Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power

被引:196
作者
Biswas, Partha P. [1 ]
Suganthan, P. N. [1 ]
Qu, B. Y. [2 ]
Amaratunga, Gehan A. J. [3 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
[2] Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou, Henan, Peoples R China
[3] Univ Cambridge, Dept Engn, Cambridge, England
基金
新加坡国家研究基金会;
关键词
Economic-environmental dispatch; Wind power generator; Solar photovoltaic; Small-hydro power unit; Uncertainty modelling; Multiobjective evolutionary algorithms; PARTICLE SWARM OPTIMIZATION; EVOLUTIONARY ALGORITHM; LOAD DISPATCH; EMISSION DISPATCH; SEARCH ALGORITHM; SYSTEM; FLOW; DECOMPOSITION; HYPERVOLUME; UNCERTAINTY;
D O I
10.1016/j.energy.2018.03.002
中图分类号
O414.1 [热力学];
学科分类号
摘要
Economic-environmental power dispatch is one of the most popular bi-objective non-linear optimization problems in power system. Classical economic power dispatch problem is formulated with only thermal generators often ignoring security constraints of the network. But importance of reduction in emission is paramount from environmental sustainability perspective and hence penetration of more and more renewable sources into the electrical grid is encouraged. However, most common forms of renewable sources are intermittent and uncertain. This paper proposes multiobjective economic emission power dispatch problem formulation and solution incorporating stochastic wind, solar and small hydro (run-of-river) power. Weibull, lognormal and Gumbel probability density functions are used to calculate available wind, solar and small-hydro power respectively. Some conventional generators of the standard IEEE 30-bus system are replaced with renewable power sources for study purpose. Network security constraints such as transmission line capacities and bus voltage limits are also taken into consideration alongwith constraints on generator capabilities and prohibited operating zones for the thermal units. Decomposition based multiobjective evolutionary algorithm and summation based multiobjective differential evolution algorithm are applied to the problem under study. An advanced constraint handling technique, superiority of feasible solutions, is integrated with both the multi objective algorithms to comply with system constraints. The simulation results of both the algorithms are summarized, analyzed and compared in this study. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1039 / 1057
页数:19
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