Cost Of Battery Energy Storage System Research Articles

Optimal sizing model of battery energy storage in a droop-controlled islanded multi-carrier microgrid based on an advanced frequency droop model

This paper introduces an optimal sizing approach for battery energy storage systems (BESS) that integrates frequency regulation via an advanced frequency droop model (AFDM). In addition, based on the AFDM, a new formulation for charging/discharging of the battery with the purpose of system frequency control is presented. The studied MG system that consists of PV units, a diesel generator (DG), a combined heat and power (CHP) unit, a gas boiler, and a BESS is designed to meet the consumers’ thermal and electrical load requirements as well as system frequency regulation. In the proposed optimization model, the net present value of expansion planning costs (EPC) over the project lifetime should be minimized according to the capacity of installed BESS. The EPC consist of four components including, (i) MG operation cost pertaining to the DG, CHP units and gas boilers, (ii) value of lost load, (iii) BESS investment cost, and (iv) replacement cost of BESS, recognizing its shorter lifetime relative to the project’s lifespan. The effectiveness of the proposed method and its advantages compared other methods are demonstrated via a case study simulation. Compared to the conventional frequency droop characteristic, the utilized AFDM can reduce the total EPC while a broader range of power/frequency control capabilities of the BESS is achieved to regulate the frequency in a desired band. Furthermore, the paper examines the impact of the AFDM on the selection of battery technology.

A bi-objective optimization framework for configuration of battery energy storage system considering energy loss and economy

To address a bi-objective optimization configuration problem of battery energy storage system (BESS) in distributed energy system (DES) considering energy loss and economy, a perturbation and observation approach (P&O) is proposed in this article. First, in a DES, the configuration model of BESS is established. Then, a novel way is designed that transforming a bi-objective optimization problem into a single objective optimization problem with variable conditions. And the P&O process of the proposed method is presented. Finally, in a simulation case, compared with a single optimization objective of energy loss or economy, the P&O method improves 5.71-fold or 2.94-fold in each direction, respectively and effectively balances the contradiction between them. In addition, with the efficiency and electricity cost of BESS increasing, the rated capacity of BESS changes by approximately 10%. And the location is a key factor affecting the configuration scheme of BESS in DES.

Design of photovoltaic and battery energy storage systems through load demand characterization: A case study in Thailand

The integration of photovoltaic (PV) system at behind the meter has gained popularity due to the growing trend toward environmentally friendly energy solutions. Coupling PV systems with battery energy storage systems (BESS) addresses the uncertainties of PV energy production while enhancing energy management. Load characteristics have influence on PV and BESS design both in technical and economic aspects. This paper presents a comprehensive analysis of load demand characterization methodologies tailored for the design of PV and BESS. The fundamental load properties such as daily maximum power, minimum power, and load energy, categorizing loads based on their daily and annual variation degrees are investigated. The techno-economic performance of PV-only and PV+BESS systems is analyzed, focusing on metrics such as excess energy, peak limiting performance, and economic feasibility using the levelized cost of energy (LCOE). The findings of this study reveal three distinct load characteristics based on daily variation: moderate (50–70 %), moderate to high (70–80 %), and high (80–90 %). Load profiles with moderate variation (50–70 %) demonstrate economic feasibility when sized with a PV-only system matching maximum power demand. With BESS prices reduced to less than 140 USD/kWh, these loads could also integrate a 1C-BESS sized at half capacity under the same control strategy, resulting in lower excess energy and LCOE. Conversely, loads with higher variation (>70 %) and lower self-consumption rates (<70 %) face fewer viable options for both PV-only and PV+BESS configurations, especially if BESS costs remain high. These loads require custom-designed solutions and more extensive load analysis. Through analysis, this study establishes the relationship between load characteristics and system design suitability for the loads studied. The findings underscore the importance of incorporating load demand characterization techniques in the design systems, highlighting the need for tailored approaches based on specific load characteristics and economic viability.

Optimizing the bidding strategy and assessing profitability of over-install renewable plants equipped with battery energy storage systems

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150%–170% and 160%–180% for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh.

Comparing the Financial and Environmental Impact of Battery Energy Storage Systems and Diesel Generators on Microgrids

This article presents a robust analysis based on the data obtained from a genuine microgrid in operation, simulated by utilizing a diesel generator (DG) in lieu of the Battery Energy Storage System (BESS) to meet the same load during periods of elevated energy costs. The study reveals that the BESS significantly outperforms the DG and the conventional electrical grid in various financial and environmental aspects. Environmentally, BESS accounts for zero CO2 emissions, compared to the 67.32 tons of CO2 emitted annually by the DG. Financially, the total cost of BESS over 20 years (USD 1,553,791.31) is lower than that of DG (USD 1,564,965.18) and the electrical grid (USD 2,726,181.09). Furthermore, BESS displays a lower Required Average Discharge Price—RADP (USD 0.38/kWh) and Required Average Price Spread—RAPS (USD 0.18/kWh) compared to DG (RADP: USD 0.39/kWh; RAPS: USD 0.22/kWh) and the electrical grid (RADP: USD 0.71/kWh; RAPS: USD 0.38/kWh). During periods of high-energy tariffs, BESS provides significant environmental benefits, but it also offers a more economically advantageous option to meet the load. It offers an energy-efficient and economically feasible solution for the operation of microgrids.

Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems.

Optimal Configuration of Battery Energy Storage for AC/DC Hybrid System Based on Improved Power Flow Exceeding Risk Index

After the fault disturbance (DC bi-polar blocking) in the AC/DC hybrid system, when the battery energy storage system (BESS) near the fault location is used to eliminate the power transfer, some sensitive and vulnerable transmission lines still have the problem of power flow exceeding the limit value. Therefore, an optimal configuration of BESS for AC/DC hybrid systems based on power flow exceeding risk index is proposed, which is used to eliminate the impact of power transfer on transmission lines. Firstly, considering the line outage distribution factor, the power flow exceeding risk index is established, which is used to judge the sensitive and vulnerable transmission lines on the shortest path power flow after the fault in the AC/DC hybrid system. The shortest path power flow is found by using the Dijkstra algorithm; the transmission lines nodes of the shortest path power flow are selected as candidate nodes for BESS configuration. Secondly, considering the safe and stable operation capability of the transmission lines, a multi-objective optimal mathematical model of BESS configuration for the AC/DC hybrid system is established, which minimizes the annual investment cost of BESS and maximizes the sum of the power flow exceeding risk index. Finally, the CEPRI36V7 power grid model in Power System Analysis Software Package (PSASP) is used for simulation analysis to verify the effectiveness of the proposed method.

Optimal battery energy storage system deployment from perspectives of private investors and system operators for enhancing power system reliability

Due to their intermittent nature, high penetration of renewable energy resources (RES) can deteriorate power system reliability. A battery energy storage system (BESS) offers an opportunity to reduce the uncertainty associated with RES and hence improve power system reliability. In the last decade, the cost of BESS has declined so its deployment has become increasingly feasible, but the decision ultimately depends on its Net-Benefit to the investor. Depending on the perspective of the investor, the deployment decision involves discovering the most optimal location, capacity, and technology of BESS. A sensitivity analysis based on the Z-bus matrix is used for selecting the optimal location. In this paper, from the narrow perspective of a private investor, the Net-Benefit function is the energy arbitrage value of BESS minus its overall cost. However, the system operator additionally values BESS because it enables more reliable system operation at reduced operating costs. After examining multiple power system reliability indices, the product of Expected Unserved Energy (EUE) and Value of Lost Load (VoLL) is incorporated into the Net-Benefit function of the system operator. MATPOWER Optimal Scheduling Tool (MOST) is used for testing the proposed Net-Benefit functions on a standard reliability test system (RTS) known as RTS-GMLC. Three diverse case studies are examined in three exploration phases; the first phase helps determine search space whereas the second one narrows it down and the third phase finds the optimal BESS solution. Presented research results demonstrate that, over a period of 15 years, optimal BESS deployment can offer as much as $M164.8 and $M33.6 Net-Benefits to the system operator and private investor respectively.

A universal optimization framework for commercial building loads using DERs from utility tariff perspective with tariff change impacts analysis

To accommodate the changes in the nature and the pattern of electricity consumption with the available resources, utilities have introduced variety of tariffs over the years. This paper develops a comprehensive optimization framework that addresses the challenges associated with the diversity of utility tariffs for commercial loads. Optimization of commercial building net load through Battery Energy Storage System (BESS) and renewable energy resources is modeled and explored for minimizing billing cost for different tariffs. Cost functions are formulated for each possible commercial utility tariff type, engendering the universal cost function format. An algorithm is developed to apply the optimization model and generate the desired optimal outputs (e.g., optimal net load and BESS power, costs and savings, etc.). The results for several building loads and all tariff types are compared and analyzed to present the findings. An impacts analysis is performed to observe how different changes in tariffs affect the optimizations results. Results exhibit that for same building load and renewable generation, the tariff type can have an overwhelming impact on costs and savings. Tariffs without Time of Use (TOU) charges or peak demand charge components offer lower savings opportunity from BESS optimization. Price change sensitivities showed the complicated dynamics among TOU price components on savings and historical trend analysis as an effective tool to predict future savings. Tradeoff between energy and demand charge heavy tariff options are also discussed for user benefit. The recent change in TOU time periods presents limited desired benefits while the modified tariff proposed in this paper exhibit significant improvement in terms of utility operation.

Customized predictions of the installed cost of behind-the-meter battery energy storage systems

Behind-the-meter (BTM) battery energy storage systems (BESS) are undergoing rapid deployment. Simple equations to estimate the installed cost of BTM BESS are often necessary when a rigorous, bottom-up cost estimate is not available or not appropriate, in applications such as energy system modeling, informing a BESS sizing decision, and cost benchmarking. Drawing on project-level data from California, I estimate several predictive regression models of the installed cost of a BTM BESS as a function of energy capacity and power capacity. The models are evaluated for in-sample goodness-of-fit and out-of-sample predictive accuracy. The results of these analyses indicate stronger empirical support for models with natural log transformations of installed cost, energy, and power as compared against widely-used models that posit a linear relationship among the untransformed versions of these variables. Building on these results, I present a logarithmic model that can predict installed cost conditional on energy capacity, power capacity, AC or DC coupling with distributed generation, customer sector, and local wages for electricians. I document how the model can be easily extrapolated to future years, either with forecasts from other sources or by re-estimating the parameters with the latest data.

Bilevel optimization model for sizing of battery energy storage systems in a microgrid considering their economical operation

Battery energy storage systems (BESSs) play extremely important roles in managing power supply and demand in a microgrid. The BESSs, on the other hand, have problems of high initial investment costs and limited lifetime. In this paper, a problem formulation and its solution are presented for sizing an aggregated BESS in a microgrid that minimizes the sum of initial costs of the aggregated BESS and operational costs of the microgrid. The target problem is originally categorized into a bilevel optimization because it comprises two optimization problems: the sizing of aggregated BESS and the operational scheduling of microgrid’s components. The authors, however, apply the Karush–Kuhn–Tucker approach and reformulate the target problem as a single-level optimization problem. This reformulation enables us to treat the problem as if it was a type of operation scheduling problems. That is, the solution techniques developed to obtain the optimal operation scheduling are applicable by the problem reformulation. As the basis for the solution method, an algorithm combining binary particle swarm optimization and quadratic programming is applied in this paper. To verify the validity of the proposal, numerical simulations are performed, and their results are discussed.

Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks

Due to fast response time and the ability to charge and discharge efficiently, the battery energy storage system (BESS) has become a promising option for ancillary services in low voltage networks. The BESS planning is more critical where only selected cost parameters were considered, and the sustainability aspects were ignored in most cases. In this paper, BESS planning is re-imagined from a circular economy perspective that primarily allows the optimal use of battery capacities without compromising the grid support. A novel method for optimal siting and sizing of commercially available residential BESS for ancillary support in low voltage residential feeders is proposed. This method incorporates the cost of BESS operation in the planning stages to provide an accurate estimate of BESS for grid support. The implications of introducing BESS operation cost at the planning stages are demonstrated by solving four different optimization problems with the costs defined as over-voltage, voltage fluctuations, BESS costs (investment, O&M costs), and multiple objective operations. To validate, the proposed method is applied to an IEEE European low voltage feeder and a modified version of this test feeder to an Australian scenario using particle swarm optimization (PSO) algorithm. It is found that the proposed method successfully reduces the BESS costs and provides an appropriate estimation of how the BESS installations can provide grid voltage support. Compared to the existing methodologies for mitigating over-voltage, the net required BESS capacity for the European and Australian feeders was reduced by 7.21 percent and 9.58 percent, respectively. The total BESS capacity in the European and Australian grids was decreased by 41.48 percent and 48.06 percent, simultaneously compared with the methodologies for reducing the voltage fluctuations. Overall, the optimal sizing and siting approach we proposed not only reduces the voltage fluctuations and required battery capacities but also promotes circular economy by lowering the resources in the BESS planing for ancillary services.

Multi-objective optimal siting and sizing of BESS considering transient frequency deviation and post-disturbance line overload

With the development of renewable energy sources, more frequent and severe active power disturbances emerge in power systems, and jeopardize the frequency stability and line loading security. The battery energy storage system (BESS) is able to adjust output power flexibly, and an attractive solution to improve frequency dynamics and power flow distribution. This paper proposes a multi-objective optimal siting and sizing scheme for the BESS to arrest frequency excursion and mitigate line overload under major disturbances. First, an extended average system frequency model is developed to estimate the required transient frequency regulation capability (TFRC) for frequency stability, and the post-disturbance power flows. Then, taking the required TFRC and line capacity as constraints, a multi-objective optimization model is established to minimize the life cycle cost of the BESS and generation cost. Furthermore, based on the linear weighted method, big-M method, and multi-cut generalized Benders decomposition, the intractable optimization model is transformed into a master problem for investment decision making, and a set of subproblems accounting for normal operation, frequency stability and line loading security. Additionally, the master problem and subproblems are solved iteratively to determine the location and capacity of the BESS. Case studies are conducted to validate the proposed scheme, showing superior performance in improving frequency nadir and alleviating post-disturbance line overload.

Multi-objective planning and optimization of microgrid lithium iron phosphate battery energy storage system consider power supply status and CCER transactions

Lithium iron phosphate battery (LIPB) is the key equipment of battery energy storage system (BESS), which plays a major role in promoting the economic and stable operation of microgrid. Based on the advancement of LIPB technology and efficient consumption of renewable energy, two power supply planning strategies and the china certified emission reduction (CCER) model are proposed respectively. Based on it, the multi-objective planning optimization model with economic benefits, environmental benefits and power supply stability as the objective function is established for the first time, and the Newton Weighted Sum Frisch method (NWSFA) solution model is adopted. In the planning process, rain flow counting method is used to research the life of BESS, which improves the accuracy of energy storage annual cost calculation. A park in northern China is taken as a case study to demonstrate the application of this model. The simulation results show that the annual economic operating cost of BESS is decreased by 18.81%, the energy supply reliability is increased by 0.15%, and the optimal electricity price adjustment ratio of the system is 15%.

Wind-plus-battery system optimisation for stacking of frequency response and black start services in the UK

The energy storage-friendly reforms of the ancillary service markets in the UK have heightened interest in co-locating battery energy storage systems (BESSs) with renewable power plants for the stacking of multiple revenue streams. This paper develops a modelling framework to simulate the operation of a wind farm (WF) and BESS co-location system where the BESS uses the existing connection point to deliver frequency responses (FRs) to the AC grid while sustaining a minimum energy level to assist the WF in supplying demand blocks in black start (BS) events. The BESS capacity and operating strategies are optimised to maximise the final net present value of the co-location project based on the UK perspective, addressing the trade-off between the BESS costs and the availability performance of FR and BS services. The modelling framework is demonstrated based on a particular transmission-connected WF in the UK combined with a weekly auctioned FR product, i.e., Dynamic Low High (DLH). The optimisation results are compared between the DLH-only provision and the stacking of BS and DLH, and discussed around the techno-economic feasibility of co-location projects, in particular, the BESS energy management and the minimum possible availability prices for DLH and BS services.

Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

Distributed energy resource allocation using multi-objective grasshopper optimization algorithm

The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.

Applying levelized cost of storage methodology to utility-scale second-life lithium-ion battery energy storage systems

The dramatic increase in electric vehicle (EV) sales has led to a rapid increase in deployed lithium-ion battery (LIB) capacity over the last decade. As EV batteries age and are retired from use in vehicles, they will require management. Second-life applications are often proposed as an environmentally and economically preferable management strategy to direct recycling or disposal. In particular, the repurposing of EV LIBs in stationary applications is expected to provide cost-effective solutions for utility-scale energy storage applications. However, the adoption of second-life battery energy storage systems (BESS) has been slow. One barrier to adoption is the lack of meaningful cost estimates of second-life BESS. Thus, this study develops a model for estimating the Levelized Cost of Storage (LCOS) for second-life BESS and develops a harmonized approach to compare second-life BESS and new BESS. This harmonized LCOS methodology predicts second-life BESS costs at 234–278 ($/MWh) for a 15-year project period, costlier than the harmonized results for a new BESS at 211 ($/MWh). Despite having a higher LCOS, the upfront costs for second-life BESS are 64.3–78.9% of new systems' costs. Results for second-life BESS are highly sensitive to assumptions of discount rate, depth of discharge, and module repurposing costs. If deemed environmentally or societally beneficial, policies should stimulate the use of second-life LIBs, such as providing incentives equal to or greater than those available for first life BESS. Further work can explore comparative economics at smaller scales and quantify non-economic benefits of second-life BESS.

Comprehensive benefits analysis of electric vehicle charging station integrated photovoltaic and energy storage

Photovoltaic–energy storage charging station (PV-ES CS) combines photovoltaic (PV), battery energy storage system (BESS) and charging station together. As one of the most promising charging facilities, PV-ES CS plays a decisive role in improving the convenience of EV charging, saving energy and reducing pollution emissions. To promote PV-ES CS and improve the network of charging facilities, it is necessary to explore its comprehensive benefits. Firstly, to make full use of peak-to-valley electricity price difference and consume the power generated by the PV, this paper introduces the energy management strategy of the station based on time-of-use (TOU) electricity price. Secondly, from the perspective of multiple beneficiaries, a comprehensive benefits analysis model of charging station is proposed, including the benefits of PV-ES CS, power grid and society. Finally, the comprehensive benefits of the new charging station are analyzed through a PV-ES CS in Beijing. The impact of the construction cost reduction (including BESS cost and PV cost) and subsidy decline on the economy of the charging station is discussed. The government should further enrich charging stations subsidies. The results show that the social and economic benefits brought by PV-ES CS are far greater than the economic benefits of the station itself. With the development of the new charging facility in the future, it will bring considerable social and economic benefits and play a vital role in saving energy and reducing emissions.

Incentive Policy for Battery Energy Storage Systems Based on Economic Evaluation Considering Flexibility and Reliability Benefits

The efficient application of battery energy storage system (BESS) technology can effectively alleviate the uncertainty and volatility caused by distributed generations (DGs) and loads, and reduce their adverse effects on the power grid. More efficient applications could delay equipment capacity upgrades, improve equipment utilization, save costs, and increase the system hosting capacity for renewable energy. However, the application of BESS is restricted by its high cost and limited policy support. It is, therefore, necessary to carry out an economic evaluation of BESS, considering its flexibility and improvement of reliability, alongside incentive policy research to promote its deployment. This study on BESS involves four key aspects: 1) It proposes a reliability-benefit model for BESS, considering the value of electricity in the national economy. 2) It describes a flexibility improvement benefit calculation model for BESS, built with the definition of flexibility indexes of distribution network related to BESS, and considering the capacity, charge, and discharge constraints. 3) A reliability improvement benefit calculation model of BESS was built, and the present study proposes a detailed calculation flow of economic evaluation model for BESS users considering net present value (NPV) index and dynamic payback period (DPP) index. 4) An impact analysis of different prices and incentive policies on BESS business models is also carried out, with the present study finally presenting an incentive policy based on flexibility and reliability improvement. The results of the IEEE 33-node test system show that flexibility and reliability improvement can effectively reflect the benefit and cost of BESS, and that incentive policies can help to promote the development of BESS technology.