Multiarea Economic Dispatch Using Evolutionary Algorithms

Abstract

Multiarea economic dispatch (MAED) is a vital problem in the present power system to allocate the power generation through dispatch strategies to minimize fuel cost. In economic dispatch, this power generation distribution always needs to satisfy the following constraints: generating limit, transmission line, and power balance. MAED is a complex and nonlinear problem and cannot be solved with classical techniques. Many metaheuristic methods have been used to solve economic dispatch problems. In this study, the dynamic particle swarm optimization (DPSO) and grey wolf optimizer (GWO) have been used to solve the MAED problem for single-area 3 generation units, a two-area system with four generating units, and four areas with 40-unit system. The hunting and social behaviors of grey wolves are implemented to obtain optimal results. During the optimization search, this algorithm does not require any information regarding the objective function’s gradient. The tunable parameters of the original PSO that are three parameters are dynamically controlled in this work that provides the efficient cost values in less execution time although satisfying all the MAED problem’s diverse constraints. In this study, the authors also implemented the GWO algorithm with two tunable parameters, and its execution is straightforward to implement for the MAED problem.

Renewable based techno-economic analysis for telecommunication system: A case study of western Himalaya

“…These are the different constraints using in the system. There are multiple evolutionary algorithm techniques used for the technoeconomic analysis of hybrid renewable energy sources (Kumar et al, 2021). The simulation results formed from HOMER, compared with different evolutionary techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Harris Hawks Optimization (HHO).…”

An Efficient Solution for Economic Load Dispatch Using Cheetah Optimizer with Opposition-Based Learning and Adaptive Weighting Factor

“…where 𝐹 𝑐 is the total fuel cost of all generators in the system, 𝑃 𝑖 0 is the earlier output power of a generatori, 𝑈𝑅 𝑖 and 𝐷𝑅 𝑖 are the up-ramp and down-ramp limits, respectively; 𝑛𝑔 is the number of generators, 𝑃 𝐷 is the total demand on system, 𝑃 𝑙𝑜𝑠𝑠 is the total transmission loss, 𝐵 𝑖𝑗 , 𝐵 0𝑖 an 𝐵 00 are the Bcoefficients of transmission system, respectively. [3] x --x ----MBGSA [11] x --x ----DNN [8] x --x ----EEWOA [9] x --x x ---ISPSO [5] x --x x ---HSSA [6] x --x x ---GWO [7] x --x x -x -OPIO [19] x…”

“…In [6], salp swarm algorithm (SSA) and βhill climbing optimizer (βHO) are hybridized to formulate hybrid salp swarm algorithm (HSSA) and solved ELDP for cost reduction. In [7], dynamic particle swarm optimization (DPSO) and grey wolf optimizer (GWO) were employed for ELDP in multiarea power system by aiming cost reduction. In [8], deep neural network (DNN) is trained by using different solution data set of ELDPs using λ-iteration optimization algorithm by considering only MW limits.…”

Apply three metaheuristic algorithms for energy storage-based integrated power system to reduce generation cost

“…In the concept of HTS, m TPPs and n HPPs are working to supply electricity to loads and the main target is to cutting TEPC of m TPPs. Normally, the TEPC caused by TPPs is approximately described by a quadratic function and the objective function is as follows [29], [30]:…”