In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability.

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Summary This paper describes a tool for an Aggregator to forecast the aggregated load demand response of a group of domestic customers subscribed to an indirect load control program based on price/volume signals. The tool employs a bottom‐up approach based on physical end‐use load models where the individual responses of a random sample of customers are combined in order to build the aggregated load demand response model. Simulation of the individual responses is carried out with an optimization algorithm based on mixed integer linear programming that minimizes the electricity bill whilst maintaining consumer's comfort level. To improve the performance of the model, a genetic algorithm for fitting the input parameters according to measured data is also provided. The tool is intended to allow the Aggregator rehearsing the impact of different control strategies and therefore choosing the most appropriate ones for market participation and portfolio optimization. To exemplify the methodological applicability of the developed algorithm, a case study based on an actual power system in eastern Spain is considered. Copyright © 2014 John Wiley & Sons, Ltd.

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