A Shapley value-driven integrated energy revenue allocation method

Abstract

This invention belongs to the field of energy management and economic settlement allocation technology for power systems, and discloses a Shapley value-driven integrated energy revenue allocation method applicable to grid-connected integrated energy systems. It establishes a set of participants and constructs metering mapping rules, aligning the electricity and heat or cooling metering data of each participant to the grid connection point time granularity and converting it into mapped power time series. It obtains time-of-use pricing, feed-in tariff, and demand billing parameters, and determines high-price periods. For any subset of participants, it sets computational boundaries and solves scheduling optimization to obtain the grid connection point power purchase and feed-in power time series and calculates the subset's net revenue, calculating the initial allocation amount based on the Shapley value. Then, based on the comparison after removing participants, it calculates the demand reduction contribution and the high-price electricity energy reduction contribution, generates participation weights, and performs conservation corrections to obtain the final allocation amount, outputting the settlement list.

Description

Technical Field

[0001] This invention belongs to the field of energy management and economic settlement allocation technology of power systems, specifically a Shapley value-driven comprehensive energy revenue allocation method. Background Technology

[0002] With the construction of distributed energy and integrated energy stations, when park-level integrated energy systems are connected to the power grid, they often involve multiple energy-consuming or energy-supplying participants. The combined energy demand and controllable energy output of each participant during the settlement period will jointly affect the power purchased, the power fed into the grid, and the maximum power purchase demand at the grid connection point. The system settlement is affected by both the time-of-use electricity price and the demand-based billing rules.

[0003] Existing revenue or cost allocation is usually based on the proportion of electricity, equipment capacity, or contractually agreed proportions of the participating parties. Although some solutions introduce scheduling optimization results as the basis for allocation, in multi-participant scenarios, they are still generally calculated using a single-caliber energy consumption or supply indicator. Since there may be multiple energy forms such as electricity, heat, and cooling within the integrated energy system, the metering caliber and sampling time granularity of different participants are different, and the conversion between energy forms is related to equipment efficiency, making it difficult for the allocation indicator to be consistent with the metering caliber of the grid connection point.

[0004] When time-of-use pricing and demand-based billing coexist, simply allocating costs based on energy or capacity ratios often fails to establish a verifiable correspondence with the settlement basis at the grid connection point. It also fails to objectively reflect the differences in the impact of the combination of participating parties on the grid connection point's electricity purchase cost, grid connection revenue, and maximum demand. At the same time, there may be discrepancies between the grid connection point's electricity metering data and the power time series integral conversion results. Without consistency verification and caliber processing, cumulative errors can easily be introduced, affecting the reliability of the settlement. Summary of the Invention

[0005] The purpose of this invention is to provide a Shapley value-driven method for allocating comprehensive energy benefits, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides the following technical solution: a Shapley value-driven integrated energy revenue allocation method, applicable to integrated energy systems operating in conjunction with the power grid. This integrated energy system has an energy metering device installed at the grid connection point, and collects the power time series of the active power at the grid connection point. The system contains two or more participants, each of whom, through their energy consumption or supply behavior during the settlement period, jointly influences the power purchased at the grid connection point, the power fed into the grid at the grid connection point, and the maximum power demand at the grid connection point. Since the integrated energy system may simultaneously contain multiple energy forms such as electricity, heat, or cooling, the metering standards of different participants differ. Furthermore, grid connection settlement is simultaneously affected by time-of-use electricity pricing and demand billing rules. If allocation is based solely on energy ratios or equipment capacity, the allocation results are difficult to establish a correspondence with the grid connection point settlement standards. To ensure consistency between the allocation process and the grid connection settlement basis, this invention uses the grid connection point metering standards as a unified reference, incorporating the metering data, scheduling results, and settlement rules of the participants into calculations at the same time granularity.

[0007] During the settlement period, a set of participants is first established, and metering mapping rules are formulated for each participant. The electricity metering data and heat metering data or cooling metering data of the participant are converted into a mapped power time series with the same time granularity as the grid connection point power time series under the metering caliber of the grid connection point. The mapped electricity is then obtained from the mapped power time series. Subsequently, the grid connection point electricity metering data, grid connection point power time series, time-of-use electricity price parameters, grid connection settlement price parameters, and demand billing parameters are obtained during the settlement period. Based on the time-of-use electricity price parameters, a set of high-price periods is determined to ensure that subsequent net income accounting and contribution assessment are carried out under the same caliber and time granularity.

[0008] To obtain the net revenue corresponding to different combinations of participants, for any subset of participants in the participant set, a computational boundary is constructed in the scheduling optimization model for that subset. Boundary constraints are applied to participants not belonging to that subset, ensuring that their controllable energy output is zero in the model and their energy demand is based on the baseline energy demand curve. The scheduling optimization is solved under this computational boundary to obtain the time series of purchased power and grid-connected power at the grid connection point. Based on the purchased power and grid-connected power time series, the purchased electricity and grid-connected electricity are converted. Combined with the time-of-use electricity price parameters, grid-connected settlement price parameters, and demand billing parameters, the net revenue of that participant subset in the settlement period is calculated. The net revenue is jointly determined by the electricity sales revenue, electricity purchase cost, and demand billing cost. Since the net revenue calculation uses the metering caliber and billing parameters of the grid connection point, the net revenue can directly correspond to the grid connection settlement rules of the settlement period, thus enabling comparison of the marginal contributions of different combinations of participants.

[0009] After obtaining the net revenue of each participant's subset, the net revenue is used as the input of the characteristic function. The initial allocation amount of each participant is calculated according to the Shapley value rule. In order to establish a verifiable correspondence between the allocation result and the grid connection billing item, a participant subset is constructed for each participant within the same settlement period, after removing that participant. The corresponding scheduling optimization is solved at the same time granularity to obtain the power purchase time series of the comparison grid connection point. The difference between the maximum value of the power purchase time series of the comparison grid connection point and the maximum value of the power purchase time series of the grid connection point of all participants is taken as the participant's contribution to the reduction of the maximum power purchase demand at the grid connection point. In the high electricity price period set, the difference between the power purchase energy corresponding to the power purchase time series of the comparison grid connection point and the power purchase energy corresponding to all participants is taken as the participant's contribution to the reduction of power purchase energy during the high electricity price period. The above two types of contributions correspond to the demand billing item and the power purchase cost item during the high electricity price period, respectively, and are used to characterize the degree of influence of the participants on the key billing items.

[0010] Based on this, the contribution of the reduction in the maximum electricity demand at the grid connection point and the contribution of the reduction in electricity energy during periods of high electricity price are combined into a participation weight. Under the premise of keeping the total allocation equal to the net income of all participants, the initial allocation amount is adjusted according to the conservation correction rule to obtain the final allocation amount of each participant. Finally, a settlement list is output. The settlement list includes at least the metering basis of the grid connection point and the final allocation amount of each participant, which is used to verify and trace the allocation results within the settlement period.

[0011] Furthermore, in this invention, to ensure that the metering results of different participants can be calculated under the same caliber as the grid connection point settlement basis, a metering mapping rule is set for the metering data of the participants. The metering mapping rule uses the sampling interval of the active power time series of the grid connection point as the time granularity, and converts the electrical energy metering data, heat metering data, or cold energy metering data of each participant into a mapped power time series consistent with the time granularity. The mapped electrical energy is obtained by integrating the mapped power time series within the settlement period. By converting the metering of the participants from the energy accumulation caliber to the power caliber consistent with the grid connection point power time series, subsequent scheduling calculations, net income accounting, and contribution comparisons can be carried out on the same time scale and under the same dimension, avoiding settlement caliber deviations caused by inconsistent time granularity or inconsistent dimensions.

[0012] Specifically, for electrical energy metering data, when the electrical energy metering data is recorded in the form of cumulative electrical energy readings, the difference between the cumulative electrical energy readings at adjacent sampling times is taken at the same time granularity and divided by the time interval between adjacent sampling times to obtain the electrical power sequence at the corresponding time granularity. For heat metering data or cold energy metering data, the difference between the heat metering value or cold energy metering value at adjacent sampling times is also taken at the same time granularity and divided by the time interval between adjacent sampling times to obtain the heat power sequence or cold power sequence. Then, the heat power sequence or cold power sequence is converted into an equivalent electrical power sequence using the energy conversion efficiency parameter, where the equivalent electrical power sequence is equal to the heat power sequence or cold power sequence divided by the energy conversion efficiency parameter. The energy conversion efficiency parameter is determined by the rated parameters of the corresponding energy conversion equipment or by the test results. The electrical power sequence and the equivalent electrical power sequence are algebraically superimposed at the same time granularity to form the mapped power time sequence of the participants at the grid connection point metering caliber, and the mapped electrical energy is obtained accordingly.

[0013] Through the above metering mapping process, the electrical energy, heat and cold energy on the participating side are unified into the power time series and its corresponding electrical energy under the metering caliber of the grid connection point. This ensures that the calculation and comparison of settlement-related quantities such as the power purchased by the grid connection point, the power fed into the grid connection point, and the maximum demand have a consistent data basis, thereby ensuring that the evaluation of the participating party's contribution is consistent with the grid connection settlement caliber.

[0014] Furthermore, in the metering mapping rules, heat metering data or cold power metering data needs to be converted into an equivalent electrical power sequence so that it can be calculated under the same dimension and time granularity as the active power time series at the grid connection point. The conversion uses energy conversion efficiency parameters, which are used to convert the heat power sequence or cold power sequence into an equivalent electrical power sequence, so that the mapped power time series of each participant can be consistently calculated under the metering caliber of the grid connection point. To ensure that the conversion parameters have traceability and match the status of the field equipment, the rated efficiency parameters of the corresponding energy conversion equipment are preferred. When the energy conversion equipment is operating under conditions that differ from the rated operating conditions, the energy conversion efficiency parameters are the efficiency parameters determined by testing under the actual operating conditions or similar operating conditions on site. By clarifying the source of the energy conversion efficiency parameter, the caliber of converting heat or cold energy into equivalent electrical power can remain consistent within the settlement period, providing a stable parameter basis for the calculation of the mapped power time series and mapped electrical energy, thereby ensuring that subsequent scheduling calculations and revenue sharing are consistent with the settlement caliber at the grid connection point.

[0015] Furthermore, when constructing the scheduling optimization model for the participant subset, for participants not included in the selected participant subset, their energy demand needs need to be given in the model calculation boundary to ensure that the boundary conditions for net benefit calculation are closed. To this end, the present invention determines a benchmark energy demand curve for each participant. The benchmark energy demand curve represents the time series of energy demand changes over time when the participant is an outside participant in the settlement period. When solving the scheduling optimization corresponding to the participant subset, the energy demand of the participant is fixed as the benchmark energy demand curve, so that different participant subsets can perform net benefit calculation and comparison under consistent boundary input conditions.

[0016] The benchmark energy consumption curve can be generated from any of the following data and uniformly converted into an energy demand time series with the same time granularity as the active power time series at the grid connection point: First, historical metering data within a predetermined number of days before the start of the settlement period, which is then aligned with the time granularity of the active power time series at the grid connection point to form an energy demand time series with the same time granularity as the settlement period, serving as the benchmark energy consumption curve for that participant; Second, the contractual load curve of the participant, which is converted into an energy demand time series with the same time granularity as the active power time series at the grid connection point, serving as the benchmark energy consumption curve for that participant. By adopting the same time granularity as the active power time series at the grid connection point, the benchmark energy consumption curve can be directly used as the calculation boundary input of the scheduling optimization model, ensuring that the comparison basis for calculating the net revenue of the participant's subset is consistent.

[0017] Furthermore, in this invention, the electricity purchase cost is jointly determined by the purchased electricity volume and the time-of-use electricity price parameter. To consistently statistically analyze the changes in purchased electricity volume by participating parties during periods of higher electricity prices within the settlement period, a set of high-price periods is determined. This set of high-price periods is based on the time-of-use electricity price parameter table, with the time granularity of the active power time series at the grid connection point as the dividing granularity. The time-of-use electricity price parameter corresponding to each time granularity within the settlement period is judged. When the time-of-use electricity price parameter is not lower than the high-price threshold, the... The time granularity is assigned to the high electricity price period set. The high electricity price threshold is determined before the start of the settlement period based on the time-of-use electricity price parameter table and remains unchanged during the settlement period to ensure that the division standard of the high electricity price period set is consistent within the same settlement period. Based on the high electricity price period set, the purchased electricity energy during the high electricity price period is statistically analyzed at the time granularity of the active power time series at the grid connection point. The statistical results are used to calculate the contribution of the purchased electricity energy reduction in subsequent high electricity price periods, so that the contribution assessment of the participants is consistent with the settlement caliber of the time-of-use electricity price.

[0018] Furthermore, in order to establish a verifiable correspondence between the revenue sharing results and the demand billing items and the electricity purchase cost items during high electricity price periods in the grid connection point settlement, this invention, after performing a comparison solution that removes the participants for each participant, calculates the contribution of the grid connection point's maximum electricity purchase demand reduction and the contribution of the electricity purchase energy reduction during high electricity price periods based on the time series of the grid connection point's electricity purchase power, and uses these as input indicators for the subsequent generation of participation weights.

[0019] Within the same settlement period, for the target participant, firstly, the time series of grid-connected power purchases at all participating parties during collaborative scheduling is solved; then, a subset of participating parties after removing the target participant is constructed, and the corresponding scheduling optimization is solved at the same time granularity as described above, resulting in a comparative grid-connected power purchase time series. The difference between the maximum value of the comparative grid-connected power purchase time series within the settlement period and the maximum value of the grid-connected power purchase time series corresponding to all participating parties within the settlement period is determined as the target participant's contribution to reducing the maximum grid-connected power demand; when this difference is less than zero, the contribution to reducing the maximum grid-connected power demand is zero.

[0020] Meanwhile, within the high-electricity-price period set, the power purchase time series of the grid-connected point and the power purchase time series of the grid-connected point corresponding to all participants are converted into purchased electricity and energy, respectively. The difference between the purchased electricity and energy of the two in the high-electricity-price period set is determined as the target participant's contribution to the reduction of purchased electricity energy during the high-electricity-price period. When the difference is less than zero, the contribution to the reduction of purchased electricity energy during the high-electricity-price period is zero. Through the above two types of contribution indicators, the influence of the target participant on the maximum power purchase demand of the grid-connected point and the purchased electricity energy during the high-electricity-price period can be quantified, providing a consistent calculation method for the subsequent generation and conservation correction of participation weights.

[0021] Furthermore, after obtaining the contribution of each participant to the reduction of the maximum electricity demand at the grid connection point and the contribution of the reduction of electricity energy purchased during the high electricity price period, the present invention generates a participation weight, and adjusts the initial allocation amount according to the participation weight in the subsequent conservation correction process, so that the correction process can reflect the relative influence of the participants on the demand billing item and the electricity purchase cost item during the high electricity price period.

[0022] The participation weight is determined as follows: First, the contribution of the grid connection point to the reduction of the maximum electricity demand is normalized: the contribution of each participant to the reduction of the maximum electricity demand at the grid connection point is divided by the sum of the contributions of all participants to obtain the proportion of that participant in the contribution to the reduction of the maximum electricity demand at the grid connection point. Secondly, the contribution of electricity purchase reduction during high-electricity-price periods is normalized: the contribution of each participant to electricity purchase reduction during high-electricity-price periods is divided by the sum of the contributions of all participants to obtain the proportion of that participant in the contribution of electricity purchase reduction during high-electricity-price periods; then, using a weighting coefficient determined before the start of the settlement period and remaining unchanged during the settlement period, the above two proportions are weighted and synthesized to obtain the participation weight of that participant. The weighting coefficient is used to set the relative influence ratio of the contribution of the maximum electricity demand reduction at the grid connection point to the contribution of electricity purchase reduction during high-electricity-price periods in the participation weight.

[0023] In the normalization process, when the sum of all participants' contributions to a certain item is zero, it indicates that the contribution cannot distinguish the differences among participants within the settlement period. In this case, the normalized value of the item is taken as the uniform weight corresponding to the reciprocal of the number of participants, so as to avoid the denominator being zero and causing the calculation to be invalid, and to ensure that the contribution does not introduce additional bias within the settlement period, thereby ensuring the computability and stability of the participation weight.

[0024] Furthermore, after obtaining the initial contribution amount for each participant based on the net income characteristic function, a conservation correction is performed on the initial contribution amount to obtain the final contribution amount. The conservation correction is based on the participation weight and uses the net income corresponding to the entire set of participants as the correction scale, so that the final contribution amount reflects the difference in participation while keeping the total contribution amount consistent with the net income.

[0025] The calculation process for the conservation correction is as follows: First, determine the uniform weight, which is the reciprocal of the number of participants. For each participant, calculate the difference between the participant's participation weight and the uniform weight to obtain the correction coefficient. Multiply the correction coefficient by the correction ratio coefficient and the net income corresponding to the set of all participants to obtain the correction amount for that participant. The correction ratio coefficient is determined before the start of the settlement period and remains unchanged during the settlement period to limit the correction intensity. Add the correction amount for that participant to the initial contribution amount for that participant to obtain the final contribution amount for that participant.

[0026] To ensure the conservation of the total contribution amount, the participation weights are normalized and the sum of the participation weights of each participant is one. The sum of the uniform weights across the participant set is also one. Therefore, the sum of the correction coefficients of each participant is zero, and thus the sum of the correction amounts of each participant is zero. This ensures that the sum of the final contribution amounts of each participant is consistent with the net income corresponding to the entire participant set. Through the above conservation correction, the final contribution amount, while maintaining the total net income unchanged, adjusts the initial contribution amount according to the relative contribution reflected by the participation weights, providing a verifiable calculation basis for the subsequent settlement list output.

[0027] Furthermore, the calculation of the net revenue of the participating subset is based on the metering caliber of the grid connection point, involving the purchased electricity, grid-connected electricity, and electricity related to demand billing within the settlement period. To ensure the consistency of the data caliber for net revenue calculation, a consistency check is performed on the electricity metering data of the grid connection point and the active power time series of the grid connection point before calculating the net revenue of the participating subset.

[0028] During consistency verification, the active power time series of the grid-connected point is aligned by time granularity and accumulated within the settlement period to obtain the power integral energy. The power integral energy is then compared with the grid-connected point energy metering data. The absolute value of the difference is compared with a tolerance set before the start of the settlement period and kept unchanged within the settlement period. If the absolute value of the difference does not exceed the tolerance, the consistency verification is deemed successful, and the grid-connected point energy metering data is used as the basis for net income calculation. If the absolute value of the difference exceeds the tolerance, the consistency verification is deemed unsuccessful. In this case, the original grid-connected point energy metering data is not changed, and consistency correction energy is generated based on the power integral energy. This consistency correction energy is used as the grid-connected point energy caliber in the net income calculation of the participating subset.

[0029] Through the above consistency verification and consistency correction processes, the grid-connected point power caliber used in net income accounting is kept consistent with the grid-connected point active power time series within the same settlement period, thereby avoiding net income deviation caused by measurement deviation or time alignment deviation, and improving the traceability and verifiability of subsequent income allocation results.

[0030] The beneficial effects of this invention are as follows: 1. This invention establishes metering mapping rules for each participating party, aligning data from different metering calibers, such as electricity, heat, or cooling, to the time granularity of active power at the grid connection point within the same settlement cycle, and obtaining a power sequence by differentiating adjacent time points. For heat or cooling, an energy conversion efficiency parameter is introduced, which can be the rated parameters of the equipment or parameters determined by testing, enabling each participating party to form a directly superimposed mapped power time sequence and mapped electrical energy. By unifying the time granularity, unifying the metering caliber, and recording the mapping basis in the settlement list, the energy consumption and supply behavior of multiple participating parties can correspond one-to-one with the electricity purchase and sale settlement at the grid connection point, and is compatible with data sources from different types of metering devices, thereby reducing manual adjustments, caliber discrepancies, and reconciliation disputes.

[0031] 2. This invention constructs a computational boundary for any subset of participants, applies boundary constraints to participants outside that subset in the scheduling optimization model, and fixes their energy demand using a benchmark energy consumption curve. It then solves for the time series of power purchased at the grid connection point and the time series of power fed into the grid. Based on this, it calculates the net revenue of the subset, which includes electricity sales revenue, electricity purchase cost, and demand billing cost. This allows the net revenue to simultaneously reflect the coupled impact of time-of-use pricing and demand billing. Furthermore, it uses the net revenue of each subset to form a characteristic function and calculates the initial allocation amount according to the Shapley value rule. This ensures that allocation no longer depends on a single electricity or capacity ratio, but rather quantifies the incremental effect of a participant's entry or exit on the subset's net revenue, thus balancing synergy and marginal contribution, and improving the fairness and stability of settlement.

[0032] 3. This invention constructs a subset of participants after removing that participant and solves a comparative scheduling optimization problem. It calculates the contribution of the grid-connected point's maximum demand reduction and the contribution of the energy reduction during high-price periods, respectively. These two types of contributions are normalized and weighted to generate participation weights, ensuring that the allocation adjustment reflects the difference in impact between demand costs and high-price costs. Furthermore, a conservation adjustment rule is used to adjust the initial allocation amount, ensuring that the sum of the final allocation amounts for each participant equals the net benefit for all participants, and the adjustment ratio remains constant throughout the settlement period, thus avoiding drift in the total allocation amount. Before calculating the net benefit, consistency verification and correction are performed on the grid-connected point's energy metering data and power integral energy, improving the verifiability and error resistance of the settlement list and reducing disputes caused by metering deviations. Attached Figure Description

[0033] Figure 1 This is a flowchart of the data preprocessing and measurement mapping process of the present invention; Figure 2 This is a flowchart illustrating the calculation of net income and initial allocation of the participating subsets in this invention. Figure 3 Flowchart for contribution assessment and final allocation adjustment of this invention. Detailed Implementation

[0034] 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.

[0035] like Figures 1 to 3As shown in the embodiment of the present invention, a Shapley value-driven integrated energy revenue sharing method is provided in this embodiment. It is applied to an integrated energy system that is connected to the power grid. The integrated energy system is equipped with an energy metering device at the grid connection point and collects the active power time series at the grid connection point. The integrated energy system includes two or more participants. Each participant affects the power purchased at the grid connection point, the power fed into the grid at the grid connection point, and the maximum power purchase demand at the grid connection point through energy consumption and energy supply behavior during the settlement period.

[0036] During the settlement period, this method performs the following processes sequentially: First, a set of participants is established, and a metering mapping rule is established for each participant. The metering mapping rule converts the electrical energy metering data, heat metering data, or cold energy metering data collected by the participant during the settlement period into a mapped power time series under the grid connection point metering caliber. The mapped power time series and the grid connection point active power time series adopt the same time granularity. Based on the mapped power time series, the mapped electrical energy of the participant is obtained by accumulating and converting according to the time granularity during the settlement period. This data is used as the data input for subsequent scheduling solutions and net income calculation.

[0037] Subsequently, the electricity metering data of the grid-connected point, the active power time series of the grid-connected point, the time-of-use electricity price parameters, the grid-connected settlement electricity price parameters, and the demand billing parameters are obtained within the settlement period. Based on the time-of-use electricity price parameters, a set of high-price periods is determined. The set of high-price periods is divided by the time granularity of the active power time series of the grid-connected point. The time granularity at which the time-of-use electricity price parameters are not lower than the high-price threshold is included in the high-price period set. The high-price threshold is determined before the start of the settlement period and remains unchanged during the settlement period.

[0038] Before calculating the net income of the participating subset, a consistency check is performed on the grid-connected point energy metering data and the grid-connected point active power time series. During the settlement period, the grid-connected point active power time series is accumulated and converted into power integral energy at the time granularity. The power integral energy is then compared with the grid-connected point energy metering data. When the absolute value of the difference exceeds the tolerance, the original grid-connected point energy metering data is not changed. Consistency correction energy is generated based on the power integral energy, and the consistency correction energy is used as the grid-connected point energy caliber in subsequent net income calculations. When the absolute value of the difference does not exceed the tolerance, the grid-connected point energy metering data is used as the grid-connected point energy caliber. The tolerance is set before the start of the settlement period and remains unchanged during the settlement period.

[0039] Next, for any subset of participants in the participant set, a computational boundary is constructed in the scheduling optimization model. For participants that do not belong to this subset, boundary constraints are applied in the scheduling optimization model to make the controllable energy output of the participant zero in the model, and to make the energy demand of the participant take the benchmark energy demand curve in the model. The benchmark energy demand curve is the energy demand time series with the same time granularity as the active power time series at the grid connection point. The benchmark energy demand curve is obtained by aligning historical metering data within a pre-set number of days before the start of the settlement period with the same time granularity, or by converting the contractual load curve of the participant with the same time granularity.

[0040] The decision variables of the scheduling optimization model include the controllable power output time series of each participant in the participant subset and the power exchange time series between the grid connection point and the power grid. The constraints include energy balance constraints, energy demand satisfaction constraints, equipment output upper and lower limit constraints, and grid connection point power exchange constraints. Among them, the energy balance constraints are used to match the power purchased by the grid connection point, the power fed into the grid connection point, and the power output and energy demand of each participant at each time granularity. The scheduling optimization is solved under the constraints with the net income in the settlement period as the optimization objective, and the power purchased by the grid connection point and the power fed into the grid connection point corresponding to the participant subset are obtained.

[0041] After obtaining the time series of power purchased and power delivered to the grid at the grid connection point, the net revenue of the participating subset within the settlement period is calculated. The purchased power is obtained by summing the power purchased time series at the grid connection point according to the time granularity, and the delivered power is obtained by summing the delivered power time series at the grid connection point according to the time granularity. The power purchase cost is determined by the purchased power and the time-of-use electricity price parameter. The power sales revenue is determined by the delivered power and the on-grid settlement price parameter. The demand billing cost is determined by taking the maximum value of the power purchased time series at the grid connection point under the demand time granularity corresponding to the demand billing parameter to obtain the maximum power purchase demand of the grid connection point, and combining it with the demand billing parameter. The net revenue of the participating subset is jointly determined by the power sales revenue, the power purchase cost, and the demand billing cost.

[0042] After obtaining the net income corresponding to each participant subset, the net income of each participant subset constitutes a characteristic function. The initial contribution of each participant is calculated according to the Shapley value rule. The initial contribution is used to characterize the marginal contribution of each participant to the net income under different participant combinations.

[0043] Within the same settlement period, a subset of participants is constructed for each participant after removing that participant, and the corresponding scheduling optimization is solved at the same time granularity to obtain the power purchase time series of the comparison grid connection points. The difference between the maximum value of the power purchase time series of the comparison grid connection points and the maximum value of the power purchase time series of the grid connection points of all participants within the settlement period is determined as the maximum power purchase demand reduction contribution of that participant. The power purchase time series of the comparison grid connection points and the power purchase time series of the grid connection points of all participants are converted into power purchase energy in the high electricity price period set, and the difference between the power purchase energy of the two in the high electricity price period set is determined as the power purchase energy reduction contribution of that participant in the high electricity price period.

[0044] Based on the contribution of each participant to the reduction of maximum electricity demand at the grid connection point and the contribution of the reduction of electricity purchased during high electricity price periods, the participation weight of each participant is generated. The contribution to the reduction of maximum electricity demand at the grid connection point is normalized to obtain the proportion, and the contribution to the reduction of electricity purchased during high electricity price periods is normalized to obtain the proportion. The two proportions are weighted and synthesized using a weighting coefficient to obtain the participation weight. The weighting coefficient is determined before the start of the settlement period and remains unchanged during the settlement period. When the sum of a certain type of contribution across all participants is zero, the normalized result corresponding to that type of contribution is taken as the uniform weight corresponding to the reciprocal of the number of participants.

[0045] After obtaining the participation weights, the initial contribution amount is adjusted according to the conservation adjustment rule to obtain the final contribution amount. The uniform weight is the reciprocal of the number of participants. For each participant, the difference between the participation weight and the uniform weight is used to obtain the adjustment coefficient. The adjustment coefficient is multiplied by the adjustment ratio coefficient and the net income corresponding to the set of all participants to obtain the adjustment amount. The adjustment ratio coefficient is determined before the start of the settlement period and remains unchanged during the settlement period. The adjustment amount is added to the initial contribution amount to obtain the final contribution amount, and the sum of the adjustment amounts of each participant is made zero so that the sum of the final contribution amounts of each participant is consistent with the net income corresponding to the set of all participants.

[0046] Finally, a settlement list is output, which includes the measurement basis of the outlets and the final share of each participant, and is used to verify and trace the share of the results within the settlement period.

[0047] In this embodiment, in order to enable the metering data of each participant to be uniformly calculated under the metering caliber of the grid connection point, and to provide consistent data input for subsequent scheduling optimization and revenue sharing, a metering mapping rule is established for each participant. The output of the metering mapping rule is the mapped power time series of that participant within the settlement period. The mapped power time series and the active power time series of the grid connection point adopt the same time granularity. The mapped electrical energy is obtained by accumulating and converting the mapped power time series according to the time granularity within the settlement period.

[0048] Data preparation and time granularity alignment During the settlement period, electrical energy metering data and its corresponding heat metering data or cold energy metering data are obtained for each participant, and the active power time series of the grid connection point is obtained as a unified time reference. The electrical energy metering data, heat metering data or cold energy metering data are all collected by the corresponding metering devices, and the metering caliber is configured before the start of the settlement period and remains unchanged during the settlement period. During time alignment, the settlement period is divided according to the time granularity of the active power time series at the grid connection point. Various energy metering data from the participating side are assigned to their respective time granularity intervals by timestamp, and an aligned energy reading sequence is formed at the boundary points of each time granularity interval. If the metering data from the participating side is a cumulative reading, the cumulative reading at the interval boundary time is used as the boundary point reading; if the metering data from the participating side is a time-segmented reading, the time-segmented reading within that time granularity interval is used as the energy increment of that interval.

[0049] When missing intervals exist, if the number of consecutively missing intervals does not exceed the missing threshold configured in the settlement, the boundary point readings are interpolated and filled in according to the readings of the adjacent valid boundary points to make the differential conversion executable; if the consecutive missing intervals exceed the missing threshold, the missing segment is marked as an invalid segment. When calculating the mapped power time series, the invalid segment is maintained by the average power value of the previous valid interval, or the invalid segment is removed from the statistical interval according to the business settlement rules and the removal range is recorded in the settlement list to ensure that the mapping results are traceable.

[0050] When an unreasonable jump in energy reading is detected due to the metering device resetting or returning to zero, the jump point is used as the segment boundary, and differential conversion is performed independently before and after the boundary. For cumulative reading scenarios, a reset offset can be added to the cumulative reading after resetting to restore the monotonicity of the cumulative reading sequence before performing differential conversion.

[0051] Conversion of electrical energy metering data to electrical power series When the electrical energy metering data is the cumulative electrical energy reading, the difference between the cumulative electrical energy readings between adjacent time granularity boundary points is taken to obtain the electrical energy increment of that time granularity interval; then, the electrical energy increment is divided by the time interval of that time granularity interval to obtain the average electrical power value of that time granularity interval, forming an electrical power sequence.

[0052] When the electrical energy metering data is time-division electrical energy readings, the time-division electrical energy readings are used as the electrical energy increment of that time granularity interval, and then divided by the time interval of that time granularity interval to obtain the average electrical power value, forming an electrical power sequence.

[0053] To maintain consistent power direction, this embodiment adopts a unified convention: when a participant obtains electrical energy from the integrated energy system, the electrical power is positive; when a participant feeds back electrical energy to the integrated energy system, the electrical power is negative. If the electricity metering device records the positive and negative electrical energy respectively, the positive and negative electrical power sequences are calculated separately, and then synthesized into an electrical power sequence according to the above-mentioned direction convention. When the incremental electrical energy obtained by differential measurement has an abnormally negative value and is inconsistent with the metering standard, the interval is marked as an abnormal interval, and the abnormal increment is replaced by the average incremental electrical energy of the adjacent valid intervals to avoid the abnormal point from generating unreasonable spikes in the power sequence.

[0054] Conversion of heat or cold measurement data to equivalent electrical power series When the participants have heat or cold measurement data, in order to unify different energy forms to the dimension of electric power, the heat or cold is first converted into a heat power sequence or a cold power sequence, and then converted into an equivalent electric power sequence. When the heat measurement data is a cumulative heat reading, the difference between the cumulative heat readings between adjacent time granularity boundary points is taken to obtain the heat increment; when the heat measurement data is a time-segmented heat reading, the time-segmented heat reading is used as the heat increment, and the heat increment is divided by the time interval of the time granularity interval to obtain the heat power sequence. The processing of the cold energy measurement data is the same to obtain the cold power sequence.

[0055] After obtaining the thermal power sequence or cold power sequence, an energy conversion efficiency parameter is introduced to convert the thermal power sequence or cold power sequence into an equivalent electrical power sequence. The energy conversion efficiency parameter is used to characterize the conversion relationship between electrical energy input and heat output or cold output of the energy conversion equipment under corresponding operating conditions. For electric heating equipment, the thermal efficiency parameter can be used, and for electric refrigeration equipment or heat pump equipment, the energy efficiency coefficient parameter can be used. The energy conversion efficiency parameter is determined before the start of the settlement period and remains unchanged during the settlement period. Its value comes from the rated efficiency parameter of the energy conversion equipment and the efficiency parameter determined by testing under actual or similar operating conditions on site. When determining the value by testing, the input electrical energy and output heat or cold output are recorded in the stable operating range, and the energy conversion efficiency parameter is determined by the ratio of output to input. At the same time, the equipment identification, test conditions and test time are archived for settlement verification and traceability.

[0056] When the same participant corresponds to multiple energy conversion devices, a unique energy conversion efficiency parameter is matched for each group of metering devices and energy conversion devices to form multiple equivalent power sequences. These sequences are then algebraically superimposed at the same time granularity to form the equivalent power sequence of the participant.

[0057] Synthesis of mapped power time series and acquisition of mapped electrical energy At the same time granularity, the power sequence and the equivalent power sequence are algebraically superimposed to obtain the mapped power time series of the participants under the grid connection point metering caliber. The mapped power time series are accumulated and converted at the time granularity within the settlement period to obtain the mapped electrical energy. The mapped electrical energy serves as the energy metering result of the participants under the grid connection point metering caliber, and is used for data input of the subsequent scheduling optimization model and for unifying the revenue accounting caliber.

[0058] In one implementation, to verify the consistency between the mapping result and the grid connection point metering caliber, the total mapped electrical energy obtained by accumulating and converting the mapped power time series of all participants within the settlement period is compared with the grid connection point electrical energy recorded by the grid connection point electrical energy metering device. When the absolute value of the difference exceeds the energy difference tolerance, the metering caliber, time alignment result, and energy conversion efficiency parameter value of the participants are checked first, and the metering mapping is re-executed after correcting the source of the anomaly. The energy difference tolerance is set before the start of the settlement period and remains unchanged during the settlement period.

[0059] In this embodiment, the energy conversion efficiency parameter is used to convert the heat or cold energy metering data of the participating party into an equivalent electrical power sequence, so that it and the electrical power sequence are combined under the same grid connection point metering caliber to form a mapped power time series. The energy conversion efficiency parameter characterizes the energy conversion relationship between the input electrical energy and the output heat or cold energy of the energy conversion equipment under the corresponding operating conditions. For electric heating equipment, this parameter corresponds to a thermal efficiency parameter; for electric refrigeration equipment or heat pump equipment, this parameter corresponds to an energy efficiency coefficient parameter. Its value is not required to be less than or equal to one. The energy conversion efficiency parameter is determined before the start of the settlement period and remains unchanged during the settlement period to ensure that the metering mapping caliber is stable and verifiable within the same settlement period.

[0060] Correspondence between energy conversion equipment and metering circuit For each piece of heat or cold measurement data, a unique energy conversion device identifier is established, and the input-side electrical energy measurement basis and output-side heat or cold measurement basis of the device are determined. The input-side electrical energy measurement basis corresponds to the electrical energy metering device of the device's input circuit, and the output-side heat or cold measurement basis corresponds to the heat or cold measurement device of the device's output circuit. The correspondence is established and fixed before the start of the settlement cycle to ensure that the source of the energy conversion efficiency parameter is consistent with the conversion object, and to avoid the mixing of metering data from different devices or different circuits.

[0061] When a single participant includes multiple energy conversion devices, the energy conversion efficiency parameters are determined for each energy conversion device, and corresponding equivalent power sequences are generated. These sequences are then algebraically superimposed at the same time granularity to form the equivalent power sequence for that participant.

[0062] Determination method of rated efficiency parameters In one implementation, the energy conversion efficiency parameter is taken as the rated efficiency parameter of the energy conversion equipment. The rated efficiency parameter is derived from the rated operating condition calibration value in the equipment nameplate parameters, factory technical documents, equipment instruction documents, or completion acceptance documents, and corresponds one-to-one with the equipment identification.

[0063] When the rated efficiency parameter is given multiple calibration values ​​depending on the operating conditions, the calibration value that matches the operating mode identifier and key operating conditions determined before the start of the settlement cycle is selected. The key operating conditions include at least the supply and return water temperature range, ambient temperature range, load rate range, and operating mode identifier. If the operating conditions during the settlement cycle are difficult to clearly match a certain calibration value, the minimum value among the multiple calibration values ​​is taken as the energy conversion efficiency parameter. The basis for the value selection, the source of the calibration value, and the equipment identifier are recorded in the metering basis section of the settlement list to ensure that the parameter selection is traceable.

[0064] The method for determining efficiency parameters is based on testing. In another implementation, the energy conversion efficiency parameter is taken as the efficiency parameter determined by testing. The test is carried out within a specified window before the start of the settlement cycle. The input-side electrical energy metering device and the output-side heat metering device or cold energy metering device used for testing should be within the verification or calibration validity period. The test data and metering mapping adopt the same time granularity, or can be aligned to the same time granularity.

[0065] The efficiency parameters determined by the test are established according to the following steps.

[0066] Test interval selection Select a test interval where the energy conversion equipment operates in a consistent and relatively stable mode. The stability of operation is judged by the fluctuation rate of the input power. The fluctuation rate is calculated as the ratio of the difference between the maximum and minimum values ​​of the input power in the test interval to the average value. The upper limit of the fluctuation rate and the minimum test duration are set before the start of the settlement period and clearly recorded in the test record.

[0067] Input-side electrical energy harvesting Within the test range, the input-side electrical energy is acquired. If the metering caliber of the input-side electrical energy metering device is inconsistent with that of the participating party's electrical energy metering data, it is converted to a consistent caliber based on the determined metering ratio relationship. The source of the metering ratio relationship should be traceable parameter records in the metering device configuration file or calibration file.

[0068] Output side heat or cold energy acquisition The output heat or output cold is measured within the test range. The metering circuit of the output-side metering device should be consistent with the output circuit of the energy conversion equipment to avoid including heat or cold from sources other than the output circuit of the equipment in the test results.

[0069] Efficiency parameter calculation and consistency verification The ratio of output heat or output cooling to input electrical energy within the test interval is calculated to obtain the efficiency parameter determined by the test. Consistency verification is performed on the input and output data within the test interval. Consistency verification includes at least time alignment check, reading abrupt change check, and abnormal missing data check. If the consistency verification fails, the test interval is changed and the calculation is repeated. If it fails twice in a row, the rated efficiency parameter is used and the reason for the backtracking is explained in the test record.

[0070] Recording and Tracing For the efficiency parameters determined by the test, record the equipment identification, the start and end times of the test interval, the basis for input side electrical energy measurement, the basis for output side heat or cold energy measurement, the operating mode identification, key operating conditions, and the verification or calibration status information of the metering device. The above records serve as the measurement basis attachments to the settlement list or archived materials of equivalent validity for settlement verification and traceability.

[0071] When the difference between the efficiency parameter determined by the test and the rated efficiency parameter exceeds the parameter difference tolerance set before the start of the settlement cycle, the validity period of the metering device, the correspondence of the metering loop and the time alignment results should be checked first. After eliminating metering abnormalities, the source of the final efficiency parameter should be determined. The parameter difference tolerance can be determined by comprehensively considering the allowable error of the metering device and the stability requirements of the test range, and the basis for setting it should be recorded in the settlement data.

[0072] Application of efficiency parameters in econometric mapping In the metering mapping process, the heat metering data or cold energy metering data is differentially divided at the boundary points of adjacent time granularities to obtain the heat increment or cold energy increment, and then divided by the time interval corresponding to the time granularity to obtain the heat power sequence or cold power sequence. Subsequently, the heat power sequence or cold power sequence is divided by the energy conversion efficiency parameter to obtain the equivalent electrical power sequence. The equivalent electrical power sequence and the electrical power sequence are algebraically superimposed at the same time granularity to form the mapped power time sequence, and further accumulated and converted according to the time granularity to obtain the mapped electrical energy.

[0073] In this embodiment, in order to ensure that the energy demand of participants that do not belong to the participant subset has an executable and verifiable reference baseline in the model when constructing the participant subset and solving the scheduling optimization, a benchmark energy demand curve is pre-determined for each participant. The benchmark energy demand curve is a time series of energy power demand, and its time granularity is consistent with the time granularity of the active power time series at the grid connection point, and remains unchanged during the settlement period.

[0074] The benchmark energy consumption curve can be generated from historical metering data. Specifically, the metering data corresponding to the participating party is read within a preset number of days before the start of the settlement period, and aligned with the timestamp of the active power time series of the grid connection point as a benchmark to form an energy reading sequence or time-segmented energy sequence consistent with the time granularity. For cumulative energy readings, the difference between adjacent time granularity boundary points is taken to obtain the energy increment of that time granularity. Then, the energy increment is divided by the time interval of that time granularity to obtain the average energy consumption value of that time granularity, thereby forming the benchmark energy consumption curve. For time-segmented energy sequences, the energy increment within that time granularity is divided by the time interval to obtain the average energy consumption value, thereby forming the benchmark energy consumption curve. The preset number of days, alignment rules, and missing data handling rules are pre-configured according to the settlement rules before the start of the settlement period and recorded in the settlement list as the metering basis.

[0075] When there is data for multiple days within a preset number of days, it can be aggregated by day type. The day type is divided into working days and non-working days according to the natural day attributes. The average energy consumption power of each time granularity within the same day type is taken to obtain the benchmark energy consumption curve for the corresponding day type. Within the settlement period, the corresponding benchmark energy consumption curve is selected as the benchmark energy consumption curve for that day according to the day type. If the historical metering data is missing, causing a certain time granularity to be unable to form a stable baseline, the average value of adjacent valid time granularities is used to make up the gap according to the settlement rules, and the gap range and gap method are recorded in the settlement list.

[0076] If the energy consumption of a participating party is more clearly defined by the contract, the benchmark energy consumption curve can also be determined by the contractually agreed load curve of that participating party. The contractually agreed load curve is a curve of energy consumption power changing over time. It is interpolated or segmented according to the time granularity of the active power time series at the grid connection point to obtain the energy demand time series at the same time granularity, which serves as the benchmark energy consumption curve. The source, scope of application, and mapping rules of the contractually agreed load curve are determined before the start of the settlement period and are retained in the settlement list.

[0077] During the settlement period, in order to identify the impact range of high electricity price periods on electricity purchase costs, a set of high electricity price periods is determined based on the time-of-use electricity price parameters. Specifically, the time granularity of the active power time series at the grid connection point is used as the dividing granularity. The time-of-use electricity price parameters corresponding to each time granularity within the settlement period are compared with the high electricity price threshold. Time granularities where the time-of-use electricity price parameters are not lower than the high electricity price threshold are included in the high electricity price period set. The high electricity price threshold and the time-of-use electricity price parameters use the same unit and caliber. The high electricity price threshold is determined before the start of the settlement period and remains unchanged during the settlement period. Its source can be the electricity price policy level, the contractual level, or the preset value of the settlement rules. The source and applicable scope of the threshold are recorded in the settlement list.

[0078] After completing the scheduling optimization solution for the set of all participants, the power purchase time series of the grid connection points for all participants is obtained. In order to characterize the impact of a single participant on the maximum power purchase demand of the grid connection point and the power purchase energy during high electricity price periods, a subset of participants is constructed for each participant within the same settlement period after removing that participant, and the corresponding scheduling optimization is solved at the same time granularity as mentioned above to obtain the power purchase time series of the grid connection points for comparison.

[0079] The contribution of the grid-connected point to the reduction of the maximum electricity purchase demand is determined according to the demand time granularity corresponding to the demand billing parameters. Specifically, the time series of the grid-connected point's electricity purchase power is aggregated according to the demand time granularity to obtain the average electricity purchase power of each demand billing interval, and the maximum value is taken as the maximum electricity purchase demand of the grid-connected point. The difference between the maximum electricity purchase demand of the grid-connected point corresponding to the time series of the grid-connected point's electricity purchase power and the maximum electricity purchase demand of the grid-connected point corresponding to all participants is determined as the contribution of the participant to the reduction of the maximum electricity purchase demand of the grid-connected point. The contribution of reduced electricity purchases during high-electricity-price periods is determined based on the difference in electricity purchases within the set of high-electricity-price periods. Within the set of high-electricity-price periods, the electricity purchase power time series of the grid-connected points and the electricity purchase power time series of the grid-connected points of all participants are converted into electricity purchases at different time granularities. The conversion method for electricity purchases is to multiply the electricity purchase power at each time granularity by the time interval of that time granularity and then sum them up. The difference between the two is taken as the contribution of the participant to the reduction of electricity purchases during high-electricity-price periods. To ensure consistency, the time granularity and time interval used for the conversion of electricity purchases are consistent with the net income calculation and remain unchanged during the settlement period.

[0080] The participation weights are generated based on the contribution of the reduction in the maximum electricity demand at the grid connection point and the contribution of the reduction in electricity purchases during periods of high electricity prices; non-negative contribution values ​​are used to generate the participation weights to ensure the determination of the normalization results; Specifically, the value of the contribution of the reduction in the maximum electricity demand at the grid connection point being less than zero is set to zero, and the value of the contribution of the reduction in electricity energy purchased during high electricity price periods being less than zero is set to zero, thus obtaining two types of non-negative contribution values ​​respectively. The non-negative contribution value of the maximum electricity purchase demand at the grid connection point of each participant is normalized to obtain the first normalized value; the non-negative contribution value of the electricity purchase energy during the high electricity price period of each participant is normalized to obtain the second normalized value. The first normalized value and the second normalized value are weighted and synthesized with a preset weight coefficient to obtain the participation weight of each participant.

[0081] The preset weight coefficients are determined before the start of the settlement period and remain unchanged during the settlement period. The preset weight coefficients are non-negative and the sum of the two types of weights is one to ensure the interpretability of the participation weights. In the normalization calculation, if the sum of a certain type of non-negative contribution value across all participants is zero, then the normalization value of that type is taken as the uniform weight corresponding to the reciprocal of the number of participants. The number of participants is the number of participants in the participant set. The uniform weight remains unchanged within the same settlement period.

[0082] After obtaining the initial contribution amount and participation weight of each participant, in order to redistribute the initial contribution amount without changing the total net income of the entire participant set, a conservation correction is performed to obtain the final contribution amount. The conservation correction is formed by the difference between the participation weight and the uniform weight, where the uniform weight is the reciprocal of the number of participants. For each participant, the correction amount is obtained by multiplying the correction coefficient by the correction ratio coefficient and the net income corresponding to the set of all participants. The correction amount is then added to the participant's initial contribution amount to obtain the final contribution amount. The correction ratio coefficient is determined before the start of the settlement period and remains unchanged during the settlement period. Since the sum of the participation weights is one and the sum of the uniform weights is one, the sum of the correction coefficients is zero, and thus the sum of the correction amounts for each participant is zero. This makes the sum of the final contributions of each participant equal to the net income corresponding to the set of all participants. The parameter values ​​for the conservation correction, the method for determining the uniform weights, and the verification result that the sum of the correction amounts is zero are recorded in the settlement list for verification.

[0083] In addition, to avoid time alignment deviations, sampling omissions, or metering errors between the grid-connected point energy metering data and the grid-connected point active power time series, which could affect the calculation of the net revenue of the participating subset, a consistency check is performed on the grid-connected point energy metering data and the grid-connected point active power time series before calculating the net revenue of the participating subset. During the consistency check, the grid-connected point active power time series is converted into power integral energy according to the time granularity. The conversion method is to multiply the active power of each time granularity by the time interval of that time granularity and then sum them up. The power integral energy is compared with the grid-connected point energy metering data, and the absolute value of the difference is compared with the preset tolerance. The preset tolerance is determined before the start of the settlement period and remains unchanged during the settlement period. Its value is determined comprehensively based on the accuracy level of the metering device, the upper limit of sampling and alignment error, and the allowable settlement error. The source and applicable scope of the tolerance are recorded in the settlement list.

[0084] When the absolute value of the difference exceeds the preset tolerance, the consistency verification is deemed to have failed, and the consistency correction energy is used as the grid connection point energy caliber for subsequent net income calculation. The consistency correction energy is taken as the power integral energy. When the absolute value of the difference does not exceed the preset tolerance, the grid connection point energy metering data is used as the grid connection point energy caliber for subsequent net income calculation. The consistency verification and consistency correction results are recorded in the settlement list, including the power integral energy, grid connection point energy metering data, difference, tolerance, and the final grid connection point energy caliber used, to ensure that the metering basis is traceable and verifiable.

[0085] Through the above implementation method, this embodiment integrates the benchmark energy consumption curve, the set of high electricity price periods, the comparative solution of removing participants, the characterization of dual indicators contribution, the generation of participation weight, the conservation correction, and the metering consistency verification into an executable link within the same settlement cycle. This ensures that the net income calculation and income allocation under the grid connection point metering caliber have consistent time granularity and metering basis, and can form verifiable records in the settlement list, thereby enabling those skilled in the art to directly implement the method according to the description in the specification.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A Shapley value-driven integrated energy revenue sharing method, applied to a grid-connected integrated energy system, wherein the system is equipped with a grid-connected point-of-connection energy metering device and collects active power time-series data of the grid-connected point, and includes at least two participants, characterized in that, include: Establish a set of participants and establish metering mapping rules for each participant, converting the metering data of each participant into the mapped power time series under the metering caliber of the grid connection point; Obtain time-of-use electricity pricing parameters, grid connection settlement electricity pricing parameters, and demand billing parameters, and determine the set of high-price periods; For any subset of participants, in the scheduling optimization model, boundary constraints are applied to participants outside the subset, such that their controllable energy output is zero and their energy demand is equal to the baseline energy demand curve. The time series of the power purchased at the grid connection point and the time series of the power fed into the grid at the grid connection point are obtained, and the net revenue of the subset is calculated. The net revenue includes the revenue from electricity sales, the cost of purchasing electricity, and the demand metering cost. The net income of each participant subset constitutes a characteristic function, and the initial allocation of each participant is obtained according to the Shapley value rule. For each participant, a subset of participants after removing the participant is constructed, and the power purchase time series of the comparison grid connection point is obtained at the same time granularity. The maximum demand reduction contribution and the power purchase energy reduction contribution of the participant during the high electricity price period are calculated. The participation weight is generated from the two types of contributions, and the initial allocation is corrected according to the conservation correction rule to obtain the final allocation, so that the sum of the final allocation is equal to the net income of all participants. Output a settlement list, which includes the measurement basis for the grid connection points and the final share of each participant.

2. The Shapley value-driven integrated energy revenue allocation method according to claim 1, characterized in that: The metering mapping rules include: for electrical energy metering data, dividing the difference in electrical energy between adjacent sampling points by a time interval to obtain an electrical power sequence, and using the electrical power sequence as part of the mapped power time series; for heat energy metering data or cold energy metering data, dividing the difference in heat or cold energy between adjacent sampling points by a time interval, and then dividing by an energy conversion efficiency parameter to obtain an equivalent electrical power sequence, and incorporating the equivalent electrical power sequence into the mapped power time series.

3. The Shapley value-driven integrated energy revenue allocation method according to claim 2, characterized in that: The energy conversion efficiency parameter is taken as the rated efficiency parameter of the energy conversion equipment or the efficiency parameter determined by testing.

4. The Shapley value-driven integrated energy revenue allocation method according to claim 3, characterized in that: The benchmark energy consumption curve is obtained by aligning the historical metering data of the participating party within a preset number of days before the settlement period with the same time granularity as the active power time-series data of the grid connection point, or by determining the load curve agreed upon in the contract by the participating party.

5. The Shapley value-driven integrated energy revenue allocation method according to claim 4, characterized in that: The set of high electricity price periods is determined as follows: during the settlement period, the time granularity at which the electricity purchase time-of-use price parameter is not lower than the high electricity price threshold is included in the set of high electricity price periods, wherein the high electricity price threshold is determined before the start of the settlement period and remains unchanged during the settlement period.

6. The Shapley value-driven integrated energy revenue allocation method according to claim 5, characterized in that: The maximum demand reduction contribution of the grid-connected point is the difference between the maximum value of the power purchase time sequence of the grid-connected point after removing the participant and the maximum value of the power purchase time sequence of the grid-connected points corresponding to all participants; the power purchase energy reduction contribution during high electricity price periods is the difference between the power purchase energy corresponding to the power purchase time sequence of the grid-connected point and the power purchase energy corresponding to the power purchase time sequence of the grid-connected points corresponding to all participants within the set of high electricity price periods.

7. The Shapley value-driven integrated energy revenue allocation method according to claim 6, characterized in that: The participation weight is determined as follows: the contribution of each participant to the reduction of the maximum demand at the grid connection point is normalized to obtain a first normalized value, the contribution of each participant to the reduction of electricity purchased during the high electricity price period is normalized to obtain a second normalized value, and the first normalized value and the second normalized value are weighted by a preset weighting coefficient to obtain the participation weight. In the normalization calculation, if the sum of the corresponding contributions is zero, then the normalized value of this item is taken as the uniform weight corresponding to the number of participants.

8. The Shapley value-driven integrated energy revenue allocation method according to claim 7, characterized in that: The conservation correction rule is as follows: the correction coefficient is formed by the difference between the participation weight of each participant and the uniform weight. The correction coefficient is multiplied by the correction ratio coefficient and the net income corresponding to the set of all participants to obtain the correction amount. The correction amount is added to the corresponding initial contribution amount to obtain the final contribution amount. The correction ratio coefficient is determined before the start of the settlement period and remains unchanged during the settlement period. The sum of the correction amounts of each participant is zero.

9. A Shapley value-driven integrated energy revenue allocation method according to claim 8, characterized in that: Before calculating the net income of the participating subset, a consistency check is performed on the grid-connected point electrical energy metering data and the grid-connected point active power time-series data. If the consistency check fails, the difference between the electrical energy obtained by integrating the grid-connected point active power time-series data within the settlement period and the grid-connected point electrical energy metering data is compared with a preset tolerance. If the difference exceeds the preset tolerance, the grid-connected point electrical energy metering data is corrected using the power integrated electrical energy, and the corrected grid-connected point electrical energy metering data is used in the calculation of the net income.

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