Optimal configuration of shared energy storage systems considering retired electric vehicle batteries laddering utilization in multi-microgrid scenarios

Optimal configuration of shared energy storage system in microgrid cluster: Economic analysis and planning for hybrid self-built and leased modes

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.

In this paper, we consider a smart grid network where customers have their own photovoltaic generation system (PVS) but an energy storage system (ESS) is shared. The energy generated in PVS located at customer n's home can be immediately used for customer n at that time or be stored in the shared ESS. Customers all belongs to the same entity or different entities with common interest and seek a common goal. We find an optimal capacity of shared ESS and individual photovoltaic generation system minimizing the total energy cost. For a predetermined daily pattern of generation and load, we formulate an liner programming problem to decide an optimal capacity of the shared ESS minimizing the total energy cost. The total energy cost is the sum of expenses to buy electricity and to install the shared ESS. Numerical examples show that as compared with the case of individually installed ESS for each customer, the shared ESS can decrease the electricity demand to the main grid and the total energy cost is slightly reduced while meet the demand of electricity by appliances.

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy.

Research on the collaborative operation strategy of shared energy storage and virtual power plant based on double layer optimization

The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy.

Shared energy storage system for prosumers in a community: Investment decision, economic operation, and benefits allocation under a cost-effective way

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

Shared energy storage systems (SESS) have been gradually developed and applied to distribution networks (DN). There are electrical connections between SESSs and multiple DN nodes; SESSs could significantly improve the power restoration potential and reduce the power interruption cost during fault periods. Currently, a major challenge exists in terms of how to consider both the efficiency of the operation and the reliability cost when formulating the SESS scheduling scheme. A SESS optimal scheduling method that considers the DN operation risk is proposed in this paper. First, a multi-objective day-ahead scheduling model for SESS is developed, where the user’s interruption cost is regarded as the reliability cost and it is the product of the occurrence probability of the expected accident and the loss of power outage. Then, an island partition model with SESS was established in order to accurately calculate the reliability cost. Via the maximum island partition and island optimal rectification, the SESS was carefully integrated into the power restoration system. Furthermore, in order to minimize the comprehensive operation cost, an improved genetic algorithm for the island partition was designed to solve the complex SESS optimal scheduling model. Finally, a case study on the improved PG&E 69 bus system was analyzed. Moreover, we found that the DN’s comprehensive operation cost decreased by 6.6% using the proposed method.

In order to improve the ability to suppress unbalanced voltage in bipolar DC microgrids, a comprehensive power regulation control of a novel shared energy storage system is proposed for a multi-scenario bipolar DC microgrid. The novel shared energy storage system is composed of an electric spring (ES) with a full-bridge DC/DC converter and non-critical load (NCL) in series, considering demand-side response. The proposed comprehensive power regulation control can enable the bipolar DC microgrid to deal with various scenarios. When operating in stand-alone mode, the unbalanced voltage caused by greater unbalanced power can still be suppressed under the proposed control of the shared energy storage. In case of distributed energy storage (DES) failure on the source side, the shared energy storage can realize DC voltage regulation and maintain system operation by reducing NCL power. In grid-connected operation, the shared energy storage can actively cooperate with the power dispatching of the utility grid for storage reduction of DES on the source side. Thus, the reliability and resilience of the bipolar microgrid have been improved. Finally, to verify the effectiveness of the proposed control strategy, hardware-in-the-loop experimental results are presented in this paper.

A multi-objective optimisation method to quantitatively identify retired points of electric vehicle (EV) batteries is proposed to minimise the life cycle cost (LCC) of EV batteries and the total annual cost (TAC) of energy storage systems (ESS). It features comprehensive considerations of battery capacity degradation characteristics and energy storage capacity optimisation. The effectiveness of the proposed method is demonstrated by a case study. The influence of the purchase cost and the profit of batteries in the second life are analysed. The Pareto front of LCC and TAC is given. The trade-off point is obtained when SOHre is 0.855 and the corresponding LCC and TAC are 28,742.2 USD and 7,905.5 USD. Results indicate that retired points are intensively dependent upon the optimal capacity, LCC and TAC. Both LCC and TAC can be reduced by decreasing the purchase cost and increasing the profit without changing the retired points.

Dynamic game optimization control for shared energy storage in multiple application scenarios considering energy storage economy

This paper proposes a nondominated sorting genetic algorithm II (NSGA-II) based approach to determine optimal or near-optimal sizing and siting of multi-purpose (e.g., voltage regulation and loss minimization), community-based, utility-scale shared energy storage in distribution systems with high penetration of solar photovoltaic energy systems. Small-scale behind-the-meter (BTM) batteries are expensive, not fully utilized, and their net value is difficult to generalize and to control for grid services. On the other hand, utility-scale shared energy storage (USSES) systems have the potential to provide primary (e.g., demand-side management, deferral of system upgrade, and demand charge reduction) as well as secondary (e.g., frequency regulation, resource adequacy, and energy arbitrage) grid services. Under the existing cost structure, storage deployed only for primary purpose cannot justify the economic benefit to owners. However, delivery of storage for primary service utilizes only 1-50% of total battery lifetime capacity. In the proposed approach, for each candidate set of locations and sizes, the contribution of USSES systems to grid voltage deviation and power loss are evaluated and diverse Pareto-optimal front is created. USSES systems are dispersed through a new chromosome representation approach. From the list of Pareto-optimal front, distribution system planners will have the opportunity to select appropriate locations based on desired objectives. The proposed approach is demonstrated on the IEEE 123-node distribution test feeder with utility-scale PV and USSES systems.

This paper proposes a minimum losses-based approach to determine optimal locations of utility-scale shared energy storage (USSES) systems. Small-scale behind-the-meter (BTM) batteries are still expensive, not fully utilized, and difficult to control for grid services. On the other hand, USSES systems have the potential to enhance grid services and save money for owners of BTM energy sources through storage leasing agreements. Also, new multi-use business models, which utilize battery storage for both primary (e.g., demand side management, deferral of system upgrade, and demand charge reduction) and secondary (e.g., frequency regulation, resource adequacy, and energy arbitrage) services, are needed to increase economic benefits for both USSES owners and investors. This paper uses a genetic algorithm (GA)-based approach to determine optimal locations of USSES systems to reduce power losses caused by customer requests of charging and discharging. This is the first step toward developing multi-use business models. The proposed approach is demonstrated on IEEE-13 and IEEE-123 node test feeders with BTM solar PV. The results show that optimal locations for loss reduction depend on charging and discharging profiles.

Energy storage is crucial for source-side renewable energy power plants for enhancing output stability and reducing mismatch between power generation and demand. However, installing large size energy storage systems for renewable energy plants may not be economic, due to high capital cost and ever-increasing human resources and maintenance cost. As a result, in this paper, a shared energy storage system among multiple wind farms is proposed to address this energy management challenge. A state-of-the-art wind power forecasting method with ensemble numerical weather prediction models is used to optimally determine the size of a shared energy storage system (ESS). A number of scenarios are performed to optimize and explore the energy storage size under different economic and storage resource sharing circumstances. The performance of ESS, namely the net revenue of power plants, is explored subject to ESS size and operating constraints of wind farms and power systems. Results of a case study show that sharing of energy storage among multiple wind farms and lower cost of storage progressively enhance the economic benefits of using storage to mitigate over-production/under-forecasting (thus curtailment) and under-production/over-forecasting scenarios.