Utilization Of Energy Storage System Research Articles

Programmable logic controlled lithium-ion battery management system using passive balancing method

Advancements in electric vehicle technologies and the widespread adoption of electric vehicles have brought energy storage systems to the forefront. However, efficient and secure utilization of energy storage systems requires Battery Management Systems (BMS).BMSs are typically designed with power electronics, electronic cards, integrated circuits, and auxiliary hardware components. Nevertheless, BMSs designed with such microcontrollers can only control a limited number of battery cells and constrained current values. Furthermore, employing a separate controller for each battery pack exacerbates these issues.In this study, a Programmable Logic Controller (PLC) - based BMS proposal for lithium-ion batteries has been presented, aiming to address the challenges in existing BMSs. The developed system is a passive balancing BMS comprised of controller PLC modules and auxiliary hardware. The Ladder Diagram (LD) programming language was employed for programming the PLC.In the conducted research, the voltage levels of the cells comprising the battery pack were monitored, and a passive balancing method was employed to equalize the cell voltages. Passive balancing has been applied to cells with voltage values of 2.70 V, 2.40 V, and 2.80 V using the minimum reference voltage cell balancing method to equalize the voltage levels. The output voltage has been balanced according to the maximum reference charge voltage set at 6 V through the maximum reference charge voltage balancing method. Additionally, a cooling circuit was integrated into the system to safeguard the Battery Management System (BMS) against thermal hazards. The developed PLC controller and algorithm can effectively control multiple battery packs with a single controller and operate at higher current values. This advantage allows the PLC-controlled BMS to have a broader control capacity. The presented test results indicate the successful performance of the PLC-based BMS.

Electric School Buses: Catalysts for Peak Shaving and Load Leveling in V2G Systems

This comprehensive research paper navigates the complexities arising from the peak demand for electric vehicle (EV) charging during traditional peak hours within the Vehicle-to-Grid (V2G) systems framework. The crux of the matter lies in the emergence of grid congestion—a critical issue leading to inefficiencies, voltage fluctuations, and potential disruptions in power supply. The paper highlights the challenges associated with grid congestion, mentioning its implications on the reliability of V2G services. Overburdened charging infrastructure not only results in delays and increased maintenance requirements but also elevates the likelihood of technical failures, further jeopardizing the stability of the entire energy distribution system. To address these challenges, the paper explores the concepts of peak shaving and load leveling as strategic interventions in energy management. Peak shaving, an approach aimed at mitigating the highest peaks in electrical load profiles, seeks to minimize electricity costs by avoiding or mitigating peak demand charges—a significant component of commercial or industrial electricity bills. On the other hand, load leveling endeavors to balance electrical loads over specific periods, ensuring a consistent and stable load profile. These strategies involve utilizing energy storage systems, demand response programs, and innovative grid technologies to optimize electrical power consumption.

A Guide to the Integration and Utilization of Energy Storage Systems with a Focus on Demand Resource Management and Power Quality Enhancement

The increasing peak electricity demand and the growth of renewable energy sources with high variability underscore the need for effective electrical energy storage (EES). While conventional systems like hydropower storage remain crucial, innovative technologies such as lithium batteries are gaining traction due to falling costs. This paper examines the diverse applications of energy storage, spanning from grid connectivity to end-user solutions, and emphasizes large-scale energy recovery and system stability. The integration of EES with various energy infrastructures and consumer strategies is explored, highlighting the use of tariffs and peak pricing systems for energy cost savings. Country-specific priorities shape EES deployment, with the U.S focusing on grid stability, Japan on emergency power, and South Korea, still in the demonstration phase, prioritizing peak demand reduction. Our analysis of the UK, U.S., and South Korea reveals the pivotal role of energy storage in achieving flexible and efficient energy systems. The industry shows promising growth, with significant commercial expansion expected around 2035, presenting profound policy and deployment implications for the future.

Enhancing the Stability of an Isolated Electric Grid by the Utilization of Energy Storage Systems: A Case Study on the Rafha Grid

A system’s stability is affected by the generation types in the interconnected power system. For example, synchronous generators usually have high inertia sharing with the power system since they have rotating mass, and they usually have primary frequency response capability. On the other hand, renewable energy sources (RES) neither provide inertia to the system nor have a primary frequency response capability; hence, adding RES will impact the power system’s voltage, angle, and frequency stability. Battery energy storage systems (BESSs) have many applications in the future electric grid. From the stability perspective, BESSs can be used to increase the power system’s stability. A case study was conducted on the Rafha microgrid in the Kingdom of Saudi Arabia (KSA) to inspect a BESS’s influence on the Rafha microgrid’s stability and the impact of changing the BESS’s location, which might cause changes in the system stability after contingencies. In addition, we investigated which dynamic stability is affected if the BESS’s capacity changes. The microgrid is tested using contingencies that affect the system’s frequency, angle, and voltage stability using the power system simulator for engineering (PSS/E) software as a simulation platform. Finally, we investigated the technical impact of utilizing a BESS and its influence on economic operation.

Potential of thermal, electricity and hydrogen storage systems for achieving sustainable buildings

The current global energy scarcity challenges and the urgent need to enhance building sustainability have emphasized the utilization of energy storage systems as a highly promising approach. This study assesses the energy, economic, and environmental (3E) potential of thermal, electricity and hydrogen storage systems in promoting the sustainability of buildings. To that end, several simulations have been performed for five energy storage systems: photovoltaic (PV) with batteries, PV with hydrogen storage, solar collectors with hot water storage, phase change materials (PCMs) and Trombe wall. The 3E performance of these systems is evaluated in three distinct climates, namely hot desert, Mediterranean and cold climates. The results show that employing batteries for electricity storage is more energy-efficient than using hydrogen in all considered climates. Moreover, combining solar thermal collectors and hot water storage yields excellent energy outcomes, especially in the hot desert climate, while integrating PCMs in exterior walls and roof exhibits higher energy savings (43.06–64.29%) than the Trombe wall technique (4.18–18.09%). Environmentally, all storage systems, except for the Trombe wall, are eco-friendly with carbon payback periods of around 2.21–2.51, 0.40–0.67, 0.55–0.64, and 2.80–4.04 years for PV with batteries, PV with hydrogen storage, solar collectors with hot water storage, and PCM, respectively. In economic terms, the best cost-effectiveness is achieved using PV with batteries, followed by solar collectors with hot water storage and PCMs, with discounted payback period/levelized cost of energy of about 4.98–5.48/0.035–0.039, 6.35–7.52/0.039–0.046 and 9.32–14.43 years/0.046–0.067 $/kWh, sequentially. In contrast, incorporating Trombe wall and PV with hydrogen storage reveals a low economic viability. Overall, these outcomes could help engineers and building owners get informed about the 3E benefits and disadvantages of each storage system, enabling them to select the most suitable ones.

Optimization of building demand flexibility using reinforcement learning and rule-based expert systems

The increasing use of renewable energy in buildings requires optimization of building demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the optimization process is generally challenging that needs to consider the on-site intermittent energy supply, dynamic building energy demand, and proper utilization of energy storage systems. Leveraging the growing availability of operational data in buildings, data-driven strategies such as reinforcement learning (RL) have emerged as effective approaches to optimizing building demand flexibility. However, training a reliable RL agent is practically data-demanding and time-consuming, limiting its practical applicability. This study proposes a new strategy that integrates a rule-based expert system (RBES) and RL agents into the decision-making process to jointly reduce building energy costs, minimize the peak-to-average ratio (PAR) of grid power, and maximize PV self-consumption. In this strategy, the RBES determines system operation directly in less complex decision-making scenarios, while, in more intricate decision-making environments, it provides a reference decision for RL to explore optimal solutions further. This integration empowers RL agents to avoid unnecessary exploration and significantly enhance learning efficiency. The proposed strategy was tested using PV generation data and energy consumption data of a low energy office building. The results demonstrated an 85.7% improvement in RL learning efficiency and this strategy can successfully avoid sub-optimal convergence during policy learning. Compared to relying solely on the RBES, the proposed strategy led to 5.4% and 19.2% reductions in the electricity costs and daily PAR of grid power at peak hours, respectively. The strategy also achieved a satisfying PV self-consumption ratio of 62.4%, which is merely 0.4% lower than the optimal value determined by the RBES strategy that prioritized maximizing PV self-consumption. Additionally, compared with a model predictive control method developed for cost reduction, the strategy achieved similar cost savings while significantly reducing the decision time.

Economic Analysis of a Redox Flow Batteries-Based Energy Storage System for Energy Savings in Factory Energy Management System

Renewable energy systems are essential for carbon neutrality and energy savings in industrial facilities. Factories use a lot of electrical and thermal energy to manufacture products, but only a small percentage is recycled. Utilizing energy storage systems in industrial facilities is being applied as a way to cut energy costs and reduce carbon emissions. However, lithium-based batteries, which are predominantly used in traditional industries, face challenges in terms of affordability and reliability. Redox flow batteries, on the other hand, offer high power output and reliability, and are economical to manufacture for installations with high capacity. Although redox flow batteries are difficult to use in general electrical systems due to their small volume-to-capacity ratio, they can be easily utilized as energy storage devices in industrial parks or renewable energy parks with relatively little space constraints. In addition, since factories use a lot of heat energy in addition to electricity, utilizing combined heat and power can further reduce heat energy. In this study, we analyzed the cost estimation and economic feasibility of utilizing photovoltaics, redox flow cells, and combined heat and power to save energy in a factory’s energy management system.

A scheduling framework for VPP considering multiple uncertainties and flexible resources

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP.

Energy Storage System Utilization, in a Distribution Power System

Energy Storage System Utilization, in a Distribution Power System

Optimal selection of energy storage system sharing schemes in industrial parks considering battery degradation

With the continuous deployment of renewable energy sources, many users in industrial parks have begun to experience a power supply–demand imbalance. Although configuring an energy storage system (ESS) for users is a viable solution to this problem, the currently commonly used single-user, single-ESS mode suffers from low ESS utilization efficiency and unsatisfactory investment costs. ESS sharing refers to the joint investment and operation of a set of ESSs by multiple users, which can effectively improve ESS utilization and economic benefits. In the industrial park environment, ESS sharing has multiple schemes that involve different ESS installation structures and energy-sharing methods. Therefore, this study determines the optimal ESS-sharing scheme in an industrial park through the construction of load optimization model and comparative analysis. Several typical ESS-sharing schemes were first established based on different ESS installation structures and energy-sharing methods for comparative analysis. Then, based on each scheme characteristic, a corresponding load optimization model that minimizes the total electricity cost in the industrial park was constructed, after which a simulation experiment was conducted using real historical data. The results indicated that the centralized ESS scheme resulted in lower electricity costs and greater utilization compared to other ESS-sharing schemes, as its cost savings and utilization could reach approximately 18.10 % and 60.75 %, respectively. Despite the resulting increase in energy losses, increasing the proportion of centralized ESS is the most effective method of improving ESS performance.

Development of an incentive model for renewable energy resources using forecasting accuracy in South Korea

AbstractThis study presents an incentive model for a renewable energy source (RES) supplier, based on forecasting accuracy. The incentive model imposes responsibility of forecasting to RES suppliers by providing an additional revenue or penalty, according to forecasting accuracy. For this, a profit analysis reflecting the current environment was performed. The structure of the incentive model is designed based on the results of profit analysis. Subsequently, the price parameter was determined using an artificially generated forecasting error of the RES. In addition, the utilization of energy storage system (ESS) is analyzed, including the operation algorithm and optimal sizing, to verify the utility of the ESS when the incentive model is applied. In the case study, the actual price data and RES data from South Korea were used. The results show that the incentive model can provide a high level of profit or penalty for the RES supplier, according to the forecasting accuracy. It is also shown that the optimal capacity of the ESS based on econometrics can be reduced by more than 50% compared with the current environment, thereby reducing the burden on the ESS investment of RES suppliers.

Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming

Penetration of renewable energy sources (RESs) in power systems increase all over the world to overcome current challenges, most importantly environmental issues. Beside advantages of RESs, their integration into the power systems have imposed various challenges considering uncertain and intermitted power output. To cope with these challenges, utilizing energy storage systems with renewable energy sources alongside the demand response (DR) programs are considered as reliable solutions. On the other hand, in an uncertain environment, minimizing worst-case cost or regret is counted as an important criterion to evaluate operation of any system under uncertain parameters. Therefore, in this paper, optimal operation of power systems is solved under penetration of wind turbines, hydrogen storage system, and DR programs in an uncertain environment. To guarantee robust operation of the system under the worst-case scenario, a novel stochastic p-robust optimization method (SPROM) is proposed which combines both stochastic programming and robust optimization approaches where minimizes the worst-case cost or regret level. The performance of the developed model is evaluated considering a six-bus test system under two cases as stochastic optimization (SO) and SPROM. Obtained results show that the maximum regret level is reduced considerably using the proposed SPROM comparing with pure stochastic method.

The coordinated operation of dual batteries energy storage system for cold areas

Utilizing energy storage systems have been considered as a feasible pathway to achieve carbon neutrality. However, the common battery type for energy storage systems is the cheap lithium iron phosphate battery, which has low output efficiency and is almost impossible to charge in cold areas. Lithium titanate battery has high output efficiency and charge efficiency in cold areas. Meanwhile, the price of a lithium titanate battery is three times that of a lithium iron phosphate battery with the same capacity. To achieves the complementary advantages of lithium iron phosphate battery and lithium titanate battery, this paper proposes the dual battery framework of energy storage systems. Based on this, the operation model of energy storage systems is formulated. Besides, five cases are designed to demonstrate the effectiveness of the proposed method and the results show that the dual battery framework has high output efficiency with a cheaper cost than lithium titanate battery.

Grid balancing challenges illustrated by two European examples: Interactions of electric grids, photovoltaic power generation, energy storage and power generation forecasting

The past decade has not only witnessed a great shift in the energy mix, with electricity gaining a more and more prominent role, but has also seen electricity coming increasingly from renewable energy sources, whose proportion is forecasted to reach 50% by 2030. As the integration of photovoltaic energy cannot be deemed successful without the electricity supply being both sustainable and secure, such far-reaching developments prompt legislations and policy makers, including those of the European Union, to make changes to accommodate not only ever-changing technologies, including energy storage solutions, but also new players in the market. Currently, battery energy storage systems are not used for enhancing the precision of photovoltaic power generation schedules, so actors in the market find it difficult to make well-grounded decisions on the viability of utilizing batteries for such a purpose. The innovative novelty of the procedure presented in this paper is that it is suitable for the planning, analysis and evaluation of the schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion-based battery energy storage systems of various capacities. By the examples of two European Union countries, this article studied the deviations of day-ahead and intraday photovoltaic power generation forecasts from the actual electricity generation of 1000 MWp photovoltaic systems. The two main objectives of the project were to investigate the amount of the photovoltaic balancing needs, based on actual data compared to the day-ahead and intraday forecasts, and to establish to what extent sodium-sulfur and lithium-ion-based battery storage systems can decrease those requirements. Furthermore, by using a model for simulation, the potentials of a number of different energy storage capacities (nominal net storage capacities from 10 MWh to 10,000 MWh) for reducing grid balancing needs were assessed. The practical use of the model is that its application makes it possible to create a dynamic design, analysis and assessment system mechanism that can allow one to establish the multilevel schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion energy storage systems linked to solar power plant systems. Real photovoltaic data from Belgium and Hungary were used to find out how the accuracy of PV power generation forecasts influence the level of the annual utilization of energy storage systems.

Impact of solar tracker and energy storage system on sizing of hybrid energy systems: A comparison between diesel/PV/PHS and diesel/PV/FC

Design of renewable energy-based hybrid systems has become a challenging task especially for electrification to off-grid regions. In the design problem, it is important to economically investigate utilization of energy storage systems (ESSs) and solar tracker. This paper focuses on optimal size of hybrid energy systems which make use of different types of tracking systems. For this aim, two hybrid energy systems, diesel generator/photovoltaic/pumped hydro storage (diesel/PV/PHS) and diesel generator/photovoltaic/fuel cell (diesel/PV/FC), have been designed with respect to different tracking systems (fixed, single-axis and double axis). In order to find optimum design of the hybrid systems, two important objectives, total net present cost (TNPC) and reliability index expressed by loss of power supply probability (LPSP) are minimized in a multi-objective framework. With respect to complexity of this optimization problem, a multi-objective efficient approach is used to solve the problem. Simulation results show that compared to FC when PHS is employed as energy storage system, TNPC of the hybrid system decreases. Moreover, using a solar tracker in diesel/PV/PHS is not cost-effective in comparison with fixed PV panels.

Optimal control and learning for cyber‐physical systems

Optimal control and learning for <scp>cyber‐physical</scp> systems

Sensitivity Analysis of Photovoltaic System Design Parameters to Passively Mitigate Ramp Rates

As solar photovoltaic (PV) generation continues to become a larger part of the energy mix, the challenges associated with their inherent resource variability need to be addressed. Approaches often considered for limiting variability are enhanced cloud forecasting and the utilization of energy storage systems. In this article, we explore addressing variability through the inherent design of the solar site, avoiding the use of additional equipment such as cloud cameras or energy storage. Additionally, we evaluate the sensitivity that three key design parameters have on a PV site's ramp rate, and how intelligent design can limit ramping. A PV power model was developed using plane of array irradiance calculations and manufacturer hardware parameters. Using high resolution environmental data from a site with a relatively large quantity of ramping, annual energy generation and ramp rate estimates for a baseline PV site are estimated as each design parameter is swept. The results show that varying design parameters from a typical “optimal” energy configuration can passively reduce ramp rates by almost 50% with the energy and financial impact also evaluated.

Exploring the Utilization of Energy Storage Systems for Frequency Response Adequacy of a Low Inertia Power Grid

The utilization of Energy Storage Systems (ESS) for improving the frequency response of a low inertia power system is investigated in this article. Substantial wind power penetration is causing the replacement of conventional synchronous generators in several power systems. Variable speed wind machines traditionally do not contribute to frequency regulation without additional control strategy. As a result, a wind dominated power grid may have inadequate inertia and governor responsive reserve. In such situation, a large contingency may yield undesirable Rate of Change of Frequency (ROCOF) and frequency deviation. To overcome this problem, deployment of ESS, namely, Superconducting Magnetic Energy Storage (SMES) and Battery Energy Storage System (BESS) can be a worthwhile solution. Since these devices are costly, their appropriate sizing and operational approach are crucial. Therefore, in this paper, analytical expressions are derived to find the minimum ratings of SMES and BESS. To this end, system frequency deviation, ROCOF, inertial response and governor response are taken into account. Also, a coordinated operational strategy is proposed to retain frequency response adequacy and minimize under-frequency load shedding where SMES is triggered when system ROCOF supersedes a certain limit and BESS is activated due to system frequency surpassing a preset threshold. The performance of the proposed strategy is explored in a low inertia network under substantial wind penetration considering several different wind penetration levels. Also, the results are validated against two existing approaches. Simulation results reveal that the proposed methodology considerably enhances the frequency response in various operating conditions.

Wind Power Smoothing With Energy Storage System: A Stochastic Model Predictive Control Approach

The utilization of energy storage system (ESS) is an effective method for dealing with the randomness and variability of wind power. Therefore, improving the coordination between ESS and wind power is a direction worthy of research. This study develops a two-layer stochastic model predictive control (SMPC) method for wind power smoothing on different time-scales. The proposed optimization framework smooths wind energy by adjusting the charge and discharge power of hybrid ESSs; moreover, it combines the SMPC approach and the chance constraints to address the uncertainties of wind energy. Furthermore, the previous states are taken into account, in addition to states within the rolling time horizon, to obtain optimal control of the hybrid ESSs, and capacity planning for hybrid ESSs is conducted simultaneously. The numerical results obtained through amplitude-frequency simulation comparison prove that the proposed method is superior to the conventional method in terms of minimizing the frequency fluctuation of wind power and the size of the ESS.

Stochastic bidding strategy of electric vehicles and energy storage systems in uncertain reserve market

This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day-Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base-load and reserve up/down capacities plans. In the Real-Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator–owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.