One can define an energy system as a system that converts one or more energy fluxes into other energy fluxes of a different kind. This definition may describe a relatively small system, for instance, a power plant, a chemical plant, or the heating and cooling apparatus of a single-family house, as well as one covering larger energy needs, for example, those of a city, a country, or even a continent. As energy systems are developed through the centuries, the way we structure these systems goes through changes affected by contextual conditions. Recently, concerns about the availability of traditional fossil energy sources and their environmental effects are revolutionizing the way energy systems are planned, designed, and operated.

In this paper, we demonstrate how latency insertion method (LIM) can be used for real-time simulation of high switching frequency power electronics systems and how this approach can be implemented for scalable Field Programmable Gate Array (FPGA) execution. We first present a summary of the LIM and how this method can be developed into a solver for high-performance FPGA execution. We then present how common power electronics topologies-buck and boost-can be modeled using the LIM approach, followed by how the LIM model of a three-phase dc/ac converter is created. Afterward, we demonstrate the accuracy of the developed FPGA solver through a set of power electronic system examples, with each example compared with near ideal results of same example provided by a traditional electromagnetic transient type solver. We complete the paper analyzing the scalability of the proposed approach on FPGA devices, both in terms of achievable time step as well as of resource usage.

Investigations of the dynamic behaviour of power electronic components integrated into electric networks require suitable and established simulation methodologies. Real-time simulation represents a frequently applied methodology for analyzing the steady-state and transient behavior of electric power systems. This work introduces a guideline on how to model power electronics converters in digital real time simulators, taking into account the trade-off between model accuracy and the required computation time. Based on this concept, possible execution approaches with respect to the usage of central processing unit and field-programmable gate array components are highlighted. Simulation test scenario, such as primary frequency regulation and low voltage ride through, have been performed and accuracy indices are discussed for each implemented real-time model and each test scenario, respectively. Finally, a run-time analysis of presented real-time setups is given and real-time simulation results are compared. This manuscript demonstrates important differences in real-time simulation modelling, providing useful guidelines for the decision making of power engineers.

In this paper we present a solution for real-time multi-FPGA simulation of energy conversion systems based on the Latency-Based Linear Multistep Compound (LB-LMC) simulation approach. The approach relies on a nodal decomposition approach derived from multiport network analysis, and fast parallel communication between Field Programmable Gate Array (FPGA) devices. The results presented in this paper – using a microgrid example – demonstrate how the developed real-time platform overcame the resource scalability limit of single FPGA solvers while achieving a time step size of 100 nanoseconds.

In this paper, an approach to perform model reduction of distribution grids for phasor measurement unit (PMU)-based state estimation (SE) purposes is presented. The method is based on ac load flow equations. An arbitrary number of lines and nodes can be selected and reduced to an equivalent one. The performance of the method is tested using the IEEE34 and IEEE123 test cases. An evaluation of the computational performance of the SE process is also reported. To conclude, the SE process based on the reduced model is tested against uncertainty in the parameters of the system and in the measurements so to evaluate how the performance of the SE is deteriorated by these phenomena.

This paper proposes a new planning framework to determine the optimal location, capacity, and types of EV charging stations (EVCSs) in multi-energy systems (MESs). We propose a two-stage stochastic programming approach -with scenario-based algorithms-that explicitly considers vehicle-to-grid (V2G) peculiarities (four-quadrant operation and stochastic human factors influence: V2G willingness, walking distance, and charging patterns). Considering those factors together with MES uncertainties -RES generation, load demands, and electricity price-enables a comprehensive study of V2G and MES impact on EVCS planning. The proposed approach is applied to both a purely electric distribution network (EDN) and an MES to analyze the interplay of EVCSs in different energy domains, in consideration of different V2G contracts. The obtained results underline that the sole consideration of the EDN can lead to non-optimal results, while the more comprehensive analysis leads to optimal planning of all energy resources and cost savings. Finally, we analyse how each considered factor individually impacts EVCSs planning.