Planning Of Energy Storage Systems Research Articles

Data-driven stochastic programming for energy storage system planning in high PV-penetrated distribution network

Energy storage systems (ESSs) facilitate the reliable and economic operation of distribution systems with high PV penetration. Establishing uncertainty models is the key to the optimal planning and operation of ESSs in distribution systems. Widely used parametric models cannot describe the variability of uncertainties thoroughly. In this paper, a data-driven method is designed for uncertainty modeling, and a distributionally robust optimization (DRO) model is developed to determine the optimal ESS planning strategy in distribution systems. First, a deterministic optimization model is established considering both ESS planning and distribution system operation. Then, the Wasserstein-metric-based ambiguity set is designed for the probability distributions of random variables. To hedge against the distributional ambiguity, the optimization problem is expanded into a two-stage DRO problem, of which the second-stage problem minimizes the expected operating cost under the worst-case probability distribution. Finally, the two-stage DRO model is reformulated as a mixed-integer second-order cone programming (MISOCP) problem, which is solved by optimization solvers. The proposed method is tested on modified 33-bus and 123-bus distribution systems with actual solar irradiance and load data. The influence of different strategies on ESS planning and operation is discussed. The ESS planning results obtained by DRO are compared with those of conventional stochastic optimization and deterministic optimization to verify the superiority of the proposed method.

Two-Layer Optimization Model for the Siting and Sizing of Energy Storage Systems in Distribution Networks

One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise between the real power loss, voltage stability margin, and the application cost of ESSs. Thereinto, an improved bat algorithm based on non-dominated sorting (NSIBA), as an outer layer optimization model, is employed to obtain the Pareto optimal solution set to offer a group of feasible plans for an internal optimization model. According to these feasible plans, the method of fuzzy entropy weight of vague set, as an internal optimization model, is applied to obtain the synthetic priority of Pareto solutions for planning the optimal siting and sizing of ESSs. By this means, the adopted fuzzy entropy weight method is used to obtain the objective function’s weights and vague set method to choose the solution of planning ESSs’ optimal siting and sizing. The proposed method is tested on a real 26-bus distribution system, and the results prove that the proposed method exhibits higher capability and efficiency in finding optimum solutions.

Optimal placement and sizing of energy storage systems to improve the reliability of hybrid power distribution network with renewable energy sources

Concept of renewable hybrid energy systems have attracted many utilities and implemented by them too. With such energy systems, customers are not only supplied more economically but also more reliably. Moreover, optimal placements of energy storage systems (ESSs) increase the reliability of such hybrid renewable system up to a great extent. Therefore, this paper presents a new approach for optimal placement and sizing of energy storage systems (ESSs) in hybrid renewable radial distribution system to improve the reliability of such system without violating the system constraints. The cost of energy not supplied (CENS) associated with power service interruption and power shortage is also been considered in the objective function during placement of ESSs. Hence, the proposed optimal placement planning of ESSs is presented with an aim of minimizing the objective function includes cost of energy not supplied (CENS), investment cost and operational cost of ESSs, and power loss in distribution system. It is to be noted that the Particle swarm optimization (PSO) technique is adopted to minimize the objective function. The presented methodology is demonstrated by considering several case studies on 11 kV, 30 bus radial distribution system. Further, a rigorous sensitivity analysis is performed by limiting the number of applied ESSs and varying the maximum capacity of participating ESSs.

Optimal planning of energy storage systems in active distribution networks considering multiple operation optimisation measures

An original three-layer planning model of energy storage systems (ESSs) in active distribution networks is proposed in this study, taking demand response (DR) and network reconfiguration (NR) into account. In order to handle the uncertainties of distributed generations and loads, a practical scenario-generation method based on seasonal characteristics is put forward. Based on the time of use price, an ESSs charging and discharging strategy is presented. The mixed-integer nonlinear programming problem is converted to a mixed-integer second-order cone (SOCP) and the SOCP and an improved hybrid intelligent algorithm are applied to solve the ESSs planning model. Finally, the feasibility and effectiveness of the proposed model and solution methods are proven through the improved IEEE 33-bus system. The case study demonstrates the positive effect of DR and NR on optimising system operation and reducing total cost.

Framework for Sizing of Energy Storage System Supplementing Photovoltaic Generation in Consideration of Battery Degradation

There is growing interest in the use of energy storage systems (ESS) to create combined “renewable energy plus storage” power plants. ESS based on lithium-ion batteries have drawn much attention due to their high energy density and low self-discharge. However, as lithium-ion batteries are still costly, a power producer should determine ESS capacity in a sophisticated manner to ensure profitability of the PV plus storage projects. During the project horizon, lithium-ion batteries undergo severe capacity degradation, which must be considered in ESS planning. The degradation rate depends on various stress factors which are affected by ESS sizes and operation. Therefore, this paper aims to propose an advanced framework for calculating the capacity of an ESS supplementing a photovoltaic system considering the effect of the size and operation of ESS on battery degradation while maximizing profitability. Depending on how batteries are used during the project horizon, two scenarios are discussed and an ESS sizing framework for each scenario is suggested. To deal with non-convexity and black-box parameters of the optimal ESS sizing problems, we introduce an iterative algorithm that finds a solution by accessing battery degradation and optimizing profitability repetitively. We adopted the South Korean market for analysis and simulation of the frameworks.

Accommodating uncertain wind power investment and coal-fired unit retirement by robust energy storage system planning

Increasing wind power integration and coal-fired unit retirement strain the power system's spinning reserve and increase the pressure on peak regulation. With the ability to stock extra power generation and supply the peak load, the energy storage system (ESS) can alleviate the rising demand on the spinning reserve and plays an increasingly important role in the power system. In this paper, a tri-level robust ESS planning model is proposed to accommodate uncertain wind power investment as well as coal-fired unit retirement. The upper-level of this model is to determine the planning scheme of ESSs, which iteratively takes the worst-case of wind power investment and coal-fired unit retirement into consideration. The middle-level and lower-level of this model are to make the optimal daily economic dispatch under the worst-case realizations of uncertainties. We derive an equivalent reformulation of the proposed robust ESS planning model and solve it with a dual column-and-constraint generation algorithm. Case studies are conducted on the IEEE RTS-79 system. The results demonstrate the superiority of the proposed planning method in comparison with other methods. Furthermore, the effects of the capital cost of ESS, the expected proportion of wind power, and the uncertainty budget on the development of ESS are studied. Taking the uncertainties of unit retirement and wind power investment into consideration achieves a better trade-off between the ESS investment cost and the operation cost.

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.

Integrated transmission and storage systems investment planning hosting wind power generation: continuous‐time hybrid stochastic/robust optimisation

In this study, a continuous-time hybrid stochastic/robust optimisation is proposed for the integrated investment in transmission lines (TLs) and energy storage systems (ESSs) with high penetration of uncertain wind power generation (WPG) sources from a central planner viewpoint. The main objective of the problem is to achieve a simultaneous expansion of transmission assets, TLs and ESSs, whereas minimising the investment cost while taking the operational aspects of a power system into account to accommodate higher shares of uncertain and intermittent WPGs. However, the integrated expansion planning of joint TL and ESS to integrate WPGs via conventional hourly discrete time model can increase the operation cost and result in a non-optimal sizing and siting of TLs and ESSs, hence, can impose an opposite effect on the favourite. Accordingly, a continuous-time model is proposed to coordinate the expansion planning of both TL and ESS to deal with sub-hourly uncertainty of WPGs. Also, the WPG uncertainty in expansion planning problem is characterised using a hybrid stochastic/robust optimisation framework. Numerical tests are implemented on a modified IEEE RTS 24-bus system and the achieved results confirm the efficiency of the proposed model.

Stochastic price based coordinated operation planning of energy storage system and conventional power plant

A generation company (GENCO) which has a conventional power plant (CPP) intends to add an energy storage system (ESS) beside the CPP to increase its flexibility and profitability. For this purpose, a new model is proposed for coordinated operation planning of the CPP and ESS in energy and spinning reserve markets in the presence of a bilateral contract. The proposed model is based on the stochastic price based unit commitment (PBUC) and price based storage commitment (PBSC). The uncertainties of the energy and spinning reserve prices and delivery requests in the spinning reserve market are modeled via scenarios based upon historical data. The proposed model maximizes the profitability of the ESS beside the CPP and encourages the GENCO to invest the ESS. ESS technology options to use beside the CPP are determined by economic assessments. Numerical results show that utilization of ESSs improves the technical operation of CPPs, as well as GENCOs’ profitability.

Investment optimization of grid-scale energy storage for supporting different wind power utilization levels

With the large-scale integration of renewable generation, energy storage system (ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty. This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load. A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS. Specifically, the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making. Then, an easy-to-implement variant of Benders decomposition (BD) algorithm is developed to solve the resulting mixed integer nonlinear programming problem. Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power. In addition, the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.

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.

Optimal planning of energy storage system using modified differential evolution algorithm

In smart distribution network, optimal planning of Energy Storage Systems (ESS) can lead to voltage profile improvement, network loss reduction and increasing the profit of distribution company (DISCO). This paper proposes the application of Modified Differential Evolution Algorithm (MDEA) which is very strong global search capability for maximizing the DISCO profit over the planning. The objective function is the difference between the benefit which is the energy arbitrage in a deregulated market and the costs which are investment cost, operation and maintenance cost. The implementation of MDEA is performed on a 30-bus radial distribution network. The simulation results demonstrate that the proposed MDEA application significantly increases the profit without any violation of network constraints.

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.

Optimal Investment Planning of Bulk Energy Storage Systems

Many countries have the ambition to increase the share of renewable sources in electricity generation. However, continuously varying renewable sources, such as wind power or solar energy, require that the power system can manage the variability and uncertainty of the power generation. One solution to increase flexibility of the system is to use various forms of energy storage, which can provide flexibility to the system at different time ranges and smooth the effect of variability of the renewable generation. In this paper, we investigate three questions connected to investment planning of energy storage systems. First, how the existing flexibility in the system will affect the need for energy storage investments. Second, how presence of energy storage will affect renewable generation expansion and affect electricity prices. Third, who should be responsible for energy storage investments planning. This paper proposes to assess these questions through two different mathematical models. The first model is designed for centralized investment planning and the second model deals with a decentralized investment approach where a single independent profit maximizing utility is responsible for energy storage investments. The models have been applied in various case studies with different generation mixes and flexibility levels. The results show that energy storage system is beneficial for power system operation. However, additional regulation should be considered to achieve optimal investment and allocation of energy storage.

Optimal planning of energy storage systems in microgrids for improvement of operation indices

The output of distributed generation resources depends on the weather conditions. This causes a fluctuation in the production of these resources and reduces the power quality. One of the solutions to overcome this problem is the use of energy storage systems. The use of these systems improves the operation indices of the grid. Energy storage systems, especially batteries, with all their technical capabilities, are high-cost systems. Therefore, installing them at any location with any random and non-optimum size can lead to higher cost. In this research, introducing the steps to realize an optimal energy management process, a method by which it can be possible to determine the optimal location, power and energy capacity of storage systems in a grid based on hourly data of the grid over a year (Includes loads and variable output power of distributed generation) is presented. the energy storage system used in this article is a vanadium redox flow battery. It is shown that the effect of storage device on the operation indices depends more on the installation location than on the storage capacity. The symbiotic organisms search algorithm has been used to solve the optimization problem. In the optimization process, in addition to improving the voltage profile, reducing losses and increasing network reliability, storage costs (Including the cost of investing, operating and repair) are minimized. The results obtained with Symbiotic algorithm are compared with other conventional algorithms such as particle swarm (PSO) and genetic algorithms. Given that Symbiotic algorithm has no specific adjusting parameters, the convergence rate increases and a more appropriate answer is obtained.

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.

Aging Cost Optimization for Planning and Management of Energy Storage Systems

In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs. The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.

Operation planning of energy storage system considering multiperiod energy supplies and demands

The energy storage system (ESS) is recently drawing an increasing attention as an efficient and affordable tool in energy systems. While most interest has been concerned with developing better ESS technology, attention should be given to constructing a rigorous decision-supporting framework for operating the ESS. Motivated by this need, a mathematical modeling framework for the operation of an ESS is proposed in this paper. The proposed framework allows us to prepare ESS operations such as when to charge or discharge by which amount over multiple time periods. Numerical examples are presented to illustrate the applicability of the proposed framework.

Optimal planning and operation of energy storage systems in radial networks for wind power integration with reserve support

Though energy storage system (ESS) is a promising approach to alleviate the variability of non-dispatchable wind power and other forms of renewable energy sources, its high investment cost has impeded its wide deployment. Aiming at exploiting the arbitrage benefit of ESS in reserve market and raising revenue of shareholders, this study explores the optimal planning and operation of ESS in radial networks. Besides load balancing, ESS is used to provide three kinds of operating reserve services in presence of high wind power penetration, including spinning reserve, upward and downward regulation reserves. In light of the capacity limitation of ESS, the time duration of reserve provision has been taken into account. In the proposed model, unit commitment and AC optimal power flow (AC-OPF) are combined together over sequential time series to find the optimal location and size of ESSs. In order to reduce the computational complexity, the extended DistFlow model of AC-OPF is adopted to convert the problem into a mixed-integer second-order cone programming. Numerical studies on the IEEE 34-bus distribution test feeder are used to investigate the effects of ESS with respect to various penetration levels of wind power and load scales.

Optimal management and planning of storage systems based on particle swarm optimization technique

In restructured electric power systems under electricity market regulations, Distribution Companies (DISCOs) mainly aim at maximizing their profit subject to safe operation of the network. In this regard, optimal planning of Energy Storage Systems (ESSs) can be a very effective method to maximize DISCOs profit. This paper addresses an optimal methodology to signify the location and capacity of ESSs in distribution network under electricity market environment. The proposed optimal ESSs planning aims at maximizing DISCO profit subject to safe and secure operation constraints (e.g., voltage and flow limits). The maximization problem is mathematically expressed as a mixed integer non-linear programming and solved using particle swarm optimization algorithm. Simulations are carried out on a typical distribution network. Simulation results demonstrate significant impact of ESSs on the network operation, security constraint, and costs. The proposed planning not only increases the DISCO profit but also guarantees the safe operation of the network. As well, several stativity analyses are carried out to indicate the impact of parameters on the planning.