Power trading methods, systems, equipment, and media based on multi-objective network-source coordination

Abstract

This invention discloses a power trading method, system, equipment, and medium based on multi-objective grid-source collaboration, belonging to the field of power trading technology. Its purpose is to provide a power trading method, system, equipment, and medium suitable for the operation of distribution networks in future new power systems. It includes: establishing a multi-source collaborative model with the goal of maximizing the comprehensive benefits of the investment entities; using an improved multi-objective particle swarm optimization algorithm to obtain the optimal solution set of the multi-source collaborative model; using a fuzzy satisfaction method to select the most suitable solution from the optimal solution combination; calculating the grid access fee based on the most suitable solution of the multi-source collaborative model; and conducting power trading based on the grid access fee. The grid-source collaborative planning takes maximizing the benefits of distributed power source investment entities as the optimization objective, with the access capacity of distributed power sources as the optimization variable. Cost accounting is performed for each stage after optimization planning, and the corresponding grid access fee for different stages is calculated.

Description

Technical Field

[0001] This invention relates to the field of power trading technology, and to a power trading method, and more particularly to a power trading method, system, equipment and medium based on multi-objective grid-source coordination. Background Technology

[0002] With the new round of power system reform and the gradual liberalization of the electricity retail side, transactions among various participants in the electricity market have become highly complex. Each participant aims to maximize its own interests, leading to competition and conflicts of interest. The generation side primarily uses electricity price as its strategy, while users primarily use electricity volume as their strategy, mainly to reduce government control over electricity prices and allow the market to determine them. The calculation mechanism for "grid access fees" is inevitably involved in the electricity trading process.

[0003] The "Notice on Conducting Pilot Projects for Market-Based Transactions of Distributed Generation" and its "Supplementary Notice" issued in 2017 stipulated that the "grid access fee" should be temporarily calculated by deducting the transmission and distribution price of the highest voltage level involved in the distributed generation market-based transaction from the transmission and distribution price corresponding to the voltage level at which the power user accesses the grid. However, the interim method for the "grid access fee" presents certain operational difficulties. Due to the small price differences between transmission and distribution levels at different voltage levels, the grid access fee calculated using the current method is relatively low, making it difficult for distribution network operators to recover their costs through the fee, thus increasing the burden on the distribution network. Furthermore, using the price differences between transmission and distribution levels at different voltage levels to determine the interim "grid access fee" standard may directly result in power users participating in the pilot transactions bearing less or no cross-subsidy costs, which is inconsistent with the idea of ​​industrial and commercial users fairly sharing the responsibility for cross-subsidies in electricity prices.

[0004] Currently, research on distributed generation grid access fee mechanisms has yielded some results. Some technical solutions propose a grid access fee mechanism adapted to the photovoltaic cost learning curve under subsidy-free conditions, dividing the grid access fee into three parts: basic electricity price, grid loss price, and cross-subsidy. However, these solutions do not consider the impact of distributed transactions on line congestion. Other solutions propose a dynamic grid access fee mechanism based on a multi-entity point-to-point trading model, where the grid access fee dynamically changes with the location, electricity volume, and time of each transaction. However, this method is difficult to implement in practice and does not consider the impact of cross-subsidies on the grid access fee. Additionally, a multi-entity collaborative planning optimization strategy based on dynamically updated grid access fees can be adopted, treating the grid access fee as an influencing factor in the planning. Alternatively, a distributed generation market point-to-point (P2P) trading model and a community (CB) trading model can be constructed from the perspective of producers and consumers, respectively. Grid access fee calculation models for the two trading models can be established using dual multipliers and coupled power trading models. However, this method has limitations in considering the impact of distributed transactions on grid companies.

[0005] The development of distributed generation and load demand are highly stochastic and phased, and the investment costs of distributed generation are difficult to recover in the short term. Therefore, reasonable planning is necessary when formulating a reasonable grid access fee mechanism. Currently, traditional power system planning is mostly static and single-phase, with little consideration for the construction sequence of distributed generation. However, in medium- and long-term planning, static single-phase planning can lead to oversupply of distributed generation in the early stages of investment, resulting in wasted resources, while in the later stages of the planning period, equipment aging and insufficient capacity lead to a decline in power supply levels, failing to meet load demand. To address this issue, some researchers have proposed a multi-phase dynamic planning method for hydrogen-electricity coupled microgrids, improving the economics of microgrid planning schemes. Other solutions address the long planning cycle of wind-thermal power bundled generation systems by proposing a multi-phase optimization planning model for wind-thermal power bundled generation systems based on life-cycle cost (LCC).

[0006] While the aforementioned existing technologies provide various grid access fee mechanisms applicable to electricity trading, each has its own advantages and disadvantages. This application aims to provide a new grid access fee mechanism for electricity trading. Summary of the Invention

[0007] The purpose of this invention is to provide a power trading method, system, equipment, and medium based on grid-source coordination and multi-objective operation suitable for the operation of distribution networks in future new power systems.

[0008] To achieve the above objectives, the present invention specifically adopts the following technical solution:

[0009] A power trading method based on grid-source coordination and multiple objectives includes the following steps:

[0010] Step 1: Using the maximization of the overall benefits of the investment entities as the objective function, build a multi-source collaborative model;

[0011] Step 2: Use the multi-objective particle swarm optimization algorithm to find the optimal solution set of the multi-source cooperative model;

[0012] Step 3: Use the fuzzy satisfaction method to select the most suitable solution from the optimal solution set;

[0013] Step 4: Calculate the grid access fee based on the most suitable solution of the multi-source collaborative model, and conduct electricity trading based on the grid access fee.

[0014] A power trading system based on grid-source coordination and multiple objectives includes:

[0015] The multi-source collaborative model building module is used to build a multi-source collaborative model with the goal of maximizing the comprehensive benefits of the investment entities.

[0016] The optimal solution set solving module is used to find the optimal solution set of the multi-source cooperative model using the multi-objective particle swarm algorithm.

[0017] The optimal solution module is used to select the most suitable solution from the set of optimal solutions using the fuzzy satisfaction method.

[0018] The grid access fee calculation module is used to calculate the grid access fee based on the most suitable solution of the multi-source collaborative model, and to conduct power trading based on the grid access fee.

[0019] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method described above.

[0020] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described method.

[0021] The beneficial effects of this invention are as follows:

[0022] 1. This invention innovatively provides a grid access fee pricing mechanism and calculation model, which is then applied to power trading. Employing a multi-stage planning approach, the planning period is divided into several stages based on load growth during the planning phase. Each stage considers load growth and the penetration rate of distributed generation based on the previous stage. Equipment investment and line upgrades are implemented at the beginning of each planning stage to meet the load demand and distributed trading needs of the entire planning phase. The upper-level model of grid-source collaborative planning aims to maximize the benefits for distributed generation investment entities, with the distributed generation access capacity as the optimization variable. The lower-level model's optimization variable is the distribution network structure, with the annual operating cost of the distribution network operator as the optimization objective. Finally, cost accounting is performed on each stage after optimization planning to calculate the corresponding grid access fee for different stages. Based on the grid access fee calculated by this model, an operation strategy suitable for the distribution network of future new power systems is proposed, contributing to the development of the distribution network and distributed generation.

[0023] 2. In this invention, an improved particle swarm optimization algorithm is adopted. This algorithm guides the value of the inertia weight by the distance between the particle and the optimal particle in the swarm, so that the inertia weight can be adaptively adjusted. The adaptively adjusted inertia weight can control the particle's search behavior by adjusting the particle's speed and position. Considering the introduction of crossover and mutation operations when the distance between the particle and the optimal particle in the swarm is minimized, it avoids getting trapped in local optima, ensures global search capability, can deal with problems with diversity and complexity, fully utilizes the advantages of swarm intelligence, and at the same time considers the particularity of the problem, thus improving the solution effect. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the present invention;

[0025] Figure 2This is a flowchart illustrating the multi-objective particle swarm algorithm in this invention;

[0026] Figure 3 This is a schematic diagram comparing net revenue and network access fees in two scenarios of this invention, where a represents dynamic network access fees and b represents single network access fees. Detailed Implementation

[0027] Example 1

[0028] This embodiment provides a power trading method based on multi-objective network-source coordination, the specific steps of which are as follows:

[0029] Step 1: Build a multi-source collaborative model with the objective function of maximizing the overall benefits of the investment entities.

[0030] The multi-source collaborative model established includes the DG planning model and the network access fee model.

[0031] The DG planning model includes the cost model F1, the power quality model F2, and the distribution network loss model F3.

[0032] From the perspective of the investor, and with the objective function of maximizing the investor's overall benefits, in the direct transaction model, the investor's revenue mainly comes from the sales revenue of electricity generated from the grid connection and government subsidies. The cost model includes investment and operation costs, grid access fees, and electricity purchase costs, discounting all costs to the beginning of the investment period. Therefore, the cost model F1 is expressed as:

[0033]

[0034] in, For the returns of the investment entity, For the cost of the investment entity, For the electricity sales revenue of the investment entity, Government subsidies for renewable energy power generation, The residual value of the equipment at the end of the planning period; The investment and construction costs incurred by the investment entity; The operating and maintenance costs for the investment entity; The cost of purchasing electricity from the grid; This refers to the network access fee that the investing entity needs to pay to the power distribution network operator.

[0035] DG power operates under three models: direct trading, grid-based power sales, and grid purchase. When DG trades electricity with users without grid purchase, its sales revenue is factored into the initial investment period. Therefore, the sales revenue... Represented as:

[0036]

[0037] in, It is the total number of years in the planning period. For the first Year The price at which DG (Distributed Generated Water) projects invested and constructed by the main investment entity sell electricity to power users. For the first Year The total power transmitted from the distributed generation (DG) plants invested and constructed by the main investment entity to the distribution network is obtained by summing the power transmitted by each DG plant. is the discount rate.

[0038] Since investment and construction costs only occur at the beginning of each stage, these costs need to be factored in towards the beginning of the entire planning period. Therefore, investment and construction costs... Represented as:

[0039]

[0040] in, The investment cost per unit capacity of DG; In the first The total installed capacity of DG invested and constructed by the main investors in each stage. is the discount rate.

[0041] Operation and maintenance costs of the investment entity Represented as:

[0042]

[0043] in, For the first The annual operating and maintenance cost per unit capacity of DG constructed by the main investment entity. For the first Total DG capacity accumulated in 2018 and earlier. is the discount rate.

[0044] Due to the uncertainty of distributed generation (DG) output, when the actual output is less than the expected output, it is necessary to purchase electricity from the grid to make up for the shortfall. Therefore, the cost of purchasing electricity from the grid... Represented as:

[0045]

[0046] in, It is the total number of years in the planning period. It is the first The unit time-of-use electricity price for electricity purchased by the annual investment entity. For the first Annual electricity purchase volume It is the first The expected output of DG, which is invested and constructed by the main investment entity in the current year, It is the first The actual output of DG constructed by the main investment entity in the year is the discount rate.

[0047] When distributed generation (DG) projects invested in by an investor engage in distributed trading, the traded electricity needs to pass through the distribution network operator's equipment, and the investor needs to pay a corresponding transmission fee to the distribution network operator. Therefore, the transmission fee... Represented as:

[0048]

[0049] in, For the i-th stage, The total number of stages. For the t-th moment of the h-th year, For the hth year of the planning period, For the hth year The total power transmitted by distributed generation to the distribution network at any given time. For the first The network access fee paid by both parties to the distributed network transaction to the distribution network operator per unit of electricity in each stage. is the discount rate.

[0050] Because the deployment times of various distributed generation (DG) systems differ during the planning period, some DG systems will not have reached their lifespan by the end of the planning period, necessitating the calculation of their residual value. Therefore, equipment residual value... Represented as:

[0051]

[0052]

[0053] in, It is the total number of years in the planning period. For the i-th stage, The total number of stages. The capacity of distributed power sources invested and constructed by the investment entity. The investment and construction cost per unit capacity of distributed power sources. For the lifespan of DG; Net residual value rate of equipment; The total runtime of the DG deployed in the i-th phase; It is in the The annual equipment depreciation cost for each stage of DG's investment. is the discount rate.

[0054] According to relevant guidelines, DG may be eligible for government subsidies. Therefore, government subsidies... Represented as:

[0055]

[0056] in, It is the total number of years in the planning period. It is the first Year, The unit power generation subsidy cost for DG, This represents the total power generation of DG. is the discount rate.

[0057] Grid connection of distributed generation (DG) will affect the power quality of the distribution network, mainly including voltage fluctuations, voltage sags, and voltage deviations. Since multi-stage planning problems are long-term problems, the average voltage deviation is used to characterize power quality. Therefore, the power quality model F2 is expressed as:

[0058] The power quality model F2 is expressed as:

[0059]

[0060] in, This represents the number of system nodes. The reference voltage has a per-unit value of 1. Indicates the first The voltage value of each node.

[0061] A well-planned grid connection for distributed generation (DG) can effectively reduce network losses and improve energy utilization. The objective function should be to minimize network losses after DG integration into the distribution network, thus determining the appropriate installation stage for DG. Therefore, the distribution network loss F3 is expressed as:

[0062]

[0063] in, For the first One line, The total number of lines. For the first Network loss rate of each line For distributed power sources in the first The power loss of each line.

[0064] The network access fee model includes a basic usage fee. Congestion surcharge and cross-subsidy fees .

[0065] Basic usage fee Represented as:

[0066]

[0067] in, This is the i-th stage; Let i be the starting year of the i-th stage. =1 indicates that the starting year of the first stage is 1; For the first Total service cost at each stage In the first Cost of loss of distribution network operating rights during a planning cycle For the first The amount of electricity used for internet access during each stage of DG.

[0068] Service Costs Represented as:

[0069]

[0070] in, It refers to the proportion of DG (Distribution Generator) electricity to the total electricity consumption of the distribution network. and They are the first The amount of electricity fed into the grid by DG and the total amount of electricity fed into the grid by the distribution network at each stage. That is in the first Total service cost at each stage.

[0071] The cost of distribution network services includes not only the costs of constructing and expanding distribution network lines and equipment for distributed generation transactions, but also the costs of distribution network losses and voltage deviation mitigation resulting from the large-scale implementation of distributed generation transactions. To address potential voltage exceedances at distribution network nodes due to power flow changes after distributed generation transactions, the generation company needs to provide reactive power support services. In this case, the distribution network pays the generation company a voltage mitigation cost as follows:

[0072]

[0073] in, It is the unit voltage offset correction cost. It is the node voltage after the transaction is generated. This is the rated voltage.

[0074] Network loss cost calculation is related to DG trading volume:

[0075]

[0076] in, Cost per unit of network loss For network loss rate, For DG in the The trading volume at each stage.

[0077] Distributed generation (DG) transactions reduce the electricity sales share of distribution network operators. Since DG is transmitted through the distribution network, its integration results in a loss of the grid company's concession rights. This means grid costs cannot be fully recovered through existing electricity sales, necessitating the inclusion of these unrecoverable costs in the calculation of transmission fees. These transmission fees are calculated at the beginning of each phase and remain constant within a planning phase. Therefore, the cost of the distribution network concession rights loss... Represented as:

[0078]

[0079] in, For the planning period Year, For the first The year's first time; For the first The years included in each stage ; Let be the provincial transmission and distribution price corresponding to time t in year h of the i-th stage. For the first The corresponding network access fee price for each stage. For the first In the first stage Year The amount of electricity supplied to the grid by the distributed power source at all times.

[0080] exist The basic network access fee that DG should share in the phase should be:

[0081] .

[0082] We also need to consider congestion surcharges. Represented as:

[0083]

[0084] in, For the trading pair of a and b, the first The contribution of each line congestion, For the first The basic network access fee that DG should share in each stage.

[0085] Contribution Represented as:

[0086]

[0087] in, For nodes Injected power, node Outflow power in the line The active power flowing through the circuit is positive when it is in the same direction as the original power flow of the circuit, and negative when it is opposite to the original power flow direction. It is a line Maximum capacity, It is a node in a distributed transaction , Trading volume , , They are stages Nodes in internal distributed transactions , The total number of lines through which power flows, the total number of distributed trading power sources and load nodes.

[0088] Due to the impact of cross-subsidies, using the "price difference method" to collect grid access fees would prevent power grid companies from obtaining their full permitted revenue. The grid access fee pricing model must consider incorporating the cross-subsidy component; for regions with already approved cross-subsidy amounts, the cross-subsidy amount should be calculated based on the approved value. Therefore, the cross-subsidy fee... Calculations are performed based on the approved values.

[0089] Finally, during the planning period Network access fees for distributed transactions at each stage Represented as:

[0090] ;

[0091] Then the internet access fee Distributed among power source and load:

[0092]

[0093] in, In the first The grid connection fee per unit of electricity that distributed power sources should share in each stage. In the first The network access fee per unit of electricity that should be shared by the loads participating in distributed transactions in each stage. This is the allocation factor. Different allocation factor values ​​can be selected based on the varying impacts of grid access fees on power supply and load in different regions. The range of values ​​for the allocation factor is... .

[0094] Step 2: Use the multi-objective particle swarm optimization algorithm to find the optimal solution set of the multi-source cooperative model.

[0095] The multi-objective particle swarm optimization algorithm has the following specific steps:

[0096] Step 2-1: Set network parameters and wind turbine parameters, and set the population size I in the algorithm to 100 and the maximum number of iterations H to 2000.

[0097] Step 2-2: Generate the initial population for the multi-stage planning model, and initialize the population's position variable x and velocity variable y;

[0098] Steps 2-3: Obtain the individual optimal position and the global optimal position;

[0099] Steps 2-4: Calculate the difference X between each particle and the optimal particle;

[0100] Steps 2-5: Update the inertial weights of each particle, and update the velocity and position components of each particle.

[0101] Steps 2-6: Calculate the objective function value of each particle, update the historical optimal solution of the particle according to the dominance relationship, form a new set of non-dominated solutions, and update it.

[0102] Steps 2-7: Select the globally optimal solution for the population;

[0103] Step 2-8: Determine whether the selected optimal solution meets the termination condition; if the termination condition is met, proceed to step 2-9; if not, proceed to step 2-5.

[0104] Steps 2-9 yield the optimal investment year, investment node, and investment capacity for each stage, and form the optimal solution set of the multi-source collaborative model;

[0105] In steps 2-5, after updating the inertia weights, velocity components, and position components of each particle, I particles are obtained, where I=100. These I particles are then combined with a typical wind power output model and sequentially compared with the power quality model F2 and the distribution network loss F3 and grid access fees. Electricity sales revenue in cost model F1 Investment and construction costs Operation and maintenance costs Equipment residual value Internet access fee and government subsidies After applying constraints, the updated position variable x and velocity variable y are obtained, and then steps 2-3 are performed. The typical wind power output model is an existing model and can be directly applied.

[0106] When applying constraints, the constraint conditions are as follows:

[0107] Network constraints are represented as:

[0108]

[0109]

[0110] in, Represents a node The set of parent nodes; Represented by node The set of branch end nodes with the first node; It is a node , The active power; , , They are nodes exist The actual wind power output, purchased power output, and load power at any given time; and They are nodes , voltage amplitude, For nodes , The resistance;

[0111] The node voltage constraint is expressed as:

[0112]

[0113] in, For the first Each node The node voltage at time t, and These represent the maximum and minimum values ​​of the node voltage, respectively.

[0114] The constraints for investment years at each stage are expressed as follows:

[0115]

[0116] in, Represents integers, The number of years contained in the final stage. The time required to recoup the costs of DG. It is the total number of years in the planning period. For the first Each stage The total number of stages. For the first The starting year of each stage;

[0117] The DG output constraint is expressed as:

[0118]

[0119] in, and These are the upper and lower limits of the contribution from the investment entity, DG. It is the first Year The actual output of DG invested and constructed by the main investment entity at all times;

[0120] The DG penetration constraint is expressed as:

[0121]

[0122] in, For the first Each stage node Maximum DG capacity allowed for access; The maximum DG penetration rate allowed by the system; For the first The maximum load of the distribution network at each stage For nodes The total number, For the first Each stage node Installed distributed power capacity.

[0123] Step 3: Use the fuzzy satisfaction method to select the most suitable solution from the set of optimal solutions.

[0124] The fuzzy satisfaction method is used to select the most suitable solution from the optimal solution set, specifically as follows:

[0125] Calculate the fuzzy satisfaction level for each objective in the optimal solution set. :

[0126]

[0127] in, It is the value of the k-th objective function; , These are the solutions in the set of _____. The maximum and minimum values ​​of the objective function;

[0128] Calculate the satisfaction level of each solution in the optimal solution set. :

[0129]

[0130] in, It is the number of objective functions corresponding to a single solution in the solution set;

[0131] Based on the satisfaction level of each solution in the optimal solution set Choose the solution with the highest satisfaction level as the most suitable solution.

[0132] Step 4: Calculate the grid access fee based on the most suitable solution of the multi-source collaborative model, and conduct electricity trading based on the grid access fee.

[0133] Based on the foregoing, the most suitable solution for the multi-source collaborative model can be obtained, and the grid access fee can be calculated accordingly. As for the subsequent electricity trading based on the obtained grid access fee, existing technologies can be directly applied, requiring no creative effort.

[0134] Simulation experiment:

[0135] In the process of allocating transmission costs, since power plants and electricity users together constitute the main body of the electricity market, they jointly use electricity resources equally according to demand. Furthermore, in the distribution network, the transmission price has a largely similar impact on both the power source and the load. Therefore, this experiment allocates congestion costs equally between the generation and load sides, with the allocation factor... The value is set to 0.5, but in actual engineering projects, the value can be chosen according to different needs.

[0136] To verify the effectiveness of the network access fee model based on network source cooperative planning proposed in this embodiment, three scenarios are selected for comparison:

[0137] Scenario 1: Considering the construction sequence of distributed power sources, the plan is divided into three phases. The planned distributed power sources will be deployed in each phase, and the grid access fee will be determined in three phases.

[0138] Scenario II: Without considering the construction sequence of distributed power sources, the pre-constructed distributed power sources are put into operation in the first year of the planned operation period, and the grid access fee is the same as in Scenario I.

[0139] Scenario III: Similar to Scenario I, considering the construction sequence of distributed power sources, the grid access fee is set as a fixed value within the three planning periods.

[0140] The simulation results are as follows:

[0141] Table 1 Planning Results for Scenario I and II

[0142]

[0143] Table 2. DG Access Nodes and Access Capacity in Scenario I and II

[0144]

[0145] Table 3. Different DG access nodes and access capacities in Scenario I and II

[0146]

[0147] Table 1 shows that the single-stage planning scheme results in a one-time investment of 2000kW of distributed wind power in the first year of the investment period to supply power to the load during the planning period. However, in the grid-source coordinated planning, considering the annual increase in system load and the expansion and upgrading of system lines and equipment, the maximum penetration rate of distributed generation (DG) in the system will gradually increase in each planning stage. Therefore, the optimal planning result is: 1100kW of distributed wind power will be invested in the first stage (year 1 of the planning period), 1150kW in the second stage (year 6 of the planning period), and 550kW in the third stage (year 12 of the planning period). Compared with the single-stage planning, the distributed wind power capacity increases by 800kW by the end of the planning period. The following explanation will focus on the economics and DG absorption capacity of the two planning scenarios.

[0148] 1. Economic Comparison

[0149] Because the multi-phase planning scheme installs more distributed power sources in the second and third phases, the operation and maintenance time required for these power sources is shorter during the planning period. Furthermore, the residual value of the equipment installed in the second and third phases is greater than that of the equipment installed at the beginning of the planning period, resulting in lower overall operation and maintenance costs. In the multi-phase planning, the average annual operation and maintenance cost of wind turbines is approximately RMB 107.3 / kW, and the average residual value of the turbine equipment is RMB 125.4 / kW. In contrast, in the single-phase planning, the average annual operation and maintenance cost of wind turbines is RMB 136.5 / kW, and the average residual value of the equipment is RMB 33 / kW. The average life-cycle capacity cost of wind turbines in Scenario I and Scenario II is RMB 6926.4 / kW and RMB 9412.5 / kW, respectively, with Scenario I showing a 26.4% reduction in life-cycle capacity cost. When different nodes are selected for distributed power source installation, the multi-phase planning scheme yields an average net benefit of approximately RMB 25.66 million, while the single-phase planning scheme yields a total net benefit of RMB 16.351 million. The net benefit of the multi-phase planning is still higher than that of the single-phase planning, as shown in Table 3.

[0150] 2. Analysis of DG Absorption Capacity

[0151] Taking the wind power output characteristics of a typical summer day as an example, the wind power consumption situation under two scenarios is presented. Table 2 compares the wind power consumption situation under the two planning schemes.

[0152] As shown in Table 4, in the early stage of the planning period, i.e., the first phase, the wind curtailment rate of Scenario I decreased by 13% compared to Scenario II. However, in the later stage, i.e., the third phase of the planning period, the wind curtailment rate of Scenario I was 18% lower than that of Scenario II. This is because Scenario II under-invested in the early stage of the planning period, resulting in a large equipment capacity configuration that far exceeded the initial load demand, leading to energy waste. In the later stage of the planning period, it was unable to meet the gradually increasing load demand. Therefore, from the perspective of the entire planning period, multi-stage planning has a lower wind curtailment rate and wind curtailment rate than single-stage planning, which can improve the wind power absorption rate and achieve better operational results.

[0153] Table 4 Comparison of Wind Power Consumption

[0154]

[0155] 3. Analysis of the results and impacts of internet access fees

[0156] Considering scenarios I and III separately, the results are shown in Table 5 below. In the multi-stage planning, when the grid access fee remains constant throughout the planning period, the calculated value is 0.204 yuan / kW. When a dynamic grid access fee is adopted, the grid access fees in stages 1 to 3 are 0.253 yuan / kW, 0.206 yuan / kW, and 0.178 yuan / kW, respectively. The comparison between net revenue and grid access fee in the two scenarios is as follows. Figure 3 As shown in (a) and (b) in the figure.

[0157] Table 5. Internet access fees at different stages

[0158]

[0159] As shown in Table 5, Scenario I involves multi-stage planning of dynamic grid access fees. Since grid access fees are closely related to provincial transmission and distribution prices, the provincial transmission and distribution price is [not specified] when the distribution network does not collect grid access fees. The higher cost results in higher grid access fees compared to the static grid access fees in Scenario III. In Phases 2 and 3, the distribution network can recover some costs by collecting grid access fees, eliminating the need for the power grid to compensate for sunk costs by increasing transmission and distribution prices. It will also gradually decrease and return to its true price; therefore, the unit price of network access fees in Scenario I will decrease accordingly. The cost gradually decreases as the cost decreases, eventually falling below the static network access fee of Scenario III.

[0160] In Scenario I, the net profit of the investment entity is negative due to the high network access fee in the first stage. However, overall, as the network access fee decreases and the DG capacity increases, the net profit in Scenario I gradually increases and exceeds the net profit in Scenario III, demonstrating the superiority of the dynamic network access fee model over the static one.

[0161] Example 2

[0162] This embodiment provides a power trading system based on multi-objective network-source coordination, which specifically includes:

[0163] The multi-source collaborative model building module is used to build a multi-source collaborative model with the objective function of maximizing the comprehensive benefits of the investment entities.

[0164] The multi-source collaborative model established includes the DG planning model and the network access fee model.

[0165] The DG planning model includes the cost model F1, the power quality model F2, and the distribution network loss model F3.

[0166] The cost model F1 is expressed as:

[0167]

[0168] in, For the returns of the investment entity, For the cost of the investment entity, For the electricity sales revenue of the investment entity, Government subsidies for renewable energy power generation, The residual value of the equipment at the end of the planning period; The investment and construction costs incurred by the investment entity; The operating and maintenance costs for the investment entity; The cost of purchasing electricity from the grid; This refers to the network access fee that the investing entity needs to pay to the power distribution network operator.

[0169] Electricity sales revenue Represented as:

[0170]

[0171] in, It is the total number of years in the planning period. For the first Year The price at which DG (Distributed Generated Water) projects invested and constructed by the main investment entity sell electricity to power users. For the first Year The total power transmitted from the distributed generation (DG) plants invested and constructed by the main investment entity to the distribution network is obtained by summing the power transmitted by each DG plant. is the discount rate.

[0172] Investment and construction costs Represented as:

[0173]

[0174] in, The investment cost per unit capacity of DG; In the first The total installed capacity of DG invested and constructed by the main investors in each stage. is the discount rate.

[0175] Operation and maintenance costs of the investment entity Represented as:

[0176]

[0177] in, For the first The annual operating and maintenance cost per unit capacity of DG constructed by the main investment entity. For the first Total DG capacity accumulated in 2018 and earlier. is the discount rate.

[0178] Cost of purchasing electricity from the grid Represented as:

[0179]

[0180] in, It is the total number of years in the planning period. It is the first The unit time-of-use electricity price for electricity purchased by the annual investment entity. For the first Annual electricity purchase volume It is the first The expected output of DG, which is invested and constructed by the main investment entity in the current year, It is the first The actual output of DG constructed by the main investment entity in the year is the discount rate.

[0181] Internet access fee Represented as:

[0182]

[0183] in, For the i-th stage, The total number of stages. For the t-th moment of the h-th year, For the hth year of the planning period, For the hth year The total power transmitted by distributed generation to the distribution network at any given time. For the first The network access fee paid by both parties to the distributed network transaction to the distribution network operator per unit of electricity in each stage. is the discount rate.

[0184] equipment residual value Represented as:

[0185]

[0186]

[0187] in, It is the total number of years in the planning period. For the i-th stage, The total number of stages. The capacity of distributed power sources invested and constructed by the investment entity. The investment and construction cost per unit capacity of distributed power sources. For the lifespan of DG; Net residual value rate of equipment; The total runtime of the DG deployed in the i-th phase; It is in the The annual equipment depreciation cost for each stage of DG's investment. is the discount rate.

[0188] According to relevant guidelines, DG may be eligible for government subsidies. Therefore, government subsidies... Represented as:

[0189]

[0190] in, It is the total number of years in the planning period. It is the first Year, The unit power generation subsidy cost for DG, This represents the total power generation of DG. is the discount rate.

[0191] The power quality model F2 is expressed as:

[0192]

[0193] in, This represents the number of system nodes. The reference voltage has a per-unit value of 1. Indicates the first The voltage value of each node.

[0194] The distribution network loss F3 is expressed as:

[0195]

[0196] in, For the first One line, The total number of lines. For the first Network loss rate of each line For distributed power sources in the first The power loss of each line.

[0197] The network access fee model includes a basic usage fee. Congestion surcharge and cross-subsidy fees .

[0198] Basic usage fee Represented as:

[0199]

[0200] in, This is the i-th stage; Let i be the starting year of the i-th stage. =1 indicates that the starting year of the first stage is 1; For the first Total service cost at each stage In the first Cost of loss of distribution network operating rights during a planning cycle For the first The amount of electricity used for internet access during each stage of DG.

[0201] Service Costs Represented as:

[0202]

[0203] in, It refers to the proportion of DG (Distribution Generator) electricity to the total electricity consumption of the distribution network. and They are the first The amount of electricity fed into the grid by DG and the total amount of electricity fed into the grid by the distribution network at each stage. That is in the first Total service cost at each stage.

[0204] The voltage regulation costs paid by the distribution network to the power generation company are:

[0205]

[0206] in, It is the unit voltage offset correction cost. It is the node voltage after the transaction is generated. This is the rated voltage.

[0207] Network loss cost calculation is related to DG trading volume:

[0208]

[0209] in, Cost per unit of network loss For network loss rate, For DG in the The trading volume at each stage.

[0210] Cost of loss of distribution network operating rights Represented as:

[0211]

[0212] in, For the planning period Year, For the first The year's first time; For the first The years included in each stage ; Let be the provincial transmission and distribution price corresponding to time t in year h of the i-th stage. For the first The corresponding network access fee price for each stage. For the first In the first stage Year The amount of electricity supplied to the grid by the distributed power source at all times.

[0213] exist The basic network access fee that DG should share in the phase should be:

[0214] .

[0215] We also need to consider congestion surcharges. Represented as:

[0216]

[0217] in, For the trading pair of a and b, the first The contribution of each line congestion, For the first The basic network access fee that DG should share in each stage.

[0218] Contribution Represented as:

[0219]

[0220] in, For nodes Injected power, node Outflow power in the line The active power flowing through the circuit is positive when it is in the same direction as the original power flow of the circuit, and negative when it is opposite to the original power flow direction. It is a line Maximum capacity, It is a node in a distributed transaction , Trading volume , , They are stages Nodes in internal distributed transactions , The total number of lines through which power flows, the total number of distributed trading power sources and load nodes.

[0221] Cross subsidy fee Calculations are performed based on the approved values.

[0222] Finally, during the planning period Network access fees for distributed transactions at each stage Represented as:

[0223] ;

[0224] Then the internet access fee Distributed among power source and load:

[0225]

[0226] in, In the first The grid connection fee per unit of electricity that distributed power sources should share in each stage. In the first The network access fee per unit of electricity that should be shared by the loads participating in distributed transactions in each stage. This is the allocation factor. Different allocation factor values ​​can be selected based on the varying impacts of grid access fees on power supply and load in different regions. The range of values ​​for the allocation factor is... .

[0227] The optimal solution set solving module is used to find the optimal solution set of a multi-source cooperative model using a multi-objective particle swarm optimization algorithm.

[0228] The multi-objective particle swarm optimization algorithm has the following specific steps:

[0229] Step 2-1: Set network parameters and wind turbine parameters, and set the population size I in the algorithm to 100 and the maximum number of iterations H to 2000.

[0230] Step 2-2: Generate the initial population for the multi-stage planning model, and initialize the population's position variable x and velocity variable y;

[0231] Steps 2-3: Obtain the individual optimal position and the global optimal position;

[0232] Steps 2-4: Calculate the difference X between each particle and the optimal particle;

[0233] Steps 2-5: Update the inertial weights of each particle, and update the velocity and position components of each particle.

[0234] Steps 2-6: Calculate the objective function value of each particle, update the historical optimal solution of the particle according to the dominance relationship, form a new set of non-dominated solutions, and update it.

[0235] Steps 2-7: Select the globally optimal solution for the population;

[0236] Step 2-8: Determine whether the selected optimal solution meets the termination condition; if the termination condition is met, proceed to step 2-9; if not, proceed to step 2-5.

[0237] Steps 2-9 yield the optimal investment year, investment node, and investment capacity for each stage, and form the optimal solution set of the multi-source collaborative model;

[0238] In steps 2-5, after updating the inertia weights, velocity components, and position components of each particle, I particles are obtained, where I=100. These I particles are then combined with a typical wind power output model and sequentially compared with the power quality model F2 and the distribution network loss F3 and grid access fees. Electricity sales revenue in cost model F1 Investment and construction costs Operation and maintenance costs Equipment residual value Internet access fee and government subsidies After applying constraints, the updated position variable x and velocity variable y are obtained, and then steps 2-3 are performed. The typical wind power output model is an existing model and can be directly applied.

[0239] When applying constraints, the constraint conditions are as follows:

[0240] Network constraints are represented as:

[0241]

[0242]

[0243] in, Represents a node The set of parent nodes; Represented by node The set of branch end nodes with the first node; It is a node , The active power; , , They are nodes exist The actual wind power output, purchased power output, and load power at any given time; and They are nodes , voltage amplitude, For nodes , The resistance;

[0244] The node voltage constraint is expressed as:

[0245]

[0246] in, For the first Each node The node voltage at time t, and These represent the maximum and minimum values ​​of the node voltage, respectively.

[0247] The constraints for investment years at each stage are expressed as follows:

[0248]

[0249] in, Represents integers, The number of years contained in the final stage. The time required to recoup the costs of DG. It is the total number of years in the planning period. For the first Each stage The total number of stages. For the first The starting year of each stage;

[0250] The DG output constraint is expressed as:

[0251]

[0252] in, and These are the upper and lower limits of the contribution from the investment entity, DG. It is the first Year The actual output of DG invested and constructed by the main investment entity at all times;

[0253] The DG penetration constraint is expressed as:

[0254]

[0255] in, For the first Each stage node Maximum DG capacity allowed for access; The maximum DG penetration rate allowed by the system; For the first The maximum load of the distribution network at each stage For nodes The total number, For the first Each stage node Installed distributed power capacity.

[0256] The optimal solution module is used to select the most suitable solution from the set of optimal solutions using the fuzzy satisfaction method.

[0257] The fuzzy satisfaction method is used to select the most suitable solution from the optimal solution set, specifically as follows:

[0258] Calculate the fuzzy satisfaction level for each objective in the optimal solution set. :

[0259]

[0260] in, It is the value of the k-th objective function; , These are the solutions in the set of _____. The maximum and minimum values ​​of the objective function;

[0261] Calculate the satisfaction level of each solution in the optimal solution set. :

[0262]

[0263] in, It is the number of objective functions corresponding to a single solution in the solution set;

[0264] Based on the satisfaction level of each solution in the optimal solution set Choose the solution with the highest satisfaction level as the most suitable solution.

[0265] The grid access fee calculation module is used to calculate the grid access fee based on the most suitable solution of the multi-source collaborative model, and to conduct power trading based on the grid access fee.

[0266] Example 3

[0267] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform steps of a power trading method based on network-source collaborative multi-objectives.

[0268] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0269] The memory includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D-interface display memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device. Of course, the memory may include both the internal storage unit and the external storage device of the computer device. In this embodiment, the memory is often used to store the operating system and various application software installed on the computer device, such as the program code of the power trading method based on network-source collaborative multi-objective. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

[0270] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is typically used to control the overall operation of the computer device. In this embodiment, the processor is used to run program code stored in the memory or process data, for example, to run the program code of the power trading method based on network-source collaborative multi-objectives.

[0271] Example 4

[0272] A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform steps of a power trading method based on network-source collaborative multi-objectives.

[0273] The computer-readable storage medium stores an interface display program, which can be executed by at least one processor to cause the at least one processor to perform the steps of the power trading method based on network-source coordination and multi-objectives as described above.

[0274] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the power trading method based on network-source coordination and multi-objectives described in the embodiments of this application.

Claims

1. A power transaction method based on network-source cooperative multi-objective, characterized in that, Includes the following steps: Step 1: Using the maximization of the overall benefits of the investment entities as the objective function, build a multi-source collaborative model; Step 2: Use the multi-objective particle swarm optimization algorithm to find the optimal solution set of the multi-source cooperative model; Step 3: Use the fuzzy satisfaction method to select the most suitable solution from the optimal solution set; Step 4: Calculate the grid access fee based on the most suitable solution of the multi-source collaborative model, and conduct electricity trading based on the grid access fee; In step 1, the multi-source collaborative model established includes the DG planning model and the network access fee model; The DG planning model includes the cost model F1, the power quality model F2, and the distribution network loss model F3. The cost model F1 is expressed as: in, For the returns of the investment entity, For the cost of the investment entity, For the electricity sales revenue of the investment entity, Government subsidies for renewable energy power generation, The residual value of the equipment at the end of the planning period; The investment and construction costs incurred by the investment entity; The operating and maintenance costs for the investment entity; The cost of purchasing electricity from the grid; This refers to the network access fee that the investing entity needs to pay to the power distribution network operator. The power quality model F2 is expressed as: in, This represents the number of system nodes. The reference voltage has a per-unit value of 1. Indicates the first Voltage values ​​at each node; The distribution network loss model F3 is expressed as follows: in, For the first One line, The total number of lines. For the first Network loss rate of each line For distributed power sources in the first The power loss of each line; The network access fee model includes a basic usage fee. Congestion surcharge and cross-subsidy fees ; Basic usage fee Represented as: in, This is the i-th stage; Let i be the starting year of the i-th stage. =1 indicates that the starting year of the first stage is 1; For the first Total service cost at each stage In the first Cost of loss of distribution network operating rights during a planning cycle For the first DG's internet usage at each stage; Congestion surcharge Represented as: in, For the trading pair of a and b, the first The contribution of each line congestion, For the first The basic network access fee that DG should share in each stage; Cross subsidy fee Calculate according to the approved values; Finally, during the planning period Network access fees for distributed transactions at each stage Represented as: ； Then the internet access fee Distributed among power source and load: in, In the first The grid connection fee per unit of electricity that distributed power sources should share in each stage. In the first The network access fee per unit of electricity that should be shared by the loads participating in distributed transactions in each stage. This is the allocation factor.

2. The power trading method based on grid-source coordination and multi-objectives as described in claim 1, characterized in that: Electricity sales revenue Represented as: in, It is the total number of years in the planning period. For the first Year The price at which DG (Distributed Generated Water) projects invested and constructed by the main investment entity sell electricity to power users. For the first Year The total power transmitted from the distributed generation (DG) plants invested and constructed by the main investment entity to the distribution network is obtained by summing the power transmitted by each DG plant. The discount rate; Investment and construction costs Represented as: in, The investment cost per unit capacity of DG; In the first The total installed capacity of DG invested and constructed by the main investors in each stage. The discount rate; Operation and maintenance costs Represented as: in, For the first The annual operating and maintenance cost per unit capacity of DG constructed by the main investment entity. For the first Total DG capacity accumulated in 2018 and earlier. The discount rate; Cost of purchasing electricity from the grid Represented as: in, It is the total number of years in the planning period. It is the first The unit time-of-use electricity price for electricity purchased by the annual investment entity. For the first Annual electricity purchases It is the first The expected output of DG, which is invested and constructed by the main investment entity in the current year, It is the first The actual output of DG constructed by the main investment entity in the year The discount rate; Internet access fee Represented as: in, For the i-th stage, The total number of stages. For the t-th moment of the h-th year, For the hth year of the planning period, For the hth year The total power transmitted by distributed generation to the distribution network at any given time. For the first The network access fee paid by both parties to the distribution network operator for each unit of electricity in each stage of the distributed transaction. The discount rate; equipment residual value Represented as: in, It is the total number of years in the planning period. For the i-th stage, The total number of stages. The capacity of distributed power sources invested and constructed by the investment entity. The investment and construction cost per unit capacity of distributed power sources. For the lifespan of DG; Net residual value rate of equipment; The total runtime of the DG deployed in the i-th phase; It is in the The annual equipment depreciation cost for each stage of DG's investment. The discount rate; Government subsidies Represented as: in, It is the total number of years in the planning period. It is the first Year, The unit power generation subsidy cost for DG, This represents the total power generation of DG. The discount rate; Service Costs Represented as: in, It refers to the proportion of DG (Distribution Generator) electricity to the total electricity consumption of the distribution network. and They are the first The amount of electricity fed into the grid by DG and the total amount of electricity fed into the grid by the distribution network at each stage. That is in the first Total service cost at each stage; Cost of loss of distribution network operating rights Represented as: in, For the planning period Year, For the first The year's first time; For the first The years included in each stage ; Let be the provincial transmission and distribution price corresponding to time t in year h of the i-th stage. For the first The corresponding network access fee price for each stage. For the first In the first stage Year The amount of electricity supplied to the grid by the distributed power source at all times; Contribution Represented as: in, For nodes Injected power, node Outflow power in the line The active power flowing through the circuit is positive when it is in the same direction as the original power flow of the circuit, and negative when it is opposite to the original power flow direction. It is a line Maximum capacity, It is a node in a distributed transaction , Trading volume , , They are stages Nodes in internal distributed transactions , The total number of lines through which power flows, the total number of distributed trading power sources and load nodes.

3. The power trading method based on multi-objective grid-source coordination as described in claim 1, characterized in that, In step 2, the multi-objective particle swarm algorithm is as follows: Step 2-1: Set network parameters and wind turbine parameters, and set the population size I in the algorithm to 100 and the maximum number of iterations H to 2000. Step 2-2: Generate the initial population for the multi-stage planning model, and initialize the population's position variable x and velocity variable y; Steps 2-3: Obtain the individual optimal position and the global optimal position; Steps 2-4: Calculate the difference X between each particle and the optimal particle; Steps 2-5: Update the inertial weights of each particle, and update the velocity and position components of each particle. Steps 2-6: Calculate the objective function value of each particle, update the historical optimal solution of the particle according to the dominance relationship, form a new set of non-dominated solutions, and update it. Steps 2-7: Select the globally optimal solution for the population; Step 2-8: Determine whether the selected optimal solution meets the termination condition; if the termination condition is met, proceed to step 2-9; if not, proceed to step 2-5. Steps 2-9 yield the optimal investment year, investment node, and investment capacity for each stage, and form the optimal solution set of the multi-source collaborative model; In steps 2-5, after updating the inertia weights, velocity components, and position components of each particle, I particles are obtained, where I=100. These I particles are then combined with a typical wind power output model and sequentially applied to the power quality model F2, the distribution network loss model F3, and the network access fee. Electricity sales revenue in cost model F1 Investment and construction costs Operation and maintenance costs Equipment residual value Internet access fee and government subsidies Apply constraints to obtain the updated position variable x and velocity variable y, and then proceed to steps 2-3.

4. The power trading method based on multi-objective grid-source coordination as described in claim 3, characterized in that, When applying constraints, the constraint conditions are: Network constraints are represented as: in, Represents a node The set of parent nodes; Represented by node The set of branch end nodes with the first node; It is a node , active power, It is a node , The active power; , , They are nodes exist The actual wind power output, purchased power output, and load power at any given time; and They are nodes , voltage amplitude, For nodes , The resistance; the node voltage constraint is expressed as: in, For the first Each node The node voltage at time t, and These represent the maximum and minimum values ​​of the node voltage, respectively. The constraints for investment years at each stage are expressed as follows: in, Represents integers, The number of years contained in the final stage. The time required to recoup the costs of DG. It is the total number of years in the planning period. For the first Each stage The total number of stages. For the first The starting year of each stage; The DG output constraint is expressed as: in, and These are the upper and lower limits of the contribution from the investment entity, DG. It is the first Year The actual output of DG invested and constructed by the main investment entity at all times; The DG penetration constraint is expressed as: in, For the first Each stage node Maximum DG capacity allowed for access; The maximum DG penetration rate allowed by the system; For the first The maximum load of the distribution network at each stage For nodes The total number, For the first Each stage node Installed distributed power capacity.

5. The power trading method based on multi-objective grid-source coordination as described in claim 1, characterized in that, In step 3, the fuzzy satisfaction method is used to select the most suitable solution from the optimal solution set, specifically as follows: Calculate the fuzzy satisfaction level for each objective in the optimal solution set. : in, It is the value of the k-th objective function; , These are the solutions in the solution set. The maximum and minimum values ​​of the objective function; Calculate the satisfaction level of each solution in the optimal solution set. : in, It is the number of objective functions corresponding to a single solution in the solution set; Based on the satisfaction level of each solution in the optimal solution set Choose the solution with the highest satisfaction level as the most suitable solution.

6. A power trading system based on multi-objective network-source coordination, characterized in that, include: The multi-source collaborative model building module is used to build a multi-source collaborative model with the goal of maximizing the comprehensive benefits of the investment entities. The optimal solution set solving module is used to find the optimal solution set of the multi-source cooperative model using the multi-objective particle swarm algorithm. The optimal solution module is used to select the most suitable solution from the set of optimal solutions using the fuzzy satisfaction method. The grid access fee calculation module is used to calculate the grid access fee based on the most suitable solution of the multi-source collaborative model, and to conduct power trading based on the grid access fee. In the multi-source collaborative model building module, the multi-source collaborative models built include the DG planning model and the network access fee model; The DG planning model includes the cost model F1, the power quality model F2, and the distribution network loss model F3. The cost model F1 is expressed as: in, For the returns of the investment entity, For the cost of the investment entity, For the electricity sales revenue of the investment entity, Government subsidies for renewable energy power generation, The residual value of the equipment at the end of the planning period; The investment and construction costs incurred by the investment entity; The operating and maintenance costs for the investment entity; The cost of purchasing electricity from the grid; This refers to the network access fee that the investing entity needs to pay to the power distribution network operator. The power quality model F2 is expressed as: in, This represents the number of system nodes. The reference voltage has a per-unit value of 1. Indicates the first Voltage values ​​at each node; The distribution network loss model F3 is expressed as: in, For the first One line, The total number of lines. For the first Network loss rate of each line For distributed power sources in the first The power loss of each line; The network access fee model includes a basic usage fee. Congestion surcharge and cross-subsidy fees ; Basic usage fee Represented as: in, This is the i-th stage; Let i be the starting year of the i-th stage. =1 indicates that the starting year of the first stage is 1; For the first Total service cost at each stage In the first Cost of loss of distribution network operating rights during a planning cycle For the first DG's internet usage at each stage; Congestion surcharge Represented as: in, For the trading pair of a and b, the first The contribution of each line congestion, For the first The basic network access fee that DG should share in each stage; Cross subsidy fee Calculate according to the approved values; Finally, during the planning period Network access fees for distributed transactions at each stage Represented as: ； Then the internet access fee Distributed among power source and load: in, In the first The grid connection fee per unit of electricity that distributed power sources should share in each stage. In the first The network access fee per unit of electricity that should be shared by the loads participating in distributed transactions in each stage. This is the allocation factor.

7. A computer device, characterized in that: It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 5.

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