Hybrid energy system design using greedy particle swarm and biogeography‐based optimisation

Abstract

Renewable energy systems (RESs) are affordable, clean and sustainable. However, their output power is intermittent. Therefore, RESs are usually combined with an energy storage system or conventional sources to make the overall operation uninterruptable. Optimal sizing of hybrid energy system components is imperative to be financially and technically feasible. In this study, a multi-objective optimisation based on a hybrid optimisation procedure, which combines the exploitation ability of the biogeography-based optimisation (BBO) with the exploration ability of the particle swarm optimisation (PSO), is used to handle the system design. This algorithm is known as greedy particle swarm and BBO algorithm (GPSBBO). Weighted sum method is added to the GPSBBO to handle the multi-objective nature of the design problem. A case study for a hybrid wind-PV energy system design in the standalone and grid-connected configurations is presented to illustrate the proposed method. Coverage of two sets, hypervolume and diversity performance indices are used to compare results of the proposed method to nondominated sorting genetic algorithm and the multi-objective PSO. These indices show an improved performance of the suggested method in finding the optimal system design.

Novel Fuzzy-Swarm Optimization for Sizing of Hybrid Energy Systems Applying Smart Grid Concepts

“…The relation between the wind speed at the hub height of the WT to the measurement height is shown in Eqn. (1). [33].…”

“…Renewable energy sources (RES) are becoming the best option for the electricity supply of remote areas, distributed generation, as well as central power stations. The share of the worldwide generated energy from RES is about 20% [1] and it may share 50% by the end of the 21 st century [2]. The intermittent nature of RES introduces reliability challenge issues to its use in the power systems.…”

“…The use of batteries and diesel generators improved the performance of RES, especially in abnormal operating conditions. Different methodologies have been introduced to schedule the charging/discharging of the battery system [1,3]. The main challenge of batteries and diesel generators is the high initial and operating costs.…”

“…[20], have been introduced for sizing and planning of HES, but the main problem with these programs are, they are inflexible for considering the modern smart grid strategies such as the DRS, or custom battery charging strategies, etc. [1]. For this reason, it is better to introduce flexible software to handle the planning of HES taking the smart grid concepts and other custom controllers into considerations which have been introduced effectively in this study.…”

“…Many SC algorithms have been used for solving the planning problem of HES such as genetic algorithm (GA) [14], particle swarm optimization (PSO) [23], biogeography-based optimisation (BBO) [1], artificial bee colony (ABC) [24], cuckoo search (CS) [25], bacterial foraging algorithm (BFA) [26], grasshopper optimization algorithm (GOA) [26], crow search algorithm (CSA) [8], firefly algorithm (FFA) [27], social mimic optimization (SMO) [6], grey wolf optimization (GWO) [15], and jellyfish search (JS) [28]. Some other researchers used two different SC optimization algorithms to gain the exploration performance of one SC algorithm at the beginning of the optimization and gain the exploitation performance of the other technique at the end of the optimization [1,29]. A detailed revision of the SC algorithms used in the sizing of HES is shown in [21,30,31].…”