Energy Storage Services Research Articles

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

Maturity assessment of grid-scale flexibility and energy storage services towards a decarbonized Europe

Background The study evaluates the maturity of grid-scale flexibility services, essential for Europe's transition to a decarbonized economy. Conducted under the Horizon-Europe SINNOGENES project, it focuses on energy storage technologies, regulatory frameworks, and stakeholder perspectives. Methods The methodology comprises three phases. First, significant grid-scale energy storage and flexibility technologies, including Battery Energy Storage Systems (BESS) and Vehicle-to-Grid (V2G) systems, were reviewed. Second, the regulatory framework for flexibility services at the EU transmission systems level was assessed, analyzing key EU directives and national regulations from Spain, Portugal, Germany, Cyprus, and Switzerland. Third, stakeholder perspectives were gathered through a survey of forty-four stakeholders in the European energy sector and bilateral interviews with companies and organizations in the energy sector. Results Technological advancements in grid-scale flexibility services have been identified, along with persistent regulatory challenges. The survey and interviews revealed a varied perception among stakeholders regarding the maturity and implementation of these services. A high demand for flexibility was noted, with 77% of stakeholders expressing a need for or active use of flexibility services. Electrochemical energy storage and demand response were highlighted as the most relevant technologies. However, high implementation costs, perceived insufficient benefits, and regulatory uncertainties were major barriers to widespread adoption. Conclusions The study concludes that despite notable technological progress and some regulatory advancements, several barriers still impede the full maturity and integration of grid-scale flexibility services in Europe. Addressing these regulatory and market challenges is crucial for achieving a fully functional and integrated flexibility service framework across the European grid. Harmonized regulations, financial incentives, and clear long-term pricing signals are essential to support the market.

Collaborative operation strategy of multiple microenergy grids considering demand‐side energy‐sharing behavior

AbstractIn recent years, as the energy market continues to change, an emerging business model is gradually emerging as an energy provider (EP). The participation of EP as an independent entity in the distribution network investment model not only helps realize a win–win situation for multiple stakeholders but also promotes the innovation and sustainable development of the energy industry. However, in the energy system, EPs, grids, microenergy grids (MEGs), and shared energy storage service providers usually belong to different stakeholders, and their respective pursuit of profit maximization can easily lead to uncontrolled competition, which greatly reduces market efficiency. To address the varying ownership structures within the energy system, a refined bilevel coordinated optimization operation model is introduced, rooted in the master–slave game theory. In this model, the EP assumes the role of the master, while the MEG operator and the shared energy storage operator (SESO) act as slaves. A peer‐to‐peer energy‐sharing mechanism between MEGs and an energy trading mechanism between MEGs and shared energy storage is used to further increase the local consumption rate of new energy. Simulation results demonstrate that the proposed method not only boosts the revenues of an EP, SESO, and MEG operators but also effectively accounts for the MEG's energy storage needs and SESOs' profitability.

Optimizing government strategy for deep decarbonization in towns: A game theory approach incorporating energy storage services

Renewable district energy systems present a promising solution for decarbonizing the energy sector. However, optimal strategies to mitigate the financial challenges of deep decarbonization are still underexplored. This study aims to optimize government subsidy strategies and users' system designs to facilitate cost-effective deep decarbonization using game theory. Two indicators, the renewable generation rate (RGR) and the self-sufficiency rate (SSR), are formulated to understand the principles of achieving deep decarbonization. Additionally, the study introduces a new strategy called subsidy plus energy storage service (SUB + ESS), contrasting it with the traditional subsidy-only strategy. Key findings indicate that merely increasing the RGR requirements does not achieve deep decarbonization due to energy mismatches. Restricting the SSR requirements proves effective in achieving deep decarbonization but imposes a significant economic burden on the government. When the SSR requirement is set at 100%, carbon emissions are reduced by 93%, but the government's subsidy expenditure increases nearly fivefold. In contrast to the subsidy-only strategy, the novel SUB + ESS strategy, with a 100% SSR requirement, reduces government expenditure by approximately 52% and lowers total expenditure by 19.7%. A two-stage decarbonization strategy is proposed: initially, offering subsidies is sufficient, but as deeper decarbonization is pursued, establishing energy storage service becomes essential.

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

Modeling hydrogen storage at room temperature: Adsorbent materials for boosting pressure reduction in compressed H2 tanks

Energy storage systems are required for an efficient integration of renewables into the grid to achieve a net zero energy system. Hydrogen compressed at 700 bar, is one of the key energy storage technologies. This study evaluates the effectiveness of solid-state hydrogen storage, particularly physisorption in porous materials, to enhance storage performance and safety at room temperature by reducing the operating tank pressure. We model dynamically the entire storage system, comparing adsorbent materials to traditional compression in terms of maximum tank pressure and round-trip storage efficiency. Different energy system applications with varied cycle frequencies and discharge durations were examined. Results indicate that porous material-based systems exhibit higher efficiency for long-duration energy storage services than the compressed hydrogen. Notably, bulk density plays a pivotal role in storage performance. For instance, IRMOF-1 with a bulk density of 500 kg/m3, shows a 70 % pressure reduction compared to compressed hydrogen systems. In contrast, when its bulk density is reduced to 130 kg/m3, the maximum tank pressure is even 30 % higher than the compressed tank. We emphasize the need for comprehensive material characterization, highlighting the significance of parameters like bulk density for determining the most performing hydrogen adsorbent material in terms of maximum tank pressure and efficiency. As general outcome, the best performing material depends on the specific target or system requirements, such as pressure, volume, cost, or weight.

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

The power grid presents several obstacles for demand forecasting and resource scheduling, such as a substantial amount of data, a growing number of factors influencing the demand profile, uncertainty in the generation profile of distributed energy from renewable sources, and a shortage of historical data. Here, we provide a unique market-oriented energy storage method based on artificial intelligence (AI) that aims to optimize operational profit in the electricity market between consumers, energy storage, and grid service providers. The approach is divided into two parts; the first is the Residual Network-50 (GR-ResNet-50) algorithm, which is used to overcome future uncertainties related to load needs and power pricing. The second uses deep learning based on the Radial Heap Basis Algorithm (RHBA) to determine the most effective charging or discharging operation considering the grid peak states, load demand, and the state of the batteries. The performance of the proposed method in this study is compared with the conventional method in terms of 0.007% of low MAPE and 20 seconds of less execution time. The research shows that the proposed strategy significantly lowers on-peak power, improves operational profit, and improves effective performance.

Optimized Battery Capacity Allocation Method for Wind Farms with Dual Operating Conditions

In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering the simultaneous execution of the dual conditions of energy storage in fluctuation smoothing and primary frequency regulation is proposed. Firstly, a two-layer model is established to optimize the capacity allocation under dual operating conditions, i.e., the planning layer takes into account the lifetime, cost, and benefit, and the operation layer considers the wind turbine reserve backup and storage control to participate in the primary frequency regulation in a cooperative manner. Then, the dual battery pack operation strategy is embedded with the variational modal decomposition method to determine the charging and discharging operation strategy of energy storage after considering the grid-optimized reference power. An improved particle swarm algorithm with inverse learning pre-optimization combined with variational crossover post-optimization is embedded in the GUROBI computation to obtain the optimal battery storage capacity allocation scheme. Finally, the superiority of the model proposed in this paper in terms of improving energy storage utilization, service life, and economic efficiency as well as reducing wind power load shedding is verified by comparing it with a single execution working condition scenario and traditional battery control strategy.

Frequency Security Control Technology for Simulated Wind Storage Integrated Power Grid

Electronic control strategies are pivotal in the evolution of power systems, which have higher requirements for power leveling and optimization, frequency safety, and frequency stability. In contrast, the core objectives of existing energy storage services are mostly limited to one function, which cannot fully meet the operational requirements of power systems. This paper presents research on a frequency security controller based on digital twin technology and aimed to enhance the safety of the system. The proposed controller can simultaneously smooth out active wind power fluctuations and optimize reactive power, participate in system frequency regulation, and improve system damping to damp low-frequency oscillations based on simulating the actual operating environment. Simulations also verify the effectiveness of the proposed controller in DIgSILENT/PowerFactory based on a two-area system. This active/reactive power-based system support service will bring new economic benefits to wind energy storage systems.

Electric truck gravity energy storage: An alternative to seasonal energy storage

AbstractThe global shift toward a sustainable and eco‐friendly energy landscape necessitates the adoption of long‐term, high‐capacity energy storage solutions. This research introduces an inventive energy storage concept involving the movement of granular materials from a lower elevation to a higher point within natural terrains such as mountains or excavated mining sites. Electrical energy is employed to charge electric batteries that elevate the granular material, thereby storing potential energy. Subsequently, this material is transported down during the electricity generation phase, and the regenerative braking mechanism converts the gravitational energy to replenish the vehicle's battery. The stored energy within these vehicles is then dispatched during peak demand periods or utilized by the vehicles for other freight transportation purposes. Our findings demonstrate a power cost of 1200 USD/kW, an energy storage expense spanning from 1 to 10 USD/kWh, a levelized cost of storage ranging from 35 to 200 USD/MWh, and a global annual potential of approximately 5.4 PWh. Electric vehicle gravity energy storage showcases its capability to bolster sustainable development by offering seasonal and multi‐year energy storage services.

Current Situation and Engineering Application Prospect of Liquid Air Energy Storage Technology

Liquid air energy storage (LAES)technology is a new-coming energy storage method using cryogenic technology as basic theory. It managed to reach consumption of new energy in a grid-connected way, absorbing low power properly, and stable output of many other forms of energy like industrial gas and electricity. This paper introduces the technical principle and advantages of LAES, analyzes and discusses the problems faced by the development of LAES technology and the corresponding solutions, summarizes the applied fields of LAES technology. It also looks forward to the development direction of LAES technology. LAES has the advantages of high energy storage density and low storage pressure, and is less restricted by geographical conditions. It can store the remaining or discontinuous power for a certain time and produce stable power output. LAES technology has a wide range of application prospects in the power system, providing a variety of energy storage services and market opportunities for the power system, the industrial field, providing a variety of energy recovery, peak-valley balance, emergency backup and energy conversion services for industrial production, and urban gas systems, providing a variety of energy reserve, peak load emergency, emergency backup and auxiliary services for urban gas systems. It is of great significance to improve the phenomenon of "wind abandonment" and "light abandonment" in the power generation industry and meet the demand of "peak cutting and valley filling" in the power grid system.

Low carbon-oriented planning of shared energy storage station for multiple integrated energy systems considering energy-carbon flow and carbon emission reduction

—With the development of energy storage technology and sharing economy, the shared energy storage in integrated energy system provides potential benefit to reduce system operation costs and carbon emissions. This paper presents a bi-level carbon-oriented planning method of shared energy storage station for multiple integrated energy systems. Firstly, the energy-carbon relationship of the multiple integrated energy systems is established, and the node carbon intensity models of power grid, integrated energy system and shared energy storage station are established. Secondly, a bi-level planning model of shared energy storage station is developed. The upper layer model solves the optimal capacity planning problem of shared energy storage station to minimize average emission reduction cost in a long time scale. The lower layer model solves the optimal operation problem of multiple integrated energy systems with the goal of minimizing the operation cost in a short time scale. Furthermore, considering the uncertainty of wind and photovoltaic power in integrated energy system, the conditional value at risk method is adopted as the risk measurement index in the lower layer operation cost. Finally, the case study verifies the advantages of the proposed method in economy and environmental friendliness through the comparative analysis of three different energy storage planning cases. Simulation results show that, compared with the energy storage planned separately for each integrated energy system, it is more environmental friendly and economical to provide energy storage services for each integrated energy system through shared energy storage station, the carbon emission reduction rate has increased by 166.53 %, and the system operation cost decreases by 33.48 %.

Addressing the Voltage and Energy Fading of Al-Air Batteries to Enable Seasonal/Annual Energy Storage

The European Commission's Strategic Energy Technology Plan (SET Plan-2018) aims to slow down anthropogenic global warming by 2030 and is seen as a crucial intermediate step to guarantee net-zero greenhouse gas emissions by 2050. Its achievement requires a significant increase in renewable energy. Various technologies have been proposed to solve this issue, including power-to-X options, i.e., converting renewable electricity into a storable form (e.g., hydrogen), and secondary-battery-based power-to-power options.1 Nonetheless, seasonal and even annual energy storage in a low-cost manner is still a challenge. In this context, employing the earth-abundant, low-cost, and easy to transport and store Al metal as the energy carrier may offer some valuable options, e.g., the combination of Al production via inert-electrode smelting (power to metal) and Al conversion to electricity via Al-air batteries (AABs, metal to power).2 However, voltage decay during discharge severely decreases the practical energy density of AABs, especially, upon long-term operation.3,4 ,5 Herein, the electrochemical behavior of AMA in KOH aqueous solutions containing various concentrations of dissolved aluminum, predominantly present as , is compared to investigate the impact of . It is observed that an increasing polarization of AMAs occurs even before any precipitate is generated on their surface. Further experimental and computational results reveal that the accumulation of greatly reduces the OH- concentration and, in turn, negatively affects the reaction kinetics. Therefore, the voltage decay issue of AABs is caused by the accumulation of in the electrolyte rather than Al(OH)3 on AMAs. Subsequently, it is demonstrated that seeded precipitation of in the electrolytes to Al(OH)3 precipitate with releasing OH- via utilizing its lower solubility at 20 °C than at 50 °C allows effective recovery of the potential of AMAs and the voltage of AABs. At last, prototype AABs are assembled and tested to evaluate the feasibility of voltage recovery via the proposed seeded precipitation process. References (1) Ersoy, H.; Baumann, M.; Barelli, L.; Ottaviano, A.; Trombetti, L.; Weil, M.; Passerini, S. Hybrid Energy Storage and Hydrogen Supply Based on Aluminum—a Multiservice Case for Electric Mobility and Energy Storage Services. Adv. Mater. Technol. 2022, 7 (8), 2101400.(2) Baumann, M.; Barelli, L.; Passerini, S. The Potential Role of Reactive Metals for a Clean Energy Transition. Adv. Energy Mater. 2020, 10 (27), 1–8.(3) Zhang, P.; Xue, J.; Liu, X.; Wang, Z.; Li, X.; Jiang, K. Electrochimica Acta Improving Energy Efficiency of Commercial Aluminum Alloy as Anodes for Al-Air Battery through Introducing Micro-Nanoscale AlSb Precipitates. Electrochim. Acta 2022, 417, 140331.(4) Wu, P.; Zhao, Q.; Yu, H.; Tang, Z.; Li, Y.; Huang, D.; Sun, D.; Wang, H.; Tang, Y. Modification on Water Electrochemical Environment for Durable Al-Air Battery: Achieved by a Low-Cost Sucrose Additive. Chem. Eng. J. 2022, 438, 135538.(5) Liu, S.; Ban, J.; Shi, H.; Wu, Z.; Shao, G. Near Solution-Level Conductivity of Polyvinyl Alcohol Based Electrolyte and the Application for Fully Compliant Al-Air Battery. Chem. Eng. J. 2022, 431 (P3), 134283.

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.

Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side

The application prospects of shared energy storage services have gained widespread recognition due to the increasing use of renewable energy sources. However, the decision-making process for connecting different renewable energy generators and determining the appropriate size of the shared energy storage capacity becomes a complex and interrelated problem when considering the multi-site spatio-temporal characteristics. This paper presents an optimal planning and operation architecture for multi-site renewable energy generators that share an energy storage system on the generation side. Furthermore, an economic-environmental model is proposed to minimize the costs associated with the energy system infrastructure while maintaining a high penetration rate of renewable energy. The centralized multi-objective model allows renewable energy generators to make cost-optimal planning decisions for connecting to the shared energy storage station, while also optimizing the size of the storage capacity to maximize renewable energy generation and minimize costs. The Non-dominated Sorting Genetic Algorithm-II is employed in a centralized manner to solve the multi-objective nonlinear model. Numerical experiments are conducted to demonstrate the economic and environmental benefits of the proposed system. Therefore, this method is highly recommended for implementation on the generation side.

Two-Stage Capacity Determination Framework for Residential Second-Life BESSs Considering Cloud Energy Storage Service

Second-life battery energy storage systems (SL-BESSs) have potential to be used as an economic and affordable energy storage solution for supporting a variety of applications, such as energy backup in residential sector. This article proposes a new capacity determination framework for SL-BESSs that are utilized in household environment. Despite the capital cost is significantly reduced, the degradation characteristics of retired batteries exhibits two-stage decline characteristics. The proposed framework first establishes the SL-BESSs model and estimates its remaining useful lifetime by identifying the “knee point” in the SL-BESSs capacity degradation curve; based on this, a two-level SL-BESS capacity determination model is formulated, which sizes the SL-BESS based on short-term home energy management under different scenarios. In particular, this article considers the dual-service modes of an SL-BESS in its capacity determination process: first, provide local energy supply to the household, and second, provide cloud energy storage service to external users. Extensive numerical simulations are conducted to validate the proposed framework.

Dynamic resistance loss of the high temperature superconducting coil for superconducting magnetic energy storage

The Superconducting Magnetic Energy Storage (SMES) has excellent performance in energy storage capacity, response speed and service time. Although it's typically unavoidable, SMES systems often have to carry DC transport current while being subjected to the external AC magnetic fields.Under this circumstance, the dynamic resistance loss occurs in conjunction with the magnetization loss. Accordingly, the dissipative heat originating from the dynamic resistance loss will cause thermal overhead and even threaten the stability of the SMES. To ensure the safe operation of the SMES, this study examines dynamic resistance losses of high temperature superconducting (HTS) coils. In addition, this study also compares it with other losses, including magnetization loss, under various conditions such as different DC transport currents, amplitudes, frequencies, and angles of external AC magnetic fields. Furthermore, the analysis of dynamic resistance loss has also taken into account thermal coupling. This research offers valuable insights into the electromagnetic-thermal characteristics of the HTS coil and can be used as a reference for designing SMES systems.

Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid

Power-to-gas (P2G) is a promising solution to the issue of non-dispatchable renewable power generation. However, the high investment costs and low energy efficiency of P2G systems pose challenges. This study designs a green hydrogen-based Energy Storage as a Service (ESaaS) mode to improve the economic efficiency of P2G systems. In this ESaaS mode, the P2G system acts as an energy trading hub. The ESaaS operator manages the system and enables microgrids to access energy storage services. In return, the ESaaS operator generates revenue through electricity and hydrogen trading. To characterize stakeholders' behaviors, we develop a cost-minimizing model for multi-energy microgrid operation and a revenue-maximizing model for P2G investment and operation. To coordinate stakeholders involved in cooperation, we employ Nash bargaining-based model reformulation to determine investment and operation decisions and benefit distribution. Then, logarithm transformation and trading price discretization are introduced to efficiently solve the non-convex and nonlinear reformulated model. Simulation experiments using Shanghai's electricity market and meteorological data demonstrate that the ESaaS mode achieves 100% recovery of surplus renewable electricity and increases the proportion of renewable energy in microgrids from 59% to 83%. The results suggest that green hydrogen-based ESaaS is a feasible solution to increase the utilization rate of renewable energy and bring considerable economic and environmental benefits to all stakeholders.

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.