Shared Energy Storage Research Articles

A new integrated energy system cluster energy sharing framework adapted to high altitude areas

Since a renewable energy is connected to a high-altitude integrated energy system (HAIES), challenges arise for system operation. Shared energy storage as a jointly operated energy hub for multi-integrated energy system (IES) can effectively improve the economy and flexibility of the system. This paper proposes a joint day-ahead and intra-day scheduling strategy for a HAIES considering a shared composite energy storage operator (SCESO) and profit clearing scheme. First, a HAIES structure suitable for residential, industrial, and commercial applications is built based on combined oxygen supply and integrated demand response (IDR). An SCESO is then proposed to provide multiple energy sharing services for HAIES cluster, including electricity–oxygen–hydrogen. Accordingly, HAIESs and SCESOs are aggregated to create a HAIES cluster energy sharing framework. Second, during the intra-day scheduling stage, which is based on real-time forecast data that considers multiple source–load–charge uncertainties, the flexible adjustment capabilities of each unit of the framework are used to achieve intra-day energy balance and ensure the safe operation of the framework. Furthermore, a profit clearing scheme is proposed, introducing different energy contribution quantification methods for different energy cooperation modes. Finally, day-ahead and intra-day simulation results under multiple scenarios are analyzed. The simulation results demonstrate that the framework is optimal in terms of economy, adaptability to different RE outputs and RE uncertainties, and energy storage utilization efficiency.

Two-Layer Optimization Method for Sharing Energy Storage and Energy considering Subjectivity

The high level of integration of distributed generation systems (DGSs), especially distributed wind and solar, significantly affects the flexibility and controllability of the power system. Aggregating local DGSs and shared energy storage systems (ESSs) within an energy community offers an economically and environmentally viable solution. However, the coupling of shared ESSs with the energy community, while considering subjectivity, is often overlooked. Therefore, this study introduces a two-layer optimization framework that enables DGSs to trade energy freely, voluntarily, and independently and to share ESSs within the energy community, considering participants’ subjectivity. The upper layer optimizes the size of shared ESSs, while the lower layer, structured as a two-layer model, simulates participant interactions. The numerical case shows that, compared to DGSs operating individually, the shared ESS case indicates that community self-sufficiency and self-consumption rates increase by 16.22% and 21.98%, respectively. Additionally, the annual operating cost is reduced by approximately 27.10%, and CO2 emissions are decreased by about 33.24%. Considering DGS’ subjectivity, the self-sufficiency and self-consumption rates are 3.04% lower, and the total operating costs and CO2 emissions are 3.26% and 6.86% higher, respectively.

Optimization Decision Study of Business Smart Building Clusters Considering Shared Energy Storage

Smart buildings have a large number of dispatchable resources, both for power production and consumption functions, and the energy consumption of intelligent building clusters has a good complementary and interactive relationship, which can better promote the local consumption of distributed energy. In order to realize the goal of “dual-carbon” and promote the construction of a new power system mainly based on renewable energy, this paper takes the Business Smart Building (BSB) cluster with photovoltaic (PV) power generation as the research object. A peer-to-peer (P2P) energy trading model with shared energy storage (SES) for BSBs is constructed, and the potential risk of the stochastic volatility of photovoltaic power generation to BSBs is evaluated using conditional value-at-risk (CVaR). Finally, the optimal strategy for P2P energy sharing among BSBs is obtained by distributed solving using the alternating direction multiplier method (ADMM). The results show that the proposed model can minimize the operating cost of the multi-BSB alliance and realize win–win benefits for building users and shared energy storage operators. Meanwhile, the proposed CVaR gives a trade-off between benefits and risks, which can satisfy the needs of decision-makers with different risk preferences in making decisions.

An evolutionary planning method for distribution networks considering prosumers and shared energy storage

An evolutionary planning method for distribution networks considering prosumers and shared energy storage

Optimization Operation Strategy for Shared Energy Storage and Regional Integrated Energy Systems Based on Multi-Level Game

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs.

Cooperative game-based energy storage planning for wind power cluster aggregation station

It is possible to cut down the investment costs in energy storage and enhance the utilization of energy storage by planning the shared energy storage in the wind farm collection station to replace the dispersed energy storage of each wind farm. Considering the cluster complementary effects of multiple wind farms, this article proposes a cooperative game-based plan for the hybrid energy storage of battery and supercapacitor in the wind power cluster. Firstly, charging and discharging strategy for batteries follows the peak shaving and valley filling approach, while the strategy for supercapacitors aims to flatten power fluctuations and track power generation plans. Then, a dual-layer planning model for the shared energy storage station is established, and evaluation indicators for the energy storage configuration results are constructed. Finally, based on the improved Shapley value method, the profits of each wind farm are allocated, and the impact of energy storage investment costs on the results is analyzed. The case study results demonstrate that under this cooperative scheme, not only can the real-time output deviation of wind power be reduced, but also the investment costs of wind farms in energy storage systems can be reduced.

Energy community with shared photovoltaic and storage systems: influence of power demand in cost optimization

AbstractEnergy management of distributed energy resources has gradually become a complex problem because of the intermittent nature of renewable energy sources, such as photovoltaic power, and the large use of energy storage systems. A way to deal with these issues is to operate within an energy community. However, the efficient management of the community in terms of costs is particularly relevant. Specifically, the minimization of the energy community costs, which consists of properly utilizing shared energy storage and renewable energy sources, becomes an important objective. In this context, a fundamental role is played by demand power characteristics which strongly influence the benefits brought by this energy management scheme. This work investigates the influence of the variability of power demand on the minimization of the operating cost problem of an energy community while determining the optimal capacity of the energy storage system that increases the self‐consumption potential of the photovoltaic source. Two main scenarios are implemented where the effects of considering the community photovoltaic capacity as a variable or a parameter on costs and energy storage system size are investigated. This analysis consists of a multi‐objective optimization coupled with a Monte Carlo framework. The community management is conducted by considering random power demand profiles of each unit belonging to the same community, and different sizes, categories of users and users' aggregations. A comparison is led among different users' categories in terms of costs, photovoltaic unit and energy storage system size. The results provide an overview of how each category benefits from taking part in an energy community both in terms of cost and energy storage and photovoltaic sizes and show how these aspects change within a multi‐category aggregation where each category makes a different contribution to the community. In particular, we find evidence of the “synergy effect” brought by multi‐category aggregations capable of exploiting differences in consumption profiles. Each building category, with its numerosity, has a different effect on the energy community, resulting in a different impact on total costs and cost savings. We also investigate how the energy storage system capacity is affected by both the available photovoltaic capacity and the consumption profiles of the categories within the energy community.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.

Optimized scheduling of smart community energy systems considering demand response and shared energy storage

Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation.

Two-stage robust transaction optimization model and benefit allocation strategy for new energy power stations with shared energy storage considering green certificate and virtual energy storage mode

In the context of the large-scale participation of renewable energy in market trading, this paper designs a cooperation mode of new energy power stations (NEPSs) and shared energy storage (SES) to participate in the power-green certificate market, which divides SES into physical energy storage and virtual energy storage. Secondly, combining the advantages of scenario generation and robust optimization (RO), a two-stage RO model with improved uncertainty interval is proposed to determine the optimal trading strategy. Then, to better align the distribution results of cooperative benefits with the actual contributions of NEPSs and SES, an entropy weight modified Shapley value benefit allocation strategy is constructed. Finally, the new energy base in Qinghai Province, China is chosen for simulation. The results show: (1) Adding energy storage and using two-stage RO are able to effectively improve the ability of NEPSs to resist uncertainty, which increases the revenue of the alliance by 22.8%. (2) The application of SES has better economic benefits than each member equipped with energy storage separately. Compared with the latter, the deviation penalty cost of the former is reduced by 66.4%, and the revenue is increased by 3.4%. (3) The proposed entropy weight modified Shapley value method embodies the important auxiliary role of SES more obviously. Based on this method, the overall satisfaction of the alliance increases by 12.6%. Generally speaking, the optimization model and benefit allocation strategy proposed in this paper can provide guidance for NEPSs and SES participating in power trading, and promote the low-carbon transformation of the power sector.

Methods for Coordinating Optimization of Urban Building Clusters and District Energy Systems

In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits.

A new shared energy storage business model for data center clusters considering energy storage degradation

In recent years, the energy consumption of data centers (DCs) has shown a sharp upward trend. Given the high investment cost of energy storage, this study introduces the concept of energy sharing within a data center cluster (DCC) and proposes a novel shared energy storage (SES) business model. The model realizes the co-optimization for DCC and SES and is divided into three main layers. Firstly, considering the SES's economy and the DCC's cleanliness, the upper layer establishes a novel multi-objective optimization configuration scheme for SES with lifetime degradation. In addition, a two-stage robust optimization technique for DCC is proposed in the middle layer, considering the demand response mechanism for data loads. Subsequently, the concept of contribution degree (CD) is defined, and a novel benefit distribution mechanism based on cooperative game and CD is presented in the lower layer. The simulation results demonstrate that the proposed business model positively enhances the economic benefits of the SES and DCC. The constructed uncertainty optimization model exhibits strong robustness, and the proposed benefit allocation scheme is fairer and more reasonable.

Optimal allocation method for MIES-based shared energy storage using cooperative game theory and CSP

To further promote the efficient use of energy storage and the local consumption of renewable energy in a multi-integrated energy system (MIES), a MIES model is developed based on the operational characteristics and profitability mechanism of a shared energy storage station (SESS), considering concentrating solar power (CSP), integrated demand response, and renewable energy output uncertainty. We propose a corresponding MIES model based on co-operative game theory and the CSP and an optimal allocation method for MIES shared energy storage. The model considers the maximum operating benefit of the SESS as the upper objective function and the minimum operating cost of the MIES as the lower objective function. First, the Karush–Kuhn–Tucker conditions of the lower-layer model are transformed into constraints of the upper-layer model, and the Big-M method is used to linearize the nonlinear problem and convert the two-layer nonlinear model into a single-layer linear model. Second, based on the Nash negotiation theory, the benefits of each IES in the MIES are allocated. Finally, the fuzzy chance constraints are used to relax the power balance constraints, and the trapezoidal fuzzy numbers are transformed into a deterministic equivalence class to assess the impact of renewable energy output uncertainty on system operation. The validity and rationality of the proposed two-layer model are verified through simulation, and the results demonstrate that the proposed shared storage capacity leasing model can effectively reduce the total operation cost, increase the profitability of the shared storage operator, and increase the utilization rate of the SESS.

Geographic information system-based multi-criteria decision-making analysis for investment assessment of wind-photovoltaic-shared energy storage power stations: a case study of Shanxi Province.

As the center of the development of power industry, wind-photovoltaic (PV)-shared energy storage project is the key tool for achieving energy transformation. This research seeks to construct a feasible model for investment appraisal of wind-PV-shared energy storage power stations by combining geographic information system (GIS) and multi-criteria decision-making (MCDM) method. Firstly, a comprehensive criteria system is established from the perspectives of orography, economy, resources, climate, and society, and the evaluation data is described using probabilistic linguistic term sets (PLTSs). Then, to avoid the weight deviation produced by the single weighting approach, a comprehensive weighting model including the best-worst method (BWM) and entropy weight method is provided to calculate the weights of criteria. Next, expert weights are calculated based on trust analysis. Finally, alternatives are ranked by the improved gained and lost dominance score (GLDS) method. To verify the validity of the model, an empirical investigation is carried out in Shanxi Province. The results show that the economy is the primary factor influencing the investment decision. Among all the projects approved by the government, alternative F4 located in Yanzhuang Town, Yuanping City is the best investment object. Furthermore, to illustrate the stability of the result, triple sensitivity analysis and comparative analysis are conducted in Shanxi Province. This study expands the application scope of GIS and MCDM method by first providing support for government and investors to identify optimal investment targets.

Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory

With the increasing demand of users for distributed energy storage (ES) resources and the emerging development of peer to peer (P2P) transaction technology, shared energy storage (SES) has great potential to contribute into new business models of demand-side ES. In order to compromise essential elements like safety, stability and efficiency of P2P trading, as well as to improve the utilization rate of demand-side ES, this paper devotes to construct a P2P transaction framework based on a partially decentralized topology and proposes a two-stage trading optimization strategy of SES in a P2P market, considering the equilibrium state of supply and demand flow. In the first stage, this paper simultaneously balances the interests of buyers and sellers and brings the carbon trading mechanism into the transaction process. The interaction of interests of bilateral parties with consideration of carbon trading mechanism has been investigated, and a SES capacity sharing model is, then, established based on the bargaining game theory. In the second stage, a unique pricing mechanism for SES leasing fee is designed based on a multi-strategy evolutionary game model, considering bounded rational decision-making for SES operators and communities. Finally, numerical simulation verified the feasibility and superiority of the proposed P2P trading strategy of SES.

Optimizing the operation and allocating the cost of shared energy storage for multiple renewable energy stations in power generation side

The concept of shared energy storage in power generation side has received significant interest due to its potential to enhance the flexibility of multiple renewable energy stations and optimize the use of energy storage resources. However, the lack of a well-set operational framework and a cost-sharing model has hindered its widespread implementation and large-scale development. This paper introduces three distinct cost-sharing methods, namely the uniform allocation method, the predictive weighted allocation method, and the dynamic weighted allocation method, which are laterly applied to develop a combined operational and cost distribution model. The objective is to improve the efficiency of the power generation system by incorporating shared energy storage assistance and allocating the associated costs based on the use of various renewable energy stations. Furthermore, the viability of the suggested operational framework for shared energy storage and the methods for allocating costs are confirmed through numerical simulation. The findings indicate that the uniform allocation method represents the most straightforward approach for distributing costs evenly. In contrast, the second model introduces a cost allocation method that addresses the issue of fairness by considering the predicted generation level of each renewable energy plant. However, the third model takes into account the actual generation level of the plants when determining their respective costs. Sensitivity analysis is further conducted to offer valuable insights into cost-saving policies for four representative regions in China. The proposed operation and cost-sharing model is anticipated to serve as a useful reference for the widespread implementation of shared energy storage in power generation side.

Research on Grid-Connected Optimal Operation Mode between Renewable Energy Cluster and Shared Energy Storage on Power Supply Side

The renewable energy cluster can reduce the total power deviation of renewable energy stations and also bring cooperative benefits to renewable energy stations. Shared energy storage can assist in tracking the power generation plan of renewable energy and has advantages in the scale of investment, utilization rate, and other aspects. Therefore, this article proposes a study on the grid-connected optimal operation mode between renewable energy cluster and shared energy storage on the power supply side. Firstly, for the complementary characteristics blurring each member’s contribution to the cluster power deviation, an improved Shapley value method is used to build a rational mechanism for apportioning the cluster power deviation penalty. Secondly, based on the Nash negotiation model and allocation mechanism, the revenue model of the renewable energy cluster is constructed, and an improved particle swarm optimization algorithm is proposed to solve the cluster operation strategy. Then, based on the charge and discharge demand of renewable energy cluster, with the goal of maximizing the benefits of shared energy storage, capacity pricing is optimized, as well as capacity allocation and participation in the frequency control auxiliary service market. Finally, the solving process of grid-connected optimal operation mode is proposed, and the rationality of the grid-connected optimal operation strategy between renewable energy cluster and shared energy storage is verified by example analysis. The results indicate that renewable energy cluster and shared energy storage can effectively increase both benefits, and a win-win situation for all parties can be realized. On the one hand, the cooperation mode and allocation mechanism can effectively guarantee the benefit of each renewable energy station. On the other hand, shared energy storage is beneficial for assisting in tracking the power generation plan of renewable energy and reducing the renewable energy power curtailment situation. There also has residual capacity in the shared energy storage system; it can participate in the frequency control ancillary service market, which can improve the capacity utilization rate and benefit of shared energy storage.

Bargaining‐based energy sharing framework for multiple CCHP systems with a shared energy storage provider

AbstractIn the present day, when centralized energy storage technology is becoming increasingly mature, the cooperative energy sharing framework between the combined cooling, heating, and power (CCHP) systems and a shared energy storage provider (SESP) can effectively promote the local consumption of renewable energy and reduce the social energy cost. In this study, the optimal operation models of CCHP and SESP are first developed by considering the electric energy trading of the CCHP and the SESP. Then the CCHP–SESP cooperative operation model is established based on Nash bargaining theory, followed by equating it into two subproblems of coalition social energy cost minimization and electric energy trading payment negotiation. The alternating direction method of multipliers approach is introduced to solve the energy‐sharing problems in a distributed way. Numerical simulation results show the effectiveness of the proposed energy‐sharing framework. Simulation results show that the proposed bargaining‐based energy‐sharing framework can significantly reduce the social energy cost and improve the utilization rate of energy storage resources.

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.

Three-side coordinated dispatching method for intelligent distribution network considering dynamic capacity division of shared energy storage system

Shared Energy Storage Systems (SESSs) are increasingly being integrated into Intelligent Distribution Networks (IDNs). IDNs are transitioning from traditional electricity distributors to multi-type energy supply platforms with SESSs and multi-type microgrids (MGs). Compared to traditional distribution networks, IDNs need to meet the integration and cooperation requirements of the flexible resources from both external agents and internal source-network-load-storage side. However, these external and internal factors have complex coupling relationships. Fully maximizing the potential benefits of SESSs for improving IDNs, SESSs, and MGs remains a challenge. This paper proposes a three-side coordinated dispatching method for IDNs. Its characteristic is a business model for SESS considering virtual MG was proposed. SESS capacity could be utilized fully, improving the economic benefits of all parties including SESS operator. First, the autonomous operation mechanism of the SESS was provided, and a dynamic capacity division model was established. The remaining SESS capacity was then used for IDN optimal dispatching in the form of a virtual MG. Second, an optimal control model of an integrated energy MG was established, and a new MG structure that allows for a multi-node grid connection was designed. Subsequently, this study developed an information exchange method between multiple agents and a solution strategy to solve the IDN-MG-SESS three-side coordinated dispatching model. Finally, an improved IEEE 33-bus system with two MGs and a SESS was analysed. The SESS dynamic capacity division method, when compared to configuring the SESS capacity according to the installed capacity ratio of distributed generation, increased the daily economic income of the SESS by 26.19 %.