Flattening Energy-Consumption Curves by Monthly Constrained Direct Load Control Contracts

Balancing electricity demand and supply is one of the most critical tasks that utility firms perform to maintain grid stability and reduce system cost. Demand-response programs are among the strategies that utilities use to reduce electricity consumption during peak hours and flatten the energy-consumption curve. Direct load control contracts (DLCCs) are a class of incentive-based demand-response programs that allow utilities to assign “calls” to customer groups to reduce their energy usage by a prespecified amount for a given length of time. Given the rapid expansion of such contracts, in this paper, we develop an integer stochastic dynamic optimization problem for executing DLCCs that minimizes total system cost subject to monthly and annual constraints on the number of times and hours customers can be called. We develop a hierarchical approximation approach, which consists of an annual problem and monthly problems, to solve the DLCC implementation problem effectively and in a reasonable amount of time. Motivated by the practice in a large utility firm in California, we incorporate a reduce-to-threshold policy that attempts to flatten energy-consumption curves whenever demand exceeds a given threshold. We verified the quality of our proposed approach on real data from the California Independent System Operator, which is the umbrella organization of the utility firms in California, and measured the quality of our solution against a lower bound. A large utility firm in California implemented our model and informed us that the additional reduction in cost was approximately 4%. Our sensitivity analysis reports the impact of managerial concerns on some policies to enhance customer experience and provides insights for improving the features of DLCC contracts.