Electric power spot market settlement method for inter-province and intra-province two-level market collaboration

Abstract

The invention discloses an inter-province and intra-province two-level market collaborative electric power spot market settlement method, and relates to the field of electric power spot market operation and settlement, and the method comprises the steps: firstly, calculating an inter-province transaction boundary based on a dynamic boundary coupling algorithm, carrying out the rolling updating, and issuing the inter-province transaction boundary to an intra-province as an external constraint; during in-province settlement, the received province takes inter-province electric energy as an external power supply, and the outgoing province performs settlement according to inter-province market price and local subsidy. And secondly, according to the power consumption type, the time period and the consumption proportion of the user, locating a benefit subject, and sharing the cross-regional network loss and the blocking cost by adopting an entropy weight method. And furthermore, inter-province and intra-province electricity prices are coordinated through a key node electricity price linkage calibration algorithm, an incoming upper limit and an outgoing lower limit are set, and an electricity price fault is eliminated. And aiming at the transaction deviation, establishing an inter-provincial collaborative correction mechanism and then an intra-provincial collaborative correction mechanism, and formulating a supplementary adjustment scheme through sorting and integer programming. And final settlement adopts a composite mode of'node marginal price + cross-regional cost apportionment price ', and multi-cycle data closed loop and fund settlement are realized.

Description

Technical Field

[0001] This application relates to the field of electricity spot market operation and settlement technology, and in particular to a method for electricity spot market settlement that coordinates inter-provincial and intra-provincial two-level markets. Background Technology

[0002] Electricity spot market settlement is a core component of electricity spot market operation and a crucial support for ensuring the standardized implementation of the electricity spot market. Current research on the electricity market mainly focuses on optimizing intra-provincial spot clearing algorithms or designing inter-provincial single-product electricity trading mechanisms, which fails to cover the multi-dimensional and systemic needs of actual operation, resulting in significant shortcomings in the adaptability and completeness of existing settlement schemes. This invention addresses the key issues of coordinated settlement between inter-provincial and intra-provincial spot markets, and develops a corresponding electricity spot market settlement method.

[0003] In the actual operation of the electricity spot market, although inter-provincial trading institutions can determine boundary parameters such as the amount of electricity transmitted / received and the marginal price at the node (LMP) through clearing, and intra-provincial trading institutions can also complete local basic settlement, there are still significant problems in the coordination and connection between inter-provincial and intra-provincial settlement links. First, inter-provincial settlement boundary parameters are mostly synchronized to the province using static, fixed values. When supply and demand fluctuations occur within the province, such as a surge in load or a sharp drop in renewable energy output, manual intervention is required to adjust the parameters to match the actual situation. This not only consumes a lot of manpower and time but is also prone to distortion of settlement results within the province due to human judgment bias. Second, cross-regional costs are allocated based on single electricity consumption without considering differences in benefit from electricity type, inter-provincial electricity consumption ratio, and electricity consumption time, which can easily lead to disputes over cost shifting and undermine market fairness. Third, the handling of supply and demand discrepancies between inter-provincial and intra-provincial markets is isolated, without a linkage correction mechanism. This may lead to power shortages in receiving provinces driving up electricity prices and losses for power generation companies in exporting provinces, posing a risk of market disorder. Fourth, the lack of effective calibration between inter-provincial and intra-provincial electricity prices can easily distort market signals if the price difference is too large. Over-reliance on low-priced electricity exports by receiving provinces may lead to the retirement of local regulating units, while electricity prices in exporting provinces falling below cost may weaken their willingness to export, affecting the optimization of cross-regional resources.

[0004] In summary, with the expansion of inter-regional power transmission from west to east and from north to south, as well as the increasing proportion of renewable energy, the complexity of the electricity spot market has increased. The traditional settlement model relying on manual intervention is no longer adequate to meet market demands. Against this backdrop, it is necessary to utilize intelligent collaborative mechanisms to facilitate settlement coordination, cost sharing, deviation correction, and electricity price calibration between inter-provincial and intra-provincial spot markets. This will standardize the settlement process, improve the accuracy of settlement results, and enhance risk control, thereby improving settlement efficiency, ensuring market fairness, and maintaining power system stability. Summary of the Invention

[0005] This invention addresses the technical problem by proposing a two-tiered electricity spot market settlement method that coordinates inter-provincial and intra-provincial markets. After the electricity spot market clearing is completed, a two-tiered collaborative process of "defining boundaries between provinces and settling details within provinces" is adopted. This method combines dynamic boundary coupling, precise cost allocation, collaborative deviation correction, and electricity price logic calibration technologies to achieve standardized, accurate, and risk-controllable electricity spot market settlement, thereby protecting the interests of market participants and ensuring the stable operation of the power system.

[0006] To solve the technical problem, the technical solution of the present invention is as follows:

[0007] A method for electricity spot market settlement that coordinates inter-provincial and intra-provincial markets, the method comprising:

[0008] Step S1: With the goal of minimizing the total operating cost of the power system, a rolling time-domain optimization algorithm is used to calculate the dynamic boundary value of inter-provincial transactions for a future preset period based on real-time power grid data;

[0009] Step S2: Using the dynamic boundary value as an external constraint, perform the clearing of the provincial electricity spot market to obtain the provincial generating unit power generation plan, the inter-provincial trading plan electricity volume, and the preliminary nodal marginal electricity price;

[0010] Step S3: Based on the provincial unit power generation plan and the inter-provincial transaction plan, and in conjunction with the power grid topology and power flow distribution, power flow analysis is used to determine the physical power transmission and attribution relationships between each generator and each load;

[0011] Step S4: Based on the physical power transmission and attribution relationship, calculate the cross-regional network loss cost and congestion cost caused by inter-provincial transactions; according to the power consumption attributes of the load, allocate the cross-regional network loss cost and congestion cost to the corresponding beneficiaries through a multi-attribute decision model;

[0012] Step S5: Based on the power grid's transmission capacity constraints and voltage security constraints, and considering the cost allocation results of the inter-regional transactions, calibrate the preliminary node marginal electricity price to generate a calibrated node marginal electricity price for final settlement.

[0013] Step S6: Compare the actual electricity consumption with the planned electricity consumption to identify the responsible parties for inter-provincial transaction deviations and intra-provincial pure deviations; with the goal of minimizing the total compensation cost, determine the optimal deviation handling scheme and the corresponding deviation correction cost through optimization algorithms;

[0014] Step S7: Integrate the calibrated node marginal electricity price, the allocated inter-regional cost, and the deviation correction cost to complete the fund settlement for all market participants; and feed back the calibration electricity price information and deviation analysis results of this settlement cycle to step S1 of the next cycle to update the calculation of the dynamic boundary value.

[0015] Furthermore, step S1 includes:

[0016] Collect real-time power grid data, including power generation plans, load forecasts, network topology, equipment parameters, and voltage, phase angle, frequency, and power flow data of inter-provincial interconnections for each province.

[0017] With the goal of minimizing the total operating cost of the power system, the dynamic boundary value is calculated based on the real-time data by jointly using dynamic boundary parameter updates and a rolling time-domain optimization algorithm.

[0018] Among them, the dynamic update of boundary parameters dynamically corrects the proxy model and feasible region describing the inter-provincial power transmission capacity through a closed-loop mechanism of sampling, contraction and expansion; the rolling time-domain optimization takes the current system state as the starting point, solves the optimal inter-provincial tie line power plan in the rolling time domain, and outputs it as the dynamic boundary value.

[0019] Furthermore, step S2 includes:

[0020] Using the aforementioned dynamic boundary values ​​as the core constraint, the provincial spot market clearing is executed;

[0021] For provinces that receive electricity, the electricity received between provinces is modeled as a fixed power source with an upper limit of output equal to the dynamic boundary value, and participates in the intra-provincial market equilibrium.

[0022] For provinces that are exporting electricity, after meeting the load demand within the province, the portion of the province's surplus power generation capacity that does not exceed the dynamic boundary value will be used for external transmission.

[0023] The price settlement mechanism for this portion of exported electricity is as follows: it will be settled first based on the inter-provincial market price. If the price is lower than the marginal generation cost within the province or the preset floor price, the provincial subsidy mechanism will be activated to make up the price difference and ensure the economic viability of the exported electricity.

[0024] The clearing results are: the power generation plans of each generator unit in the province, the planned power volume of inter-provincial transmission / reception, and the preliminary calculated marginal electricity price of the nodes in the province.

[0025] Furthermore, step S3 includes:

[0026] Based on the provincial unit power generation plan and inter-provincial transaction plan obtained in step S2, and combined with the power grid topology, power flow calculation is performed to generate power flow section results that include the power flow direction and magnitude of each branch.

[0027] Based on the power flow section, the power flow tracing method is adopted. Starting from each load node, the power flow direction and proportion of each branch are used to trace back to the power supply node that supplies it, so as to determine the physical power transmission and attribution relationship between each generator and each load.

[0028] The final output is a physical energy attribution matrix that characterizes the energy contribution relationship between the power source and the load.

[0029] Furthermore, step S4 includes:

[0030] Based on the physical power allocation relationship determined in step S3, the total cross-regional cost generated by inter-provincial transactions is calculated, including cross-regional network loss cost and cross-regional congestion cost.

[0031] Construct a multi-attribute decision model with the electricity consumption attributes of the load as input, wherein the electricity consumption attributes include at least the proportion of inter-provincial electricity consumed, electricity consumption type, and electricity consumption period;

[0032] In the multi-attribute decision-making model, the entropy weight method is used to determine the objective weights of each electricity consumption attribute;

[0033] Based on the weights, the total cross-regional costs are allocated to the corresponding beneficiaries, resulting in a cost allocation outcome for each entity.

[0034] Furthermore, step S5 includes:

[0035] Based on the power grid topology, a node-branch correlation matrix covering the sending-end province, receiving-end province and inter-provincial interconnection lines is constructed to form a unified power grid model.

[0036] For the receiving province, identify the main power landing points and key transmission sections between provinces, and determine the upper limit of the received power volume based on the power flow calculation of the unified power grid model;

[0037] For provinces that export electricity, identify the main power generation nodes and the sending nodes, and set a minimum export price based on the province's power generation cost and the marginal electricity price of the nodes;

[0038] The upper limit of the received electricity volume and the lower limit of the external electricity price are added as new constraints and fed back to the provincial market clearing model in step S2 for re-clearing, or as boundary conditions for price adjustment after clearing, thereby generating the calibrated nodal marginal electricity price.

[0039] Furthermore, step S6 includes:

[0040] Obtain the actual electricity consumption data, calculate the deviation between it and the corresponding planned electricity consumption in step S2, and classify the deviation into inter-provincial transaction deviation and intra-provincial pure deviation.

[0041] Set processing priority rules: inter-provincial deviations take precedence over intra-provincial pure deviations, and within the same category, deviations with larger magnitudes take precedence over those with smaller magnitudes;

[0042] With the goal of minimizing the total system replenishment cost, a mixed-integer linear programming model is established with inter-provincial and intra-provincial replenishment electricity as integer decision variables to solve the problem and determine the optimal replenishment scheme.

[0043] Based on the optimal adjustment scheme and the attribution of deviations, the responsible parties for each deviation and the deviation correction costs they should bear are identified.

[0044] Furthermore, step S7 includes:

[0045] Settlement Calculation: Based on the calibrated node marginal electricity price generated in step S5, the cost allocation results of each entity generated in step S4, and the deviation correction costs of each entity generated in step S6, the settlement revenue of the power generation enterprise and the settlement expenditure of the power consumption enterprise are calculated respectively.

[0046] Multi-cycle settlement execution: Set up three-level settlement cycles: real-time, daily, and monthly; wherein, real-time settlement achieves complete synchronization with the boundary update cycle of step S1 and the clearing cycle of step S2 through the data interface between the scheduling automation system and the transaction clearing system; daily settlement summarizes the real-time data of the day to generate a daily settlement statement; monthly settlement summarizes the data within the cycle, processes deviations to correct the final payment and generates the final settlement voucher to complete the fund transfer;

[0047] Closed-loop feedback: The calibrated node marginal electricity price information and deviation analysis results generated in this settlement cycle are sent as feedback information to step S1 of the next settlement cycle to optimize the dynamic boundary value calculation of the next cycle.

[0048] This application has the following advantages:

[0049] This invention provides a method for electricity spot market settlement with inter-provincial and intra-provincial market coordination. The method includes: determining the pre-boundary of inter-provincial settlement based on a dynamic boundary coupling algorithm. Using a dynamic update algorithm for inter-provincial boundary parameters based on real-time power flow data and a rolling time-domain optimization algorithm, the inter-provincial transaction boundary is calculated every 15-30 minutes, and this result is synchronized to the intra-provincial trading institutions of both provinces as an external constraint condition for intra-provincial settlement. Intra-provincial local settlement based on the external constraint condition. For receiving provinces, the electricity received from inter-provincial sources is considered an external power source and included in the total provincial power supply; local power generation enterprises settle at the intra-provincial spot clearing price. For exporting provinces, the exported electricity is considered as locally generated electricity transferred out, and local power generation enterprises settle at the inter-provincial market price and intra-provincial subsidies. Precise identification of beneficiaries based on electricity consumption. Based on the electricity consumption type, user time period, and inter-provincial electricity consumption ratio within the province, the beneficiaries causing network losses and increased congestion costs are identified. Cost allocation weights based on multi-attribute decision-making. Input attributes such as user electricity consumption type, inter-provincial electricity consumption ratio, and user electricity consumption time period. The entropy weight method is used to determine the weight of each attribute, accurately allocating inter-regional network losses and congestion costs for beneficiaries. Multi-node electricity price collaborative calibration and settlement are based on inter-provincial and intra-provincial electricity price differences. Using an intra-provincial key node electricity price linkage calibration algorithm, upper limits for inter-provincial incoming electricity volume and lower limits for outgoing electricity price are set for receiving and sending provinces respectively, eliminating price gaps caused by grid topology differences. For inter-provincial outgoing / receiving deviations and intra-provincial generation and consumption deviations, a collaborative mechanism of "prioritizing inter-provincial correction, then linking intra-provincial adjustments" is constructed. The adjustment scheme is solved through deviation ranking and integer programming to avoid risk transmission. Based on inter-provincial dynamic boundary parameters, inter-regional cost allocation results, and deviation correction schemes, a composite model of "node marginal price as the core and inter-regional cost allocation price as a supplement" is adopted for entity-specific settlement, achieving data closure and fund clearing through multiple cycles. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating the electricity spot market settlement method for inter-provincial and intra-provincial two-tier market coordination, as described in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] Example 1:

[0054] This embodiment provides a two-tiered electricity spot market settlement method that coordinates inter-provincial and intra-provincial markets. After the electricity spot market clearing is completed, a two-tiered collaborative process of "defining boundaries between provinces and settling details within provinces" is adopted. Combined with dynamic boundary coupling, precise cost allocation, deviation collaborative correction and electricity price logic calibration technology, the settlement of the electricity spot market is standardized, accurate and risk-controllable, thus ensuring the interests of market participants and the stable operation of the power system.

[0055] Figure 1 This is a flowchart of a method for electricity spot market settlement that coordinates inter-provincial and intra-provincial markets, according to an embodiment of the present invention. The method includes:

[0056] Step S1: Determine the pre-settlement boundary between provinces based on the dynamic boundary coupling algorithm. Using a dynamic update algorithm for inter-provincial boundary parameters based on real-time power flow data and a rolling time-domain optimization algorithm, the inter-provincial transaction boundary is calculated every 15-30 minutes. This result is then synchronized to the intra-provincial transaction institutions of both provinces as an external constraint for intra-provincial settlement.

[0057] Step S2: Intra-provincial local settlement based on external constraints. For receiving provinces, the electricity received from other provinces is considered as an external power source and included in the total electricity supply within the province. Local power generation companies settle accounts based on the intra-provincial spot clearing price. For exporting provinces, the electricity exported is considered as local power generation transferred out. Local power generation companies settle accounts based on inter-provincial market prices and intra-provincial subsidies.

[0058] Step S3: Precisely identify beneficiaries based on electricity consumption. Based on the electricity consumption type of users within the province, the time period of user electricity consumption, and the inter-provincial electricity consumption ratio, determine the beneficiaries that cause network losses and increase congestion costs.

[0059] Step S4: Cost allocation weighting based on multi-attribute decision-making. Input attributes such as user electricity consumption type, inter-provincial electricity consumption ratio, and user electricity consumption time period. Determine the weight of each attribute using the entropy weight method to accurately allocate the cross-regional network loss and congestion costs of the beneficiary.

[0060] Step S5: Multi-node electricity price collaborative calibration and settlement based on inter-provincial and intra-provincial electricity price differences. Using the intra-provincial key node electricity price linkage calibration algorithm, upper limits for inter-provincial incoming electricity volume and lower limits for outgoing electricity price are set for receiving provinces and sending provinces respectively, eliminating the price gap caused by differences in grid topology.

[0061] Step S6: Distinguish between inter-provincial deviations and intra-provincial pure deviations, sort them according to "inter-provincial high amplitude → inter-provincial low amplitude → intra-provincial high amplitude → intra-provincial low amplitude", use integer programming to find the optimal adjustment amount, and clarify the responsible party to bear the cost.

[0062] Step S7: Calculate the revenue and expenditure of power generation and power consumption enterprises based on the node marginal price and allocated price. Real-time synchronization of boundary period data generation, daily summary bill verification, and month-end processing of final payments, generation of settlement vouchers, and fund clearing.

[0063] For example, step S1 includes:

[0064] Step S11: Obtain power generation plans, load forecasts, network topology and equipment parameters from the energy management systems of each province, and collect real-time data such as voltage, phase angle and frequency of inter-provincial interconnection lines and key substations.

[0065] Step S12: Employing a dynamic boundary parameter update algorithm and a rolling time-domain optimization algorithm, with the goal of minimizing total operating cost, while simultaneously optimizing the output of generating units within the province and the power of inter-provincial tie lines, the optimal inter-provincial transaction boundary value is calculated and synchronously sent to the provincial power trading centers of both provinces. The specific implementation process is as follows:

[0066] 1. Boundary parameter dynamic update algorithm: Through a closed loop of "sampling-shrinking-expanding", the search space evolves synchronously with the surrogate model / optimization state, which can significantly reduce the number of expensive function evaluations and improve global optimization capabilities.

[0067] (1) Generate initial samples, train the first round of proxy model, and calculate the initial boundary and initial feasible region.

[0068] (2) Using the Expected Improvement method in the feasible region B of the kth generation k Find the most promising point inside x new Call the actual function to evaluate x new Add it to the sample library.

[0069] (3) Filter feasible points by removing samples that violate the constraint condition g(x)≤0; and filter by I j k+1 =min{x j |x j ∈Feasible},u j k+1 =max{x j | x j ∈Feasible} calculates the shrinkage of the hyperrectangular volume for each dimension j, where Feasible is the feasible point, i.e., the point that satisfies all constraints. jk+1 The j-th lower boundary, u, is calculated from the feasible point. j k+1 It is the j-th dimension upper boundary calculated from feasible points; if I j k+1 If the value is too close to the current lower bound, extrapolate δ. j =ε k (u j k –I j k To prevent omissions, extrapolation and expansion are implemented, where ε k It is the decay coefficient of the k-th iteration; update B k+1 =∏ j (I j k+1 -δ j , u j k+1 +δ j ), among which, B k+1 It is the new search space in the (k+1)th iteration, ∏ j It is the Cartesian product (direct product) over all dimensions j, I j k+1 The j-th lower boundary, u, is calculated from the feasible point. j k+1 The j-th dimension upper boundary is calculated from the feasible point, δ j It is the extrapolation expansion of the j-th dimension, x j This represents a specific variable value in the j-th dimension.

[0070] (4) Retrain the surrogate model with new samples; ε k Decays exponentially or linearly.

[0071] (5) If ε k If the number of evaluations reaches the maximum number of iterations (τ), where τ is the convergence threshold, then stop; otherwise, proceed to step (2).

[0072] 2. Rolling Time Domain Optimization Algorithm: Through finite time domain prediction and online rolling refresh, it transforms complex global problems into local quadratic programming problems that can be solved quickly, combining real-time performance and robustness.

[0073] (1) Obtain the current true state s(t) c ).

[0074] (2) Based on the model and future disturbance estimation, generate the state trajectory in the interval [c, c+n], where n is the predicted future interval length.

[0075] (3) Solve the above quadratic programming problem; if the system contains integer variables, then a mixed integer programming problem is formed.

[0076] (4) Only the optimal control input U*c is applied to the controlled object.

[0077] (5) Change c to c+1, return to step (1), and implement scrolling.

[0078] For example, step S2 includes:

[0079] Step S21: Model the inter-provincial received electricity as a fixed-output power source, whose maximum output limit is the received inter-provincial transaction boundary value, and whose settlement price is the intra-provincial clearing price. Perform intra-provincial local settlement based on the intra-provincial received electricity volume and the winning bid electricity volume of intra-provincial power generation enterprises.

[0080] Step S22: After meeting the needs within the province, the remaining power generation capacity is used for external transmission. The group of power generation companies participating in external transmission and the external transmission quota allocated to each company are determined. The revenue from this portion of electricity is jointly determined by the inter-provincial market price and the intra-provincial subsidy mechanism.

[0081] For example, step S3 includes:

[0082] Step S31: Based on the different load curves and power reliability requirements of the users, the users are divided into different types such as industrial, commercial and residential, and the connection nodes of each high-voltage user in the power grid model and the power supply transformer nodes to which the low-voltage users are aggregated are accurately located.

[0083] Step S32: Employing the power flow tracing method, starting from the load node and tracing upstream against the power flow direction until reaching the generator node, the contribution of each generator to the target load is determined, achieving precise identification of beneficiaries at different electricity consumption periods. The specific implementation process is as follows:

[0084] 1. Power Flow Source Tracing Method: Based on proportional allocation and graph search, it can quickly and verifiably provide detailed power distribution from generators to loads under any complex topology and voltage level.

[0085] (1) Construct the node power splitting coefficient matrix. For any node r, define the proportion of its power flowing out to branch h as follows: ,in For all nodes to all corresponding outflow branches, P rh Let be the active power flowing out of node r through branch h.

[0086] (2) Form the unit injection matrix. If unit g injects power P at node r g Then the matrix element G g,r =P g The rest are 0.

[0087] (3) For each directed path R from unit g to load L g, LCalculate the cumulative diversion coefficient. , The definition is given about R. g, L The function, This indicates that the h-th branch (r, h) of the r-th node belongs to the directed path R. g, L , If it is a product symbol, then the load L receives power P from unit g. g, L =P g H(R g, L ).

[0088] (4) For each unit g, the required H(R) values ​​for all loads L are given. g, L The summation is 1, ensuring that no allocation is missed.

[0089] For example, step S4 includes:

[0090] Step S41: Collect the electricity usage time, electricity type, and cross-provincial electricity consumption ratio of all beneficiaries, encode and preprocess these attribute data, and thus construct the input matrix of the hierarchical cost allocation algorithm.

[0091] Step S42: Input the attribute matrix and determine the weight of each attribute using the entropy weight method. Calculate the total network loss and congestion cost based on inter-provincial transaction electricity volume, network loss coefficient, settlement price, clearing price, and reference price. Calculate the allocated cost for different beneficiaries using the attribute weights and total cost. The specific implementation process is as follows:

[0092] (1) Calculate the numerical weight W of the qth attribute for the p-th user. pq

[0093] (1)

[0094] Among them, Z pq Let q be the original value of the p-th user on the q-th attribute, and M be the total number of users.

[0095] (2) Calculate the normalization constant K and the entropy e of the q-th attribute. q :

[0096] (2)

[0097] (3)

[0098] (3) Calculate the variation index d of the q-th attribute. q :

[0099] (4)

[0100] (4) Calculate the weight β of the q-th attribute. q :

[0101] (5)

[0102] For example, step S5 includes:

[0103] Step S51: Establish a unified power grid model covering the sending-end province, the receiving-end province, and the interconnections between them, and obtain the preliminary cleared inter-provincial transaction prices and intra-provincial spot market node prices.

[0104] Step S52: For the receiving province, identify the nodes of the main power landing points and the nodes of the main load centers between provinces. Based on the power grid security analysis, determine the maximum power receiving capacity of the receiving power grid at the key section and set the upper limit of the power received between provinces.

[0105] Step S53: For provinces that export electricity, identify the nodes of major power sources and the outgoing nodes of the transmission channels. Based on the power generation costs within the exporting province and the marginal electricity prices of key nodes, set a minimum export price while ensuring the economic rationality of the exported electricity volume. The specific implementation process is as follows:

[0106] 1. Power Grid Topology Modeling Based on Graph Theory Matrix Method

[0107] (1) Abstract the busbar as a vertex v∈V, where V is the set of all vertices; the branch / transformer is an edge e∈E, where E is the set of all edges; generate the node-branch association matrix A. n×l ∈{0,±1};

[0108] (2) Automatically extract node and component information from BPA / PSD data files, use breadth-first search to span the tree, record the topological relationship, and form matrix A. The time taken is <1s.

[0109] (3) Further, we can obtain the weighted Laplacian L=diag(w)-A, which can be used for subsequent PTDF and OTDF fast calculation.

[0110] 2. Weighted Least Squares Method

[0111] (1) Establish the model and perform ordinary least squares to obtain the initial residual e.

[0112] (2) Estimate σ based on residuals i 2 Construct w i= 1 / σ i 2 , where σ i 2 For the error variance estimate of the i-th observation, w i Let be the weight of the i-th observation.

[0113] (3) Construct a diagonal matrix W=diag(w).

[0114] (4) Calculate β WLS =(F T WF) -1 F T WF, where F T WF is a weighted information matrix, β WLS These are the coefficients of the weighted least squares estimate.

[0115] (5) If iterative reweighting is performed, return to step (2) to update the weights until ||β|| k+1 -β k ||<ε1, where β k ε1 is the coefficient estimate for the k-th iteration, and ε1 is the convergence threshold.

[0116] For example, step S6 includes:

[0117] Step S61: Based on the Rolling Time-Domain Optimization (RTO) algorithm, the dynamic boundary parameters synchronized by the inter-provincial trading institutions are refreshed every 15-30 minutes. The difference between the actual electricity transmitted / received and the planned electricity transmitted / received in each province (region, municipality) is obtained, and it is noted whether the deviation exceeds the threshold. Simultaneously, the intra-provincial trading institutions calculate the difference between the actual power generation of local power generation enterprises, the actual electricity consumption of users, and their respective planned amounts, deducting the inter-provincial transmission / received portion to avoid double counting. Deviations are divided into two categories: inter-provincial deviations and intra-provincial pure deviations, each associated with a responsible party. The responsible party for inter-provincial deviations is the power generation enterprise in the transmitting / receiving province, while the responsible party for intra-provincial pure deviations is the local power generation enterprise or user. The specific calculation formula is as follows:

[0118] Inter-provincial deviation of outbound shipments:

[0119] (1)

[0120] In the formula, For the first Inter-provincial deviation of each exporting province This represents the actual amount of electricity transmitted from the province. This refers to the planned electricity volume to be transmitted from this province.

[0121] Inter-provincial deviations among accepted provinces:

[0122] (2)

[0123] In the formula, For the first Inter-provincial deviation of each receiving province This represents the actual amount of electricity received by the receiving province. This refers to the planned electricity volume received by the receiving province.

[0124] The provincial trading institution calculates the difference between the actual power generation of local power generation enterprises, the actual electricity consumption of users, and their respective planned amounts, deducting the portion transmitted / received from other provinces to avoid double counting. The net deviation within the province after deduction is calculated using the following formula:

[0125] Pure deviation in power generation within the province:

[0126] (3)

[0127] In the formula, For the first The pure deviation of power plants within a province or This refers to the actual or planned power generation of the power plant. The power plant's electricity generation accounts for a significant portion of the province's power transmission output. The proportion of total electricity transmitted to other regions.

[0128] Pure deviation of electricity consumption within the province:

[0129] (4)

[0130] In the formula, For the first Pure deviation of users within a province or This refers to the user's actual or planned electricity consumption. The user's electricity consumption accounts for a certain percentage of the total electricity consumption in the receiving province. The proportion of total received electricity.

[0131] Deviations are divided into two categories: inter-provincial deviations and intra-provincial pure deviations. The responsible parties for deviations are respectively associated with the power generation companies in the sending / receiving provinces, while the responsible parties for intra-provincial pure deviations are the local power generation companies or users.

[0132] Step S62: Based on the principle of "priority of impact scope > priority of deviation magnitude," a sorting rule is established. Inter-provincial deviations, due to their impact on cross-regional power resource allocation and the power supply security of the receiving province, have a higher priority than intra-provincial pure deviations. Among deviations of the same category, those with deviation magnitudes exceeding the aforementioned set threshold are considered high-amplitude deviations, and their priority is higher than low-amplitude deviations with deviation magnitudes within the threshold. Accordingly, a deviation processing list is formed in the order of inter-provincial high-amplitude deviations, inter-provincial low-amplitude deviations, intra-provincial high-amplitude pure deviations, and intra-provincial low-amplitude pure deviations, ensuring that high-risk deviations are corrected first.

[0133] Step S63: With the objective of minimizing the total cost of deviation correction, define inter-provincial supplementary adjustment volume as X and intra-provincial supplementary adjustment volume as Y, both in MWh and as integers to meet the minimum volume unit requirement for electricity trading. Set the objective function as: Total deviation correction cost = Inter-provincial supplementary adjustment unit cost × X + Intra-provincial supplementary adjustment unit cost × Y. Constraints: Inter-provincial supplementary adjustment volume cannot exceed the remaining available capacity of the supplementary adjustment channel; intra-provincial supplementary adjustment volume cannot exceed the maximum output of local standby generating units; inter-provincial supplementary adjustment volume is not less than the absolute value of the inter-provincial deviation; intra-provincial supplementary adjustment volume is not less than the absolute value of the intra-provincial pure deviation. After obtaining the optimal supplementary adjustment volumes X and Y by solving the model using an integer programming algorithm, the inter-provincial trading institution coordinates cross-regional supplementary adjustments, and the intra-provincial trading institution schedules local generating units for supplementary adjustments. The correction cost is borne by the corresponding deviation responsible party. The specific implementation method is as follows:

[0134] 1. Define the objective function as:

[0135] (5)

[0136] In the formula, The total cost of deviation correction (in yuan). To supplement unit costs between provinces, To supplement unit costs within the province.

[0137] 2. Constraints include:

[0138] (1) Inter-provincial power replenishment The remaining available capacity of the adjustment channel shall not exceed the limit.

[0139] (2) Provincial supplementary power supply The output shall not exceed the maximum output of the local standby unit.

[0140] (3) Inter-provincial power replenishment Not lower than the absolute value of the inter-provincial deviation.

[0141] (4) Provincial supplementary power supply Not lower than the absolute value of the pure deviation within the province.

[0142] The optimal replenishment power was obtained by solving the model using an integer programming algorithm. , Subsequently, inter-provincial trading institutions coordinate cross-regional adjustments, while intra-provincial trading institutions dispatch local units for adjustments, with the correction costs borne by the corresponding responsible party for the deviation.

[0143] For example, step S7 includes:

[0144] Step S71: Calculate settlement revenue based on the province of the power generation enterprise and the type of transaction participation, combined with the node marginal price, inter-regional cost allocation results, and deviation correction costs. Revenue of power generation enterprises in exporting provinces = (Inter-provincial node marginal price + Intra-provincial subsidy) × Exported electricity volume + Intra-provincial node marginal price × Local power generation - Allocated inter-regional cost. The formula is:

[0145] (6)

[0146] In the formula, Revenue settlement for power generation companies in provinces that export electricity. The marginal price between provincial nodes. Subsidies are provided within the province (only activated when the marginal price at the inter-provincial node is lower than the local marginal cost). For external power transmission, The marginal price of nodes within the province. For local power generation, The cross-regional costs allocated to the enterprise.

[0147] Revenue of power generation enterprises receiving investment from other provinces = Provincial intra-provincial nodal marginal price × Local power generation - Allocated inter-regional costs - Deviation correction costs, the formula is:

[0148] (7)

[0149] In the formula, For the settlement revenue of power generation enterprises in the receiving province, The unit deviation correction cost, deviation correction cost The deduction is only made when the enterprise is the sole responsible party for pure deviations within the province.

[0150] Step S72: Calculate the settlement expenditure based on the province where the electricity-consuming enterprise is located and the inter-provincial electricity consumption situation, combined with the nodal marginal price, inter-regional cost allocation results, and deviation correction costs. The expenditure of the electricity-consuming enterprise in the exporting province = intra-provincial nodal marginal price × actual electricity consumption + allocated inter-regional cost + deviation correction cost, as shown in the formula:

[0151] (8)

[0152] In the formula, Settlement of payments for electricity consumption enterprises in provinces that export electricity. The actual electricity consumption of the user This is the cost allocation coefficient (taken only when the enterprise is an indirect beneficiary of inter-provincial electricity, and determined by multi-attribute decision-making).

[0153] The expenditure of electricity-consuming enterprises in the receiving province = (marginal price at the provincial node + allocated cross-regional cost / total electricity consumption in the province) × actual electricity consumption + deviation correction cost, the formula is:

[0154] (8)

[0155] In the formula, For the settlement of expenses of electricity-consuming enterprises in the receiving province, To account for the total cross-regional costs of entering the province, Total electricity consumption of the receiving province. Deviation correction cost. Add this only when the enterprise is the responsible party for pure deviations within the province.

[0156] Step S73: Set up three-level settlement cycles: "real-time, daily, and monthly". Real-time settlement is synchronized with the inter-provincial boundary parameter update cycle and the intra-provincial spot clearing cycle. The inter-provincial boundary parameter update cycle is 15-30 minutes, generating instantaneous power generation and consumption, node marginal price, and real-time cross-regional cost data.

[0157] Daily settlement involves summarizing real-time data to generate a "Daily Settlement Bill," clearly specifying the day's electricity consumption, costs, and deviations for market participants to verify. Monthly settlement involves processing deviation corrections at the end of each month, adjusting the final amount based on the node electricity price calibration results, generating a "Monthly Final Settlement Voucher," and simultaneously completing fund transfers with the clearing institution. Node electricity price calibration is calculated based on graph theory combined with weighted least squares.

[0158] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0159] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for electricity spot market settlement that coordinates inter-provincial and intra-provincial two-tier markets, characterized in that, The method includes: Step S1: With the goal of minimizing the total operating cost of the power system, a rolling time-domain optimization algorithm is used to calculate the dynamic boundary value of inter-provincial transactions for a future preset period based on real-time power grid data; Step S2: Using the dynamic boundary value as an external constraint, perform the clearing of the provincial electricity spot market to obtain the provincial generating unit power generation plan, the inter-provincial trading plan electricity volume, and the preliminary nodal marginal electricity price; Step S3: Based on the provincial unit power generation plan and the inter-provincial transaction plan, and in conjunction with the power grid topology and power flow distribution, power flow analysis is used to determine the physical power transmission and attribution relationships between each generator and each load; Step S4: Based on the physical power transmission and attribution relationship, calculate the cross-regional network loss cost and congestion cost caused by inter-provincial transactions; according to the power consumption attributes of the load, allocate the cross-regional network loss cost and congestion cost to the corresponding beneficiaries through a multi-attribute decision model; Step S5: Based on the power grid's transmission capacity constraints and voltage security constraints, and considering the cost allocation results of the inter-regional transactions, calibrate the preliminary node marginal electricity price to generate a calibrated node marginal electricity price for final settlement. Step S6: Compare the actual electricity consumption with the planned electricity consumption to identify the responsible parties for inter-provincial transaction deviations and intra-provincial pure deviations; with the goal of minimizing the total compensation cost, determine the optimal deviation handling scheme and the corresponding deviation correction cost through optimization algorithms; Step S7: Integrate the calibrated node marginal electricity price, the allocated inter-regional cost, and the deviation correction cost to complete the fund settlement for all market participants; and feed back the calibration electricity price information and deviation analysis results of this settlement cycle to step S1 of the next cycle to update the calculation of the dynamic boundary value.

2. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S1 includes: Collect real-time power grid data, including power generation plans, load forecasts, network topology, equipment parameters, and voltage, phase angle, frequency, and power flow data of inter-provincial interconnections for each province. With the goal of minimizing the total operating cost of the power system, the dynamic boundary value is calculated based on the real-time data by jointly using dynamic boundary parameter updates and a rolling time-domain optimization algorithm. Among them, the dynamic update of boundary parameters dynamically corrects the proxy model and feasible region describing the inter-provincial power transmission capacity through a closed-loop mechanism of sampling, contraction and expansion; the rolling time-domain optimization takes the current system state as the starting point, solves the optimal inter-provincial tie line power plan in the rolling time domain, and outputs it as the dynamic boundary value.

3. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S2 includes: Using the aforementioned dynamic boundary values ​​as the core constraint, the provincial spot market clearing is executed; For provinces that receive electricity, the electricity received between provinces is modeled as a fixed power source with an upper limit of output equal to the dynamic boundary value, and participates in the intra-provincial market equilibrium. For provinces that are exporting electricity, after meeting the load demand within the province, the portion of the province's surplus power generation capacity that does not exceed the dynamic boundary value will be used for external transmission. The price settlement mechanism for this portion of exported electricity is as follows: it will be settled first based on the inter-provincial market price. If the price is lower than the marginal generation cost within the province or the preset floor price, the provincial subsidy mechanism will be activated to make up the price difference and ensure the economic viability of the exported electricity. The clearing results are: the power generation plans of each generator unit in the province, the planned power volume of inter-provincial transmission / reception, and the preliminary calculated marginal electricity price of the nodes in the province.

4. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S3 includes: Based on the provincial unit power generation plan and inter-provincial transaction plan obtained in step S2, and combined with the power grid topology, power flow calculation is performed to generate power flow section results that include the power flow direction and magnitude of each branch. Based on the power flow section, the power flow tracing method is adopted. Starting from each load node, the power flow direction and proportion of each branch are used to trace back to the power supply node that supplies it, so as to determine the physical power transmission and attribution relationship between each generator and each load. The final output is a physical energy attribution matrix that characterizes the energy contribution relationship between the power source and the load.

5. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S4 includes: Based on the physical power allocation relationship determined in step S3, the total cross-regional cost generated by inter-provincial transactions is calculated, including cross-regional network loss cost and cross-regional congestion cost. Construct a multi-attribute decision model with the electricity consumption attributes of the load as input, wherein the electricity consumption attributes include at least the proportion of inter-provincial electricity consumed, electricity consumption type, and electricity consumption period; In the multi-attribute decision-making model, the entropy weight method is used to determine the objective weights of each electricity consumption attribute; Based on the weights, the total cross-regional costs are allocated to the corresponding beneficiaries, resulting in a cost allocation outcome for each entity.

6. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S5 includes: Based on the power grid topology, a node-branch correlation matrix covering the sending-end province, receiving-end province and inter-provincial interconnection lines is constructed to form a unified power grid model. For the receiving province, identify the main power landing points and key transmission sections between provinces, and determine the upper limit of the received power volume based on the power flow calculation of the unified power grid model; For provinces that export electricity, identify the main power generation nodes and the sending nodes, and set a minimum export price based on the province's power generation cost and the marginal electricity price of the nodes; The upper limit of the received electricity volume and the lower limit of the external electricity price are added as new constraints and fed back to the provincial market clearing model in step S2 for re-clearing, or as boundary conditions for price adjustment after clearing, thereby generating the calibrated nodal marginal electricity price.

7. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S6 includes: Obtain the actual electricity consumption data, calculate the deviation between it and the corresponding planned electricity consumption in step S2, and classify the deviation into inter-provincial transaction deviation and intra-provincial pure deviation. Set processing priority rules: inter-provincial deviations take precedence over intra-provincial pure deviations, and within the same category, deviations with larger magnitudes take precedence over those with smaller magnitudes; With the goal of minimizing the total system replenishment cost, a mixed-integer linear programming model is established with inter-provincial and intra-provincial replenishment electricity as integer decision variables to solve the problem and determine the optimal replenishment scheme. Based on the optimal adjustment scheme and the attribution of deviations, the responsible parties for each deviation and the deviation correction costs they should bear are identified.

8. The electricity spot market settlement method with inter-provincial and intra-provincial two-tier market coordination as described in claim 1, characterized in that, Step S7 includes: Settlement Calculation: Based on the calibrated node marginal electricity price generated in step S5, the cost allocation results of each entity generated in step S4, and the deviation correction costs of each entity generated in step S6, the settlement revenue of the power generation enterprise and the settlement expenditure of the power consumption enterprise are calculated respectively. Multi-cycle settlement execution: Set up three-level settlement cycles: real-time, daily, and monthly; wherein, real-time settlement achieves complete synchronization with the boundary update cycle of step S1 and the clearing cycle of step S2 through the data interface between the scheduling automation system and the transaction clearing system; daily settlement summarizes the real-time data of the day to generate a daily settlement statement; monthly settlement summarizes the data within the cycle, processes deviations to correct the final payment and generates the final settlement voucher to complete the fund transfer; Closed-loop feedback: The calibrated node marginal electricity price information and deviation analysis results generated in this settlement cycle are sent as feedback information to step S1 of the next settlement cycle to optimize the dynamic boundary value calculation of the next cycle.

Images