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Medium and long term load forecasting is important for power system planning and construction. Affected by the New Economy normal and other relevant factors, various regions of China have entered a new period of economic development mode and industrial structure changing. Load structure and load growth pattern, to a certain extent, has changed, which undoubtedly makes the long-term load forecasting more complex and uncertain. To study the models and methods of Medium and long term load forecasting in the market environment is more important. This paper deeply analyzes the new economy normal state's features, refines the factors which can properly characterize the new normal state's changing, and applies them to the medium and long term load forecasting's models; in accordance with the actual data of one province, uses a variety of methods and models to calculate and compare the predictions, and gets the applicability of long-term load forecasting model in new economy normal state.

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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.