Park energy scheduling method and system based on supply and demand balance

Abstract

The invention provides a park energy scheduling method and system based on supply and demand balance, and relates to the technical field of data processing. The method comprises the following steps: constructing a park energy network, wherein the park energy network comprises a state grid, photovoltaic power generation equipment, candidate equipment and pure electric equipment; acquiring an electrical load predicted quantity; obtaining photovoltaic power generation predictive quantity; determining the pre-purchased electric quantity purchased from the State Grid Corporation of China according to the electrical load predicted quantity and the photovoltaic power generation predicted quantity by taking supply-demand balance as a target; acquiring device data of each candidate device in the current park energy network environment; determining a priority parameter of each candidate device according to the device data of each candidate device; and the pre-purchased electric quantity and the photovoltaic power generation amount are preferentially used for power supply, and the candidate equipment is scheduled to charge and discharge in combination with the priority parameters of the candidate equipment by taking the minimum power consumption cost and the most balanced supply and demand as targets so as to supply power to the park. According to the invention, the balance and stability of power supply can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a park energy dispatching method and system based on supply and demand balance. Background Art

[0002] With the popularization and development of clean energy, more and more industrial parks are deploying photovoltaic power generation equipment for cost considerations. On the one hand, it can reduce the demand for purchasing electricity from the national grid and reduce the energy cost of the industrial park. On the other hand, it can reduce greenhouse gas emissions and improve the environmental quality of the park.

[0003] However, due to the dual uncertainty of power load and photovoltaic power generation, there will be a mismatch between power supply and demand. For example, in certain periods of time, photovoltaic power generation may exceed the actual power demand of the industrial park, resulting in the inability to effectively use excess power, which can only be abandoned and waste resources; while in other periods of time, the power load may exceed the photovoltaic power generation, and additional power needs to be purchased from the national grid, increasing costs. At the same time, it will also affect the stability and reliability of the energy supply of the industrial park, and may even cause power outages and shutdowns, affecting production and operations. Summary of the invention

[0004] In order to solve the problem of mismatch between electricity supply and demand due to the dual uncertainty of electricity load and photovoltaic power generation in the prior art. For example, in certain periods of time, photovoltaic power generation may exceed the actual electricity demand of the industrial park, resulting in the inability to effectively utilize excess electricity, which can only be abandoned and waste resources; while in other periods of time, the electricity load may exceed the photovoltaic power generation, and additional electricity needs to be purchased from the national grid, increasing costs. At the same time, it will also affect the stability and reliability of the energy supply of the industrial park, and may even cause power outages, shutdowns and other problems, affecting production and operation. Technical problems, the present invention provides a park energy scheduling method and system based on supply and demand balance.

[0005] The technical solution provided by the embodiment of the present invention is as follows:

[0006] First aspect

[0007] An embodiment of the present invention provides a park energy scheduling method based on supply and demand balance, including:

[0008] S1: Building a park energy network, which includes: the national power grid, photovoltaic power generation equipment, standby equipment and pure power-consuming equipment, wherein the standby equipment includes battery equipment, boiler equipment and electric vehicles in charging state;

[0009] S2: Obtain the predicted power load;

[0010] S3: Obtaining the predicted amount of photovoltaic power generation;

[0011] S4: with the goal of balancing supply and demand, determining the amount of pre-purchased electricity purchased from the national grid according to the predicted amount of electricity load and the predicted amount of photovoltaic power generation;

[0012] S5: Obtain device data of each candidate device in the current campus energy network environment;

[0013] S6: Determine the priority parameter of each candidate device according to the device data of each candidate device;

[0014] S7: giving priority to using the pre-purchased electricity and photovoltaic power generation for power supply, combining the priority parameters of the standby equipment, with the goal of minimizing electricity cost and balancing supply and demand, scheduling the standby equipment for charging and discharging to supply power to the park.

[0015] Second aspect

[0016] An embodiment of the present invention provides a park energy dispatching system based on supply and demand balance, including:

[0017] processor;

[0018] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the park energy scheduling method based on supply and demand balance as described in the first aspect is implemented.

[0019] The third aspect

[0020] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the park energy scheduling method based on supply and demand balance as described in the first aspect is implemented.

[0021] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0022] (1) In the present invention, by predicting the power load and photovoltaic power generation, and determining the pre-purchased electricity from the State Grid based on the predicted power load and photovoltaic power generation, the park can better match power supply and demand, help avoid excess or insufficient power supply, optimize energy scheduling plans, and reduce costs.

[0023] (2) In the present invention, the pre-purchased electricity and photovoltaic power generation are preferentially used for power supply, which helps to reduce the energy cost of the park. Afterwards, with the goal of minimizing the electricity cost and balancing the supply and demand, the standby equipment is scheduled for charging and discharging to supply power to the park. Through reasonable scheduling and utilization, the balance of supply and demand can be better achieved. During peak electricity consumption periods, photovoltaic power generation and pre-purchased electricity are preferentially used for power supply to reduce dependence on the national power grid. When photovoltaic power generation is insufficient or electricity consumption is at a low point, the standby equipment can be reasonably scheduled for charging and discharging to ensure the balance and stability of the power supply, improve the stability and reliability of the park's energy supply, and ensure production and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0025] Figure 1 A schematic diagram of a flow chart of a park energy scheduling method based on supply and demand balance provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the structure of a campus energy network provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a flow chart of a method for determining an energy scheduling scheme provided by an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of the structure of a park energy dispatching system based on supply and demand balance provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0031] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0032] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0033] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0034] Reference Manual Attached Figure 1 , showing a flow chart of a park energy scheduling method based on supply and demand balance provided by an embodiment of the present invention.

[0035] The embodiment of the present invention provides a method for energy scheduling in a park based on supply and demand balance, which can be implemented by a park energy scheduling device based on supply and demand balance, and the park energy scheduling device based on supply and demand balance can be a terminal or a server. The processing flow of the method for energy scheduling in a park based on supply and demand balance can include the following steps:

[0036] S1: Build a campus energy network.

[0037] Reference Manual Attached Figure 2 , showing a schematic diagram of the structure of a campus energy network provided by an embodiment of the present invention.

[0038] The park's energy network includes: the national power grid, photovoltaic power generation equipment, backup equipment and pure electricity-consuming equipment. The backup equipment includes battery equipment, boiler equipment and electric vehicles in charging state.

[0039] Furthermore, the construction of the park energy network can realize the centralized management and monitoring of energy. Through the intelligent system, the generation, consumption and storage of energy can be monitored in real time, which helps the park managers to better understand the energy usage and make reasonable scheduling and decisions.

[0040] It should be noted that photovoltaic power generation equipment is a clean energy source. By incorporating it into the park's energy network, the park can use solar power generation to reduce its reliance on high-carbon energy sources such as traditional coal-fired power generation and reduce environmental pollution. The batteries and boiler equipment in the standby equipment can be used for energy storage and deployment. When the photovoltaic power generation equipment generates excess electricity, the excess electricity can be stored in the battery for use at night or on rainy days. The boiler equipment can also be used to burn and generate electricity when needed to provide additional energy supply. The batteries, boilers and electric vehicles in the standby equipment can serve as backup and emergency means of energy. When photovoltaic power generation is insufficient or the power supply of the national grid is unstable, these devices can be quickly started to meet the energy needs of the park.

[0041] In the present invention, building a park energy network can provide many benefits such as diversified energy supply, utilization of clean energy, energy storage and allocation, response to energy fluctuations, reduction of energy costs, improvement of energy management efficiency, environmental protection and sustainable development, which is conducive to building a more intelligent, efficient and sustainable industrial park.

[0042] S2: Obtain the predicted power load.

[0043] Specifically, the power load forecast can be obtained through technologies such as recurrent neural network (RNN), LSTM long short-term memory neural network, etc.

[0044] In a possible implementation, S2 specifically includes: obtaining a predicted power load through GRU-RNN.

[0045] Among them, GRU-RNN (Gated Recurrent Unit-Recurrent Neural Network) is a variant of recurrent neural network (RNN) designed to solve the long-term dependency problem in traditional RNN. Compared with the standard RNN, GRU introduces two important gating units: Update Gate and Reset Gate. The role of these gating units is to control the flow of information so that the model can selectively remember or forget information based on the input data.

[0046] The traditional GRU-RNN will retain part of the original signal when updating the state, which leads to an abnormally large number of model parameters and a complex calculation process, which affects the model calculation efficiency and is not conducive to the real-time scheduling of energy. Therefore, the present invention proposes the following improved GRU-RNN, which only considers the previous hidden state and bias when updating the gate and resetting the gate to update the state, and discards part of the original signal. This is because the power load is uncertain, and the original signal is not very meaningful for the state update. By discarding part of the original signal, more attention is paid to the changing trend and pattern of historical data, which helps to deal with the uncertainty of the power load, can greatly improve the calculation efficiency of the model, and is more conducive to the real-time scheduling of energy.

[0047] Specifically, the power load forecast is obtained through the improved GRU-RNN, including:

[0048] In the input layer, the electricity load sequence is obtained.

[0049] In the update gate, the output vector of the update gate is updated:

[0050] z t =σ(W hz h t-1 +b z )

[0051] Among them, z t represents the output vector of the update gate at time t, σ() represents the activation function, and W hz represents the self-connection weight matrix of the update gate between time t and time t-1, h t-1 represents the hidden state vector at time t-1, b z Represents the bias term for the update gate.

[0052] It should be noted that through the update gate, the model can selectively retain the hidden state information of the previous time step, which helps to deal with long-term dependencies. In some cases, the information of the previous time step may be very important for the prediction of the current time step, and the update gate can effectively learn and retain this long-term dependent information.

[0053] In the reset gate, the output vector of the reset gate is updated:

[0054] r t =σ(W hr h t-1 +b r )

[0055] Among them, r t represents the output vector of the reset gate at time t, W hr represents the self-connection weight matrix between the reset gate at time t and time t-1, b rRepresents the bias term for the reset gate.

[0056] It should be noted that by resetting the gate, the model can dynamically adjust to ignore or retain the hidden state information of the previous time step according to the characteristics of the input sequence. This helps to alleviate the gradient vanishing problem and improve the model's learning ability on long sequences.

[0057] In the candidate layer, the output vector of the candidate layer is updated:

[0058] c t =σ(W uc u t +W hc (r t ⊙h t-1 )+b c )

[0059] Among them, c t represents the output vector of the candidate layer at time t, W uc represents the weight matrix between the input layer and the candidate layer, u t Represents the voltage signal at time t, W hc represents the self-connection weight matrix between the candidate layer at time t and time t-1, b c represents the bias term of the candidate layer, and ⊙ represents the element-wise product operation.

[0060] It should be noted that the update method of the candidate layer combines the current input and past hidden state information, and introduces a forgetting mechanism and a nonlinear activation function, which help improve the stability and convergence speed of the model during training. The model is more likely to learn and remember patterns and regularities in the sequence.

[0061] In the hidden layer, the output state vector of the hidden layer is updated:

[0062] h t =(1-z t )⊙h t-1 +z t ⊙c t

[0063] Among them, h t Represents the hidden state vector at time t.

[0064] It should be noted that the update of the hidden layer combines current and past information, fully utilizes the role of the update gate and candidate hidden states, avoids the problems of gradient vanishing and explosion, enhances the memory and forgetting mechanism, and reduces the risk of overfitting, thereby improving the effect and performance of the GRU-RNN model in sequence data prediction and modeling tasks.

[0065] In the output layer, the power load is predicted:

[0066]

[0067] in, represents the predicted power load at time t, W yh represents the weight matrix between the hidden layer and the output layer, b y Represents the bias term of the output layer.

[0068] In the present invention, the power load forecast is obtained by using the improved GRU-RNN, which can improve the accuracy, computational efficiency and generalization ability of the model, help optimize energy scheduling, improve the efficiency of park energy management, and promote the use of clean energy, thereby realizing intelligent and efficient management of park energy.

[0069] S3: Obtain the predicted amount of photovoltaic power generation.

[0070] In a possible implementation, S3 specifically includes: obtaining the predicted amount of photovoltaic power generation through GRU-RNN.

[0071] Similarly, the traditional GRU-RNN will retain part of the original signal when updating the state, which leads to an abnormally large number of model parameters, a complex calculation process, affects the model calculation efficiency, and is not conducive to the real-time scheduling of energy. Therefore, the present invention proposes the following improved GRU-RNN, which only considers the previous hidden state and bias when updating the gate and resetting the gate to update the state, and discards part of the original signal. This is because photovoltaic equipment is easily affected by the environment and has uncertainty. The original signal is not very meaningful for state updates. Discarding part of the original signal pays more attention to the changing trends and patterns of historical data, which helps to deal with the uncertainty generated by photovoltaic equipment, can greatly improve the calculation efficiency of the model, and is more conducive to the real-time scheduling of energy.

[0072] Furthermore, the photovoltaic power generation forecast can be obtained based on the photovoltaic power generation sequence in the same manner as the power load forecast. To avoid repetition, the present invention will not go into details.

[0073] In the present invention, the photovoltaic power generation forecast is obtained by using the improved GRU-RNN, which can reduce the model complexity and computational cost, cope with the uncertainty of photovoltaic equipment, reduce the impact of noise, speed up the model training, and better adapt to real-time scheduling needs, thereby improving the efficiency and reliability of park energy management.

[0074] S4: With the goal of balancing supply and demand, determine the amount of pre-purchased electricity from the State Grid based on the forecast of electricity load and photovoltaic power generation.

[0075] In a possible implementation, S4 specifically includes: determining the pre-purchased electricity amount purchased from the State Grid according to the following formula:

[0076] G=αY-G f

[0077] Among them, G represents the pre-purchased electricity, Y represents the predicted electricity load, G f represents the predicted photovoltaic power generation, α represents the load balancing guarantee coefficient, α>1, and the load balancing guarantee coefficient is used to ensure that the pre-purchased electricity can meet the electricity demand of pure power-consuming equipment under ideal circumstances.

[0078] Among them, those skilled in the art can set the size of the load balancing guarantee coefficient α according to actual conditions, and the present invention does not limit it.

[0079] In the present invention, by dynamically determining the pre-purchased electricity according to actual conditions, the park can more effectively plan and utilize electricity purchase resources. If the photovoltaic power generation is sufficient, the demand for electricity purchase from the national grid can be reduced, reducing the energy procurement cost of the park. At the same time, the adjustment of the load balancing guarantee coefficient can be flexibly adjusted according to actual needs, further reducing the energy cost of the park.

[0080] S5: Obtain the equipment data of each candidate equipment in the current campus energy network environment.

[0081] The device data includes the current charging capacity and discharging capacity of each candidate device.

[0082] S6: Determine the priority parameter of each candidate device according to the device data of each candidate device.

[0083] In a possible implementation, S6 specifically includes sub-steps S601 to S603:

[0084] S601: Obtain the current charging capacity and discharging capacity of each candidate device.

[0085] S602: Calculate the priority parameters of each candidate device when charging according to the current charging capacity of each candidate device:

[0086]

[0087] Among them, λ ci represents the priority parameter of the i-th candidate device when charging, c i represents the current charging capacity of the i-th candidate device, and n represents the total number of candidate devices.

[0088] In the present invention, by calculating the ratio of the current charging capacity of each candidate device to the total charging capacity, the priority parameters of each device when charging are obtained. Such priority parameters can intuitively reflect the energy reserve of each device. The higher the charging capacity of the device, the larger the corresponding priority parameter. In actual energy scheduling, the system can reasonably allocate charging tasks according to these priority parameters, and give priority to charging devices with sufficient energy reserves, so as to ensure that the park can maximize the use of available energy under the balance of supply and demand. At the same time, by setting priority parameters, the system can give priority to candidate devices with higher charging capacity to ensure that these devices can be fully charged in time. This helps to maximize the use of the energy reserves of each device in the park and avoid the problem of insufficient energy supply due to insufficient power of some devices. Under the condition of supply and demand balance, making full use of available energy reserves helps to reduce the energy cost of the park and improve energy utilization efficiency.

[0089] S603: Calculate the priority parameters of each candidate device when discharging according to the current discharge capacity of each candidate device:

[0090]

[0091] Among them, λ fi represents the priority parameter of the discharge of the i-th candidate device, f i Represents the current discharge capacity of the i-th candidate device.

[0092] In the present invention, similar to the situation during charging, the priority parameters of each device during discharge are obtained by calculating the ratio of the current discharge capacity of each candidate device to the total discharge capacity. The higher the discharge capacity of a device, the larger its corresponding priority parameter. This priority allocation based on energy status can ensure that in the case of excess energy supply, devices with higher discharge capacity are given priority for discharge, thereby effectively utilizing the energy reserves of various devices in the park. At the same time, in the case of supply and demand balance, by setting the priority parameters during discharge, the system can reasonably arrange the discharge tasks of each device according to the discharge capacity of different devices. Devices with higher discharge capacity are given higher priority, which is conducive to giving priority to these devices for discharge when energy needs to be released, thereby ensuring the balance and stability of energy supply.

[0093] S7: Pre-purchased electricity and photovoltaic power generation are used for power supply first. Combined with the priority parameters of the standby equipment, the standby equipment is scheduled for charging and discharging to provide power to the park with the goal of minimizing electricity costs and balancing supply and demand.

[0094] It should be noted that, with the goal of minimizing electricity costs and balancing supply and demand, standby equipment is dispatched for charging and discharging to supply power to the park. Through reasonable dispatch and utilization, supply and demand balance can be better achieved. During peak hours, photovoltaic power generation and pre-purchased electricity are given priority to supply power, reducing dependence on the national grid. When photovoltaic power generation is insufficient or electricity consumption is low, standby equipment can be reasonably dispatched for charging and discharging to ensure the balance and stability of power supply, improve the stability and reliability of the park's energy supply, and ensure production and operation.

[0095] Reference Manual Attached Figure 3 , showing a flow chart of a method for determining an energy scheduling scheme provided in an embodiment of the present invention.

[0096] In a possible implementation, S7 specifically includes sub-steps S701 to S704:

[0097] S701: Construct an objective function with the goal of minimizing electricity cost and balancing supply and demand.

[0098] Optionally, the objective function is specifically:

[0099] F(X)=min(μ1C+μ2τ)

[0100] Among them, F() represents the objective function, X represents the energy scheduling plan, which includes the amount of electricity purchased from the national grid in real time and how each backup device is charged and discharged, min represents the minimum value, C represents the electricity cost, τ represents the supply and demand balance, μ1 represents the weight coefficient of the electricity cost, and μ2 represents the weight coefficient of the supply and demand balance.

[0101] Among them, those skilled in the art can set the size according to actual conditions, and the present invention does not limit it.

[0102] It should be noted that minimizing electricity costs and optimizing the balance between supply and demand can help reduce energy waste. Reasonable energy scheduling can ensure the efficient use of energy in the park, avoid excessive consumption or waste of energy, and achieve the best balance between electricity demand and power supply capacity in the park, thereby improving energy efficiency.

[0103] Furthermore, since the parameters in the objective function can be adjusted according to the actual situation of the park, it has certain flexibility and customization. Park managers can adjust the weights and parameters in the objective function according to different economic, environmental and equipment conditions to make energy scheduling more in line with actual needs.

[0104] The electricity cost is as follows:

[0105]

[0106] Among them, C represents the electricity cost, C g represents the pre-order price, G represents the pre-order power, C tg represents the real-time electricity price at time t, C q represents the cost of power abandonment, Q represents the amount of power abandoned, G t represents the real-time electricity purchase amount at time t, T represents the preset time scale, C tf represents the real-time natural gas price at time t, F k represents the natural gas combustion amount when the kth boiler equipment is generating electricity, λ k represents the priority parameter of the kth boiler equipment, and introduces (1-λ k ) is used to ensure that boiler equipment with higher priority parameters is used first, K represents the total number of boiler equipment, a j represents the number of charge and discharge cycles of the jth electric vehicle, C e represents the battery life loss cost under each charge and discharge cycle, λ j represents the priority parameter of the jth electric vehicle, and introduces (1-λ j ) is used to ensure that electric vehicles with higher priority parameters are used first, and J represents the total number of electric vehicles.

[0107] Among them, the electricity cost includes a variety of cost factors, such as pre-purchase price, real-time electricity price, abandoned electricity cost, natural gas price, electric vehicle charging and discharging cycle loss cost, etc. This comprehensively considers the various costs of the park's energy operation, making energy scheduling more comprehensive and accurate.

[0108] It should be noted that since the purpose of the objective function is to minimize the function value, during the charging process, the candidate equipment with a larger priority parameter should be used first, and the value range of the priority parameter is 0 to 1. Therefore, 1-λ is used to make the candidate equipment with a larger priority parameter compatible with the minimum function value. By introducing the priority parameter, it can ensure that the candidate equipment with higher priority is used more effectively during the energy scheduling process, improve the power efficiency and supply and demand balance of the park, and thus achieve the goal of minimizing the power cost.

[0109] The specific supply-demand balance is:

[0110]

[0111] Among them, τ represents the balance between supply and demand, T represents the preset time scale, and R t It represents the supply-demand ratio at time t. When the supply-demand ratio is 1, it is the optimal state, and the supply-demand balance degree τ=0.

[0112] It should be noted that the supply-demand balance can be used as an indicator to help evaluate the supply-demand balance of the park energy system at different times. When the supply-demand balance is small or close to zero, it means that the supply-demand balance is good and the system supply and demand are basically balanced; when the supply-demand balance is large, it means that the supply-demand imbalance is large and there may be an oversupply or undersupply of energy. Therefore, in the process of determining the energy scheduling plan, the goal should be to minimize the supply-demand balance.

[0113] Furthermore, by monitoring the balance between supply and demand, the imbalance between supply and demand of the energy system can be discovered in time, thus guiding the decision-making of energy dispatch. For example, when a large imbalance between supply and demand is found, the supply and demand balance can be achieved by adjusting the charging and discharging strategy, increasing or decreasing the amount of electricity purchased from the national grid, or adjusting the operating status of the standby equipment.

[0114] The specific supply-demand ratio is:

[0115]

[0116] Among them, R represents the supply and demand ratio at time t, X it represents the power supply of the i-th power supply at time t, S represents the power supply set, and Y jt represents the power consumption of the jth power-consuming device at time t, D represents the set of power-consuming devices, L t Represents the net load at time t.

[0117] S702: Setting constraints for energy scheduling.

[0118] Optionally, the constraints specifically include:

[0119] Load balancing guarantee constraints are used to adjust the pre-purchased power so that, ideally, the pre-purchased power can meet the power demand of pure power-consuming devices:

[0120] 1<α≤α max

[0121] Among them, α represents the load balancing guarantee coefficient, α max Indicates the maximum load balancing guarantee factor.

[0122] Battery output power constraint is used to ensure that the battery can meet its capacity limit when supplying power, avoiding over-discharge or operation beyond capacity:

[0123] 0≤P bat ≤C bat

[0124] Among them, P bat Indicates the battery output power, C bat Indicates the battery capacity.

[0125] Battery energy storage constraints are used to ensure that the battery does not exceed its capacity range during charging and discharging, ensuring the safe and efficient operation of the battery:

[0126] 0≤C st ≤C f

[0127] Among them, C st Indicates the battery storage energy, C f Represents photovoltaic power generation.

[0128] Boiler equipment output power constraints are used to ensure that the boiler equipment does not exceed its load capacity during power generation, avoiding low power generation efficiency or equipment damage:

[0129] 0≤P g ≤P gmac

[0130] Among them, P g Indicates the output power of boiler equipment, P gmax Indicates the maximum output power of the boiler equipment.

[0131] Electric vehicle charging and discharging constraints are used to ensure that electric vehicles do not exceed the rated power range of their charging or discharging piles during the charging and discharging process, ensuring the safety and effectiveness of charging and discharging:

[0132] 0≤P ec ≤P cmax

[0133] 0≤P ed ≤P dmax

[0134] Among them, P ec Indicates the discharge power of electric vehicles, P cmax Indicates the maximum discharge power of electric vehicles, P ed Indicates the charging power of electric vehicles, P dmax Indicates the maximum charging power of an electric vehicle.

[0135] The above constraints play a limiting and guiding role in the energy dispatch of the park, ensuring the safe, stable and effective operation of the energy system. Through these constraints, power resources can be reasonably allocated, equipment operation can be controlled, the power demand of the park can be met to the greatest extent, and energy costs and energy waste can be reduced while ensuring the quality of power supply.

[0136] S703: Under the constraints of the constraints, the optimal energy scheduling plan is determined with the goal of minimizing the function value of the objective function.

[0137] Optionally, S703 specifically includes: under the constraints of the constraints, with the goal of minimizing the function value of the objective function, determining the optimal energy scheduling plan through an improved particle swarm optimization algorithm.

[0138] Among them, the Particle Swarm Optimization (PSO) algorithm is an optimization algorithm based on swarm intelligence, inspired by the behavior of groups such as bird flocks or fish schools. The algorithm simulates the movement of individuals (particles) in the solution space and uses the collaboration and information sharing between individuals to search for the optimal solution. The PSO algorithm is usually used to solve continuous optimization problems. It is simple and easy to implement, has good global search capabilities and fast convergence speed, so it is widely used in many fields.

[0139] However, the traditional particle swarm optimization algorithm has a "clustering effect" during the search process, which causes particles to tend to move towards the local optimal solution, which may miss the global optimal solution and easily fall into the local optimal solution. The improved particle swarm optimization algorithm proposed in the present invention introduces the crossover and mutation operations of the genetic algorithm to solve the problems of the traditional particle swarm optimization algorithm. The specific execution methods include:

[0140] Initialize the particle swarm, which contains multiple particles. The location of each particle represents a feasible energy scheduling plan. The energy scheduling plan includes: how much electricity to purchase from the national grid in real time and how to charge and discharge each candidate device, etc.

[0141] The inverse of the objective function is used as the fitness function of the particle swarm.

[0142] Calculate the fitness value of each particle in the particle swarm.

[0143] According to the fitness value of each particle, the individual optimal position and the global optimal position are determined.

[0144] Among them, the individual optimal position refers to the optimal position experienced by each particle itself, that is, the optimal solution reached by the particle during the search process. The global optimal position is the optimal position currently experienced by the entire particle group, that is, the position with the best fitness among all particles.

[0145] Generate a random number and compare it with the crossover probability p e Compare the size, if the random number is less than the crossover probability p e , then the position of the current particle is cross-operated with the individual optimal position to update the position of the current particle:

[0146] Z new =rZ+(1-r)Z best

[0147] Among them, Znew represents the updated position of the current particle, Z represents the original position of the current particle, r represents a random number between 0 and 1, and Z best represents the optimal position of an individual.

[0148] It should be noted that the crossover operation allows information to be transmitted between individuals. The current particle can obtain information about a better solution by crossing with the optimal position of the individual. By crossing the position of the current particle with the optimal position of the individual, the diversity of the population can be increased to a certain extent, the convergence speed of the algorithm can be accelerated, information transmission can be promoted, and exploration can be increased, thus helping to find a better solution faster.

[0149] If the fitness value of the updated current particle is greater than the individual optimal position, the individual optimal position is replaced by the position of the current particle.

[0150] Among them, the crossover probability P e Specifically:

[0151]

[0152] Among them, P e represents the crossover probability, P e,max represents the maximum crossover probability, P e,min represents the minimum crossover probability, δ represents the fitness value of the current particle, δ avg represents the average fitness value of the population, δ max Represents the maximum fitness value among the particles.

[0153] It should be noted that the crossover probability is dynamically adjusted by comparing the fitness value of the current particle with the average fitness value of the population. When the overall fitness of the population is high, it means that the current solution is close to the global optimal solution. At this time, reducing the crossover probability is conducive to maintaining diversity and avoiding premature convergence. When the fitness is low, it means that the search space has not been fully explored. At this time, increasing the crossover probability helps to speed up the search process.

[0154] Continue to generate a new random number and compare it with the crossover probability P e Compare the size, if the random number is less than the crossover probability P e , then the current particle position is cross-operated with the global optimal position to update the current particle position:

[0155] Z new =rZ+(1-r)P best

[0156] Among them, Z new represents the updated position of the current particle, Z represents the original position of the current particle, r represents a random number between 0 and 1, and P bestrepresents the global optimal position.

[0157] It should be noted that the crossover operation allows information to be transmitted between individuals. The current particle can obtain information about a better solution by crossing with the global optimal position. By crossing the current particle position with the global optimal position, the diversity of the population can be increased to a certain extent, the convergence speed of the algorithm can be accelerated, information transmission can be promoted, and exploration can be increased, thus helping to find a better solution faster.

[0158] If the fitness value of the updated current particle is greater than the individual optimal position, the individual optimal position is replaced by the position of the current particle.

[0159] Continue to generate a new random number and compare it with the mutation probability P m Compare the size. If the random number is less than the mutation probability P, m , then the position of the current particle is mutated and updated:

[0160]

[0161] Among them, Z new represents the updated position of the current particle, Z represents the original position of the current particle, r represents a random number between 0 and 1, and Z best represents the optimal position of an individual, Z worst Indicates the worst position of an individual.

[0162] It should be noted that mutation operation helps to prevent particles from falling into the local optimal solution. When the algorithm falls into the local optimal solution, mutation operation can give individual particles a chance to jump out of the local optimal solution and continue to explore the search space.

[0163] Among them, the mutation probability P m Specifically:

[0164]

[0165] Among them, P m represents the mutation probability, P m,max represents the maximum mutation probability, P m,min represents the minimum mutation probability, δ represents the fitness value of the current particle, and δ avg represents the average fitness value of the population, δ max Represents the maximum fitness value among the particles.

[0166] It should be noted that the mutation probability is dynamically adjusted by comparing the fitness value of the current particle with the average fitness value of the population. When the overall fitness of the population is high, it means that the current solution is close to the global optimal solution. At this time, reducing the mutation probability is conducive to maintaining diversity and avoiding premature convergence. When the fitness is low, it means that the search space has not been fully explored. At this time, increasing the mutation probability helps to speed up the search process.

[0167] If the fitness value of the updated current particle is greater than the individual optimal position, the individual optimal position is replaced by the position of the current particle.

[0168] Update the global optimal position.

[0169] Determine whether the number of iterations has reached the maximum number of iterations. If so, output the energy scheduling solution represented by the position of the particle with the highest fitness value. Otherwise, return to continue iterating.

[0170] In the present invention, the optimal energy scheduling scheme is determined by an improved particle swarm optimization algorithm, which can comprehensively consider the constraints and objective functions, efficiently search for the global optimal solution, and provide economic and feasibility guarantees for the operation of the actual energy system, which has important practical significance and application prospects.

[0171] S704: Provide power to the park according to the optimal energy scheduling plan.

[0172] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0173] (1) In the present invention, by predicting the power load and photovoltaic power generation, and determining the pre-purchased electricity from the State Grid based on the predicted power load and photovoltaic power generation, the park can better match power supply and demand, help avoid excess or insufficient power supply, optimize energy scheduling plans, and reduce costs.

[0174] (2) In the present invention, the pre-purchased electricity and photovoltaic power generation are used for power supply first, which helps to reduce the energy cost of the park. Then, with the goal of minimizing the electricity cost and balancing the supply and demand, the standby equipment is dispatched for charging and discharging to supply power to the park. Through reasonable scheduling and utilization, the balance of supply and demand can be better achieved. During peak hours of electricity consumption, photovoltaic power generation and pre-purchased electricity are used for power supply first to reduce dependence on the national power grid. When photovoltaic power generation is insufficient or electricity consumption is low, the standby equipment can be reasonably dispatched for charging and discharging to ensure the balance and stability of the power supply, improve the stability and reliability of the energy supply in the park, and ensure production and operation.

[0175] Reference Manual Attached Figure 4 , showing a structural schematic diagram of a park energy dispatching system based on supply and demand balance provided by the present invention.

[0176] The present invention further provides a park energy dispatching system 20 based on supply and demand balance, which is applied to the above-mentioned park energy dispatching method based on supply and demand balance, and comprises:

[0177] Processor 201.

[0178] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, a campus energy scheduling method based on supply-demand balance as in the method embodiment is implemented.

[0179] The park energy dispatching system 20 based on supply and demand balance provided by the present invention can execute the above-mentioned park energy dispatching method based on supply and demand balance and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0180] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0181] (1) In the present invention, by predicting the power load and photovoltaic power generation, and determining the pre-purchased electricity from the State Grid based on the predicted power load and photovoltaic power generation, the park can better match power supply and demand, help avoid excess or insufficient power supply, optimize energy scheduling plans, and reduce costs.

[0182] (2) In the present invention, the pre-purchased electricity and photovoltaic power generation are preferentially used for power supply, which helps to reduce the energy cost of the park. Afterwards, with the goal of minimizing the electricity cost and balancing the supply and demand, the standby equipment is scheduled for charging and discharging to supply power to the park. Through reasonable scheduling and utilization, the balance of supply and demand can be better achieved. During peak electricity consumption periods, photovoltaic power generation and pre-purchased electricity are preferentially used for power supply to reduce dependence on the national power grid. When photovoltaic power generation is insufficient or electricity consumption is at a low point, the standby equipment can be reasonably scheduled for charging and discharging to ensure the balance and stability of the power supply, improve the stability and reliability of the park's energy supply, and ensure production and operation.

[0183] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0184] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0185] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0186] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0187] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0188] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0189] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0190] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0191] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0192] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0193] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0194] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0195] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for energy scheduling in a park based on supply and demand balance as described in the method embodiment is implemented.

[0196] A computer-readable storage medium provided by the present invention can implement the steps and effects of the park energy scheduling method based on supply and demand balance in the above method embodiment. To avoid repetition, the present invention will not go into details.

[0197] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0198] In the present invention, based on the sparse reconstruction algorithm, the abnormal score of the network transmission data is determined. When the abnormal score of the network transmission data is greater than the preset abnormal score, based on the K nearest neighbor algorithm, the abnormal category of the network transmission data is accurately determined, which can be competent for network abnormal behavior detection in any scenario, adapt to the evolution of security threats, and improve the adaptability and expansibility of network abnormal behavior analysis. The sparse reconstruction algorithm is combined with the K nearest neighbor algorithm. Only when the abnormal score of the network transmission data is greater than the preset abnormal score will further abnormal identification be performed, which can save computing resources, and the abnormal category identification by the K nearest neighbor algorithm can quickly and accurately identify abnormal behavior and improve the accuracy of abnormal behavior detection. Once the abnormal category of the network transmission data is determined, the system can automatically perform corresponding bypass blocking according to the preset rules. This automated response capability enables the system to respond to abnormal situations quickly and effectively, reduces the need for manual intervention, and improves safety and efficiency. Compared with the traditional rule-based detection method, the bypass blocking system combining the sparse reconstruction algorithm and the K nearest neighbor algorithm can provide more multi-level and multi-dimensional network security protection, and has better comprehensiveness and accuracy when capturing and processing network anomalies.

[0199] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0200] There are a few points to note:

[0201] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0202] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0203] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0204] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A park energy scheduling method based on supply and demand balance, characterized in that: include: S1: Building a park energy network, which includes: the national power grid, photovoltaic power generation equipment, standby equipment and pure power-consuming equipment, wherein the standby equipment includes battery equipment, boiler equipment and electric vehicles in charging state; S2: Obtain the predicted power load; S3: Obtaining the predicted amount of photovoltaic power generation; S4: with the goal of balancing supply and demand, determining the amount of pre-purchased electricity purchased from the national grid according to the predicted amount of electricity load and the predicted amount of photovoltaic power generation; S5: Obtain device data of each candidate device in the current campus energy network environment; S6: Determine the priority parameter of each candidate device according to the device data of each candidate device; S7: giving priority to using the pre-purchased electricity and photovoltaic power generation for power supply, combining the priority parameters of the standby equipment, with the goal of minimizing electricity cost and balancing supply and demand, scheduling the standby equipment for charging and discharging to supply power to the park.

2. The park energy dispatching method based on supply and demand balance according to claim 1 is characterized in that: The S2 is specifically: The power load forecast is obtained through GRU-RNN.

3. The park energy dispatching method based on supply and demand balance according to claim 1 is characterized in that: The S3 is specifically: The predicted photovoltaic power generation is obtained through GRU-RNN.

4. The park energy dispatching method based on supply and demand balance according to claim 1 is characterized in that: The S4 is specifically: The pre-purchased electricity volume purchased from the national grid is determined according to the following formula: G=αY-G f Among them, G represents the pre-purchased electricity, Y represents the predicted electricity load, G f represents the predicted photovoltaic power generation, α represents the load balancing guarantee coefficient, α>1, and the load balancing guarantee coefficient is used to ensure that the pre-purchased electricity can meet the electricity demand of pure power-consuming equipment under ideal circumstances.

5. The park energy dispatching method based on supply and demand balance according to claim 4 is characterized in that: The S6 specifically includes: S601: Obtaining the current charging capacity and discharging capacity of each candidate device; S602: Calculate the priority parameters of each candidate device when charging according to the current charging capacity of each candidate device: Among them, λ ci represents the priority parameter of the i-th candidate device when charging, c i represents the current charging capacity of the i-th candidate device, and n represents the total number of candidate devices; S603: Calculate the priority parameters of each candidate device when discharging according to the current discharge capacity of each candidate device: Among them, λ fi represents the priority parameter of the discharge of the i-th candidate device, f i Represents the current discharge capacity of the i-th candidate device.

6. The park energy dispatching method based on supply and demand balance according to claim 1 is characterized in that: The S7 specifically includes: S701: construct an objective function with the goal of minimizing electricity cost and balancing supply and demand; S702: Setting constraints for energy scheduling; S703: Under the constraint of the constraint condition, with the minimum function value of the objective function as the goal, determine the optimal energy scheduling plan; S704: Power the park according to the optimal energy scheduling plan.

7. The park energy dispatching method based on supply and demand balance according to claim 6 is characterized in that: The objective function is specifically: F(X)=min(μ1C+μ2τ) Wherein, F() represents the objective function, X represents the energy dispatching scheme, which includes the amount of electricity purchased from the national grid in real time and how each candidate device is charged and discharged, min represents the minimum value, C represents the electricity cost, τ represents the balance between supply and demand, μ1 represents the weight coefficient of the electricity cost, and μ2 represents the weight coefficient of the balance between supply and demand; The electricity cost is as follows: Among them, C represents the electricity cost, C g represents the pre-order price, G represents the pre-order power, C tg represents the real-time electricity price at time t, C q represents the cost of power abandonment, Q represents the amount of power abandoned, G t represents the real-time electricity purchase amount at time t, T represents the preset time scale, C tf represents the real-time natural gas price at time t, F k represents the natural gas combustion amount when the kth boiler equipment is generating electricity, λ k represents the priority parameter of the kth boiler equipment, and introduces (1-λ k ) is used to ensure that boiler equipment with higher priority parameters is used first, K represents the total number of boiler equipment, a j represents the number of charge and discharge cycles of the jth electric vehicle, C e represents the battery life loss cost under each charge and discharge cycle, λ j represents the priority parameter of the jth electric vehicle, and introduces (1-λ j ) is used to ensure that electric vehicles with higher priority parameters are used first, and J represents the total number of electric vehicles; The supply-demand balance is specifically: Among them, τ represents the balance between supply and demand, T represents the preset time scale, and R t It represents the supply-demand ratio at time t. When the supply-demand ratio is 1, it is the best state, and the supply-demand balance degree τ=0; The specific supply-demand ratio is: Among them, R represents the supply and demand ratio at time t, X it represents the power supply of the i-th power supply at time t, S represents the power supply set, and Y jt represents the power consumption of the jth power-consuming device at time t, D represents the set of power-consuming devices, L t Represents the net load at time t.

8. The park energy dispatching method based on supply and demand balance according to claim 6 is characterized in that: The constraints specifically include: Load balancing guarantee constraints: 1<α≤α max Among them, α represents the load balancing guarantee coefficient, α max Indicates the maximum load balancing guarantee factor; Battery output power constraints: 0≤P bat ≤C bat Among them, P bat Indicates the battery output power, C bat Indicates the battery capacity; Battery energy storage constraints: 0≤C st ≤C f Among them, C st Indicates the battery storage energy, C f Represents photovoltaic power generation; Boiler equipment output power constraints: 0≤P g ≤P gmax Among them, P g Indicates the output power of boiler equipment, P gmax Indicates the maximum output power of the boiler equipment; Electric vehicle charging and discharging constraints: 0≤p ec ≤p cmax 0≤p ed ≤P dmax Among them, P ec Indicates the discharge power of electric vehicles, P cmax Indicates the maximum discharge power of electric vehicles, P ed Indicates the charging power of electric vehicles, P dmax Indicates the maximum charging power of an electric vehicle.

9. The park energy dispatching method based on supply and demand balance according to claim 6 is characterized in that: The S703 is specifically as follows: Under the constraints of the constraints, with the goal of minimizing the function value of the objective function, the optimal energy scheduling plan is determined through an improved particle swarm optimization algorithm.

10. A park energy dispatching system based on supply and demand balance, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, a park energy scheduling method based on supply-demand balance as described in any one of claims 1 to 9 is implemented.

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