Distributed energy virtual power plant optimization method based on ectopic quantum evolution

Abstract

The invention discloses a distributed energy virtual power plant optimization method based on ectopic quantum evolution, and relates to the technical field of virtual power plant control. At present, research on economic optimization of a virtual power plant is insufficient. The method comprises the following steps: constructing virtual power plant power generation main body cost according to distributed energy characteristics; according to the charging and discharging characteristics of the energy storage system, constructing the operation cost of the energy storage block of the virtual power plant; based on the cost of pollution to the environment and the cost of abandoning new energy, the environment cost is constructed in a combined manner; according to the real-time dynamic change of the electricity price to the electricity demand of the user, the demand response cost is constructed; building a cost objective function according to the cost factors of various virtual power plants, and constructing an optimization model of the low-cost virtual power plant by taking the cost objective function as an optimization objective; optimizing the low-cost virtual power plant optimization model by using an ectopic quantum evolutionary algorithm; and obtaining a virtual power plant system according to an optimization result. The diversity of the distributed energy and the system stability are both considered, and efficient utilization and economical efficiency improvement of the distributed energy are promoted.

Description

Technical Field

[0001] The present invention relates to the field of control technology of virtual power plants, and in particular to a distributed energy virtual power plant optimization method based on heterogeneous quantum evolution. Background Art

[0002] With the large-scale application of new energy, the energy structure of the traditional power grid system has undergone profound changes, and the power grid system is showing a trend of decentralization, flattening and decentralization. Although new energy has the characteristics of distribution, small installed capacity, unstable energy and high cost, its long-term environmental protection benefits are significant and it has huge economic benefit potential. Especially in the context of responding to climate change and energy transformation, the proportion of new energy has gradually increased and become an important part of the power grid operation.

[0003] However, with the integration of a large number of distributed energy sources into the power grid, although it has brought significant environmental and economic benefits, it has also put forward higher requirements for the stability and security of the power grid system. When faced with unstable new energy output, traditional power grid systems often find it difficult to cope with its volatility, which may lead to insufficient load regulation capacity of the power grid, thus causing system operation risks.

[0004] In order to effectively solve these problems, virtual power plants (VPPs) have attracted widespread attention in recent years as an innovative energy management method. Virtual power plants integrate and optimize the scheduling of multiple distributed energy devices, energy storage systems, load demand and other resources through modern information and communication technologies, especially the Internet of Things technology, to achieve the coordinated operation of different energy sources in virtual power plants, thereby improving the stability, flexibility and energy efficiency of the power grid.

[0005] Although virtual power plants have shown great potential in improving system stability and energy scheduling, current research mainly focuses on the diversity of energy types, the stability of system operation, and technical implementation, and the research on the optimization of the economic cost of virtual power plants is relatively weak. Specifically, how to reduce operating costs and improve economic benefits while ensuring the stability of the power grid through reasonable cost control and economic scheduling is still an important issue that needs to be solved urgently. Therefore, in-depth research on the economic optimization methods of virtual power plants is of great significance to promoting the efficient use of distributed energy and reducing overall energy costs. Summary of the invention

[0006] The technical problems to be solved and the technical tasks proposed by the present invention are to improve and perfect the existing technical solutions, and provide a distributed energy virtual power plant optimization method based on heterogeneous quantum evolution, which can fully optimize the various cost factors of the virtual power plant, while ensuring the stability of the power grid system, and minimize the operating costs of the virtual power plant. To this end, the present invention adopts the following technical solutions.

[0007] A distributed energy virtual power plant optimization method based on heterogeneous quantum evolution comprises the following steps:

[0008] 1) According to the distributed energy characteristics of photovoltaic units, wind turbine units and steam turbine units, a cost model of the power generation entity of the virtual power plant is established;

[0009] 2) Establish an operating cost model for the energy storage block of the virtual power plant based on the charging and discharging characteristics of the energy storage system;

[0010] 3) Comprehensively consider the environmental pollution costs and the cost of abandoning new energy when the generator sets are in operation, and build an environmental cost model;

[0011] 4) Establish a demand response cost model based on the real-time dynamic changes in electricity prices to user electricity demand;

[0012] 5) Based on various cost factors, the total cost objective function of the virtual power plant is constructed, and the total cost objective function is used as the optimization target to establish an optimization model of the low-cost virtual power plant;

[0013] 6) The low-cost virtual power plant optimization model is optimized using an off-site quantum evolutionary algorithm; the optimization process includes initialization of quantum individuals, off-site operations, quantum gate updates, and global searches;

[0014] 7) According to the optimization results, an optimized distributed energy ex-situ quantum optimized low-cost virtual power plant system is provided, and various types of generators and energy storage systems are dispatched according to the optimization plan to achieve low-cost operation.

[0015] This technical solution comprehensively considers various economic costs in the operation of virtual power plants by establishing a multi-dimensional cost model covering photovoltaic units, wind turbines, steam turbines, energy storage systems, environmental pollution costs, and abandoned new energy costs. Compared with single cost optimization, this method can more comprehensively and meticulously evaluate the operating benefits and costs of different energy types, ensuring that the optimization results are more economical and operational.

[0016] The ex situ quantum evolutionary algorithm is used for optimization. Compared with traditional optimization methods, the quantum evolutionary algorithm can perform global optimization in a wider search space. The algorithm can avoid falling into the local optimal solution, and through the ex situ operation of quantum individuals and the global search strategy, it can effectively improve the solution efficiency and solution quality, ensuring that the global optimal or near-optimal low-cost virtual power plant operation plan can be obtained.

[0017] This method introduces the real-time dynamic changes of electricity prices on user electricity demand and the consideration of demand response costs into the optimization model, so that the virtual power plant can adjust the power generation and energy storage strategies in real time according to electricity price fluctuations. This flexibility not only improves the economic benefits of the virtual power plant, but also enhances the system's ability to respond to changes in grid load and fluctuations in electricity demand.

[0018] In the optimization process, this method takes into account the environmental pollution cost and the cost of abandoning new energy. This means that in the process of optimizing power generation and dispatching, virtual power plants can reduce operating costs while promoting the full utilization of renewable energy and reducing negative impacts on the environment, which is in line with the concept of green development and sustainable development.

[0019] By incorporating various energy forms such as photovoltaics, wind power, and steam turbines into the optimization model, we can give full play to the advantages of various distributed energy sources, reduce the system's dependence on a single energy source, and reduce the impact of new energy fluctuations on the power grid. At the same time, the optimization plan can achieve efficient coordinated operation of various energy sources and improve overall energy utilization efficiency while ensuring the stability of the power grid.

[0020] This technical solution is not only applicable to common energy forms such as photovoltaics, wind power, and steam turbines, but can also be extended to other new energy types according to demand, such as biomass energy, tidal energy, etc. In addition, as the power market environment and grid demand change, this method can flexibly adjust the optimization objectives and constraints to adapt to changes in different power markets and environments, and has strong adaptability and scalability.

[0021] Through the optimization process of the ex-situ quantum evolutionary algorithm, accurate scheduling of various types of power generating units and energy storage systems can be achieved, ensuring that the virtual power plant can meet the grid load demand at the lowest cost, thereby reducing energy consumption and operating costs and improving the economy of the virtual power plant.

[0022] This technical solution combines the characteristics of a variety of advanced optimization technologies and new energy systems, fully considers the dynamic needs of the electricity market, environmental protection and resource optimization, has significant economic and environmental benefits, and promotes the application of virtual power plant technology in the direction of low cost, high efficiency and diversification, providing strong support for the realization of future smart grids and sustainable energy.

[0023] As a preferred technical means: the power generation of photovoltaic units depends on the sunlight The radiation intensity and ambient temperature, the power generation is:

[0024]

[0025] in, is the photovoltaic power generation, β is the conversion rate, S is the effective illumination area, I is the illumination intensity; T t is the ambient temperature;

[0026] The power generation of a wind turbine depends on the size of the wind and the area receiving the wind. The power generation is:

[0027]

[0028] in is the wind power generation power, Q rated is the rated power generation, ω1, ω2, ω3 are transmission efficiency, power factor and power generation efficiency respectively, v t is the wind speed, V in ,V rated ,V out are the cut-in, rated and cut-out wind speeds, ρ is the air density, and d is the blade diameter;

[0029] The power generation capacity of the steam turbine unit is determined by the number of starting units; the power generation capacity is expressed as

[0030] The main cost of virtual power plant power generation is C MT It means that it includes the fuel cost C f and startup costs C qt ;

[0031] C MT =C f +C qt

[0032]

[0033] in, is a Boolean variable for the status of the steam turbine; is the power generation capacity; a, b, c are the power generation cost coefficients; SU and SD are the startup and shutdown costs.

[0034] The power generation calculation formulas for photovoltaic units, wind turbine units and steam turbine units in this technical solution are modeled based on actual environmental conditions and technical parameters, and can accurately reflect the power generation characteristics of different types of units. Photovoltaic power generation is closely related to light intensity and ambient temperature, while wind turbines are closely related to factors such as wind speed and technical parameters of wind turbines (such as blade diameter and power coefficient). The power generation of steam turbine units depends on the number of starting units and can dynamically adapt to load requirements. This precise modeling method makes the power generation model of the virtual power plant more in line with actual operating conditions and enhances the accuracy of system optimization and scheduling.

[0035] This solution divides the power generation cost of the virtual power plant into fuel cost and startup cost. The fuel cost takes into account the relationship between power generation and fuel consumption, while the startup cost takes into account the impact of the start and stop of the steam turbine unit on the cost, which can accurately describe the economic efficiency of the steam turbine unit operation. Through this detailed cost decomposition, cost control and optimized scheduling can be carried out more effectively to ensure the economic operation of the virtual power plant.

[0036] Startup and shutdown costs are an important cost factor that is often overlooked in traditional power generation systems. This solution solves the start-up and shutdown cost problem of steam turbine units under dynamic loads by introducing startup and shutdown costs and combining Boolean state variables of steam turbine units to accurately model startup and shutdown. This can effectively reduce the overall operating cost of virtual power plants, especially when load fluctuations are large.

[0037] This technical solution is based on different types of distributed energy generators such as photovoltaic, wind, and steam turbines, and adopts a power generation model that conforms to their respective operating characteristics, so that the virtual power plant can better adapt to the diversity and complexity of the energy structure. The characteristics of different generators (such as the volatility of photovoltaic and wind power, and the startup flexibility of steam turbines) can be fully reflected in the optimization model, thereby optimizing the overall power dispatch and improving the overall stability and economy of the system.

[0038] Through accurate cost models, this solution can not only optimize the economic operation of power plants, but also take into account environmental protection needs. For example, fuel consumption and pollution costs can be quantified through corresponding models, thereby promoting the use of new and clean energy. This dual optimization solution for environmental protection and economy not only meets the needs of modern power industry for cost control, but also conforms to the trend of green and low-carbon development.

[0039] This technical solution ensures that the virtual power plant can be flexibly dispatched under changing loads and external conditions by accurately modeling different units (photovoltaic, wind, steam turbines) and energy storage systems. By quantifying factors such as power generation, fuel costs, and start-stop costs, the virtual power plant can efficiently adjust the operating status of the generator set, optimize the use of power resources, and improve the stability and energy utilization of the power grid.

[0040] In the context of large-scale grid connection of distributed energy, system stability has become an important challenge. This solution can not only improve the utilization rate of renewable energy, but also effectively balance the volatility of new energy and the fluctuation of grid load demand, thereby improving the stability of the grid, by accurately modeling various energy units and cost factors and optimizing the operation strategy of virtual power plants.

[0041] This technical solution is highly practical and operational. It can not only play a role in the current energy production system, but also adapt to the diversified needs of the future energy structure. The solution provides a comprehensive optimization framework for the operation of virtual power plants by comprehensively considering multi-dimensional factors such as energy supply and demand, cost, and environment.

[0042] As a preferred technical means: in step 2), the energy storage system has bidirectional charging and discharging, and the cost expression of the energy storage system in the virtual power plant is:

[0043]

[0044] Among them, C ESS is the energy storage system cost, λ is the energy storage system operation cost coefficient, are the charging and discharging power of the energy storage system respectively.

[0045] Energy storage systems are an indispensable part of modern power systems because they have the bidirectional nature of charging and discharging. By introducing the charging power and discharging power of energy storage into the cost model, the actual operating cost of the energy storage system can be more accurately reflected. Since energy storage plays a very flexible role in the power grid and can store and release energy, considering this bidirectional characteristic helps optimize the scheduling of energy storage, thereby maximizing the economic benefits and system stability of the energy storage system.

[0046] By using the energy storage system operating cost coefficient λ to control the cost of the energy storage system, the energy storage dispatch strategy can be flexibly adjusted according to different market electricity prices, demand response and grid load conditions. The flexible cost coefficient λ helps to control the frequency of use and charging and discharging efficiency of energy storage, so that the energy storage system can meet the needs of the grid while minimizing operating costs when providing power support.

[0047] The important role of energy storage systems in the power grid is to balance the difference between power generation and demand. In this technical solution, the bidirectional modeling of the charging and discharging process can ensure that the energy storage system plays the greatest role in meeting load demand, regulating electricity price fluctuations and reducing power abandonment. Through this refined management, energy storage not only helps to improve the economic benefits of virtual power plants, but also avoids energy waste and improves the utilization rate of new energy.

[0048] In the case of large fluctuations in electricity demand and supply, the charging and discharging strategy of the energy storage system is a dynamic process. The bidirectional expression of charging and discharging power can be flexibly adjusted according to grid demand and energy storage status in different time periods. In this way, the virtual power plant can quickly respond and optimize scheduling according to changes in grid load, photovoltaic / wind power generation, and electricity prices to ensure the stability and reliability of power supply.

[0049] Due to the output volatility of new energy sources (such as photovoltaic and wind power) in the power grid, energy storage systems can provide smooth regulation. Through this bidirectional charging and discharging modeling, energy storage can be charged when electricity prices are low and discharged when electricity prices are high or when loads are high. This not only improves the virtual power plant's ability to respond to demand fluctuations, but also enhances the system's ability to adapt to uncertainties and emergencies. The best match between grid demand and renewable energy is achieved.

[0050] By optimizing the charging and discharging timing of energy storage, the procurement and production costs of energy can be effectively reduced. The optimization of the charging and discharging strategy of the energy storage system not only helps to balance the load of the power grid, but also can use the low electricity price period for charging and the high electricity price period for discharging, thereby reducing the expenditure of the virtual power plant on purchasing electricity and increasing the proportion of self-generation for self-use.

[0051] Through more precise energy storage scheduling, the phenomenon of wind and solar power abandonment can be reduced, and the volatility of new energy can be fully utilized. This has positive significance for the consumption of renewable energy such as photovoltaic and wind power. When there is excess green energy generation, the excess electricity can be stored through the energy storage system and released when the demand for electricity increases or the new energy generation is insufficient, maximizing the utilization rate of renewable energy.

[0052] As a preferred technical means: In step 3), the cost of environmental pollution caused by the operation of the generator set is C pf and abandoned new energy cost C qd , combined to build an environmental cost model:

[0053]

[0054] Where K is the type of pollutant, m k is the environmental pollution penalty coefficient, V k ,Y k is the pollution value and the amount of fine; p w ,p pv They are the punitive unit prices for abandoning the use of wind power and photovoltaic power; and They are the upper limits of wind power and photovoltaic output respectively.

[0055] Environmental pollution and the cost of abandoning new energy sources are factors that cannot be ignored in the optimization of modern power systems. This solution can clearly quantify the negative impact of power plants on the environment during operation by building pollution cost models and abandoning new energy cost models. This precise cost calculation method allows virtual power plants to consider environmental costs during the optimization process, thereby promoting more green and low-carbon power production methods.

[0056] By introducing pollution costs, the model can encourage the reduction of pollutant emissions and reduce negative impacts on the environment. The introduction of environmental pollution penalty coefficients, pollution values, and fines can help provide economic incentives for pollutant emissions and encourage power plants to adopt cleaner power generation methods. In addition, the quantification of the cost of abandoned wind power and photovoltaic power (abandoned new energy) can prompt the system to dispatch and utilize new energy more efficiently during operation, avoiding the waste of new energy caused by system mismatch.

[0057] The problem of power abandonment (especially wind and solar abandonment) is very prominent in the context of a large number of new energy sources connected to the grid. By introducing the cost of abandoning new energy sources, economic constraints can be imposed on wind and solar abandonment, prompting the power system to better dispatch when there is excess wind and photovoltaic power generation, and to utilize these excess energies through energy storage systems or other means to reduce energy waste. This not only improves the absorption capacity of new energy, but also optimizes the efficiency of energy use.

[0058] This plan promotes the power system to pay more attention to environmental protection and green development and promotes the optimization of sustainable energy structure through quantitative treatment of pollutant emissions, waste of new energy and other issues. This is consistent with the current global policy direction of promoting green and low-carbon transformation, which helps to achieve higher environmental protection standards and energy utilization efficiency, and meets the needs of modern society for clean and low-carbon energy.

[0059] The optimization goal of this solution is not only to pursue low-cost operation, but also to combine environmental protection factors. By accurately quantifying the cost of environmental pollution and the cost of abandoning new energy, it is possible to ensure the economic efficiency of the virtual power plant (i.e. reduce the overall operating cost) while also improving environmental benefits. This can minimize the economic losses caused by environmental pollution and abandonment of new energy, and improve the overall economic benefits of the system.

[0060] When the volatility and uncertainty of renewable energy generation (such as wind power and photovoltaic power) are large, the introduction of environmental costs can enable virtual power plants to respond to the fluctuations of renewable energy more flexibly during the scheduling process. For example, when photovoltaic or wind power generation reaches its upper limit, the system will give priority to storing or scheduling excess electricity, thereby reducing pollution to the environment and avoiding waste of renewable energy. This flexible scheduling helps optimize the operating strategy of generator sets and improve system stability.

[0061] In C qdThe penalty unit price introduced in w ,p pv ) makes the cost of abandoning wind power and photovoltaic power generation clear. This mechanism effectively avoids the phenomenon of power abandonment due to unreasonable scheduling, and at the same time promotes the system to prioritize renewable energy. Through this economic incentive mechanism, the system can not only improve the utilization rate of renewable energy, but also further reduce fossil energy consumption and carbon emissions.

[0062] As a preferred technical means: In step 4), the demand response cost C DR Including opportunity cost C P and the interruptible load compensation cost C I , the corresponding expression is:

[0063]

[0064] Where D t ,p t denote incentive load and compensation price respectively; They are the total demand response load and price; They are interruptible load and price.

[0065] Demand response (DR) is one of the important means to optimize the power system. By adjusting the load during peak hours, it can effectively relieve the pressure on the power system and improve the reliability and stability of the system. The solution introduces the cost of demand response, including opportunity cost and interruptible load compensation cost, so that the economic impact of demand response is clearly quantified. This can provide more accurate cost forecasting and optimization decisions for the operation of virtual power plants.

[0066] By quantifying the opportunity cost and the interruptible load compensation cost, the power system can fine-tune the dispatch of demand response. The opportunity cost takes into account the electricity substitution benefit brought by demand response, while the interruptible load compensation cost reflects the compensation fee that needs to be paid due to the interrupted load. By considering these two costs together, the system can more scientifically balance the relationship between demand response and other factors such as power generation and energy storage, thereby achieving the optimal configuration of system resources and economic operation.

[0067] The demand response cost model includes the design of compensation prices, which can effectively motivate users to participate in the demand response process. Through reasonable price incentives, users are willing to adjust or reduce their electricity load during peak hours, thereby effectively balancing the load of the power grid and improving the stability of the system. This economic incentive mechanism can better guide consumers' participation in the power system and improve the efficiency of demand response.

[0068] Demand response can balance the supply and demand of the power grid by adjusting the load. Especially during peak hours or when new energy sources fluctuate greatly, demand response can effectively supplement power supply and reduce system pressure. Introducing the consideration of opportunity cost and compensation cost can help achieve supply and demand balance while controlling the additional costs brought about by the demand response process, thus improving the overall economy and reliability of the system.

[0069] This solution can adjust the load more accurately by subdividing interruptible loads and demand response loads. Interruptible loads are loads that can be temporarily interrupted but do not affect critical demand (such as industrial loads), while demand response loads are loads that need to participate in scheduling to avoid system overload. Differentiating these two types of loads can make load regulation more flexible and accurate, and improve the adaptability of demand response.

[0070] By optimizing demand response scheduling and reasonably allocating opportunity costs and interruptible load compensation costs, the overall operating cost of the power system can be effectively reduced. For example, during periods of high electricity prices, the cost of purchasing electricity during peak hours can be reduced by adjusting the load and reducing the peak-to-valley difference; during periods of excess renewable energy generation, demand response can be used to promote flexible load adjustment, thereby avoiding power abandonment and waste of resources, further reducing system costs.

[0071] The optimization of demand response is not only to reduce economic costs, but also to promote the efficient use of clean energy to a certain extent. For example, by adjusting the load to match the power generation of new energy, the consumption of fossil energy can be reduced, which helps to reduce carbon emissions. In addition, demand response helps to improve the adaptability of the power grid to renewable energy and further promote the development of green and low-carbon power systems.

[0072] This solution combines demand response with optimization of other links such as generators and energy storage systems to promote intelligent dispatching of virtual power plants in complex power systems. The power system can not only be flexibly dispatched based on real-time electricity prices, load conditions and demand response prices, but also dynamically adjust and optimize the configuration of various resources to improve the intelligence level of virtual power plants.

[0073] As a preferred technical means: in step 5), the distributed energy characteristics of various virtual power plants are used to construct a total cost objective function, and the total cost objective function is used as the optimization target:

[0074]

[0075] Among them, X1, X2, and X3 represent the weight coefficients of the generator set, energy storage system, and demand response, respectively. Q G They are the price and amount of electricity that the virtual power plant purchases from the power grid.

[0076] This solution incorporates various cost elements of virtual power plants (such as generator set costs, energy storage costs, demand response costs, environmental costs, etc.) into the total cost objective function, thereby ensuring that factors such as power production, energy storage, demand response, and environmental protection are fully considered during optimization. This global optimization strategy can ensure that virtual power plants can meet power demand while minimizing overall operating costs.

[0077] By introducing weight coefficients (X1, X2, X3), the weights of various costs in the objective function can be flexibly adjusted according to actual needs. This setting enables the virtual power plant to optimize the costs of generator sets, energy storage systems, and demand response to varying degrees according to specific economic, environmental or policy goals. For example, when environmental protection requirements are high, the weight of environmental costs can be increased, and priority can be given to reducing pollution emissions; when economic benefits are prioritized, the operating efficiency of energy storage and generator sets can be optimized more.

[0078] The total cost objective function in this scheme also includes the power purchase cost of the power grid That is, the product of the amount of electricity that the virtual power plant needs to purchase from the grid and the electricity price. This design allows the virtual power plant to not only consider its own power generation, energy storage and other aspects during the optimization process, but also dynamically adjust its power purchase behavior according to the fluctuation of the grid's electricity price, thereby effectively reducing the cost of purchasing electricity. Through this mechanism, the virtual power plant can intelligently determine when to purchase electricity from the grid and when to use its own power generation or energy storage, further improving its economic efficiency.

[0079] By comprehensively considering multiple cost factors and taking them as optimization targets, virtual power plants can minimize costs while ensuring power supply through reasonable scheduling, optimized operation strategies, etc. For example, optimizing the start and stop of generator sets, reasonably scheduling the charging and discharging timing of energy storage systems, coordinating demand response and system load, and avoiding the abandonment of new energy, thereby reducing total costs.

[0080] The optimization objective function not only focuses on economic benefits (such as the cost of power generation, energy storage, and electricity purchase), but also takes into account environmental benefits (such as reducing pollution and the cost of abandoning new energy). By incorporating environmental costs and the cost of abandoning new energy into the objective function, virtual power plants can reduce pollution emissions, avoid wasting renewable energy, and reduce environmental-related punitive costs during the optimization process. This green development strategy is in line with the direction of future energy transformation and helps virtual power plants achieve the dual goals of environmental protection and economic benefits.

[0081] The objective function structure of this technical solution is suitable for combination with intelligent optimization algorithms. Through the algorithm, various cost weights and optimization strategies are continuously adjusted to improve the dispatch flexibility of virtual power plants. In different periods, different electricity prices, and different grid load conditions, virtual power plants can flexibly adjust their operating modes to ensure optimal economic and environmental benefits.

[0082] In the optimization process, virtual power plants not only pursue the lowest cost, but also improve the stability and reliability of the power grid by coordinating the role of multiple resources such as power generation, energy storage, and demand response. For example, by intelligently scheduling the charging and discharging of energy storage systems, it is possible to balance demand during peak load periods, reduce the burden on the power grid, and improve system stability.

[0083] As the proportion of renewable energy continues to increase, electricity price fluctuations in the power grid and power market may become more frequent and drastic. This optimization model introduces the power purchase cost of the power grid and the cost of renewable energy abandonment, allowing the virtual power plant to dynamically adjust its operating strategy according to market price changes and energy supply conditions. This flexibility enables the virtual power plant to maximize its economic benefits in the face of uncertainty in the energy market.

[0084] As a preferred technical means: photovoltaic units are subject to upper and lower limits of output: in They are the upper limit of the output of the photovoltaic unit, the output and the upper limit of the output;

[0085] The power generation capacity of a wind turbine depends on the size of the wind and the area receiving the wind, and is subject to the upper and lower limits of the output: in They are the wind turbine output upper limit, output and output upper limit;

[0086] During the operation of the steam turbine unit, it is subject to climbing constraints, output constraints and start-stop constraints:

[0087]

[0088] in: It is a Boolean variable of the steam turbine status, 0 means stop; 1 means start; is the lower limit of turbine power generation, is the power generation, is the upper limit of power generation; and are the down-ramp rate and up-ramp rate of the steam turbine unit;

[0089] The energy storage system constrains the storage capacity and charges and discharges equally within a 24-hour cycle:

[0090] SOC min ≤SOC t ≤SOC max

[0091] SOC1=SOC 24

[0092]

[0093] Among them, SOC min ,SOC max are the upper and lower limits of energy storage respectively; is the charge and discharge amount; SOC1 is the energy storage capacity in the first hour, SOC 24 is the energy storage capacity at the 24th hour;

[0094] The overall balance of the virtual power plant is constrained by:

[0095]

[0096] Electricity prices will have a positive and negative impact on user electricity demand. The demand response cost C DR The opportunity cost of C P and the interruptible load compensation cost C I Constraints of composition: Where: η is the interruptibility compliance rate of the virtual power plant; it means that the reduction in interruptibility compliance must exceed the maximum allowed value;

[0097] According to various constraints and combined with the optimization goal, the optimization model is obtained.

[0098]

[0099] Where U is the entire list of uncertain variables, X is the entire list of decision variables; l and q are the number of uncertain constraints and the number of uncertain variables respectively.

[0100] Photovoltaic units and wind turbines are subject to upper and lower output limits, ensuring that each unit does not exceed its capacity in actual operation. This not only avoids waste of resources, but also enables virtual power plants to be flexibly dispatched according to actual weather conditions and output levels.

[0101] Constraints are set for the climbing, output, start and stop of the steam turbine unit (such as the climbing rate, the descending rate, etc.). These constraints can ensure that the steam turbine unit can meet the power demand while ensuring a smooth transition during the startup and shutdown process, and avoid grid fluctuations caused by sudden startup or shutdown.

[0102] The energy storage system needs to meet the requirement of equal charge and discharge and operate within the specified capacity range. Such a design can ensure that the energy storage system can effectively adjust to changes in grid load within 24 hours, while avoiding exceeding the maximum charge and discharge capacity of the energy storage equipment, thereby ensuring the safety and stability of the battery.

[0103] Through the virtual power plant balance constraint, the supply and demand balance between all generators, energy storage systems and purchased electricity is ensured, avoiding system instability or grid security risks caused by excess or insufficient energy.

[0104] This plan refines the demand response mechanism, takes into account the compensation and opportunity cost of interruptible loads, ensures that flexible loads at the user end can be effectively mobilized during peak electricity consumption or when the power grid is tight, and provides reasonable compensation measures to optimize the cost performance of virtual power plants under different load demands.

[0105] This optimization model can adapt to the impact of changes in electricity prices and flexibly adjust demand response behavior. It gives priority to the use of grid electricity when electricity prices are low, and increases the proportion of self-generation or dispatches energy storage systems for self-sufficiency when electricity prices are high, thereby maximizing cost-effectiveness.

[0106] By introducing multi-level constraints (such as photovoltaic, wind power, steam turbines, energy storage, power grid purchase, etc.) and incorporating them into a unified optimization objective function, the operation of the virtual power plant can achieve global optimization under multiple constraints. This method can avoid local optimization under the constraints of a single resource and ensure the economy and safety of the entire system.

[0107] This scheme takes into account the impact of uncertain variables (such as weather changes, load fluctuations, etc.), and ensures that the optimization model can maintain a certain degree of robustness in the face of uncertain factors through uncertain constraints, thereby ensuring the stable operation of the virtual power plant.

[0108] This solution allows the virtual power plant to adjust its operation strategy according to actual conditions by flexibly setting different constraints (such as interruptible load rate η, demand response price, generator status, etc.). This flexibility enables the virtual power plant to respond quickly and optimize its operation according to external factors such as grid demand and market prices in an uncertain environment.

[0109] Through dynamic optimization based on real-time data, virtual power plants can automatically adjust power generation, energy storage and power purchase strategies according to external changes, avoiding the system's over-reliance on preset static models. This not only improves the operating efficiency of virtual power plants, but also enhances their ability to adapt to market changes and uncertainties.

[0110] This technical solution takes environmental protection goals into account during the optimization process by setting environmental pollution costs (such as CO2 emissions, wind and solar power abandonment and other punitive costs). In the process of optimizing scheduling, the virtual power plant will consider reducing pollution emissions and avoiding the waste of new energy, thereby achieving dual optimization of environmental and economic benefits.

[0111] By considering the punitive costs of wind and solar power curtailment in the optimization model, virtual power plants can reduce the waste of new energy, make full use of the power generation of renewable energy, reduce energy curtailment, and reduce the economic losses caused by the waste of new energy.

[0112] Through the dynamic dispatch of each generator set and energy storage system and the energy balance constraint of the virtual power plant, the grid load can be effectively balanced, grid overload or frequent fluctuations can be avoided, and the stability and reliability of the grid can be improved. Especially in the face of peak demand or grid tension, the virtual power plant can maintain the balance and safety of the grid through flexible dispatch and demand response mechanisms.

[0113] This technical solution can adapt to the complex and dynamic environment of future smart grids by integrating the dispatch optimization of diversified resources such as distributed energy, energy storage, and demand response. This highly integrated and optimized model is suitable for more complex energy networks and smart grid systems in the future, and can effectively promote the green, low-carbon, and sustainable development of energy.

[0114] As a preferred technical means: in step 6), an off-site quantum evolutionary algorithm is used to optimize the low-cost virtual power plant optimization model, specifically including:

[0115] 6.1) Convert each variable in the variable array into N-bit binary;

[0116] 6.2) Each variable parameter generates a population of M quantum individuals, all of which have a length of N and are all equilibrium quantum gates, that is, the initial state of all quantum gates is θ = 45°;

[0117] 6.3) Perform translocation processing on each quantum individual of the population corresponding to all variables: perform translocation on a random quantum gate of each individual in the population, that is, exchange it with the corresponding quantum gate of another random individual;

[0118] 6.4) Generate a sample using each new quantum individual: Each quantum gate randomly generates a θ. If θ is greater than the quantum gate, it is edited to 1, and then the binary individual is compiled into a decimal individual;

[0119] 6.5) Substitute the obtained decimal array into the optimization model, screen out the optimal individual, and mark the corresponding quantum individual;

[0120] 6.6) Take the optimal quantum individual in 6.5) as the temporary optimal solution, and let the other quantum individuals in the population rotate Δθ toward the temporary optimal solution;

[0121] 6.7) The first iteration is now complete, and we return to 6.3) for the next iteration;

[0122] 6.8) Continue iterating until the required number of iterations is met or the optimization goal is reached, and finally the low-cost virtual power plant optimization solution is obtained.

[0123] The quantum evolutionary algorithm can explore multiple solution spaces in parallel through the characteristics of quantum superposition states, avoiding the problem that traditional optimization algorithms may fall into the local optimal solution. This enables the algorithm to effectively find the global optimal solution in complex virtual power plant optimization models, thereby ensuring cost minimization and maximization of system performance. The introduction of out-of-situ operations further improves the diversity of the search. By randomly exchanging quantum gates in the population, the variability of the solution is increased, avoiding falling into the local optimal solution during the search process, and increasing the depth and breadth of the global search.

[0124] Through the random transformation and exchange of quantum individuals and quantum gates, the heterotopic quantum evolutionary algorithm can accelerate the convergence process and is more efficient than traditional optimization methods such as genetic algorithms and particle swarm algorithms. Especially when dealing with high-dimensional optimization problems, the quantum evolutionary algorithm can find the optimal solution more quickly and reduce the calculation time. Compared with traditional evolutionary algorithms, the quantum evolutionary algorithm can explore multiple solutions simultaneously in each iteration in a quantum parallel computing manner, thereby significantly improving the efficiency of optimization.

[0125] The heterositu quantum evolutionary algorithm can not only efficiently calculate the optimization of virtual power plants under various constraints, but also has strong adaptability. In cases of high uncertainty or rapid environmental changes (such as fluctuations in new energy sources, changes in demand response, etc.), the algorithm can quickly adjust and find new optimal solutions to ensure the stable operation of virtual power plants in dynamic environments. The algorithm can handle large-scale, complex and high-dimensional optimization problems, so that the optimization model of the virtual power plant can still find the most appropriate solution in the face of multiple resource scheduling and constraints.

[0126] Quantum superposition and quantum entanglement: Quantum computing can process multiple solution spaces in parallel through the superposition state of quantum bits, which can effectively improve computing efficiency and accuracy and reduce computing costs, especially when dealing with large-scale, multi-constrained optimization problems. The quantum evolutionary algorithm can search in multiple solution spaces simultaneously through quantum gate operations, which not only reduces the number of calculation steps, but also improves the accuracy of the solution and global search capabilities.

[0127] The quantum rotation operation can accurately adjust the convergence of quantum individuals in the population to the optimal solution, thereby improving the accuracy of the optimization results. By continuously adjusting the rotation angle of the quantum individuals, the algorithm can accurately guide the convergence process of the solution, thereby effectively avoiding premature convergence and ensuring that the optimal solution is found. In each iteration, by adjusting the quantum individuals, the search direction and the convergence speed during the optimization process can be accurately controlled, so that the optimization results achieve the expected effect.

[0128] In each iteration, the algorithm can dynamically adjust the status of other individuals in the population according to the results of the previous round (i.e., the optimal quantum individual) to ensure that the optimization direction is always close to the optimal solution. Especially when faced with dynamic environments such as changes in electricity prices and energy fluctuations, the ex situ quantum evolution algorithm can flexibly adapt and quickly adjust the optimization strategy. This optimization method has strong flexibility and can adjust the parameters of the quantum gate according to actual needs to adapt to different optimization goals and constraints, thereby ensuring that the optimal solution is always obtained in a changing power grid and energy scheduling environment.

[0129] Virtual power plant optimization involves a large number of variables and complex constraints. Traditional optimization methods are less efficient under high dimensions and complex constraints. The out-of-situ operation and quantum gate mechanism of quantum evolutionary algorithm can efficiently handle multi-dimensional optimization problems and quickly find the optimal solution, which is particularly suitable for the optimization of such complex distributed energy systems.

[0130] Quantum computing can essentially achieve more efficient solution search based on large-scale parallel computing. For virtual power plant optimization problems that require processing large amounts of data and complex calculations, the heterositu quantum evolutionary algorithm can effectively reduce the consumption of computing resources and improve optimization efficiency and computing speed.

[0131] Through the optimization of the ex situ quantum evolutionary algorithm, the virtual power plant can more accurately dispatch various types of generators, energy storage systems, and demand response mechanisms to reduce overall operating costs. The algorithm can ensure the efficient use of energy and avoid energy waste (such as abandonment of wind and solar power), thereby improving overall economic benefits. At the same time, the operation of the system can be adjusted in real time according to environmental conditions, market electricity prices and other factors, thereby optimizing the cost structure of the virtual power plant, maximizing energy utilization, and reducing operating costs.

[0132] Beneficial effects: This technical solution takes into account the diversity of distributed energy and balances the stability of the system. According to the characteristics of power generation, energy storage, environment and demand response costs, it uses excellent heterogeneous quantum algorithms to optimize system costs and effectively reduce the cost of distributed energy virtual power plants. Compared with other algorithms, heterogeneous quantum evolutionary algorithms have better robustness, more stable performance in multi-objective or multi-variable systems, and more wide-area convergence, so they can be better applied to virtual power plant systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] Figure 1 It is a flow chart of the present invention.

[0134] Figure 2 It is the flow chart of the heterositu quantum optimization of the present invention. DETAILED DESCRIPTION

[0135] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings.

[0136] Embodiment 1:

[0137] like Figure 1 As shown, the present invention comprises the following steps:

[0138] S1: According to the distributed energy characteristics of photovoltaic units, wind turbine units and steam turbine units, a cost model of the power generation entity of the virtual power plant is established;

[0139] The power generation of a photovoltaic unit depends on the intensity of sunlight and the ambient temperature. Its power generation is:

[0140]

[0141] in, is the photovoltaic power generation, β is the conversion rate, S is the effective illumination area, I is the illumination intensity; T t is the ambient temperature;

[0142] The power generation of a wind turbine depends on the size of the wind and the area receiving the wind. The power generation is:

[0143]

[0144] in is the wind power generation power, Q rated is the rated power generation, ω1, ω2, ω3 are transmission efficiency, power factor and power generation efficiency respectively, v t is the wind speed, V in ,V reted ,V out are the cut-in, rated and cut-out wind speeds, ρ is the air density, and d is the blade diameter;

[0145] The power generation capacity of the steam turbine unit is determined by the number of starting units; the power generation capacity is expressed as

[0146] The main cost of virtual power plant power generation is C MT It means that it includes the fuel cost C f and startup costs C qt ;

[0147] C MT =C f +C qt

[0148]

[0149] in, is a Boolean variable for the status of the steam turbine; is the power generation capacity; a, b, c are the power generation cost coefficients; SU and SD are the startup and shutdown costs.

[0150] This technical solution provides an efficient, low-cost and environmentally friendly virtual power plant optimization method through accurate power generation model, multi-dimensional cost optimization and flexible scheduling mechanism, which adapts to the complexity and dynamics of modern distributed energy systems.

[0151] S2: Establishing an operating cost model for the energy storage block of the virtual power plant based on the charging and discharging characteristics of the energy storage system;

[0152] The energy storage system has bidirectional charging and discharging. The cost expression of the energy storage system in the virtual power plant is:

[0153]

[0154] Among them, C ESS is the energy storage system cost, λ is the energy storage system operation cost coefficient, are the charging and discharging power of the energy storage system respectively.

[0155] By finely modeling the bidirectionality of charging and discharging of energy storage systems, this technical solution can optimize the operation of energy storage systems and improve the economy, flexibility and stability of virtual power plants. It has significant advantages in balancing supply and demand, reducing costs, increasing the utilization rate of new energy and reducing power abandonment, and provides effective technical support for building a more efficient and environmentally friendly power system.

[0156] S3: Comprehensively consider the environmental pollution cost caused by the operation of the generator set and the cost of abandoning new energy, and build an environmental cost model;

[0157] The cost of environmental pollution caused by the operation of the generator set is C pf and abandoned new energy cost C qd , combined to build an environmental cost model:

[0158]

[0159] Where K is the type of pollutant, m k is the environmental pollution penalty coefficient, V k ,Y k is the pollution value and the amount of fine; p w ,p pv They are the punitive unit prices for abandoning the use of wind power and photovoltaic power; and They are the upper limits of wind power and photovoltaic output respectively.

[0160] By introducing pollution costs and the cost of abandoning new energy, this technical solution can effectively promote the implementation of environmental protection policies, reduce environmental pollution, improve the absorption capacity of new energy, and optimize the operation and dispatching strategy of the power system. Through economic incentive mechanisms, more green and low-carbon energy production and use are encouraged, and virtual power plants are promoted to develop in a more efficient and sustainable direction, which meets the needs of the current green transformation of the power system.

[0161] S4: Establish a demand response cost model based on the real-time dynamic changes of electricity prices on user electricity demand;

[0162] Demand response cost C DR Including opportunity cost C P and the interruptible load compensation cost C I , the corresponding expression is:

[0163]

[0164] Where D t ,p t denote incentive load and compensation price respectively; They are the total demand response load and price; They are interruptible load and price.

[0165] By introducing the demand response cost model, this technical solution can effectively integrate demand response measures in the virtual power plant optimization process, accurately adjust the system load, optimize the allocation of power resources, reduce system operating costs, and improve the reliability and economy of the power system. By considering compensation prices and opportunity costs, the solution can encourage more users to participate in demand response, balance supply and demand, promote the development of green and low-carbon power systems, and enhance the intelligence and flexibility of virtual power plants.

[0166] S5: Based on various cost factors, a total cost objective function of the virtual power plant is constructed, and the total cost objective function is used as the optimization target to establish an optimization model of the low-cost virtual power plant;

[0167] By utilizing the distributed energy characteristics of various virtual power plants, a total cost objective function is constructed and the total cost objective function is used as the optimization target:

[0168]

[0169] Where X1, X2, and X3 represent the weight coefficients of the generator set, energy storage system, and demand response, respectively. Q G They are the price and amount of electricity that the virtual power plant purchases from the power grid.

[0170] Photovoltaic units are subject to upper and lower output limits: in They are the upper limit of the output of the photovoltaic unit, the output and the upper limit of the output;

[0171] The power generation capacity of a wind turbine depends on the size of the wind and the area receiving the wind, and is subject to the upper and lower limits of the output: in They are the wind turbine output upper limit, output and output upper limit;

[0172] During the operation of the steam turbine unit, it is subject to climbing constraints, output constraints and start-stop constraints:

[0173]

[0174] in: It is a Boolean variable of the steam turbine status, 0 means stop; 1 means start; is the lower limit of turbine power generation, is the power generation, is the upper limit of power generation; and are the down-ramp rate and up-ramp rate of the steam turbine unit;

[0175] The energy storage system constrains the storage capacity and charges and discharges equally within a 24-hour cycle:

[0176] SOC min ≤SOC t ≤SOC max

[0177] SOC1=SOC 24

[0178]

[0179] Among them, SOC min ,SOC max are the upper and lower limits of energy storage respectively; is the charge and discharge amount; SOC1 is the energy storage capacity in the first hour, SOC 24 is the energy storage capacity at the 24th hour;

[0180] The overall balance of the virtual power plant is constrained by:

[0181]

[0182] Electricity prices will have a positive and negative impact on user electricity demand. The demand response cost C DR The opportunity cost of C P and the interruptible load compensation cost C I Constraints of composition: Where: η is the interruptibility compliance rate of the virtual power plant; it means that the reduction in interruptibility compliance must exceed the maximum allowed value;

[0183] According to various constraints and combined with the optimization goal, the optimization model is obtained.

[0184]

[0185] Where U is the entire list of uncertain variables, X is the entire list of decision variables; l and q are the number of uncertain constraints and the number of uncertain variables respectively.

[0186] By integrating multiple cost factors such as generator sets, energy storage systems, demand response, and power purchase from the power grid into the total cost objective function and minimizing them through optimization algorithms, this technical solution can achieve triple optimization of the economic efficiency, environmental benefits, and system stability of virtual power plants. This comprehensive optimization strategy helps improve the operating efficiency of virtual power plants, reduce overall costs, promote better use of renewable energy, and further enhance the flexibility and reliability of virtual power plants in smart grids.

[0187] By comprehensively considering multiple constraints, a comprehensive optimization model was constructed to optimize the operation of the virtual power plant while meeting system stability, environmental protection requirements and economic benefits. The advantages of the solution include comprehensive consideration of costs, constraints and resource utilization, while being able to flexibly adapt to uncertainties and dynamically adjust operating strategies to achieve efficient, environmentally friendly and sustainable power production.

[0188] S6: Using the ex situ quantum evolutionary algorithm to optimize the low-cost virtual power plant optimization model; the optimization process includes initialization of quantum individuals, ex situ operations, quantum gate updates and global search;

[0189] The low-cost virtual power plant optimization model is optimized using an off-site quantum evolutionary algorithm. Figure 2 As shown, it specifically includes the steps of:

[0190] S6.1: Convert each variable in the variable array into 16-bit binary;

[0191] S6.2: Each variable parameter generates a population of 1000 quantum individuals, all of which have a length of 16 and are all balanced quantum gates, that is, the initial state of all quantum gates is θ = 45°;

[0192] S6.3: Perform out-of-place processing on each quantum individual of the corresponding population of all variables: randomly perform out-of-place processing on a quantum gate of each individual in the population, that is, generate a random number from 1 to 16, use it as out-of-place, and exchange it with the corresponding quantum gate of another random individual;

[0193] S6.4: Generate a sample using each new quantum individual: each quantum gate randomly generates a random number θ between 0 and 90. If θ is greater than the quantum gate, it is edited to 1, and then the binary individual is compiled into a decimal individual;

[0194] S6.5: Substitute the obtained decimal array into the optimization model, screen out the optimal individual, and mark the corresponding quantum individual;

[0195] S6.6: Take the optimal quantum individual in S6.5 as the temporary optimal solution, and let the other quantum individuals in the population rotate Δθ towards the temporary optimal solution, Δθ = 0.1°;

[0196] S6.7: The first iteration is now completed, and the process returns to S6.3 for the next iteration;

[0197] S6.8: Continue iterating until the required number of iterations is met or the optimization target is reached, and finally a low-cost virtual power plant optimization solution is obtained. In this embodiment, the number of iterations is 10,000.

[0198] This technical solution introduces the ex situ quantum evolutionary algorithm to optimize the virtual power plant optimization model efficiently and accurately, with the following advantages:

[0199] Efficient global search capability avoids local optimal solutions and ensures that the optimal solution is found.

[0200] The optimization efficiency is greatly improved, especially when dealing with high-dimensional complex constraint problems, which can accelerate the convergence process.

[0201] Strong adaptability and robustness, able to cope with environmental changes and uncertainties.

[0202] The parallel computing advantage of quantum computing can improve the computing efficiency of large-scale problems.

[0203] Precise control and flexibility to adapt to dynamically changing power demand and resource scheduling needs.

[0204] S7: Based on the optimization results, provide an optimized distributed energy ex-situ quantum optimized low-cost virtual power plant system, and dispatch various types of generators and energy storage systems according to the optimization plan to achieve low-cost operation.

[0205] Low-cost operation: Through precise optimization, the operating cost of virtual power plants can be minimized. Improve energy utilization, reduce wind and solar power abandonment, and increase the utilization rate of renewable energy. Enhance system stability and reliability to ensure the balance and stable operation of the power grid. Adapt to dynamic changes and respond quickly to electricity price fluctuations and market demand. Reduce environmental pollution and carbon emissions, and promote green and low-carbon development. Global optimal solution: avoid local optimality through ex-situ quantum optimization to ensure system optimization effect. Improve computing efficiency, reduce the computational complexity of traditional algorithms, and improve overall optimization efficiency.

[0206] This embodiment combines the characteristics of multiple advanced optimization technologies and new energy systems, fully considers the dynamic demand of the electricity market, environmental protection and resource optimization, has significant economic and environmental benefits, promotes the application of virtual power plant technology in the direction of low cost, high efficiency and diversification, and provides strong support for the realization of future smart grids and sustainable energy.

[0207] Embodiment 2:

[0208] This embodiment is basically the same as the first embodiment, but adds more distributed energy, including biomass power generation and tidal power generation. At the same time, the parameters of the ex situ quantum optimization are modified, including taking 1000 samples, ex situ probability 0.1, quantum gate update step 0.01°, and using 100,000 iterations as the termination optimization condition. The process of this embodiment is the same as that of the first embodiment, and will not be repeated here.

[0209] The above-mentioned distributed energy virtual power plant optimization method based on ex-situ quantum evolution is a specific embodiment of the present invention, which has embodied the substantial characteristics and progress of the present invention. According to actual use needs and under the guidance of the present invention, equivalent modifications in shape, structure, etc. can be made to the method, which are all within the protection scope of this scheme.

Claims

1. A distributed energy virtual power plant optimization method based on heterogeneous quantum evolution, characterized by The following steps are involved: 1) Based on the distributed energy characteristics of photovoltaic units, wind turbines, and steam turbines, a cost model for the power generation entities of virtual power plants is established; 2) Establish an operating cost model for the energy storage block of the virtual power plant based on the charging and discharging characteristics of the energy storage system; 3) Comprehensively consider the environmental pollution costs caused by the operation of the generator set and the cost of abandoning new energy, and build an environmental cost model; 4) Establish a demand response cost model based on the real-time dynamic changes in electricity prices to user electricity demand; 5) Based on various cost factors, the total cost objective function of the virtual power plant is constructed, and the total cost objective function is used as the optimization target to establish an optimization model for the low-cost virtual power plant; 6) Using an ex-situ quantum evolutionary algorithm to optimize the low-cost virtual power plant optimization model; the optimization process includes quantum individual initialization, ex-situ operation, quantum gate update and global search; 7) Based on the optimization results, an optimized distributed energy heterogeneous quantum optimized low-cost virtual power plant system is provided, and various types of generators and energy storage systems are dispatched according to the optimization plan to achieve low-cost operation.

2. The distributed energy virtual power plant optimization method based on ex situ quantum evolution according to claim 1 is characterized by: The power generation capacity of a photovoltaic unit depends on the intensity of sunlight and the ambient temperature. Its power generation capacity is: in, is the photovoltaic power generation power, β is the conversion rate, S is the effective illumination area, I is the illumination intensity; T t is the ambient temperature; The power generation capacity of a wind turbine depends on the size of the wind and the area exposed to the wind. The power generation capacity is: in is the wind power generation power, Q rated is the rated power generation, ω1, ω2, ω3 are transmission efficiency, power coefficient and power generation efficiency respectively, v t is the wind speed, V in , V rated , V out are the cut-in, rated and cut-out wind speeds, ρ is the air density, and d is the blade diameter; The power generation capacity of the steam turbine unit is determined by the number of starting units; the power generation capacity is expressed as The cost of virtual power plant power generation is expressed in C MT Indicates that it includes fuel cost C f and startup costs C qt ; C MT =C f +C qt in, is a Boolean variable for the status of the steam turbine; is the generated power; a, b, c are the power generation cost coefficients; SU and SD are the startup and shutdown costs.

3. The distributed energy virtual power plant optimization method based on ex situ quantum evolution according to claim 2 is characterized by: In step 2), the energy storage system has bidirectional charging and discharging. The cost expression of the energy storage system in the virtual power plant is: Among them, C ESS is the energy storage system cost, λ is the energy storage system operation cost coefficient, are the charging and discharging power of the energy storage system respectively.

4. The distributed energy virtual power plant optimization method based on ex situ quantum evolution according to claim 3 is characterized by: In step 3), the cost of environmental pollution caused by the generator set during operation is C pf and abandoned new energy cost C qd , combined to build an environmental cost model: Where K is the type of pollutant, m k is the environmental pollution penalty coefficient, V k , Y k is the pollution value and the amount of fine; p w , p pv They are the punitive unit prices for abandoning the use of wind power and photovoltaic power; and They are the upper limits of wind power and photovoltaic output respectively.

5. The distributed energy virtual power plant optimization method based on heterositu quantum evolution according to claim 4 is characterized by: In step 4), the demand response cost C DR Including opportunity cost C P and the interruptible load compensation cost C I , the corresponding expression is: Among them D t , p t denote the incentive load and compensation price respectively; are the total demand response load and price, respectively; They are interruptible load and price.

6. The distributed energy virtual power plant optimization method based on ex situ quantum evolution according to claim 5 is characterized by: In step 5), the distributed energy characteristics of various virtual power plants are used to construct a total cost objective function, and the total cost objective function is used as the optimization target: Where X1, X2, and X3 represent the weight coefficients of the generator set, energy storage system, and demand response, respectively. Q G They are the price and amount of electricity purchased by the virtual power plant from the power grid.

7. The distributed energy virtual power plant optimization method based on ex situ quantum evolution according to claim 6 is characterized by: Photovoltaic units are subject to upper and lower output limits: in They are the upper limit of PV unit output, output and output limit; The power generation capacity of a wind turbine depends on the wind strength and the wind-receiving area, and is subject to upper and lower limits of output: in They are wind turbine output upper limit, output and output upper limit; During operation, the steam turbine unit is subject to ramp constraints, output constraints, and start-stop constraints: in: It is a Boolean variable of the steam turbine status, 0 means stop; 1 means start; is the lower limit of turbine power generation, is the power generation power, is the upper limit of power generation; and are the down-ramp rate and up-ramp rate of the turbine unit; The energy storage system constrains the storage capacity and charges and discharges equally within a 24-hour cycle: SOC min ≤SOC t ≤SOC max SOC1=SOC 24 Among them, SOC min ,SOC max are the upper and lower limits of energy storage, respectively; is the charge and discharge capacity; SOC1 is the energy storage capacity in the first hour, SOC 24 is the energy storage capacity at the 24th hour; The overall balance of the virtual power plant is constrained by: Electricity prices will have a positive and negative impact on user electricity demand. The demand response cost C DR The opportunity cost of C P and the interruptible load compensation cost C I Constraints of the composition: Where: η is the interruptibility compliance rate of the virtual power plant; it means that the reduction in interruptibility compliance must exceed the maximum allowed value; According to various constraints and combined with the optimization goal, the optimization model is obtained. min f(X,U) Where U is the entire list of uncertain variables, X is the entire list of decision variables; l and q are the number of uncertain constraints and the number of uncertain variables, respectively.

8. The distributed energy virtual power plant optimization method based on ex situ quantum evolution according to claim 7 is characterized by: In step 6), the low-cost virtual power plant optimization model is optimized using an ex situ quantum evolutionary algorithm, specifically including: 6.1) Convert each variable in the variable array into N-bit binary; 6.2) Each variable parameter generates a population of M quantum individuals, all of which have length N and are all equilibrium quantum gates, i.e., all quantum gates have an initial state of θ = 45°; 6.3) Perform translocation processing on each quantum individual in the corresponding population of all variables: translocate a random quantum gate of each individual in the population, that is, exchange it with the corresponding quantum gate of another random individual; 6.4) Generate a sample using each new quantum entity: Each quantum gate randomly generates a θ. If θ is greater than the quantum gate, it is edited to 1, and then the binary entity is compiled into a decimal entity; 6.5) Substitute the obtained decimal array into the optimization model, screen out the optimal individual, and mark the corresponding quantum individual; 6.6) Take the optimal quantum individual in 6.5) as the temporary optimal solution, and let all other quantum individuals in the population rotate Δθ towards the temporary optimal solution; 6.7) This completes the first iteration and returns to 6.3) for the next iteration; 6.8) Continue iterating until the required number of iterations is met or the optimization goal is achieved, and finally a low-cost virtual power plant optimization solution is obtained.

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