Search

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models' errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3). [ABSTRACT FROM AUTHOR]

This article emphasizes the intermittent nature of RESs and finds how virtual inertia (VI) works to enhance the operation of the automatic generation control (AGC) mechanism of the interconnected hybrid power system (HPS). The challenge of controlling frequency deviation becomes more difficult as the complexity of a HPS increases. The robustness of the controller significantly influences the stability of an HPS. Because of HPS hybridization, PID, FOPID, and TID, the basic AGC controllers, are insufficient to provide a plant with maximum performance. So, a robust controller is needed for this. To get the best performance in terms of overshoot, undershoot, and settling time, a modified TID–MPC controller has been suggested and evaluated by comparison with a few existing controllers. The suggested controller can manage the frequency deviation effectively; it can stabilize it in 7 s for area 1, 9 s for area-2, and an average of 8 s for both areas. However, the average time for MPC is 12.5 s, and 19.5 s for TID. A newly modified OSHO approach has been proposed to optimize the different controller settings. The OSHO has been compared with a few well-known, existing metaheuristic optimizations to show its superiority. Due to higher penetration of RESs reduced the system inertia, further deteriorating the frequency response in HPS. To overcome these challenges, VI is implemented with RESs. The suggested controller's flexibility and reliability have been evaluated under different scenarios, such as step, multiple load disturbances, and modified IEEE-39 bus. The comprehensive research results provide strong evidence for the effectiveness and efficiency of the suggested control techniques and demonstrate the possibility of their use in actual power systems to enhance the stability and performance of modern HPS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption. [ABSTRACT FROM AUTHOR]

Abstract 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.

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety. [ABSTRACT FROM AUTHOR]

In recent years, Poland has implemented substantial changes to its energy mix, resulting in an increased proportion of energy production from photovoltaics (PV). However, the photovoltaic energy market's development is determined by several factors, and still requires further analysis. Therefore, the study's main objective was to comprehensively understand the PV phenomenon and its development in Poland. Furthermore, a PESTEL analysis was undertaken to assess the macroeconomic context of the photovoltaic industry in Poland. A systematic literature review methodology was employed to achieve this. The study's principal findings identified a number of pivotal opportunities and barriers to PV development. The environmental benefits of CO2 reduction and the economic advantages, including cost savings and subsidies, were identified as significant opportunities, as were social acceptance and enhanced energy security. However, obstacles to progress include outdated grid infrastructure, high investment costs, environmental concerns during the PV lifecycle, and political uncertainties. Technical challenges like grid stability and high battery costs also impede growth. Potential strategies for improvement involve better public awareness campaigns, enhanced self-consumption through storage systems, and optimised system placement. Addressing these factors could transform current neutral aspects into either opportunities or threats for PV deployment. [ABSTRACT FROM AUTHOR]

The electrical power system (EPS) has been heavily stressed due to high load demand. It operates close to the total capacity limits, resulting in voltage instability that can lead to voltage collapse. In this regard, incorporating flexible alternating current transmission system (FACTS) devices and renewable energy sources (RESs) to obtain the optimum values of the generator voltage, reactive compensation, and transformer tab in optimal reactive power dispatch (ORPD) is essential in increasing the reliability and safety of the system. ORPD involves discrete and continuous variables, which are nonlinear, noncontinuous, non-convex, and complex problems. The objective functions of ORPD are reduction in active power loss (Ploss), voltage deviation, and voltage profile enhancement. This paper presents a recent advancement of the ORPD problem, mathematical formulation of the objectives function, and a summary of various metaheuristic optimization methods (single and hybrid) used to solve the ORPD problems. The hybrid method combines two or more methods to improve the demerits of one method to obtain a quality solution to a problem. This review covered incorporating FACTS devices and RESs used in solving the ORPD problem to reduce the active Ploss and improve the voltage profile in the EPS. The benefits of FACTS devices and RESs are also discussed. Also, various metaheuristic algorithms (single, modified, and hybrid) employed to solve the ORPD problem were discussed. The future direction for researchers in this field was provided to give insight into the applicability and performance. Overall, this research explores different techniques used in solving ORPD problems from the optimization point of view to incorporating RESs and FACTS devices to obtain quality solutions. Some existing methods do not guarantee an optimum solution, but incorporating RESs and FACTS devices will help attain the best solution to the problem for better power system operation to improve system reliability and voltage profile. Based on the review journal, it can be concluded that hybrid techniques offer efficient quality solutions to the ORPD problem. [ABSTRACT FROM AUTHOR]

Microgrid systems face challenges in preserving frequency stability due to the fluctuating nature of renewable energy sources (RESs), underscoring the importance of advanced frequency stabilization strategies. To ensure power system stability in situations where renewable energy significantly contributes to the energy mix, it is essential to implement load frequency controllers (LFCs). Moreover, with the widespread use of electric vehicles (EVs), leveraging battery storage from EVs for microgrid frequency control is becoming increasingly crucial. This integration enhances grid stability and offers a sustainable solution by utilizing renewable energy more efficiently and reducing dependency on traditional power sources. Therefore, this paper proposes an innovative approach to LFCs, using fractional-order control techniques to boost the resilience of the interconnected microgrid systems. The approach centers on a centralized control scheme with a tilt fractional-order integral-derivative featuring an accelerated derivative (TFOID-Accelerated) controller. The accelerated derivative component of this controller is tailored to mitigate high-frequency disturbances, while its tilt feature and fractional integration effectively handle disturbances at lower frequencies. As a result, the proposed controller is expected to efficiently counteract disturbances caused by variability in RESs and/or load changes, achieving a high level of disturbance rejection. Additionally, this paper employs the recent growth optimizer (GO) method for the optimal design of the controller's parameter set, avoiding the need for complex control theories, elaborate disturbance observers, filters, and precise power system modeling. The GO algorithm enhances fractional-order capabilities, offering a robust solution to the challenges of renewable energy variability and demand fluctuations. This is accomplished by optimizing parameters and simplifying the control system design across different microgrid scenarios. The proposed TFOID-Accelerated LFC demonstrates superior performance in enhancing frequency stability and minimizing oscillations compared to existing controllers, including traditional proportional-integral-derivative (PID), PID-Accelerated (PIDA), and tilt-integral-derivative (TID) controllers. [ABSTRACT FROM AUTHOR]

Over the past two decades, Renewable Energy Sources (RESs) have gained global popularity. Control issues are becoming more difficult as the system inertia decreases due to the absence of typical synchronous generators. Innovative methods like fault current limiters, energy storage devices, and alternative control systems are utilized to deal with these difficulties. This study provides a summary of the challenges associated with incorporating high-level RESs into the existing grid. The increased penetration of the RESs has a negative impact on the system oscillations and harmonics, generating the need for power quality improvement techniques, such as adaptive control, adding energy systems, power stream assessment, or weight stream examination. This paper presents a framework of the power stream issue, its arrangement as well as different game plan methods. The power stream model of a power structure can be built using the significant association, weight, and age data. [ABSTRACT FROM AUTHOR]

The challenge of controlling frequency deviation becomes more difficult as the complexity of a power network increases. The robustness of the controller has a major impact on the stability of a Modern Power system (MPS). Due to the hybridization of MPS basic AGC controllers (PID, FOPID, and TID) are insufficient to give optimal performance of a plant. This requires a robust controller. So, a novel MPC + PIDN controller has been proposed and evaluated by comparing it with several existing controllers, which gives optimal performance in terms of overshoot, undershoot, and settling time. A new modified Opposition‐based Sea‐horse Optimization (OSHO) method has been suggested to optimize the various controller settings. To demonstrate the OSHO's superiority, it is compared with a few popular, existing meta‐heuristic optimizations. The higher penetration levels of RESs reduced system inertia which further deteriorate frequency response in MPS. To overcome these challenges virtual inertia (VI) is implemented with MPC. VI is applied to improve the performance of the AGC of the interconnected MPS along with emphasizing the nature of intermittent renewable energy sources (RESs) of PV and wind energy. To determine the reliability and flexibility of the proposed controller, analysis has been done under a different situation, including step, random disturbances, and modified IEEE‐39 bus. Finally, the stability analysis is performed on a bode plot and the proposed results are compared with previously published literature. The extensive study demonstrates strong evidence that the suggested control approach is efficient and effective. [ABSTRACT FROM AUTHOR]

The need to contain climate change and improve energy security has increased the interest in agricultural biomass as a renewable energy source (RES). Given the complexity of the issue of energy production and its environmental impact, the main objective of this study was to assess the significance and potential of the agriculture of the European Union Member States in terms of the capability of producing renewable energy. Using the multi-criteria TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, we designed a synthetic measure based on several diagnostic characteristics for 2010–2021, obtaining a ranking for EU countries reflecting their agriculture's RES potential. The research showed that the agricultural sectors with the highest potential for renewable energy production were in the Netherlands, Lithuania, Latvia, and Hungary during the study period. Bulgaria, Denmark, and Spain joined this group in 2021. A comprehensive assessment was conducted using the TOPSIS method to identify the leaders and areas in need of support in leveraging the potential of agriculture for energy in the EU. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Revenue optimization is a main consideration in auction design. While it is a first-order objective in most auction settings, that is not the case for rebate auctions that use monetary rewards to incentivize auction participants to perform a task, where revenue optimization is secondary to successful task completion. This paper considers the case of VCG-based rebate auctions used to incentivize cloud workload migrations between datacenters to correct power-grid energy imbalances, and proposes a revenue maximization approach that takes into account the task completion objective (i.e., power-grid balancing) before a specified deadline. The proposed approach uses a task-completion constraint in the rebate auction optimization problem to ensure that the required task is completed on time, and uses predictions for the trend of future bid valuations (assumed to be gathered via a separate prediction module) to adjust the task-completion constraint to maximize revenue. Existing VCG-based revenue maximization approaches are not suitable for rebate auctions since they do not consider task completion deadlines (as regular auctions are not associated with tasks), and assume that bid valuations are randomly drawn from a probability distribution, which is not the case in rebate auctions. The proposed approach is compared against the existing rebate auction implementation (that does not consider the task completion deadline, nor the variations in bid valuations over time) in terms of its monetary effect on the auction participants and its ability to complete the required task on time. Simulation results show that the proposed approach improves the auctioneer’s revenue and consistently completes the energy-balancing task on time.

By considering the influence of renewable energy sources (RESs) integration on multi-area interconnected hybrid power systems, this paper proposes an equivalent input disturbance (EID)-based load frequency control (LFC) strategy, which can effectively overcome the factors of random disturbance, model uncertainties and communication delay. First, an equivalent mathematical LFC model of an interconnected system is constructed. Then, the proposed robust controllers, based on the idea of EID, are designed to suppress the randomness and volatility of the renewable energy grid connection and coordinate the frequency fluctuation of the interconnected power system. Finally, the validity and superiority of the established topology structure and the superiority of the proposed strategy are demonstrated by dynamic time domain response experiments under the condition of high penetration of renewable energy.

The increasing penetration of renewable energy sources brings about severer frequency fluctuation problem, and the recovery speed and probability of frequency crossing dead band are very important dynamics for frequency regulation performance. In this paper, a comprehensive frequency regulation strategy of energy storage is proposed to improve the frequency dynamic performance. Firstly, the system frequency response model is introduced, and the frequency variation characteristics faced with high penetration of renewable energy sources are analyzed, based on which the principles of proposed strategy are elaborated. Then, an inverse inertia is designed to improve the frequency recovery speed after nadir, of which the stability margin is carefully analyzed to determine the inertia coefficient value. Further, a revised dead band of energy storage participating in frequency regulation is proposed to improve the frequency dynamic performance, of which the nonlinear characteristics is analyzed by describing function method to guarantee the system stability. The performance of the proposed control strategy is validated by the simulation cases with different operating scenarios. The results show that the proposed strategy can smooth the frequency fluctuation, reduce the probability of frequency deviation crossing the threshold, and speed up the frequency recovery speed without increasing the energy storage capacity demand.

In recent years, Poland has implemented substantial changes to its energy mix, resulting in an increased proportion of energy production from photovoltaics (PV). However, the photovoltaic energy market’s development is determined by several factors, and still requires further analysis. Therefore, the study’s main objective was to comprehensively understand the PV phenomenon and its development in Poland. Furthermore, a PESTEL analysis was undertaken to assess the macroeconomic context of the photovoltaic industry in Poland. A systematic literature review methodology was employed to achieve this. The study’s principal findings identified a number of pivotal opportunities and barriers to PV development. The environmental benefits of CO2 reduction and the economic advantages, including cost savings and subsidies, were identified as significant opportunities, as were social acceptance and enhanced energy security. However, obstacles to progress include outdated grid infrastructure, high investment costs, environmental concerns during the PV lifecycle, and political uncertainties. Technical challenges like grid stability and high battery costs also impede growth. Potential strategies for improvement involve better public awareness campaigns, enhanced self-consumption through storage systems, and optimised system placement. Addressing these factors could transform current neutral aspects into either opportunities or threats for PV deployment.

Microgrid systems face challenges in preserving frequency stability due to the fluctuating nature of renewable energy sources (RESs), underscoring the importance of advanced frequency stabilization strategies. To ensure power system stability in situations where renewable energy significantly contributes to the energy mix, it is essential to implement load frequency controllers (LFCs). Moreover, with the widespread use of electric vehicles (EVs), leveraging battery storage from EVs for microgrid frequency control is becoming increasingly crucial. This integration enhances grid stability and offers a sustainable solution by utilizing renewable energy more efficiently and reducing dependency on traditional power sources. Therefore, this paper proposes an innovative approach to LFCs, using fractional-order control techniques to boost the resilience of the interconnected microgrid systems. The approach centers on a centralized control scheme with a tilt fractional-order integral-derivative featuring an accelerated derivative (TFOID-Accelerated) controller. The accelerated derivative component of this controller is tailored to mitigate high-frequency disturbances, while its tilt feature and fractional integration effectively handle disturbances at lower frequencies. As a result, the proposed controller is expected to efficiently counteract disturbances caused by variability in RESs and/or load changes, achieving a high level of disturbance rejection. Additionally, this paper employs the recent growth optimizer (GO) method for the optimal design of the controller’s parameter set, avoiding the need for complex control theories, elaborate disturbance observers, filters, and precise power system modeling. The GO algorithm enhances fractional-order capabilities, offering a robust solution to the challenges of renewable energy variability and demand fluctuations. This is accomplished by optimizing parameters and simplifying the control system design across different microgrid scenarios. The proposed TFOID-Accelerated LFC demonstrates superior performance in enhancing frequency stability and minimizing oscillations compared to existing controllers, including traditional proportional-integral-derivative (PID), PID-Accelerated (PIDA), and tilt-integral-derivative (TID) controllers.

Abstract The hybrid AC/DC distribution networks will have a crucial role in the future of smart grids due to their adaptiveness to a variety of DC loads and energy resources. Thus, it is necessary to assess and enhance the Hosting Capacity (HC) of these hybrid networks for the integration of Renewable Energy Sources (RESs). One way to increase the HC without further expansion in the grid is using the potential of Network Reconfiguration (NR). This paper proposes an HC enhancement model that includes AC/DC distribution NR. Two types of NR are considered, namely static, that is, embedding the NR capabilities at the planning stage, and dynamic, that is, using remotely controlled switches as an Active Network Management (ANM) scheme. Besides, two unique potential sources of hybrid AC/DC distribution networks that could be used for the HC enhancement are included, that is, reactive power control of Voltage Source Converters (VSCs) and the mesh operation capability. The problem is formulated as a mixed‐integer linear optimal power flow that aims to maximize the RESs HC under thermal and voltage constraints. Also, stochastic programming is utilized for managing the RESs uncertainties. The simulation results show the accuracy and efficiency of the proposed formulation from various perspectives.

This study proposes an advanced control strategy for the coordination of an energy storage system (ESS) based on fuel cells (FCs) and renewable energy sources (RESs) to enhance frequency dynamic performance in hybrid power systems (HPSs). The proposed coordination control strategy is based on the nonlinear proportional-integral (NPI) controller, which increases the system's flexibility in dealing with disturbances and changing operating conditions. In addition, it improves the system's dynamic response and attempts to address system weakness caused by highly penetrating RESs. The proposed NPI controller is optimally designed using a new optimization algorithm, called dandelion optimizer (DO), whose proficiency and effectiveness are verified by comparing its performance with other well-known optimization algorithms used in the literature; particle swarm optimization (PSO), grey wolf optimization (GWO), and ant lion optimization (ALO) algorithms considering various standard objective functions. Furthermore, the proposed NPI controller performs better than other control strategies used in the literature under load/RESs fluctuations. The effectiveness of the proposed nonlinear coordination control strategy is examined and investigated through a self-contained HPS that includes a diesel generator, RESs (i.e., photovoltaic and wind power plants), battery ESS, flywheel ESS, aqua electrolyzer for hydrogen production, FCs, electric vehicles, and customer loads. The simulation results carried out by the MATLAB software demonstrate the superior performance of the proposed DO-optimized NPI controller for HPS frequency regulation, even when the power system's parameters have substantial variations. Moreover, the results revealed that the proposed strategy significantly reduces the frequency deviation by approximately 95% compared to the conventional coordination strategy based on the fixed contribution of RESs and by 90% compared to the adaptive coordination control based on the PI controller.

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided.

The integration of renewable rnergy sources (RESs) into the power grid involves operational challenges due to the inherent RES energy-production variability. Imbalances between actual power generation and scheduled production can lead to grid instability and revenue loss for RES operators and aggregators. To address this risk, in this paper, we introduce a mutually beneficial bilateral trading scheme between a RES and a DR aggregator to internally offset real-time energy imbalances before resorting to the flexibility market. We consider that the DR aggregator manages the energy demand of users, characterized by uncertainty in their participation in DR events and thus the actual provision of flexibility, subject to their offered monetary incentives. Given that the RES aggregator faces penalties according to dual pricing for positive or negative imbalances, we develop an optimization framework to achieve the required flexibility while addressing the trade-off between maximizing the profit of the RES and DR aggregators and appropriately incentivizing the users. By using appropriate parameterization of the solution, the achievable revenue for the imbalance offsetting can be shared between the RES and the DR aggregators while keeping users satisfied. Our analysis highlights the interdependencies of the demand–production energy imbalance on user characteristics and the RES and DR aggregator profits. Based on our results, we show that a win–win outcome (for the RES and DR aggregators and the users) is possible for a wide range of cases, and we provide guidelines so that such bilateral agreements between RES and DR aggregators could emerge in practical settings. [ABSTRACT FROM AUTHOR]

This research proposes a novel solution for the optimal day-ahead scheduling problem in the GAMS environment using the BARON approach. The challenge is extended to include Renewable Energy Sources (RESs) and Electric Vehicles (EVs), making it more complex and practical. EVs serve as loads, energy suppliers, and storage during RESs' uncertainties. The framework improves cost savings, quality, reliability, and stability of the power supply system by modeling solar, wind, and EV power in the scheduling problem. The solution is tested on a 10 -unit thermal system considering RESs and EVs under deterministic and stochastic environments. Stochastic scenarios are generated using Monte Carlo simulation, and the simultaneous scenario reduction approach enhances results. The BARON solver outperforms other solvers, achieving profits of $205,321 with wind, solar, and EVs, and $187,297 when considering uncertainty, resulting in a reduction of $18,024. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the enormous stress of energy lack and air pollution, renewable energy sources such as photovoltaic sources become an effective solution to solve these problems. The penetration of inertia-less photovoltaic sources into power system has adverse effects on the overall system inertia which threatens system stability. As a solution for this problem, virtual inertia technique can be used as a system controller in order to enhance the system performance and maintain its stability. In this paper, an adaptive virtual synchronous generator (VSG) controller based on the oscillation motion of the synchronous machine is introduced. Then, a proposed VSG with adaptive neuro-fuzzy inference system (ANFIS) is presented as an inverter controller. The system response is investigated and compared with other control methods under different operating scenarios. To verify the effectiveness of the proposed adaptive VSG, an experimental setup is presented with real-time implementation for the system using dSPACE DS1104 interfacing with MATLAB software, and the system response is investigated under different operating scenarios. Upon the presented results, there is an enhancement in the system response when the proposed adaptive VSG with ANFIS controller is used, and this emphasizes the superiority of using such controller in PV systems over other techniques. [ABSTRACT FROM AUTHOR]

Microgrid technology offers a new practical approach to harnessing the benefits of distributed energy resources in grid-connected and island environments. There are several significant advantages associated with this technology, including cost-effectiveness, reliability, safety, and improved energy efficiency. However, the adoption of renewable energy generation and electric vehicles in modern microgrids has led to issues related to stability, energy management, and protection. This paper aims to discuss and analyze the latest techniques developed to address these issues, with an emphasis on microgrid stability and energy management schemes based on both traditional and distinct approaches. A comprehensive analysis of various schemes, potential issues, and challenges is conducted, along with an identification of research gaps and suggestions for future microgrid development. This paper provides an overview of the current state of the field and proposes potential areas of future research. [ABSTRACT FROM AUTHOR]

Solar energy is one of the most suggested sustainable energy sources due to its availability in nature, developments in power electronics, and global environmental concerns. A solar photovoltaic system is one example of a grid-connected application using multilevel inverters (MLIs). In grid-connected PV systems, the inverter's design must be carefully considered to improve efficiency. The switched capacitor (SC) MLI is an appealing inverter over its alternatives for a variety of applications due to its inductor-less or transformer-less operation, enhanced voltage output, improved voltage regulation inside the capacitor itself, low cost, reduced circuit components, small size, and less electromagnetic interference. The reduced component counts are required to enhance efficiency, to increase power density, and to minimize device stress. This review presents a thorough analysis of MLIs and a classification of the existing MLI topologies, along with their merits and demerits. It also provides a detailed survey of reduced switch count multilevel inverter (RSC-MLI) topologies, including their designs, typical features, limitations, and criteria for selection. This paper also covers the survey of SC-MLI topologies with a qualitative assessment to aid in the direction of future research. Finally, this review will help engineers and researchers by providing a detailed look at the total number of power semiconductor switches, DC sources, passive elements, total standing voltage, reliability analysis, applications, challenges, and recommendations. [ABSTRACT FROM AUTHOR]

The hybrid AC/DC distribution networks will have a crucial role in the future of smart grids due to their adaptiveness to a variety of DC loads and energy resources. Thus, it is necessary to assess and enhance the Hosting Capacity (HC) of these hybrid networks for the integration of Renewable Energy Sources (RESs). One way to increase the HC without further expansion in the grid is using the potential of Network Reconfiguration (NR). This paper proposes an HC enhancement model that includes AC/DC distribution NR. Two types of NR are considered, namely static, that is, embedding the NR capabilities at the planning stage, and dynamic, that is, using remotely controlled switches as an Active Network Management (ANM) scheme. Besides, two unique potential sources of hybrid AC/DC distribution networks that could be used for the HC enhancement are included, that is, reactive power control of Voltage Source Converters (VSCs) and the mesh operation capability. The problem is formulated as a mixed‐integer linear optimal power flow that aims to maximize the RESs HC under thermal and voltage constraints. Also, stochastic programming is utilized for managing the RESs uncertainties. The simulation results show the accuracy and efficiency of the proposed formulation from various perspectives. [ABSTRACT FROM AUTHOR]

The power system for large-scale adoption of hybrid electric vehicles can benefit from a distributed reserve provided by the vehicle-to-grid (V2G) concept. This study suggests a V2G technology that can effectively control frequency on a microgrid throughout a 24-h cycle. When usage is at its lowest in the spring or fall, a microgrid is intended to be large enough to simulate a community of 2000 households. A 1:5 ratio of cars to households is realized by modelling 400 electric vehicles (EVs) as a basic model, indicating a typical case in the future. An in-depth analysis of the voltage, current, reactive, and active power is carried out for a microgrid. By coordinating control of diesel generation, renewable energy source (RES) generation, power exchange, and EV generation, the system frequency of a microgrid can be managed by regulating load demand with V2G devices. The proposed microgrid with V2G effectively manages energy and reduces the uncertain and variable nature of RES power generation with enhanced performance. System parameter variations have been investigated for various operating scenarios, and it has been discovered that error is confined to less than 5%. [ABSTRACT FROM AUTHOR]

Due to the great uncertainty of the output power of renewable energy sources (RESs), the storage and reuse of RESs is the focus of new energy technology. The bidirectional DC-DC resonant converter is preferred in the new energy technology. However, the efficiency of traditional bidirectional DC-DC resonant converter will be greatly affected by the wide voltage gain range. By applying the adaptive excitation inductance, a CLLCLC resonant converter is proposed. The $L-C$ branch is introduced to replace the fixed excitation inductance in traditional CLLLC resonant converter. In the design of transformer, the turns ratio and excitation inductance of transformer are decoupled to reduce the difficulty of transformer design. Simultaneously, compared with the traditional transformer, the impedance of equivalent excitation inductance will adjust automatically against the working frequency, decreasing the circulating current loss of resonant tank. Therefore, the proposed converter solves the contradiction between wide voltage gain range and high efficiency existed in the traditional converter. Finally, an experimental platform with 200 W rated power is built to verify the effectiveness of proposed topology.

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient load frequency control (LFC) technique based on a fractional-order proportional–integral–derivative–accelerator with a low-pass filter compensator (FOPIDA-LPF) controller, which can also be accurately referred to as the PIλDND2N2 controller. A trustworthy metaheuristic optimization algorithm, known as the gray wolf optimizer (GWO), is used to fine-tune the suggested PIλDND2N2 controller parameters. Moreover, the proposed PIλDND2N2 controller is designed for the LFC of a self-contained hybrid maritime microgrid system (HMμGS) containing solid oxide fuel cell energy units, a marine biodiesel generator, renewable energy sources (RESs), non-sensitive loads, and sensitive loads. The proposed controller enables the power system to deal with random variations in load and intermittent renewable energy sources. Comparisons with various controllers used in the literature demonstrate the excellence of the proposed PIλDND2N2 controller. Additionally, the proficiency of GWO optimization is checked against other powerful optimization techniques that have been extensively researched: particle swarm optimization and ant lion optimization. Finally, the simulation results performed by the MATLAB software prove the effectiveness and reliability of the suggested PIλDND2N2 controller built on the GWO under several contingencies of different load perturbations and random generation of RESs. The proposed controller can maintain stability within the system, while also greatly decreasing overshooting and minimizing the system's settling time and rise time. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal stochastic operation strategy for renewable energy (RE) supported isolated microgrids (IMGs). It incorporates an emission-averse model to reduce the negative impact of CO2 emission on the environment by optimally utilizing carbon capture-based technology. The emission from dispatchable sources, viz. diesel engines used to produce electrical energy, adversely affects the environment. Although imposing a penalty cost on carbon emissions reduces its production from such sources, but still, it cannot be avoided. The proposed work introduces a carbon capture-based reduced emission model that incorporates a small-scale carbon capture unit (CCU) incorporated with a fossil fuel-based unit. Depending upon the CCU system efficiency, a fractional penalty has also been imposed on carbon emissions. From the analysis point of view, the efficiency of the CCU is considered as 90%. The overall problem is formulated as a multivariable constrained cost optimization problem to obtain the optimal dispatch of various connected sources and is solved through a hybrid function approach using the 'fmincon' solver in the MATLAB environment. The results are analyzed for the techno-economically viability of the IMG system with different RE penetration levels and the carbon emission factor (EF). It is found that the microgrid is operated most economically with a 40% RE penetration (corresponding optimal cost is 1.521e + 03 $), and it is also obtained that with an increase in emission factor, the overall economics of the system is adversely affected considering a certain RE penetration level in the system. [ABSTRACT FROM AUTHOR]

The emerging smart-grid and microgrid concept implementation into the conventional power system brings complexity due to the incorporation of various renewable energy sources and non-linear inverter-based devices. The occurrence of frequent power outages may have a significant negative impact on a nation's economic, societal, and fiscal standing. As a result, it is essential to employ sophisticated monitoring and measuring technology. Implementing phasor measurement units (PMUs) in modern power systems brings about substantial improvement and beneficial solutions, mainly to protection issues and challenges. PMU-assisted state estimation, phase angle monitoring, power oscillation monitoring, voltage stability monitoring, fault detection, and cyberattack identification are a few prominent applications. Although substantial research has been carried out on the aspects of PMU applications to power system protection, it can be evolved from its current infancy stage and become an open domain of research to achieve further improvements and novel approaches. The three principal objectives are emphasized in this review. The first objective is to present all the methods on the synchro-phasor-based PMU application to estimate the power system states and dynamic phenomena in frequent time intervals to observe centrally, which helps to make appropriate decisions for better protection. The second is to discuss and analyze the post-disturbance scenarios adopted through better protection schemes based on accurate and synchronized measurements through GPS synchronization. Thirdly, this review summarizes current research on PMU applications for power system protection, showcasing innovative breakthroughs, addressing existing challenges, and highlighting areas for future research to enhance system resilience against catastrophic events. [ABSTRACT FROM AUTHOR]

Renewable energy has attained enormous attention in recent years due to the exhaustion of many energy resources, and the pollution caused by fossil fuels. However, the adoption of renewable energy sources (RESs) is a complicated process influenced by multifarious factors. This study aims to investigate factors influencing the adoption of RESs by the Palestinian industrial and commercial sectors and their effect of this adoption on sustainable performance, besides examining if the government plays a role in moderating the relationship between the adoption of RESs and such sustainable performance. To this end, a quantitative method was used to collect data through questionnaires from 100 top managers in the Palestinian commercial and industrial sectors. The data analysis was conducted using the Smart PLS software to test the formulated hypotheses. The findings support the positive relationship between the adoption of RESs and sustainable performance. There is also a positive relationship between the adoption of RESs and the government's role. However, the relationship between the government's role and sustainable performance is not established, but the government's role is found to be a moderator between adopting RESs and sustainable performance.

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]