Abstract
Demand response (DR) flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a demand response optimization dispatch control strategy for flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) with stochastic renewable power injection. Firstly, a chance constraint look-ahead programming model is proposed to maximize the social welfare of both units and load agents, through which the optimal power scheduling for 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. It has been shown that the proposed dispatch and control strategy can coordinate the flexible load agents and the renewable power injection. Finally, the simulation results on a modified IEEE 39 bus system demonstrate the effectiveness of the proposed demand response strategy.
Highlights
With the rapid increase in energy consumption and environmental pollution, renewable energy power generation is becoming more and more popular and various kinds of distributed generations are connecting into the power grid
This paper investigated the optimal dispatch and demand response control problems of flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) loads in the smart grid with renewable energies
The dispatch and control strategy is realized by a chance-constraint optimization model and the aggregated TCL/PEV control models
Summary
With the rapid increase in energy consumption and environmental pollution, renewable energy power generation is becoming more and more popular and various kinds of distributed generations are connecting into the power grid. The incorporation of renewable energy units gives rise to the increasing need for resource capacity or ancillary services if there exists major forecast uncertainty. The widely adopted flexible loads for these services include thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs), which can respond the power dispatch or electricity price timely. To address these concerns, there have been several valuable studies on the coordination and interaction of traditional units, renewable units, and flexible loads of source–load systems [1,2,3,4]. The power produced by these renewable energies largely depends on natural environmental conditions, such as wind speed or illumination intensity, which are stochastic and cannot be precisely predicted
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