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

A hybrid energy system (HES) is a perfect option for supplying electric energy to remote areas. A HES normally uses renewable energy sources such as wind and PV. Owing to the intermittent nature of these sources, HES should have batteries and/or conventional energy sources. HES proposed in this study is having wind, PV, batteries, and diesel generators. The design and operation of HES are considerably improved with the use of smart grid concepts. This study introduced a fuzzy logic controller to implement a new demand response strategy (DRS) where the electricity tariff is determined based on the state of charge of the battery, the charging/discharging power from the battery, and the previous response from the customers. A modified cuckoo search (MCS) optimization algorithm is introduced for sizing HES components for the lowest cost of energy (CoE) and loss of load probability (LOLP). A multiobjective function consisting of the CoE and LOLP is used to get the optimal design of HES. The MCS reduces the number of times that the optimization algorithm executes the objective function. The continuous reduction of the swarm size proposed in this paper enhances the exploration in the beginning and enhances exploitation at the final stage. The MCS is compared with 10 state-of-the-art optimization algorithms. The results from using MCS reduced the convergence time to 25-63% of the time needed by other optimization algorithms and the DRS introduced in this study reduced the CoE by 34% compared with the flat-rate pricing.