Optimal Sizing Of Energy Storage Research Articles

Optimal sizing of energy storage in generation expansion planning of new power system with high penetration of renewable energies

With the consumption of fossil fuels and the impact of the greenhouse effect, renewable energies are ushering in a huge development opportunity, thus the optimal configuration of energy storage is essential to cope with the intermittence and fluctuation of renewable energies. This paper establishes a mathematical model for optimal sizing of energy storage in generation expansion planning (GEP) of new power system with high penetration of renewable energies. The objective function is to achieve the lowest total cost of investment and operation under the comprehensive consideration of various generation technologies and energy storage technologies. Carbon peaking and carbon neutrality goals are transformed into constraints of the GEP model to achieve high penetration of renewable energies in a power system. And 8760h operation curve are adopted to deal with the intermittence and fluctuation of renewable energies and obtains a more reasonable and realistic GEP results. Meanwhile, the optimal sizing of energy storage is solved in GEP model by detailed operation optimization and constraints of penetration rate and curtailment rate of renewable energies. Finally, the solving flow chart of GEP model and flow chart of optimal sizing of energy storage are given and the validity of this GEP model is proved in case analysis. In addition, carbon peaking goal may achieve in advance by vigorously developing wind power and PV power and configuring appropriate energy storage.

Impact of Storage Devices with Renewable Integrated Distribution Network for Power Loss Minimization

The intermittent behaviour of renewable energy generation has become an essential issue for power deficiency in the distribution network. The high penetration of wind and solar became the primary task for the optimal size of energy storage to support the power mismatch. In the present work, the impact of the energy storage device with distribution generation (DGs) have been determined in a renewable integrated distribution system for power loss minimization. The main contribution of this paper is: (i) optimal location of DGs and battery are obtained by solving single and multi-objective functions. (ii) Determination of DG and battery size for minimization of power loss and system cost. (iii) Impact of battery energy storage device on loss profile and total cost of the system. The impact of day load variation has been considered in the study. The results have been obtained for IEEE-33 bus test system using a hybrid GAMS and particle swarm optimization (PSO) algorithm. The power loss is reduced to 47.60% with single DG and battery energy storage (BES). In addition, the power loss is reduced to 59.285% with two DGs and BES. The simulation results of the test system have been compared with other existing results.

Optimization of Energy Storage in Hybrid Wind and Photovoltaic Energy Systems

Возобновляемые источники энергии играют незаменимую роль в отдаленных районах, где электросеть недоступна. Гибридная система, предлагаемая в этой статье, включает в себя два возобновляемых источника энергии, а именно: фотоэлектрические системы, ветряные турбины. Комбинация этих энергий для удовлетворения требований к нагрузке с надежным, экономичным и устойчивым энергоснабжением считается критической проблемой. Гибридные системы считаются наиболее прагматичным решением для удаленных районов за пределами сети.Предлагается новый метод определения оптимального размера накопления энергии в фотоэлектрических и ветряных гибридных системах генерации энергии. Эти компоненты размещены в схеме из трех блоков для прогнозирования, измерения и распределения (управления) потоками мощности во всей системе для удовлетворения требований стороны спроса. Данные о мощности электрической нагрузки, мощности солнечного излучения, температуре окружающей среды, скорости ветра и других погодных условиях должны прогнозироваться с высокой точностью. Алгоритм определения оптимального размера разработан на основе данных прогнозирования, ограничений, соотношения величин во всей системе и способности заряжать/разряжать энергию накопления. Оптимальный размер в этом исследовании помогает переупорядочить диаграммы нагрузки, которые полностью компенсируют дефицит энергии на этапах с высоким и средним уровнями цен.Данный подход может быть применен на каждой шине, чтобы уменьшить стоимость покупки электричества от электрической системы. Новое предложение иллюстрируется результатами моделирования в тематическом исследовании, проведенном в MATLAB 2019a.

Economics of energy storage options to support a conventional power plant: A stochastic approach for optimal energy storage sizing

Is an energy storage system (ESS) an economic choice to enhance the flexibility and controllability of a conventional power plant (CPP)? So far, most literatures have investigated the profitability of ESSs beside renewable power plants (RESs). The focus of this paper is on economic assessment of an ESS alongside a CPP in a price taker generation company (GENCO). This work presents a method to determine economic storage technology options and their optimal power and energy capacities. A stochastic price based unit commitment (PBUC) conjugated with a stochastic price based storage commitment (PBSC) is developed in the ESS sizing problem. The output determines the optimal participation of a power plant and its optimum sized ESS in day-ahead energy and spinning reserve markets and bilateral contracts. Numerical results confirm the capability of the proposed model to improve the operational characteristics of the CPPs and to increase the profitability of GENCOs. The results show that the ESS increases the GENCOs' profit by up to 36%. The ESS, using the proposed model, can reduce the number of on line hours of the CPP about 13%. Also, the GENCO can offer about 10% lower price for bilateral contracts.

Optimal sizing of user-side energy storage considering demand management and scheduling cycle

Battery energy storage systems (BESSs) can play a key role in obtaining flexible power control and operation. Ensuring the profitability of the energy storage is the prerequisite to realize its reasonable applications in the power system. This paper establishes a bi-level optimal sizing of energy storage participating in demand management and energy arbitrage for industrial users. The BESS scheduling cycle and lifetime are considered in the optimization model. The proposed bi-level model is derived from a life-cycle economic analysis of energy storage based on the maximization of net profit over the entire life-cycle and profit over the scheduling cycle as upper- and lower-level objective functions, respectively. The Karush–Kuhn–Tucher (KKT) conditions are combined with a mixed-integer linear programming (MILP) approach to solve the optimization model. Case studies based on realistic industrial load data are used to validate the usefulness of the proposed method, with the simulation results confirming that the method can effectively improve the benefits of the energy storage system. Finally, the effect of the load characteristics and electricity price policies on the model results is analyzed.

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.

Optimal Energy Storage Sizing for Networked Microgrids Considering Reliability and Resilience

In this paper, an optimal energy storage sizing method is proposed for networked microgrids (NMGs) considering reliability and resilience enhancement. A bi-level optimization model for energy storage sizing is developed. The upper-level model is focused on the optimal energy storage sizing problem, aiming at maximizing annual profit. The lower-level problem is aimed at operation optimization for profit maximization under multiple operating scenarios, i.e., normal operation, typical fault, and extreme fault scenarios. The bi-level model is converted into a mixed-integer linear program (MILP), which can be solved by off-the-shelf optimization solvers such as CPLEX. For comparison, the optimal energy storage size schemes are obtained for the NMGs and non-networked microgrids (NNMGs), respectively. The results indicate that the required energy storage size can be reduced while the operating profit is improved by interconnecting the microgrids (MGs). The results also indicate that the energy interaction in NMGs enables the enhancement of both reliability and resilience during grid outages.

Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach

The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization. To evaluate that, a multi-stage stochastic program model designed to minimize community electricity purchase cost per day is used to support decision-making by creating control policies for energy management. Two different strategies are created to represent the interest of a single household (the individual energy management - IEM) and households that share their assets with the community (shared energy management - SEM). Our strategies consider time-of-use rates (ToU), load and resource variation during different seasons, with their distinct days of the year, to calculate net present value (NPV) associated with the energy savings. IEM and SEM are then used in a framework designed to establish the requirement of optimal energy storage size for each house of the community based on NPV values. The results of this study for an analysis considering a community with five houses show that the proposed SEM strategy reduces the overall electricity purchase costs for a summer day up to 11% and 3% compared with heuristic and IEM control respectively. Moreover, our results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%.

Optimal Energy Storage Sizing in Photovoltaic and Wind Hybrid Power System Meeting Demand-Side Management Program in Viet Nam

This paper proposes a new method to determine optimal energy storage sizing in photovoltaic and wind hybrid power generation systems. These generations are placed in a scheme of three blocks to forecast, measure, dispatch/control and distribute power flows in whole system to meet requirements of the demand-side management program in Viet Nam. Data about electric load power, power of solar irradiance, ambient temperature, wind speed and other weather conditions must be forecasted in a high accuracy. An algorithm to determine the optimal sizing is designed basing on forecasting data, constraints, the relation of quantities in whole system and the capability to charge/discharge energy of energy storage. The optimal sizing in this research helps to rearrange load diagrams that compensates deficient energy completely in stages having high and medium price levels. It can be applied at each bus to reduce cost for buying electricity from electric power system. The new proposal is illustrated by simulation results in a case study carried out by MATLAB 2017a.

Energy storage sizing methodology for mass-transit direct-current wayside support: Application to French railway company case study

In the context of direct-current electrified railway systems, power supply quality issues are a major concern for the railway network operators. Indeed, after signaling systems and track equipment, voltage issues may be the most challenging factor for the capacity of a direct-current railway line. These issues and the high cost of new substations motivate the implementation of wayside energy storage systems to support the system. Since an appropriate sizing methodology is a key step towards an energy storage system implementation in a railway line, in this paper a sizing methodology is presented in detail. As hypothesis, the sizing methodology is defined such that the storage system is able to support the railway line under projected conditions of the rolling stock traffic, along with other technical criteria relevant to the railway operator. A real-time simulation oriented, direct-current railway network modeling approach is proposed and exploited in this sizing methodology. Using this modeling method, the optimal energy storage sizing formulation is described. The objective function to minimize is the trade-off between energy storage capacity and charging power. The proposed sizing method is applied to a real railway line with known power supply quality issues. This case study is introduced by analyzing real voltage measurements on two key sites of the line over several days. Then, the sizing methodology is applied and the results are discussed for the study case, defining the minimal technical requirements to install. The obtained minimal technical requirements for the storage system are considered by the operator to take a cost-effective decision in contrast with the reinforcement of the line with new substations.

Optimal energy storage sizing and offering strategy for the presence of wind power plant with energy storage in the electricity market

Optimal energy storage sizing and offering strategy for the presence of wind power plant with energy storage in the electricity market

Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems

From an environment perspective, the increased penetration of wind and solar generation in power systems is remarkable. However, as the intermittent renewable generation briskly grows, electrical grids are experiencing significant discrepancies between supply and demand as a result of limited system flexibility. This paper investigates the optimal sizing and control of the hydrogen energy storage system for increased utilization of renewable generation. Using a Finnish case study, a mathematical model is presented to investigate the optimal storage capacity in a renewable power system. In addition, the impact of demand response for domestic storage space heating in terms of the optimal sizing of energy storage is discussed. Finally, sensitivity analyses are conducted to observe the impact of a small share of controllable baseload production as well as the oversizing of renewable generation in terms of required hydrogen storage size.

Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling

Microgrid is universally accepted as a new approach to solve the global energy problem. In a microgrid, the optimal sizing of energy storage is necessary to ensure reliability and improve economic efficiency. Its sizing results are impacted by uncertainty on natural resources, energy storage as well as load, and it is hard to coordinate these factors. Therefore, microgrid needs more improved strategies for optimal sizing. In this paper, we present a power source sizing strategy with integrated consideration of characteristics of distributed generations, energy storage and loads. Distributed generations consist of wind turbine, photovoltaic panels, combined heat and power generation (CHP) as well as electric vehicles. A two-layer hybrid energy storage system with three storage types (i.e. super capacitor, li-ion battery, lead-acid battery) is constructed based on their power density, energy density, response speed and lifetime, as well as load classification. Power load differences among different time intervals which are supplied by different types of storage leads to allocation of energy storage. An objective function is established based on life cycle cost (LCC) theory, which includes construction cost, operation maintenance cost, recycling profit, environment cost, and energy shortage compensation. Three scenarios, in which particle swarm optimization (PSO) is used for the optimal sizing, modeling and results calculating. From the simulations results analysis, it is found that the proposed model and strategy are feasible and practical.

A Framework for Stacked-Benefit Analysis of Distribution-Level Energy Storage Deployment

This paper presents a planning framework for integrating energy storage (ES) systems into the distribution system. An ES system is deployed to simultaneously provide multiple benefits, also known as stacked-benefits, for the feeder. The primary and secondary application scenarios for the feeder are identified. The proposed ES deployment approach includes the following steps: (1) size the ES system for primary application; (2) identify optimal ES locations based on both primary and secondary application scenarios; (3) calculate the ES accommodation capacity for each potential location; and (4) develop control methods for ES units and conduct grid impact analysis to demonstrate ES applications. For the selected feeder, the primary application for ES deployment is to provide the N-1 contingency requirement. During normal operating conditions, ES is programmed for multiple secondary applications: voltage management and ancillary services by frequency regulation. A probabilistic approach is presented to obtain the optimal ES size for providing the N-1 contingency requirement. Optimal ES locations are obtained based on secondary application scenarios. Real and reactive power control methods are developed to demonstrate the viability of deploying an ES system for simultaneously providing multiple applications. The simulation results show that ES can successfully provide the stacked-benefits for the distribution circuit. The proposed framework is generic and can be employed for the ES integration analysis of any feeder, with different sets of primary and secondary applications.

Co-Optimization of Storage System Sizing and Control Strategy for Intelligent Photovoltaic Power Plants Market Integration

Energy storage systems (ESS) when integrated with large-scale photovoltaic (PV) plants, constituting a so-called Intelligent PV (IPV) power plant, are able to contribute to improve the economic viability of these power plants and to help them participate in the electricity markets as other traditional generators. In this paper, the sizing and control strategy co-optimization for an existing IPV power plant is proposed and implemented. A global linear programming (LP) optimization algorithm is developed, where the optimal components sizing is computed directly in the same optimization as the operating management of the storage system. In the IPV power plant design stage, the LP optimization is applied to obtain the optimal energy storage sizing parameters (capacity and power rate). In the operation stage, the same LP algorithm optimizes the control strategy to participate to electricity markets. This participation is based on the online model predictive control (MPC) to counteract PV forecast errors by participating in intraday markets. The proposed approach (optimal ESS sizing and online MPC) increases the daily economic benefits around 20%.

Optimal Sizing of Energy Storage for Regenerative Braking in Electric Railway Systems

<?Pub Dtl=""?> The problem of optimally sizing hybrid energy storage systems (HESS) installed in electric railway systems, considering the effect of regenerative braking is studied in this paper. HESSs combine traditional batteries and newly developed ultracapacitors, taking advantage of the high energy capacity of batteries and of the flexibility and ability to capture high power density of ultracapacitors. A novel mixed integer linear programming formulation that includes the counting of battery cycles is presented. Some particularities of battery operation are included in the model, like the dependence of its performance on the number of cycles and the depth of discharge (DOD). Results are reported first for a illustrative case study, allowing us to perform sensitivity studies for several parameters; results are also reported and for a larger case, closely resembling the problem faced by railway energy systems planners and operators.

Optimal Energy Storage Sizing and Control for Wind Power Applications

The variable output of a large wind farm presents many integration challenges, especially at high levels of penetration. The uncertainty in the output of a large wind plant can be covered by using fast-acting dispatchable sources, such as natural gas turbines or hydro generators. However, using dispatchable sources on short notice to smooth the variability of wind power can increase the cost of large-scale wind power integration. To remedy this, the inclusion of large-scale energy storage at the wind farm output can be used to improve the predictability of wind power and reduce the need for load following and regulation hydro or fossil-fuel reserve generation. This paper presents sizing and control methodologies for a zinc-bromine flow battery-based energy storage system. The results show that the power flow control strategy does have a significant impact on proper sizing of the rated power and energy of the system. In particular, artificial neural network control strategies resulted in significantly lower cost energy storage systems than simplified controllers. The results show that through more effective control and coordination of energy storage systems, the predictability of wind plant outputs can be increased and the cost of integration associated with reserve requirements can be decreased.