Intelligent power grid multi-energy complementary scheduling method and system

Abstract

The invention discloses a smart power grid multi-energy complementary scheduling method and system, and relates to the technical field of power grid scheduling, and the method comprises the steps: interacting power grid distribution data and multi-energy network access data of a pre-jurisdiction area; traversing the power grid distribution data, and mining a scheduling constraint condition and a multi-energy game function; taking multi-domain network coordination as an upper-layer optimization structure and multi-energy coordination as a lower-layer optimization structure, and generating a dual-drive decision model; reading power grid supply and demand data, wherein the power grid supply and demand data comprises real-time supply and demand data and predicted supply and demand data; transmitting the power grid supply and demand data to the dual-drive decision model, and determining a complementary coordination strategy; and performing power grid dispatching management of the pre-jurisdiction area. The technical problem of poor operation efficiency and stability of the power grid in the prior art is solved, and the technical effects of high-efficiency multi-energy complementary scheduling of the power grid and improvement of the operation efficiency and stability of the power grid are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a smart grid multi-energy complementary dispatching method and system. Background Art

[0002] With the continuous growth of energy demand and the continuous transformation of energy structure, modern power grids are facing increasingly complex challenges. Various forms of energy, including traditional energy such as coal, natural gas, etc., and renewable energy such as wind energy, solar energy, hydropower, etc., are constantly connected to the power grid. Different energy sources have different power generation characteristics and laws. Traditional energy generation is relatively stable but there are problems of environmental pollution and limited resources. Although renewable energy is environmentally friendly, it is intermittent, random and uncertain. Under the traditional power grid dispatching mode, it is often difficult to fully coordinate and integrate different types of energy to meet the complex and changing power demand, which is prone to problems such as imbalance between supply and demand, energy waste, low power grid operation efficiency and poor stability.

[0003] The existing technology has technical problems of poor power grid operation efficiency and stability. Summary of the invention

[0004] The present application provides a smart grid multi-energy complementary scheduling method and system for solving the technical problems of poor grid operation efficiency and stability in the prior art.

[0005] In view of the above problems, the present application provides a smart grid multi-energy complementary scheduling method and system.

[0006] In a first aspect of the present application, a smart grid multi-energy complementary scheduling method is provided, the method comprising: Interact with the power grid distribution data and multi-energy grid access data of the pre-jurisdiction area; traverse the power grid distribution data, delineate the multi-domain network, and combine the multi-energy grid access data to mine the dispatching constraints and multi-energy game functions, wherein the dispatching constraints at least include the unit output limit and the power grid transmission limit; take the multi-domain network coordination as the upper optimization structure and the multi-energy coordination as the lower optimization structure, combine the dispatching constraints and the multi-energy game function to perform data-driven modeling, and generate a dual-drive decision model; read the power grid supply and demand data, the power grid supply and demand data includes real-time supply and demand data and predicted supply and demand data; transmit the power grid supply and demand data to the dual-drive decision model, and the upper and lower layers cooperate to make multi-energy complementary dispatching decisions and determine the complementary coordination strategy; delegate the complementary coordination strategy to the power system to perform power grid dispatching management in the pre-jurisdiction area.

[0007] The second aspect of the present application provides a smart grid multi-energy complementary dispatching system, the system comprising: A data interaction module, the data interaction module is used to interact with the power grid distribution data and multi-energy access data of the pre-jurisdiction area; a power grid distribution data traversal module, the power grid distribution data traversal module is used to traverse the power grid distribution data, delineate the multi-domain network, and combine the multi-energy access data to mine the scheduling constraints and the multi-energy game function, wherein the scheduling constraints at least include the unit output limit and the power grid transmission limit; a dual-drive decision model generation module, the dual-drive decision model generation module is used to use the multi-domain network coordination as the upper optimization structure and the multi-energy coordination as the lower optimization structure, combine the scheduling constraints with the multi-energy game function The game function performs data-driven modeling to generate a dual-drive decision model; a power grid supply and demand data reading module, the power grid supply and demand data reading module is used to read power grid supply and demand data, and the power grid supply and demand data includes real-time supply and demand data and predicted supply and demand data; a complementary coordination strategy determination module, the complementary coordination strategy determination module is used to transmit the power grid supply and demand data to the dual-drive decision model, and the upper and lower layers cooperate to make multi-energy complementary scheduling decisions and determine the complementary coordination strategy; the power grid dispatching management module, the power grid dispatching management module is used to delegate the complementary coordination strategy to the power system, and perform power grid dispatching management in the pre-jurisdiction area.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Interactive pre-jurisdiction area power grid distribution data and multi-energy network access data; traverse the power grid distribution data, delineate multi-domain networks, and mine scheduling constraints and multi-energy game functions; combine the scheduling constraints and the multi-energy game functions to perform data-driven modeling and generate a dual-drive decision model; read power grid supply and demand data, transmit it to the dual-drive decision model, and determine the complementary coordination strategy; delegate the complementary coordination strategy to the power system to perform power grid dispatch management in the pre-jurisdiction area. The technical effect of achieving efficient multi-energy complementary dispatch of the power grid and improving the efficiency and stability of power grid operation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a smart grid multi-energy complementary scheduling method flow chart provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a smart grid multi-energy complementary dispatching system provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: data interaction module 10 , power grid distribution data traversal module 20 , dual-drive decision model generation module 30 , power grid supply and demand data reading module 40 , complementary coordination strategy determination module 50 , power grid dispatching management module 60 . DETAILED DESCRIPTION

[0012] The present application provides a smart grid multi-energy complementary scheduling method and system to solve the technical problems of poor grid operation efficiency and stability in the prior art.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a smart grid multi-energy complementary scheduling method, the method comprising: S100: Interact with the power grid distribution data and multi-energy grid access data of the pre-jurisdiction area.

[0015] Specifically, the interactive grid distribution data and multi-energy grid access data in the pre-jurisdiction area are of vital significance. For grid distribution data, understanding the distribution of power stations is the basis. Clarifying the geographical location of each power station in the pre-jurisdiction area can accurately grasp the spatial layout of energy production. The location of different power stations will affect the path planning of power transmission and the allocation of power between regions. Grid access location information is also extremely critical. It determines how electricity is connected to the grid. Different grid access locations will face different technical requirements and restrictions. Installed capacity data is even more indispensable. It reflects the power generation capacity of each power station. Power stations with larger installed capacity usually play a more important role in power supply and have a more significant impact on the grid. The power generation potential and support capacity of different power stations for the grid can be evaluated based on the installed capacity. The interaction of multi-energy grid access data also includes comprehensive consideration of multiple energy types. For example, for wind power stations, their grid access data will involve the impact of meteorological conditions such as wind speed and wind direction on power generation; solar power stations will involve factors such as light intensity and sunshine time. These factors will directly affect the power generation efficiency and stability of different energy power stations, and thus affect the operation of the entire grid. By interacting with these detailed grid distribution data and multi-energy access data, a comprehensive power system information framework is established, providing a solid data foundation and decision-making basis for subsequent grid dispatch and energy management, so that more scientific and reasonable decisions and operations can be made when conducting grid planning, optimizing dispatch and responding to various emergencies.

[0016] S200: traverse the power grid distribution data, delineate a multi-domain network, combine the multi-energy access data, and mine scheduling constraints and multi-energy game functions, wherein the scheduling constraints at least include unit output restrictions and power grid transmission restrictions.

[0017] Specifically, we start to traverse the grid distribution data, delineate the multi-domain network by traversing the grid distribution data, and combine the multi-energy access data to mine the dispatching constraints and multi-energy game functions. Dispatching constraints are key factors to ensure the stable operation of the power grid. The unit output limit ensures that the output power of the generator unit is within a reasonable range to avoid exceeding its rated power or safe operating range; the grid transmission limit takes into account factors such as the transmission capacity and voltage stability of the grid line to prevent problems caused by transmission overload or voltage instability. The determination of the multi-energy game function comprehensively considers factors such as the priority grid connection and consumption rules of energy and the game mode of the multi-node equivalent load curve, providing mathematical model support for subsequent energy dispatch decisions.

[0018] S300: Taking multi-domain network coordination as the upper optimization structure and multi-energy coordination as the lower optimization structure, combining the scheduling constraints and the multi-energy game function to perform data-driven modeling, and generating a dual-drive decision model.

[0019] Specifically, multi-domain network coordination is used as the upper optimization structure, which is mainly responsible for the coordination between multi-domain networks, including energy transmission between regions and power balance. Multi-energy coordination is used as the lower optimization structure, focusing on the complementarity between different energy sources, energy conversion and storage. Combined with the previously determined scheduling constraints and multi-energy game functions, a dual-drive decision model that can accurately reflect the operation of the power grid and the characteristics of energy complementarity is generated through data-driven modeling, providing strong support for the multi-energy complementary scheduling of smart grids.

[0020] S400: Reading power grid supply and demand data, where the power grid supply and demand data includes real-time supply and demand data and predicted supply and demand data.

[0021] Specifically, reading the supply and demand data of the power grid is of vital importance. Real-time supply and demand data reflects the current actual situation of the power grid. Real-time demand data includes the immediate demand for electricity in various regions and different user groups. These demands will fluctuate dynamically over time and are affected by many factors, such as the operating status of industrial production, the busyness of commercial activities, and the daily electricity consumption patterns of residents. Real-time supply data shows the actual output power of various energy power stations in the current power grid, the discharge status of energy storage equipment, and the transmission capacity of the power grid. By monitoring these data in real time, it is possible to grasp the operating status of the power grid in a timely manner so as to respond quickly when there is an imbalance between supply and demand or an emergency. Forecast supply and demand data provides forward-looking guidance for the future planning and scheduling of the power grid. Forecast demand data is based on comprehensive analysis and prediction of historical electricity consumption data, meteorological information, economic development trends, and social activities. For example, before the arrival of high temperature weather in summer, based on the peak electricity consumption data and weather forecasts in the same period in history, it is predicted that the electricity demand for refrigeration equipment such as air conditioners will increase significantly. Forecast supply data takes into account factors such as the power generation potential of various energy sources, equipment maintenance plans, and the commissioning progress of new energy projects. For example, for wind power generation, its power generation capacity needs to be evaluated based on future wind speed forecasts; for solar power generation, the forecast of light intensity and duration is crucial to estimating its power generation. By integrating real-time supply and demand data and forecast supply and demand data, comprehensive and accurate information support can be provided for the stable operation and optimized dispatch of the power grid, ensuring the reliable supply and efficient use of electricity.

[0022] S500: The power grid supply and demand data is transmitted to the dual-drive decision model, and the upper and lower layers cooperate to make multi-energy complementary scheduling decisions and determine the complementary coordination strategy.

[0023] Specifically, the grid supply and demand data is transmitted to the dual-drive decision model, and the upper-level multi-domain network coordination structure begins to play a role. It makes inter-domain scheduling decisions based on the macro information in the grid supply and demand data, such as the difference in power supply and demand between multi-domain networks in different regions, the overall capacity and load of grid transmission, etc. Considering factors such as the energy complementarity potential between multi-domain networks and grid transmission limitations, the upper-level structure will preliminarily determine a scheduling strategy that can achieve inter-regional energy balance and optimal configuration. For example, if a multi-domain network has a power supply shortage and another adjacent multi-domain network has surplus power, the upper-level structure will plan the cross-regional power transmission path and transmission volume based on factors such as the transmission capacity and cost of the grid. The lower-level multi-energy coordination structure receives the preliminary scheduling strategy passed down from the upper layer, and further refines and optimizes it based on its own detailed information. It will deeply analyze the characteristics of various energy sources in the region, power generation costs, output limitations, and real-time supply and demand changes. For example, for regions containing wind energy, solar energy, hydropower, and traditional energy, the lower-level structure will dynamically adjust the power generation ratio of various energy sources based on the current power generation capacity and future forecasts of different energy sources, as well as real-time fluctuations in power demand. At the same time, considering the multi-energy game function, different energy sources will compete and cooperate on the premise of meeting the supply and demand balance to achieve the lowest cost and most efficient energy combination. The upper and lower structures work together throughout the process. The upper layer provides a macro framework and guidance for the lower layer, and the lower layer provides more detailed support and feedback for the upper layer's decision-making. Through this collaboration, the dispatch strategy is continuously adjusted and optimized, and finally a complementary coordination strategy is determined that can not only meet the current power grid supply and demand balance, but also adapt to future trends, while fully considering the complementary characteristics and constraints of various energy sources. This strategy will guide the production, transmission and distribution of various energy sources in the power system, ensuring the stable operation of the power grid and efficient use of energy.

[0024] S600: Decentralize the complementary coordination strategy to the power system to perform dispatching management of the power grid in the pre-jurisdiction area.

[0025] Specifically, delegating the complementary coordination strategy to the power system is the key execution link to realize the multi-energy complementary dispatching of smart grid. When the complementary coordination strategy is transmitted to the power system, it will first have a direct guiding role in the power generation link. For different types of power stations, whether it is traditional thermal power generation, hydropower generation, or emerging wind power generation, solar power generation, etc., the strategy will adjust their power output according to the current power grid supply and demand situation and the complementary needs of various energy sources. In the transmission link, the strategy will optimize the dispatching of the transmission lines and substations of the power grid, and reasonably arrange the path and capacity of power transmission according to the differences in power supply and demand and energy distribution characteristics in different regions. In the process of power grid dispatching and management, the operation status of the power system will be monitored in real time so as to dynamically adjust the complementary coordination strategy in time. The real-time data collected by sensors and monitoring equipment, such as voltage, current, power factor, etc., will be compared and analyzed with the expected goals in the strategy. If it is found that the actual operation situation deviates from the strategy goal, such as the imbalance of power supply and demand due to sudden equipment failure or changes in energy supply, the emergency plan will be quickly activated, and the strategy will be re-evaluated and adjusted to ensure that the power grid always maintains stable operation. In short, by delegating the complementary coordination strategy to the power system and conducting effective grid dispatching management, it is possible to achieve the coordinated complementarity of multiple energy sources, improve the reliability, stability and energy utilization efficiency of the power grid, and meet the ever-changing electricity demand in the pre-jurisdiction area.

[0026] In a possible implementation, step S200 further includes: Step S210: Identify the grid distribution data, and identify multiple microgrids based on the grid-connected system.

[0027] Step S220: Identify the power grid distribution data, and based on a preset difference threshold between the demand-side consumption and the supply-side consumption, traverse the pre-jurisdiction area to divide the area and determine the divided power grid.

[0028] Step S230: Fitting the multiple microgrids and the divided power grids to determine the multi-domain network.

[0029] Specifically, first of all, in the process of identifying multiple microgrids based on the grid-connected system, the grid distribution data is the key analysis basis. The grid-connected system is the architecture that connects different power generation units, energy storage equipment and loads, and its rules and interaction methods provide the basis for identification. From these data, select the parts that meet the characteristics of the microgrid, such as units with power generation capacity, energy storage facilities and local loads and can operate independently to a certain extent. For example, in an industrial park, a system consisting of distributed photovoltaic power generation, energy storage batteries and industrial power loads in the park is identified as a microgrid.

[0030] Next, for the division of the power grid based on the preset difference threshold generated by the demand-side consumption and the supply-side, this difference threshold must be set first. It is a measurement standard determined based on the power grid operation experience and demand. When the difference between the demand side and the supply side exceeds the threshold, the power grid needs to be divided. Then, the power grid distribution data of the pre-jurisdiction area is comprehensively traversed, and the power supply and demand status of each area is analyzed from the power supply end to the load end. If the difference in a certain area reaches or exceeds the threshold, it will be treated as a separate division object. For example, in residential areas during peak electricity consumption, the demand for electricity increases greatly, but the local power supply is insufficient. When the difference exceeds the threshold, the power grid area where the residential area is located can be divided separately, and finally it is determined to be a divided power grid.

[0031] From the perspective of multi-microgrids, multi-microgrids are composed of multiple relatively independent small grid units with certain characteristics. Each microgrid has its own power supply (such as distributed power generation equipment), energy storage device and specific load. These microgrids can operate independently under normal circumstances to meet the power demand of local areas, and can also interact with external power grids. For the divided power grid, it is based on the preset difference threshold generated by the demand side consumption and the supply side, and is obtained by traversing and dividing the pre-jurisdictional area. This division takes into account factors such as power supply and demand balance, power grid stability and regional characteristics, so that each divided power grid area has relatively unique power supply and demand characteristics and operation requirements. In the fitting process, it is first necessary to conduct in-depth analysis and understanding of the various characteristics of multi-microgrids and divided power grids, including their power production capacity, power consumption mode, power supply stability, load change law, etc. For example, when the power production of a multi-microgrid is surplus in a specific time period, and there is a power demand gap in the adjacent divided power grid area, the channel and mechanism for power transmission can be established through the fitting operation to achieve optimal allocation of power. Through precise fitting operations, the finalized multi-domain network can fully integrate the advantages of multi-microgrids and divided power grids, and improve the adaptability, flexibility and energy efficiency of the entire power grid system. It can better cope with the changes in power supply and demand in different regions and at different times, realize the coordinated complementarity and optimized scheduling of multiple energy sources, and provide strong support for the stable operation and sustainable development of smart grids.

[0032] In a possible implementation, step S200 further includes: Step S240: Determine energy priority grid connection and consumption rules.

[0033] Step S250: setting a game mode of multi-node equivalent load curves, where the equivalent load curve of each node is the total load curve minus the equivalent load of the top priority energy source.

[0034] Step S260: Based on the energy priority grid connection and consumption rules and the game mode, and taking the multi-energy grid access data as a benchmark, determine the multi-energy game function.

[0035] Specifically, the determination of energy priority grid connection and consumption rules is a key link, which is formulated based on a variety of factors. First, consider the environmental friendliness of energy. For example, renewable energy such as wind energy and solar energy usually have lower carbon emissions and environmental impacts, so they may be given higher priority grid connection rights under the same conditions. Secondly, the stability and reliability of energy are also important factors. For example, traditional thermal power generation, although not a completely clean energy, has a relatively stable power generation capacity and can ensure the stable operation of the power grid to a certain extent. Therefore, in some cases, even if renewable energy is abundant, a certain proportion of stable energy needs to be retained to maintain the reliability of the power grid. Furthermore, cost factors will also affect the priority grid connection and consumption rules. Some energy with lower power generation costs are more economically advantageous and may get higher priority in the competition. For example, large hydropower stations have relatively low power generation costs and may be given priority in power grid dispatching.

[0036] In the game mode of the set multi-node equivalent load curve, the principle of priority grid connection and consumption of new energy is followed. For example, new energy such as photovoltaic and wind power have priority in grid connection. First, the total load curve is determined, which represents the total power demand of the entire power grid system in a specific time period. In order to obtain the equivalent load curve of each node, the equivalent load of the energy with the previous priority will be subtracted from the total load curve. For example, the output of photovoltaic power is subtracted first, then the output of wind power, and so on. In the subsequent peak-shaving arrangement, the peak load of the equivalent load will be carried out according to the logical rules of hydropower priority peak-shaving and thermal power auxiliary peak-shaving. This model reflects the order and relationship of different energy sources in meeting the load demand of the power grid, and embodies a dynamic game and coordination mechanism based on energy characteristics and power grid demand. Its purpose is to maximize the advantages of various energy sources and achieve efficient utilization and optimal allocation of energy on the premise of ensuring the stable operation of the power grid.

[0037] Combine the previously determined energy priority grid connection and consumption rules with the set game mode, and determine the multi-energy game function based on the multi-energy grid access data. The multi-energy grid access data contains key information such as the characteristics, power generation capacity, and access points of various energy sources. According to the energy priority grid connection and consumption rules, different energy sources are given different weights or priorities. For example, renewable energy has a higher priority weight, while some high-pollution and high-cost energy sources have lower weights. Then, combined with the game mode, consider the competition and cooperation relationship between different energy sources in meeting equivalent load requirements. For example, in a certain period of time, when renewable energy is sufficient, it will occupy a larger share of power generation; when renewable energy is insufficient, other energy sources need to play a greater role. By combining these factors, a multi-energy game function is constructed. Through this function, the optimal combination and dispatching strategy of different energy sources can be calculated under given grid conditions and requirements to achieve stable operation of the grid, efficient use of energy, and cost optimization.

[0038] In a possible implementation, step S400 further includes: Step S410: Identify the power grid supply and demand data, make inter-domain scheduling decisions based on the upper-layer optimization structure, and determine a first scheduling strategy; Step S420: Transfer the first scheduling strategy to the lower-level optimization structure, make a decision on multi-energy complementarity within the domain based on the multi-energy game function, and determine the complementary coordination strategy through full-domain integration, wherein the lower-level optimization structure takes energy flow, electricity trading, and demand response as decision-making objectives.

[0039] Specifically, identifying grid supply and demand data is the primary task. Grid supply and demand data includes real-time power demand and supply information, as well as future supply and demand trends based on forecasting models. By accurately identifying and analyzing these data, we can clearly understand the current operating status of the power grid and possible future changes. Inter-domain scheduling decisions are made based on the upper-level optimization structure. The upper-level optimization structure mainly focuses on the coordination and balance between multi-domain networks. When considering inter-domain scheduling, multiple factors need to be considered comprehensively. For example, the power transmission capacity and limitations between different multi-domain networks, including line capacity, voltage stability, etc. If one multi-domain network has a power supply shortage and another multi-domain network has surplus power, it is necessary to decide whether to transmit power and how much power to transmit based on the connection relationship and transmission capacity of the power grid. In the process of determining the first scheduling strategy, the distribution and characteristics of energy resources in different regions also need to be considered. For example, some regions have abundant renewable energy, while others rely mainly on traditional energy. By combining these factors, a preliminary inter-domain scheduling strategy is formulated to achieve resource optimization and power balance between multi-domain networks.

[0040] After the first dispatching strategy is transferred to the lower optimization structure, the lower optimization structure makes a decision on multi-energy complementarity within the domain based on the multi-energy game function. The multi-energy game function describes the competition and cooperation relationship between different energy sources, and it takes into account the characteristics, cost, reliability and other factors of various energy sources. When energy flow is used as the decision-making target, it is necessary to consider the transmission and distribution of different energy sources in the region, for example, how to effectively integrate distributed energy such as wind energy and solar energy into the power grid, and how to optimize the energy transmission path to reduce losses and improve efficiency. Electricity trading is another important decision-making target. In the region, there is electricity trading between different power generation entities and users. It is necessary to determine reasonable electricity trading prices and trading volumes based on market rules and supply and demand relationships to promote efficient use of energy and fair competition in the market. Demand response is also a factor that needs to be considered in the lower optimization structure. By guiding users to adjust their electricity consumption behavior according to electricity prices and grid conditions, the optimization of grid load can be achieved. For example, when the power supply is tight, users are encouraged to reduce unnecessary electricity consumption or adjust electricity consumption time to non-peak hours. Finally, through full-domain integration, the upper-level inter-domain dispatching strategy and the lower-level intra-domain multi-energy complementary decision-making are comprehensively considered. This includes coordinating the complementary relationship between different energy sources and ensuring that the dispatching strategies between and within domains match and work together. For example, when determining the energy dispatching strategy for a certain area, it is necessary to consider the supply and demand of different energy sources in the area, as well as the power transmission and coordination relationship with other areas, and finally determine a complementary coordination strategy that can meet the stable operation of the entire power grid system and efficient use of energy.

[0041] In a possible implementation, step S400 further includes: Step S430: Balance cost, resource utilization and environmental friendliness to determine the policy baseline.

[0042] Step S440: Evaluate the complementary coordination strategy. If the strategy baseline is not met, perform strategy iteration optimization to obtain an optimized complementary coordination strategy.

[0043] Specifically, balancing costs, resource utilization and environmental friendliness is a multi-dimensional consideration process. In terms of cost, it is necessary to comprehensively consider the costs of energy procurement, equipment operation, management and personnel. For example, energy procurement costs involve market price fluctuations of different energy sources and the impact of long-term supply stability; equipment operation costs include energy consumption, maintenance and repair costs of equipment such as generators and transmission lines. In terms of resource utilization, both traditional and renewable energy sources must be evaluated for their actual utilization efficiency. For traditional energy, attention should be paid to its energy conversion efficiency, while for renewable energy sources such as wind and solar energy, the ratio of its actual power generation capacity to its potential power generation capacity under the influence of natural factors such as weather should be considered. At the same time, the rational use of energy storage equipment is also critical to improving the overall resource utilization rate. In terms of environmental friendliness, pollutants generated by traditional fossil energy power generation have a negative impact on the environment. Therefore, it is necessary to attach importance to the use of renewable energy, reduce pollutant emissions, and reduce the impact on the climate. By comprehensively weighing these factors, a reasonable strategy baseline is determined, which provides a benchmark reference for subsequent evaluation and optimization.

[0044] Evaluating the complementary coordination strategy is a key step to ensure the effectiveness of the strategy. Compare the current complementary coordination strategy with the strategy baseline. If it is found that the strategy cannot meet the requirements of the strategy baseline, it is necessary to perform strategy iteration and optimization. When using genetic algorithms for strategy iteration and optimization, the complementary coordination strategy is first encoded and converted into a chromosome form that can be processed by the algorithm. Then the fitness function is determined. This function comprehensively considers factors such as cost, resource utilization, and environmental friendliness. The fitness value is calculated by calculating the deviation between the actual value and the ideal value, which is used to measure the pros and cons of each strategy. Then, a selection operation is performed, just like the roulette selection method, so that chromosomes with high fitness have a greater chance of entering the next generation of populations. After that, a crossover operation is performed to select crossover points on the chromosome for partial exchange to produce new offspring. Then a mutation operation is performed to change the gene positions on the chromosome with a certain probability to increase population diversity. The process of selection, crossover and mutation is repeated continuously. With each iteration, the population is constantly updated. When the preset number of iterations is reached or the fitness of the population is no longer significantly improved, the iteration stops. At this time, the strategy represented by the chromosome with the highest fitness in the population is the optimized complementary coordination strategy. This strategy achieves a better balance between cost, resource utilization and environmental friendliness, and can better meet the needs of multi-energy complementary scheduling of smart grids.

[0045] In a possible implementation, step S400 further includes: Step S450: Interact with the grid dispatch records of the preset time zone to mine periodic dispatch uncertainties and dispatch risk characteristics.

[0046] Step S460: traverse the scheduling uncertainty factors and scheduling risk characteristics, and determine the unstable smoothing relationship based on the recorded adjustment information.

[0047] Step S470: Based on the unstable smoothing relationship, perform scheduling fault tolerance analysis and determine a fault tolerance redundancy mechanism.

[0048] Step S480: Based on the fault-tolerant redundancy mechanism, assist in multi-energy complementary scheduling management.

[0049] Specifically, interactive preset time zone grid dispatch records are basic work. These dispatch records contain rich information such as the operation status of the grid at different time points in the past, dispatch decisions, and actual power supply and demand. Through in-depth analysis of these records, periodic dispatch uncertainties can be unearthed. For example, the periodic fluctuations in electricity demand in some regions due to seasonal changes, or the regular changes in electricity demand due to the production cycle of a specific industry; at the same time, dispatch risk characteristics can also be identified, such as the increased risk of grid failures due to equipment aging, sudden weather changes, etc. in certain specific periods of time.

[0050] Traversing the dispatch uncertainties and dispatch risk characteristics mined, recording the regulation information is a record of the regulation measures and corresponding effects taken in the past for these uncertainties and risks. By analyzing this information, the instability suppression relationship can be determined. For example, when it is found that the peak electricity consumption in a certain season leads to grid instability, the relationship between the regulation measures taken in the past, such as increasing the output of a specific power station or adjusting the power distribution strategy, and the stability of the grid can be clarified, thereby clarifying the role and effect of different regulation measures on suppressing unstable factors.

[0051] Based on the previously determined unstable leveling relationship, the fault tolerance analysis of dispatching is carried out. In this process, the possible failures of the power grid and their impact on dispatching are carefully analyzed under various uncertain factors and risk scenarios. For example, when a key power generation equipment fails due to an emergency, the scope of the power supply interruption caused by it and the impact on the frequency and voltage stability of the power grid will be evaluated based on its position in the power grid and its power generation capacity. At the same time, for failures caused by natural disasters that damage transmission lines or equipment aging, the potential chain reaction is analyzed. Then, the fault-tolerant redundancy mechanism is determined based on these analysis results. This mechanism involves many aspects of arrangements, such as setting up backup power supplies. When the main power supply fails, the backup power supply can be quickly put into use to maintain the basic operation of the power grid; planning redundant transmission lines. When a line has problems, other lines can share the power transmission task in time to ensure the continuous supply of electricity; it also includes the formulation of emergency plans. When a major failure occurs, it can be quickly handled according to the plan process and coordinate resources from all parties to restore the normal operation of the power grid. These measures provide a solid guarantee for the stable dispatch of the power grid, so that the power grid has stronger adaptability and recovery capabilities when facing various emergencies.

[0052] The fault-tolerant redundancy mechanism provides important auxiliary support for multi-energy complementary dispatching management. The fault-tolerant redundancy mechanism establishes a safety net to deal with emergencies and potential failures. When multi-energy complementary dispatching management is carried out, various energy sources need to work together to meet the dynamic changes in power demand. For example, wind energy, solar energy, hydropower and traditional energy (such as coal, natural gas, etc.) play different roles at different times and under different conditions. Under normal circumstances, multi-energy complementary dispatching will reasonably allocate the power generation ratio and dispatching priority of different energy sources based on factors such as energy availability, cost-effectiveness and environmental protection requirements. However, when emergencies occur, the fault-tolerant redundancy mechanism begins to play a key role. If an energy generation system fails, such as wind power generation equipment is damaged by strong winds or solar panels have a sharp drop in power generation efficiency due to bad weather, the fault-tolerant redundancy mechanism can respond quickly, and the backup power supply will be started in time to fill the power generation gap caused by the failure of the energy source and ensure the continuity of power supply. At the same time, in multi-energy complementary dispatching management, redundant transmission lines and equipment can also provide additional protection. When a transmission line has a problem, other backup lines can be put into use immediately to maintain the normal transmission and distribution of electricity, avoiding power outages in local areas or unstable power supply due to line failures. In addition, as part of the fault-tolerant redundancy mechanism, the emergency plan provides a clear response process and decision-making basis for multi-energy complementary dispatching management. In the face of complex emergencies, reasonable dispatching adjustments can be made quickly according to the emergency plan to coordinate the switching and complementarity between different energy sources, and minimize the impact of faults on power grid operation and user electricity consumption. In short, the fault-tolerant redundancy mechanism assists multi-energy complementary dispatching management to achieve a more stable, efficient and flexible power supply by providing backup resources, enhancing the reliability of the system and the ability to respond to emergencies.

[0053] In a possible implementation, step S600 further includes: Step S610: monitor and track the response status of the complementary coordination strategy, and determine the strategy response distribution.

[0054] Step S620: traverse the strategy response distribution, perform abnormal response judgment and over-limit alarm based on the response vector deviation and response trend bias, and determine abnormal scheduling data.

[0055] Step S630: Based on the abnormal dispatch data, tracing and locating the source of abnormal dispatch of the power grid and feedback dispatch management are performed.

[0056] Specifically, it is crucial to monitor and track the response status of the complementary coordination strategy. By setting up sensors, monitoring equipment and data acquisition systems at various key nodes and links of the power grid, relevant data on the operation of the power grid can be obtained in real time. These data include parameters such as the flow direction, voltage, current, power, as well as the operating status of various energy generation equipment, the charging and discharging status of energy storage devices, etc. Then, these data are analyzed and processed to determine the strategy response distribution. The strategy response distribution reflects the actual response of each part of the entire power grid after the implementation of the complementary coordination strategy. It can show the response degree and time difference of different regions and different devices to the strategy. For example, some areas may have a certain delay in responding to the strategy due to the long distance from the power source or the large line impedance; and some devices may have a deviation from the expected response degree due to their own performance or aging.

[0057] For the traversal strategy response distribution, we must first clarify the concepts of response vector deviation and response trend bias. Response vector deviation usually refers to the difference between the actual response and the expected response in the vector direction, including both magnitude and direction. For example, for a certain power generation equipment, it is expected that it should increase a certain amount of power generation to respond to the strategy, but the actual increased power is different from the expected value, which is the response vector deviation. Response trend bias refers to the deviation between the change trend of the actual response and the expected trend. For example, it is expected that the load of the power grid should gradually increase within a certain period of time, but the actual monitored load change trend does not match the expectation. By analyzing these factors, abnormal response judgment can be made. If the response vector deviation exceeds a certain threshold, or the response trend bias obviously deviates from expectations, it can be considered that an abnormal response has occurred. At this time, an over-limit alarm will be issued to remind the management staff to pay attention. In this way, the response situation that does not meet the expectations in the operation of the power grid can be discovered in time, the abnormal dispatching data can be determined, and accurate information can be provided for subsequent processing.

[0058] Based on the determined abnormal dispatch data, the abnormal dispatch of the power grid is traced and located, which requires the comprehensive use of data analysis, fault diagnosis technology, and topological structure information of the power grid. By analyzing the characteristics of abnormal data, propagation paths, and associations with other devices and nodes, the scope of the abnormality is gradually narrowed, and the source of the abnormality is finally determined. For example, if the voltage in a certain area is abnormal, it is necessary to trace whether it is caused by the power generation equipment, transmission lines, load changes, or other factors in the area. Once the source of the abnormality is determined, feedback dispatch management can be carried out, which includes taking appropriate measures to correct the abnormality, such as adjusting the output of the power generation equipment, switching transmission lines, adjusting load distribution, etc. At the same time, the complementary coordination strategy is appropriately corrected and optimized to avoid similar abnormalities from happening again. Feedback dispatch management also needs to update the monitoring data and operating status of the power grid in a timely manner to ensure that the entire power grid can be restored to a normal operating state and continuously improve the stability and reliability of the power grid.

[0059] Embodiment 2, based on the same inventive concept as the smart grid multi-energy complementary scheduling method in the above embodiment, Figure 2 As shown, the present application provides a smart grid multi-energy complementary dispatching system, and the system and method embodiments in the present application embodiments are based on the same inventive concept. Among them, the system includes: The data interaction module 10 is used to interact with the power grid distribution data and multi-energy grid access data of the pre-jurisdictional area.

[0060] The power grid distribution data traversal module 20 is used to traverse the power grid distribution data, delineate multi-domain networks, and combine the multi-energy access data to mine scheduling constraints and multi-energy game functions, wherein the scheduling constraints at least include unit output limitations and power grid transmission limitations.

[0061] The dual-drive decision model generation module 30 is used to use multi-domain network coordination as the upper optimization structure and multi-energy coordination as the lower optimization structure, combine the scheduling constraints and the multi-energy game function to perform data-driven modeling, and generate a dual-drive decision model.

[0062] The power grid supply and demand data reading module 40 is used to read power grid supply and demand data, and the power grid supply and demand data includes real-time supply and demand data and predicted supply and demand data.

[0063] The complementary coordination strategy determination module 50 is used to transmit the power grid supply and demand data to the dual-drive decision model, and the upper and lower layers cooperate to make multi-energy complementary scheduling decisions and determine the complementary coordination strategy.

[0064] The power grid dispatching management module 60 is used to delegate the complementary coordination strategy to the power system and perform power grid dispatching management in the pre-jurisdiction area.

[0065] Furthermore, the power grid distribution data traversal module 20 includes: A multi-microgrid identification unit is used to identify the power grid distribution data and identify multiple microgrids based on the grid-connected system.

[0066] A grid division determination unit is used to identify the grid distribution data, traverse the pre-jurisdiction area for division based on a preset difference threshold between the demand side consumption and the supply side, and determine the grid division.

[0067] A multi-domain network determination unit is used to fit the multiple microgrids and the divided power grids to determine the multi-domain network.

[0068] Furthermore, the power grid distribution data traversal module 20 includes: A rule determination unit, wherein the rule determination unit is used to determine energy priority grid connection and consumption rules.

[0069] A game mode setting unit is used to set a game mode of a multi-node equivalent load curve, wherein the equivalent load curve of each node is the total load curve minus the equivalent load of the top priority energy source.

[0070] A multi-energy game function determination unit, which determines the multi-energy game function based on the energy priority grid connection and consumption rules and the game mode and taking the multi-energy grid access data as a benchmark.

[0071] Furthermore, the complementary coordination strategy determination module 50 includes: The first scheduling strategy determination unit is used to identify the power grid supply and demand data, make inter-domain scheduling decisions based on the upper-layer optimization structure, and determine a first scheduling strategy.

[0072] A complementary coordination strategy determination unit, wherein the complementary coordination strategy determination unit is used to transfer the first scheduling strategy to the lower-level optimization structure, and make multi-energy complementary decisions within the domain based on the multi-energy game function, and determine the complementary coordination strategy through full-domain integration, wherein the lower-level optimization structure takes energy flow, electricity trading, and demand response as decision-making objectives.

[0073] Furthermore, the complementary coordination strategy determination module 50 includes: The policy baseline determination unit is used to balance cost, resource utilization and environmental friendliness to determine the policy baseline.

[0074] The optimized complementary coordination strategy unit is used to evaluate the complementary coordination strategy. If the strategy baseline is not met, the strategy is iterated and optimized to obtain the optimized complementary coordination strategy.

[0075] Furthermore, the complementary coordination strategy determination module 50 includes: A power grid dispatch record interaction unit is used to interact with power grid dispatch records in a preset time zone and to mine periodic dispatch uncertainty factors and dispatch risk characteristics.

[0076] The unstable stabilization relationship determination unit is used to traverse the scheduling uncertainty factors and scheduling risk characteristics, and determine the unstable stabilization relationship based on the recorded adjustment information.

[0077] A fault-tolerant redundant mechanism determination unit is configured to perform scheduling fault-tolerant analysis based on the unstable smoothing relationship to determine a fault-tolerant redundant mechanism.

[0078] A complementary scheduling management unit, wherein the complementary scheduling management unit assists in multi-energy complementary scheduling management based on the fault-tolerant redundancy mechanism.

[0079] Furthermore, the power grid dispatching management module 60 includes: A strategy response distribution determination unit is used to monitor and track the response status of the complementary coordination strategy and determine the strategy response distribution.

[0080] The abnormal scheduling data determination unit is used to traverse the policy response distribution, respond to the vector deviation and the response trend deviation, perform abnormal response judgment and over-limit alarm, and determine the abnormal scheduling data.

[0081] A source tracing and positioning unit is used to perform source tracing and positioning of abnormal power grid scheduling and feedback scheduling management based on the abnormal scheduling data.

[0082] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0084] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A smart grid multi-energy complementary dispatching method, characterized in that: The method comprises: Interactive pre-jurisdiction area power grid distribution data and multi-energy grid access data; Traversing the power grid distribution data, delineating a multi-domain network, combining the multi-energy access data, mining scheduling constraints and multi-energy game functions, wherein the scheduling constraints at least include unit output restrictions and power grid transmission restrictions; Taking multi-domain network coordination as the upper optimization structure and multi-energy coordination as the lower optimization structure, combining the scheduling constraints and the multi-energy game function to perform data-driven modeling, and generating a dual-drive decision model; Reading power grid supply and demand data, wherein the power grid supply and demand data includes real-time supply and demand data and predicted supply and demand data; The power grid supply and demand data is transmitted to the dual-drive decision model, and the upper and lower layers cooperate to make multi-energy complementary scheduling decisions and determine the complementary coordination strategy; The complementary coordination strategy is decentralized to the power system to carry out the dispatching management of the power grid in the pre-jurisdiction area.

2. The smart grid multi-energy complementary scheduling method according to claim 1, characterized in that: Traversing the power grid distribution data, delineating a multi-domain network, including: Identify the grid distribution data, and identify multiple microgrids based on the grid-connected system; Identify the power grid distribution data, and based on a preset difference threshold between the demand side consumption and the supply side, traverse the pre-jurisdiction area to divide the area and determine the divided power grid; The multi-microgrids and the divided power grids are fitted to determine the multi-domain network.

3. The smart grid multi-energy complementary scheduling method according to claim 1, characterized in that: Get multi-energy game functions, including: Determine the rules for priority grid connection and consumption of energy; The game mode of multi-node equivalent load curve is set, and the equivalent load curve of each node is the total load curve minus the equivalent load of the top priority energy; Based on the energy priority grid connection and consumption rules and the game mode, the multi-energy game function is determined based on the multi-energy grid access data.

4. The smart grid multi-energy complementary scheduling method according to claim 1, characterized in that: The determining of complementary coordination strategies comprises: Identify the power grid supply and demand data, make inter-domain scheduling decisions based on the upper-layer optimization structure, and determine a first scheduling strategy; The first scheduling strategy is transferred to the lower-level optimization structure, and based on the multi-energy game function, a multi-energy complementary decision is made within the domain, and the complementary coordination strategy is determined by full domain integration, wherein the lower-level optimization structure takes energy flow, electricity trading, and demand response as decision-making objectives.

5. The smart grid multi-energy complementary scheduling method according to claim 4, characterized in that: After determining the complementary coordination strategy, it includes: Balance costs, resource utilization and environmental friendliness to determine the strategy baseline; The complementary coordination strategy is evaluated, and if the strategy baseline is not met, the strategy is iterated and optimized to obtain an optimized complementary coordination strategy.

6. The smart grid multi-energy complementary scheduling method according to claim 1, characterized in that: The method further comprises: Interactively preset the grid dispatch records in time zones to mine periodic dispatch uncertainties and dispatch risk characteristics; Traversing the scheduling uncertainties and scheduling risk characteristics, and determining the unstable smoothing relationship based on the recorded adjustment information; Based on the unstable smoothing relationship, a scheduling fault tolerance analysis is performed to determine a fault tolerance redundancy mechanism; Based on the fault-tolerant redundancy mechanism, multi-energy complementary scheduling management is assisted.

7. The smart grid multi-energy complementary scheduling method according to claim 1, characterized in that: After the power grid dispatching management in the pre-jurisdiction area is carried out, it includes: Monitor and track the response status of the complementary coordination strategy and determine the strategy response distribution; Traversing the strategy response distribution, based on the response vector deviation and response trend bias, making abnormal response judgments and over-limit alarms, and determining abnormal scheduling data; Based on the abnormal dispatch data, source tracing and positioning of abnormal power grid dispatch and feedback dispatch management are performed.

8. Smart grid multi-energy complementary dispatching system, characterized in that: The system comprises: A data interaction module, the data interaction module is used to interact with the power grid distribution data and multi-energy network access data of the pre-jurisdiction area; A power grid distribution data traversal module, which is used to traverse the power grid distribution data, delineate multi-domain networks, and mine scheduling constraints and multi-energy game functions in combination with the multi-energy access data, wherein the scheduling constraints at least include unit output restrictions and power grid transmission restrictions; A dual-drive decision model generation module, wherein the dual-drive decision model generation module is used to perform data-driven modeling based on multi-domain network coordination as an upper optimization structure and multi-energy coordination as a lower optimization structure, and to generate a dual-drive decision model by combining the scheduling constraints and the multi-energy game function; A power grid supply and demand data reading module, the power grid supply and demand data reading module is used to read power grid supply and demand data, the power grid supply and demand data includes real-time supply and demand data and predicted supply and demand data; A complementary coordination strategy determination module, which is used to transmit the power grid supply and demand data to the dual-drive decision model, and the upper and lower layers cooperate to make multi-energy complementary scheduling decisions and determine the complementary coordination strategy; A power grid dispatching management module is used to delegate the complementary coordination strategy to the power system and perform power grid dispatching management in the pre-jurisdiction area.

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