Using MultiAgent Systems for Demand Response Aggregators: Analysis and Requirements for the Development Demand response is an important mechanism to support the integration of renewable energies. It helps to stabilize the power grid by controlling energy consumption and generation at strategic times. There are different demand response markets and different ways to gain access to these markets but all have one thing in common: increasing or decreasing the load of the power grid on the consumer side. In order to offer this, demand response providers must be able to offer flexibility in their energy consumption andor generation, and also technical and market wise. Demand response providers are often accumulated in a virtual power plant and managed by demand response aggregators. These aggregators pool and manage technical units of industrial manufacturers that can provide energy flexibility and establish the connection to the demand response markets. The management of these loads is a complex task with great optimization potential. This distributed system is subject to various technical and organizational requirements, depending on the market the provider supplies. The current technical implementation has open potential for a more automated and flexible structure. The idea for a multiagent system for virtual power plants of demand response aggregators to combine the different demand response mechanisms, to automate and optimize underlying processes and transforming them into an expandable generic architecture is proposed in this paper. It shows the requirements of the demand response markets in Germany and the implementation via aggregators. The requirements for such a multiagent system are listed and further considerations for development are made.

Incentivizing Strategy for Demand Response Aggregator Considering Market Entry Criterion: A Game Theoretical Approach This paper presents a novel incentivizing strategy for a demand response (DR) aggregator to participate in an intraday market organized by the grid operator (GO). By taking the market entry criterion into consideration, the proposed incentivizing strategy is able to help the aggregator determine the optimal incentive for each customer based on their identified characteristics or preferences. The hierarchical decision making procedure between the aggregator and customers is casted into a Stackelberg game. In accordance, an optimal incentivizing strategy can be ascertained for the aggregator, which further helps the aggregator elicit optimal load reductions from enrolled customers meanwhile maximizing its potential profit. Simulation results show that the proposed incentivizing strategy is effective in helping the aggregator determine optimal incentive for each customer in face of different external factors.