User-side time-of-use electricity right transaction control method and system considering bias assessment

Abstract

The application provides a user-side time-of-use electricity right transaction control method and system considering deviation examination, relates to the field of power market, and comprises the following steps: obtaining time-of-use deviation data of each market subject according to day-ahead clearing electricity and load prediction data; calculating ultra-immune examination deviation electricity, deviation marginal loss and feasible transaction price interval based on the time-of-use deviation data, immune examination deviation proportion and deviation penalty coefficient, obtaining time-of-use electricity right declaration data; constructing and solving a day-ahead-day-in two-stage joint optimization model according to the time-of-use electricity right declaration data and load adjustment parameter, obtaining day-ahead declaration electricity, day-in electricity right transaction electricity, clearing price and load adjustment amount; generating time-of-use electricity right transaction results according to the day-in electricity right transaction electricity and clearing price, generating a load adjustment plan according to the load adjustment amount, and correcting the deviation examination base according to the day-in electricity right transaction electricity and load adjustment amount.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, specifically to a user-side time-of-use electricity rights trading control method and system that takes into account deviation assessment. Background Technology

[0002] With the development of the electricity spot market and demand response mechanisms, user-side market participants are no longer simply passively accepting electricity prices, but are gradually participating in market operation processes such as day-ahead declarations, intraday adjustments, load regulation, and deviation assessments. In practice, market participants typically need to declare their electricity consumption plans during the day-ahead phase and adjust them intraday based on actual load changes and real-time electricity prices.

[0003] In some cases, one market participant may have a positive bias, meaning their predicted electricity consumption exceeds the day-ahead clearing capacity, necessitating additional electricity usage rights; another market participant may have a negative bias, meaning their predicted electricity consumption is lower than the day-ahead clearing capacity, thus possessing transferable electricity usage rights. If the two participants can engage in time-of-use electricity usage rights trading within the same day, the risk of bias assessment for both can be reduced, and the matching degree between the electricity trading plan and the actual load can be improved.

[0004] However, existing user-side transaction control methods still have the following problems: First, they do not fully consider the exemption deviation ratio and deviation penalty coefficient, resulting in a lack of reasonable correlation between the declared price and the deviation loss; second, they do not jointly optimize day-ahead declarations, intraday electricity right transactions, and load regulation, which can easily lead to a disconnect between day-ahead plans and intraday adjustments; and third, they lack a control mechanism to correct the deviation assessment base based on the transaction volume and load regulation, making it difficult to form executable time-of-use electricity right transaction results and load regulation plans. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing a user-side time-of-use electricity rights trading control method and system that takes into account deviation assessment, thereby solving the problem of lack of coordination between deviation assessment, time-of-use trading and load regulation in existing user-side trading control methods.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] Obtain the day-ahead cleared electricity volume, real-time electricity price, load forecast data, exemption from assessment deviation ratio, deviation penalty coefficient and load adjustment parameters of each market entity in the target electrical zone for multiple settlement periods, and obtain the time-of-use deviation data of each market entity based on the day-ahead cleared electricity volume and load forecast data; Based on the time-of-use deviation data, the proportion of deviations exempt from assessment, and the deviation penalty coefficient, the excess deviation electricity, the marginal loss of deviation, and the feasible transaction price range are calculated to obtain the time-of-use electricity right application data. Based on the time-of-use electricity right application data and load adjustment parameters, a two-stage joint optimization model for day-ahead and intraday periods is constructed and solved to obtain the day-ahead applied electricity volume, intraday electricity right transaction volume, clearing price and load adjustment amount; The time-of-use electricity rights trading results are generated based on the intraday electricity rights transaction volume and clearing price. The load adjustment plan is generated based on the load adjustment amount. The deviation assessment base is corrected based on the intraday electricity rights transaction volume and load adjustment amount.

[0008] In some embodiments, the process of obtaining time-of-use deviation data for each market participant based on the day-ahead clearing power and load forecast data includes: The day is divided into multiple consecutive settlement periods; For each market participant and each settlement period, the difference between the load forecast data and the day-ahead cleared electricity is calculated to obtain the forecast deviation electricity. Based on the positive or negative direction of the predicted deviation in electricity volume, the corresponding market entity is determined as a candidate for electricity right buyer or a candidate for electricity right seller. The time-of-use deviation data is composed of the predicted deviation electricity, the candidate entities for electricity use rights buyers, and the candidate entities for electricity use rights sellers.

[0009] In some embodiments, the process of calculating the excess deviation electricity, marginal loss of deviation, and feasible transaction price range based on the time-of-use deviation data, the exemption deviation ratio, and the deviation penalty coefficient to obtain the time-of-use electricity right application data includes: Based on the aforementioned cleared electricity volume and the exemption deviation ratio, the exemption deviation range is obtained; The time-sharing deviation data is compared with the exemption deviation range to obtain the positive excess exemption deviation power and the negative excess exemption deviation power. The marginal loss of deviation is determined based on the positive excess deviation amount, the negative excess deviation amount, and the deviation penalty coefficient. The bid boundaries for electricity rights buyers and sellers are determined based on the marginal loss of the deviation, and the feasible transaction price range is obtained from the bid boundaries for electricity rights buyers and sellers.

[0010] In some embodiments, the process of obtaining time-of-use electricity right application data includes: The buyer's bid price and the seller's bid price are determined based on the feasible transaction price range; The buyer's declared electricity volume and the seller's declared electricity volume are determined based on the excess excess electricity volume. The time-of-use electricity right application data is formed from the buyer's declared electricity volume, the buyer's declared price, the seller's declared electricity volume, and the seller's declared price.

[0011] In some embodiments, the process of obtaining the intraday electricity consumption rights transaction volume and clearing price includes: The time-of-use electricity right application data are sorted according to price priority and time priority rules to obtain the buyer application sequence and the seller application sequence; Based on the buyer's and seller's bid sequences, a centralized bidding process is conducted to clear the candidate transaction results. Apply the order quantity constraint and transaction mutual exclusion constraint to the candidate transaction results to obtain the intraday electricity consumption right transaction volume and clearing price.

[0012] In some embodiments, the process of constructing and solving the day-to-day two-stage joint optimization model includes: An objective function is constructed with the goal of minimizing the expected total electricity purchase cost for each market participant throughout the entire cycle. The reported electricity volume before the day will be used as a decision variable for the day-ahead stage. The intraday electricity consumption rights transaction volume, clearing price, and load adjustment volume are used as intraday stage decision variables; Based on the day-ahead reporting constraints, intraday trading constraints, market equilibrium clearing constraints, and flexible entity load adjustment constraints, the aforementioned day-ahead-intraday two-stage joint optimization model is obtained.

[0013] In some embodiments, the process of solving the day-to-day two-stage joint optimization model includes: Based on ADMM, the day-to-day two-stage joint optimization model is decomposed into multiple main sub-problems; Each market participant solves its corresponding sub-problem to obtain the candidate buy volume and candidate sell volume for each market participant. The clearing price is updated based on the candidate buy volume and candidate sell volume of each market participant; Calculate the market supply and demand residual. When the market supply and demand residual meets the preset convergence condition, obtain the day-ahead declared electricity volume, intraday electricity consumption right transaction volume, clearing price, and load adjustment volume.

[0014] This invention proposes a user-side time-of-use electricity rights trading control system that takes into account deviation assessment, comprising: The acquisition unit is configured to acquire the day-ahead cleared electricity volume, real-time electricity price, load forecast data, exemption from assessment deviation ratio, deviation penalty coefficient and load adjustment parameters of each market entity in the target electrical zone during multiple settlement periods, and to obtain the time-of-use deviation data of each market entity based on the day-ahead cleared electricity volume and load forecast data. The time-sharing unit is configured to calculate the excess deviation electricity, deviation marginal loss, and feasible transaction price range based on the time-sharing deviation data, the exemption deviation ratio, and the deviation penalty coefficient, thereby obtaining time-sharing electricity right application data. The optimization unit is configured to construct and solve a two-stage joint optimization model based on the time-of-use electricity right application data and load adjustment parameters, so as to obtain the day-of-use electricity application, the electricity transaction volume of the day-of-use electricity right, the clearing price and the load adjustment amount. The correction unit is configured to generate time-of-use electricity rights trading results based on the intraday electricity rights transaction volume and clearing price, generate a load adjustment plan based on the load adjustment amount, and correct the deviation assessment base based on the intraday electricity rights transaction volume and load adjustment amount.

[0015] This invention proposes a computer device, comprising: At least one processor; and a memory storing a computer program executable on the processor, wherein the processor, when executing the program, performs the steps of the user-side time-of-use electricity rights trading control method that takes into account deviation assessment.

[0016] This invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the user-side time-of-use electricity rights trading control method that takes into account deviation assessment.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention obtains time-of-use deviation data based on day-ahead cleared electricity volume and load forecast data, enabling the identification of the direction and scale of electricity consumption deviations for each market participant at the settlement period level, providing a data foundation for subsequent matching of electricity rights trading. A two-stage joint optimization model is constructed, integrating day-ahead declared electricity volume, intraday electricity rights transaction volume, clearing price, and load adjustment, avoiding the disconnect between day-ahead planning, intraday trading, and load control. The deviation assessment base is adjusted based on intraday electricity rights transaction volume and load adjustment, ensuring that trading results are reflected in deviation assessment and load adjustment execution, improving the executability of trading control results. Convergence is determined by judging market supply and demand residuals.

[0018] This invention reduces data mismatch between different data sources and settlement periods by uniformly processing data into time-segmented intervals and calculating time-segmented deviations, thereby improving the accuracy of time-segmented deviation calculations. By transforming deviation constraints into calculable interval data, it reduces invalid values ​​in the optimization model input, improving the consistency and computability of the model input data. A two-stage joint optimization model for the day-ahead and intraday phases is constructed and solved, enabling the joint solution of day-ahead declared electricity volume, intraday electricity right transaction volume, clearing price, and load adjustment volume under the same constraint system. This reduces the computational fragmentation between day-ahead and intraday data, improving constraint coordination and convergence stability during model solving. Dynamically adjusting the deviation assessment baseline improves the continuity of deviation calculations and the data consistency of the execution plan. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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 embodiments can be obtained based on these drawings without creative effort.

[0020] Figure 1 The flowchart of the user-side time-of-use electricity rights trading control method that takes into account deviation assessment provided by the present invention is shown.

[0021] Figure 2 This invention provides a module diagram of a user-side time-of-use electricity rights trading control system that takes into account deviation assessment.

[0022] Figure 3 A schematic diagram of the structure of an embodiment of the computer device provided by the present invention.

[0023] Figure 4 This is a schematic diagram of an embodiment of the computer-readable storage medium provided by the present invention.

[0024] Figure 5 This is a schematic diagram illustrating the formation of deviation assessment, deviation electricity volume, and electricity right trading in the user-side time-of-use electricity right trading control method that takes into account deviation assessment provided by the present invention.

[0025] Figure 6 The flowchart shows the optimization solution of the user-side time-of-use electricity rights trading control method that takes into account deviation assessment provided by the present invention.

[0026] Figure 7 A comparison chart of the total market electricity purchase cost and deviation penalty cost under different scenarios for the user-side time-of-use electricity rights trading control method that takes deviation assessment into account, provided by the present invention.

[0027] Figure 8 A comparison chart of the reduction in electricity purchase costs for different types of entities in the user-side time-of-use electricity rights trading control method that takes into account deviation assessment provided by the present invention.

[0028] Figure 9 A time-of-use electricity deviation comparison chart for the entire market in different scenarios under the user-side time-of-use electricity rights trading control method that takes into account deviation assessment provided by the present invention.

[0029] Figure 10 The deviation migration distribution diagram is provided for the user-side time-of-use electricity rights trading control method that takes into account deviation assessment provided by the present invention.

[0030] Figure 11A comparison chart of the system daily load curves under different scenarios for the user-side time-of-use electricity rights trading control method that takes into account deviation assessment provided by the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention. It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application.

[0032] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0033] This invention proposes a user-side time-of-use electricity rights trading control method that takes into account deviation assessment. Please refer to [link / reference]. Figure 1 , Figure 5 and Figure 6 ,include: Obtain the day-ahead cleared electricity volume, real-time electricity price, load forecast data, exemption from assessment deviation ratio, deviation penalty coefficient and load adjustment parameters of each market entity in the target electrical zone for multiple settlement periods, and obtain the time-of-use deviation data of each market entity based on the day-ahead cleared electricity volume and load forecast data; Based on the time-of-use deviation data, the proportion of deviations exempt from assessment, and the deviation penalty coefficient, the excess deviation electricity, the marginal loss of deviation, and the feasible transaction price range are calculated to obtain the time-of-use electricity right application data. Based on the time-of-use electricity right application data and load adjustment parameters, a two-stage joint optimization model for day-ahead and intraday periods is constructed and solved to obtain the day-ahead applied electricity volume, intraday electricity right transaction volume, clearing price and load adjustment amount; The time-of-use electricity rights trading results are generated based on the intraday electricity rights transaction volume and clearing price. The load adjustment plan is generated based on the load adjustment amount. The deviation assessment base is corrected based on the intraday electricity rights transaction volume and load adjustment amount.

[0034] It should be noted that, in this embodiment of the invention, based on the intraday electricity consumption rights transaction volume and clearing price, the transaction direction, transaction volume and transaction amount of each market participant in the corresponding settlement period are determined to obtain the time-of-use electricity consumption rights transaction results; based on the load adjustment amount, the load adjustment direction and adjustment volume of each market participant in the corresponding settlement period are determined to obtain the load adjustment plan; and the deviation assessment base is corrected based on the intraday electricity consumption rights transaction volume and load adjustment amount.

[0035] The deviation assessment base refers to the benchmark electricity volume used to calculate the deviation assessment electricity volume for market participants, ensuring that the final assessment result is consistent with the intraday trading and load adjustment results. Intraday electricity right trading volume refers to the electricity volume actually transferred through time-of-use electricity right trading. Load adjustment volume refers to the electricity volume that market participants increase, decrease, or transfer based on optimization results. Time-of-use electricity right trading results include the trading entity, trading period, trading volume, and clearing price. Load adjustment plans include the adjustment entity, adjustment period, adjustment direction, adjustment volume, and execution requirements. The system corrects the deviation assessment base based on the intraday electricity right trading volume and load adjustment volume, so that the deviation assessment of market participants is no longer solely based on the original daily cleared electricity volume, but rather reflects the actual control results after intraday trading and load adjustment.

[0036] In this method, time-of-use electricity rights trading is preferably limited to the same target electrical zone. The target electrical zone can be divided according to the same substation, the same feeder, the same transformer area, the same electricity price node, or the same dispatch management area. After the trading results are generated, the trading control platform can perform a safety check based on transformer capacity, feeder current carrying capacity, node voltage limit, zone net load limit, and adjustable load response capability. If the check does not meet the requirements, the traded electricity volume or load adjustment amount will be reduced, adjusted, or re-cleared to avoid local line overload or voltage exceeding the limit caused by time-of-use electricity rights trading.

[0037] my country's electricity spot market generally adopts a core operating mechanism of centralized bidding and clearing of all electricity volume in the day-ahead market, settlement of deviations in the real-time market, and assessment of time-of-use deviations. Both settlement and assessment use a minimum metering period of 15 minutes, dividing the day into 96 consecutive periods (T=96), achieving precise spatiotemporal matching of electricity volume, price, and physical generation and consumption. This mechanism achieves price discovery and benchmark locking through the day-ahead market, while ensuring real-time power balance of the power system through the real-time market, and represents the current common regulatory framework for the domestic spot market.

[0038] In some embodiments, a target electrical zone refers to a user-side electrical area with a unified settlement or coordinated control relationship in terms of grid topology, price nodes, distribution areas, or transaction organization; market participants include industrial users, commercial users, load aggregators, park users, or other user-side entities participating in electricity rights trading; the settlement period refers to the time interval in the electricity market used for metering, settlement, and deviation assessment; day-ahead cleared electricity refers to the planned electricity consumption determined by market participants in the day-ahead market; real-time electricity price refers to the electricity price formed during the day or real-time operation phase; load forecast data refers to the electricity demand predicted based on historical load, production plans, meteorological information, or user behavior; the exemption deviation ratio refers to the range within which the actual electricity consumption of market participants is allowed to deviate from the day-ahead cleared electricity without triggering deviation assessment; the deviation penalty coefficient refers to the coefficient used to calculate the deviation cost after exceeding the exemption deviation range; load regulation parameters include adjustable load capacity, regulation cost, regulation upper limit, regulation lower limit, ramping capability, and response time. By comparing day-ahead cleared electricity and load forecast data, the system can determine the electricity rights gap or surplus of market participants in different settlement periods, thereby obtaining time-of-use deviation data.

[0039] In some embodiments, excess deviation electricity refers to the portion of the predicted deviation electricity that exceeds the exemption deviation range; positive excess deviation electricity refers to the electricity whose load forecast data is higher than the day-ahead cleared electricity and exceeds the exemption deviation range; negative excess deviation electricity refers to the electricity whose load forecast data is lower than the day-ahead cleared electricity and exceeds the exemption deviation range; deviation marginal loss refers to the change in deviation assessment cost that may be caused by a unit excess deviation electricity; feasible transaction price range refers to the range of time-of-use electricity right transaction prices that the buyer is willing to pay and the seller is willing to accept; time-of-use electricity right declaration data includes the buyer's declared electricity volume, the buyer's declared price, the seller's declared electricity volume, and the seller's declared price. The system uses time-of-use deviation data, the exemption deviation ratio, and the deviation penalty coefficient to determine transaction declaration data, so that the declaration behavior corresponds to the deviation assessment risk.

[0040] In some embodiments, the buyer's bid sequence refers to the set of buyer bids arranged according to price priority and time priority rules; the seller's bid sequence refers to the set of seller bids arranged according to price priority and time priority rules; centralized bidding clearing refers to the market clearing method of centrally matching buyer and seller bids to determine the transaction volume and clearing price; candidate transaction results refer to the transaction results formed after bid sorting and preliminary matching; bid volume constraint means that the transaction volume cannot exceed the buyer's bid volume or the seller's bid volume; transaction mutual exclusion constraint means that the same market participant cannot repeatedly trade for the same electricity right as both buyer and seller in the same settlement period. The system obtains the intraday electricity right transaction volume and clearing price through centralized bidding clearing, enabling the transfer of time-of-use electricity rights among market participants.

[0041] In some embodiments, the day-ahead-intraday two-stage joint optimization model refers to an optimization model that simultaneously describes day-ahead declaration decisions and intraday transaction adjustment decisions. Day-ahead stage decision variables include day-ahead declared electricity volume; intraday stage decision variables include intraday electricity consumption rights transaction volume, clearing price, and load adjustment volume; the objective function is a mathematical expression used to evaluate the quality of the optimization results; the expected total electricity purchase cost includes day-ahead electricity purchase cost, intraday transaction cost, deviation assessment cost, and load adjustment cost; day-ahead declaration constraints include upper and lower limits on day-ahead declared electricity volume and planning consistency constraints; intraday transaction constraints include transaction volume constraints, transaction direction constraints, and transaction price constraints; market equilibrium clearing constraints include the balance relationship between buyer and seller transaction volumes; and flexible load adjustment constraints include upper and lower limits on load adjustment volume, adjustment costs, and response capacity constraints. By constructing this joint optimization model, the system enables unified decision-making for day-ahead declaration, intraday transactions, and load adjustment.

[0042] In some embodiments, the main subproblem refers to the local optimization problem formed by splitting the overall joint optimization model according to market participants; the candidate purchase quantity refers to the electricity volume to be purchased by market participants based on their own deviations and costs; the candidate sale quantity refers to the electricity volume to be sold by market participants based on their own surplus electricity volume and revenue; the market supply and demand residual refers to the difference between the total candidate purchase quantity and the total candidate sale quantity in the current iteration; the preset convergence condition refers to the condition used to determine whether the market supply and demand residual is small enough, whether the clearing price is stable, or whether the number of iterations has reached the required level. The system decomposes the joint optimization model into multiple main subproblems, which helps to reduce the difficulty of centralized solution when there are many market participants.

[0043] This invention proposes a user-side time-of-use electricity rights trading control system that takes into account deviation assessment. Please refer to [link / reference]. Figure 2 ,include: The acquisition unit is configured to acquire the day-ahead cleared electricity volume, real-time electricity price, load forecast data, exemption from assessment deviation ratio, deviation penalty coefficient and load adjustment parameters of each market entity in the target electrical zone during multiple settlement periods, and to obtain the time-of-use deviation data of each market entity based on the day-ahead cleared electricity volume and load forecast data. The time-sharing unit is configured to calculate the excess deviation electricity, deviation marginal loss, and feasible transaction price range based on the time-sharing deviation data, the exemption deviation ratio, and the deviation penalty coefficient, thereby obtaining time-sharing electricity right application data. The optimization unit is configured to construct and solve a two-stage joint optimization model based on the time-of-use electricity right application data and load adjustment parameters, so as to obtain the day-of-use electricity application, the electricity transaction volume of the day-of-use electricity right, the clearing price and the load adjustment amount. The correction unit is configured to generate time-of-use electricity rights trading results based on the intraday electricity rights transaction volume and clearing price, generate a load adjustment plan based on the load adjustment amount, and correct the deviation assessment base based on the intraday electricity rights transaction volume and load adjustment amount.

[0044] In some embodiments, please refer to the figure. The present invention first establishes a settlement model with day-ahead full power locking and real-time deviation adjustment. Secondly, it introduces a deviation ratio exempt from assessment. and positive and negative deviation penalty coefficients By dividing the electricity consumption into exemption zones and excess amounts exceeding the exemption threshold, a full-volume electricity fee settlement model with two-way penalties is established. This part provides a clear cost calculation and assessment basis for the subsequent electricity rights trading mechanism.

[0045] 1. Day-ahead-Real-Time Market Settlement Model: User-side electricity billing adopts a two-stage system of day-ahead full electricity volume locking + real-time deviation adjustment. Market Participants During the period The total electricity consumption is used to calculate the electricity cost: (1); of which the day-ahead market settlement electricity fee: (2); Real-time market deviation settlement of electricity fees: (3) (4); where, Indicates market entities During the period The total electricity consumption will be used to calculate the electricity cost, in yuan; Representing the subject During the period The day-ahead market settlement electricity fee is the base electricity fee locked for the full amount of electricity, in yuan; Representing the subject During the period The real-time market deviation settlement electricity fee is the difference adjustment fee for the deviation portion, in yuan; Representing the subject During the period The contracted electricity volume declared and cleared by the market before the date of declaration, in MWh, will be used as the benchmark for subsequent deviation assessment. Indicates time period The day-ahead market clearing price or regional node marginal price, RMB / MWh, is formed by centralized bidding in the day-ahead market and is locked after the day-ahead market clearing on D-1 day, and is not affected by real-time market operation. Representing the subject During the period The time-of-use deviation of electricity, in MWh; when A positive deviation indicates that the user's actual electricity consumption exceeds the amount reported the day before; when A negative deviation indicates that the user's actual electricity consumption is lower than the amount reported the day before. as the main body During the period The meter measures the actual electricity consumption, in MWh, which serves as the physical basis for electricity billing and deviation assessment. Indicates time period The real-time market-wide unified settlement price, denoted as yuan / MWh, is formed through real-time market clearing and reflects the real-time supply and demand relationship of the power system within a given time period. 2. Deviation Assessment Mechanism: Set a percentage of deviations exempt from assessment. (Typically 3%~5%), the upper and lower limits of the deviation in electricity consumption exempt from assessment are: (5); The positive and negative deviations in electrical quantity exceeding the exemption range are decomposed as follows: (6); The full electricity bill settlement model including deviation assessment is as follows: (7); where, This is the positive deviation penalty coefficient, with a value greater than 1; This is the negative deviation penalty coefficient, with a value less than 1; , The main body During the period The upper limit of positive and negative deviation in electricity consumption within the exemption from assessment, in MWh; as the main body During the period Positive deviation in electricity consumption exceeding the exemption range, MWh, with a non-negative value; as the main body During the period The negative deviation in electricity volume, MWh, exceeding the exemption range, is a non-positive value.

[0046] User-side deviation loss mechanism and feasible range of electricity right trading: 1. Positive / negative deviation loss: under positive deviation scenario ( The marginal loss beyond the exemption range is always... In conjunction with electricity market rules, ; Negative bias scenario ( Marginal loss is always In conjunction with electricity market rules, Both of these factors result in fixed additional costs, which constitute the fundamental motivation for users to participate in electricity rights trading.

[0047] 2. Feasible price range for electricity rights trading: The lower limit of the reservation price for users with negative deviations (sellers) is... The maximum reservation price for users with positive deviation (buyers) is... Therefore, the feasible trading price range is: (8) In conjunction with domestic market rules, This feasible price range always exists, and both parties in the transaction always have room for Pareto improvement. Within the feasible price range, the profit per unit of electricity for both the buyer and seller can be quantified as follows: (9); The total welfare increase achieved through transactions across the entire market is: (10); where, The seller's profit per unit of electricity. The profit per unit of electricity generated by the buyer is always positive, which proves that the transaction is a strict cost optimization for both parties and that neither party suffers any loss of interest. The transaction price is the revenue the seller receives per unit of electricity when participating in the transaction. The total increase in welfare achieved through transactions across the entire market; For users who sell their electricity usage rights; For users who purchase the right to use electricity.

[0048] Time-of-use electricity rights trading mechanism design: 1. Trading targets and subject access: Trading targets: Electricity rights corresponding to a 15-minute time slot in the spot market (the right to use and the obligation to consume electricity that has been cleared up in the previous day), which cannot be carried over to the next day or time slot. Subject classification: Users with rigid demand (without load regulation capabilities) and flexible subjects (with controllable load, energy storage and other regulation capabilities).

[0049] 2. Rolling Trading Sequence: The first trading window opens at 00:00 on the trading day (D day), with one round opening every hour, and each round lasting 30 minutes; for each trading target in each time period, the window closes 4 hours before real-time operation. Entities can submit multiple rounds of applications to gradually correct deviations.

[0050] 3. Centralized Auction Matching Model: Employs a unified marginal clearing mechanism. Buy and sell orders are prioritized by price and then by time, forming a supply and demand curve. The clearing problem can be expressed as: (11) (12) (13) (14); Equation (12) is an individual rationality constraint, ensuring that only orders with a buyer's bid price not lower than the clearing price and a seller's bid price not higher than the clearing price can be executed, which is in line with the participation intentions of market participants; Equation (13) is a bid volume constraint, ensuring that the transaction volume does not exceed the bid volume of the participants; Equation (14) is a transaction mutual exclusion constraint, ensuring that the same participant cannot simultaneously execute buy and sell orders in the same period of the same round of transactions. Based on the deviation assessment model, the final deviation assessment base for users is defined as: (15). In the formula, The final deviation assessment base for subject i in time period t, MWh; This represents the total number of trading rounds corresponding to time period t. Let MWh represent the buy volume and sell volume of subject i in the k-th round of trading during time period t.

[0051] Safety Verification: Transactions are only permitted within the same electrical zone, and the dispatching agency conducts safety verification of the clearing results. Combined Electricity Bill Settlement: Electricity fees from electricity rights transactions and spot electricity fees are combined and settled daily and monthly.

[0052] Day-ahead and intraday two-stage joint optimization decision-making model: The two-stage joint optimization decision-making model for day-ahead and intraday electricity right trading proposed in this invention takes into account load uncertainty. It aims to solve the strong coupling problem of decision-making among market participants in the three stages of day-ahead declaration, intraday trading and load adjustment in the electricity market, and to unify the impact of output load uncertainty on decision-making.

[0053] 1. Handling Load Uncertainty: The actual load is a random variable, expressed as: (16), where, The relative error of load forecasting follows a normal distribution. , The standard deviation of the prediction error is determined based on the historical prediction accuracy of the subject. Subsequently, Latin hypercube sampling (LHS) is used to generate initial scenarios, which are then reduced to S typical scenarios using a synchronous back-substitution reduction method. The probability of occurrence of each scenario s is given by... It satisfies the normalization condition: (17).

[0054] 2. Objective Function (Minimize the expected total electricity purchase cost over the entire lifecycle): The unified objective function for all market participants is as follows: (18); Among them, the cost of electricity right transactions: (19); Deviation settlement and penalty costs: (20); Load adjustment cost: (21); where the actual load is a random variable. ; The relative error of load forecasting follows a normal distribution. , The standard deviation of the prediction error is determined based on the historical prediction accuracy of the subject. The probability of occurrence of each scenario s after reduction using the synchronous back-substitution reduction method; The expected total cost of electricity purchase over the entire lifecycle; The net cost of electricity rights transactions for the main entity in scenario s; For real-time deviation settlement and penalty costs; The marginal adjustment costs incurred by flexible entities adjusting their own load; as the main body The unit load regulation cost; This is the absolute value of the load adjustment.

[0055] 3. Constraint System: Day-ahead Decision Constraints: Upper and Lower Limits of Declared Electricity Volume Constraints: (22); Nonnegativity constraint: (23); Intraday decision-making constraints: Transaction mutual exclusion constraints: (24); Maximum sales volume constraint: (25); Transaction price constraints: (26); Nonnegativity constraint: (27); Market equilibrium clearing constraints: (28); Flexible main body constraints: 1 Adjustable load upper and lower limit constraints: (29); Timing ramp constraint: (30). In the formula, These are the lower and upper limits of the daily reporting volume of subject i in time period t, respectively, MWh, which is a deterministic parameter determined by market rules; Let MWh be the transaction volume of subject i in this round of trading, representing the transaction volume of buy and sell orders, and take a non-negative value. Let MWh be the sell order volume of subject i in the k-th round window for time period t. The price quoted by subject i for time period t in the k-th round window, in yuan / MWh; Let MWh be the total number of transactions completed by subject i in the m-th round window for time period t. The real-time electricity price forecast for time period t is released by the trading center before each window opens to provide a price reference for market participants; Ω represents the set of all compliant market participants; T represents the total number of 15-minute time periods per day. A collection of typical scenarios; For a flexible and adaptable set of entities, satisfying ; Subject i in time period t and scene respectively s The lower and upper limits of load adjustment, in MWh; Adjust the ramp rate for the maximum load of subject i, MWh / period.

[0056] 4. Model linearization: Linearization of value terms: For any real number x, introduce non-auxiliary variables. ,satisfy: (31); Linearization of max / min functions: The max and min functions of the excess exemption deviation in the model are linearized using the Big M method. The positive excess exemption deviation... For example, let's introduce 0-1 binary variables. ,satisfy: (32) (33) (34) (35). Linearization of the bilinear product term: Linearization is performed using 0-1 binary variables. Taking the mutual exclusion constraint of transactions as an example, 0-1 binary variables are introduced. ,satisfy: (36) (37) (38).

[0057] 5. Distributed solution algorithm based on ADMM (1) Core Mechanism: Lower layer: Each market participant independently solves its own two-stage stochastic optimization to minimize the expected electricity purchase cost and reports the optimal buying and selling volume to the trading center. Upper layer: The trading center collects the buying and selling volumes of all participants, updates the electricity clearing price according to the supply and demand balance, and notifies all participants.

[0058] (2) Construction of augmented Lagrange function: (39). In the formula, For the Lagrange multiplier constrained by supply and demand balance, the clearing price for electricity rights trading is applied; For the globally augmented Lagrangian function; as the main body Its own objective function; This is a penalty factor used to improve the convergence speed of the algorithm; The original residual of the market supply and demand imbalance; This is the preset convergence accuracy.

[0059] (3) Iterative process: In each iteration, each subject solves the sub-problem in parallel to obtain the optimal buying and selling volume. The trading center updates prices accordingly. Notify all market participants; calculate residuals ,like If it converges, then it converges; otherwise, continue iterating.

[0060] The two-stage joint optimization decision-making model designed in this invention unifies the three types of decisions: day-ahead reporting, intraday trading, and load adjustment. It adopts a scenario-based approach to handle load and electricity price uncertainties, constructs a unified objective function and constraint system covering both rigid users and flexible entities across the entire market, ensures the model is solvable through linearization, and finally uses distributed algorithms such as ADMM to solve the problem efficiently, taking into account both privacy protection and computational efficiency.

[0061] To verify the effectiveness of the method of this invention, based on the actual rules and operating data of the long-term trial operation of the electricity spot market in a certain province of my country, the effectiveness of the two-stage stochastic joint optimization decision-making model of the user-side time-of-use electricity rights trading mechanism that takes into account deviation assessment proposed in this paper is fully verified through multi-scenario simulation examples.

[0062] Deviation penalty and significant reduction in electricity purchase cost: Comparison of total electricity purchase cost and total deviation penalty cost across the entire market under different scenarios is as follows: Figure 7 As shown in Table 1, the specific cost quantification data and optimization rate statistics are as follows. The results show that, compared to the baseline scenario without transactions, in the full-mechanism scenario proposed in this invention, the total daily deviation penalty cost for the entire market decreased from RMB 112,600 to RMB 34,800, a reduction of 69.10%; the total daily electricity purchase cost decreased from RMB 1,324,800 to RMB 1,221,700, a reduction of 7.78%; and the average cost per unit of electricity decreased from RMB 345.2 / MWh to RMB 318.4 / MWh.

[0063] Meanwhile, it can be observed that the pure financial hedging scenario can only achieve a reduction of 39.25% in total penalty costs and a reduction of 3.74% in total electricity purchase costs. However, this mechanism, through the physical load response of the flexible entity, further increases the reduction of penalty costs to nearly 70%, proving the synergistic optimization effect of transaction + physical response, and breaking through the limitation that pure financial mutual guarantee can only achieve cost redistribution.

[0064] Table 1. Quantitative Statistics on Cost Optimization Effects Across the Entire Market

[0065] Meanwhile, to verify the universality of the proposed mechanism for both users with rigid demand and flexible entities, the unit electricity cost and optimization effect of different types of entities in three scenarios were statistically analyzed. The results are shown in Table 2, which compares the cost reduction of various types of entities. Figure 8 As shown in the results, the proposed mechanism achieves significant cost reduction for both users with rigid demand and those with flexible needs.

[0066] Table 2. Statistics on Cost Optimization Effects of Different Types of Entities

[0067] The effectiveness of the time-sharing trading mechanism in mitigating deviation risk throughout the entire trading period: The time-of-use comparison results of the deviation in electricity consumption during the exemption period across the entire market under Scenario 1 and Scenario 3 are as follows: Figure 9 and Figure 10 As shown in the results, after the implementation of the mechanism proposed in this invention, the deviation in electricity volume during the entire market's exemption period was reduced throughout the entire time period, proving the hedging capability of the time-sharing trading mechanism against deviation risk throughout the entire time period.

[0068] Optimization of system load characteristics and incremental value to power grid operation: The comparison results of the system daily load curves under the three scenarios are as follows: Figure 11As shown in Table 3, the optimization effect of key indicators of system load characteristics is statistically analyzed. The results show that in the pure financial hedging scenario, the system load curve is almost identical to the baseline scenario, proving that pure financial mutual guarantee only achieves cost redistribution among users and has no significant optimization effect on system load characteristics. However, after the implementation of the mechanism proposed in this invention, the system peak load decreased from 486.2MW to 453.7MW, a reduction of 6.68%, the valley load increased from 182.5MW to 210.2MW, an increase of 15.18%, the daily peak-valley difference decreased from 303.7MW to 243.5MW, a reduction of 12.37%, and the daily load factor increased from 0.582 to 0.635, an increase of 9.11%, achieving a reasonable peak shaving and valley filling effect. During off-peak hours, the supply of electricity rights exceeds demand, and the trading price is low. Flexible entities buy electricity rights at low prices and actively increase their load to achieve valley filling in the system. During peak hours, the supply of electricity rights falls short of demand, and the trading price is high. Flexible entities sell electricity rights at high prices and actively reduce their load to achieve peak shaving in the system. The optimization of the system load characteristics can effectively reduce the peak-shaving pressure caused by the peak-valley difference in the power grid, reduce the number of peak-shaving start-ups and shutdowns of thermal power units, and improve the stability of power system operation, proving the core incremental value of the proposed mechanism for system operation.

[0069] Table 3 Statistical Analysis of System Load Characteristics Optimization Effect

[0070] Based on the same inventive concept, according to another aspect of the present invention, such as Figure 3 As shown, an embodiment of the present invention also provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, it performs the steps of the method described above.

[0071] Based on the same inventive concept, according to another aspect of the present invention, such as Figure 4 As shown, embodiments of the present invention also provide a computer-readable storage medium 40, which stores a computer program 410 that, when executed by a processor, performs the methods described above.

[0072] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A user-side time-of-use electricity rights trading control method that takes into account deviation assessment, characterized in that, include: Obtain the day-ahead cleared electricity volume, real-time electricity price, load forecast data, exemption from assessment deviation ratio, deviation penalty coefficient and load adjustment parameters of each market entity in the target electrical zone for multiple settlement periods, and obtain the time-of-use deviation data of each market entity based on the day-ahead cleared electricity volume and load forecast data; Based on the time-of-use deviation data, the proportion of deviations exempt from assessment, and the deviation penalty coefficient, the excess deviation electricity, the marginal loss of deviation, and the feasible transaction price range are calculated to obtain the time-of-use electricity right application data. Based on the time-of-use electricity right application data and load adjustment parameters, a two-stage joint optimization model for day-ahead and intraday periods is constructed and solved to obtain the day-ahead applied electricity volume, intraday electricity right transaction volume, clearing price and load adjustment amount; The time-of-use electricity rights trading results are generated based on the intraday electricity rights transaction volume and clearing price. The load adjustment plan is generated based on the load adjustment amount. The deviation assessment base is corrected based on the intraday electricity rights transaction volume and load adjustment amount.

2. The user-side time-of-use electricity rights trading control method considering deviation assessment as described in claim 1, characterized in that, The process of obtaining the time-of-use deviation data of each market participant based on the day-ahead clearing electricity and load forecast data includes: The day is divided into multiple consecutive settlement periods; For each market participant and each settlement period, the difference between the load forecast data and the day-ahead cleared electricity is calculated to obtain the forecast deviation electricity. Based on the positive or negative direction of the predicted deviation in electricity volume, the corresponding market entity is determined as a candidate for electricity right buyer or a candidate for electricity right seller. The time-of-use deviation data consists of the predicted deviation electricity volume, the candidate entities for electricity use rights buyers, and the candidate entities for electricity use rights sellers.

3. The user-side time-of-use electricity rights trading control method considering deviation assessment as described in claim 1, characterized in that, The process of calculating the excess electricity consumption exceeding the exemption threshold, the marginal loss of the deviation, and the feasible transaction price range based on the time-of-use deviation data, the exemption deviation ratio, and the deviation penalty coefficient, to obtain the time-of-use electricity right application data includes: Based on the aforementioned cleared electricity volume and the exemption deviation ratio, the exemption deviation range is obtained; The time-sharing deviation data is compared with the exemption deviation range to obtain the positive excess exemption deviation power and the negative excess exemption deviation power. The marginal loss of deviation is determined based on the positive excess deviation amount, the negative excess deviation amount, and the deviation penalty coefficient. The bid boundaries for electricity rights buyers and sellers are determined based on the marginal loss of the deviation, and the feasible transaction price range is obtained from the bid boundaries for electricity rights buyers and sellers.

4. The user-side time-of-use electricity rights trading control method considering deviation assessment according to claim 3, characterized in that, The process of obtaining time-of-use electricity right application data includes: The buyer's bid price and the seller's bid price are determined based on the feasible transaction price range; The buyer's declared electricity volume and the seller's declared electricity volume are determined based on the excess excess electricity volume. The time-of-use electricity right application data is formed from the buyer's declared electricity volume, the buyer's declared price, the seller's declared electricity volume, and the seller's declared price.

5. The user-side time-of-use electricity rights trading control method considering deviation assessment according to claim 1, characterized in that, The process of obtaining the intraday electricity consumption rights transaction volume and clearing price includes: The time-of-use electricity right application data are sorted according to price priority and time priority rules to obtain the buyer application sequence and the seller application sequence; Based on the buyer's and seller's bid sequences, a centralized bidding process is conducted to clear the candidate transaction results. Apply the order quantity constraint and transaction mutual exclusion constraint to the candidate transaction results to obtain the intraday electricity consumption right transaction volume and clearing price.

6. The user-side time-of-use electricity rights trading control method considering deviation assessment according to claim 1, characterized in that, The process of constructing and solving the day-to-day two-stage joint optimization model includes: An objective function is constructed with the goal of minimizing the expected total electricity purchase cost for each market participant throughout the entire cycle. The reported electricity volume before the day will be used as a decision variable for the day-ahead stage. The intraday electricity consumption rights transaction volume, clearing price, and load adjustment volume are used as intraday stage decision variables; Based on the day-ahead reporting constraints, intraday trading constraints, market equilibrium clearing constraints, and flexible entity load adjustment constraints, the aforementioned day-ahead-intraday two-stage joint optimization model is obtained.

7. The user-side time-of-use electricity rights trading control method considering deviation assessment according to claim 6, characterized in that, The process of solving the day-to-day two-stage joint optimization model includes: Based on ADMM, the day-to-day two-stage joint optimization model is decomposed into multiple main sub-problems; Each market participant solves its corresponding sub-problem to obtain the candidate buy volume and candidate sell volume for each market participant. The clearing price is updated based on the candidate buy volume and candidate sell volume of each market participant; Calculate the market supply and demand residual. When the market supply and demand residual meets the preset convergence condition, obtain the day-ahead declared electricity volume, intraday electricity consumption right transaction volume, clearing price, and load adjustment volume.

8. A user-side time-of-use electricity rights trading control system that takes into account deviation assessment, characterized in that, include: The acquisition unit is configured to acquire the day-ahead cleared electricity volume, real-time electricity price, load forecast data, exemption from assessment deviation ratio, deviation penalty coefficient and load adjustment parameters of each market entity in the target electrical zone during multiple settlement periods, and to obtain the time-of-use deviation data of each market entity based on the day-ahead cleared electricity volume and load forecast data. The time-sharing unit is configured to calculate the excess deviation electricity, deviation marginal loss, and feasible transaction price range based on the time-sharing deviation data, the exemption deviation ratio, and the deviation penalty coefficient, thereby obtaining time-sharing electricity right application data. The optimization unit is configured to construct and solve a two-stage joint optimization model based on the time-of-use electricity right application data and load adjustment parameters, so as to obtain the day-of-use electricity application, the electricity transaction volume of the day-of-use electricity right, the clearing price and the load adjustment amount. The correction unit is configured to generate time-of-use electricity rights trading results based on the intraday electricity rights transaction volume and clearing price, generate a load adjustment plan based on the load adjustment amount, and correct the deviation assessment base based on the intraday electricity rights transaction volume and load adjustment amount.

9. A computer device, comprising: At least one processor; The processor also includes a memory storing a computer program that can run on the processor, characterized in that the processor executes the program to perform the steps of the user-side time-of-use electricity rights trading control method that takes into account deviation assessment as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it performs the steps of the user-side time-of-use electricity rights trading control method that takes into account deviation assessment as described in any one of claims 1 to 7.

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