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In order to improve the operation optimization effect of the microgrid system, this paper combines the intelligent system model to construct the operation optimization model of the microgrid system. Moreover, this paper conducts a quantitative calculation and research on the maximum compensation capacity of the active power filter device and proposes a harmonic power adaptive compensation control strategy in the case of the limited output capacity of the APF device. In addition, this paper adopts the method of extracting the harmonic current command in the synchronous coordinate system based on the band-pass filter composed of SOGI and combines the RDFT algorithm to determine the number of harmonics that can be compensated and the degree of compensation for the harmonic number. Finally, this paper combines simulation experiments and comprehensive evaluation to study and verify that the operation optimization effect of the microgrid system based on the intelligent system model is better and can effectively improve the operation effect of the microgrid system. [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]

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]

Summary: The distributed generations (DGs) of an islanded microgrids (MGs) comprises of interconnecting primary and secondary control layers. The interactions among these controllers may yield new oscillatory modes with low damping, which decrease the stability margins. As the distribution network is dynamic in nature, the maintenance of system stability is of great concern, mainly while operating in autonomous mode. This paper proposes the optimal internal model control (IMC)‐based droop control scheme to improve the small‐signal stability and transient‐response of an inverter‐based MGs (IMGs) under different operating conditions. The proposed optimal scheme is achieved with the following approach: (a) first, comprehensive small signal model is prepared for the study system, and the interaction of the control parameters which influence the system stability are investigated through eigenvalue analysis, (b) the critical values of the significant parameters of the IMC and droop controllers are sorted out, and their corresponding stability domain is formulated, (c) based on the stability domain, PSO optimization technique is employed to generate optimal values, which delivers effective coordination among the crucial control parameters to improve the stability, (d) the developed optimal control scheme is cross‐validated by comparing with the existing methods from the literature. The effectiveness of the proposed optimal scheme is subsequently assessed quantitatively with the help of time‐domain specifications. [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]

In this paper, two-level decentralized optimization power dispatch control strategies are proposed in an islanded microgrid without communication network to ensure frequency security and economic benefit. First, the secondary-level decentralized optimization control strategy is designed only by using local measurement regarding frequency deviation. It is able to implement the optimal power dispatch for all dispatchable distributed generation units in a coordinated way in order to completely restore the system frequency with minimum operation cost. Then, the first-level decentralized droop compensation control strategy is designed to implement real-time optimal power sharing among all energy storage units in order to quickly compensate large supply-demand imbalance and ensure frequency security. Moreover, the proposed control strategies are proved to be able to meet the requirements of the 'plug and play' operation in the absence of communication network. Simulation results verify the validity of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes the new concept of preplanned microgrid islanding (PMI) in the smart distribution system. PMI is a special form of controlled islanding that can be used to provide demand-side service for electrical users under the scheduled distribution operation, especially for the preventive maintenance. A simulation-based multi-objective optimization framework is developed to decide the starting time and duration of PMI. The optimization objectives include the minimization of load curtailment, operating cost, and pollutant emission. On one hand, the stochastic short-term simulation method is developed to assess the performance of candidate PMI schemes considering multiple uncertainties and optimal scheduling of microgrid operation. On the other hand, the weighted-sum approach is used to compromise the conflicting objectives according to the decision preferences of users. Furthermore, the response surface methodology is adopted to improve the efficiency of scheme selection with the help of experimental design technique and regression analysis. Finally, the effectiveness and high efficiency of the proposed method is verified in case studies. Sensitivity analysis is also performed to investigate the impact of decision preference, season scenario, and sizing alternative on optimization result. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Flexible AC transmission systems (FACTS) and optimal power‐flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear‐nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem‐based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi‐ and single‐OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30‐bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm. [ABSTRACT FROM AUTHOR]

The establishment of isolated microgrid is of great significance in solving power supply problems in offshore islands or remote mountainous areas. Aiming at the isolated microgrid containing photovoltaic, photothermal, wind, diesel, and energy storage, a three-objective sizing optimization model of the microgrid is proposed considering comprehensive economy cost, deficiency of power supply probability (DPSP), and renewable energy discard rate (REDR). The three-objective sizing optimization model was solved by the improved multiobjective grey wolf optimization algorithm. An island was taken as an example to optimize the sizing of the microgrid, and the rationality of the proposed three-objective model was verified. The feasibility of the improved multiobjective grey wolf optimization (IMOGWO) was verified by comparing with the multiobjective grey wolf optimization (MOGWO). Three representative solution sets and a set of compromise solution sets are obtained by simulation, and the results satisfied the load demand. And the DPSP and the REDR are reduced by 7.55% and 6.29% by using the IMOGWO. The designed and analyzed hybrid renewable energy system model might be applicable to around the world having similar climate conditions. [ABSTRACT FROM AUTHOR]

GIS (gas-insulated switchgear) is important equipment in the substation system in a complex microgrid network. Due to the long-term service in harsh operation environments, the electrical performance of GIS equipment will be seriously affected. Therefore, the regular maintenance of GIS equipment in the substation system is a routine task so as to ensure the normal operation of the microgrid networks. The traditional method relies on manual labor, where not only with the low operation efficiency but also regarding some maintenance, labors cannot reach the inside of the GIS equipment, which seriously restricted the completion of GIS equipment maintenance operation. Based on the above analysis, a basic configuration of a GIS equipment maintenance robot with smart structure, convenient control, and stable motion characteristic has been proposed in this paper. Through the optimization design of the chassis adsorption system, the GIS inspection robot can achieve hanging and vertical adsorption inside the GIS equipment and some special motion status. In this way, the entire GIS equipment can be inspected without blind spots. Through establishing the mechanical model of the GIS inspection robot under the hanging and vertical motion state, the robustness and stability of the GIS inspection robot in the special status have been analyzed, and the corresponding GIS inspection robot robust stability motion control method has also been proposed. Finally, through the integrated design of mechanical system and hardware and software control system, the GIS inspection robot physical prototype system has been developed and the maintenance operation experiment has been carried out in Hunan Electric Power Company Maintenance Company through simulating the internal GIS equipment. The physical prototype can realize the basic movement and special movement in internal GIS equipment without any blind spots and complete the inspection task of GIS equipment operation; the movement of GIS robot is dexterous and stable in the whole operation process, which has strong robust stability. The research of GIS inspection robot in the complex microgrid networks has important theoretical significance and practical application value for the intelligent operation and maintenance management of substation system in complex microgrid networks. [ABSTRACT FROM AUTHOR]

The wide use of renewable energy sources stimulates the implementation of microgrid (MG) systems. MG is an independent power system that consists of various distributed generations (DGs), storage devices, and loads. The optimization problems of MG have gained wide spread interest considering the economic and environmental operation management of the system. To address the multi-variable non-linear programming problem with multiple hybrid constraints, this paper proposes a novel hybrid multi-objective fireworks algorithm with gravitational search operator (MFAGSO). In the algorithm, the operator, as a new search mechanism, directs the sparks in the clustered region to exchange positional information with Pareto-optimal solutions at each iteration process for obtaining accurate results. The operation management of typical grid-connected MG, which comprises various DGs and storage battery, was optimized within 24 hours to evaluate the performance of the proposed algorithm. Considering operation cost and pollutant emission individually or simultaneously, 3 scenarios were involved in operation management for power market planning and practical considerations. The simulation results of the proposed algorithm were compared with those from other methods employed in previous literature and original fireworks algorithm. The results demonstrated the superior performance of the proposed method to solve operation management for optimizing problems of MG. [ABSTRACT FROM AUTHOR]

To solve problems such as the high cost of microgrids (MGs), balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS) and harmony search algorithm (HSA) are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA) can effectively improve the stability and economy of MG operation with an ESS. [ABSTRACT FROM AUTHOR]

Summary. When the microgrid topology changes, the power output of the inverter cannot be adaptively adjusted by traditional droop control, and the dynamic performance and steady-state accuracy of the inverter are affected. To solve this problem, a three-partition multistrategy adaptive fruit fly optimization algorithm (MSAD-FOA) is proposed, which performs a real-time optimization of the PI parameters to realize microgrid droop control. The fruit fly population is divided into three regions according to the ranking of the fitness values of the algorithm. Next, the multistrategy model is automatically updated according to the difference in the fruit fly performance in each region. The local fine search in zone I ensures that the population does not degenerate. Zone II pertains to the adaptive adjustment to ensure the diversity and convergence of the algorithm. Zone III guides the fruit flies to accelerate convergence. The effectiveness of the algorithm and feasibility of the proposed control strategy are verified through a theoretical simulation and microgrid droop control simulation. The comparison with other algorithms demonstrates the superiority of the development and exploration ability of the proposed algorithm. The response speed of the inverter is 40 times higher when the proposed control strategy is used, and the steady-state error is reduced by 4.3%. [ABSTRACT FROM AUTHOR]

Summary: Energy management system (EMS) is a modern methodology in smart households' advancement that build collaboration among end‐users with their home appliances to operate automatically, multifunctionally, adaptably, and efficiently. EMS is categorized into several applications; battery energy management is an eminent application on smart grid applications. Such an approach is responsible for cost minimization during peak hour operated loads with various optimization methods and different adaptive controllers, which are described. Applications of EMS based different topologies are recommended with issues on these techniques have also been analyzed. Results obtained on EMS using optimization methods and battery EMS using fuzzy and PI controllers are demonstrated. Finally, based on the review, potential future scopes in this topic have been identified, and future research directions in this context are pointed out. [ABSTRACT FROM AUTHOR]

Stand-alone hybrid energy systems based on solar and energy storage are an effective option for rural areas to meet the load demand. The objective of the current work is to the optimal configuration of a stand-alone hybrid photovoltaic/battery energy storage system with the help of an efficient metaheuristic algorithm, improved harmony search, to supply electrical of a residential load in Iran. The objective function is a minimization of total life cycle cost (TLCC) subject to the reliability index (loss of load probability). The optimal configurations of hybrid systems are compared in respect of different losses of load probability (0 to 20%). Sensitivity analysis and effects of economic parameters based on photovoltaic and battery prices are carried out to study the possibility of the suggested scheme. The results show that, by increasing the reliability index from 0 to 20%, the optimal number of panels and batteries decreases by 52 and 1202. Also, it is found that the TLCC of the system and cost of system components are increased by decreasing of the reliability index value. [ABSTRACT FROM AUTHOR]

A grid connected renewable energy based hybrid microgrid, incorporating solar PV, biomass generators, and Gravity Energy storage system (GESS) has been proposed in this article. This is done to provide uninterrupted energy to a hilly village of North‐East India. A bi‐directional energy transaction with the central grid is considered for handling uncertainties and to gain monetary benefit through energy sale. The GESS is only charged through solar PV and biomass generators, to reduce the carbon footprint. The outward energy transaction with the central grid helps in reducing the energy cost for consumers inside the microgrid. Transaction price of energy from the central grid is considered from historical data available in the website of Indian Energy Exchange Limited (IEX). For different combinations of loading pattern and GESS initial storage, the Levelized cost of energy (LCOE) is found to vary in between INR 2.71/kWh to INR 3.41/kWh, which is lower than the current Nagaland state government approved tariff of INR 3.55/kWh. Any subsidy from governmental or non‐governmental organizations has not been considered in the present study while calculating the installation cost of Solar PV and Biomass generators to portray the actual scenario. Whale optimization algorithm (WOA) has been used to find the optimal solution of the proposed system. [ABSTRACT FROM AUTHOR]

Summary: This study aims to provide a comprehensive review about the configurations, operation, and integration of multiple energy sources for microgrid (MG) system. The applications of renewable and non‐renewable energy sources have been discussed and analysed. Similarly, the several control strategies, protection schemes, and communication networks used for MG have been defined to provide a unique and distinct perspective for an optimal and real‐time project. During the review analysis, different used software tools and techniques were observed and applied broadly. However, the most frequently used software and technique are hybrid optimization model for electric renewables (HOMER) Pro and particle swarm optimization algorithm. Additionally, the total global installed capacity for renewable sources is enhanced up to 2588 GW, including 627 GW of PV source, along with the global investment of USD 316.7 Billion on clean energy sources. Furthermore, the least levelized cost of energy for on‐grid and off‐grid hybrid systems using HOMER Pro was found as $0.072 and $0.145/kWh, respectively. Finally, the work has provided thorough study and future directions about MGs to assist engineers and researchers for their upcoming prospects in the field of renewable energy. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method. [ABSTRACT FROM AUTHOR]