A virtual power plant wide-area aggregation optimization scheduling method

Abstract

The application relates to the technical field of power dispatch optimization, and discloses a virtual power plant wide-area aggregation optimization dispatching method. The method releases a day-ahead dispatching demand through a power grid virtual power plant dispatching platform, preliminarily screens and aggregates distributed resources related to aggregation nodes based on geographical area correlation after receiving the dispatching demand, calls an aggregation optimization service interface 1, further aggregates and optimizes according to a feedback preliminary screening resource list, generates an optimal distributed resource list, takes the dispatching demand aggregation node and the optimal distributed resource list as input, outputs a resource total amount and an effectiveness sequence list, carries out economic benefit evaluation, judges whether the optimal distributed resource list needs to be adjusted, uploads the optimal resource list participating in the dispatching to an upper power grid virtual power plant dispatching platform, waits for feedback results of safety checking and a dispatching out-clear plan, sends corresponding execution plans to each distributed resource response control unit according to the dispatching out-clear plan, and starts execution monitoring and checking.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power dispatch optimization, in particular to a virtual power plant wide-area aggregation optimization dispatch method. BACKGROUND

[0002] In recent years, the power source structure and network structure of the power industry in China have undergone major changes, the installed capacity of power has continued to expand, clean energy has developed rapidly, the construction of the auxiliary service market is facing new challenges, the complexity of system operation and management is continuously improving, the demand for auxiliary services has significantly increased, the existing auxiliary service varieties need to further adapt to the needs of system operation, and only through unilateral bearing of the entire system auxiliary service cost on the power generation side cannot bear the demand generated by the large-scale access of renewable energy to the system, the scale of cross-provincial and cross-regional trading electricity is increasing, and the inter-provincial auxiliary service market mechanism and cost sharing principles need to be improved. New industries such as new energy storage and electric vehicle charging networks also need market-oriented mechanisms to guide and promote development.

[0003] With renewable energy becoming the main direction of future global energy development, the purpose of the research on virtual power plants in China is mainly to provide a framework and technical support for the access of large-scale new energy power, and to realize the complementary and collaborative dispatching between traditional energy and new energy and the optimized operation of the power grid through the operation mechanism of the virtual power plant, so as to maximize the suppression of the strong random volatility of new energy power and improve the utilization rate of new energy.

[0004] The virtual power plant business has emerged in recent years, and the digital platform for domestic project application is mainly provided by foreign manufacturers. With the rapid development of the domestic market, the policies, regulations, practical paths, and landing steps of various places present a unified target, diverse development, and promoting situation, which is quite different from the foreign market. Therefore, the research and development of a self-controlled virtual power plant aggregation optimization dispatch platform that supports the development of the business market as the goal is the inevitable demand at the current stage and an important aid for future development. SUMMARY

[0005] (I) Technical problems solved

[0006] In view of the deficiencies of the prior art, the virtual power plant wide-area aggregation optimization scheduling method provided by the present application has the advantages that the power grid virtual power plant scheduling platform issues day-ahead scheduling requirements, the virtual power plant aggregation optimization platform preliminarily screens and aggregates nodes related distributed resources based on geographical area correlation after receiving the scheduling requirements, calls the aggregation optimization service interface 1, takes the scheduling requirement aggregation node and the preliminary screening distributed resource list as input, outputs the feedback preliminary screening resource list, the virtual power plant aggregation optimization platform further aggregates and optimizes according to the feedback preliminary screening resource list, considers the influence factors such as business contract, resource scale and historical execution qualified rate, mainly takes economic benefit and execution evaluation as the basis, generates an optimized distributed resource list including resource account number and adjustment plan curve, calls the aggregation optimization service interface 2, takes the scheduling requirement aggregation node and the optimized distributed resource list as input, outputs the total resource amount and effectiveness sequence list, evaluates the economic benefit according to the total resource amount and the optimized resource effectiveness sequence list, judges whether the optimized distributed resource list needs to be adjusted, the virtual power plant aggregation optimization platform uploads the optimized resource list participating in scheduling to the upper power grid virtual power plant scheduling platform, waits for the feedback results of safety checking and scheduling clearing plan, sends the corresponding execution plan to each distributed resource response control unit according to the scheduling clearing plan, and starts execution monitoring and checking, the distributed wide-area energy resource aggregation and optimization scheduling can be realized according to the above method steps, the reliability and energy saving of the power grid are improved, the sustainable energy development is contributed, the economic value of various scattered resources is realized, the economic benefit of the load aggregation trader is improved, and the above problems are solved.

[0007] (II) Technical solutions

[0008] To achieve the above object, the present application provides the following technical solutions: a virtual power plant wide-area aggregation optimization scheduling method, comprising the following steps:

[0009] S1, the power grid virtual power plant scheduling platform issues day-ahead scheduling requirements, including aggregation nodes, scheduling time period, scheduling type and scheduling requirement amount, and calculates load prediction Fhyc and renewable energy power generation capacity prediction Kzyc, which are used to evaluate the effectiveness and adjustable capacity of distributed resources;

[0010] S2, the virtual power plant aggregation optimization platform preliminarily screens and aggregates nodes related distributed resources based on geographical area correlation after receiving the scheduling requirements, calls the aggregation optimization service interface 1, takes the scheduling requirement aggregation node and the preliminary screening distributed resource list as input, outputs the feedback preliminary screening resource list, and calculates the resource adjustable power Ktgl and the resource historical adjustment qualified rate Zytl to ensure that the selected resources have the ability to execute scheduling;

[0011] S3, the virtual power plant aggregation optimization platform performs further aggregation optimization according to the feedback preliminary screening resource list, considers business contracts, resource scale, and historical execution qualification rate, etc. influencing factors, mainly generates an optimal distributed resource list including resource account number and adjustment plan curve, calls the aggregation optimization service interface 2, takes the scheduling demand aggregation node and the optimal distributed resource list as input, outputs the total resource amount and the effectiveness sequence list, and calculates the resource historical adjustment benefit Lssy and the response coefficient Xyxs;

[0012] S4, economic benefit evaluation is performed according to the total amount of effective resources and the optimal resource effectiveness sequence list, whether the optimal distributed resource list needs to be adjusted is judged, if so, step S3 is repeated until the optimal resource list participating in scheduling is determined, or whether to participate in the invitation is evaluated, if participating, the next step is entered, and the scheduling benefit Ddsy and the resource adjustment cost Ztcb are calculated;

[0013] S5, the virtual power plant aggregation optimization platform uploads the optimal resource list participating in scheduling to the upper-layer power grid virtual power plant scheduling platform, waits for the feedback result of safety check and the scheduling out-clear plan, and returns to repeat step S4 when the safety check fails, at this time, the safety check index Ajzb is calculated, which is used to identify that the scheduling scheme will not exceed the operation limit that the power grid can bear;

[0014] S6, according to the scheduling out-clear plan, the corresponding execution plan is sent to each distributed resource response control unit, and the execution monitoring check is started.

[0015] Preferably, the S1 power grid virtual power plant scheduling platform issues day-ahead scheduling demand, including aggregation node, scheduling time period, scheduling type and scheduling demand amount, and calculates load prediction Fhyc, the calculation formula is as follows:

[0016] Fhyc(t) = x + a1*T(t) + a2*D(t) + a3*H(t) + C(t)

[0017] In the formula, Fhyc(t) represents the load prediction at time point t, T(t) represents the temperature at time point t, D(t) represents the sunshine duration at time point t, H(t) represents the humidity at time point t, x represents a constant term, a1, a2 and a3 are regression coefficients of temperature, sunshine and humidity, and C(t) represents an error term.

[0018] Preferably, the S1 power grid virtual power plant scheduling platform issues day-ahead scheduling demand, including aggregation node, scheduling time period, scheduling type and scheduling demand amount, and calculates renewable energy power generation capacity prediction Kzyc, the calculation formula is as follows:

[0019] Kzyc(t) = Xtxl*Yxmj(t)*cos(θ)

[0020] In the formula, Kzyc(t) represents the renewable energy power generation capacity at time point t, Xtxl represents the system electric efficiency, Yxmj represents the effective area of the renewable energy device, G(t) represents the irradiance at time point t, and cos(θ) represents the cosine inclination angle of the photovoltaic panel.

[0021] Preferably, the S2 virtual power plant aggregation optimization platform receives the scheduling demand, preliminarily screens and aggregates the distributed resources related to the aggregation node based on the geographical area correlation, and calculates the resource adjustable power Ktgl. The calculation formula is as follows:

[0022] Ktgl(i) = P max (i) - P current (i)

[0023] In the formula, Ktgl(i) represents the adjustable power of the resource i, P max (i) represents the maximum output power of the resource i, and P current (i) represents the current output power of the resource i.

[0024] Preferably, the S2 virtual power plant aggregation optimization platform receives the scheduling demand, preliminarily screens and aggregates the distributed resources related to the aggregation node based on the geographical area correlation, and calculates the resource historical adjustment qualified rate Zytl. The calculation formula is as follows:

[0025]

[0026] In the formula, Zytl(i) represents the historical adjustment qualified rate of the resource i, Cgtj(i) represents the number of successful adjustments of the resource i, and Ztjs(i) represents the total number of adjustments of the resource i.

[0027] Preferably, the S3 virtual power plant aggregation optimization platform further optimizes the preliminary screening resource list according to the feedback, and calculates the resource historical adjustment benefit Lssy. The calculation formula is as follows:

[0028]

[0029] In the formula, Lssy(i) represents the historical adjustment benefit of the resource i, Tjgl(i,j) represents the adjustable power of the resource i in the adjustment j period, Scjg(j) represents the market price in the adjustment j period, Cm(i) represents the operation cost of the resource i, and N represents the total number of adjustment periods.

[0030] Preferably, the S3 virtual power plant aggregation optimization platform further optimizes the preliminary screening resource list according to the feedback, and calculates the responsiveness coefficient Xyxs. The calculation formula is as follows:

[0031]

[0032] In the formula, Xyxs(i) represents the responsiveness coefficient of resource i, AKtgl(i) represents the change amount of the adjustable power of resource i, and ADdxq represents the change amount of the scheduling demand.

[0033] Preferably, the S4 performs economic benefit evaluation according to the total effective resource amount and the preferred resource effectiveness sequence list, calculates the scheduling benefit Ddsy, and the calculation formula is as follows:

[0034]

[0035] In the formula, Ddsy(t) represents the scheduling benefit at the time point t, M represents the number of resources participating in scheduling, Tjgl(i, t) represents the adjustable power of resource i at the time point t, Scjg(t) represents the market price at the time point t, Gdcb(i) represents the fixed cost of resource i, and Kbcb(i) represents the variable cost of resource i at the time point t.

[0036] Preferably, the S4 performs economic benefit evaluation according to the total effective resource amount and the preferred resource effectiveness sequence list, calculates the resource adjustment cost Ztcb, and the calculation formula is as follows:

[0037] Ztcb(i, t) = Gdcb(i) + Kbcb(i) + Qdcb(i) + Whcb(i)

[0038] In the formula, Ztcb(i, t) represents the total adjustment cost of resource i at the time point t, Gdcb(i) represents the fixed cost of resource i, Kbcb(i) represents the variable cost of resource i at the time point t, Qdcb(i) represents the start-up cost of resource i at the time point t, and Whcb(i) represents the maintenance cost of resource i at the time point t.

[0039] Preferably, the S5 virtual power plant aggregation optimization platform uploads the preferred resource list planned to participate in scheduling to the upper-layer power grid virtual power plant scheduling platform, waits for the feedback result of safety checking and the scheduling out-clear plan, calculates the safety checking index Ajzb, and the calculation formula is as follows:

[0040]

[0041] In the formula, Ajzb represents the safety checking index, represents the sensitivity of power to voltage, Q load represents the total apparent power of all loads in the system, represents the reactive power generated by generator i, and N represents the number of generators in the system.

[0042] Compared with the prior art, the virtual power plant wide-area aggregation optimization scheduling method has the following beneficial effects:

[0043] The virtual power plant scheduling platform of the power grid publishes a day-ahead scheduling demand, and the virtual power plant aggregation optimization platform preliminarily screens and aggregates nodes related to distributed resources based on geographical area correlation after receiving the scheduling demand, calls the aggregation optimization service interface 1, takes the scheduling demand aggregation node and the preliminarily screened distributed resource list as input, and outputs the preliminarily screened resource list. The virtual power plant aggregation optimization platform further aggregates and optimizes according to the feedback preliminarily screened resource list, considers business contracts, resource scale, historical execution qualification rate and other influencing factors, mainly takes economic benefits and execution evaluation as the basis, generates an optimized distributed resource list including resource account numbers and adjustment plan curves, calls the aggregation optimization service interface 2, takes the scheduling demand aggregation node and the optimized distributed resource list as input, and outputs the total amount of resources and the effectiveness sequence list. According to the total amount of effective resources and the optimized resource effectiveness sequence list, the economic benefits are evaluated to determine whether the optimized distributed resource list needs to be adjusted. The virtual power plant aggregation optimization platform uploads the optimized resource list participating in scheduling to the upper-layer power grid virtual power plant scheduling platform, waits for the feedback results of safety checking and scheduling clearing plans, sends corresponding execution plans to each distributed resource response control unit according to the scheduling clearing plans, and starts execution monitoring and checking. According to the above method steps, the aggregation and optimization scheduling of distributed wide-area energy resources can be realized, the reliability and energy saving of the power grid can be improved, the sustainable energy development can be contributed, the economic value of various scattered resources can be realized, and the economic benefits of the load aggregation business can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The figure is a schematic diagram of the method steps of the present application.

[0045] Figure 2 The figure is a schematic diagram of the workflow of the virtual power plant aggregation optimization platform. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] In view of the problem that the entire power system auxiliary service cost is currently only borne by the power generation side, which cannot bear the demand of a large number of renewable energy access to the system, reduces the development space of sustainable energy, and increases the power grid operation load, a virtual power plant wide-area aggregation optimization scheduling method is proposed, please refer to Figure 1The method comprises the following steps:

[0048] S1, the grid virtual power plant dispatching platform issues day-ahead dispatching demand, including aggregation nodes, dispatching time periods, dispatching types and dispatching demand quantity, and calculates load forecast Fhyc and renewable energy power generation capacity forecast Kzyc, for evaluating effectiveness and adjustable capacity of distributed resources;

[0049] The load forecast calculation formula is as follows:

[0050] Fhyc(t) = x + a1*T(t) + a2*D(t) + a3*H(t) + C(t)

[0051] By understanding load demand in different time periods, the dispatching of power generation resources is optimized to ensure that power generation capacity matches demand and reduce the risk of excess or shortage. In the formula, Fhyc(t) represents load forecast at time point t, T(t) represents temperature at time point t, D(t) represents sunshine duration at time point t, H(t) represents humidity at time point t, x represents a constant term, a1, a2 and a3 are regression coefficients of temperature, sunshine and humidity, and C(t) represents an error term. Accurate load forecast can avoid unnecessary standby power sources from being turned on, reduce power generation cost and fuel consumption, and improve economic efficiency;

[0052] The renewable energy power generation capacity forecast calculation formula is as follows:

[0053] Kzyc(t) = Xtxl*Yxmj*G(t)*cos(θ)

[0054] Prediction of renewable energy power generation capacity can help the system adapt to variable weather conditions, better balance supply and demand, and improve the reliability and stability of the grid. In the formula, Kzyc(t) represents renewable energy power generation capacity at time point t, Xtxl represents system electrical efficiency, Yxmj represents effective area of renewable energy equipment, G(t) represents irradiance at time point t, and cos(θ) represents cosine inclination angle of photovoltaic panels. Prediction and dispatching of renewable energy help enterprises better fulfill their obligations and improve the proportion of clean energy;

[0055] S2, after the virtual power plant aggregation optimization platform receives the dispatching demand, it preliminarily screens and aggregates distributed resources related to the aggregation node based on geographical area correlation, calls aggregation optimization service interface 1, takes the dispatching demand aggregation node and the preliminarily screened distributed resource list as input, outputs the preliminarily screened resource list, and calculates resource adjustable power Ktgl and resource historical adjustment qualification rate Zytl to ensure that the selected resources have the ability to execute dispatching;

[0056] The resource adjustable power calculation formula is as follows:

[0057] Ktgl(i) = P max (i) - P current (i)

[0058] Resource adjustable power refers to the ability of power generation and demand-side resources to adjust, and clarifying this ability makes scheduling more flexible, optimizing the response ability of the power system. In the formula, Ktgl(i) represents the adjustable power of resource i, P max (i) represents the maximum output power of resource i, P current (i) represents the current output power of resource i. Clear adjustable power target enables the power system to respond quickly when facing sudden load changes, ensuring stable operation of the power grid.

[0059] Resource historical adjustment qualification rate

[0060]

[0061] By analyzing the historical data of different resources, dispatchers can better select suitable adjustment resources according to past performance, thereby improving the reliability of overall scheduling strategies. In the formula, Zytl(i) represents the historical adjustment qualification rate of resource i, Cgtj(i) represents the number of successful adjustments of resource i, and Ztjs(i) represents the total number of adjustments of resource i. Historical adjustment qualification rate data can support future expectations for resource deployment, thereby effectively reducing uncertainty and enhancing the quantitative analysis capability of scheduling.

[0062] S3, the virtual power plant aggregation optimization platform further aggregates and optimizes according to the feedback of the preliminary screening resource list, considers business contracts, resource scale, and historical execution qualification rate, etc. Influencing factors, mainly economic benefits and evaluation execution, to generate an optimized distributed resource list, including resource account number and adjustment plan curve, call aggregation optimization service interface 2, take the scheduling demand aggregation node and the optimized distributed resource list as input, output the resource total amount and effectiveness sequence list, and calculate the resource historical adjustment benefit Lssy and the response coefficient Xyxs;

[0063] The resource historical adjustment benefit calculation formula is as follows:

[0064]

[0065] By calculating the historical adjustment benefit, the actual economic benefit brought by the adjustment measures can be intuitively presented, which helps to evaluate the effectiveness of policies and decisions. In the formula, Lssy(i) represents the historical adjustment benefit of resource i, Tjgl(i,j) represents the adjustable power of resource i in adjustment period j, Scjg(j) represents the market price in adjustment period j, Cm(i) represents the operation cost of resource i, and N represents the total number of adjustment periods. Understanding the historical adjustment benefit can help identify which resources or time periods produce the highest economic benefit, thereby optimizing future resource allocation strategies.

[0066] The responsiveness coefficient calculation formula is as follows:

[0067]

[0068] The responsiveness coefficient reflects the sensitivity of the system to load changes, which helps to identify the flexibility and responsiveness of resources under different operating conditions. In the formula, Xyxs(i) represents the responsiveness coefficient of resource i, ΔKtgl(i) represents the change in adjustable power of resource i, and ΔDdxq represents the change in scheduling demand. By evaluating the responsiveness coefficient, dispatchers can continuously optimize management strategies to enhance the responsiveness of the entire system and improve power supply reliability.

[0069] S4, according to the total amount of effective resources and the preferred resource effectiveness sequence list, evaluate the economic benefit, judge whether it is necessary to adjust the preferred distributed resource list, if necessary, repeat step S3 until the preferred resource list participating in scheduling is determined, or evaluate whether to participate in this invitation, if participating, go to the next step, and calculate the dispatching benefit Ddsy and the resource adjustment cost Ztcb;

[0070] The dispatching benefit calculation formula is as follows:

[0071]

[0072] The calculation of dispatching benefit helps virtual power plants evaluate the economic benefit brought by different dispatching strategies, ensuring that the overall benefit is maximized on the basis of supply-demand balance. In the formula, Ddsy(t) represents the dispatching benefit at time point t, M represents the number of resources participating in dispatching, Tjgl(i,t) represents the adjustable power of resource i at time point t, Scjg(t) represents the market price at time point t, Gdcb(i) represents the fixed cost of resource i, and Kbcb(i) represents the variable cost of resource i at time point t. The analysis of dispatching benefit can promote the utilization of renewable energy, as these resources often have lower marginal costs and higher contribution to benefit in optimized dispatching.

[0073] The resource adjustment cost calculation formula is as follows:

[0074] Ztcb(i, t) = Gdcb(i) + Kbcb(i) + Qdcb(i) + Whcb(i)

[0075] The calculation of the adjustment cost can help to evaluate the real-time response ability and economy of the resource, so as to optimize the scheduling time and sequence, improve the adaptability of the power system to the load fluctuation, and the formula is that Ztcb(i, t) represents the total adjustment cost of the resource i at the time point t, Gdcb(i) represents the fixed cost of the resource i, Kbcb(i) represents the variable cost of the resource i at the time point t, Qdcb(i) represents the starting cost of the resource i at the time point t, Ehcb(i) represents the maintenance cost of the resource i at the time point t, by understanding the adjustment cost of different resources, the resource combination can be optimized, the resources required for adjusting the load can be effectively allocated, and unnecessary waste can be avoided;

[0076] S5, the virtual power plant aggregation optimization platform uploads the preferred resource list participating in the scheduling to the upper-layer power grid virtual power plant scheduling platform, waits for the feedback result of the safety check and the scheduling out-clear plan, when the safety check fails, returns to repeat step S4, at this time, the safety check index Ajzb is calculated, which is used to identify that the scheduling scheme will not exceed the operation limit that the power grid can bear;

[0077] The safety check index calculation formula is as follows:

[0078]

[0079] The calculation of the safety check index can ensure that the power system meets various operation safety standards in the scheduling process, effectively prevents possible failures and accidents, and ensures the stability of the power grid, and the formula is that Ajzb represents the safety check index, represents the sensitivity of power to voltage, Q load represents the total apparent power of all loads in the system, represents the reactive power generated by the generator i, and N represents the number of generators in the system, by evaluating the safety check index, problems existing in the operation of the equipment can be found in time, so that preventive maintenance can be carried out, and the reliability and operation efficiency of the overall equipment can be improved;

[0080] S6, according to the scheduling out-clear plan, the corresponding execution plan is sent to each distributed resource response control unit, and the execution monitoring check is started;

[0081] The dispatching out-clear plan is generated based on the analysis results of power demand prediction and renewable energy generation capacity, and is calculated by an optimization algorithm. The plan contains the scheduling requirements of each distributed resource in a specific time period, including the power output required by each distributed generation unit, the load reduction or increase amount of the load response unit, and the charging and discharging strategy of the energy storage unit. Once the dispatching out-clear plan is generated, the next step is to convey the execution plan to the response control unit of each distributed resource. In order to ensure that the information between different devices can be correctly parsed, the dispatching instructions need to be converted into a unified format, including power demand, predicted power generation, and adjustment instructions, etc. At the same time of transmission, the dispatching system ensures that the execution plan is correctly received and interpreted in the distributed resources through the monitoring mechanism. During the monitoring process, if there is a large difference between the actual execution and the plan, problem identification needs to be carried out in time, including device failure, communication delay or load change, etc. Based on the monitoring feedback, rapid adjustment and optimization can be carried out, for example, notifying the relevant resources to make further adjustment. After the execution process is completed, the effectiveness of the entire dispatching execution is checked and analyzed, the effectiveness of the dispatching execution is evaluated, and a regular report is generated, including key indicators such as dispatching benefit, response accuracy, resource utilization rate, etc., to provide data support for operation decision-making. According to the checking results, the dispatching algorithm is further optimized to improve the accuracy and reliability in future dispatching process. By effectively conveying the dispatching out-clear plan to each distributed resource and starting the execution monitoring and checking mechanism, the virtual power plant can ensure the accuracy and effectiveness of the formulated dispatching strategy in implementation, which not only improves the stability and flexibility of the power system, but also provides continuous improvement basis for future dispatching, forming an effective information feedback and optimization cycle, and finally realizing the efficient utilization and optimized dispatching of power resources.

[0082] According to the above method steps, the aggregation and optimized dispatching of distributed wide-area energy resources can be realized, the reliability and energy saving of the power grid can be improved, the sustainable energy development can be contributed, the economic value of various dispersed resources can be realized, and the economic benefits of the load aggregator can be improved.

[0083] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A virtual power plant wide-area aggregation optimal scheduling method, characterized in that, The method comprises the following steps: S1, the grid virtual power plant dispatch platform issues day-ahead dispatch requirements, including aggregation nodes, dispatch time periods, dispatch types, and dispatch requirement amounts, and calculates load forecasts with renewable energy generation capacity forecasts for assessing the effectiveness and adjustability of distributed resources; The computing load prediction The computing formula is shown as follows: in the formula, denotes the time point of the load prediction, denotes the time point of the temperature, denotes the time point of the sunshine duration, denotes the time point of the humidity, denotes the constant term, , , the regression coefficients for temperature, sunshine and humidity, denotes the error term; The computing a renewable energy generation capacity forecast The formula is as follows: In the formula, Indicates a point in time renewable energy generation capacity Indicates the system's electrical efficiency. Indicates the effective area of ​​renewable energy equipment. Indicates a point in time Irradiance, Indicates the cosine tilt angle of the photovoltaic panel; S2, the virtual power plant aggregation optimization platform receives the scheduling demand, preliminarily screens and aggregates the distributed resources related to the node based on the geographical area correlation, calls the aggregation optimization service interface 1, takes the scheduling demand aggregation node and the preliminarily screened distributed resource list as the input, outputs the feedback preliminarily screened resource list, and calculates the adjustable power of the resource With the historical regulation qualification rate of the resource , to ensure that the selected resource has the ability to execute the scheduling; S3, the virtual power plant aggregation optimization platform further aggregates and optimizes according to the feedback of the preliminary screening resource list, considers the business contract, resource scale and historical execution qualified rate influence factor, takes economic benefit and assessment execution as the main factors, generates an optimal distributed resource list including resource account number and adjustment plan curve, calls the aggregation optimization service interface 2, takes the scheduling demand aggregation node and the optimal distributed resource list as input, outputs the resource total amount and effectiveness sequence list, and calculates the historical adjustment benefit of the resource with the response coefficient ; S4, according to the total amount of effective resources and the preferred resource effectiveness sequence list, economic benefit evaluation is carried out to determine whether the preferred distributed resource list needs to be adjusted, if so, repeat step S3 until the preferred resource list participating in the dispatch is determined, or evaluate whether to participate in the invitation, if so, go to the next step and calculate the dispatch benefit with resource regulation cost ; S5, the virtual power plant aggregation optimization platform sends the preferred resource list participating in the scheduling to the upper grid virtual power plant scheduling platform, waits for the feedback result of the safety check and the scheduling out plan, and returns to step S4 when the safety check fails, at which time the safety check index is calculated for identifying that the scheduling scheme does not exceed the operation limit that the grid can bear; S6. According to the dispatching cleaning plan, a corresponding execution plan is sent to each distributed resource response control unit, and execution monitoring and checking are started. 2.The virtual power plant wide-area aggregation optimization scheduling method of claim 1, wherein: The S2 virtual power plant aggregation optimization platform receives the scheduling demand, preliminarily screens the distributed resources related to the aggregation node based on geographical area correlation, and calculates the adjustable power of the resources The calculation formula is as follows: In the formula, denotes the adjustable power of the resource denotes the maximum output power of the resource denotes the current output power of the resource ​​​ 3.The virtual power plant wide-area aggregation optimization scheduling method of claim 2, wherein: The S2 virtual power plant aggregation optimization platform receives the scheduling demand, preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, and calculates the resource historical adjustment qualification rate The calculation formula is as follows: In the formula, represents the historical adjustment eligibility rate of the resource represents the historical adjustment eligibility rate of the resource represents the number of successful adjustments of the resource represents the number of successful adjustments of the resource represents the total number of adjustments of the resource represents the total number of adjustments of the resource 4.The virtual power plant wide-area aggregation optimization scheduling method of claim 3, wherein: The S3 virtual power plant aggregation optimization platform performs further aggregation optimization according to the feedback preliminary screening resource list, and calculates resource historical adjustment benefits The calculation formula is as follows: In the formula, denotes the historical regulation benefit of the resource , denotes the operation cost of the resource , denotes the adjustable power of the resource in the regulation period, denotes the price of the market price in the regulation period, denotes the operation cost of the resource , denotes the total number of regulation periods.

5. The virtual power plant wide-area aggregation optimization scheduling method according to claim 4, characterized in that: The S3 virtual power plant aggregation optimization platform performs further aggregation optimization according to the feedback preliminary screening resource list, and calculates a response coefficient The calculation formula is as follows: In the formula, denotes the responsiveness coefficient of the resource , denotes the change amount of the adjustable power of the resource , denotes the change amount of the scheduling demand. 6.The virtual power plant wide-area aggregation optimization scheduling method of claim 5, wherein: The S4 performs economic benefit evaluation according to the total amount of effective resources and the preferred resource effectiveness sequence list, and calculates scheduling benefits The calculation formula is as follows: In the formula, denotes the scheduling benefit at time point , denotes the number of resources participating in scheduling, denotes the available power of the resource at time point , denotes the market price at time point , denotes the fixed cost of the resource , denotes the variable cost of the resource at time point .

7. The virtual power plant wide-area aggregation optimization scheduling method of claim 6, wherein: The S4 carries out economic benefit evaluation according to the total amount of effective resources and the preferred resource effectiveness sequence list, and calculates resource regulation cost The calculation formula is as follows: in the formula, denotes the resource at the point in time the total regulation costs, denotes the resource the fixed costs, denotes the resource the variable costs at the point in time , the variable costs at the point in time , the variable costs at the point in time , the variable costs at the point in time , the variable costs at the point in time , the variable costs at the point in time , the variable costs at the point in time , the variable costs at the point in time 8.The virtual power plant wide-area aggregation optimization scheduling method of claim 7, wherein: The S5 virtual power plant aggregation optimization platform sends a preferred resource list participating in scheduling to an upper-layer power grid virtual power plant scheduling platform, waits for feedback results of safety checking and a scheduling result, and calculates a safety checking index The calculation formula is as shown below: in the formula, denotes a security check index, denotes the sensitivity of the power to the voltage, denotes the total apparent power of all loads in the system, denotes the generator generated reactive power, denotes the number of generators in the system.

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