Forecast-based mechanisms for demand response
We study mechanisms to incentivize demand response in smart energy systems. We assume agents that can respond (reduce their demand) with some probability if they prepare prior to the real-ization of the demand. Both preparation and response incur costs to agents. Previous work studies truthful mechanisms that select a minimal set of agents to prepare and respond such that a fixed demand reduction target is achieved with high probability. In this work we additionally consider the balancing responsibility of a retailer under a given demand forecast and imbalance price: The retailer is responsible to purchase additional reserve capacity at a high imbalance price to cover any excess in the demand. In this extended setting we study mechanisms that request only a subset of prepared agents to respond since the reduction target depends on the realization of the demand: We propose: (i) a sequential mechanism that in each round embeds a second-price auction and is truthful under some mild assumptions for the setting, and (ii) a truthful combinatorial mechanism that runs in polynomial time and uses VCG payments. We show that both mechanisms guarantee non-negative utility in expectation for both agents and the retailer (mechanism), and can further be used for simultaneous downward and upward flexibility. Last, we verify our theoretical findings in an empirical evaluation over a wide range of mechanism parameters.
|International Conference on Autonomous Agents and Multi-Agent Systems|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam, The Netherlands|
Methenitis, G, Kaisers, M, & La Poutré, J.A. (2019). Forecast-based mechanisms for demand response. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS (pp. 1600–1608).