A third-party platform-based transformer area photovoltaic on-site consumption listing transaction and deviation assessment method, device, equipment and medium

Abstract

The present application relates to the field of photovoltaic power generation technology, and particularly relates to a method and device for listing transaction and deviation assessment of on-site consumption of photovoltaic power in a transformer area based on a third-party platform, equipment and medium. First, the load management system predicts the net load of the transformer area, determines the risk of reverse overload and determines the photovoltaic power reduction. The listing quantity and price are optimized to maximize the expected income and reported to the third-party platform. The flexible resource subject combines and picks up multiple listing demands according to its own adjustment cost and benchmark power consumption curve. The platform clears according to the listing power and picking-up sequence, and generates a financial contract. After the execution of the contract, the photovoltaic operator is assessed for the deviation of the additional power generation, and the flexible resource subject is assessed for the adjustment deviation based on the historical response capacity. Based on the deviation assessment results and actual measurement data, the platform collects service fees after combined settlement and includes the fines into the risk management fund. The present application realizes the marketization matching and closed-loop assessment of on-site consumption of photovoltaic power in a transformer area.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, specifically to a method, device, equipment, and medium for listing and trading photovoltaic power generation in a distribution area and assessing deviations based on a third-party platform. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) power generation in distribution networks continues to increase, a large number of residential and small-scale distributed PV systems are being connected to transformer substations, significantly altering the power supply and demand characteristics within these substations. During the midday period when sunlight is abundant and load is relatively light, PV output often exceeds the actual electricity demand of the substation, causing the net load to shift from positive to negative. This can lead to reverse overload or even overvoltage issues in the substation, seriously threatening the safe and stable operation of the distribution network. Traditional solutions mainly rely on grid dispatching agencies to rigidly limit or force curtailment of PV power. This approach not only reduces the utilization efficiency of renewable energy but also harms the power generation revenue of PV operators, making it difficult to strike a balance between ensuring grid security and promoting the consumption of clean energy.

[0003] Currently, some regions are attempting to guide user-side load adjustment through demand response or orderly electricity consumption to alleviate the reverse overload pressure on distribution transformer areas. However, existing response mechanisms are mostly geared towards large industrial users or dedicated transformer users, with insufficient coverage for small-scale flexible resources such as energy storage facilities and charging piles within the distribution transformer area. Furthermore, the lack of market-based price signals makes it difficult for flexible resource entities to independently decide their participation intentions based on their own adjustment costs and economic benefits. Simultaneously, the lack of direct interactive trading channels between photovoltaic operators and flexible resource entities hinders efficient matching between consumption demand and regulation capacity, resulting in the underutilization of local consumption potential within the distribution transformer area. Regarding transaction organization, the current model often relies on unified dispatch by the grid company or one-way subsidy incentives for photovoltaic consumption within the distribution transformer area, failing to establish a market-based trading mechanism based on public listing and independent bidding. Particularly for markets involving small and micro-sized entities, the lack of a third-party platform capable of handling functions such as quantity and price declaration, clearing confirmation, and contract generation leads to low transparency, high transaction costs, and insufficient trust among participants. Furthermore, due to the inherent uncertainty in distributed photovoltaic (PV) output and the influence of various factors on the actual adjustment capabilities of flexible resources, scientifically assessing the performance of transaction contracts and defining liability for deviations has become a significant bottleneck hindering the implementation of market-based consumption models. Existing methods for deviation assessment mostly focus on the performance deviations of a single type of entity, lacking a refined and differentiated assessment mechanism for the flexible resource side and failing to incorporate factors such as benchmark electricity consumption curves and historical response capabilities into the differentiated evaluation system. Simultaneously, the settlement process often relies on grid companies acting as intermediaries for collection and payment, resulting in cumbersome procedures and long cycles that are ill-suited to the high-frequency, small-batch needs of distribution area transactions. Unclear deviation liability identification and inefficient settlement pathways lead to a lack of binding force in transaction contracts, and the willingness and credibility of market participants to fulfill their obligations cannot be effectively guaranteed.

[0004] Therefore, there is an urgent need to propose a method, device, equipment, and medium for listing and trading photovoltaic power generation in the distribution area and for deviation assessment based on a third-party platform. By constructing a market-oriented trading model for micro and small entities, the autonomous matching of photovoltaic power generation demand and flexible adjustment capabilities can be achieved. In addition, a two-way deviation assessment and combined settlement mechanism should be established to improve the level of photovoltaic power generation in the distribution area, ensure the safe operation of the power grid, and provide a fair and transparent trading environment and reasonable returns for various entities. Summary of the Invention

[0005] The purpose of this invention is to provide a method, device, equipment, and medium for on-site photovoltaic power consumption and deviation assessment in a distribution area based on a third-party platform. Through listing and bidding matching, two-way deviation assessment, and combined settlement, the surplus photovoltaic power in the distribution area can be autonomously consumed.

[0006] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution: A method for listing and trading photovoltaic power generation in a distribution area and assessing deviations based on a third-party platform includes the following steps: S1: Based on the load management system, predict the distributed photovoltaic output of the transformer area before the operating date (D-1). Energy storage charging / discharging power Charging load power and the load power of the transformer area Calculate the net load power of the transformer area at different times. If the net load is negative and If the risk of reverse overload exists during that period, step S2 is triggered; otherwise, the process ends. S2: The load management system determines the total photovoltaic power reduction of the transformer area for each time period. The permitted power generation capacity will be allocated to each photovoltaic operator based on their installed capacity. and power reduction The photovoltaic operators, aiming to maximize their own profits, optimize their decision-making regarding the listed electricity volume for that period, taking into account the uncertainty of photovoltaic output. and listing price And submit it to a third-party platform; the listed price is negative and its absolute value is not higher than the photovoltaic feed-in tariff; S3: The third-party platform pushes the listing quantity and price parameters to the connected flexible resource entities, which include energy storage and charging loads; each flexible resource entity, based on its own adjustment costs and benchmark electricity consumption curve, aims to maximize its own revenue within its own physical adjustment capacity boundary, and combines multiple listing demands to generate a bidding quantity and price combination. S4: After the third-party platform aggregates all bidding requests, it determines the transaction entity, transaction volume, and transaction price based on the listed electricity volume and the order of bidding applications (matched sequentially according to the order of bidding application time), and generates a financial contract; if the request of either the listing or bidding party is fully satisfied, the transaction result for that period is locked; if neither party's request is fully satisfied, the process returns to step S3 to re-bid for the unsuccessful portion. S5: Conduct deviation assessments after contract execution: For photovoltaic operators, assess the deviation of their actual increased power generation from the contracted power generation and the expected power generation; for flexible resource entities, assess the deviation of their actual adjustment from the baseline of the awarded power generation and the execution deviation. S6: Based on the deviation assessment results and the actual output and electricity metering data of each entity, the power grid company and the third-party platform will conduct a combined settlement for each entity. Among them, the net income of the photovoltaic operator is the actual power generation income paid by the power grid company at the on-grid electricity price minus the transaction costs paid by the operator to the third-party platform. The net expenditure of the flexible resource entity is the electricity purchase cost paid to the power grid company minus the response income paid by the third-party platform. The third-party platform charges a service fee based on a percentage of the total transaction amount and transfers the penalties generated by the deviation assessment to the platform as operation, maintenance and risk management funds.

[0007] Furthermore, in step S2, the photovoltaic operator considers the uncertainty of photovoltaic output by introducing a confidence adjustment factor. As decision variables, with the objective function of maximizing expected returns, and under the premise of satisfying power balance constraints, the optimal decision is found. To achieve the optimal combination of listed electricity volume and price: Objective function: in In the formula, For photovoltaic operators exist Expected total return for the period; For photovoltaic operators exist Cost of assessing expected deviations during a given period; This is the price sensitivity coefficient; This is the average penalty price for the same period in history; This is the allowable deviation coefficient for the same period in history; The expected listing price; Assume the actual output of photovoltaic power is a random variable Follows a normal distribution , The standard deviation of photovoltaic power output forecast. Expected output value for photovoltaic power generation; The following constraints must be met: Decision variable range constraints The listing price constraint adopts a negative bidding mechanism. In the formula, For photovoltaic feed-in tariffs; Physical constraints on listed electricity volume Power balance constraints Photovoltaic operators obtain the optimal confidence adjustment coefficient by solving the objective function. This allows for the determination of the optimal price combination submitted by the third-party platform. .

[0008] Furthermore, in step S3, each flexible resource entity generates a combined bidding demand based on the photovoltaic listing price and its own adjustment costs through the following steps: Flexible resource entities For each listing requirement First, we need to calculate the unit net revenue under this single listing demand. : All listing requests are sorted in descending order of net revenue per unit to obtain a response priority sequence. , making ,main body According to the sequence Decisions are made sequentially regarding the delisting response power for each listing request. and update the allocation to the first When the main body of the item demand Remaining responsive power : The initial state refers to the remaining power before processing the first item listed. : In the formula, For resources The maximum regulating power that can be provided under ideal conditions; For resources exist The reference power consumption at any given time; when or At this point, the iteration process ends, and the final combination of bidding volume and price is compiled and submitted to the platform. .

[0009] Furthermore, the combined settlement described in step S6 specifically includes: For photovoltaic operators, their net income equals the revenue from the power grid company's metering and settlement of their actual total power generation based on their on-grid tariff, minus the consumption fees paid by them through third-party platforms for listing on exchanges, the assessment costs incurred due to execution deviations, and the service fees paid to third-party platforms, which are charged as a certain percentage of the total transaction amount. For flexible resource entities, their net expenditure equals the metering and settlement cost of their actual total electricity consumption by the power grid company at the market purchase price, minus the bidding response revenue they obtain by providing adjustable capacity.

[0010] Furthermore, step S5, which assesses the deviation of flexible resource entities, also includes the step of determining the allowable deviation rate in advance: The allowable deviation rate is determined based on the deviation between the actual response capability of the flexible resource entity in the same period in history and the target response capability agreed in the current contract. Specifically, if the deviation between the contract power and the historical benchmark power is less than a first threshold, a first allowable deviation rate is adopted; if the deviation between the contract power and the historical benchmark power is greater than or equal to the first threshold but less than a second threshold, a second allowable deviation rate is adopted, and the second allowable deviation rate is greater than the first allowable deviation rate; if the deviation between the contract power and the historical benchmark power is greater than or equal to the second threshold, the contract is determined to be unreasonable and the third-party platform will refuse to sign it.

[0011] Furthermore, after determining the allowable deviation rate, based on the deviation relationship between the actual adjustment power and the contracted power of the flexible resource entity, its response results are divided into the following four categories, and differentiated settlement rules are adopted for each category: If the absolute value of the deviation between the actual adjusted power and the contracted power does not exceed the allowable deviation rate, it is determined to be a valid response, and the full amount shall be settled according to the contracted power. If the actual adjustment power is greater than the contract power and exceeds the upper limit of the allowable deviation rate, it is judged as a capped response, and the settlement is made by multiplying the contract power by the preset capping coefficient. The excess part is settled according to the market price. If the actual adjustment power is less than the contract power and exceeds the lower limit of the allowable deviation rate but is still within the preset partial response range, it is determined to be a partial response, and the settlement is made by multiplying the preset partial response coefficient by the actual adjustment power. If the actual adjusted power is significantly less than the contracted power and exceeds the preset invalid threshold, it will be judged as an invalid response, and the deviation penalty mechanism will be activated to charge a fine.

[0012] Furthermore, the specific implementation of the negative bid constraint is as follows: the listed price declared by the photovoltaic operator is negative, so that the third-party platform can directly settle the difference based on the clearing result without the need for the power grid company to collect and pay on its behalf; the optimal listing quantity and price combination of the photovoltaic operator is obtained by solving the objective function containing the confidence adjustment degree, wherein the normal distribution parameters of the actual photovoltaic output are obtained based on the statistics of historical prediction errors.

[0013] Furthermore, the self-regulation cost includes the opportunity cost or energy efficiency loss incurred by the flexible resource entity due to changes in its electricity consumption behavior; the maximum regulating power is determined by the physical rated parameters of the flexible resource entity, and the benchmark electricity consumption curve is the typical electricity consumption curve of the entity under no-trade conditions.

[0014] Furthermore, the penalty collected by the deviation penalty mechanism is positively correlated with the degree of deviation between the contract power and the actual adjustment power, and the penalty is included in the risk management fund pool of the third-party platform; the preset capping coefficient is not less than 1, the preset partial response coefficient is not greater than 1, and the preset invalid threshold is a multiple of the lower limit of the allowable deviation rate.

[0015] On the other hand, the present invention also provides a device for listing and trading photovoltaic power generation in a distribution area and for assessing deviations based on a third-party platform, comprising: The risk assessment module is used to predict the net load power of the transformer area based on the load management system, determine whether there is a risk of reverse heavy overload and trigger subsequent processes. The listing application module is used by photovoltaic operators to optimize the decision-making of the listed electricity volume and listing price and submit the application to a third-party platform, with the goal of maximizing their own profits and taking into account the uncertainty of photovoltaic output. The combined bidding module allows resource entities to flexibly combine bidding for multiple listing requests based on their own adjustment costs and benchmark electricity consumption curves, generating a combination of bidding quantity and price. The clearing and contract generation module is used by third-party platforms to clear the market based on the listed electricity volume and the order of bidding, determine the transaction entity, the transaction volume and the transaction price and generate financial contracts. The deviation assessment module is used to assess the execution deviation of photovoltaic operators' actual increased power generation relative to the contracted power generation and the expected power generation, and to assess the baseline deviation and execution deviation of flexible resource entities' actual adjustment amount relative to the winning bid power based on their historical response capabilities. The combined settlement module is used by the power grid company and a third-party platform to conduct combined settlements for various entities based on deviation assessment results and actual metering data.

[0016] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for on-site consumption and trading of photovoltaic power in distribution areas based on a third-party platform and deviation assessment.

[0017] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for on-site consumption and trading of photovoltaic power in distribution areas based on a third-party platform and deviation assessment.

[0018] The beneficial effects of this invention are: Steps S2 to S4 of this invention establish a standardized process of listing-delisting-clearing. Photovoltaic operators, aiming to maximize their own profits, decide on the listing quantity and price and submit applications. Flexible resource entities (energy storage, charging piles, etc.) combine and delist multiple listing demands within the physical regulation capacity boundary based on their own adjustment costs and benchmark electricity consumption curves. The platform clears the market according to the listed electricity volume and the order of delisting applications, generating financial contracts. Step S3 designs a combined delisting strategy: Flexible resource entities calculate the unit net profit (listing price minus their own marginal adjustment cost) for each listing demand, form a response priority sequence in descending order, and sequentially decide on the delisting response power while updating the remaining responsive power in real time until all demands are processed or the remaining power is zero. This greedy decision-making process is computationally lightweight, yet in a many-to-many transaction structure, it allows each entity to approximate the globally optimal match with only local information, avoiding the computational explosion caused by centralized combination optimization. Meanwhile, third-party platforms undertake functions such as information release, price and volume matching, and contract generation, enabling small and micro entities such as household photovoltaic and small-scale energy storage, which have long been excluded from the traditional market, to obtain low-cost trading channels. The photovoltaic consumption demand and flexible adjustment capabilities within the distribution area can be autonomously matched through price signals.

[0019] In step S2 of this invention, the photovoltaic operator uses maximizing the expected total revenue as the objective function. This expected total revenue equals the expected listing revenue minus the expected deviation assessment cost. The expected deviation assessment cost is calculated based on the historical average penalty unit price for the same period, the historical deviation allowance coefficient for the same period, and the probability distribution parameters of the actual photovoltaic output (assuming a normal distribution). The key innovation lies in introducing a confidence adjustment degree as a decision variable. The listed electricity volume equals this confidence adjustment degree multiplied by the allocated reduction power, and the listing price is negatively correlated with the confidence adjustment degree. The operator obtains the optimal confidence adjustment degree by solving the objective function, thereby determining the declared quantity and price. The confidence adjustment degree is essentially an active discount coefficient for the operator regarding the uncertainty of photovoltaic output—the higher the value, the closer the listed electricity volume is to the predicted maximum value, and the higher the expected listing revenue, but the higher the probability of actual output being lower than the contracted electricity volume and the corresponding expected assessment cost; the lower the value, the more conservative the listing, the lower the revenue but the lower the risk. The integral term in the objective function accurately characterizes this risk-return marginal substitution relationship. Unlike existing technologies where photovoltaic systems passively accept curtailment orders or use fixed-ratio applications, this invention moves uncertainty management responsibility to the transaction decision-making stage. Operators independently determine the optimal application strategy based on their own historical prediction error statistics (normal distribution parameters can be fitted based on historical data), making the listed quantity and price inherently risky and reasonable, and also providing quantifiable confidence for the distribution area consumption plan.

[0020] In steps S5 and S6 of this invention, deviation assessment rules are established for both parties. For photovoltaic operators, the deviation of the actual increased power generation relative to the contracted power generation and the expected power generation is assessed. For flexible resource entities, the deviation of the actual adjustment amount relative to the baseline of the awarded power generation and the execution deviation are assessed. For flexible resource entities, step S6 further provides a method for determining the allowable deviation rate in advance: based on the deviation between their historical actual response capability and the current contract target response capability, the degree of deviation of the contracted power relative to the historical benchmark power is calculated, and three levels of allowable deviation rates are set accordingly—a strict threshold is used when the deviation is small, a lenient threshold is used when the deviation is moderate, and the platform directly rejects the contract when the deviation is large. After execution, according to the deviation relationship between the actual adjusted power and the contracted power, the response results are divided into four categories: valid response, capped response, partial response, and invalid response, and are respectively settled in full, capped coefficient, partial response coefficient, and penalized with fines. The pre-emptive differentiated threshold filters out contracts that are not executable from the source, avoiding the imposition of adjustment requirements on resources that exceed their historical capabilities; the post-event graded handling suppresses over-adjustment, tolerates reasonable deviations, and punishes serious defaults through continuous reward and punishment gradients. Compared with the single threshold assessment method in existing technologies, this solution respects the physical characteristics and historical behavior patterns of different resources, and provides enforceable constraints for transaction contracts.

[0021] In step S2 of this invention, a negative bid constraint is adopted, where the listed price declared by the photovoltaic operator is negative. This allows the third-party platform to directly settle the difference based on the clearing result, without the need for the power grid company to collect and pay on its behalf. Step S6 designs a combined settlement structure: the photovoltaic operator's net income equals the metering and settlement revenue from the power grid company based on the on-grid tariff for its actual total power generation, minus the consumption fee paid to the third-party platform (corresponding to the absolute value of the negative bid), deviation assessment costs, and service fees. The flexible resource entity's net expenditure equals the electricity purchase cost paid to the power grid company, minus the response revenue paid to it by the third-party platform. The third-party platform collects a service fee based on a percentage of the total transaction amount and transfers the penalties generated from deviation assessments to the platform as operating, maintenance, and risk management funds. The negative bid internalizes the photovoltaic operator's payment to the resource entity as a listed price signal. After clearing, the platform directly calculates the net amount for each party. The power grid company only needs to execute electricity bill collection and expenditure according to the original metering system, without intervening in the fund settlement of market-based transactions, thus avoiding the lengthy process and excessively long settlement cycle caused by the power grid's collection and payment in the traditional model. Meanwhile, service fees provide the platform with stable operating revenue, while deviation penalties are pooled into a risk management fund, forming a self-sustaining economic model. Penalties, essentially economic punishments for non-compliance, are used for systemic risk buffering and operational investment, enabling the platform to operate sustainably without external subsidies. This design creates a financially closed loop between transaction matching, deviation assessment, fund settlement, and platform maintenance, making it suitable for the high-frequency, small-batch transaction characteristics of the distribution area.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1

[0027] The method for listing and trading photovoltaic power generation in a distribution area and assessing deviations based on a third-party platform, as described in this embodiment, specifically includes the following steps: S1: Based on the load management system, predict the distributed photovoltaic output of the transformer area before the operating date (D-1). Energy storage charging / discharging power Charging load power , area load power Calculate the net load power of the transformer area at different times. If the net load is negative and If the signal is received, it is determined that there is a risk of reverse overload in the transformer area during that period, and the subsequent step S2 is triggered. Otherwise, the process ends directly.

[0028] S2: The total photovoltaic power reduction of the transformer area is determined by the load management system for each time period. The permitted power generation capacity will be allocated according to the installed capacity of each photovoltaic operator. and power reduction Photovoltaic operators aim to maximize profits, and considering the uncertainty of photovoltaic output, they optimize their decisions regarding the listed electricity volume for a given period. and listing price And report to a third-party platform.

[0029] S3: The platform will simultaneously push the listing quantity and price parameters to the connected flexible resource entities such as energy storage and charging loads. Each flexible resource entity, based on its own adjustment costs and benchmark electricity consumption curve, will combine and bid on multiple listing demands within the boundaries of its own physical adjustment capabilities, with the goal of maximizing its own benefits.

[0030] S4: After the platform aggregates all bidding requests from day D-1, it determines the successful bidder based on the listed volume and bidding order. If neither the listing nor bidding requests are fully met, the process re-enters S3 to re-bid the unsuccessful portion. If either the listing or bidding request is fully met, the successful bidder for that period on day D is locked in. Transaction volume and transaction price And generate financial integration contracts.

[0031] S5: After the contract is executed, an assessment will be conducted. For photovoltaic operators, the assessment will be the deviation of their actual increased power generation from the contracted power generation and the expected power generation. For bidders such as energy storage and charging loads, the assessment will be the deviation of their actual adjustment from the baseline of the bid-winning power and the execution deviation.

[0032] S6: Settlement is based on assessment results, the output and electricity consumption of each resource entity, and a combined settlement between the power grid company and a third-party platform. Specifically, for photovoltaic operators, the power grid company pays them based on the distributed photovoltaic feed-in tariff and actual power generation; the operators then pay the third-party platform based on the contract price and deviation assessment results. For flexible resource entities, they pay the power grid company based on actual electricity consumption and price; the third-party platform pays them a response fee based on response status and deviation assessment results. For the third-party platform, a service fee is charged based on a percentage of the total transaction amount, and any penalties collected are also refunded. The funds will be transferred to the platform for operation, maintenance, and risk management.

[0033] In this embodiment, the uncertainty of photovoltaic output is considered in step S2 by introducing a confidence adjustment degree. As the decision variable, with the objective function being the maximization of the expected revenue of photovoltaic operators, and under the premise of satisfying power balance constraints, the optimal decision is achieved through optimization. To achieve the optimal combination of listed electricity volume and price: In the formula, For photovoltaic operators exist Expected total return for the period; For photovoltaic operators exist Cost of assessing expected deviations during a given period; This is the price sensitivity coefficient; This is the average penalty price for the same period in history; This is the allowable deviation coefficient for the same period in history; The expected listing price; assume the actual photovoltaic output is a random variable. Follows a normal distribution , The standard deviation of photovoltaic power output forecast. Expected output value for photovoltaic power forecast.

[0034] The following constraints must be met: (1) Constraints on the range of values ​​for decision variables. (2) The listing price constraint adopts a negative bidding mechanism. In the power market clearing logic, this method allows third-party platforms to directly settle the difference based on the clearing results without going through the power grid company's collection and payment process, which improves the convenience of settlement.

[0035] In the formula, This refers to the feed-in tariff for photovoltaic power.

[0036] (3) Physical constraints on the listed electricity volume (4) Power balance constraints Photovoltaic operators obtain the optimal confidence adjustment coefficient by solving the objective function. This allows for the determination of the optimal price combination submitted by the third-party platform. .

[0037] In this embodiment, in step S3, each flexible resource entity generates a combined bidding demand based on the photovoltaic listing price and its own adjustment costs through the following steps: Flexible resource entities For each listing requirement First, we need to calculate the unit net revenue under this single listing demand. : All listing requests are sorted in descending order of net revenue per unit to obtain a response priority sequence. , making ,main body According to the sequence Decisions are made sequentially regarding the delisting response power for each listing request. and update the allocation to the first When the main body of the item demand Remaining responsive power : Specifically, the remaining power in the initial state (i.e., before processing the first listing). : In the formula, For resources The maximum regulating power that can be provided under ideal conditions; For resources exist The reference power consumption at any given time.

[0038] when or At this point, the iteration process ends, and the final combination of bidding volume and price is compiled and submitted to the platform. .

[0039] In this embodiment, the settlement in step S6 involves photovoltaic operators and flexible resource entities (energy storage, charging load). The power grid company and a third-party platform conduct unified settlement for each entity. The specific steps are as follows: (1) Photovoltaic operators Net income of photovoltaic operators Revenue from grid connection Platform-side costs It consists of two parts: the former is the metering and settlement revenue from the total electricity generated by photovoltaic power being fed into the grid, and the latter is the fees paid for local consumption through the platform's organized listing and trading, related assessment fees, and service fees. in, For photovoltaic operators exist Actual deviation assessment cost for the time period: For photovoltaic operators Transaction service fees paid to third-party platforms: In the formula, For photovoltaic operators exist The actual total power generation during the period; For flexible resource entities For photovoltaic operators Provided contracted absorption capacity; for Time-of-use deviation assessment penalty unit price; This refers to the photovoltaic deviation tolerance factor. This is the service fee percentage coefficient.

[0040] Flexible resource entities Flexible resource entities Net expenditure Electricity purchase cost from the grid side Platform-side response revenue It consists of two parts: the former is the electricity purchase expenditure settled by metering, and the latter is the market-based bidding revenue obtained through the platform's provision of adjustable capabilities. In the formula, For flexible resource entities exist Total power consumption during the time period; as the main body Market electricity purchase price during the specified time period; The absorption benefits corresponding to different responses.

[0041] Based on the actual response capabilities of flexible resource entities in the same historical period With target response capability The deviation is used to correct for the degree of deviation of the contract power from the baseline power: according to Determine the allowable deviation rate :like ,but The contracted power is very close to the historical benchmark power, and the execution should be the most stringent, requiring the actual adjusted power to closely match the contracted power; if ,but The contracted power output deviates somewhat from the historical benchmark power output; therefore, the assessment criteria should be appropriately relaxed to allow for some flexibility in execution. ,but The contracted power is significantly inconsistent with the historical benchmark power, indicating that the resources may have exaggerated adjustment capabilities and are not feasible. Such contracts are unreasonable and the platform should reject them to avoid creating unfulfillable transaction contracts. This is used to verify the reasonableness of the resource commitments in advance.

[0042] Determining the permissible deviation rate Then, adjust the power according to the actual situation. The responses from flexible resource entities are further categorized into four types: valid, capped, partial, and invalid responses. 1) If the actual adjustment power satisfy If so, it is considered a valid response; 2) If If so, it is determined to be a capped response; 3) If If so, it is determined to be a partial response; 4) If A response that does not meet these criteria will be considered invalid. Based on the four response outcomes described above, the specific settlement methods are as follows: 1) Effective response: The actual response power will be fully included in the settlement process as agreed in the contract.

[0043] 2) Capping Response: (According to...) The contract response power is capped at a factor of 1, and any amount exceeding the contract response power is settled at market price.

[0044] Partial Response: By The contract response power is calculated as a multiple of the contract response power.

[0045] 4) Invalid response: The platform will calculate the penalty according to the deviation penalty mechanism. .

[0046] In summary, this invention proposes a method for on-site photovoltaic (PV) grid connection trading and deviation assessment based on a third-party platform. First, the load management system predicts the net load of the grid area, determines the risk of reverse overload, and identifies the PV power reduction. PV operators, considering output uncertainty, optimize their bidding volume and price with the goal of maximizing expected revenue and submit their bids to the third-party platform. Flexible resource entities combine multiple bidding demands based on their own adjustment costs and benchmark electricity consumption curves. The platform clears bids according to the listed electricity volume and bidding order, generating financial contracts. After contract execution, PV operators are assessed for their increased power generation deviation, and flexible resource entities are assessed for their adjustment deviation based on historical response capabilities. Finally, based on the deviation assessment results and actual metering data, the power grid company and the third-party platform conduct a combined settlement. The platform collects a service fee and incorporates penalties into risk management funds. This invention achieves market-based matching and closed-loop assessment for on-site PV grid connection.

[0047] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for listing and trading photovoltaic power generation in a distribution area and assessing deviations based on a third-party platform, characterized in that, Includes the following steps: S1: Based on the load management system, predict the distributed photovoltaic output, energy storage charging / discharging power, charging load power and load power of the transformer area before the operation date, and calculate the net load power of the transformer area in each time period; if the net load is negative and its absolute value exceeds the preset reverse overload threshold, it is determined that there is a risk of reverse overload in that time period and step S2 is triggered; otherwise, the process ends. S2: The load management system determines the total photovoltaic power reduction of each time period and allocates the permitted power and reduction power to each photovoltaic operator according to their installed capacity; the photovoltaic operator aims to maximize its own revenue, considers the uncertainty of photovoltaic output, optimizes the decision on the listed electricity volume and listed price for that time period, and submits it to a third-party platform; the listed price is negative and its absolute value is not higher than the photovoltaic feed-in tariff. S3: The third-party platform will push the listing quantity and price parameters to the connected flexible resource entities, which include energy storage and charging loads; Each flexible resource entity, based on its own adjustment costs and benchmark electricity consumption curve, aims to maximize its own profits within the boundaries of its own physical adjustment capabilities by combining and bidding on multiple listing demands, generating a combination of bidding quantity and price. S4: After the third-party platform aggregates all bidding requests, it determines the transaction entity, transaction volume, and transaction price based on the listed electricity volume and the order of bidding applications, and generates a financial contract; if the request of either the listing or bidding party is fully satisfied, the transaction result for that period is locked; if neither party's request is fully satisfied, the process returns to step S3 to re-bid for the unsuccessful portion. S5: Conduct deviation assessments after contract execution: For photovoltaic operators, assess the deviation of their actual increased power generation from the contracted power generation and the expected power generation; for flexible resource entities, assess the deviation of their actual adjustment from the baseline of the awarded power generation and the execution deviation. S6: Based on the deviation assessment results and the actual output and electricity metering data of each entity, the power grid company and the third-party platform will conduct a combined settlement for each entity. Among them, the net income of the photovoltaic operator is the actual power generation income paid by the power grid company at the on-grid electricity price minus the transaction costs paid by the operator to the third-party platform. The net expenditure of the flexible resource entity is the electricity purchase cost paid to the power grid company minus the response income paid by the third-party platform. The third-party platform charges a service fee based on a percentage of the total transaction amount and transfers the penalties generated by the deviation assessment to the platform as operation, maintenance and risk management funds.

2. The method as described in claim 1, characterized in that, The specific methods by which photovoltaic operators, considering the uncertainty of photovoltaic output and aiming to maximize expected returns, optimize the decision-making process for listing quantity and price in step S2 include: The objective function is to maximize the expected total revenue, which is equal to the expected listing revenue minus the expected deviation assessment cost. The expected deviation assessment cost is calculated based on the historical average penalty unit price, the historical deviation allowance coefficient, and the probability distribution parameters of the actual photovoltaic output. A confidence level is introduced as a decision variable, the actual photovoltaic output follows a preset probability distribution, and the expected deviation assessment cost is related to the confidence level. The optimization process must meet the following constraints: the range of confidence adjustment degree, the negative bid constraint that the listed price is lower than the photovoltaic feed-in tariff, the physical constraint that the listed power volume does not exceed the allocated permitted power, and the power balance constraint that the winning bid power volume of the bidder is equal to the listed power volume. The optimal confidence level is obtained by solving the objective function, and then the optimal combination of listing volume and price to be submitted to the third-party platform is determined.

3. The method as described in claim 1, characterized in that, The specific methods by which each flexible resource entity generates combined bidding requirements in step S3 include: For each listing request, the unit net revenue is calculated based on the listing price minus its own adjustment costs, and all listing requests are arranged in descending order of unit net revenue to form a response priority sequence. The bidding response power for each listing demand is decided sequentially according to the priority sequence, and the remaining responsive power of the entity is updated after each decision. The initial value of the remaining responsive power is equal to the maximum adjustment power that the entity can provide under ideal conditions. The iteration ends when all listing requests have been processed or the remaining responsive capacity is zero, and the final combination of bidding volume and price is summarized and submitted to the third-party platform. The self-regulation cost includes the opportunity cost or energy efficiency loss incurred by the flexible resource entity due to changes in its electricity consumption behavior; the maximum regulating power is determined by the physical rated parameters of the flexible resource entity, and the benchmark electricity consumption curve is the typical electricity consumption curve of the entity under no-trade conditions.

4. The method as described in claim 1, characterized in that, The combined settlement mentioned in step S6 specifically includes: For photovoltaic operators, their net income equals the revenue from the power grid company's metering and settlement of their actual total power generation based on their on-grid tariff, minus the consumption fees paid by them through third-party platforms for listing on exchanges, the assessment costs incurred due to execution deviations, and the service fees paid to third-party platforms, which are charged as a certain percentage of the total transaction amount. For flexible resource entities, their net expenditure equals the metering and settlement cost of their actual total electricity consumption by the power grid company at the market purchase price, minus the bidding response revenue they obtain by providing adjustable capacity; The third-party platform collects a service fee based on a percentage of the total transaction amount and incorporates the penalties generated from deviation assessments into its operations management and risk fund pool.

5. The method as described in claim 1, characterized in that, Step S5, the deviation assessment of flexible resource entities, also includes the step of determining the allowable deviation rate in advance: The allowable deviation rate is determined based on the deviation between the actual response capability of the flexible resource entity in the same period in history and the target response capability agreed in the current contract. Specifically, if the deviation between the contract power and the historical benchmark power is less than a first threshold, a first allowable deviation rate is adopted; if the deviation between the contract power and the historical benchmark power is greater than or equal to the first threshold but less than a second threshold, a second allowable deviation rate is adopted, and the second allowable deviation rate is greater than the first allowable deviation rate; if the deviation between the contract power and the historical benchmark power is greater than or equal to the second threshold, the contract is determined to be unreasonable and the third-party platform will refuse to sign it.

6. The method as described in claim 5, characterized in that, After determining the allowable deviation rate, based on the deviation relationship between the actual adjustment power and the contracted power of the flexible resource entity, its response results are divided into the following four categories, and differentiated settlement rules are applied to each category: If the absolute value of the deviation between the actual adjusted power and the contracted power does not exceed the allowable deviation rate, it is determined to be a valid response, and the full amount shall be settled according to the contracted power. If the actual adjustment power is greater than the contract power and exceeds the upper limit of the allowable deviation rate, it is judged as a capped response, and the settlement is made by multiplying the contract power by the preset capping coefficient. The excess part is settled according to the market price. If the actual adjustment power is less than the contract power and exceeds the lower limit of the allowable deviation rate but is still within the preset partial response range, it is determined to be a partial response, and the settlement is made by multiplying the preset partial response coefficient by the actual adjustment power. If the actual adjusted power is significantly less than the contracted power and exceeds the preset invalid threshold, it will be judged as an invalid response, and the deviation penalty mechanism will be activated to charge a fine. The penalty collected by the deviation penalty mechanism is positively correlated with the degree of deviation between the contract power and the actual adjustment power, and the penalty is included in the risk management fund pool of the third-party platform; the preset capping coefficient is not less than 1, the preset partial response coefficient is not greater than 1, and the preset invalid threshold is a multiple of the lower limit of the allowable deviation rate.

7. The method as described in claim 2, characterized in that, The specific implementation of the negative bid constraint is as follows: the listed price declared by the photovoltaic operator is negative, so that the third-party platform can directly settle the difference based on the clearing result without the need for the power grid company to collect and pay on its behalf; the optimal listing quantity and price combination of the photovoltaic operator is obtained by solving the objective function containing the confidence adjustment degree, wherein the normal distribution parameters of the actual photovoltaic output are obtained based on the statistics of historical prediction errors.

8. A device for listing and trading photovoltaic power generation in a distribution area and assessing deviations based on a third-party platform, characterized in that, include: The risk assessment module is used to predict the net load power of the transformer area based on the load management system, determine whether there is a risk of reverse heavy overload and trigger subsequent processes. The listing application module is used by photovoltaic operators to optimize the decision-making of the listed electricity volume and listing price and submit the application to a third-party platform, with the goal of maximizing their own profits and taking into account the uncertainty of photovoltaic output. The combined bidding module allows resource entities to flexibly combine bidding for multiple listing requests based on their own adjustment costs and benchmark electricity consumption curves, generating a combination of bidding quantity and price. The clearing and contract generation module is used by third-party platforms to clear the market based on the listed electricity volume and the order of bidding, determine the transaction entity, the transaction volume and the transaction price and generate financial contracts. The deviation assessment module is used to assess the execution deviation of photovoltaic operators' actual increased power generation relative to the contracted power generation and the expected power generation, and to assess the baseline deviation and execution deviation of flexible resource entities' actual adjustment amount relative to the winning bid power based on their historical response capabilities. The combined settlement module is used by the power grid company and a third-party platform to conduct combined settlements for various entities based on deviation assessment results and actual metering data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for listing and trading photovoltaic power generation in the substation area and deviation assessment based on a third-party platform as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for listing and trading photovoltaic power generation in the substation area and deviation assessment based on a third-party platform as described in any one of claims 1 to 7.

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