A power distribution network voltage reactive power optimization control method considering multi-party benefit balance

Abstract

The application discloses a power distribution network voltage reactive power optimization control method considering multi-party benefit balance, which comprehensively considers voltage reactive power optimization control of a plurality of subjects of a power distribution network, coordinates and optimizes reactive power output of three benefit subjects of a distributed photovoltaic power supply, the power distribution network (a capacitor bank, a static reactive power compensator and an on-load voltage regulating transformer) and an energy storage system, reduces line network loss, alleviates voltage out-of-limit problems caused by photovoltaic and electric heating load access, and realizes safe and efficient operation of the power distribution network. In addition, the market plays a decisive role in resource allocation, promotes a policy system with low-carbon development as the overall command, matches energy saving and efficiency improvement targets, promotes orderly development of new energy such as wind power and photovoltaic power, improves new energy consumption capacity, realizes matching and cooperation of power supply and the power grid and power generation and power consumption, and promotes power marketization development.

Description

Technical Field

[0001] This invention is applicable to the operation and dispatching of urban power distribution networks in public institutions in my country, belonging to the field of urban power grid operation and management. Specifically, it relates to a power distribution network voltage and reactive power optimization control method that considers the balance of interests among multiple parties. Background Technology

[0002] To address the escalating energy crisis and worsening environmental problems, countries worldwide are constantly seeking new avenues and solutions. Renewable energy, due to its unique advantages, has garnered widespread attention. Developing distributed energy sources such as photovoltaics, natural gas, wind power, biomass energy, and geothermal energy has become a crucial aspect of my country's efforts to address climate change and ensure energy security. With the continuous improvement of photovoltaic power generation technology, its share of my country's energy generation is steadily increasing. Integrating photovoltaic systems into end-use energy consumption not only complements loads but also significantly reduces urban pollutant emissions and improves the quality of the living environment. Furthermore, to reduce environmental pollution, the State Grid Corporation of China has proposed an electricity substitution strategy.

[0003] In recent years, the proportion of distributed photovoltaic (PV) power generation systems and user-end electric heating loads connected to my country's distribution networks has been continuously increasing, leading to increasingly serious voltage exceedance problems in some areas of the grid. To alleviate the uncertainty brought to the distribution network by the increasing proportion of distributed PV power generation systems and user-end electric heating loads, it is proposed to improve the volatility and intermittency caused by PV grid connection by adding energy storage devices, and to achieve demand-side management, peak shaving and valley filling, and improve power flow distribution. However, the large-scale connection of distributed PV power sources and power electronic equipment has made the reactive power optimization model of the distribution network more complex, leading to greater difficulty in solving the problem. As PV and energy storage systems are continuously connected to the distribution network, the investment entities are becoming more diversified. Distributed PV power generation operators involved in grid construction and management, and energy storage companies involved in demand response, have also become new stakeholders in the distribution network, changing the traditional grid management model and the influencing factors of various stakeholders. Against this backdrop, how to achieve voltage and reactive power optimization operation of the distribution network with multiple stakeholders under this new situation has become an urgent problem to be solved.

[0004] Currently, game theory is generally used to coordinate the interests of multiple stakeholders. Game theory, also known as strategy theory or game state theory, mainly studies how multiple stakeholders influence and constrain each other, and how each decision-maker, based on their available resources and capabilities, maximizes their own or group's interests. Under market-based operating mechanisms, photovoltaic power output and energy storage systems are affected by incentives and peak-valley electricity prices. They can adjust the reactive power supplied to the grid by the inverter to achieve voltage and reactive power balance. Therefore, in the context of high-proportion renewable energy integration and urban re-electrification, it is necessary to study voltage and reactive power optimization control methods for distribution networks that balance the interests of multiple stakeholders, combining photovoltaic power plants and energy storage systems under market mechanisms. This can effectively improve the absorption capacity of distributed photovoltaic power sources, reduce voltage fluctuations, and provide effective technical means for the safe operation of distribution networks. Summary of the Invention

[0005] Distributed photovoltaic (PV) power generation operators and energy storage operators, who participate in power grid construction, have become new stakeholders in the distribution network, changing the traditional power grid management model. To comprehensively consider the balance of interests among multiple parties and maximize their benefits, this paper proposes a voltage and reactive power optimization control strategy based on auction game theory. This method considers the balance of interests among multiple stakeholders in the distribution network (source, grid, and storage), comprehensively considering the impact of distributed PV power generation operators and energy storage operators on reactive power distribution after their integration into the distribution network. It then conducts targeted reactive power optimization and coordination control for these stakeholders to reduce voltage exceedance issues, minimize line losses, and maximize the interests of all parties. This aims to provide guidance for the planning and construction of the distribution network power market, improve the regulatory level of the power market, and promote its healthy development.

[0006] To achieve the objectives of this invention, the present invention provides a voltage and reactive power optimization control method for distribution networks that considers the balance of interests among multiple parties, comprising the following steps:

[0007] (1) Establish a voltage and reactive power control framework for the distribution network that takes into account the balance of interests of multiple parties;

[0008] (2) Establish models for the main components of voltage and reactive power optimization control of distribution networks that take into account the balance of interests among multiple parties;

[0009] (3) Solve the established multi-party interest balance distribution network voltage reactive power optimization control model.

[0010] This invention discloses a voltage and reactive power optimization control method for distribution networks that considers the balance of interests among multiple parties. This method comprehensively considers the voltage and reactive power optimization control of multiple stakeholders in the distribution network (source, grid, and energy storage), coordinating and optimizing the reactive power output of distributed photovoltaic power sources, the distribution network (capacitor banks, static var compensators, and on-load tap changers), and the energy storage system. This reduces line losses and alleviates problems such as voltage exceeding limits caused by the connection of photovoltaic and electric heating loads, achieving safe and efficient operation of the distribution network. Furthermore, it fully leverages the decisive role of the market in resource allocation, promotes a policy system guided by low-carbon development and aligned with energy conservation and efficiency goals, facilitates the orderly development of new energy sources such as wind power and photovoltaics, improves the absorption capacity of new energy sources, achieves coordinated development between power sources and the grid, and between power generation and consumption, and promotes the market-oriented development of the electricity sector.

[0011] Compared with existing technologies, this invention can effectively coordinate the voltage and reactive power distribution of the main entities of "source, grid, and storage" in the distribution network, reduce voltage fluctuations, promote the consumption of new energy sources, and achieve efficient operation of the distribution network. Attached Figure Description

[0012] Figure 1 The diagram shown is a schematic representation of the interaction relationship in the active distribution network voltage reactive power optimization control model based on benefit balance in this application.

[0013] Figure 2 The diagram shown is a schematic of the solution process for the distribution network voltage reactive power optimization control model that balances the interests of multiple parties in this application.

[0014] Figure 3 The diagram shown is a typical power distribution network structure. Detailed Implementation

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0017] This invention relates to a method for optimizing voltage and reactive power control in a distribution network that considers the balance of interests among multiple parties, and mainly includes the following steps:

[0018] (1) Establish a voltage and reactive power control framework for distribution networks that considers the balance of interests among multiple parties.

[0019] A multi-party benefit-balancing voltage and reactive power optimization dispatch model for distribution networks, while ensuring the safe operation of the power grid, fully leverages the autonomy of distributed photovoltaic (PV) power sources and energy storage in reactive power regulation, considering the coordinated operation and control of distributed PV power sources, distributed energy storage stations, and the distribution network. New stakeholders such as distributed PV power sources and energy storage actively participate in and cooperate with the power grid's reactive power dispatch operation, providing some reactive power support to the grid and obtaining corresponding economic benefits. Using each stakeholder participating in reactive power dispatch as a basic optimization unit, the reactive power control of each stakeholder is coordinated according to their respective power generation and consumption plans and operational constraints to maximize the benefits for each participant. Therefore, a method needs to be developed to coordinate and control the conflicting variables of each stakeholder, ultimately achieving consistency. The interaction relationships of the benefit-balancing active distribution network voltage and reactive power optimization control model are as follows: Figure 1 As shown.

[0020] according to Figure 1 As shown in the framework diagram, this invention will establish a three-party optimization control model for the power distribution network's "source, grid, and storage" entities, and propose corresponding solution algorithms.

[0021] (2) Establish models for the main components of voltage and reactive power optimization control of distribution networks that take into account the balance of interests among multiple parties.

[0022] Currently, the main participants in reactive power optimization of the power grid are the reactive power equipment in the distribution network. In actual operation, distributed power sources and energy storage systems can both provide reactive power to the power grid through inverters. To effectively utilize resources, this invention establishes a voltage reactive power optimization control model for the distribution network that considers the balance of interests among distributed photovoltaic operators, distribution network operators, and distributed energy storage operators. The specific models for the three main parties are as follows:

[0023] 1) Distributed photovoltaic power generation operator model

[0024] Traditional distributed photovoltaic (PV) power operators primarily analyze economic factors from the perspective of active power pricing. Unlike traditional methods, this invention focuses on the reactive power capacity provided by distributed PV power to the distribution network and the resulting economic benefits as economic and technical indicators. Since distributed PV power is connected to the grid via inverters, reactive power can be supplied to the grid by adjusting the inverters. From an economic standpoint, the revenue C of the distributed PV power operator is considered. PV The objective function is:

[0025]

[0026] In equation (1), C PV N represents the revenue of distributed photovoltaic (PV) power operators; PV Indicates the number of distributed photovoltaic power sources; C PVQ,i The reactive power sales revenue of the i-th distributed photovoltaic power source is expressed as follows: Where C sel Q represents the price of reactive power sold by distributed photovoltaic (PV) power operators to the distribution network. PVi,t C represents the reactive power that the i-th distributed photovoltaic power source can sell to the distribution network at time t; PVB,i C represents the government subsidy for the i-th distributed photovoltaic power source; PVY,i C represents the operation and maintenance cost of the i-th distributed photovoltaic power source; PVF,i This represents the cost of reactive power generated by the i-th distributed photovoltaic power source.

[0027] Constraints on distributed photovoltaic (PV) power sources include operational constraints, reactive power constraints that can be supplied to the grid, power factor constraints, and grid connection point voltage constraints.

[0028]

[0029] In equation (2), P PVi,t and Q PVi,t S represents the active power output and available reactive power capacity of the i-th distributed photovoltaic power source at time t, respectively; PVi,t Let Q be the operating capacity of the distributed photovoltaic power generation at time t (i-th time). PVi,tmax and Q PVi,tmin These represent the maximum and minimum reactive power outputs of the distributed photovoltaic power generation at time t, respectively; Q PVi,min U is the grid-connected voltage of the i-th distributed photovoltaic power source; PVi,max and U PVi,min φ represents the maximum and minimum allowable voltage fluctuations at the grid connection point of the i-th distributed photovoltaic power source, respectively; φ is the power factor.

[0030] 2) Power Distribution Company Model

[0031] The distribution company, as the builder and operator of the power grid, operates under the constraint of ensuring the safe operation of the power grid. Its economic model primarily considers the reactive power capacity of the reactive power equipment configured in the distribution network and the resulting costs, as well as the costs of purchasing reactive power from distributed photovoltaic power operators and distributed energy storage operators. Its objective function aims to maximize the reactive power revenue of the distribution company.

[0032] maxC DNQ =C GDQ -(C PVQ +C STQ +C CQ +C SVCQ +C TQ (3)

[0033] In equation (3), C DNQ For the reactive power revenue of the power distribution company; C GDQ C. Reactive power cost converted from electricity purchased for users; PVQand C STQ These are the costs incurred by the distribution network in purchasing reactive power from distributed photovoltaic power operators and distributed energy storage operators, respectively; C CQ and C SVCQ The capacitor bank and static var compensator respectively provide reactive power switching costs to the distribution network; C TQ To adjust the cost of transformer taps.

[0034] ① Constraints on safe operation of power distribution network

[0035] In power flow constraints in distribution networks, reactive power constraints mainly consider the reactive power supplied to the grid by reactive capacitor banks, static var compensators, and tap changers of on-load tap-changing transformers. The constraint equations are as follows:

[0036]

[0037] In equation (4), P i,t and Q i,t Let P be the active power and reactive power at node i at time t, respectively; PVi,t and Q PVi,t P represents the active power and reactive power injected by the distributed power source at node i during time period t; Li,t and Q Li,t Let Q be the active power and reactive power of the load at node i at time t, respectively; Ci,t and Q SVCi,t These represent the reactive power capacity connected to the capacitor bank and static var compensator at system node i during time period t; Q Ti,t G represents the reactive power capacity connected to the on-load tap-changing transformer at system node i during time period t; ij,t and B ij,t These represent the conductance and susceptance values ​​between system node i and node j during time period t; U i,t Let θ be the voltage amplitude at system node i during time period t; ij The phase difference between the voltages of node i and node j.

[0038] ② Line safety operation constraints

[0039] During operation, the line must meet the constraints of branch current, voltage, and radial safe operation.

[0040]

[0041] In equation (5), I i and I i.max U represents the amplitude of the current in branch i and the maximum amplitude of the current in branch i, respectively, where n is the number of branches; i.max and U i.min These represent the maximum and minimum allowable voltage values ​​for node i, respectively, and g p and G PThese represent the current network structure and the allowed radial network configurations, respectively.

[0042] ③ Operating constraints of grouped switching capacitors and static var compensators

[0043] In actual operation, both capacitor banks and static var compensators (SVCCs) supply reactive power to the power grid. Capacitor banks have strict limitations on the number of operations and switching times within a single cycle, while SVCCs have no such limitations. Therefore, capacitor banks must meet not only compensation capacity constraints but also switching frequency and operation time constraints, while SVCCs must meet capacity constraints. Capacitor banks use traditional constraint equations, meaning the number of switching operations, time constraints, and reactive power capacity must not exceed limits; SVCC constraints primarily consider capacity constraints.

[0044] ④ Transformer operating constraints

[0045] For voltage control in distribution networks containing distributed power sources, voltage regulation can also be achieved by changing the transformer taps, with the following constraint equation:

[0046]

[0047] In equation (6), 24 represents the calculation time for one day, and G Ti,min and G Ti,max These are the lowest and highest tap positions of the on-load tap-changing transformer i, respectively. T This represents the number of on-load tap-changing transformers.

[0048] 3) Distributed energy storage operator model

[0049] Traditional distributed energy storage operators, like distributed photovoltaic (PV) power operators, primarily consider active power pricing. This invention, however, uses economic and technical indicators that mainly consider the reactive power capacity provided by the distributed energy storage system to the distribution network and the resulting economic benefits. Since the distributed energy storage system also needs to be connected to the grid via an inverter, reactive power can also be sent to the grid by adjusting inverter parameters. From an economic perspective, this is based on the distributed energy storage operator's revenue C. ST The objective function is:

[0050]

[0051] In equation (7), C ST N represents the revenue of distributed energy storage operators; ST Indicates the number of distributed energy storage power stations; C STQ,i The reactive power sales revenue of the i-th distributed energy storage power station is expressed as follows: Where C sel This represents the price of reactive power sold by distributed energy storage operators to the distribution network; QPVi,t C represents the reactive power that the i-th distributed energy storage power station can sell to the distribution network at time t; STY,i C represents the operation and maintenance cost of the i-th distributed energy storage power station; STF,i This represents the cost of reactive power generated by the i-th distributed energy storage power station.

[0052] The constraints of distributed energy storage power stations mainly include the operation constraints of distributed energy storage power stations, the reactive power and power factor constraints that can be provided to the grid, and the voltage constraints at the grid connection point.

[0053]

[0054] In equation (8), P STi,t and Q STi,t S represents the active power output and available reactive power of the i-th distributed energy storage station at time t, respectively; STi,t Let Q be the operating capacity of the i-th distributed energy storage power station at time t; STi,tmax and Q STi,tmin These represent the maximum and minimum reactive power outputs of the i-th distributed energy storage power station at time t, respectively; Q STi,min U is the grid-connected voltage of the i-th distributed photovoltaic power source; STi,max and U STi,min φ represents the maximum and minimum allowable voltage fluctuations at the grid connection point of the i-th distributed energy storage power station, respectively; φ is the power factor.

[0055] (3) Solve the established distribution network voltage and reactive power optimization control model that balances the interests of all parties.

[0056] The auction process for optimizing voltage and reactive power control in a distribution network that balances the interests of multiple parties is as follows:

[0057] ① Initialization data. This includes the capacity S, active power P, reactive power Q, voltage V, and number of nodes N of distributed photovoltaic power sources, capacitor banks, static var compensators, on-load tap changers, and energy storage systems. The number of iterations is also set. The initial data is calculated based on the power flow of the actual power grid or a typical power grid structure.

[0058] ② Determine the auction agents and the initial auction price. Based on the operating constraints formulas (4) and (5), the distribution network combines nodes that do not meet the safe operation requirements to form a set of tasks T to be completed and a set of auction agents A to auction the proxy tasks. The initial auction price is determined based on the electricity market clearing price of the day.

[0059] T = {T1, T2, ..., T} m} (9)

[0060] A = {A1, A2, ..., A} m} (10)

[0061] ③ The bidding agents calculate their respective bid prices and submit bids to the auction agents. Distributed photovoltaic operators, on-load tap-changing transformers, and distributed energy storage operators submit bids based on their own constraint formulas (2), (6), and (8), as well as the constraint conditions for the safe operation of reactive power equipment in the distribution network, and the reactive power clearing price in the electricity market. This forms a set of auction agents B participating in the auction, as well as a set of benefits U and costs C incurred by the agents to complete the task. Sets U and C can be calculated using formulas (1), (3), and (7), and form a set of bid prices P for the bidding agents.

[0062]

[0063]

[0064]

[0065] In equations (11), (12), and (13), Indicates agent B i Complete task T j Benefits gained; Indicates agent B i Complete task T j The price to be paid; Indicates agent B i Auction Task T j The bid price.

[0066] ④ The auction agent continuously updates the auction price based on the result of the bid price set P obtained in ③, according to the greedy principle.

[0067] ⑤ Determine whether the objective function is satisfied. If not, return to step 3 and the bidding agent adjusts its bid price and resubmits the bid. If the objective function is satisfied, proceed to the next step.

[0068] ⑥ Notify the bidding intelligent entities and obtain the allocation results to realize the transaction. The power distribution company will issue reactive power adjustment instructions to each participating operator and reactive power equipment involved in the dispatch. Each operator, reactive power equipment and transformer will respond to achieve optimized operation of reactive power.

[0069] The complete solution process for the distribution network voltage reactive power optimization control model that balances the interests of multiple parties is as follows: Figure 2 As shown.

[0070] This invention takes a typical power distribution network structure as an example, such as... Figure 3As shown, the maximum allowable range of voltage deviation in the 10kV distribution network is -7% to +7%. A photovoltaic power plant with a capacity of 1.2MVA is connected at nodes [5, 11, 14, 21, 28, 36, 43, 52, 59, 65]; a storage battery with a capacity of 1.5MVA is connected at nodes [4, 8, 13, 20, 29, 43, 51, 61, 67]; a capacitor bank with a capacity of 330kVA is connected at nodes [16, 23, 35, 38, 54]; an on-load tap-changing transformer with a capacity of 200kVA is connected at nodes [16, 23, 35, 39, 54]; and a static var compensator with a capacity of 3MVA is connected at node [2]. The total load of the typical distribution network is (3802.19 + j2694.6)kVA.

[0071] Because photovoltaic power generation is affected by the environment, voltage exceeding limits is prone to occur in actual operation. Furthermore, voltage exceeding limits often occur at the end of the line. Therefore, several typical photovoltaic grid-connected points and line end nodes were selected as key nodes for testing. The voltage conditions of some key nodes before and after optimization are shown in Tables 1 and 2.

[0072] Table 1. Limit Exceeding Status of Key Nodes in the Distribution Network Before Optimization

[0073]

[0074] As shown in Table 1, during the period from 9:00 to 15:00, nodes 7, 27, 41, 58, and 69 all exceeded their upper limits, with nodes 7 and 27 exhibiting more severe exceedances, remaining at the upper voltage limit throughout the period. During the period from 21:00 to 23:00, nodes 7, 21, 27, 41, and 65 exceeded their lower limits, with node 27 showing particularly severe exceedances.

[0075] Table 2. Limit Exceeding Status of Key Nodes in the Optimized Distribution Network

[0076]

[0077] As shown in Table 2, during the period from 9:00 to 15:00, nodes 7, 27, 41, 58, and 69 were all kept within a reasonable range, with no nodes exceeding the upper limit across the entire network. The exceedance issues of nodes 7 and 27 were significantly improved. From 21:00 to 23:00, the exceedances of nodes 7, 21, 27, 41, and 65 disappeared, and all nodes across the network were kept within a reasonable range.

[0078] In this invention, the distribution network operator, photovoltaic operator, and energy storage operator aim to maximize their own interests. They formulate corresponding strategies through auction game theory and implement reactive power and voltage control. By adjusting the reactive power output within each operator, they regulate the voltage at over-limit nodes. Based on this algorithm, the optimal solution for the example is derived. The reactive power price of the distribution network, the reactive power output of photovoltaic and energy storage, the compensation costs and expenses for reactive power output at each time point, and the reactive power benefits are shown in Tables 3 and 4. The revenue of each entity is shown in Table 5.

[0079] Table 3. Photovoltaic reactive power output and power distribution network reactive power price compensation.

[0080]

[0081] Table 4. Reactive power output from energy storage and reactive power price compensation in the distribution network.

[0082]

[0083] Table 5. Comparison of Costs / Benefits for Various Stakeholders Before and After Optimization (Table 5)

[0084]

[0085] As shown in Tables 3, 4 and 5, through this reactive power optimization control of various entities in the distribution network, without adding any additional equipment, not only is the grid voltage constrained within a safe and reasonable operating range and voltage fluctuations reduced, but the economic benefits of each entity are also improved, maximizing the interests of each entity.

[0086] It should be noted that any technical solutions not detailed in this application employ publicly known technologies.

[0087] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing voltage and reactive power control in a distribution network that considers the balance of interests among multiple parties, characterized in that, Includes the following steps: (1) Establish a voltage and reactive power control framework for the distribution network that takes into account the balance of interests of multiple parties; (2) Establish models for the main components of voltage and reactive power optimization control of distribution networks that take into account the balance of interests among multiple parties; (3) Solve the established multi-party interest balance distribution network voltage reactive power optimization control model; Step (2) specifically includes establishing a voltage reactive power optimization control model for the distribution network that considers the balance of interests among distributed photovoltaic operators, distribution network operators, and distributed energy storage operators; The distributed photovoltaic power generation operator model is as follows: Since distributed photovoltaic (PV) power sources are connected to the grid via inverters, reactive power can be sent to the grid by adjusting the inverters. From an economic perspective, the revenue C of distributed PV power source operators can be calculated. PV The objective function is: In equation (1), C PV N represents the revenue of distributed photovoltaic (PV) power operators. PV Indicates the number of distributed photovoltaic power sources; C PVQ,i The reactive power sales revenue of the i-th distributed photovoltaic power source is expressed as follows: Where C sel Q represents the price of reactive power sold by distributed photovoltaic (PV) power operators to the distribution network. PVi,t C represents the reactive power that the i-th distributed photovoltaic power source can sell to the distribution network at time t; PVB,i C represents the government subsidy for the i-th distributed photovoltaic power source; PVY,i C represents the operation and maintenance cost of the i-th distributed photovoltaic power source; PVF,i This represents the cost of the reactive power generated by the i-th distributed photovoltaic power source; Constraints on distributed photovoltaic power sources include operational constraints, reactive power constraints that can be supplied to the grid, power factor constraints, and grid connection point voltage constraints. In equation (2), P PVi,t and Q PVi,t S represents the active power output and available reactive power capacity of the i-th distributed photovoltaic power source at time t, respectively; PVi,t Let Q be the operating capacity of the distributed photovoltaic power generation at time t (i-th time). PVi,tmax and Q PVi,tmin These represent the maximum and minimum reactive power outputs of the distributed photovoltaic power generation at time t, respectively; Q PVi,min U is the grid-connected voltage of the i-th distributed photovoltaic power source; PVi,max and U PVi,min These are the maximum and minimum allowable voltage fluctuations at the grid connection point of the i-th distributed photovoltaic power source, respectively; φ is the power factor. The power distribution company model is as follows: Its objective function aims to maximize the reactive power revenue of the power distribution company. maxC DNQ =C GDQ -(C PVQ +C STQ +C CQ +C SVCQ +C TQ )(3) In equation (3), C DNQ For the reactive power revenue of the power distribution company; C GDQ C. Reactive power cost converted from electricity purchased for users; PVQ and C STQ These are the costs incurred by the distribution network in purchasing reactive power from distributed photovoltaic power operators and distributed energy storage operators, respectively; C CQ and C SVCQ The capacitor bank and static var compensator respectively provide reactive power switching costs to the distribution network; C TQ To adjust the cost of transformer taps; ① Constraints on safe operation of power distribution network In the power flow constraints of distribution networks, reactive power constraints consider the reactive power supplied to the grid by reactive capacitor banks, static var compensators, and tap changers of on-load tap-changing transformers. The constraint equations are as follows: In equation (4), P i,t and Q i,t Let P be the active power and reactive power at node i at time t, respectively; PVi,t and Q PVi,t P represents the active power and reactive power injected by the distributed power source at node i during time period t; Li,t and Q Li,t Let Q be the active power and reactive power of the load at node i at time t, respectively; Ci,t and Q SVCi,t These represent the reactive power capacity connected to the capacitor bank and static var compensator at system node i during time period t; Q Ti,t G represents the reactive power capacity connected to the on-load tap-changing transformer at system node i during time period t; ij,t and B ij,t These represent the conductance and susceptance values ​​between system node i and node j during time period t; U i,t Let θ be the voltage amplitude at system node i during time period t; ij The phase difference between the voltages at node i and node j; ② Line safety operation constraints The line must meet the constraints of branch current, voltage, and radial safe operation during operation; In equation (5), I i and I i.max U represents the amplitude of the current in branch i and the maximum amplitude of the current in branch i, respectively, where n is the number of branches; i.max and U i.min These represent the maximum and minimum allowable voltage values ​​for node i, respectively, and g p and G P These represent the current network structure and the allowed radial network configurations, respectively. ③ Operating constraints of grouped switching capacitors and static var compensators In actual operation, both capacitor banks and static var compensators (SVCs) provide reactive power to the power grid. Capacitor banks have strict limitations on the number of operations and switching times within a single cycle, while SVCs have no such limitations. Therefore, capacitor banks must meet not only compensation capacity constraints but also switching frequency and operation time constraints during operation, while SVCs must meet capacity constraints. Capacitor banks use traditional constraint equations, meaning the number of switching times, time, and reactive power capacity must not exceed limits; SVCs consider capacity constraints. ④ Transformer operating constraints For voltage control in distribution networks containing distributed power sources, voltage regulation is achieved by changing the transformer taps. The constraint equation is as follows: In equation (6), 24 represents the calculation time for one day, and G Ti,min and G Ti,max These are the lowest and highest tap positions of the on-load tap-changing transformer i, respectively. T This refers to the number of on-load tap-changing transformers; The distributed energy storage operator model is as follows: C, the revenue of distributed energy storage operators ST The objective function is: In equation (7), C ST N represents the revenue of distributed energy storage operators; ST Indicates the number of distributed energy storage power stations; C STQ,i The reactive power sales revenue of the i-th distributed energy storage power station is expressed as follows: Where C sel This represents the price of reactive power sold by distributed energy storage operators to the distribution network; Q PVi,t C represents the reactive power that the i-th distributed energy storage power station can sell to the distribution network at time t; STY,i C represents the operation and maintenance cost of the i-th distributed energy storage power station; STF,i This represents the cost of reactive power generated by the i-th distributed energy storage power station; The constraints of distributed energy storage power stations include the operation constraints of distributed energy storage power stations, the reactive power and power factor constraints that can be supplied to the grid, and the voltage constraints at the grid connection point. In equation (8), P STi,t and Q STi,t S represents the active power output and available reactive power of the i-th distributed energy storage station at time t, respectively; STi,t Let Q be the operating capacity of the i-th distributed energy storage power station at time t; STi,tmax and Q STi,tmin These represent the maximum and minimum reactive power outputs of the i-th distributed energy storage power station at time t, respectively; Q STi,min U is the grid-connected voltage of the i-th distributed photovoltaic power source; STi,max and U STi,min These are the maximum and minimum allowable voltage fluctuations at the grid connection point of the i-th distributed energy storage power station, respectively; φ is the power factor. In step (2), the solution process for the voltage reactive power optimization control auction of the distribution network with balanced interests of multiple parties is as follows: ① Initial data, including the capacity S, active power P, reactive power Q, voltage V and number of nodes N of distributed photovoltaic power sources, capacitor banks, static var compensators, on-load tap changers and energy storage systems, and the number of iterations, are set. The initial data is calculated based on the power flow of the actual power grid or typical power grid structure. ② Determine the auction agent and the initial auction price. According to the operating constraint formulas (4) and (5), the distribution network combines the nodes that do not meet the safe operation requirements to form the task set T to be completed and the auction agent set A to be auctioned on behalf of the task. The initial auction price is determined based on the electricity market clearing price of the day. T={T1,T2,…,T m } (9) A={A1, A2, …, A m (10) ③ The bidding agents calculate their respective bid prices and submit bids to the auction agents; the distributed photovoltaic operators, on-load tap-changing transformers, and distributed energy storage operators submit bids according to their own constraint formulas (2), (6), and (8), as well as the power grid reactive power equipment safety operation constraint conditions, based on the power market reactive power clearing price, forming a set of auction agents B participating in the auction, and a set of benefits U and costs C that the agents obtain to complete the task. Sets U and C can be calculated according to formulas (1), (3), and (7), and form a set of bid prices P for the bidding agents; In equations (11), (12), and (13), Indicates agent B i Complete task T j Benefits gained; Indicates agent B i Complete task T j The price to be paid; Indicates agent B i Auction Task T j The bid price; ④ Based on the result of the set of bid prices P obtained in ③, the auction agent continuously updates the auction price according to the greedy principle; ⑤ Determine if the objective function is met. If not, return to step 3, and the bidding agent adjusts its bid price and resubmits. If the objective function is met, proceed to the next step. ⑥ The bidding intelligent entity is notified and the allocation result is obtained to realize the transaction; the power distribution company will issue reactive power adjustment instructions to each participating operator and reactive power equipment, and each operator, reactive power equipment and transformer will respond to achieve optimized operation of reactive power.

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