Optimal Energy Storage System Research Articles

PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect

The load demand modeling of Plug-in Electric Vehicles (PEVs) has been taken more attention in today's power system studies. Big-data should be handled for accurate modeling of PEVs load demand. Therefore, the utilization of data mining tools will be helpful for PEVs data analytics and clustering. In this paper, a Factor Analysis (FA) based method is introduced for the PEVs data mining. The load profiles of PEVs that are extracted by Monte Carlo Simulation (MCS) are clustered in some groups optimally using FA method. The clustered data is applied on Energy Storage Systems (ESSs) and Distributed Generation (DGs) planning procedure, separately. The simulation results show the power demand of PEVs effect on both ESSs and DGs planning, however, the temporal feature of PEVs profiles affects only on ESS planning, but not considerably on DG planning. This temporal feature, here called Storage to Storage (S2S) effect, reflects the nature of PEVs and ESS long-term memory which is discussed in this paper. The simulation results show that the optimal ESSs capacity is reduced if the PEVs data are clustered especially in high PEVs penetration. However, the optimal capacities of DGs is the same with and without PEVs data clustering scenarios.

A Stochastic Optimization Method for Energy Storage Sizing Based on an Expected Value Model

Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method.

Estimation of the optimal capacity of energy storage system with consideration to REC policy

The Korean Government is promoting the distribution of energy storage systems (ESS) that use new and renewable energy sources by applying the Renewable Energy Certificate (REC) weight factor in relation to the new and renewable energy source. This study estimated the optimal ESS capacity by conducting an economic analysis of the REC weight factor. In the case of solar energy, the weight factors of 5.0 and 4.0 were applied to ESSs set to be charged during the specific time of 10:00-16:00 and due to be installed by 2019 and 2020, respectively. The formula for calculating the optimal capacity is E.C=3.64*P.C-0.49 for a weight factor of 5.0 and 4.0. Here, P.C refers to the solar generator capacity while E.C refers to the optimal ESS capacity. Considering the rate of increase of the charged amount per capacity of the solar power generator installed in Daejeon Metropolitan City, the optimal capacity of the ESS installed in the solar power generator is about 3.6 times that of the solar power generator when the weight factor is 5.0.

Optimal sizing of energy storage considering the spatial‐temporal correlation of wind power forecast errors

Energy storage is considered as an effective approach to deal with the power deviation that caused by the stochastic wind power forecast error. Because of the spatial-temporal correlation of forecast errors for wind farms, which locate close to each other and integrate into the same regional power grid, energy storage could be deployed effectively and economically. Therefore, this study proposes an optimisation method to size the capacity of energy storage system (ESS) considering the spatial-temporal correlation of forecast errors for multiple nearby wind farms. The copula theory based correlation model of high-dimensional forecast errors is built to capture the spatial-temporal characteristics of forecast error. Then, according to multiple scenarios technique, an optimal ESS sizing model is established to minimise the investment and operation costs of the ESS. Meanwhile, an operation strategy considering the trend of prediction and correlation of errors is used to implement rolling operation strategy of ESS. The case studies demonstrate the effectiveness of the new method and illustrate the significant impact of the correlation of forecast error on the capacity of ESS.

Deterministic System Analysis to Guarantee Worst Case Performance for Optimal ESS and PV Sizing

In this paper, we propose a deterministic system analysis that guarantees a profit by developing a system calculus with deterministic bounds. A system analysis model is required to guarantee a minimum profit and ensure stable investments in the energy storage system (ESS) and photovoltaic (PV) by the third-party operators. The deterministic system analysis guarantees the worst case performance and determines the optimal ESS and PV capacity with an optimization problem through a smart grid system analysis. The proposed approach can be used as an analytical tool to determine the investment cost for a third-party operator and guarantee investment-based return by presenting the optimal ESS and PV capacity in a smart grid system.

Optimal Energy Storage System Operation Model for Peak Reduction with Prediction Uncertainty

Abstract This study is aimed at determining the optimal energy storage system (ESS) operation schedule to minimize the peak load on the feeder of a distribution network. To reduce the peak load, the feeder load needs to be predicted. However, a deterministic prediction is not reliable because it may contain errors. This study proposes the use of the prediction interval (PI) of the error estimated based on prior predictions. An algorithm is used to determine the optimal ESS schedule using the PI. To demonstrate the method’s validity, a case study is presented, where the proposed optimal ESS schedule determined based on PI reduces the peak load during network operations over a one-year period. The performance of the proposed method is superior to that of the conventional method which uses deterministic load prediction.

Efficient Allocation Strategy of Energy Storage Systems in Power Grids Considering Contingencies

This paper addresses the allocation of Energy Storage Systems (ESSs) in power grids by finding the optimal number of ESSs and their locations and sizes with the goal of improving reliability in contingency states. We propose a contingency-sensitivity-based heuristic to decide the optimal number of ESSs and the most effective locations for ESSs support, while circumventing the combinatorial nature of the siting problem. A contingency sensitivity index (CSI) is proposed which represents the impacts of contingencies on the network buses. The CSI ranks the buses, such that those with higher impacts have the privilege for installing ESSs. For the ESSs being fixed, the sizing is formulated as a multi-period AC optimal power flow (OPF) problem and solved by Self-Organizing Hierarchical Particle Swarm Optimization with Time Varying Acceleration Coefficients (HPSO TVAC). The optimal ESSs sizes are selected by minimizing a total cost, which includes investment cost of storage devices, bus voltage deviation cost and average network losses cost. Uncertainties of the renewable generation are accounted by considering different realizations of the generation profiles, then, ESSs sizes are selected by taking the worst case approach. The proposed methodology has been demonstrated on the modified IEEE 30-bus system and Tunisian Grid. The obtained results show the effectiveness of the proposed methodology and the related reliability merits.

Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids

In the face of the radical revolution of energy systems, there is a gradually held consensus regarding the adoption of distributed renewable energy resources, represented by Photovoltaic (PV) and wind generation. Consequently, the distributed Energy Storage Systems (ESSs) have become increasingly important in the distribution networks, as they provide the arbitrage and ancillary services. Determining the optimal installation site and the capacity of the distributed ESSs will defer the network reinforcements, reduce the investment of ESSs, and improve the reliability, flexibility, and efficiency of distribution grids. In order to investigate the optimal ESS configuration and to solve voltage fluctuations brought by the increased penetration of PV, in this study a two-stage heuristic planning strategy has been proposed, which considers both the economic operation and the lifetime of the distributed ESSs, to determine the optimal sitting and sizing of the ESSs, in the distribution grids. The first stage decides the optimal installation site and the economic scheduling of the ESSs, aiming to minimize the fabricating cost of the distributed ESSs and the network losses. Based on the output of the first stage, the second stage planning is further delivered to achieve the optimal ESS capacity, considering the Life-Cycle Cost (LCC) minimization. Finally, the feasibility and effectiveness of the proposed method is verified on a typical distribution case study network.

A two-stage stochastic model for energy storage planning in a microgrid incorporating bilateral contracts and demand response program

In recent years, application of energy storage technologies in smart grids has attracted huge attention. In this paper a cost-benefit analysis is carried out to evaluate and quantify the benefit of installing an energy storage system (ESS) in a typical Microgrid (MG). The model aims to yield the optimal ESS size by finding the least cost energy scheduling over a year under an uncertain environment. Therefore, a two-stage stochastic programming (SP) is defined to address the probabilistic nature of the parameters. The moment matching method is incorporated to create samples form probability distribution functions (PDFs) as nodes of a scenario tree. Furthermore, the fast forward selection (FFS) approach is utilized for scenario reduction. Lead acid battery (LAB), flywheel (FW) and pumped hydro energy storage (PHES) are chosen as ESS candidates. Simulation results demonstrate that whether operating as islanded or grid-connected, favorable yearly cost savings are achieved by optimal ESS planning. In this regard, the scheduled load responses and signed bilateral contracts (BCs) play important roles. The effectiveness of the proposed approaches is verified through comparisons with the deterministic and common stochastic methods. Finally, a sensitivity analysis is carried out on ESS cost to examine its effect on optimal sizing.

Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences

Under the context of low-carbon economy development, the utilization of renewable energy is deemed as an effective way for energy conservation and emission reduction. Considering about the intermittent and volatile characteristics of renewable energy, the selection of the optimal energy storage system (ESS) among various kinds of alternatives is of critical significance for giving impetus to the development of renewable energy. Therefore, it is essential to develop a comprehensive assessment technique for prioritizing various battery energy storage systems and selecting the optimal one. This paper proposed an integrated fuzzy-MCDM (multi-criteria decision making) model combining Fuzzy-Delphi approach, the Best-Worst method (BWM), and fuzzy-cumulative prospect theory (CPT) for the comprehensive assessment of battery energy storage systems. The comprehensive assessment index system consists of 15 sub-criteria from the perspectives of technology, economy, environment, performance, and sociality based on Fuzzy-Delphi method. The optimal weights are determined by the BWM based on experts'judgments which emphasized the importance of technology and environment impacts. Fuzzy theory is employed to convert interval values and crisp values to triangular fuzzy numbers (TFNs) to maximize the use of objective data information, and then the CPT model is utilized to prioritize the rankings of various alternatives considering risk preferences of decision makers and investors. The empirical result shows that the Li-ion battery is the priority selection for micro-grid demonstration projects, followed by NaS battery and NiMH battery. Sensitivity analysis discusses the influences of risk preferences on alternatives rankings. Results demonstrate that even if decision makers and investors have various risk preferences, considering about the technological, environmental, economic, social, and performance criteria, the Li-ion battery is still the optimal, followed by NaS battery.

Comprehensive Performance Assessment on Various Battery Energy Storage Systems

With the increasing development of renewable resources-based electricity generation and the construction of wind-photovoltaic-energy storage combination exemplary projects, the intermittent and fluctuating nature of renewable resources exert great challenges for the power grid to supply electricity reliably and stably. An energy storage system (ESS) is deemed to be the most valid solution to deal with these challenges. Considering the various types of ESSs, it is necessary to develop a comprehensive assessment framework for selecting appropriate energy storage techniques in establishing exemplary projects combining renewable resources-based electricity generation and an ESS. This paper proposes a multi-criteria decision making (MCDM) model combining a fuzzy-Delphi approach to establish the comprehensive assessment indicator system, the entropy weight determination method, and the best-worst method (BWM) to calculate weights of all sub-criteria, and a Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) comprehensive evaluation model to choose the optimal battery ESS. In accordance with the comprehensive evaluation results, the Li-ion battery is the optimal battery ESS to apply to wind-photovoltaic-energy storage combination exemplary projects. Based on the discussion on the comprehensive evaluation results, policy implications are suggested to improve the applicability of battery ESSs and provide some references for decision makers in related fields.

Optimizing the technical and economic value of energy storage systems in LV networks for DNO applications

Abstract Electrical energy production from renewable energy sources and electrification of consumer energy demand are developments in the ongoing energy transition. These developments urge the demand for flexibility in low voltage distribution networks, on the one hand caused by the intermittency of renewable energy sources, and on the other hand by the high power demand of battery electric vehicles and heat pumps. One of the foremost ways to create flexibility is by using energy storage systems. This paper proposes a method to first optimize the siting, power and capacity rating, technology, and operation of energy storage systems based on the technical and economic value. Secondly the method can be used to make cost- and time-based network planning decisions between network upgrades and network upgrade deferral by energy storage systems. To demonstrate the proposed method, study cases are analyzed of five low voltage distribution networks with different penetrations of photovoltaics, heat pumps and battery electric vehicles. The optimal energy storage systems in the study cases are: flow batteries sited at over 50% of the cable length with a high capacity rating per euro. With the current state of energy storage system development, network upgrade deferral is up to 61% cheaper than network upgrades in the study cases. The energy storage systems can offer additional value by reducing the peak loading of the medium voltage grid which is not taken into account in this research.

Distribution energy storage investment prioritization with a real coded multi-objective Genetic Algorithm

Energy Storage Systems (ESSs) are progressively becoming an essential requisite for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. A rapid improvement in ESS technology efficiency has been seen, but not yet sufficient to drastically reduce the high investments associated. Thus, optimal planning and management of these devices are crucial to identify specific configurations that can justify ESSs installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal ESS location and the optimal ESS schedule. In the paper, a novel multi-objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm – II integrated with a real codification that allows joining in a single optimization all the main features of an optimal ESS implementation project: siting, sizing and scheduling. The methodology has been tested on a real-size rural distribution network.

Methodology for ESS‐type selection and optimal energy management in distribution system with DG considering reverse flow limitations and cost penalties

As there are different types of energy storage systems (ESS), it is important to have a tool that determines the best alternative to be installed in the distribution systems. This study presents a methodology for optimal determination of size, type and site of ESS in distribution systems with distributed generation (DG). The methodology uses a genetic algorithm and a power flow calculated by the Electric Power Research Institute software OPENDSS. For the optimal ESS charge and discharge management uses a non-linear optimisation model that has as objective an energy cost and loss reduction. In addition, the methodology makes an energy management of the system by limiting the reverse flow with a load shifting process and a cost penalty. Solar and wind power are considered as DG modelled in Homer. Then, a genetic algorithm is used to select the size, type and site of the ESS, considering different lifecycles and costs. This methodology is tested in the IEEE 123 nodes system simulated and adapted in the OPENDSS and the formulation is implemented in MATLAB via COM interface. Finally, results of the methodology are presented and discussed.

Energy Storage System Control Algorithm by Operating Target Power to Improve Energy Sustainability of Smart Home

As energy issues are emerging around the world, a variety of smart home technologies aimed at realizing zero energy houses are being introduced. Energy storage system (ESS) for smart home energy independence is increasingly gaining interest. However, limitations exist in that most of them are controlled according to time schedules or used in conjunction with photovoltaic (PV) generation systems. In consideration of load usage patterns and PV generation of smart home, this study proposes an ESS control algorithm that uses constant energy of energy network while making maximum use of ESS. Constant energy means that the load consumes a certain amount of power under all conditions, which translates to low variability. The proposed algorithm makes a smart home a load of energy network with low uncertainty and complexity. The simulation results show that the optimal ESS operating target power not only makes the smart home use power constantly from the energy network, but also maximizes utilization of the ESS. In addition, since the smart home is a load that uses constant energy, it has the advantage of being able to operate an efficient energy network from the viewpoint of energy providers.

Modeling the temporal correlation of hourly day-ahead short-term wind power forecast error for optimal sizing energy storage system

Due to the inherent stochastic and intermittent nature of wind generation, it is very difficult to sharply improve the wind power forecasting accuracy. The sizing of energy storage system (ESS) for wind farm can effectively reduce the uncertainty caused by the inevitable forecast error of wind power. However, optimal sizing of ESS is a multi-period decision-making problem and it is the key point that exactly captures the variation magnitude and speed of forecast error. This paper proposes a method to establish the multivariate joint cumulative distribution function (JCDF) of hourly day-ahead short-term multi-period forecast errors using Normal/t copula. Based on the proposed JCDF and multiple scenarios technique, a model of optimal sizing of ESS is proposed in which the temporal correlation relationship between different time period forecast errors and probability distribution of each time period forecast error are both considered. The simulation results verify the effectiveness of the proposed methods and show that if the temporal correlation of wind power short-term forecast error is ignored, the rated energy and power capacity of sizing of ESS will be significantly misestimated.

Stochastic Optimal Planning of Battery Energy Storage Systems for Isolated Microgrids

A novel stochastic planning framework is proposed to determine the optimal battery energy storage system (BESS) capacity and the year of installation in an isolated microgrid using a new representation of the BESS energy diagram. The BESS enhances microgrid operation via load leveling and reserve provisions in conjunction with the spinning reserve from dispatchable distributed generation (DG) units. A decomposition-based approach is proposed to solve the problem of stochastic planning of BESS under uncertainty. The optimal decisions minimize the net present value (NPV) of total expected costs over a multiyear horizon taking into consideration the optimal BESS operation considering a novel matrix representing BESS energy capacity degradation. The proposed approach is solved in two stages as mixed-integer linear programming problems to ensure the convergence of the stochastic optimization problem. The optimal ratings of the BESS are determined in the first stage, while the optimal installation year is determined in the second stage. The approach ensures utilizing the allocated budget effectively by performing several iterations. Extensive studies considering four types of BESS technologies for deterministic, Monte Carlo Simulations, and stochastic cases are presented to demonstrate the effectiveness of the proposed approach.

Novel Adaptive Multi-Clustering Algorithm-Based Optimal ESS Sizing in Ship Power System Considering Uncertainty

The optimal sizing of an energy storage system (ESS) in a power generation system that incorporates photovoltaic (PV) generation is crucial in a power grid for which the reduction of CO2 emissions is important. This problem is particularly challenging when it relates to the power system of a ship because it involves uncertain meteorological and load data along a navigation route. This paper proposes a novel method for multi-objective minimization of investment/replacement cost, fuel cost, and CO2 emissions, to find the optimal size of the ESS considering life-span of the ESS. The generation of power by PV modules on a ship is affected by temporal and geographical variations of irradiation along the navigation route. In particular, operating load conditions and irradiation are uncertain. This paper proposes a novel algorithm for partitioning high-dimensional uncertain data into tractable clusters solved by deterministic optimization method. Case studies of an all-electric ship along a route from Dalian in China to Aden in Yemen are shown to demonstrate the applicability of the proposed clustering-based stochastic optimization method.

Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

Energy storage systems (ESS) play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs) and supercapacitors (SCs) is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS) of 14-ton underground load-haul-dump vehicles (LHDs). Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP)-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

Energy storage system scheduling for peak demand reduction using evolutionary combinatorial optimisation

This paper is concerned with finding an optimal energy storage system (ESS) schedule for peak demand reduction and load-levelling given only the information certainly available to and controllable by the Distribution Network Operator (DNO) which are the substation demand profile information and the DNO-owned ESS parameters. Methods such as set-point control are usually suboptimal and can create new peaks. Other methods require more parameters and actions than the DNO can fully control to form the basis for optimisation and can be computationally complex. The method presented in this paper uses simple heuristics to find possible optimal operation points for the ESS and improves the solutions found using genetic algorithm optimisation. A case study is presented showing a UK distribution network with a peak capacity violation which is resolved using the method and the results are compared to a closed-loop set-point control method.