Distributed Energy Storage Research Articles

Promoting Reaction Kinetics of the Air Cathode for Neutral Zinc-Air Batteries by the Photothermal Effect.

Rechargeable neutral zinc-air batteries are attracting enormous research interest due to their long lifetime and low cost. However, the sluggish kinetics of the air cathode in the neutral electrolyte reduces the catalytic performance and impedes the power density and energy efficiency of zinc-air batteries. In this work, we propose a universal, sustainable, and effective approach to improve the kinetics of the air cathode through a solar-energy-induced photothermal effect. By illumination of the cost-effective carbon-black-based cathode, the temperature of the air cathode increases from room temperature to 40.5 °C, thereby accelerating the kinetics for the electrocatalytic reaction and reducing the interfacial resistance of the zinc-air battery. Attributed to the sunlight-promoted reaction kinetics, the neutral zinc-air battery exhibits a higher power density of 2.89 mW cm-2 and longer cycling durability over 250 h, 116 and 156% times the one without light illumination, significantly improved in contrast to the one without light illumination. Furthermore, we demonstrated solar energy-driven and solar-enhanced charging in the daytime and discharging at night, potentially applying in distributed energy storage applications. The proposed sustainable solar-energy-promoted reaction kinetics of air cathodes will drive the development of efficient zinc-air batteries and also inspire the rational design of electrocatalytic electrodes in other electrochemical devices.

An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform.

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Optimizing Federated Reinforcement Learning Algorithm for Data Management of Distributed Energy Storage Network

Optimizing Federated Reinforcement Learning Algorithm for Data Management of Distributed Energy Storage Network

Large-scale energy storage for carbon neutrality: thermal energy storage for electrical vehicles

AbstractThermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure.

Empowering energy management in smart buildings: A comprehensive study on distributed energy storage systems for Sustainable consumption

The increment of photovoltaic generation in smart buildings and energy communities makes the use of energy storage systems desired to increase the self-consumption efficiency. This paper proposes and explores a model for energy storage systems management that considers local renewable generation, local demand, and retailer energy prices. The proposed model was tested at both energy community-level and the smart building level, demonstrating their capabilities of deployment. To validate the proposed model, a case study with two scenarios, including a 251 members energy community, was executed. The results demonstrate significant cost reductions for community members when adopting energy storage systems and the proposed management model. Regarding the smart building application four scenarios were tested, it is demonstrated that the demand for energy from the retailer could be set to zero during periods of time to enable its participation in demand response events. Overall, this paper contributes to the state-of-the-art by identifying and evaluating a model that manages the energy storage systems charge and discharge operation to actively reduce energy costs at the community-level (19.26%) and building level (11.75%) and to demonstrate that part of the loads can be optimized to understand if the building can be energy net-zero.

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System

A low voltage load balancing distribution method considering street information and V2G technology application

The low-voltage distribution network (LVDN) is the final stage in delivering electric energy from power plants to consumers, and its operational condition greatly impacts many power users. While medium-voltage and high-voltage distribution networks can be managed through intelligent digital systems, load imbalance issues in LVDNs often rely on planners’ experience, leading to significant limitations. With advancements in electric vehicle (EV) charging technology and vehicle-to-grid (V2G) technology, where EVs act as distributed energy storage units, bidirectional energy exchange between vehicles and the grid can now contribute to LVDN operation. This paper proposes a low-voltage load distribution planning method that integrates street information and V2G technology. A two-stage stochastic programming mixed-integer model is developed to tackle load imbalance in LVDNs, with the planning scheme derived from solving this model. A case study is presented to verify the effectiveness of the method, demonstrating that incorporating V2G technology enhances load distribution accuracy and reduces reliance on manual planning, improving network stability and operational efficiency.

Research on Optimal Scheduling Strategy of Differentiated Resource Microgrid with Carbon Trading Mechanism Considering Uncertainty of Wind Power and Photovoltaic

Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access have led to the threat against the safe and stable operation of the current power system. With the increasing uncertainty on both sides of power supply and demand, the microgrid (MG) is needed to effectually aggregate, coordinate, and optimize resources, such as adjustable resources, distributed power supply, and distributed energy storage in a certain area on the demand side. Therefore, in this paper, the uncertainty of wind power and PV is first dealt with by Latin hypercube sampling (LHS). Secondly, differentiated resources in the MG region can be divided into adjustable resources, distributed power supply, and energy storage. Adjustable resources are classified according to demand response characteristics. At the same time, the MG operating cost and carbon trading mechanism (CTM) are comprehensively considered. Finally, a low-carbon economy optimal scheduling strategy with the lowest total cost as the optimization goal is formed. Then, in order to verify the effectiveness of the proposed algorithm, three different scenarios are established for comparison. The total operating cost of the proposed algorithm is reduced by about 30%, and the total amount of carbon trading in 24 h can reach nearly 600 kg, bringing economic and social benefits to the MG.

Optimal price-taker bidding strategy of distributed energy storage systems in the electricity spot market

Optimal price-taker bidding strategy of distributed energy storage systems in the electricity spot market

Optimal configuration of distributed energy storage based on the identification of voltage-vulnerable nodes

Optimal configuration of distributed energy storage based on the identification of voltage-vulnerable nodes

CPS-based power tracking control for distributed energy storage aggregator in demand-side management

The deployment of distributed energy storage on the demand side has significantly enhanced the flexibility of power systems. However, effectively controlling these large-scale and geographically dispersed energy storage devices remains a major challenge in demand-side management. In this paper, we propose a CPS-based framework for controlling a distributed energy storage aggregator (DESA) in demand-side management. Within this framework, a distributed power tracking control algorithm is designed to ensure both power tracking and state-of-charge (SoC) balancing among the energy storage units (ESUs) within the DESA. The proposed algorithm utilizes a distributed observation-based approach that relies solely on local communication. It is demonstrated that the algorithm achieves power tracking convergence within a fixed time, while asymptotically achieving SoC balancing when assuming a connected communication network among the storage units. To validate the theoretical analysis and demonstrate the effectiveness of the proposed control strategy, an example scenario comprising six ESUs is presented.

Electric vehicle aggregator as demand dispatch resources: Exploring the impact of real-time market performance on day-ahead market

As the integration of variable renewable energy sources (VREs) into power grids escalates, effectively managing the unpredictability of VRE generation becomes crucial. Electric vehicles, utilized as distributed energy storage, emerge as a potential solution. However, it remains unclear whether electric vehicle aggregators (EVAs), acting as demand dispatch resources, have a favorable impact on the overall electricity market, especially when considering their performance in real-time markets during day-ahead energy allocations. Therefore, this paper proposes a decentralized clearing mechanism (DCM) relying on an multi-attribute evaluation model of EVAs for optimal bidding strategies in the day-ahead market. The EVAs multi-attribute evaluation model employs an integration of the Entropy-Analytic Hierarchy Process, allowing for the simultaneous consideration of real-time performance and day-ahead bidding prices of EVAs. Consequently, the decentralized clearing mechanism supports strategic electricity planning in the day-ahead market by incorporating the actual market performance of EVAs. Furthermore, a real-time market bidding model is developed to balance dynamic mismatches between VRE generation and demand, aiming to maximize profits through optimized real-time commitment and real-time dispatch. System simulations on the IEEE39 bus system demonstrate that the DCM enhances overall profits by 18.69% compared to the centralized clearing mechanism.

A novel and cost-effective model for cloud energy storage based on a dual storage setup in active distribution grid with resilience consideration

The rising occurrences of natural disasters, terrorist actions, and cyber-attacks that result in extensive, long-lasting, and expensive disruptions have necessitated a shift in focus towards the resilience of electrical grids for network operators. However, the task of designing a resilient network remains complex and costly. One potential solution to bolster resilience is the deployment of battery energy storage devices on the consumer side, known as distributed energy systems (DES). Despite its effectiveness, the high construction costs and lengthy payback period associated with investing in energy storage devices have led consumers to exhibit reluctance in adopting them. Cloud energy storage (CES) is an innovative and cost-effective solution to address those challenges. In the CES platform, investors install storage facilities in the network which can be rented by consumers to fulfill their needs and they become holders of the virtual batteries. By adopting this approach, consumers are relieved from the burden of maintenance, repair, and installation. While a single CES facility offers reduced costs and increased comfort for consumers, it compromises the resilience of the grid when compared to the Distributed Energy Storage (DES) mechanism. In order to bridge this gap, this paper proposes a dual CES model which serves as an intermediate solution between DES and single CES. The dual CES model strikes a balance between the resilience of the grid and cost-effectiveness. It provides a higher level of resilience compared to a single CES and a lower level compared to DES. Additionally, the costs associated with the dual CES model fall between that of a single CES and DES. This model not only increases the profit margin for investors but also enhances the overall comfort and well-being of consumers compared to the single CES. To validate the proposed model, a Mixed Integer Linear Programming (MILP) problem is formulated and simulated on the IEEE 33 bus network. Three cases are considered: DES, single CES, and dual CES. The results indicate that the dual CES reduces consumers' costs by 28 %, losses by 27 %, unsupplied load costs by 45 %, and return on investment by 33 %. Moreover, it increases the stability margin time by 2 intervals and improves robustness by 47 %.

The Design and Implementation of Blockchain Smart Contract in Hybrid Energy Storage Management of Photovoltaic System

As the main power supply mode of new energy, photovoltaic system has the characteristics of wide distribution and scattered energy collection Traditional management methods cannot realize the effective processing of distributed energy storage. Therefore, finding an effective intelligent method is the focus of current research, and the design of blockchain smart contracts can theoretically realize the centralized processing of photovoltaic energy storage systems. In this paper, the blockchain smart contract system is taken as the research object, and the energy storage management of the photovoltaic system is quantified through mapping, so as to realize the massive quantitative processing of data. Then, the reduction function is used to differentially reduce the distributed functional energy storage system, complete the centralized management of energy storage, and simplify the unnecessary data. Finally, combined with the hybrid energy storage situation, the key indicators in the data are mined, and the completion rate of the contract and the stability analysis of energy storage management are calculated. The results show that blockchain can mine the characteristics of hybrid energy storage, reduce the impact of distributed features on energy storage, improve the energy storage efficiency by 20%, and make the energy storage stability reach more than 90%. At the same time, the completion rate of smart energy storage contracts can be enhanced, the fitting degree can reach 90%, and the grid connection with the thermal grid can be effectively connected, the utilization rate of distributed energy resources will reach 85%, and the cost saving rate will be increased by 10%. Therefore, blockchain can solve the problem of hybrid energy storage in integrated systems, improve the completion rate of smart contracts, and promote the development of new energy.

A Fast State-of-Charge (SOC) Balancing and Current Sharing Control Strategy for Distributed Energy Storage Units in a DC Microgrid

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios.

Optimal dispatching of distributed photovoltaic system based on operating costs

Abstract This article investigates the method of distributed photovoltaic optimal dispatching method. The goal is to minimize the operating costs of the distribution network system by coordinating electric heating and distributed energy storage when adjusting the demand-side resources of the distribution network. The analysis is conducted on the operating costs of the distribution network system under different penetration rates of photovoltaics to further promote the accommodation capacity of distributed photovoltaics. The research results show that when the penetration rate of photovoltaics is less than or equal to 90%, only electric heating needs to participate in the accommodation capacity of distributed photovoltaics; when the photovoltaic penetration rate exceeds or equals 100%, both distributed energy storage and electric heating need to participate in the accommodation capacity of distributed photovoltaics. As the penetration rate of photovoltaics gradually increases, the total operating costs and purchased electricity costs show a downward trend. When the penetration rate of photovoltaics increases from 70% to 100%, the network loss costs decrease first, but when the penetration rate reaches 110%, photovoltaics cannot be completely integrated, leading to an increase in network loss costs and dispatching costs. This method fully utilizes the flexible adjusting capabilities of electric heating loads and distributed energy storage, promotes the accommodation capacity of distributed photovoltaics, and improves the economic efficiency of distribution network operation.

Distributed energy storage participates in reactive power optimization strategy research of new distribution system

Abstract We studied the reactive power control strategy of distributed energy storage in distribution systems, improved reactive power support capacity, and enhanced system reliability and new energy carrying capacity. Firstly, the principles and methods of reactive power optimization in distribution networks are studied. Then, the principles and mechanisms of distributed energy storage participating in reactive power control in distribution networks are studied. Finally, the genetic algorithm optimization method for reactive power optimization in distribution networks is studied, including parameter setting, target selection, and genetic strategy improvement, providing support for fine control of reactive power and voltage in distribution systems and efficient operation. Through simulation analysis of the IEEE33 node system, it is shown that distributed energy storage can improve the reactive voltage level of the distribution system and promote the consumption of new energy.

A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system

Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

Optimizing domestic energy management with a wild Mice colony-inspired algorithm: Enhancing efficiency and coordination in smart grids through dynamic distributed energy storage

Energy management (EM) is a critical strategy that spans the production and consumption of electricity, enhancing the stability of the electricity network. Smart grid technology significantly improves the electrical system's energy efficiency (EE), facilitating a transformation from a conventional power grid (PG) to a smart PG. This paper presents a key strategy for modeling the EE of the smart grid tailored to domestic demand, establishing smart coordination between domestic demand, energy production, and storage to reduce energy waste and costs. Our model integrates various energy sources, including renewable energy (RE), photovoltaic (PV) systems, wind power, and an energy storage system (ESS), interconnected with the PG. The model's structure ensures the coordinated flow of electricity in a residential house through an optimal control method (OCM). To develop a robust closed-loop control model, we employ Demand Response (DR) schemes within the Real-Time Electricity Pricing (RTEP) framework. We construct a dynamic model of the ESS to compute the System Performance Index (SPI), corresponding to energy costs. To enhance our model, we introduce a Dynamic Distributed Energy Storage Strategy (DDESS). Additionally, we introduce a novel optimization algorithm inspired by the behavioral patterns of wild mice, called the Wild Mice Colony (WMC). By analyzing the targeted and advantageous behaviors of wild mice in colonies, we propose that these behaviors can serve as a model for addressing complex, uncertain problems. This strategy is highly advantageous, capable of reducing total energy consumption (EC) from the main grid by over 100 % of the load demand, optimizing the energy system, and ensuring synchronization. The performance of DDESS optimizes energy flow (EF) during the repayment plan, leading to minimized EC costs from the PG.