Category-Based Multiobjective Approach for Optimal Integration of Distributed Generation and Energy Storage Systems in Distribution Networks

Abstract

Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective; hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the Whale Optimization Algorithm and Genetic Algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic Algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.

Modeling and Optimization of Combined Heating, Power, and Gas Production System Based on Renewable Energies

“…To show the efficiency and superiority of the proposed method, the results have been compared with other meta-heuristic methods. As can be seen in Table 5, the whale optimization algorithm [34], the particle swarm optimization algorithm [35], and the refrigeration simulation algorithm [36] have been selected for comparison with the proposed method. The solution is performed by meta-heuristic algorithms in the Julia programming To check and correct the absolute optimality of the results and also to verify the obtained results, in addition to solving the problem with the latest version of Grooby, it has also been implemented with the latest version of Muzak solver, and the results have been compared.…”

“…To show the efficiency and superiority of the proposed method, the results have been compared with other meta-heuristic methods. As can be seen in Table 5, the whale optimization algorithm [34], the particle swarm optimization algorithm [35], and the refrigeration simulation algorithm [36] have been selected for comparison with the proposed method. The solution is performed by meta-heuristic algorithms in the Julia programming language and the "meta-heuristic" optimization package [37].…”

“…The solution is performed by meta-heuristic algorithms in the Julia programming language and the "meta-heuristic" optimization package [37]. The parameters of these algorithms are set based on references [34][35][36].…”

PRESEP: Cluster Based Metaheuristic Algorithm for Energy-Efficient Wireless Sensor Network Application in Internet of Things

“…A game theory inspired Threshold Game Theory Algorithm based DEEC considered weighted CH and member energy consumption for threshold [51]. Whale Optimization Algorithm optimally search the nodes with highest fitness value based on hunting behavior of humpback whales for CH election [52]. Rooster based classification used for CH selection proposed in Chicken Swarm Optimization algorithm [53].…”

Overview of Loss Sensitivity Analysis in Modern Distribution Systems

“…However, the increased engagement of consumers in such activities may impose new challenges to the operation of the system. If such consumer activities are not properly accounted for, technical challenges like increased power losses [4], nodal under-voltages [5], and reverse power flow [6] will arise. High levels of power losses in the distribution system results in economic losses and degradation of overall system efficiency.…”