Multi-target scheduling optimization method and system for virtual power plant to participate in electricity market

Abstract

The invention relates to the technical field of virtual power plants, in particular to a multi-target scheduling optimization method and system for a virtual power plant to participate in a power market, and the method comprises the steps: carrying out the dynamic integration of distributed energy, an adjustable load and an energy storage unit in the virtual power plant, and constructing a resource model; establishing a distributed resource identity authentication and data storage mechanism; constructing a multi-objective optimization function, comprehensively considering a plurality of objectives such as resource utilization rate, carbon emission and load balance, and designing a dynamic weight adjustment mechanism; calculating a multi-objective optimization result, determining the optimal output and operation state of virtual power plant resources, and generating a virtual power plant optimization scheduling strategy; and cooperative optimization of internal resource scheduling and external power grid scheduling of the virtual power plant is realized by combining a game theory and a swarm intelligence algorithm. According to the method, the utilization efficiency of distributed resources can be improved, source-network load-storage coordination and new energy consumption are promoted, and the flexibility and comprehensive benefits of a power system are enhanced.

Description

Technical Field

[0001] This application relates to the technical field of virtual power plants, and specifically to a multi-objective scheduling optimization method and system for virtual power plants to participate in the electricity market. Background Technique

[0002] As an emerging distributed resource aggregation and scheduling mode, a virtual power plant can better cope with the impacts brought by new energy access and user demand changes by integrating and optimizing distributed energy, adjustable loads, and energy storage units.

[0003] Currently, the distributed resources in virtual power plants are diverse in type and characteristics, lacking a unified modeling method and interoperability standard, making it difficult to achieve plug-and-play and flexible scheduling of resources. There are a large number of distributed resources, which are widely distributed, lacking a reliable identity authentication and data security mechanism, and there is a risk of false identity access and data tampering. The operation of virtual power plants involves multiple objectives and constraints, such as environmental protection and grid security, etc., and it is necessary to balance and optimize among multiple objectives. There is an interactive influence and game relationship between the internal resource scheduling of virtual power plants and the external grid scheduling, lacking an effective coordination and optimization mechanism, and it is difficult to achieve the coordinated interaction between virtual power plants and the grid.

[0004] In view of this, this application proposes a multi-objective scheduling optimization method and system for participating in the electricity market. Summary of the Invention

[0005] To achieve the above object, this application provides a multi-objective scheduling optimization method and system for virtual power plants to participate in the electricity market. The specific technical solutions are as follows: The multi-objective scheduling optimization method for virtual power plants to participate in the electricity market includes: Dynamically integrate distributed energy, adjustable loads, and energy storage units to construct a virtual power plant resource model; Establish a distributed resource identity authentication mechanism to authenticate the identity and store data of the distributed energy, adjustable loads, and energy storage units of the virtual power plant; Based on multiple objectives of resource utilization rate, carbon emissions, and load balance, construct a multi-objective optimization function, design a dynamic weight adjustment mechanism, and dynamically adjust the weights of optimization objectives according to grid scheduling requirements; Calculate the multi-objective optimization results of the virtual power plant, determine the output and operating status of the virtual power plant resources, and establish an optimized scheduling strategy for the virtual power plant in combination with grid scheduling requirements; Combine game theory and swarm intelligence algorithms to optimize the coordination between the internal resource scheduling of the virtual power plant and the external grid scheduling.

[0006] Preferably, the steps of constructing the virtual power plant resource model include: Obtain the distributed energy information in the virtual power plant, and establish a mathematical model based on the physical characteristics and operation constraints of the distributed energy. The distributed energy includes photovoltaic power plants, wind farms, and micro gas turbines; Obtain the adjustable load information in the virtual power plant, and establish a mathematical model based on the electricity consumption characteristics and regulation potential of the adjustable load. The adjustable load includes industrial production load and electric vehicle charging load; Obtain the energy storage unit information in the virtual power plant, and establish a mathematical model based on the energy storage characteristics and operation constraints of the energy storage unit. The energy storage unit includes battery energy storage system and flywheel energy storage system; Consider the physical characteristics, operation constraints, and interaction relationships of the internal resources of the virtual power plant, and construct a virtual power plant resource model.

[0007] Preferably, the distributed resource identity authentication mechanism includes a resource identity identification mechanism and a data deposit mechanism; The resource identity identification mechanism includes: establishing a unique identity for each distributed resource in the virtual power plant, and generating an identification code by performing a hash calculation on the resource characteristic information; The data deposit mechanism includes: organizing and storing the resource data using a Merkle tree structure to generate a data deposit record.

[0008] Preferably, the multi-objective optimization function includes a resource utilization optimization function, a carbon emission optimization function, and a load balance optimization function; The resource utilization optimization function is used to maximize the utilization rate of distributed energy, the adjustable load regulation rate, and the utilization rate of energy storage units; the carbon emission optimization function is used to minimize the carbon emission intensity per unit of electricity during the operation of the virtual power plant; the load balance optimization function is used to minimize the deviation between the actual operating power of the virtual power plant and the grid dispatching plan.

[0009] Preferably, the dynamic weight adjustment mechanism includes: Dynamically adjust the weight coefficient of the resource utilization target according to the real-time operating status of the internal resources of the virtual power plant; dynamically adjust the weight coefficient of the carbon emission target according to the requirements of the grid for renewable energy consumption; dynamically adjust the weight coefficient of the load balance target according to the volatility of the grid load.

[0010] Preferably, to calculate the multi-objective optimization result of the virtual power plant, an improved particle swarm optimization algorithm is used, introducing non-dominated sorting and crowding distance to obtain the Pareto optimal solution set of the target optimization result.

[0011] Preferably, according to the Pareto solution set of the multi-objective optimization of the virtual power plant, combined with the grid dispatching requirements and the operating characteristics of the virtual power plant, determine the optimal output and operating status of the virtual power plant resources.

[0012] Preferably, a virtual power plant optimal dispatching strategy model is constructed, with the goal of tracking the grid dispatching power and under the conditions of the output constraints of the virtual power plant resources and the energy storage SOC constraint, to generate the optimal output curve of the virtual power plant.

[0013] Preferably, the internal and external dispatching of the virtual power plant is coordinated and optimized, including: Construct an internal game model and an external game model to describe the game process of the internal resources of the virtual power plant and the game process between the virtual power plant and the grid dispatching respectively; use the swarm intelligence algorithm to realize the optimization of the internal resource dispatching of the virtual power plant and the optimization of the external dispatching response of the virtual power plant; and realize the collaborative solution of the optimization at the internal and external levels of the virtual power plant through the iterative interaction mechanism.

[0014] A multi-objective dispatching optimization system for a virtual power plant participating in the electricity market, which is used to implement the multi-objective dispatching optimization method of the virtual power plant participating in the electricity market, including: a resource integration module, a resource authentication module, a multi-objective optimization module, a dispatching strategy module, and a collaborative optimization module; The resource integration module is used to dynamically integrate distributed energy, adjustable loads, and energy storage units to construct a virtual power plant resource model; The resource authentication module is used to establish a distributed resource identity authentication mechanism to authenticate the identity and store data of the distributed energy, adjustable loads, and energy storage units of the virtual power plant; The multi-objective optimization module constructs a multi-objective optimization function based on multi-objectives such as resource utilization rate, carbon emissions, and load balance, designs a dynamic weight adjustment mechanism, and dynamically adjusts the weights of the optimization objectives according to the grid dispatching requirements; The dispatching strategy module is used to calculate the multi-objective optimization results of the virtual power plant, determine the output and operating status of the virtual power plant resources, and establish an optimal dispatching strategy for the virtual power plant in combination with the grid dispatching requirements; The collaborative optimization module combines game theory and swarm intelligence algorithm to coordinate and optimize the internal resource dispatching of the virtual power plant and the external grid dispatching. This application realizes the unified modeling and collaborative management of multiple types of resources in the virtual power plant through the dynamic integration of distributed energy, adjustable loads, and energy storage units.

[0015] This application realizes the identity trustworthiness and data traceability of various resources in the virtual power plant by establishing a distributed resource identity authentication mechanism.

[0016] This application realizes the comprehensive optimization of resource utilization rate, carbon emissions, and load balance of the virtual power plant by constructing a multi-objective optimization function and introducing a dynamic weight adjustment mechanism.

[0017] This application realizes the intelligent configuration of the output and operating status of the virtual power plant resources by calculating the multi-objective optimization results and formulating a dispatching strategy.

[0018] This application combines game theory and swarm intelligence algorithms to collaboratively optimize the scheduling process, achieving the collaborative coordination between the internal scheduling of the virtual power plant and the external scheduling of the power grid. Description of the Drawings

[0019] Figure 1 It is the flowchart of the multi-objective scheduling optimization method for the virtual power plant participating in the power market provided by this application; Figure 2 It is the flowchart of constructing the virtual power plant resource model provided by this application; Figure 3 It is the flowchart of the distributed resource identity authentication provided by this application; Figure 4 It is the flowchart of constructing the multi-objective optimization function for this step provided by this application; Figure 5 It is the flowchart of the game optimization collaborative scheduling provided by this application; Figure 6 It is the structural diagram of the multi-objective scheduling optimization system for the virtual power plant participating in the power market provided by this application. Detailed Embodiments

[0020] To make the above objects, features, and advantages of this application more obvious and understandable, the following will describe the detailed embodiments of this application in conjunction with the drawings in the specification.

[0021] Many specific details are set forth in the following description to facilitate a thorough understanding of this application, but this application can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of this application. Therefore, this application is not limited by the specific embodiments disclosed below.

[0022] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of this application. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0023] Embodiment 1 Referring to Figures 1 to 5 , the first embodiment of this application provides a multi-objective scheduling optimization method for the virtual power plant participating in the power market.

[0024] Step 1: Dynamically integrate distributed energy sources, adjustable loads, and energy storage units to construct a virtual power plant resource model. Refer to Figure 2 , which is the flowchart of constructing the virtual power plant resource model for this step.

[0025] Model the distributed energy sources in the virtual power plant, including photovoltaic power plants, wind farms, and micro gas turbines; establish a mathematical model for each distributed energy source according to its physical characteristics and operating constraints.

[0026] Output power of the photovoltaic power plant: ; where is the output power of the photovoltaic power plant at time is the conversion efficiency of the photovoltaic module, is the total area of the photovoltaic module, is the solar radiation intensity at time

[0027] Output power of the wind farm: ; where is the output power of the wind farm at time is the air density, is the swept area of the wind turbine, is the wind energy utilization coefficient, related to the tip speed ratio and the pitch angle ; is the wind speed at time

[0028] Output power of the micro gas turbine: ; where is the output power of the micro gas turbine at time is the power generation efficiency of the micro gas turbine, is the fuel consumption of the micro gas turbine at time

[0029] Model the adjustable loads in the virtual power plant, including industrial production loads and electric vehicle charging loads; establish a mathematical model for each adjustable load according to its electricity consumption characteristics and regulation potential for display.

[0030] Industrial production load regulation: ; where is the electricity consumption power of the industrial production load at time is the basic electricity consumption power of the industrial production load at time is the regulation ratio of the industrial production load at time, with a value range of [0, 1].

[0031] Electric vehicle charging load regulation: ; where is the total electricity consumption power of the electric vehicle charging load at time is the number of electric vehicles in the virtual power plant, is the charging power of the th electric vehicle at time is the charging state of the th electric vehicle at time

[0032] Model the energy storage units in the virtual power plant, including battery energy storage systems and flywheel energy storage systems; establish a mathematical model for each energy storage unit according to its energy storage characteristics and operation constraints.

[0033] Dynamic model of battery energy storage system: ; where is the state of charge of the battery energy storage system at time is the charging and discharging power of the battery energy storage system at time is the time step, is the rated capacity of the battery energy storage system.

[0034] Dynamic model of flywheel energy storage system: ; where is the energy stored in the flywheel energy storage system at time is the charging and discharging power of the flywheel energy storage system at time is the self-discharge coefficient of the flywheel energy storage system.

[0035] On the basis of establishing the mathematical models of distributed energy, adjustable load and energy storage units, comprehensively consider the physical characteristics, operation constraints and interaction relationships of the internal resources of the virtual power plant, and construct a virtual power plant resource model.

[0036] Total power generation of virtual power plant: ; where is the total power generation of the virtual power plant at time is the number of distributed energy sources in the virtual power plant, is the power generation of the th distributed energy source at time is the number of energy storage units in the virtual power plant, is the charging and discharging power of the th energy storage unit at time

[0037] Total power consumption of virtual power plant: ; where is The total power consumption of the virtual power plant at a certain moment, is the number of loads in the virtual power plant, is the power consumption of the th load at a certain moment, is the number of adjustable loads in the virtual power plant, is the regulation power of the th adjustable load at a certain moment.

[0038] Virtual power plant energy balance constraint: ; where is the exchange power between the virtual power plant and the external power grid at a certain moment (a positive value indicates purchasing electricity from the grid, and a negative value indicates selling electricity to the grid), is the power loss in the virtual power plant at a certain moment.

[0039] Virtual power plant reserve capacity constraint: ; where is the available reserve capacity of the virtual power plant at a certain moment, is the reserve capacity demand of the virtual power plant at a certain moment.

[0040] Virtual power plant network security constraint: ; where is the line power flow from the th node to the th node inside the virtual power plant at a certain moment, is the transmission capacity limit of this line, is the set of internal lines of the virtual power plant.

[0041] The virtual power plant resource model established in this step not only reflects the diversity and flexibility of the internal resources of the virtual power plant but also embodies the characteristics of the virtual power plant as a whole participating in the power market dispatching, providing an important theoretical basis and decision-making support for subsequent multi-objective optimal dispatching.

[0042] Step 2: Establish a distributed resource identity authentication mechanism to authenticate and store data for the distributed energy, adjustable loads, and energy storage units of the virtual power plant. See Figure 3 , which is the flow chart of the distributed resource identity authentication for this step.

[0043] Establish a distributed resource identity authentication mechanism, including a resource identity identification mechanism and a data storage and certification mechanism.

[0044] Specifically, the resource identity identification mechanism includes: constructing resource identity identifications, establishing unique identity identifications for each distributed resource within the virtual power plant. The resource identity identification includes a resource type code, a geographical location code, a capacity level code, and a registration timestamp, and is calculated through a hash function to generate a unique resource identification code.

[0045] The data deposit and certification mechanism includes: generating digital certificates for each distributed resource. The certificate content includes a resource identification code, a resource public key, a certificate validity period, and digital signature information of the certification center. Among them, the certificate signature is obtained by performing a hash calculation on the resource identification, public key, and validity period using the private key of the certification center.

[0046] The identity authentication process of the distributed resources in the virtual power plant is divided into two stages: resource registration authentication and resource operation status authentication. In the resource registration authentication stage, first collect the characteristic information of the resources, including the installed capacity, power generation efficiency, and operation parameters of distributed energy, the electricity consumption capacity, regulation characteristics, and response time of adjustable loads, and the energy storage capacity, charge-discharge efficiency, and cycle life of energy storage units, and verify the identity information submitted by the resources. The digital certificate is verified using the public key of the certification center. In the resource operation status authentication stage, the resource operation status is periodically authenticated, and a status authentication code is obtained through the resource operation status, timestamp, and resource private key signature calculation.

[0047] In terms of the data deposit and certification mechanism, the Merkle tree structure is used to organize and store resource data. By performing a hash calculation on data blocks, the root hash value of the Merkle tree is generated to ensure the integrity and immutability of the data. The data deposit and certification process includes two steps: data packaging and deposit record generation. When packaging, the resource operation data and timestamp information are encapsulated into data blocks, and the Merkle root hash value is calculated. Then, a hash operation is performed on the data block hash, timestamp, and virtual power plant private key signature to generate the final deposit record.

[0048] This step can effectively ensure the identity authenticity and data credibility of various resources within the virtual power plant through the identity authentication and data deposit and certification of distributed resources, providing a solid foundation for the safe and reliable operation of the virtual power plant.

[0049] Step 3: Based on resource utilization rate, carbon emissions, and load balance objectives, construct a multi-objective optimization function, design a dynamic weight adjustment mechanism, and dynamically adjust the weights of optimization objectives according to grid dispatching requirements. See Figure 4 , which is the flow chart for constructing the multi-objective optimization function in this step.

[0050] Considering the three objectives of resource utilization rate, carbon emissions, and load balance in the operation of the virtual power plant, construct a multi-objective optimization function, including a resource utilization rate optimization function, a carbon emissions optimization function, and a load balance optimization function.

[0051] The resource utilization optimization function is used to maximize the comprehensive utilization rate of the internal resources of the virtual power plant, including the utilization rate of distributed energy, the regulation rate of adjustable load, and the utilization rate of energy storage units: Among them, is the resource utilization target function, is the total number of optimization periods, is the number of distributed energy sources in the virtual power plant, is the time the output of the the installed capacity of the is the number of adjustable loads in the virtual power plant, is the time the actual power consumption of the the rated power consumption of the is the number of energy storage units in the virtual power plant, is the time the charge and discharge power of the the rated power of the

[0052] The carbon emission optimization function is used to minimize the carbon emission intensity per unit of electricity during the operation of the virtual power plant and promote the consumption of new energy: Among them, is the carbon emission target function, is the time total power generation of the virtual power plant, is the time new energy power generation in the virtual power plant, including photovoltaic power plants, wind farms, etc., is the time fossil energy power generation in the virtual power plant, mainly referring to micro gas turbines, is the carbon emission coefficient of new energy, is the carbon emission coefficient of fossil energy.

[0053] The load balance optimization function is used to minimize the deviation between the actual operating power of the virtual power plant and the grid dispatching plan, and ensure the response ability of the virtual power plant to grid dispatching instructions: Among them, is the load balancing objective function, For the moment The actual operating power of the virtual power plant, For the moment The dispatch plan power issued by the power grid to the virtual power plant.

[0054] Construct a multi-objective optimization function: .

[0055] In order to balance the importance of multiple optimization objectives, a dynamic weight adjustment mechanism is designed to dynamically adjust the weight coefficients of each objective according to the grid dispatching requirements: ;in, , and Separately for the moment Weight coefficients for resource utilization, carbon emission intensity, and load balance objectives.

[0056] By constructing a multi-objective optimization function and designing a dynamic weight adjustment mechanism, the efficiency, environmental protection and flexibility of virtual power plant operation can be comprehensively considered, and the importance of multiple objectives can be dynamically balanced according to the actual dispatching needs of the power grid, providing a scientific decision-making basis for the optimal dispatching of virtual power plants.

[0057] Regarding the adjustment of resource utilization target weight, the weight coefficient of resource utilization target is dynamically adjusted according to the real-time operating status of internal resources of the virtual power plant.

[0058] For example, when the internal resource utilization rate of the virtual power plant is generally low, the weight of the resource utilization rate target is increased; the internal resource utilization rate of the virtual power plant can be compared by setting a threshold, and if it is less than the threshold, it is determined that the internal resource utilization rate of the virtual power plant is generally low; when the internal resource utilization rate of the virtual power plant is generally high, the weight of the resource utilization rate target is reduced, and the internal resource utilization rate of the virtual power plant can be compared by setting a threshold, and if it is greater than the threshold, it is determined that the internal resource utilization rate of the virtual power plant is generally high: in, For the moment The average utilization of all resources in the virtual power plant, is the lower limit of the reasonable interval of virtual power plant resource utilization, is the upper limit of the reasonable range of virtual power plant resource utilization, is the resource utilization weight adjustment coefficient, The maximum value of the resource utilization target weight.

[0059] For the adjustment of the carbon emission target weight, according to the requirements of the power grid for the consumption of renewable energy, dynamically adjust the weight coefficient of the carbon emission target; when the requirements of the power grid for the consumption of renewable energy increase, increase the weight of the carbon emission target; when the requirements of the power grid for the consumption of renewable energy decrease, reduce the weight of the carbon emission target: Among them, is the requirement for the proportion of renewable energy consumption in the power grid at time , and are the minimum and maximum values of the requirements for the consumption of renewable energy in the power grid respectively, and are the minimum and maximum values of the carbon emission target weight respectively.

[0060] For the adjustment of the load balancing target weight, according to the volatility of the power grid load, dynamically adjust the weight coefficient of the load balancing target; when the power grid load fluctuates greatly, increase the weight of the load balancing target and require the virtual power plant to operate strictly according to the dispatching plan; when the power grid load is relatively stable, reduce the weight of the load balancing target and allow the virtual power plant to appropriately adjust the operating power: Among them, is the volatility rate of the power grid load at time , reflecting the volatility of the load, is the threshold of the power grid load volatility rate, and are the minimum and maximum values of the load balancing target weight respectively.

[0061] In this step, according to the dispatching requirements of the power grid, dynamically optimize multiple objectives of the virtual power plant operation from aspects such as resource utilization efficiency, new energy consumption, and load response ability, and balance the relative importance of each objective through the weight adjustment mechanism to achieve the dynamic adaptation of the virtual power plant to the power grid demand; at the same time, introduce piecewise functions and dynamic thresholds to make the weight adjustment strategy more flexible and variable, and be able to adapt to the complex and changeable external environment and internal state.

[0062] Step 4: Calculate the multi-objective optimization result of the virtual power plant, determine the output and operating state of the virtual power plant resources, and establish an optimal dispatching strategy for the virtual power plant in combination with the power grid dispatching requirements.

[0063] To solve the multi-objective optimal dispatching problem of the virtual power plant, this step uses an improved particle swarm optimization algorithm (MOPSO); compared with the traditional particle swarm optimization algorithm, MOPSO introduces the ideas of non-dominated sorting and crowding distance, and can effectively handle multiple conflicting optimization objectives to obtain the Pareto optimal solution set; the MOPSO algorithm process is as follows: Initialize particle swarm: randomly generate an initial particle swarm, each particle represents an output combination of virtual power plant resources; the position and speed of the particles are randomly initialized, the position dimension is equal to the number of all resources in the virtual power plant, and the speed dimension is the same as the position dimension.

[0064] Evaluate particle swarm: For each particle in the particle swarm, calculate its fitness value on the three objectives of resource utilization, carbon emission intensity and load balance to form a fitness vector.

[0065] Update local optimal solution and global optimal solution: for each particle, compare its current position with its historical optimal position. If the current position of the particle is better than the individual historical optimal position in terms of the objective function, update the individual historical optimal position to the current position. ,According to the fitness vector and crowding distance of the particles, the global optimal solution set is updated.

[0066] Update particle speed and position: For each particle, update the particle speed and position according to its current speed, current position, historical optimal position and global optimal solution.

[0067] Particle out-of-bounds processing: Perform an out-of-bounds check on the position vector of each particle. If a dimension exceeds the upper or lower limit of the corresponding resource output, it will be set to the upper or lower limit.

[0068] Termination condition judgment: If the maximum number of iterations is reached, the iteration is stopped and the global optimal solution set is output as the Pareto optimal solution set for multi-objective optimization of the virtual power plant; otherwise, it returns to the evaluation particle swarm step and continues the step.

[0069] According to the Pareto solution set of multi-objective optimization of virtual power plants, combined with the grid dispatching requirements and the operating characteristics of virtual power plants, the optimal output and operating status of virtual power plant resources are determined.

[0070] Exemplarily, the scheduling solution selection strategy based on load priority and multi-objective compromise is as follows: Prioritize solutions with higher load balance target fitness in the Pareto solution set frontier to ensure the virtual power plant's ability to track grid dispatch instructions. On the basis of satisfying the load balance constraint, select solutions that balance resource utilization targets and carbon emission targets to ensure the environmental friendliness of the virtual power plant. Comprehensively consider the changes in grid dispatch demand and the flexibility of virtual power plant operation, appropriately select solutions in different areas of the Pareto frontier to improve the diversity and adaptability of virtual power plant dispatching schemes.

[0071] Based on the optimal output and operating status of virtual power plant resources, an optimal dispatching strategy model of the virtual power plant is constructed; the optimal dispatching strategy model aims to track the grid dispatching power and generates the optimal dispatching curve of the virtual power plant under the conditions of the output constraints of virtual power plant resources and the energy storage SOC constraints; the optimal dispatching strategy model is expressed as follows: Among them, is the output of the virtual power plant at time under the optimal dispatching strategy, is the power instruction issued by the grid dispatching agency, and are the upper and lower limits of the output of the virtual power plant respectively, is the energy storage unit at time The state of charge, and are the upper and lower limits of the state of charge of the energy storage unit respectively, is the set of energy storage units in the virtual power plant, is the optimal output of the distributed power source , is the optimal charging and discharging power of the energy storage unit , is the load The power consumption of.

[0072] By solving the above optimal dispatching strategy model, the optimal output curve and resource operating status of the virtual power plant in different time periods can be obtained, realizing the dynamic response and tracking of the virtual power plant to the grid dispatching instructions; at the same time, this dispatching strategy comprehensively considers multiple operating objectives of the virtual power plant and has strong feasibility.

[0073] This step uses an improved MOPSO algorithm to solve the multi-objective optimal dispatching problem of virtual power plant resources, and obtains the optimal output combination and operating status of the internal resources of the virtual power plant; on this basis, this step also combines the grid dispatching requirements to construct an optimal dispatching strategy for the virtual power plant, generates the optimal output curve of the virtual power plant, and realizes the dynamic interaction and coordination between the virtual power plant and the grid dispatching.

[0074] Compared with the traditional single-objective optimal dispatching method, the method in this step can more comprehensively consider multiple operating objectives of the virtual power plant, improve the environmental protection and flexibility of the virtual power plant operation; the intelligent optimization algorithm and rolling dispatching strategy adopted in this step also help to improve the efficiency and adaptability of the virtual power plant dispatching, providing strong support for the virtual power plant to participate in the power market.

[0075] Step 5: Combine game theory and swarm intelligence algorithms to conduct collaborative optimization of the internal resource dispatching of the virtual power plant and the external grid dispatching, seeFigure 5 , is the flow chart of collaborative optimization of internal and external dispatching of the virtual power plant in this step.

[0076] A hierarchical game model is constructed for the collaborative optimization problem of internal resource dispatching and external grid dispatching of the virtual power plant, including two levels: internal game and external game.

[0077] The internal game model describes the game process among various resources within the virtual power plant. By establishing a resource utility function and constraint conditions, solving the game equilibrium, the optimal dispatching strategy of the internal resources of the virtual power plant is obtained.

[0078] The goal of the internal game model is to maximize the utility function of each resource, while satisfying the output balance constraint of the virtual power plant, the upper and lower limits of resource output, the upper and lower limits of energy storage capacity, and the output deviation constraint of the virtual power plant.

[0079] The external game model describes the game process between the virtual power plant and the grid dispatching. By constructing the virtual power plant utility function and the grid dispatching objective function, solving the Stackelberg game model, the optimal strategy for the virtual power plant to respond to the grid dispatching instruction is obtained.

[0080] The goal of the external game model is to maximize the utility function of the virtual power plant, while minimizing the objective function of the grid dispatching, and satisfying the upper and lower limits of the virtual power plant output and the non - negative constraint of the penalty factor.

[0081] Based on the constructed game model, a distributed optimization solution method based on swarm intelligence algorithm is designed to realize the hierarchical decoupling collaborative optimization of internal and external dispatching of the virtual power plant.

[0082] The adaptive weight particle swarm optimization algorithm (AWPSO) is used to solve the internal resource dispatching problem of the virtual power plant; the utilization rate of various adjustable resources and the load balance deviation in the virtual power plant are used as fitness indicators. By designing the adaptive position and velocity update mechanism of particles, introducing the adaptive inertia weight and learning factor, the global search efficiency and local convergence ability of the algorithm are improved, so as to realize the optimal dispatching strategy of the optimal allocation of internal resources of the virtual power plant.

[0083] The adaptive differential evolution algorithm is used to solve the external dispatching problem of the virtual power plant. By designing the mutation, crossover and selection operations of the population individuals, introducing the adaptive scaling factor and crossover probability, the global search ability and local development ability of the algorithm are enhanced; the mutation and crossover operations of the population individuals adopt the binary crossover and random mutation strategies respectively, and the selection operation adopts the greedy selection strategy; the scaling factor and crossover probability adopt the adaptive adjustment strategy and change dynamically with the increase of the evolution generation.

[0084] By designing an iterative interaction mechanism for internal resource scheduling and external grid scheduling in the virtual power plant, distributed collaborative optimization at two levels is achieved; the grid dispatching agency issues multiple optional dispatching plans to the virtual power plant according to the system operation status, the virtual power plant determines the optimal output combination of various resources through internal game negotiation, then selects the optimal response plan through external game comparison, and feeds the result back to the grid dispatching agency; the grid dispatching agency comprehensively considers the response situations of each virtual power plant and updates the dispatching plan for the next time period.

[0085] The iterative interaction mechanism can be expressed as: ; where is the dispatching plan issued by the grid to the virtual power plant at the -th iteration, is the optimized output within the virtual power plant at the -th iteration, is the grid dispatching plan adjustment coefficient.

[0086] This step comprehensively considers the internal resource characteristics of the virtual power plant and the external grid demand, constructs a hierarchical game model to describe the interaction mechanism between internal and external dispatching of the virtual power plant, and designs a distributed optimization solution method combined with the swarm intelligence algorithm. While ensuring the optimal internal resource scheduling of the virtual power plant, it maximally meets the grid dispatching requirements and improves the flexibility of the virtual power plant to participate in grid dispatching.

[0087] Embodiment 2 Referring to Figure 6 , which is the second embodiment of this application, a multi-objective scheduling optimization system for the virtual power plant to participate in the power market is provided.

[0088] The system includes: a resource integration module, a resource authentication module, a multi-objective optimization module, a scheduling strategy module, and a collaborative optimization module.

[0089] The resource integration module is used to dynamically integrate distributed energy, adjustable load, and energy storage units to construct a virtual power plant resource model.

[0090] The resource authentication module is used to establish a distributed resource identity authentication mechanism to authenticate the identity and store data of the distributed energy, adjustable load, and energy storage units of the virtual power plant.

[0091] The multi-objective optimization module constructs a multi-objective optimization function based on multi-objectives such as resource utilization rate, carbon emission, and load balance, designs a dynamic weight adjustment mechanism, and dynamically adjusts the weights of optimization objectives according to grid dispatching requirements.

[0092] The scheduling strategy module is used to calculate the multi-objective optimization results of the virtual power plant, determine the output and operation status of the virtual power plant resources, and establish an optimal scheduling strategy for the virtual power plant in combination with grid dispatching requirements.

[0093] The collaborative optimization module combines game theory and swarm intelligence algorithms to perform collaborative optimization on the internal resource scheduling of the virtual power plant and the external power grid scheduling.

[0094] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0095] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make changes, modifications, substitutions and variations to the above embodiments without departing from the purpose of the present application and the scope protected by the claims. These all fall within the protection scope of the present application.

Claims

1. Multi-objective dispatch optimization method for virtual power plants participating in the power market, characterized by ,include: Dynamically integrate distributed energy, adjustable loads and energy storage units to build a virtual power plant resource model; Establish a distributed resource identity authentication mechanism to authenticate and store data for distributed energy, adjustable loads and energy storage units of virtual power plants; Based on the multi-objectives of resource utilization, carbon emissions and load balance, a multi-objective optimization function is constructed, and a dynamic weight adjustment mechanism is designed to dynamically adjust the weights of the optimization objectives according to the grid dispatching requirements; Calculate the multi-objective optimization results of the virtual power plant, determine the output and operating status of the virtual power plant resources, and establish the optimal dispatching strategy of the virtual power plant in combination with the grid dispatching requirements; Combining game theory with swarm intelligence algorithms, the internal resource scheduling of the virtual power plant and the external power grid scheduling are collaboratively optimized.

2. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 1 is characterized in that: The steps of constructing a virtual power plant resource model include: Obtaining information on distributed energy in a virtual power plant and establishing a mathematical model based on the physical characteristics and operating constraints of distributed energy, wherein the distributed energy includes photovoltaic power stations, wind farms, and micro gas turbines; Obtaining adjustable load information in the virtual power plant and establishing a mathematical model based on the power consumption characteristics and regulation potential of the adjustable load, wherein the adjustable load includes industrial production load and electric vehicle charging load; Acquire information of energy storage units in the virtual power plant, and establish a mathematical model based on energy storage characteristics and operation constraints of the energy storage units, wherein the energy storage units include a battery energy storage system and a flywheel energy storage system; Considering the physical characteristics, operation constraints and interaction relationships of the internal resources of the virtual power plant, a virtual power plant resource model is constructed.

3. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 2 is characterized in that: The distributed resource identity authentication mechanism includes a resource identity identification mechanism and a data evidence storage mechanism; The resource identification mechanism includes: establishing a unique identification for each distributed resource in the virtual power plant, and generating an identification code by performing a hash calculation on resource feature information; The data evidence storage mechanism includes: using a Merkle tree structure to organize and store resource data and generate data evidence records.

4. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 3 is characterized in that: The multi-objective optimization function includes a resource utilization optimization function, a carbon emission optimization function and a load balance optimization function; The resource utilization optimization function is used to maximize the utilization of distributed energy, adjustable load regulation rate and energy storage unit utilization; The carbon emission optimization function is used to minimize the carbon emission intensity per unit of electricity during the operation of the virtual power plant; The load balancing optimization function is used to minimize the deviation between the actual operating power of the virtual power plant and the grid dispatching plan.

5. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 4 is characterized in that: The dynamic weight adjustment mechanism includes: According to the real-time operating status of the internal resources of the virtual power plant, the weight coefficient of the resource utilization target is dynamically adjusted; according to the requirements of the power grid for renewable energy consumption, the weight coefficient of the carbon emission target is dynamically adjusted; according to the volatility of the power grid load, the weight coefficient of the load balance target is dynamically adjusted.

6. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 5 is characterized in that: The multi-objective optimization results of the virtual power plant are calculated, and the improved particle swarm optimization algorithm is used to introduce non-dominated sorting and congestion distance to obtain the Pareto optimal solution set of the objective optimization results.

7. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 6 is characterized in that: According to the Pareto solution set of multi-objective optimization of virtual power plants, combined with the grid dispatching requirements and the operating characteristics of virtual power plants, the optimal output and operating status of virtual power plant resources are determined.

8. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 7 is characterized in that: A virtual power plant optimization dispatching strategy model is constructed to track the grid dispatching power as the goal, and the optimal output curve of the virtual power plant is generated based on the virtual power plant resource output constraints and energy storage SOC constraints.

9. The multi-objective scheduling optimization method for virtual power plants participating in the power market according to claim 8 is characterized in that: Collaborative optimization of internal and external dispatch of virtual power plants, including: An internal game model and an external game model are constructed to describe the internal resource game process of the virtual power plant and the game process between the virtual power plant and the power grid dispatching respectively; a swarm intelligence algorithm is used to realize the optimization of the internal resource dispatching of the virtual power plant and the external dispatching response of the virtual power plant; and an iterative interaction mechanism is used to realize the collaborative solution of the optimization of the internal and external levels of the virtual power plant.

10. A multi-objective dispatch optimization system for virtual power plants to participate in the power market, which is used to implement the multi-objective dispatch optimization method for virtual power plants to participate in the power market as described in any one of claims 1 to 9, characterized in that , including: resource integration module, resource authentication module, multi-objective optimization module, scheduling strategy module and collaborative optimization module; The resource integration module is used to dynamically integrate distributed energy, adjustable loads and energy storage units to build a virtual power plant resource model; The resource authentication module is used to establish a distributed resource identity authentication mechanism to authenticate and store data on distributed energy, adjustable loads and energy storage units of the virtual power plant; The multi-objective optimization module constructs a multi-objective optimization function based on the multi-objectives of resource utilization, carbon emissions and load balance, designs a dynamic weight adjustment mechanism, and dynamically adjusts the weights of the optimization objectives according to the grid dispatching requirements; The dispatch strategy module is used to calculate the multi-objective optimization results of the virtual power plant, determine the output and operating status of the virtual power plant resources, and establish an optimized dispatch strategy for the virtual power plant in combination with the grid dispatch requirements; The collaborative optimization module combines game theory with swarm intelligence algorithms to collaboratively optimize the internal resource scheduling of the virtual power plant and the external power grid scheduling.

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