Capacity Of Energy Storage System Research Articles

Guest editorial: Application of cloud energy storage systems in power systems

Guest editorial: Application of cloud energy storage systems in power systems

Optimal allocation method of hybrid energy storage capacity of multi-energy system under low-carbon background

Abstract The energy dispatching and distribution ability is improved by optimizing the configuration of hybrid energy storage capacity of multi-energy system in low-carbon background, and an optimal configuration method of hybrid energy storage capacity of multi-energy system in low-carbon background based on equilibrium control and dynamic optimization algorithm is proposed. The data structure model of hybrid energy storage capacity distribution of multi-energy system is constructed. Under the condition that the energy storage optimal allocation model based on cost analysis meets the system performance index, the energy storage optimal allocation model is established with the objective function of minimizing the cost of configuring energy storage system, and with the objective of minimizing the fluctuation of active power of distributed power sources, such as wind and light. Taking the penalty cost of wind energy storage combined output power exceeding the fluctuation limit as the objective function, low-pass filtering method is adopted to stabilize the fluctuation of new energy power, and the optimal configuration capacity of energy storage system is determined according to the allowable frequency deviation and voltage stability of the system. Balanced control and dynamic optimization algorithm are adopted to realize the optimal configuration of hybrid energy storage capacity of multi-energy system under low-carbon background by combining different wind and solar energy combinations, different sampling intervals and different number of power stations. The simulation results show that the hybrid energy storage capacity allocation of multi-energy system has strong adaptability and high environmental adaptability, which effectively improves the transient stability of power grid system and further promotes the safe and stable operation of power grid system.

Measurement of shrinkage void and identification of solid-liquid phases in phase change materials: Ultrasound-based approach and simulated predictions

Phase change materials in thermal energy storage systems undergo solidification and melting during cyclic operations. During solidification, internal shrinkage voids are formed randomly in phase change materials due to density differences in the solid and liquid phases. These shrinkage voids can severely affect the heat transfer performance and storage capacity of thermal energy storage systems. Additionally, the containers in which phase change materials are kept are usually metallic, making them opaque. Therefore, identification of the solid–liquid phase by direct visualisation during the phase change is limited. The present work aims to develop a non-destructive ultrasonic scanning approach to detect and measure internal shrinkage voids in solid phase change materials and the solid–liquid phases during melting and perform multi-phase numerical simulations that simultaneously capture solidification/melting and the associated shrinkage/expansion. The most commonly used phase change materials, n-eicosane, and n-octadecane, are used in this study. The effectiveness of the ultrasonic technique is evaluated by measuring cavities of known sizes. Further, three cases are considered for examining and measuring the shrinkage void. Case A involves two-dimensional solidification with a surface void, while Case B and C involve three-dimensional solidification with an internal shrinkage void. The numerically obtained shrinkage cavity profile shows a maximum deviation of 7.8% with experimental results for case C. Furthermore, the developed approach is implemented to uniquely detect and identify the solid and liquid phases during melting in an opaque container.

Optimal planning of hybrid electric-hydrogen energy storage systems via multi-objective particle swarm optimization

Abstract: In recent years, hydrogen is rapidly developing because it is environmentally friendly and sustainable. In this case, hydrogen energy storage systems (HESSs) can be widely used in the distribution network. The application of hybrid electric-hydrogen energy storage systems can solve the adverse effects caused by renewable energy access to the distribution network. In order to ensure the rationality and effectiveness of energy storage systems (ESSs) configuration, economic indicators of battery energy storage systems (BESSs) and hydrogen energy storage systems, power loss, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, multi-objective particle swarm optimization (MOPSO) is used to solve Pareto non-dominated set of energy storage systems’ optimal configuration scheme, in which the technique for order preference by similarity to ideal solution (TOPSIS) based on information entropy weight (IEW) is used select the optimal solution in Pareto non-dominated solution set. Based on the extended IEEE-33 system and IEEE-69 system, the rationality of energy storage systems configuration scheme under 20% and 35% renewable energy penetration rate is analyzed. The simulation results show that the power loss can be reduced by 7.9%–22.8% and the voltage fluctuation can be reduced by 40.0%–71% when the renewable energy penetration rate is 20% and 35% respectively in IEEE-33 and 69 nodes systems. Therefore, it can be concluded that the locations and capacities of energy storage systems obtained by multi-objective particle swarm optimization can improve the distribution network stability and economy after accessing renewable generation.

ADMM-Based Multi-Objective Control Scheme for Mitigating the Impact of High Penetration DER Integration in the Modern Distribution Systems

The high penetration of renewable energy sources in modern distribution networks poses challenges for grid voltage regulation. In this study, a multi-agent distributed voltage control strategy based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM) is proposed for distribution power systems with a high penetration of photovoltaic (PV) resources coordinated with battery energy storage systems (BESS). In this context, all active and reactive power outputs of the PV are locally optimized through smart inverters to improve the grid voltage with minimum power loss. Uncertainties associated with the solar energy generation and load demands are considered in the defined scenarios. This study consists of two phases. In the first phase, the voltage control problem is formulated as an optimization problem to regulate the voltages within an acceptable limit with fast convergence. In the second phase, a coordinated voltage control strategy for smart PV inverters and BESS is proposed to allocate the power capacity of the battery energy storage systems and the active power loss reduction. Finally, the proposed method is tested on modified IEEE 13-bus, 33-bus and 141-bus distribution systems using MATLAB/Simulink and MATPOWER. A comparison of the results of the voltage profiles with and without the control algorithm demonstrated the efficacy, robustness, and scalability of the distributed scheme for voltage improvement and optimal utilization of PV power under different scenarios.

Optimal Capacity and Cost Analysis of Hybrid Energy Storage System in Standalone DC Microgrid

Optimal Capacity and Cost Analysis of Hybrid Energy Storage System in Standalone DC Microgrid

Life Cycle Environmental Impact of Pumped Hydro Energy Storage

Pumped hydro energy storage (PHES) is one of the energy storage systems to solve intermittent renewable energy and support stable power generation of the grid. About 95% of installed capacity of the global energy storage system is contributed by PHES. Life cycle assessment (LCA) is used to analyse the environmental impact of PHES construction and operation phase in this study, and 1 MWh of electricity delivered from PHES to the power grid is set as the functional unit. The results show that the electricity power structure and electricity loss caused by the charging-discharging of PHES are the main environmental burden contributors, contributing 80 to 99% of the total environmental emissions. And environmental impacts during the construction phase is mainly due to the use of concrete, steel, and cement. In the future, as the proportion of renewable energy in the grid structure increases, the environmental impacts caused by PHES will decrease accordingly.

Study of Long-Term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

Distributed generation equipment improves renewable energy utilization and economic benefits through an energy storage system (ESS). However, dominated by short-term data, the configuration of long-period ESS capacity is absent based on the dynamic change of load, which leads to a large deviation from the expected return. Considering the system characteristics of lack of data and less information, after introducing the grey theory, we propose a new long-term capacity configuration method for ESS and establish the long-term grey forecasting model (GFM) of user load, improving the basic forecasting model to improve the accuracy of the long-term forecasting model. Then, the scheduling model is established with the maximum economic and social benefits as the optimization objective. Based on the forecast data of the improved grey forecasting model (IGFM), the hierarchical solution method is used to solve the scheduling model. Finally, the parameters are configured based on the service life of the equipment and the expected rate of return. The simulation results show that higher accuracy is realized in the improved prediction model, and the improved algorithm gets higher convergence speed and precision. Apart from that, the nonlinear correlation trend of the EES return rate between the capacity and life cycle is revealed. Compared with the ESS configuration in a short period, this study provides more comprehensive and accurate data support for the capacity configuration of the ESS, reducing the error between the actual return and the expected return significantly.

Calculation of Regional Optimal Energy Storage System Capacity for Advancement of Renewable Energy Cloud Platform

Calculation of Regional Optimal Energy Storage System Capacity for Advancement of Renewable Energy Cloud Platform

Aggregate demand response strategies for smart communities with battery-charging/switching electric vehicles

Demand response (DR) plays an important role in the peak shaving and load-shifting of a community, but the avalanche effect creates a huge obstacle in DR with uncoordinated usage of appliances and charging of electric vehicles (EVs). Recognizing the remarkably rising deployment of EVs, there is great potential to coordinate EVs' charging in a charging station or a battery switching station for providing DR. However, the potential benefits of the battery-switching EVs for providing DR and their impacts on the aggregate load of the community have not been fully studied. In this regard, the present study proposes two novel aggregate community-level DR strategies, i.e., a strategy employing battery-charging EVs and a strategy employing battery-switching EVs. The proposed strategies are compared with the conventional strategy that only incorporates community battery energy storage for enabling peak shaving. The cost optimization is conducted with optimization parameters including demand limits during different periods and the power and energy capacity of the battery energy storage system. A stochastic bottom-up demand model for households, a probabilistic model for predicting loads of EVs, and a redistribution time-of-use tariff are implemented in the optimization. A group with 20 households and 20 EVs in New York, USA is used in the case study. The results in the case study show that the total annual costs (electricity tariff cost and battery energy storage cost if any) are reduced by 63.3 %, 54.0 %, and 14.7 % under the battery-switching EV strategy, the battery-charging EV strategy, and the battery energy storage strategy compared to the base case (i.e., DR-free strategy), respectively. Additionally, the battery-switching EV strategy enables DR throughout the day with the maximum temporal flexibility. Last, the developed two EV strategies can provide a good stability of the household loads on the grid and avoid the avalanche effect.

Optimal clustering-based operation of smart railway stations considering uncertainties of renewable energy sources and regenerative braking energies

The smart railway stations (SRSs), as prosumer microgrids, are considered active users in smart grids. By utilizing regenerative braking energy (RBE) and renewable energy resources (RERs) along with energy storage systems (ESSs), these SRSs can participate in the prosumer market. The uncertainties of RERs in SRSs due to meteorological factors have been studied in the literature. However, there is a research gap in developing a stochastic method for optimized operating of SRSs considering the RBE uncertainties besides the RER, load, and number of passengers’ uncertainties. In this paper, a new probabilistic clustering-based framework for the optimal operation of SRSs is presented. By applying Monte Carlo Simulations (MCS), several scenarios are generated and then clustered by the k-means algorithm. The introduced method is applied to an actual SRS in Tehran Urban and Suburban Railway Operation Company. The test results of the MCS, deterministic, and proposed scenario-based approaches are compared to illustrate the proposed method. Test results imply that the related error of the scenario-based method under the real-time pricing can be less than 4.4%, while the computation time significantly decreases. Furthermore, sensitivity analysis is done to determine how the exchanging power constraints and ESS capacity might influence the SRS operation.

The power balancing benefits of wave energy converters in offshore wind-wave farms with energy storage

With many countries planning to significantly increase grid renewable energy penetration levels, we consider the role of wave energy in supply–demand matching. We investigate how incorporating wave power into an offshore wind farm affects farm power predictability, smoothness, required energy storage capacity, and cost. In this paper, we do a first-order cost analysis of an offshore farm comprised of floating wind turbines and wave energy converters that are both standalone and combined and onshore compressed air energy storage. Then, we do a parameter sweep investigation of an isolated power network supplied by varied grid renewable energy penetration levels supplemented by natural gas, varied distribution of renewable energy between wind and wave power, and varied power capacity of a compressed air energy storage system supplying power to a shoreline community. For each parameter set, we consider the historical hourly electricity demand and wind-sea data of a coastal California community over a year, and optimize the energy storage schedule to reduce curtailed power, stored energy, and base gas plant operational cost. We show that a co-located wind-wave farm has smoother power supply, less energy curtailment, and higher farm-to-grid efficiency than a solely wind farm. That is, a 50%–50% wind-wave farm has a 15% smaller coefficient of variation in the power supply, 6% less curtailed power, and 2% higher farm-grid efficiency than a 100% wind farm when the grid is 100% renewable energy. These benefits of wave power potentially decrease the need for interconnecting regional transmission lines to match power supply with demand. The intent of this paper is to provide baseline system technical results to help future researchers and policy makers make decisions about offshore hybrid wind-wave-storage farms.

Optimal Allocation of Energy Storage Capacity of High-Permeability Photovoltaic Power Generation System Based on Elastic Neural Network

The energy storage capacity configuration of high permeability photovoltaic power generation system is unreasonable and the cost is high. Taking the constant capacity of hybrid energy storage system (Hess) composed of high permeability wind frame and super capacitor as the standard, in order to ensure smooth and stable output of photovoltaic power equipment, this paper adopts the power suppression method based on empirical mode decomposition to decompose the original power into high and low frequency components, and uses super capacitor and energy storage battery to stabilize the high and low frequency components, Reduce output power fluctuation; Taking the minimum energy storage cost as the objective function, the optimal allocation of system energy storage capacity is realized by the recursive algorithm of elastic neural network. The experimental results show that when the supercapacitor capacity and photovoltaic electric field energy storage cell capacity are 0.262 mw· h and 2.889 mw· h respectively, the minimum cost is 16987 yuan, and the smoothness index is less than 5%; Compared with before the optimal configuration, the output power of the photovoltaic power generation system after the optimal configuration is smoother, the power peak valley difference is lower, and the loss can be reduced.

Optimal integration of Vehicle to Building (V2B) and Building to Vehicle (B2V) technologies for commercial buildings

The advent of vehicle-to-building (V2B) technology provides an option to export electricity from the battery in electric vehicles (EVs) into buildings to assist in meeting building electric demands. However, there are some constraints on operating the EV fleets as flexible energy storage for buildings, including (i) stochastic and limited availability of EVs for building energy management; and (ii) necessity of bi-directional flow of EVs to operate in both V2B and building-to-vehicle (B2V) modes. Hence, these constraints and the changing degree of electric energy exchange provided by the different numbers of EVs could affect the building energy management system and the dynamic energy usage. The main objectives of this paper are to study the optimal configurations of the number of EVs interacting with a building by examining (1) the impact of different numbers of EVs on the daily electricity bills in a grid-connected commercial building microgrid, (2) the range of allowable EV numbers that enable optimal V2B capacity and reasonable charging loads for enhancing the cost-effectiveness of building energy management; (3) the role of different EV numbers in affecting the required stationary battery energy storage system (BESS) capacity in an isolated commercial building microgrid. It is observed that optimal building connected EV numbers are determined by EV driving patterns, seasonal solar output, building demand profiles, and the price of grid electricity. Thus, a scenario analysis is conducted to determine a range of suitable EV numbers in each scenario. Simulation results indicate that the optimal number of EVs varies greatly between summer and winter, as well as across different solar irradiance scenarios. In a case study where a building’s peak electric consumption is 30 kW and the PV system’s rated capacity is 50 kW, the allowable EV number, depending on the amount of available solar energy, varies from 8 to 29 in summer and changes from 2 to 15 in winter. These results could be used to suggest the optimal EV numbers based on the day-ahead solar energy forecasting, enabling day-ahead planning to either attract more vehicle owners to boost V2B capacity or to limit the number of integrated EVs in order to avoid a spike in total EV charging demand.

배전계통에 있어서 태양광전원의 수용성 향상을 위한 SVR 및 ESS의 운용방안

According to the 3020 RE policy of korean government, renewable sources including PV and WP systems have been actively installed and operated in distribution systems. However, if large-scale PV systems are interconnected in distribution system, hosting capacity in PV systems may be reduced because they may violate the allowable voltage limits(220±13V) due to reverse power flow of PV systems. Under these circumstances, this paper proposes the operation algorithms of SVR(step voltage regulator) and ESS(energy storage system) to improve the hosting capacity of PV systems. Specifically, optimal LDC setting values are obtained by calculation algorithm of LDC setting values for SVR using the Least-squares method that minimizes the error for the entire time interval based on the distribution characteristics of upper and lower limits for the sending voltage of SVR considering the reverse power flow by PV systems. In addition, the minimum introduction capacity of ESS is obtained by evaluation algorithm of the required capacity of ESS to maintain the customer voltage within the allowable voltage limits for each time interval. From the simulation results, it is confirmed that the proposed algorithms are useful and practical tool to improve the hosting capacity of PV system.

Analysis of the Hybridization of PV Plants with a BESS for Annual Constant Power Operation

The electrical generation of photovoltaic systems is variable and non-dispatchable. Energy storage systems can provide the system with energy management capabilities. In particular, with a hybrid system that combines a photovoltaic system and an energy storage system, it is possible to deliver firm power to the grid, if it is correctly dimensioned and operated. The objective of this work is to study the most appropriate relationship between the capacity of the battery energy storage system (BESS) and the peak power of the photovoltaic generator that enables the delivery of constant power throughout the year. Analysis parameters are presented that help to decide the most convenient energy injection constant value (PV-CPG set point) and the size of the storage system. As a case study, the most suitable battery capacity for a 1 MWp photovoltaic system with a battery located in Zaragoza (Spain), and the most convenient annual setpoint values for its operation are analyzed.

Analysis of the problem of optimal placement and capacity of the hydrogen energy storage system in the power system

Nowadays the trend of increasing the generation units based on renewable energy sources in the electric power system can be observed. Obviously, this is due to the intensifying level of consumer load and demand for electricity. However, renewable generation is characterized by intermittent energy production, which can cause and potential imbalance between generation and demand, especially during off-peak periods. Therefore, in order to ensure a reliable power supply to consumers, it is necessary to use a maneuverable reserve of capacity, such as energy storage systems, in conjunction with the renewable energy source unit. Over the past 10 years, the energy storage market has grown by almost 50%: the installed capacity of energy storage system in the world is about 5 GW. Analysis of the literature on the subject determines the need to study the impact of these devices on the parameters of electric power systems and one of the primary tasks is to determine the optimal location and capacity of energy storage system in the power system. This paper presents the result of solving the task of determining the optimal parameters of a hydrogen energy storage system using the particle swarm optimization method for example a test scheme radial distribution system – 33 bus IEEE. The choice of the type of energy storage is based on such advantages of a hydrogen energy storage system as environmental friendliness, high energy capacity and the ability to store electricity for a long period of time. In addition, compared to lithium-ion batteries, hydrogen energy storage systems have a long life time of about 25 years, during this period of time there is no degradation and significant deterioration of its properties. All these advantages of hydrogen as an energy carrier allow to take into account not only the criterion of total value of active power losses and its maximum reduction respectively, but the possibility and economic efficiency of partial use of the stored hydrogen for other needs when determining the optimal scenario of their operation in the process of discharge.

Two-Stage stochastic optimization for operating a Renewable-Based Microgrid

• A conventional MILP algorithm does not consider the uncertainty in operating a renewable-based Microgrid. • The two-Stage stochastic optimization algorithm takes into consideration the potential future PV profile. • The proposed method improves the Microgrid reliability by a 19.4% decrease in the Energy not supplied. • The proposed method decreases the operation cost by 18.8%. Carbon emissions are increasing as a result of urbanization and population growth all over the world. Scientists agree that these emissions are one of the main causes of climate change and global warming. The power industry is shifting to renewable energy sources (RES) such as solar power (PV) and employing different Energy Storage Systems (ESS) to enable a clean energy future and compensate for the scarcity of fossil fuel. However, the intermittent behaviour of renewable resources causes some obstacles such as power fluctuation, committing extra reserve units, and load shedding. Microgrid (MG) technology is introduced as a promising solution for integrating different RES and loads into the grid. Operating the MG in islanded mode with a limited ESS capacity requires a sophisticated scheduling method. Previous studies on MG addressed the operation issue, neglecting the supply–demand uncertainty or adopting the worst-case scenario. Uncertainty is an inherent characteristic in power systems; on the other hand, considering the worst-case scenario may unnecessarily increase the operation and planning costs. To address the uncertainty and fluctuant characteristics of RES-based MG, this paper proposes a two-stage stochastic optimization integrated with a novel ANN-based prediction model. A new model for PV power prediction is proposed by which the predicted data is in a probability density function (PDF) form. A stochastic optimization (SO) method is proposed to minimize the operation cost and load shedding during the islanding mode. In the proposed SO, the optimal scheduling decision is made in the current moment taking into account the probability of potential supply, load, and ESS capacities in the near future. For this purpose, an ANN-based prediction model is developed to represent the PV output uncertainty in the SO problem. The proposed prediction model proves efficient in the prediction with nRMSE of 9.7% and nMAE of 9.1%. The proposed method is applied to a real Microgrid designed by the Natural Energy Laboratory of Hawaii Authority (NELHA) and compared to a conventional optimization method. The proposed scheduling method reduces both the average Energy Not Supplied (ENS) and operation costs by 19.4% and 18.8%, respectively, with no additional investment cost. A sensitivity study is also conducted to assess the performance of the proposed method in terms of ENS, cost, and simulation time.

Study on the Structural Degradation of the Polyaniline(PANi) Coated LiNi0.90Co0.85Mn0.15o Particles

The nickel content of the layered lithium transition metal oxide cathodes is proportional to the discharge capacity of energy storage systems. Ni2+ ion, the oxidation state of Ni, moves to the Li sites when Li+ ion is deintercalated during discharge process due to the similar size of ion (Ni2+: 0.069 nm, Li+: 0.076 nm). The spatial transition of Ni2+ ion causes gradual cation mixing and structure degradation of cathode, resulting in the failure of the battery. Also, thick cathode-electrolyte interphase (CEI) is formed on the cathode surface because of the transition metal dissolution, the degradation of electrolyte, and the residual gas such as H2O and CO2.Coating technology is one of the strategies for the physical protection of the cathode surface and the control of CEI layer thickness. Polyaniline(PANi) is a conductive polymer with stable oxidation state. PANi can enhance the electrochemical property and structural stability as a protective layer. In this study, we synthesized PANi material by the oxidative polymereization and coated NCM with the synthesized PANi by sonication method.The PANi-coated NCM(PANi@NCM) has uniform coating layer with 5 nm thickness. PANi@NCM exhibits an initial discharge capacity of 208 mAhg-1 and a capacity retention of 81% after 50 cycles at 45 oC in Figure 1(a) and (b). After the cycling test, pristine and coated cathodes were disassembled for the comparison of the microstructure. Pristine NCM only shows the structural transition from layered structure to structure in the XRD pattern of Figure 1(c). Cross-sectional SEM image of the pristine NCM after cycling test shows micro and macro cracks are distributed from the bulk to the surface of the cathode as shown in Figure 1(d). On the other side, cracks are barely observed on the PANi@NCM particles in Figure 1(e). The layered structure of PANi@NCM is maintained even after cycling test, which are confirmed by TEM analysis with FFT patterns and HAADF images. Figure (f) and (g) show the FFT pattern of the two cathode surfaces after cycling test. Mixed phase is observed on the pristine cathode surface, while the layered structure is maintained on the PANi@NCM surface. Also, the PANi@NCM maintains a thinner passivation layer compared with that of the pristine NCM (6 nm vs 35 nm). In conclusion, the PANi coating layer prevents the degradation of structure and the formation of thick CEI layer. The structural stability enhances the electrochemical performance. Figure 1

Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty

The power systems of today seem inseparable from clean energy sources such as wind turbines (WTs) and photovoltaics (PVs). However, due to their uncertain nature, operational challenges are expected when WT and PV energy is added to the electricity network. It is necessary to introduce new technologies to compensate for the intermittent nature of renewable energy sources (RESs). Therefore, rationally implementing a demand response (DR) program with energy storage systems (ESSs) in a virtual power plant (VPP) environment is recommended as a way forward to minimize the volatile nature of RESs and improve power system reliability. Our proposed approach aims to maximize social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). Our method assesses the impact of the DR program on SW maximization. Two scenarios are examined, one with and one without a DR program. Stochastic programming theory is used to address the optimization problem. The uncertain behavior of WTs, PVs, and load demand is modeled using a scenario-based approach. The correctness of the proposed approach is demonstrated on a 16-bus UK generic distribution system. Our results show that SW and active power dispatch capacity of WT, PV, and ESS are fairly increased using the proposed approach.