Optimal Dispatching of Virtual Power Plants Considering Comprehensive Demand Response of Electricity and Heat Loads

doi:10.3969/j.issn.1000-7229.2019.12.008

Abstract

Abstract: Aiming at the rapid growth of multi-type loads represented by electricity and heat load in distribution network， and the coordinated scheduling of distributed energy such as controllable units， energy storage devices and fans， an optimal scheduling model of virtual power plant (VPP) considering multi-type load comprehensive demand response is proposed. Firstly， wind turbines， cogeneration system， various energy storage devices， electric boilers and electric heating load are integrated into a virtual power plant. On the user side， a comprehensive demand response model for electric heating load is established on the basis of the combination of electricity price type and incentive demand response measures. Then， aiming at maximizing the operating profit of the virtual power plant， the opportunity constraint model is used to describe the uncertainty of the wind turbine， load forecasting and internal power balance， and the operational constraints and network security constraints of each unit are considered. Scheduling scheme is generated on the basis of reasonable control and coordination of the output of each component. The quantum particle swarm optimization algorithm with adaptive inertia weight adjustment is used to solve the model. In the example， the effects of different demand response schemes on load curve optimization results， network security and virtual power plant economy are compared. The dispatching results of virtual power plant under different confidence levels are compared. Therefore， the feasibility of the model is verified.

Key words: integrated energy system, comprehensive demand response, virtual power plant(VPP), quantum particle swarm, optimal scheduling

CLC Number:

TM 62

JIANG Yefeng， XIONG Hao， HU Yu， LIU Yu. Optimal Dispatching of Virtual Power Plants Considering Comprehensive Demand Response of Electricity and Heat Loads[J]. Electric Power Construction, 2019, 40(12): 61-69.

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