Self-Adaptive Dragonfly Algorithm for Optimal Allocation of Multiple Energy Storage Systems with VAr Support for Islanded Microgrid Operation

Abstract

Microgrid (MG) operation is one of the operational requirements of modern utilities for not only maintaining reliable and uninterrupted supply under faulty conditions but also for achieving desired economic goals in the competitive electricity market environment. Under faulty conditions, the electrical distribution system (EDS) becomes islanded microgrid (IMG) with either micro turbine (MT) or renewable energy (RE) based distribution generation (DG) units. However, the power generation from RE based DGs is stochastic nature and it may become either surplus or deficit to the network connected load. Also, the reactive power (VAr) support from DGs is limited. Under this scenario, there is a need for the integration of energy storage systems (ESSs) and reactive power compensators like switched/ fixed capacitor banks (CBs). In this paper, a novel optimization approach for determining the locations and capacities of ESSs combined with CBs along with DGs is proposed based on improved variant of dragonfly algorithm (DFA). Different performance variables of basic DFA are tuned by a self-adaptive mechanism in SADFA for attaining the global solution with least computational efforts. Simulation results on IEEE 33-bus are compared with literature works and also other algorithms namely basic DFA, PSO, BOA, and FSA. The islanded network is suffered with a loss of 97.1229 kW, whereas, it is reduced to 74.2305 kW, 47.3380 kW and 37.6792 kW, by integrating CBs, DGs and simultaneous CBs and DGs, respectively. Based on the comparative analysis, SAFDA is dominated literature works and all other simulated algorithms. Also, the proposed method for ESSs, CBs and DGs integration is shown its effectiveness for serving the IMG energy requirements with reduced losses and increased economic benefits and its suitability for practical applications.

Quad Tournament Optimizer: A Novel Metaheuristic Based on Tournament Among Four Strategies

“…Metaheuristics have become a popular method for solving various optimization problems in recent decades, particularly in the engineering field. In the power system, metaheuristic has been used to optimize the solar cell and photovoltaic system [1], multiple energy storage system [2], capacitor bank [3], optimal power flow [4], and so on. In the biomedical works, metaheuristic has been used to optimize the detection of COVID-19 severity [5], lung cancer [6], brain tumor [7], anterior cruciate ligament deficiency [8], melanoma [9], and so on.…”