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An introduction is presented in which the editor discusses articles in the issue on topics including distributed generation of electric power; low-voltage distribution network; and wind-battery energy storage hybrid systems.

The development of renewable energy will increase the demand for flexible resources in power systems due to the strong uncertainties. To allocate resources and cope with these uncertainties, it is beneficial to apply robust coordinated energy storage and transmission expansion planning (ES&TEP). The large candidate line set can significantly increase the computational complexity of robust optimization models. However, there is currently no suitable network search space reduction (NSSR) method to address this problem. This paper designs a NSSR method based on redundancy constraint identification that only requires information on component power ranges rather than fixed power curves, which can be well adapted to robust optimization. Additionally, this method requires only basic numerical calculations and ensures convergence within a finite number of iterations. The classical stochastic robust ES&TEP model and the column and constraint generation (C&CG) algorithm are used to validate the effectiveness of the NSSR method. In the case studies, the numerical results confirm the rationality and effectiveness of the approach proposed in this paper. By selecting appropriate parameters, the candidate line set can be reduced by over 70% and the computational efficiency can be improved by an order of magnitude. [ABSTRACT FROM AUTHOR]

Driven by the demand for carbon emission reduction and environmental protection, battery swapping stations (BSS) with battery energy storage stations (BESS) and distributed generation (DG) have become one of the key technologies to achieve the goal of emission peaking and carbon neutrality. Therefore, this paper proposes a strategy to optimize the operation of BSS with photovoltaics (PV) and BESS supplied by transformer spare capacity. Firstly, it introduces the operation mechanism of BSS and uses the spare capacity of building special transformers and the roof PV to supply power to BSS to avoid the investment of transformers. Secondly, this paper establishes the load model of BSS and proposes the charging rules of battery swapping. Thirdly, a segmented pricing mechanism for the rental price of special transformers is formulated to guide BSS operators to preferentially rent spare capacity during low load rate periods. Aiming at the maximum daily profit of BSS, an optimization model is established to optimize the number of batteries to be charged and the charging status of BESS in each period; on this basis, the demand response model is further proposed. Simulation results show that the proposed strategy can improve the daily profit of BSS through shifting load. And the configuration of BESS can improve the battery swapping capacity and peak‐shaving ability. Moreover, the exponential segmented pricing mechanism can greatly reduce the number of high load periods and reduce the burden on the power supply. [ABSTRACT FROM AUTHOR]

In recent years, the interest in using DC microgrids has greatly increased due to their higher efficiency, less complexity, and greater transmission power compared to AC microgrids. To address challenges in DC microgrids in the presence of electrical vehicles (EVs) and the uncertainty of charging EVs, researchers have used PV/EV combination systems with energy storage systems (ESS). Controlling DC microgrids, including PV/EV/ESS, is crucial to cope with the existing challenges. On the other hand, the research on DC microgrids' protection systems is in the early stages, and there are still many challenges in this field. In addition, differences in control systems can pose different challenges for the protection system. The simulation results in this paper demonstrate that considering the best case (use of unipolar resistance grounding system) the DC bus voltage is improved by 22.3% (based on MAPE% data comparison). In the protection part, the empirical‐mode decomposition (EMD) method and selecting a suitable intrinsic mode functions (IMF) have been used to protect the DC microgrid. The proposed protection method has been tested under pole‐to‐pole and pole‐to‐ground fault conditions. The results show that the proposed method is capable of detecting various fault types in the studied microgrid. [ABSTRACT FROM AUTHOR]

In addition to providing mobility, plug‐in hybrid electric vehicles (PHEVs) provide a two‐sided energy exchange opportunity which makes them highly flexible distributed energy storage systems for the future of energy systems. This paper analyzes PHEVs' performance from the perspective of urban traffic and energy using a decentralized multichannel blockchain network based on the hyperledger model. This network using a layered design and local management of energy sources can significantly contribute to urban management and optimal use of its infrastructures. Then, dynamic modelling of PHEVs in this network is performed, and their data is added to the network to evaluate the network performance compared with the current centralized networks. The results indicated that the proposed blockchain network could simultaneously optimize PHEVs' performance, urban traffic management, and energy systems. Furthermore, by utilizing smart contracts, it can consider and optimize multiple challenges, such as congestion in the electricity network, urban traffic, and limited fuel, simultaneously. Therefore, it gives a strong tool to study the impact of mass deployment of PHEVs and their value and role in the sustainable cities and communities of the future while helping to support the global efforts toward affordable and clean energy for all. [ABSTRACT FROM AUTHOR]

During the operation of the ITER machine, hundreds of MW/Var of active and reactive power will be exchanged with the grid. The E‐STATCOM scheme composed of the Modular Multilevel Converter (MMC) and split supercapacitor energy storage has been proposed to improve the power compensation performance of the existing reactive power compensation system in the previous study. However, one of the main technical challenges which is lack of research is to precharge all submodule capacitors and supercapacitors from zero to their nominal voltage values efficiently during a startup process. As the capacitance and operating voltage of supercapacitors are much different from capacitors in each submodule, the startup of E‐STATCOM is a more complicated process. To coordinate the energy exchange between submodule capacitors and supercapacitors, submodule capacitors and the grid, this paper presents an optimized four‐stage AC side startup strategy for the E‐STATCOM. The proposed method minimizes the use of current‐limiting resistors while suppressing the surge current in the zero‐voltage startup process of supercapacitors, in addition to optimizing the energy consumption. The pulse current charging, constant current charging and constant power charging strategies of supercapacitors are adopted in different charging stages, and the detailed coordinated control scheme between submodule capacitors and supercapacitors are described and analyzed. The effectiveness and performance of the proposed method are verified by simulation results and hardware‐in‐the‐loop (HIL) experiments. [ABSTRACT FROM AUTHOR]

Cloud energy storage system (CESS) technology is a novel idea to eliminate the distributed energy storage systems from the consumers into a cloud service centre, where CESS acts as a virtual energy storage capacity instead of the actual devices. This combination forms a grid-forming battery-supercapacitor cloud hybrid energy storage system (CHESS) which is responsible for maintaining the voltage stability and power balance at the common DC bus of the multiple NG system. By establishing such an access to CESS, the proposed model allocates optimal shares of charge/discharge capacities for home owners, minimizes the daily operation cost of each home and grants an optimal operation of household appliances. [Extracted from the article]

Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large‐scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small‐scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long‐distance transmission. This paper proposes a deep reinforcement learning (DRL)‐based energy management system (EMS) called DRL‐MG to process and schedule energy purchase requests from customers in real‐time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q‐learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real‐time scheduling methods significantly. [ABSTRACT FROM AUTHOR]

Residential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers' costs in the proposed model. [ABSTRACT FROM AUTHOR]

Contemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a fully distributed scheme to solve the day‐ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud‐fog‐based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud‐fog‐based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small‐scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24‐h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods. [ABSTRACT FROM AUTHOR]

The ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios. [ABSTRACT FROM AUTHOR]

With the increasing rate of population in big cities around the world, the tendency to build new buildings in the suburb of main cities or to build large apartments in the main cities has been highlighted. In this regard, building residential complexes has seen a dramatic increase in these areas as it makes it possible to build a large number of residential units within a reasonable space. Although these complexes have brought numerous benefits, they are some challenges regarding their construction processes. One main concern associated with these complexes is how to optimally install energy components such as transformers, combined heat and power (CHP) units, boilers etc., in the shared area of apartments in the residential complex. To address this issue, this paper models the energy system of a residential complex as an energy hub and proposes a novel framework to obtain the optimal planning of such an energy hub. In order to address the conflicting desires of the residential complex's builders and the future residents of the residential units, a multi‐objective (MO) optimization problem has been considered in the proposed method that simultaneously optimizes the investment costs, operation costs, and the reliability of energy supply. In this regard, a Multi‐objective Particle Swarm Optimization (MOPSO) algorithm combined with classical linear programming (LP) optimization method has been proposed to solve the MO optimization problem. In order to demonstrate the effectiveness of the proposed method, a case study including a residential complex with 300 residential units is considered, and the proposed method is implemented in this case study. The numerical results show that the proposed framework can appropriately optimize investment costs, operation costs, and the reliability index simultaneously, and the obtained Pareto frontier gives the investors the freedom to opt for any point from this surface. [ABSTRACT FROM AUTHOR]

This paper proposes and develops the idea of using a community supercapacitor (SC) in an islanded DC multiple nano‐grids (MNG) system. In the proposed structure, the community SC works in tandem with the community/cloud battery energy storage system (CBESS) of the DC MNG. This combination forms a grid‐forming battery‐supercapacitor cloud hybrid energy storage system (CHESS), which is responsible for maintaining the voltage stability and power balance at the common DC bus of the MNG system. Also, to effectively utilize the SC capacity, this paper proposes a modified control structure for each DC nano‐grid enabling the local BESS units to coordinate with the community SC. Then, it is shown that, in the proposed grid‐forming CHESS technology, the output power of all the local and community BESS units has significantly smoother power variations leading to a higher battery lifetime. Additionally, it is shown that the proposed CHESS technology can improve the voltage stability of the system leading to higher voltage quality. Moreover, it is discussed analytically that the proposed CHESS technology requires less energy storage capacity for the community SC compared to its equivalent MNG with a distributed SC architecture. Finally, these results are verified by simulating two case‐study MNGs in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Cloud energy storage (CES) in the power systems is a novel idea for the consumers to get rid of the expensive distributed energy storages (DESs) and to move to using a cloud service centre as a virtual capacity. Although the different characteristics and applications of the energy storages are reviewed in some papers, there is no review study on the CES concepts, formulations, applications, and challenges. Therefore, the main contribution of this paper is to review the applications of the CES and its technical challenges in the power systems. For this purpose, the concept and fundamentals of the CES, as well as their role in supporting the consumers and the power network, are described first. The flow of information in a CES is then discussed, and the roles of the operator, consumers, and facilities, as the main sectors of the CES are explained. The existing studies are classified and discussed regarding the different applications of the CES in the power systems and their drawbacks are highlighted. The operation and planning (feasibility) problems of the CES are investigated. Reviewing the existing studies shows that comprehensive models are required to address the energy management (EM) and feasibility analysis of the CES applications. To address this challenge, the general formulations are presented for the planning and the operation scheduling problems of the CES. In addition, addressing different CES applications in the power systems leads to some technical challenges which are described. Finally, future directions are suggested for potential researchers to continue the studies on the CES integration and application. [ABSTRACT FROM AUTHOR]

The fault of the tie line between the photovoltaic (PV) station and the grid is a serious fault for the PV station. It will cause the PV station to operate into an unintentional island. The uncontrolled Island voltage and frequency will inevitably lead to the disconnection of the inverter in the station. This situation will bring great losses to PV operators and impact to the power grid. In order to deal with the tie line fault, this paper analyzes the operation characteristics of PV stations in case of tie line fault firstly. Then a tie line fault ride‐through method based on cooperative strategy of small capacity energy storage (ES), relay protection and PV inverters is proposed. The islanding switching control strategies of PV and ES are designed respectively. The cooperative strategy of protection, PV controller and ES controller is formulated as well. The real‐time digital simulator (RTDS) closed‐loop test platform including line protection device, ES controller and PV controller is built to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The article discusses incentivised by environmental concerns and energy security risks, converter-interfaced generation and a fleet of renewable energy sources have extensively penetrated for the world. Topics include maintaining rate-of-change of-frequency and voltage angle within satisfactory boundaries has critical to prevent unnecessary operation of protective relays; and the proposed control scheme adjusts the droop factors dynamically for the transient events.

Anticipating and relieving congestions is an ongoing challenge for transmission system operators. Distributed grid‐scale battery energy storage systems enable operators to shift power flows and remedy congestion through virtual power lines and grid boosters. This paper includes battery energy storage systems in a combined preventive and curative congestion management optimization. First, it analyzes the impact of the two operational strategies in a case study of the German transmission grid. Furthermore, it outlines curative ad‐hoc measures to overcome uncertainties during operational planning and real‐time operation. The simulation results indicate that battery energy storage systems further increase the use of curative measures and reduce congestion management costs. [ABSTRACT FROM AUTHOR]

The indirect benefits of battery energy storage system (BESS) on the generation side participating in auxiliary service are hardly quantified in prior works. Nevertheless, the configuration of BESS could be affected by its indirect benefits. In this paper, the authors purpose a quantitative economic evaluation method of BESS considering the indirect benefits from the reduction in unit loss and the delay in investment. First, the authors complete further the cost model of BESS for frequency and peak regulation based on the whole life cycle theory. Second, the authors quantify the indirect benefits of BESS in thermal power plants based on the theory of rotor fatigue life loss and establish a benefits model that considers the unit loss reduction during frequency regulation and the delay in investment during peak regulation. Finally, the authors propose a set of indexes for economic evaluation of the thermal power plant with BESS. The simulation results show that the total benefits of BESS can be improved effectively by considering the indirect benefits from unit loss reduction and the delay in investment, proving the effectiveness of the proposed approach which can be meaningful for the future investment in BESS on the generation side. [ABSTRACT FROM AUTHOR]

This paper presents a multi‐objective restoration scheme for improving the resilience of integrated electricity and natural gas distribution systems against extreme weather events. The coupling constraints of electricity and gas networks are tackled properly using a linearized optimal power flow (OPF). Distributed generators, power‐to‐gas facility, rescheduling of generation/storage units, and microgrid formation are employed as operational resources/measures for restoring the integrated energy system after the event landfall. An adaptable directed multi‐commodity flow‐based microgrid formation is utilized, that is, the network configuration is dynamically changed in accordance with time‐variant load priority weights. The proposed method was successfully examined on an integrated electricity and natural gas distribution system comprised of the modified IEEE 33‐bus distribution network and a 14‐node natural gas distribution network. Numerical results showed that using microgrid formation increased the supplied critical load of integrated electricity and natural gas distribution system by about 16%. Moreover, due to making benefit of the power‐to‐gas unit, the supplied critical load increased by about 12.3%. respectively. While utilizing energy storage systems along with the power‐to‐gas unit facilitated the exchange of energy between the power distribution network and natural gas distribution network regarding time‐variant load priority weights, the supplied critical load increased by about 13%. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive under frequency load shedding (UFLS) model that can be implemented in a multi‐area power system with real network characteristics. Conventional single‐area UFLS models operate on the basis of an equivalent center‐of‐inertia (COI) model, which ignores the local dynamics of system frequency response (SFR) and the impacts of load shedding location. Unlike the single‐SFR model, that is commonly utilized in previous works, the suggested multi‐area or multi‐SFR UFLS plan of this research has the distinct benefit of taking into account the dynamics of power transfer across different electric areas. The proposed multi‐area UFLS design incorporates a flywheel energy storage system (FESS) to support the inertial system frequency response and alleviate more than 30% load shedding while improving the frequency nadir by 25%. In order to investigate the performance of the proposed method under high penetrations of inertia‐free renewables, the inertia of the power network is reduced by around 30%; therefore the proposed UFLS scheme is assessed under a low inertia scenario. The proposed multi‐stage UFLS scheme is formulated as a mixed‐integer linear programming problem, and the optimal settings, including frequency set‐points and load shedding, are then optimized. The efficiency of the proposed model is verified using the IEEE‐118 Bus dynamic test system. [ABSTRACT FROM AUTHOR]

According to the necessity of cost‐effectiveness, improving efficiency, and decreasing emissions to supply the various demands, the concept of energy hubs (EHs) has emerged in recent years. This paper aims to ensure the optimal operation of an EH to meet electrical, thermal, and hydrogen demands, considering the issues of pollutant emission and operating costs. In the proposed structure for the EH, an solid oxid fuel cell (SOFC) converter with ammonia fuel is used, and thus a combined hydrogen, heat, and power (CHHP) system is formed. Moreover, a basic EH structure is also examined and compared to show the capability of the presented structure. The proposed economic‐environmental framework for the operation of the EH is multi‐objective as a stochastic mixed‐integer linear programming (MILP). Also, in addition to modelling the uncertainty of renewable sources production as stochastic scenarios, demand response programs (DRPs) have been implemented for electrical and thermal demands. The ε‐constraint method is used to solve the multi‐objective problem, and the fuzzy technique is applied to select the best solution among the Pareto solutions. The simulation results of the proposed EH compared to the basic EH show that the operation cost and emission are decreased by 15.14% and 5.9%, respectively, in the presence of ammonia and DRP. [ABSTRACT FROM AUTHOR]

Islanded AC–DC hybrid microgrids composed of new energy sources, constant power loads and energy storage system are typical non‐linear systems, and guaranteeing large signal stability is a key issue. In this paper, the non‐linear model of islanded AC–DC hybrid microgrids is established, and large signal stability criteria are obtained. Considering reference current limitation of the energy DC–AC converter, the derived large signal stability criterion gives important constraints on the constant power loads power, the power of energy storage unit, DC bus voltage, the inductor and equivalent resistance of the AC filter, DC capacitor, the proportional parameter of the inner current controller and the maximum instantaneous power of the DC–AC converter. The paper is summarized as follows. Firstly, the equivalent model of the AC–DC hybrid microgrids in DQ rotating frame is established. Then based on mixed potential theory, the mixed potential model is constructed, and large signal stability criterion is derived and optimized considering the reference current limitation of the DC–AC converter. Finally, the presented criterion is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Increasing the intermittent outputs of renewable energy sources (RESs) has forced planners to define a new concept named flexibility. In this regard, some short‐ and long‐term solutions, such as transmission expansion planning (TEP) and energy storage systems (ESSs) have been suggested to improve the flexibility amount. A proper optimization procedure is required to choose an optimal solution to improve flexibility. Therefore, a mixed‐integer linear programming (MILP) direct‐optimization TEP versus ESSs co‐planning model is presented in this paper to enhance power system flexibility. In doing so, a novel RES‐BESS‐based grid‐scale system flexibility metric is proposed to investigate the improvement of flexibility amount via ESSs modules in the numerical structure. In this paper, a novel repetitive fast offline method has been proposed to quickly reach the desired amount of flexibility by defining an engineering price/benefit trade‐off to finally find the best investment plan. Also, multiple uncertainties associated with wind farms and demanded loads and a practical module‐type battery energy storage system (BESS) structure for each node are defined. The proposed model is applied to the modified IEEE 73‐bus test system including wind farms, where the numerical results prove the model efficiency as BESS impacts on flexibility, investment plans and power system economics. [ABSTRACT FROM AUTHOR]

The multi‐scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system. This paper first considers the interaction mechanism of multi‐type storage peak regulation time sequences based on the Euclidian distance, dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source‐grid‐load‐storage (SGLS) was constructed. To attain a low‐carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time‐series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K‐means clustering and dual‐structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy. [ABSTRACT FROM AUTHOR]

This paper presents a two‐stage adaptive robust optimization framework for day‐ahead energy and intra‐day flexibility self‐scheduling of a technical virtual power plant (TVPP). The TVPP exploits diverse distributed energy resources' (DERs) flexibility capabilities in order to offer flexibility services to wholesale flexibility market as well as preserving the distribution network's operational constraints in the presence of DER uncertainties. The TVPP aims at maximizing its profit in energy and flexibility markets considering the worst‐case uncertainty realization. In the proposed framework, the first stage models the TVPP's participation strategy in day‐ahead energy market and determines the DERs' optimal energy dispatch. The second stage addresses the TVPP's strategy in intra‐day flexibility market to determine the DERs' optimal flexibility capability provision by adjusting their energy dispatch for the worst‐case realization of uncertainties. The uncertainty characteristics associated with photovoltaic units, electric vehicles, heating, ventilation and air conditioning systems, and other responsive loads as well as the transmission network's flexibility capability requests are considered using an adaptive robust approach. Adopting the duality theory, the model is formulated as a mixed‐integer linear programming problem and is solved using a column‐and‐constraint generation algorithm. This model is implemented on a standard test system and the model effectiveness is demonstrated. [ABSTRACT FROM AUTHOR]

The continuous growth of renewable energy sources (RESs) has increased the demand for flexibility in managing uncertainties of RES generation. Energy storage systems (ESSs) are recognized as one of the promising methods to address this challenge. For multi‐area power system planning problems, capacity allocations of RESs can vary considerably among areas accounting for the geographic diversities in RES generation and load patterns. This paper presents a hierarchical coordinated planning model considering the interaction of local‐area (LA) planning entities and the system‐wide (SW) planner. A novel multi‐objective LA planning model is proposed to compute optimal capacity configuration ratios of RESs and ESSs based on regional resource characteristics. The SW planner acts as a coordinator and further optimizes specific capacities of generation assets. Optimal configuration ratios produced by LA planning entities are important indices reflecting LA planning preferences and serve as a link between the two planning layers. Numerical results on a modified NREL‐118 test system show that the proposed planning model can reduce carbon emission by an average of 10.5% and improve the RES utilization rate by an average of 1.8% compared with planning models without considering configuration ratios and ESS installations. [ABSTRACT FROM AUTHOR]

With large‐scale integration of renewable generation, energy storage is expected to play an important role in providing flexibility to energy systems. In this paper, the authors construct a trilevel Stackelberg game model to study the co‐investment of merchant and regulated storage in energy and reserve markets. The upper‐level problem is a profit‐maximizing storage investment problem with a desired rate‐of‐return solved by a merchant investor. In the middle‐level problem, the system operator (SO) makes regulated storage investment decisions to minimize system cost. In the lower‐level problem, the SO clears energy and reserve markets. The proposed model captures interactions of regulated and merchant storage investment. Also, it clarifies how different ownership structures of storage influence merchant storage profitability and system cost structures in different capital cost of storage investment and wind penetration level scenarios. The numerical results conducted on a 6‐bus illustrative example and the IEEE 24‐bus Reliability test case validate the proposed model. The results show that both regulated and merchant storage can increase social welfare, and social welfare remains almost the same under different ownership structures of storage. [ABSTRACT FROM AUTHOR]

The access of large‐scale distributed generation (DG) easily leads to energy imbalance in distribution network. To deal with this issue, this paper proposes an energy optimal schedule method for distribution network considering the participation of source‐load‐storage aggregation groups (SAGs). Firstly, the system model consisting of distribution network layer and SAGs layer is established, and the schedule objectives and constraints of each layer are also given. Secondly, considering the fluctuation on the load side, a forecasting method based on Adaboost integrated convolutional neural networks and bidirectional long‐short term memory is proposed. Then, the improved sparrow search algorithm (ISSA) is proposed by using the tent map and Levy flight on the original sparrow search algorithm. At the same time, by introducing Pareto dominance relation and adaptive grid algorithm, the multi‐objective sparrow search algorithm (MOSSA) is derived. After that, a two‐layer optimization framework (ISSA–MOSSA) is proposed to solve the studied system. The simulation results verify the accuracy of the proposed load forecasting model, the superiority of ISSA as well as MOSSA, and the effectiveness of ISSA–MOSSA in solving the energy optimal schedule problem of the distribution system with the access of DG. [ABSTRACT FROM AUTHOR]

Keywords: AC-AC power convertors; AC-DC power convertors EN AC-AC power convertors AC-DC power convertors 3343 3344 2 08/04/23 20230801 NES 230801 INTRODUCTION With the rapid development of large-scale new energy and the increasingly extensive application of energy storage, the problems of high loss of AC and DC energy conversion and poor control flexibility in traditional transmission and distribution systems has become increasingly prominent. Fei Liu, et al. propose an LLC type DC-DC converter based on IGCT and magnetically integrated transformer, which can significantly increase the capacity, efficiency and power density of DC-DC converter. Guest Editorial: AC/DC transmission and distribution technology supporting the new-type power system. [Extracted from the article]

Nowadays, with the development of the global economy, traditional non‐renewable energy resources can not only meet the increasing energy demand but also bring severe ecological and environmental problems. Wind power, as one of the most economical types of renewable energies source, has become one of the essential resources for clean energy production. Because of the randomness, intermittency, and fluctuation of natural wind, the output power of wind turbines is greatly affected by wind uncertainty. Therefore, wind power generation forecasting and scheduling may be challenging. In this paper, for the successful and accurate presence of wind power producers in the electricity energy market, a method based on forecasting wind power production, the electricity price, and Financial Loss/Gain (FLG) in coordination with energy storage is proposed. To predict the electricity price, wind power production, and FLG for the next 24 h, the hybrid method based on deep learning time series prediction based on the LSTMs method and input selection based on the MRMI method has been used. For this purpose, first, based on historical data, the wind power producer forecasts the electricity price and wind power production for the next 24 h. According to the same predicted values, initial offers are set for participation in the day‐ahead electricity market. After that, wind power producers modify their wind power production based on the FLG prediction method. Since the FLG signal has highly volatile behaviour, therefore it is not efficient to forecast and apply it directly to the proposed wind power production offers. Therefore, classifying FLG by the FCM method and predicting the FLG class labels is much more helpful in improving the proposed bid of wind power producers to the electricity market. Finally, the wind power producer can improve its profit from participating in the electricity market by interacting with the energy storage unit. The presented numerical results demonstrate the efficiency of the proposed method. For example, if the initial offers are modified based on the FLG method, it has caused the expected profit to improve by 4.44–27.69% in the desired months of the year 2018. Also, in three months, the total profit of the wind power producer and energy storage will be higher than if the profit of the wind power producer participates in the day‐ahead market with 100% accuracy. [ABSTRACT FROM AUTHOR]

The rapid growth in the number of electric vehicles (EVs), driven by the 'double‐carbon' target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles' low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model. [ABSTRACT FROM AUTHOR]

Economic operation and reliable supply‐demand balance are problems of paramount importance in power grids with a massive share of intermittent renewable energy sources (RESs) of great interest. This paper sought an optimal coordinated generation scheduling for day‐ahead power system operation considering RESs and energy storage units. Renewable power generation, particularly, wind and photovoltaic are uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, a hyperparameter‐optimized long short‐term memory (LSTM) regression model is employed to forecast the day‐ahead weather from the historical time‐series weather data. Eventually, an empirical formula is used to estimate the power conversion from the day‐ahead weather forecasts for a selected PV module and wind turbine. The objective of the scheduling framework is to keep a delicate supply‐demand balance at the lowest possible cost of generation while maintaining the prevailing generation and system constraints. A variance measure uncertainty handling‐based grey wolf optimizer (GWO) technique is used to find the optimal day‐ahead generation schedules and dispatches under RESs forecast uncertainty. The proposed generation scheduling framework is examined on the IEEE 6 and 30‐bus systems. In the studied scenarios, the coordinated operation of generators can decrease the total day‐ahead operating cost for the modified IEEE 6‐bus system by 2.57% compared to supplying electricity generation with conventional generators alone. Likewise, the total operating cost from the coordinated operation of all generation portfolios was reduced by 6.93% from the operating cost of generation during base case simulation (supply only from dispatchable thermal units) on the modified IEEE 30‐bus system. Moreover, the case studies show that coordinated generation scheduling can mitigate the RESs power variability problem, provide secure supply‐demand operation, and minimize the operating cost of electricity generation. [ABSTRACT FROM AUTHOR]

Economic efficiency is the ultimate goal of all markets, including the electricity market. Several technical and pecuniary restrictions known as externalities in economics literature can significantly affect the economic efficiency of the electricity market. Negative externalities resulting from the operational restrictions of generation units are inherent to electricity markets. In this paper, after reviewing the effects of externalities on the day‐ahead electricity markets' economic efficiency using a unit commitment‐based model, an innovative and theoretically efficient service‐based procedure aimed at internalizing negative externalities in the day‐ahead electricity markets is presented. In this way, a new service procured by the energy storage system to provide energy interchange possibilities in the electricity market is introduced. The proposed service uses both price and quantity adjustment methods to internalize externalities. A new discriminatory method for pricing the service and a bi‐level optimization problem for determining the capacity of the energy storage system required to provide the service are considered. The consideration of the proposed method facilitates reaching the first‐best optimal market solution by alleviating negative externalities existing in the sub‐optimal second‐best solution in the presence of generation sector operational constraints. Numerical case studies demonstrate the functioning of the proposed externalities internalization scheme. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve the goal of green and sustainable development. To accomplish this goal, electric vehicles (EVs) are considered system‐schedulable energy storage devices, suppressing the negative impact of the randomness and fluctuation of renewable energy on the system's operation. In this paper, a coordination control strategy aimed at minimising the carbon emissions of a distribution network between EVs, energy storage devices, and static var compensators (SVCs) is proposed. A model‐free deep reinforcement learning (DRL)‐based approach is developed to learn the optimal control strategy with the constraint of avoiding system overload caused by random EV access. The twin‐delayed deep deterministic policy gradient (TD3) framework is applied to design the learning method. After the model learning is completed, the neural network can quickly generate a real‐time low‐carbon scheduling strategy according to the system operating situation. Finally, simulation on the IEEE 33‐bus system verifies the effectiveness and robustness of this method. On the premise of meeting the charging demand of electric vehicles, this method can optimise the system operation by controlling the charge‐discharge process of EVs, effectively absorbing the renewable energy in the system and reducing the carbon emissions of the system operation. [ABSTRACT FROM AUTHOR]

Cloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large‐scale energy storage compared with the requirements of small‐scale localized users. In this paper, to satisfy the small‐ and medium‐scale timely energy storage requirement from localized users, the concept of the cloud‐based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long‐term seasonally dynamic transformer capacity increment and short‐term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two‐step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud‐based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits. [ABSTRACT FROM AUTHOR]

This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control framework for the flexible and effective management and control of clustered DC nano‐grids (NGs) in an islanded DC microgrid system. It is assumed that each NG contains a photovoltaic (PV) system, a battery energy storage system (BESS), local loads, and a gateway (GW) module. Each NG has a hierarchical control system consisting of a decision‐making module and low‐level controllers. The decision‐making module ensures various desirable features including plug‐and‐play operation of NGs, maximum utilization of PV power generations, and avoiding state of charge (SoC) violation of batteries. Moreover, an adaptive model predictive control (AMPC) strategy is proposed to regulate the voltage of the NG local DC buses in the presence of non‐linear loads. This approach improves the performance of the NG voltage control system and reduces the current ripples of BESSs, thereby enhancing the lifetime of the batteries. In addition, a smart switching consensus‐based control strategy is designed that provides flexible power sharing among the NGs to balance the SoC of BESSs in which the BESSs altogether imitate the behaviour of a single cloud energy storage system (ESS). Finally, the performance of the proposed control system is verified by simulating the DC microgrid in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Cloud energy storage system (CESS) can effectively improve the utilization rate of the energy storage system (ESS) and reduce the cost. However, there is a lack of a model designed for large‐scale renewable energy power plants (REPPs). Due to the volatility and intermittency of renewable energy power generation, as well as the demand of following scheduling plan and market arbitrage, it is also necessary to configure ESS for REPPs. However, if the REPP builds ESS by itself, the investment is relatively high. Therefore, the application of CESS on the renewable energy generation side can reduce the investment cost and increase the revenue by utilizing the difference between actual output and demand. Considering the uncertainties of renewable energy, this paper proposes a robust optimal configuration model of CESS based on the cooperative game. Firstly, the CESS model on the generation side is developed to describe the formation mechanism of ESS supply and demand. Then, the proposed model aims at maximizing the revenue of REPPs. The participants of the coalition are each REPP. By taking the renewable power uncertainty into consideration, the novel nested column‐and‐constraint generation (nested C&CG) method is utilized to solve the proposed model based on the min–max–min form. Furthermore, the Shapley‐value method is used to distribute the benefits to each member of the grand coalition. Finally, case studies verify the rationality and validity of the proposed model. [ABSTRACT FROM AUTHOR]

Presenting cloud energy storage system (CESS) in the landscape of storage devices exposes microgrids (MGs) to a substantial change. Employing a specific type of inverter namely synchronverter as the connecting interface for CESS gains high inertial response in MGs with high penetration level of inverter‐based distributed generations (IBDGs). However, high fault contribution of synchronverters can lead to challenging issues. Nevertheless, presence of fault resistance which results in malfunctioning of the recloser‐fuse pair intensifies the cruciality of the dilemma. In this paper, at the outset, the effects of contribution of synchronverter‐based CESS and also fault resistance on recloser‐fuse protection are explored and then, a new algorithm is proposed for tackling these bottlenecks. To overcome these hurdles, the conventional recloser‐fuse scheme is applied at first. Then, by using the obtained schemes for fast and slow operations of the recloser, a specific part of them is set in a manner that the fuse‐saving task is performed properly even when the synchronverter‐based CESS is deployed. On the other hand, the other part of the recloser scheme is altered in a way that the maloperation of the recloser‐fuse pair owing to the fault resistance is alleviated. Adequate numerical analyses are conducted to evaluate the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper explores residential microgrids (MG) which not only deploy smart algorithms and energy management strategies for appliances scheduling but also make benefit of insulated wall structures and modern windows ending to smart buildings with sustainable architecture. This amendment is shown to have a remarkable effect on avoiding thermal energy losses and hence lowering energy consumption of heat, ventilation, and air conditioning system (HVAC) in smart and sustainable homes. Moreover, frequency selective surfaces applied in modern windows are shown to greatly improve in‐service telecommunication signal transfer ratio, assuring a reliable communication for inhabitants. The proposed model adopts the most recent concept of cloud energy storage system (CESS) unit to provide a public access to charge/discharge capacity for smart home owners. Accordingly, a simple but applicable capacity sharing strategy of CESS is developed for the energy exchanges of smart homes in the MG. By establishing such an access to CESS, the proposed model allocates optimal shares of charge/discharge capacities for home owners, minimizes the daily operation cost of each home and grants an optimal operation of household appliances. The proposed model is formulated as a mixed integer linear programming (MILP) problem and is assessed in several operating strategies. [ABSTRACT FROM AUTHOR]

Energy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi‐lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation‐based bi‐lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi‐lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG. [ABSTRACT FROM AUTHOR]

This paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system. [ABSTRACT FROM AUTHOR]

This paper proposes an inertia‐emulation‐based cooperative control strategy for the multi‐parallel energy storage system (ESS) to meet the requirements of state‐of‐charge (SoC) balance, inertia enhancement and zero‐steady‐state voltage deviation. The inertia emulation loop (IEL) is constructed by analogy with DC motors to dampen voltage oscillation, while the secondary voltage recovery loop is derived from the circuit equivalence of an inductor to indicate the system stiffness. Moreover, to equalize SoCs of energy storage units (ESUs) dynamically, a SoC self‐balance algorithm is developed. The redefined SoC mismatch degree and balance speed adjustment factor k are introduced into the droop resistance, adjusting the SoC self‐balance rate and eliminating the SoC deviation among ESUs. The dynamic performance of the SoC self‐balance algorithm is analyzed and the small signal model of the DC microgrid (DC‐MG) with proposed strategy is established. Based on eigenvalue analysis and step response, the system stability is assessed, and the influence of control parameters on transient characteristics and stability margin is investigated. Considering power constraint, voltage deviation constraint and dynamic stability constraint, the optimal design method of k is given. Finally, simulation and experiment verify that the proposed control, without modifying hardware, performs better dynamic and static characteristics and can equalize SoC among ESUs in charge and discharge mode. [ABSTRACT FROM AUTHOR]

Current trends in the modern grid are leading to the development and deployment of energy storage to help integrate increasing variable renewable energy sources into the grid. This paper studies a pumped thermal energy storage (PTES) system for multiple grid services including energy arbitrage, frequency regulation, spinning and non‐spinning reserve, and resource adequacy. Optimal dispatch methods are proposed for individual services as well as value stacking from multiple services to maximize the economic benefits. Assessment results demonstrate the superiority of value stacking. Specifically, the study shows the maximum revenue from an individual grid service with a 30‐MWh PTES system was $522,520, while the value stacking could increase the benefits to $678,477. In addition, sensitivity analyses were conducted to explore the cost‐effectiveness of a PTES system with different combinations of power transfer limits and energy capacity. It was found that the power transfer limit had a greater impact than the energy capacity on the benefits. The proposed method could help determine the optimal duration of a future PTES system. [ABSTRACT FROM AUTHOR]

In this paper, a new structure of a non‐isolated multi‐input converter is presented that is capable of generating high voltages to increase the efficiency of the converter, which uses a combination of a coupled inductor and a voltage multiplier cell. By combining these two methods, it is possible to use switches with low voltage stress and therefore low conductivity. This type of structure is suitable for photovoltaic (PV) applications. The proposed converter has two distinct phases for each input, which can be used to properly control the energy received from each source using two separate phases. The performance of the proposed converter depends on the charging or discharging mode of the energy storage system (ESS). To design a high step‐up non‐isolated multi‐input converter, first the structure and performance of the proposed converter are thoroughly investigated. The exact design method is presented for the correct operation of the converter and the simulation results for different modes of the converter operation are shown. Finally, in order to confirm the accuracy of the simulation results of the proposed converter, a laboratory sample of the proposed converter to supply a 400 W–400 V load is implemented and a comparison between the theoretical and practical results is performed. [ABSTRACT FROM AUTHOR]

Faced with the uncertainty of wind and photovoltaic power output and load fluctuation caused by the increase of new energy penetration in active distribution network, the demand for operational flexibility and the construction demand for flexible resources of distribution network are gradually increasing. The flexible operation of active distribution network can be realized by coordinated planning of the soft open point integrated with energy storage system (ESOP) and flexible resources. Firstly, the flexibility resource adjustability evaluation and margin indicators are proposed for the response model of typical flexibility resources. Secondly, a two‐stage distributionally robust coordinated planning model considering the coordination planning scheme of distributed generation, flexibility resource, and ESOP as well as the comprehensive norm uncertainty of wind power and photovoltaic outputs multi‐operation scenarios is established with the distribution network construction cost, annual operation cost, and annual power sales revenue as the objective functions meanwhile the investment and flexibility resource operation as constraints. Finally, the column constraint generation algorithm is used to solve the problem, and the effectiveness of the proposed model is verified by the modified IEEE33 node system. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear decentralized double‐integral sliding mode controller (DI‐SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI‐SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC‐bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor‐in‐loop validation, including a comparative study with a proportional‐integral (PI) controller, verify the applicability and effectiveness of the EMS‐based DI‐SMC under different operating conditions of the DCMG. [ABSTRACT FROM AUTHOR]

This paper studies the hybrid energy storage system to provide frequency support for the interconnected AC grid through MTDC systems interfacing renewable resources. A hybrid energy storage structure was created using a supercapacitor (SC) and battery storage systems (BESs). To coordinate the sharing of power between SC and BESS, an improved droop controller based on virtual inductance, capacitance, and resistance gain (VICRC) is suggested. Meanwhile, the droop controller's steady‐state deviation of the DC link voltage is immediately suppressed. The SC will be in charge of compensating the high‐frequency demand, whereas the BESS will be in charge of compensating the low‐frequency power demand, thanks to a decentralized energy management process. In addition, the suggested method aids in the rehabilitation of the SC's SOC. Finally, the simulation results with detailed models of the wind farms and AC–DC grid converters are performed on IEEE tests systems in Simulink/SPS and the results extensively discussed to evaluate the proposed structure. [ABSTRACT FROM AUTHOR]