Optimization of household energy consumption towards day-ahead retail electricity price in home energy management systems

In this paper, a novel approach is proposed to optimize the behavior of household appliances towards retail electricity price. At the supply-side, a distributed generation-owning retailer participates in the wholesale electricity market, i.e. day-ahead and intraday trading floors. Considering the uncertainties associated with electricity price and wind/solar power, the retailer determines the retail price using stochastic programming. At the demand-side, smart household prosumers take the advantage of two kinds of storage capacities: (1) thermal storage capacity of thermostatic devices and (2) electrical storage capacity of batteries integrated with roof-top photovoltaic panels. The Home Energy Management System (HEMS) determines the operational strategies of appliances, including thermostatically controlled, uninterruptible and curtailable appliances, in response to the retail price. The HEMS uses a heuristic Forward-Backward Algorithm (F-BA) to minimize the energy cost of the thermal appliances satisfying the residents' comfort. To prevent from creating peak demands in the power system, a Peak Flattening Scheme (PFS) is suggested. Investigating the interaction between household consumption and network security, the household demands are located at different buses of a distribution network relieving congestion in weak lines. Finally, the proposed approach is implemented as a case for Danish sector of Nordic Electricity Market.