Energy Storage Sizing Research Articles

Resilience improvement planning of power-water distribution systems with multiple microgrids against hurricanes using clean strategies

This paper proposes a comprehensive planning framework including a main problem and two sub-problems to enhance the resilience of power distribution network (PDN) and water distribution network (WDN) with multiple microgrids against hurricanes. The main problem which is formulated in stochastic programming aims to minimize the investment cost of resilience improvement strategies and the expected inaccessibility values of loads to power and water under hurricanes. Line hardening in PDN, upgrading the energy storage size in microgrids and water tanks in WDN are considered as three clean candidate strategies. In analyzing each scenario of the main problem, the microgrids which are connected to the PDN are modeled as emergency sources through the first stochastic sub-problem that can restore disconnected loads and water pumps. Water pumps as critical loads are equipped with emergency generators with limited fuel capacity. If there are some water pumps which cannot be restored in each scenario of the main problem, their emergency generators will be scheduled with the second sub-problem of the model. The proposed model is tested on the modified IEEE 33-bus PDN with multiple microgrids and a designed WDN, and the effectiveness of the proposed method is validated accordingly.

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.

Analysis and optimization the size of heliostat field and thermal energy storage for solar tower power plants

Abstract The optimal sizing of solar tower power (STP) plants with thermal energy storage (TES) is critical for increasing the system reliability and reducing the investment cost. However, it is still unclear to determine the key design parameters for sizing the STP system, including design direct normal irradiance (DNI), solar multiple (SM) and TES hours. In this study, a thermo-economic analysis of the effects of these parameters on the capacity factor and levelized cost of energy (LCOE) of a 50 MW STP plant is presented using System Advisor Model. Optimal combinations of these parameters are obtained with respect to the minimal LCOE at two locations in China. The results show that the optimal design DNI is dependent on both the annual radiation level and the distribution of solar irradiance, which differs from the recommended values obtained from traditional methods. It is found that the irradiation received by heliostats at the optimal design condition accounts for a specific percentage of annual irradiation. The percentage is close to 73% for the cases in this study, and the corresponding optimal SM and TES hours are 2.7-2.8 and 15-16 hours respectively. The results can provide a theoretical reference for the optimal sizing of STP with TES systems under different solar resources.

Parametric performance maps for design and selection of Liquid Air Energy Storage system for mini to micro-grid scale applications

This paper aims to deliver new performance maps for “microgrid scale” Liquid Air Energy Storage system with a liquid air production of 1000 kg/h. By means of the performance maps, the impact of the main Liquid Air Energy Storage operative parameters, as well as the effect of the cold/warm thermal energy storage utilization factor, over the key performance indicators has been assessed and analyzed. The thermodynamics and sub-processes of the Liquid Air Energy Storage system are described in details and simulated by means of the software Aspen Hysys. Each performance map has been modelled by means of a sensitivity analysis carried out for the system operative parameters. Such a new methodology allows to select Liquid Air Energy Storage size and its related performance by means of a simple tool without the implementation of any complex numerical model.

Coordination of optimal sizing of energy storage systems and production buffer stocks in a net zero energy factory

Green smart factories will be one of the key elements of the future smart grids. They will play an active role in managing the volatility of power generated by renewable energy sources (RES). In order to do that, the green smart factories must set up energy management systems to control the production processes according to both the on-site power generation by RES and external signals sent by the system operators. The aim of the study is to size energy storage systems and production buffer stocks as the flexibility options, allowing the highest integration of power generated by volatile energy within a manufacturing system. The concept of the net zero energy factory is considered. Algorithms for coordinating the flexibility options optimally have been developed. These algorithms are based on mixed-integer linear programming and aim to maximize the matching between RES power generation and flexible demand. A case study is used to verify the framework results. Key performance indices, such as the system self-sufficiency index and the system self-consumption index have been considered for evaluating the solutions developed. Finally, sensitivity analysis has been performed to assess the robustness of the solutions found.

Optimal sizing strategy for energy storage system considering correlated forecast uncertainties of dispatchable resources

This paper, from the perspective of hourly dispatch, proposes an approach of sizing Energy Storage System (ESS) in an isolated grid. The objective is to minimize the total cost by optimizing the size of ESS, thermal units generation power, wind curtailment and load demand response. While transforming the chance constraints, such as the network constraints, into the deterministic constraints, the model takes into account the correlations of the forecast errors among wind farms, and load at different buses. In addition, two types of transmission line overloading probability (TLOP) constraints are considered in the ESS sizing models. In particular, an approach of linearizing the scenario generation network constraints is proposed, so that the commercial software can solve the optimization problem. Meanwhile, the performance of the proposed sizing method is validated on a 4-Bus system and a modified IEEE RTS-79 system.

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.

Frequency Constrained Optimal Siting and Sizing of Energy Storage

Frequency Constrained Optimal Siting and Sizing of Energy Storage

Day-Ahead Electricity Price Forecasting and Scheduling of Energy Storage in LMP Market

This paper proposes an optimization strategy for the day-ahead planning of utility owned energy storage operation while considering the electricity price volatility in locational marginal pricing (LMP) market. The proposed optimization strategy works upon the predicted day-ahead electricity prices while considering the consequences of energy storage scheduling on the next day’s electricity prices. In this study, a piecewise linear relation between load and LMP is established using historical load and LMP data. The piecewise linear relation results are provided to the next stage for the proposed energy storage optimization which is solved using Quadratic Programming. This study is performed using the real historical data from New York Independent System Operator which provides price-based demand response program including day-ahead dynamic pricing which is suitable for the proposed study. The simulation is carried out for two energy storage sizes. The simulation result of our proposed optimization strategy, considering impact of energy storage charging/discharging on LMP, was compared with the optimization result considering no impact of energy storage on LMP. The results show that the proposed optimization strategy not only increases the revenue for the utility but also helps in smoothing the next day’s electricity price curve.

Optimal storage sizing of energy storage for peak shaving in presence of uncertainties in distributed energy management systems

Optimal storage sizing of energy storage for peak shaving in presence of uncertainties in distributed energy management systems

Optimal storage sizing of energy storage for peak shaving in presence of uncertainties in distributed energy management systems

The rapid development of eco-friendly technologies such as energy storage system (ESS) and peak-shaving technology in smart grid plays a significant role and shapes the future electricity consumpti...

Robust energy storage allocation for transmission grid integrated by multiple wind farms

Since optimal planning for energy storage sizing and locating is critical to meet the requirement of total wind power absorption, a robust optimization theory based energy storage allocation model for transmission grid with multiple wind farms integrated is proposed in this paper. The model fully considers the transmission capacity limitation, and uses linear decision rule to optimize the allocation of the reserve capacity of the adjustable units and energy storage devices to cope with the fluctuation of wind power, which makes full use of the regulation capacity in the system. Some statistical information of wind power, such as expectation and interval, is used to describe the uncertainty of wind power. The uncertainty model is transformed into a deterministic model by using the duality optimization theory, which provides convenience for engineering application. The Garver’s 6-bus transmission system model is used for example analysis, which shows that the proposed method can effectively solve the energy storage planning problem for uncertain wind power integrated power systems.

Mobile Energy Storage Sizing and Allocation for Multi-Services in Power Distribution Systems

A mobile energy storage system (MESS) is a localizable transportable storage system that provides various utility services. These services include load leveling, load shifting, losses minimization, and energy arbitrage. A MESS is also controlled for voltage regulation in weak grids. The MESS mobility enables a single storage unit to achieve the tasks of multiple stationary units at different locations. The MESS is connected to the grid at specific substations (or buses) known as MESS stations. This paper proposes an optimization algorithm for sizing and allocation of a MESS for multi-services in a power distribution system. The design accounts for load variation, renewable resources intermittency, and market price fluctuations. A realistic dynamic model for the MESS is adopted to consider the capacity and lifetime constraints. A detailed network power flow model is utilized to include voltage constraints, feeders, and transformers ampacity in the problem formulation. By considering all these constraints, the resulting sizing problem is a mixed-integer nonlinear problem. This paper presents the problem formulation and proposes a solution using a hybrid optimization technique. The adopted technique is based on the particle swarm algorithm and mixed-integer convex programming. A case study is conducted on a real 41-bus radial feeder to validate the proposed sizing technique, and investigate the MESS profitability to the system operator.

Sizing Energy Storage Based on a Life-Cycle Saving Dispatch Strategy to Support Frequency Stability of an Isolated System With Wind Farms

Wind power fluctuations usually cause grid frequency deviations, which could have a significant impact on an isolated power system. This paper proposes a dispatch strategy of the energy storage (ES) to support grid frequency stability of the isolated power system. The dispatch strategy of the ES is based on the forecasted mean wind speed and the corresponding standard deviation. Based on the dispatching scheme, a power spectrum density (PSD)-based method has been developed for sizing the ES and validated through system simulations based on the actual wind speed data and real isolated system in China. Results presented in this paper have shown demonstrated that the cycle-life of the ES can be significantly saved by using the proposed dispatch strategy. The developed ES sizing method has taken the random characteristics of wind power fluctuations and the frequency responses of the system into full consideration. Therefore, the proposed dispatch strategy and sizing method of the ES is general and can be applied to other isolated power systems with the ES being used to support grid frequency stability.

Stochastic optimal sizing of integrated cryogenic energy storage and air liquefaction unit in microgrid

This paper investigates the optimal sizing of cryogenic energy storage (CES) in a microgrid (MG). Nowadays, energy storage units have been considered as a viable solution to solving the peak load problems and output power fluctuation of renewable energy resources. At this paper, the CES technology has been presented as large-scale energy storage. In the CES process, the cryogenic liquid (nitrogen and oxygen) is used for storing the energy of electricity. The CES recovers electricity by expanding the cryogen liquid in peak periods. In this respect, the optimal sizing problem of adding CES to an existing air liquefaction unit (ALU) in an MG system is investigated in order to minimize storage unit investment cost as well as the MG operation cost. The problem is modeled as a two-stage stochastic optimization problem and is solved by general algebraic modeling system, in which the pool price market, MG load and wind speed are considered as stochastic parameters.

Optimal planning of renewable energy source and energy storage in a medium‐ and low‐voltage distributed AC/DC system in China

As the quantity of DC load and distributed generation system increases in China, AC/DC distribution system is arousing more and more research interest for its high-efficiency distribution ability. In this study, the optimal size and location of renewable energy source (RES) and energy storage in a medium- and low-voltage distributed AC/DC system is studied. A modelling method for the optimisation of such hybrid AC/DC system is developed. The objective of the proposed optimisation method is to minimise the life cycle cost of the system with consideration of the life span of RES. The planning method is based on energy balances and constraints of system operation. The utility requirements and subsidy in China for such system are also considered in the planning process. The method is demonstrated in a 10 kV and low-voltage distributed AC/DC system considering installing photovoltaic system, wind turbines, and electric storage system. The provided results indicate that the method is effective and applicable for the optimisation of the size and location of RES in distributed AC/DC system. The life cycle cost of such system is significantly reduced with the subsidy of the Chinese government compared with the system without RES.

Simulation of sizing of energy storage for off-grid decentralized wastewater treatment units: A case study in the Netherlands

Abstract Constant energy supply for decentralized wastewater treatment plants (DWWTPs) is crucial in order to ensure its functionality and prevent contamination of rivers and human illnesses due to pollution. However, power blackouts are a common problem in rural areas, which can affect the reliability of wastewater treatment plants. This paper presents a simulation study of sizing of solar photovoltaics and Sea-Salt batteries for powering a DWWTP working in 100% off-grid mode. The analysis is performed for two different DWWTPs: a prototype membrane bioreactor (MBR) and a Bever III compact wastewater aerobic system. The study is performed using the simulation package DEMKit developed at the University of Twente in the Netherlands. Results show that a solar photovoltaic system of 15 kWp coupled with a 20 kWh Sea-Salt battery may provide 100% of the electricity necessary during summer and up to 75% during winter in the Netherlands for the Bever III. In the case of the MBR, a photovoltaic system of 30 kWp in combination with a Sea-Salt battery of 50 kWh meets 100% of the electricity needs during summer and up to 65% during the winter in the Netherlands. Furthermore, in order to power the DWWTPs during the months of low sunlight, the dimensions of the solar photovoltaic system and the Sea-Salt battery needs to be increased by a factor of three.

Monodisperse nitrogen-doped carbon spheres with superior rate capacities for lithium/sodium ion storage

Carbon spheres (CS) with submicron size have demonstrated great potential in both lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs), in virtue of their tunable microstructure, facile dispersion in slurry, enabling densely packing along with enough interspace for effective electrolyte transportation. However, the synthesis of monodisperse CS with both uniform size and excellent energy storage performance at high charge/discharge rates is still very challenging. Herein, a surfactant-assisted hydrothermal growth strategy is proposed to produce monodisperse CS. Moreover, an ammonia treatment is adopted to boost up its capacity especially at high current densities. As-synthesized N-doped carbon spheres (NCS) exhibit excellent lithium/sodium ions storage ability which delivers reversible capacities of 580 mAh g−1 and 270 mAh g−1 for LIBs and SIBs respectively at a current density of 20 mA g−1. Moreover, NCS can retain 194 mAh g−1 at 4 A g−1 for LIBs and 90 mAh g−1 at 5 A g−1 for SIBs. The improving rate capacities of NCS can be attributed to the short diffusion lengths of Li+ and Na+ ions of uniform submicron structure, as well as large specific surface area (SSA), additional active sites and good conductivity provided by ammonia treatment.

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%.