A distributed wind-solar resource admission capacity planning method and system

Abstract

The application discloses a kind of distributed wind and light resource admission capacity planning method and system, by with the target function of distribution network system operation cost target function and distribution network system wind and light resource admission capacity target function, obtain the target function of admission capacity planning model, according to the data such as to be planned node, to be planned capacity, admission capacity planning model is solved, and planning result is obtained, according to flexibility evaluation index and new energy consumption quality index, the planning result that evaluation result meets preset condition is selected as final planning result, the method is calculated distributed wind and light resource planning result by allowing distribution network system to abandon wind and light in certain period, and distributed wind and light resource planning result is evaluated using clean energy consumption quality index and flexibility evaluation index, improve the accuracy and practicality of planning result, avoid resource waste.

Description

Technical Field

[0001] This invention relates to the field of distributed wind and solar resource access capacity analysis technology, and in particular to a method and system for distributed wind and solar resource access capacity planning. Background Technology

[0002] Clean energy generation, primarily wind and solar photovoltaic power, is gradually replacing traditional energy generation, mainly thermal power. The large-scale integration of clean energy into the power grid is becoming a major future development trend. Distributed wind and solar power integration into the distribution network system enables the local consumption of generated energy. Its flexible operation can also meet the growing load demand, reduce the difficulty of grid expansion and the overall investment and operating costs of the transmission network, and improve the reliability of the distribution network system.

[0003] Compared to traditional passive distribution network systems, active distribution networks can effectively control various devices and connected distributed generators (DGs), maximizing the flexibility of resources on the source, grid, and load sides, thereby improving the flexibility of the distribution network system. While the large-scale integration of distributed wind and solar resources into the distribution network system brings economic, technical, and environmental benefits, it also presents challenges to the system's operation and planning. Due to the randomness, volatility, and intermittency of wind and solar power output, the uncertainties introduced by wind and solar power generation, as well as the uncertainty of load forecasting, result in a significant mismatch between the output of distributed wind and solar power and the temporal characteristics of the load, leading to insufficient flexibility in the distribution network system. Simultaneously, it also restricts the system's capacity to accommodate distributed wind and solar resources, resulting in the inability to fully absorb wind and solar power output, thus causing a certain degree of wind and solar curtailment. Summary of the Invention

[0004] This invention provides a method and system for distributed wind and solar resource access capacity planning. By allowing the distribution network system to curtail wind and solar power during certain periods, the method calculates the distributed wind and solar resource planning results for the distribution network system. Furthermore, it evaluates the distributed wind and solar resource planning results using clean energy consumption quality indicators and flexibility evaluation indicators, thereby improving the accuracy and practicality of the planning results and avoiding resource waste.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a distributed wind and solar resource access capacity planning method, comprising: Acquire data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data; The pre-defined access capacity planning model is invoked to iteratively solve for the nodes to be planned, the capacity to be planned, energy storage equipment data, and power operation data to obtain the planning results. The planning results are compared with the pre-defined planning results to obtain the planning error. The iterative solution stops when the planning error is less than the pre-defined error, and the planning results are output. The access capacity planning model includes an access capacity planning objective function and system constraints. The access capacity planning objective function is constructed by the distribution network system operation cost objective function and the distribution network system wind and solar resource access capacity objective function. The system constraints include system operation constraints and flexibility constraints. The planning results are evaluated based on flexibility evaluation indicators and new energy consumption quality indicators. If the evaluation results meet the preset conditions, the planning results are determined as the final planning results. If the evaluation results do not meet the preset conditions, the iterative calculation is performed again based on the preset access capacity planning model to obtain new planning results. The evaluation results of the new planning results are then calculated. This process is repeated until the evaluation results of the new planning results meet the preset conditions, and the new planning results are determined as the final planning results. This allows the distribution network system to plan the access capacity of distributed wind and solar resources based on the final planning results.

[0006] This embodiment acquires data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data. It then constructs an access capacity planning model using the objective function derived from the distribution network system's operating cost objective function and the distribution network system's wind and solar resource access capacity objective function. The model is then solved based on the data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data to obtain the planning results. These results are then evaluated using flexibility evaluation indicators and new energy consumption quality indicators. The final planning result is selected based on the evaluation results that meet preset conditions. This method allows for the calculation of distributed wind and solar resource planning results in the distribution network system under conditions where wind and solar curtailment occurs at certain times. Furthermore, it evaluates the distributed wind and solar resource planning results using clean energy consumption quality indicators and flexibility evaluation indicators, thereby improving the accuracy and practicality of the planning results and avoiding resource waste.

[0007] As a preferred option, the flexibility evaluation index is obtained by weighting the electrical power of the tie line and the allowable fluctuation rate of the system power, and the specific calculation method is as follows: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward volatility of electrical energy during time period t, respectively, and their calculation method is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. The quality index for renewable energy consumption is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

[0008] In this embodiment, the final planning results are evaluated based on flexibility evaluation indicators and new energy consumption quality indicators to obtain evaluation results. This allows the distribution network system to determine whether the final planning results can be used to plan the access capacity of distributed wind and solar resources. This method improves the accuracy of distributed wind and solar resource planning results by introducing new energy consumption quality indicators on the basis of the original safe operation constraints and comprehensively analyzing the planning results.

[0009] As a preferred option, the predicted wind power output is calculated by subtracting the predicted wind power output from the actual wind power output. The calculation formula is as follows: in, This represents the predicted wind power output. This represents the actual output value of wind power. The wind power output prediction error is represented by the probability density function of the wind power output prediction error: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The predicted photovoltaic output value is calculated by subtracting the actual photovoltaic output value from the predicted photovoltaic output error. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. The photovoltaic output prediction error is represented by the probability density function of the photovoltaic output prediction error: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

[0010] As a preferred option, the access capacity planning objective function is constructed by the distribution network system operating cost objective function and the distribution network system wind and solar resource access capacity objective function, specifically as follows: After normalizing the objective functions for the operating cost of the distribution network system and the wind and solar resource access capacity of the distribution network system, we obtain the normalized objective functions for the operating cost of the distribution network system and the normalized objective functions for the wind and solar resource access capacity of the distribution network system. The objective function for the operating cost of the distribution network system is as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function for the wind and solar resource access capacity of the power distribution network system is: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. The normalized objective function for the operating cost of the distribution network system and the normalized objective function for the wind and solar resource access capacity of the distribution network system are added together with different weight ratios to obtain the planning objective function. The formula for the access capacity planning objective function is as follows: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively.

[0011] In this embodiment, the objective functions for the operating cost of the distribution network system and the wind and solar resource access capacity of the distribution network system are normalized to obtain normalized objective functions for the operating cost of the distribution network system and normalized objective functions for the wind and solar resource access capacity of the distribution network system. The normalized objective functions for the operating cost of the distribution network system and normalized objective functions for the wind and solar resource access capacity of the distribution network system are then added together according to different weight ratios to obtain the planning objective function. This planning objective function can significantly improve the accuracy of the planning results for wind and solar resources and avoid resource waste by allowing the system to appropriately curtail wind and solar power in certain periods.

[0012] As a preferred embodiment, the flexibility constraints are derived based on the net load, the upper limit of flexibility resource output, and the lower limit of flexibility resource output, wherein the flexibility constraints are: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error.

[0013] As a preferred solution, to address the same technical problem, embodiments of the present invention also provide a distributed wind and solar resource access capacity planning system, including an acquisition module, a planning result calculation module, and an evaluation module. The acquisition module is used to acquire data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data. The planning result calculation module is used to call the preset access capacity planning model to iteratively solve the planning nodes, planning capacity, energy storage equipment data and power operation data to obtain the planning results. The planning results are compared with the preset planning results to obtain the planning error. The iterative solution stops when the planning error is less than the preset error and the planning results are output. The access capacity planning model includes the access capacity planning objective function and system constraints. The access capacity planning objective function is constructed by the distribution network system operation cost objective function and the distribution network system wind and solar resource access capacity objective function. The system constraints include system operation constraints and flexibility constraints. The evaluation module is used to evaluate the planning results based on flexibility evaluation indicators and new energy consumption quality indicators. If the evaluation results meet the preset conditions, the planning results are determined as the final planning results. If the evaluation results do not meet the preset conditions, the system iteratively calculates the new planning results based on the preset access capacity planning model to obtain new planning results. The evaluation results of the new planning results are then calculated. This process is repeated until the evaluation results of the new planning results meet the preset conditions, and the new planning results are determined as the final planning results. This allows the distribution network system to plan the access capacity of distributed wind and solar resources based on the final planning results.

[0014] As a preferred option, the evaluation module includes an evaluation index construction unit. The evaluation index construction unit is used to obtain the flexibility evaluation index by weighting the electrical power of the tie line and the allowable fluctuation rate of the system power. The specific calculation method is as follows: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward volatility of electrical energy during time period t, respectively, and their calculation method is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. The quality index for renewable energy consumption is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind and solar power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

[0015] As a preferred option, the evaluation module also includes a wind power output prediction unit and a photovoltaic power output prediction unit. The wind power output prediction unit is used to calculate the predicted wind power output value by comparing the actual wind power output value with the wind power output prediction error. The calculation formula is as follows: in, This represents the predicted wind power output. This represents the actual output value of wind power. Let represent the wind power output prediction error. The probability density function of the wind power output prediction error is: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The photovoltaic output prediction unit is used to calculate the predicted photovoltaic output value by comparing the actual photovoltaic output value with the photovoltaic output prediction error. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. This represents the photovoltaic power output prediction error, and the probability density function of the photovoltaic power output prediction error is: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

[0016] As a preferred solution, the planning result calculation module includes a normalization unit and a planning objective function unit. The normalization unit is used to normalize the objective function of the distribution network system operating cost and the objective function of the distribution network system wind and solar resource access capacity, respectively, to obtain the normalized objective function of the distribution network system operating cost and the normalized objective function of the distribution network system wind and solar resource access capacity. The objective function of the distribution network system operating cost is as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function for the wind and solar resource access capacity of the power distribution network system is: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. The planning objective function unit is used to add the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function according to different weight ratios to obtain the planning objective function. The formula for the access capacity planning objective function is: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively.

[0017] As a preferred option, the planning result calculation module also includes a flexibility constraint unit. The flexibility constraint unit is used to obtain flexibility constraints based on net load, the upper limit of flexibility resource output, and the lower limit of flexibility resource output. The flexibility constraints are as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error.

[0018] This method acquires data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data. It constructs an access capacity planning model using objective functions derived from the distribution network system's operating cost objective function and the distribution network system's wind and solar resource access capacity objective function. The model is then solved based on the data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data to obtain planning results. These results are then evaluated using flexibility evaluation indicators and new energy consumption quality indicators. The final planning result is selected based on the evaluation results that meet preset conditions. This method allows for the calculation of distributed wind and solar resource planning results in the distribution network system under certain periods of wind and solar curtailment. Furthermore, it utilizes clean energy consumption quality indicators and flexibility evaluation indicators to evaluate the distributed wind and solar resource planning results, thereby improving the accuracy and practicality of the planning results and avoiding resource waste. Attached Figure Description

[0019] Figure 1 : A flowchart illustrating an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention; Figure 2 This is a schematic diagram of the radial operation of an active distribution network, representing an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention. Figure 3 This is a schematic diagram illustrating the relationship between wind speed and power in one embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention. Figure 4 : A schematic diagram illustrating the flexible supply and demand balance of an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention; Figure 5 : A schematic diagram of an active distribution network system structure according to an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention; Figure 6 : A schematic diagram of the system wind and solar resource consumption situation in Scheme 1 of an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention; Figure 7 : A schematic diagram of the system wind and solar resource consumption situation in Scheme 3 of an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention; Figure 8 : A schematic diagram of the system wind and solar resource consumption situation in Scheme 2 of an embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention; Figure 9 : A schematic diagram of the system structure of another embodiment of the distributed wind and solar resource access capacity planning method provided by the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 Please refer to Figure 1 The distributed wind and solar resource access capacity planning method provided in this embodiment of the invention includes steps 101 to 103, each step of which is as follows: Step 101: Obtain data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data.

[0022] In this embodiment, the data of nodes to be planned, planned capacity, energy storage equipment, and power operation data are obtained. It should be noted that the data required before planning the access capacity of distributed wind and solar resources includes, but is not limited to, the following: grid data, equipment data, load data, wind and solar data, and the nodes to be planned for wind and solar equipment and their planned capacity.

[0023] Step 102: Call the preset access capacity planning model to iteratively solve for the nodes to be planned, the capacity to be planned, the energy storage equipment data, and the power operation data to obtain the planning results. Compare the planning results with the preset planning results to obtain the planning error. Stop iterative solving when the planning error is less than the preset error and output the planning results. The access capacity planning model includes the access capacity planning objective function and system constraints. The access capacity planning objective function is constructed by the distribution network system operation cost objective function and the distribution network system wind and solar resource access capacity objective function. The system constraints include system operation constraints and flexibility constraints.

[0024] Optionally, the access capacity planning objective function is constructed using the distribution network system operating cost objective function and the distribution network system wind and solar resource access capacity objective function, specifically as follows: After normalizing the objective functions for the operating cost of the distribution network system and the wind and solar resource access capacity of the distribution network system, we obtain the normalized objective functions for the operating cost of the distribution network system and the normalized objective functions for the wind and solar resource access capacity of the distribution network system. The objective function for the operating cost of the distribution network system is as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function for the wind and solar resource access capacity of the power distribution network system is: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. The normalized objective function for the operating cost of the distribution network system and the normalized objective function for the wind and solar resource access capacity of the distribution network system are added together with different weight ratios to obtain the planning objective function. The formula for the access capacity planning objective function is as follows: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively.

[0025] Optionally, the flexibility constraints are derived from the net load, the upper limit of the flexibility resource output, and the lower limit of the flexibility resource output. The flexibility constraints are as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error.

[0026] In this embodiment, the steady-state operation mode of the active distribution network is mainly radial operation, and its simplified structural diagram is as follows: Figure 2 As shown. For a radial topology network, it can be described by a directed graph, where the direction of power flow from node i to node j is the positive direction of power flow in branch ij. Let j be the set of the end nodes of the branch. Let j be the set of the starting and ending nodes of the branch with node j as the ending node. Figure 2 It can be seen from this that and For complex power, as well as And branch complex impedance Let set B represent the set of all nodes in the network; node 0 is a substation node. In traditional optimal power flow, the voltage remains constant. If an on-load tap changer is installed, the voltage will change with the OLTC transformer ratio; E represents the set of all branches in the network. The network has... Substation Each node and A side road.

[0027] The general model of optimal power flow based on the branch power flow model is as follows: Where p and q represent the active and reactive power injected into the node, respectively; P and Q represent the active and reactive power transmitted through the branch, respectively; V represents the node voltage; and I represents the branch current. , Let B be the set of the first and last nodes of the branches connected to node j, and let E be the set of the branches. , Let the resistance and reactance of branch ij be represented respectively. , , , These represent the power flow of branch ij during time period t. , These represent the active power and reactive power consumed by node j during time period t, respectively. This represents the current in branch ij during time period t. This represents the voltage at node j during time period t. , These represent the upper and lower limits of the current in branch ij, respectively. , These represent the upper and lower voltage limits of node j, respectively.

[0028] The output power of a wind turbine is mainly determined by wind speed, and the wind speed-power function relationship of a wind turbine is as follows: Figure 3 As shown, from Figure 3 It can be seen that when the wind speed At that time, the wind turbine output was zero. At this critical point, the wind turbine begins to generate power, and as the wind speed increases, the output power of the wind turbine shows a non-linear increasing trend; this is called the critical value. To determine the cutoff wind speed. When When the output power of the wind turbine remains constant at its rated output power, this is called the critical value. This is the rated wind speed. When At times, the wind speed is too high, and continuing to operate the wind turbine would pose a certain risk. Therefore, the wind turbine will be shut down. This wind speed is referred to as the "wind speed limit". To determine the cutoff wind speed, considering the wind curtailment behavior of wind turbines, the above relationship is used as the relationship between the predicted power generation of the wind turbine and the wind speed. The wind turbine model can be expressed as: in, This refers to the output power of the wind turbine. The rated output power of the wind turbine is used as its installed capacity for simplified calculations. This refers to the actual wind speed. To cut into wind speed, Rated wind speed, To cut off the wind speed.

[0029] The output power of photovoltaic (PV) power generation equipment is mainly related to the solar irradiance received by the PV array, operating conditions, and ambient temperature. Since PV power generation is most significantly affected by solar irradiance, a power output model that ignores the effect of temperature can be used to simplify calculations. Considering the curtailment of solar power, the PV power generation model can be expressed as: in, The maximum power generation of photovoltaic power predicted at time t is generally taken as... , .

[0030] Energy storage devices in power distribution networks are mainly electrochemical energy storage devices, and their energy storage and release processes can be represented by the following model: in, The energy stored by the energy storage device during time period t. The energy self-loss rate of energy storage equipment. , These refer to the energy storage efficiency and energy release efficiency of energy storage devices, respectively. , These refer to the energy storage and release capacity of the energy storage device. , These are the energy storage and release flags for the energy storage device, respectively. Each flag is a 0-1 variable; a value of 1 indicates that the energy storage device stores or releases energy during time period t, while a value of 0 indicates that the energy storage device does not operate. , These refer to the upper limits of the energy storage and release power of energy storage devices. , These represent the upper and lower limits of energy storage for the energy storage device.

[0031] Gas turbines convert the chemical energy in natural gas into electrical energy output. Their energy conversion and operational constraints are as follows: in, Let be the electrical power output of the gas turbine during time period t. This represents the power generation efficiency coefficient of the gas turbine. This refers to the gas consumption of the gas turbine. , These represent the upper and lower limits of the gas turbine's output, respectively. , The maximum upward and downward climbing power of the gas turbines are distinguished.

[0032] The objective function is to select the lowest operating cost of the distribution network, which is as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function is to maximize the access capacity of wind and solar resources in the power distribution network. The specific objective function is as follows: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. Since the two objective functions have different dimensions, they cannot be compared. Therefore, it is necessary to normalize the two objective functions. The normalization method is as follows: The objective function is processed as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. The cost of generating electricity from gas turbines, For network loss costs, The penalty for abandoning wind and solar power, The cost of generating electricity from wind and solar power. , These represent the maximum and minimum theoretical operating costs of the distribution network, respectively.

[0033] The objective function 2 is processed as follows: in, , These represent the maximum and minimum theoretical wind and solar resource access capacity of the distribution network, respectively.

[0034] Adding the normalized objective function one and the normalized objective function two according to different weight ratios yields the planning objective function, where the formula for the admission capacity planning objective function is: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively.

[0035] It should be noted that the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function can be selected as needed.

[0036] Flexibility supply and demand balance can be achieved through a coordinated and complementary approach involving power generation, grid, load, and storage. Due to the randomness of renewable energy generation and the uncertainty of load forecasting, sufficient margin must be maintained in day-ahead dispatching to accommodate system flexibility. Because of various uncertainties, the power system experiences dynamic changes in net load during operation, as described below. Figure 4 As shown. From Figure 4 It can be observed that the current net load curve lies between the envelopes of the upper and lower limits of the flexibility resource output, thus ensuring that the flexibility resources can meet the system's flexibility requirements and achieve a balance between the supply and demand of flexibility. If the operating state subsequently changes, and power fluctuations occur in the following three different scenarios, the following three situations will arise: In situation one, the fluctuation range of the net load exceeds the maximum value of the flexibility resource output. In this case, due to a lack of flexibility, the system's supply and demand relationship cannot be well balanced. In situation three, the fluctuation range of the net load is less than the lower limit of the flexibility resource. In this case, due to a lack of system flexibility, the system's supply and demand relationship cannot be well balanced. In situation two, the fluctuation of the net load does not exceed the upper and lower limits of the flexibility resource output. In this case, the system's flexibility supply and demand relationship can be balanced. Based on this, the flexibility supply and demand constraints are set as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error.

[0037] Step 103: Evaluate the planning results based on the flexibility evaluation index and the quality index of new energy consumption, and obtain the evaluation results. If the evaluation results meet the preset conditions, the planning results are determined as the final planning results. If the evaluation results do not meet the preset conditions, iterative calculation is performed again based on the preset access capacity planning model to obtain new planning results. The evaluation results of the new planning results are then calculated. This step is repeated until the evaluation results of the new planning results meet the preset conditions, and the new planning results are determined as the final planning results, so that the distribution network system can plan the access capacity of distributed wind and solar resources based on the final planning results.

[0038] Optionally, the flexibility evaluation index is obtained by weighting the electrical power of the tie line and the allowable fluctuation rate of the system power, and the specific calculation method is as follows: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward volatility of electrical energy during time period t, respectively, and their calculation method is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. The quality index for renewable energy consumption is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

[0039] Optionally, the predicted wind power output is calculated by subtracting the predicted wind power output from the actual wind power output, using the following formula: in, This represents the predicted wind power output. This represents the actual output value of wind power. Let represent the wind power output prediction error. The probability density function of the wind power output prediction error is: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The predicted photovoltaic output value is calculated by subtracting the predicted photovoltaic output value from the actual photovoltaic output value. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. This represents the photovoltaic power output prediction error, and the probability density function of the photovoltaic power output prediction error is: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

[0040] In this embodiment, flexibility resources serve as the basis for analyzing flexibility demand response and establishing flexibility evaluation indicators. Therefore, the various flexibility resource models within the distribution network are as follows: Gas turbine units constitute a significant portion of the flexibility adjustment margin provided by system flexibility resources, making them the most widely used flexibility resource. Because flexibility is directional, the flexibility margin of a gas turbine unit is divided into upward and downward flexibility margins. The upward flexibility margin of a gas turbine unit at a given moment should be the minimum value between the unit's upward ramp limit and the difference between its maximum generating power and current power output. Similarly, the downward flexibility margin of a gas turbine unit at a given moment should be the minimum value between its downward ramp limit and the difference between its current power output and minimum generating power. Based on this, the electrical energy flexibility provided by the unit can be expressed as follows: in, , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , These represent the upward and downward ramp rates of the gas turbine, respectively. , These represent the maximum and minimum electrical power output of the gas turbine, respectively.

[0041] In power distribution networks, energy storage devices can serve as a flexibility resource to regulate power fluctuations. Because flexibility is directional, the flexibility margin of energy storage devices is divided into upward and downward flexibility margins. The upward flexibility margin of energy storage devices at a given moment should be the minimum of the difference between the energy output provided by the remaining battery storage capacity and the maximum discharge power and the current discharge power. Similarly, the downward flexibility margin of energy storage devices at a given moment should be the minimum of the difference between the energy output provided by the remaining battery charging capacity and the maximum charging power and the current charging power. Therefore, the energy flexibility provided by the battery is expressed as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These are the maximum discharge and charging power of the battery, respectively. , These represent the maximum and minimum capacities of the battery, respectively. , These represent the discharge and charging power of the battery during time period t, respectively. This represents the state of the battery during time period t.

[0042] To quantitatively analyze the adequacy of distribution network flexibility and better measure its sufficiency, the flexibility evaluation index of the distribution network is defined as the allowable energy volatility rate. The upward and downward flexibility of the system is reflected by the allowable upward and downward volatility rates, respectively. A larger value indicates a more sufficient system flexibility margin. For example, when both the allowable upward and downward volatility rates reach 100%, the system flexibility margin is sufficient; when both allowable upward and downward volatility rates are 0, the system has no flexibility margin. During the day-ahead dispatch phase, a 24-hour dispatch cycle is used, with each hour constituting a dispatch period. At the hourly level, the expression for the system's allowable energy volatility rate is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. Let be the electrical power of the tie line in system time period t, and its specific expression is shown in the following formula: in, Let be the electrical power of the photovoltaic power transmission line during time period t. Let be the electrical power of the wind power on the tie line during time period t. Let be the electrical power output of the gas turbine during time period t. Let t represent the electrical power of the interconnect line for the energy storage device during time period t.

[0043] When evaluating system flexibility, both upward and downward flexibility should be considered. Therefore, the overall flexibility of the system at each time point can be obtained by weighting both, as shown in the following expression: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward fluctuations of electrical energy during time period t, respectively, and their calculation method is shown in the following formula: Within each dispatch interval of the optimized operation of the distribution network, the balance between supply and demand for flexibility must be maintained. Maintaining this balance primarily depends on the adjustment rate of the output of flexibility resources within the system. Simultaneously, the responsiveness of flexibility resource output adjustment also depends on whether the system has sufficiently robust transmission lines. Therefore, as the supporting platform for the flexibility resources of the entire system, the distribution network structure needs to consider the current margin of its branches during operation. The difference between the maximum current a line can withstand and the current flowing through the line at the current moment can be used to represent the line current margin. During system operation, the line current margin of the distribution network cannot be negative. Therefore, the local flexibility constraints of the distribution network can be expressed as follows: in, Let be the current flowing through branch ij at time t. Let be the maximum current allowed to flow through branch ij.

[0044] The maximum allowable capacity for wind and solar resources in the distribution network, obtained under flexible constraints, reflects the limit state of the distribution network under these constraints. Since this constraint does not consider the possibility of wind and solar curtailment, and appropriate curtailment of wind and solar resources at certain times will increase the distribution network's absorption capacity for distributed wind and solar resources, this state usually does not mean that the current distribution network's absorption capacity for wind and solar resources has reached its maximum. Therefore, based on satisfying the flexibility constraints, a strategy for assessing the maximum allowable capacity for distributed wind and solar resources in an active distribution network, considering new energy absorption quality indicators, is proposed.

[0045] The quality indicators for renewable energy consumption are mainly measured by the renewable energy utilization rate. The renewable energy utilization rate is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, Indicates the utilization rate of new energy sources. , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

[0046] The predicted wind power output is calculated by subtracting the predicted wind power output from the actual wind power output. The calculation formula is as follows: in, This represents the predicted wind power output. This represents the actual output value of wind power. Let represent the wind power output prediction error. The probability density function of the wind power output prediction error is: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The predicted photovoltaic output value is calculated by subtracting the predicted photovoltaic output value from the actual photovoltaic output value. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. This represents the photovoltaic power output prediction error, and the probability density function of the photovoltaic power output prediction error is: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

[0047] Furthermore, the forecasting of distribution network flexibility demand is mainly caused by fluctuations in system net load and forecasting errors in wind, solar, and load. Net load is the difference between the sum of the loads at all nodes in the active distribution network and the total output of wind and solar power. In practice, the net load fluctuation of the distribution network system is determined by the actual values ​​of wind, solar, and load, while the forecasting error is determined by the difference between the forecasted results of wind, solar, and load and the actual output, both of which are affected by many factors. Since the forecasting errors of wind, solar, and load output are minimally affected by various random factors, it can be assumed that the forecasting errors of wind, solar, and load output all follow a normal distribution. The forecasting models for wind, solar, and load, and the forecasting model for distribution network flexibility demand are as follows: Within each scheduled period t before the date, the predicted value of photovoltaic power output is the sum of its actual output value and the prediction error: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. The photovoltaic output prediction error is represented by the standard deviation. For a 24-hour photovoltaic output prediction, the average value (expectation) of the photovoltaic prediction error within each scheduling period t can be considered as 0, and the standard deviation can be approximately expressed as: Therefore, photovoltaic prediction error The probability density function can be expressed as: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

[0048] Existing wind power output data indicates that wind power prediction errors do not follow a normal distribution. However, due to the large number of wind turbines and their relatively dispersed geographical distribution, it can be proven using the Central Limit Theorem that wind power prediction errors approximately follow a normal distribution. Therefore, within each day-ahead scheduling period t, the predicted wind power output is the sum of its actual output and the prediction error: in, This represents the predicted wind power output. This represents the actual output value of wind power. The wind power output prediction error represents the average value (expected value) of the prediction error within each scheduling period t for a 24-hour period. The standard deviation can be approximated as: in, This is expressed as the standard deviation of the wind power output prediction error. This refers to the total installed capacity of the wind turbine units. This represents the predicted wind power output.

[0049] Therefore, the probability density function of wind power prediction error can be expressed as: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. This indicates the error in wind power prediction.

[0050] Net load forecasts are typically derived from local historical net load data. It is assumed that the net load forecast consists of the actual net load value and the net load forecast error, which follows a mean of 0 and a standard deviation of [missing value]. If the net load follows a normal distribution, then within each scheduling period t before the day, the predicted net load value is the sum of the actual net load value and the prediction error. The sum of: in, This indicates the net load forecast error. This represents the projected net load. This represents the actual net load value.

[0051] Since current net load forecasting methods are quite mature, and net load exhibits periodicity, it can be assumed that within the net load forecasting range, the standard deviation of the net load forecast for each scheduling period t is equal to the percentage of the predicted net load value. in, This represents the standard deviation of the net load forecast. This represents the predicted net load.

[0052] Therefore, the probability density function of the net load forecast error can be expressed as: in, This indicates the net load forecast error.

[0053] The net load forecast value of the distribution network system is the sum of the wind, solar, and load forecast values. Since the forecast errors of wind, solar, and load are independent and all three follow a normal distribution with an expected value of 0, the net load forecast error of the distribution network system should also follow a normal distribution with an expected value of 0 and a standard deviation of 0. The net load forecast value of the distribution network system is the sum of the actual net load value and the net load forecast error. in, This is expressed as net load forecast error. Expressed as standard deviation, This is expressed as a net load forecast. This is expressed as the actual net load value, which is the actual load value minus the actual wind and solar power output values. in, Represented as the actual net load value, This represents the predicted photovoltaic power output. This represents the predicted wind power output. The standard deviation of the net load forecast error within each scheduling period t is: in, This is expressed as net load forecast error. This represents the standard deviation of the net load forecast. This is expressed as the standard deviation of the wind power output prediction error. It is expressed as the standard deviation of the photovoltaic power output prediction error.

[0054] Therefore, the probability density function of the net load forecast error can be expressed as: in, This indicates the net load forecast error.

[0055] As defined by flexibility requirements, in day-ahead scheduling, each scheduling period... Within this context, the forecast value for the flexibility demand of the distribution network system should be the sum of the fluctuation of the net load forecast value and the net load fluctuation forecast error, i.e. in, This indicates the error in the net load fluctuation forecast. This indicates the fluctuation in the net load forecast. This represents the forecast value for flexibility requirements. This represents the fluctuation in the net load forecast. The expression for the fluctuation in the net load forecast is: in, Indicates the net load forecast value at The fluctuation amount at any given moment Indicates the net load forecast value at The fluctuation amount at any given moment.

[0056] In each scheduling period Within this period, the standard deviation of the net load fluctuation forecast error is: in, This indicates the error in the net load fluctuation forecast. Represented as net load at The square of the prediction error at time 1. Represented as net load at The square of the prediction error at time t.

[0057] Assuming that the net load forecast errors of adjacent scheduling periods are uncorrelated, the net load fluctuation forecast error for each scheduling period should follow a normal distribution with an expected value of 0 and a standard deviation of . Its probability density function can be expressed as: in, This indicates the error in the net load fluctuation forecast. This represents the standard deviation of net load fluctuation.

[0058] Furthermore, since the probability of significant forecast errors in wind, solar, and load forecasts is very small, considering these forecast errors in day-ahead dispatching could lead to excessively large flexibility margins, resulting in increased operating costs and wasted resources. Therefore, to obtain a reasonable distribution network flexibility demand forecasting model, a certain confidence level needs to be considered. Assume the confidence level considered by the decision-maker is... Then, within each scheduling period t, the predicted value of the flexibility demand of the distribution network system is... The scope should be: in, , These represent the upper and lower confidence limits for the predicted flexibility requirements of the distribution network system, respectively. , Considering confidence levels of At that time, the maximum upward flexibility demand and maximum downward flexibility demand of the distribution network system caused by the net load fluctuation prediction error are expressed as follows: Within each scheduling period t, their expressions are: in, , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error.

[0059] As an example of this embodiment, such as Figure 5As shown, this active distribution network system includes two wind and solar access zones. Areas with abundant wind and solar resources are defined as wind and solar access zones, where nodes can connect to wind and solar turbines. Gas turbines with a capacity of 1200kW are connected at nodes 17, 22, and 32, with a ramp rate of 600kW / h and a power generation efficiency of 0.4. Energy storage devices with a capacity of 4000kW are connected at nodes 4, 5, 6, 7, 9, 14, 15, 17, 32, and 33. These energy storage devices have a charge / discharge efficiency of 0.98, an energy self-loss rate of 0.01, and a maximum charge / discharge capacity of 2000kW / h. Continuous reactive power compensation devices with an adjustment range of -0.1 to 0.5MVar are located at nodes 6 and 30.

[0060] To verify the effectiveness of the proposed solution, the following comparative examples are set up.

[0061] (1) Option 1: Only consider objective function 2, that is, the wind and solar resources access capacity of the distribution network is required to be maximized. Calculate the access capacity of wind and solar resources and the clean energy consumption quality index of the system at this time.

[0062] (2) Option 2: Taking into account both objective function 1 and objective function 2, calculate the access capacity of wind and solar resources and the quality index of clean energy consumption of the system at this time.

[0063] (3) Option 3: Without considering wind and solar curtailment, take into account objective function 1 and objective function 2, and calculate the access capacity of wind and solar resources and the quality index of clean energy consumption of the system at this time.

[0064] This led to the determination of the configuration of wind and solar turbine units at each node of the system under three different schemes.

[0065] Table 1. Configuration of wind and solar turbine units at each node in Scheme 1 Table 2. Configuration of wind and solar turbine units at each node in Scheme 3 Table 3. Configuration of wind and solar turbine units at each node in Scheme 2 As mentioned earlier, nodes 7, 8, 9, 10, 30, 31, 32, and 33 of the system are connected to distributed power sources, with a maximum output of 200kW for a single wind turbine and 180kW for a single photovoltaic unit.

[0066] The data in the table shows that since Scheme 1 only requires the maximum access capacity of wind and solar resources without considering the operating cost of the system, the number of wind turbines and photovoltaics connected to each node in Scheme 1 is very large. This indicates that it is unreasonable to only consider the access capacity of wind and solar resources without considering the operating cost of the system.

[0067] Figure 6 , 7 Figures 8 and 9 respectively show the absorption of wind and solar resources under three different schemes. The left side of the figure represents the absorption value of wind and solar resources in each time period, and the right side represents the actual output value of wind and solar power in each time period. It can be seen from the figure that in the period from 8h to 17h, the difference between the output value of wind and solar power in each time period and the absorption value of wind and solar resources in each time period is large in Scheme 1, indicating that the wind and solar curtailment phenomenon is serious in this period. In Scheme 2, the absorption value of wind and solar resources is lower than the output value of wind and solar power only in a few time periods, such as 17h and 21h, indicating that the wind and solar curtailment phenomenon only exists in Scheme 2. In Scheme 3, the absorption value of wind and solar resources is equal to the output value of wind and solar power in any time period, indicating that the wind and solar curtailment phenomenon does not exist in Scheme 3.

[0068] The following are the system wind and solar resource access capacity, wind and solar curtailment, and clean energy consumption quality indicators obtained from the three schemes: Table 4 Results of each scheme Comparing the results of the three schemes reveals the following: Scheme 1, which prioritizes the maximum wind and solar resource access capacity without considering distribution network operating costs, results in severe wind and solar curtailment, low renewable energy utilization, and poor renewable energy absorption quality, despite having the maximum system wind and solar resource access capacity. Scheme 2, which considers both wind and solar resource access capacity and distribution network operating costs, achieves a significantly lower system wind and solar resource access capacity compared to Scheme 1, but also significantly reduces the amount of wind and solar curtailment, resulting in a significantly higher renewable energy utilization rate and improved renewable energy absorption quality. Scheme 3, which requires the system to prevent wind and solar curtailment, achieves a 100% renewable energy utilization rate compared to Scheme 2, but its system wind and solar resource access capacity is significantly lower than both Scheme 2 and Scheme 1.

[0069] Table 5 shows the transaction costs, gas purchase costs, grid loss costs, wind and solar curtailment costs, and wind and solar power generation costs generated by the system under the three schemes.

[0070] Table 5. Costs of Various Options The data in the table shows that Scheme 1 has the highest penalty for wind and solar curtailment, at 947,797 yuan, indicating that the curtailment phenomenon in Scheme 1 is very serious. Scheme 1 also has the highest network loss cost, at 61,316 yuan, and its operating cost is also the highest, at 1,142,173 yuan. The daily operating cost of Scheme 2 is only 11,290 yuan higher than that of Scheme 3. However, combined with the data in Table 4, it can be seen that the wind and solar resource access capacity of Scheme 2 is 59,780 kW higher than that of Scheme 3. This shows that allowing the system to curtail wind and solar, while sacrificing some economic efficiency, can significantly increase the system's wind and solar resource access capacity.

[0071] The simulation results above show that simply requiring the maximum access capacity of wind and solar resources without considering the operating costs of the distribution network will result in high losses during system operation, severe wind and solar curtailment, low utilization rate of new energy sources, and poor absorption quality. Allowing the system to appropriately curtail wind and solar resources during certain periods can significantly improve the access capacity of wind and solar resources while ensuring good absorption quality, thus improving the accuracy and practicality of the scheduling structure.

[0072] Example 2 Accordingly, see Figure 9 , Figure 9 This is a schematic diagram of a distributed wind and solar resource access capacity planning system provided by the present invention. As shown in the figure, the distributed wind and solar resource access capacity planning system includes an acquisition module 901, a planning result calculation module 902, and an evaluation module 903. The specific units of each module are as follows: The acquisition module 901 is used to acquire data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data. The planning result calculation module 902 is used to call a preset access capacity planning model to iteratively solve the node to be planned, the capacity to be planned, the energy storage equipment data, and the power operation data to obtain the planning result. The planning result is compared with the preset planning result to obtain the planning error. The iterative solution is stopped when the planning error is less than the preset error, and the planning result is output. The access capacity planning model includes an access capacity planning objective function and system constraints. The access capacity planning objective function is constructed by the distribution network system operation cost objective function and the distribution network system wind and solar resource access capacity objective function. The system constraints include system operation constraints and flexibility constraints. Evaluation module 903 is used to evaluate the planning results based on flexibility evaluation indicators and new energy consumption quality indicators, and obtain evaluation results. If the evaluation results meet the preset conditions, the planning results are determined to be the final planning results. If the evaluation results do not meet the preset conditions, the system iteratively calculates the new planning results based on the preset access capacity planning model to obtain new planning results. The evaluation results of the new planning results are then calculated. This process is repeated until the evaluation results of the new planning results meet the preset conditions, and the new planning results are determined to be the final planning results, so that the distribution network system can plan the access capacity of distributed wind and solar resources based on the final planning results.

[0073] Optionally, the evaluation module 903 includes an evaluation index construction unit 9031. Among them, the evaluation index construction unit 9031 is used to obtain the flexibility evaluation index by weighting the electrical power of the tie line and the allowable fluctuation rate of the system power. The specific calculation method is as follows: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward volatility of electrical energy during time period t, respectively, and their calculation method is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. The quality index for renewable energy consumption is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind and solar power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

[0074] Optionally, the evaluation module 903 also includes a wind power output prediction unit 9032 and a photovoltaic output prediction unit 9033. Among them, the wind power output prediction unit 9032 is used to calculate the wind power output prediction value by comparing the actual wind power output value with the wind power output prediction error. The calculation formula is as follows: in, This represents the predicted wind power output. This represents the actual output value of wind power. The wind power output prediction error is represented by the probability density function of the wind power output prediction error: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The photovoltaic output prediction unit 9033 is used to calculate the photovoltaic output prediction value obtained by comparing the actual photovoltaic output value with the photovoltaic output prediction error. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. The photovoltaic output prediction error is represented by the probability density function of the photovoltaic output prediction error: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

[0075] Optionally, the planning result calculation module 902 includes a normalization unit 9021 and a planning objective function unit 9022. The normalization unit 9021 is used to normalize the distribution network system operating cost objective function and the distribution network system wind and solar resource access capacity objective function, respectively, to obtain the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function. The distribution network system operating cost objective function is as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function for the wind and solar resource access capacity of the power distribution network system is: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. The planning objective function unit 9022 is used to add the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function according to different weight ratios to obtain the planning objective function. The formula for the access capacity planning objective function is as follows: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively.

[0076] Optionally, the planning result calculation module 902 also includes a flexibility constraint unit 9023. Flexibility constraint unit 9023 is used to obtain flexibility constraints based on net load, the upper limit of flexibility resource output, and the lower limit of flexibility resource output. The flexibility constraints are as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error.

[0077] The aforementioned distributed wind and solar resource access capacity planning system can implement one of the distributed wind and solar resource access capacity planning methods described in the above-described method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0078] For a more detailed explanation of the working principle and procedures of this embodiment, please refer to the relevant description in Embodiment 1.

[0079] Compared to existing technologies, this method obtains data on planned nodes, planned capacity, energy storage devices, and power operation data. It constructs an access capacity planning model using the objective functions of the distribution network system's operating cost and wind and solar resource access capacity. The model is then solved based on the planned nodes, planned capacity, energy storage device data, and power operation data to obtain the planning results. These results are then evaluated using flexibility and renewable energy absorption quality indicators. The final planning result is selected based on the evaluation results that meet preset conditions. This method allows for the calculation of distributed wind and solar resource planning results even when the distribution network system experiences wind and solar curtailment during certain periods. Furthermore, it evaluates the distributed wind and solar resource planning results using clean energy absorption quality and flexibility indicators, thereby improving the accuracy and practicality of the planning results and avoiding resource waste.

[0080] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A distributed wind and solar resource access capacity planning method, characterized in that, include: Acquire data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data; The pre-defined access capacity planning model is invoked to iteratively solve the node to be planned, the capacity to be planned, the energy storage device data, and the power operation data to obtain the planning result. The planning result is compared with the pre-defined planning result to obtain the planning error. The iterative solution stops when the planning error is less than the pre-defined error, and the planning result is output. The access capacity planning model includes an access capacity planning objective function and system constraints. The access capacity planning objective function is constructed through the distribution network system operating cost objective function and the distribution network system wind and solar resource access capacity objective function, specifically: After normalizing the objective function of the distribution network system operating cost and the objective function of the distribution network system wind and solar resource access capacity, the normalized objective functions of the distribution network system operating cost and wind and solar resource access capacity are obtained. The objective function of the distribution network system operating cost is as follows: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function for the wind and solar resource access capacity of the power distribution network system is: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. The normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function are added together with different weight ratios to obtain the planning objective function, wherein the formula for the access capacity planning objective function is: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively. The system constraints include system operation constraints and flexibility constraints. The flexibility constraints are derived from the net load, the upper limit of flexibility resource output, and the lower limit of flexibility resource output. The flexibility constraints are as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error; The planning results are evaluated based on flexibility evaluation indicators and new energy consumption quality indicators to obtain evaluation results. If the evaluation results meet the preset conditions, the planning results are determined as the final planning results. If the evaluation results do not meet the preset conditions, the iterative calculation is performed again according to the preset access capacity planning model to obtain new planning results. The evaluation results of the new planning results are then calculated. This process is repeated until the evaluation results of the new planning results meet the preset conditions, and the new planning results are determined as the final planning results, so that the distribution network system can plan the access capacity of distributed wind and solar resources according to the final planning results.

2. The distributed wind and solar resource access capacity planning method as described in claim 1, characterized in that, The flexibility evaluation index is obtained by weighting the electrical power of the tie line and the allowable fluctuation rate of the system power, and the specific calculation method is as follows: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward volatility of electrical energy during time period t, respectively, and their calculation method is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. The aforementioned quality index for new energy consumption is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

3. The distributed wind and solar resource access capacity planning method as described in claim 2, characterized in that, The predicted wind power output is calculated by subtracting the predicted wind power output from the actual wind power output, and the calculation formula is as follows: in, This represents the predicted wind power output. This represents the actual output value of wind power. The wind power output prediction error is represented by the probability density function of the wind power output prediction error: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The predicted photovoltaic output value is calculated by subtracting the actual photovoltaic output value from the predicted photovoltaic output error. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. The photovoltaic output prediction error is represented by the probability density function of the photovoltaic output prediction error: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

4. A distributed wind and solar resource access capacity planning system, characterized in that, include: The acquisition module is used to acquire data on nodes to be planned, capacity to be planned, energy storage devices, and power operation data. The planning result calculation module is used to call a preset access capacity planning model to iteratively solve the node to be planned, the capacity to be planned, the energy storage equipment data, and the power operation data to obtain the planning result. The planning result is compared with the preset planning result to obtain the planning error. Iterative solving stops when the planning error is less than the preset error, and the planning result is output. The access capacity planning model includes an access capacity planning objective function and system constraints. The access capacity planning objective function is constructed through the distribution network system operating cost objective function and the distribution network system wind and solar resource access capacity objective function. The system constraints include system operation constraints and flexibility constraints. The planning result calculation module includes a normalization unit and a planning objective function unit. The normalization unit is used to normalize the distribution network system operating cost objective function and the distribution network system wind and solar resource access capacity objective function respectively to obtain normalized distribution network system operating cost objective function and normalized distribution network system wind and solar resource access capacity objective function. The distribution network system operating cost objective function is: in, The cost of purchasing and selling electricity between the transformer node and the upstream power grid. For real-time electricity prices, This refers to the power exchanged between the transformer and the upstream power grid. The cost of generating electricity from gas turbines, For natural gas prices, The calorific value of natural gas. For the output power of the gas turbine, For the number of gas turbines connected, For network loss costs, For network loss power, The penalty for abandoning wind and solar power, To punish prices, , These represent the power of wind and solar power curtailment, respectively. The cost of generating electricity from wind and solar power. , The prices are for wind and solar power generation, respectively. , These represent the power generated by wind and solar power, respectively. This refers to the number of wind turbine units connected to the grid. This refers to the number of photovoltaic power generation units connected to the grid. The objective function for the wind and solar resource access capacity of the power distribution network system is: in, This represents the actual power absorbed by the wind and solar resources at node i to be planned. This is the set of nodes corresponding to the wind and solar resources. The planning objective function unit is used to add the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function according to different weight ratios to obtain the planning objective function, wherein the formula for the access capacity planning objective function is: in, This represents the objective function for access capacity planning. This represents the normalized objective function for the operating cost of the distribution network system. This represents the normalized objective function for the wind and solar resource access capacity of the distribution network system. , These represent the weights of the normalized distribution network system operating cost objective function and the normalized distribution network system wind and solar resource access capacity objective function, respectively. The planning results calculation module also includes a flexibility constraint unit. The flexibility constraint unit is used to obtain flexibility constraint conditions based on net load, the upper limit of flexibility resource output, and the lower limit of flexibility resource output. The flexibility constraint conditions are as follows: in, , These represent the upward and downward adjustment flexibility margins of the battery's power supply during time period t. , These represent the upward and downward adjustment flexibility margins of the electrical energy that the gas turbine can provide during time period t. , Considering confidence levels of At that time, the maximum upward flexibility requirement and the maximum downward flexibility requirement of the distribution network system due to the net load fluctuation prediction error; The evaluation module is used to evaluate the planning results based on flexibility evaluation indicators and new energy consumption quality indicators, and obtain evaluation results. If the evaluation results meet the preset conditions, the planning results are determined to be the final planning results. If the evaluation results do not meet the preset conditions, the system iteratively calculates the new planning results based on the preset access capacity planning model to obtain new planning results. The evaluation results of the new planning results are then calculated. This process is repeated until the evaluation results of the new planning results meet the preset conditions, and the new planning results are determined to be the final planning results, so that the distribution network system can plan the access capacity of distributed wind and solar resources based on the final planning results.

5. The distributed wind and solar resource access capacity planning system as described in claim 4, characterized in that, The evaluation module includes an evaluation index construction unit. The evaluation index construction unit is used to obtain the flexibility evaluation index by weighting the electrical power of the tie line and the allowable fluctuation rate of the system power. The specific calculation method is as follows: in, Let t be the allowable fluctuation rate of electrical energy. , These are the weighting coefficients for the allowable upward and downward volatility of electrical energy during time period t, respectively, and their calculation method is as follows: in, , These represent the allowable upward and downward volatility of electrical energy during time period t, respectively. The aforementioned quality index for new energy consumption is the ratio of the actual to the predicted value of wind and solar power output, taking into account wind and solar curtailment. Specifically: in, , These are the predicted values ​​for wind power output and solar power output, respectively. , These are the actual wind power output and the actual solar power output, respectively. The actual wind power output equals the predicted wind and solar power output minus the amount of wind power curtailment, and the actual solar power output equals the predicted solar power output minus the amount of solar power curtailment. in, , These are the actual output values ​​of wind power and photovoltaic power, respectively. , These are respectively the amount of wind curtailment and the amount of solar curtailment.

6. The distributed wind and solar resource access capacity planning system as described in claim 4, characterized in that, The evaluation module also includes a wind power output prediction unit and a photovoltaic power output prediction unit. The wind power output prediction unit is used to calculate the predicted wind power output value by comparing the actual wind power output value with the wind power output prediction error. The calculation formula is as follows: in, This represents the predicted wind power output. This represents the actual output value of wind power. The wind power output prediction error is represented by the probability density function of the wind power output prediction error: in, This is expressed as the standard deviation of the wind power output prediction error. , This refers to the total installed capacity of the wind turbine units. The photovoltaic output prediction unit is used to calculate the predicted photovoltaic output value by comparing the actual photovoltaic output value with the photovoltaic output prediction error. The calculation formula is as follows: in, This represents the predicted photovoltaic power output. This represents the actual value of photovoltaic power output. The photovoltaic output prediction error is represented by the probability density function of the photovoltaic output prediction error: in, This is expressed as the standard deviation of the photovoltaic power output prediction error. .

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