Optimal Clustering of Time Periods for Electricity Demand-Side Management

Several pure binary integer optimization models are developed for clustering time periods by similarity for electricity utilities seeking assistance with pricing strategies. The models include alternative objectives for characterizing various notions of within-cluster distances, admit as feasible only clusters that are contiguous, and allow for circularity, where time periods at the beginning and end of the planning cycle may be in the same cluster. Restrictions upon cluster size may conveniently be included without the need of additional constraints. The models are populated with a real-world dataset of electricity usage for 93 buildings and solutions and run-times attained by conventional optimization software are compared with those by dynamic programming, or by a greedy algorithm applicable to one of the models, that run in polynomial time. The results provide time-of-use segments that an electricity utility may employ for selective pricing for peak and off-peak time periods to influence demand for the purpose of load leveling.