Energy Storage Resources Research Articles

Optimization Strategy for Shared Energy Storage Operators-Multiple Microgrids with Hybrid Game-Theoretic Energy Trading

To address the issue of low utilization rates, constrained operational modes, and the underutilization of flexible energy storage resources at the end-user level, this research paper introduces a collaborative operational approach for shared energy storage operators in a multiple microgrids (ESO-MGs) system. This approach takes into account the relation of electricity generated by MGs and the integration of diverse energy storage resources managed by ESO. A hybrid game-theoretic energy trading strategy is employed to address the challenges associated with energy trading and revenue distribution in this joint operational mode. Firstly, a multi-objective master–slave game optimization model is developed with the objective of maximizing the revenue earned by shared energy storage operators while simultaneously minimizing the operational costs of multiple microgrids. Secondly, acknowledging the peer-to-peer (P2P) energy sharing dynamics inherent in the multiple microgrid system, a non-co-operative game model is formulated. This model seeks to establish a multi-microgrid Nash equilibrium and equitable income allocation. Finally, leveraging the Karush–Kuhn–Tucker (KKT) conditions and drawing upon the principles of strong duality theory, precise dimensionality reduction is executed on the master–slave game model. The non-co-operative income is iteratively determined using the alternating direction multiplier algorithm. The empirical findings of this study indicate that the integration of electric vehicle clusters contributes to flexible storage resources for shared energy storage operators. Moreover, the proposed hybrid game optimization strategy enhances the overall benefits for shared energy storage operators and multiple microgrids, thereby affirming the economic viability and reliability of this innovative strategy.

Benefit Evaluation of Carbon Reduction and Loss Reduction under a Coordinated Transportation–Electricity Network

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

Distribution network restoration supply method considers 5G base station energy storage participation

This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output.

Dispatching Method of Distributed Energy Storage Resources in Substation Based on Peak-shaving Compensation Response Model

The conventional distributed energy storage resource scheduling method is mainly based on automatic load demand response scheduling, and the load response gap between peak hours and low hours is large, which affects the economic benefits of energy storage resource scheduling. Therefore, a distributed energy storage resource scheduling method based on a peak shaving compensation response model is designed. The cluster scheduling characteristics of distributed energy storage resources are extracted, and the distributed energy storage resources are charged and discharged synchronously at the same scheduling node. Combined with the resource scheduling strategy of the substation, the scheduling load of energy storage resources is limited so that the scheduling impact on the substation power grid is reduced. Based on the peak-shaving compensation response model, the dispatching objective function of distributed energy storage resources in the substation is optimized, the dispatching of distributed energy storage resources in the substation is optimized from the perspective of safe and economic operation, and the stochastic error of distributed energy storage resource scheduling is avoided in the form of a compensation response constraint to meet the resource scheduling requirements. Comparative experiments show that this method is more economical and can be applied to real life.

A Two-Stage Scheduling Strategy for Electric Vehicles Based on Model Predictive Control

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs’ uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations.

High temporal resolution generation expansion planning for the clean energy transition

As power systems integrate increasing quantities of wind, solar and energy storage resources, it is important to revisit power system capacity expansion modeling methods and assumptions that have been utilized in thermal-dominated systems. We conduct a series of case study analyses using a simplified representation of the Electric Reliability Council of Texas (ERCOT) system to demonstrate how least-cost capacity expansion outcomes are impacted by changes in model resolution across two temporal dimensions: 1) the number of considered representative periods, and 2) the system dispatch interval. First, we find that the least-cost generation portfolio can differ significantly for small changes in the number of representative days, but largely converges to the 365-day result once 104 representative days are considered. Furthermore, systems with wind, solar and storage resources were more sensitive to changes in the number of representative days than a thermal-dominated system. Second, we find that considering five-minute dispatch resolution consistently results in least-cost generation portfolios with less solar capacity and more energy storage capacity than corresponding scenarios with hourly dispatch intervals. This suggests that hourly dispatch representation fails to capture the intra-hour volatility of solar generation, and therefore also overlooks opportunities for storage resources to provide system value by balancing this volatility. Collectively these results indicate that capacity expansion modelers should revisit conventional approaches to temporal representation when conducting analyses of deeply decarbonized power systems to ensure that such analyses are robust and actionable. To our knowledge, this is the first study to analyze capacity expansion outcomes with five-minute dispatch resolution in this manner.

Energy Management System for Smart Grid in the Presence of Energy Storage and Photovoltaic Systems

Today, the desire to use renewable energy as a source of clean and available energy in the grid has increased. Due to the unpredictable behavior of renewable resources, it is necessary to use energy storage resources in the microgrid structure. The power generation source and the storage source in microgrids should be selected in such a way that it has the ability to respond to the maximum demand in the state connected to the grid and operate independently. In this article, the optimal capacity and economic performance of a microgrid based on photovoltaic and battery system have been investigated. In this way, first, using the iterative optimization method, the optimal microgrid capacity has been obtained. Then, the dynamic planning method has been used for optimal microgrid energy management. The simulation results show the accuracy and efficiency of the proposed solutions. The proposed controller, while automatically and dynamically adapting to the solar cell output changes, is capable of responding to external requests, such as price signals or satisfying power system constraints or operator requests. In addition, the results indicate that by using the proposed energy management system, the microgrid system can regain stability during one to two cycles, during the occurrence of PV system radiation changes as well as ESS charge changes. And also, according to the ESS charge changes, the voltage changes should be within the defined permissible range between 0.95 and 1.05 pu, which is the result of the unique efficiency of the proposed energy management system.

Optimized scheduling study of user side energy storage in cloud energy storage model.

With the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, the author introduced the concept of cloud energy storage and proposed a system architecture and operational model based on the deployment characteristics of user-side energy storage devices. Additionally, a cluster scheduling matching strategy was designed for small energy storage devices in cloud energy storage mode, utilizing dynamic information of power demand, real-time quotations, and supply at the load side. Subsequently, numerical analysis was conducted to verify that the proposed operational mode and optimal scheduling scheme ensured the maximum absorption of renewable energy, improved the utilization rate of energy storage resources at the user side, and contributed to peak shaving and load leveling in the power grid. The model put forward in this study represents a valuable exploration for new scenarios in energy storage application.

Optimization of configurations and scheduling of shared hybrid electric‑hydrogen energy storages supporting to multi-microgrid system

As the energy structure undergoes transformation and the sharing economy advances, hydrogen energy and shared energy storage will become the new norm for addressing future energy demand and user-side storage applications, in order to better meet the flexibility and sustainability requirements of the energy system. This paper focuses on shared energy storage that links multiple microgrids and proposes a bi-layer optimization configuration method based on a shared hybrid electric‑hydrogen storage station for microgrids, combining cooling, heating, and power systems, to better achieve efficient energy utilization and promote sustainable development. The upper-layer model solves the energy storage station capacity configuration problem, while the lower-layer model solves the optimization operation problem of the multi-microgrid system. The lower-layer model is transformed into a constraint condition of the upper-layer model based on the Karush-Kuhn-Tucher condition of the lower-layer optimization model, and the Big-M method is used to linearize nonlinear problems. The rationality and effectiveness of the proposed bi-layer planning model are verified through analytical examples in three different scenarios. The results showed that compared to individual energy storage, shared power storage achieved an average daily net income of $430.00, reduced battery capacity by 75.94 %, and reduced daily operating costs of the microgrids by 11.53 %. Hybrid energy storage increased the daily net income of the energy storage side by 61.67 %, further reduced battery capacity by 67.13 %, and further reduced daily operating costs of the microgrid by 3.39 %. In addition, the optimal capacity allocation of energy storage systems and the operation optimization of multi microgrid systems have been achieved. The results demonstrate that the proposed hybrid energy storage services can effectively reduce user costs, save energy storage resources, and achieve mutual benefits for both users and energy storage operators.

The Importance of Sustainable Biomass Sources in the Development of Porous Carbonaceous Materials-Synthesis and Energy Applications: A Review

In this review, a variety of planning methods for porous organic materials using different types of biomasses are compiled, and their functions are discussed. Improved pore architectures and variable surfaces were created in porous carbons manufactured from biomass. It is effective and sustainable to use biomass waste, which mostly consists of cellulose, hemicellulose, and lignin. They assemble microtextured structures around one another in multi-channel configurations. The possibility of incorporating porous carbon compounds derived from biomass is being explored due to their distinct surface and structure. These "old type" carbons, which are produced through direct pyrolysis or physical or chemical activation, represent the instantaneous usage of biomass. There has been a lot of research on the chemical activation of biomass to create extremely effective activated porous carbons for gas collection. They consist of a variety of small patterns, pore dispersions, and conductive spines and have adaptive physicochemical properties that permit their application as dynamic stage networks or electroactive materials. The structure of carbon-based materials obtained from biomass has the potential to improve electrochemical energy storage. Energy storage resources are required more frequently every day. Energy storage is essential to meet the energy demands resulting from the interruption of energy sources due to several circumstances. There are many uses for carbon materials, including the storage of energy, the filtration of water and air, and the storage of gases like carbon dioxide (CO2), nitrogen (N2), methane (CH4), and hydrogen (H2). In this study, the advantages of cellulose-based systems as well as the latest developments and methods for creating high-capacity electronics are highlighted.

Chance-Constrained Generic Energy Storage Operations Under Decision-Dependent Uncertainty

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.

Techno-environmental-economical performance of allocating multiple energy storage resources for multi-scale and multi-type urban forms towards low carbon district

In addition to battery and thermal storage, electric vehicle shows increasingly great potential as energy storage according to the growth trend. More importantly, natures of urban form including size and type mix can greatly influence the sharing performance. Investigating the influence of various urban forms can benefit to decision making and implementation policy for city-scale low carbon planning. Therefore, this study has built an optimization model to allocate multiple energy storage resources based on their characteristics for cooperation, and perform the model on multi-scale and multi-type urban forms for analysis. Results show that, for multi-scale district, annual cost decreases by up to 55.8 % after optimization. Annual cost from buildings unit to community unit decreases 27.3 %, while only 1.9 % annual cost reduction is achieved from community unit to district unit. Urban forms with higher type entropy have weaker dependence of storage resources. Combining all energy storage resources can reduce at most 29.5 % annual cost compared with using one type only. Sensitivity analysis shows that PV generation ranks the top in optimization. When PV generation doubles, the improvement of annual cost reduction is 30.0 %. With PV volume increases, performance improves with carbon prices and EV number rising.

Improving data accessibility and application for underground climate re-search at the British Geological Survey

Improving data accessibility and application for underground climate re-search at the British Geological Survey

Stackelberg game-based three-stage optimal pricing and planning strategy for hybrid shared energy storage

Inspired from sharing economy and advanced energy storage technologies, hybrid shared energy storage (HSES), as an innovative business model, can provide flexible storage leasing services to new energy stations (NESs) and bring additional profits to the energy storage owner. Under this business model, pricing and planning issues are the main focus of the HSES operator to increase revenues but are rarely considered in current studies. Therefore, a Stackelberg game-based three-stage optimal pricing and planning strategy of HSES is formulated for the operator. First, an HSES model considering two leasing options is developed to provide two kinds of short-term use rights of energy storage resources for NESs. Then, the interactions between selfish NESs and the HSES operator are characterized as a Stackelberg game, and a bi-level pricing and planning strategy optimization model is developed to help the HSES operator make optimal decisions. Finally, considering different characteristics in each stage of the Stackelberg game, a three-stage solution method based on the genetic algorithm (GA) and mixed-integer linear programming (MILP) models is proposed to solve the optimization problem. Case studies on six NESs in a certain region are taken to verify the effectiveness of the proposed strategy. Simulation results show that the HSES operator can obtain maximum profit under the proposed pricing and planning strategy. In addition, the proposed HSES leasing model can provide additional benefits to both the operator and NESs.

Operation control technology of source network load storage area based on flexible application of energy storage

This paper studies the operation control technology of source-network-load-storage area. Firstly, the flexible application mode of energy storage in the source-network-load-storage area is analyzed. Secondly, the definitions of absolute mileage and relative mileage are proposed, and the power coordination control model of energy storage units based on mileage balance is established to achieve the purpose of efficient utilization of energy storage resources. Finally, the operation control strategy of the source network load storage area is proposed, which verifies the effectiveness and practicability of the power coordination control model of the energy storage unit and the operation control strategy of the source network load storage area.

Role of Acetonitrile Additive in Enhanced Performances of Aqueous Zn Anodes

Zinc-ion batteries have been proposed as promising candidates to address ever-growing demands for energy storage and limited resources available for the currently used lithium-battery systems. Despite their promise, the apparent advantages of aqueous zinc-ion batteries (low cost and high energy density) are still overshadowed by uncontrolled water interactions at the zinc metal-electrolyte interface, such as the hydrogen evolution reaction and electrode corrosion. A number of different strategies – e.g., water-in-salt, addition of chelating agents and/or co-solvents – have been proposed to overcome the aforementioned interfacial problems. Among them, the addition of acetonitrile to aqueous ZnSO4-based electrolytes has shown great promise, with reports of improved Coulombic efficiencies and increased cycle life. However, the actual role of this additive remains unclear. In this work, we determine the mechanistic principles behind the participation of acetonitrile in enhancing the performance of aqueous Zn batteries. For this purpose, we utilized a combination of X-ray absorption and Raman spectroscopies, cyclic voltammetry, and galvanostatic coin cell cycling. Our results indicate that the addition of acetonitrile perturbs the interfacial solvation structure, rather than the bulk. Specifically, acetonitrile does not penetrate the first solvation shell of the Zn2+ ion, but does modify interfacial solvation behavior, likely by physisorption to the electrode surface. The Coulombic efficiency of half cells was found to depend on electrode material, employed current density and capacity, and only moderately on acetonitrile concentration. Overall, this works points strongly to the need for systematic evaluation of battery performance in co-solvent systems, and provides a new framework for future efforts to optimize ion transport and performance in zinc-ion batteries._______________________The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan

Optimal planning of energy storage system under the business model of cloud energy storage considering system inertia support and the electricity-heat coordination

As the penetration rate of renewable energy increases in the electric power system, the issues of renewable power curtailment and system inertia shortage become more severe. Innovative solutions such as Cloud Energy Storage (CES) can be employed to address this challenge. However, the energy storage resources aggregated by the traditional CES business model mainly concentrate on Electrical Energy Storage (EES), which is still limited and expensive. It necessitates the exploration of new approaches to enhance the flexibility and cost-effectiveness of energy storage utilization, in which using District Heating System (DHS) as an equivalent energy storage resource of the power system is an effective method. Therefore, this paper proposes an optimal planning strategy of energy storage system under the CES model considering inertia support and electricity-heat coordination. Firstly, the system components and business model of the CES are described, and the framework of energy storage planning problem from the perspective of CES operator is formulated. Then the evaluation methods of energy storage utilization demand from CES users are proposed, including the evaluation of the renewable power curtailment, system minimum inertia requirement, and the equivalent energy storage ability of DHS. Based on this evaluation results, a bi-layer optimal energy storage planning model for the CES operator is established, where the upper-layer model determines the installed capacity of lithium (Li-ion) battery station and the lower-layer model determines the optimal schedules of the CES system. The numerical tests based on the operation profile of a typical city in China are carried out to demonstrate the effectiveness of the proposed method. The simulation result shows that the annual profit of the CES system can be improved by 15.26% after installing the energy storage system whose capacity is determined by the proposed method.

Low-Carbon Economic Optimization of Integrated Energy System Considering Refined Utilization of Hydrogen Energy and Generalized Energy Storage

In order to improve the level of new energy consumption in the system and utilize the clean and efficient characteristics of hydrogen energy, an integrated energy system (IES) scheduling model considering refined utilization of hydrogen energy and generalized energy storage is proposed. Firstly, the two-stage hydrogen energy utilization model of power-to-gas (P2G) is finely modeled, and the waste heat of the P2G methanation reaction is innovatively coupled with the Kalina cycle to improve the thermoelectric decoupling capability of the combined heat and power (CHP) unit. Secondly, integrated demand response, electric vehicles, and hydrogen-containing multi-source energy storage equipment are used as generalized energy storage resources to cut peaks and fill valleys. Then, on the basis of considering the ladder-type carbon trading mechanism, the IES conventional operation model is constructed with the minimum operating cost of the system as the objective function. Furthermore, considering the source-load uncertainty of IES operation, a multi-energy complementary optimal scheduling model of hydrogen-containing IES based on conditional value-at-risk was established. Through simulation analysis, it can be seen that the proposed model takes into account both economic and environmental benefits and improves the system’s ability to “peak cutting and valley filling” and measure risk levels.

Pricing method of electric-thermal heterogeneous shared energy storage service

Energy storage system (ESS) has been considered as a pivotal technology enjoying a wide range of applications in different levels of power systems. However, the investment of ESS is still relatively high. Thus, shared energy storage (SES) is consequently recognized as a promising business model. Considering the dynamic virtual energy storage characteristics of heat network, this paper proposes a power-thermal heterogeneous SES model to meet the practical need of participants. Firstly, a quasi-dynamic model of heat energy transport is conducted to analyze the virtual energy storage potential of heat network. Then, the power-thermal heterogeneous SES model is developed to describe the formation mechanism of ESS supply and demand with heat network as the schedulable energy storage resource. Furthermore, a pricing method of electric-thermal heterogeneous SES is proposed. The upper-level problem of it targets at maximizing the benefits of the SES operator, while the lower-level problem aims to minimize the costs of users. Simultaneously, the service price and user purchase capacity can be obtained as optimization variables. Finally, case studies are adapted to verify the rationality and validity of the proposed model.

Reconnaissance Survey for Potential Energy Storage and Carbon Dioxide Storage Resources of Petroleum Reservoirs in Western Europe

Energy producers and utilities use oil and gas reservoirs for gas storage to meet peak seasonal demand or to supplement intermittent energy production. These reservoirs are also suitable for the long-term storage of carbon dioxide (CO2), a greenhouse gas. This study reports on a reconnaissance analysis of the potential magnitude of storage resources in 9424 known oil and gas reservoirs from 24 countries within highly industrialized western Europe. To standardize the storage resources of the oil and gas reservoirs, their volumetric capacity is expressed in terms of metric tons (mass) of CO2. Estimates of recoverable oil and gas at the surface are converted to subsurface volumes and then converted to the equivalent mass of CO2 at reservoir conditions. The results indicate 36.7 gigatons of CO2 could be stored, with oil reservoirs accounting for 32% of that total and natural gas reservoirs comprising the remaining 68%. About four-fifths of the reservoir storage resource is offshore, with about three-fourths of that offshore resource at water depths of 200 m or less. Most countries do not have the reservoir storage resources to store 15 years of CO2 at 2017 emission levels. With few exceptions the bulk of the storage is offshore for countries that do have at least 15 years of storage. The expansion of natural gas storage for strategic purposes in abandoned onshore gas reservoirs is not expected to seriously impact CO2 storage. The contribution of this analysis is the description of the spatial distribution of potential storage and physical accessibility.