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CN-121981853-A - Coordination optimization method for energy system

CN121981853ACN 121981853 ACN121981853 ACN 121981853ACN-121981853-A

Abstract

The invention discloses an energy system coordination optimization method, which belongs to the technical field of energy system coordination optimization and comprises the following steps of building a multi-level coordination optimization framework, namely building a multi-time scale coordination optimization framework comprising a daily optimization scheduling layer, a daily rolling correction layer and a real-time balance control layer, wherein the daily optimization scheduling layer makes a reference plan with the optimal economy of the whole day as a target, the daily rolling correction layer carries out rolling adjustment on a follow-up time period plan based on latest prediction data, the real-time balance control layer responds to ultra-short-term fluctuation from a second level to a minute level, and building a multi-energy flow unified optimization model. According to the invention, through constructing a day-ahead, day-in and real-time multi-time-scale coordinated optimization framework, the whole process closed-loop optimization from long-term planning to second-level response of the energy system is realized, the capability of the system for coping with renewable energy fluctuation and load uncertainty is remarkably improved, and the running toughness and economy are enhanced.

Inventors

  • YANG YING
  • YANG GUANFENG

Assignees

  • 丽水朝旭新能源科技有限公司

Dates

Publication Date
20260505
Application Date
20260105

Claims (9)

  1. 1. The energy system coordination optimization method is characterized by comprising the following steps of: The method comprises the steps of S1, constructing a multi-level coordination optimization framework, namely, establishing a multi-time scale coordination optimization framework comprising a daily optimization scheduling layer, a daily rolling correction layer and a real-time balance control layer, wherein the daily optimization scheduling layer makes a reference plan with the optimal total-day economy as a target, the daily rolling correction layer carries out rolling adjustment on a follow-up time period plan based on latest prediction data, and the real-time balance control layer responds to ultra-short-term fluctuation from a second level to a minute level; s2, building a multi-energy coupling system model comprising an electric power network, a natural gas network and a heat supply network, wherein the model is constrained with network transmission through an energy conversion equipment coupling equation to form an electric, gas and heat multi-energy flow integrated operation constraint set so as to represent space-time conversion and complementary relation among different energy sources; And S3, executing multi-time scale closed loop optimization, namely executing a closed loop optimization flow of planning, tracking, feedback and correction based on the multi-level coordinated optimization framework and the multi-energy flow unified optimization model, firstly solving at a day-ahead optimization scheduling layer to obtain a reference planning curve of each subsystem, then starting periodic rolling optimization at a day-ahead rolling correction layer, finally calling a preset rapid adjustment strategy at a real-time balance control layer to cope with actual operation deviation, and feeding deviation information back to an upper optimization model.
  2. 2. The method for coordinated optimization of an energy system according to claim 1, wherein in step S1, the specific step of performing the rolling optimization adjustment by the intra-day rolling correction layer includes: S4, collecting ultra-short-term prediction data of renewable energy power generation and multi-element load demands at each rolling optimization starting moment, and receiving operation deviation statistical information from a real-time balance control layer; s5, aiming at minimizing the total running cost of the system in a rolling window in the future, simultaneously taking the running deviation statistical information as a punishment item, and re-solving an optimization model under the condition of meeting the multi-energy flow integrated running constraint set; And S6, outputting the energy subsystem scheduling instructions of which the first time period is corrected in the rolling window, and transmitting the energy subsystem scheduling instructions to the real-time balance control layer for execution, and updating the pre-scheduling plans of the subsequent time period.
  3. 3. The method for coordinated optimization of an energy system according to claim 1, wherein in step S3, the specific step of invoking the fast adjustment policy by the real-time balance control layer includes: S7, continuously monitoring actual operation parameters of key nodes of the system, comparing the actual operation parameters with a short-term or ultra-short-term plan value at the current moment, and calculating operation deviation values of power, pressure or temperature types; S8, when the running deviation exceeds a preset first threshold value, a preset flexible regulating resource in the system is preferentially called to quickly stabilize, wherein the flexible regulating resource comprises the charging and discharging power of an energy storage device, the regulating capability of an interruptible load and the conversion rate of energy conversion equipment; and S9, recording deviation and duration exceeding the flexible adjustment resource capacity range, summarizing the deviation into the running deviation statistical information, and uploading the running deviation statistical information to the intra-day rolling correction layer for triggering or correcting the next rolling optimization.
  4. 4. The method for coordinated optimization of an energy system according to claim 1, further comprising a step S10 of online adaptive updating of the unified optimization model of multiple energy flows; S10, in the continuous operation process of the system, periodically collecting actual historical operation data of each energy subsystem, wherein the actual historical operation data at least comprises equipment output, pipe network tide, energy conversion efficiency and load data; S11, comparing the actual historical operation data with the model prediction data of the corresponding period, and calculating the long-term deviation rate and the confidence interval of the key equipment and the network model parameters; And S12, when the long-term deviation rate of the specific parameter continuously exceeds the corresponding confidence interval, triggering a model parameter calibration mechanism, identifying and updating the corresponding parameter in the unified optimization model of the multi-functional flow by utilizing the actual historical operation data, and replacing the original parameter with the updated model parameter for subsequent optimization calculation.
  5. 5. The method according to claim 2, wherein in step S5, the rolling window length and the penalty weight of the rolling optimization are dynamic adaptive parameters, and the determining step includes: S13, evaluating short-term operation risk level faced by the system based on uncertainty intensity of renewable energy prediction at the current moment, real-time load fluctuation rate of the system and network key section margin; S14, according to the short-term operation risk level, self-adaptively matching with preset configuration in a rolling optimization strategy library, when the risk level is high, automatically shortening the length of a rolling window and increasing the punishment weight of operation deviation to enhance the adjustment frequency and conservation; And S15, executing the daily rolling correction layer optimization calculation of the current period by adopting the dynamically adjusted rolling window length and penalty term weight.
  6. 6. The method for coordinated optimization of an energy system according to claim 3, wherein in step S8, the first threshold is a dynamic adjustment threshold, and the adjustment resources are not limited to a preset list, and the specific enhancing step includes: S16, dynamically calculating and generating a floating range of the first threshold based on the overall inertia level, the standby capacity and the network power flow distribution of the system at the current moment, so that the threshold is relaxed when the system has strong adjusting capability, frequent actions are reduced, and the threshold is tightened when the system is fragile and intervened in advance; s17, when the adjustment resource needs to be called, automatically generating and issuing a real-time market invitation containing adjustment requirements and compensation signals besides the preset flexible adjustment resource; And S18, aggregating generalized distributed resources responding to the real-time market invitation, wherein the generalized distributed resources comprise but are not limited to an electric automobile cluster, a user side temperature control load group and distributed energy storage, and integrating the aggregated regulating capacity into a rapid regulating strategy of a real-time balance control layer for uniform calling.
  7. 7. The method according to claim 4, wherein in step S12, the model parameter calibration mechanism fuses prospective environmental information, and the method specifically comprises: s19, synchronously acquiring multi-dimensional prospective information of a future key period before triggering parameter calibration, wherein the information comprises refined weather forecast, a daily price clearing curve of an electric power market and a known important event calendar; S20, establishing a mapping relation between the prospective information and similar scenes in the historical operation data, and carrying out scene weighting and trend enhancement processing on the currently acquired actual historical operation data based on the mapping relation to generate an enhanced training data set for parameter identification; And S21, calibrating model parameters by adopting the enhanced training data set, so that the updated unified optimization model of the multi-energy flow not only fits the historical operation rule, but also embeds the trend response capability for foreseeable future environmental changes.
  8. 8. The method for coordinated optimization of an energy system according to claim 5, wherein the rolling optimization strategy library has self-evolution capability based on reinforcement learning, and the specific steps include: S22, after each rolling optimization execution, recording the adopted rolling window length, punishment item weight, corresponding system operation risk level, and actually generated optimization objective function result and real-time balance pressure value under the strategy; S23, taking each strategy selection and a corresponding operation result as a sample, and inputting the sample into an online operation reinforcement learning agent for training, wherein the reinforcement learning agent takes the minimum long-term comprehensive operation cost as a target, takes the system operation state as input, and takes the optimized strategy parameters as output action; And S24, regularly using the trained reinforcement learning agent to perform strategy evaluation and screening on the preset strategy library, and outputting an evaluation result.
  9. 9. The method for coordinated optimization of an energy system according to claim 6, wherein the method for coordinated clarification of market physical security is performed when generalized distributed resources are aggregated, and specifically comprises: s25, synchronously issuing key network topological structure and safety constraint margin information related to the adjustment requirement when the real-time market invitation is generated; S26, after receiving quotes and adjustment capability declarations of generalized distributed resources, pre-evaluating the influence of response behaviors of each resource on power flow, voltage and air network pressure distribution of the power network before executing market clearing calculation, and screening out resource response combinations which can induce or aggravate local safety risks; And S27, executing collaborative clearing optimization, namely taking the lowest total adjustment cost as an economic target, taking the newly added safety risk of the system as a rigid constraint, and carrying out joint solution, wherein the final clearing result is only a resource calling combination which simultaneously meets the economy and the whole network safety.

Description

Coordination optimization method for energy system Technical Field The invention relates to the technical field of energy system coordination optimization, in particular to an energy system coordination optimization method. Background Energy systems are an important infrastructure supporting economic and social developments. With the continuous improvement of the permeability of renewable energy sources and the increasing of the coupling degree of multi-energy systems such as electric power, natural gas, heat supply and the like, the traditional operation mode based on single energy source and independent scheduling is difficult to adapt to the safe, economic and flexible operation demands of the system under the access of high-proportion new energy sources. At present, more researches have been carried out on the aspect of energy system optimization scheduling at home and abroad, mainly focusing on aspects of economic scheduling before the day, daily plan correction, multi-energy collaborative modeling and the like, and aiming at improving the system operation efficiency through the improvement of models and algorithms. However, the prior art still has the defects that firstly, the coordination of multiple time scales is insufficient, an effective closed loop feedback mechanism is often lacking between daily planning, daily correction and real-time control, rapid fluctuation of new energy output and load is difficult to cope with, secondly, the modeling of a multi-energy system is still based on independent optimization or simple coupling, deep fusion of electricity, gas and heat in network transmission, conversion efficiency, space-time complementation and the like is not fully reflected, thirdly, model parameters are fixed, self-adaption is lacking, dynamic correction cannot be carried out according to the actual running state of the system and the change of external environment, so that an optimization result is disjointed from the actual running, and fourthly, the type of a regulating resource is single, a response mechanism is stiff, and a cooperative regulating system covering distributed resources, a market mechanism and safety constraint is not formed. Based on the above, the invention designs an energy system coordination optimization method to solve the above problems. Disclosure of Invention The invention aims to provide an energy system coordination optimization method for solving the problems in the background technology. An energy system coordination optimization method comprises the following steps: The method comprises the steps of S1, constructing a multi-level coordination optimization framework, namely, establishing a multi-time scale coordination optimization framework comprising a daily optimization scheduling layer, a daily rolling correction layer and a real-time balance control layer, wherein the daily optimization scheduling layer makes a reference plan with the optimal total-day economy as a target, the daily rolling correction layer carries out rolling adjustment on a follow-up time period plan based on latest prediction data, and the real-time balance control layer responds to ultra-short-term fluctuation from a second level to a minute level; s2, building a multi-energy coupling system model comprising an electric power network, a natural gas network and a heat supply network, wherein the model is constrained with network transmission through an energy conversion equipment coupling equation to form an electric, gas and heat multi-energy flow integrated operation constraint set so as to represent space-time conversion and complementary relation among different energy sources; And S3, executing multi-time scale closed loop optimization, namely executing a closed loop optimization flow of planning, tracking, feedback and correction based on the multi-level coordinated optimization framework and the multi-energy flow unified optimization model, firstly solving at a day-ahead optimization scheduling layer to obtain a reference planning curve of each subsystem, then starting periodic rolling optimization at a day-ahead rolling correction layer, finally calling a preset rapid adjustment strategy at a real-time balance control layer to cope with actual operation deviation, and feeding deviation information back to an upper optimization model. Preferably, in step S1, the specific step of executing the rolling optimization adjustment by the intra-day rolling correction layer includes: S4, collecting ultra-short-term prediction data of renewable energy power generation and multi-element load demands at each rolling optimization starting moment, and receiving operation deviation statistical information from a real-time balance control layer; s5, aiming at minimizing the total running cost of the system in a rolling window in the future, simultaneously taking the running deviation statistical information as a punishment item, and re-solving an optimization model under the condition of meeti