During the last two decades, the use of residential photovoltaic (PV) systems has been widely promoted by governments through various support mechanisms such as feed‐in‐tariffs, net‐metering, net‐billing, etc. These support schemes have developed a secure investment environment, increasing the penetration level of PVs in low‐voltage distribution grids. Nonetheless, increased PV integration may introduce several technical problems regarding the secure operation of distribution grids. Battery energy storage (BES) systems can mitigate such challenges, but the high capital cost is one of the most important limiting factors towards the widespread use of these systems. In fact, the financial viability of integrated PV and BES systems under different support schemes remains an open issue. In this study, the profitability of PV and BES systems is evaluated through an advanced techno‐economic model, that provides the optimal size of PV‐BES system in terms of net present value, based on the electricity production and consumption profile of the installation, PV and BES systems costs, and electricity charges. The proposed model may be a useful tool for prosumers, grid operators and policymakers, to assess the impact of various incentive policy schemes and different BES operation strategies on the economic viability of PV‐BES systems.

In this paper, a sizing method is proposed for photovoltaic (PV) and battery energy storage systems (BESSs) for buildings with demand side management capability. Three objective functions (OF) are defined, describing the self-sufficiency of the building, the net-present value of the investment in PV and BESS, and their combination in a joint expression, respectively. The latter OF provides cost-effective solutions that are potentially more profitable in case of future high increase of the electricity cost. Two analysis methods are developed to find the optimal solution under a predefined load shifting strategy. The first one applies an exhaustive search by examining all possible combinations of PV and BESS capacity. The drawback of this method is the high computational burden due to unnecessary access to the solution space. The second method is implemented with a particle swarm optimization (PSO) variant, namely the unified-PSO. Under this approach, the analysis algorithm is guided in the solution space toward efficient solutions under lower computational time. The comparative assessment of the two methods in both residential and industrial sites verifies that via the PSO algorithm the optimal solutions are found under lower execution time.