An Evolutionary Algorithm for the Optimization of Residential Energy Resources

Important changes are currently underway in electric power systems, namely concerning the integration of distributed generation based on renewables to cope with Green-House Gas (GHG) emissions and external energy dependency. Moreover, the introduction of new loads such as electric vehicles and other storage systems, as well as local micro-generation and the possibility of using demand as a manageable resource create new challenges for the overall power system optimization. The deployment of smart metering and advanced communications capabilities will allow power systems to be managed more in accordance with generation availability, demand needs, and network conditions. A key issue for this optimal management is the existence of dynamic tariffs, according to the availability of several resources, congestion situations, generation scheduling, etc. Dynamic tariffs foreshadow a more active role for the consumer / prosumer concerning electricity usage decisions (consumption, storage, generation, and exchanges with the grid), namely in the residential sector. Demand Response (DR) can be used in this context by residential end-users to make the most of energy price information, weather forecasts, and operational requirements (e.g., comfort) to minimize the electricity bill. Nevertheless, the implementation of DR actions require the time availability of residential end-users, data processing capability, and the need to anticipate the corresponding impacts on the electricity bill and end-users satisfaction regarding the quality of energy services in use. Energy management systems (EMS) capable of offering decision support should be used to assist end-users optimizing the integrated usage of all energy resources. A multi-objective model has been developed aimed at minimizing the electricity bill and the possible dissatisfaction caused to the end-user by the implementation of DR actions. An evolutionary algorithm to cope with the multi-objective and combinatorial nature of the model has been developed, which is tailored to the physical characteristics of the problem, namely using adequate solution encoding schemes and customized operators. Simulation results show that significant savings might be achieved by optimizing load scheduling, local micro-generation, and storage systems including electric vehicles (EVs) in both grid-to-vehicle (G2V) and V2G (vehicle-to-grid) modes.