Electrical Storage Systems Research Articles

Integration of smart buildings with high penetration of storage systems in isolated 100 % renewable microgrids

The operation of smart buildings is an important feature in energy-transition programs. Nowadays, smart buildings typically use renewable energy resources and energy storage systems to feed their energy demands. In this research, a bi-level stochastic model is put forward for integration of smart buildings with high penetration of storage systems in isolated 100 % renewable microgrids. In the first level, each smart building solves its operational planning model and submits its desired power exchange to the microgrid; then, in the second level, microgrid operator solves its operational planning model, considering all requests from smart buildings. Each smart building performs as a prosumer having an electric water heater, air conditioner, photovoltaic modules, storage system and electric vehicles. A dynamic demand response (DR) program is used in which microgrid operator awards incentive for shift-down of demand of a building at times and scenarios in which demand of the building is higher than its average demand. The incentives to be paid for shift-down and shift-up of non-thermal demand of buildings are exponential functions of the difference between demand and average demand of the building. The findings approve the efficiency of the proposed bi-level methodology; the results confirm that thanks to storage systems, renewable energy resources and demand response program, the operation of the smart buildings is profitable; moreover, the microgrid operator exploits the responsiveness of smart buildings through DR program to decrease its operational planning cost. The results show that storage systems increase the daily profit of the smart buildings by 95 %. The results also show that DR and building-to-grid capability respectively increase the daily profit of the smart buildings by 43.8 % and 164 %.

Algorithmic trading of real-time electricity with machine learning

Algorithmic trading is becoming the dominant approach in many electricity spot and futures markets. This paper focuses on the emerging interest in the less documented real-time imbalance markets, by developing reinforcement learning agents to find profit-making opportunities algorithmically. We develop a repeatable experimental setting to compare different market participants and explore the applications of Q-learning with neural networks for three types of market participants: a non-physical trader, a gas generator, and a battery electricity storage system. We backtest all three agents using British data across summer and winter months to compare their profits, risks and various experimental design considerations.

Energy Management System for Polygeneration Microgrids, Including Battery Degradation and Curtailment Costs.

Recent advancements in sensor technologies have significantly improved the monitoring and control of various energy parameters, enabling more precise and adaptive management strategies for smart microgrids. This work presents a novel model of an energy management system (EMS) for grid-connected polygeneration microgrids that allows optimizing the management of electrical storage systems, electric vehicles, and other deferrable loads such as heat pumps. The main novelty of this model is that it incorporates both climate comfort variables and the consideration of the degradation of the energy storage capacity in the control strategy, as well as a penalty for the dumping of surpluses. The model has been applied to a smart, sustainable building as a case study. The results show that the proposed model is highly adaptable to diverse weather conditions, minimizing renewable energy losses while satisfying the energy demand and providing comfort to the building's users. The study shows (i) that EVs' dynamic charging schedules play a crucial role, (ii) that it is possible to minimize a battery's degradation by optimizing its cycling, averaging one cycle per day, and (iii) the critical impact of seasonal weather patterns on microgrid energy management and the strategic role of EVs and storage systems in maintaining energy balance and efficiency.

Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study

This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning (DRL) to optimize the management of flexible energy systems in SES, including heating, cooling and electricity storage systems along with District Heating and Cooling Systems (DHCS). The proposed approach is applied on Meridia Smart Energy (MSE), a french demonstration project for SES. The proposed DRL framework, based on actor–critic architecture, is first applied on a Modelica digital twin that we developed for the MSE SES, and is benchmarked against a rule-based approach. The DRL agent learnt an effective strategy for managing thermal and electrical storage systems, resulting in optimized energy costs within the SES. Notably, the acquired strategy achieved annual cost reduction of at least 5% compared to the rule-based benchmark strategy. Moreover, the near-real time decision-making capabilities of the trained DRL agent provides a significant advantage over traditional optimization methods that require time-consuming re-computation at each decision point. By training the DRL agent on a digital twin of the real-world MSE project, rather than hypothetical simulation models, this study lays the foundation for a pioneering application of DRL in the real-world MSE SES, showcasing its potential for practical implementation.

Greening Your Drive: A Method of Effective Conversion from IC Engine Based Vehicle to EV

This research presents the most efficient method for turning vehicles with internal combustion engines (IC engines) into electric vehicles (EVs). With an emphasis on reducing the environmental footprint and optimizing energy efficiency, our strategy includes cutting-edge technology in electric powertrain parts, power electronics, and battery storage systems. This paper assesses performance indicators by thoroughly analyzing the integration of new electric drivetrain components and the retrofitting of old ones, including motor starting and energy usage. To produce energy-efficient EVs, we suggest a supercapacitor bank and an effective battery cooling system. The recommended approach, Safety concerns, environmental advantages, and economic viability are also covered. The suggested approach shows promise in terms of lower production of greenhouse gases, increased range, and cost-effectiveness. The results provide an efficient way of battery cooling system and efficient use of battery storage by integrating a supercapacitor bank. The outcomes support the worldwide trend towards environmentally sound methods of transportation by providing important insights that will accelerate the widespread use of sustainable EVs.

An overview on building-integrated photovoltaics: technological solutions, modeling, and control

The advancement of renewable and sustainable energy generation technologies has been driven by environment-related issues, energy independence, and high costs of fossil fuels. Building-integrated photovoltaic systems have been demonstrated to be a viable technology for the generation of renewable power, with the potential to assist buildings in meeting their energy demands. This work reviews the current status of novel PV technologies, including bifacial solar cells and semi-transparent solar cells. This review discusses the various constructions of PV technologies, recent advances in these products, the influence of key design factors on electrical and thermal performance, and their potential in the design of energy-efficient smart buildings. The attention is focused on bifacial and semi-transparent PV systems, given the high level of interest of the scientific community in their current and potential applications.Focus is also devoted to the analysis of the electrical, optical, and thermal modeling procedures developed for sizing, designing, and integrating photovoltaics into larger building simulations. The development of these models has a positive impact on the implementation of next-generation smart buildings. The latest innovative developments and key issues in the application of bifacial PV solutions in buildings are also summarized and analyzed. Special attention is paid to rear side electrical performance, which can be evaluated by means of illuminance/optical backside modeling. Finally, energy management and control of PV-equipped buildings via both model-based and data-driven approaches are discussed, as well as the integration of electric storage systems in a multi-building context.

Numerical Investigation of the Thermal Performance of Air-Cooling System for a Lithium-Ion Battery Module Combined with Epoxy Resin Boards

Lithium-ion batteries (LIBs) have the lead as the most used power source for electric vehicles and grid storage systems, and a battery thermal management system (BTMS) can ensure the efficient and safe operation of lithium-ion batteries. Epoxy resin board (ERB) offers a wide range of applications in LIBs due to its significant advantages such as high dielectric strength, electrical insulation, good mechanical strength, and stiffness. This study proposes an air-cooled battery module comprised of sixteen prismatic batteries incorporating an ERB layer between the batteries. To compare the performance of the ERB-based air-cooling system, two other air-cooling structures are also assessed in this study. Three-dimensional numerical models for the three cases are established in this paper, and the heat dissipation processes of the battery module under varying discharge rates (1C, 2C, and 5C) are simulated and analyzed to comprehensively evaluate the performance of the different cooling systems. Comparative simulations reveal that incorporating ERB into the battery assembly significantly reduces battery surface temperatures and promotes temperature uniformity across individual batteries and the entire pack at various discharge rates. Notably, under 5C discharge conditions, the ERB-based thermal management system achieves a maximum battery surface temperature increase of 16 °C and a maximum temperature difference of 8 °C between batteries. Additionally, this paper also analyzes the impact of battery arrangement on air-cooling system performance. Therefore, further optimization of the structural design or the integration of supplementary cooling media might be necessary for such demanding conditions.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems

A promising energy carrier and storage solution for integrating renewable energies into the power grid currently being investigated is hydrogen produced via electrolysis. It already serves various purposes, but it might also enable the development of hydrogen-based electricity storage systems made up of electrolyzers, hydrogen storage systems, and generators (fuel cells or engines). The adoption of hydrogen-based technologies is strictly linked to the electrification of end uses and to multicarrier energy grids. This study introduces a generic method to integrate and optimize the sizing and operation phases of hydrogen-based power systems using an energy hub optimization model, which can manage and coordinate multiple energy carriers and equipment. Furthermore, the uncertainty related to renewables and final demands was carefully assessed. A case study on an urban microgrid with high hydrogen demand for mobility demonstrates the method’s applicability, showing how the multi-objective optimization of hydrogen-based power systems can reduce total costs, primary energy demand, and carbon equivalent emissions for both power grids and mobility down to −145%. Furthermore, the adoption of the uncertainty assessment can give additional benefits, allowing a downsizing of the equipment.

Study of two thermally integrated pumped thermal electricity storage systems using distinct working fluid groups: Performance comparison

As a competitive new energy storage technology, the thermally integrated pumped thermal electricity storage (TI-PTES) system has received much attention. In this work, two TI-PTES systems are constructed: the RHP-BORC system, which consists of a heat pump cycle (HP) with a regenerator and a basic organic Rankine cycle (BORC), and the RHP-EOFC system, which consists of a HP with a regenerator and an organic flash cycle with ejectors. Meanwhile, R1233zd(E) and Cis-butene are selected to form R1233zd(E)/R1233zd(E) (denoting the working fluid used in the charge and discharge cycles, respectively), R1233zd(E)/Cis-butene, Cis-butene/Cis-butene, and Cis-butene/R1233zd(E) groups. Thermodynamic and economic analyses as well as multi-objective optimization are performed for both systems. The results indicate that for the two systems with different working fluid groups, different parameters have distinct effects on the system performance, and the thermodynamic and economic performance of the RHP-BORC system is better than those of the RHP-EOFC system. For the RHP-BORC system, with the variations of thermal storage temperature, evaporation temperature of heat pump and pinch point temperature difference, the values of different performance metrics of the system with different working fluid groups are close respectively, such as power-to-power efficiency, energy storage density and annual emission reduction. For the RHP-EOFC system under different parameter conditions, when the system uses R1233zd(E)/Cis-butene and Cis-butene/Cis-butene, the performance is better. From the results of the multi-objective optimization, the optimal working fluid group of the RHP-BORC and RHP-EOFC systems are Cis-butene/Cis-butene and R1233zd(E)/Cis-butene, and the values of the power-to-power efficiency and the levelized cost of storage under the optimal solution conditions are 75.76 %, 0.234 $·kWh−1, and 84.31 %, 0.227 $·kWh−1, respectively. The results of the exergy destruction analysis under the optimal solution conditions show that the largest exergy destruction in the RHP-BORC and RHP-EOFC systems are in the condenser (480 kW) and compressor (1275 kW) of the HP cycle. This work expands the configuration of the TI-PTES system, showing promising development prospects. It also provides theoretical support for the further advancement of the TI-PTES system and offers valuable guidance for the design of such systems.

Comparative study of an innovative coldly integrated pumped thermal electricity storage system: Thermo-economic assessment and multi-objective optimization

Energy storage is an important means of solving the instability of renewable energy sources. As a novel energy-storage technology, thermally integrated pumped thermal electricity storage systems have gained considerable attention owing to their capacity to enhance the power-to-power efficiency of pumped thermal electricity storage systems by integrating low-grade heat sources. However, previous studies have predominantly focused on the integration of low-grade heat sources during the charging phase. In this paper, a novel coldly integrated pumped thermal electricity storage system that integrates liquefied natural gas on the discharge side is proposed. Seawater and R1233zd(E) were utilised as the system’s heat source and working fluid, respectively. The performance of the proposed system was compared with that of a previous thermally integrated pumped thermal electricity storage system that used geothermal energy as the heat source on the charge side. System models were constructed in MATLAB, and thermodynamic and economic comparative analyses were conducted. The Genetic Algorithm was adopted for the multi-objective optimisation of the two systems. The results indicate that, within given temperature ranges for thermal and cold storage temperatures, the proposed system exhibits a higher power-to-power efficiency and has a lower levelised cost of storage compared to the existing system. The optimal power-to-power efficiency and levelised cost of storage solutions obtained via multi-objective optimisation were 1.45, 0.297 $·kWh−1 and 0.807, 0.411 $·kWh−1 for the coldly integrated and thermally integrated pumped thermal electricity storage systems, respectively. The proposed system outperformed the thermally integrated pumped thermal electricity storage system under comparison in terms of thermodynamic and economic performance. The findings of this study shed light on the design and performance of coldly integrated pumped thermal electricity storage systems.

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

A three-level model for integration of hydrogen refuelling stations in interconnected power-gas networks considering vehicle-to-infrastructure (V2I) technology

The coupling with natural gas networks creates an excellent opportunity for renewable-rich power systems to facilitate the utilisation of renewable energy resources; on the other hand, with the increase in employment of fuel cell vehicles (FCVs), the number of hydrogen refuelling stations (HRSs) is increasing. The integration of HRSs in electric distribution systems may impact the operation of electric distribution systems. Through vehicle-to-infrastructure (V2I) technology, vehicles may exchange information with HRSs and know hydrogen prices. The operation of the coupled power and natural gas networks with integration of green HRSs, considering the model of FCVs has not been investigated in the literature, so, it has been set as the goal of this paper. To avoid the challenges of nonlinear gas flow model, they are linearized through piecewise linearisation. The case study is a 33-bus electric distribution system, coupled with a 14-node gas distribution network; each HRS includes a battery, a hydrogen tank, an electrolyzer, a PV and a wind turbine. MILP models have been used for FCVs, HRSs and power-gas networks. CPLEX solver is used for solving the developed MILP models. The results show that the total cost of the coupled electricity-gas network is $760.37 and the refuelling cost of each FCV is $17.30. The results indicate that both electric and hydrogen storage systems enhance the flexibility of HRSs, enabling efficient utilisation of PV modules and wind turbines, so, only in few hours, HRSs need to purchase electricity from electric distribution system; that is why each HRS makes a considerable profit of $519 per day. The achieved results also indicate that the pressure of all gas nodes and gas flow of all pipelines fall within their prespecified range.

Multi-objective modeling of price and pollution in large-scale energy hubs with load management

This study introduces a novel multi-objective model that addresses emission costs, large-scale user operational expenses, and the efficiency of the electric storage system within an integrated energy hub encompassing heating, water, power, and gas sources. A key objective of this model is to maximize the performance of the electrical storage system. The study employs the epsilon-constraint method to construct the Pareto front, aiming to balance profitability and emission reduction from virtual power plant units. The final decision-making process utilizes a fuzzy decision-making technique. Additionally, demand response program (DRP) is incorporated to optimize peak-hour demand and align the load profile with defined objectives. The proposed approach is evaluated across various operational scenarios within a sample system, demonstrating its effectiveness and potential benefits in terms of cost reduction and environmental impact mitigation. Focusing on environmental impact, the carbon dioxide (CO2) emissions are also lower under the DRP scenario, amounting to 10253.92 kg without DRP versus 10127.74 kg with DRP. This reduction in emissions aligns with sustainable energy management goals, showcasing DRP's capability to mitigate environmental impacts associated with energy generation and consumption. The percentage improvements in total costs and emissions further highlight the advantages of employing DRP. The reductions in total costs range from approximately 1.2%–1.4%, demonstrating cost savings across different cost categories. Similarly, the decrease in CO2 emissions by approximately 1.2% underscores DRP's role in promoting environmentally friendly energy practices.

Sustainable Battery Lifecycle: Non-Destructive Separation of Batteries and Potential Second Life Applications

Large quantities of battery systems will be discarded from electric vehicles in the future. Non-destructive separation of used electric vehicle (EV) traction batteries enables a second life of battery components, extraction of high value secondary materials, and reduces the environmental footprint of recycling and separation processes. In this study, the key performance indicators (KPIs) for the second life application of spent EV batteries are identified. Three battery packs are analyzed in terms of the joining techniques used—and possible separation techniques—considering only direct recycling methods. The components that can be recovered from these batteries are evaluated against the KPIs. This study shows that all the batteries analyzed allow a second life in stationary and semi-stationary electrical storage systems and marine applications when used at the pack and module levels. Two packs can be reused in electric vehicles such as forklifts. However, the feasibility of re-use in micro-mobility and consumer electronics is very limited. This study shows that technically feasible separation methods are dictated and constrained by the joining techniques used. As welding and adhesive bonding pose challenges to separation processes, future efforts should prioritize ‘design for disassembly’ to ensure sustainable battery life cycle management.

Design of the pistol P1 weapon storage system shelf using fingerprint electronic system in the TNI-AD units

One of the most important aspects of preventing illegal usage of firearms is safe storage. An inventive way to guarantee that only authorized owners can access the firearms is to incorporate fingerprint technology into the design of electric firearm storage systems. The use of fingerprint technology to increase the security of gun storage is desperately needed. However, issues with the finger's health, such as stains, cuts, and dampness, can impair the accuracy of the detection. This study employs a quantitative methodology using five fingerprint detection situations to test the accuracy of the system. The Arduino ESP was used as the foundation for the system implementation throughout the nine months that the research was conducted at the Kodiklatad Poltekad Electronics Laboratory. According to the test results, the system has 100% accuracy in identifying five different fingerprint detecting scenarios. But limitations are implied by the system's incapacity to identify damp, damaged, or dusty fingertips. Consequently, further thought must be given to these circumstances in order to improve system reliability. The design of an electric gun storage unit using Arduino ESP-based fingerprint technology has the potential to increase the security of gun storage, but more work is needed to address some issues that limit detection accuracy

Approaching zero emissions in ports: implementation of batteries and supercapacitors with smart energy management in hybrid ships

The urgent need to reduce energy consumption and environmental impact in the shipping industry has prompted research and industry to explore new solutions for minimizing fuel usage. This study examines the potential effects and benefits of integrating electrical energy storage systems, such as lithium-ion batteries and supercapacitors, into short sea shipping ships during port stay. Specifically, a novel dynamic simulation tool is developed to conduct suitable analyses that investigate the feasibility of charging electrical storage systems while at navigation and utilizing them as alternatives to traditional diesel generators while in port. The analysis reveals that lithium-ion batteries and supercapacitors are effective tools for reducing pollutant emissions by minimizing diesel generator usage during port stopovers, resulting in a significant reduction in fuel consumption from 1.148 kt/year to 0.511 kt/year. Moreover, under identical conditions, the use of supercapacitors increases the lifespan of batteries from 10.6 years to 11.9 years. Additionally, there is a 55 % reduction in carbon dioxide emissions during port stays, decreasing from 2.98 kt/year to 1.64 kt/year.

Maximizing renewable energy and storage integration in university campuses

The worldwide demand increase and climate change constraints are leading to integration and maximization of renewable energy sources (RESs) share in the energy production. This represents a key aspect to reduce the pollutant emissions and facilitate the transition to a cleaner future. Many agreements and incentives were discussed and imposed in order to limit the fossil fuel use and thus reducing the pollutant emissions to limit global warming. With this considerations, the renewable energy sources are installed with continuous share increase. To support this implementation and used simultaneously with providing a power reserve for these intermittent power sources, energy storage systems are becoming more and more used and necessary. Simultaneously with these changes, hydrogen is becoming more and more used, being a means by which electricity can be produced without generating greenhouse gas emissions (GGE). A power supply system with multiple sources is presented and its operation is analysed, the system contains photovoltaic panels, wind system, fuel cell, hydrogen generator, electricity and hydrogen storage system to supply the consumption of a student dormitory from a university campus in Romania with prosumer possibilities.

Working fluid pair selection of thermally integrated pumped thermal electricity storage system for waste heat recovery and energy storage

Global issues such as the energy crisis and carbon emissions impulse the development of waste heat recovery and energy storage technologies. In most practical industrial scenarios, the electricity supply and consumption cannot be perfectly matched and effective utilization of waste heat is in urgent need. In the present study, we develop a mathematical model to evaluate the thermally integrated pumped thermal electricity storage (TI-PTES) system to achieve off-peak electricity storage along with low-grade waste heat recovery. A double-layer optimization for screening working fluid pairs with high round-trip efficiency is carried out from 24 fluids of the heat pump and 21 fluids of the Organic Rankine cycle (ORC). In the first-layer multi-objective optimization, 3 types of working fluid pair combination strategies are compared and the great improvement of round-trip efficiency by using zeotropic fluids is proved. Among 7 energy storage temperatures covering from 393.15 K to 423.15 K with an increment interval of 5 K, the highest round-trip efficiency of 101.29% is achieved by adopting the zeotropic fluid pair [90Diethyl ether_10Pentane - 80Butane_20Pentane] at 398.15 K. Furthermore, in the second-layer single-objective optimization, the thermo-economic performance indicators of TI-PTES is evaluated and compared under different designing weighting factor groups, which effectively contributes to the screening of working fluids according to designer's trade-off. Finally, through varying energy storage temperatures and designing weighting factors, optimal working fluid pair recommendations including pure fluids and zeotropic ones were proposed to the fluid selection of TI-PTES.

Investigating Electricity Usage Profile for Residential Consumers in India

Electricity has become one of the essential needs in modern society. The demand and necessity for electricity have increased with population, trade, and industry, which has resulted in increased stress on the grid. As the development of an electricity storage system is costly, utilities are working on customer segmentation to classify the consumers according to their usage patterns to understand their demand and further develop resources accordingly. In this work, the K-Means clustering algorithm has been applied to a two-year monthly consumption data set (January 2019 to December 2020) of 100 residential consumers from Kutheda village Hamirpur (Himachal Pradesh, India) to visualize the diverse consumption behaviour. It resulted in four different clusters of consumers. It also showed that there is a group having a large number of consumers whose consumption is low and consumption pattern is very less flexible in different seasons. Another group of consumers with high consumption also shows high flexibility in consumption patterns, but their number is less. Also, the winter season shows the highest consumption of electricity and the monsoon season shows the lowest consumption of electricity in this particular region. This analysis will certainly help the electricity provider to make production planning for a different group of consumers in a specific season based on electricity consumption patterns.