Hardware-in-the-Loop Validation of Energy Management Systems for Microgrids: A Short Overview and a Case Study

Proper management of the tasks in a microgrid makes the energy management system successful. These tasks are based on analysis, control, and predictions in real-time, which makes the system capable of autonomous and guarantees its reliability and validity. In this paper, an experimental Microgrid testbed is proposed to allow emulating tasks in real-time that involve predicting energy consumption and generation in an emulated Microgrid cluster with an on-cloud energy management system. It involves machine learning methods in order to solve economic dispatch problems. Since the use of the supervised machine learning technique to estimate future consumption and generation in a system, helps the control system verify and modify itself. The Microgrid testbed includes system hardware in the loop interface, hardware power in the loop, and real inverters to have close emulation of the Microgrid cluster. A communication protocol is also implemented to connect to the on-cloud energy management system and the system hardware in the loop.

ABSTRACTThis paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm.

The real-time (RT) hardware-in-the-loop (HIL) simulation-based testing is getting popular for power systems and power electronics applications. The HIL testing provides the interactive environment between the actual power system components like control and protection devices and simulated power system networks including different communication protocols. Therefore, the results of the RT simulation and HIL testing before the actual implementation in the field are generally more acceptable than offline simulations. This paper reviews the HIL testing methods and applications in the recent literature and presents a step-by-step documentation of a new HIL testing setup for a specific case study. The case study evaluates real-time implementation of previously proposed communication-dependent logically selective adaptive protection algorithm of AC microgrids using HIL testing of IEC 61850 generic object-oriented substation event (GOOSE) protocol. The RT model of AC microgrid including the converter-based distributed energy resources and battery storage along with IEC 61850 GOOSE protocol implementation is created in MATLAB/Simulink and RT-LAB software using OPAL-RT simulator platform. Local area network (LAN) at the laboratory acts as IEC 61850 station bus for exchanging GOOSE Boolean signals between the RT target and the actual digital relay. The evaluation of the round-trip delay using the RT simulation has been performed. It is found that the whole process of fault detection, isolation and adaptive setting using Ethernet communication is possible within the standard low voltage ride through curve maintaining the seamless transition to the islanded mode. The signal monitoring inside the relay is suggested to avoid false tripping of the relay.

The recent advancements in the Internet of Things (IoT) and telecommunication infrastructure have significantly increased the reliability and effectiveness of communication protocols in microgrid environment. Nowadays, the equipment in a smart microgrid not only exchange information with one another much faster but also control, monitor and diagnose faults much faster and more reliably. Despite the benefits of new technologies, they are tailored to satisfy specific requirements, and therefore, they can be only used for certain applications. The microgrid communication model consists of a three-layer architecture, where the energy management system (EMS) sits in the top layer and controls the overall operations of the island of microgrids. The middle layer includes the local controllers (LCs) that regulate operations within the local grid. The bottom layer includes IoT devices, such as smart meters, fault recorders and protective relays, which continuously capture and transmit the stream of sensed data. Such hierarchical architecture introduces specific computation and latency requirements for each layer of microgrid communication. To meet these requirements, each layer must use different communication equipment and protocols. This chapter provides an insight into communication requirements, system architecture, standards, protocols and tools used in microgrid communications. The chapter concludes with a case study, where wireless technology is utilised for reliable and optimal operations in a microgrid.

Compared to conventional engine-based powertrains, electrified powertrain exhibit increased energy efficiency and reduced emissions, making electrification a key goal for the automotive industry. For a vehicle with hybrid energy storage system, its performance and lifespan are substantially affected by the energy management system. Reinforcement learning-based methods are gaining popularity in vehicle energy management, but most of the literature in this area focus on pure simulation while hardware implementation is still limited. This paper introduces the digital twin methodology to enhance the Q-learning-based energy management system for battery and ultracapacitor electric vehicles. The digital twin model can exploit the bilateral interdependency between the virtual model and the actual system, which improves the control performance of the energy management system. The physical model is established based on a hardware-in-the-loop simulation platform. In addition, battery degradation is also considered for prolonging the battery lifespan to reduce the operating cost. The validation results of the trained reinforcement learning agent illustrate that the digital twin-enhanced Q-learning energy management system improves the energy efficiency by 4.36% and the battery degradation is reduced by 25.28%.

This article proposes an energy management system (EMS) for a fuel cell (FC) hybrid electric vehicle. The EMS is based on nonlinear model predictive control (NMPC) and employs a recurrent neural network (RNN) for modeling a proton exchange membrane FC. The NMPC makes possible the formulation of control objectives not allowed by a linear model predictive control (MPC), such as maximum efficiency point tracking of the FC, while the RNN can accurately predict the FC nonlinear dynamics. The EMS was implemented on a low-cost development board, and the experiments were performed in real time on a hardware-in-the-loop test bench equipped with a real 3-kW FC stack. The experimental results demonstrate that the NMPC EMS is able to meet the vehicle's energy demand, as well as to operate the FC in its most efficient region. Moreover, a comparative study is performed between the proposed NMPC, a linear MPC, and hysteresis band control. The results of this comparative study demonstrate that the NMPC provides a better fuel economy and can reduce FC degradation.

The present work focuses on the development of computational algorithms to determine on-line energy-based driving guidance for an Electric Vehicle (EV) endowed with regenerative breaking system capabilities. A predictive decision support system is designed to optimally distribute the energy flow between the instantaneous power demand requested by the driver for the powertrain engine and the different auxiliaries relating to comfort performance, such as the heating system. The proposed methodology uses an on-line iterative optimization process algorithm to search for global optimum relatively to specific objective functions, which take into account the battery autonomy, driving comfort indexes and the travel time. Our methodology has been validated for a heavy motorized quadricycle vehicle using Hardware In the Loop (HIL) simulations, for which the Energy Management System has been implemented in a DSP board communicating through a CAN protocol.

This paper presents the benefits of transitioning from traditional generator employment in a Forward Operating Base (FOB) to an alternative architecture using a power electronics based Energy Management System (EMS). The EMS provides an interface between power sources, loads, and energy storage elements to form a microgrid. Using power electronics and programmable logic, the EMS enables more efficient generator utilization by matching real time load demand to the smallest capable power source, reducing overall fuel consumption. The EMS offers redundancy as it can connect any one of multiple power sources to critical loads. A hardware-based laboratory experiment demonstrates the ability to transition from one power source to another while providing uninterrupted current to the load. The results of the experiment validate a Simulink model of the EMS. An example load profile was applied to the model to compare overall fuel consumption between the traditional architecture and the EMS-enabled microgrid.

This paper introduces a centralized reactive powermanagement strategy for the real-time (RT) operation of power systems. The proposed approach reformulates the Optimal Power Flow (OPF) problem to minimize system losses and redispatch costs while ensuring compliance with technical constraints. These include node voltage limits, line capacity, and operational limits of generating units. The strategy has been developed to integrate with the current operation of power systems, which typically relies on market mechanisms or Energy Management Systems (EMS). These solutions calculate the setpoints for dispatchable generation based on day-ahead forecasts, without actively managing reactive power and thus operating in a suboptimal point. To address this, the proposed approach enables continuous, real-time adjustment of plant setpoints within the defined optimization interval, leveraging more accurate forecasts. This aspect becomes increasingly crucial with the growth of renewable energy sources (RES). The method is tested in a real-time hardware-in-the-loop (HIL) simulation environment, evaluating its performance over varying optimization intervals (5, 15, 30, and 60 minutes) for the Lanzarote-Fuerteventura power system. Results from a two-hour real-time experimental simulation demonstrate that the proposed strategy reduces power losses compared to the current power system operation approach, with further reductions as the optimization interval decreases. Key words: Reactive power management, voltage control, optimal control, real-time systems, hardware-in-the loop simulation.

Korea Power Exchange (KPX) plays a pivotal role in South Korea's electric power industry. As a neutral and independent organization, KPX coordinates the flow of electricity through all transmission networks of South Korea, ensure reliable operation of the grid, operate cost-based competitive electricity market, and plan long-term generation/transmission expansion to secure reliable power supply. To run real time power system, EMS (Energy Management System) and MOS (Market Operation System) are run periodically. Here I will focus on introducing EMS, MOS main functions to operate reliable power system and power market. This paper will discuss on the operation status of 4 axes of EMS applications; SCADA, AGC/ED, NA, DTS, MOS-EMS relationship, and IT system enhancement.

Energy management system (EMS) is able to coordinate the generation and load to ensure economic and reliable operation and improve performance metrics of microgrids (MGs). However, most the previous studies have focused mainly on the optimal dispatch of the gas turbines and/or energy storage systems inside a normal MG. Very few works have been carried out to address the potential power outage in MGs. In this study, a novel optimal approach in the EMS based on online forecasts is proposed to minimise the system‐wide outages of MG, specifically through pre‐emptive load shedding. Online distributed generations' output forecasts are updated to predict the state of system and characterise potential upcoming outages. Proper control strategies based on receding horizon methods are introduced in this study. Outage mitigation actions using pre‐emptive load shedding are then performed to avoid power outages or minimise the duration of unavoidable outages. The effectiveness of proposed approach can be demonstrated in the presence of various fluctuations, which can be easily integrated in the EMS for both islanded and grid‐connected MGs.

The usage of batteries and supercapacitors in the field of electric vehicles is becoming increasingly prominent in both research and development. Due to the complementary advantages of the two systems, high energy density, and high power density, a combined battery/supercapacitor system offers potential. To effectively utilise the potential of such a hybrid energy storage system (HESS), one requires an intelligent energy management system (EMS). The EMS is responsible for controlling the electrical power between the battery and the supercapacitor in such a way that the required power can be optimally distributed at all times (currently and in the future). For this purpose, the energy management system utilises information on the driving route and, based on this information, shall calculate a global strategy for the continuous power distribution. The controlled power distribution should take place in real time and be robust against discrepancies so that unpredictable or unreliably predictable events do not significantly influence the functionality. For the implementation of the concept, a rule-based power distribution is implemented in combination with a predictive energy management. Here, the energy management is combined with a rule-based strategy calculation based on data on the route to be driven with a global optimization for the calculation of a route-specific strategy. Depending on the selected objective, the increase in energy efficiency, or lifetime, the operation of the power control is optimised. Due to the functional separation, the continuous power control can operate in real time, while more computational time can be spent on the calculations of the power management strategy, which accordingly does not need to be executed in real time. The results show that by using the presented EMS, especially in combination with a route-specific parameterisation, a significant effect on the energy efficiency and/or battery lifetime can be achieved. The average battery energy consumption can be reduced by up to 9.14% on urban routes. Regarding battery lifetime, the average battery usage can be reduced up to 13.35% and the battery energy losses even up to 62.72%.

Microgrid control methods toward achieving sustainable energy management: A bibliometric analysis for future directions

An Energy Management System (EMS) is a complex computer system for controlling the real time operation of electric power systems. An EMS consists of a set of subsystems that are composed of various applications, tools, and data stores. These different parts of the system interface with each other in terms of function invocation and data exchange. An EMS may also need to interact with external systems or external data stores. Different types of interactions have their own characteristics and requirements. This paper presents possible interface paradigms in the EMS and feasible ways of realizing such interfaces.

Preliminary design and validation of a Real Time model for hardware in the loop testing of bypass valve actuation system