Virtual power plant wide area aggregation optimization scheduling method

Abstract

The invention relates to the technical field of power dispatching optimization, and discloses a virtual power plant wide area aggregation optimization dispatching method. The method comprises the following steps: issuing a day-ahead scheduling demand through a virtual power plant scheduling platform of a power grid, preliminarily screening distributed resources related to aggregation nodes based on geographic region correlation after receiving the scheduling demand, calling an aggregation optimization service interface 1, performing further aggregation optimization according to a fed-back preliminarily screened resource list, generating an optimal distributed resource list, and sending the optimal distributed resource list to the virtual power plant scheduling platform of the power grid. And taking the scheduling demand aggregation node and the optimal distributed resource list as input, outputting a total resource amount and an effectiveness sequence table, performing economic benefit evaluation, judging whether the optimal distributed resource list needs to be adjusted, and uploading the optimal resource list planned to participate in scheduling to an upper-layer power grid virtual power plant scheduling platform. And waiting for a feedback result of security check and a scheduling clearing plan, sending a corresponding execution plan to each distributed resource response regulation and control unit according to the scheduling clearing plan, and starting execution of monitoring check.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatch optimization, and in particular to a wide-area aggregation optimization dispatch method for a virtual power plant. Background Art

[0002] In recent years, my country's power industry has undergone major changes in its power supply structure and grid structure. The scale of installed power capacity has continued to expand, clean energy has developed rapidly, and the construction of the ancillary service market has faced new challenges. The complexity of system operation and management has continued to increase, and the demand for ancillary services has increased significantly. The existing types of ancillary services need to further adapt to the needs of system operation. Only by unilaterally bearing the cost of ancillary services for the entire system on the power generation side can it no longer bear the demand for large-scale access to renewable energy in the system. The scale of cross-provincial and cross-regional electricity transactions is increasing, and the inter-provincial ancillary service market mechanism and cost-sharing principles need to be improved. New industries and new formats such as new energy storage and electric vehicle charging networks also urgently need market-oriented mechanisms to guide and promote development.

[0003] As renewable energy becomes the main direction of future global energy development, the research purpose of virtual power plants in my country is mainly to provide a framework and technical support for the access of large-scale new energy electricity. Through the operation mechanism of virtual power plants, the complementary and coordinated scheduling between traditional energy and new energy and the optimized operation of the power grid can be realized, so as to minimize the strong random volatility of new energy electricity and improve the utilization rate of new energy.

[0004] The virtual power plant business has emerged in recent years, and the digital platforms used in domestic projects are mainly provided by foreign manufacturers. With the rapid development of the domestic market, local policies, regulations, practice paths, and implementation steps have shown a unified goal and diverse development trend, which is quite different from foreign markets. Therefore, the research and development of a domestically-produced, independent and controllable virtual power plant aggregation optimization and dispatching platform aimed at supporting business market development is an inevitable demand at the current stage and an important boost to future development. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies of the prior art, the present invention provides a method for wide-area aggregation optimization scheduling of a virtual power plant, which has the function of publishing the day-ahead scheduling demand through the virtual power plant scheduling platform of the power grid. After receiving the scheduling demand, the virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation 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 input, and outputs the feedback preliminarily screened resource list. The virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback preliminarily screened resource list, considers the influencing factors such as commercial contracts, resource scale, and historical execution qualification rate, and mainly generates a preferred distributed resource list based on economic benefits and assessment execution, including resource household numbers and adjustment plan curves, calls the aggregation optimization service interface 2, takes the scheduling demand aggregation node and the preferred distributed resource list as input, and outputs the feedback preliminarily screened resource list. Select the distributed resource list as input, output the total amount of resources and the effectiveness sequence list, perform economic benefit evaluation based on the total amount of effective resources and the effectiveness sequence list of preferred resources, and determine whether it is necessary to adjust the preferred distributed resource list. The virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the scheduling to the upper-level power grid virtual power plant scheduling platform, waits for the feedback results of the safety verification and the scheduling clearing plan, and sends the corresponding execution plan to each distributed resource response control unit according to the scheduling clearing plan, and starts execution monitoring and verification. According to the above method steps, the aggregation and optimized scheduling of distributed wide-area energy resources can be realized, the reliability and energy saving of the power grid can be improved, and contributions can be made to the development of sustainable energy. The economic value of various types of decentralized resources can be realized, and the economic benefits of load aggregators can be improved, thereby solving the above problems.

[0007] (II) Technical solution

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

[0009] S1. The grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the load forecast Fhyc and renewable energy generation capacity forecast Kzyc to evaluate the effectiveness and adjustable capacity of distributed resources;

[0010] S2. After receiving the dispatch demand, the virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, calls the aggregation optimization service interface 1, takes the dispatch demand aggregation node and the preliminarily screened distributed resource list as input, outputs the feedback preliminarily screened resource list, and calculates the resource adjustable power Ktgl and the resource historical adjustment qualification rate Zytl to ensure that the selected resources have the ability to execute the dispatch;

[0011] S3. The virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback of the initial screening resource list, taking into account influencing factors such as commercial contracts, resource scale, and historical execution qualification rate, and mainly focusing on economic benefits and assessment execution, to generate a list of preferred distributed resources, including resource account numbers and adjustment plan curves, and call the aggregation optimization service interface 2, taking the scheduling demand aggregation node and the preferred distributed resource list as input, outputting the total resource amount and effectiveness sequence table, and calculating the historical resource adjustment benefit Lssy and the responsiveness coefficient Xyxs;

[0012] S4. Perform economic benefit evaluation based on the total amount of effective resources and the effectiveness sequence list of preferred resources, and determine whether it is necessary to adjust the preferred distributed resource list. If necessary, repeat step S3 until the preferred resource list participating in the scheduling is determined, or evaluate whether to participate in the invitation. If yes, proceed to the next step and calculate the scheduling benefit Ddsy and the resource adjustment cost Ztcb.

[0013] S5. The virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the dispatch to the upper-level power grid virtual power plant dispatch platform, waiting for the feedback results of the safety check and the dispatch clearing plan. If the safety check fails, it returns to repeat step S4, at which time the safety check index Ajzb is calculated to identify that the dispatch plan will not exceed the operating limit that the power grid can bear;

[0014] S6. According to the scheduling and clearing plan, the corresponding execution plan is sent to each distributed resource response control unit, and execution monitoring and verification is started.

[0015] Preferably, the S1 power grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the load forecast Fhyc, and 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 forecast 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 the constant term, a1, a2, a3 correspond to the regression coefficients of temperature, sunshine and humidity, and C(t) represents the error term.

[0018] Preferably, the S1 power grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the renewable energy generation capacity forecast Kzyc, and the calculation formula is as follows:

[0019] Kzyc(t)=Xtxl*Yxmj(t)*cos(θ)

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

[0021] Preferably, after receiving the dispatching demand, the S2 virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, and calculates the resource adjustable power Ktgl, and 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 resource i, P max (i) represents the maximum output power of resource i, P current (i) represents the current output power of resource i.

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

[0025]

[0026] 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.

[0027] Preferably, the S3 virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback of the initial screening resource list, and calculates the resource historical adjustment income Lssy, and the calculation formula is as follows:

[0028]

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

[0030] Preferably, the S3 virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback initial screening resource list, and calculates the responsiveness coefficient Xyxs, and the calculation formula is as follows:

[0031]

[0032] In the formula, Xyxs(i) represents the responsiveness coefficient of resource i, ΔKtgl(i) represents the change in the adjustable power of resource i, and ΔDdxq represents the change in scheduling demand.

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

[0034]

[0035] In the formula, Ddsy(t) represents the scheduling revenue at time point t, M represents the number of resources involved in the scheduling, 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.

[0036] Preferably, S4 performs economic benefit evaluation based on the total amount of effective resources and the preferred resource effectiveness sequence table to calculate 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 time point t, Gdcb(i) represents the fixed cost of resource i, Kbcb(i) represents the variable cost of resource i at time point t, Qdcb(i) represents the startup cost of resource i at time point t, and Whcb(i) represents the maintenance cost of resource i at time point t.

[0039] Preferably, the S5 virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the dispatch to the upper-level power grid virtual power plant dispatch platform, waits for the feedback results of the safety check and the dispatch clearing plan, and calculates the safety check index Ajzb. The calculation formula is as follows:

[0040]

[0041] In the formula, Ajzb represents the safety verification index, Indicates 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 present invention provides a method for wide-area aggregation optimization scheduling of a virtual power plant, which has the following beneficial effects:

[0043] The present invention releases the day-ahead dispatch demand through the grid virtual power plant dispatch platform. After receiving the dispatch demand, the virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, calls the aggregation optimization service interface 1, takes the dispatch demand aggregation node and the preliminarily screened distributed resource list as input, and outputs the feedback preliminarily screened resource list. The virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback preliminarily screened resource list, considers the influencing factors such as commercial contracts, resource scale, and historical execution qualification rate, and mainly generates a preferred distributed resource list based on economic benefits and assessment execution, including resource account numbers and adjustment plan curves, calls the aggregation optimization service interface 2, takes the dispatch demand aggregation node and the preferred distributed resource list as input, and outputs the feedback preliminarily screened resource list. Input, output the total amount of resources and the effectiveness sequence table, perform economic benefit evaluation based on the total amount of effective resources and the effectiveness sequence table of preferred resources, and judge whether it is necessary to adjust the preferred distributed resource list. The virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the dispatch to the upper-level power grid virtual power plant dispatch platform, waits for the feedback results of the safety verification and the dispatch clearing plan, and sends the corresponding execution plan to each distributed resource response control unit according to the dispatch clearing plan, and starts execution monitoring and verification. According to the above method steps, the aggregation and optimized dispatch of distributed wide-area energy resources can be realized, the reliability and energy saving of the power grid can be improved, and contributions can be made to the development of sustainable energy. The economic value of various decentralized resources is realized and the economic benefits of load aggregators are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the steps of the method of the present invention.

[0045] Figure 2 Schematic diagram of the workflow of the aggregation optimization platform for virtual power plants. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] At present, the auxiliary service cost of the entire power system is borne unilaterally by the power generation side, which can no longer bear the demand for the large-scale access of renewable energy to the system, and reduces the development space of sustainable energy and increases the operating load of the power grid. Therefore, a wide-area aggregation optimization scheduling method for virtual power plants is proposed. Please refer to Figure 1, the method comprises the following steps:

[0048] S1. The grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the load forecast Fhyc and renewable energy generation capacity forecast Kzyc to evaluate the 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 the load demand in different time periods, optimizing the scheduling of power generation resources, ensuring that the power generation capacity matches the demand, and reducing the risk of surplus or shortage, in the formula, Fhyc(t) represents the load forecast 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 the constant term, a1, a2, a3 correspond to the regression coefficients of temperature, sunshine and humidity, and C(t) represents the error term. Accurate load forecasting can avoid unnecessary backup power supply startup, reduce power generation costs and fuel consumption, and improve economy.

[0052] The calculation formula for forecasting renewable energy generation capacity is as follows:

[0053] Kzyc(t)=Xtxl*Yxmj*G(t)*cos(θ)

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

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

[0056] The formula for calculating the adjustable power of resources 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 be adjusted. Clarifying this ability makes scheduling more flexible and optimizes the response capacity 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. The clear and adjustable power target enables the power system to respond quickly to sudden load changes and ensure the stable operation of the power grid.

[0059] Resource historical adjustment pass rate

[0060]

[0061] By analyzing the historical data of different resources, the dispatcher can better select the appropriate adjustment resources according to the past performance, thereby improving the reliability of the overall scheduling strategy. 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. The data of historical adjustment qualification rate can support the expectation of resource allocation in the future, thereby effectively reducing uncertainty and enhancing the quantitative analysis ability of scheduling.

[0062] S3. The virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback of the initial screening resource list, taking into account influencing factors such as commercial contracts, resource scale, and historical execution qualification rate, and mainly focusing on economic benefits and assessment execution, to generate a list of preferred distributed resources, including resource account numbers and adjustment plan curves, and call the aggregation optimization service interface 2, taking the scheduling demand aggregation node and the preferred distributed resource list as input, outputting the total resource amount and effectiveness sequence table, and calculating the historical resource adjustment benefit Lssy and the responsiveness coefficient Xyxs;

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

[0064]

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

[0066] The calculation formula of the response coefficient is as follows:

[0067]

[0068] The responsiveness coefficient reflects the sensitivity of the system to load changes and 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 the adjustable power of resource i, and ΔDdxq represents the change in dispatch 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. Perform economic benefit evaluation based on the total amount of effective resources and the effectiveness sequence list of preferred resources, and determine whether it is necessary to adjust the preferred distributed resource list. If necessary, repeat step S3 until the preferred resource list participating in the scheduling is determined, or evaluate whether to participate in the invitation. If yes, proceed to the next step and calculate the scheduling benefit Ddsy and the resource adjustment cost Ztcb.

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

[0071]

[0072] The calculation of dispatch revenue helps virtual power plants evaluate the economic benefits brought by different dispatch strategies and ensures that the overall revenue is maximized on the basis of supply and demand balance. In the formula, Ddsy(t) represents the dispatch revenue at time point t, M represents the number of resources involved in the dispatch, 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 dispatch revenue can promote the use of renewable energy because these resources often have lower marginal costs in optimal dispatch and contribute more to revenue.

[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 regulation cost can help evaluate the real-time response capability and economy of resources, thereby optimizing the dispatching time and sequence and improving the adaptability of the power system to load fluctuations. In the formula, Ztcb(i,t) represents the total regulation cost of resource i at time point t, Gdcd(i) represents the fixed cost of resource i, Kbcb(i) represents the variable cost of resource i at time point t, Qdcb(i) represents the startup cost of resource i at time point t, and Ehcb(i) represents the maintenance cost of resource i at time point t. By understanding the regulation costs of different resources, we can optimize the resource combination, effectively allocate the resources required for load regulation, and avoid unnecessary waste.

[0076] S5. The virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the dispatch to the upper-level power grid virtual power plant dispatch platform, waiting for the feedback results of the safety check and the dispatch clearing plan. If the safety check fails, it returns to repeat step S4, at which time the safety check index Ajzb is calculated to identify that the dispatch plan will not exceed the operating limit that the power grid can bear;

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

[0078]

[0079] The calculation of safety check index can ensure that the power system meets various operation safety standards during the dispatching process, effectively prevent possible failures and accidents, and ensure the stability of the power grid. In the formula, Ajzb represents the safety check index, Indicates the sensitivity of power to voltage, Q load represents the total apparent power of all loads in the system, It represents the reactive power generated by generator i, and N represents the number of generators in the system. By evaluating the safety verification index, problems in equipment operation can be discovered in time, so as to carry out preventive maintenance and improve the reliability and operation efficiency of the overall equipment.

[0080] S6. According to the scheduling and clearing plan, the corresponding execution plan is sent to each distributed resource response control unit, and execution monitoring and verification are started;

[0081] The dispatching and clearing plan is generated based on the analysis results of power demand forecast and renewable energy generation capacity after optimization algorithm calculation. This plan includes the dispatching requirements of each distributed resource in a specific period of time, including the power output that each distributed generation unit needs to provide, the load that the load response unit should reduce or increase, and the charging and discharging strategy of the energy storage unit. Once the dispatching and clearing plan is generated, the next step is to convey the execution plan to the response and control units 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, which includes power demand, predicted power generation, and adjustment instructions. During the transmission, the dispatching system uses a monitoring mechanism to ensure that the execution plan is correctly received and interpreted in the distributed resources. During the monitoring process, if it is found that there is a large difference between the actual execution and the plan, it is necessary to identify the problem in a timely manner, including equipment failure, communication failure, etc. Signal delays or load changes, etc., can be quickly adjusted and optimized based on monitoring feedback, for example, relevant resources can be notified for further adjustment. After the execution process is completed, the entire dispatch execution effect is verified and analyzed, the effect of dispatch execution is evaluated, and regular reports are generated, including dispatch revenue, response accuracy, resource utilization and other key indicators, to provide data support for operational decisions. According to the verification results, the dispatch algorithm is further optimized to improve the accuracy and reliability of future dispatch processes. By effectively communicating the dispatch clearing plan to each distributed resource and starting the execution monitoring and verification mechanism, the virtual power plant can ensure the accuracy and effectiveness of the formulated dispatch strategy in implementation, which not only improves the stability and flexibility of the power system, but also provides a basis for continuous improvement in future dispatch, forming an effective information feedback and optimization cycle, and ultimately achieving efficient utilization of power resources and optimized dispatch.

[0082] According to the above method steps, the aggregation and optimal dispatch of distributed wide-area energy resources can be realized, the reliability and energy saving of the power grid can be improved, contributions can be made to the development of sustainable energy, the economic value of various decentralized resources can be realized, and the economic benefits of load aggregators can be improved.

[0083] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A virtual power plant wide-area aggregation optimization scheduling method, characterized in that: The following steps are involved: S1. The grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the load forecast Fhyc and renewable energy generation capacity forecast Kzyc to evaluate the effectiveness and adjustable capacity of distributed resources; S2. After receiving the dispatch demand, the virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, calls the aggregation optimization service interface 1, takes the dispatch demand aggregation node and the preliminarily screened distributed resource list as input, outputs the feedback preliminarily screened resource list, and calculates the resource adjustable power Ktg l and the resource historical adjustment qualification rate Zytl to ensure that the selected resources have the ability to execute the dispatch; S3. The virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback of the initial screening resource list, taking into account influencing factors such as commercial contracts, resource scale, and historical execution qualification rate, and mainly focusing on economic benefits and assessment execution, to generate a list of preferred distributed resources, including resource account numbers and adjustment plan curves, and call the aggregation optimization service interface 2, taking the scheduling demand aggregation node and the preferred distributed resource list as input, outputting the total resource amount and effectiveness sequence table, and calculating the historical resource adjustment benefit Lssy and the responsiveness coefficient Xyxs; S4. Perform economic benefit evaluation based on the total amount of effective resources and the effectiveness sequence list of preferred resources, and determine whether it is necessary to adjust the preferred distributed resource list. If necessary, repeat step S3 until the preferred resource list participating in the scheduling is determined, or evaluate whether to participate in the invitation. If yes, proceed to the next step and calculate the scheduling benefit Ddsy and the resource adjustment cost Ztcb. S5. The virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the dispatch to the upper-level power grid virtual power plant dispatch platform, waiting for the feedback results of the safety check and the dispatch clearing plan. If the safety check fails, it returns to repeat step S4, at which time the safety check index Ajzb is calculated to identify that the dispatch plan will not exceed the operating limit that the power grid can bear; S6. According to the scheduling and clearing plan, the corresponding execution plan is sent to each distributed resource response control unit, and execution monitoring and verification is started.

2. A virtual power plant wide-area aggregation optimization scheduling method according to claim 1, characterized in that: The S1 grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the load forecast Fhyc. The calculation formula is as follows: Fhyc(t)=x+a1*T(t)+a2*D(t)+a3*H(t)+C(t) In the formula, Fhyc(t) represents the load forecast 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 the constant term, a1, a2, a3 correspond to the regression coefficients of temperature, sunshine and humidity, and C(t) represents the error term.

3. A virtual power plant wide-area aggregation optimization scheduling method according to claim 2, characterized in that: The S1 grid virtual power plant dispatching platform publishes the day-ahead dispatching demand, including aggregation nodes, dispatching period, dispatching type and dispatching demand, and calculates the renewable energy generation capacity forecast Kzyc. The calculation formula is as follows: Kzyc(t)=Xtxl*Yxmj*G(t)*cos(θ) In the formula, Kzyc(t) represents the renewable energy generation capacity at time point t, Xtxl represents the system electrical efficiency, Yxmj represents the effective area of ​​the renewable energy equipment, G(t) represents the irradiance at time point t, and cos(θ) represents the cosine tilt angle of the photovoltaic panel.

4. A virtual power plant wide-area aggregation optimization scheduling method according to claim 3, characterized in that: After receiving the dispatch demand, the S2 virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, and calculates the resource adjustable power Ktg l. The calculation formula is as follows: Ktgl(i)=P max (i)-P current (i) 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.

5. A virtual power plant wide-area aggregation optimization scheduling method according to claim 4, characterized in that: After receiving the dispatch demand, the S2 virtual power plant aggregation optimization platform preliminarily screens the distributed resources related to the aggregation node based on the geographical area correlation, and calculates the resource historical adjustment qualification rate Zytl. The calculation formula is as follows: 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.

6. A virtual power plant wide-area aggregation optimization scheduling method according to claim 5, characterized in that: The S3 virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback of the initial screening resource list and calculates the resource historical adjustment income Lssy. The calculation formula is as follows: In the formula, Lssy(i) represents the historical regulation income of resource i, Tjgl(i, j) represents the adjustable power of resource i in regulation period j, Scjg(j) represents the market price in regulation period j, Cm(i) represents the operating cost of resource i, and N represents the total number of regulation periods.

7. A virtual power plant wide-area aggregation optimization scheduling method according to claim 6, characterized in that: The S3 virtual power plant aggregation optimization platform performs further aggregation optimization based on the feedback of the initial screening resource list and calculates the responsiveness coefficient Xyxs. The calculation formula is as follows: In the formula, Xyxs(i) represents the responsiveness coefficient of resource i, ΔKtg l(i) represents the change in the adjustable power of resource i, and ΔDdxq represents the change in scheduling demand.

8. A virtual power plant wide-area aggregation optimization scheduling method according to claim 7, characterized in that: The S4 performs economic benefit evaluation based on the total amount of effective resources and the preferred resource effectiveness sequence table to calculate the scheduling benefit Ddsy, and the calculation formula is as follows: In the formula, Ddsy(t) represents the scheduling revenue at time point t, M represents the number of resources involved in the scheduling, 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.

9. A virtual power plant wide-area aggregation optimization scheduling method according to claim 8, characterized in that: S4 performs economic benefit evaluation based on the total amount of effective resources and the preferred resource effectiveness sequence table, and calculates the resource adjustment cost Ztcb. The calculation formula is as follows: Ztcb(i,t)=Gdcb(i)+Kbcb(i)+Qdcb(i)+Wh cb(i) In the formula, Ztcb(i, t) represents the total adjustment cost of resource i at time point t, Gdcb(i) represents the fixed cost of resource i, Kbcb(i) represents the variable cost of resource i at time point t, Qdcb(i) represents the startup cost of resource i at time point t, and Whcb(i) represents the maintenance cost of resource i at time point t.

10. A virtual power plant wide-area aggregation optimization scheduling method according to claim 9, characterized in that: The S5 virtual power plant aggregation optimization platform sends the preferred resource list planned to participate in the dispatch to the upper-level power grid virtual power plant dispatch platform, waits for the feedback results of the safety verification and the dispatch clearing plan, and calculates the safety verification index Ajzb. The calculation formula is as follows: In the formula, Ajzb represents the safety verification index, Indicates 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.

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