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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: 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: 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]

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]

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]

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]