Optimized Household Demand Management with Local Solar PV Generation
It addresses the problem of optimizing residential energy consumption for end-users and grid operators, but the approach is incremental, applying an existing algorithm to a known problem.
This paper proposes an optimal scheduling solution for household demand side management with local solar PV generation using the Clonal Selection Algorithm, achieving reductions in electricity bills and grid energy import under dynamic pricing and voltage constraints.
Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid.