Allocation Of Energy Storage System Research Articles

Review on the optimal placement, sizing and control of an energy storage system in the distribution network

Abstract Energy storage system (ESS) has developed as an important element in enhancing the performance of the power system especially after the involvement of renewable energy based generation in the system. However, there are a few challenges to employ ESS in distribution network, one of which is to ensure the best location and capacity so as to take the full advantage of installing ESS in the grid. In this paper, an extensive literature review on optimal allocation and control of ESS is performed. Besides, different technologies and the benefits of the ESS are discussed. Some case studies of ESS application in different part of the world are also presented. Finally, this paper emphasizes the future improvement of ESS control and performance to solve more complicated problems originated from the participation of renewable energy generation in the power system.

Characteristic analysis of operation curve of energy storage system considering typical weather conditions to suppress photovoltaic power fluctuation

By mining the typical operating curve of an energy storage system, an understanding of the overall characteristics of the charge–discharge power of the system can be attained and a decisive support for the capacity allocation of the system can be provided. This paper proposes a typical operation curve mining algorithm based on a cloud model for the application scenario of using an energy storage system to suppress the power fluctuation of a photovoltaic (PV) power station. The frequency distribution of the charge–discharge power of the energy storage system in a longitudinal time series is decomposed into several cloud models with different granularities. By weighting and summing the expected values of the normal cloud model group, the typical operating curve of the energy storage system is obtained. To analyze the power distribution of the energy storage system and the typical operating curve under six typical weather types, a nonparametric estimation method was adopted to fit the power value distribution and the fitting deviation was calculated. The rationality of the extracted typical operating curve is verified and the capacity of the energy storage system is determined according to the obtained curve. The typical operating curve is used to configure the energy storage capacity of a 40 MWp PV plant and the result is 4.4984 MW·h, i.e., approximately 4.5 MW·h, which represents 11.25% of the installed capacity of the PV plant. This energy storage capacity affords a 95% probability of meeting the daily capacity requirements of the system. This shows that mining the typical operating curve is a valid method for configuring the energy storage capacity of a PV power station. Using the simulation examples, the effectiveness and the correctness of the method proposed in this paper are verified to provide theoretical support for the capacity allocation of energy storage systems. The authors of the article agree to the retraction of the article effective AUGUST 25, 2021.

Optimal allocation of energy storage systems considering wind power uncertainty

Abstract Energy storage systems (ESSs) play a major role in power system planning and operation. As evolution of the storage technologies continues, planners will be regarded the ESSs in future power systems more than ever. Simultaneous determination of size and site of ESSs is a non-deterministic, and non-convex problem, which should take into account the uncertain nature of today's power systems. This paper, for the first time, investigates uncertain optimal allocation of ESSs considering practical constraints, including prohibited zones, and ramp rate, as well as simultaneous reduction of three different and incompatible objective functions of operation cost, voltage deviation, and air emission. Due to complexity of the problem, two multi-objective hybrid algorithms called MOGSA and MOPSO-NSGA_II were proposed. Furthermore, five-point estimation method is utilized in order to model the wind power uncertain nature. The simulation results on IEEE 30-bus test system are detailed. To increase the accuracy and ensure selection of the best solution from among the set of optimal solutions, the multi-criteria decision-making techniques (TOPSIS) are used. The simulation results clearly show the efficiency and effectiveness of the proposed method.

Distributed generation and energy storage system planning for a distribution system operator

The smart distribution system architecture provides value-based control techniques that facilitate bi-directional power flows and energy transactions. Although consensus and understanding continue to develop around peer-to-peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two-stage of power scheduling. The proposed model is verified on the modified IEEE 15-bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk-seeking operation strategy is recommended if wind power penetration increases.

Optimal allocation of a hybrid energy storage system considering its dynamic operation characteristics for wind power applications in active distribution networks

This paper aims at specifying the optimal allocation of a hybrid supercapacitor-vanadium redox flow battery (VRB) energy storage system (ESS) for maintaining power balance of active distribution networks (ADNs) for wind power applications. Correspondingly, an optimal allocation approach for the hybrid ESS considering the application scenarios of supercapacitor ESS and VRB ESS was proposed. The supercapacitor ESS was used for restraining the high-frequency components of net load power and a net load power spectrum analysis method based on dividing the variations of net load power was used to obtain the optimal capacity of supercapacitor ESS. For the allocation of hybrid ESS, a mathematical framework was proposed considering both the economy and reliability of ADNs associated with VRB′ dynamic operation characteristics. Finally, the proposed optimal allocation approach was validated and tested by a modified IEEE 33-bus system. Test results have demonstrated the effectiveness of the proposed optimal allocation approach associated with the power flow operation characteristics of ADNs.

Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective bi-level optimization

A fuzzy multi-objective bi-level optimization problem is proposed to model the planning of energy storage system (ESS) in active distribution systems (ADS). The proposed model enables us to take into account how optimal operation strategy of ESS in the lower level can affect and be affected by the optimal allocation of ESS in the upper level. The power characteristic model of micro-grid (MG) and typical daily scenarios are established to take full consideration of time-variable nature of renewable energy generations (REGs) and load demand while easing the burden of computation. To solve the bi-level mixed integer problem, a multi-subgroup hierarchical chaos hybrid algorithm is introduced based on differential evolution (DE) and particle swarm optimization (PSO). The modified IEEE-33 bus benchmark distribution system is utilized to investigate the availability and effectiveness of the proposed model and the hybrid algorithm. Results indicate that the planning model gives an adequate consideration to the optimal operation and different roles of ESS, and has the advantages of objectiveness and reasonableness.

Optimal Allocation method on Distributed Energy Storage System in Active Distribution Network

With distributed generations (DG) including the wind and photovoltaic power generations continually connected to the distribution network, reasonable allocation of energy storage system is extremely important to active distribution network (ADN). In this paper, a method to optimize the location and capacity of the embedded distributed energy storage system (DESS) is proposed to meet the needs of ADN and DGs. Firstly, the model of DGs is built according to the natural characteristics of wind and solar energy. Secondly, the model of allocating the DESS is established to achieve the best economical investment results with the constraints of ADN, DG and DESS themselves. Finally, a test ADN with wind and photovoltaic power generations is used to verify the efficiency of the method proposed in this paper.

Optimal Allocation of Energy Storage Systems for Voltage Control in LV Distribution Networks

This paper addresses the problem of finding the optimal configuration (number, locations, and sizes) of energy storage systems (ESSs) in a radial low voltage distribution network with the aim of preventing over- and undervoltages. A heuristic strategy based on voltage sensitivity analysis is proposed to select the most effective locations in the network where to install a given number of ESSs, while circumventing the combinatorial nature of the problem. For fixed ESS locations, the multi-period optimal power flow framework is adopted to formulate the sizing problem, for whose solution convex relaxations based on semidefinite programming are exploited. Uncertainties in the storage sizing decision problem due to stochastic generation and demand, are accounted for carrying out the optimal sizing over different realizations of the demand and generation profiles, and then taking a worst-case approach to select the ESS sizes. The final choice of the most suitable ESS configuration is done by minimizing a total cost, which takes into account the number of storage devices, their total installed capacity and average network losses. The proposed algorithm is extensively tested on 200 randomly generated radial networks, and successfully applied to a real Italian low voltage network and a modified version of the IEEE 34-bus test feeder.

Multi-Agent Optimal Allocation of Energy Storage Systems in Distribution Systems

A variety of optimal methods for the allocation of a battery energy storage system (BESS) have been proposed for a distribution company (DISCO) to mitigate the transaction risk in a power market. All the distributed devices are assumed to be owned by the DISCO. However, in future power systems, more parties in a distribution system will have incentives to integrate BESS to reduce operational cost. In this paper, an enhanced BESS optimal allocation method is proposed for multiple agents in a distribution system. First, the electricity market mechanism is extended to a distribution system, and the corresponding energy transaction process is modeled for different agents, such as wind farms, solar power stations, demand aggregators, and the DISCO. The uncertainties of renewable energy and demand are addressed using stochastic methods. In the proposed transaction model, the integration of BESS can help an agent to reduce the operational cost, also defined as the payoff function. Next, game theory is introduced in this paper to investigate the interactions among the agents and to determine the BESS integration plans. The agents are built as players who are willing to minimize their payoff functions in the proposed non-cooperative game. The Nash equilibrium, which is the best strategy for the players, is proved to exist. Such equilibrium can be solved using an iterative algorithm. The proposed BESS allocation method for the multi-agent system is verified for two cases, and the payoff reductions are quantified based on the proposed distribution energy transaction mechanism.

Optimal ESS Allocation for Benefit Maximization in Distribution Networks

Smart grids have been emerging nowadays as an initiative to operate modern distribution systems in a more economic and efficient way. Energy storage systems (ESSs) are one of the promising technologies that can achieve the goals of smart grids via facilitating the connection of renewable sources, improving system reliability, and controlling the net demand through peak load shaving, etc. In this paper, a comprehensive planning framework is introduced for ascertaining the most cost effective siting and sizing of ESSs that maximize their benefits in distribution networks. A probabilistic approach is further adopted that includes the consideration of the stochastic nature of system components. Such approach allows determining the optimal operation of ESS at each load state. Moreover, contingency planning decisions, in the form of load points to be shed during contingencies, are identified through the approach proposed.

Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms

With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms.

Bi-Level Programming Approach for the Optimal Allocation of Energy Storage Systems in Distribution Networks

Low-CO2-emission wind generation can alleviate the world energy crisis, but intermittent wind generation influences the reliability of power systems. Energy storage might smooth the wind power fluctuations and effectively improve system reliability. The contribution of energy storage to system reliability cannot be comprehensively assessed by the installed capacity of energy storage. The primary goal of this paper is to investigate the impact of the installed location and capacity of energy storage on power system reliability. Based on a bi-level programming approach, this paper presents a bi-level energy storage programming configuration model for energy storage capacity and location configuration. For upper-level optimization, a depth search method is utilized to obtain the optimal installed location of energy storage. For the lower-level optimization, the optimal capacity of energy storage is solved to meet the system reliability requirements. The influence of the contribution of energy storage location to system reliability is analyzed. The proposed model and method are demonstrated using the RBTS-Bus6 System and Nanao (NA) island distribution system in China. The results show the effectiveness and practicability of the proposed model and method.

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

Scheme to improve the load characteristics in DC distribution networks based on optimal allocation of energy storage systems

Scheme to improve the load characteristics in DC distribution networks based on optimal allocation of energy storage systems

Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system

Owing to low efficiency of traditional ships and the serious environmental pollution that they cause, the use of solar energy and an energy storage system (ESS) in a ship’s power system is increasingly attracting attention. However, the swinging of a ship raises crucial challenges in designing an optimal system for a large oil tanker ship, which are associated with uncertainties in solar energy. In this study, a series of experiments are performed to investigate the characteristics of a photovoltaic (PV) system on a moving ship. Based on the experimental results, an interval uncertainty model of on-board PV generation is established, which considers the effect of the swinging of the ship. Due to the power balance equations, the outputs of the diesel generator and the ESS on a large oil tanker are also modeled using interval variables. An interval optimization method is developed to determine the optimal size of the ESS in this hybrid ship power system to reduce the fuel cost, capital cost of the ESS, and emissions of greenhouse gases. Variations of the ship load are analyzed using a new method, taking five operating conditions into account. Several cases are compared in detail to demonstrate the effectiveness of the proposed algorithm.

Review of energy storage allocation in power distribution networks: applications, methods and future research

Changes in the electricity business environment, dictated mostly by the increasing integration of renewable energy sources characterised by variable and uncertain generation, create new challenges especially in the liberalised market environment. The role of energy storage systems (ESS) is recognised as a mean to provide additional system security, reliability and flexibility to respond to changes that are still difficult to accurately forecast. However, there are still open questions about benefits these units bring to the generation side, system operators and the consumers. This study provides a comprehensive overview of the current research on ESS allocation (ESS sizing and siting), giving a unique insight into issues and challenges of integrating ESS into distribution networks and thus giving framework guidelines for future ESS research.

Optimal energy storage system allocation and operation for improving wind power penetration

In recent years, the energy storage system (ESS) has been demonstrated to be involved in many aspects of the integration of wind power. For ESS application, ESS allocation of the installation location, power rating, and energy rating is the first concern. Different from previous studies, this study emphasises the significance of the ESS operation in the study of ESS allocation. A bi-level-programming-based model is proposed to take the interaction of allocation and operation into consideration at the same time, with the external level optimising allocation and the internal level optimising operation. The complexity assessment and solution algorithm of the model is also discussed. Next, a genetic numerical algorithm is proposed to solve the bi-level model. The authors’ results were tested on a modified IEEE 39 bus system and a provincial regional power system to verify both the flexibility and applicability of the proposed model and algorithm. This model is useful for various types of ESS and provides a foundation for ESS application.

Optimal Tilt Angle of Photovoltaic Arrays and Economic Allocation of Energy Storage System on Large Oil Tanker Ship

This study optimizes the tilt angle of photovoltaic (PV) panels on a large oil tanker ship system and considers the impact of partial shading to improve the performance of the PV system. This work presents a novel method that considers the difference between the expected and real outputs of PV modules to optimize the size of energy storage system (ESS). The method also takes into account the cost of wasted power, the capital cost of the system, fuel cost and the CO2 emissions. Unlike on land, power generation using a PV on a ship depends on the date, latitude and longitude of the navigation. Accordingly, this work considers a route from Dalian in China to Aden in Yemen, accounting for the seasonal and geographical variations of solar irradiation. This proposed method adopts five conditions associated with the navigation route to model the total shipload. Various cases are discussed in detail to demonstrate the effectiveness of the proposed algorithm.

Optimal sizing of energy storage system for microgrids

Microgrids (MGs) are Low Voltage distribution networks comprising various distributed generators (DG), storage devices and controllable loads that can operate either interconnected or isolated from the main distribution grid as a controlled entity. Energy storage system (ESS) is a vital part of an MG. In this paper, a methodology is proposed for the optimal allocation and economic analysis of ESS in MGs on the basis of net present value (NPV). As the optimal operation of an MG strongly depends on the arrangement and allocation of its ESS, economic operation strategies and optimal allocation methods of the ESS devices are required for the MG. Self-adaptive Bee Swarm Optimization (SBSO) algorithm is applied to optimize the operation strategies and capacities of ESS in MGs in order to find maximal NPV, the generation schedule of ESS and distributed generation sources. This paper is to suggest, among those available ESS, the optimal sizes and types of them and their optimal arrangement, such that the total NPV achieved during the system operational lifetime period is maximized. After introducing the methodology, a case study is presented for illustration.

Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated.