Cross-price-area medium and long term transaction ATC explicit distribution method and device and medium

Abstract

The invention provides an ATC explicit distribution method and device for cross-price-area medium and long term transactions and a medium, and relates to the technical field of electricity markets. The method comprises the following steps: firstly, acquiring line operation information of a regional power grid, and dividing the regional power grid into a plurality of price zones based on the line operation information; secondly, calculating cross-price region parameter information among different price regions in a preset time period of medium and long term transaction based on a direct current power flow model, and determining a target ATC transaction feasible region based on a calculation result; and finally, according to the target ATC transaction feasible region and the power generation cost sorting typical scene set, establishing a power transaction model, and based on the power transaction model, determining a cross-price-region ATC target transaction value corresponding to each price region so as to realize cross-price-region medium-and-long-term transaction ATC explicit distribution. According to the method and the device, the optimal ATC value between the transactions of each price interval can be obtained before the formal clearing of the medium-and-long-term transaction of the electric power, so that the market economic benefit maximization is realized while the operation safety of the power grid is met.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power market, and in particular to an ATC explicit allocation method, device and medium for medium- and long-term transactions across price zones. Background Art

[0002] In the analysis and research of power systems, Available Transfer Capability (ATC) is an important parameter used to measure the power system's ability to transfer power from one node to another while meeting reliability constraints. The calculation of ATC aims to determine the maximum amount of power transmission that the power grid can support while meeting reliability requirements. This not only involves the safe operation of the power system, but also how to solve it efficiently and accurately. With the development of the power market, the application of ATC has gradually expanded to market transactions, especially power transactions between different nodes or regions. This allocation mechanism helps to optimize resource allocation and avoid market risks.

[0003] In related technologies, there are two main methods for ATC allocation: implicit auctions and explicit auctions. Implicit auctions, such as PJM's SCUC, SCED and Europe's Flow-Based method, are widely used in the electricity spot market, but their drawback is that there is a complex coupling relationship between the calculation and allocation of ATC, which makes the calculation process cumbersome and difficult to meet the needs of the real-time market. Explicit auctions simplify the transaction process and improve market transparency and liquidity by fixing the ATC values ​​of the boundaries. However, how to ensure the security of the power grid while taking into account the needs of market players and making effective medium- and long-term explicit allocations of different boundaries remains a challenge that needs to be solved. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a method, device and medium for explicit allocation of ATC for medium- and long-term transactions across price zones, which can obtain the optimal ATC value between transactions in each price zone before the medium- and long-term electricity transactions are officially cleared, thereby achieving maximization of market economic benefits while ensuring the safety of power grid operation.

[0005] The embodiment of the present disclosure provides a method for explicitly allocating ATC for mid- to long-term transactions across price ranges, including:

[0006] Acquiring line operation information of a regional power grid, and dividing the regional power grid into a plurality of price zones based on the line operation information;

[0007] Based on the DC power flow model, the cross-price zone parameter information between different price zones in the preset time period of medium and long-term transactions is calculated, and the target ATC transaction feasible domain is determined based on the calculation results;

[0008] An electricity trading model is established according to the target ATC transaction feasible domain and a set of typical scenarios for power generation cost ranking, and the cross-price zone ATC target transaction values ​​corresponding to each price zone are determined based on the electricity trading model to achieve explicit allocation of ATC for medium- and long-term transactions across price zones.

[0009] The embodiment of the present disclosure provides an ATC explicit allocation device for mid- and long-term transactions across price ranges, comprising:

[0010] A price zone division module, used for obtaining line operation information of a regional power grid, and dividing the regional power grid into a plurality of price zones based on the line operation information;

[0011] A feasible domain calculation module is used to calculate the cross-price zone parameter information between different price zones in the preset time period of medium and long-term transactions based on the DC power flow model, and determine the target ATC transaction feasible domain based on the calculation results;

[0012] The value determination module is used to establish an electricity trading model according to the target ATC transaction feasible domain and a set of typical scenarios ranked by power generation cost, and determine the cross-price zone ATC target transaction values ​​corresponding to each price zone based on the electricity trading model to realize the explicit allocation of ATC for medium- and long-term transactions across price zones.

[0013] An embodiment of the present disclosure provides a computer device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the ATC explicit allocation method for medium- and long-term transactions across price ranges as described in any possible implementation manner described above is performed.

[0014] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for explicitly allocating ATC for mid- to long-term transactions across price ranges as described in any of the above possible implementations is implemented.

[0015] The ATC explicit allocation method, device and medium for medium- and long-term transactions across price zones provided in the embodiments of the present disclosure achieve effective management of the complexity of the power grid by acquiring line operation information of the regional power grid and dividing the power grid into multiple price zones accordingly, so that the power grid operator can more accurately understand the power supply and demand conditions, transmission capacity and its dynamic changes between different regions; a DC power flow model is used to calculate the cross-price zone parameter information between different price zones within a preset time period of medium- and long-term transactions, and the target ATC transaction feasible domain is determined, which significantly improves the prediction accuracy and efficiency of cross-price zone power transactions; finally, a power transaction model is established based on a set of typical scenarios ranked by the target ATC transaction feasible domain and power generation costs, and the cross-price zone ATC target transaction value corresponding to each price zone is determined based on the model, which not only improves the transparency and fairness of power transactions, but also can obtain the optimal ATC value between transactions in each price interval before the medium- and long-term power transactions are officially cleared, thereby achieving the maximization of market economic benefits while meeting the safety of power grid operation.

[0016] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required to be cited in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flowchart of an ATC explicit allocation method for mid- to long-term transactions across price ranges provided by an embodiment of the present disclosure is shown;

[0019] Figure 2 A flowchart of a method for determining a typical scenario set for power generation cost ranking in an ATC explicit allocation method for medium- and long-term transactions across price zones provided by an embodiment of the present disclosure is shown;

[0020] Figure 3 A schematic diagram showing a method for determining a typical scenario set of power generation cost ranking in the ATC explicit allocation method for medium- and long-term transactions across price zones provided by an embodiment of the present disclosure;

[0021] Figure 4 A schematic diagram of the structure of an ATC explicit allocation device for medium- and long-term transactions across price ranges provided by an embodiment of the present disclosure is shown;

[0022] Figure 5 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0025] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0026] In the analysis and research of power systems, Available Transfer Capability (ATC) is an important parameter used to measure the power system's ability to transfer power from one node to another while meeting reliability constraints. The calculation of ATC aims to determine the maximum amount of power transmission that the power grid can support while meeting reliability requirements. This not only involves the safe operation of the power system, but also how to solve it efficiently and accurately. With the development of the power market, the application of ATC has gradually expanded to market transactions, especially power transactions between different nodes or regions. This allocation mechanism helps to optimize resource allocation and avoid market risks.

[0027] According to research, most of the current power spot markets use implicit auctions to allocate ATC, such as PJM's SCUC, SCED and Europe's flow-based method (FBATC). In my country's power market environment, the research on ATC mainly focuses on the inter-provincial medium- and long-term trading model and clearing algorithm considering ATC, solving the problem of simplifying the consideration of grid constraints in medium- and long-term transactions, but they all belong to implicit auction models; the corresponding to implicit auctions is explicit auctions, that is, ATC is allocated and auctioned separately, independent of the transaction of electric energy, such as Europe's net transfer capacity (NTC) / available transfer capacity (ATC) method (NTC). The main advantage of explicit auctions is that the ATC values ​​of different boundaries are fixed, and the coupling relationship between ATCs in different types of transactions does not need to be considered during the auction. The transaction organization is simple and can support decentralized electric energy transactions, which is conducive to market transparency and liquidity. How to explicitly allocate medium- and long-term ATC for different boundaries while taking into account the security of the power grid and the needs of market entities is an issue that has not yet been fully resolved.

[0028] Based on the above research, a method, device and medium for explicit allocation of ATC for medium- and long-term transactions across price zones are provided in the embodiments of the present disclosure. By acquiring the line operation information of the regional power grid and dividing the power grid into multiple price zones accordingly, effective management of the complexity of the power grid is achieved, and the power grid operator can more accurately understand the power supply and demand conditions, transmission capacity and its dynamic changes between different regions; a DC power flow model is used to calculate the cross-price zone parameter information between different price zones within a preset time period of medium- and long-term transactions, and the target ATC transaction feasible domain is determined, which significantly improves the prediction accuracy and efficiency of cross-price zone power transactions; finally, a power trading model is established based on a set of typical scenarios ranked by the target ATC transaction feasible domain and power generation cost, and the cross-price zone ATC target transaction value corresponding to each price zone is determined based on the model, which not only improves the transparency and fairness of power trading, but also can obtain the optimal ATC value between transactions in each price interval before the medium- and long-term power transactions are officially cleared, thereby achieving the maximization of market economic benefits while meeting the safety of power grid operation.

[0029] To facilitate understanding of this embodiment, the execution subject of the ATC explicit allocation method for medium- and long-term transactions across price zones provided by the embodiment of the present disclosure is first introduced in detail. The execution subject of the ATC explicit allocation method for medium- and long-term transactions across price zones provided by the embodiment of the present disclosure is a computer device. The computer device can be a server. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms.

[0030] The following is a detailed description of the ATC explicit allocation method for mid- and long-term transactions across price ranges provided by the embodiment of the present application in conjunction with the accompanying drawings. Figure 1 As shown, it is a flow chart of a method for explicit allocation of ATC for mid- and long-term transactions across price ranges provided by an embodiment of the present disclosure, and the method includes the following S101 to S103:

[0031] S101, acquiring line operation information of a regional power grid, and dividing the regional power grid into a plurality of price zones based on the line operation information.

[0032] Specifically, line operation information is the core basis for power system monitoring, dispatching, planning, safety assessment, market transactions and other aspects of the work, and is crucial to ensure the stable, efficient and safe operation of the power grid. Here, line operation information may include line information or section blockage information, among which line information may include but is not limited to the voltage level, transmission capacity, current load conditions, and geographical location and topological structure of the line. The "section" in the power grid usually refers to the power transmission restriction point on a specific area or path in the power grid, such as the bottleneck of the transmission line, the capacity limitation of the substation, etc. The section blockage information reflects whether the power transmission capacity at these key locations is close to or has reached its physical or economic limit.

[0033] It is understandable that the main purpose of dividing the regional power grid into multiple price zones based on line information or section congestion information is to divide the power grid into different economic areas according to the actual operation status of the power grid, so as to implement differentiated electricity price policies or dispatch strategies, thereby optimizing resource allocation and improving the economy and efficiency of power grid operation. Specific division methods may include: dividing the areas in the power grid with limited transmission capacity or prone to congestion into separate price zones according to the transmission capacity of the line and the degree of section congestion; or, considering the load characteristics of different regions (such as peak-to-valley difference, seasonal changes, etc.), dividing areas with similar load characteristics into the same price zone.

[0034] S102, calculating the cross-price zone parameter information between different price zones in a preset time period of medium- and long-term transactions based on the DC power flow model, and determining the target ATC transaction feasible domain based on the calculation results.

[0035] Among them, the DC power flow model is a simplified power system analysis method that ignores the impact of line reactance and voltage amplitude changes on power flow, only considers the case of zero resistance (i.e., pure resistance network), and assumes that the voltage amplitudes of all nodes are equal and nominal. This model greatly simplifies the computational complexity, making it possible to perform fast power flow calculations in large power systems. Medium- and long-term transactions refer to agreements reached between buyers and sellers in the power market on power supply and demand over a period of time in the future (such as a quarter, year, or longer).

[0036] Exemplarily, the cross-price zone parameter information refers to various parameters that affect power transmission between price zones, and may include cross-price zone GSDF and cross-price zone PTDF. Among them, the cross-price zone GSDF represents the cross-price zone power transfer factor (Generator Shift Distribution Factor, GSDF), which can be used to quantitatively analyze the feasibility, economy and technical difficulty of power transmission between different electricity price areas; the cross-price zone PTDF represents the cross-price zone power transfer distribution factor (Power Transfer Distribution Factor, PTDF), which is an important parameter used in power system analysis to quantify the impact of power transmission between nodes or price zones on system flow, and mainly focuses on the impact of changes in power transactions between different electricity price areas on system branch flows.

[0037] Here, the DC power flow model includes the cross-price zone GSDF calculation formula and the cross-price zone PTDF calculation formula. Therefore, when calculating the cross-price zone parameter information between different price zones in the preset time period of medium- and long-term transactions based on the DC power flow model, the following (1) to (2) can be included:

[0038] (1) determining the key branch in the price region and the nodes in the key branch, and obtaining the node GSDF corresponding to the node;

[0039] (2) Calculating the inter-price zone GSDF based on the node GSDF and inter-price zone GSDF calculation formula, and calculating the inter-price zone PTDF based on the inter-price zone GSDF and inter-price zone PTDF calculation formula.

[0040] It is understandable that in the power network, not all branches (such as transmission lines, transformers, etc.) have a significant impact on cross-price zone power transmission. Therefore, those branches that connect different price zones and are significantly affected by cross-price zone transactions under a certain operating mode can be defined as key branches. Next, determine the nodes in these key branches, which are usually physical interfaces for power transactions or important power transmission nodes. Then obtain the node GSDF corresponding to each node, and then calculate the cross-price zone GSDF based on the node GSDF and the cross-price zone GSDF calculation formula. Here, the cross-price zone GSDF calculation formula is expressed as:

[0041]

[0042] here,

[0043] in, It is represented as the GSDF of node n on the critical branch c; It is expressed as the GSDF of the price zone i on the critical branch c; It is represented as the GSK of generator set g in price zone i (the distribution of unit change of power in price zone among different generation nodes); n is the node number; N is the set of all nodes in price zone i; I is the set of all price zones; c is the key branch number; C is the key branch set; g is the generator set number; G i Represented as the set of generators in price zone i.

[0044] After calculating the cross-price zone GSDF, the cross-price zone PTDF can be calculated according to the cross-price zone GSDF and cross-price zone PTDF calculation formulas. The cross-price zone PTDF calculation formula can be expressed as:

[0045]

[0046] in, It represents the PTDF of the key branch c for transactions in price zone i and price zone j; i and j represent the price zone numbers respectively.

[0047] It can be understood that after the topological structure of the transmission network is known and the reference node and related parameters are determined, the node GSDF is a constant, but the cross-price zone GSDF is affected by the node GSK. When the power generation combination in the price zone is different, the cross-price zone GSDF value will be different.

[0048] Specifically, after calculating the cross-price zone parameter information between different price zones, the key branch data information can be determined based on the calculation results. In the present disclosure, the key branch data information can include the power flow reliability margin of the key branch, the transmission capacity of the key branch occupied by the medium- and long-term transactions within the price zone, and the key branch cross-price zone medium- and long-term transactions The allocated agreement flow.

[0049] For example, the power flow reliability margin of the key branch may correspond to the power flow reliability margin value at the day-ahead time scale, or may be determined by a certain proportion of the maximum transmission capacity of the key branch; the transmission capacity of the key branch occupied by medium- and long-term transactions within the price zone may be calculated by the following formula:

[0050]

[0051] in, It is expressed as the transmission capacity of the key branch c occupied by medium- and long-term transactions in the price zone; is the actual used power flow of the key branch c; is the net power of price zone i generated by medium and long-term transactions across price zones.

[0052] For example, the allocated agreement flow of medium- and long-term transactions in the key branch straddle price zone is determined by the allocated agreement of medium- and long-term transactions in the straddle price zone, such as medium- and long-term annual, monthly, and intra-month transaction contracts in the straddle price zone, and the calculation expression is:

[0053]

[0054] in, The medium and long-term transactions in the cross-price zone represented by the key branch c have been assigned the agreement flow; It refers to the transmission power of the price range of the allocated agreement for medium and long-term transactions across the price range.

[0055] It can be understood that after determining the key branch data information, the ATC calculation formula is used in combination with the key branch data information to calculate the target ATC transaction feasible domain. Here, ATC represents available transmission capacity, which is a measure of the ability to further reliably transmit electric energy between two points (or regions) in the power system; the ATC transaction feasible domain represents the parameter range within which a feasible transaction occurs. By determining the ATC feasible domain, it can be determined which transactions are safe, economical, and can be actually executed. Among them, the ATC calculation formula can be expressed as:

[0056]

[0057] in, It is expressed as the available transmission capacity in the medium and long term across the price zone; It is expressed as the maximum transmission capacity of the key branch c; It is expressed as the power flow reliability margin of the critical branch c.

[0058] The target ATC transaction feasible domain is expressed as:

[0059]

[0060] Among them, Q i,tIt is expressed as the power generation in zone i during time period t.

[0061] S103, establishing an electricity trading model according to the target ATC transaction feasible domain and a set of typical scenarios ranked by power generation cost, and determining the cross-price zone ATC target transaction value corresponding to each price zone based on the electricity trading model, so as to realize the explicit allocation of ATC for medium- and long-term transactions across price zones.

[0062] Reference Figure 2 As shown, before establishing the power trading model according to the target ATC transaction feasible domain and the typical scenario set of power generation cost ranking, the following steps S1031 to S1033 may be included:

[0063] S1031, obtaining a historical data set, and determining a typical pattern set of power generation costs based on the historical data set.

[0064] Here, the historical data set includes historical power generation data of multiple power generation groups, and the power generation cost curve corresponding to the historical power generation data of each power generation group is divided into different power generation cost typical modes, so as to obtain a power generation cost typical mode set including a first number of power generation cost typical modes. Figure 3 As shown in the figure, taking the generator set G as an example, it includes multiple generator sets such as g1, g2, g3, etc. The generator set may include multiple sets of historical power generation data, and then the typical power generation cost model g corresponding to the historical power generation data of each generator set can be obtained. 1,1 , g 1,2 , g 2,1 , g 2,2 Etc., multiple typical power generation cost models Figure 3 The typical pattern set G' of power generation cost is shown in .

[0065] S1032, based on the typical pattern set of power generation costs, using artificial intelligence algorithm and preset uncertainty factor information to determine a typical pattern set of predicted power generation costs.

[0066] Exemplarily, based on the set of typical power generation cost patterns, an artificial intelligence algorithm is used to predict the power generation cost pattern taking into account the preset uncertainty factor information, that is, a set of predicted power generation cost typical patterns including a first number of predicted power generation cost typical patterns is obtained. Among them, the predicted power generation cost typical patterns are in a one-to-one correspondence with the power generation cost typical patterns, that is, Figure 3 Shown g 1,1 , g 1,2 with a 1,1 、a 1,2 Corresponding to, g 2,1 , g 2,2 with a 2,1 、a 2,2Correspondingly, multiple typical patterns for predicting power generation costs together constitute a typical pattern set X for predicting power generation costs. Here, a major source of the preset uncertainty factor information is the uncertainty of power generation costs: the power supply structure in different regions is different, and under different new energy resources and different fuel prices, the power generation cost characteristics in different regions are quite different. Based on the known set of typical patterns of power generation costs, artificial intelligence algorithms can deeply explore the inherent connections and laws between data, consider uncertainty factor information, and realize the prediction of power generation cost patterns that take into account uncertainty factor information through machine learning, deep learning and other technologies.

[0067] In some other embodiments, the preset uncertainty factor information may also include changes in the policy environment (such as the introduction of environmental protection laws and regulations, adjustments to subsidy policies), the impact of climate conditions on power generation efficiency (such as the constraints of extreme weather on wind and solar power generation), etc., which are not specifically limited here.

[0068] S1033: Determine the typical scenario set for ranking power generation costs based on the typical mode set for predicting power generation costs and a weight calculation formula.

[0069] Specifically, after obtaining the set of typical patterns of predicted power generation costs, a set of typical scenarios for ranking power generation costs is determined by considering the typical scenarios of power generation costs corresponding to each typical pattern of predicted power generation costs and a weight calculation formula, wherein the set of typical scenarios for ranking power generation costs includes a second number of typical scenarios of power generation costs, and one typical scenario of power generation costs corresponds to at least one typical pattern of predicted power generation costs. Here, in the case where multiple typical patterns of predicted power generation costs correspond to one typical scenario of power generation costs, the value of the second number is less than the value of the first number, for example, the number of typical patterns of predicted power generation costs is 6 (i.e., the first number is 6), and the corresponding number of typical scenarios of power generation costs is only 2 (i.e., the second number is 2); in addition, if a situation occurs where a typical pattern of predicted power generation costs corresponds to a typical scenario of power generation costs, then the value of the second number is equal to the value of the first number. Reference Figure 3 , predicting the typical model of power generation cost a 1,1 、a 2,1 、a 3,1 Corresponding to m 1,1 、m 2,1 、m 3,1 , which belongs to typical scenario 1; typical model a for predicting power generation cost 1,2 、a 2,2 、a 3,2 Corresponding to m 1,2 、m 2,2 、m 3,2 , which belong to typical scenario 2; typical scenario 1 and typical scenario 2 and other scenarios together constitute the typical scenario set M for power generation cost ranking.

[0070] Exemplarily, the weight coefficients of the typical scenarios of power generation cost ranking are set according to the probability of occurrence of each predicted power generation cost typical mode, and the sum of the weight coefficients is 1, and the expression is:

[0071]

[0072] Among them, m represents the sequence number of the typical scenario of power generation cost; M represents the set of typical scenarios of power generation cost ranking; represents the weight of the typical scenario m of power generation cost; G represents the set of all power generation units; x represents the sequence number of the typical model of predicted power generation cost; a g,x It is represented by the probability of the generator group g appearing under the typical mode x of predicted power generation cost; X is represented by the set of typical modes of predicted power generation cost.

[0073] In some other embodiments, it may be impossible to obtain real data from the system operator, so a quadratic function is used to simulate the power generation cost to achieve the purpose of obtaining historical data. The expression of the power generation cost function is:

[0074] f g (Q g )=Q g ·c(Q g );

[0075] Among them, f g (Q g ) is expressed as the power generation cost function of the generator set g, which can be expressed as Q g indicates; c(Q g ) is expressed as the power generation bidding function of generator group g.

[0076] It is understandable that, under the premise of ensuring the safe operation of the system, the price range ATC is allocated for maximizing social welfare from the perspective of considering the economic factors of the system. Therefore, after obtaining the feasible domain of the target ATC transaction and the set of typical scenarios for power generation cost ranking, the minimum total power generation cost objective function and the constraints corresponding to the minimum total power generation cost objective function (including system constraints and price range constraints) can be established based on the feasible domain of the target ATC transaction and the set of typical scenarios for power generation cost ranking, and then the power trading model can be established based on the minimum total power generation cost objective function and the constraints.

[0077] Specifically, the total power generation cost minimization objective function includes:

[0078]

[0079] Among them, t represents a certain period of time; T represents the set of all time periods; It is represented by the power generation of generator g in price zone i in time period t under the typical scenario m of power generation cost ranking; It is expressed as the power generation cost function of generator g in price zone i during time period t.

[0080] Here, the system constraints include system power balance constraints, system unit output upper and lower limit constraints, and system line flow constraints. The price zone constraints include price zone power balance constraints, price zone unit output upper and lower limit constraints, and price zone line flow constraints.

[0081] Exemplarily, the system power balance constraints include:

[0082]

[0083] in, It is represented by the net power generation in price zone i in time period t under the typical scenario m of power generation cost ranking; It is represented by the power generation power in price zone i in time period t under the typical scenario m of power generation cost ranking; It is expressed as the predicted load in price zone i during time period t.

[0084] Exemplarily, the upper and lower limit constraints of the system unit output include:

[0085]

[0086] in, It is expressed as the lower limit of the power generation of the generator group g in the price zone i; It is expressed as the upper limit of the power generation capacity of the generator group g in the price zone i.

[0087] Exemplarily, the system line flow constraints include:

[0088]

[0089] Exemplarily, the price zone power balance constraint includes:

[0090] When i=j:

[0091]

[0092] When the net power of the ATC sending price zone should be less than the power delivered in the price zone:

[0093]

[0094] When the power received by the ATC receiving end price zone should be greater than the net power of the price zone:

[0095]

[0096] in, It is expressed as the ATC distribution value between price zone i and price zone j in time period t.

[0097] Exemplarily, the upper and lower limits of the unit output in the price zone include:

[0098]

[0099] Exemplarily, the price zone line flow constraints include:

[0100]

[0101] in, It is represented by the ATC allocation value of the critical branch c in time period t.

[0102] After obtaining the electricity trading model, the cross-price zone ATC target transaction value corresponding to each price zone can be determined based on the electricity trading model to achieve explicit allocation of ATC for medium- and long-term transactions across price zones.

[0103] This disclosure considers the physical transmission constraints of key lines, takes the decoupled ATC in different price intervals as the optimization variable, allocates ATC with the minimum expected value of the total power generation cost of the system as the optimization goal, and determines the ATC limits of different boundaries that are independent and uncoupled in advance, that is, the upper limit value of each price interval that can be freely traded in the power market. In this way, it can provide an important reference for the active dispatch decision of the power grid and the transaction decision of the market subject, and maximize the market economic benefits while meeting the safety of power grid operation.

[0104] In addition, the present disclosure also conducts simulation analysis on the power generation cost deviation caused by inaccurate prediction of typical power generation cost ranking scenarios of different ATC allocation models in related technologies, specifically including the following (a) to (d):

[0105] (a) Given a typical scenario of power generation cost ranking and its weight coefficient, optimal scheduling is performed based on each ATC allocation model to obtain the corresponding ATC allocation scheme;

[0106] (b) Perform error analysis on each ATC allocation model and obtain the model W with the minimum error;

[0107] (c) When the actual market clearing result is unknown, since the simulation error of model W is the smallest, the total power generation cost calculated by model W is taken as the actual total power generation cost;

[0108] (d) Substitute each ATC allocation scheme into model W and calculate the total power generation cost corresponding to each ATC allocation scheme; and compare the total power generation cost of each ATC allocation scheme with the total power generation cost of model W to obtain its total power generation cost deviation.

[0109] Through the above steps, it can be concluded that the present disclosure deeply analyzes the performance and impact of different ATC allocation models when the prediction of typical scenarios of power generation cost ranking is inaccurate, and on this basis proposes an electricity trading model to obtain the ATC target trading value corresponding to the minimum total power generation cost.

[0110] The ATC explicit allocation method, device and medium for medium- and long-term transactions across price zones provided in the embodiments of the present disclosure achieve effective management of the complexity of the power grid by acquiring line operation information of the regional power grid and dividing the power grid into multiple price zones accordingly, so that the power grid operator can more accurately understand the power supply and demand conditions, transmission capacity and its dynamic changes between different regions; a DC power flow model is used to calculate the cross-price zone parameter information between different price zones within a preset time period of medium- and long-term transactions, and the target ATC transaction feasible domain is determined, which significantly improves the prediction accuracy and efficiency of cross-price zone power transactions; finally, a power transaction model is established based on a set of typical scenarios ranked by the target ATC transaction feasible domain and power generation costs, and the cross-price zone ATC target transaction value corresponding to each price zone is determined based on the model, which not only improves the transparency and fairness of power transactions, but also can obtain the optimal ATC value between transactions in each price interval before the medium- and long-term power transactions are officially cleared, thereby achieving the maximization of market economic benefits while meeting the safety of power grid operation.

[0111] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0112] Based on the same inventive concept, an explicit allocation device for medium- and long-term transactions ATC across price zones corresponding to the explicit allocation method for medium- and long-term transactions ATC across price zones is also provided in the embodiments of the present disclosure. Since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to the explicit allocation method for medium- and long-term transactions ATC across price zones mentioned above in the embodiments of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0113] Reference Figure 4 FIG. 4 is a schematic diagram of an ATC explicit allocation device 400 for medium- and long-term transactions across price ranges provided by an embodiment of the present disclosure, the device comprising:

[0114] The price zone division module 401 is used to obtain line operation information of a regional power grid, and divide the regional power grid into a plurality of price zones based on the line operation information;

[0115] A feasible domain calculation module 402 is used to calculate the cross-price zone parameter information between different price zones in the preset time period of medium- and long-term transactions based on the DC power flow model, and determine the target ATC transaction feasible domain based on the calculation results;

[0116] The value determination module 403 is used to establish an electricity trading model based on the target ATC transaction feasible domain and a set of typical scenarios ranked by power generation cost, and determine the cross-price zone ATC target transaction values ​​corresponding to each price zone based on the electricity trading model to realize the explicit allocation of ATC for medium- and long-term transactions across price zones.

[0117] In some possible embodiments, the cross-price zone parameter information includes the cross-price zone GSDF and the cross-price zone PTDF, the DC power flow model includes the cross-price zone GSDF calculation formula and the cross-price zone PTDF calculation formula; the feasible domain calculation module 402 is specifically used to:

[0118] Determine the key branch in the price region and the nodes in the key branch, and obtain the node GSDF corresponding to the node;

[0119] Calculating the inter-price zone GSDF based on the node GSDF and inter-price zone GSDF calculation formula, and calculating the inter-price zone PTDF based on the inter-price zone GSDF and inter-price zone PTDF calculation formula;

[0120] The cross-price range GSDF calculation formula includes:

[0121]

[0122] in, It is represented as the GSDF of node n on the critical branch c; It is expressed as the GSDF of the price zone i on the critical branch c; It is represented as the GSK of generator set g in price zone i; n is the node number; N is the set of all nodes in price zone i; I is the set of all price zones; c is the key branch number; C is the key branch set; g is the generator set number; G i It is represented as the set of generators in price zone i;

[0123] The cross-price zone PTDF calculation formula includes:

[0124]

[0125] in, It represents the PTDF of the key branch c for transactions in price zone i and price zone j; i and j represent the price zone numbers respectively.

[0126] In some possible embodiments, the feasible domain calculation module 402 is specifically used to:

[0127] Determine the key branch data information based on the calculation result, and calculate the target ATC transaction feasible domain according to the key branch data information and the ATC calculation formula; wherein the key branch data information includes the power flow reliability margin of the key branch, the transmission capacity of the key branch occupied by the medium- and long-term transactions within the price zone, and the key branch cross-price zone medium- and long-term transactions The allocated agreement flow;

[0128] The ATC calculation formula includes:

[0129]

[0130] in, It is expressed as the available transmission capacity in the medium and long term across the price zone; It is expressed as the maximum transmission capacity of the key branch c; It is expressed as the transmission capacity of the key branch c occupied by medium- and long-term transactions in the price zone; The medium and long-term transactions in the cross-price zone represented by the key branch c have been assigned the agreement flow; It is expressed as the power flow reliability margin of the critical branch c;

[0131] The target ATC transaction feasible domain includes:

[0132]

[0133] Among them, Q i,t It is expressed as the power generation in zone i during time period t.

[0134] In some possible embodiments, the value determination module 403 is further configured to:

[0135] Acquire a historical data set, and determine a set of typical power generation cost patterns based on the historical data set; wherein the historical data set includes historical power generation data of a plurality of power generation groups, and the set of typical power generation cost patterns includes a first number of typical power generation cost patterns;

[0136] Based on the typical pattern set of power generation cost, an artificial intelligence algorithm and preset uncertainty factor information are used to determine a typical pattern set of predicted power generation cost; wherein the typical pattern set of predicted power generation cost includes a first number of typical patterns of predicted power generation cost;

[0137] Determine the typical scenario set for ranking power generation costs based on the typical mode set for predicted power generation costs and the weight calculation formula; wherein the typical scenario set for ranking power generation costs includes a second number of typical scenarios for power generation costs, and one typical scenario for power generation costs corresponds to at least one typical mode for predicted power generation costs;

[0138] The weight calculation formula includes:

[0139]

[0140] Among them, m represents the sequence number of the typical scenario of power generation cost; M represents the set of typical scenarios of power generation cost ranking; represents the weight of the typical scenario m of power generation cost; G represents the set of all power generation units; x represents the sequence number of the typical model of predicted power generation cost; a g,x It is represented by the probability of the generator group g appearing under the typical mode x of predicted power generation cost; X is represented by the set of typical modes of predicted power generation cost.

[0141] In some possible embodiments, the value determination module 403 is specifically used to:

[0142] Establishing a total power generation cost minimum objective function and constraints corresponding to the total power generation cost minimum objective function according to the target ATC transaction feasible domain and the power generation cost ranking typical scenario set, and establishing the power trading model based on the total power generation cost minimum objective function and the constraints;

[0143] The total power generation cost minimum objective function includes:

[0144]

[0145] Among them, t represents a certain period of time; T represents the set of all time periods; It is represented by the power generation of generator g in price zone i in time period t under the typical scenario m of power generation cost ranking; It is expressed as the power generation cost function of generator g in price zone i during time period t.

[0146] In some possible embodiments, the constraint conditions include system constraint conditions, and the system constraint conditions include system power balance constraint, system unit output upper and lower limit constraint and system line flow constraint;

[0147] The system power balance constraints include:

[0148]

[0149] in, It is represented by the net power generation in price zone i in time period t under the typical scenario m of power generation cost ranking; It is represented by the power generation power in price zone i in time period t under the typical scenario m of power generation cost ranking; It is expressed as the predicted load in price zone i during time period t;

[0150] The upper and lower limit constraints of the system unit output include:

[0151]

[0152] in, It is expressed as the lower limit of the power generation of the generator group g in the price zone i; It is expressed as the upper limit of the power generation capacity of the generator set g in price zone i;

[0153] The system line flow constraints include:

[0154]

[0155] In some possible embodiments, the constraint conditions further include price zone constraint conditions, and the price zone constraint conditions include price zone power balance constraint, price zone unit output upper and lower limit constraint and price zone line flow constraint;

[0156] The price zone power balance constraints include:

[0157]

[0158]

[0159] in, It is represented as the ATC distribution value between price zone i and price zone j in time period t;

[0160] The upper and lower limits of the unit output in the price zone include:

[0161]

[0162] The power flow constraints of the price zone lines include:

[0163]

[0164] in, It is represented by the ATC allocation value of the critical branch c in time period t.

[0165] Based on the same technical concept, the embodiment of the present disclosure also provides a computer device. Figure 5 , which is a schematic diagram of the structure of a computer device 500 provided in an embodiment of the present disclosure, including a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions, including a memory 5021 and an external memory 5022; the memory 5021 is also called an internal memory, which is used to temporarily store the operation data in the processor 501 and the data exchanged with the external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the memory 5021.

[0166] In the embodiment of the present application, the memory 502 is specifically used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 501. That is, when the computer device 500 is running, the processor 501 communicates with the memory 502 through the bus 503, so that the processor 501 executes the application code stored in the memory 502, and then executes the method described in any of the above embodiments.

[0167] Among them, the memory 502 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0168] Processor 501 may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0169] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the computer device 500. In other embodiments of the present application, the computer device 500 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0170] The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the ATC explicit allocation method for medium- and long-term transactions in cross-price zones described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0171] The present disclosure also provides a computer program product that carries a program code. The program code includes instructions that can be used to execute the steps of the method for explicit allocation of ATC for medium- and long-term transactions across price ranges described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0172] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0173] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0174] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0176] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0177] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for explicit ATC allocation for mid- to long-term transactions in a cross-price range, characterized in that: include: Acquiring line operation information of a regional power grid, and dividing the regional power grid into a plurality of price zones based on the line operation information; Based on the DC power flow model, the cross-price zone parameter information between different price zones in the preset time period of medium and long-term transactions is calculated, and the target ATC transaction feasible domain is determined based on the calculation results; An electricity trading model is established according to the target ATC transaction feasible domain and a set of typical scenarios for power generation cost ranking, and the cross-price zone ATC target transaction values ​​corresponding to each price zone are determined based on the electricity trading model to achieve explicit allocation of ATC for medium- and long-term transactions across price zones.

2. The method according to claim 1, characterized in that The cross-price zone parameter information includes the cross-price zone GSDF and the cross-price zone PTDF, and the DC power flow model includes the cross-price zone GSDF calculation formula and the cross-price zone PTDF calculation formula; the calculation of the cross-price zone parameter information between different price zones in the preset time period of medium- and long-term transactions based on the DC power flow model includes: Determine the key branch in the price region and the nodes in the key branch, and obtain the node GSDF corresponding to the node; Calculating the inter-price zone GSDF based on the node GSDF and inter-price zone GSDF calculation formula, and calculating the inter-price zone PTDF based on the inter-price zone GSDF and inter-price zone PTDF calculation formula; The cross-price range GSDF calculation formula includes: in, It is represented as the GSDF of node n on the critical branch c; It is expressed as the GSDF of the price zone i on the critical branch c; It is represented as the GSK of generator set g in price zone i; n is the node number; N is the set of all nodes in price zone i; I is the set of all price zones; c is the key branch number; C is the key branch set; g is the generator set number; G i It is represented as the set of generators in price zone i; The cross-price zone PTDF calculation formula includes: in, It represents the PTDF of the key branch c for transactions in price zone i and price zone j; i and j represent the price zone numbers respectively.

3. The method according to claim 2, characterized in that Determining the target ATC transaction feasible domain based on the calculation result includes: Determine the key branch data information based on the calculation result, and calculate the target ATC transaction feasible domain according to the key branch data information and the ATC calculation formula; wherein the key branch data information includes the power flow reliability margin of the key branch, the transmission capacity of the key branch occupied by the medium- and long-term transactions within the price zone, and the key branch cross-price zone medium- and long-term transactions The allocated agreement flow; The ATC calculation formula includes: in, It is expressed as the available transmission capacity in the medium and long term across the price zone; It is expressed as the maximum transmission capacity of the key branch c; It is expressed as the transmission capacity of the key branch c occupied by medium- and long-term transactions in the price zone; The medium and long-term transactions in the cross-price zone represented by the key branch c have been allocated with the agreement flow; It is expressed as the power flow reliability margin of the critical branch c; The target ATC transaction feasible domain includes: Among them, Q i,t It is expressed as the power generation in zone i during time period t.

4. The method according to claim 3, characterized in that: Before establishing the power transaction model according to the target ATC transaction feasible domain and the typical scenario set of power generation cost ranking, the method further includes: Acquire a historical data set, and determine a set of typical power generation cost patterns based on the historical data set; wherein the historical data set includes historical power generation data of a plurality of power generation groups, and the set of typical power generation cost patterns includes a first number of typical power generation cost patterns; Based on the typical pattern set of power generation cost, an artificial intelligence algorithm and preset uncertainty factor information are used to determine a typical pattern set of predicted power generation cost; wherein the typical pattern set of predicted power generation cost includes a first number of typical patterns of predicted power generation cost; Determine the typical scenario set for ranking power generation costs based on the typical mode set for predicted power generation costs and the weight calculation formula; wherein the typical scenario set for ranking power generation costs includes a second number of typical scenarios for power generation costs, and one typical scenario for power generation costs corresponds to at least one typical mode for predicted power generation costs; The weight calculation formula includes: Among them, m represents the sequence number of the typical scenario of power generation cost; M represents the set of typical scenarios of power generation cost ranking; represents the weight of the typical scenario m of power generation cost; G represents the set of all power generation units; x represents the sequence number of the typical model of predicted power generation cost; a g,x It is represented by the probability of the generator group g appearing under the typical mode x of predicted power generation cost; X is represented by the set of typical modes of predicted power generation cost.

5. The method according to claim 4, characterized in that The power trading model is established according to the target ATC transaction feasible domain and the typical scenario set of power generation cost ranking, including: Establishing a total power generation cost minimum objective function and constraints corresponding to the total power generation cost minimum objective function according to the target ATC transaction feasible domain and the power generation cost ranking typical scenario set, and establishing the power trading model based on the total power generation cost minimum objective function and the constraints; The total power generation cost minimum objective function includes: Among them, t represents a certain period of time; T represents the set of all time periods; It is represented by the power generation of generator g in price zone i in time period t under the typical scenario m of power generation cost ranking; It is expressed as the power generation cost function of generator g in price zone i during time period t.

6. The method according to claim 5, characterized in that The constraint conditions include system constraint conditions, which include system power balance constraint, system unit output upper and lower limit constraint and system line flow constraint; The system power balance constraints include: in, It is represented by the net power generation in price zone i in time period t under the typical scenario m of power generation cost ranking; It is represented by the power generation power in price zone i in time period t under the typical scenario m of power generation cost ranking; It is expressed as the predicted load of price zone i in time period t; The upper and lower limit constraints of the system unit output include: in, It is expressed as the lower limit of the power generation of the generator group g in the price zone i; It is expressed as the upper limit of the power generation capacity of the generator set g in price zone i; The system line flow constraints include:

7. The method according to claim 6, characterized in that The constraint conditions also include price zone constraint conditions, and the price zone constraint conditions include price zone power balance constraint, price zone unit output upper and lower limit constraint and price zone line flow constraint; The price zone power balance constraints include: in, It is represented as the ATC distribution value between price zone i and price zone j in time period t; The upper and lower limits of the unit output in the price zone include: The power flow constraints of the price zone lines include: in, It is represented by the ATC allocation value of the critical branch c in time period t.

8. A device for explicit allocation of ATC for medium and long-term transactions across price ranges, characterized in that: include: A price zone division module, used for obtaining line operation information of a regional power grid, and dividing the regional power grid into a plurality of price zones based on the line operation information; A feasible domain calculation module is used to calculate the cross-price zone parameter information between different price zones in the preset time period of medium and long-term transactions based on the DC power flow model, and determine the target ATC transaction feasible domain based on the calculation results; The value determination module is used to establish an electricity trading model according to the target ATC transaction feasible domain and a set of typical scenarios ranked by power generation cost, and determine the cross-price zone ATC target transaction values ​​corresponding to each price zone based on the electricity trading model to realize the explicit allocation of ATC for medium- and long-term transactions across price zones.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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