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To tackle the carbon emission problem, the exploitation of renewable energy resources (RESs) with zero emissions has gained much importance. In this context, hydrogen has become an important resource to involve itself in distributed energy resources (DERs) to generate electricity, energize vehicles, and heat our living areas. The utilization of hydrogen technologies is indispensable due to their procurement, environmental friendliness, and high efficiency for DERs. A challenging area in the field of hydrogen energy technologies is extracting efficient power and integration to DERs. This paper investigates how to model and operate a proton exchange membrane fuel cell (PEMFC) stack in a DER application to establish a living lab facility for educational and research purposes by covering Power-to-X (P2X). The PEMFC model of the studied system is designed in a MATLAB/Simulink environment and implemented in an experimental testbed. Thus, a fuel cell (FC) unit as a complementary source is preferred to be used near a photovoltaic (PV) array, a small-scale wind turbine (WT), and a lead acid battery energy storage system (BESS) by creating a P2X concept to form a living lab platform. Beyond the model, regarding the experimental part, a PV array, a WT, and a BESS are combined with a PEMFC module that is rated output of 1200 W. Expect for all systems, the FC part is emphasized in this study. Also, a dynamic model of the mentioned PEMFC is proposed and verified. • Importance of hydrogen energy is focused on its advantages, future role, potential, and challenges. • FC unit as a complementary source is evaluated by creating a P2X concept. • This platform is designed to meet the energy needs of research-education, diversify the supply, increase energy independence. • Energy procurement is supplied via green energy during a lesson, which is noteworthy and instructive. • Feasibility of combined PV/WT/BESS/PEMFC system is investigated. [ABSTRACT FROM AUTHOR]

This paper presents a powerful performance optimization approach for load frequency control in the off-grid mode of a delayed fuel cell microgrid with PI controller. The new approach based on stability boundary locus is used. Thanks to this, the search is only carried out in PI control parameter space keeping stable the system in the proposed optimization. The superiority of the proposed approach in terms of iteration history, fitness values, and time responses of the system is presented by comparing the results obtained. According to the results obtained, the proposed approach reaches the global minimum in a very short number of iterations and achieves this by both guaranteeing the stability of the system and providing the desired time response performances compared to the other method. • The stability of a delayed fuel cell microgrid system is a very important issue, especially in off grid mode. • A delayed fuel cell microgrid system should provide the desired quality of energy to the load. • The optimization approach provides the desired quality of energy to the load ensuring the stability of the microgrid. • Proposed optimization approach has great advantages in terms of iteration history and fitness value. [ABSTRACT FROM AUTHOR]

The necessity of employing renewable energy sources within microgrid-based Power-to-X (P2X) systems has been emphasized by the imperatives of reducing global warming and fossil fuel consumption. Due to their numerous advantages, solar and wind energy have acquired prominence in contemporary power systems. However, their intermittent nature presents challenges. To overcome these challenges, energy storage devices are utilized. Batteries, however, are plagued by issues such as high maintenance needs, self-discharge, and diminishing storage capacity with age. Consequently, investigating alternatives such as hydrogen storage has become a financially viable option, especially for high-power applications. However, the intrinsically slow dynamics of hydrogen production from electricity contrast with the rapid responses of contemporary power electronic devices. This paper presents a versatile technique for optimizing hydrogen production in the presence of stochastic fluctuations in solar irradiance and wind speed. The proposed method combines a modified version of the Symbiotic Differential Whale Optimization Algorithm (m-SDWOA) with power converters to maximize production from renewable sources and electrolyzers. Using extensive simulations to demonstrate the efficacy of the proposed method by comparing its performance to that of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The results demonstrate the superior performance of the m-SDWOA-integrated method and pave the way for improved hydrogen generation in microgrid-based P2X systems. • Innovative control framework coordinates WT, PV, BESS, and HESS. • Maximizes hydrogen production while maintaining power quality. • Efficient controllers stabilize DC-bus voltage amidst WT/PV variability. • Ensures consistent, reliable power supply in microgrid. • Efficient energy management via m-SDWOA for resource optimization. [ABSTRACT FROM AUTHOR]

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%. • Embedding network security constraints to ensure that each microgrid operates within safe voltage levels and avoids line capacity. • Examining the impacts of flexible loads and the charging patterns of EVs and FCVs on operational costs. • Incorporating hydrogen storage systems and refueling stations in the model to mitigate the adverse effects of renewable energy fluctuations. • Establishing a multi-objective model to consider both economic and environmental aspects of energy management in the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective framework for sustainable scheduling of hybrid hydrogen-power systems. In the proposed model, the microgrid system incorporated renewable energy systems, battery energy storage systems, non-renewable resources, power-to-hydrogen, hydrogen-to-power, demand response programs, and hydrogen storage systems to present a highly flexible structure in the energy systems. The purpose of the proposed model is to provide a complete operation mechanism for the daily scheduling of microgrids considering environmental, economic, and sustainable problems. To this end, a stochastic three-objective optimization model is proposed to consider the intermittent behavior of renewable generation, loads, and market prices and simultaneously tries to minimize the carbon emission, total cost, and peak loads. The peak load reduction is important because it increases the reliability, renewable energy integration, power quality, and reduced cost of reserve capacity. Therefore, a min-max approach is presented to reduce the peak load in the proposed model. Also, the hydrogen-to-power and power-to-hydrogen resources are integrated as flexible resources to increase the efficiency of microgrids during operation scheduling. The proposed model is evaluated on a standard case study and the numerical results show the multi-objective model reduces the carbon emission by 28.05 kg and increases the load factor by 6.26%, respectively. • Description of a multi-objective problem that integrates electricity and hydrogen. • Proposing an enviro-economic model to reduce carbon emission and operating cost. • The proposed model significantly uniforms the load profile by min-max approach. • Hydrogen storage tank and DR programs are considered to increase the flexibility. [ABSTRACT FROM AUTHOR]

Renewable energy production through electrolysis of water to obtain "green hydrogen" has gained interest as a clean energy storage option. However, integrating variable renewable energy resources into the grid poses challenges to maintaining energy balance and stability. In this paper, a hybrid energy storage system combining short-term battery energy storage system and long-term hydrogen-based energy storage system is proposed for an isolated DC microgrid with a structure similar to a hydrogen refueling station. Passivity-Based Control (IDA-PBC) is utilized for power converters regulation, ensuring global stability based on Lyapunov theory while accounting for the nonlinear characteristics of the physical system. The control strategy is designed to ensure a reliable supply of electrical energy to the loads, preserve the quality of electrical variables in the network, and secure the safe operation of system components. Additionally, to safeguard devices from overvoltage, a Sliding Mode Reference Conditioning loop (SMRC) is proposed. It intervenes only when there is a potential risk of exceeding predefined boundaries. Simulations results under demanding conditions demonstrate the effectiveness of incorporating hydrogen energy storage systems with IDA-PBC and SMRC for this class of problems. • An isolated MG with H2 storage, typical structure of a refueling station, was modeled. • The IDA-PBC ensures the stability of the nonlinear and complex MG. • SMRC ensures overvoltage protection in the bus. • The control confirms that H2 storage integrated with RES contributes to MG stability. [ABSTRACT FROM AUTHOR]

This paper provides a cost-effective energy management method for the microgrid's standalone and on-grid operational modes. The considered microgrid based on Green-to-Green systems such as a photovoltaic array (PV), wind turbine (WT) as renewable generators, hydrogen fuel cell (FC) system, microturbine (MT), and battery storage system. The suggested technique is based on the marine predator algorithm (MPA) that and created for a scheduling horizon of one day. The main objective of this paper is to reduce operating costs while satisfying the demand power. To approve the performance of the suggested management strategy, the simulation results are compared with other recent optimization algorithms. These algorithms include particle swarm optimization (PSO), salp swarm algorithm (SSA), and coot optimization algorithm (COOT). To approve the performance of the proposed EMS, the system will be evaluated for three cases: free renewable generation, restricted generation mode, and unlimited main grid power mode. For the first case, the proposed EMS reduces the operating costs by 0.8732%. For case 2, the economic benefits have been raised by 1.0815% and by 0% for the last case because of the reduced problem complexity. • A cost-effective energy management strategy for the microgrid based PEM fuel cells is proposed. • Implementation of marine predator algorithm to minimize the operating costs. • The performance has been investigated under various operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, certain issues envisaged in the framework of development of design methodology for intelligent autonomous distributed hybrid power complexes (ADHPC) with green hydrogen and energy storage, functioning in grid mode and in the mode of interaction with the global (national) grid (GN) have been solved. Depending on their energy deficit or surplus, relative to the global grid, ADHPCs can operate as a load or as an energy source, respectively, as follows: based on the analysis of the energy balance management process in the ADHPC, a reasonable choice of the structure of the system of accumulation and distribution of power flows (SADCF) was made from the point of view of increasing the reliability of its functioning and ensuring the physical feasibility of the energy balance management process in this structure, i.e. keeping the actual power consumption of the consumers close to the required rated power at each given time t. This is achieved by including a condenser connected to the SADCF system on its assembly and distribution bus and a storage system BS with double-level ((BS1, BS2), double-circuit ((BS1(1), BS1(2)), (BS2(1), BS2(2))) structure, whereby: BS1of the level 1 - to manage the capacity balance in the SADCF under normal ADHPC regime and the variation of green hydrogen and consumption capacities within their confidence intervals assessed at the design stage; BS2 of the level 2 - to coordinate ADHPC and GN modes of operation and to control, together with BS1 of the level 1, GN, diesel generator (DG), the power balance in the SADCF when the ADHPC fails and when RES and consumption power are outside their confidence intervals; alternating charge/discharge operation of the parallel circuits will extend the life of the BS system; – a comprehensive definition of optimal ADHPC system situational energy balance management task is formulated. [ABSTRACT FROM AUTHOR]

With the extensive utilization of fossil fuels, environmental concerns have been growing. Hydrogen-powered vehicles (HPVs) as well as hydrogen refueling stations (HRSs) are expected to proliferate to alleviate the pollution. In order to promote the economic valibity of HRSs, this paper proposes an optimal scheduling model for distributed HRSs to maximize HRSs' revenue during a day. HRSs serves both the mobility sector with fuel individually and the power system with reserve demand response collaboratively. The elaborated operating of electrolysers and relaxed control of hydrogen storage are involved to enhance practicability and flexibility of the scheduling. Considering that this model has multiple variables, intractable high-dimension nonlinear constraints, a transfer multi-agent reinforcement learning algorithm is developed. The algorithm consists of two parts: the one is the multi-agent optimization method by transforming multi-decision optimization into a multi-agent optimization; the other is to transfer the empirical knowledge of the source task to reinforcement learning according to the similarity between the source task and the target task, making it cut down the blindness of exploration and accelerate convergence. Numerical studies reveal that participating in the reserve services market collaboratively increases overall revenues of HRSs by up to 32.90%, and the computation time is sharply shortened by approximately 34 times compared to the basic reinforcement learning. • A joint operation model of distributed HRSs is proposed to lift revenue by 32.90%. • Relaxed control of hydrogen storage is utilized to minimize service deviation. • An evolutionary transfer reinforcement learning shortens solving time by 34 times. [ABSTRACT FROM AUTHOR]

Large-scale hydrogen production through water electrolysis is recognized as a promising way to promote the utilization of renewable energy. However, the integration of massive electrolyzers will lead to low computational efficiency in their scheduling problem, thus an aggregation modeling method for electrolyzer clusters is urgently needed. In this paper, a refined operation model of alkaline water electrolyzers is established to accurately describe their operation characteristics. Moreover, to solve 'the curse of dimensionality' in their large-scale applications, an approximated aggregated model for electrolyzer clusters which can improve the computational efficiency is proposed. The proposed model is incorporated into the optimal scheduling of power systems, and its effectiveness is validated via numerical experiments on the modified IEEE 118-bus test system. The results show that the proposed method can improve the computational efficiency by at least 1–2 orders of magnitude while ensuring high modeling accuracy. • A refined operation model for alkaline water electrolyzers is established. • An approximated aggregated model for electrolyzer clusters is proposed. • The proposed model is incorporated into the optimal scheduling of power systems. • The computational efficiency can be improved by at least 1–2 orders of magnitude. [ABSTRACT FROM AUTHOR]

In renewable-based microgrids, intermittency caused by some energy sources highlights the important role of energy storage systems. Nowadays, hydrogen and thermal energy storage are expected to play key roles in grid-scale applications, for facilitating effective penetration of clean energy sources. This paper, through a multilevel control framework for the energy management of a renewable and reversible solid oxide based microgrid, develops medium level controls accounting for the dynamic behaviour of storage systems. The storage systems considered are a hydrogen storage tank and a thermal energy storage based on phase change material technology. In particular, the proposed algorithm is helpful in the microgrid design phase as well as for clearly assessing high-level energy management strategies, since dynamic and transient behaviours of tanks during both charging and discharging phases are validated. In this way, top-level energy management strategies can be further refined and optimized in addition to a techno-economic design being pursued, primarily through shrinking the initial sizes of storages and thereby achieving significantly lower initial capital costs. For the analyzed microgrid, its feasibility is demonstrated by reducing by as much as 40% the size of the hydrogen tank and by up to 20% the energy capacity of the thermal storage. Finally, the proposed medium level control in a multilevel algorithm framework for a hydrogen-based microgrid is also seen to be fruitful for avoiding waste energy, which can be beneficial against the background of distributed energy systems acting in virtual power plants. • An rSOC-microgrid relying on renewables and innovative storages (i.e., HST and PCM-TES). • Multilevel control strategy checking storages resiliency in terms of energy satisfaction. • HST and TES sub-models considering transient phases for charging and discharging. • Scenario analyses aimed at optimizing both energy and economic aspects of energy storages. [ABSTRACT FROM AUTHOR]

The electricity production from Renewable Energy (RE) in isolated locations requires long-term energy storage systems. To that end, Hybrid Energy Storage Systems (HESS), through a combination of hydrogen and batteries, can benefit from the different advantages of both technologies. This paper presents a hybrid Power-to-Power (PtP) Optimal Power Dispatch (OPD) model for isolated systems with no electric grid access. Currently, the electricity supply in such cases is usually based on a mix of RE as the primary energy source sustained by a diesel genset acting as a backup generator. In this context, the model delivers the hourly energy flows between renewable production sources, energy storage devices and the electrical load, which minimises costs and Green House Gases (GHG) emissions. For validation purposes, the model was tested through its application to a case study in an isolated area in the Canary Islands, Spain. The results show that the algorithm calculates the hourly OPD successfully for a given plant sizing, considering the defined operational states of the different assets. These operational constraints showed a decrease in the PtP round-trip efficiency of 5.4% and a reduction of the hydrogen production of 9.7%. Finally, the techno-economic analysis of the results proves that the combination of hydrogen and batteries with RE production is a feasible alternative to phasing out fossil fuels for the selected case study – reducing the diesel generator usage down to 1.2% of the yearly energy supply. • Novel multi-state optimal power dispatch model for power-to-power energy systems. • Multi-year techno-economic evaluation of an off-grid case study. • Phasing out fossil fuels with renewable energy-based hybrid energy storage systems. • Hydrogen technology contributes to a lower levelized cost of energy. • High initial investment cost was a significant entry barrier to deploying hydrogen. [ABSTRACT FROM AUTHOR]

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices' operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy. • Design of control algorithms for islanded and grid-connected microgrids. • Development of hierarchical MPCs to target long-term and short-term operations. • Tracking the load demand and maximizing the revenue via electricity markets. • Minimization of the HESS operational costs. [ABSTRACT FROM AUTHOR]

After a natural disaster, electrical grids might go into a self-healing mode to restore service and reconnect any customers that lost power. In the self-heal system, the fast operation and restoring the critical loads (CLs) during a fault by available resources can increase the flexibility and resilience of the network. In addition to renewable energy sources and electric vehicles (EVs) storage devices, the hydrogen storage system and the process of converting hydrogen into electric energy by micro-gas turbine (MGT) have been modeled in order to assess the impact of this storage system in electrical loads restoration in critical conditions. Self-healing functionality is enhanced by using alternative and novel energy sources. In such a situation, how to interact with the stochastic nature of network components will be challenging. This paper formulates the optimal service restoration (SR) problem using chance-constrained Adaptive Distributionally Robust Optimization (ADRO) to address the ambiguities introduced by the uncertain parameters. A tri-level robust optimization model is developed such that the optimal switching in the network until clearing the fault is determined at the upper level, and the maximization of the SR based on the load demand values is determined at the lower level. The proposed method is demonstrated by simulation results on the modified Civanlar test system. In an equal operating condition and considering β = 3, it can be seen that the initial hydrogen State of the Tank (SoT) by 50 % can improve the load restoration by 7.5 % compared to the uncharged hydrogen tank. By increasing the initial SoT to the nominal value (10 Kg), the load restoration can increase to 13.2 %. [Display omitted] • Customized hydrogen into electric energy conversion model is developed in the energy management field. • Each uncertain parameter is considered ambiguous in the manner that follows an unknown but bounded distribution. • EVs, PHEVs, flexible loads, and the coordinated operation of the hydrogen-to-power system are all taken into account simultaneously. • A tri-level MILP model is formulated to determine the service restoration schedule. • CVaR is employed to effectively deal with the uncertainty of the parameters. [ABSTRACT FROM AUTHOR]

In this paper, a two-layer hierarchical control strategy for an isolated DC microgrid with a hybrid energy storage system is considered. The DC microgrid studied is composed of a short-term battery energy storage system, long-term hydrogen-based energy storage system and renewable energy resources. The proposed control strategy, combines concepts of Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) as a primary level and Model Predictive Control (MPC) as a supervisory level. The IDA-PBC layer guarantees a correct power balance and ensures global stability according to Lyapunov theory, considering the non-linear behavior of the physical system. On the other hand, the MPC layer calculates the reference signals for the primary level, ensuring null stationary error in the desired variables. As known, it is important to keep the electrolyzer, the fuel cell and the batteries in a safe operating mode to increase their useful life and guarantee the quality of the hydrogen produced. In this sense, the benefits of MPC are exploited by considering system constraints in the controller design. The main objective of the two-layer control strategy is to ensure the supply of electrical energy to the loads and quality in the electrical variables of the network. Representative simulation results under demanding conditions verify the effectiveness of combining IDA-PBC and MPC for this class of problems. • An isolated MG with H2 storage, typical structure of a refueling station, was modeled. • The hierarchical control strategy successfully combines IDA-PBC and MPC techniques. • The IDA-PBC ensures the stability of the nonlinear and complex MG. • Supervisor MPC ensures zero stationary error and safe operating mode of the MG. • The control confirms that H2 storage integrated with RES contributes to MG stability. [ABSTRACT FROM AUTHOR]

Extreme events such as floods, hurricanes, earthquakes, and wildfires pose significant threats to the uninterrupted supply of electricity to consumers, as they can cause the failure of numerous power system components. The resilience of a power system is defined by its ability to withstand extreme events and continue meeting demand. While the placement of multi-carrier microgrids (MGs) in power systems can impact resilience, the literature lacks research on resilience-oriented placement. This paper aims to address this gap by proposing a resilience-oriented placement strategy for multi-carrier MGs, utilizing switchable transmission lines to enhance the resilience of power systems. The study focuses on a heat-power-hydrogen MG that obtains gas from a gas network, exchanges power with the main power system, and incorporates combined heat and power (CHP), electrolyzer, and thermal storage. The developed model employs a stochastic mixed-integer linear programming (MILP) approach, ensuring the attainment of the global optimum. Resilience is evaluated using the metric of expected load not supplied (ELNS). The results demonstrate that when the bus connected to the MG is isolated, the MG generates electricity through its CHP unit to meet local demand, reducing the total demand shed in the power system and improving system resilience. Specifically, the MG reduces ELNS from 4955.48 MWh to 2356.64 MWh, indicating a remarkable 52% improvement in ELNS. Furthermore, the study shows that transmission line switching further decreases ELNS from 2356.64 MWh to 848.68 MWh. Several experiments are conducted to analyze the sensitivity of power system resilience to the number of MGs, the MG-power system exchange limit, and the limit on gas import from the gas network. • Heat-power-hydrogen microgrids (MG's) are used to improve power system resilience. • Resilience-oriented optimal placement of MGs has been done. • Transmission line switching is used to improve the resilience of power systems. • The effect of the number of MGs on power system resilience is assessed. • Sensitivity of system resilience to MG-power system exchange limit is assessed. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]