Dynamic economic emission dispatch of thermal-wind-solar system with constriction factor-based particle swarm optimization algorithm

Abstract

Purpose Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA). Design/methodology/approach Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically. Findings The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established. Research limitations/implications Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution. Practical implications The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible. Social implications As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact. Originality/value In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm

“…The model can reduce the waste of wind and solar energy, and realize the system to minimize the cost of running by adjusting the power purchased from the grid, as well as the battery charge and discharge power, under the time-sharing price mechanism. Behera et al [24] compared constriction factor-based particle swarm optimization (CFBPSO) with PSO, improved PSO and the red deer algorithm (RDA). This obtained the optimal economic and emission scheduling level, as well as an optimal solution for the renewable energy hybrid power generation system, which is composed of thermal, wind and solar generators, when the dynamic load changes within a day.…”

A new multi‐objective hybrid optimization algorithm for wind‐thermal dynamic economic emission power dispatch

“…In other literatures, thermal power combined with wind power, solar power or electric vehicles constitute an integrated energy system. Behera et al 14 presented a DEED model involving stochastic wind power and solar power, and proposed a constriction factor‐based particle swarm optimization (CFBPSO) algorithm to solve the DEED problem. Qiao and Liu 15 incorporated electric vehicles and wind power with thermal units to establish the dynamic optimal economic emission dispatch model, and adopted the multi‐objective differential evolution algorithm with the self‐adaptive parameter operator and local search operator (SaMODE_LS) to solve the problem model.…”