Cost Of Battery Energy Storage System Research Articles

Deep reinforcement learning‐based optimal data‐driven control of battery energy storage for power system frequency support

A battery energy storage system (BESS) is an effective solution to mitigate real-time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high-operating costs. This study proposes a deep reinforcement learning-based data-driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three-area power system.

Per-use-share rental strategy of distributed BESS in joint energy and frequency control ancillary services markets

The battery energy storage system (BESS) plays an increasingly important role in the electricity sector around the world. BESS can not only smooth the outputs of renewable energy, but also provide a variety of benefits to the electricity grid and markets. At this stage though, the capital cost of BESS is still high. Thus, the BESS sharing economy offers an opportunity to apply BESS services for customers. In this paper, the per-use-share rental strategy of BESS is proposed to support customers and increase the profit for the battery sales firm in the joint energy and the Frequency Control Ancillary Services markets. The rental pricing model is optimized for the firm and participating customers, and the proofs of the rental strategy are analyzed to assess its feasibility of the proposed rental strategy. The IEEE 33-bus distribution system is used to verify the proposed per-use-share rental strategy of BESS. The case studies use real historical data from the Australian Energy Market Operator. Moreover, the feasibility analysis of the per-use-share rental model is studied considering the fluctuations of clearing prices in the spot and Frequency Control Ancillary Services markets. The results show that the proposed per-use-share rental strategy of BESS provides a win–win situation for customers and the battery sales firm.

Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study

Abstract Residential battery energy storage system (BESS) adoption is hindered with its expensive price in current market. Optimally sized BESS can excel the fiscal benefits and thus can be economically sensible. An optimization problem, which targets to minimize the total annual cost including both energy and battery degradation-based costs, is formulated to investigate the added benefits of operational optimization of a BESS over the baseline operation of BESS based on self-consumption maximization (SCM). A case study is conducted considering a Victorian electricity customer in Australia. Results revealed that installation of residential BESSs are fiscally beneficial at current market prices only if adequate rebates are available. A fiscally beneficial upper limit of BESS capacity that can be installed with the prevailing government incentives depending on the PV system capacity is also found. For a given installed cost of BESS (AU$/kWh), optimal BESS capacity is larger when operational optimization of BESS is considered. However, with a time-of-use (TOU) tariff scheme, BESSs based on operational optimization outperforms BESSs based on SCM in terms of the minimum total annual cost, return on investment and PV curtailment avoidance which excels the fiscal benefits of a BESS and results in indirect greenhouse gas emission reduction.

A bi-level optimization framework for investment planning of distributed generation resources in coordination with demand response

ABSTRACT In this study, a multiyear distributed generation (DG) and battery energy storage system (BESS) investment planning with the coordination of demand response (DR) is presented for the distribution system. This coordinated system planning problem aims to maximize the net present value (NPV) profit related to the cost of energy purchased from the grid, energy losses, emission penalty cost, demand deviation penalty, operation and maintenance (OM) cost as well as investment cost of renewable power generation. To handle the high complexity of the investment planning problem, a bi-level optimization framework is used. The level-1 determines the optimal sizing of multiple BESS in presence of high penetration of PVs into the system. This level is used to minimize the net present cost (NPC) for energy consumption, greenhouse gas (GHG) emission, load deviation penalty, energy loss, investment and OM cost of PVs and BESS. In level-2, the optimal BESS power dispatch in coordination with the DR aggregator is obtained. The level-2 framework is used to minimize NPC for voltage deviation penalty and network losses with the scheduling of BESS and DR only. The proposed framework is simulated on the 33-bus distribution system to obtain the best investment plan for the distribution system operator (DSO). The results show that the NPV benefits of the proposed DR model with optimal integration of BESSs and PVs are 17.94 M$ and payback period of 9 years of the 20 years of planning horizons which is very significant compared to non-DR-based investment planning besides other technical benefits of DR implementation such as improvement in mean voltages, power loss, etc.

Impact of policy incentives on the promotion of integrated PV and battery storage systems: a techno‐economic assessment

During the last two decades, the use of residential photovoltaic (PV) systems has been widely promoted by governments through various support mechanisms such as feed-in-tariffs, net-metering, net-billing, etc. These support schemes have developed a secure investment environment, increasing the penetration level of PVs in low-voltage distribution grids. Nonetheless, increased PV integration may introduce several technical problems regarding the secure operation of distribution grids. Battery energy storage (BES) systems can mitigate such challenges, but the high capital cost is one of the most important limiting factors towards the widespread use of these systems. In fact, the financial viability of integrated PV and BES systems under different support schemes remains an open issue. In this study, the profitability of PV and BES systems is evaluated through an advanced techno-economic model, that provides the optimal size of PV-BES system in terms of net present value, based on the electricity production and consumption profile of the installation, PV and BES systems costs, and electricity charges. The proposed model may be a useful tool for prosumers, grid operators and policymakers, to assess the impact of various incentive policy schemes and different BES operation strategies on the economic viability of PV-BES systems.

Economic feasibility of battery energy storage systems for replacing peak power plants for commercial consumers under energy time of use tariffs

Abstract This work assesses the economic feasibility of replacing conventional peak power plants, such as Diesel Generator Sets (DGS), by using distributed battery energy storage systems (BESS), to implement Energy Time Shift during peak hours for commercial consumers, whose energy prices vary as a function of energy time of use (ToU tariffs). The economic analysis is performed by calculating the break-even point (BEP) for four different battery technologies: (i) lead-acid (OPzS); (ii) nickel-cadmium (NiCd); (iii) lithium-ion (Li-NCA); and (iv) Redox Flow (FeCr), using the HOMER Energy software in the simulations. One case study is analyzed for a commercial consumer connected to the MV network (Campinas/Brazil). The results show that, considering the updated 2018 BESS costs, none of the types of battery analyzed is economically attractive as a replacement for DGS. However, it can be observed that a 31%, 38% and 26% reduction in the costs of OPzS, Li-NCA and FeCr batteries, respectively, makes the installation of BESS viable. It is estimated that, in a 5- to 6-year horizon, these technologies would become economically attractive as a result of the strong decrease of the expected costs for the years to come.

Mitigating voltage-sag and voltage-deviation problems in distribution networks using battery energy storage systems

This paper proposes a framework for solving voltage-sag and voltage-deviation problems in distribution networks using battery energy storage systems (BESSs). The proposed framework is divided into two parts. In the first part, a proposed stochastic planning algorithm determines the optimal sizes and locations of the BESSs that mitigate voltage sags in distribution systems (DSs). The objective of the planning algorithm is to minimize the system annualized costs that include the BESS costs as well as the economic losses associated with voltage-sag events. In the second part, a post-fault network-reconfiguration (PFNR) algorithm is proposed to guarantee the optimal and reliable post-fault operation of the system. The objective function is to minimize the post-fault operation costs that include the generation costs, load-curtailment costs, and BESS costs. The main goal of the second part is to use the BESSs obtained from the planning algorithm to ensure the full satisfaction of the system operational constraints that include the voltage limits. The two parts proposed in this paper have been tested on IEEE 33-bus and IEEE 69-bus distribution systems. The obtained results demonstrate the effectiveness of the proposed algorithms in mitigating voltage-sag and voltage-deviation problems at the system level using BESSs.

Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting

Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise short-term LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.

Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics

The problems associated with the deployment of intermittent, unpredictable and uncontrollable solar photovoltaics (PV) can be feasibly solved with battery energy storage systems (BESS), particularly in terms of optimizing the available capacity, increasing reliability and reducing system losses. Consequently, the degree of importance of BESS increases in proportion to the level of PV penetration. Nevertheless, the respective high cost of BESS imposes a huge concern and the need to establish a techno-economic solution. In this paper, we investigate the system losses and power quality issues associated with the high deployment of PV in a grid network and hence formulate BESS capacity optimization and placement methodology based on a genetic algorithm. The concept of the proposed methodology has been tested and validated on a standard IEEE 33 bus system. A brief stepwise analysis is presented to demonstrate the effectiveness and robustness of the proposed methodology in reducing the incremental system losses experienced with increased PV penetration. Furthermore, based on the proposed optimization objectives, a comparative study has also been performed to quantify the impact and effectiveness of aggregated and distributed placement of BESS. The results obtained exhibit a substantial reduction in system losses, particularly in the case of distributed BESS placement.

Sizing Battery Energy Storage Systems: Using Multi-Objective Optimization to Overcome the Investment Scale Problem of Annual Worth

The financial objective, when sizing a Battery Energy Storage System (BESS) for installation in a microgrid, is to maximise the difference between discounted BESS benefits and discounted BESS costs. This may be described as maximising Annual Worth (AW). However, one drawback of sizing microgrid BESS using AW is that the scale of investment is not taken into consideration. This can lead to unrealistic BESS sizes. This article presents two multi-objective optimisation (MOO) models to account for the scale of investment required in sizing BESS. The first model, Paired Comparison, utilises two objective functions: Daily Worth (DW), which maximises daily benefit cost differences a BESS installation provides a microgrid; and Daily Cost (DC), which minimises the daily cost of a BESS installation. The second model, called Rating Method, uses the objective functions DW and Daily Benefit-Cost Ratio (DBCR), the latter of which maximises the relative measure of BESS benefit and BESS cost. Both models are solved for a test microgrid system under three different scenarios using Compromise Programming (CP). For system designers who rank objective functions by importance, the Rating Method is the appropriate approach, whereas system designers who rank objective functions by absolute values should use Paired Comparison.

Methodology of Power Distribution System Design for Hybrid Short Sea Shipping

Application of energy storage (ES) in power distribution systems (PDS) of short sea shipping marine vessels is gaining ground. The main drivers are the increasing demand for higher energy efficiency and the decreasing costs of battery ES systems (BESS). The feasible chemistry and dimensioning of the BESS depend heavily on the vessel type, the PDS topology, and the load profile. Thus, no general rules for battery dimensioning that would fit all vessels can be presented, but the analysis must be performed by using an advanced modeling approach, which takes into account the abovementioned factors. This paper presents a modeling methodology for dimensioning marine vessel ES and hybrid PDS components. The methodology has been tested for two distinct vessels, a cruiseferry and an off-shore support vessel.

Optimal operation management of grid-connected microgrids under uncertainty

<span>This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.</span>

Optimal Investment Strategies for Solar Energy Based Systems

Solar energy, as an inexhaustible renewable energy, can be used to produce heat and electricity. It is of great importance to examine the strategy for investment on solar energy technology. In response to varying electricity price in the electricity market, the battery energy storage system (BESS) can be used to get price arbitrage. This paper proposes an optimal configuration model for a photovoltaic (PV) system, solar heating system, and BESS in order to obtain maximum profit for investors. The investment potential of these systems is compared and analyzed based on return on investment (ROI) index which is defined to evaluate economic profitability. A bi-level programming is adopted to optimize the operation strategy of batteries (inner layer), the size of PV system and solar heating system, and the size of batteries (outer layer) including their maximum discharge/charge power and capacity. Sequential quadratic programming (SQP) method and particle swarm optimization (PSO) are used as optimization methods. In the case study, five investment strategies are investigated in order to decide how to invest in PV modules, batteries, and solar thermal collectors. The results show that the BESS may be a preferable choice for the investors if the investment cost of BESS goes down a lot in the future. Investing in solar energy for both heat and power may be not reasonable because the ROI of this strategy is always higher than either investing in heat or in power. The optimal strategy may be changed with the fluctuation of heat and electricity prices.

Assessment of Battery Storage Technologies for a Turkish Power Network

Population growth has brought an increase in energy demand and cost that has a meaningful impact on personal and government expenses. In this respect, governments attach importance to investments in renewable energy resources (RER), which are a sustainable and clean energy source. However, the unpredictable characteristics of RER are a major problem for these clean sources and RER need auxiliary assets. Battery energy storage systems (BESS) are one of the promising solutions for these issues. Due to the high investment cost of BESS, governments act cautiously about accepting and implementing BESS in their power network. Recently, with the improvement of technology, the cost of BESS has been reduced, and therefore battery technologies have begun to be applied to conventional systems. In this study, first, we will review and discuss the current globally state-of-the-art BESS and their applications. Later, attention will be turned to a country-specific study for Turkey.

Technical and economic assessment of a battery storage system for a nZEB in the Mediterranean climate

In recent years, the promotion of nearly-Zero Energy Buildings (nZEB), has become a priority for European member states. In order to get this label, typically, a monthly or yearly energy balance of energy use through the exploitation of renewable energy sources (RES) is required, but not a hourly balance. This approach may determine a low exploitation of the RES on site in the case of solar and wind energy, because they are intermittent by nature. Moreover, energy production and energy use peaks are often in mismatch during the day. Battery energy storage systems (BESS) offer a solution to better integrate RES into buildings as well as into the grid, increasing its reliability and minimizing interactions. The cost of these systems is rapidly decreasing, opening new economic opportunities for investors. However, for many applications they do not yet represent the optimal cost-effective solution due to the lifespan of their short-lived components. The paper investigates the technical and economic feasibility of integrating a BESS into a high-performance residential building in the Mediterranean climate based on the outcomes of an original case study based research. The existing photovoltaic system combined with the BESS may substantially optimize the energy use, maximizing the self-consumption and minimizing grid interactions; nevertheless, the pay-back time may become fully-attractive for the analysed building, only if BESS costs will halve by 2030.

Optimal Sizing and Operation of Battery Energy Storage Systems Connected to Wind Farms Participating in Electricity Markets

A Battery Energy Storage System (BESS) is a reliable resource to provide energy for various power system applications. The BESS can increase the flexibility and reliability of the renewable energy dispatch. Wind energy has the largest contribution among renewable energy resources and its control has become a research focus in power systems area. This paper introduces a novel BESS control to manage the net energy exchange between a wind farm and the grid in an electricity market. A Receding Horizon Control (RHC) scheme is proposed for optimal operation of the BESS in the presence of operational constraints. The proposed method seeks a decision policy to manage operation of the BESS to increase daily profits. Utilizing short-term wind and price forecasts provide valuable information for the BESS controller to obtain the best times to charge batteries, discharge the stored energy, or purchase energy from the DA market. An optimization problem is formulated considering BESS costs and operational constraints. This optimization problem, at each time step, is solved using the RHC scheme. All wind and electricity price data and case studies in this paper are based on MISO energy market data.

Assessment of economic benefits of battery energy storage application for the PV‐equipped households in Finland

Significant growth in residential solar photovoltaic (PV) installations and the ongoing decline in battery costs have increased interest in household solar battery energy storage projects in Finland in recent years. Among various potential applications, considerable attention is drawn to the use of the battery energy storage system (BESS) for the purpose of the households’ self-consumption enhancement. This study presents the results of a techno-economic study of the LiFePO4-based battery storage added to residential roof-top PV installations in Finland to maximise self-utilisation of on-site solar energy generation. Using a comprehensive DC model of BESS, the battery charge and discharge levels under the typical household load and PV production conditions are simulated. The estimates of the average retail electricity prices are used to assess the value that the customer-sited solar battery storage can provide to the household end-users in Finland. The economic attractiveness of the battery storage projects is evaluated considering the present and forecasted BESS costs and the electricity tariff levels in Finland and the conditions for profitable operation of the solar energy storage systems are determined.

An Economic Analysis of Load Leveling with Battery Energy Storage Systems (BESS) in an Electricity Market Environment: The Korean Case

The capacity of battery energy storage systems (BESS) is expected to increase for power system applications. However, as the cost of BESS is high, economic feasibility must be considered when using BESS in grid applications. Load leveling with BESS is one such application for which the economic implications have been analyzed in the literature. However, these studies do not sufficiently consider the fact that the leveled loads will lead to a change in electricity prices, thereby modifying charging/discharging operations of BESS. Additionally, in a competitive electricity market, electricity prices are not determined by the generator cost functions. Market participants’ strategic decisions also affect prices. Therefore, we conducted an economic analysis of load leveling with BESS in an electricity market from the perspective of a utility company and/or a government agency. In our analysis of the Korean market, we examine whether the leveled loads necessarily lead to economic benefits. Load leveling performance and the associated economic benefit are quantitatively analyzed for varying sizes of BESS. Further, the policy implications related to using BESS are derived from the analysis results.

Energy cost minimization for net zero energy homes through optimal sizing of battery storage system

Whilst a net zero energy (NZE) home produces the same amount of energy as it consumes it still exchanges significant amount of energy with the grid due to mismatch between the generation and load patterns. Consequently, the homeowner has to pay an annual electric bill because the cost of imported energy is usually higher than that of exported energy. Installing a local battery energy storage system (BESS) can reduce the electric bill by exchanging less energy with the grid. This paper proposes a method of determining the optimal size of a BESS for a typical NZE home with rooftop solar photovoltaic (PV) system to minimize the annual net payment for electricity and battery cost. The optimal battery size is determined through solving an optimization problem which is formulated using hourly load and PV generation data for a South Australian home, battery annual payment rate, retail price (RP), and feed-in tariff (FIT). The effects of interest rate, RP and FIT on the annual net payment are investigated. The results obtained are thoroughly analysed and clearly indicate that, with current installation cost of BESS and South Australian RP and FIT, the use of a local BESS is economically beneficial for the homeowner.

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.