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The integration of distributed energy resources and smart grid technologies has increased the vulnerability and complexity of modern power systems, especially in the face of extreme events. Enhancing the resilience of power systems is crucial to ensure continuous and reliable electricity supply during and after such events. This paper proposes a preventive framework to enhance the resilience of networked microgrids (NMGs) during extreme events. The framework integrates optimal energy management strategies and preventive resilience enhancement measures, focusing on the coordination and utilization of hydrogen (H2) energy systems and controllable distributed generators (CDGs). The framework leverages capacity‐based signals to prompt H2 system owners and CDG operators to prepare for disruptions by fully charging H2 storage tanks and pre‐scheduling CDG commitments. By utilizing this proactive energy scheduling, critical loads can be supplied through stationary fuel cells and CDGs, improving overall network resilience. The proposed resilient energy management approach combines optimization techniques to improve the resilience of NMGs and reduce their dependence on the main grid. Numerical simulations demonstrate the effectiveness of the proposed linear model in enhancing the resilience of NMGs and improving their operational performance during extreme events. The contributions of this paper include advancements in understanding NMGs, the development of a preventive framework, integration of H2 energy systems and CDGs, and proposal of effective signalling models. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for jointly optimizing the sizing and operation of AC microgrids. The methodology is applied to the CAMPUSGRID microgrid, which is currently being developed at the State University of Campinas (UNICAMP) in Brazil. The distributed energy resources (DERs) considered in the study include a battery energy storage system (BESS), a dispatchable gas generator, and photovoltaic (PV) generation. The proposed energy management system (EMS) minimizes the costs associated with day-ahead operation of the DERs in an unbalanced three-phase AC microgrid. To develop the EMS as a mixed-integer linear programming (MILP) model, the MATLAB Link module of HOMER Pro and the AMPL API are used. Heuristic rules are also developed for contingency and post-contingency operations in case of unintentional disconnection from the main grid. The sizing simulations combine tariff regimes, contingencies, and dispatch strategies. The results demonstrate the influence of the tariff on the net present cost (NPC) and the importance of resilience in sizing DERs. Additionally, the proposed model is capable of assessing the impact of DERs on the electrical grid. Finally, the joint strategy proposed in this paper leads to a reduction in microgrid investment cost, improvement in voltage magnitude profile, and reduction in active power losses when compared to the default dispatch strategies of HOMER Pro. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently. [ABSTRACT FROM AUTHOR]

One of the main issues in power systems relates to scheduling of energy resources. With the ever-increasing penetration of renewable energies with intermittent power output, this issue has turned into an even more significant problem. Renewable energy sources (RESs) have captured attention due to their low environmental emission and also low running cost. One drawback that may be brought into power systems is the surplus power generation by such generation technologies that should be carefully addressed in power system-related problems. This paper proposes the unscented transform modeling to consider the stochastic behavior of charge and discharge of EVs, random performance of photovoltaic, load demand and wind turbine systems. Due to the unpredictable nature of solar and wind power outputs, as well as plug-in electric vehicle owners' behavior when supplying or receiving power from the grid, a stochastic programming-based approach is proposed to operate microgrids in grid-connected configuration mode. The integration of vehicle to grid (V2G) has a good ability to minimize the operating cost of the MG. An integrated optimization model is presented in this study for optimal operation of the MG with high penetration of PEVs and RESs. Modified sunflower optimization algorithm (MSFO) algorithm is applied in this paper to address the optimization problem. The single-objective stochastic optimization is used for minimizing the total operating cost over the day taking into consideration the uncertainties due to the RESs' power output intermittency, including wind speed and solar irradiance and load demand forecast error. Several case studies are taken into account to show the efficiency of the optimal operation with PEVs. [ABSTRACT FROM AUTHOR]

It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management. [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]

One of the main objectives of the Energy Management System (EMS) is to achieve a very high level of flexibility, stability, and the system must be able to adapt to most changes in the distribution network. This paper proposes a multi-agent system-based microgrid energy management to balance the energy supply and demand by feasibly integrating the energy storage system and demand response. An overall power management strategy is necessary to manage power flows among all interconnected elements of the microgrid. The principal contribution of this paper is an energy management system based on intelligent agents; each agent uses the microgrid data to manage the power flow in the microgrid. The interaction between agents proposed is simulated using JADE (Java Agent Development Environment) and the microgrid model is simulated using MATLAB/Simulink, both platforms are connected through the MACSimJX middleware for a dynamic simulation. [ABSTRACT FROM AUTHOR]

In recent years, renewable energy has seen widespread application. However, due to its intermittent nature, there is a need to develop energy management systems for its scheduling and control. This paper introduces a multi-stage constraint-handling multi-objective optimization method tailored for resilient microgrid energy management. The microgrid encompasses diesel generators, energy storage systems, renewable energy sources, and various load types. The intelligent management of generators, batteries, switchable loads, and controllable loads ensures a reliable power supply for the critical loads. Beyond operational costs, our model also considers grid dependency as a key objective, making it particularly suited for energy management in extreme environments such as islands, border regions, and military bases. Managing complex controls of generators, batteries, switchable loads, and controllable loads presents challenging constraints that the management strategy must meet. To tackle this challenge, we propose an multi-objective optimization algorithm with multi-stage constraint-handling strategy to handle the high-dimensional complex constraints of the resilient energy management problem. Our proposed approach demonstrates superior performance compared to nine leading constrained multi-objective optimization algorithms across various test scenarios. Furthermore, the benefits of our method become increasingly evident as the complexity of the problem increases. Compared to the classical NSGA-II, the proposed NSGA-II-MC method achieved a 49.7% improvement in the Hypervolume metric on large-scale problems. [ABSTRACT FROM AUTHOR]

The global electricity network and natural gas have been developed worldwide. So far, various investigations in relation to energy infrastructures have been performed; however, the combination of these systems has not been examined much. For this reason, electric and thermal loads have been interconnected by CHP units, and the combined study of power and heat has helped the operator to propose a more optimal structure for energy management. Therefore, integrating different energy carriers and using the benefits of each one can increase the efficiency and reliability along with optimizing system performance. Such a capability in structure and operation requires a comprehensive framework to describe the effects of combining different energy carriers on the economic and environmental indicators of energy systems. In this paper, the microgrid has been modeled as an energy hub in order to combine different energy carriers and perform conversions in them to provide the optimal electric and thermal load required by the consumer. This structure has been created by managing the incoming carriers of gas, electricity and renewable energy and managing their conversion and storage in order to minimize the cost of energy supply and environmental pollutants. In addition to managing generation units, in order to improve the performance of the microgrid and help the operator of the microgrid, a responsive load management strategy and charging and discharging of electric vehicles have been proposed. Epsilon constraints and fuzzy decision-making methods have been used to solve this issue. The results of the study indicated that in the optimal state with the application of responsive loads, the total cost of operation has been reduced by more than 6% and the cost of environmental pollutants has been mitigated by about 13%. [ABSTRACT FROM AUTHOR]

The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users' thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation. [ABSTRACT FROM AUTHOR]

The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi‐carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a new energy management system (EMS) for an islanded microgrid (MG) to increase power system security cost‐effectively. The small size of MGs, variations in renewable energy sources (RESs) output power, and low inertia of power electronic interfaced distributed energy resources (DERs) convert voltage fluctuations into a vital issue for the MG EMS. This paper proposes a two‐stage stochastic multiobjective framework to simultaneously attain economic, environmental, and technical aims. In the first stage, many scenarios related to RESs output power variations, load demand changes, and DERs contingency outages are generated using Monte Carlo simulation. Afterwards, generated scenarios are reduced and applied to the second stage. A novel objective function based on the expected voltage fluctuations (EVFs) caused by active and reactive power variations is proposed, which is minimized alongside the active and reactive costs and MG emission. The proposed model is applied to a test MG, and active and reactive power and primary and secondary reserve scheduling are correctly accomplished for five different cases. Precise analysis confirms the notability and effectiveness of MG voltage fluctuations management in energy and reserve scheduling. Numerical results show more than 60% reduction in voltage fluctuations. [ABSTRACT FROM AUTHOR]

This paper proposes an Energy Management System (EMS) of an off-grid residential microgrid comprised of a solar photovoltaic array, wind turbine, and a battery-based energy storage system for a residential building in a remote area. The aim is to design a less complex energy management system that addresses energy resources dispatch challenge in a hybrid residential microgrid. It equitably dispatches resources and manages the generated energy to meet the residential load. The management control system algorithm is implemented using the state machine and simulated in MATLAB/Simulink environment for validation. Three scenarios are simulated, they emulate various contingencies scenarios that might rise due to the intermittency nature of the renewable resources. Simulation results show that the proposed energy management system achieves its design objectives. It maintains the power supply and demand balance, through an appropriate scheduling of resources even during contingency due to the intermittence nature of the renewable resources. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources (RES) such as wind and solar power is increasing rapidly to meet growing electricity demand. However, the intermittent nature of RES poses a challenge to grid stability. Energy storage (ES) technologies offer a solution by adding flexibility to the system. With the emergence of distributed energy resources (DERs) and the transition to prosumer-based electricity systems, energy management systems (EMSs) have become crucial to coordinate the operation of different devices and optimize system efficiency and functionality. This paper presents an EMS for a residential photovoltaic (PV) and battery system that addresses two different functionalities: energy cost minimization, and self-consumption maximization. The proposed EMS takes into account the operational requirements of the devices and their lower-level controllers. A genetic algorithm (GA) is used to solve the optimization problems, ensuring a desired State of Charge (SOC) at the end of the day based on the next day forecast, without discretizing the SOC transitions allowing a continuous search space. The importance of adhering to the manufacturer's operating specification to avoid premature battery degradation is highlighted, and a comparative analysis is performed with a simple tariff-driven solution, evaluating total cost, energy exchange, and peak power. Tests are carried out in a detailed model, where Power Electronics Converters (PECs) and their local controllers are considered together with the EMS. [ABSTRACT FROM AUTHOR]

In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

In this paper, a decentralized energy management system is presented for intelligent microgrids with the presence of distributed resources using reinforcement learning. Due to the unpredictable nature of renewable energy resources, the variability of load consumption, and the nonlinear model of batteries, the design of a microgrid energy management system is associated with many challenges. In addition, centralized control structures in large-scale systems increase computational volume and complexity in control algorithms. In this paper, a fully decentralized multi-agent structure for a microgrid energy management system is proposed and the Markov decision process is used to model the stochastic behavior of agents in the microgrid. Electrical and thermal distributed resources, batteries, and consumers are considered intelligent and independent agents. They have the learning ability to explore and exploit the environment in a fully decentralized manner and achieve their optimal policies. The proposed method for hourly microgrid management is model-independent and based on learning. The method maximizes the profits of all manufacturers, minimizes consumer costs, and reduces the dependence of the microgrid on the maingrid. Finally, using real data from renewable energy sources and consumers, the accuracy of the proposed method in the Iranian electricity market is simulated and verified. [ABSTRACT FROM AUTHOR]

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate. [ABSTRACT FROM AUTHOR]

Different types of distributed generation (DG) units based on renewable and non-renewable energy sources can create a local energy system in microgrids. The widespread penetration of distributed energy resources (DERs) has affected many power system issues, such as the control and operation of these networks. For the optimal operation of microgrids, optimal energy planning and management in the new space governing the distribution system requires extensive research and analysis. Getting acquainted with the latest research about the evaluation of the problems and challenges in the design of control systems plays an important role in providing a guidance map for researchers to find the recent challenges and propose new solutions. This paper tried to list the challenges of distributed generation sources for MG applications, opportunities, and solutions. These challenges are reported in hierarchical control strategies and power-sharing categories. Therefore, Model Predictive Control (MPC)-based approaches are reviewed for different recent control levels and power sharing strategies in a comprehensive and simple point of view. The performance comparison of MPC methods together and different allocated fitness functions and implementation algorithms are dedicated. Another hand, the potential of MPC methods to control inverters for increasing the reliability of the grid, which this feature could not be achieved by using conventional strategies, while has not been investigated by researchers widely, is introduced in a short review. Therefore, this paper shows an intersection guidance map for readers to facilitate future research works in these exciting and undiscovered fields. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

This paper presents a novel fully decentralized and intelligent energy management system (EMS) for a smart microgrid based on reinforcement learning (RL) strategy. The purpose of the proposed EMS is to maximize the benefit of all microgrid entities comprising customers and distributed energy resources (DERs). Due to unpredictable features of renewable energy sources and variability of consumers' demands, designing the microgrid EMS is a complicated task. To overcome this issue, the multi‐agent hour‐ahead energy management problem is modelled as a finite Markov decision process. The microgrid entities are considered as intelligent agents. The optimal policy of agents is obtained through a newly developed framework of the model‐free Q‐learning algorithm to maximize the benefit of all renewable and non‐renewable energy resources and battery energy storage system. The degradation model of the battery is considered to reduce the number of battery replacements. To ensure customers' comfort, customers' expenses are decreased without demand curtailment via introducing two types of load shifting techniques. The microgrid operation is analysed under four scenarios comprising no‐learning, generator‐learning, customer‐learning, and whole‐learning. the performance of the proposed algorithm is compared to the Monte Carlo method and simulation results on the real power‐grid dataset show the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

In order to effectively make up for the defect of service life of re-tired power battery echelon used in microgrids (MGs) and improve the reliability of MGs system, an energy management method based on the energy hub of the electricity heat hydrogen multi-energy storage system (EHH-MESS) is proposed the in the paper. Firstly, considering the uncertain factors such as energy output, load fluctuation, retired power battery life defects and multi energy flow impact, MGs supply-demand balance model is built. Secondly, taking the economy of energy supply as the goal, a control method based on energy hub is proposed, which considers the weight of multi-objective optimization to control MGs. Finally, the effectiveness of the proposed energy management method is verified by simulation analysis, which improves the power supply reliability and economy of the system. [ABSTRACT FROM AUTHOR]

This paper aims to investigate energy management of the hybrid AC/DC microgrid with the high penetration of distributed energy resources (DERs), such as electrical vehicles, heat pumps, and photovoltaics. In the previous studies, energy management of the hybrid microgrid is usually carried out by the system operator in a centralized manner, which suffers from the compromise of privacy information protection and the risk of single-point failure. Therefore, this paper proposes a distributed energy management scheme of the hybrid microgrid using the projection function-based alternating direction method of multipliers (P-ADMM), which allows each subgrid, i.e., AC subgrid and DC subgrid, to make day-ahead schedules independently with information exchanges while obtaining the optimal energy management solution. The energy management problem of the hybrid microgrid is formulated as a mixed-integer quadratic programming (MIQP) model, considering DER and energy storage system operation constraints, system operation constraints, and converter operation constraints. Then, the MIQP model is decomposed and distributed into smaller-scale QP models between subgrids using the P-ADMM algorithm, which can handle binary variables through projection functions. The numerical results conducted on the hybrid microgrid demonstrate that the proposed distributed scheme can effectively achieve optimal energy management for the hybrid AC/DC microgrid in a distributed manner. [ABSTRACT FROM AUTHOR]

Microgrid technology has recently gained global attention over increasing demands for the inclusion of renewable energy resources in power grids, requiring constant research and development in aspects such as control, protection, reliability, and management. With an ever-increasing scope for maximizing renewable energy output, there is also a need to reduce the curtailment of power on both the generation and demand sides by increasing forecasting accuracies and using resources more effectively. This paper proposes a dual-stage dispatch employing a novel "split-horizon" strategy, in a bid to enhance energy management in a standalone microgrid. The split-horizon is essentially the considered time horizon split into equal operational periods of the dual-stage dispatch. The proposed strategy utilizes a custom-designed novel variant of the inertia-weight-based particle swarm optimization (PSO), termed customized PSO, to perform the optimal schedule and dispatch operation by benefitting from the simplicity of PSO and customization as per the considered objectives. A modified IEEE 34-node test system is derived into a standalone microgrid with added distributed energy resources to test the proposed strategy, while another standalone microgrid, a modified IEEE 69-node test feeder, is also considered for scalability. Furthermore, the validation of the strategy is performed appropriately with a case study while also validating the proposed optimization algorithm. It is observed that the proposed energy management strategy provides approximatelya 7% reduction in costs. [ABSTRACT FROM AUTHOR]

Owing to the wide distribution of rural areas, the construction of power facilities is not perfect, which poses a great challenge to the power supply stability of the rural power grid. As an important supplement, the microgrid (MG) plays a complementary role in improving the power supply quality. However, the addition of the MG increases the complexity, and how to balance the interests of the participants has become a substaintial problem. Based on the analysis of the power consumption characteristics of rural residential, agricultural and industrial areas, a game relationship model between MG operators and power users is established, and the overall optimal strategy is obtained in this paper. For consumers, it can effectively achieve the effect of 'peak shaving and valley filling' and reduce the cost of electricity. For MG operators, it can improve the consumption rate of renewable energy and increase revenue. [ABSTRACT FROM AUTHOR]

This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting. [ABSTRACT FROM AUTHOR]

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system for an effective operation of micro-grids. In recent years, the penetration of the distributed energy resources such as photovoltaic and wind turbine generators into the electrical networks has been increasing. These resources generate perturbations and instability in the electrical grid. Battery energy storage system is the most used recently on micro-grids in order to manage the integration of intermittent of energy resources and help the control system to reduce the undesirable effects like power fluctuations because of perturbations (fault, short-circuit,...etc). However, for control and management purposes, a power distribution network can be modeled as a discrete or hybrid (discrete events and continuous variables) event system. This distributed topology will enhance the resilience of the power grid, facilitate the integration of distributed energy resources, especially renewable sources, accommodate energy storage units, and enhance the quality of delivered power. The main objective of this paper is to highlight a methodology for surveillance and performance analysis of a micro-grid during a perturbation based on discrete event systems. This systematic control method is effective in managing many operations in smart grids. As result, the formulation of power network control problems in discrete event system is useful for energy management, control, modeling, and monitoring with diagnosis applications. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear economic model predictive control based energy management of a multi-energy microgrid is presented. The microgrid is composed of the district heating system with a cogeneration power plant supplying both the heat and the electricity demand of residential buildings with photovoltaic sources. Furthermore, access to liberalized heat and electricity market is assumed. This kind of environment requires consideration of the economical aspects, the technical aspects, the safety/quality of supply, and consumer preferences. The major contributions introduced in the presented approach are the following. A nonlinear formulation is adopted to capture the nonlinear thermal models and the nonlinear algebraic model of the heat supply network. The second major contribution is the economic character of the objective following the assumption of a liberalized market environment. Such formulation accounts for the specifics of the energy market organization. Finally, the stochastic nature of the planning problem is introduced through a practical robust formulation. The results obtained on a representative case study clearly show the benefits of the proposed approach compared to the traditional energy management approach. [ABSTRACT FROM AUTHOR]

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases. [ABSTRACT FROM AUTHOR]

This research paper presents a novel technique for improving load-shedding in DC microgrids by incorporating a binary number generator (BNG). Load shedding, a controlled strategy utilized to maintain equilibrium between power supply and demand within a microgrid when the aggregate demand surpasses generated power, is widely employed. Nevertheless, conventional load-shedding methods frequently encounter challenges, notably over-shedding and voltage deviations, particularly in scenarios involving numerous controllable loads. To overcome these challenges, an enhanced algorithm is proposed, which integrates the BNG technique. Simulation results conclusively demonstrate the effectiveness of the proposed algorithm in ensuring microgrid stability during periods of supply-demand imbalances. [ABSTRACT FROM AUTHOR]

• Introducing an energy management in renewable based MGs with DRP considering tidal and solar units in real-time digital twin case. • Developing a GWOA to find the optimal economic operating point in a nonlinear constraint multi-objective framework. • Proposing an intelligent deep GAN based model to predict the output power of tidal and solar units. • Critical and technical analysis of different algorithms in the microgrid operation and management. In this research study, a novel demand response program (DRP) has been proposed for renewable-based microgrids (MGs) which takes into account the high penetration of tidal units and solar energy as important, prevalent renewable resources in the power systems. To this end, multi-objective problem (MOP) structure is a promising solution to reduce the whole operating costs of the scheduling problem as well as decrease the high risk of failure in electrical power transmission because of component increasing failure rates and extended repair time. The complexities and nonlinearity of the problem necessitate an innovative heuristic solution which is derived from the Grey Wolf optimization algorithm to help for resolving the problem without making any assumptions or compromising precision. This paper also proposes the dynamic 3-phase correction (DPC) formulation to boost the layout convergence ability by increasing the global search features. Through such a modification, the diversity of the members in the algorithm population increases which would result in low computational burden and low possibility of trapping in local optima. Moreover, it is necessary to have a clear and accurate estimation of the output power of renewable sources in the system. Considering that solar irradiance is hard to anticipate, this paper develops a deep learning layout that uses generative adversarial networks (GAN) to forecast the hourly power generation of the tidal and solar agents. GAN model consists of two competing networks which help to enhance the training process by increasing the accuracy of distinguishing real data from fake data. At the end, IEEE standard test system is used to evaluate the efficiency and effectiveness of the suggested multi-layer problem. The simulation results display that the suggested deep model outperforms other well-known algorithms in smart microgrids. [ABSTRACT FROM AUTHOR]

The rising demand for electricity, economic benefits, and environmental pressures related to the use of fossil fuels are driving electricity generation mostly from renewable energy sources. One of the main challenges in renewable energy generation is uncertainty involved in forecasting because of the intermittent nature of renewable sources. The demand also varies according to the time of day, the season, the location, the climate, and the availability of resources. Microgrids offer a potential solution for the integration of small-scale renewable energy sources and loads along with energy storage systems and other non-renewable sources. However, intermittent generation and varying demand need to be matched to provide stable power to consumers. Therefore, it is crucial to design an energy management system to effectively manage the energy sources and supply loads for reliable and efficient operation. This paper reviews different techniques proposed in the literature to achieve the objectives of a microgrid energy management system. The benefits of existing energy management systems and their challenges are also discussed. The challenges associated with uncertainties and methods to overcome them are critically reviewed. [ABSTRACT FROM AUTHOR]

DC-DC converter plays a major role in microgrid and energy storage system using operational stability and synchronised power delivery. In this paper, an energy management control algorithm is proposed based on closed-loop proportional-integral (PI) to solve and analyse the issues of unstable DC link voltage. During grid-connected mode, a hybrid energy storage system (HESS), i.e. super-capacitor (SC) and battery units are charged from different hybrid energy resource, i.e. solar and wind. In the absence of energy resources (solar or wind), the energy storage units (supercapacitor and battery) need to supply the grid, which is the main aim of this work. Initially, the energy management control is used to control the generated power from wind and solar for the stability requirement. The MPPT technique has been considered here to make system flexibility and compatibility with different PV cell and wind energy. This proposed system successfully tracked the maximum power, which is analysed using the combined development of galactic swarm optimisation (GSO) based incremental conductance (IC) algorithm. Using GSO in MPPT should reduce the steady-state oscillation and get high convergence rate. The whole work is implemented in Matlab/Simulink platform and the performance of proposed is compared with existing algorithms. Abbreviation: HESS: Hybrid Energy Storage System; MPT: Maximum PowerPoint; PWM: Pulse Width Modulation; SOC: State of Charge; PMSG: Permanent Magnet Synchronous Generator; PI: Proportional Integral; AC: Alternating Current; DC: Direct Current; GSO: Galactic Swarm Optimization [ABSTRACT FROM AUTHOR]

The papers in this special section focus on the latest research efforts in modelling, control and value proposition of distributed energy resources (DERs). Future power grids will face tremendous challenges in operation and planning as a massive number of distributed energy resources (DER) are integrated into the system. Due to their limited visibility and controllability, it is imperative to study the impacts of a high penetration of DERs over the power grid, and to analyze and evaluate their reliability and economic benefits especially when they are enabled to provide essential reliability services in a large scale. The lack of DER modelling method is another barrier to the efficient integration ofDERs into the existing operation, planning and market paradigm. This is also complicated by increasing complexities in the management systems developed to coordinate and control a large number of heterogeneous DERs. [ABSTRACT FROM AUTHOR]

Summary: In a power system, distributed energy resources, including renewables, are advantageous for reducing power transmission losses and environmental pollution. However, increasing penetration of distributed renewable energy sources is challenging as they may pose security issues to the grid and centralized control of quantum number of such sources is quite impossible. Microgrid based distribution system is facilitating integration of distributed renewable energy sources as they can supply the local loads and reduce burden on the grid. Moreover, distributed control mechanism of different microgrids enhances the functioning efficiency of the grid. However, intermittent and uncertain nature of renewables leads into demand‐supply imbalance in a microgrid. Networked microgrids is new concept where the energy sharing between the microgrids may encourage renewable integration and reduce demand‐supply imbalance. In order to manage such energy sharing among the different components in a microgrid or different microgrids in a networked structure, an appropriate energy management approach is necessitated. Transactive energy is an emerging technology for energy management in the distribution system, which uses market mechanisms for energy management. This paper discusses various types of market structures and market clearing approaches in transactive energy framework for energy management. Moreover, techno‐economic perspectives of utility in transactive energy trading, such as network constraints and network usage charges, are elaborated. Finally, this review details the challenges and future scope in the transactive energy management of microgrids and networked microgrids. [ABSTRACT FROM AUTHOR]

Low-voltage grid-connected microgrids rely on the exploitation of inverter-interfaced distributed energy resources (DERs) in order to feed loads and to achieve bidirectional power flow controllability at their point of common coupling (PCC) with the upstream grid. However, adverse operational conditions, such as the existence of DERs of different operation natures, DERs of non-equal power ratings, as well as the occurrence of non-steady and non-sinusoidal grid voltage scenarios, bring complications to microgrid energy management. Consequently, control strategies employed to coordinate DERs in dispatchable microgrids need to be resilient to such non-ideal conditions. Hence, this paper demonstrates that a multi-purpose strategy, so-called the Generalized Current-Based Control (GCBC) approach, is capable of steering DERs under such adverse operational scenarios, ensuring proportional current sharing among them while also regulating the microgrid power dispatchability at the PCC. The discussions are supported by an extensive experimental validation on a laboratory-scale single-phase microgrid prototype, demonstrating that the GCBC approach allows DERs of different operational natures to be coordinated, respecting their power ratings, and allowing the single-controllable microgrid to endure operation under distorted voltages and support voltage ride-through conditions. [ABSTRACT FROM AUTHOR]

Energy generation is currently evolving into a smart distribution system that incorporates several green energy resources at a distributed level, ensuring that clean energy is generated without releasing harmful gases, that operational procedures are consistent, and that energy management and supervision arrangements are improved. This paper proposes a multi-agent system-based microgrid energy management and proper control in distributed systems. For the complexity of energy management in distributed systems, a multi-agent system-based decentralized control architecture was developed. The proposed technique is based on several smart agents, each agent is based on the microgrid data for energy management and frequency control. The proposed energy management system based on the multi-agent system was tested by simulation under renewable resource fluctuations and seasonal load demand. The simulation results show that the proposed energy management system proved to be more resilient and high-performance controls than conventional centralized energy control systems. [ABSTRACT FROM AUTHOR]

In this paper, a joint scheduling scheme for the electric supply and demand of Home Energy Management System (HEMS) and Building Energy Management System (BEMS) via energy management in houses and buildings is presented. To reflect ongoing policy, the Korea Electric Power COoperation (KEPCO) tariff is employed to formulate the optimization problem in terms of the sequential management procedure of prediction, supply control and demand control. As a result, an optimal electric supply pattern to produce the minimum energy cost can be obtained under a given curtailment. From simulation, the proposed scheduling scheme demonstrates greater reliability and adaptation for the interruptible load (IL) than the day-ahead method. [ABSTRACT FROM AUTHOR]

Because of the problems of low operation efficiency and poor energy management of multienergy input and output system with complex load demand and energy supply, this paper uses the double-layer nondominated sorting genetic algorithm to optimize the multienergy complementary microgrid system in real-time, allocating reasonably the output of each energy supply end and reducing the energy consumption of the system on the premise of meeting the demand of cooling, thermal and power load, so as to improve the economy of the whole system. According to the system load demand and operation mode, the first layer of this double-layer operation strategy calculates the power required by each node of the microgrid system to reduce the system loss. The second layer calculates the output of each equipment by using nondominated sorting genetic algorithm with various energy values calculated in the first layer as constraint conditions, considering the operation characteristics of various equipment and aiming at economy and environmental protection. In this paper, a typical model of energy input-output is established. This model combines with the operation control strategy suitable for multienergy complementary microgrid system, considers the operation mode and equipment characteristics of the system, and uses a double-layer nondominated sorting genetic algorithm to optimize the operation of each equipment in the multienergy complementary system in real time, so as to reduce the operation cost of the system. [ABSTRACT FROM AUTHOR]

The incorporation of distributed energy resources and technical innovations has restructured the electrical power system network from conventional grid to microgrid. Consequently, significant impacts on both distribution and utilization of power have been observed triggered by diverse operating condition and enhanced power demand. In planning of microgrid, energy management is a critical issue which is related to efficient utilization of available resources at minimum operating cost. Distributed and intermittent nature of generations in microgrid demands efficient and optimal allocation of existing resources. An allocation technique is necessary to attain optimal utilization of assets and to accomplish decision making for optimal power flow route through shortest path. This paper proposes a multi-objective optimization-based resource allocation technique to efficiently control available generation at any load condition to achieve minimum operating cost while maintaining all system constraints. In proposed framework, line flows are maximized to its load-carrying capability and by controlling power flow through shortest possible path. Proposed methodology suggests proper controller position to sustain desired power flow through the shortest path between each generator and load pair. An interconnected microgrid network topology is considered to demonstrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Conventional energy generation sources mainly provide energy supply to remote areas nowadays. However, because of growing concerns over greenhouse gas emissions, the integration of renewable energy sources is mandatory to meet power demands and reduce climatic effects. The advancements in renewable generation sources and battery storage systems pave the way for microgrids (MGs). As a result, MGs are becoming a viable solution for power supply shortage problems in remote-area applications, such as oceanic islands. In this paper, an islanded MG, which consists of PV system, tidal turbine (TT), diesel generator (DG), and Li-ion battery, is considered for Ouessant island in Brittany region in France. The economic operation of the MG is achieved by including battery degradation cost, levelized costs of energy of the PV system and TT, operating and emission costs of DG, and network constraints. The developed model leads to a non-linear and non-convex problem, which unfortunately can converge to a local optimum solution. The problem has, therefore, been relaxed and converted to a convex second-order cone model to achieve an optimal decision strategy for islanded MG operations with a global or near-global solution. Numerical simulations are carried out to prove the effectiveness of the proposed strategy in reducing the operating and emission costs of the islanded MG. It is shown that the developed convex energy management system formulation has an optimality gap of less than 1% with reduced computational cost. [ABSTRACT FROM AUTHOR]

Energy conservation, emission reduction and vigorous development of new energy are inevitable trends in the development of the power industry, but factors such as energy storage loss, solar energy loss and line loss in real power situations have led the problem to a complex direction. To address these intricacies, we use a more precise modeling approach of power loss and propose a collaborative optimization method integrating the Deep-Q-Network (DQN) algorithm with the multi-head attention mechanism. This algorithm calculates weighted features of the system's states to compute the Q-values and priorities for determining the next operational directives of the energy system. Through extensive simulations that replicate real world microgrid (MG) scenarios, our investigation substantiates that the optimization methodology presented here effectively governs the distribution of energy resources. It accomplishes this while accommodating uncertainty-induced losses, ultimately achieving the economic optimization of MG. This research provides a new approach to deal with problems such as energy loss, which is expected to improve economic efficiency and sustainability in areas such as microgrids. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

The most important challenge in microgrids is the coordination of distributed energy resources (DERs), due to the existence of several DERs with fugacious characteristics. In this paper, a robust frame associated with a quantum version of the Teaching-Learning-Based Optimization (quantum TLBO) algorithm is proposed for the first time for the microgrid optimal energy management problem. Uncertainties in the load and the output power of renewable energy sources are modeled using robust optimization (RO). The operation cost of the microgrid is considered as an objective function. The problem is formulated as a bi-level minimum-maximum optimization problem and is solved in two levels iteratively. First, by maximizing the operation cost of the microgrid, the worst case for the uncertain parameters is determined using Particle Swarm Optimization (PSO). Then, according to the results obtained in the first level, by minimizing the operation cost of the microgrid, the final optimal solution is obtained using the Quantum TLBO (QTLBO). This approach is applied to a grid-connected microgrid consisting of renewable energy sources, microturbines, fuel cells, and battery systems. The obtained simulation results demonstrate that the QTLBO is significantly superior to the TLBO, Differential Evolution, and Real-Coded Genetic Algorithm in terms of both achieving the final optimal solution and convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive sliding mode controller (ASMC) based on fuzzy logic is applied to control hybrid smart microgrid power systems under uncertainty. The chattering effects can occur if not adjusted properly due to the sliding mode amplitude control law. The approximation of the ASMC law function is performed by Fuzzy logic, and chattering effects are reduced through a low pass filter. The Lyapunov theory proves the stability of the control system and is also applied to stabilise the system's control mechanism. The hybrid micro-grid system operation, which consists of distribution generation, loads, PWM IGBT inverter and low pass filter, is simulated using MATLAB 2017b. The reference speed is compared with the hybrid microgrid real speed, which was 1.0 p.u. and gradually controlled through ASMC-fuzzy until the speed desired is achieved for power supply stability. A comparison of results between the simulated ASMC-Fuzzy and PI controller indicated a drastic reduction of THD, which satisfies the required 'below 5% THD level according to IEEE 1547 standard' required for operation. [ABSTRACT FROM AUTHOR]

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. Note to Practitioners—This work is motivated by the emerging need for effective energy management approaches in multi-carrier microgrids. The inherent difficulty of scheduling simultaneously the operations of various energy infrastructures (e.g., electricity, natural gas) is exacerbated by the inevitable presence of uncertainties that affect the inter-dependent dynamics of different energy resources and equipment. The proposed robust MPC-based control strategy allows the energy manager to effectively determine an optimal energy scheduling of multi-faceted system components, making a tradeoff between performance and protection against data uncertainty. The presented strategy is comprehensive and generic, as it can be applied to different microgrid frameworks integrating various types of system components and sources of uncertainty, while at the same time being implementable in any energy management system. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel control scheme on AC/DC ring bus islanded microgrid under unbalanced and non‐linear loads. The DC bus is integrated with the fuel cell, one wind turbine, and two batteries. The DC bus can be divided into two sub‐sections, where the first zone is supported by the fuel cell and battery 1, and the second zone is installed with the wind turbine and battery 2. The AC bus is also able to be sectionalized into two sub‐sections where the diesel generator and the unbalanced load are located on the first section and the non‐linear load with one wind turbine are placed on the other section. The DC bus is connected to the AC bus through two parallel three‐phase lines each one configured by three single‐phase inverters. The proposed control scheme in the ringed and separated modes presents several contributions including balancing the unbalanced loads, compensating the harmonics of non‐linear loads, improving the voltage stability and sag/swells, enabling partial operation under outages, and enhancing resilience. In the ring mode, wind turbine 1 and diesel generator inject balanced and linear P‐Q powers to the system as 39 kW/6 kVAr and 7 kW/9 kVAr, respectively. In the separated mode, the P‐Q powers of wind turbine 1 are 36 kW/9 kVAr and they are 15 kW/9 kVAr for diesel generators. In both modes, the unbalanced and non‐linear powers are supplied by the DC bus. The results verify that the developed control strategy efficiently deals with harmonic components, unbalanced currents and voltage issues. [ABSTRACT FROM AUTHOR]

A microgrid is a concept that has been developed with the increasing penetration of distributed generators. With the increasing penetration of distributed energy resources in the microgrids, along with advanced control and communication technologies, the traditional microgrid concept is being transited towards the concept of microgrid clustering. It decomposes the distribution system into several interconnected microgrids, effectively reducing problems such as voltage rise, harmonics, poor power factor, reverse power flow and failure of the conventional protection schemes. Microgrid clusters effectively coordinate power sharing among microgrids and the main grid, improving the stability, reliability and efficiency of the distribution network at the consumption premises. Despite the evident benefits of microgrid clusters to the consumers and the electrical utility, there are challenges to overcome before adopting the microgrid cluster concept. This paper is aimed at critically reviewing the challenges in design aspects of microgrid clustering. Categorization of multimicrogrids into different architectures based on the layout of the interconnections, evaluation of reported control techniques in microgrid clustering and multi-microgrid protection aspects are presented, highlighting the possible areas of future research that would improve the operational aspects of microgrid clusters. [ABSTRACT FROM AUTHOR]

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about 80% of global CO2 emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modelling of Power Exchanges, Algorithms for Local Energy Market (LEM), Competitiveness of Combined Heat and Power Plant (CHP) and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a district energy management system (DEMS), a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an LEM by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible CHPs in low-carbon power systems to balance the volatility of renewable energy. An Energy Signal Light (ESL) was developed as an energy feedback device, which is integrated into the DEMS in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation. [ABSTRACT FROM AUTHOR]