Effect Of Energy Storage System Research Articles

Energy storage mechanism and modeling method of underground aquifer to meet the demand of large-capacity new energy consumption

With the increasing scale of new energy, the ability to rationally utilize new energy power abandonment has become key to improving the operational efficiency of new power systems in the future. Existing new energy power abandonment consumption methods have problems such as limited consumption capacity and large-scale applications. Therefore, to consume the large-scale power abandonment of new energy and enable it to be stored for a long time, this study proposed a method in which electro-thermal conversion devices are used to convert electric energy into thermal energy, and the energy is stored in an underground aquifer. A finite element numerical analysis model was constructed to analyze and calculate this process. A multi-physical field coupling numerical analysis model, considering the seepage of groundwater and the heat transfer of the heat exchangers and porous media, was constructed by combining the distribution characteristics of power abandonment and a theoretical analysis of the heat transfer principle of an aquifer. A scaled test platform was constructed based on the similarity principle to verify the accuracy of the numerical analysis model. The results of the numerical analysis and simulated test showed that the energy storage system could store power abandonment in the form of thermal energy in the aquifer. At the same time, the annual energy loss of the system is only 14.89%, indicating that the system has long-term storage capabilities. In addition, a comparative analysis of the consumption effects of energy storage systems of different sizes showed that an aquifer energy storage system can be configured according to the capacity of power abandonment. Thus, a large-capacity new energy consumption space can be constructed to realize the complete consumption of power abandonment.

Energy-Efficient Storage System Optimization and Recent Trends in Enhancing Energy Management and Access Microgrid: A Review

Energy is a vital component of our everyday lives, driving advancements in human development, economic expansion, and efficiency. Implementing sustainable energy storage systems is an economically viable approach to improving energy efficiency and regulating the generation and consumption of power. This study extensively examines energy storage, delving into several techniques, their practical uses, and the most recent progress. It focuses on environmentally sustainable alternatives for the electricity sector, including batteries, flywheels, thermal storage, and pumped hydro storage. The influence of includes compelling case examples, such as using vanadium redox flow batteries in maritime hybrid propulsion systems and advancing portable hydrogen energy storage devices. The results are encouraging, indicating that these technologies have the potential to be adequate replacements for traditional fuel sources, particularly in isolated areas where there is a critical requirement for energy storage. Moreover, this study thoroughly examines the integration options for energy storage in microgrids. It explores the complex aspects of the models, assessment indices, and optimization methods used in designing energy storage systems. This paper explores the capabilities of software used to determine the optimal size for energy storage systems. Furthermore, this study introduces and examines uncertainty analysis in modeling energy storage devices. This state-of-the-art technology has been created to demonstrate the effectiveness of energy storage systems in microgrids, providing crucial data for future progress in the industry.

Optimum exploitation of multiple energy system using IGDT approach and risk aversion strategy and considering compressed air storage with solar energy

The concept of an energy hub within a microgrid has garnered significant attention from investors due to its potential for efficiently managing diverse energy sources and accurately forecasting energy prices. This study presents a model for an energy hub that facilitates the optimal operation of a microgrid with multiple energy carrier infrastructures under future-day scenarios. The primary objective of the optimization process is to minimize operating costs, environmental impacts, and technical limitations. The proposed energy hub model integrates both distributable generation, such as a Combined Cooling, Heating, and Power (CCHP) unit, and non-distributable generation sources, including wind turbines and photovoltaic systems. Additionally, it incorporates various energy storage components, namely Ice Storage Conditioner (ISC), Thermal Energy Storage (TES) systems, and Electric Vehicles (EVs). Furthermore, the study investigates the impact of a novel energy storage system called Solar Powered Compressed Air Energy Storage (SPCAES) on the operational and environmental performance of the energy hub. To address uncertainties stemming from predicted and actual variables, a two-level stochastic optimization problem is formulated based on the Information Gap Decision Theory (IGDT) with a Risk Aversion (RA) strategy. The overarching aim is to mitigate the risks associated with the information gap confronted by microgrid operators. Subsequently, the two-level stochastic optimization problem is transformed into a single-level problem using the Karush-Kuhn-Tucker method. The proposed framework employs the RA-IGDT method and utilizes the Elephant Herd Optimization (EHO) algorithm to effectively solve the optimization problem. By applying this proposed framework to a typical energy hub, this research demonstrates the effectiveness of energy storage systems in reducing operating costs and greenhouse gas emissions during daily energy management. The study highlights the potential of energy hubs and storage systems for cost-effective and eco-friendly microgrid energy management. Scenario 4, featuring the proposed energy hub with SPCAES and ISC storage, demonstrates a 15 % reduction in operating costs and a notable 20 % increase in peak-hour electrical power for sale, underscoring its economic and operational benefits.

Challenges and Solutions for Integration of Energy Storage Systems in Hot Desert Regions

The increasing utilization of solar renewable energy sources in the Gulf Cooperation Council (GCC) regions, including countries such as Saudi Arabia, the UAE, and Qatar, highlights the pressing need for energy storage systems (ESS) to ensure grid stability. ESS can store excess energy during peak generation and supply it during periods of low generation or high demand. However, the harsh environmental conditions and extreme temperatures that are typical of hot desert regions can impact the performance and lifespan of ESS, creating unique integration challenges.Our work explores these challenges and offers solutions for integrating energy storage systems in hot desert regions. Our research shows that while the integration of ESS in hot desert regions is technically feasible and economically viable, it requires careful planning, design, and maintenance to ensure optimal performance and longevity. The solutions we present in this work can provide valuable guidance for engineers, policymakers, and stakeholders who are working towards a sustainable and resilient energy future in hot desert regions.During the presentation, I will delve into the specific challenges that the harsh environmental conditions and extreme temperatures in hot desert regions pose for the integration of energy storage systems. I will provide examples of how these challenges can impact the performance and lifespan of ESS. Furthermore, I will highlight the solutions that we have identified and presented in our research, which can help overcome the challenges and improve the effectiveness of energy storage systems in hot desert regions.

Optimized energy management for photovoltaic/wind hybrid micro-grid using energy storage solution

ABSTRACT One of the key strategies for reducing the rate of environmental pollution is decarbonizing the power industry. In this work, we investigate the effects of energy storage systems (ESS) and fluctuations in renewable energy on climate change mitigation in a grid-connected microgrid. This analysis has been carried out by utilizing an improved energy management system (EMS) and optimal economic dispatch using computational models of mixed-integer linear programming (MILP). In addition to battery deterioration analysis, long short-term memory (LSTM) is developed to estimate photovoltaic and wind power renewable production, energy price, and load requirement. A sensitivity assessment is also performed to evaluate the influence of different input constraints on the model. The output results demonstrated that the EMS could schedule power effectively while considering electricity pricing. By up to 1636.96 $/hr. in day-ahead revenue with the degradation effect and 1811.96 $/hr. without the degradation effect, the analysis confirmed the usefulness of the proposed framework. Through the case studies explained, the new objective function observed minimum power costs with battery degradation by up to 1.10% less as compared to without battery degradation effect. Furthermore, the second case analysis indicates the significance of considering forecasted electrical parameters for realistic microgrid power dispatch.

The role of risk-based demand response in resource management of a grid-connected renewable-based large-scale microgrid with stationary and mobile energy storage systems and emission tax

Integration of energy storage systems with local electricity systems, like microgrids, is emerging due to their economical, reliability, and environmental benefits. These advantages can be achieved by load management subject to the related condition, such as the hour of the day, electricity price, etc. From an economic point of view, energy storage systems can reduce the share of costly power generators in supplying the peak demand. In other words, they can be charged in low-demand hours and discharged in peak-demand hours, when the electricity price is high. In this work, the impact of stationary battery storage and electric vehicles on the resource management of a large-scale microgrid is assessed through a stochastic model. The understudy microgrid includes also renewable energy sources (namely solar photovoltaic systems and wind turbines) as well as diesel generators. This study considers the uncertainty in the electricity price, where a scenario-based risk management approach is employed to control the risk associated with price uncertainty. The resulting model is implemented in the GAMS software package to be solved by a suitable solver. Numerical simulation of a large-scale microgrid is accomplished to validate the effectiveness of energy storage systems in the economic improvement of the understudy microgrid.

Renewable energy effects on energy management based on demand response in microgrids environment

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications.

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.

Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

ОЦІНКА ЕКОНОМІЧНОГО ЕФЕКТУ ВІД НАДАННЯ СИСТЕМАМИ НАКОПИЧЕННЯ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ ПОСЛУГИ БАЛАНСУВАННЯ В ОЕС УКРАЇНИ

The article presents a simulation model for estimating the economic effect of the energy storage systems providing the balancing electricity to the Transmission System Operator in the balancing market segment. The model simulates the serial charge and discharge of storage (i.e., sequential provision of unloading and loading services). Peculiarities of considering the cost of purchase, installation, and maintenance of energy storage systems with reduction both to the guaranteed service life and the guaranteed resource of charge/discharge cycles are given. An example of the application of the simulation model for estimating the economic effect and payback period of energy storage systems in the provision of electricity balancing services in the balancing market segment is shown. Ref. 15.

전기추진 선박의 연료비용 분석에 따른 최적 발전시스템 구성방안 및 에너지 저장장치 적용의 효용성 분석

This paper presents how to design the configuration of generation system for shipboard power system based on fuel cost analysis. We propose a sophisticated economic analysis method with detailed fuel cost evaluation, which can be obtained from actual specific fuel consumption (SFC) characteristics of the gas-turbine and diesel generators installed in existing ships. Using the proposed method, we analyze economic benefits for optimal configurations of shipboard generation system for both hybrid propulsion ships such as CODLOG (COmbined Diesel-eLectric Or Gas-turbine) and COGLOG (COmbined Gas-turbine eLectric Or Gas-turbine) and full electric propulsion ships. In addition, we analyze the effects of energy storage system (ESS) in shipboard power systems in terms of spinning reserve management and single generator operation mode to meet the reliability and survivability requirement. The effectiveness of the proposed method is verified through multiple case studies with actual measurement data in ships that are currently in operation.

Security constrained optimal power flow in a power system based on energy storage system with high wind penetration

This study is focused on assessing the effect of energy storage system (ESS) presence on security improvement of power systems hosting remarkable renewable energy resources. To this end, ESS presence is suitably included in security-constrained optimal power flow (SCOPF) model; the required technical amendments are hence considered. To launch a realistic model, ramping constraints of thermal units are also taken into account which limit the generators from completely responding to power shortfalls. Considering a high penetration level of renewable generations, different scenarios of outages in transmission lines and generators are simulated to measure the line outage distribution factor (LODF) and power transfer distribution factor (PTDF). also, in order to illustrate the economic impact of wind power generation curtailment and load shedding, two penalty parameters VWC and VOLL are considered in the model. Two test systems, including a PJM 5-bus system and an IEEE 24-bus RTS, are put under numerical studies to assess the possible impact of ESS on security improvement of the investigated systems. The obtained results are discussed in depth.

Effects of Energy Storage Systems on Fuel Economy of Hybrid-Electric Vehicles

Three energy storage systems, namely Nickel Zinc, Nickel Metal Hydride and Lithium ion batteries were simulated on ADVISOR (Advanced vehicle simulator) to determine their impact on fuel economy. ADVISOR, a drivetrain analysis tool developed in MATLAB/Simulink for comparing fuel economy and emissions performance and designed by the National Renewable Energy Laboratory by Ford, GM, and Chrysler was used for the simulations. In choosing the batteries for simulations, only the latest technological advanced batteries of NiZn, Li ion and NiMH were used. The results showed that NiZn battery influence in fuel economy and system efficiency far exceeds the other batteries especially for the combined Powertrain. While a lithium ion battery is seen to be well suited for Parallel and Series powertrains at higher speeds, average values for all drive cycle singles out NiZn as a better performing battery. NiMH showed the worst performance. This confirms NiMH, which is the predominant energy storage system today in the HEV industry, is deficient in advancing the growth of HEV’s.Keywords: power trains; hybrid energy storage; hybrid electric vehicle; combined hybrid; parallel hybrid

Research on Comprehensive Strategy of Microgrid PV Absorption Capability Improvement

As a typical renewable energy, photovoltaic (PV) has developed rapidly due to its permanent, clean and flexible characteristics. However, in the microgrid with a high proportion of PV installed, the difficulty of absorption has always been a hot issue. This paper constructs a photovoltaic microgrid system containing an electric vehicle and introducing a demand response model. Electric vehicles are used as energy storage systems and transferable loads. With the constraints of user satisfaction and the goal of maximizing photovoltaic absorption, the economy of microgrid is also taken into account. The peak, flat and valley time periods of time-sharing tariff are optimized to construct the tariff incentive model, which promotes the movement of the transferable load to the photovoltaic output. Comparing the comprehensive and single effects of energy storage system and demand response and the improved simulated annealing algorithm is used to solve the problem of multivariable nonlinearity. The results show that the combined effect of the two methods can promote the consumption rate and reduce the cost of power generation side and user side, while reducing the emission of polluting gases.

Capacity Optimization Based on Energy Storage to Restrain Severe Fluctuation of Wind Power

In order to solve the coordination problem between the economy and the stabilization effect of energy storage system, a capacity optimization model based on energy storage to suppress the violent fluctuation of wind power is proposed, and a multi-objective function with the maximum wind power dissipation capacity and the minimum operating cost of energy storage system is established. Considering the average annual cost and penalty cost of the whole life cycle, an evaluation index with correlation coefficient as the fitting degree is proposed, and the reference power of wind farm grid connection is optimized by particle swarm algorithm. Using the operation data of Qidong Wind Farm in Jiangsu Province, the theoretical validity is verified, the coordination is improved to the greatest extent, and the capacity demand for energy storage system is reduced.

Effect of energy storage systems on automatic generation control of interconnected traditional and restructured energy systems

During major disturbances in electric power system (PS) penetrated with renewable energy sources, primary and supplementary automatic generation control (AGC) strategies usually show inefficiency in mitigating the frequency and power oscillations because of sluggish control action. The frequency and power deviations should be controlled to retain the generation-demand balance, which reinforce the quality and stability of overall PS. The fast-acting energy storage systems (ESSs) having very small time constants like capacitive energy storage (CES) and redox flow battery (RFB) are utilised in this study to improve these dynamic responses. To conduct the analysis, initially, a two-area nonreheat thermal PS with extra generations from wind turbine system (WTS) and dish-stirling solar thermal system (DSTS) is explored extensively, and then to validate the efficacy of the method, the approach is tested on two-area nonreheat thermal system having governor deadband (GDB) nonlinearity, reheat thermal, and restructured multisource thermal gas systems. An imperialist competition algorithm (ICA) optimised fuzzy PID-filter-(1 + PI) controller named as FPIDF-(1 + PI) is utilised as supplementary controller, and its performance with CES/CES-RFB is compared with ICA-optimised FPIDF with/without CES and existing optimal PI/PID/PIDF/FPID controller without CES. Investigation of dynamic responses for sudden variation in power demand unveils the superiority of the control approach compared with others regarding settling time, peak undershoot, and performance index. Analysing the impact of ESSs on the responses divulges that the amalgamation of CES-RFB in PS imparts better system dynamics. The robustness analysis suggests that ICA-optimised controller with ESSs performs excellently and robustly for ±25% variation in PS parameters, random load disturbances, and nonlinearities.

A novel voltage stability and quality index demonstrated on a low voltage distribution network with multifunctional energy storage systems

This work examines the effect of energy storage systems (ESSs) operation on the voltage stability and quality of the local power system. The variation of these two voltage dimensions is expressed in a collective manner by the novel voltage stability and quality index (VSQI). For the calculation of the VSQI, a complete voltage stability curve is required, and the coordinates associated with voltage stability and quality are identified. The distance between the abovementioned coordinates, that collectively indicates the effect of ESSs operation on the two voltage dimensions, is represented by the VSQI. The process for VSQI identification is repeated for each operating scenario, with the studied cases including grid operation prior to ESSs utilisation, grid support performed by a community level (CL) ESS, and grid operation with distributed (DIST) ESSs. The ESSs are powered by renewable energy sources (RESs), and a future scenario of 70% photovoltaic (PV) penetration was examined. Other ESSs applications presented in this work, include time-shifting of the energy produced by RESs, peak load shaving, and reducing the loading levels of transmission lines and of the substation transformer. The above stack of ESSs applications, highlights the multifunctional role of ESSs as support to the grid. A real low voltage (LV) distribution network located in south-east Queensland, Australia was employed for the study.

Operational Reliability Studies of Power Systems in the Presence of Energy Storage Systems

This paper mainly focuses on operational reliability studies of modern power systems taking into consideration the effects of energy storage systems (ESSs). The aim is to develop a new evaluation tool to assess the effects of different factors such as penetration rate, operational strategies, and capacities of the ESSs in determining the role of these systems as a source of operating reserve. In this regard, at first, some modifications are made to the Pennsylvania-New Jersey-Maryland (PJM) method aimed to precisely model the short-term variability in output generation of wind farms in reliability studies. Then, this algorithm is employed to examine the effects of ESSs operating strategies in operational reliability level of the system. Finally, a new formulation for ESSs scheduling problem is proposed to optimally gain the benefits of ESSs in improving the mid-term reliability level of the system. The usefulness of the proposed framework is illustrated using different case studies.

Stochastic valuation of energy storage in wholesale power markets

Energy storage systems are well poised to mitigate uncertainties of renewable generation outputs. Grid-scale energy storage projects are major investments which call for rigorous valuation and risk analysis. This paper provides a stochastic energy storage valuation framework in wholesale power markets which considers all key revenue streams simultaneously. As part of this framework, an operational optimization model is developed to determine the energy storage system's optimal dispatch sequences. A future curve model is built to capture the volatilities of electricity prices. In addition, a frequency regulation service price forecasting model is developed. Simulation results with a realistic battery storage system reveal that the majority of the market revenues comes from frequency regulation services. Simulation results also show that both round-trip efficiency and power-to-energy ratio are crucial to the cost effectiveness of energy storage systems.

Appliances considered demand response optimisation for smart grid

Demand side management can bring a lot of benefits to smart grid, including reducing peak load demand, reshaping demand file and increasing grid sustainability. To obtain a more practical and rational demand side response optimisation, the authors first build a power network model with suppliers, utility companies, consumers with appliances and energy storage systems. Unlike other power network models previously proposed, power consumptions and work patterns of appliances have been considered in the authors’ work. They classify appliances into elastic and inelastic ones according to their distinct constraints on power consumptions, while all appliances’ work patterns are modelled based on their functions and common users’ habits in reality. With the help of convex optimisation, a distributed load scheduling algorithm is proposed subject to social welfare maximisation. Simulation results confirm that the proposed load scheduling algorithm can lead to a suitable power consumption schedule to improve the social welfare than other load scheduling algorithm does. The effect of energy storage system on reducing the fluctuation of power network is also tested and proved to be effective. At last, a strategy of charge rate is also given to satisfy power consumption and avoid energy waste, which is further verified to be competent.