Study of energy management system for distributed generation systems

The author describes how traditional energy management system (EMS) power system applications are undergoing changes, in order to adapt to the users needs of the deregulated energy markets environment.

Introduction: Integrating renewables in the distribution sector is a rapidly growing reality in many countries, amongst which Chile’s stands out with an increasingly diversifiable electricity matrix. However, incorporating RES into the electricity distribution sector is altogether a steep climb at present, and seen by some as a formidable challenge for utilities. Likewise, the introduction of the Smart Grid agenda in Chile is imposing new challenges to electric utilities, mainly from a regulatory and technical viewpoint. In spite of this, big players like ENEL are moving forward decisively to meet this challenge, together with academia experts.Methods: We model a sustainable energy system in the form of a smart microgrid operated by ENEL Chile comprising a hypothetical community we term a Sustainable Block™ representing an average residential building in Santiago. We then run simulations under different operating scenarios. The model takes into account the most recent innovation in the legal regulatory framework that governs the energy market in Chile ―Law 20,571―which allows for benefits to those that generate and consume part or all of their energy needs while connected to the grid. Thus, the community considers the option of consuming green energy from the microgrid with an energy storage unit to supply electricity to the 60-apartment complex of various sizes. Under this scenario, a set of energy homeostasis strategies that comprise the homeostatic control and energy management systems help balance the electricity supply versus demand.Results: The model proposed comprises a set of energy homeostasis management strategies that have been designed in the power control and energy management system to balance supply and demand while optimizing the availability and use of green energy. Thus, the energy homeostasis model optimizes the microgrid supply while injecting excess power to the grid. In this context, the community residents exhibit different consumption profiles, therefore they may willingly participate of the sustainable energy strategy as prosumers, displaying a thriftier consumption, and enjoying a lower electric bill while using more renewable energy. The model’s energy homeostasis control and energy management system, especially designed for electric power systems, seeks to maintain a dynamic balance between supply and demand and is being currently discussed with ENEL Chile as part of the intelligent control options for the introduction of distributed generation systems tied to the grid, in order to complement their electric power distribution services.Discussion: The model being proposed comprises a community of residents that we term a sustainable block™ representing an average residential building in Santiago, Chile, which aims to take advantage of Law 20,571 in Chile that allows independent electric power generators to benefit by selling electricity to the grid and also allows independent consumers (mostly residential) to generate part or all of their energy needs while connected to the grid. The community may consume electricity from the microgrid with energy storage, operated by the local electric company, supplying electricity to the 60-apartment complex of various sizes. In his regard, just like in the human body where the brain, particularly the hypothalamus, is primarily responsible for the regulation of energy homeostasis, by monitoring changes in the body’s energy state through various mechanisms, the role of energy storage as well as the role of prosumers are the key enabling factors of energy homeostasis and their interaction are highlighted in the overall analysis.

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

The transition to sustainable and intelligent energy systems has intensified the development of smart microgrids, which offer decentralized, resilient, and efficient power solutions. This review critically examines the integration of Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) into smart microgrid platforms, focusing on their role in optimizing sustainable energy management. Traditional energy management systems often struggle to adapt to the dynamic nature of modern energy demands, renewable energy intermittency, and grid complexity. AI-driven solutions, particularly DRL, provide adaptive, autonomous, and data-driven mechanisms for real-time decision-making and predictive control within microgrids. DRL, by learning optimal policies through interaction with the environment, is capable of handling multi-objective problems, including demand-response optimization, energy storage control, load forecasting, and distributed generation scheduling. This paper synthesizes recent advancements and applications of DRL algorithms such as Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) in smart microgrids. It also explores hybrid models that combine DRL with other AI techniques, such as fuzzy logic and neural networks, to improve performance under uncertainty and nonlinearity. Furthermore, the review evaluates benchmark testbeds, simulation tools, and real-time platforms used to implement and validate these intelligent systems. Challenges such as high computational costs, model generalizability, real-time implementation issues, and cybersecurity vulnerabilities are discussed. The study also highlights recent research trends emphasizing decentralized control, edge computing, and federated learning to enhance scalability and privacy in DRL-based microgrid applications. Future directions suggest the need for explainable AI (XAI), robust training environments, and standardization to facilitate the wider adoption of DRL in real-world microgrid systems. The integration of DRL into smart microgrids represents a transformative shift toward resilient, efficient, and sustainable energy ecosystems. This review offers a comprehensive understanding of how AI and DRL are revolutionizing energy management, providing a foundation for future research and practical deployment in smart energy infrastructures.

Optimized droop controller based energy management for stand-alone micro-grid using hybrid monarch butterfly and sine-cosine algorithm

The increasing penetration of EVs is providing new opportunities for various Energy Management Systems (EMS) due to their flexibilities and energy storage capabilities. However, traditional EMS based on centralized control strategies requires huge investment in Information & Communications Technologies (ICTs) to coordinate the households demands and achieve grid level goals. Alternatively, decentralized EMS require less computation burden and communications but are mostly focused on customer energy cost saving and comfort level, ignoring the grid goals. This paper develops a variancebased optimization algorithm for a decentralized Home Energy Management Systems (HEMS) to achieve centralized performance without excessive computational burdens and coordination communications and to achieve energy bills saving for households at the same time. The performances of the proposed HEMS are assessed against different households at different aggregated grid levels. Results show that the proposed system can reduce system reinforcement investment for network operators by reducing 30% system peak demand and more than 50% demand uncertainty compared to traditional HEMS. Consumers can achieve nearly 12% energy bill saving by only accessing energy market. Further cost savings can be expected, given access to other markets.

EMDSS: An optimization-based decision support system for energy systems management under changing climate conditions – An application to the Toronto-Niagara Region, Canada

Preface. 1. IPPs Get VAMMoed in Texas R. Baldick. 2. Competition and Direct Access in New Zealand's Electricity Market M.E. Bergara, P.T. Spiller. 3. Competition and Access in Electricity Markets: ECPR, Global Price Cap, and Auctions M. Boyer, J. Robert. 4. Periodic Regulatory Review in UK Electricity Markets Developments Within a Deregulated System P. Burns, T. Weyman-Jones. 5. Challenges for Effective Competition in Large-Hydro Dominated Markets: The Case of Quebec P. Dunsky, P. Raphals. 6. Hydroelectricity and the State in Quebec and Ontario: Two Different Historical Paths R. Dupre, M. Patry. 7. Restructuring and Competitive Arrangements in the Electricity Supply Industry Towards a Better Efficiency? D. Finon. 8. Electricity Deregulation in England and Wales R. Green. 9. Improving the Performance of Electricity Industries in Developing Countries: Is World Bank Policy on Deregulation the Way Forward? D. Hawdon. 10. Demand-Side Management Expenditures and the Market Value of U.S. Electric Utilities: Strategic Investment or Disinvestment? D.A. Houston. 11. Introducing Competition to the Electricity Industry in Spain: The Role of Initial Conditions E. Kahn. 12. Peak Load Problem, Deregulation and Reliability Pricing P.-O. Pineau. 13. An Integrated Energy and Reserve Market for New Zealand E.G. Read, et al. 14. Transmission Contracts May also Hinder Detrimental Network Investments in Oligopolistic Electricity Markets Y. Smeers, Wei Jing-Yuan. Appendix 1. Appendix 2.

The direct current (DC) systems have outstanding advantages compared to the alternating current (AC), especially when it comes to renewable energy resources (RER) and microgrids (MGs). Therefore, the DC microgrid (DCMG) has become popular and drawn the attention of researchers. Due to the integration of different power sources in the single DC bus, including solar photovoltaics (PV), wind farms, diesel generators, and energy storage systems, maintaining stable bus voltage requires a complicated control system to ensure a fast-decision-making procedure. This paper presents an energy management strategy for the DCMG using a fuzzy logic-based controller. The proposed DCMG uses a hybrid energy storage system (HESS) for energy balancing and a diesel generator as a backup. The proposed strategy uses generation and loads demand and battery state of charge (SoC) to control the power flow within the MG. The energy management system keeps the SoC in a suitable range to increase the battery life. By controlling the charging and discharging of the HESS, the proposed method has a fast response to the load variation and RER intermittency to prevent the fluctuation of the voltage level of the DC bus and keep it constant. The obtained results validate the efficacy of the proposed fuzzy logic controller.

Nowadays the control and stability of DG system are important topics that researchers in both academia and industry have been addressing. Small and large signal analyses for stability studies on various systems have been done in papers and books. In this paper, at first models of an inverter-based Distributed Generation (DG) subsystems are created and after the linearization if be required the small-signal stability analysis of the DG which is controlled with a voltage and frequency control scheme based on model predictive control (MPC) that has been used previously is established. In this control scheme, load currents at the point of common coupling (PCC) of the DG are considered as disturbances and used as feed-forward signals. This technique enhances the performance of the DG control system in transient and steady-state conditions for a wide range of loads. The stability of the DG system under various loads (such as one phase load as imbalanced load, rectifier load as nonlinear load and induction motor load as dynamic load) is demonstrated by the eigenvalues trajectory. Also the sensitivity analysis and robustness assessment of the control scheme are conducted and discussed. For more performance consideration, the DG system is simulated with MATLAB/SIMULINK software and implemented in the lab and then suitable performance of the system is demonstrated by the simulation and experimental studies.

On March 1, 2000, Maine will offer electric power through open competition, a restructuring that poses both advantages and disadvantages to residential and small retail consumers. While electric restructuring in Maine has been thoughtfully developed, the basic question of whether electricity rates will be lower for the average consumer will remain uncertain for some time. This uncertainty is linked not only to Maine’s electricity rate bidding process but also to potentially oligopolistic national trends. In addition, whether individual consumers achieve savings in their electricity costs will be determined, in part, by their choice of electricity supplier. While some consumers may prefer a higher-cost supplier because of the value-added services that accompany that option, others may make no choice and, by default, receive the standard option—where rates are determined by the Maine Public Utilities Commission (MPUC). In this article, the authors describe the factors that initiated the push toward restructuring, the history of the enabling legislation, and relevant portions of the MPUC’s Consumer Education Program. To consumers, the authors emphasize the importance of aggregation—clusters of buying groups—and detail how the nature of open competition may affect them. In particular, they call attention to the additional services that may be provided by electricity suppliers. Finally, in discussing the implications of deregulation, they lay out the uncertainties that lie ahead for consumers, policymakers, and regulators as Maine opens itself up to competition in the electric power.

Timidity in electric utility deregulation

Summary form only given. Benchmarking is a practice of assessing a company's performance relative to that of other companies, or with respect to some theoretical "best that can be done". Companies can be compared with respect to almost to any metric, but it is common to compare financial results, system performance such as reliability, level of customer service, or customer satisfaction. In the electric utility industry, deregulation and other changes have focused more attention on the practice of benchmarking. Utility management has turned to benchmarking as a way of measuring how their company stacks up against other companies. Regulators are investigating the use of distribution company benchmarking as a means of establishing regulatory policy. Even customers are using benchmarking to determine the level of service and reliability provided by their distribution company, comparing their performance to the level provided by other companies, and using this information as leverage with their present service provider. In re-regulated markets, some distribution companies are exploring options for providing premium services, such as increased reliability, service quality, green power, or new customer service options. However, none of these can be properly designed and priced, and the markets cannot be developed, unless the corresponding baseline service levels are defined. In addition to electricity providers and carriers, such baselines are also being considered by the regulators in setting performance standards. One approach is to use benchmarking of various companies to establish baseline service levels.

The transition to electric vehicles (EVs) is a pivotal step towards achieving sustainability in transportation. However, maximizing the environmental benefits of EVs relies not only on their adoption but also on efficient energy management strategies. This chapter reviewed the different approach to optimize sustainability by leveraging artificial intelligence (AI) for personalized electric vehicle energy management. Traditional energy management systems for EVs often employ generic, one-size-fits-all approaches, neglecting the diverse needs and behaviours of individual drivers. In contrast, our study harnesses the power of AI to tailor energy management strategies to the specific preferences, driving patterns, and charging behaviours of each EV owner. The core of our approach lies in the utilization of machine learning algorithms to analyse vast amounts of data collected from individual EVs, including driving habits, charging patterns, environmental conditions, and energy prices.

The continuous change of energy structure and the continuous renewal of demand make the traditional energy management system no longer adapt to the current demand. The latest development urgently needs to combine the new generation of information technology such as Internet, cloud computing and big data to build smart energy systems and put them into practical use. This is the top priority of national development and innovation. This paper summarizes the current smart energy applications at home and abroad, and further investigates and analyzes the energy situation of Yunnan Province and the smart energy application cases of Yunnan Province.