Static compensators (STATCOMs) are able to provide rapid and dynamic reactive power support within a power system for voltage stability enhancement. While most of previous research focuses on only an either static or ...
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Static compensators (STATCOMs) are able to provide rapid and dynamic reactive power support within a power system for voltage stability enhancement. While most of previous research focuses on only an either static or dynamic (short-term) voltage stability criterion, this study proposes a multi-objective programming (MOP) model to simultaneously minimise (i) investment cost, (ii) unacceptable transient voltage performance, and (iii) proximity to steady-state voltage collapse. The model aims to find Pareto optimal solutions for flexible and multi-objective decision-making. To account for multiple contingencies and their probabilities, corresponding risk-based metrics are proposed based on respective voltage stability measures. Given the two different voltage stability criteria, a strategy based on Pareto frontier is designed to identify critical contingencies and candidate buses for STATCOM connection. Finally, to solve the MOP model, an improved decomposition-based multi-objective evolutionaryalgorithm is developed. The proposed model and algorithm are demonstrated on the New England 39-bus test system, and compared with state-of-the-art solution algorithms.
decomposition-based Multi-Objective evolutionaryalgorithms (DBMOEA), such as Multiple Single Objective Pareto Sampling (MSOPS) and multiobjectiveevolutionaryalgorithmbased on decomposition (MOEA/D), have been succ...
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ISBN:
(纸本)9781479938414
decomposition-based Multi-Objective evolutionaryalgorithms (DBMOEA), such as Multiple Single Objective Pareto Sampling (MSOPS) and multiobjectiveevolutionaryalgorithmbased on decomposition (MOEA/D), have been successfully applied in finding Pareto-optimal fronts in multiobjective Optimization Problems (MOPs), two or three-objective in general. DBMOEA decomposes one MOP into multiple Single-objective Optimization Problems (SOPs) where the convergence of approximated front is facilitated by finding the optimal solution of each SOP and its diversity is preserved by a group of well distributed SOPs. However, when solving problems with many objectives, one single solution can be the optimal solution of multiple SOPs which inadvertently leads to a severe loss of population diversity. In this paper, we propose a new diversity improvement method incorporated into a modified DBMOEA to directly handle this challenge. The design includes two steps. First, a few number of weight vectors guide the whole population towards a small number of solutions nearby the true Pareto front. Afterwards, initialize a subpopulation around each solution and diversify them toward well distribution. As a case study, a new algorithmbased on this design is compared with three state-of-the-art DBMOEAs, MOEA/D, MSOPS, and MO-NSGA-II. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the chosen competitors for solving many-objective optimization problems.
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