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This paper presents a powerful performance optimization approach for load frequency control in the off-grid mode of a delayed fuel cell microgrid with PI controller. The new approach based on stability boundary locus is used. Thanks to this, the search is only carried out in PI control parameter space keeping stable the system in the proposed optimization. The superiority of the proposed approach in terms of iteration history, fitness values, and time responses of the system is presented by comparing the results obtained. According to the results obtained, the proposed approach reaches the global minimum in a very short number of iterations and achieves this by both guaranteeing the stability of the system and providing the desired time response performances compared to the other method. • The stability of a delayed fuel cell microgrid system is a very important issue, especially in off grid mode. • A delayed fuel cell microgrid system should provide the desired quality of energy to the load. • The optimization approach provides the desired quality of energy to the load ensuring the stability of the microgrid. • Proposed optimization approach has great advantages in terms of iteration history and fitness value. [ABSTRACT FROM AUTHOR]

The necessity of employing renewable energy sources within microgrid-based Power-to-X (P2X) systems has been emphasized by the imperatives of reducing global warming and fossil fuel consumption. Due to their numerous advantages, solar and wind energy have acquired prominence in contemporary power systems. However, their intermittent nature presents challenges. To overcome these challenges, energy storage devices are utilized. Batteries, however, are plagued by issues such as high maintenance needs, self-discharge, and diminishing storage capacity with age. Consequently, investigating alternatives such as hydrogen storage has become a financially viable option, especially for high-power applications. However, the intrinsically slow dynamics of hydrogen production from electricity contrast with the rapid responses of contemporary power electronic devices. This paper presents a versatile technique for optimizing hydrogen production in the presence of stochastic fluctuations in solar irradiance and wind speed. The proposed method combines a modified version of the Symbiotic Differential Whale Optimization Algorithm (m-SDWOA) with power converters to maximize production from renewable sources and electrolyzers. Using extensive simulations to demonstrate the efficacy of the proposed method by comparing its performance to that of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The results demonstrate the superior performance of the m-SDWOA-integrated method and pave the way for improved hydrogen generation in microgrid-based P2X systems. • Innovative control framework coordinates WT, PV, BESS, and HESS. • Maximizes hydrogen production while maintaining power quality. • Efficient controllers stabilize DC-bus voltage amidst WT/PV variability. • Ensures consistent, reliable power supply in microgrid. • Efficient energy management via m-SDWOA for resource optimization. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs' automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]

There has been a notable increase in the frequency and severity of extreme events, such as hurricanes, floods, wildfires, and storms. These events, although infrequent, have a significant disruptive effect, causing prolonged outages and compromising essential services, thereby severely affecting customers' safety. As a result, there is an urgent requirement to enhance the resilience of distribution networks by quickly restoring the loads during and after a disaster. In this regard, this paper reviews the existing studies on black-start service restoration in active distribution systems and microgrids. A comprehensive review is conducted for each aspect of the restoration problem, encompassing various proposed methods and modeling techniques found in the existing literature. The aim of this review is to consolidate the knowledge and findings from previous studies, providing a valuable resource for researchers and practitioners in the field. Also, some key research directions for the future work in this field are recommended for developing more practical and reliable methods. [ABSTRACT FROM AUTHOR]

The paper presents a grid-connected microgrid with a photovoltaic system and a battery as a storage element. The optimal design and control of storage elements and power quality improvement are enhanced using sigmoid-function-based variable step size (SFB-VSS) adaptive LMS control. The DC-link voltage and battery current are enhanced using an ILA-optimization-based PI controller. Comparative analysis shows that an ILA-optimized PI controller improves battery stress and DC-link voltage fluctuations, enhancing overall system stability. The relative percentage error of Vdc is only 0.5714% for ILA-optimized values as compared to GA, PSO, and manually tuned PI gains which are 0.857%, 1.14285%, and 0.86%, respectively. ILA-optimized parameters also enhance battery current, reducing stress on the battery. The system was studied under various dynamic conditions, achieving power balance in all conditions. The system has the capability of seamless transfer of control from GC mode to SA mode when the grid is disconnected. The proposed VSC control shows better performance in steady-state and dynamic conditions, maintaining a THD under 5%, which follows IEEE standard 519, and providing better DC offset rejection, fewer oscillations in the weight component of the load, and better convergence. The proposed control also enhances the frequency of the grid, ensuring a smooth transition between modes. The system is simulated in the MATLAB Simulink environment, and all the optimization techniques were carried out offline. [ABSTRACT FROM AUTHOR]

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids. [ABSTRACT FROM AUTHOR]

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques. [ABSTRACT FROM AUTHOR]

This paper explores the strategic planning required for a zero-carbon-emission AC/DC microgrid, which integrates renewable energy sources and electric vehicles (EVs) within its framework. It considers the rapidly growing adoption of EVs and the advent of vehicle-to-grid (V2G) technology, which allows EVs to return energy to the grid during peak demand. The study aims to apply optimization techniques to minimize the installation cost associated with various microgrid components. In the case of microgrids, there are decision-making scenarios where multiple alternatives are present; optimization is a valuable technique for efficiently planning and designing microgrids. This work showcases case studies and sensitivity analysis plots, illustrating how output power fluctuates due to uncertainty in renewable energy sources and the absence of EVs. The findings show how V2G contributes to the demand when renewable generation is low. The sensitivity analysis also provides insights into how the unit cost is affected by demand fluctuations. In summary, the principal contribution of this study is developing a comprehensive planning framework for AC/DC microgrids. This framework considers the escalating adoption of EVs and offers practical solutions for future microgrid designs. [ABSTRACT FROM AUTHOR]

Methods for optimization of islanded microgrid systems are usually based on hourly models where each subcomponent is described by a simple algebraic model. There are many studies on this topic, which are usually based on the minimization of total lifetime cost by determining the number of required batteries, wind turbines, PV panels, the positioning of PV panels, etc. In this paper, we further improve the modeling of the microgrid system optimization process by developing a simplified algebraic model that uses one parameter to simulate accelerated battery degradation with respect to depth of discharge. The model consists of simply linearly increasing the degradation of the battery when the state of charge (SOC) becomes lower than a fixed value, and the only model constant is the factor of degradation f. The objective of the paper is to examine the effect of the degree of degradation on the obtained optimal microgrid system parameters. The analysis was performed for several different systems, and the results show that optimal parameters of the system and the overall system cost strongly depend on battery degradation characteristics. The overall system cost can be reduced by 1–6% for lower battery degradation rates and up to 20% for high degradation factor cases. Increasing the degradation factor also has an influence on the ratio of wind turbines to PV panels, and the optimal size of the battery system. [ABSTRACT FROM AUTHOR]

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed. [ABSTRACT FROM AUTHOR]

• This paper presents a novel application of AHA to tune the controllers parameters. • The super twisting sliding mode controller is applied to microgrids. • The results of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. It is essential to improve optimal control for an autonomous microgrid operation. This paper presents a novel application of an artificial hummingbird algorithm for achieving optimal control of an autonomous microgrid. It also suggests a comparative analysis for the optimal super-twisting sliding mode control with both optimal fuzzy logic control and optimal proportional-integral control. The proposed super-twisting sliding mode controller parameters are adequately designed by using the AHA optimization method; meanwhile, the FLC and PI controllers are designed by using the African vultures and the Gorilla troops optimization algorithms, respectively. A MATLAB/Simulink environment is used to verify the effectiveness of the suggested microgrid control methods. The overall system detailed approaches for modeling, designing, and controlling are introduced. The investigation of the ST-SMC approach is also tested in a real-time setting. OPAL-RT 4510 rapid control prototyping and OPAL-RT 8660 data acquisition are used to implement the suggested controller. The verification results prove the superiority of the ST-SMC based on the AHA optimization technique compared with other optimal control techniques. [ABSTRACT FROM AUTHOR]

• Importance and challenges of relay coordination in Microgrid. • Overview of different adaptive schemes opted for relay coordination. • Different optimization techniques which have been used for the calculation of relay settings. • Evolvement of algorithms from conventional to Hybrid methods which combines two different metaheuristic methods to solve a complex, non- linear optimization problem in an efficient way for optimized relay coordination. • Future directions of research for further modifications possible in Algorithms are stated at the end. Concept of microgrids (MGs) have gained significant attention in order to enhance the resilience and efficiency of modern Power Systems. MG offers bi-directional power flow so an effective protection and control becomes a vital requirement to ensure the reliable operation of MGs. Relay coordination being an integral part of the protection system, plays a critical role in prevention of power disturbances and equipment damage due to the presence of bi-directional power flow. This review paper focuses on the optimization of relay coordination in MGs, and highlights the importance of adaptive and intelligent approaches involved for the efficacy of MG operation. This paper overviews different schemes used to adapt while determining the DOCRs settings with different contingencies considered. The paper depicts the key challenges associated with relay coordination in MGs; varying fault currents, and bidirectional power flows. To overcome these challenges, recent researches have explored advanced techniques that includes intelligent algorithms to operate on collected real-time data. Additionally, the impact of communication technologies for relay coordination in real-time has been discussed for Grid connected and Islanded mode of operation. This review covers a wide range of optimization methods such as; Traditional, Meta-Heuristic, and Hybrid algorithms, which allows adaptive and reliable protection schemes during different modes of MG operations. [ABSTRACT FROM AUTHOR]

This paper discusses the importance of microgrids in power systems and introduces a new method for enhancing their performance by improving the transient voltage response in the face of disturbances. The method involves using a hybrid optimization approach that combines driving training-based and particle swarm optimization techniques (HDTPS). This hybrid approach is used to fine-tune the system's cascaded control scheme parameters, based on proportional–integral–accelerator (PIA) and proportional–integral controllers. The optimization problem is formulated using a central composite response surface methodology (CCRSM) to create an objective function. To validate the suggested control methodology, PSCAD/EMTDC software is used to carry out the simulations. The simulations explore various scenarios wherein the microgrid is transformed into an islanded system and is subjected to various types of faults and load changes. A comparison was made between the two proposed optimized controllers. The simulation results demonstrate the effectiveness of using a PIA-optimized controller; it improved the microgrid performance and greatly enhanced the voltage profile. In addition, the two controllers' gains were optimized using only PSO to ensure that the outcomes of the HDTPS model demonstrated the same results. Finally, a comparison was made between the two optimization techniques (HDTPS and PSO); the results show a better impact when using the HDTPS model for controller optimization. [ABSTRACT FROM AUTHOR]

Simultaneous operation of different energy generation and transmission infrastructures is a subject that has been considered under the concept of energy hub. This subject is highly regarded in the field of microgrids. One of the basic issues for investors is to properly utilize the energy hub for optimally managing energy carriers, especially in the energy price prediction. In the present paper, a new strategy is introduced for the energy hub in order to achieve the optimal performance of a microgrid (MG) that includes different energy carriers for each day. The objective of this strategy is to minimize the operation cost and consider the environmental issues. The proposed energy hub consists of a combined cooling-heating-power (CCHP) system along with a wind turbine and photovoltaic cells. The studied energy hub system is composed of an ice storage conditioner (ISC) system and an energy storage system (ESS) as the energy storage resource (ESR). One of the goals of the present work is to investigate the effect of solar-powered compressed-air energy storage (SPCAES) on the performance of the energy hub. The proposed strategy takes into account the uncertainty of the energy resources such as the wind and sun for meeting the electric, thermal, and cooling needs in different scenarios. In the present paper, to produce various scenarios, the Latin hypercube sampling (LHS) method is used. Also, the k-means method is used to reduce the number of scenarios. The objective function is solved using the modified grasshopper optimization algorithm (MGOA). According to the modeling results, the ESS can exhibit successful performance in the energy management strategy. • Considering the effects of the SPCAES and ISC on optimal performance of the energy hub. • Considering the effects of the SPCAES and ISC on the optimal performance. • Application of generations sources as PV, WT, CHP system, and TESS. [ABSTRACT FROM AUTHOR]

Energy is very important in daily life. The smart power system provides an energy management system using various techniques. Among other load types, campus microgrids are very important, and they consume large amounts of energy. Energy management systems in campus prosumer microgrids have been addressed in different works. A comprehensive study of previous works has not reviewed the architecture, tools, and energy storage systems of campus microgrids. In this paper, a survey of campus prosumer microgrids is presented considering their energy management schemes, optimization techniques, architectures, storage types, and design tools. The survey is comprised of one decade of past works for a true analysis. In the optimization techniques, deterministic and metaheuristic methods are reviewed considering their pros and cons. Smart grids are being installed in different campuses all over the world, and these are considered the best alternatives to conventional power systems. However, efficient energy management techniques and tools are required to make these grids more economical and stable. [ABSTRACT FROM AUTHOR]

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively. [ABSTRACT FROM AUTHOR]

• A combined EHOA and SVM has been used to perform an efficient DSM. EHOA has been applied to optimize the battery's energy storage and determine its capacity. • Using optimal data from the EHOA, SVMs are trained. This type of training can be applied to batteries in small-scale industries; • EHOA and SVM can be combined to improve the electricity cost when regarding the battery capacity. • Low-cost production per unit is provided by the suggested method in Digital Twin. The key element of tomorrow's smart islanded microgrids (SMG) is Demand Side Management (DSM). Global energy awareness has led to an increase in SMGs' performance and peak balancing capabilities. DSMs are the control layout in these grids, and they aim at optimizing loads in various ways. The SMG includes batteries and distributed photovoltaics. This paper combines an Elephant Herding optimization algorithm (EHOA) and support sector machine (SVM) to the decision-making method in batteries for reducing the electric bill. EHOA could be an effective method for finding a nearly optimum solution to the load scheduling issue in order to reduce the consumers' expenses. Particularly, energy costs could be decreased if the consumer responds to prices that vary with the time of day. Therefore, The EHOA has been applied to assign the battery's optimum energy storage range. The SVM has been trained as a powerful machine learning method using optimal information from the EHOA. It has been employed to determine the amount of energy that has been transmitted into and out of batteries in order to take the lowest possible electric bills. In comparison to the current approach of 2.3 at the consumption of 8.2 kWh/day, the cost of the suggested method of mean generation oscillation (G o s c ) has been equated to 2.27 dollars for the residential load. EHOA-SVM decreases 11.2% of energy costs, thereby assisting the decision-maker to balance the stability of demand-side measures. [ABSTRACT FROM AUTHOR]

Microgrid operations planning is one of the keys to ensuring the safe and efficient outputs of distributed energy resources (DERs) and the stable operation of a power system in a microgrid (MG). In this study, for the symmetry in renewable energy and microgrid systems, and coordinated control based on a storage battery system, an MG dispatching model with DER conditions and integrated costs is established in grid-connected mode, on the basis of MG operation costs, interaction costs, and pollutant emissions costs. Moreover, an optimization objective for minimizing integrated costs is established. Therefore, based on the original whale optimization algorithm (WOA), an improved whale optimization algorithm (IWOA) with adaptive weight strategy and Levy flight trajectory is proposed in this paper, to solve the optimal operations planning problem of MGs. Finally, in computing comparisons with methods such as the genetic algorithm (GA), particle swarm algorithm (PSO), WOA, wild horse optimizer (WHO), and enhanced whale optimization algorithm (EWOA), the results show that the IWOA computation had lower integrated costs and higher operational efficiency. Moreover, it is verified that the IWOA performed better in solving the MG operations planning problem. [ABSTRACT FROM AUTHOR]

Generation expansion planning (GEP) is a widely studied problem in the literature. However, with increasing participation of renewable energy sources (RES), the problem has to be looked upon considering intermittency associated with these sources. Integration of storage devices is seen as a viable option for counteracting the intermittency, particularly in isolated micro-grid (IMG). This paper puts forth a generalized mathematical structure for GEP centred on techno-economic criteria which duly addresses uncertainties associated with RES and correspondingly proposes a plan for addition of RES and storage units. As GEP problem is a highly constrained, large-scale problem involving assessment of enormous combinations, a decentralized multi-agent system (MAS) is used along with butterfly particle swarm optimization (BFPSO) technique for parallel processing. Hybridization of features of MAS with BFPSO called as multi-agent-based butterfly PSO algorithm (MABFPSO) presents a time effective and more accurate alternative to existing optimization techniques for addressing GEP problem. The formulation has been applied on IMG comprising of RES (solar and wind) and battery storage system. Results obtained from MABFPSO are compared with PSO with the purpose of establishing superiority of MABFPSO. It has been found that MABFPSO provides a cost benefit of 3.5% in comparison with classic PSO. For the same reliability standards, the capacity addition required for solar and wind generators is similar for both the techniques. However, the storage capacity requirement with PSO is 100% higher in comparison with that of MABFPSO. The comparison of convergence characteristics of two techniques clearly demonstrates that on the iteration scale, MABFPSO presents 29.6% faster convergence in comparison with that of PSO. [ABSTRACT FROM AUTHOR]

Rural electrification represents a great worry for developing nations. Isolated microgrids establishment is the brightest solution for this problem. Optimization strategies for mathematical simulation and modelling are accomplished in this research to size an isolated residential microgrid in Egypt consists of wind generators (WG), photovoltaic (PV), battery energy storage system (BESS) and Biomass generator. This paper's novelty relies on the techno-economic performance comparison of three sizing strategies of isolated microgrid based on renewable sources, considering the sources' uncertainty. Also, the environmental and economic effects of using rice straw in biomass gasification are introduced to handle the problem of dark clouds from its open-air burning. Three categories with nine combinations of renewable energy sources (RES) are introduced in this paper to select the ideal option. Multi-objective genetic algorithm (MOGA) optimized the systems under both deterministic and stochastic conditions, taking into consideration the uncertainties of solar radiation and wind speed using Monte-Carlo techniques. The obtained results are compared with the results of epsilon multiobjective genetic algorithm ("-MOGA). Simulation outcomes show that PV with BESS is the most economical option for microgrid sizing with a total system cost of $ 4.04 million. Besides, Scenario 6's hybrid system provides an appropriate ecological-reliable configuration, especially when considering sources' uncertainties. [ABSTRACT FROM AUTHOR]

The optimal operation of microgrids consists of renewable energy sources (RESs) play a key role in reducing greenhouse gasses and costs of operation. This paper suggests a stochastic optimal operation for an MG consisting of several RESs, storage systems and plug-in hybrid electric vehicles (PHEVs). The uncertainties of the MG are modeled through Monte Carlo simulation. The grasshopper optimization algorithm is employed here for optimizing the power management of the MG and various charging uncertain characteristics of PHEVs. Several simulations are provided to confirm the usefulness of the proposed model. The results validate that the recommended model can properly minimize the operation cost of the MG and reduce environmental pollution. Moreover, the optimal operation of the MG is promoted with several economic and technical benefits when integrating storage and PHEVs into the system. [ABSTRACT FROM AUTHOR]

Nowadays, the influences of the communication structure on the frequency regulation performance in a multi-area power system are barely studied. In this paper, a decentralized frequency regulation method for a multi-area power system considering optimization of communication structure is presented, and the influence of the communication structure on the frequency regulation performance is studied. Firstly, the communication network model is described and the multi-area power system model considering communication structure is presented. Then, the optimization model of communication structure during a decentralized frequency regulation process is constructed. This model aims to speed up the convergence speed of the control together with ensuring the high algebraic connectivity of the communication structure. Quantum binary particle swarm optimization (QB-PSO) algorithm is introduced to solve this model and, based on this, the communication structure optimization process and frequency regulation method are proposed. The simulation results show that the proposed method could greatly improve the frequency control efficiency through the optimization of the communication structure. [ABSTRACT FROM AUTHOR]

The sizing of microgrids depends on the type of load and its operational hours. The significance of understanding the load operational characteristics in special purpose islanded microgrids is much needed for economic system sizing. The load operation of special-purpose microgrids often consumes high power for a short duration and remains idle most of the time, thus reducing the load factor. The inclusion of such loads in microgrid sizing causes huge capital costs making islanded microgrids an unfeasible solution. The islanded microgrid under study is an agricultural microgrid in a village having a small Crab Processing Plant (CPP) and a Domestic Sector (DS). The CPP constitutes the major power consumption. The community has a unique load consumption trend that is dependent on the highly uncertain parameter of availability of the crabs. Interestingly, crab availability is an independent parameter and cannot be accurately scheduled. The existing system sizing of the microgrid is performed based on the conventional methods that consider the CPP for full-day operation. However, the microgrid sources, especially the storage system may be reflected as oversized if the crab processing plants do not operate for several days due to the uncertain behavior of CPP causing enormous power wastage. In this paper, an integrated fixed and operational mode strategy for uncertain heavy loads is formulated. The proposed algorithm is based on the optimal sizing methodology aided by the load scheduling control strategy. The Particle Swarm Optimization technique is used for the optimal sizing integrated with the fuzzy logic controller to manage the available load. The membership functions are available excess power and the state of the charge of storage that defines the operational conditions for CPP. Based on input membership functions, the fuzzy controller decides on power dispatch in DS or CPP, keeping considerable SoC available for night hours. The simulation result shows that the time-dependent fuzzy controller approach manages to provide power to both sectors under optimal sizing while reducing the overall cost by 24% less than the existing microgrid. [ABSTRACT FROM AUTHOR]

In this paper, renewable resources, namely photovoltaic panels (PV), are placed in a specific configuration to obtain the maximum reliability and availability of a microgrid and study the subcomponent-level reliability and availability. The reliability of components can be increased by trying different configurations of the components. We identify the preferred configuration used for the PV panels as bridged linked. The overall reliability of the microgrid is increased when component-wise reliability is considered. Even components are further divided into subcomponents, and the multiple faults of each component are considered. The method used for the reliability evaluation and availability study is Markov state transition modeling. The microgrid's reliability and availability are plotted concerning time using Matlab. The optimization of reliability and availability is conducted through optimization techniques such as the genetic algorithm (GA) and artificial neural networks (ANN). The results are compared and validated for the optimal values of mean time to failure (MTTF) and mean time to repair (MTTR). Using a genetic algorithm, there is a 96% of improvement in the reliability, and after applying the neural networks, a significant improvement of 97% along with quick results is achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, an improved Reinforcement Learning (RL) based approach for adaptive Virtual Synchronous Generator (VSG) control of grid-forming inverters is proposed. High penetration of inverter-based resources causes recurrent frequency stabilization issues. For this matter, a novel model-free Actor–Critic policy optimization technique is proposed to improve frequency transients metrics such as frequency nadir and Rate of Change of Frequency (RoCoF). Stability analysis of the VSG controlled system is conducted through small signal modeling to determine the stability margins of both the virtual inertia parameter J and damping coefficient D p. The control problem is formulated in RL terms using a new technique for state, action and reward construction that improves the learning process of the agents. The proposed approach's efficiency is tested for three RL algorithms, namely, Deep Deterministic Policy Gradient (DDPG), Soft Actor–Critic (SAC), and Twin-Delayed DDPG (TD3) with special attention given to the latter. Various performance metrics such as training time and maximum cumulative episodic reward are used to compare the three algorithms. The resulting agent based VSG adaptive controllers are simulated and then compared for power quality and frequency stabilization capabilities. Finally, a comparison between non-adaptive VSG and TD3 based VSG is presented to conclude on the efficiency improvements achieved by the proposed approach. • Conventional VSG Controllers necessitate recurrent parameter adjustments. • Adaptive VSG parameter adjustment is mandatory during power disturbance events. • Model-free RL-based Adaptive VSG Controllers provide more frequency stabilization. • The TD3 Algorithm is better than DDPG and SAC for Virtual Inertia Optimization. • RL Based Adaptive VSG Methods can be improved by using a novel state formulation. [ABSTRACT FROM AUTHOR]

This paper presented an effective technique for considering optimal scheduling of microgrid (MG) with islanding constraints. The proposed optimal scheduling approach is the combination of both the modified dragonfly algorithm (MDA) and whale optimization algorithm (WOA)-based approach named as (MDAWO). In this work, the searching conduct of the dragonflies is altered by the effective WOA approach. This WOA is used to the optimal scheduling of MG and also significantly reduces the computational burden. In this paper, the MDAWO is the appraisal technique used to set up the definite schedule of the MG coupling subject to the power assortment. The equality and inequality constraints are utilized to characterize the target capacity of a projected approach using the system information. Batteries act as an energy source to adjust the MG with continual running at an unfaltering and stable output control. Finally, the performance of the proposed technique is executed through the MATLAB/Simulink working platform with two different scenarios. With these two different scenarios, the proposed technique performance is compared with existing techniques such as dragonfly algorithm, firefly and gravitational search algorithm. Furthermore, the statistical analysis of the proposed technique based on cost and fitness are analyzed. Numerical simulations demonstrate the effectiveness of the proposed MG optimal scheduling model and explore its economic and reliability merits. [ABSTRACT FROM AUTHOR]

As decentralized energy supply units, microgrids can make a decisive contribution to achieving climate targets. In this context, it is particularly important to determine the optimal size of the energy components contained in the microgrids and their optimal operating schedule. Hence, mathematical optimization methods are often used in association with such tasks. In particular, mixed-integer linear programming (MILP) has proven to be a useful tool. Due to the versatility of the different energetic components (e.g., storages, solar modules) and their special technical characteristics, linear relationships can often only inadequately describe the real processes. In order to take advantage of linear solution techniques but at the same time better represent these real-world processes, accurate and efficient approximation techniques need to be applied in system modeling. In particular, nonlinear-bivariate functions represent a major challenge, which is why this paper derives and implements a method that addresses this issue. The advantage of this method is that any bivariate mixed-integer nonlinear programming (MINLP) formulation can be transformed into a MILP formulation using this comprehensive method. For a performance comparison, a mixed-integer quadratic constrained programming (MIQCP) model—as an MINLP special case—is applied and transformed into a MILP, and the solution of the transformed problem is compared with the one of the MIQCP. Since there are good off-the-shelf solvers for MIQCP problems available, the comparison is conservative. The results for an exemplary microgrid sizing task show that the method delivers a strong performance, both in terms of approximation error (0.08%) and computation time. The method and its implementation can serve as a general user-tool but also as a basis for further methodological developments and research. [ABSTRACT FROM AUTHOR]

Load and supply parameters may be uncertain in microgrids (MGs) due for instance to the intermittent nature of renewable energy sources among others. Guaranteeing reliable and stable MGs despite parameter uncertainties is crucial for their correct operation. Their stability and dynamical features are directly related to the controllers' parameters and power-sharing coefficients. Hence, to maintain power good quality within the desirable range of system parameters and to have a satisfactory response to sudden load changes, careful selection of the controllers and power-sharing coefficients are necessary. In this paper, a simple design approach for the optimal design of controllers' parameters is presented in an islanded MG. To that aim, an optimization problem is formulated based on a small-signal state-space model and solved by three different optimization techniques including particle swarm optimization (PSO), genetic algorithm (GA), and a proposed approach based on the combination of both PSO and GA. The optimized coefficients are selected to guarantee desirable static and dynamic responses in a wide range of operations regardless of the number of inverters, system configuration, output impedance differences, and load types. Through the proposed design and tuning method, the performance of the MG is improved as compared to those obtained using state-of-art techniques. This fact is demonstrated by using numerical simulations performed on a detailed model implemented in PSIM© software. [ABSTRACT FROM AUTHOR]

Summary: Energy management of the grid connected microgrid (MG) systems with low cost using a novel hybrid algorithm is implemented in this paper. The novel proposed hybrid technique is the combined performance of both the Random Forest (RF) and Coral Reefs Optimization (CRO) algorithm and in this way it is named as RFCRO. Optimum operation of micro‐sources for decreasing the electricity production cost by hourly day‐ahead and real‐time scheduling is the main objective of the paper. The proposed hybrid technique is to manage the power flows between the energy sources and the grid. To achieve this point, demand response and minimum cost of energy are determined. This technique is involved with the mathematical optimization problems that necessitate more than one fitness function to be optimized simultaneously. By using the inputs of MG‐like wind turbine, photovoltaic array, battery, and fuel cell with corresponding cost functions, the RF is employed to predict the load demand. CRO clarifies the squirrel in optimizing the configuration of MG based on the load demand. The proposed hybrid technique is implemented in MATLAB/Simulink working platform and compared with other solution techniques like BFOANN, ALO, GOAPSNN, and RBFNN‐SSA technique. The comparison result reveals that the superiority of the proposed technique confirms its ability to solve the problem. [ABSTRACT FROM AUTHOR]

This paper is treated with optimum energy management in a DC/AC Microgrid (MG) containing hybrid power sources to supply the load within cost minimization. In this hybrid electrical networks, energy sources are exploited as DC and AC manner, in which the Independent System Operator (ISO) should provide a practical coordination between them in order to procure the demand load optimally. This paper presents a framework that all available resources are formulated mathematically in hybrid microgrid with full constraints along with Demand Response (DR) programs implementation. The network under consideration can operate both in grid-tied and autonomous modes to manage power exchanging. Uncertainty and intermittent of Photovoltaic (PV) with Maximum Power Point Tracker (MPPT) equipped, Wind Turbine (WT), Energy Storage Systems (ESS) and DR programs are also considered to achieve the optimal control and operation. The ESSs are capable to connect both DC and AC links and the State of Charge (SOC) is maintained within permissible range. The proposed DG control framework and operation scheduling has facilitated the energy management of renewables using dynamic programing approach. A 24-hour time horizon simulation and discussion through three scenarios verified on a IEEE 33 bus distribution network, is done to represent the effectiveness of proposed energy management strategy to keep the whole hybrid grid stable. [ABSTRACT FROM AUTHOR]

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively. • The article proposes a hybrid MG with renewable energy sources and energy storage systems (battery and fuel cell). • Develop a novel energy management system using modified grey wolf optimizer (MGWO) with multi objective functions. • Analyzes the load demand and the cost of electricity from the power grid for effective designing of MG at test locations. • A significant savings of 30.88% and 49.99% of the rolling cost in comparison with FL and MILP based EMS respectively. [ABSTRACT FROM AUTHOR]

Summary: Recently, the microgrid (MG) structure day‐ahead scheduling is an important aspect and achieved an optimal operation by maximizing the utility function. In this paper, a day‐ahead scheduling of MG and their optimal operation are analyzed with the help of the proposed adaptive algorithm. For the optimal analysis of MG, adaptive grasshopper algorithm (AGOA) with cuckoo search (CS) is proposed. The CS algorithm is utilized to update the learning functions of the GOA, and the optimal performances are evaluated. Here, the photovoltaic (PV), wind turbine (WT), battery, and diesel generator (DG) are considered to analyze the optimal scheduling issues, and the main aim is to minimize their generating and operational cost functions. In addition, to maximize the profit of operations in MG, the load demand must be satisfied according to their constraints and objectives. The multiobjective function is defined as the cost functions of MG such as the fuel cost, generation cost, state of charge (SOC), direct cost, reserve cost, and penalty cost, respectively. The proposed method is implemented in MATLAB/Simulink platform and tested with the IEEE 57‐bus system and IEEE 118‐bus system. In order to verify the effectiveness of the proposed method, this is compared with the existing methods such as whale optimization algorithm (WOA) and cuttlefish algorithm (CFA), respectively. Before the comparative study, the real‐time data of PV and WT are analyzed for the 24 hours. The SOC of the proposed method is analyzed and is about 80%. [ABSTRACT FROM AUTHOR]

This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration. [ABSTRACT FROM AUTHOR]

Due to load shedding, circumvent equipment damage, and possible power outages in the electric power system. The continuity of power supply under the Islanded mode of operation in microgrid creates shrink in the power of the tie‐line, and frequency oscillation in the event of small load perturbations. Hence, there needs a sufficient capacity of distributed energy resources, proper power management, and a grid dynamic behavior to satisfy the load demand. Therefore, in this paper, an intelligent predictive feedback fuzzy controller (PFF) together with a conventional proportional–integral–derivative (PID) controller (PFFPID) using hybrid elephant herd virtual inertia (EVI) control optimization algorithm is proposed. The proposed controller is designed to optimize the operation based on considering the uncertainties of the grid situation in terms of weather conditions, energy backup capacity, and load profile. The three inputs provided to PFF are renewable power, battery power, and load power, as a membership function in different forms to find the power variation in terms of voltage, frequency, and current. Then to estimate the gain of each output power, the PID controller is used. The purpose of the EVI algorithm is used as a controller optimization algorithm to tune the PFFPID controller to compensate the load based on generated power to meet the load demand. Finally, the simulation is performed with several existing controllers and the result indicates that the proposed controller is robust with respect to random load perturbations and change in the parameter. [ABSTRACT FROM AUTHOR]

Modern Alternating Current (AC) microgrids (ACMGs) are highly complex because of the indeterminate nature of distributed energy generation and load causes frequency fluctuations. It is essential to maintain the constant power amongst load and generations by the proper design of an efficient controller to stabilize frequency fluctuations. In this paper, the type-2 fuzzy fractional-order tilt integral derivative controller structure is designed and analyzed for frequency control of an ACMG by using an enhanced Harris hawks optimization (EHHO). The modified optimization technique is analyzed employing test functions and compared with similar methods. It is noticed that the modified optimization technique is outperforming the other methods in most of the test functions. The frequency control performance of an ACMG is demonstrated and compared with the original Harris hawks optimization (HHO) and other optimization methods. The proposed type-2 fuzzy fractional-order tilt integral derivative controller tuned with EHHO technique is demonstrated to be effective and superior to any of the alternatives by application to a typical two-area test system. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) is an essential part of any power system network. ED is howto schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators' incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software. [ABSTRACT FROM AUTHOR]

Fossil fuels are used in the system to cause environmental pollution and global warming. Thus, nowadays, the use of renewable energy sources is becoming very popular in countries around the world. In addition to being environmentally friendly, installing of renewable energy sources has shown significant impacts on the power quality as well as on the operating costs of the system. In this paper, an effective, nature-inspired meta-heuristic approach is applied to research the optimal siting and sizing of distributed photovoltaic units (PVUs) in radial distribution systems. The main objective of this research is to focus on minimizing the power loss of the system while harmonic distortions are kept in the permissible limits of the IEEE Std. 519 and voltage profile is kept within the best range of voltage. The approach is called the Coyote Optimization Algorithm (COA), which is developed based on the behavioral characteristics of the coyote community, is introduced to ensure maximum benefits from the grid integrated PVUs system. This is a simple algorithm in application with few control parameters and it has the ability to expand the search zones and avoid local traps, resulting in a high performance and stability. The effectiveness of the method is demonstrated in comparison to other methods as Particle Swarm Optimization (PSO) and Salp Swarm Algorithm (SSA) in the IEEE 85-bus radial distribution system. The obtained results indicate the outstanding efficiency of the proposed method in maximizing the economic and technical benefits by optimal installation of distributed PVUs. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the power on an hourly basis for a day among the committed generating units to satisfy the feasible load demand. Emission of pollutants from the combustion fossil fuel and gradual depletion of fossil fuel encourages the usage of renewable energy sources. Implementation of renewable energy sources with the reinforcement of green energy transforms the fossil fuel-based plant into a hybrid generating plant. The increase in power production with the increase in electricity demand implicates challenges for economical operation. The proposed algorithm is applied to the DED problem for fossil fuel based and renewable energy system to find economic schedule of generated power among the committed generating units. The proposed optimization algorithm is inspired by the huddling behavior of the emperor penguin. The exploration strategy is enhanced by adapting oppositional based learning. Chaotic mapping is used to maintain a proper balance between exploration and exploitation in the entire search space, which minimizes the cost of generation in the power system. [ABSTRACT FROM AUTHOR]

This paper develops a resilience analysis framework to study the fault ride-through capability of direct current (DC) microgrids in unknown denial of service (DoS) cyber incidents. DoS can be a frequent threat to DC microgrids with advanced controllers that hinge on active information exchanges: it can paralyze data communications and cause control ineptness or even system instability. Furthermore, we show that temporal DoS incidents render the DC microgrid cyber-physical topology and parameters time-varying and cause them to jump between faulty conditions. As existing DC microgrid stability analysis results are primarily developed from time-invariant systems, they might not be valid for the DoS interrupted systems. In this paper, we seek to study whether a DC microgrid is resilient against time-varying unknown DoS incidents. The problem is formulated as a stability analysis problem for a system with randomly switching dynamics. Scalable conditions are developed to provide provable resilience guarantees. In addition, we exploit the special structure of the conditions to yield quantified resilience measure by convex optimization techniques. The measure can be used for the evaluation of the resilience of different DC microgrid design against DoS incidents. We demonstrate the effectiveness of the proposed work using simulation case studies. [ABSTRACT FROM AUTHOR]

The paper raises the issue of controlling rural low voltage microgrids in an optimal manner. The impact of different criterion functions, related to the amount of energy exchanged with the distribution system operator network, the level of active power losses, the amount of energy generated by different energy sources and the value of financial performance measures regarding the microgrid operation, on the choice of operating points for devices suggested by the optimization algorithm has been analyzed. Both island and synchronous microgrid operation modes are being considered. We propose two variants of the optimization procedure: the first one is based on the particle swarm optimization algorithm and centralized control logic, and the second one takes advantage of the decentralized approach and Monte Carlo methods. A comparison of the simulation results for two sample rural microgrids, obtained for different objective functions, microgrid operation modes and optimization procedure variants, with the use of prepared algorithm implementations, has been provided. The results show that the proper choice of an objective function can have a crucial impact on the optimization algorithm's behavior, the choice of operating points and, as a consequence, on microgrid behavior as well. The choice of the proper form of the objective function is the responsibility of the person in charge of both the microgrid itself and its operation. This paper can contribute towards making correct decisions in this area. Generally, slightly better results have been achieved for the centralized control mode of operation. Nevertheless, the results also suggest that in many cases the approach based on distributed logic can return results that are better or sufficiently close to the ones provided by the centralized and more sophisticated approach. [ABSTRACT FROM AUTHOR]

In view of Internet of Energy, advanced operational optimization in microgrid energy management system (MEMS) is expected to be scalable to accommodate various participants, support plug-and-play, and optimize energy utilization. Based on potential game, this paper presents a fully distributed operational optimization for MEMS with high penetration of renewable energy and demand response. After analyzing the microgrid and potential game, the establishment and proof of an exact potential game model is presented in detail. Then the best-strategy-response iterative algorithm is proposed to find the game Nash equilibrium in a distributed way. Finally, the proposed approach is simulated in an islanded microgrid to verify its efficiency and feasibility. The significances of this paper are as follows. First, the scheme and algorithm are fully distributed. All heterogeneous individuals are treated as independent decision-making entities without a central coordinator/processor. Second, the optimization process is open and dynamic. The convergence is achieved with no limits to type or number of players, even if the process of execution is corrupted with delay or losses in communication information. Third, the consistency between individual rationality and overall importance is guaranteed, and individual best-strategy-response behaviors necessarily improve overall optimality. [ABSTRACT FROM AUTHOR]

Highlights • A near-optimal energy cost optimization algorithm, named ECO-Trade, is proposed. • The resulting trading strategy minimizes the total cost of all households. • Results show that unrestrained peer-to-peer trading may increase the energy cost. • Any potentially unfair cost distribution is addressed by ensuring Pareto optimality. Abstract This paper evaluates the impact of Peer-to-Peer (P2P) energy trading among the smart homes in a microgrid. Recent trends show that the households are gradually adopting renewables (e.g., photovoltaics) and energy storage (e.g., electric vehicles) in their premises. This research addresses the energy cost optimization problem in the smart homes which are connected together for energy sharing. The contributions of this paper is threefold. First, we propose a near-optimal algorithm, named Energy Cost Optimization via Trade (ECO-Trade), which coordinates P2P energy trading among the smart homes with a Demand Side Management (DSM) system. Our results show that, for real datasets, 99% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Second, P2P energy trading in the microgrid potentially results in an unfair cost distribution among the participating households. We address this unfair cost distribution problem by enforcing Pareto optimality, ensuring that no households will be worse off to improve the cost of others. Finally, we evaluate the impact of renewables and storage penetration rate in the microgrid. Our results show that cost savings do not always increase linearly with an increase in the renewables and storage penetration rate. Rather they decrease gradually after a saturation point. [ABSTRACT FROM AUTHOR]

This paper proposes a new stochastic optimization framework for day-ahead (DA) energy management of a renewable-based isolated rural microgrid. Non-dispatchable distributed generations (DGs) including wind and photovoltaic (PV) power plants have been operated by the microgrid in order to meet consumers' demand. The main challenge in this condition is to keep balance between generation and demand at all times. In order to cope with this challenge, a pumped-storage unit and an incentive-based demand response program have been used in this work. Moreover, the accurate model of pumped-storage unit, responsive demand and distribution network and also the inherent uncertainty of wind and PV power generation have been considered in this paper. Finally, the effectiveness of the proposed optimization framework has been evaluated through extensive numerical studies. The results show that the pumped-storage unit optimal scheduling and demand response implementation, significantly improve economic and technical performance indexes. [ABSTRACT FROM AUTHOR]

Summary: In battery swapping station (BSS), the battery swapping of electric vehicle (EV) is not synchronous with the centralized charging of BSS. Based on new energy electricity generation and battery swapping demand information of EVs, how to formulate charging–discharging strategies of centralized charging stations is essential in BSS energy management system. Different from traditional BSS scheduling issues, considering that the supply–demand balance of power in isolated microgrid (IMG) could be regulated by the discharging of backup batteries, a service model of EV BSS is proposed with strong constraints, coupling relationships and randomness. Because the microgrid‐BSS capacity configuration optimization issue is nonlinear and multivariable, if traditional optimization algorithms are utilized, it would take a long time and the optimal solution may not be obtained. Moreover, there is no guarantee of complete convergence even with the standard particle swarm optimization (PSO) algorithm. In this paper, the quantum behavior particle swarm optimization (QPSO) algorithm is used to handle operation control issues of BSS. As shown in case results, with the QPSO algorithm proposed, both the reliability of battery swapping service and the maximum profit of BSSs could be implemented. [ABSTRACT FROM AUTHOR]

The rapid increasing of data centers calls for an efficient method to reduce high operation costs and carbon emissions. This paper proposes a cooperative online schedule framework for local interconnected data centers considering shared energy storage. A time-average optimization problem is built to reduce the overall operation cost with essential operational constraints. Multiple randomness and the temporal coupling constraints of energy system levels make it challenging to solve the stochastic problem. Adapting the Lyapunov optimization theory with relaxation and perturbation technique, an online algorithm is proposed to find an acceptable solution to the problem to achieve both good service coordination and efficient energy coordination. The algorithm does not require prediction of random parameters, and has a fast convergence rate. Especially, the theoretical conditions for convergence and the distance between the acceptable and real optimal solution are derived in a closed form. Simulation studies show that the algorithm can effectively reduce operation cost of data centers, carbon emissions, and dependence on the main grid. • A new microgrid consisting of local data centers and shared ESS is investigated. • A cooperative framework to realize service and energy coordination is provided. • The proposed online algorithm finds an acceptable solution without predictions. • The algorithm is compared with forward-looking algorithm and rolling algorithm. [ABSTRACT FROM AUTHOR]