Search Results (2,228)

Urban integrated energy systems (UIESs) are increasingly exposed to uncertainties arising from wind and photovoltaic variability, load fluctuations, and equipment failures, highlighting the need for refined reserve assessment and coordinated operation. This study develops a unified framework that jointly models renewable and load deviations together with a load-dependent failure probability model, using Monte Carlo sampling and K-means scenario reduction to obtain representative system states. A reserve-capacity-oriented optimisation model is formulated to minimise total operating cost—including thermal generation, energy-storage operation, and reserve cost—while satisfying power balance, reserve adequacy, unit operating limits, and state-of-charge constraints. Application to a UIES comprising a 1000 kW load, 800 kW photovoltaic unit, 100 kW wind turbine, five thermal power units (total capacity 1000 kW), and a 250 kW/370 kWh energy storage system shows that reserve requirements fluctuate between −100 kW (downward) and 500 kW (upward) across different scenarios, with uncertainty-driven reserves dominating and failure-related reserves remaining below 100 kW. The optimisation results indicate coordinated operation between thermal units and storage, with storage absorbing surplus renewable output, supporting peak shaving, and providing most upward and all downward reserves. The total operating costs under typical summer and winter scenarios are 2264.02 CNY and 3122.89 CNY, respectively, confirming the method’s ability to improve reserve estimation accuracy and support economical and reliable UIES operation under uncertainty. Full article

This paper presents the design, modeling and experimental validation of an on-grid photovoltaic system with self-consumption, sized for the sustainable supply of a water pumping station. The system, composed of 68 photovoltaic panels, uses an architecture based on a Boost DC-DC converter controlled by the Perturb and Observe algorithm, raising the operating voltage to a high-voltage DC bus to maximize the conversion efficiency. The study integrates dynamic performance analysis through simulations in the Simulink environment, testing the stability of the DC bus under sudden irradiance shocks, with rigorous experimental validation based on field production data. The simulation results, which indicate a peak DC power of approximately 34 kW, are confirmed by real monitoring data that records a maximum of 35 kW, the error being justified by the high efficiency of the panels and system losses. Long-term validation, carried out over three years of operation (2023–2025), demonstrates the reliability of the technical solution, with the system generating a total of 124.68 MWh. The analysis of energy flows highlights a degree of self-consumption of 60.08%, while the absence of chemical storage is compensated for by injecting the surplus of 49.78 MWh into the national grid, which is used as an energy buffer. The paper demonstrates that using the grid to balance night-time or meteorological deficits, in combination with a stabilized DC bus, represents an optimal technical-economic solution for critical pumping infrastructures, eliminating the maintenance costs of the accumulators and ensuring continuous operation. Full article

The increasing penetration of photovoltaic (PV) generation, energy storage systems, and flexible loads within modern buildings demands advanced control strategies capable of harnessing dynamic assets while maintaining grid reliability. This Perspective article presents a comprehensive overview of reinforcement learning-driven (RL-driven) control methods for DC flexible microgrids—focusing in particular on building-integrated systems that shift from AC microgrid architectures to true PV–Energy storage–DC flexible (PEDF) systems. We examine the structural evolution from traditional AC microgrids through DC microgrids to PEDF architectures, highlight core system components (PV arrays, battery storage, DC bus networks, and flexible demand interfaces), and elucidate their coupling within building clusters and urban energy networks. We then identify key challenges for RL applications in this domain—including high-dimensional state and action spaces, safety-critical constraints, sample efficiency, and real-time deployment in building energy systems—and propose future research directions, such as multi-agent deep RL, transfer learning across building portfolios, and real-time safety assurance frameworks. By synthesizing recent developments and mapping open research avenues, this work aims to guide researchers and practitioners toward robust, scalable control solutions for next-generation DC flexible microgrids. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

The large-scale integration of electric vehicles (EVs) presents both challenges and opportunities for power grid stability and renewable energy utilization. Vehicle-to-Grid (V2G) technology enables EVs to serve as mobile energy storage units, facilitating peak shaving and valley filling while promoting the local consumption of photovoltaic and wind power. However, uncertainties in renewable energy generation and EV arrivals complicate the scheduling of bidirectional charging in stations equipped with hybrid energy storage systems. To address this, this paper proposes a multi-stage rolling optimization framework combined with a fuzzy logic-based decision-making method. First, a bidirectional charging scheduling model is established with the objectives of maximizing station revenue and minimizing load fluctuation. Then, an EV charging potential assessment system is designed, evaluating both maximum discharge capacity and charging flexibility. A fuzzy controller is developed to allocate EVs to unidirectional or bidirectional chargers by considering real-time predictions of vehicle arrivals and renewable energy generation. Simulation experiments demonstrate that the proposed method consistently outperforms a greedy scheduling baseline. In large-scale scenarios, it achieves an increase in station revenue, elevates the regional renewable energy consumption rate, and provides an additional equivalent peak-shaving capacity. The proposed approach can effectively coordinate heterogeneous resources under uncertainty, providing a viable scheduling solution for EV-aggregated participation in grid services and enhanced renewable energy integration. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

The aim of this paper is to present the transformation of the network tariff system in Slovenia using a comprehensive assessment methodology for the techno-economic evaluation of electricity costs for households. The novelty of the proposed approach lies in the combined assessment of the previous and new network tariff systems, explicitly accounting for power-based network tariff components, time-block-dependent charges, and different support schemes for household photovoltaic systems, including net metering and credit note-based schemes. The results show that the transition from an energy-based to a more power-based network tariff system, introduced primarily to mitigate congestion in distribution networks, is not inherently disadvantageous for consumers and prosumers. When tariff structures are appropriately designed, the new framework can support efficient grid utilization and maintain favorable conditions for prosumers, particularly those integrating battery storage systems. Overall, the proposed methodology provides a transparent and robust framework for evaluating the economic impacts of network tariff reforms on residential consumers and prosumers, offering relevant insights for tariff design and the development of future low-carbon household energy systems. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

Greenhouse gas emissions are a primary contributor to climate change and the observed rise in global temperatures. To reduce these emissions, renewable energy sources (RESs) must replace fossil fuels in power generation. Because of the mismatch between production and demand, the increase in RES is limited. To address this phenomenon, the addition of renewable energy generation should be accompanied by storage systems. In this paper, the island of Crete is examined for various renewable energy generations and storage capacities using the PowerWorld Simulator software. Four main scenarios are studied in which the installed renewable energy generation is increased to reach substation limits. For every scenario, different renewable energy generation mixes are considered between wind farms and photovoltaics. Furthermore, for all sub-scenarios, different storage capacities are considered, ranging from 1.6 GWh to 12.8 GWh. This study proves that storage systems are mandatory to increase renewable energy penetration. In certain scenarios, a battery energy storage system can further increase renewable energy penetration from 6.15% to 28.07%. Although the battery energy storage system enhanced renewable penetration, increasing transmission line capacities should also be considered regarding the scenario. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

High penetration and output volatility of island wind and photovoltaics (PV) pose challenges to energy consumption and supply–demand balance, and cost-effective energy storage configuration. A coupled dispatch model for a wind–PV–storage system is proposed, which treats multiple canal units as virtual ‘loads’ that switch between generation and pumping under constraints of power balance and available water head model. Considering the variable reservoir–irrigation feature, a multi-objective model framework is developed to minimize both economic cost and storage capacity required. An augmented Lagrangian–Nash product enhanced NSGA-II (AL-NP-NSGA-II) algorithm enforces constraints of irrigation shortfall and overflow via an augmented Lagrangian term and allocates fair benefits across canal units through a Nash product reward. Moreover, updates of Lagrange multipliers and reward weights maintain power balance and accelerate convergence. Finally, a case simulation (3.7 MW wind, 7.1 MW PV, and 24 h rural load) is performed, where 440.98 kWh storage eliminates shortfall/overflow and yields 1.5172 × 10⁴ CNY. Monte Carlo uncertainty analysis (±10% perturbations in load, wind, and PV) shows that increasing storage to 680 kWh can stabilize reliability above 98% and raise economic benefit to 1.5195 × 10⁴ CNY. The dispatch framework delivers coordination of irrigation and power balance in island microgrids, providing a systematic configuration solution. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

Green technologies are strongly present in the energy mixes of countries around the world. In addition to the need to reduce the extraction of non-renewable raw materials and the harmful environmental impact associated with energy production, the trend towards renewable energy development should also be linked to the need to minimize energy poverty stemming from high electricity prices and the need to increase the energy efficiency of existing solutions. These issues formed the basis for the study’s objective, which was to examine the regulatory framework for the development of Poland’s energy system, with particular emphasis on sustainable development. A particularly important aspect of the study was the exploration of the market for green technologies introduced into the energy system in Poland, with a primary focus on solutions dedicated to small, individual consumers (households). The cognitive value of the study and its original character is created by the cognitive aspect in terms of the interests and consumer preferences of households in this area, motivated by economic considerations related to the energy efficiency aspect of RES solutions. In this regard, there is a relatively limited number of current studies conducted for the reference country (Poland), justifying the choice of the research topic and theme. For the purposes of the study, a literature review, as well as legal standards and industry reports, was conducted. A practical study was conducted based on the results of surveys conducted by selected companies involved in the sale and installation of heating solutions. Detailed research was supported by statistical instruments using PQstat software version 1.8.4.164. Key findings confirm significant household interest in green electricity production technologies, which enable improved energy efficiency of home energy installations. Importantly, the potential for lower electricity bills, which can be attributed to low system maintenance costs and the ability to manage consumption, is a factor in choosing renewable energy solutions. Current interest in renewable energy solutions focuses on heat pumps, photovoltaics, and energy storage. Renewable energy users are interested in integrating renewable energy technology solutions into energy production and management to optimize energy consumption costs and increase household energy independence. Full article

South Africa faces high energy costs, highlighting the urgent need for sustainable and cost-effective energy solutions. This study investigates the design of a cost-effective photovoltaic energy system that maximises savings and revenue for the school through energy trading. In this study, the school trades with 14 neighbouring households and 125 electric motorbikes. This research first applies Latin Hypercube Sampling to explore the solution space and determine which system parameters have a significant impact on supply reliability, investment costs, revenue and savings. Optimal solutions are generated using Non-Dominated Sorting Genetic Algorithm II for a range of system scenarios. Following this, the most promising scenario is selected and applied to 53 schools in the Western Cape. The results show that number of panels strongly correlates with both supply reliability and revenue, thus reducing the break-even years, while battery capacity affects investment costs and, to some extent, break-even years. Among the configurations tested, scenarios where schools traded with both households and electric motorbikes, particularly when both included their own battery systems, achieved the most favourable financial performance for the school, with break-even periods of less than five years under sufficient roof area and improved reliability for the external entities, with an average improvement of 60%. These findings demonstrate that peer-to-peer energy trading between schools and communities can enhance the financial feasibility and sustainability of decentralised solar systems, offering a scalable model for improving energy access and affordability in South Africa and possibly other developing countries. Full article

This article analyzes the case of a typical modern residential area, which was built following current legal regulations in Poland. For the purposes of the calculations, a housing estate consisting of 32 houses was assumed, with a connection power of 36 kW each. The three variants evaluate power consumption and photovoltaic system operation: Variant I assumes no PV installations and fluctuating consumer power demands; Variant II involves PV installations in all estate buildings with a total capacity matching the building’s 36 kW connection power and minimal consumption; and Variant III increases installed PV capacity per building to 50 kW, aligning with apartment connection powers, also with minimal consumption. The simulations performed indicated that there may be problems with voltage levels and current overloads of network elements. Although in case I the transformer worked properly, after connecting the PV installation in an extreme case, it was overloaded by about 117% (Variant II) or even about 180% (Variant III). The described case illustrates the impact of changes in regulations on the stability of the electricity distribution network. A potential solution to this problem is to oversize the distribution network elements, introduce power restrictions for PV installations or to oblige prosumers to install energy storage facilities. Full article