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Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests.Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.

The microgrid concept provides a flexible power supply to the utility where the conventional grid is unable to supply. The microgrid structure is based on renewable energy sources known as distributed generators (DGs) and the power network. Nevertheless, the power quality (PQ) is a great challenge in the microgrid concept. Particularly the inclusion of renewable energy sources into the conventional grids increases the problems in the quality of power, like voltage sag/swell, oscillatory transient, voltage flickering, and voltage notching which reduces the quality and reliability of the power supply. In this paper, a microgrid is considered which consists of PV cells as DG, battery energy storage system (BESS), and a novel control strategy known as the nonlinear autoregressive exogenous model (NARX). The proposed controller is an improved artificial neural network (ANN). The various case studies like sag/swell, unbalanced condition, and voltage deviation have been simulated with the model. The comprehensive simulation results are compared with the proportional-integral (PI) controller. Hence in this paper, the robustness of the proposed controller has been studied through different situations.

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly.

Hybrid fast-charging stations with battery storage and local renewable generation can facilitate low-carbon electric vehicle (EV) charging, while reducing the stress on the distribution grid. This paper proposes energy management strategies for a novel multi-battery design that directly connects its strings to other DC components through a busbar matrix without the need for interfacing power converters. Hence, the energy management system has two degrees of control: (i) allocating strings to other DC microgrid components, in this case a photovoltaic system, two EV fast chargers, and a grid-tie inverter, and (ii) managing the energy exchange with the local distribution grid. For the grid exchange, a basic droop control is compared to an enhanced control including forecasts in the decision making. To this end, this paper evaluates results from multiple Monte Carlo simulations capturing the uncertainty of EV charging. For a realistic charging behaviour in each simulation run, random fast-charging profiles were created based on probability distributions of actual fast-charging data for arrival time, charging duration, and requested energy. The impact of different utilisation levels of the chargers was assessed by varying the average charging instances from 1 to 30 EVs per day. Using actual photovoltaic measurements from different months, the numerical analyses show that the enhanced control increases self-sufficiency by reducing grid exchange, and decreases the number of battery cycles. However, the enhanced control operates the battery closer to its charge limits, which may accelerate calendar ageing., Comment: Preprint submitted to Elsevier eTransportation

One of the challenging issues in DC microgrids attracted by many researchers is the current sharing enhancement among distributed generations (DGs) with different rated currents and/or lines impedances. In this paper, DG topology is considered comprising photovoltaic (PV) system as power resource and battery energy storage system (BESS). By inspecting the frequency droop control of synchronous generators, a unique corrective sinusoidal term is added to the output voltage in DC-DC converters of each DG with purpose of current sharing control between DC DGs based on their nominal capacities. This virtual decentralized current sharing scheme named as the frequency droop method (FDM) can accurately regulate the point of common coupling (PCC) voltage without any communication link. Moreover, a decentralized power management scheme with different operating modes for PV system and BESS is proposed in this paper based on the state of charge of BESS and the frequency obtained from FDM scheme. The performance of FDM and power management schemes is investigated by time domain off-line and real-time simulations in MATLAB/SIMULINK environment. Seven case studies are implemented considering the effects of load and solar irradiance variations under equality and inequality of lines impedances as well as rated currents, mode transition, and plug-and-play ability. By applying the proposed control schemes, the purposes of precise current sharing and DC-link voltage regulation are achieved smoothly with an acceptable transient compared to an existing power sharing method. Also, FDM scheme is more reliable than available methods due to its independence on line impedance.

Increased behind-the-meter (BTM) solar generation causes additional errors in short-term load forecasting. To ensure power grid reliability, it is necessary to consider the influence of the behind-the-meter distributed resources. This study proposes a method to estimate the size of behind-the-meter assets by region to enhance load forecasting accuracy. This paper proposes a semi-supervised approach to BTM capacity estimation, including PV and battery energy storage systems (BESSs), to improve net load forecast using a probabilistic approach. A co-optimization is proposed to simultaneously optimize the hidden BTM capacity estimation and the expected improvement to the net load forecast. Finally, this paper presents a net load forecasting method that incorporates the results of BTM capacity estimation. To describe the efficiency of the proposed method, a study was conducted using actual utility data. The numerical results show that the proposed method improves the load forecasting accuracy by revealing the gross load pattern and reducing the influence of the BTM patterns.

The mismatch between load demand and supply power may increase when distributed generation based on renewable energy sources is connected to the distribution system (DS). This paper shows the optimal battery energy storage system (BESS) placement problem on the DS to minimize the electricity cost. Diverse electricity prices are considered for normal, off-peak and peak hours in a day. Wild geese algorithm (WGA) is applied to optimize the location and power of the BESS. The problem and the efficiency of WGA is validated on the 18-bus DS four scenarios consisting of the DS without BESS placement, the DS with BESS placement, the DS existing photovoltaic system (PVS) without BESS placement and the DS existing PVS with BESS placement. The numerical results show that optimal BESS placement is an effective solution for minimizing electricity cost on the DS with and without PVS. In addition, the results have also shown that WGA is a potential method for the BESS placement problem.

In stand-alone dc microgrids, battery energy storage systems (BESSs) are conventionally used for regulating the dc-link voltage, causing a continuous battery operation. Though operating the photovoltaic (PV) system at its maximum power point yields minimum battery discharge current, the opposite is true for battery charging current. Therefore, reducing the battery charging current based on its state-of-charge (SoC) and the amount of available PV surplus power (which can be treated as virtually stored energy) is an opportunity for improving the battery life. The main objective of the control strategy proposed in this paper is to prolong the battery lifetime by reducing the charging current and keeping the battery SoC at lower values if the PV power is enough to supply the loads. Additionally, the PV system is used as the primary asset to regulate the microgrid voltage. The dynamic performance of the proposed control strategy is validated with experimental tests under various operating conditions. Furthermore, its effectiveness in prolonging the battery lifetime is evaluated using an aging model of a Li-ion battery (without loss of generality) by simulated case studies. National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by the Republic of Singapore's National Research Foundation (NRF) through “Distributed Energy Resource Management System for Energy Grid 2.0” project at Energy Research Institute @ Nanyang Technological University, and in part by the Office of Naval Research Global under Grant N62909-19-1-2081.

Simple control of a stand-alone variable speed wind turbine with a permanent magnet synchronous generator (PMSG) is presented in this paper. PMSG fed different types of loads by means of a converter where switched mode rectifier is connected to the PMSG, boost DC-DC converter, and voltage source inverter (VSI). The wind turbine is operated at measured wind speed and it is tested at estimated wind speed which is based on the power calculation of turbine power, generating electric power, losses power, and the dynamic power during the variation of wind speed. The estimation of wind speed would provide a scope of what a control method is applicable in practice and this would increase the performance by scheduling the connected loads. The bidirectional battery charger is controlled and used during the shortage of wind speed. The converter of the battery charger is controlled and implemented on an embedded system by using an STM32-based microcontroller. During the shortage or fluctuation of wind speed, the load voltage is controlled within the acceptable limit, where the voltage has been compensated. An extensive simulation has been conducted by MATLAB/Simulink, and the system shows a good dynamic response to the variation and decrease in wind speed.

The oscillation of frequency can cause the generator to run out of sync in a power system. Therefore, load frequency control (LFC) is needed to reduce frequency oscillation and prevent out of sync operation. The LFC regulates the governor to balance the turbine speed with changes in the load of existing. In this paper, a proportional integral differential (PID) controller and a battery energy storage system (BESS) are added to an LFC to improve the frequency stability of a power system. The parameters of the PID controller and BESS are optimized using the Bat algorithm (BA) to attain a good coordination. The frequency performance analysis is done by introducing disturbance in the form of changes in load power. The simulation results show that the frequency deviation of the system with the PID controller and BESS, has a faster settling time and smaller overshoot value. The system performs better than those with only the PID controller or the BESS. In conclusion, the BA algorithm can be used to find optimal parameter values of the PID controller and BESS for a synchronized coordination of an LFC.

This research paper reveals the design of controller with optimization techniques for automatic generation control (AGC) in multi-area power system incorporated with diverse energy sources. The diverse sources are conventional thermal, hydro and gas power station (THG). The frequency deviations should be remains constant in modern power systems. Optimization techniques used for proposed model for proportional integral (PI) controller which is tuned for AGC with diverse energy sources. The battery energy storage system (BESS) and extra high voltage alternating current/ direct current (EHVAC/DC) link investigated with proposed model. The proposed model has been tested with 1% step load perturbation (SLP) and MATLAB/Simulink verified their results. All the simulation results justified the area control error (ACE) with frequency deviation & tie-line power flow for area-1 and area-2. The comparative study done for PI controller with GA & PSO in the proposed model. It has shown its efficacy with proposed model including BESS and EHVAC/DC-link using GA and PSO.

This paper proposes an optimization methodology for sizing and operating battery energy storage systems (BESS) in distribution networks. A BESS optimal operation for both frequency regulation and energy arbitrage, constrained by battery state-of-charge (SoC) requirements, is considered in the proposed optimization algorithm. We use utility historical data as input in a case study on a real-world distribution circuit. The results indicate that a BESS generally brings high profits by participating in the frequency regulation market and should be sized to its highest allowable power capacity rather than its energy capacity.

The increased integration of renewable energy sources in the power grid reduces system stability, which translates to more frequency fluctuation. The UK National Grid Electricity Transmission (NGET) has introduced different frequency regulation strategies—Enhanced​ Frequency Response (EFR), and more recently Dynamic Containment (DC) to mitigate this issue. Compared to DC, the EFR includes an adjustable envelope for state of charge (SOC) management which results in better flexibility of the battery energy storage system (BESS). This paper conducts technical analysis and comparison on the two EFR services designed for BESS, and further proposes a cooperative control strategy combining the advantages of the two services based on EFR in order to achieve a better solution for both power grid and BESS. Case studies were carried out using real NGET frequency data to illustrate the schemes and verify the proposed strategy. EFR service 2 shows better frequency performance for frequency regulation while EFR service 1 behaves better in SOC management. The proposed strategy manages to incorporate the strength of both EFR services, which can be beneficial to the grid and BESS.

Battery energy storage systems (BESS) are widely used for renewable energy applications, especially in stabilizing the power system with ancillary services. The objective of this paper is to propose an active and reactive power controller for a BESS in microgrids. The proposed controller can operate the BESS with active and reactive power conditions and realize power smoothing and voltage regulation. The demanded active power flow generated through a low pass filter (LPF) is used to control the power smoothing for microgrids. LPF design is presented based on the BESS sizing and genetic algorithm (GA). The demanded reactive power flow generated according to the reactive current calculation is used to compensate the microgrid voltage to fulfill the voltage regulation. The analysis of the vector diagram is adopted to validate the proposed controller and to explain the applicability and functionality of the voltage regulation for microgrids. To verify the validity of the proposed controller, a 1-kW grid-connected inverter is built to demonstrate the ability of power smoothing and voltage regulation. Both simulations and hardware experimental results agree well with the theoretical analysis.

Battery Energy Storage Systems typically procure their primary revenues from regulated energy and ancillary services markets; nonetheless, they have great potential in supporting distribution network operators and their users. This paper evaluates the potential business case of battery storage systems integrating market application and services to a photovoltaic assisted electric vehicle fast-charging station. A mathematical deterministic optimization problem is formulated using mixed-integer linear programming to combine battery storage system operation in the day-ahead and frequency regulation market and the remunerated services offered to the charging station. The technical and economic feasibility of the solution and the applicability of the proposed framework is verified through a case study reflecting an existing photovoltaic assisted charging station in the Netherlands and considering the Dutch energy market framework and prices. The study shows that such battery storage system implementation is economically and technically advantageous for the players involved. The battery storage system can stack additional revenues on top of the market revenues. The charging station benefits from a reduced peak power and a 30% tariff reduction, and the system operator would indirectly benefit from the shaved charging station profile. Furthermore, the analysis shows that providing services to the charging station from the battery storage system does not significantly impact its market-related revenues.

Battery energy storage systems (BESSs) rely on battery sensor data and communication. It is crucial to evaluate the trustworthiness of battery sensor and communication data in (BESS) since inaccurate battery data caused by sensor faults, communication failures, and even cyber-attacks can not only impose serious damages to BESSs, but also threaten the overall reliability of BESS-based applications (e.g., electric vehicles (EVs), power grids). This paper proposes a battery data trust framework that enables detect and classify false battery sensor data and communication data by using a deep learning algorithm. The proposed convolutional neural network (CNN)-based false battery data detection and classification (FBD2C) model could potentially improve safety and reliability of the BESSs. The proposed algorithm is validated by simulation and experimental results.

Over the last decade, the electricity sector has seen a significant increase in the number of residential battery systems, as well as increasing interest in using them to reduce demand during network peaks. Although there is an abundance of literature assessing this ability using modelled residential batteries, there is a lack of detailed assessment using deployed residential batteries. This paper analyses 1-min resolution data from 15 non-coordinated residential batteries deployed in Australia across 6 network peak demand periods. A novel metric was used to quantify errors in BESS load-following, which occurred when the batteries did not completely mitigate grid import and export even when they had sufficient energy capacity and rated power. On average the 15 batteries discharged around 25% of their rated power during network demand peaks, whereas those that load-followed discharged around 40%. Despite the small sample size, these results suggest that the outcomes from modelled batteries represent the ideal upper bound and the actual performance of some batteries is likely to be lower. This there is a need for more research into the actual operation of deployed batteries, and what this means for the current modelled findings regarding their ability to reduce demand during network peaks.

The growing global electricity demand and the upcoming integration of charging options for electric vehicles is creating challenges for power grids, such as line over loading. With continuously falling costs for lithium-ion batteries, storage systems represent an alternative to conventional grid reinforcement. This paper proposes an operation strategy for battery energy storage systems, targeted at industrial consumers to achieve both an improvement in the distribution grid and electricity bill savings for the industrial consumer. The objective is to reduce the peak power at the point of common coupling in existing distribution grids by adapting the control of the battery energy storage system at individual industrial consumer sites. An open-source simulation tool, which enables a realistic simulation of the effects of storage systems in different operating modes on the distribution grid, has been adapted as part of this work. Further information on the additional stress on the storage system is derived from a detailed analysis based on six key characteristics. The results show that, with the combined approach, both the local peak load and the global peak load can be reduced, while the stress on the energy storage is not significantly increased. The peak load at the point of common coupling is reduced by 5.6 kVA to 56.7 kVA and the additional stress for the storage system is, on average, for a six month simulation, period only 1.2 full equivalent cycles higher.

This paper considers the co-operation of distributed generators (DGs), battery energy storage systems (BESSs) and voltage regulating devices for integrated peak shaving and voltage regulation in distribution grids through a co-optimization framework, which aims to minimize the operational costs while fulfilling the operational constraints of network and devices. To account for the uncertainties of load demand and generation, we then convert the co-optimization model into a two-stage stochastic program where state-of-charge (SoC) trajectories of BESSs and operation of voltage regulating devices are optimized at the first stage for day-ahead scheduling, while the reactive powers of DGs and BESSs are left at the second stage for potential intra-day scheduling to handle short-term voltage issues. The proposed co-optimization scheme is validated on the IEEE 37-node test feeder and compared with other practices.

This paper proposes a methodology to identify the best locations to install battery energy storage systems (BESS) in radial distribution networks. Such batteries are mainly intended for improving the reliability of the distribution network. The methodology uses utility historical load, outage and project-cost data, and relies on a mixed-integer linear programming optimization tool. The power and energy capacities of any of the BESS considered can be pre-specified based on the system's load and outage data. On the other hand, such parameters can be optimally determined, which generally results in a large number of small BESSs distributed throughout the distribution system. We analyze both alternatives. The optimal siting of BESSs is illustrated and discussed via sensitivity analysis using a small distribution system. In addition, a case study based on a real-worlddistribution network is analyzed. Outcomes show the validity and efficacy of the proposed methodology.

This paper proposes the network-aware operational strategies to provide (flexibility) services from Local Energy Community (LEC) by maximizing their profit based on the optimized operation of battery energy storage system (BESS), considering the uncertainty between forecasted and actual load net in LEC. The proposed method takes into account the capacity limitation constrain in the contract between LEC and distribution system operator in order to avoid penalties, and provides insightful data to support LEC in making the decision to join the balancing reserve. Three different case studies, which investigate the hypothetical LEC including 01 BESS (100kW/200kWh) and 55 houses with each installed photovoltaic system, are performed using a mixed-integer linear programming to validate the effectiveness of the proposed method. The results show that, by considering the registered limitation, the benefit is increased up to 30%, compared to the case without the capacity limitation constrain.

The paper conceives a flexible new generation naval ship Destroyer (DDX, Destroyer, Experimental) with a primary focus on low environmental footprint, high efficiency, and reliability. The ship implements an innovative propulsion power generation and storage system based on a CODOGOL (COmbined Diesel Or Gas Or eLectric) architecture and a Battery Energy Storage System (BESS). The proposed modular solution is also suitable for retrofitting applications and represents an innovation in the state of the art of hybrid propulsion systems for big, front-line naval ships. The shipboard BESS is used as a backup power source to ensure minimum generator operation (MGO) mode reliability requirements. The benefits of the proposed solution are discussed in detail, highlighting a reduction of the operating costs and fuel consumption, as well as low pollutant emissions and Life Cycle Costs. Eventually, dynamical simulation is used to assess the effectiveness of the proposed solution in critical conditions.

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively.

Substation (SS) auxiliary systems (SAux) are facilities responsible for hosting the alternating (AC) and direct current (DC) busbar to serve the equipment and systems that perform the substation’s protection, control, and supervision. External and internal power supplies typically ensure the continuity of such a facility. The electricity support will be restricted to diesel emergency generators (DG) if the external power supply is unavailable due to a contingency. The DG present a slower response time and are susceptible to starting failures. Microgrids with Battery Energy Storage Systems (BESS) paired with photovoltaic systems (PV) are presented as an innovative and reliable solution for powering the SAux. In this article, tests were carried out on the microgrid of the Edson Mororó Moura Institute of Technology (ITEMM) in Brazil to support the use of microgrids BESS/PV in the SAux of a transmission SS of the São Francisco Hydroelectric Company (Chesf). Without an external power supply, BESS commands the action of islanded operation, maintaining both voltage and frequency requirements of the microgrid without load shedding. It was possible to observe all operations of the microgrid. The experimental results showed that the solution proposed in the paper implements a dependable self-dispatchable autonomous power supply.

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

Electric ships have become popular for the purpose of reducing greenhouse gas emissions and improving energy efficiency. The DC shipboard microgrid (SMG) provides a good solution to integrate the battery energy storage system (BESS), generators, electric propulsion system, and shipboard loads. In order to avoid over-discharging and over-charging of batteries and prolong the battery lifetime, it is necessary to balance the battery state-of-charge (SoC). This paper proposes a decentralized SoC balance method based on droop control in the DC SMG. The battery SoC balancing speed is regulated by an exponent parameter n, and larger n leads to faster SoC balance. In addition, the voltage drop caused by the proposed SoC-based droop control can be ensured within the allowed value in the entire SoC range. Verification of the proposed method is implemented in Matlab/Simulink model, which consists of two batteries. The simulation results verify the effectiveness of the proposed method.

The paper presents a study on the simultaneous determination of the optimal allocation of the battery storage system and renewable distributed generation units in power distribution systems using the co-simulation approach. A method for determining the optimal sizes/powers and locations of battery storage systems and distributed generation units based on a metaheuristic optimization technique is proposed. In addition to the optimal allocation, the optimal deployment of battery storage units is also considered and included in the optimization problem. The fuzzy based controller for battery storage charging/discharging is proposed and optimized simultaneously with allocation optimization. Minimization of system losses is considered as the objective of the optimization problem. The cosimulation approach is used to simulate the power system model and the optimization procedure. The presented optimization framework is applied to the IEEE 37 node test distribution feeder to present the procedure. The power losses are compared for three scenarios. The achieved reduction in power losses compared to the base case is 28.5 and 33.1 percent for scenarios 2–3, respectively.

Battery energy storage systems (BESSs) for residential photovoltaic (PV) prosumers are becoming more popular with the reduction of battery prices, as a means of increasing self-consumption. When considering prosumers that also own an electric vehicle (EV), smart charging can also fulfill the same objective. This paper assesses the benefits of a PV prosumer with an EV under two options: installing a BESS or applying smart charging. Smart charging is applied with the goal of reducing energy imports by charging the EV when there is excess of production. The effect of using a BESS of varying inverter and storage size is also investigated, when operated under a simple energy import minimization logic. We use a yearly dataset of real 5-min measurements collected from a Danish household with a 6 kWp PV plant. The prosumer also owns a 62 kWh Nissan LEAF. We find that smart charging increases self-consumption from 29% to 54%, leading to yearly savings of 220 e . Through a sensitivity analysis we find that a BESS of 8 kWh / 2 kW yields similar yearly results. Given the approximately 20 times higher payback period of the BESS, we conclude that smart charging is economically much more attractive.

In a modular multilevel converter (MMC) based battery energy storage system (BESS), a fault tolerant design ensures uninterrupted operation of the MMC when a given number of submodules (SMs) have faulty batteries or no batteries. This paper quantitatively investigates the fault tolerance improvement in MMC-based BESSs with different numbers of redundant SMs, which is commonly practiced to improve the fault tolerance in modular converter topologies. Simulation results are obtained to verify the proposed analysis. The presented analysis provides guidelines for designers to choose an appropriate number of redundant SMs to achieve a desired fault tolerance. Accepted version This work was supported by the Office of Naval Research U.S. under DUNS Code: 595886219.

DC microgrids with photovoltaic (PV) systems often incorporate a battery energy storage system (BESS) to regulate the dc bus voltage fluctuations due to the intermittent irradiance changes and load transients. Traditional control schemes do not consider the battery charging rate, leading to high current stress and shortening the battery lifetime. In this paper, a coordinated control for a PV system and the BESS is proposed, which implements a deterministic rule-based control strategy that regulates the battery charging rate depending on the available PV power reserve and the battery state-of-charge. Furthermore, the proposed control considers the PV system as the primary dc voltage regulator and treats the BESS as a secondary voltage regulating resource. The performance of the proposed control is investigated via simulation results. Ministry of Education (MOE) Submitted/Accepted version This work was supported by the Singapore Ministry of Education Academic Research Fund Tier 1 under Grant No: 2019-T1-001-168. It was also supported by the Basque Country Government under the project ENSOL2 (Development of Advanced PV Technologies Grant No: KK-2020/00077).

The task of CO 2 neutrality in 2050 with high targets for 2030 ask for a large deployment of Renewable Energy Sources. However, the volatility of RES asks also for a high flexibility in the system, to be provided with a high share of storage resources. Traditional Pumped Hydro Powerplants (PHP) and newer Battery Energy Storage Systems (BESS) are competing technologies which need to co-participate to this effort. The paper is analyzing a combination of these resources, with the purpose to be able to cover the need of flexibility, by giving also a preliminary view for Republic of Moldova. The analysis is using the strengths of each technology, and investigates appropriate business models and opportunities for BESS, to highlight that they have many opportunities to be exploited in a future high-RES scenario. It is shown that these options are able to have a complementarity which brings synergies and provide meaningful solutions for the high need of storage in an decarbonized energy paradigm.

Increasing environmental restrictions, create stringent regulations for the use and management of rivers and water bodies, thereby jeopardizing the operational flexibility of hydropower plants. The national plan for new environmental regulations in Sweden is expected to be implemented in 2025. Hence, it is imperative for the hydropower producers to consider alternatives which can overcome the environmental restrictions for providing flexibility. In this paper, a Mixed Integer Linear Programming (MILP) based algorithm for the short-term regulation of hydropower plants with energy storage is proposed. There are two stages of the algorithm: Future electricity market prices model using machine learning techniques, and the combined hydropower plant and battery system (CHBS)’s operation model using MILP. The designed algorithm is used to analyze the techno-economic feasibility of the operation of three hydropower plants Skattungbyn, Unnån and Hansjö, located in lower Oreälven river in Sweden. Three different electricity market scenarios were developed based on Swedish nuclear energy targets for 2040. The forecasted market prices act as inputs for the designed optimization algorithm. The algorithm optimizes the operation of hydropower plants with an objective to maximize revenue through harnessing flexibility enabled by storage. It is observed from the results that with the current battery costs (0.36 million €/MWh), the CHBS for short-term regulation is not profitable with NPV ranging from −1 to −4 million € and the cost of battery needs to be less than 50 000 €/MWh to make it economically feasible. Publishers version

This paper proposes a novel hierarchical optimal control framework to support frequency and voltage in multi-area transmission systems, integrating battery energy storage systems (BESSs). The design is based on the coordinated active and reactive power injection from the BESSs over conventional synchronous generator-based control for fast and timely mitigation of voltage and frequency deviations. The principle of this new idea is to use two hierarchical schemes, one physical and one logical. The objective of the first scheme prioritises the power injection from the BESSs installed in the area where a contingency occurs, consequently reducing the disturbance of the dynamics in the neighbouring areas. In the second scheme, operational rules for aggregated BESSs in each are incorporated, increasing the safety of the asset. The proposed approach exploits the advantages of time-synchronised measurements, the eigensystem realisation algorithm (ERA) identification technique, the optimal linear quadratic Gaussian (LQG) controllers and a new aggregating agent that coordinates the power injection of BESSs in a hierarchical and scalable scheme to precisely regulate frequency and voltage of modern transmission grids, increasing their reliability and stability. The feasibility and robustness of the proposal is demonstrated using simulated scenarios with significant load changes and three-phase, three-cycle faults on a modified Kundur-system with four interconnected areas, mitigating frequency and voltage contingencies in less than 450 ms.

Integration of renewable energy sources in the electric power system (EPS) can introduce some uncertainties to the EPS operation. In this case, normal and secure operation of the system requires information about its flexibility. This paper explores the flexibility of the EPS with high share of wind generation supported by the battery energy storage system using artificial neural networks. The flexibility metric is expressed as the difference between the desired (target) loads at the considered nodes and the largest loads that the EPS can handle. In this study, the flexibility of a 6-node EPS is calculated for the ten minutes ahead operational point.

Integration of battery energy storage system (BESS) has been remarkably increasing in microgrids as a consequence of the high penetration level of renewable energy resources. A major challenge with BESS application is capacity degradation. The degradation of BESS is a complex mechanism that mainly affects the lifetime of the battery. Despite the importance of the topic, there is a lack of comprehensive models of degradation and hence the service lifetime in the literature. This paper presents a comprehensive and timely review on battery lifetime models in planning studies of microgrids. The existing models are deeply discussed, and the technical challenges are introduced.

This paper discusses the crucial aspects of Flywheel Energy Storage System into distributed renewable energy sources based microgrid. The study primarily focuses on the contribution of Flywheel Energy Storage System towards distributed energy sources based microgrid's reliability as well as conducts performance comparison with Battery Energy Storage System Applications. The comparative analysis takes consideration of different rotational speed ranges and corresponding losses regarding the Flywheel Energy Storage System into the reliable operation assessment of microgrid. The comparative reliability analysis between Flywheel Energy Storage System and Battery Energy Storage System Applications integrated in distributed energy sources based microgrid is demonstrated in the IEEE RTS-79 bus system. The simulation outcomes exhibit the effects of Flywheel Energy Storage System as well as carry out performance assessment of Flywheel Energy Storage System on the architecture's reliability aspects based on considered operational strategies.

Three-phase power imbalances may occur in the distribution network due to high electric vehicle (EV) charging demand. The imbalances become severe with the increasing number of EVs in the future and may be addressed with coordinated charging strategies. In this paper, we propose a phase-balancing and peak-shaving scheme for a community in the three-phase power distribution system by managing the charging and discharging strategies for EVs and grid battery energy storage systems (BESS). The proposed scheme includes energy transactions and distributed optimization models. In the transaction model, vehicle-to-vehicle (V2V) energy trading is considered. In the optimization model, the distribution system operator (DSO) optimizes the energy management strategy of the grid BESS while the EV owners optimize their charging strategies. Financial incentives are introduced to encourage the EV owners to assist the distribution system operator in reducing the phase imbalance. Simulation results show that the proposed scheme could mitigate the phase imbalance and reduce the difference between peak and valley load of the load curves in the three-phase distribution system. Both EV owners and the distribution system benefit financially. Moreover, the power quality and system reliability of the distribution network can also be improved. Brunel University London BRIEF Funding; the Department of Finance and Education of Guangdong Province 2016 [202]: Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]. We are grateful for support from the DTE Network+ funded by EPSRC grant reference EP/S032053/1.

This paper presents a modern yearly based park-and-ride management scheme. The electric vehicles’ owners are encouraged to keep their cars away from the crowded areas in cities and use the public facilities such as bus, train, and metro. This action will help the owners to reach their work on time inside these crowded cities. Electric vehicle charging stations are designed to charge 1000 electric vehicles using the proposed park-and-ride control approach. A case study of Fuxin, China is considered. The electric vehicle charging stations demand is met using renewable energy sources, namely photovoltaic and battery energy storage systems. Meeting the load demand and minimizing the total life cycle cost are considered two objective functions to formulate a multiobjective approach. The optimal sizes of the photovoltaic and battery energy storage systems are obtained using a multiobjective genetic algorithm and ε-MOGA. The robustness and effectiveness of the proposed control methodology are verified by detailed analysis and comparison using MATLAB®.

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered.

In order to improve the operational reliability and economy of the battery energy storage system (BESS), the topology and fault response strategies of the battery system (BS) and the power conversion system (PCS) have been emphatically studied. First, a new type of BS topology is proposed, which can greatly improve the reliability and economy of the system when single or multiple battery cells fail. Compared with the conventional topology, the maximum number of faulty battery cells that the new topology can bear and the economic gains will increase. In addition, by comparing with the centralized PCS topology, the advantages of the distributed PCS topology are analyzed from the four aspects of consistency, efficiency, BS reliability and PCS reliability. The consistency, efficiency and reliability of the latter are greatly improved. Therefore, the research results obtained in this paper will be helpful to improve the intelligent operation and maintenance of BESS.

The efficiency of hybrid microgrid systems is drastically affected by the number of power electronics converters interfacing with its components. Integrating distributed energy sources with microgrids with the optimal number of converters is crucial to minimizing the switching losses and power conversion stages, thereby improving the efficiency of the systems. This paper considers an efficient and economical configuration for a wind/solar photovoltaic (PV) system integrated with a battery energy storage system (BES). The PV system is connected directly to the DC-link, thus lowering the losses and cost by eliminating the PV boost converter. In the literature, only a few publications have investigated this effective microgrid configuration. In addition, none of the publications have developed a nonlinear control approach for the microgrid configuration. Due to the greater flexibility of fractional calculus in speeding up the system response and improving the robustness, this article proposes a global sliding-mode control method with fractional-order terms (GSMCFO) to enhance the transient, steady-state, and robust operation of the hybrid microgrid. This controller provides the maximum power point tracking (MPPT) of both the solar PV and wind power generators, regulates the DC-link voltage, ensures proper power transfer to the grid, and maintains the power balance. In addition, the GSMCFO guarantees the global stability of the hybrid microgrid. Furthermore, considering the simplicity, robustness, few control variables, and fast convergence rate of the differential evolution (DE) optimization method, it is utilized to optimize the performance of the GSMCFO. The proposed hybrid microgrid configuration under the action of the GSMCFO was simulated in MATLAB/SIMULINK. Various scenarios were investigated to illustrate the feasibility of the proposed scheme. The simulation results show that the GSMCFO can achieve superior dynamic performances than the proportional–integral (PI) controller with zero overshoot, a shorter settling time, and stronger robustness, thus improving the power balance of the hybrid microgrid.

This paper deals with the multi-objective operation of battery energy storage systems (BESS) in AC distribution systems using a convex reformulation. The objective functions are CO2 emissions, and the costs of the daily energy losses are considered. The conventional non-linear nonconvex branch multi-period optimal power flow model is reformulated with a second-order cone programming (SOCP) model, which ensures finding the global optimum for each point present in the Pareto front. The weighting factors methodology is used to convert the multi-objective model into a convex single-objective model, which allows for finding the optimal Pareto front using an iterative search. Two operational scenarios regarding BESS are considered: (i) a unity power factor operation and (ii) a variable power factor operation. The numerical results demonstrate that including the reactive power capabilities in BESS reduces 200 kg of CO2 emissions and USD 80 per day of operation. All of the numerical validations were developed in MATLAB 2020b with the CVX tool and the SEDUMI and SDPT3 solvers.

Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

The power system is experiencing an increasing share of renewable and intermittent energy production and increasing electrification. However, these changes are creating high power peaks, are straining the grid and call for expensive investments in expansions and improvements. This paper examines how the operational strategy of shared battery energy storage systems (s-BESS) can address these issues for commercial buildings with relatively high power peaks. Due to the uncertainty in long-term costs when subject to a measured peak (MP) grid tariff, the scheduling of the battery is optimised with a receding horizon control algorithm. The optimisation model is used on a Norwegian real-life case study to find the best possible configuration with an already existing battery. Although current Norwegian regulations challenge the possibility for shared metering and billing for a s-BESS configuration, the results show that the total system cost was reduced by 19.2% compared to no battery. The community peak was reduced by 17.8% compared to no battery and 6.22-17.5% compared to individual storage, which indicates that s-BESS is of value for the DSO as well.

A multi-unit battery energy storage system (BESS) which aggregates individual battery units can be used to provide auxiliary services in power grid. Existing state-of-charge (SOC)-based power sharing strategies for multi-unit BESSs rarely consider the unit-level battery aging characteristics and may lead to high aging rates. This paper proposes a rule-based health-aware power sharing strategy to cope with the above challenge. An aging function considering impacts of initial SOCs and charge–discharge power is used in the proposed strategy to calculate the aging rates of battery units. According to the characteristics of aging function, multi-unit BESSs are classified into three types. Three operation rules based on type parameters and initial SOCs are designed to reduce the aging rate of the whole multi-unit BESS. Rule-based power sharing algorithms and corresponding parameter conversion approaches are developed to implement the operation rules. One-hour and 24-hour testing cases based on BESSs with different aging characteristics are carried out to validate the effectiveness of the proposed strategy. Simulation results show that, compared to the existing SOC-based power sharing strategies, the proposed strategy is able to reduce the overall aging rate of the whole multi-unit BESS. It is also shown that the advantages of the proposed strategy are more prominent when the regulation flexibility of BESS is higher or the aging function is more nonlinear.

Under different operating conditions, the influence of energy storage battery on the power grid is different, and the location and capacity determination methods are also different. Therefore, how to scientifically and reasonably select the location is of great significance. This paper proposes a candidate set identification method for the most vulnerable branch and critical cut set of power system transient stability based on branch potential energy, and uses the improved comprehensive index to identify the critical cut set. This method does not need to wait for the end of time domain simulation to determine the most vulnerable lines and critical cut sets, thus providing the basis for the location of battery energy storage system (BESS). Through the power system comprehensive analysis program (PSASP) simulation, the accuracy of theoretical analysis and engineering practicability are verified.

In this paper, the effects of fluctuating demand on a low voltage electric utility system equipped with electric vehicular charging station have been studied and mitigation strategy has been proposed. By applying Battery Energy Storage System (BESS) and demand management (DMM), the stored electric energy during an off-peak period is controlled to supply the spiky demand during the EV charging period. Employing different approaches of discharging control, the BESS can response and shape the peaky demand into the smoothed one with an elevated load factor. Therefore, the result is that energy cost of EV charging has been reduced by cutting the demand charge during the on-peak period and the power quality of the system has also been improved. These findings are confirmed and illustrated by simulation results.

The propagation of hybrid power systems (solar–diesel–battery) has led to the development of new energy management system (EMS) strategies for the effective management of all power generation technologies related to hybrid microgrids. This paper proposes two novel EMS strategies for isolated hybrid microgrids, highlighting their strengths and weaknesses using simulations. The proposed strategies are different from the EMS strategies reported thus far in the literature because the former enable the real-time operation of the hybrid microgrid, which always guarantees the correct operation of a microgrid. The priority EMS strategy works by assigning a priority order, while the optimal EMS strategy is based on an optimization criterion, which is set as the minimum marginal cost in this case. The results have been obtained using MATLAB/Simulink to verify and compare the effectiveness of the proposed strategies, through a dynamic microgrid model to simulate the conditions of a real-time operation. The differences in the EMS strategies as well as their individual strengths and weaknesses, are presented and discussed. The results show that the proposed EMS strategies can manage the system operation under different scenarios and help power system operator obtain the optimal operation schemes of the microgrid. This work was supported by the Autonomous Community of Madrid under the PROMINT-CM project (S2018/EMT-4366).

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.