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CN-121981340-A - Space-time collaborative configuration method for renewable energy supply-transmission-demand-storage system

CN121981340ACN 121981340 ACN121981340 ACN 121981340ACN-121981340-A

Abstract

The invention provides a space-time collaborative configuration method of a renewable energy supply-transmission-demand-storage system, which relates to the technical field of intelligent energy, and comprises the steps of obtaining data of a plurality of areas, calculating time mismatching degree, space mismatching degree and space-time coupling mismatching degree of each area, decomposing an optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension, constructing a multi-objective optimization function of each time layer, decomposing the optimization problem of each time layer in the space dimension, adopting a distributed iteration method to alternately solve a local optimization sub-problem and a global coordination problem of each area, outputting a configuration result of the current time layer when an algorithm converges, and executing a decision of a first time period by each time layer and taking the decision as a constraint condition of a next time layer to obtain the power generation installed capacity, the power transmission line capacity, the energy storage capacity and the operation strategy of each area. The invention can improve the renewable energy consumption rate and reduce the system operation cost.

Inventors

  • YU SHIWEI
  • ZHENG SHUHONG
  • ZHOU SHUANGSHUANG
  • YOU LIMIN

Assignees

  • 中国地质大学（武汉）

Dates

Publication Date
20260505
Application Date
20260407

Claims (10)

  1. 1. A method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system, comprising: s1, acquiring space topology data of a plurality of areas, historical time series data and electric power system parameters, wherein the space topology data comprises area geographic coordinates, inter-area transmission line connection relations and inter-area distances, the historical time series data comprises load demand data and renewable energy output data of each area, and the electric power system parameters comprise existing installed capacity, equipment investment cost and running cost; S2, calculating the time mismatching degree of each region by adopting a similarity calculation method based on renewable energy output data and load demand data of each region, calculating the energy-load proportion according to renewable energy resource potential and load demand data of each region, calculating the space weight coefficient based on the load demand data and inter-region distance of each region, and calculating the space mismatching degree by adopting a weighted deviation method to obtain the space mismatching degree and the load ratio of each region; s3, carrying out weighted amplification on the time mismatch degree according to the load occupation ratio of each region, and fusing the weighted time mismatch degree and the space mismatch degree to obtain space-time coupling mismatch degree; S4, decomposing the optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension, and establishing a capacity constraint relation and a power transfer relation between the time layers; s5, constructing a multi-objective optimization function of each time layer; S6, decomposing the optimization problem of each time layer in the space dimension, and decomposing the global optimization problem into a local optimization sub-problem and a global coordination problem of each region; s7, initializing iteration parameters according to the time mismatch degree and the inter-region distance of each region, and adopting a distributed iteration method to alternately solve the local optimization sub-problem and the global coordination problem of each region, wherein the iteration parameters are dynamically adjusted according to the optimization residual errors in the iteration process, and the distributed iteration method comprises a local variable optimization step, a global variable coordination step and a dual variable updating step; s8, judging the convergence between the local optimization sub-problem and the global coordination problem and the change stability of the time-space coupling mismatch degree, and outputting the configuration result of the current time layer when the convergence and the change stability meet preset conditions; And S9, executing steps S6 to S8 on a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in sequence from long to short according to a time scale, and executing a decision of a first period by each time layer and taking the decision as a constraint condition of a next time layer to obtain the power generation installed capacity, the power transmission line capacity, the energy storage capacity and the operation strategy of each region.
  2. 2. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 1, wherein in step S2, a similarity calculation method is adopted to calculate the time mismatch degree of each region, and the method specifically comprises: Normalizing the renewable energy output data and the load demand data of the region s, and calculating the normalized renewable energy output And load demand ; Calculating the time mismatch degree of the region s by adopting the inverse index of cosine similarity : ; Wherein t is a time index, As a total number of the historical time periods, The value range of (2) is [0,2].
  3. 3. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 1, wherein in step S2, a weighted deviation method is adopted to calculate the degree of spatial mismatch, and the method specifically comprises: Calculating the proportion of renewable energy resource potential of the region s to the whole system And the proportion of load demand to the whole system ; Calculating the load weighted geometric center coordinates of the whole system, calculating the distance from the region s to the load center, and normalizing to obtain a space weight coefficient ; Calculating the space mismatching degree SMD: ; Where S is the total number of regions and S is the region index.
  4. 4. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 1, wherein in step S3, a calculation formula for obtaining a space-time coupling mismatch degree STCM is: ; In the formula, For the time-mismatched weights, For the spatial mismatch weight(s), , Is the load deviation factor for the region s, As a result of the coupling coefficient, For the degree of temporal mismatch of the region s, Is the degree of spatial mismatch.
  5. 5. The space-time collaborative configuration method of the renewable energy supply-output-demand-storage system is characterized in that in step S4, the optimization problem is decomposed into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension, specifically, the optimization time domain of the long-term planning layer is 1 year, the optimization granularity is 1 month, the decision variable is the power generation installed capacity increment, the power transmission line capacity increment and the energy storage capacity increment of each region, the optimization time domain of the medium-term scheduling layer is 7 days, the optimization granularity is 1 hour, the decision variable is the power generation output, the energy storage charge-discharge power and the inter-region power transmission power of each region, the optimization time domain of the short-term real-time layer is 4 hours, the optimization granularity is 15 minutes, and the decision variable is the output adjustment amount, the energy storage adjustment amount and the power transmission adjustment amount.
  6. 6. The method of claim 5, wherein in step S5, the multi-objective optimization function of each time layer includes a space-time coupling mismatch term, and the space-time coupling mismatch term is: ; In the formula, For the degree of space-time coupling mismatch, For the decision variables for each region, S is the total number of regions, For the total energy storage capacity of the region s, For the energy storage and the coefficient of closure, In order to prevent a small amount of zero removal, And For renewable energy output and load demand at time t for region s, For the degree of temporal mismatch of the region s, In order to be a degree of spatial mismatch, For the time-mismatched weights, For the spatial mismatch weight(s), , Is the load deviation factor for the region s, Is the coupling coefficient.
  7. 7. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 1, wherein in step S6, the global optimization problem is decomposed into a local optimization sub-problem and a global coordination problem of each region, and an augmentation lagrangian function is constructed by adopting an alternate direction multiplier method: ; In the formula, Representing an augmented lagrangian function, For decision variables of each region, S is the total number of regions, z is a global coordination variable, representing inter-region transmission power, In order to be a lagrange multiplier vector, As a local cost function of the region s, As a function of the global cost, Is the transpose of the lagrangian multiplier vector for region s, And In order to be an association matrix, Is a penalty parameter.
  8. 8. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 7, wherein in step S7, iteration parameters are initialized according to the degree of time mismatch and inter-zone distance of each zone, and for a zone pair (i, j) where a transmission line connection exists, initial penalty parameters are initialized The method comprises the following steps: ; In the formula, As a reference penalty parameter, For the time-mismatched coupling coefficient, For the degree of temporal mismatch of region i, For the degree of temporal mismatch of region j, As the distance coefficient, the distance coefficient is used, For the distance between region i and region j, Is the maximum inter-zone distance in the power system; In the iterative process, dynamically adjusting iteration parameters according to the optimized residual error to define an original residual error And dual residual Updating penalty parameters based on residual ratio : ; Where k is the iteration round, Is the residual ratio threshold value, And Is an adjustment factor.
  9. 9. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 8, wherein in step S8, the convergence between the local optimization sub-problem and the global coordination problem and the stability of the variation of the degree of mismatch of the space-time coupling are determined, specifically comprising: Calculating original residual norms of the full system after the kth iteration And dual residual norms ; Calculating the space-time coupling mismatch degree variation of two adjacent iterations STCM denotes the degree of space-time coupling mismatch; calculating the degree of mismatch of space-time coupling the change rate and the stability are judged; When (when) 、 、 And terminating the iteration when the rate of change is stable, wherein 、 And The original residual error threshold value, the dual residual error threshold value and the space-time mismatch degree change threshold value are respectively defined.
  10. 10. The method for space-time collaborative configuration of a renewable energy supply-transmission-demand-storage system according to claim 5, wherein in step S9, rolling optimization is performed on a long-term planning layer, a medium-term scheduling layer, and a short-term real-time layer in order from long to short according to a time scale, specifically comprising: The long-term planning layer performs annual optimization at the initial planning moment to obtain a installed capacity deployment plan of each month in the future, only performs construction decisions of the first month, takes the plans of the other months as a predictive scheme, and transmits the installed capacity increment of the first month to the medium-term scheduling layer as the upper capacity limit; Performing Zhou Du optimization at the day-ahead time of each day by the medium-term scheduling layer to obtain an hour-level operation plan of 7 days in the future, performing only the scheduling plan of the first day, taking the rest of the scheduling plans of 6 days as a predictive scheme, and transmitting the power set value of the first day to the short-term real-time layer; the short-term real-time layer performs 4 hours of optimization at real-time moments per hour, performing only the adjustment amount of the first period.

Description

Space-time collaborative configuration method for renewable energy supply-transmission-demand-storage system Technical Field The invention relates to the technical field of intelligent energy, in particular to a space-time collaborative configuration method of a renewable energy supply-transmission-demand-storage system. Background Currently, the duty ratio of renewable energy sources in an electric power system is continuously improved. However, the space-time distribution characteristics of renewable energy sources have a remarkable mismatch with the load demands, namely, the photovoltaic power generation peak appears at noon and the load peak usually appears at evening in the time dimension, and the wind-light resources are rich but the load demands are small in northwest areas and the renewable energy sources are concentrated but limited in eastern coastal areas in the space dimension. The space-time mismatch causes difficult renewable energy source digestion and serious wind and light discarding phenomenon, and restricts the low-carbon transformation process of the electric power system. The existing supply-transmission-demand-storage collaborative configuration method mainly focuses on system economy optimization and equipment capacity configuration, lacks quantitative characterization of the degree of time-space mismatching, and is difficult to guide energy storage configuration and power transmission network planning in a targeted manner to bridge the time-space mismatching. The Chinese patent application CN118100175A discloses an energy management method of an energy system based on distributed optimization, and the method provides an energy management model of pre-real-time two-stage scheduling, wherein the first stage is pre-economic scheduling, the optimization targets of economic benefit maximization, efficiency maximization and carbon emission cost minimization are adopted, and the second stage is real-time optimal scheduling, and the optimization targets of interactive power deviation punishment cost, wind abandoning punishment cost and user satisfaction loss cost minimization are adopted. The method adopts an improved multi-agent reinforcement learning method to solve a pre-economic dispatch model, and adopts YALMIP tool boxes to solve a real-time optimization model. However, the optimization target of the method is focused on economy and carbon emission, the space-time mismatch degree of renewable energy sources and loads is not taken into an objective function as an explicit optimization index, and the energy storage and power transmission collaborative configuration optimization aiming at space-time mismatch characteristics is difficult to realize. Disclosure of Invention In view of the above, the invention provides a space-time collaborative configuration method of renewable energy supply-transmission-demand-storage systems, which is to construct a time mismatching degree, a space mismatching degree and a space-time coupling mismatching degree quantization model, take the space-time mismatching degree as an explicit optimization target, establish a multi-scale optimization framework covering three time scale layers of long-term planning, medium-term scheduling and short-term real-time, adopt a distributed iterative solution algorithm based on an alternate direction multiplier method, realize collaborative configuration optimization of energy storage closing time mismatching and transmission relieving space mismatching, improve the renewable energy consumption rate and reduce the system operation cost. The technical scheme of the invention is realized as follows: The invention provides a space-time collaborative configuration method of a renewable energy supply-transmission-demand-storage system, which comprises the following steps: s1, acquiring space topology data of a plurality of areas, historical time series data and electric power system parameters, wherein the space topology data comprises area geographic coordinates, inter-area transmission line connection relations and inter-area distances, the historical time series data comprises load demand data and renewable energy output data of each area, and the electric power system parameters comprise existing installed capacity, equipment investment cost and running cost; S2, calculating the time mismatching degree of each region by adopting a similarity calculation method based on renewable energy output data and load demand data of each region, calculating the energy-load proportion according to renewable energy resource potential and load demand data of each region, calculating the space weight coefficient based on the load demand data and inter-region distance of each region, and calculating the space mismatching degree by adopting a weighted deviation method to obtain the space mismatching degree and the load ratio of each region; s3, carrying out weighted amplification on the time mismatch degree according to the load occupation r