Distributed Energy Storage Research Articles

Distributed energy storage system planning in relation to renewable energy investment

Distributed energy storage system planning in relation to renewable energy investment

Assessment of vehicle-side costs and profits of providing vehicle-to-grid services

The rapid expansion of electric vehicle market brings a huge stock of batteries, which can potentially serve as distributed energy storage systems to provide grid services through Vehicle-to-Grid (V2G) technology. Existing research on V2G's economic viability often simplifies intricate technical details and neglects the influence of key parameters on the results. To address these gaps, a technology-rich model was developed to evaluate the vehicle-side costs and profits of V2G. Given the current state of V2G-related technologies and costs, V2G's levelized cost of storage ranges from $0.085/kWh to $0.243/kWh, and its net present value ranges from $-1,317 to $3,013, depending on the operational strategies implemented. The variations in assessment results due to changes in key parameters were further evaluated to analyze the impacts of technological advancements and user behavior. With advancements in battery technologies, the net present value of V2G is expected to reach approximately $7,000. These findings underscore V2G's potential cost competitiveness against mainstream stationary energy storage technologies and suggest that, with appropriate technological development and usage scenarios, V2G could play a pivotal role in the new electricity system with renewable energy sources as the main component, offering substantial profitability.

Robust planning for distributed energy storage systems considering location marginal prices of distribution networks

Energy storage plays an important role in integrating renewable energy sources and power systems, thus how to deploy growing distributed energy storage systems (DESSs) while meeting technical requirements of distribution networks is a challenging problem. This paper proposes an area-to-bus planning path with network constraints for DESSs under uncertainty. First, a distribution location marginal price (DLMP) formulation with maximum fluctuation boundaries of uncertainties is designed to select vulnerable areas exceeding voltage limits and higher line losses that occur in distribution networks. Different from simple multi-scenario power flow calculation and sensitivity analysis, DLMP with time and regional characteristics could be more intuitive to reflect line losses and voltage limits of distribution networks through price signals. After that, a two-stage stochastic robust optimization based planning method is developed to determine locations and capacities of DESSs in vulnerable areas. To make the uncertainty problem more tractable, stochastic scenarios are used to portray upper and lower boundaries of uncertainties, which avoids too-conservative decisions for robust optimization. Finally, numerical tests are implemented to testify the reasonability and validity of the proposed area-to-bus planning path under uncertainty. Compared with the DESSs planning framework without DLMP, the costs of DESSs are observably reduced with DLMP. With same budgets of uncertainty, investment costs of DESSs for the stochastic robust optimization with 30 and 50 scenarios are 3.91% and 4.45% lower than classical adaptive robust optimization (ARO).

Effects of thermal insulation layer material on thermal runaway of energy storage lithium battery pack

The safety accidents of lithium-ion battery system characterized by thermal runaway restrict the popularity of distributed energy storage lithium battery pack. An efficient and safe thermal insulation structure design is critical in battery thermal management systems to prevent thermal runaway propagation. An experimental system for thermal spreading inhibition of lithium-ion battery modules was set up, in order to achieve the goal of zero spreading of thermal runaway between lithium-ion batteries in the module by using thermal insulation layer. And the effects of six different materials of thermal insulation layer on the thermal spreading process of lithium-ion battery modules were investigated. The results showed that the use of thermal insulation layers can effectively inhibit the thermal spread in the battery module. The average spreading time of each cell in the module with nanofiber insulation increased by 5.27 and 7.36 times, compared with that of the module without insulation. Compared with the use of nanofiber insulation layer, the thermal spreading between lithium batteries in the module is completely suppressed by the use of composite phase change insulation layer. The goal of zero spreading of thermal runaway within the module has been realized. The thermal spreading interval between the thermal runaway battery and the neighboring batteries in the module is increased to an infinite length, and only the thermal runaway battery shows the phenomenon of spraying valve such as fire and smoke. It is expected to have a guidance for the design of thermal insulation in lithium-ion battery modules.

Research on the control strategy of DC microgrids with distributed energy storage

As a supplement to large power grids, DC microgrids with new energy access are increasingly widely used. However, with the increasing proportion of new energy in DC microgrids, its output fluctuations directly affect the overall stability of the microgrids. Distributed energy storage can smooth the output fluctuation of distributed new energy. In this paper, an AC-DC hybrid micro-grid operation topology with distributed new energy and distributed energy storage system access is designed, and on this basis, a coordinated control strategy of a micro-grid system based on distributed energy storage is proposed. To maintain the voltage stability of the DC bus and make each station have the power-sharing ability, the AC/DC flexibly interconnected converter should adopt two control strategies. The power can flow bidirectional in the power scheduling and distribution of the energy storage station; At the same time, different power distribution schemes will generate different scheduling costs. To optimize the operation of energy storage power stations, an improved particle swarm optimization algorithm is adopted in this paper to optimize the scheduling task allocation scheme. The optimization objective is the lowest scheduling cost, to realize the optimal scheduling of energy storage power stations. In this paper, based on a Matlab/Simulink environment, a microgrid system based on an AC-DC hybrid bus is built. The simulation results verify the effectiveness of the proposed microgrid coordinated control strategy.

Auction-based single buyer energy trading framework in grid-tied microgrid with distributed energy storage and demand response using a multi-agent approach

This paper presents a modified single-buyer energy trading model for a grid-tied microgrid that uses a multi-agent system (MAS) approach in a two-stage market auction for multiple sellers and multiple buyers (MSMB).Additionally, a new linear bidding algorithm (LBA) is made available for use by stakeholders (sellers and buyers) in order to select the quote prices for day-ahead trade. By utilising available distributed generation (DG), distributed storage systems (DSS), and demand response (DR) in the first stage (local) market, if not forwarded to the second stage (global) market, this strategy seeks to reduce peak demand and maximise cost benefits for stakeholders and aggregators. The suggested market framework is simulated using MATLAB and Simulink. The simulation results of a test system, which indicate the differences before and after DR and DSS (DRaDS) are taken into consideration, are used to emphasise the effects of DRaDS on market clearing prices from the proposed two-stage market auction.

A Risk Assessment Method of New-type Power System Based on TabRAM

Background:: Under the "Dual Carbon" strategy, the new-type power system integrates various complex equipment, especially the addition of wind power, photovoltaic power generation, distributed energy storage and other systems, which not only brings clean and efficient energy to the power system, but also leads to potential risks in the power grid. Objective:: Traditional methods for assessing the risks of power systems mainly focus on the characteristics of traditional power grids. With the emergence of new features in the new power system, these traditional assessment methods are no longer effective, which could affect the safe and stable operation of the power system. To address this issue, this article proposes a new vulnerability index system for power systems. It also introduces the analytic hierarchy based on Moody's order graph (MAHP) quantification analysis method, and develops the Tabular data risk assessment methodology(TabRAM) risk assessment model based on an improved Transformer method. Method:: MAHP is a comprehensive evaluation method combining Moody's Chart and Analytic Hierarchy (AHP), which has high comprehensiveness and operability. TabRAM uses Bayesian neural networks (BNN) to complete prior data fitting and model complex feature dependencies and potential causal mechanisms on tabular data. The network is trained using an improved Transformer, and after pre-training, it can approximate new prior probabilistic inference in a forward pass to achieve prediction and classification tasks for new datasets. Results:: After multiple rounds of iterations and parameter adjustments, the model achieved the desired performance and was successfully applied to the risk assessment of the new power system. Conclusion:: According to the experimental results, the proposed TabRAM model demonstrates superior performance in the field of risk assessment for the new-type power system, as compared to traditional deep learning and machine learning algorithms.

Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch

This paper focuses on the dynamic economic emission dispatch (DEED) problem, to coordinate the distributed energy resources (DERs) in a power system and achieve economical and environmental operation. Distributed energy storages (ESs) are introduced into problem formulation in which charging/discharging efficiency is taken into account. By relaxing the nonconvexity induced by the charging/discharging model of ESs and network losses, we convert the non-convex DEED problem into its convex equivalency. Then, through a Lagrangian duality reformulation, an equivalent unconstrained consensus optimization model is established—a novel consensus-based decentralized algorithm, where the incremental cost is chosen as the consensus variable. At each iteration, only one primal variable requires sub-optimization, and it is completely locally updated. This is different from the well-known alternating direction method of multiplier (ADMM)-based algorithms where more than one subproblem needs to be solved at each iteration. The results of the comparative experiments also reflect the algorithm’s advantage in terms of computational efficiency. The simulation results validate the effectiveness of the proposed algorithm, achieving a balance between emissions and economic considerations.

Multi-Energy Cooperative Primary Frequency Regulation Analysis of a Hybrid Plant Station for Wind Power and Hydrogen Production Based on Ensemble Empirical-Mode Decomposition Algorithm

Wind curtailment and inadequate grid-connected frequency regulation capability are the main obstacles preventing wind power from becoming more permeable. The electric hydrogen production system can tackle the wind curtailment issue by converting electrical energy into hydrogen energy under normal operating circumstances. It can be applied as a load-reducing method during frequency regulation to help the system restore the power balance. First, this study proposes the concept of a hybrid plant station that combines the production of hydrogen and wind energy. This plant station will be referred to as a hybrid station with centralized hydrogen production and distributed energy storage. By mimicking the synchronous generator’s frequency control features, the primary frequency regulation mechanism of a hybrid plant station is examined. Secondly, due to the frequency regulation requirements of the power grid’s full-time domain hybrid power station, this paper proposes a hybrid plant station control strategy based on the EEMD variable parameter control algorithm. In frequency regulation power, the electric hydrogen production device load reduction responds to the low-frequency component, and the supercapacitor responds to the high-frequency component. The impact of the dynamic characteristics of alkaline electrolyzers on the frequency regulation effect is analyzed in this article, along with a comparison of the matching of various energy storage devices and electrolyzers in power grid frequency regulation. Finally, the feasibility and soundness of the proposed control strategy are confirmed by creating a simulation model representing a hybrid plant station involved in primary frequency management under various operational scenarios.

Battery Cell Balancing of V2G-Equipped Microgrid in the Presence of Energy Storage Aggregator

High electricity consumption in Sarawak’s cities is a concern, driven by residential, industrial, and commercial users. Greater demand for electrical energy requires more electrical resources, affecting grid stability. To prevent blackouts and enhance grid resilience, electric vehicles can serve as distributed energy storage in microgrids. The biggest problem facing energy storage is that not all batteries within energy storage are the same. Every battery cell in the battery pack has a different rate of self-discharge, capacity, internal resistance, and aging, even though they have the same chemical and physical characteristics. These variations lead to power imbalances in energy storage. Therefore, to address this issue, a unique voltage equalization method using a modular flyback converter is presented. Its core objective is to correct voltage imbalances among batteries. The proposed method is modelled using MATLAB, and the performance of the system has been evaluated. Furthermore, the developed model is implemented in a microgrid and tested for two different cases (with and without energy storage for electric cars). From the simulation outcomes, it has been verified that the developed cell balancing technique performs well in equalizing the voltage of cells than the conventional methods.

SOC Balance Control Strategy for Distributed Energy Storage System in DC Microgrid

By integrating a distributed energy storage system (ESS), a standalone DC microgrid can maintain power balance and voltage stability between distributed energy sources and loads. However, without establishing the relationship between the output power of the distributed energy storage units and their state of charge (SOC), inconsistent SOC among the storage units can occur. This inconsistency can lead to issues such as overcharging and over-discharging, significantly impacting the ESS lifespan. To address this, the paper proposes a SOC droop control scheme utilizing the arctangent function to rapidly achieve SOC balancing of the energy storage units. During the SOC balancing process, as the SOC difference between units decreases, the balancing speed also decreases. To overcome this, a variable acceleration factor is introduced in the arctangent function SOC droop control strategy. This can ensure a progressive increment in the acceleration factor as the SOC gap between energy storage units diminishes. In addition, the integration of DC bus voltage compensation control effectively mitigates voltage deviations within the bus. Finally, the paper presents and simulates a variable acceleration factor-based arctangent function SOC droop control. It demonstrates its effectiveness in achieving SOC balance and maintaining stable bus voltage during charging and discharging scenarios in distributed energy storage systems.

Dynamic Reactive Power Compensation Optimization Strategy for Distribution Networks with Distributed Generators and Energy Storage Systems

To balance the output and load dynamic changes of distributed power generators (DG) and energy storage systems (ESS), a demand-side response-based dynamic reactive power optimization (RPO) strategy for distribution networks (DN) is proposed. This strategy adopts a phased optimization method to coordinate and optimize different types of reactive variables within the same period. By adjusting the states of parallel capacitor banks (SCBs) on load voltage transformers (OLTC), DGs, and ESS inverters, the RPO of the DN is carried out. Based on the characteristics of the model, an improved particle swarm optimization algorithm (IPSO) is used to solve different types of variables, and an IPSO based on tent chaotic mapping and levy flight strategy is proposed to improve the efficiency of collaborative solving. The simulation results show that the proposed strategy, considering the change of new energy output and demand side response, can reduce the solution scale while obtaining high satisfaction RPO results.

Distributed Cooperative Event-Triggered Control of Cyber-Physical AC Microgrids Subject to Denial-of-Service Attacks

This paper addresses the event-triggered distributed cooperative secure secondary control for islanded cyber-physical inverter-based ac microgrids (MGs) under the energy-limited denial of service (DoS) attacks. The DoS attack refers to the prevention of information exchange among Distributed Energy Resources (DERs) in the secondary control level. In this paper, an event-triggered mechanism (ETM) is employed to improve communication efficiency and reduce control command updates. Based on the last successful local and neighboring transmission attempt, an estimator is proposed which is only activated over attack periods. In addition, this study investigates the contribution of both DERs and Distributed Energy Storage Systems (DESS) in ac MGs. Finally, the performance of the proposed control scheme is evaluated by an offline digital time-domain simulation on a test MG system through different scenarios in MATLAB/Simulink environment. Also, the effectiveness and accuracy of the controller are verified by comparison with several previous studies.

CASL: A Novel Collusion Attack Against Distributed Energy Management Systems

Recently, the security issues of smart grids have received wide attention. In particular, false data injection attacks against distributed energy management systems (DEMSs) are considered of high importance as they are able to cause economic losses or even damages to system stability in subtle ways. Existing studies have identified many such attacks and proposed corresponding countermeasures. However, in this paper, we show that DEMS is still insecure and in risk of economic loss by proposing and analyzing a novel attack called Collusion Attack between a Storage and a Load (CASL). In CASL, a distributed energy storage device colludes with a load by injecting an abnormal extra power supply to the load in a secret way through using two secret functions. In this way, the colluding pair behaves as normal ones in both computational and communicational viewpoints. We theoretically prove the convergence of DEMS in presence of the attack, and derive an upper bound of the social welfare losses of DEMS caused by CASL. We demonstrate the effectiveness of CASL through simulations.

Optimal operation of microgrid with consideration of V2G's uncertainty

AbstractThe aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA.

Coordinated design of multi-stakeholder community energy systems and shared energy storage under uncertain supply and demand: A game theoretical approach

Shared energy storage plays an important role in achieving sustainable development of renewable-based community energy systems. In practice, the independent or disordered planning of community energy systems and shared storage systems can lead to suboptimal design without considering the complex interactions between neighboring energy systems. Therefore, a coordinated design approach for community energy systems and shared energy storage is proposed, and a pricing mechanism for storage sharing based on bounded rationality theory is developed. A Stackelberg game is introduced to enable consideration of storage sharing among energy systems at the design phase. The uncertainties of solar irradiation, wind speed, and electrical demand on both the supply and demand sides are also considered. Then, an uncertainty model is formed to optimize the system configurations, operation strategies, and storage sharing strategies of each stakeholder, simultaneously. Furthermore, the influence of the profit margin, storage investment cost, and carbon tax on the storage rental price and system design strategies are discussed. Illustrative examples highlight the feasibility and applicability of the shared storage pricing mechanism and coordinated design approach. This paper provides references for the government with respect to the problem of coordinated design for multi-stakeholder distributed energy systems and storages in a market-oriented environment.

Study on the cooling performance of a new secondary flow serpentine liquid cooling plate used for lithium battery thermal management

To improve the thermal and economic performance of liquid cooling plate for lithium battery module in the distributed energy storage systems, on the basis of the traditional serpentine liquid cooling plate, the unidirectional secondary channels and grooves are added, combined to three kinds of serpentine cold plates for the battery module. By contrast, the cold plate with both elliptical groove and secondary channel has the best comprehensive performance. Though the thermal performance is slightly reduced, the consumption of pump power is reduced by 92.6 % and the cooling efficiency coefficient is improved by 12.32 times compared to the original cold plate. The cooling temperature targets of the battery pack are that Tmax less than 40℃ and ΔTavg less than 3 K. In order to achieving the best performance of the cold plate, the influence of the structural parameters and hydrodynamic parameters on the performance index is analyzed, and the sensitivity analysis is carried out which clear the operational mechanism and key design parameters of the cold plate. The results show that different design parameters would generate the change of eddy current which is influential to the cooling performance. Besides, v and D have a significant effect on the performance of the cold plate, followed by d and n, and a has almost no effect on the cooling performance.

Optimal Dispatch Strategy for a Distribution Network Containing High-Density Photovoltaic Power Generation and Energy Storage under Multiple Scenarios

To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage.

Model-based battery thermal parameter optimization using symbolic regression

Lithium-ion battery packs are commonly used in electromobility and distributed energy storage. However, keeping both cell temperature and inter-cell temperature differences within operating ranges is important to avoid a negative impact on performance and lifespan. For this reason, modeling the thermal behavior of battery packs in different operating conditions is crucial for both design and practical use.In this work, we optimize the parameters of a battery thermal model using a symbolic regression method based on evolutionary algorithms. We obtain power-type expressions for the drag coefficient, friction factor, and heat transfer correlation (Nusselt number), as a function of the Reynolds number and the cell spacing factor, plus the Prandtl number in the case of the Nusselt number. The model is adjusted with CFD simulations of battery modules containing up to 102 cells to obtain the steady-state temperature distribution. Compared to CFD simulations, the proposed model gets a mean absolute percentage error of 2.39 % in estimating the temperature in a 53-cell battery pack. The adjusted parametric thermal model is validated with experimental data for cooling down a battery pack and a single cell, obtaining a mean absolute percentage error of 1.26 % and 4.68 %, respectively, in estimating the dynamic temperature balance.The adjusted parametric thermal model and the mathematical expressions obtained for the drag coefficient, friction factor, and Nusselt number, can be useful for designing battery thermal management systems in electromobility and distributed energy storage.

Research on Distributed Energy Storage Planning-Scheduling Strategy of Regional Power Grid Considering Demand Response

Distributed energy storage and demand response technology are considered important means to promote new energy consumption, which has the advantages of peak regulation, balance, and flexibility. Firstly, this paper introduces the carbon trading market and the new energy abandonment penalty mechanism. Taking the energy storage cost, distribution network operation cost, network loss cost, carbon transaction cost, and new energy abandonment cost as the objective functions, the distributed energy storage optimization allocation model is established. Considering the scheduling problems of various units in the system, the operation strategy of distributed energy storage and demand response linkage is proposed. The improved bat algorithm is used to solve the problem. The example analysis shows that under the combined action of energy storage and demand response, the annual total cost of the distribution network is effectively reduced, the consumption rate of new energy is improved, and the voltage quality and network loss of the power grid are improved to ensure the reliability and stability of the distribution network.