In order to fully mobilize user-side resources in an increasingly open energy trading market, this paper proposes an optimal allocation strategy for electricity/heat/gas shared energy storage based on the probability prediction method. The proposed optimized configuration establishes an energy hub structure with electricity/heat/gas shared energy storage, and a twobody optimized model with two-layer from the view of users and providers participating in the shared energy storage business model is established. The bottom layer describes the uncertainty of new energy output based on the probability prediction method based on long-term and short-term memory and Bayesian neural network, a user-side shared energy storage charging and discharging model, which is optimized aiming to minimize the user's total cost, is established, and the decision information will be informed to the shared energy storage provider. At the top level, aiming to minimize the investment and construction cost of shared energy storage providers, concentrates on optimizing the allocation of energy storage power and capacity of decision-making entities. The big M method is adopted to relax and linearize the nonlinear part of the objective and constraints, and then it is transformed into a mixed-integer linear optimization problem. Finally, three typical application scenarios are established. As to the verification of the superiority of the strategy, the CPLEX optimization solver is called through the YALMIP toolbox in Matlab to solve the models in different scenarios, and the overall costs and benefits are jointly compared. From the case analysis, we can draw the conclusion that compared with the traditional buying and selling model, the shared energy storage business model in this paper effectively reduces the investment and construction scale of user-side energy storage, correspondingly reducing the investment and construction cost of user-built energy storage and the time cost of operating and maintaining physical energy storage.
KEYWORDS: Data centers, Solar energy, System on a chip, Photovoltaics, Power supplies, Absorption, Data modeling, Wind energy, Instrument modeling, Distributed interactive simulations
In order to solve the problem of optimal operation and revenue distribution of multi-energy power stations (data centers, electric vehicle charging stations, distributed photovoltaics, wind power, and energy storage stations), this paper proposes a multi-station integration collaborative optimization strategy. First, exploring the energy supply mode for the data center under the multi-station integration scenario, and the subjects in the multi-station integraion mode are divided into three subjects: the power grid company, the equipment investor and the data center. Second, various deveices involved in multistation integration are modeled ,and the modeling of energy storage station is combined with special requirements for the reliability of the data center. Practical demand is taken into account in the modeling of charging station as the electric car is divided into three kinds of charging methods. Considering the randomness of electric vehicles, the interconnection time and initial SOC are randomly generated, and subject to the charging pile number and power limit. Later, optimal dispatch is carried out with the maximum benefit of the equipment investor as the objective function. Finally, the analysis of the results shows that the multi-station integration mode can bring benefits to the above three subjects, and it is a win-win mode, which has a development prospect.
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