• Current frameworks lack incentives for prosumer participation in ancillary markets and fail to address dynamic pricing complexities. Policy gaps hinder the adoption of multi-energy systems and cross-sector revenue models.
• Communication protocol fragmentation complicates data exchange between DERs, grid operators, and market platforms. No unified standards exist for multi-energy asset interoperability, limiting flexibility potential.
• Methods for rural and under-resourced area planning remain underdeveloped, often relying on oversimplified models. Equity impacts of DER integration, such as grid cost allocation and energy justice, are poorly quantified.

These trends highlight a shift toward adaptive, data-integrated systems, while unresolved gaps underscore the need for interdisciplinary collaboration to address technical, regulatory, and social challenges in the energy transition.

The thematic summary of trends observed in related scientific work based on five criteria of classification has been applied to characterize each paper reviewed in Section 3. These characteristics are presented in

Table 2. Summary of the work related to distributed energy resources (DERs) integration in expansion planning.

Ref.	Methodological Approach	Technical Focus Area	Market/Economic Dimension	Uncertainty/Resilience Management	Stakeholder/Coordination
[48]	Hybrid optimization (ILS + TDSOPF)	MV/LV joint planning, RES, BESS integration	Investment optimization	-	-
[49]	Holistic uncertainty modeling, time series, cost-benefit	Grid planning, DR, reconfiguration, flexibility	Probabilistic cost-benefit investment	RES, EV, load uncertainty	-
[50]	Stochastic programming, MAR-OPF, sequential algorithm	DNEP, ESS/line investment, dispatchable grids	-	Stochastic generation/demand	-
[51]	Game theory, bilevel programming	DER siting, market participation, network impact	Market equilibrium, investment incentives	Strategic uncertainty (market/player)	DSO, DER owner, market operator
[52]	Heuristic, metaheuristic (GA/PSO), multi-stage planning	DER/ESS placement, grid reinforcement	Investment and operational cost analysis	Scenario-based DER/load uncertainty	DSO, third-party DER providers
[53]	Robust optimization, decomposition algorithm	Distribution system resilience, DER integration	Investment under resilience constraints	Extreme event scenario modeling	DSO, emergency services, DER operator
[54]	DC OPF, decision-support tool	DSO investment planning, demand-side flexibility	Economic impact of flexibility contracts	-	DSO planning support
[55]	Receding horizon OPF, asset allocation	BTM flexibility, congestion management	-	Asset/geographic uncertainty	-
[56]	Multi-objective optimization, scenario analysis	Microgrid expansion, DER siting/sizing, network reconfiguration	Economic/environmental trade-offs	Scenario-based uncertainty (load, RES)	Microgrid operator, DSO coordination
[57]	Heuristic decomposition, robust modeling, reformulation	AMI planning, grid observability	-	Redundancy, resilience	-
[58]	Double-nested game, tri-layer decomposition	Robust expansion, stakeholder interaction	Dynamic pricing, deregulated market	Worst-case scenario planning	DSO-stakeholder coordination
[59]	Mathematical optimization	DNEP, prosumer/ESS/PV integration	Ancillary service markets, cost reduction	Load/net generation/planning uncertainty	Prosumer participation
[60]	Convex three-phase OPF, role decoupling	MV-LV DER-rich network, prosumer integration	-	-	DSO-prosumer role separation
[61]	Co-optimization, market-integrated planning	Joint DSO-EVCS-PV planning, grid-EV integration	Dynamic/cost-reflective pricing, market	-	-
[62]	Robust virtual battery, iDLMP optimization	EVs as prosumer flexibility, spinning reserve	Price signal design	Driving behavior uncertainty	Prosumer resource management
[63]	ARMA modeling, optimization framework	EV-RES coordination, capacitor sizing	Bilateral contracts, revenue maximization	RES generation uncertainty	EV aggregator–RES coordination
[64]	Data-driven (probabilistic, scenario-based) robust optimization	DER hosting capacity assessment, PV/EV integration, grid expansion	Cost-benefit analysis, investment planning	Probabilistic scenario modeling, DER uncertainty	DSO planning, prosumer/EV aggregator roles
[65]	MILP, scenario generation	DER/EV/ESS expansion, grid upgrade planning	Investment planning, cost minimization	Scenario-based DER/EV uncertainty	DSO, city planner, energy community
[66]	Hierarchical optimization, rolling horizon	Multi-level DER/ESS planning, operational scheduling	Multi-period investment and operation	Rolling horizon (temporal) uncertainty	DSO, aggregator, asset owner
[67]	LSTM-based forecasting, five-phase planning	Load forecasting, scenario generation, grid expansion	Techno-economic evaluation	Forecasting uncertainty	DSO decision support
[68]	Convex optimization, distributed control	Real-time DER dispatch, voltage regulation	Ancillary service markets, cost allocation	Real-time operational uncertainty	DSO, aggregator, prosumer
[69]	Multi-criteria analysis, demand modeling, DSM	HRES design, grid-tied deployment, DR	Multi-domain economic/environmental/societal	-	Stakeholder-aligned planning
[70]	Stochastic programming, Benders decomposition	DNEP with DER/ESS, distribution automation	Investment cost minimization, reliability	Stochastic DER/load modeling	DSO, automation vendor interaction
[71]	Agent-based modeling, distributed optimization	Peer-to-peer DER trading, local market integration	Local market pricing, trading efficiency	Behavioral and market uncertainty	Prosumers, local market operator
[72]	MCDMA, stakeholder engagement	Integrated DER planning, social/environmental impact	Socio-economic assessment, investment prioritization	Qualitative uncertainty, stakeholder preferences	DSO, municipality, public stakeholders
[73]	Three-stage algorithm, strategic bidding, uncertainty	NDERs in ORDSEP, resilience post-shock	Market power, post-contingency restoration	Five uncertainty dimensions	-
[74]	Robust optimization, chance-constrained programming	Active distribution network expansion, PV/EV integration	Cost-risk trade-off, reliability pricing	Chance constraints, robust uncertainty	DSO, market participant coordination
[75]	Data-driven forecasting, time-series analysis	Load/DER forecasting for expansion planning	Cost-benefit of forecasting accuracy	Forecasting/modeling uncertainty	DSO, forecasting service provider
[76]	Stochastic dual dynamic programming (SDDP)	Long-term grid expansion with high DER penetration	Long-term investment planning	Multi-stage stochastic uncertainty	DSO, regulatory authority
[77]	Machine learning (clustering, regression), scenario reduction	DER clustering for planning, grid impact assessment	Planning efficiency, cost reduction	Scenario reduction, data-driven uncertainty	DSO, data analyst, planning consultant

.

4. Protections in Modern Power Systems

The structure of a conventional low-voltage grid, as well as a contemporary/future one based on prosumer energy, can be expressed as shown in Figure 2. In the conventional approach to designing LV grids, the flow of power/current was considered only in one direction—from the transformer substation to the consumers (Figure 2a). When it comes to LV grids based on prosumer energy, the power flow is bidirectional (Figure 2b). In addition, it can only take place inside the local prosumer network/installation (also only in the area of the local DC installation).

Modern grids (Figure 2b) contain many RES, which is beneficial from the point of view of environmental protection. This type of network should be fully developed and promoted. However, it should be remembered that there are also negative aspects of such grids, and this should be given special attention at the stage of new investments. One of the negatives is uncontrolled voltage increases in low-voltage grids with PV sources. According to the standard [78], the permissible voltage deviation is ±10% of the nominal value, which in the 230/400 V grid means the phase voltage ranges from 207 V to 253 V. As it results from the measurements discussed in [79], the real voltage values may reach 262 V, which significantly exceeds the upper permissible level of 253 V. Such voltage increases may cause disconnection of the PV sources or damage to receivers sensitive to voltage parameters. When planning the construction of a new LV grid or the development of an existing one with DG, it is necessary to install on-load tap-changer MV/LV transformers [80] or use voltage regulators deep within the low-voltage grid [79,81].

The presence of distributed power sources is also associated with the problem of using overcurrent protection devices in the appropriate place in the circuit. According to the requirements of the standards [11,82], protections should be placed at the beginning of the circuit, but in many cases this “beginning of the circuit” is a controversial/ambiguous term. In the case of a prosumer system containing a local distribution line (Figure 3a), overcurrent protection devices in this “local distribution line” should be installed at both ends (F1_D and F2_D protection devices). Current flow can occur in both directions—both during normal operation and in the event of a short-circuit.

To avoid using protections at both ends of the line, it is better to design the installation according to the structure shown in Figure 3b. In this case, all circuits (sources and loads) are connected directly to the main distribution board (without the use of a local distribution line).

A multi-source grid/installation also complicates the issue of selectivity between protections in individual circuits. Typically, in the case of a single-sided power supply to the grid (Figure 4a) and current flow in only one direction, and also in the case of a short-circuit, for reliable power supply of final circuits, selectivity between F1_L/F2_L/F3_L protection(s) and F1_D protection is mainly required. Only the protection in the faulty circuit should operate. For example, in the case of a short-circuit as shown in Figure 4a, the F1_L protection should trip, and the F1_D protection should not react. The same effect will be obtained when local sources/energy storage are not utilized (disconnected from the installation—Figure 4b). In the case of a short-circuit as in Figure 4c (supply of the short-circuit point from the transformer, energy storage, and local power supplies), the short-circuit current flowing through the F1_L protection is greater (sum of currents from all sources) than the short-circuit current flowing through F1_D (transformer component). This favors selective operation between F1_L and F1_D. It will be similar for the selectivity between F1_L and F1_ES/F1_PV. Selective operation of protections (F1_L vs. F1_ES/F1_PV) can also be expected in the case of island power supply, as in Figure 4d. Islanding of the system and short circuits, as in Figure 4e,f, do not provide certainty regarding the selective operation of the F1_ES vs. F1_PV protections. If the rated current of these protections is the same or similar, then theoretically they may operate simultaneously (no selectivity).

Another important issue to consider in the case of grids with RES is the value of the short-circuit current from the point of view of the response of protection devices to this current and automatic disconnection of supply. In the case of conventional grids supplied from a transformer, short-circuit currents in residential installations are on the order of several hundred amperes to several kiloamperes. These are values many times greater than the rated current of the overcurrent protection devices used there, which are usually 10 A or 16 A. High short-circuit current values facilitate quick reaction of the protection, which makes it relatively easy to obtain effective protection against electric shock in the event of a fault to the earth (disconnection of supply in, for example, less than 0.4 s). If local sources (diesel generators, energy storage, and PV power sources) are connected to the network, the short-circuit current value from such sources is significantly lower. Figure 5 shows the illustrative relationship between the short-circuit current I_k and the load current I_L in normal conditions for selected power sources. A short-circuit in a circuit powered by a transformer can result in, for example, the ratio I_k/I_L = 100. In the case of diesel generators, the ratio is about I_k/I_L = 3, and in the case of sources powered by inverters, it is I_k/I_L = 1.0–1.4.

The most difficult conditions for detecting a short-circuit by overcurrent protections are in the case of supplying the circuit by power electronic converters (inverters). Example values of continuous/rated currents and short-circuit currents in circuits with inverters are given in

Table 3. Example values of continuous/rated currents and short-circuit currents in circuits with inverters (own elaboration based on [83,84]).

Rated Continuous Current per Phase Ir (A)	Peak Short-Circuit Current ip (A)		Symmetrical Initial Short-Circuit Current; rms Ik″ (A)		Symmetrical Steady State Short-Circuit Current; rms Ik (A)
-	<40 μs		<30–50 ms		-
(1) 14.6	82.87	(ip/Ir = 5.7)	14.6	(Ik″/Ir = 1)	14.6
(2) 36.2	116.37	(ip/Ir = 3.2)	40.06	(Ik″/Ir = 1.12)	36.2
(3) 40.0	92.4	(ip/Ir = 2.3)	43.5	(Ik″/Ir = 1.1)	43.5
(4) 120.0	277.2	(ip/Ir = 2.3)	130.5	(Ik″/Ir = 1.1)	130.5

Comment: (1), (2), (3), (4)—numbers of subsequent inverters.

. For these inverters, the symmetrical initial short-circuits current I_k^″ is up to 12% higher than the continuous/rated current I_r. The symmetrical steady-state short-circuit current I_k has similar values to the current I_k^″. In conditions of such a low value of short-circuit current in relation to the rated current, there is no possibility of reaction by overcurrent protective devices, especially in the event of an earth fault. Therefore, to detect this earth fault, an RCD should be used.

It should be remembered, however, that RCDs have to be selected to the expected shape of the residual current, and in circuits with electronic converters, residual currents are usually non-sinusoidal. The circuits with selected electronic converters and the expected residual current waveforms in such circuits are depicted in Figure 6.

Taking into account the expected residual current waveforms, recommendations can be made regarding the use of RCDs in circuits with converters, as shown in

Table 4. Systems with electronic converters/inverters from Figure 6 and suitable appropriate residual current devices (RCDs). Own elaboration based on [13,14,22,23,24,34,85].

System/Converters (Figure no.)	Appropriate RCD Type	Comments
single-phase rectifier (Figure 6a)	A, F, B *	-
single-phase rectifier with smoothing (Figure 6b)	B *	-
six-pulse bridge rectifier (Figure 6c)	B *	-
inverter in the PV system (Figure 6d)	A, F, B *	type B is necessary, unless: at least a simple separation between the AC side and the DC side is included in the inverter, or at least a simple separation between the RCD and the inverter by a transformer is applied, or the inverter properties ensure that an RCD of type B is not necessary; this should be indicated by the inverter manufacturer
converter in the EV charging system (Figure 6e)	A, F, B * RDC-DD	in charging stations with dedicated connectors according to IEC 62196, it is required to apply type B RCD, or type A RCD together with the DC detection module (RDC-DD according to IEC 62955 [34]), or type F RCD together with the DC detection module (RDC-DD according to IEC 62955 [34])
converter in the variable-speed drive system—single-phase powered (Figure 6f)	F, B *	type F—only in systems without power factor correction; otherwise type B
converter in the variable-speed drive system—three-phase powered (Figure 6g)	B *	-

Comments: * Instead of a type B RCD, a type B + RCD compliant with the standard [24] can be used. The standard classification, as well as the properties and parameters of different types of RCDs, are described in detail in the book [21].

. In many cases, RCDs of type B should be used, which are relatively expensive, but only they can detect a unidirectional residual current with a low pulsation or a residual current composed of high-order harmonics (due to high pulse width modulation (PWM) frequencies in converters).

5. Local Energy Balancing and Voltage Stability

5.1. Background

The development of distributed electricity generation technologies, the growing role of prosumers, and the creation of energy clusters and energy cooperatives are causing a significant change in the role and function of distribution networks. Medium-voltage networks, which were designed for unidirectional energy transmission from the transmission network to end users, must be adapted to new technical conditions related to local changes in the flow direction. Failure to adapt the network to such a situation causes problems with network capacity, local problems with maintaining the voltage level, and limitations in the possibility of receiving energy from prosumers. These new challenges must be solved by technological modification of the network, but also by commercial activities that support the local power balancing. Traditionally, in the power system, the balancing of power and active energy is carried out in a centralized manner, and the most important functions in this respect are performed by the Balancing Market and the market of regulatory system services.

After changing the energy mix and increasing the role of DG and new local structures integrating consumers, prosumers, and small producers, it is necessary to introduce market mechanisms into the system that organize area balancing and take over some tasks from the Balancing Market. The need for local balancing results from the need to solve technical and commercial problems. Solving technical problems can be understood as reducing the challenges for the distribution network, consisting of limiting the flows between the transmission and distribution networks and achieving greater predictability of flows, allowing for easier network traffic and reducing the need for central regulatory and balancing activities.

Commercial problems that could be solved by local balancing include simplifying settlements within local communities, better use of local resources, and generating market impulses that support the creation of local energy communities. Local energy balancing in distribution network areas is a significant technical challenge for the power system and, in particular, for DSOs. Existing distribution networks were not designed with the need to implement such tasks in mind. They were designed to ensure a one-way flow of energy from the transmission network to end users, and the main task of the operators was to ensure the continuity of power supply to recipients, with appropriate quality parameters of electricity. The introduction of area balancing therefore creates not only organizational and commercial challenges but also technical ones: the need to implement new tasks using the existing technical infrastructure and configuration of network systems.

These issues have a direct impact on new expectations regarding the methods of planning the development of distribution networks, which are changing their role. While in the past, distribution networks and their development were viewed in terms of energy supply and meeting the growing needs of recipients, today their role should be perceived as servicing the comprehensive needs of prosumers, for whom it is also important to feed energy into the system. When planning networks, enabling local consumption of surplus energy produced by prosumers and limiting energy exchange with the system area are becoming increasingly important. This is of significant importance for reducing network losses and using local energy resources. In this context, the aspects of network reliability and certainty of supply are important, they are becoming important criteria for network development.

In conditions of bidirectional energy flows in networks with limited regulatory capabilities, maintaining appropriate voltage levels becomes a key technical challenge. Voltage problems are one of the key factors forcing the need for local power balancing.

This new approach to the development of distribution networks, taking into account local energy balancing, is presented in several articles [88,89,90,91,92,93,94,95,96,97,98,99,100,101,102].

5.2. Review of Related Work

Riaz et al. [88] investigate how increasing levels of distributed battery storage affect the balancing and voltage stability of the electric grid, focusing on the National Electricity Market (NEM). The authors employ a hierarchical optimization framework, modeled as a Stackelberg game, to capture the interactions between the ISO and aggregated prosumer behavior. Using a 14-generator model of the NEM and real-world weather and load data, the paper analyzes scenarios with varying levels of residential battery penetration. The results detail how increasing distributed storage improves grid balancing, enhances loadability, and supports voltage stability.

Bischi et al. [89] discuss how to integrate distributed RES into the energy market using blockchain technology for local, peer-to-peer electricity markets. The authors address the challenges of using variable RES. They focus on small-scale RES, such as rooftop solar panels, and how these resources can participate in local electricity markets to reduce reliance on centralized systems. The paper also considers the role of Internet of Things (IoTs) technologies, such as smart meters and smart appliances, in enabling more flexible and controllable energy consumption and production. The authors propose a two-step market design with a day-ahead and a quasi-real-time session to handle electricity imbalances. They assess the economic feasibility of using Ethereum-based blockchain platforms, including the Energy Web Chain, and suggest using a custom Ethereum-based consortium blockchain for the local market. They also create a proof-of-concept implementation to validate their solution, which includes the blockchain, a smart meter, and various loads.

The study by Zhou and Lund [90] identifies key gaps in peer-to-peer energy trading, such as the lack of consideration for costs related to renewable system depreciation, transmission losses, and infrastructure in pricing models. It also highlights that current cost-benefit analyses often overlook fairness in distribution, which can lead to stakeholder dissatisfaction. Additionally, the research points out the limited exploration of dynamic pricing equilibrium through collaborative operations among multiple stakeholders. To address these gaps, the paper reviews recent advancements in decentralized P2P trading systems, including modeling, energy sharing mechanisms, and pricing strategies that support decarbonization, improve efficiency, and manage grid challenges such as congestion and voltage stability. It also systematically examines dynamic trading strategies aimed at balancing power quantities and prices. Finally, the study explores cooperative approaches among prosumers, consumers, retailers, and utilities to better understand how mutual economic benefits can be achieved through agent-based energy sharing and economic dispatch methods.

Agostini et al. [91] address the problem of integrating small-scale variable distributed energy resources (V-DERs) into the energy market and balancing services. They highlight that the increasing diffusion of non-dispatchable RES makes grid management more challenging. The paper aims to evaluate the impacts of different market frameworks on the overall cost of balancing services when allowing for V-DER participation. They contribute to scientific discourse by analyzing two different market designs for V-DER provision of balancing services: one prioritizing economic dispatching at the high voltage level and another prioritizing balancing at the distribution level. They evaluate the impact of these market frameworks on upward and downward balancing energy provision and on network externalities, considering the social cost function of the balancing service. They use a theoretical simulation reference model of the LV network to establish the impact of different market frameworks on network externalities and social welfare.

The paper by Alabri et al. [92] addresses the growing challenge of voltage regulation in power distribution systems due to the rapid adoption of rooftop PV generation. The main problems identified are voltage imbalance between phases and overvoltage at network nodes, which existing voltage management devices and centralized control methods struggle to address effectively and economically, especially given the fast fluctuations of PV output. Current scientific gaps include the limited effectiveness of active power curtailment for voltage unbalance, the inadequacy of linear droop-based reactive power control for both voltage rise and unbalance, and the lack of solutions that consider real-world unbalanced networks with mutual line coupling. Previous approaches often ignore the impact of high PV penetration or system imbalance, or require impractical operational conditions. The major contribution and novelty of this study lie in proposing a supervisory reactive power control strategy for PV inverters that uses advanced step control and local voltage measurements. This method aims to maintain voltage magnitude within standards and keep voltage imbalance under specified limits while also minimizing inverter wear. The approach is validated using real metered PV and load data on an unbalanced distribution system, and its performance is tested under different PV penetration levels and weather conditions. Results are compared against conventional linear droop control, demonstrating the superiority of the proposed method in both voltage regulation and unbalance mitigation.

Rao et al. [93] address the lack of a comprehensive methodology for generating active power setpoints at each node in low-voltage distribution grids, particularly when multiple RES and diverse load types are present. Existing methods do not effectively calculate grid hosting capacity at a granular level, nor are they compatible with LEMs for real-time settlement and grid stability. There is also a scientific gap in accommodating all flexibility types with distributed energy resources in a unified, online system that can respond quickly to the variability of RES and loads. To fill these gaps, the paper introduces an Optimal Capacity Management (OCM) control scheme that integrates multiple flexibilities and load types to manage grid capacity in real time. The OCM is linked with a local P2P energy market settlement process, allowing for coordinated market operations and grid management. The methodology employs a holomorphic embedding load flow method (HELM) and genetic algorithms (GA) to generate operational limits and setpoints, considering various objectives and constraints. The proposed approach is validated through both simulation and real-world testing on an Austrian feeder with actual measurement data, demonstrating its effectiveness and practical applicability. This holistic and real-time solution advances the state-of-the-art in grid capacity management and market integration for low-voltage networks.

Malik et al. [94] aim to solve the problem of limited coordination between the economic benefits of both consumers and prosumers in P2P energy trading within local energy communities (LECs). The identified scientific gap lies in the tendency of existing research to focus on optimizing single parameters, such as energy demand or transaction zoning, without considering a holistic approach that balances the interests of all participants. To address this, the paper proposes a novel cooperative game theory-based framework for P2P energy trading, encouraging peers to form a grand coalition by maximizing economic benefits for both prosumers and consumers. The framework includes an algorithm that enables an aggregator to select the best trading priority (energy demand, geographical distance, or trading price) for each time interval, optimizing the use of community energy storage (CES). The study evaluates the revenues earned by prosumers and the electricity bill savings for consumers. This mechanism is demonstrated and evaluated using an IEEE European low-voltage test feeder dataset with 100 households, 15 EV charging points, and a CES, analyzing the best priority for the grand coalition over a 24-h period. The novelty of the approach lies in its dynamic and holistic optimization of trading priorities to maximize the overall economic benefits for all members of the local energy community.

Yang et al. [95] address the challenge of integrating network constraints into P2P electricity markets, which are increasingly important due to the rise of DERs. A significant scientific gap exists in developing fully decentralized P2P market models that incorporate AC-modeled network constraints (AMNC), including nodal voltage, network losses, and power flow, while also protecting user privacy. This paper introduces an innovative decentralized electricity distribution market model with AMNC, enabling parallel computation for market clearing. The nodal voltage variables serve as the only public information, ensuring user privacy. Unlike existing methods using DistFlow or DC power flow models, the authors employ Taylor expansion for linearization and reformulate constraints as second-order cone constraints, resulting in a second-order cone optimization problem for each user’s decision-making. The model demonstrates stable convergence and guarantees global social welfare maximization on the IEEE 33-node system, highlighting its effectiveness in integrating network constraints within a fully decentralized P2P market framework.

The scientific gap recognized by Spiliotis et al. [96] lies in the underutilization of demand-side flexibility—specifically, the potential for residential consumers to shift their electricity usage without sacrificing comfort, thus helping balance supply and demand more efficiently. The major contribution of the paper is the proposal of a fair mechanism that enables residential consumers to offer their demand flexibility to distribution system operators, incentivized through a combination of dynamic price savings and fixed benefits, minimizing consumer risk. The paper’s methods include developing both a conceptual market model and an empirical planning model, the latter implemented via mixed integer linear programming in the GAMS software system. The study specifically investigates how mobilizing residential demand-side resources can defer or replace the need for physical grid expansions, offering a market-oriented alternative to capital-intensive infrastructure investments. The regulated, hybrid incentive mechanism is designed to balance DSO investment minimization with consumer protection, acknowledging the limited liquidity and potential market power issues in small local flexibility markets.

Nousdilis et al. [97] address the problem of over-voltages in low-voltage distribution networks caused by increasing residential PV systems, which inject power back into the grid when generation exceeds local demand. While grid reinforcement and reactive power control are potential solutions, they are often costly or ineffective in LV networks. Existing active power control (APC) schemes mitigate over-voltages, but they curtail PV power, leading to a loss of green energy and not considering the prosumers consumption profile. The scientific gap lies in the lack of active power management methods that avoid renewable energy curtailment while accounting for individual prosumer’s self-consumption behavior. The proposed solution is an active power management method based on the self-consumption ratio (SCR) of each PV owner, creating a power schedule that prosumers can follow using controllable loads or storage systems, thus maximizing the utilization of generated PV energy. The effectiveness of the proposed approach is demonstrated on the IEEE European LV Test Feeder.

Wasiak et al. [98] tackle the problem of maintaining voltage stability in low-voltage networks with increasing numbers of electricity consumers and prosumers, particularly those with PV systems and energy storage. The existing scientific gap lies in the need for a comprehensive, coordinated control system that can manage the distribution transformer load, regulate power exchange, and ensure voltage regulation across all network nodes while minimizing interference with existing prosumer devices. The proposed solution is an innovative centralized control system that uses controllable devices at prosumer installations, leveraging ancillary services and cooperating with local control strategies, primarily using reactive power from ESS for voltage regulation before resorting to active power adjustments or PV power curtailment. The main contribution of this work is a comprehensive system designed to work with existing LV distribution networks and prosumer devices (PV inverters and BESS) with minimal interference. The system applies both central control to manage voltage, network exchange, and transformer load and local power management tailored for both the distribution system operator and prosumer benefits. The central and local controllers allow for active and reactive control of BESS and PV inverters. The system is also designed to work with supervisory control and can participate in system ancillary services. Computer simulations and hardware-in-the-loop (HIL) tests validated all main system features.

The increasing adoption of residential PV systems, driven by subsidies and regulations such as the nearly zero energy buildings (NZEB) mandate in Europe, presents challenges for LV networks, including line and transformer overloading, reverse power flow, and over-voltage issues. Mello and Villar [99] introduce the “marginal Self-Consumption Ratio “metric to establish the minimum SCR required by all prosumers in a feeder to avoid over-voltages, enabling fairer distribution of network costs. It proposes a novel active power management methodology for overvoltage mitigation in LV feeders with high PV penetration. This method rewards prosumers with high SCR, minimizing curtailment, and assigns higher weighting factors to low-SCR prosumers, increasing their responsibility for voltage regulation. The methodology constructs an active power profile that can act as a power schedule for the prosumers, who can comply with this schedule by utilizing either controllable loads or storage systems. The effectiveness of the proposed approach is demonstrated on the IEEE European LV Test Feeder.

The scientific gap identified by Cortés Borray et al. [100] is that most previous studies have analyzed the impacts of either EVs or PVs separately, rather than their combined effect on LV networks, and have not adequately addressed the fair distribution of PV power curtailment among aggregators. The major contribution of this work is the development of a centralized coordination strategy at the DSO level that jointly manages the aggregated operation of EVs and PVs per feeder, optimizing both loading and voltage profiles. The proposed solution extends previous mixed-integer linear programming formulations by introducing a mixed-integer quadratic programming approach, which enables more equitable curtailment of surplus PV power among all aggregators and ensures network operational limits are respected. Two models are presented: one that optimally manages PV curtailment and another that incorporates device-level constraints for both EVs and PVs, including loading and voltage restrictions per phase. The methodology also introduces an energy-boundary model to guarantee that EV charging requirements are met for each aggregator. The DSO uses these models to dictate optimal power profiles for both individual and aggregated EV and PV operations, thereby preventing network overload and voltage violations.

The scientific gap identified by Ölmez et al. [101] lies in the insufficient understanding of how large-scale ESSs interact with electricity market dynamics, renewable integration, and the economic and policy frameworks needed to support decarbonization. Existing research has highlighted the need for backup capacity to manage renewable intermittency and the importance of market-oriented frameworks but has not provided flexible, universally applicable simulation tools to assess ESS impacts across diverse market structures. The major contribution of this study is the development of a pragmatic simulation model that evaluates the influence of ESS on electricity market prices and renewable energy integration, offering clear guidance for market design and energy policy. The model is designed to be adaptable to various market configurations and provides insights into the economic and functional viability of ESS. By simulating the complex interactions between storage, renewables, and market actors, the study informs policymakers, investors, and researchers about optimal strategies for supporting sustainable, flexible, and reliable energy systems.

The study by Małkowski et al. [102] addresses the technical and economic viability of operating hybrid PV-BESS within a balancing group to participate in day-ahead electricity markets. It develops a comprehensive methodology to optimize BESS sizing, market bidding strategies, and cost analysis while validating simulations against real-world conditions. Key contributions include a simulation model in DIgSILENT PowerFactory, created to determine optimal BESS capacity for coordinated PV-BESS operation, enabling effective bidding strategies in day-ahead markets based on weather forecasts and generation profiles. In addition, the team conducted live tests at Gdańsk University of Technology’s LINTE^2 laboratory to compare simulation results with real-world PV-BESS performance, enhancing model accuracy for grid applications. Finally, researchers developed a 25-year levelized cost model incorporating battery aging, PV degradation, hourly generation profiles, and forecast errors while quantifying balancing costs from deviations in committed electricity production.

5.3. Section Summary

Table 5. Summary of the work related to local energy balancing and voltage stability considerations in expansion planning.

Ref.	Methodological Approach	Technical Focus Area	Market/Economic Dimension	Uncertainty/Resilience Management	Stakeholder/Coordination
[88]	Hierarchical optimization (Stackelberg game)	Distributed battery storage impact on grid balancing and voltage stability	Economic viability of small-scale batteries	Real-world weather and load data	ISO, aggregated prosumer
[89]	Market design, proof-of-concept implementation (blockchain)	P2P energy markets with distributed RES	Economic evaluation of Ethereum-based blockchain platforms	Variable RES impact on grid stability	Local market participants
[90]	Literature Review: Decentralized P2P trading systems	Costs for renewable systems, transmission losses, and infrastructure	Cost-benefit analyses	Dynamic trading strategies	Prosumers, consumers, retailers, and utilities
[91]	Two market models organized with (1) commercial aggregation and (2) technical aggregation.	Integrate small-scale V-DER	Analysis of market design for balancing services	Impacts of different market frameworks	DSOs
[92]	Supervisory reactive power control strategy	Voltage regulation in distribution systems with rooftop PV	Minimizing inverter wear	Real metered PV and load data, unbalanced distribution system	N/A
[93]	OCM, GA	Active power setpoints in LV grids with multiple RES	LEMs for real-time settlement	Variability of RES and loads	N/A
[94]	Cooperative game theory-based framework	P2P energy trading within LECs	Maximizing economic benefits for prosumers and consumers	CES	Aggregators, consumers, and prosumers in local energy communities
[95]	Decentralized distribution electricity market model with AMNC	P2P electricity markets with network constraints	Social welfare maximization	Nodal voltage, network losses, and power flow	N/A
[96]	Conceptual and empirical (MILP) market model	Residential demand-side flexibility	Dynamic price savings, fixed benefits	Limited liquidity and potential market power issues	DSOs, residential consumers
[97]	Active power management method based on SCR	Over voltages in LV distribution networks with residential PV	Maximizing utilization of generated PV energy	Individual prosumer’s self-consumption behavior	Prosumers
[98]	Centralized coordinated control system	Voltage stability in LV networks with PV and energy storage	Transformer load, power exchange	N/A	N/A
[99]	Literature Review	Key gaps in P2P energy trading: costs, losses, infrastructure	Analysis of pricing models, fairness	Dynamic trading strategies	Prosumers, consumers, retailers, utilities
[100]	Mathematical models of PVs, EVs, and their aggregators	Balancing services with V-DERs	Market designs for V-DER participation	Impact of market frameworks	DSOs
[101]	Supervisory reactive power control strategy	Voltage regulation with rooftop PV	Minimizing inverter wear	Real metered PV and load data, unbalanced distribution system	N/A
[102]	OCM, GA	Active power setpoints in LV grids with multiple RES	LEMs for real-time settlement	Variability of RES and loads	N/A

presents a summary of the work related to local energy balancing and voltage stability considerations in expansion planning analyzed in this section. From the DSO perspective, the development of prosumer participation, saturation of the grid with distributed, unstable sources, and the need to cooperate with local structures also cause new technical challenges. The nature of the operation of distribution grids is changing fundamentally, as in the past they implemented a one-way flow of energy (from the transmission system to recipients), and now they must be adapted to bidirectional flows (from and to the transmission system). With the variability of energy flow directions, numerous technical problems appear in distribution grids, such as problems with maintaining appropriate voltage levels and periodic overloads of key parts of the grid infrastructure. These problems create new challenges for the method of planning the development of the distribution grid, which is visible in the articles discussed in this section.

Much attention is paid in publications to the use of energy storage for local balancing purposes. Currently, there are numerous commercial energy storage technologies available. The use of electricity storage is no longer limited to low power but includes solutions with medium and high power, suitable for use in local structures. Practically all available technologies are characterized by technical parameters that allow their use in balancing the energy of a separate structure.

Unfortunately, energy storage remains relatively expensive, and its use for commercial energy balancing is rarely justified in small, separate structures within the distribution system. In practical applications, electrochemical storage devices with limited durability are most often used. Their efficiency can be achieved with a relatively large number of storage charging and discharging cycles. Taking this into account, they allow for short-term storage of electricity and can only solve balancing tasks in the short term (in cycles of several hours, daily, and at most weekly). They cannot be effectively used to seasonally balance the energy balance, and this is often a problem with seasonal variable production of renewable sources, e.g., PVs.

Local structures are created not only in the power system but also within other network energy systems: heating and gas. The nature of these other systems and their physical features are more conducive to the creation of local structures, and in the case of heating systems, their local nature is a fundamental feature. The potential integration of local structures of different energy systems (heating, gas and power) creates new opportunities for the local use of surpluses of produced electricity. Conversion of excess electricity to heat can be an alternative to sending it to the system area outside the reach of the local structure. A similar solution is the conversion of electricity to hydrogen, which can be consumed locally, fed into the gas network or stored. Conversion of electricity to other forms of energy can be reversible (at least partially), which would enable covering local electricity deficits in other time periods. In particular, the development of the hydrogen economy may create many opportunities for using surplus electricity and obtaining a new, effective way of storing energy. A development impulse that creates new opportunities for local energy structures may turn out to be solving the problem of meeting energy needs in a comprehensive manner, taking into account various carriers and integrated local energy infrastructure of various nature. This is one of the potential ways to reduce the demand for energy balancing within the power system itself and to obtain new possibilities for its local balancing.

The popularization of energy storage technologies will significantly facilitate the functioning of local balancing, and new opportunities may be created by the integration of local systems of various energy carriers [103]. In this context, the implementation of hydrogen economy solutions creates a number of new, potential technological possibilities. Integrated planning of local energy systems is an important research challenge that will require new methods currently poorly described in the literature.

There remain significant research problems at the interface of various local energy systems, which should be solved in the near future in order to improve the efficiency of planning their development.

6. Summary

Traditional distribution system expansion planning prioritized grid infrastructure optimization and capacity enhancement to meet growing consumer demand, driven primarily by economic efficiency and reliability criteria.

Modern methodologies increasingly adopt multi-criteria decision-making frameworks, recognizing that grid development transcends purely technical challenges. Contemporary planning now integrates technological innovation, organizational restructuring, and market mechanisms to address emerging demands-shifting focus from territorial network expansion to systemic adaptability. Key drivers include: (1) Integration of DERs; (2) Prosumer participation in energy markets; (3) Localized supply-demand balancing.

Low- and medium-voltage networks now face heightened complexity due to bidirectional power flow requirements, necessitating revised technical standards and reliability metrics. Optimization problems have grown multidimensional, yet current approaches often fragment these challenges into partial solutions focusing on isolated aspects (e.g., DER hosting capacity or line reinforcement). While such decomposition enables tractable analysis, optimization theory cautions that locally optimal partial solutions may not converge to global system optima.

Partial optimization methods serve as preliminary steps toward integrated research synthesis. The field anticipates advanced techniques that unify developmental criteria, addressing limitations of current oversimplified multi-criteria models, which remain more effective for strategic guidance than detailed operational planning.

Future planning must reconcile electricity networks with multi-carrier energy systems (e.g., natural gas, district heating, biogas, hydrogen). This necessitates holistic approaches to meet consumer and prosumer demands through: (1) cross-sectoral infrastructure coordination; (2) Unified optimization of energy vectors; (3) Interoperable market frameworks.

The integration of distributed multi-energy systems introduces novel challenges requiring interdisciplinary solutions, including: (1) Coupled optimization models for heterogeneous energy networks; (2) Dynamic pricing mechanisms for cross-carrier flexibility; (3) Standardized protocols for grid-device interoperability.

Based on the above, the following suggestions should be considered by policy makers:

• Policies should facilitate prosumer participation by creating regulatory frameworks that recognize and compensate distributed energy resources fairly. This includes P2P energy trading platforms with transparent pricing mechanisms and clear rules to ensure grid stability and fairness. Incentives such as feed-in tariffs, net metering, or dynamic rewards for flexibility services can encourage prosumer engagement.
• Implementing time-of-use tariffs, real-time pricing, or critical peak pricing can incentivize consumers and prosumers to shift consumption and generation patterns, enhancing grid flexibility. Policies need to ensure that pricing signals are clear, equitable, and supported by adequate metering and communication infrastructure.
• Governments and regulators should promote open standards for data exchange and interoperability among market participants, prosumer devices, and grid operators. This facilitates integration of diverse technologies and stakeholder inputs into decision-making frameworks.
• Policy frameworks must include provisions for community engagement and equitable access to benefits from distributed energy resources and dynamic pricing schemes. This may involve targeted subsidies, education programs, and mechanisms to protect vulnerable consumers from price volatility.
• Support development and deployment of advanced communication and control infrastructures to facilitate real-time information exchange among grid operators, prosumers, and market participants.
• Develop policies that foster cross-sectoral infrastructure coordination and investments in multi-energy carriers (electricity, gas, heating, hydrogen). This includes financial incentives, streamlined permitting, and standards for interoperable infrastructure and market mechanisms.
• Mandate open and standardized data formats and communication protocols for grid devices, DERs, and market platforms to ensure seamless interoperability and integration across energy vectors and stakeholders
• Promote the use of distributionally robust model predictive control and other advanced optimization techniques in planning regulations to better handle uncertainties from renewable integration and prosumer behavior.
• Encourage utilities and planners to adopt multi-objective frameworks balancing cost, reliability, flexibility, and environmental goals.