Article
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Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles
Version 1
: Received: 31 August 2021 / Approved: 1 September 2021 / Online: 1 September 2021 (12:25:48 CEST)
A peer-reviewed article of this Preprint also exists.
Hu, J.; Cao, J. Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies. Fractal Fract. 2021, 5, 140. Hu, J.; Cao, J. Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies. Fractal Fract. 2021, 5, 140.
Abstract
Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.
Keywords
Chance constraint programming; Source-load systems; Demand response control; Thermostatically controlled loads (TCLs); Plug-in electric vehicles (PEVs).
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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