Power factor segmentation-based pricing method for active support optimization of reactive voltage in power supply area

Abstract

The application discloses a power factor segmentation-based active support optimization method for reactive voltage of a power supply area, and belongs to the technical field of active distribution network optimization operation and reactive voltage control. The method obtains power supply area network, load, distributed power supply, electricity price and adjustment equipment operation data, determines the power factor according to active power and reactive power exchanged between the power supply area and a superior power grid, and determines the reactive power purchase and sale price according to the power factor. A target function containing network loss, active and reactive power purchase and sale, reactive compensation and abandoned power cost is constructed, on-load voltage regulation transformer, shunt capacitor group, static reactive compensator, energy storage system and distributed power supply operation constraint are established, and branch power flow constraint is second-order cone relaxed. The mixed integer second-order cone optimization model is solved, and equipment control amounts of each dispatching period are obtained. The application can realize local reactive power balance, active voltage support, network loss reduction and new energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of active distribution network optimization operation and reactive voltage control technology, specifically involving an active support optimization method for reactive voltage in power supply areas based on power factor segmented pricing. Background Technology

[0002] As the penetration rate of distributed photovoltaic and wind power and other new energy sources in the distribution network continues to increase, the power flow of the distribution network is gradually changing from traditional unidirectional power supply to multi-source bidirectional interaction. The fluctuations in new energy output and load changes are superimposed, which can easily cause node voltage exceeding limits, increased reactive circulating current in lines, and increased network losses. In order to improve the regulation capability of the power supply area, existing technologies usually divide the distribution network into several power balancing units and utilize on-load tap changers (OLTC), capacitor banks (CB), static var compensators (SVC), battery energy storage systems (BESS), and distributed power sources to participate in reactive power and voltage regulation.

[0003] However, existing power supply area optimization methods still have the following problems: Firstly, reactive power is usually only used as a constraint to meet voltage safety requirements, without taking the cost of reactive power purchase and sale as an independent economic variable. This makes it difficult to guide the power supply area to reduce the purchase of reactive power and achieve local reactive power balance through price signals. Secondly, active power dispatch and reactive power optimization are often carried out separately, without fully considering the coupling relationship between renewable energy output, energy storage charging and discharging, power factor and reactive power purchase and sale costs, making it difficult to balance voltage safety, renewable energy consumption, grid loss reduction and operation economy. Third, discrete control devices such as OLTC and CB are often processed continuously, which cannot accurately reflect gear changes, switching groups and action times constraints, and it is also difficult to coordinate control with continuous control resources such as SVC.

[0004] Therefore, there is an urgent need for an active reactive voltage support optimization method that can comprehensively consider power factor segmented pricing, bidirectional exchange of active and reactive power, and the coordinated operation of various discrete and continuous adjustment resources. Summary of the Invention The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a reactive power and voltage active support optimization method for power supply areas based on power factor segmented pricing. This method solves the technical problems of existing power supply area optimization methods failing to utilize power factor segmented pricing to guide local reactive power balance, disconnecting active power dispatch and reactive power and voltage optimization, and difficulty in accurately coordinating discrete and continuous regulation resources, resulting in insufficient node voltage regulation capability, large reactive power transmission loss, and poor operational economy.

[0005] The present invention adopts the following technical solution: The active reactive voltage support optimization method for power supply areas based on power factor segmented pricing includes the following steps: S1. Obtain the network topology, line parameters, load forecast data, distributed power generation forecast output data, active power purchase and sale price, and operating parameters of each regulating device of the power balance unit in the power supply area. S2. Determine the power factor for each scheduling period based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid, and determine the reactive power purchase price and reactive power sales price for the corresponding scheduling period based on the preset power factor interval to which the power factor belongs. The reactive power purchase price and reactive power sales price are used as reactive power purchase and sales cost parameters to participate in the solution of the mixed integer second-order cone optimization model. S3. With the goal of minimizing the total operating cost of the power balance unit in the power supply area, establish an objective function. The total operating cost includes network loss cost, active power purchase and sale cost, reactive power purchase and sale cost determined based on the reactive power purchase price and reactive power sale price, reactive power compensation device operating cost, and distributed power curtailment cost. S4. Establish the operating constraints of the power balance unit in the power supply area. The operating constraints include node active and reactive power balance constraints, branch power flow constraints obtained through second-order cone relaxation, node voltage constraints, line current constraints, on-load tap changer tap position and number of operations constraints, parallel capacitor bank switching capacity and number of operations constraints, static var compensator reactive power output constraints, energy storage system charging and discharging state and state of charge constraints, and distributed power output constraints. S5. Using the active and reactive power exchange, on-load tap changer tap position, number of parallel capacitor banks switched on and off, reactive power output of static var compensator, charging and discharging power of energy storage system, and actual output of distributed power source as decision variables, construct a mixed integer second-order cone optimization model based on the objective function and operating constraints, and solve it to obtain the operating control quantity of the regulating equipment for each scheduling period. Then, determine the operating control quantity as the corresponding scheduling setpoint for on-load tap changer, parallel capacitor bank, static var compensator, and energy storage system.

[0006] Further, in step S2, the power factor range is divided by a preset power factor threshold; when the power factor is not lower than the preset power factor threshold, the reactive power purchase price and the reactive power sales price are set as a first price; when the power factor is lower than the preset power factor threshold, the reactive power purchase price and the reactive power sales price are set as a second price and a third price that are higher than the first price, respectively, and the second price is higher than the third price.

[0007] Further, in step S3, the network loss cost is determined based on the square of the current amplitude of each branch, the branch resistance, and the network loss price; the active power purchase and sale cost and the reactive power purchase and sale cost are determined based on the active power and reactive power exchanged with the upper-level power grid during each dispatch period and the corresponding purchase and sale price; the distributed power curtailment cost is determined based on the difference between the predicted output and the actual output of photovoltaic and wind power.

[0008] Furthermore, in step S4, the constraints on the tap position and number of operations of the on-load tap-changing transformer include: The on-load tap changer tap position is determined by the tap position identifier variable for each scheduling period. The up-shift or down-shift action is determined based on the tap position change between adjacent scheduling periods, and the total number of tap position actions within the optimization cycle is limited to not exceeding the preset maximum number of actions.

[0009] Furthermore, in step S4, the constraints on the switching capacity and number of operations of the parallel capacitor bank include: The reactive power compensation of the parallel capacitor bank is determined based on the number of operating groups and the compensation capacity of a single group, and the number of operating groups and the number of switching cycles within the optimization period are limited; the reactive power output constraint of the static var compensator includes limiting its reactive power compensation power between a preset lower limit and a preset upper limit.

[0010] Furthermore, in step S4, the constraints on the charge / discharge state and state of charge of the energy storage system include: The energy storage system is restricted to not charge and discharge simultaneously within the same scheduling period by using charging state variables and discharging state variables. The energy storage capacity between adjacent scheduling periods is updated based on charging power, discharging power, charging efficiency, and discharging efficiency, and the energy storage capacity is limited to between a preset lower limit and a preset upper limit.

[0011] Further, in step S4, the distributed power output constraint includes: The actual active power output of photovoltaic and wind power during each dispatch period is limited to the corresponding predicted maximum output, and the correspondence between the reactive power output and active power output of photovoltaic and wind power is determined according to the preset power factor angle.

[0012] Further, in step S4, the branch power flow constraints obtained through second-order cone relaxation include node voltage drop constraints, branch active power balance constraints, branch reactive power balance constraints, and second-order cone constraints. The second-order cone constraints are used to limit the product of the square of the branch current amplitude and the square of the voltage amplitude at the branch's head node to be no less than the sum of the square of the branch's active power and the square of the reactive power.

[0013] Furthermore, in step S5, the mixed integer second-order cone optimization model is jointly solved according to multiple consecutive scheduling periods to obtain the on-load tap changer tap position, the number of parallel capacitor banks in operation, the reactive power output of the static var compensator, the charging and discharging power of the energy storage system, the actual output of the distributed power source, and the active and reactive power exchange power with the upper-level power grid corresponding to each scheduling period.

[0014] Secondly, embodiments of the present invention provide a reactive power and voltage active support optimization system for power supply areas based on power factor segmented pricing, comprising: The data acquisition module is used to acquire the network topology, line parameters, load forecast data, distributed power generation forecast output data, active power purchase and sale price, and operating parameters of various regulating devices of the power balance unit in the power supply area. The segmented pricing module is used to determine the power factor based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid, and to determine the reactive power purchase price and reactive power sales price based on the preset power factor interval to which the power factor belongs. The reactive power purchase price and reactive power sales price are used as reactive power purchase and sales cost parameters to participate in the solution of the mixed integer second-order cone optimization model. The model building module is used to establish objective functions including network loss costs, active power purchase and sale costs, reactive power purchase and sale costs, reactive power compensation device operating costs, and distributed power curtailment costs, and to establish node power balance constraints, branch power flow constraints, voltage constraints, current constraints, and operating constraints of each regulating device. The optimization and solution module is used to construct and solve the mixed integer second-order cone optimization model based on the objective function and running constraints; The control output module is used to output the on-load tap changer tap position, the number of parallel capacitor banks in operation, the reactive power output of the static var compensator, the charging and discharging power of the energy storage system, and the actual output of the distributed power source for each scheduling period.

[0015] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for optimizing reactive voltage support in power supply areas based on power factor segmented pricing.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for optimizing reactive voltage support in power supply areas based on power factor segmented pricing.

[0017] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described active reactive voltage support optimization method for power supply areas based on power factor segmented pricing.

[0018] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described active support optimization method for reactive voltage in power supply areas based on power factor segmented pricing.

[0019] Compared with the prior art, the present invention has at least the following beneficial effects: The reactive power and voltage active support optimization method for power supply areas based on power factor segmented pricing incorporates power factor segmented reactive power pricing, bidirectional active and reactive power exchange, network losses, and various adjustment resources into a unified optimization framework. This allows for the simultaneous determination of power exchange and equipment control quantities based on the actual operating status of the power supply area. It changes the previously separate operation mode of active power dispatch and reactive power and voltage control, achieving synergistic optimization among local reactive power balance, active node voltage support, network loss reduction, and renewable energy consumption.

[0020] Furthermore, by determining the reactive power purchase price and reactive power sales price based on the power factor range, the cost of purchased reactive power can be increased when the power factor decreases, and the revenue from electricity sales can be used to incentivize local reactive power resources to participate in regulation. By changing the price parameter in the optimization model's selection of reactive power exchange schemes, the excessive dependence of the power supply area on the reactive power of the upstream power grid can be reduced.

[0021] Furthermore, incorporating network losses, active power purchase and sale, reactive power purchase and sale, compensation device costs, and curtailment costs into the objective function can prevent simply reducing power purchase costs from leading to increased network losses, excessive equipment operation, or curtailment of renewable energy. Different dispatch schemes are evaluated from the perspective of overall system operating costs.

[0022] Furthermore, by constraining the OLTC's range and number of operations, the OLTC can implement graded voltage regulation based on changes in node voltage, while avoiding frequent range switching. This balances voltage regulation capability, mechanical contact life, and equipment operational reliability.

[0023] Furthermore, by combining discrete switching of the CB with continuous regulation of the SVC, the CB is responsible for basic compensation and coarse adjustment, while the SVC is responsible for dynamic compensation and fine adjustment. This balances a wide range of reactive power compensation capabilities with continuous voltage regulation accuracy, while limiting frequent switching of the CB.

[0024] Furthermore, by employing charging and discharging mutual exclusion, power limiting, energy recursion, and state-of-charge range constraints, BESS can respond to time-of-use pricing and changes in renewable energy output while avoiding overcharging and over-discharging. Energy storage can be used for peak shaving and valley filling, source-load matching, and to ensure the safe operation of energy storage.

[0025] Furthermore, the actual output of photovoltaic and wind power will be limited to within the predicted maximum output, and a power factor relationship between active and reactive power will be established, enabling distributed power sources to participate in reactive power regulation. This will improve the absorption capacity of new energy sources and fully utilize the reactive power regulation capabilities of inverter-type power sources.

[0026] Furthermore, by employing second-order cone relaxation to address the non-convex relationship between branch currents, voltages, and power, the original non-convex power flow model can be transformed into a convex optimization model that can be solved efficiently. This improves the solution efficiency of multi-time-period optimization problems while maintaining the physical relationship between branch power flow and node voltage constraints.

[0027] Furthermore, by jointly solving multiple consecutive scheduling periods, constraints such as changes in energy storage capacity, the number of OLTC actions, and the number of CB switching can be considered simultaneously. This avoids conflicts in control quantities between different time periods caused by independent hourly optimization, ensuring that the resulting scheduling scheme has temporal continuity and is executable.

[0028] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0029] In summary, this invention incorporates bidirectional active and reactive power exchange, second-order cone power flow, and various regulation resources into a unified optimization through power factor-based segmented reactive power pricing. It coordinates OLTC, CB, SVC, and energy storage to implement tiered voltage regulation and local reactive power balance, taking into account renewable energy consumption, reduced grid losses, equipment lifespan, and economic operation of the power supply area.

[0030] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0031] Figure 1 A schematic diagram of an improved 33-node distribution network; Figure 2 This is a schematic diagram of the electricity purchase and sale price. Figure 3 This is a schematic diagram illustrating the photovoltaic power consumption situation. Figure 4 This is a schematic diagram illustrating the wind power consumption situation. Figure 5 A schematic diagram illustrating the purchase and sale of active and reactive power. Figure 6 is a schematic diagram of the charging and discharging of the energy storage units, where (a) shows the charging and discharging of energy storage unit 1 and (b) shows the charging and discharging of energy storage unit 2. Figure 7 This is a schematic diagram showing the switching status of the reactive power compensation device; Figure 8 This is a schematic diagram showing the on-load tap changer switching operation. Figure 9 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 10 This is a block diagram of a chip according to an embodiment of the present invention; Figure 11 This is a flowchart of the method of the present invention.

[0032] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0033] 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, not all, of the embodiments of the present invention. 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.

[0034] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0035] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0037] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0038] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0039] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0040] This invention provides an active reactive power and voltage support optimization method for power supply areas based on power factor segmented pricing. Taking the improved IEEE 33-bus system as an example, an active reactive power and voltage support optimization model considering various reactive power regulation resources is constructed. First, the second-order cone relaxation (SOCR) technique is introduced to relax the non-convex power flow constraints of distribution network branches into convex constraints, transforming the original problem into a convex optimization problem that can be solved efficiently. Second, a hybrid integer optimization framework considering active power time-of-use pricing and reactive power factor segmented pricing is established, in which the reactive power purchase price increases in a stepwise manner as the power factor of the power supply area decreases, guiding local reactive power balance and voltage quality improvement through price signals. Finally, the discrete switching of parallel capacitor banks, continuous adjustment of static var compensators, tap position operation of on-load tap changers, and state of charge management of energy storage are comprehensively considered to achieve spatiotemporal coordination of multiple discrete and continuous regulation resources.

[0041] Please see Figure 11 This invention discloses an active optimization method for reactive power and voltage support in power supply areas based on power factor-based segmented pricing. Taking the power balance unit of the power supply area as the optimization object, and considering active power purchase and sale, reactive power purchase and sale, network losses, reactive power compensation costs, and renewable energy curtailment costs, it coordinates the operating states of on-load tap changers (OLTC), capacitor banks (CB), static var compensators (SVC), battery energy storage systems (BESS), photovoltaic (PV), and wind turbines (WT). The optimization cycle in this embodiment is 24 hours, with each scheduling period lasting 1 hour. The specific steps include: S1. Obtain power supply area operation data Obtain the network topology, line resistance, line reactance, line current carrying capacity, node voltage limits, load forecast data, photovoltaic forecast output, wind power forecast output, active power purchase and sale price, and operating parameters of OLTC, CB, SVC, and BESS for the power balance unit in the power supply area.

[0042] Among them, OLTC is used to perform graded regulation of substation outlet voltage by changing the tap position; CB is used to provide discrete reactive power compensation; SVC is used to perform continuous reactive power regulation; BESS is used to perform charging and discharging power regulation; PV and WT are used to provide active power to the power supply area and participate in reactive power regulation according to the preset power factor.

[0043] S2. Determine the reactive power purchase and sale price. The power factor is determined based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid, and the reactive power purchase price and reactive power sales price are determined according to the range in which the power factor is located.

[0044] In this embodiment, 0.85 is used as the power factor threshold, and the reactive power purchase and sale price is expressed as follows:

[0045] in, For the first The reactive power purchase price for each dispatch period, For the first The reactive power sales price for each dispatch period, For the power balancing unit of the power supply area in the first Power factor for each scheduling period.

[0046] When the power factor is below the threshold, the reactive power purchase price is increased and a reactive power sales revenue is set to suppress the excessive acquisition of reactive power from the upper-level power grid by the power supply area, and to guide the power supply area to call on local reactive power regulation resources to achieve local reactive power balance.

[0047] The power factor for each scheduling period is determined based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid. The reactive power purchase price and reactive power sales price for the corresponding scheduling period are determined based on a comparison between the power factor and a preset power factor threshold. When the power factor is not lower than the preset power factor threshold, the reactive power purchase price and reactive power sales price adopt a first price. When the power factor is lower than the preset power factor threshold, the reactive power purchase price is increased and a corresponding reactive power sales price is determined, so that the reactive power purchase and sales prices participate in the calculation of the power supply area's operating costs and the optimization solution of the regulation resource control quantity.

[0048] S3. Constructing the objective function The optimization objective is to minimize the sum of network loss costs of the power balance unit in the power supply area, active power purchase and sale costs to the upper-level power grid, reactive power purchase and sale costs, reactive power compensation device costs, and wind and solar power curtailment costs. The objective function is expressed as:

[0049] in, For scheduling time period identification; For the set of scheduling periods; The scheduling period is 1 hour in this embodiment; For branch set; Price for network loss; branch road In the The square of the current amplitude over each time period; branch road The resistance; The cost of purchasing and selling electricity; The cost of reactive power purchase and sale; Cost of reactive power compensation device; Costs associated with curtailing wind and solar power.

[0050] 1. Cost of reactive power compensation device The discounted cost of a parallel capacitor bank is expressed as follows:

[0051] in, This refers to the number of capacitor banks connected in parallel. The fixed investment cost per unit capacity of parallel capacitor banks is expressed in yuan / kvar. For nodes Parallel capacitor bank at the first The change in reactive power compensation over a given period; The total service life of the capacitor bank is typically 10 to 15 years. To represent the average utilization rate, this embodiment takes... .

[0052] 2. Active power purchase and sale cost The active power purchase and sale cost to the upstream power grid is expressed as:

[0053] in, and The first The active power purchase price and active power sales price for each dispatch period; and They are nodes In the The active power purchased and active power sold are exchanged with the upper-level power grid during each dispatch period.

[0054] 3. Reactive power purchase and sale costs The reactive power purchase and sale cost to the upstream power grid is expressed as:

[0055] in, and They are nodes In the The reactive power purchased and the reactive power sold during each dispatch period.

[0056] 4. Costs of wind and solar power curtailment The costs of wind and solar power curtailment are expressed as follows:

[0057] in, and These are the unit curtailment penalty costs for photovoltaic and wind power, respectively. and They are nodes exist Actual and projected power output of PV during the time period; and They are nodes exist The actual and predicted power output of WT during the time period.

[0058] S4. Establish operational constraints 1. OLTC constraint OLTC transforms the balancing node voltage into an optimized variable that can be adjusted according to changes in load and distributed power output by changing the tap position. A 0-1 identifier variable represents the OLTC tap position for each scheduling period, and up-tap and down-tap identifier variables represent the tap position changes between adjacent periods.

[0059] OLTC can perform at most one gear shifting action within the same scheduling period, and the number of actions within the entire optimization cycle will not exceed the preset maximum number of actions.

[0060]

[0061] in, All are 0-1 identifier variables, respectively OLTC gear positions 1, 2, and highest gear during the time period ; It is a set of nodes that includes transformers; and They are respectively The indicator for changes in transformer tap position adjustment during different time periods is a 0-1 variable. For transformer tap position; This is the highest gear setting; for The maximum number of times the transformer tap can be adjusted within a given time period.

[0062] By limiting the range of gears and the number of actions, the impact of frequent actions on the lifespan and operational reliability of mechanical contacts can be reduced while achieving node voltage regulation.

[0063] 2. CB constraint node The reactive power compensation at CB is expressed as:

[0064] in, For nodes During the period The total power of the CBs put into operation in the interior; For nodes exist The number of operational units during a given time period; The compensation power for each CB group; It is the set of nodes including CB; For nodes Maximum number of connected CB groups. To ensure equipment lifespan and operational economy, the total number of CB operations within multiple time periods should also be limited, i.e. 3. SVC Constraints The reactive power compensation of the SVC satisfies:

[0065] in, For nodes SVC in The magnitude of the time-period compensation power; and They are nodes The upper and lower limits of SVC compensation power; Let be the set of nodes including SVC. To address the voltage limit violation issue caused by a high proportion of distributed power sources, let the lower limit constraint for SVC compensation be . .

[0066] In this embodiment, the lower limit of reactive power of the SVC is less than zero, so that the SVC can both inject reactive power into the grid and absorb reactive power, thereby adapting to the different voltage regulation requirements during the peak generation period of distributed power sources and the peak load period.

[0067] 4. BESS constraint Energy storage systems cannot be in a charging and discharging state simultaneously.

[0068] The charging and discharging power meets the following requirements:

[0069] Energy storage capacity is updated according to the following formula:

[0070] The optimization cycle satisfies the cyclic constraint at both ends:

[0071] Energy storage capacity meets:

[0072]

[0073] in, It is a set of nodes that includes energy storage devices; and They are nodes Energy storage The charging and discharging status during a given period; and Representing nodes respectively Energy storage Charging and discharging power during a given time period; and These are the maximum values ​​of the charging power and discharging power of the energy storage device, respectively. For nodes Energy storage Electricity consumption during the period; and They are nodes The maximum and minimum energy storage lifetime values ​​at the energy storage location; The charging efficiency is [value missing]. This represents the discharge efficiency coefficient.

[0074] 5. PV and WT constraints Distributed power generation integration schemes primarily consider the operating conditions that allow for the curtailment of electricity. With advancements in power electronics technology, some distributed power sources utilize constant power factor control strategies to achieve coordinated active and reactive power output. The reactive power output varies proportionally with the active power output according to a fixed power factor, thereby influencing the reactive voltage characteristics of the power grid.

[0075] in, It is a set of nodes including PV; It is a set of nodes including WT; For nodes exist The amount of reactive power of PV during the time period; For nodes exist The amount of reactive power of WT during the time period; and These are the power factor angles for PV and WT, respectively.

[0076] 6. Node Injection Power Constraints The active power injected into a node is expressed as:

[0077] The reactive power injection at a node is expressed as:

[0078] in, and These are the active and reactive loads of the nodes, respectively.

[0079] 7. Second-order cone power flow constraints The relationship of branch voltage drop is expressed as follows:

[0080] Applying a second-order cone relaxation to the non-convex equation between branch current and power, we obtain:

[0081] The active and reactive power flow balance of a node are expressed as follows:

[0082] in, These are the first and last nodes of the line; For nodes During the period The square of the voltage amplitude; For the line The resistance; For the line The reactance; and The lines are respectively During the period The active and reactive power within the system; For the line During the period The square of the branch current amplitude; , They are nodes During the period Internally injected active and reactive power; Represents a node The child nodes. The direction of power is indicated by Flow direction .

[0083] 8. Voltage constraint Node voltages satisfy:

[0084] in, and They are nodes The upper and lower limits of the square of the voltage amplitude.

[0085] 9. Current constraint Branch current satisfies:

[0086] in, For the line The square of the upper limit of the current amplitude constraint for the segment.

[0087] S5. Solve the optimization model Using active and reactive power exchange, OLTC tap position, number of CB units in operation, SVC reactive power output, BESS charging and discharging power, actual photovoltaic power output, and actual wind power output as decision variables, a mixed integer second-order cone optimization model is constructed based on the objective function and the above operating constraints.

[0088] The model is solved jointly over multiple time periods to obtain the OLTC tap position, the number of CB groups in operation, the reactive power output of SVC, the charging and discharging power of BESS, the actual output of distributed power sources, and the active and reactive power exchange power with the upper-level power grid for each scheduling period. Based on this, a reactive voltage active support scheduling scheme for the power supply area is formed.

[0089] This invention sets segmented reactive power purchase and sale prices based on the power factor of the power supply area, and uses these prices as parameters for reactive power purchase and sale costs in the optimization solution. This allows the power supply area to prioritize the use of local reactive power regulation resources, reducing line losses and node voltage deviations caused by long-distance reactive power transmission. By incorporating the discrete regulation of OLTC and CB, the continuous regulation of SVC, and the cross-time-period charging and discharging regulation of BESS into a mixed integer second-order cone optimization model, it achieves coordinated voltage level regulation, coarse and fine reactive power adjustment, and active power peak shaving and valley filling, thereby improving the power supply area's active reactive power voltage support capability and renewable energy absorption capacity.

[0090] In another embodiment of the present invention, a power supply area reactive voltage active support optimization system based on power factor segmented pricing is provided. This system can be used to implement the above-mentioned power supply area reactive voltage active support optimization method based on power factor segmented pricing. Specifically, the power supply area reactive voltage active support optimization system based on power factor segmented pricing includes a data acquisition module, a segmented pricing module, a model construction module, an optimization solution module, and a control output module.

[0091] The data acquisition module is used to acquire the network topology, line parameters, load forecast data, distributed power generation forecast output data, active power purchase and sale price, and operating parameters of various regulating devices of the power balance unit in the power supply area. The segmented pricing module is used to determine the power factor based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid, and to determine the reactive power purchase price and reactive power sales price based on the preset power factor range to which the power factor belongs. The model building module is used to establish objective functions including network loss costs, active power purchase and sale costs, reactive power purchase and sale costs, reactive power compensation device operating costs, and distributed power curtailment costs, and to establish node power balance constraints, branch power flow constraints, voltage constraints, current constraints, and operating constraints of each regulating device. The optimization and solution module is used to construct and solve the mixed integer second-order cone optimization model based on the objective function and running constraints; The control output module is used to output the on-load tap changer tap position, the number of parallel capacitor banks in operation, the reactive power output of the static var compensator, the charging and discharging power of the energy storage system, and the actual output of the distributed power source for each scheduling period.

[0092] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used in the operation of a power supply area reactive voltage active support optimization method based on power factor segmented pricing, including: The system acquires the network topology, line parameters, load forecast data, distributed generation forecast output data, active power purchase and sale price, and operating parameters of each regulating device for the power balancing unit in the power supply area. Based on the active and reactive power exchanged between the power balancing unit and the upper-level grid, it determines the power factor for each scheduling period and, according to the preset power factor range to which the power factor belongs, determines the reactive power purchase price and reactive power sale price for the corresponding scheduling period. With minimizing the total operating cost of the power balancing unit as the optimization objective, an objective function is established. The total operating cost includes network loss cost, active power purchase and sale cost, reactive power purchase and sale cost determined based on the reactive power purchase and sale prices, reactive power compensation device operating cost, and distributed generation curtailment cost. Operating constraints for the power balancing unit are established, including node active power and reactive power consumption. The system incorporates the following constraints: reactive power balance constraints, branch power flow constraints obtained through second-order cone relaxation, node voltage constraints, line current constraints, on-load tap changer tap positions and number of operations constraints, parallel capacitor bank switching capacity and number of operations constraints, static var compensator (SVC) reactive power output constraints, energy storage system charging / discharging state and state of charge constraints, and distributed generation output constraints. Using the active and reactive power exchange power, on-load tap changer tap positions, number of parallel capacitor banks switched, SVC reactive power output, energy storage system charging / discharging power, and actual distributed generation output during each scheduling period as decision variables, a mixed-integer second-order cone optimization model is constructed and solved based on the objective function and operational constraints. This yields the operating control quantities of the regulating equipment for each scheduling period, and these operating control quantities are determined as the corresponding scheduling setpoints for the on-load tap changer, parallel capacitor banks, SVC, and energy storage system.

[0093] Please see Figure 9 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the active reactive voltage support optimization method for power supply areas based on power factor segmented pricing in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the active reactive voltage support optimization system for power supply areas based on power factor segmented pricing in this embodiment. To avoid repetition, these details are not elaborated here.

[0094] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 9This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0095] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0096] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0097] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0098] Please see Figure 10 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0099] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 11 The steps are shown in the figure.

[0100] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0101] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0102] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0103] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0104] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0105] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0106] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0107] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the active reactive voltage support optimization method for power supply areas based on power factor segmented pricing in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: The system acquires the network topology, line parameters, load forecast data, distributed generation forecast output data, active power purchase and sale price, and operating parameters of each regulating device for the power balancing unit in the power supply area. Based on the active and reactive power exchanged between the power balancing unit and the upper-level grid, it determines the power factor for each scheduling period and, according to the preset power factor range to which the power factor belongs, determines the reactive power purchase price and reactive power sale price for the corresponding scheduling period. With minimizing the total operating cost of the power balancing unit as the optimization objective, an objective function is established. The total operating cost includes network loss cost, active power purchase and sale cost, reactive power purchase and sale cost determined based on the reactive power purchase and sale prices, reactive power compensation device operating cost, and distributed generation curtailment cost. Operating constraints for the power balancing unit are established, including node active power and reactive power consumption. The system incorporates the following constraints: reactive power balance constraints, branch power flow constraints obtained through second-order cone relaxation, node voltage constraints, line current constraints, on-load tap changer tap positions and number of operations constraints, parallel capacitor bank switching capacity and number of operations constraints, static var compensator (SVC) reactive power output constraints, energy storage system charging / discharging state and state of charge constraints, and distributed generation output constraints. Using the active and reactive power exchange power, on-load tap changer tap positions, number of parallel capacitor banks switched, SVC reactive power output, energy storage system charging / discharging power, and actual distributed generation output during each scheduling period as decision variables, a mixed-integer second-order cone optimization model is constructed and solved based on the objective function and operational constraints. This yields the operating control quantities of the regulating equipment for each scheduling period, and these operating control quantities are determined as the corresponding scheduling setpoints for the on-load tap changer, parallel capacitor banks, SVC, and energy storage system.

[0108] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0109] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0110] Please see Figure 1 This embodiment uses an improved 33-node distribution network. To improve the model's applicability to scenarios with a high proportion of distributed resource access, OLTC, CB, SVC, BESS, WT, and PV are integrated into the basic network structure.

[0111] The ratio range of the OLTC is set to CB access nodes 18 and 31, with a single group capacity of 50 kvar; SVC access nodes 24 and 30, with compensation ranges respectively. kvar and kvar; BESS access nodes 10, 24, 29, and 30 have maximum power caps of 200 kW, 200 kW, 300 kW, and 300 kW, respectively, and maximum capacities of 400 kWh, 400 kWh, 600 kWh, and 600 kWh, respectively, with a charge / discharge efficiency of 0.95 for all. In the diagram, both ESS and BESS represent energy storage systems. Figure 1 Only some of the devices configured on the nodes are shown.

[0112] Please see Figure 2 The system displays the 24-hour active power purchase price and active power sales price.

[0113] Please see Figure 3 The actual power output curves of PV1 and PV2 almost completely coincide with their theoretical maximum power output curves, and the curtailment rate remains zero. This indicates that under the proposed second-order cone relaxation dynamic optimal power flow (SOCP-DOPF) optimization scheduling strategy, distributed photovoltaic power achieves 100% full absorption within 24 hours. This result verifies that the reactive voltage support model effectively solves the voltage limit exceeding problem under high-penetration photovoltaic access by coordinating the coordinated control of on-load tap changers (OLTC), parallel capacitor banks (CB), static var compensators (SVC), and energy storage systems, ensuring node voltage quality without taking curtailment measures.

[0114] Please see Figure 4 The actual output curves of wind power 1 and wind power 3 almost completely overlap with their theoretical maximum output curves, and the wind curtailment rate remains at zero. This indicates that under the optimized scheduling of the power balance unit reactive power voltage support model of the constructed power supply area, distributed wind power has achieved 100% full consumption 24 hours a day.

[0115] Please see Figure 5 The system operates in a clear "two-way interaction" mode, with active and reactive power purchases and sales occurring simultaneously within a specific time period. This indicates that the constructed power balance unit in the power supply area has flexible power regulation and two-way interaction capabilities in scenarios with high penetration of distributed power sources.

[0116] Please refer to Figure 6. During the 24-hour optimized scheduling cycle, the two electrochemical energy storage units exhibit typical "peak shaving and valley filling" and "source-load matching" operation modes, effectively supporting the power balance of the power supply area's power balancing unit and the absorption of new energy sources. Analysis of the charging and discharging timing and electricity price linkage characteristics shows that the energy storage units strictly adhere to the economic optimization strategy of "low-price charging, high-price discharging." Analysis of the state of charge (SOC) management characteristics shows that the SOC of both units is strictly maintained within the safe operating range of 10%-90%, and the depth of charging and discharging is positively correlated with the intensity of electricity prices: the greater the peak-valley difference in electricity prices, the greater the depth of charging and discharging power and SOC adjustment.

[0117] Please see Figure 7 The parallel capacitor bank (CB) and static var compensator (SVC) exhibit a "discrete-continuous collaborative, hierarchical-smooth complementary" regulation strategy during the 24-hour operating cycle, effectively supporting the voltage quality and reactive power balance of the power supply area's power balance unit. The proposed optimization model constructs a "coarse adjustment + fine adjustment" reactive power control architecture by coordinating the discrete hierarchical switching of the CB and the continuous fine adjustment of the SVC: the CB undertakes basic reactive power compensation and coarse adjustment of the voltage level, while the SVC undertakes dynamic reactive power balance and fine voltage regulation. Especially during the peak photovoltaic power generation period from 10:00 to 11:00, when the output of distributed power sources fluctuates dramatically, the two work together to achieve rapid reduction of reactive power output and active voltage suppression. This verifies the value of the reactive power compensation device in the hierarchical and zoned coordinated configuration of the power supply area's power balance unit in addressing fluctuations in high-penetration renewable energy sources and ensuring voltage quality.

[0118] Please see Figure 8The on-load tap-changing transformer (OLTC) exhibits a stepped discrete regulation characteristic during its 24-hour operation cycle, switching between taps 6 and 9, and performing a total of 5 tap actions throughout the day, precisely meeting the daily limit constraint for equipment actions. This reverse regulation strategy of "suppressing voltage at low taps and boosting voltage at high taps," together with the reactive power switching of the parallel capacitor bank (CB) and static var compensator (SVC), forms a "voltage-reactive power" coordinated control system: the OLTC achieves hierarchical management and coarse adjustment of system voltage through changes in transformer ratio, while the CB and SVC achieve local fine adjustment through reactive power output. The three work together to ensure that the voltage at each node of the system fluctuates smoothly within a safe range, verifying the accurate characterization and optimized control capability of the proposed model for the discrete regulation characteristics of the on-load tap-changing transformer, and demonstrating the multi-timescale coordinated optimization efficiency of discrete control equipment and continuous regulation resources in the power balance unit of the power supply area.

[0119] To quantify the economic performance of the proposed reactive power voltage support model, Table 1 lists the details of the optimized operating costs of the power balance unit in the power supply area under a typical daily scenario. It covers various expenditures and benefits, including network loss costs, investment and operating costs of reactive power compensation devices, active and reactive power purchase and sale costs, and curtailment penalty costs, providing a data foundation for subsequent economic analysis.

[0120] Table 1 Costs and Expenses of Each Item

[0121] As shown in Table 1, the total daily operating cost of the optimized power supply area power balance unit is RMB 1995.56, exhibiting a structural characteristic of "electricity purchase cost as the main factor, electricity sales revenue as the offset, and network loss and equipment investment as the supplement." This verifies the comprehensive optimization efficiency of the proposed reactive power voltage support model in terms of economy and new energy consumption.

[0122] Specifically, the active power purchase cost (2783.71 yuan) constitutes the main expenditure for system operation, accounting for more than 90% of the total cost (before revenue is included), reflecting the basic attribute of the power balance unit as a power receiving-end network. However, through the full absorption of distributed photovoltaic and wind power (with zero cost of curtailment for both solar and wind power) and the peak shaving and valley filling regulation of the energy storage system, the system achieved active power sales revenue of 641.43 yuan, effectively offsetting 23.0% of the power purchase expenditure. More significantly, reactive power sales revenue reached 309.57 yuan, accounting for 32.6% of the total revenue, indicating that the synergistic optimization of reactive power compensation devices (CB, SVC) and distributed power sources not only achieved local reactive power balance, but also created additional economic value by feeding reactive power back to the main grid, demonstrating the potential of "reactive power resource utilization" and "reactive power marketization" in the economic dispatch of the power balance unit. Furthermore, both network loss costs and reactive power compensation equipment operation and maintenance costs remained at low levels, indicating that the second-order cone relaxation dynamic optimal power flow (SOCP-DOPF) model effectively reduced line losses and equipment investment sharing costs by optimizing power flow distribution and reactive power compensation device switching. The results of zero wind curtailment and zero solar curtailment verified that the model achieved 100% local consumption of renewable energy while ensuring voltage quality, thus balancing economic efficiency and low-carbon operation goals.

[0123] The model constructs a multi-objective collaborative control strategy that balances full absorption of new energy sources, minimization of grid losses, and optimal operational economy by coordinating active and reactive power joint optimization, source-grid-load-storage interaction, and time-of-use pricing response. This provides effective theoretical support and practical paradigm for the economical and safe operation of power balance units in power supply areas.

[0124] To verify the superiority of the model proposed in this invention, another comparative model was set up for verification analysis: Model 1 is the model proposed in this invention; Model 2 only considers active power purchase from the main grid. To quantitatively evaluate the technical and economic performance of different configuration strategies, both models were optimized under a unified solution framework, and the comparison results of the key indicators are shown in Table 2.

[0125] Table 2. Costs of each item in the two models

[0126] As shown in Table 2, the comparative analysis reveals that the proposed power balance unit operation model (Model 1), which considers the reactive power regulation resources of the system, exhibits significant advantages in both economy and operational efficiency compared to the traditional one-way power purchase model (Model 2). Although the active power purchase cost of Model 1 (2783.71 yuan) is slightly higher than that of Model 2 (2548.87 yuan), it generates a total revenue of 950.99 yuan by selling active power (641.43 yuan) and reactive power (309.57 yuan) to the main grid. Ultimately, the total daily operating cost is reduced to 1995.56 yuan, a 27.7% decrease compared to Model 2 (2759.24 yuan), thus verifying the economic feasibility of the strategy of "replacing one-way power receipt with exchange".

[0127] More significant in terms of engineering implications is the improvement in network loss indicators: the network loss cost of Model 1 is only 108.73 yuan, a 45.3% reduction compared to Model 2 (198.95 yuan). This indicates that through the coordinated optimization of OLTC, CB, and SVC, local reactive power balance and reasonable power flow distribution are achieved, significantly reducing line transmission losses. Although the investment and maintenance costs of reactive power compensation devices in Model 1 are higher than those in Model 2, this incremental investment is more than compensated for by the reduction in network losses and the revenue from reactive power sales, reflecting the marginal benefits of "active reactive power investment in exchange for system-level economic optimization".

[0128] Furthermore, both models achieve full absorption of renewable energy (zero cost for both solar and wind curtailment), but Model 1, through bidirectional interaction and energy storage coordination, creates additional market revenue while improving the quality of renewable energy absorption. Overall, the proposed model, by constructing a power balance unit operation mode for the power supply area that integrates active and reactive power optimization, bidirectional interaction between power purchase and sales, and coordinated control of power generation, grid, load, and storage, achieves coordinated optimization of grid loss minimization and operational economy, providing an effective paradigm for the economical and safe operation of power balance units under high-penetration distributed power source access.

[0129] In terms of optimizing dispatch strategies, the model constructs a reactive power and voltage collaborative control system of "hierarchical coarse adjustment + local fine adjustment" by coordinating OLTC, CB, and SVC. Simultaneously, it guides the energy storage system to implement "low storage, high release" arbitrage operation in response to time-of-use pricing, achieving peak shaving and valley filling of active power and local balance of reactive power. Compared with the traditional model that only considers one-way power purchase, the proposed model, through active-reactive power joint optimization and bidirectional power exchange, reduces the total daily operating cost by 27.7%, reduces network loss costs by 45.3%, and generates 309.57 yuan in reactive power sales revenue, verifying the significant advantages of "source-grid-load-storage" collaborative optimization in improving the economic efficiency of power balance units and the capacity for renewable energy absorption. This research provides a theoretical framework and practical path for the active reactive power and voltage support and economic optimization operation of power balance units in power supply areas with high-penetration distributed power sources.

[0130] In summary, this invention presents an active reactive power and voltage support optimization method for power supply areas based on power factor-based segmented pricing. Taking the power balance unit of the power supply area as the optimization object, it introduces power factor-based segmented reactive power pricing into distribution network dispatching. This ensures that reactive power is no longer merely an auxiliary variable for satisfying voltage constraints, but participates in cost decision-making alongside active power purchase and sale. When the power factor decreases, by increasing the reactive power purchase price and setting reactive power sales revenue, the power supply area is guided to prioritize the use of local reactive power resources, reducing long-distance reactive power transmission and the resulting line losses and voltage deviations. The model simultaneously considers discrete constraints such as OLTC tap positions, the number of CB switching groups and the number of operations, as well as continuous constraints such as SVC continuous reactive power regulation, energy storage charging and discharging, and state of charge, forming a control method combining hierarchical voltage regulation, basic compensation, and dynamic fine-tuning. Second-order cone relaxation transforms the non-convex branch power flow relationship into a solvable convex constraint, which is beneficial for improving the solution efficiency and executability of multi-time period joint optimization. In the example, both photovoltaic and wind power are fully absorbed, energy storage is used for peak shaving and valley filling according to time-of-use pricing, and OLTC, CB and SVC operate collaboratively within the equipment operation boundary. Compared with the model of purchasing electricity only from the main grid, the total daily operating cost is reduced by 27.7%, the grid loss cost is reduced by 45.3%, and reactive power sales revenue is obtained, thus taking into account voltage safety, renewable energy absorption, equipment life and operating economy.

[0131] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0132] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0134] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for active reactive voltage support optimization in power supply areas based on power factor-based segmented pricing, characterized in that, Includes the following steps: S1. Obtain the network topology, line parameters, load forecast data, distributed power generation forecast output data, active power purchase and sale price, and operating parameters of each regulating device of the power balance unit in the power supply area. S2. Determine the power factor for each scheduling period based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid, and determine the reactive power purchase price and reactive power sales price for the corresponding scheduling period based on the preset power factor interval to which the power factor belongs. The reactive power purchase price and reactive power sales price are used as reactive power purchase and sales cost parameters to participate in the solution of the mixed integer second-order cone optimization model. S3. With the goal of minimizing the total operating cost of the power balance unit in the power supply area, establish an objective function. The total operating cost includes network loss cost, active power purchase and sale cost, reactive power purchase and sale cost determined based on the reactive power purchase price and reactive power sale price, reactive power compensation device operating cost, and distributed power curtailment cost. S4. Establish the operating constraints of the power balance unit in the power supply area. The operating constraints include node active and reactive power balance constraints, branch power flow constraints obtained through second-order cone relaxation, node voltage constraints, line current constraints, on-load tap changer tap position and number of operations constraints, parallel capacitor bank switching capacity and number of operations constraints, static var compensator reactive power output constraints, energy storage system charging and discharging state and state of charge constraints, and distributed power output constraints. S5. Using the active and reactive power exchange, on-load tap changer tap position, number of parallel capacitor banks switched on and off, reactive power output of static var compensator, charging and discharging power of energy storage system, and actual output of distributed power source as decision variables, construct a mixed integer second-order cone optimization model based on the objective function and operating constraints, and solve it to obtain the operating control quantity of the regulating equipment for each scheduling period. Then, determine the operating control quantity as the corresponding scheduling setpoint for on-load tap changer, parallel capacitor bank, static var compensator, and energy storage system.

2. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing as described in claim 1, characterized in that, In step S2, the power factor range is divided by a preset power factor threshold. When the power factor is not lower than the preset power factor threshold, the reactive power purchase price and the reactive power sales price are set as a first price. When the power factor is lower than the preset power factor threshold, the reactive power purchase price and the reactive power sales price are set as a second price and a third price that are higher than the first price, respectively, and the second price is higher than the third price.

3. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing according to claim 1, characterized in that, In step S3, the network loss cost is determined based on the square of the current amplitude of each branch, the branch resistance, and the network loss price; the active power purchase and sale cost and the reactive power purchase and sale cost are determined based on the active power and reactive power exchanged with the upper-level power grid during each dispatch period and the corresponding purchase and sale price; the distributed power curtailment cost is determined based on the difference between the predicted output and the actual output of photovoltaic and wind power.

4. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing as described in claim 1, characterized in that, In step S4, the constraints on the tap position and number of operations of the on-load tap-changing transformer include: The on-load tap changer tap position is determined by the tap position identifier variable for each scheduling period. The up-shift or down-shift action is determined based on the tap position change between adjacent scheduling periods, and the total number of tap position actions within the optimization cycle is limited to not exceeding the preset maximum number of actions.

5. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing according to claim 1, characterized in that, In step S4, the constraints on the switching capacity and number of operations of the parallel capacitor bank include: The reactive power compensation of the parallel capacitor bank is determined based on the number of operating groups and the compensation capacity of a single group, and the number of operating groups and the number of switching cycles within the optimization period are limited; the reactive power output constraint of the static var compensator includes limiting its reactive power compensation power between a preset lower limit and a preset upper limit.

6. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing according to claim 1, characterized in that, In step S4, the constraints on the charging / discharging state and state of charge of the energy storage system include: The energy storage system is restricted to not charge and discharge simultaneously within the same scheduling period by using charging state variables and discharging state variables. The energy storage capacity between adjacent scheduling periods is updated based on charging power, discharging power, charging efficiency, and discharging efficiency, and the energy storage capacity is limited to between a preset lower limit and a preset upper limit.

7. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing according to claim 1, characterized in that, In step S4, the distributed power output constraints include: The actual active power output of photovoltaic and wind power during each dispatch period is limited to the corresponding predicted maximum output, and the correspondence between the reactive power output and active power output of photovoltaic and wind power is determined according to the preset power factor angle.

8. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing according to claim 1, characterized in that, In step S4, the branch power flow constraints obtained through second-order cone relaxation include node voltage drop constraints, branch active power balance constraints, branch reactive power balance constraints, and second-order cone constraints. The second-order cone constraints are used to limit the product of the square of the branch current amplitude and the square of the voltage amplitude at the branch's head node to be no less than the sum of the square of the branch's active power and the square of the reactive power.

9. The method for active reactive voltage support optimization of power supply areas based on power factor segmented pricing according to claim 1, characterized in that, In step S5, the mixed integer second-order cone optimization model is jointly solved according to multiple consecutive scheduling periods to obtain the on-load tap changer tap position, the number of parallel capacitor banks in operation, the reactive power output of the static var compensator, the charging and discharging power of the energy storage system, the actual output of the distributed power source, and the active and reactive power exchange power with the upper-level power grid corresponding to each scheduling period.

10. A reactive power and voltage active support optimization system for power supply areas based on power factor segmented pricing, characterized in that, include: The data acquisition module is used to acquire the network topology, line parameters, load forecast data, distributed power generation forecast output data, active power purchase and sale price, and operating parameters of various regulating devices of the power balance unit in the power supply area. The segmented pricing module is used to determine the power factor based on the active and reactive power exchanged between the power balance unit of the power supply area and the upper-level power grid, and to determine the reactive power purchase price and reactive power sales price based on the preset power factor interval to which the power factor belongs. The reactive power purchase price and reactive power sales price are used as reactive power purchase and sales cost parameters to participate in the solution of the mixed integer second-order cone optimization model. The model building module is used to establish objective functions including network loss costs, active power purchase and sale costs, reactive power purchase and sale costs, reactive power compensation device operating costs, and distributed power curtailment costs, and to establish node power balance constraints, branch power flow constraints, voltage constraints, current constraints, and operating constraints of each regulating device. The optimization and solution module is used to construct and solve the mixed integer second-order cone optimization model based on the objective function and running constraints; The control output module is used to output the on-load tap changer tap position, the number of parallel capacitor banks in operation, the reactive power output of the static var compensator, the charging and discharging power of the energy storage system, and the actual output of the distributed power source for each scheduling period.

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