Battery Energy Storage System Capacity Research Articles

경제성 평가를 통한 태양광발전기와 연계된 배터리 에너지저장장치의 최적 용량 산정

In situations, where the weight of a renewable energy certificate (REC) for a battery energy storage system (BESS) linked to a photovoltaic (PV) system is applied, it is necessary to evaluate the optimal capacity of the BESS to maximize the profit and prepare for a change in future support policy. The levelized cost of energy (LCOE) was calculated for a range of conditions using the hourly power generation data and charge/discharge guidelines of the nationwide PV plants. As a result, the optimal capacity of BESS under the current REC weight 5.0 was determined to be 2.7 times that of the PV capacity nationwide. In the reference cases of Jeju, Gyeongnam, and Jeonnam, which have the lowest, average, and highest solar radiation of the country, it was confirmed that the economic benefit by BESS linkage was higher in cases of lower solar radiation. If the REC weight is downgraded in the future, the optimal capacity of BESS will also be reduced. If there is no reduction in the BESS facility unit cost, the advantage of the REC weighted value of 3.5 or less will be lower than that of a PV single operation.

Design optimisation for a hybrid renewable microgrid: Application to the case of Faial island, Azores archipelago

The integration of intermittent renewable energy sources (RES) represents a great challenge for any energy system. In particular, islanded microgrids with a high penetration of renewables experience a strong need for technologies that allow to match demand and production at any moment. This work aims at proposing an optimised design for the energy system of Faial, an island in the Azores archipelago, featuring the highest possible renewable energy penetration that can be obtained respecting the technological and financial feasibility constraints. To this purpose, a model has been developed, using weather and electric demand measured data to combine and size optimally the components of a hybrid energy system. The model can be varied in its constraints to fit at best the multi-objective nature of the problem, where the conflicting objectives are the Net Present Value, its Renewable Energy Fraction and the Energy Index of Reliability. Once a set of possible optimal design has been determined, a system design featuring 5504 kW of geothermal installed power and 6208 kWh of BESS (Battery Energy Storage System) capacity, together with the already present thermal generators and 4250 kW of wind turbines, has been analysed more in detail. Monte Carlo simulations with synthetic time series have been performed to investigate the impact on the project of the variability in wind speed and in energy demand, highlighting the robustness of the selected design.

A Multi-Objective Planning Framework for Coordinated Generation From Offshore Wind Farm and Battery Energy Storage System

This paper proposes a novel multi- objective planning framework to determine optimal capacity of battery energy storage system (BESS) for coordinated operation of large scale offshore wind farm (OWF) and BESS. Multiple objectives such as battery cost and lifetime, availability of wind turbines (WTs), expected energy not supplied (EENS), loss of load hour (LOLH) and curtailment of wind energy have been considered for determining optimal capacity of BESS for OWF. Wake effect loss in OWF and uncertainty of wind speed have been also incorporated the optimal BESS framework for determining aforementioned objectives. Moreover, as WTs are susceptible to random failure, availability of WTs is an important issue which has been also considered in the planning stage for determining optimal size of BESS in this study. One of the main features of the proposed approach is that it provides flexibility to decision makers in selecting best available alternative for BESS (among different types) to minimize mismatch between forecasted generation schedule and actual generation of OWF, while taking into account economical and operational reliability issues. The effectivene ss of the proposed planning strategy has been validated through different case studies conducted on Horns Rev- 1 OWF situated in Denmark.

Hierarchical Optimal Allocation of Battery Energy Storage Systems for Multiple Services in Distribution Systems

This paper addresses the optimal allocation of battery energy storage systems (BESS) in radial distribution systems for bus voltage regulation and energy cost reduction. A hierarchical planning model is proposed to obtain the optimal configuration (number, locations, and sizes) of BESS. The three interacting levels in the proposed model are: (1) determination of the optimal BESS number; (2) use of a heuristic strategy based on voltage sensitivity analysis to select the most effective locations for a given BESS number in the network; and (3) sizing the BESS capacity to realize optimal operation objectives. The final BESS configuration is selected based on minimization of total life cycle cost, which takes into account the investment cost, residual value of BESS, and system operation cost. The voltage regulation objectives are modeled as constraints. The proposed method reduces computational burden significantly and provides a comprehensive sensitivity analysis and locate the most effective buses to mitigate voltage violation. The effectiveness of this optimal allocation method was demonstrated for a modified IEEE 15-bus and IEEE 69-bus distribution network.

Hierarchical control system for a flexible microgrid with dynamic boundary: design, implementation and testing

The design, implementation, and testing of a control system for a flexible microgrid (MG) is presented in this study. The MG controllers can be implemented in a real-world MG with multiple smart switches, photovoltaic panel system, and battery energy storage systems (BESSs). With the benefits from smart switches, the MG has unique characteristics such as dynamic boundary and flexible point of interconnection (POI) concepts. To control such a unique MG and realise the dynamic boundary, an MG central controller and two types of local controllers are implemented. Compared to the MG with fixed boundary, the MG with dynamic boundary can have smaller BESS capacity, better utilisation of renewable energy, and multiple POI options. Also, compared with IEEE Std 2030.7–2017, the topology identification and active and reactive power balance functions are newly designed to realise the dynamic boundary concept. The planned islanding and reconnection functions are modified to realise the flexible POI concept. These functions are introduced including the software architecture, cooperation, and interaction among them. Finally, a hardware-in-the-loop testing platform based on the Opal-RT real-time simulator is set up to verify the performance, realisation of the dynamic boundary, and flexible POI concepts with four comprehensive test scenarios.

PV-Wind-Battery Hybrid Power Systems: Power Management Control Strategy Using P2D Battery Model

A standalone hybrid power system (SHPS) using renewable energy resources (RERs) like wind, solar, hydro and biomass can be installed at any remote location where power grid extension is either not possible or too expensive. However, these RERs have drawbacks like unpredictable nature, intermittency, dependence on location and mismatch of generated power with load demand. To overcome the above limitations SHPSs are often integrated with battery energy storage system (BESS) [1]. Lithium-ion batteries (LiBs) are one of the most popular and emerging options for energy storage for SHPSs [2]. The financial feasibility of the SHPSs depends on the operation and performance of LiBs which is one of the most expensive components. Improperly designed BESS leads to a high life cycle cost or failure of the system to provide a reliable power supply and hence the power management and control in the SHPS is an active area of research. It is essential to manage the power flow between the load, the power generated by RERs and the battery to ensure the power stability in the system and guarantee continuous power supply to the end user [3]. Till now various researchers successfully implemented empirical/equivalent circuit models (ECM), which are less reliable under dynamic operating conditions, to design battery management systems and control algorithms due to the ease of their implementation in the control and operation of SHPSs [4, 5]. We present the use of physics-based battery (pseudo-2D) models representing the transport and kinetic processes that take place in the LiBs [6] which provides better control and prediction of battery performance to design robust power management control strategy for the SHPS consisting of PV-wind based generators integrated with Li-ion battery. The wind velocity is negatively correlated to solar insolation [7] and therefore we consider the solar-wind combination to utilize their complementary nature. This framework comprises of PV panels, wind turbines, Maximum Power Point Tracking (MPPT) controller, power electronics, and LiBs. A single-diode equivalent circuit-based model is used to model a solar cell. A differential algebraic equation based MPPT algorithm is implemented to track the maximum power point of the solar array [8]. The power output from the wind turbine generator is predicted using wind speed and speed characteristics of the turbine. The power management strategy guarantees 0% excess output power production, and ensuring no energy is transferred to the dump load. A case study will be presented for a year-long real-world data which would consider the seasonal variability in load demand and power generation. The load demand and power generated (by RERs) and power supplied by the battery for a sample 7-day data is shown in Figure 1. These models are used to study the effects of RERs and load demand uncertainties and variations in the operating conditions of hybrid systems. Further, we explore the possibilities of using a physics-based battery model for a Li-ion battery in designing optimal BESS capacity that minimizes capacity fade and thermal degradation.

Optimizing Capacity Configuration of Photovoltaic and Battery Energy Storage Systems in EV Charging Station based on Time-of-Use Pricing

In order to improve the utilization efficiency of photovoltaic system (PV system) and battery energy storage system (BESS) in photovoltaic and battery energy storage integrated electric vehicle (EV) charging stations, the capacity of PV system and BESS need to be allocated reasonably. In this paper, the maximization of the net annual financial value is used as the objective function of photovoltaic and battery energy storage integrated EV charging station in its whole life cycle. The control strategy of BESS is proposed based on time-of-use pricing. And then the optimization configuration model of PV system and BESS capacity is built. The economic efficiency of EV charging stations is compared and analyzed with actual test system when PV system and BESS configurating. The results indicate that the income and investment payback period of charging stations can be significantly improved through the reasonable configuration of PV system and BESS.

Strategies for combined operation of PV/storage systems integrated into electricity markets

The increasing share of photovoltaic (PV) power in the global energy mix presents a great challenge to power grid operators. In particular, PV power's intermittency can lead to mismatches between energy production and expectation. Battery energy storage systems (BESSs) are often put forward as a good technological solution to these problems, as they can mitigate forecast errors. However, the investment cost of such systems is high, which questions the benefits of using these systems in operational contexts. In this work, we compare several strategies to manage a PV power plant coupled with a BESS in a market environment. They are obtained by stochastic optimisation using a model predictive control approach. This study proposes an approach that takes into account the aging of the BESS, both at the day-ahead level and in the real-time control of the BESS, by modelling the cost associated with BESS usage. As a result, the BESS arbitrates between compensating forecast errors and preserving its own life expectancy, based on both PV production and price scenarios derived from probabilistic forecasts. A sensitivity analysis is also carried out to provide guidelines on the optimal sizing of the BESS capacity, depending on market characteristics and BESS prospective costs.

Assessing the Effect of Wind Farm Layout on Energy Storage Requirement for Power Fluctuation Mitigation

Optimization of wind farm (WF) layout has been studied in the literature with the objective of maximizing the wind energy capture. Based on the power spectrum density theorem, this paper shows that the WF layout affects not only the total harvested energy but also the level of power fluctuation, which, in turn, influences required capacity of battery energy storage system (BESS) needed to mitigate the inherent power fluctuation of the WFs. Since, both harvested energy level and BESS capacity directly influence the profit of WF owner, the effect of WF layout on these quantities is taken into account simultaneously, and WF layout optimization problem is redefined. Genetic algorithm is then employed in order to optimize the resulting objective function. The proposed method and optimization process are performed on the layout of an actual offshore WF using real wind data. A new index is introduced to quantify the power fluctuations, and energy curtailment is assessed. The comparative analysis between the actual layout performance and the optimal layout in different scenarios is conducted, showing the reduction of power fluctuations and improvement of energy curtailment. In addition, different BESS technologies have been analyzed to study the impact of their parameters on the optimization results.

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.

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.

Feasibility study on Coordinated Control of Heating Ventilation and Air Conditioning Loads and Battery Energy Storage System for Improved Performance of FastADR Response

Abstract Fast Automatic Demand Response (FastADR) system is one of the expected measures to control electricity demand of consumers so that the electricity supply – demand balancing in a short time horizon is improved. Due to their high power consumption in business buildings, Heating Ventilation and Air-conditioning (HVAC) loads are one of the promising candidates on demand-side for FastADR. However, HVAC loads do not perfectly follow external control signals due to the operational characteristics. Although Battery Energy Storage System (BESS) can be useful for supplementing power consumption of an HVAC load in following FastADR signal, its State of Charge (SOC) should be recovered quickly in order to prepare for the next FastADR signal or other purposes, and to reduce the energy capacity of BESS. This paper proposes and numerically demonstrates a coordinated control of HVAC loads and BESS to restore BESS’s SOC without affecting total electricity load and indoor temperature.

Optimal Battery Capacity for Residential Rooftop PVs With Consideration of Net-Metering Scheme Compensation Period

With the significant increase of the prosumers in the world, the battery energy storage system (BESS) has become an important device to enhance the performance of renewable energy generation. By focusing on residential scale of rooftop PVs, BESS has been developed and applied in many aspects especially in terms of electricity charge saving. Many papers propose the methodology to control the operation of BESS, while many papers propose the methodology to determine the battery capacity. As f or this paper, the proposed methodology will simultaneously determine the operation schedule and battery capacity of BESS for rooftop PVs under a net-metering scheme (NMS). The main objective is to maximize the net present value (NPV) of the prosumers. The battery capacity will be constrained by the period of the NMS compensation which will be effective for only one year of each electricity bill. The numerical results show that , for the TOU structure without demand charge, an appropriate BESS should have C NOM,DC as high as possible to increase the revenue from BESS and have P NOM,DC as low as possible to reduce the total investment cost (TTC). Also, by considering the limit period of the NMS compensation, an appropriate battery capacity should not be higher than monthly residential load consumption. Lastly, the sensitivity of electricity tariff shows that the NPV will increase proportionally by the increment of electricity price, which will reduce the break-even year of the investment.

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.

ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs

One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods.

Optimization of Battery Energy Storage System Capacity for Wind Farm with Considering Auxiliary Services Compensation

An optimal sizing model of the battery energy storage system (BESS) for large-scale wind farm adapting to the scheduling plan is proposed in this paper. Based on the analysis of the variability and uncertainty of wind output, the cost of auxiliary services of systems that are eased by BESS is quantized and the constraints of BESS accounting for the effect of wind power on system dispatching are proposed. Aiming to maximum the benefits of wind-storage union system, an optimal capacity model considering BESS investment costs, wind curtailment saving, and auxiliary services compensation is established. What’s more, the effect of irregular charge/discharge process on the life cycle of BESS is considered into the optimal model by introducing an equivalent loss of the cycle life. Finally, based on the typical data of a systems, results show that auxiliary services compensation can encourage wind farm configuration BESS effectively. Various sensitivity analyses are performed to assess the effect of the auxiliary services compensation, on-grid price of wind power, investment cost of BESS, cycle life of BESS, and wind uncertainty reserve level of BESS on this optimal capacity.

Effect of BESS Response on Frequency and RoCoF During Underfrequency Transients

Battery energy storage system (BESS) services will be of great help when operating power systems at high renewable energy penetrations. This paper demonstrates the requirements for effective frequency and inertia services. Some system operators currently apply scalars to financially reward improved performance. This paper demonstrates a method of validating and improving these scalars by considering the delay time and ramp time of the BESS. The importance of the method used to measure the rate of change of frequency is discussed in the paper. While the rate of change of frequency services improves with reduced delivery times, limits are identified where further reductions produce a negligible impact. This demonstrates the tangible benefits of a rapid response, and that current technology could meet the criteria. A variety of BESS capacities are trialed, which demonstrate consistency in performance and the occurrence of diminishing returns

Multi-Objective Optimization Technique for the Operation of Grid tied PV Powered EV Charging Station

The wide deployment of grid connected large scale photovoltaic (PV) systems and the rapid growth in the electric vehicle (EV) market opens the avenue for the PV based EV stations to participate in the energy as well as the EV arbitrage markets. This participation should comply with the utility restrictions with respect to power oscillations at the point of common coupling (PCC). Consequently, there is a need for battery energy storage system (BESS) to fulfill this task. This paper proposes a multi-objective optimization (MOO) methodology that aims at maximizing the revenues of the PV based EV (PV-EV) station while minimizing the BESS capacity fading simultaneously. The objectives are to be met under the power fluctuations restriction imposed by the hosting grid at the point of common coupling (PCC). The proposed methodology provides detailed modelling of the dynamics of the BESS as well as the EV parking garage behavior. The MOO problem is solved by the augmented ε-constrained (AUGMECON2). The results showed the effectiveness of the proposed methodology in achieving the aforementioned objectives. Furthermore, sensitivity analysis was performed to show the effect of neglecting one of the objectives on the other, and the effect of the power fluctuations limit constraint on the objectives of concern in this study. Moreover, the results showed the adverse effect of neglecting the battery’s detailed modelling on the battery’s lifetime. Additionally, the effect of uncertainties in the BESS’s internal parameters coefficients of both objectives was investigated.

Non‐cooperative and cooperative optimisation of battery energy storage system for energy management in multi‐microgrid

Multi-microgrid is an integrated system of microgrids, distributed generations, and battery energy storage system (BESS). As the significant equipment in microgrid, BESS can perform multitasking, such as load management and peak shaving. This study mainly focuses on the energy consumption scheduling of multi-microgrid considering the optimisation of BESS capacity. Energy management with BESS optimisation is studied by considering the cost of distributed generations, cost of BESS, and bidirectional energy trading. The optimisation problem is tackled from two different aspects: an individual-oriented optimisation and a coalition-based optimisation. In the first approach, each microgrid is optimised individually with a non-cooperative game; while in the second approach, the joint optimisation of all microgrids is formulated through cooperation among multi-microgrids. In order to achieve the optimal energy consumption strategy and BESS capacity, distributed algorithms for two formulations are presented, which combine particle swarm optimisation and interior point method. Simulation results show that both approaches can contribute to peak shaving and reducing the daily cost of multi-microgrid.

Optimal sizing and operating strategies for VSG based BESS in ancillary service market

In response to growing power demand and rising environmental awareness, renewable energy (RE) resources are becoming increasingly common. However, the power output of RE is intermittent, as it strongly depends on the weather conditions; this increases the difficulties for the system operator (SO) to maintain the system frequency. This paper proposes an optimal sizing and operating strategy for a battery energy storage system (BESS) which integrates with virtual synchronous generator (VSG) technologies to participate in ancillary service (AS) market. The proposed method improves the traditional dual-stage architecture of BESS capacity planning problem to speed-up computation. However, the dimension and complexity of the optimization problem would increase accordingly. Therefore, the Electron Drifting Algorithm (eDA) is proposed to solve the proposed optimization problem and improve the quality of solution. To verify the feasibility, the IEEE 30-bus test system is employed for dynamic analysis by using the dynamic power system simulation software DIgSILENT PowerFactory. This paper provides a new business model to promote investments in BESS, not only yielding economic benefits, but also effectively increasing the robustness of the grid and further accommodating additional RE.