A new indirect load control approach is developed for managing financial risks in electricity markets. These risks are generated by price volatility and demand uncertainty regarding distributed renewable generation as negative load. The proposed method is based on risk-limiting dynamic contracts between a load-serving entity and its customers. The contract framework allows the load-serving entity to incentivize the customers to control their loads in a way that is beneficial to itself, while ensuring the risk transferred to each customer be less than or equal to a pre-specified threshold. The difficulty arises due to the fact that the load-serving entity has very limited information: it has no capability of monitoring the states and controls of the customers' loads. The proposed contract approach resolves this difficulty by providing an appropriate compensation to each customer such that the customer has no incentive to hide his or her private information. The risk-limiting capability is achieved by explicitly taking into account the variance of each customer's payoff. We propose a dynamic programming-based approach to design such a contract. By using locational marginal price data from the Electricity Reliability Council of Texas as well as data on the electric energy consumption of customers in Austin, Texas, we estimate models necessary for designing the proposed contract and demonstrate the performance of the indirect load control program.
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