Size Of Battery Energy Storage System Research Articles

Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

Abstract This paper proposes an approach for optimal placement and sizing of battery energy storage system (BESS) to reduce the power losses in the distribution grid. A meta-heuristic optimization algorithm known as Whale Optimization Algorithm (WOA) is introduced to perform the optimization. In this paper, two different approaches are presented to achieve the optimal allocation of the BESS. The first approach is to obtain the optimal location and sizing in two steps while the second approach optimizes both location and sizing simultaneously. The performance of the proposed technique has been validated by comparing with two other algorithms namely firefly algorithm and particle swarm optimization. The results show that WOA has outstanding performance in attaining the optimal location and sizing of BESS in the distribution network for power losses reduction.

The role of intelligent generation control algorithms in optimizing battery energy storage systems size in microgrids: A case study from Western Australia

Battery energy storage systems can play a substantial role in maintaining low-cost operation in microgrids, and therefore finding their optimal size is a key element of microgrids’ planning and design. This paper explores the optimal sizing options for batteries in microgrids that include wind turbines, solar photovoltaics, synchronous machines and a grid connection supply under various types of retail tariff schemes. The optimal size of batteries is hypothesized to be significantly related to the intelligent control rules applied to dispatch the microgrid sources. This problem can be formulated as a mixed linear integer problem and can be solved using linear/non-linear solvers depending on the complexity of the generation control plan. The main objective of this work is to apply online intelligent adaptation mechanism to tune the economic generation control (dispatch) rules of the microgrid. This tuning objectives are maintaining secure operation, maximizing profitable utilization of batteries and managing their charging life-cycles. While sizing options exploration has been formulated as a linear programming based optimization problem, Fuzzy-Logic is proposed to control the charging/discharging time and quantity for batteries. For the sake of performance comparison, various optimization techniques, i.e., Particle Swarm Optimization, Genetic Algorithm and Flower Pollination Algorithm are applied to perform the economic dispatch calculation. As a case study, a commercial type load connected to the 22 kV distribution network in south Western Australia was used in the testing and validation if the results of the proposed sizing method. The operation condition data was obtained from Western Power the distribution and transmission company in south Western Australia, the Australian Bureau Of Meteorology (BOM) and the Australian Energy Market Operator (AEMO). The results showed that employing intelligent batteries in operation can reduce the annual generation cost of microgrids. However, the decision on selecting the size of batteries depends heavily on the amount of upfront investment cost. The simulation results showed that the intelligence added to batteries’ control could achieve 6.5%, 7.6% and 11.5% of the annual generation cost in the Islanded, Grid-connected with no-export and Grid-connected with export operating modes respectively. Also, intelligent batteries operation control was proven to minimize their payback time to 2.8, 2.7 and 2.7 years in the Islanded, Grid-connected with no-export and Grid-connected with export operating modes respectively.

Planning and Operation of Isolated Microgrids Based on Repurposed Electric Vehicle Batteries

Battery energy storage systems (BESSs) can be very beneficial to power systems and microgrids for various applications. With increasing sales of electric vehicles (EV), the availability of used electric vehicle batteries (EVBs) is on the rise, which has received significant attention in recent years. The retired EVBs, after repurposing, can serve as an alternative option to new batteries in a BESS. In addition, when a microgrid operator desires to install a BESS, the optimal decisions such as installation year, energy and power size, replacement year, and the number of cycles to failure, that are normally overlooked, have to be determined. Therefore, this paper proposes a comprehensive and novel framework for planning and operation of the BESS based on repurposed EVBs. A novel linearized BESS sizing model is proposed that obtains the BESS optimal decisions regarding design and operation. Various new, modified, and linearized relationships for the BESS have been included in the planning model to ensure that the replacement year of the BESS is optimally determined. Several classes of EVs with multiple drive cycles are clustered and integrated within the proposed framework.

Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

Energy class dependent residential battery storage sizing for PV systems in Cyprus

Battery energy storage systems (BESS) have started to be part of the photovoltaic (PV) system design to allow the further penetration of PV into the grid. This study deals with the sizing (power and energy capacity) of a BESS for residential households which are represented by a typical load consumption profile, they have electrical air conditioning and electrical water heating (water boiler) resembling a common residential house in Cyprus. The BESS sizing is done considering the local energy consumption profile and the energy efficiency class of the house and the water boiler. The BESS is sized in order to achieve zero PV grid export and to increase self-consumption.

Sizing battery energy storage systems for industrial customers with photovoltaic power

The battery energy storage system (BESS) helps reduce the electricity bill of industrial customers (IC) with photovoltaic power (PV). Given the current high investment cost of BESS, the detailed cost-benefit analysis of BESS considering PV uncertainty is needed for enterprise owners to judge whether the profits can be obtained by incorporating BESS. In this context, a bi-level stochastic programming model is proposed to determine the optimal power and capacity of BESS. The upper model aims at deciding the optimum size of BESS, and the lower model focuses on optimizing BESS operation. The benefit from reducing electricity bills, the benefit from financial subsidies, the investment cost, as well as the operation and maintenance cost of BESS are assessed carefully, the impacts of the lifetime characteristics of BESS on which are fully taken account of. Simulation results on an actual IC with PV validate the effectiveness of the proposed methodology.

Reuse of electric vehicle batteries in buildings: An integrated load match analysis and life cycle assessment approach

The increasing use of renewable energy technologies for electricity generation in buildings will require a growing number of battery energy storage systems (BESS) to enhance the reliability of electricity supply. The increasing number of retired electric vehicle (EV) batteries, expected from the automotive sector, can match this demand as EV batteries can be used as BESS, considering that they have about 80% of their original energy capacity.In this context, the study aims at examining the system, consisting of a BESS made by retired Li-ion EV batteries, a photovoltaic plant (20 kW) and the electricity grid, that provides the electricity required by an existing nearly net zero residential building (25.000 kWh/year). The goal is to identify the optimal BESS size, expressed as energy capacity, for load match optimization and environmental impacts in a life cycle perspective.A BESS of around 46 kWh of energy capacity allows achieving significant results in terms of load match increase and environmental sustainability.The study includes an environmental assessment combining the load match analysis and the life cycle approach. It highlights the potential synergy inspired to the principles of the circular economy and of the industrial symbiosis, between the building and the automotive sector.

A BESS Sizing Strategy for Primary Frequency Regulation Support of Solar Photovoltaic Plants

This paper proposes a strategy for sizing a battery energy storage system (BESS) that supports primary frequency regulation (PFR) service of solar photo-voltaic plants. The strategy is composed of an optimization model and a performance assessment algorithm. The optimization model includes not only investment costs, but also a novel penalty function depending on the state of charge (SoC). This function avoids the existence of a potential inappropriate SoC trajectory during BESS operation that could impede the supply of PFR service. The performance assessment algorithm, fed by the optimization model sizing results, allows the emulation of BESS operation and determines either the success or failure of a particular BESS design. The quality of a BESS design is measured through number of days in which BESS failed to satisfactorily provide PFR and its associated penalization cost. Battery lifetime, battery replacements, and SoC are also key performance indexes that finally permit making better decisions in the election of the best BESS size. The inclusion of multiple BESS operational restrictions under PFR is another important advantage of this strategy since it adds a realistic characterization of BESS to the analysis. The optimization model was coded using GAMS/CPLEX, and the performance assessment algorithm was implemented in MATLAB. Results were obtained using actual frequency data obtained from the Colombian power system; and the resulting BESS sizes show that the number of BESS penalties, caused by failure to provide PFR service, can be reduced to zero at minimum investment cost.

Optimal strategies of siting, sizing, and scheduling of BESS: Voltage management solution for future LV network

With the object of reducing CO2 emission and the reliance on fossil fuels, solar power generation has received special support and is being deployed rapidly in many distribution networks. Battery energy storage system (BESS), an effective voltage management solution for low‐voltage (LV) networks to deal with the high penetration of rooftop photovoltaic (PV) systems, is investigated in this paper considering the degradation of BESS due to aging. Two optimal strategies of BESS cost minimization are proposed for long‐term planning and operation planning, where the utility is the BESS owner. As for the long‐term planning, a strategy for siting and sizing of BESS is formulated based on the characteristics of the network, typical load, and PV generation profiles. Then, for the operation planning, schedules of BESSs and their cost estimation are provided based on the load and PV generation forecasts of 1 day ahead. The effectiveness of the proposed methods is demonstrated on the simplified network of the Metropolitan Electricity Authority, Thailand, using MATLAB (Natick, Mssachusetts, USA) 2016a and MATPOWER 6.0. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Разработка усовершенствованной методики выбора параметров и мест размещения систем накопления электроэнергии в распределительных электрических сетях

State of the art. In the context of digitalization of electric grids, distribution grids remain the largest-scale and most problematic component. Technological solutions to improve the energy efficiency of these grids consist managing the voltage and transmission capacity of transmission lines using distributed generation and sto­rage-based controls. However, to date, there is no universal method for appropriate sizing and placement of battery energy storage systems (BESS) in distribution grids that would take into account the varying daily load schedules, which is the subject matter of this paper. Materials and methods. Researchers have reviewed Russian and international scientific and technical publications on sizing and placement of BESS in distribution grids. Besides, this research uses well-known analytical methods for calculating the characteristic daily loads. Mathematical modeling was crucial to the experimental part and was done by means of PSS SINCAL. Results. The paper analyzes and presents state-of-the-art methods for sizing and placement of BESS in distribution grids. The authors have devised optimality criteria that help tailor the parametrization and placement of BESS to the application. The criteria are proven solid by mathematical modeling. Conclusions. The methodology herein presented can be used for the reconstruction and new construction of storage-enabled distribution grids of better economic efficiency and performance.

Wind Power Economic Dispatch – Impact of Radial Basis Functional Networks and Battery Energy Storage

This paper proposes the development, implementation, and impact of an innovative real-time forecast model on the cost and operational revenues of a wind generation (WG) microgrid with an associated battery energy storage system (BESS). An economic dispatch scheme is formulated using a predictive optimization policy called receding horizon control, in order to sell energy to the electricity grid through an energy market. This power dispatch framework is able to incorporate multi-step ahead forecasts of wind power and energy price, needed to determine the income and operational profits of the WG microgrid. An innovative intelligent forecast model is presented using the radial-basis functional network (FN), that offers more accuracy as compared to benchmark and conventional intelligent models, and consequently, the income of the WG microgrid is maximized. Since the inaccuracy of the forecasting can also lead to inadequate BESS sizing which subsequently mitigates the operational profits. Hence, at one hand, this research work strongly advocates the impact of power forecast accuracy on the economic aspects of a WG microgrid, while on the other hand also provides the necessary tools to the wind power producers in order to maximize their profits.

Photovoltaic active power control based on BESS smoothing

Abstract The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only acquires the capacity of BESS accurately, but also improves the effect to smoothing power fluctuation of PV effectively. Finally, a case is proposed to verify correctness of the theory.

Battery Storage-Based Frequency Containment Reserves in Large Wind Penetrated Scenarios: A Practical Approach to Sizing

This paper focuses on the sizing of a battery energy storage system providing frequency containment reserves in a power system with a large wind power penetration level. A three-stage sizing methodology including the different aspect of battery energy storage system performance is proposed. The first stage includes time-domain simulations, investigating battery energy storage system dynamic response and its capability of providing frequency reserves. The second stage involves lifetime investigation. An economic assessment of the battery unit is carried out by performing the last stage. The main outcome of the proposed methodology is to choose the suitable battery energy storage system size for providing frequency containment reserve from augmented wind power plants while fulfilling relevant evaluation criteria imposed for each stage.

Economic Capacity Planning of BESS for Electricity Customers Charged Based on Both Energy and Peak Demand

Electricity customers can achieve economic benefits from cost reduction and/or revenue increase using battery energy storage system (BESS). However, different conditions of each customer require different methods to determine the suitable size of BESS. Additionally, different operation strategies of BESS result in the different cost/benefit analysis. Thus, in this study, authors have identified and focused on the electricity customers with TOU rate, varying operation mode with the seasons, and charging scheme based on both energy consumption and the peak demand. The procedures to find the optimal power and energy capacities for the selected customer are formulated. The optimal operation strategy with priority, that is, peak shaving–DR–scheduling modes in summer/winter and DR–scheduling mode–peak shaving modes in spring/autumn, is also proposed. The proposed method is applied to a specific type of customer in Korea in the case study. The results showed that an optimal capacity can be found and the proposed operation strategy with priority is optimal. Moreover, it has been found out that, for this type of customer, the large energy capacity of BESS mostly leads to large economic benefit; in contrast, the small power capacity mostly leads to small economic benefit.

Life cycle planning of battery energy storage system in off‐grid wind–solar–diesel microgrid

For off-grid microgrids in remote areas (e.g. sea islands), proper configuring the battery energy storage system (BESS) is of great significance to enhance the power-supply reliability and operational feasibility. This study presents a life cycle planning methodology for BESS in microgrids, where the dynamic factors such as demand growth, battery capacity fading and components' contingencies are modelled under a multi-timescale decision framework. Under a yearly timescale, the optimal DER capacity allocation is carried out to meet the demand growth, while the investment decisions of BESS are made periodically to yield the optimal sizing, type selection and replacement plans of BESS during the entire lifetime of the microgrid. Then, under an hourly timescale, the long-term probabilistic sequential simulation is adopted to comprehensively evaluate the investment decisions and derive detailed operation indicators. Moreover, a decomposition-coordination algorithm is developed to address the presented planning model, which iteratively strengthens the feasible space of investment-decision model by substituting the operation indicators until an acceptable sub-optimal solution is obtained. Case studies on a wind-solar-diesel microgrid in Kythnos Island, Greece illustrate the effectiveness of the proposed method. This study provides a practical and meaningful reference for BESS planning in off-grid microgrids.

A Fuzzy Optimization Model for Distribution System Asset Planning With Energy Storage

The distribution system asset (DSA) planning process for a new or existing subdivision involves a comparison of several feasible plans and selection of the least costly plan. The costs comprise the cost of new assets and their replacements, operation and maintenance costs, as well as reliability costs. DSA planning tools have been used for distribution utilities for decades, so that mature methods are available for traditional distribution systems. However, most of the DSA planning tools fail to include battery energy storage systems (BESS) as an asset solution for systems with high penetration of renewable energy sources. In addition, minimizing the investment cost and maximizing the reliability performance, which can be measured by the system average interruption duration index (SAIDI), are contradicting objectives. With a large investment, the system will have a small SAIDI, and vice versa. In this paper, a multi-objective algorithm that uses fuzzy optimization technique to handle these contradicting objectives is proposed. The algorithm includes traditional types of assets as well as BESS. One of the main advantages of the proposed method is that the customer interruption costs are not explicitly necessary, as these values cannot be precisely determined for all loads. In the tool, the annual investment cost and SAIDI are minimized, and the optimal size of BESS is obtained. Its performance is demonstrated on a 33-bus distribution system and the results are compared with other methods available in the literature.

Techno-economic evaluation of a hybrid CSP + PV plant integrated with thermal energy storage and a large-scale battery energy storage system for base generation

Chile presents a combination of favorable climatic conditions which result in the highest levels of solar irradiation in the world. In this paper, the performance of a hybrid CSP + PV plant at utility-scale integrated with a large-scale Battery Energy Storage System (BESS) located in northern Chile was studied. The model considered a solar power tower integrated with a two-tank molten salt direct TES and a fixed-angle PV plant coupled to a BESS to deliver a baseload of 100 MWe. A parametric analysis was carried out in terms of the PV size, solar multiple, TES hours capacity, and BESS size. The hybrid plant performance was studied in terms of the LCOE and the capacity factor. The minimum LCOE configurations were identified and a sensibility analysis was carried out to evaluate the effect of a cost reduction of the BESS in the hybrid plant configuration for different BESS sizes. Results showed that current investment costs of the storage section of the BESS make unprofitable its integration to the hybrid plant, so, it is required a reduction of approximately 60–90% of the storage cost (based on a reference value of 300 USD/kWh) to achieve competitive LCOEs in comparison to those obtained for a hybrid plant without BESS. Under this BESS cost reduction scenario, it was found a solutions domain with different hybrid plant configurations, which allow to integrate and complement the production of both CSP and PV plants with both storage types in a synergetic operation. Also, a comparative study evaluating the differences between implementing a fixed-tilt PV configuration or a one-axis tracking system in the hybrid plant scheme was carried out, obtaining that the annual production of the hybrid plant increases by no more than 5% with the tracking system, while the LCOE is reduced up to -1.98% due to a decrease of the BESS participation in the total output. These results indicate that the minimum LCOE configurations of a hybrid CSP + PV plant with tracked PV arrays (given a BESS size) could result in smaller PV plants, since the PV production increases in comparison to that with a fixed-tilt PV configuration.

Placement and sizing of battery energy storage for primary frequency control in an isolated section of the Mexican power system

Increasing penetration levels of inverter-interfaced generation impose challenging frequency control problems to power system operation since frequency response capabilities are reduced as conventional generation is displaced by renewable energy sources. To face these challenges, alternatives such as Battery Energy Storage Systems (BESSs) are necessary to provide advanced and enhanced frequency support. However, improper location and size of BESSs may greatly affect the performance and economic cost of their particular application. Based on this, an approach for the placement and sizing of BESS for primary frequency support in an isolated power system is presented in this study. BESS location and size are determined according to the most severe contingency for generation outage and different penetration levels of converter based renewable generation in the test system. Under these considerations, the transmission system bus with the larger frequency decline is identified for BESS placement. On the other hand, BESS sizing is formulated as a constrained optimization problem, with a defined cost function to be minimized. An iterative process based on the Bat Optimization Algorithm (BOA) is used in this work to determine the selected parameters to be optimized. For comparison purposes, a Genetic Algorithm (GA) approach is also included to deal with the formulated optimization problem. Simulation results show that system frequency response can be improved with the approach proposed in this study. Besides, the use of BOA based alternative is seen to perform relatively better than the GA approach in this case.

Optimal Size and Location of Battery Energy Storage Systems for Reducing the Wind Power Curtailments

Both the lack of the downward regulation capacity and the transmission capacity limit have led to considerable wind power curtailments in China. The battery energy storage system (BESS) provides a new solution to reduce the wind power curtailments due to its relatively high energy density and flexible installed location. In this paper, a new bi-level programming model is proposed to optimize the size and location of BESS for reducing the wind curtailments. The revenues of BESS, when it is located either at the wind farm or the load center, are considered comprehensively. Moreover, a numerical optimization algorithm is developed to solve the proposed model. Simulation results demonstrate the validity of the proposed model and developed algorithm. They also reveal that the BESS located at the load center offers more net profit compared to the one located at the wind farm. Subsequently, sensitivity analysis is performed to assess the effect of key parameters on the optimal values of BESS.

Optimal Sizing of PV and BESS for a Smart Household Considering Different Price Mechanisms

The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: 1) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT); 2) the effect of subsidies of PV; and 3) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find an optimal solution. Finally, six cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. Only when the subsidies of PV reach a certain level will the installation of PV be considered.