Virtual power plant optimization scheduling method based on whale algorithm

Abstract

The invention provides a virtual power plant optimal scheduling method based on a whale algorithm. The method comprises the steps that an electric heating comprehensive system economic scheduling target with the lowest comprehensive operation cost as the target is determined; constructing constraint conditions of the virtual power plant optimization scheduling model; the whale algorithm is used to solve the economic dispatch target; and analyzing a model result, and evaluating the operation benefit of the virtual power plant. In order to solve the problems that virtual power plant optimization scheduling is high in calculation complexity, low in convergence speed and prone to falling into a local optimal solution, the whale algorithm scheme is adopted, the convergence speed and precision of the algorithm are improved through natural predation behaviors simulated by the whale algorithm and optimization of contraction factors and expansion factors, the calculation time is effectively shortened, and the optimal scheduling efficiency is improved. And the adaptability and the flexibility of the algorithm are enhanced through the shrinkage factor and the expansion factor which can be dynamically adjusted, so that the effects of optimizing the operation cost of the power system, improving the resource configuration efficiency and enhancing the stability of the power grid are achieved, and the development of sustainable energy is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and scheduling, and in particular to a virtual power plant optimization and scheduling method based on a whale algorithm. Background Art

[0002] With the rapid development of renewable energy, virtual power plants (VPPs) as a new type of energy management system are playing an increasingly important role in power systems. Virtual power plants integrate distributed energy resources, such as photovoltaic power stations, CHP units, electric vehicles, energy storage systems, etc., to achieve optimal resource allocation and scheduling, and improve the operating efficiency and economy of power systems.

[0003] At present, the optimization scheduling methods of virtual power plants mainly include traditional optimization methods, intelligent optimization algorithms, etc. Traditional optimization methods have high computational complexity and slow convergence speed; while intelligent optimization algorithms, such as genetic algorithms and particle swarm algorithms, have high computational efficiency but are prone to fall into local optimal solutions. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a virtual power plant optimization scheduling method based on the whale algorithm, which provides an efficient and accurate optimization scheduling strategy for virtual power plants by adopting the whale algorithm, thereby improving the overall performance and economic benefits of virtual power plants in modern power systems. Solve the problems raised in the above background technology.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: a virtual power plant optimization scheduling method based on whale algorithm, comprising:

[0006] S1. Establish the economic dispatch target of the electric and thermal integrated system with the lowest comprehensive operating cost as the goal;

[0007] S2. Construct the constraints of the virtual power plant optimization dispatch model to ensure that the virtual power plant can quickly respond to system failures, keep the units of the virtual power plant within the safe and efficient operation range, and smoothly respond to load changes to ensure the stable operation of the power system;

[0008] S3. Apply the whale algorithm to solve the economic dispatch target, which performs the following operations: first, initialize the whale algorithm parameters to simulate the power plant load and photovoltaic output under different operating conditions; and initialize the equipment parameters of the virtual power plant to ensure the accuracy of the basic data of the model; second, determine the current optimal operation strategy of the power plant through fitness evaluation and leader selection, simulate the whale's behavior of surrounding prey, and construct a weighted distance formula to update the distance between individuals in the prey surrounding stage to simulate the whale's search behavior; third, in the spiral bubble network attack stage, update the individual position by constructing a spiral equation, and perform boundary processing to ensure the legitimacy of solving the economic dispatch target, and update the population to retain the dominant solution; finally, after each iteration, determine whether to continue or end the iteration according to the preset termination condition. If the termination condition is not met, return to the above steps and iterate until the optimal operation strategy is found;

[0009] S4. Analyze the model results and evaluate the operating benefits of the virtual power plant.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] In order to solve the problems of high computational complexity, slow convergence speed and easy falling into local optimal solutions in the prior art of virtual power plant optimization scheduling, the present invention adopts the whale algorithm solution, which significantly improves the ability to search for the global optimal solution through its simulated natural predation behavior. At the same time, by optimizing the contraction factor and expansion factor, the convergence speed and accuracy of the algorithm are improved, the calculation time is effectively shortened, and the dynamically adjustable contraction factor and expansion factor enhance the adaptability and flexibility of the algorithm, thereby achieving the effect of optimizing the operating cost of the power system, improving resource allocation efficiency and enhancing the stability of the power grid, and promoting the development of sustainable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0013] Figure 1 A schematic diagram of a framework structure of a virtual power plant proposed in one embodiment of the present invention;

[0014] Figure 2 A schematic diagram of a flow chart of a virtual power plant optimization scheduling method proposed in one embodiment of the present invention;

[0015] Figure 3 The figure is a schematic diagram of the flow of solving a model based on the whale algorithm proposed in one embodiment of the present invention. DETAILED DESCRIPTION

[0016] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0017] The present invention is further described in detail below with reference to the accompanying drawings, but is not intended to limit the present invention.

[0018] As an understanding of the technical concept and implementation principle of the present invention, the virtual power plant optimization scheduling method based on the whale algorithm proposed in the present invention is mainly aimed at solving the problems of high computational complexity, slow convergence speed and easy to fall into local optimal solution in the virtual power plant optimization scheduling in the prior art, which is mainly reflected in:

[0019] 1. The existing calculation needs to comprehensively consider the scheduling strategies of multiple energy resources, meet various operating constraints, and achieve the objective function of maximizing cost-effectiveness. These factors lead to the large scale of optimization problems and cumbersome calculation processes;

[0020] 2. The problems of slow convergence and easy falling into local optimal solutions are reflected in the fact that traditional optimization algorithms and some intelligent optimization algorithms require a large number of iterations to find solutions when dealing with complex virtual power plant scheduling problems. These solutions may not be globally optimal, but are trapped in local optimal areas, which limits the optimization quality and efficiency of the scheduling scheme.

[0021] Therefore, if Figure 1 - Figure 3 As shown, as an embodiment of the present invention, a virtual power plant optimization scheduling method based on a whale algorithm is proposed, comprising the steps of:

[0022] S1. Establish the economic dispatch target of the electric-thermal integrated system with the lowest comprehensive operating cost as the goal. It should be noted that the constructed economic dispatch target is an objective function that includes the start-up and shutdown costs of the cogeneration unit, the cost of power generation and heating, the dispatch cost of interruptible loads, the compensation cost of electric vehicle owners participating in the dispatch, and the cost of treating pollutants;

[0023] S2. Construct the constraints of the virtual power plant optimization dispatch model to ensure that the virtual power plant can quickly respond to system failures, keep the units of the virtual power plant within the safe and efficient operation range, and smoothly respond to load changes to ensure the stable operation of the power system;

[0024] S3. Apply the whale algorithm to solve the economic dispatch target, which performs the following operations:

[0025] First, initialize the Whale algorithm parameters to simulate the power plant load and photovoltaic output under different operating conditions; and initialize the equipment parameters of the virtual power plant to ensure the accuracy of the basic data of the model;

[0026] Secondly, through fitness evaluation and leader selection, the optimal operation strategy of the power plant is determined to simulate the behavior of whales surrounding their prey. In the stage of surrounding their prey, a weighted distance formula is constructed to update the distance between individuals and simulate the search behavior of whales.

[0027] Thirdly, in the spiral bubble network attack phase, the individual positions are updated by constructing the spiral equation, while boundary processing is performed to ensure the legitimacy of solving the economic dispatch target, and the population is updated to retain the dominant solution;

[0028] Finally, after each iteration, it is determined whether to continue or end the iteration according to the preset termination condition. If the termination condition is not met, for example, the objective function value converges to a certain degree, the above steps are returned to iterate repeatedly until the optimal operation strategy is found.

[0029] S4. Analyze the model results and evaluate the operating benefits of the virtual power plant.

[0030] like Figure 1 As shown, based on the above technical concept, it can be understood that the virtual power plant takes into account the optimal scheduling of virtual power plants including electric vehicles, energy storage batteries, photovoltaic power generation units, multi-stage loads, heat storage tanks, and cogeneration units. The incentive demand response (IDR) considered mainly guides the flexible load on the user side to change its operating mode by compensating for the cost, and then participates in the optimal scheduling of the combined electric and thermal system.

[0031] The above-mentioned economic dispatch objective function is the net profit of the VPP within one trading day taking into account the IDR, including load energy supply income, heat sales income, power generation and heating costs of the cogeneration unit, environmental protection costs, flexible loads and compensation costs of electric vehicles. However, since the cost of building PV units, heat storage tanks, EB and other equipment is fixed and the daily maintenance cost is relatively small, it is negligible in this embodiment.

[0032] The constraints take into account power balance constraints, heat balance constraints, EV-related constraints, transferable and interruptible load constraints, thermal system spinning reserve constraints, unit output upper and lower limit constraints, and unit ramp rate constraints, making the established virtual power plant model more in line with the actual situation.

[0033] In one embodiment of the present invention, the process of ensuring the accuracy of the basic data of the whale algorithm model is as follows:

[0034] First, set the population size N to generate a corresponding number of individuals, where each individual needs to integrate the output of the photovoltaic power station CHP unit output Electric vehicle charging and discharging status and power chr,t ,d dis,t ; Energy storage system charging and discharging power Battery energy storage charging and discharging power and power consumption Transferable load P t trant and interruptible load capacity operating parameter P t DR,d ;

[0035] Secondly, the number of search steps of the whale algorithm is planned by setting the number of iterations L, and the contraction factor a is dynamically adjusted to control the local search accuracy in this search process according to the maximum number of iterations Lmax. At the same time, the expansion factor b is set to 1 to control the distance at which the whale spiral bubble net attacks the prey, so as to ensure that the whale algorithm has a stable global search capability. It can be understood that the contraction factor L max is the maximum number of iterations;

[0036] Finally, the number of scenarios ω is determined to cover the predicted changes in load and PV output, and the scenario probability P is calculated based on historical data or prediction models to reflect the actual probability of each scenario, so as to find the most cost-effective scheduling plan. During specific implementation, the number of scenarios ω considered in the model is determined. Each scenario represents different load forecasts, PV output forecasts and other information, and representative typical days are selected as scenarios. When calculating the scenario probability P, the probability of each scenario is determined, which is usually estimated based on historical data or prediction models.

[0037] In the process of initializing the virtual power plant equipment parameters, the process of initializing the virtual power plant equipment parameters is as follows:

[0038] First, the number and capacity of photovoltaic power stations need to be set, and the corresponding power curve needs to be defined for each power station to describe its power generation capacity; secondly, the parameters of the CHP unit need to be configured, and the configuration parameters include at least the statistical number, set capacity and power curve, and the thermal efficiency and pollutant emissions also need to be determined; thirdly, the parameters of the electric vehicle EV need to be configured, and the configuration parameters include at least the number, battery capacity, and the setting of the charging and discharging power and state of charge limits; at the same time, the setting parameters of the energy storage system ESS need to be initialized for the number, capacity, charging and discharging power limit and state of charge limit; from then on, the parameters of the battery energy storage EB need to be configured, and the configuration parameters include at least the number, capacity, power curve and thermal efficiency; finally, the operating parameters need to be set, and the operating parameters include at least the time-of-use electricity price, heating price, pollutant emission penalty cost, transfer load adjustment cost, interruption load subsidy cost and other cost parameters, and other cost parameters such as equipment operation and maintenance costs. Through the above steps, it is ensured that the optimization scheduling model is based on accurate and reliable basic data when performing algorithm search and decision-making, thereby improving the effectiveness and practicality of the optimization results.

[0039] In one embodiment of the present invention, by comprehensively considering the economic performance of the virtual power plant, such as operating costs, benefits, and operating constraints, such as constraints including thermal system spinning reserve constraints, CHP unit output upper and lower limits constraints, and unit ramp rate constraints, the objective function established evaluates the pros and cons of each individual on multiple objectives to complete the fitness evaluation step. In specific implementation, multiple objectives refer to multiple aspects that need to be considered and optimized simultaneously when determining the economic dispatch objective function, including at least the lowest comprehensive operating cost, optimal resource allocation, system stability, environmental sustainability, and environmental sustainability.

[0040] It should be noted that completing the fitness assessment process ensures that the system has sufficient backup capacity when facing failures or load fluctuations, while ensuring that the unit output is maintained within a safe range and can smoothly respond to load changes. The ultimate goal is to quantify the individual's degree of adaptation to these goals and provide a decision-making basis for selecting the optimal operating strategy.

[0041] The above-mentioned minimum comprehensive operating cost refers to the economic dispatch objective function in minimizing the overall operating cost of the virtual power plant, which includes the start-up and shutdown costs of the cogeneration unit, the cost of power generation and heating, the dispatching cost of interruptible loads, the compensation cost of electric vehicle owners participating in the dispatching, and the cost of system treatment of pollutants; optimal resource allocation refers to ensuring the reasonable allocation and use of various energy resources and equipment (such as photovoltaic power stations, CHP units, electric vehicles, energy storage systems, etc.) in the virtual power plant to maximize energy efficiency; system stability refers to ensuring that the virtual power plant can respond quickly to system failures or load fluctuations and maintain stable operation of the power system by satisfying the rotating reserve constraints of the thermal system, the upper and lower limits of the unit output, and the unit ramp rate constraints; environmental sustainability refers to considering reducing pollutant emissions and promoting the development of sustainable energy during the dispatching process, which involves evaluating the treatment costs of pollutants such as nitrogen oxides emitted by CHP units; economic benefits refer to pursuing the maximization of the economic benefits of the virtual power plant while meeting the above conditions, including attracting electric vehicle owners to participate in the dispatching through incentive mechanisms, and optimizing the power market participation strategy through energy storage systems and demand response.

[0042] In one embodiment of the present invention, the individual with the highest fitness after completing the fitness evaluation is selected as the leader to represent the current optimal operation strategy. At the same time, an individual is randomly selected as the prey, and the distances between other individuals in the population and the prey and the leader are updated using the weighted distance formula:

[0043]

[0044] Where W i (i=1, 2, ..., 7) represents the weight of the parameters contained in each individual; X and Y represent the two individuals whose distance needs to be calculated; It is expressed as the electric power of the CHP unit at time t; P t PV Indicates the actual output of the PV unit at time t; They are respectively expressed as EV charging and discharging power; Respectively represent the charging and discharging power of ESS; It is expressed as EB of electrical power consumption. It can be understood that in this way, the algorithm simulates the behavior of whales searching for prey, guides individuals to move to areas with better solutions, thereby increasing the probability of finding the global optimal solution and improving the search accuracy and efficiency of the algorithm, which not only optimizes the search process, but also improves the potential of virtual power plant operation benefits.

[0045] Based on the above technical concept, it can be understood that by using the weighted distance formula to update the distance between other individuals and prey and leaders, the algorithm can better simulate the behavior of whales when searching for prey, and at the same time update the position of individuals according to the distance and shrinkage factor, simulating the behavior of whales surrounding prey, so that the algorithm can gradually focus on the potential optimal solution during the search process, improve the accuracy and efficiency of the search, and such an introduction effect will help improve the performance of the whale algorithm in the optimization and scheduling of virtual power plants, so that it can find the optimal operation strategy more quickly, thereby improving the operating efficiency of virtual power plants. In specific implementation,

[0046] In the spiral bubble attack phase,

[0047] First, we need to use the distance and shrinkage factor a to update the position of each individual according to the distance between the current solution and the leader and prey:

[0048] X new =X old +a*D(X leader ,X old )+b*D(X prey ,X old )(2)

[0049] In the formula, X new , X old They represent the next generation individuals and the current generation individuals after the update respectively; X learder , X Prey Represented as leader and prey, respectively, it can be understood that this step helps the algorithm to focus the search for potential optimal solution areas by simulating the natural behavior of whales surrounding prey;

[0050] Secondly, for each individual, its new position in the search space needs to be calculated using the spiral equation:

[0051] X new =X old +D(X prey ,X old )*e bl cos(2πl)(3)

[0052] Where l is a random number between -1 and 1. It can be understood that the spiral equation usually involves a random number l, whose value is between -1 and 1, to ensure the randomness and exploration of the search process.

[0053] Thirdly, after checking all updated individual positions and ensuring that they are within the defined solution space and satisfy all constraints of economic dispatch, such as spinning reserve, output upper and lower limits, and ramp rate, etc., boundary processing adjustments are performed for individuals that exceed the boundary to ensure their legality;

[0054] Finally, when updating the population, based on the results of the fitness evaluation, the individuals with the highest fitness are selected to remain in the population, and the individuals with lower fitness are replaced to ensure that the overall quality of the population gradually improves and approaches the optimal solution.

[0055] As an embodiment of the present invention, the objective function formula is established as follows:

[0056] maxC margin =C income -C cost (4)

[0057] In the formula, C margin , C income , C cost Respectively represent the gross profit, revenue and cost of the virtual power plant VPP;

[0058] During specific implementation, the gross profit, revenue and cost of the virtual power plant VPP are calculated by comprehensively considering the power supply and heat supply income of the virtual power plant VPP in a certain period, the fuel and operating costs of the CHP units, the energy consumption and maintenance costs of the energy storage system and heat storage tanks, the electric vehicle charging and discharging scheduling costs, the costs incurred due to scheduling loads and interrupted loads, the environmental-related pollutant treatment costs, the marginal costs and battery losses in grid operations, and the inconvenience costs incurred by users due to participating in demand response.

[0059] The calculation formula is:

[0060]

[0061] In the formula, I load It represents the benefit obtained by VPP in supplying power to the load; I heat represents the heating revenue of VPP; C CHP represents the fuel cost of CHP unit; C EP represents the cost of VPP to treat pollutants; C EV Indicates the number of vehicles required to dispatch EV load; C ESS represents the operating cost of the energy storage power station and the heat storage tank; C EB Represents the operation and maintenance cost of the electric boiler; C DR1 , C DR2 They represent the costs required for dispatching load and interrupting load respectively.

[0062]

[0063] In the formula, E t load1 、E t load2 It indicates the remaining load of primary and secondary users after interrupting part of the load; p tL1 ,p t L2 Indicates the electricity cost of each level of load.

[0064] I heat =H t load p heat (7)

[0065] In the formula, H t load represents the total heat output of VPP; p heat Represents the heat output cost.

[0066]

[0067] In the formula, C MG , C bat represents the marginal cost of the power grid and the battery loss cost; C UD Indicates user inconvenience cost Indicates the charging and discharging power transferred between the EV and the grid.

[0068] Since photovoltaic power generation is clean and thermal power units are equipped with desulfurization devices, the cost of cleaning up pollutants only needs to consider the pollution cost caused by nitrogen oxides emitted by thermal power units:

[0069]

[0070] In the formula, Indicates the heat output of CHP; N CHP Represents the total number of CHP units; α z , β z , γ z , z Indicates the relevant parameters of CHP nitrogen oxide emissions.

[0071]

[0072] In the formula, e i (i=1, 2, ..., 5) represents the energy cost coefficient of CHP; They represent the electrical power and thermal power of CHP at time t respectively.

[0073]

[0074] Where P TOU，t represents the time-of-use electricity price at time t; c hs represents the output cost of the heat storage tank; Indicates the charging and discharging power of ESS; Indicates the charging and discharging heat power of the heat storage tank.

[0075]

[0076] In the formula, c eb It represents the unit output cost of EB; Represents the output consumed by EB at time t.

[0077] C DR1 =P t trans *C t trans (13)

[0078] C DR2 =P t DR,d *C t DR (14)

[0079] Where P t trans , P t DR,d Indicates the amount of load transfer and interruption; C t trans , C t DR It represents the unit load call cost and the electricity price discount of the interrupted load.

[0080] As an embodiment of the present invention, in order to ensure the safe and stable operation of the VPP, relevant operation constraints must be met. In specific implementation, the operation constraints include charge and discharge power balance constraints: ensuring that the charging and discharging power of the energy storage system is balanced at any time to avoid grid frequency fluctuations; thermal balance constraints: ensuring the balance between thermal energy supply and demand to meet the needs of cogeneration; electric vehicle (EV) constraints: covering the transfer power and charge state restrictions of electric vehicles, as well as the owner's expected charge state, to ensure the effective scheduling of EVs; transferable load constraints and interruptible load constraints: ensuring the effective implementation of demand response, by adjusting the load to respond to changes in grid demand; thermal system spinning reserve constraints: ensuring that there is sufficient reserve capacity to deal with system failures or load fluctuations to maintain system reliability; unit output upper and lower limit constraints: ensuring that the output power of the power generation and heating units is within the range of safety and efficiency, and unit ramp rate constraints: controlling the rate of change of the unit output power to ensure that the rate of change is within a safe range to avoid damage to the equipment.

[0081] In specific implementation, the charge and discharge power balance constraint is expressed as:

[0082]

[0083] Where P t PVIndicates the actual output of the PV unit at time t. By ensuring the balance of charging and discharging power, VPP can avoid grid frequency fluctuations caused by energy imbalance and maintain stable operation of the grid.

[0084] The thermal balance constraint is expressed as:

[0085]

[0086] In the formula, H t EB Indicates the value of EB converting electrical energy into thermal energy.

[0087] The EV constraint is expressed as:

[0088]

[0089] Where P t EV represents the transfer power of the electric vehicle at time t; Indicates the maximum transfer power of EV; SOC t Indicates the state of charge of the EV; SOC min , SOC max Indicates the minimum and maximum state of charge of an EV; SOC out , SOC demand Indicates the EV's state of charge at the end of charging and the owner's desired state of charge.

[0090] The transferable load constraint is expressed as:

[0091]

[0092] Where P t in_max , P t out_max It represents the maximum load that can be transferred in and out among users participating in demand response at time t.

[0093] The interruptible load constraint is expressed as:

[0094]

[0095] In the formula, Indicates the minimum and maximum values ​​of the interruptible load of the D-level load; It indicates the maximum power reduction that the adjustable load of level D can provide within two consecutive moments.

[0096] The thermal system spinning reserve constraint is expressed as:

[0097]

[0098] Where ΔH represents the steam enthalpy drop; Indicates the maximum heat release power of the heat storage tank; R s Indicates system spinning reserve.

[0099] The upper and lower limits of the unit output are expressed as:

[0100]

[0101]

[0102] In the formula, It represents the predicted active power output of the photovoltaic power station at time t; represents the maximum value of the electric power output by the jth CHP at time t; It represents the maximum thermal power output of the jth CHP at time t.

[0103] The unit ramp rate constraint is expressed as:

[0104]

[0105] In the formula, r up 、r down Indicates the climbing and sliding power of the PV unit per unit time; r CHP,up 、r CHP,down Indicates the climbing and ramping power of the CHP unit per unit time.

[0106] The technical scope of the present invention is not limited to the contents in the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. A virtual power plant optimization scheduling method based on the whale algorithm, characterized by: Includes steps: S1. Establish the economic dispatch target of the electric and thermal integrated system with the lowest comprehensive operating cost as the goal; S2. Construct the constraints of the virtual power plant optimization dispatch model to ensure that the virtual power plant can quickly respond to system failures, keep the units of the virtual power plant within the safe and efficient operation range, and smoothly respond to load changes to ensure the stable operation of the power system; S3. Apply the whale algorithm to solve the economic dispatch target, which performs the following operations: First, initialize the Whale algorithm parameters to simulate the power plant load and photovoltaic output under different operating conditions; and initialize the equipment parameters of the virtual power plant to ensure the accuracy of the basic data of the model; Secondly, through fitness evaluation and leader selection, the optimal operation strategy of the power plant is determined to simulate the behavior of whales surrounding their prey. In the stage of surrounding their prey, a weighted distance formula is constructed to update the distance between individuals and simulate the search behavior of whales. Thirdly, in the spiral bubble network attack phase, the individual positions are updated by constructing the spiral equation, while boundary processing is performed to ensure the legitimacy of solving the economic dispatch target, and the population is updated to retain the dominant solution; Finally, after each iteration, it is determined whether to continue or end the iteration according to the preset termination condition. If the termination condition is not met, the above steps are returned to iterate repeatedly until the optimal operation strategy is found. S4. Analyze the model results and evaluate the operating benefits of the virtual power plant.

2. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 1 is characterized in that: In step S3, the process of ensuring the accuracy of the model's basic data is as follows: First, the population size N is set to generate a corresponding number of individuals, wherein each individual needs to integrate the output of the photovoltaic power station, the output of the CHP unit, the charging and discharging status and power of the electric vehicle, the charging and discharging power of the energy storage system, the electric power consumed by the battery energy storage, the transferable load and the interruptible load operation parameters; Secondly, the number of search steps of the whale algorithm is planned by setting the number of iterations L, and the contraction factor a is dynamically adjusted to control the local search accuracy in this search process according to the maximum number of iterations Lmax. At the same time, the expansion factor b is set to 1 to ensure that the whale algorithm has a stable global search capability; Finally, the number of scenarios ω is determined to cover the predicted changes in load and PV output, and the scenario probability P is calculated based on historical data or prediction models to reflect the actual probability of occurrence of each scenario, so as to find the most cost-effective scheduling solution.

3. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 1 is characterized by: In step S3, the objective function is established by comprehensively considering the economic performance and operation constraints of the virtual power plant to evaluate the pros and cons of each individual on multiple objectives, completing the fitness evaluation step, wherein: The multiple objectives refer to multiple aspects that need to be considered and optimized simultaneously when determining the economic dispatch objective function, including at least the lowest comprehensive operating cost, optimal resource allocation, system stability, environmental sustainability, and environmental sustainability.

4. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 1 or 3 is characterized in that: The individual with the highest fitness after completing the fitness evaluation is selected as the leader to represent the current optimal operation strategy. At the same time, an individual is randomly selected as the prey, and the distance between other individuals in the population and the prey and the leader is updated using the weighted distance formula: Where W i (i=1, 2, ..., 7) represents the weight of the parameters contained in each individual; X and Y represent the two individuals whose distance needs to be calculated; It is expressed as the electric power of the CHP unit at time t; P t PV Indicates the actual output of the PV unit at time t; They are respectively expressed as EV charging and discharging power; Respectively represent the charging and discharging power of ESS; Indicates the electrical power consumed by EB.

5. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 1 or 2 is characterized in that: In the spiral bubble attack phase, First, we need to use the distance and shrinkage factor a to update the position of each individual according to the distance between the current solution and the leader and prey: X new =X old +a*D(X leader ,X old )+b*D(X prey ,X old ) Where, X new , X old They represent the next generation individuals and the current generation individuals after the update respectively; X learder , X Prey Represented as leader and prey respectively; Secondly, for each individual, its new position in the search space needs to be calculated using the spiral equation: X new =X old +D(X prey ,X old )*e bl cos(2πl) In the formula, l represents a random number in [-1, 1]; Thirdly, after checking all updated individual positions and ensuring that they are within the defined solution space and satisfy all constraints of economic dispatch, boundary processing adjustments are performed on individuals that exceed the boundary to ensure their legality. Finally, when updating the population, based on the results of the fitness evaluation, the individuals with the highest fitness are selected to remain in the population, and the individuals with lower fitness are replaced to ensure that the overall quality of the population gradually improves and approaches the optimal solution.

6. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 1 is characterized in that: In step S3, during the process of initializing the virtual power plant equipment parameters, First, the number and capacity of PV power plants need to be set, and the corresponding power curves need to be defined for each plant to describe its power generation capacity; Secondly, the parameters of the CHP unit are configured. The configuration parameters include at least statistical quantity, set capacity and power curve. The thermal efficiency and pollutant emissions must also be determined. Thirdly, the electric vehicle EV is configured with parameters including at least the quantity, battery capacity, and the setting of the charging and discharging power and state of charge limits; at the same time, the energy storage system ESS needs to initialize the setting parameters of quantity, capacity, charging and discharging power limit and state of charge limit; Next, the battery energy storage EB is configured with parameters, which include at least quantity, capacity, power curve and thermal efficiency; Finally, the operating parameters are set, which at least include time-of-use electricity prices, heating prices, pollutant emission penalty costs, load transfer adjustment costs, interrupted load subsidy costs and other cost parameters.

7. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 3 is characterized by: The objective function formula is as follows: maxC margin =C income -C cost In the formula, C margin , C income , C cost Respectively represent the gross profit, revenue and cost of the virtual power plant VPP, Among them, the gross profit, revenue and cost of the virtual power plant VPP are calculated by comprehensively considering the power supply and heat supply income of the virtual power plant VPP in a certain period, the fuel and operating costs of the CHP unit, the energy consumption and maintenance costs of the energy storage system and the heat storage tank, the electric vehicle charging and discharging scheduling costs, the costs generated by scheduling loads and interrupted loads, the environmental-related pollutant treatment costs, the marginal costs and battery losses in power grid operations, and the inconvenience costs incurred by users due to participating in demand response.

8. The virtual power plant optimization scheduling method based on the whale algorithm according to claim 3 is characterized by: The operating constraints include charge and discharge power balance constraints, heat balance constraints, electric vehicle (EV) constraints, transferable load constraints and interruptible load constraints, thermal system spinning reserve constraints, unit output upper and lower limit constraints, and unit climbing rate constraints.

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