Smart Monitoring and Management of Local Electricity Systems with Renewable Energy Sources

Abstract

Smart monitoring of local electricity systems (LESs) with sources based on renewable energy resources (RESs) from the point of view of the requirements of the functions of an intelligent system are hardware and software systems that can solve the tasks of both analysis (optimization) and synthesis (design, planning, control). The article considers the following: a functional scheme of smart monitoring of LESs, describing its main components and scope of application; an assessment of the state of the processes and the state of the equipment of generators and loads; dynamic pricing and a dynamic assessment of the state of use of primary fuel and/or current costs of generators; economic efficiency of generator operation and loads; an assessment of environmental acceptability, in particular, the volume of CO₂ emissions; provides demand-side management, managing maximum energy consumption; a forecast of system development; an assessment of mutual flows of electricity; system resistance to disturbances; a forecast of metrological indicators, potential opportunities for generating RESs (wind power plants, solar power plants, etc.); an assessment of current costs; the state of electromagnetic compatibility of system elements and operation of electricity storage devices; and ensures work on local electricity markets. The application of smart monitoring in the formation of tariffs on local energy markets for transactive energy systems is shown by conducting a combined comprehensive assessment of the energy produced by each individual power source with graphs of the dependence of costs on the generated power. Algorithms for the comprehensive assessment of the cost of electricity production in a transactive system for calculating planned costs are developed, and the calculation of the cost of production per 1 kW is also presented. A visualization of the results of applying this algorithm is presented.

1. Introduction

The growing integration of renewable energy sources (RESs) into local electricity systems is transforming the way communities produce, consume, and manage energy [1]. This shift is driven by global efforts to reduce greenhouse gas emissions, combat climate change, and decentralize power generation. Local electricity systems (LESs), when smartly monitored and managed, can enhance the efficiency and resilience of energy infrastructure, especially in remote or underserved regions [2]. They allow for real-time data collection, better load balancing, and improved fault detection, which are crucial for maintaining grid stability [3]. LESs also play a significant role in empowering local communities to actively participate in energy decision-making and self-sufficiency [1,2,3,4]. The research gains strong relevance considering escalating energy demands, the increasing frequency of climate-related disasters, and the urgent need for sustainable, adaptive, and decentralized energy models [4]. Thus, investigating smart monitoring and management of LESs with RESs directly addresses one of the most pressing challenges of the contemporary energy transition.

The focus on ecological security and environmental management underscores the necessity of aligning technological innovation with environmental protection goals [1,3,5]. By embedding ecological considerations into the operation of LES, this research contributes to minimizing the environmental footprint of energy systems, protecting biodiversity, and promoting sustainable land use [4,6]. Smart systems enable real-time data collection and adaptive control, which are essential for managing variable renewable outputs, optimizing energy storage, and balancing supply–demand dynamics [7]. These systems can also reduce dependency on fossil fuels, lower emissions at the local level, and enhance the overall sustainability of energy operations [8]. The integration of environmental monitoring tools within LESs enables continuous assessment of air, water, and soil quality in surrounding areas and the topology of the area [9]. These capabilities support early detection of ecological risks, more efficient use of resources, and proactive environmental governance [8,10]. As a result, smart LESs become not only technological assets but also instruments of environmental responsibility and resilience.

Decarbonization of the energy sector requires a comprehensive and integrated approach that coordinates all components of the system [11]. This includes generating assets, the transmission and distribution grid, and end-use loads. These elements must work in synergy to ensure efficient electricity production while minimizing carbon emissions [12]. In this context, local electricity systems (LESs), also known as microgrids, play a vital role [13]. The microgrid is a decentralized energy system generation that operates either connected to the main grid or independently [14]. One of its primary objectives is the optimized management of distributed renewable energy sources (RESs), enhancing sustainability and resilience [11,13,14,15].

Innovation solutions proposed within this research framework further elevate their practical importance. The integration of digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and blockchain in managing local energy flows opens new avenues for community-level participation, peer-to-peer trading, and transparent energy governance [9,16]. These technologies enable real-time monitoring, predictive maintenance, and automated decision-making, which contribute to the overall efficiency and reliability of local electricity systems [17]. By decentralizing control and enhancing transparency, they empower prosumers and local stakeholders to become active participants in energy markets [18]. These innovations not only improve system performance but also foster social trust, economic inclusivity, and policy responsiveness [19]. They support the development of flexible, adaptive energy networks that can respond to evolving environmental and societal needs [17,20]. Overall, the study is highly topical, providing a vital bridge between renewable energy development, environmental stewardship, and the broader transition toward smart, secure, and sustainable energy futures.

The constant increase in renewable energy source (RES) generation capacity creates new challenges for maintaining the stability of power systems. To address these challenges, it becomes necessary to introduce new balancing capacities that can respond quickly to fluctuations in supply and demand. This development also requires a corresponding transformation of the existing structure of generating capacities to ensure greater flexibility and adaptability [14]. Without such adjustments, the integration of a growing share of RESs could compromise the reliability and efficiency of the overall energy system.

Local electricity systems (LESs) require accurate system modeling to support effective planning, operation, and decision-making. Modeling allows for the evaluation of different configurations, control strategies, and operational scenarios under varying conditions. Depending on its operational strategy, an LES can deliver a wide range of benefits across economic, technical, environmental, and social dimensions [15]. These benefits can positively impact both internal stakeholders, such as system operators and consumers, and external ones, including utilities and the broader community [12]. Realizing these advantages depends on the ability to predict performance, assess trade-offs, and optimize system behavior. Therefore, advanced modeling techniques are essential for maximizing the value and sustainability of LES implementation [13].

The economic values created by the microgrid have the advantages of locality and selectivity [12]. Local advantages are mainly explained by the creation of an internal “supersystem” energy market within the microgrid. In turn, when forming management strategies for LESs in the form of an extended system of characteristics and indicators that are subject to monitoring, it is necessary to introduce additional components [21].

The advantages of dynamic pricing are most fully manifested at the local level. The modern interaction of participants in the ancillary services market involves an increased role of distributed generation aggregators and distributed consumption aggregators.

The implementation of LES (microgrid) control strategies is carried out in technical, economic, and environmental directions [14], while establishing the following:

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technical benefits: reliability; power losses; grid voltage and load; limitation of power sources; energy balance (island mode);

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economic benefits: outage cost; loss cost; emission cost; cost and income of power sources;

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environmental benefits: greenhouse gas emissions.

Without the use of modern monitoring systems and equipment functionality or using the advantage of software algorithms that provide greater automation and control, grid operators lose the opportunity to determine the best option for connection and organization of management, and therefore, the opportunity to optimize the operation of the grid [5,11,12,13,21,22].

Today, the main functions of the monitoring system are laid down in the standard IEC TS 62898-3-4:2023: Technical requirements—microgrid monitoring and control systems [23], which contains technical requirements for monitoring and control of microgrids. This document applies to non-isolated or isolated microgrids integrated with renewable energy resources [24]. For a small microgrid with an installed capacity of less than 100 kW, according to the standard, the monitoring and energy management systems are usually combined into a single device called the Microgrid controller.

The microgrid monitoring and control module integrated into the microgrid controller is used as the second level of the control system. It is configured to work with signals with short time scales (minute, second). It is about data collection and processing, the regulation of active and reactive power, the regulation of frequency/voltage during normal operation of the isolated microgrid, the detection of island mode, and the switching of modes. The scheme of functional interaction of the microgrid monitoring and control system is presented in Figure 1 [23,25,26].

Smart monitoring for LESs is considered to be a comprehensive and systemic monitoring that provides observations of the current technological and economic efficiency of the functioning of individual elements of the system and the system as a whole, and when assessing the state, it provides identification of phenomena, processes, signals, and situations in the electricity system in accordance with the expanded requirements for the functioning of energy markets, primarily local markets [22,27].

The management of local electricity systems (LESs) with renewable energy sources (RESs) increasingly relies on the integration of smart monitoring, dynamic pricing, and demand-side management (DSM) to optimize performance [28]. Smart monitoring enables continuous data acquisition on generation, consumption, and system health, allowing for real-time decision making and predictive control [29]. Dynamic pricing uses this real-time information to form electricity tariffs that reflect actual generation costs, fuel usage, and market conditions, encouraging cost-efficient and environmentally responsible consumption patterns [30]. DSM strategies, such as load shifting, flexible load shaping, and energy arbitrage align consumer behavior with renewable energy availability, reducing peak demand and maximizing self-consumption of RESs [31]. The combined approach enhances technical performance by improving energy balance and grid stability, economic performance by lowering operational costs, and environmental performance by minimizing fossil fuel dependence. Case studies, such as SonnenCommunity [32,33], Brooklyn Microgrid [34,35], and the Yuri-Honjo Offshore Wind Project [36,37], illustrate how similar concepts succeed under diverse regulatory and market contexts. Overall, integrating these three components creates a scalable, adaptive LES framework capable of supporting the energy transition while meeting local community needs and sustainability targets.

The actuality of this research lies in the urgent global need to integrate renewable energy sources (RESs) into decentralized and adaptive local electricity systems (LESs) to ensure ecological security, environmental sustainability, and system resilience. As the capacity of RESs continues to expand, energy systems face new challenges in balancing generation and demand, maintaining grid stability, and reducing dependence on fossil fuels. LESs, supported by smart monitoring and control technologies, enable real-time data collection, predictive analytics, and decentralized energy management, which are essential for optimizing technical performance, economic efficiency, and environmental protection. These systems not only enhance operational reliability and community participation but also serve as instruments for achieving strategic energy, climate, and environmental goals. International standards such as IEC TS 62898-3-4:2023 reflect the growing importance of smart microgrid control architectures and justify the need for advanced modeling, dynamic pricing, and ancillary service optimization at the local level [23,27]. Consequently, this research addresses a critical intersection of technological advancement, ecological responsibility, and energy market transformation, contributing directly to the sustainable development agenda and future-proof energy infrastructure.

The novelty of this work lies in uniting these elements into a cohesive system that supports both technical optimization and active participation in local transactive energy markets. The proposed approach enables more accurate tariff formation, improved energy balance management, and reduced operating costs while maintaining ecological responsibility. By integrating cost modeling, performance diagnostics, and control into a single architecture, this research offers a scalable and adaptable solution to one of the pressing challenges of the energy transition—how to maximize the efficiency, resilience, and sustainability of decentralized energy systems.

2. Materials, Methods, and Methodology for LES Smart Monitoring

The research is based on both technical and analytical materials related to the structure and functioning of local electricity systems (LESs) integrated with renewable energy sources (RESs). Key components include photovoltaic panels, wind turbines, energy storage systems (ESSs), inverters, smart meters, and microgrid controllers compliant with IEC TS 62898-3-4:2023 [23,38]. Digital infrastructure such as sensors, Internet of Things (IoT) modules, and communication protocols (e.g., MQTT, Modbus) are also considered [22,39]. For software tools, energy management systems (EMSs), simulation environments (e.g., MATLAB/Simulink R2023a, DIgSILENT PowerFactory 15.1.7), and GIS-based modeling interfaces are used for system modeling and scenario analysis [22,38,39,40]. Empirical data for load profiles, weather conditions, RES generation, and consumption behavior are gathered from real case studies, pilot projects, and open databases [13,21,22,41]. Environmental and economic indicators are evaluated to reflect system performance, while ecological data are used to assess environmental impact.

The methodological approach includes a combination of system modeling, simulation, and multi-criteria analysis [20,42]. Mathematical modeling techniques are used to represent the physical and operational behavior of LESs, focusing on power flow, voltage regulation, load forecasting, and energy balance in both grid-connected and islanded modes [20,43]. Simulation-based testing is applied to assess system response under different operational scenarios, including varying weather conditions, demand fluctuations, and fault events [43,44]. Optimization algorithms, such as linear programming, genetic algorithms, or particle swarm optimization, are employed to improve energy dispatch, minimize losses, and maximize economic and environmental benefits [22,45]. Life cycle assessment (LCA) and carbon footprint evaluation are conducted to quantify the environmental impacts of the system and ensure its ecological sustainability [20,22,46]. Sensitivity analysis is used to identify key performance drivers, assess the robustness of system configurations, and improve decision-making under uncertainty [35,47]. The methodological structure ensures that both technical efficiency and environmental responsibility are considered in the development and operation of smart, resilient LESs with integrated renewable energy sources.

The overall research methodology is structured around a systems engineering framework, combining technical diagnostics with sustainability evaluation [13,21,48]. The study begins with problem identification and system boundary definition, followed by data acquisition and modeling [22,48,49]. A modular design is adopted, enabling the integration of various RESs, storage units, and control devices [48,50]. The monitoring and control logic is based on layered control architecture, distinguishing between local (device-level), supervisory (system-level), and market-interfacing (aggregator-level) functions [48,50,51]. Methodological emphasis is placed on dynamic system behavior and the role of smart control algorithms in maintaining ecological balance, reliability, and user participation [12,49,52]. The study also applies stakeholder mapping and socio-economic impact assessment to ensure that technical innovation aligns with community needs and policy objectives. Validation of results is achieved through comparison with reference models, field data, and expert evaluation.

Generally, the application levels of smart monitoring of local electricity systems (LESs) are divided into several hierarchical layers based on system complexity and functional roles. These typically include the device level (e.g., sensors, meters), system level (e.g., energy management platforms), and network or market level (e.g., aggregators, grid operators). Each level plays a critical role in ensuring real-time visibility, efficient control, and seamless integration of renewable energy sources within the LES framework [22]:

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Basic level (LES level): indication; making operational decisions; solving optimization procedures;

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Upper level (electricity transmission and distribution systems; local energy market): general system issues regarding the functioning of the higher-level system, in particular, energy management systems (EMSs);

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Lower level (subsystems of a dedicated LES, loads, individual generating capacities): control.

In general, smart monitoring solves the tasks of both analysis (optimization) and synthesis (design, planning, control). At the same time, two-way communication with objects is carried out, the forecasting of the management of technical and financial and economic activities is carried out, and integrated information, analytical support, and the identification of information are implemented. The functional scheme of smart monitoring consists of several basic system procedures [22].

Basic procedures of smart monitoring at the first level include calculation, measurement (without changing the type of signal), control, pattern recognition (with syntactic adequacy of information), and diagnostics with identification. These functions are primarily performed at the level of direct data acquisition and immediate system response, where raw signals are processed in their original form to ensure real-time control, stability, and operational continuity. The emphasis at this level is on fast and accurate interpretation of signals without structural transformation, allowing for quick reaction to system changes.

Basic procedures of smart monitoring at the second level involve measurement with a change in the type of signal, typically enabled by intelligent sensors and advanced signal processing techniques. This includes indirect measurement—where the values of one or more measured quantities are determined after the transformation of the type of quantity—and mediated measurement, where a quantity is measured through an intermediate transformation. These procedures allow for more flexible and adaptive monitoring by extending the range of observable parameters and improving the interpretability of system states. Both cumulative and compatible measurements are applied, depending on the objectives of the analysis and the structure of the monitoring architecture.

Comparison with the “reference tariff” when forming a quasi-optimal tariff plan, for example, will allow for the evaluation of the tariff change during Smart monitoring not by time interval, but by current values of fuel consumption and generation and consumption capacity (in particular, estimates of active and reactive power, losses in electrical grids, profiles of load schedules). It will also allow for the calculation of the instantaneous cost as an integral (averaged) instantaneous characteristic of the cost of electricity over a given “small” time interval of operation of the LES.

Controlling (with pragmatic adequacy of information) is an interfunctional process aimed at forming management actions and detecting events that determine control interventions. Pattern recognition, based on the semantic adequacy of information, enables the system to identify meaningful structures and trends within data. Diagnosis with forecasting supports the early identification of system faults and the prediction of future states or anomalies. Core system monitoring procedures include indication (visualization), registration, transformation, and information transfer, all of which ensure the continuous and coherent flow of operational data across the monitoring architecture.

New smart-monitoring technologies in LESs should provide an effective assessment of the system’s functioning and the creation of data flows (with distributed and multi-level processing) in real time. With the help of this, it will be possible to monitor the technical and economic efficiency of the functioning, the dynamics of processes in the system, and the use operations with big data within the framework of the Data as a Service (DaaS) mechanism—in particular, within the framework of the mechanism for ensuring flexibility and balancing of energy flows in the system [53,54]. At the same time, the main requirements for the construction of technical and software tools are the modular principle, openness of the architecture, separation of the functions of control and management of the facility, and compliance with requirements for ensuring cybersecurity [53,55,56,57,58,59]. In modern LES, despite the diversity of energy consumers and distributed energy production or consumption, it is predicted that the optimal use of local resources and their advantages due to their physical proximity to the load can be a good factor for the interaction of local stakeholders through the local energy market for local energy communities [60]. In this case, transactive energy is defined to manage consumption rates and generate resources on the demand side through the local market. Interactive nodes are defined as connection points between different parts of the electricity transmission grid [61].

Transactive energy systems combine both economical and control mechanisms. The scale and size of transactive energy systems can be limited to a residential neighborhood, microgrid, or distribution feeder. Such a system can also consist of a cluster of local communities, microgrids, or low-voltage feeders, and the concept of transactive energy (TE) can be applied to a local distribution grid or extended to a regional energy system. TE also allows for energy exchange between residential communities, microgrids, or individual LESs, and manages both the supply and demand sides for a dynamic energy balance in a changing environment with high-RES penetration [62,63,64,65]. Smart monitoring, unlike classic monitoring, conducts a comprehensive analysis, not only of energy indicators and technical, but also of economic ones. The authors propose a smart-monitoring structure for LESs with TE elements (Figure 2).

Modern energy systems are hierarchical systems where an energy system can be part of a larger energy system, and where the use of smart monitoring will in the future provide such systems with the ability to interact at different levels. The cost of operating the system and the cost of energy produced are economic indicators that are often used to optimize the size of multi-source wind farms and to determine the profitability of such systems. The cost of electricity is the main indicator for TE systems. The economic assessment considers the total current cost of the system, which includes the sum of capital costs, operating and maintenance costs, and replacement of various components.

Total annual cost including the grid for the LES is as follows [62]:

$$C_{tot}=CRF\cdot ({\displaystyle \sum C_{cap}+C_{O\&M}+C_{rep})+C_{cha}+C_{totaldisiel}, \mathrm{USD},}$$

(1)

where ${\displaystyle \sum C_{cap}}$ is the sum of the capital costs for photovoltaic panels and batteries [USD]; $C_{O\&M}$ is the cost of PV operation and maintenance [USD]; $C_{rep}$ is the cost of replacing the battery [USD]; $C_{cha}$ is the cost of grid energy used during the exchange of battery energy [USD]; $CRF$ is the capital recovery factor; and $C_{totaldisiel}$ is the cost of diesel generator energy [USD].

When operating without connection to a centralized power grid, the component $C_{cha}$ is missing in expression (1).

The energy cost of a diesel generator can be calculated as follows:

$$C_{totaldiesel}=C{C}_{diesel}+OM{G}_{diesel}+fuel{C}_{diesel}, \mathrm{USD},$$

(2)

where $C{C}_{diesel}$ is the annual capital cost of the diesel generator [USD]; $OM{G}_{diesel}$ is the annual cost of operating and maintaining a diesel generator [USD]; and $fuel{C}_{diesel}$ is the annual cost of diesel fuel [USD].

The fuel consumption characteristics of a DG are determined by its specific fuel consumption curve and efficiency, which are functions of the generator output power [63,64,65].

Specific fuel consumption can be described by polynomials of order 2 or 3 as follows:

$$F=a{P}_{gen}^3+b{P}_{gen}^2+c{P}_{gen}+d, \mathrm{L/kWh},$$

(3)

where $a, b, c, d$ are the coefficients of the polynomial, and $P_{gen}$ is the current power of the DG.

Diesel generator efficiency [20]:

$${\eta }_{diesel}={\displaystyle \frac{3600P_{gen}}{F{\rho }_{fuel}LH{V}_{fuel}}},$$

(4)

where ${\rho }_{fuel}$ is density [kg/m³] and $LH{V}_{fuel}$ is the lower heating value of diesel fuel [kJ/kg].

Capital Recovery Factor (CRF):

$$CRF={\displaystyle \frac{i{(1+i)}^N}{{(1+i)}^N-1}},$$

(5)

where i is the interest rate [%] and N is the system lifetime [year] (25 years).

The annual cost of energy (COE) is the average cost (in USD) of the actual energy produced and delivered (in kWh), determined by the following formula:

$$COE={\displaystyle \frac{C_{tot}}{{\displaystyle \sum _{t=1}^m{P}_g\mathsf{\Delta }t}}}, \mathrm{USD/kWh},$$

(6)

where $P_g$ is the power of generators [kW].

For further calculations, the Industrial Diesel Generator Set—J130 diesel generator was selected. According to the specified fuel consumption levels for specific values of output power, the function of specific fuel consumption from the generated power was obtained based on the results of approximation of tabular regulatory data.

$$F=4\cdot {10}^{-8}{P}_{gen}^3+2\cdot {10}^{-6}{P}_{gen}^2-0.0013P_{gen}+0.3721, \mathrm{L/kWh}.$$

(7)

Thus, the implementation of Smart monitoring within local electricity systems (LESs) operating in both grid-connected and island modes proves essential for optimizing energy flows, ensuring system balance, and enabling effective participation in transactive energy markets. The ability to dynamically monitor and control power generation from solar panels, diesel generators, and storage systems facilitates accurate tariff formation and cost management, while supporting energy exchange and load scheduling tailored to real-time conditions.

Fuel costs are the main component of the price of electricity produced by DG. The cost $T_{dis}$ of generating energy by DG consists of the costs of fuel and its transportation, as well as taking into account expression (3) in the conversion of costs per 1 kW.

$$T_{dis}=F{P}_{gen}{C}_{ldis}+C_{totaldiesel}, \mathrm{USD},$$

(8)

The fuel cost $C_{ldis}$ is taken as the average per 1 L for April 2024 according to the data [66].

The cost of 1 kW of solar energy $C_{pv}=0.033$ USD according to the data [67]. Total cost of energy generation:

$$T_{pv}=k_{pv}{P}_{pv}{C}_{pv}, \mathrm{USD},$$

(9)

where $k_{pv}$ is the coefficient of energy loss and $P_{pv}$ is the power of PV.

The cost of 1 kW of storage system 0.112 USD according to data [67]. Total cost of electricity:

$$T_B=k_B{P}_B{C}_B, \mathrm{USD},$$

(10)

where $k_B$ is the coefficient of energy loss of storage system and $P_B$ is the power of storage system.

In general, the total cost of system generation was as follows:

$$T_{\sum }=T_{pv}+T_B+T_{dis}, \mathrm{USD},$$

(11)

An algorithm and a tariff formation program based on this have been developed for local energy markets of transactive energy systems with smart monitoring in LESs, where the smart-monitoring system estimates the generated energy of each individual energy source using graphs of the dependence of costs on the generated power and other parameters of the sources. When forming a price, the power of each individual generator should be determined to establish variable costs. The generalized algorithm for the functioning of smart monitoring when forming tariffs in LESs consists of the following enlarged steps:

• Sensors in the nodes of the local electricity system record indicators of electricity consumption $P$ and production $P_{gen}$, the data obtained are then stored on LES servers.
• The monitoring system evaluates the generated energy of each individual power source with graphs of the dependence of costs on the generated power, taking into account the time of day and season.
• Total generation costs are calculated.
• The generated power and the power of consumers are compared. The quality indicators of electric energy are determined and compliance with standards is checked. The fuel consumption of diesel generators is estimated to be using the coefficient k, which shows the optimality of energy consumption $W_E$ and fuel consumption $W_F$ [66]:

  $$k={\displaystyle \frac{W_F}{W_E}},W_E={\displaystyle \underset{0}{\overset{T}{\int }}F(P(t))dt},W_E={\displaystyle \underset{0}{\overset{T}{\int }}P(t)dt}.$$

  (12)
• Calculation of the tariff for consumers, the cost of electricity generation in general $T_{\sum }$, and for individual generators $T_{dis}, T_{pv}, T_B$ according to expressions (8)–(11).
• Formation of tariff policy (dynamic tariffing, day-ahead tariffing, etc.) when the system is functioning on the local electricity market.
• Use of a specific demand response program based on monitoring results.

The presented methodology and mathematical framework enable comprehensive modeling, monitoring, and optimization of local electricity systems (LESs) integrated with renewable energy sources. By combining advanced system simulation, multi-level smart monitoring, and dynamic tariff formation, this approach facilitates efficient energy management, cost optimization, and reliable operation under varying conditions. The integration of real-time data acquisition, intelligent control algorithms, and economic–environmental assessments supports both grid-connected and islanded modes, ensuring sustainability, system resilience, and active participation in local energy markets. Overall, this methodology provides a robust foundation for designing smart, adaptive, and economically viable LESs with high renewable penetration. The implementation of smart monitoring for the coordinated operation of energy communities (with their formation into clusters) to support seamless integration with distribution networks is promising. The algorithm can be used when planning the operation of LESs at the stages of its implementation for technical and economic assessment.

3. Results and Discussion

The results of the study demonstrate that smart monitoring and management of local electricity systems (LESs) with renewable energy sources significantly enhance system efficiency, economic viability, and environmental sustainability. The research is structured around two core areas: monitoring dynamic pricing for participation in local markets of the transactive system and smart-monitoring-driven dynamic pricing and demand-side management in local electricity systems with renewable energy sources. In the first part, the application of dynamic pricing mechanisms, based on real-time data from smart monitoring, enables accurate cost formation that reflects actual generation, fuel use, storage costs, and time-based load profiles. This allows consumers and prosumers to participate in local energy markets through transactive energy models, improving transparency and market responsiveness. In the second part, three demand-side management (DSM) strategies, load shifting, flexible load shape, and energy arbitrage, were tested using predictive analytics and consumption forecasting. The DSM3 strategy (energy arbitrage) yielded the highest reduction in total energy cost (10.6%) by optimizing the use of solar generation during low-price periods. Overall, the findings confirm that smart monitoring not only improves control and visibility over local energy systems but also facilitates active market participation and economically motivated load management, contributing to the resilience and adaptability of LESs within the green energy transition framework.

3.1. Monitoring Dynamic Pricing for Participation in Local Markets of the Transactive System

The use of local and transactive energy markets is essential for the functioning of local electricity systems (LESs), enabling decentralized energy exchange and more flexible system operation. The effective performance of these systems relies heavily on the implementation of smart monitoring procedures, which ensure real-time data acquisition, system diagnostics, and adaptive control. By continuously evaluating consumption, generation, and market signals, Smart monitoring supports dynamic pricing, demand-side response, and optimal utilization of renewable resources. The general scheme of Smart monitoring, tailored to the specific characteristics and architecture of LESs, is presented in Figure 3.

As an example, let us consider the implementation of such an important component of smart monitoring as calculating the tariff for the participation of an LES in local TES markets, which consists of a solar generator (PV), an energy storage system—a battery (B), and a diesel generator (DG), with the ability to connect to a centralized power grid.

In a dedicated LES, the conditions for ensuring energy balance are met by using electricity from solar power plants and DG by ensuring the necessary electricity consumption schedules with the organization of energy exchange with the storage during electricity shortages. The use of DG in systems with an electric grid is more expensive than the cost of grid energy, but DG is necessary to compensate for grid instability and during its outages (Figure 4a,b).

The condition for the system to function is the prohibition of purchasing energy from the centralized electricity grid for the purpose of its subsequent resale. To comply with this rule, it is prohibited to charge the battery using energy from the main grid, as this may result in a mode where the energy stored in the battery is sold to the grid.

For the scheme presented in Figure 4a, the following ways of organizing energy flow can be determined:

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power generated by solar generators (Ppv) can be used to power loads (Ppc), charge the storage system (Ppb), and/or sell energy to the centralized grid Ppg;

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power from the storage system (Pb) can be used to power loads (Pbc) and/or transmission and sale in the grid (Pbg);

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power from a diesel generator (Pd) can be used to power loads (Pbc) and/or transmit to the storage system (Pdb);

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the grid is used only for load consumption (Pgc).

In local electricity systems operating in island mode (Figure 4b), we can distinguish the following energy flows:

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the power generated by solar generators (Ppv) is used to power loads (Ppc) and charge the storage system (Ppb);

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the power from solar generators can also be lost when the storage system is charged and the load is provided;

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the power from the storage system (Pb) is used to power loads (Pbc);

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the power from the diesel generator (Pd) can be used to power loads (Pbc) and/or be sent to the battery (Pdb).

For the considered LES, in accordance with the smart-monitoring procedures, we will consider the procedure for forming the cost price for subsequent dynamic tariffing. To do this, it is necessary to conduct the following:

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comprehensive monitoring from the point of view of forming the energy balance, as well as monitoring the needs of various users of the transactive system;

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monitoring the operation of distributed energy sources and the quality of electricity, for example, voltage, current, power, frequency, etc. (monitoring data can be selective, according to the operating conditions of each energy source);

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load monitoring, including load distribution by individual categories, for example, the load of important users, sensitive users, large users, etc.

Comprehensive monitoring of distributed energy sources, load characteristics, and electricity quality underpins economic optimization by integrating capital, operational, and fuel costs into a dynamic pricing framework. This approach not only improves the economic viability of LESs but also enhances their flexibility, resilience, and sustainability by prioritizing renewable generation and minimizing dependency on centralized grids. The mathematical models for fuel consumption, capital recovery, and cost of energy offer a robust foundation for assessing system performance and guiding operational decisions. Ultimately, the smart monitoring and management framework presented here lays the groundwork for scalable, adaptive LESs that can meet future energy demands while aligning with environmental and market requirements.

3.2. Smart-Monitoring-Driven Dynamic Pricing and Demand-Side Management in Local Electricity Systems with Renewable Energy Sources

When implementing Smart monitoring, we will consider a dynamic pricing system, the prices of which are set close to the real-time electricity consumption for a dedicated LES with renewable energy resources, which is represented by a transactive system. In general, the price of electricity for the consumer consists of the cost of producing 1 kW of electricity by each type of generation, the cost of transportation, and taxes. It is the sum of all these costs that is determined as the final price for electricity charged to the consumer when operating on the local market.

Using expressions (1)–(12), it was established that the implementation of smart monitoring and dynamic tariff formation within local electricity systems enables accurate calculation of generation costs, optimal fuel consumption of diesel generators, and effective balancing of energy supply and demand. This comprehensive approach supports real-time system control, enhances economic efficiency, and ensures reliable operation in both grid-connected and islanded modes, thereby promoting sustainable and cost-effective energy management.

In the electricity market, the analysis of the behavior of all participants in the local system (not only consumers) taking into account demand response is essential for the deployment of distributed energy sources, energy conservation, and emission reduction. The analysis of the energy consumption and production profile directly affects economic efficiency. The transactive system consists of power sources: from renewable power sources and a diesel generator; storage systems; and consumers. Visualization of the results was obtained in the Matlab program. Let us consider an example of how an algorithm for the functioning of smart monitoring when forming tariffs in LESs functions.

The daily load profile of consumers, divided into 24-h intervals, is presented in Figure 5. The goal of the strategy of distributing power by generators, which smart monitoring should provide, is to maximize the consumption of solar energy of the LES. This strategy provides for the priority use of energy generated by the solar generator to power electrical loads.

According to the presented strategy of applying smart monitoring in LESs for further control, the distribution of power by each type of system generation was obtained (Figure 5).

According to expressions (8)–(11), we estimate the total cost of electricity generated in the LES and the cost of electricity taken from each generator. The results of the analysis are presented in Figure 6.

For the specified case, the cost of electricity per 1 kW for each measured (hourly) time interval is presented in Figure 7.

The cost of electricity generated by DH is variable, since the specific fuel consumption is nonlinear. The highest cost is in areas 1–5 and 21–24. The lowest price occurs when using only a solar generator. The operation of DH at capacities above nominal leads to additional fuel consumption.

The average daily cost for 1 kW of energy is USD 0.23/kWh.

Load management provides more options for actions to maintain network stability, i.e., greater flexibility and reliability (reducing peak demand by eliminating electricity use, or by shifting it to off-peak times, etc.) by identifying the load and predicting the energy consumption of the load.

Let us consider the application of DSM demand management mechanisms based on the results of smart monitoring, using three DSM strategies [54,64,65]: load shifting, flexible load shape, and energy arbitrage.

The main idea behind the flexible load shifting strategy is for the consumer to form the necessary electricity load schedules in exchange for financial incentives. In turn, energy arbitrage involves shifting the load to times of day when prices are low.

Thus, in accordance with the specified Smart monitoring, the LES, after analyzing the predicted consumption schedules, offers the consumer to optimize the load schedule by transferring power to other time intervals, while the condition is met that the average value of power consumption by the load remains unchanged (see Figure 8).

Three options for shifting electricity consumption relative to the base curve P(t) are proposed (see Figure 5).

DSM1—partial load shifting from evening hours to daytime hours for greater use of solar energy (load shifting strategy);

DSM2—load shifting partially from evening and partially morning hours to daytime hours (flexible load shape);

DSM3—load shifting in such a way that uses all the forecasted power of solar generators to cover the consumer’s costs (energy arbitrage strategy).

The percentage change relative to the base load schedule of the total cost T (in percent) for DSM1 is 3.2%, for DSM2 it is6.1%, and for DSM3 it is 10.6%. It should be noted that according to the information received, consumers subsequently choose an acceptable option based on the offers provided to them and their priorities.

The prospective areas of use of smart monitoring are as follows: use the results for the creation of practical recommendations on the architecture, construction, and operation of local electric power systems of LESs, highlighting the features of the interaction of DER; use in dynamic control systems for energy processes; application in consideration of LESs as cellular energy structures with the subsequent unification of LESs into cluster systems to increase their stability and reliability of functioning; study of the use of guaranteed power sources to ensure reliability and increase stability; planning the operation of LESs at the stages of their implementation for technical and economic assessment.

Within the scope of the discussion, it is worth reviewing the experience of LES-related projects. Descriptions and analyses of such projects are well represented in specialized literature, providing valuable insights. As an example, we propose to consider the implemented projects such as SonnenCommunity (Germany), Brooklyn Microgrid (USA), and the Yuri-Honjo Offshore Wind Project (Japan). This body of work offers comprehensive information on methodologies, challenges, and outcomes associated with LES implementations. By examining these studies, one can better understand best practices and potential pitfalls.

Comparable international initiatives further underscore the practical applicability and global relevance of the proposed LES framework. The SonnenCommunity in Germany integrates decentralized photovoltaic generation with home battery systems (SonnenBatterie) and a virtual trading platform, enabling peer-to-peer (P2P) energy sharing within a supportive regulatory environment [32,33]. This model demonstrates how advanced monitoring, market incentives, and active prosumer engagement can maximize local renewable energy use and reduce reliance on centralized grids.

In the United States, the Brooklyn Microgrid employs blockchain-based transactive energy platforms to facilitate local electricity exchange through smart contracts and auction mechanisms [34,35]. Despite operating under more restrictive regulations, the project showcases the potential for decentralized markets to improve grid resilience and foster community-level energy autonomy.

Similarly, the Yuri-Honjo Offshore Wind Project in Akita Prefecture, Japan, illustrates significant progress in large-scale renewable energy deployment, while also revealing challenges in integrating such generation into local energy use under current market regulations [36,37]. The project underscores the importance of adequate grid connection capacity, benefit-sharing schemes for local stakeholders, and transparent policy frameworks to ensure social acceptance and economic viability.

Comparative characteristics of selected international LES-related projects, SonnenCommunity, Brooklyn Microgrid, and the Yuri-Honjo Offshore Wind Project, are presented in

Table 1. Comparative characteristics of selected international LES-related projects [32,35,36,37].

Feature	LES-Related Project
	SonnenCommunity (Germany)	Brooklyn Microgrid (USA)	Yuri-Honjo Offshore Wind Project (Japan)
Main technology	Virtual P2P trading platform with home batteries and PV systems	Blockchain-based local energy market with PV and storage	Offshore wind farm (820 MW planned) integrated into regional grid
Generation source	Distributed rooftop PV and storage	Distributed rooftop PV and storage	Offshore wind (large-scale generation)
Market model	Peer-to-peer energy sharing	Peer-to-peer energy trading via blockchain	Wholesale market participation and local supply initiatives
Regulatory environment	Supportive, enabling P2P trading	Limited; operates as pilot/demonstration	Evolving; offshore wind regulations and grid code reforms underway
Community role	Active prosumer participation in energy sharing	Community prosumers trade energy locally	Local governments and fishing cooperatives as stakeholders
Scalability potential	High, backed by national energy transition policy	Moderate; regulatory changes required	High; contingent on infrastructure upgrades and market reform
Key benefit	Maximizes self-consumption and local renewable penetration	Demonstrates blockchain and community market models	Boosts renewable capacity and regional economic development
Key challenge	Requires ongoing incentives and regulatory support	Regulatory and utility integration barriers	Grid connection limits; market access for local energy use

. This table summarizes key features and distinctions among these projects. It provides a clear overview to facilitate further analysis and discussion.

Collectively, these cases highlight that while technological readiness is a key enabler, the success and scalability of LESs depend equally on institutional design, regulatory flexibility, and socio-economic engagement. Lessons from these international experiences can inform the adaptation of the proposed framework to diverse energy, market, and community contexts.

The implementation of LESs with renewable energy sources enables dynamic pricing that closely reflects real-time electricity consumption and production costs from diverse generation units such as solar panels, diesel generators, and storage systems. By integrating detailed cost components, including fuel, transportation, and operational expenses, the system can accurately calculate tariffs and support flexible tariff policies tailored to local market conditions. The application of demand-side management (DSM) strategies like load shifting, flexible load shape, and energy arbitrage further optimizes electricity consumption patterns, maximizing the use of solar energy and reducing overall costs by up to 10.6%. These smart monitoring-driven approaches enhance grid stability, economic efficiency, and environmental benefits by aligning consumer incentives with renewable energy availability and system constraints. Ultimately, this framework fosters an adaptive, consumer-responsive, and economically viable LESs that advances the transition to sustainable energy futures.

4. Conclusions

The study demonstrates that Smart monitoring and management of local electricity systems (LESs) integrated with renewable energy sources (RESs) substantially improve operational efficiency, economic feasibility, and environmental sustainability. By implementing dynamic pricing mechanisms based on real-time smart monitoring data, the system enables precise cost calculation that reflects actual generation, fuel consumption, storage expenses, and demand profiles. This allows consumers and prosumers to actively participate in local energy markets through transactive energy models, promoting transparency and market responsiveness while prioritizing renewable generation.

The research also highlights the importance of demand-side management (DSM) strategies to optimize energy consumption and reduce costs within LES. Among the tested strategies, load shifting, flexible load shape, and energy arbitrage, the latter showed the greatest benefit, decreasing total energy costs by 10.6% through optimized use of solar generation during low-price periods. This confirms that smart monitoring combined with predictive analytics and consumption forecasting empowers consumers to manage loads more effectively, enhancing system flexibility, resilience, and economic performance in both grid-connected and islanded modes.

The development of a modular, layered smart monitoring framework ensures comprehensive real-time data acquisition, system diagnostics, and adaptive control across all system levels. This framework integrates technical, economic, and environmental indicators, enabling dynamic tariff formation and energy balancing that supports decentralized energy exchanges and transactive market participation. The applied mathematical models and algorithms provide a robust basis for fuel consumption optimization, cost management, and sustainable operation, laying the groundwork for scalable and adaptive LES architectures.

Finally, the findings align well with international LES projects such as SonnenCommunity (Germany), Brooklyn Microgrid (USA), and the Yuri-Honjo Offshore Wind Project (Japan), demonstrating the global applicability of the proposed approach. The proposed smart monitoring system and demand management mechanisms support not only improved energy efficiency and cost savings but also foster active stakeholder engagement and policy alignment, contributing to the green energy transition and the evolution of intelligent, resilient local power systems worldwide.

Future research should focus on enhancing the integration of advanced machine learning and artificial intelligence techniques within smart monitoring systems to improve predictive accuracy and autonomous decision-making. Expanding the scalability of these systems to larger, more complex LES networks with diverse energy resources and consumers will be critical, alongside developing robust cybersecurity measures to protect data integrity and system resilience. Exploring the socio-economic impacts of widespread adoption of transactive energy markets and demand-side management strategies can provide valuable insights for policy development and stakeholder engagement. Finally, incorporating emerging technologies such as blockchain for secure energy transactions and investigating the potential of vehicle-to-grid (V2G) systems could further optimize energy flows and support the transition toward sustainable, decentralized energy systems.