In future electric distribution networks, demand flexibility offered by controllable loads will play a key role for the effective transition towards the smart grids. Electric heat pumps are flexible loads whose operation can be controlled, to some extent, to foster the efficient operation of the distribution grids. This paper presents an optimization algorithm that defines a smart day-ahead scheduling of electric heat pumps aimed at achieving power peak shaving in the distribution grid, while providing customers with the desired thermal comfort over the day. The proposed optimization relies upon a Mixed Integer Linear Programming approach and allows defining both the time schedule and the operating points of the heat pump, guaranteeing an energy efficient solution for the customers. Performed tests show the benefits achievable by means of the proposed optimal scheduling both at the distribution grid level and at the customer side, proving the goodness of the conceived solution.

Final customers are expected to play an active role in the Smart Grid scenario by offering their flexibility to allow a more efficient and reliable operation of the electric grid. Among the household appliances, heat pumps used for space heating are commonly recognized as flexible loads that can be suitably handled to gain benefit in the Smart Grid context. This paper proposes an optimization algorithm, based on a Mixed-Integer Linear Programming approach, designed to achieve power peak shaving in the distribution grid while providing at the same time the required thermal comfort to the end-users. The developed model allows considering a continuous operation mode of the heat pumps and different comfort requirements defined by the users over the day. Performed simulations prove the proper operation of the proposed algorithm and the technical benefits potentially achievable through the devised management of the heating devices.

In the smart grid scenario, Demand Response (DR) and Demand Side Management (DSM) programs are considered as strategic to obtain a more efficient operation of the grid. The flexibility given by the final customers plays a key role to unlock the potential benefits offered by the application of these schemes. A classical example of flexible load that can be exploited for DR and DSM purposes is the electric heat pump. This paper aims at evaluating the main factors affecting the flexibility available in the management of electric heat pumps for space heating. The performed analysis allows identifying some indexes to quantify the available flexibility and highlights how the thermal comfort requirements of the customers affect the provided level of flexibility. Sample simulations show the impact of these flexibility terms on the results of a DSM program designed for power peak shaving at grid level. The possible use of the defined indexes for sorting the customers flexibility and for estimating the potential benefits offered by the adopted DSM scheme is also investigated and discussed.