Peak Shaving Algorithms for Residential Consumers. A Comparative Study

摘要

The aim of this paper is to compare the day-ahead electricity consumption optimization algorithms for smart houses with the widely used linear programming techniques. Both algorithms are designed to work on real data acquired from a small residential community with 11 houses and are validated in simulation. The main purpose of both optimization algorithms is peak shaving, meaning the flattening of the electricity consumption vector. The flattening capacity of both algorithms is evaluated through two indexes, namely the flattening index and the peak-to-average ratio. Whereas the peak shaving is beneficial for the energy producer and providers, it can also bring savings to the consumers if specific pricing plans are imposed. The pricing plans assume expensive energy at peak hours and cheap energy at off-peak hours. Both algorithms were also compared in terms of savings.