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Single widespread employment of renewable energy sources (RESs) contributes to the shortage in the inertia of the microgrid (MG). After this, frequency stability may regress as a result of power imbalance or minor load fluctuations. This paper proposes an explicit adaptive modified virtual inertia control (VIC) based on an online Archimedes optimization algorithm (AOA) identifier for MG containing thermal, wind, and solar photovoltaic power plants. The Rung Kutta approach is used to construct the proposed online identifier, which acts as a model of the MG. AOA predicts the coefficients of the online identifier based on the input and output of MG to mimic the frequency deviation of the MG online. AOA estimates online the inertia and damping coefficients of the VIC system with an energy storage device based on online AOA identifier coefficients. The frequency deviation of the MG based on the proposed explicit adaptive modified VIC is compared with the conventional VIC based on fixed parameters and the VIC system based on optimal parameters using AOA offline under mutation in loads, weather-dependent input, and MG parameters using MATLAB/Simulink software. Furthermore, the proposed explicit adaptive modified VIC based on an online AOA identifier is evaluated with the adaptive VIC system based on fuzzy logic control, which adjusts only the inertial gain online. The simulation results demonstrate the capabilities of the proposed explicit adaptive modified VIC to improve the frequency stability and enhance low-inertia islanded MGs with RESs.

The article presents an analysis of the statistical relationship between the determinants of and barriers to the development of renewable energy sources (RESs) in the macroeconomic system and the development of renewable energy source consumption in individual European Union countries. The article considers four key categories of RES development barriers in the European Union: political, administrative, grid infrastructural, and socioeconomic. The work is based on publicly available historical data from European Union reports, Eurostat, and the Eclareon RES Policy Monitoring Database. The empirical analysis includes all 27 countries belonging to the European Union. The research aimed to determine the impact of all four types of factors, including socioeconomic, on the development of RESs in European Union countries. The analysis uncovered that describing the European Union as a consistent region regarding the speed of renewable energy advancement and the obstacles to such progress is not accurate. Notably, a significant link exists between a strong degree of societal development and the integration of renewable energy sources. In less prosperous EU nations, economic growth plays a pivotal role in renewable energy development. Barriers of an administrative nature exert a notable influence on renewable energy development, especially in less affluent EU countries, while grid-related obstacles are prevalent in Southern–Central Europe. In nations where the proportion of renewable energy sources in electricity consumption is substantial, an excess of capacity in the renewable energy market significantly affects its growth.

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS.

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

This study introduces an enhanced self-adaptive wild goose algorithm (SAWGA) for solving economical-environmental-technical optimal power flow (OPF) problems in traditional and modern energy systems. Leveraging adaptive search strategies and robust diversity capabilities, SAWGA distinguishes itself from classical WGA by incorporating four potent optimizers. The algorithm's application to optimize an OPF model on the different IEEE 30-bus and 118-bus electrical networks, featuring conventional thermal power units alongside solar photovoltaic (PV) and wind power (WT) units, addresses the rising uncertainties in operating conditions, particularly with the integration of renewable energy sources (RESs). The inherent complexity of OPF problems in electrical networks, exacerbated by the inclusion of RESs like PV and WT units, poses significant challenges. Traditional optimization algorithms struggle due to the problem's high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study's simulation results underscore the efficacy of SAWGA in achieving optimal solutions for OPF, notably reducing overall fuel consumption costs in a faster and more efficient convergence. Noteworthy attributes of SAWGA include its remarkable capabilities in optimizing various objective functions, effective management of OPF challenges, and consistent outperformance compared to traditional WGA and other modern algorithms. The method exhibits a robust ability to achieve global or nearly global optimal settings for decision parameters, emphasizing its superiority in total cost reduction and rapid convergence.

The increasing penetration of large-scale Renewable Energy Sources (RESs) has raised several challenges for power grid operation. Power management solutions supporting the integration of RESs, such as those based on energy storage technologies, are generally costly. Alternatively, promoting a more proactive role of the Distribution System Operator (DSO) to successfully manage RESs’ uncertainty, and take advantage of their flexible resources for the provision of ancillary services, can avoid installing expensive devices in the network and reduce costs. In this line, improved coordination between Transmission System Operators (TSOs) and DSOs is highly desirable. In this paper, the feasibility of solving different aspects of the integration of RESs through an improved TSO/DSO coordination is evaluated. In particular, a Systematic Literature Review (SLR) is conducted to study the most relevant TSO/DSO coordination approaches, exclusively focused on integrating distributed RESs, currently available in the literature. Their main operational, managerial, economic, and computational challenges, advantages, and disadvantages are discussed in detail to identify the most promising research trends and the most concerning research gaps to pave the way for future research toward developing a solid TSO/DSO coordination mechanism for integrating RESs efficiently. The main results of the SLR show a clear trend in implementing decentralized TSO/DSO coordination models since they provide efficient facilitation of RESs’ services, while reducing computational burden and communication complexity and, consequently, reducing operative costs. In addition, while different aspects of the TSO/DSO coordination implementation, such as reactive power and voltage regulation, operational cost minimization, operational planning, and congestion management, have been thoroughly addressed in the literature, further research is needed regarding data exchange mechanisms and RESs’ uncertainty modeling and prediction. In this line, the development of standardized communication solutions, based on the Common Grid Model Exchange Standard (CGMES) of the International Electrotechnical Commission (IEC), has shown promising interoperability results, whereas the use of learning-based approaches to predict RESs’ uncertain behavior and distribution networks’ responses, using only historical data, which relieves the need for access to commercially sensitive and proprietary network data, has also shown itself to be a promising research direction.

Renewable energy sources (RESs) offer key transformative potential from a societal point of view due to their modularity and ability to generate energy at the local level, allowing for the development of grassroots democratic and participatory initiatives. The paper aims to share insights into the processes of creating RES cooperatives in Poland. One of the first cooperatives to be established in the Upper Silesian region in Poland was the energy cooperative (EC) “Our Energy”. This study presents an in-depth empirical analysis of a community-based renewable energy cooperative. The study employed a case study methodology, including a SWOT analysis framework, to describe the research subject and identify its strengths, weaknesses, opportunities, and threats. Key findings indicate that members benefit from stable energy prices and full recovery of the energy produced, and the cooperative is at the forefront of energy-sharing practices that minimize costs through direct transactions with the local municipality. The strategic goals of the EC focus on expanding membership, increasing the number of photovoltaic installations, implementing energy balancing, combating energy poverty, and reducing emissions. Challenges such as financial constraints and a lack of real-time monitoring of energy distribution are acknowledged, and carbon footprint reduction innovations and stakeholder engagement are highlighted as forward-looking approaches. The study highlights the role of cooperatives as a model for community-led sustainable energy initiatives. However, the study acknowledges the limitations of its small sample size, suggesting the need for broader research to understand the impact of collaborative energy on decarbonization. Future research directions are proposed, focusing on the long-term sustainability and socioeconomic impacts of energy cooperatives. This study contributes to the scholarly discourse on renewable energy cooperatives by offering insight into their potential to bridge the gap between energy producers and consumers and support sustainable community development. The main novelty of this paper lies in its detailed examination of a specific renewable energy cooperative, incorporating SWOT analysis, stakeholder perspectives, quantitative assessments, and a forward-thinking approach. This multifaceted analysis contributes to the existing literature on renewable energy initiatives, providing a valuable reference for researchers, policymakers, and practitioners in the field.

This manuscript explores the relationship between the economic awareness (as a part of energy awareness) of Polish households and their sustainable energy consumption practices. Sustainable consumption is measured by the frequency of behaviors such as turning off electrical devices when not in use, removing mobile device chargers from sockets, switching off lights when leaving a room, preferring showers over baths, using washing machines and dishwashers only when full, and purchasing energy-efficient appliances and light bulbs. Economic awareness is gauged through variables such as knowledge of electricity tariffs, understanding of electric bill components, awareness of electricity prices, exact knowledge of electricity expenses, electricity usage in kWh, knowledge of effective energy-saving methods, and familiarity with the energy efficiency classes of appliances and light bulbs. This study presents profiles of households with high and low economic awareness regarding their electricity expenditures and examines how these profiles differ in their sustainable energy consumption behaviors. This research is based on a survey of 1407 Polish households conducted online in 2023. Data collected from the survey were subjected to statistical analysis and are presented in tables and graphs. The findings are discussed in the context of the existing literature in the field, highlighting the implications of economic awareness on sustainable energy consumption practices. This research contributes to understanding how economic knowledge influences energy-saving behaviors among Polish households, providing insights for policymakers and energy conservation initiatives. One of the key findings of this paper is the significant association between economic awareness, energy-saving knowledge, and the adoption of sustainable energy consumption behaviors among Polish households. This study reveals that households with higher levels of economic awareness demonstrate a notably higher frequency of practices related to sustainable energy consumption compared to those with lower economic awareness. Similarly, households equipped with greater knowledge about energy-saving techniques exhibit a higher propensity to adopt energy-efficient behaviors. This underscores important roles of economic literacy and education in fostering behavioral changes towards more sustainable energy practices, highlighting the importance of targeted interventions and educational campaigns aimed at enhancing economic awareness and promoting energy-saving knowledge among consumers.

Energy storage systems (ESSs) and demand-side management (DSM) strategies have significant potential in providing flexibility for renewable-based distribution networks. Therefore, combining ESSs and DSM strategies with renewable energy sources (RESs) to solve economic, operational, environmental, and power-related political issues has received special attention from power system planners around the world. In this regard, developed countries, which are pioneers in renewable technologies, have proposed various supportive policies and practices for the widespread use of ESSs and DSM strategies in the context of distribution networks. Hence, this paper performs a comprehensive review of the most recent actions taken to build the infrastructure necessary to achieve 100% renewable energy. On this basis, this paper firstly surveys the necessity of using ESSs and DSM strategies in renewable-based distribution networks. Then, the existing policies and incentive programs implemented in different countries for the development of RESs in optimal coordination with ESSs and DSM strategies are presented. The impacts of utilizing ESSs and DSM strategies in improving the economic performance of the renewable-based distribution networks are also investigated. Finally, prevalent energy management strategies, which are proposed to optimize utilization of ESSs and DSM strategies in renewable-based distribution networks, are investigated from the perspective of optimization approaches.

This paper presents an advanced control strategy for energy storage–integrated DC microgrids to improve DC-link voltage stability and transient performance under operating uncertainties. The proposed approach emulates virtual synchronous generator (VSG) behavior through coordinated primary and secondary control layers, where virtual inertia and damping are introduced to enhance dynamic response. To improve robustness against load changes and renewable-generation fluctuations, a fractional-order control law is designed to regulate the rate of change of the DC-link voltage (RoCoV), offering additional tuning flexibility compared with conventional integer-order VSG schemes. Controller parameters are optimally selected using a metaheuristic optimization method to further enhance regulation quality and efficiency. Extensive simulations benchmark the proposed fractional-order VSG against representative integer-order, derivative-based VSG controllers. The results demonstrate improved DC-link voltage regulation during load variations and renewable power disturbances, with the proposed method achieving up to 80% reduction in DC-link voltage overshoot in some test scenarios compared with the baseline controller.