A Constraint Programming approach for collective smart building scheduling improved by blockchain structure

Abstract

Demand Side Management (DSM) is an effective strategy for balancing the supply and demand of electricity and improving the reliability of the smart grid by addressing current grid constraints. In this study, a novel methodology that leverages artificial intelligence and computer science techniques is proposed to solve the problem of cooperative energy demand planning. Specifically, Constraint programming (CP) is used to minimize the Peak-to-Average ratio (PAR), optimize cost savings, and ensure secure energy trading within a community of heterogeneous smart homes. To guarantee the integrity of information exchanges during energy trading, a blockchain structure is employed. The efficiency and computing performance of the CP method are compared with Mixed integer programming (MIP) solutions for a range of load profiles. Simulations demonstrate that both techniques effectively handle the proposed scheduling of collective smart buildings in a community of up to 100 smart homes. In particular, both approaches can effectively reduce the cost of electricity by 10% and 7% respectively, and lower PAR by 25%. However, the CP algorithm outperforms the MIP-based solutions in terms of efficiency and speed in dealing with large-scale optimization issues.