Abstract
As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms.
Highlights
Compared with the traditional fossil energy, wind power has the advantages of no energy consumption, no emission and no pollution, and its strategic status is gradually increasing as an alternative energy source and even the dominant energy source [1]
T =1 where KRt is the price of the green certificate trading within the unit period of t; KHt is the penalty price of the green certificate of the amount of the vacancies within the unit period of t; RLt is the amount of the green certificate that can be purchased by the system within the unit period of t; φ is the margin of the amount of the green certificate that can be purchased; FRt is the green certificate trading costs of the system within the unit period of t; T is the total number of periods of the day-ahead dispatch, taking 24 as the value; FR is the green certificate trading costs of the system in the scheduling cycle T
T =1 where KCt is the price of the carbon emission rights trading within the unit period of t; KLt is the penalty price of the carbon emission rights of the amount of the excesses within the unit period of t; ECt is the carbon emission rights that can be purchased by the system within the unit period of t; ρ is the margin of the carbon emission rights that can be purchased; FCt is the carbon trading costs of the system within the unit period of t; FC is the carbon trading costs of the system in the scheduling cycle T
Summary
Compared with the traditional fossil energy, wind power has the advantages of no energy consumption, no emission and no pollution, and its strategic status is gradually increasing as an alternative energy source and even the dominant energy source [1]. Hetzer et al developed a model with wind energy conversion system generators, in which a Weibull probability density function was used to characterize the stochastic wind speed Both the risk of overestimation and cost of underestimation of available wind power have been considered by introducing reserve cost and penalty cost into the objective function [10]. Established a wind-thermal economic emission dispatch (WTEED) model, where the wind power cost was considered as a part of the optimization objective function. Azizipanah-Abarghooee et al proposed a multi-objective stochastic search algorithm to analyze the probabilistic WTEED problem considering both overestimation and underestimation of available wind power [24].
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