A Case Study of Control and Improved Simplified Swarm Optimization for Economic Dispatch of a Stand-Alone Modular Microgrid

Abstract

Due to the complex configuration and control framework, the conventional microgrid is not cost-effective for engineering applications with small or medium capacity. A stand-alone modular microgrid with separated AC bus and decentralized control strategy is proposed in this paper. Each module is a self-powered system, which consists of wind and solar power, a storage battery, load and three-port converter. The modules are interconnected by three-port converters to form the microgrid. Characteristics, operation principle, control of the modular microgrid and the three-port converter are analyzed in detail. Distributed storage batteries enable power exchanges among modules to enhance economic returns. Economic dispatch of the stand-alone modular microgrid is a mixed-integer programming problem. A day-ahead operation optimization model including fuel cost, battery operation cost, and power transmission cost is established. Because there are so many constraints, it is difficult to produce a feasible solution and even more difficult to have an improved solution. An improved simplified swarm optimization (iSSO) method is therefore proposed. The iSSO scheme designs the new update mechanism and survival of the fittest policy. The experimental results from the demonstration project on DongAo Island reflect the effectiveness of the stand-alone modular microgrid and the economic dispatch strategy based on the iSSO method.

A novel boundary swarm optimization method for reliability redundancy allocation problems

“…Numerous studies have shown the competency of SSO and the critical role the algorithm plays in a myriad of research areas including: the RAP which was solved by the first orthogonal SSO [25], the RRAP using the SSO hybrid with the PSO [5], the disassembly sequencing problem in the green supply chain domain with the first self-adaptive SSO [26,27], the training of artificial neural network in mining time series data which is based on the first continuous SSO [28], the high-dimensional numerical continuous functions that use an improved continuous SSO in [29], SSO with the macroscopic indeterminacy in [30], SSO combined with the glowworm swarm optimization in [31], the RFID network problem in health care management [32], the dispatch problems solving by hybrid bacterial foraging and SSO [33] and by gradient-based SSO [34], and the network security problem in detecting network intrusions [35], etc. Experimental results have confirmed that the SSO and its variants outperform PSO, GA, EDA, and ANN in RAP problems [25,36,37], RRAP problems [5,7,8], and other problems [26][27][28][29][30][31][32][33][34][35][37][38][39][40][41][42].…”

“…Similar to all major AI, in the first generation of SSO, all solutions are randomly initialized [5,7,8,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. The basic idea of the update mechanism SSO in updating xi,j is based on the following stepwise functions [5,7,8,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

“…3. With pBest [5, 7, 8, 25-29, 31-40, 43] vs. without pBest [29,41,42] The pBest of a solution, e.g., Xi, is the best solution in the update process of the solution thus far. In PSO [9,11,12] and the traditional SSO [5, 7, 8, 25-29, 31-40, 43], changing pBest can prevent X from being updated to a poor solution in the next generation.…”

Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm

“…The SSO has also played a very significant solution role in relevant studies of artificial intelligence. Furthermore, the SSO has been applied by many papers to solve different types of problems in various fields [35][36][37][38]43].…”

“…[31][32][33][34][35][36][37][38]. For example, genetic algorithms [39], artificial bee colony algorithms [40], particle swarm optimization [34,37,41,42], simplified swarm optimization (SSO) [34][35][36][37][38][42][43][44][45], grey wolf [46], neural network [13,41,45,47], harmony search algorithm [47], Sugeno-Type Fuzzy Inference [48], etc. Among these algorithms, SSO proposed by Yeh is the most simple one.…”