DEMAND RESPONSE OF A HOUSEHOLD WITH DISTRIBUTED ENERGY RESOURCES

Due to uncontrollable renewable generation, future households have to adapt their consumption to match the production. This master's thesis explores the optimization of demand response for a prosumer equipped with distributed energy resources (DER). The research focuses on solving the energy consumption demand response problem for a household. In this study, the problem has been formulated as an optimization task. To address the problem, a Python code was written using the Pyomo optimization modeling language and the GLPK solver for MILP-type optimization problems. The prosumer, located near Helsinki, Finland, owns a villa, and the analysis spans an optimization window with hourly resolution. Local climate data, including outdoor temperature and irradiation, are incorporated to model environmental conditions affecting renewable energy production and building energy consumption. The prosumer system integrates various components, including photovoltaic (PV) panels, whose generation was calculated using measured irradiance data, an electrical energy storage system (BESS), an electric vehicle (EV) with a charging station, and a storage and heating system for domestic hot water (EWH). Additionally, a two-capacity thermal model for the building considers air conditioning through an HVAC system with an air-to-water heat pump (HP) and thermal storage (TESS). The prosumer interacts with the grid, adhering to governmental limits on energy export and import. The optimization problem aims to minimize total costs, factoring in energy prices based on market trends (Day-Ahead price) and controllable variables. After outlining the prosumer's energy model, the thesis scrutinizes results obtained by modifying input data, facilitating the identification of daily expenditure or gain based on the analyzed time periods. This analysis enhances understanding of the impact of variables on system behavior and identifies optimal strategies for energy resource management. Conducted over the course of one year (2022 price data), the analysis code is versatile and easily adaptable to obtain new analyzable results by adjusting input data. Applicable to any prosumer scenario, the code holds potential for broader community use, allowing communication among users producing and consuming energy. Consequently, this thesis significantly contributes to the field of energy optimization and the management of distributed energy resources.