This paper presents an approach to extend the capabilities of smart grid laboratories through the concept of Power Hardware-in-The-Loop (PHiL) testing by re-purposing existing grid-forming converters. A simple and cost-effective power interface, paired with a remotely located Digital Real-Time Simulator (DRTS), facilitates Geographically Distributed Power Hardware Loop (GD-PHiL) in a quasi-static operating regime. In this study, a DRTS simulator was interfaced via the public internet with a grid-forming ship-To-shore converter located in a smart-grid testing laboratory, approximately 40 km away from the simulator. A case study based on the IEEE 13-bus distribution network, an on-load-Tap-changer (OLTC) controller and a controllable load in the laboratory demonstrated the feasibility of such a setup. A simple compensation method applicable to this multi-rate setup is proposed and evaluated. Experimental results indicate that this compensation method significantly enhances the voltage response, whereas the conservation of energy at the coupling point still poses a challenge. Findings also show that, due to inherent limitations of the converter s Modbus interface, a separate measurement setup is preferable. This can help achieve higher measurement fidelity, while simultaneously increasing the loop rate of the PHiL setup.@en

As future power systems become increasingly complex and interconnected to other energy carriers, a single research infrastructure can rarely provide the required test-beds to study a complete energy system, especially if different types of real power hardware are expected to be in-the-loop. Therefore, virtual interconnection of laboratories for large-scale systems plays an important role for geographically distributed realtime simulation. This paper presents the improvements made in simulation fidelity as well as usability for establishing future simulator and laboratory connections. A general procedure is proposed and analyzed for geographically distributed real-time simulation, which allows users easily to adapt this procedure to specific test cases. A systematic and comprehensive analysis of a dynamic phasor based co-simulation interface algorithm and its improvements are provided to demonstrate the advantages as well as limitations of this approach.@en

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.@en

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.@en

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.@en

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.@en