Application of Particle Swarm Optimization for Transmission Network Expansion Planning with Security Constraints

Abstract

In this study, a new discrete parallel Particle Swarm Optimization (PSO) method is presented for long term Transmission Network Expansion Planning (TNEP) with security constraints. The procedure includes obtaining the expansion planning with the minimum investment cost using a model based on DC load flow formulation. (N-1) contingency is included in this model. The Particle Swarm Optimization algorithm presented in this study is used to solve the planning problem for two different models: without security constraints and with security constraints. Also to solve the problem of transmission expansion planning for a medium network, new improved particle swarm optimization algorithms, the so called, Parallel Particle Swarm Optimization (PPSO) is proposed in this research. The algorithm presents high performances for such networks. The performances with this new algorithm are shown to be better than the ones with the standard PSO. Simulation results show the effectiveness of the parallel particle swarm optimization algorithm.

Optimal placement of fixed series compensation and phase shifting transformer in the multi‐year generation and transmission expansion planning problem at the pool‐based market for maximizing social welfare and reducing the investment costs

“…Constraint (13) f ti pq demonstrate the total power injected to the bus p and the total output power through transmission lines connected to the bus p, in the year t and the scenario i.…”

“…With the formation of the electricity market, new targets were added to the problem of GTEP, making it a multi-objective and complex problem on a large scale. Some meta-heuristic methods such as particle swarm optimization (PSO) [13], Genetic Algorithm (GA) [14], modified gases Brownian motion optimization(GBMO) [15], Benders Decomposition (BD) algorithm [16], high-performance hybrid genetic algorithm (HPHGA) [17], simulated annealing(SA) [18], Tabu Search Optimization Algorithm (TS) [19], Grey Wolf Optimization (GWO) [20], and Harmony Search (HS) [21] has been applied to solve this problem.…”

Engenharia de Produção: paradigmas e aplicabilidades no setor produtivo

“…Isto é devido ao efeito de "valve point" em funções de custo para as unidades geradoras, dos limites de gradiente de velocidade e as perdas de transmissão. Assim, a proposta de um método de solução eficaz para este problema de otimização é de grande interesse [31] afirmam que a solução dos problemas do Despacho Econômico (ED) depende, principalmente, da modelagem dos geradores térmicos. As variações físicas, tais como o envelhecimento e temperatura ambiente afetam os parâmetros de modelagem e são inevitáveis.…”