Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic

Abstract

In recent years, many studies have studied economic dispatch problem in power systems. However, most of them have not considered the environmental pollution caused by fossil fuels. In this study, the use of an evolutionary search algorithm called multi-objective particle swarm optimization algorithm is proposed to solve the economic dispatch problem in power systems while considering environmental pollution. The proposed method is validated in terms of its accuracy and convergence speed based on comparisons with the results obtained using the classic nonlinear programming method. The proposed strategy is applied to a realistic power system under various conditions. Overall, six generating units are investigated along the corresponding constraints. The results obtained reveal that costs of operation and pollution with/ without power loss are reduced significantly by the proposed approach. Obtained results show a good compromise can be established between two contradicting functions of exploitation cost and pollution by optimizing them simultaneously.

A Cost-Effective Solution for Non-Convex Economic Load Dispatch Problems in Power Systems Using Slime Mould Algorithm

“…Heuristic techniques, which are known for their adaptability and flexibility, have received a lot of attention in recent years for solving a range of real-time economic load dispatch issues. Such techniques include Orthogonal Learning Competitive Swarm Optimizer(OLCSO) [35], Water Cycle Algorithm (WCA) [36], Moth Flame Optimizer (MFO) [37], Opposition-Based Krill Herd Algorithm (OKHA) [38], Two-Stage Artificial Bee Colony (TSABC) [39], Modified Crow Search Algorithm (MCS) [40], Chaotic Improved Harmony Search Algorithm (CIHSA) [41], Improved Fireworks Algorithm with Chaotic Sequence Operator (IFWA-CSO) [42], Exchange Market Algorithm (EMA) [43], Distance-Based Firefly Algorithm (DFA) [44], Root Tree Optimization Algorithm (RTO) [45], Backtracking Search Algorithm (BSA) [46], Adaptive Charged System Search Algorithm (ACSS), Ant Lion optimizer (ALO) [47], Grey Wolf Optimization (GWO) [48], Improved Differential Evolution (IDE) [49], Improved Bird Swarm Algorithm (IBSA) [50], Chaotic Bat Algorithm (CBA) [51], Particle Swarm Optimization (PSO) [52,53], Island Bat Algorithm (IBA) [54], Dual-Population Adaptive Differential Evolution (DPADE) [55], and Chaotic Teaching-Learning-Based Optimization (CTLBO) [56], which are used to solve economic load dispatch problems. To summarize, the Artificial Cooperative Search Algorithm (ACS) [57] was recently proposed on the basis of a co-evolution method that may find an optimal solution for the problematical economic load dispatch issue with a high degree of probability.…”

Filter feeding allogenic engineering optimization algorithm for economic dispatch

“…Hybrid methods seek to take advantage of specific strengths of each of the individual methods. Examples include chaotic particle swarm optimization and sequential quadratic programming, differential evolution and genetic algorithm, hybrid krill heard algorithm and bee algorithm and tabu search, particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic [24], hybrid of firefly and Bat algorithms [25], firefly and the levy flights algorithm [26], an hybrid of ant colony optimization-artificial bee colony-harmony search [27], hybrid Grey Wolf optimization [28] and a combination of continuous grasp algorithm and differential evolution [29].…”

Dual-Layer Optimal Dispatching Strategy for Microgrid Energy Management Systems considering Demand Response

“…Guo et al [2] proposed a multiobjective optimization model for isolated microgrid system, which aimed at the confliction of interests between the distribution company and the distributed generation owners in the isolated microgrid system. The economic scheduling objective function in [3,4] considered the operation costs, environmental pollution of the power system, and the equipment type, while Chen et al [5] also considered the extra cost of battery. Chen et al [6] proposed that the economic scheduling problem of isolated microgrid for reducing the total cost of distributed generators and satisfying the demand and supply constraints.…”