In the residential sector, natural gas has been the main consumed energy resource for surface heating (SH) and domestic hot water (DHW) during cold seasons, and substituting this energy carrier with electricity from renewable resources imposes challenges not only in economic but also technical terms. Due to the intermittent nature of renewable sources, the electricity production will fluctuate causing a mismatch between the electricity supply and demand. Therefore, this fluctuation in the electricity supply must be mitigated to prevent instability in the transmission and distribution grids. In this context, the integration of flexible energy devices such as heat pumps (HP), electric vehicles (EV), and batteries (BES) within demand response programs present as a promising option to reduce the effects of intermittent electricity production from renewable resources for the residential and transportation sectors. In this thesis, an integrated energy system formed by PV panels, EV, BES, and a HP coupled with thermal storage tanks (TES) has been studied. The research aimed to minimize the total energy costs by scheduling the optimal power consumption of each device using a demand response program based on electricity price signals. This control scheme allowed to determine the optimal energy consumption of the HP and its flexibility potential. This has been achieved by developing a HP-TES model to satisfy the SH and DHW demands of a typical Dutch household. Then, the HP-TES model was implemented into a second model developed by Wiljan Vermeer and Gautham Ram which described the functioning of the PV-BES-EV systems. With this, an NLP optimization problem to minimize the total energy costs of the all-electric system was formulated and solved in GAMS. Three different scenarios were studied: a base case where no demand response program is used, a demand response case with a high feed-in tariff (FIT), and a demand response case with a reduced FIT. The HP coupled to a TES produced a load shifting potential, where its power consumption was optimally scheduled to happen at times of low electricity prices to charge the storage tanks. Thus, at times of high electricity prices, the HP remained OFF and the thermal demand of the building was entirely met by the storage system. It was calculated that the HP did not operate for 9.45 h/per day during 5 days in winter season, shifting in total 56 kWh of energy towards low demand times. Finally, it was found that in the high FIT scenario, the system’s strategy to minimize the energy costs consisted of purchasing and injecting energy at low and high prices, respectively. It was calculated that a 49% in cost savings could be achieved in this scenario compared to the base case. On the other hand, in the reduced FIT case, the system’s energy intake was reduced, and no energy was injected to the grid, resulting in 44% in cost reduction.