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Recent development of renewable generation and increasing penetration of electric vehicles have led to large volumes of residential battery storage systems connected at distribution networks. In this paper, we propose a control algorithm for residential batteries that determines optimal day-ahead battery scheduling and operation with the aim to minimize household energy bills and in the context of dynamic Time of Use (ToU) electricity tariffs. The proposed formulation of the optimization problem takes into consideration the battery’s depreciation cost, which is determined by the accurate enumeration of battery cycles, including partial cycling i.e. battery cycles that do not start or end at 100% of State of Charge (SoC). A key advantage of the proposed formulation is that the problem can be solvable by use of linear programming. In addition, we study and compare the benefits of the optimisation-based algorithm with lifespan consideration to a simple heuristic-based battery control scheme and an optimisation-based algorithm without battery lifecycle consideration. Results show that battery lifespan consideration in the optimization algorithm does not necessarily yield to lower prosumer energy bills, when compared to other approaches, but it can lead to a lower depreciation cost of the battery.

The day-ahead PV power generation scenarios, which represent the possible output of PV, have a significant impact on the scheduling of the available flexibility in a smart building. In this paper, a scenario generation approach for the day-ahead production of a single PV system is presented. The proposed method is important in the context of single buildings where the self-consumption has to be optimized. LASSO, which is a data-driven method, is used in order to select relevant quantiles to capture CDF. Moreover, a statistical-based scenario generation is applied in order to compare the performance of the proposed method. The generated scenarios for day-ahead PV generation are used in a stochastic problem to minimize the expected operational cost of a building and manage the flexibility, which is battery in this case study. Finally, the proposed method has been applied to a real PV installation on the rooftop of EnergyVille-1, a research institute. The simulation results demonstrate that the proposed method is able to capture the dynamics of the system even with low number of scenarios, which leads to reduce the computational time of stochastic problem.