Full-constraint shadow price information processing method based on electric power spot market parameters

Abstract

The invention relates to a full-constraint shadow price information processing method based on electric power spot market parameters. The method comprises the steps of obtaining regional electric power spot market parameters; taking the sum of the total power generation cost, the trans-provincial power transmission cost and the cross-section power flow constraint out-of-limit penalty cost of the minimized power system as an optimization target, and taking the output of each unit, the power transmission power of each trans-provincial transaction component, and the forward power flow slack variable and the reverse power flow slack variable of each cross section meeting corresponding constraints as constraint conditions; establishing a regional electric power spot market clearing model; executing a solving operation on the regional power spot market clearing model to obtain shadow price information corresponding to the constraint condition and an association relationship between the regional power spot market parameters and the shadow price information; and outputting an abnormal electricity price tracing result according to the shadow price information and the association relationship. According to the above content, the method does not need to depend on manpower through the analysis and processing of the incidence relation, and improves the tracing efficiency of the abnormal electricity price.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power spot market, in particular to a full-constraint shadow price information processing method based on power spot market parameters. BACKGROUND

[0002] With the accelerated construction of the national unified power market system, the price theory research of the unified power spot market has become a hot spot of common concern in the academic and engineering circles. The regional power spot market is an important form of the unified power spot market, and the analysis method of its price characteristics is crucial for market operation agencies to locate the causes of abnormal electricity prices, for market participants to deepen their understanding of the power market, and for optimizing the formulation of trading strategies.

[0003] The power spot market is based on security-constrained unit commitment and security-constrained economic dispatch, uses a complete full-network node model, and the cross-provincial priority plan power transmission constraint is coupled with the security constraints of the full-network units and the cross-section devices, and the spot price is affected by multiple sensitive factors.

[0004] However, in the related art, the abnormal electricity price tracing is mainly based on manual experience, resulting in low tracing efficiency. SUMMARY

[0005] Therefore, it is necessary to provide a full-constraint shadow price information processing method based on power spot market parameters, device, computer equipment and computer readable storage medium, which can improve the efficiency of abnormal electricity price tracing.

[0006] In a first aspect, the application provides a full-constraint shadow price information processing method based on power spot market parameters, comprising:

[0007] Obtaining regional power spot market parameters; the regional power spot market parameters are used to record the bids of each unit in the power system, the transmission prices of each cross-provincial transaction component, and the over-limit penalty factors of the cross-section power flow constraints;

[0008] Establishing a regional power spot market clearing model, taking the sum of the total power generation cost, the cross-provincial transmission cost and the cross-section power flow constraint over-limit penalty cost of the power system as the optimization target, and taking the output of each unit, the transmission power of each cross-provincial transaction component, the positive power flow relaxation variable and the negative power flow relaxation variable of each cross-section as the constraint conditions;

[0009] Performing a solving operation on the regional power spot market clearing model to obtain the shadow price information corresponding to the constraint conditions and the correlation between the regional power spot market parameters and the shadow price information; the correlation is used to represent that the regional power spot market parameters are a linear combination of the shadow price information;

[0010] According to the shadow price information and the correlation relationship, an abnormal electricity price tracing result is output.

[0011] In one of the embodiments, a solution operation is performed on the regional electricity spot market clearing model to obtain the shadow price information corresponding to the constraint conditions and the correlation relationship between the regional electricity spot market parameters and the shadow price information, including:

[0012] According to the regional electricity spot market clearing model, a Lagrange function is constructed;

[0013] According to the preset optimization solution condition, a solution operation is performed on the Lagrange function to obtain the shadow price information corresponding to the constraint conditions and the correlation relationship between the regional electricity spot market parameters and the shadow price information.

[0014] In one of the embodiments, according to the shadow price information and the correlation relationship, an abnormal electricity price tracing result is output, including:

[0015] In the case of the abnormal electricity price being an electricity price peak abnormality, according to the shadow price information and the correlation relationship, it is determined that the abnormal electricity price tracing result is a shadow price abnormality corresponding to the section flow constraint or a shadow price abnormality corresponding to the unit climbing constraint;

[0016] In the case of the abnormal electricity price being an electricity price average abnormality, according to the shadow price information and the correlation relationship, it is determined that the abnormal electricity price tracing result is a shadow price abnormality corresponding to the unit output upper limit constraint.

[0017] In a second aspect, the application further provides a full-constraint shadow price information processing device based on electricity spot market parameters, including:

[0018] The market parameter acquisition module is configured to acquire regional electricity spot market parameters, and the regional electricity spot market parameters are used to record the bids of various units in the power system, the transmission prices of various inter-provincial transaction components, and the section flow constraint overrun penalty factors.

[0019] The clearing model establishment module is configured to establish a regional electricity spot market clearing model by taking the minimization of the sum of the total power generation cost, the inter-provincial transmission cost and the section flow constraint overrun penalty cost of the power system as an optimization target, and taking the power output of each unit, the transmission power of each inter-provincial transaction component, and the positive and negative forward flow relaxation variables of each section satisfying the corresponding constraints as constraint conditions.

[0020] The shadow price information acquisition module is configured to perform a solution operation on the regional electricity spot market clearing model to obtain the shadow price information corresponding to the constraint conditions and the correlation relationship between the regional electricity spot market parameters and the shadow price information, and the correlation relationship is used to represent that the regional electricity spot market parameters are a linear combination of the shadow price information.

[0021] The traceability result output module is configured to output an abnormal electricity price traceability result according to the shadow price information and the correlation relationship.

[0022] In a third aspect, the present application further provides a computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the method steps of the first aspect when executing the computer program.

[0023] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the method steps of the first aspect.

[0024] The above full-constraint shadow price information processing method, device, computer device and computer readable storage medium based on power spot market parameters, by acquiring regional power spot market parameters, the regional power spot market parameters are used to record the offers of each unit in the power system, the transmission price of each inter-provincial transaction component and the out-of-limit penalty factor of the cross-section flow constraint, taking the sum of the total power generation cost, the inter-provincial transmission cost and the cross-section flow constraint out-of-limit penalty cost of the power system as the optimization target, taking the output of each unit, the transmission power of each inter-provincial transaction component, the positive forward flow slack variable and the reverse flow slack variable of each cross-section satisfying the corresponding constraint as the constraint condition, a regional power spot market clearing model is established; the regional power spot market clearing model is solved to obtain the shadow price information corresponding to the constraint condition and the correlation between the regional power spot market parameters and the shadow price information; the correlation is used to represent that the regional power spot market parameters are a linear combination of the shadow price information; according to the shadow price information and the correlation, an abnormal electricity price traceability result is output. According to the above content, by establishing a regional power spot market clearing model and solving the clearing model, the shadow price information corresponding to the constraint condition and the correlation between the regional power spot market parameters and the shadow price information are obtained; by analyzing and processing the correlation, the cause of the abnormal electricity price can be automatically and quickly traced, without relying on manual work, and the efficiency of abnormal electricity price traceability is improved. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0026] Figure 1 An application environment diagram of the full-constraint shadow price information processing method based on power spot market parameters in one embodiment;

[0027] Figure 2 a flowchart of a full-constraint shadow price information processing method based on power spot market parameters in an embodiment;

[0028] Figure 3 a topology structure diagram of a power spot market in a full-constraint shadow price information processing method based on power spot market parameters in an embodiment;

[0029] Figure 4 a structure block diagram of a full-constraint shadow price information processing device based on power spot market parameters in an embodiment;

[0030] Figure 5 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0032] It should be noted that the terms "first", "second" and the like used in the present application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application means two and more than two. The term "and / or" used in the present application means one of the options or any combination of a plurality of options.

[0033] The full-constraint shadow price information processing method based on power spot market parameters provided by the embodiments of the present application can be applied to, for example, Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The terminal 102 obtains the regional electricity spot market parameters; the regional electricity spot market parameters are used to record the offers of various units in the power system, the transmission prices of various inter-provincial trading components, and the over-limit penalty factors of the cross-section flow constraints; a regional electricity spot market clearing model is established, with the minimum sum of the total power generation cost, the inter-provincial transmission cost and the cross-section flow constraint over-limit penalty cost of the power system as the optimization objective, and the output of each unit, the transmission power of each inter-provincial trading component, the positive and negative forward flow relaxation variables of each cross-section satisfying the corresponding constraints as the constraint conditions; the regional electricity spot market clearing model is solved to obtain the shadow price information corresponding to the constraint conditions and the correlation between the regional electricity spot market parameters and the shadow price information; the correlation is used to represent that the regional electricity spot market parameters are a linear combination of the shadow price information; according to the shadow price information and the correlation, the abnormal electricity price tracing result is output. The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smartphones, tablet computers, drones, low-altitude aircraft, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart TV, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0034] In one embodiment, as shown in Figure 2 A method for processing full-constraint shadow price information based on electricity spot market parameters is provided. This embodiment illustrates the method applied to a terminal. It should be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and can be realized through the interaction of the terminal and the server. In this embodiment, the method includes the following steps:

[0035] Step S210, obtaining regional electricity spot market parameters; the regional electricity spot market parameters are used to record the offers of various units in the power system, the transmission prices of various inter-provincial trading components, and the over-limit penalty factors of the cross-section flow constraints.

[0036] Among them, the power system is an electric energy production and consumption system composed of power plants, transmission and distribution lines, power supply and distribution stations, and power consumption.

[0037] The offer of a unit refers to the combination of generation output and corresponding electricity price proposed by a power generation enterprise in the electricity spot market for a certain operation period (such as 15 minutes, 1 hour, or a day). The same electricity price can be used for the entire period, or differential electricity prices can be set according to the output range of different units or different time periods (such as a higher electricity price during peak hours).

[0038] The transmission price of a cross-province transaction component is the price of services such as power transmission, grid interconnection, and security provided by a power grid enterprise through cross-province and cross-region special projects, and is composed of a sending-province transmission price, a regional grid transmission price, a cross-province and cross-region special project transmission price, and losses.

[0039] The cross-section flow constraint overrun penalty factor is a penalty parameter in a power market clearing model that quantifies the overrun degree of a cross-section flow and adjusts the value of an objective function. When the cross-section flow exceeds its transmission limit, the penalty factor penalizes the overrun behavior by increasing the value of the objective function (such as total generation cost or load shedding amount), thereby guiding the system to operate within a safe range.

[0040] Specifically, as shown in Figure 3 A topological structure diagram of an electricity spot market is provided. Among them, the load of province A is low, the installed capacity of power generation is large, and the surplus power can be sent to province B through the tie line (A5→B6); Province B is the center of the whole network load and the hub of cross-region power exchange, with high load, low-cost and high-cost units coexisting; The load of province C is low, and it accepts external power through the tie line (B10→C11). Among them, the intra-provincial line and the inter-provincial tie line are represented by solid lines and dashed lines respectively, and the "→" of the inter-provincial tie line represents the direction of the flow.

[0041] In the embodiments of the present application, the pre-set offers of each unit, the transmission prices of each cross-province transaction component, and the cross-section flow constraint overrun penalty factor can be received.

[0042] In step S220, a regional electricity spot market clearing model is established with the sum of the total generation cost of the power system, the cross-province transmission cost, and the cross-section flow constraint overrun penalty cost as the optimization objective, and with the output of each unit, the transmission power of each cross-province transaction component, and the positive flow relaxation variable and the negative flow relaxation variable of each cross-section satisfying the corresponding constraints as the constraint conditions.

[0043] Among them, the total generation cost of the power system is the sum of the offers of each unit in different time periods, the cross-province transmission cost is the product sum of the transmission power of each cross-province transaction component in different time periods and the corresponding transmission price, and the cross-section flow constraint overrun penalty cost is the product sum of the sum of the positive flow relaxation variable and the negative flow relaxation variable of each cross-section in different time periods and the cross-section flow constraint overrun penalty factor.

[0044] In the power system, the forward flow slack variable and the reverse flow slack variable of the section are mathematical tools for relaxing the section flow constraint, which allows the section flow to exceed the limit for a short time under certain conditions by introducing non-negative variables, so as to find a feasible solution or a better solution in the optimization model. Specifically, in the section flow constraint, the introduction of the forward flow slack variable and the reverse flow slack variable can convert the strict inequality constraint into an equality constraint.

[0045] The regional power spot market clearing model is a mathematical model for determining the power transaction price and transaction volume in the regional power spot market, and the core goal is to find a resource allocation scheme that maximizes social welfare by an optimization algorithm under the premise of meeting the power grid safety constraint.

[0046] In the embodiment of the present application, the regional power spot market clearing model includes an objective function (i.e. optimization target) and a series of constraint conditions (i.e. the output of each unit, the power transmission of each cross-provincial transaction component, the corresponding constraints that the forward flow slack variable and the reverse flow slack variable of each section need to meet, respectively).

[0047] In step S230, a solving operation is performed on the regional power spot market clearing model to obtain shadow price information corresponding to the constraint conditions and the association relationship between the regional power spot market parameters and the shadow price information; the association relationship is used to represent that the regional power spot market parameters are a linear combination of the shadow price information.

[0048] The shadow price information reflects the marginal benefit or cost brought by the increase or decrease of one unit of power resources under the optimal allocation of resources and the safety constraint of the power system. In the power market clearing process, the model takes the maximization of social welfare (such as the minimization of total generation cost or the maximization of total surplus) as the goal, considers the grid safety constraint, generator output limit, load demand, etc., and the optimal solution corresponding to the dual variable obtained by solving is the shadow price information.

[0049] In the regional power spot market clearing model, each constraint condition corresponds to a shadow price information, which reflects the marginal influence of the constraint condition on the objective function in the regional power spot market clearing model.

[0050] In the embodiment of the present application, the solving operation is performed on the regional power spot market clearing model to obtain the regional power spot market clearing result. The regional power spot market clearing result includes the output value of each unit, the power transmission value of each cross-provincial transaction component, the forward flow slack variable value and the reverse flow slack variable value of each section, the shadow price information corresponding to the constraint condition, and the association relationship between the regional power spot market parameters and the shadow price information.

[0051] Step S240, according to the shadow price information and the correlation relationship, output abnormal electricity price tracing result.

[0052] Wherein, by analyzing the shadow price information of each constraint condition and the correlation relationship between the regional power spot market parameters and the shadow price information, it can be identified which factors have a significant impact on the electricity price, so as to trace the cause of abnormal electricity price.

[0053] Wherein, the abnormal electricity price refers to the abnormal shadow price information (system marginal price, also called market clearing price) corresponding to the load balance constraint. When the system marginal price is significantly higher than the reasonable level (such as far exceeding the real cost of the unit or the historical same period value), the system marginal price is determined to be abnormal.

[0054] In the embodiment of the application, the offer of each unit is a linear combination of the system marginal price and the shadow price information of the unit output constraint. Therefore, when the system marginal price is abnormal, it can be caused by the increase of the offer of the unit, or by the increase of the shadow price information of the unit output constraint.

[0055] The above method, by acquiring regional power spot market parameters; the regional power spot market parameters are used to record the offer of each unit in the power system, the transmission price of each inter-provincial trading component and the over-limit penalty factor of the inter-area flow constraint; taking the sum of the total power generation cost, the inter-provincial transmission cost and the over-limit penalty cost of the inter-area flow constraint of the power system as the optimization objective, taking the output of each unit, the transmission power of each inter-provincial trading component, the positive flow relaxation variable and the reverse flow relaxation variable of each inter-area satisfying the corresponding constraint as the constraint condition, a regional power spot market clearing model is established; performing a solving operation on the regional power spot market clearing model to obtain the shadow price information corresponding to the constraint condition and the correlation relationship between the regional power spot market parameters and the shadow price information; the correlation relationship is used to represent that the regional power spot market parameters are a linear combination of the shadow price information; according to the shadow price information and the correlation relationship, the abnormal electricity price tracing result is output. According to the above content, the application establishes a regional power spot market clearing model, and performs a solving operation on the clearing model to obtain the shadow price information corresponding to the constraint condition and the correlation relationship between the regional power spot market parameters and the shadow price information; by analyzing and processing the correlation relationship, the cause of abnormal electricity price can be automatically and quickly traced, without relying on manual work, and the efficiency of abnormal electricity price tracing is improved.

[0056] In one embodiment, the expression of the optimization objective in the regional power spot market clearing model is:

[0057]

[0058] Wherein, represents the offer of unit i, Pitdenotes the output of unit i at time period t, Pikdenotes the transmission price of inter-provincial trading component k, Piktdenotes the transmission power of inter-provincial trading component k at time period t, Pfdenotes the out-of-limit penalty factor of the cross-section power flow constraint, Pfsdenotes the forward power flow slack variable of cross-section power flow s at time period t, Pfsdenotes the reverse power flow slack variable of cross-section power flow s at time period t, N denotes the total number of units, T denotes the total number of time periods, NKdenotes the total number of inter-provincial trading components, NSdenotes the total number of cross-section power flows.

[0059] In the embodiments of the present application, the offer of unit i is Pitdenotes the output of unit i at time period t, Pitdenotes the offer cost function of the output of unit i at time period t. It is usually a convex function, such as a quadratic function.

[0060] In one embodiment, the constraint conditions in the regional power spot market clearing model include:

[0061] provincial load balance constraint, provincial positive reserve constraint, provincial negative reserve constraint, unit output upper and lower limit constraint, unit ramping constraint, cross-section power flow constraint, trading component and tie-line power coupling constraint, inter-provincial priority plan transmission power constraint, inter-provincial trading component power non-negative constraint, power upper and lower limit constraint of inter-provincial DC tie-line, and cross-section power flow constraint slack variable non-negative constraint;

[0062] wherein the provincial load balance constraint is:

[0063]

[0064] wherein, Pitdenotes the output of unit i in province a at time period t, Pamtdenotes the transmission power of DC tie-line m in province a at time period t, Paldenotes the total power of load in province a, Paldenotes the shadow price information of load balance constraint in province a at time period t, Pdenotes the province area set, Pdenotes the number of units in province a, Pdenotes the number of DC tie-lines connected to province a, Tdenotes the total number of time periods;

[0065] The provincial positive reserve constraint is:

[0066]

[0067] wherein, Pimaxdenotes the maximum output of unit i in province a, positive reserve requirement of province a, shadow price information of positive reserve constraint of province a at time period t;

[0068] negative reserve constraint of province a is:

[0069]

[0070] wherein, minimum output of unit i of province a, negative reserve requirement of province a, shadow price information of negative reserve constraint of province a at time period t;

[0071] upper and lower output limit constraint of unit is:

[0072]

[0073] wherein, lower output limit of unit i, upper output limit of unit i, shadow price information of lower output limit of unit i at time period t, shadow price information of upper output limit of unit i at time period t;

[0074] unit ramping constraint is:

[0075]

[0076] wherein, upper ramping limit of unit i, lower ramping limit of unit i, shadow price information of upper ramping of unit i at time period t, shadow price information of lower ramping of unit i at time period t;

[0077] sectional power flow constraint is:

[0078]

[0079] wherein, upper limit of sectional power flow s, lower limit of sectional power flow s, sensitivity of unit i to sectional power flow s, sensitivity of inter-provincial DC tie-line m to sectional power flow s, sensitivity of load d to sectional power flow s, transmission power of DC tie-line m at time period t, transmission power of load d at time period t, shadow price information of the lower bound constraint of the sectional flow s at time period t, shadow price information of the upper bound constraint of the sectional flow s at time period t, positive flow slack variable of the sectional flow s at time period t, negative flow slack variable of the sectional flow s at time period t;

[0080] The transaction component and tie-line power coupling constraint is:

[0081]

[0082] wherein, transmission power of the transaction component k at time period t, shadow price information of the coupling constraint of the transaction component k at time period t;

[0083] The cross-province priority plan transmission power constraint is:

[0084]

[0085] wherein, transmission power lower bound, shadow price information of the transmission power constraint of the cross-province transaction component k;

[0086] The cross-province transaction component power non-negative constraint is:

[0087]

[0088] wherein, shadow price information of the power non-negative constraint of the cross-province transaction component k at time period t;

[0089] The power upper and lower bound constraint of the cross-province DC tie-line is:

[0090]

[0091] wherein, transmission power lower bound of the tie-line, transmission power upper bound of the tie-line, shadow price information of the transmission power lower bound of the cross-province DC tie-line m at time period t, shadow price information of the transmission power upper bound of the cross-province DC tie-line m at time period t;

[0092] The sectional flow constraint slack variable non-negative constraint is:

[0093]

[0094] wherein, shadow price information of a non-negativity constraint of a forward relaxation variable of the section flow s at the time period t, shadow price information of a non-negativity constraint of a backward relaxation variable of the section flow s at the time period t.

[0095] In one embodiment, the solving operation is performed on the regional power spot market clearing model to obtain the shadow price information corresponding to the constraint condition and the correlation between the regional power spot market parameters and the shadow price information, including steps S302-S304, and the details are as follows:

[0096] In step S302, the Lagrange function is constructed according to the regional power spot market clearing model.

[0097] In the embodiment of the application, the objective function is extracted from the optimization objective in the regional power spot market clearing model, and the objective function is combined with the constraint condition by means of the Lagrange multiplier to construct the Lagrange function. The objective function is the sum of the total power generation cost, the cross-province transmission cost and the out-of-limit penalty cost of the section flow constraint of the power system.

[0098] In step S304, the solving operation is performed on the Lagrange function according to the preset optimization solving condition to obtain the shadow price information corresponding to the constraint condition and the correlation between the regional power spot market parameters and the shadow price information.

[0099] The preset optimization solving condition is the KKT (Karush-Kuhn-Tucker) condition, which is a set of necessary conditions in optimization theory and is applicable to solving the nonlinear programming problem with equality and inequality constraints. When the objective function and the constraint condition are convex, the KKT condition is also a sufficient condition for finding the optimal solution.

[0100] In the embodiment of the application, the partial derivatives of the Lagrange function with respect to the output of each unit, the transmission power of each cross-province transaction component, the forward flow relaxation variable and the backward flow relaxation variable of each section are calculated, and the results are set to 0, so that the shadow price information corresponding to the constraint condition and the correlation between the regional power spot market parameters and the shadow price information can be obtained.

[0101] In one embodiment, the expression of the Lagrange function corresponding to the regional power spot market clearing model is as follows:

[0102]

[0103] wherein, , , , are matrices composed of the output of each unit at each time period, the power of each cross-province transaction component, and the relaxation variable of the section flow constraint, representing the power transmission of the transaction component k at time period t, representing the offer of unit i, representing the output of unit i at time period t, representing the transmission price of transaction component k across provinces, representing the power transmission of transaction component k across provinces at time period t, representing the out-of-limit penalty factor of the section flow constraint, representing the positive flow slack variable of section flow s at time period t, representing the negative flow slack variable of section flow s at time period t, N represents the total number of units, T represents the total number of time periods, NK represents the total number of transaction components across provinces, NS represents the total number of section flows, representing the output of unit i of province a at time period t, representing the power transmission of DC tie-line m of province a at time period t, representing the total power of load of province a, representing the province area set, representing the number of units of province a, representing the number of DC tie-lines connected to province a, ND represents the total number of loads, representing the maximum output of unit i of province a, representing the positive reserve requirement of province a, representing the minimum output of unit i of province a, representing the negative reserve requirement of province a, representing the lower limit of output of unit i, representing the upper limit of output of unit i, representing the upper ramping limit of unit i, representing the lower ramping limit of unit i, representing the upper limit of section flow s, representing the lower limit of section flow s, representing the sensitivity of unit i to section flow s, representing the sensitivity of DC tie-line m across provinces to section flow s, representing the sensitivity of load d to section flow s, representing the power transmission of transaction component k at time period t, representing the power transmission of DC tie-line m at time period t, representing the lower limit of power transmission, representing the lower limit of power transmission of tie-line, representing the upper limit of power transmission of tie-line, representing the power transmission of load d at time period t, representing the shadow price information of load balance constraint of province a at time period t, shadow price information of positive reserve constraint of province a in time period t, shadow price information of negative reserve constraint of province a in time period t, shadow price information of lower limit of output of unit i in time period t, shadow price information of upper limit of output of unit i in time period t, shadow price information of upper ramp of unit i in time period t, shadow price information of lower ramp of unit i in time period t, shadow price information of lower limit constraint of sectional flow s in time period t, shadow price information of upper limit constraint of sectional flow s in time period t, shadow price information of coupling constraint of transaction component k in time period t, shadow price information of power transmission constraint of transaction component k across provinces, shadow price information of power non-negative constraint of transaction component k across provinces in time period t, shadow price information of lower limit of power transmission of DC tie-line m across provinces in time period t, shadow price information of upper limit of power transmission of DC tie-line m across provinces in time period t, shadow price information of positive non-negative constraint of sectional flow s in time period t, shadow price information of negative non-negative constraint of sectional flow s in time period t.

[0104] In the embodiments of the present application, the inequality in each constraint condition is converted into an equation by means of a Lagrange multiplier (shadow price information) and added to the Lagrange function.

[0105] In one embodiment, the association between the regional power spot market parameters and the shadow price information includes: a first association between the bid of each unit in the power system and the shadow price information corresponding to the satisfaction of the corresponding constraint by the output of each unit; a second association between the transmission price of each transaction component across provinces and the shadow price information corresponding to the satisfaction of the corresponding constraint by the transmission power of each transaction component across provinces; and a third association between the over-limit penalty factor of the sectional flow constraint and the shadow price information corresponding to the satisfaction of the corresponding constraint by the positive flow relaxation variable and the negative flow relaxation variable of each section.

[0106] The first association is:

[0107]

[0108] The second association is: The bid of unit i, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t,

[0109] The second association relationship is:

[0110]

[0111] wherein, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t, Pij(t) represents the output of unit i at time period t,

[0112] The third association relationship is:

[0113]

[0114] or,

[0115]

[0116] wherein, represents the shadow price information of the non-negative constraint of the positive relaxation variable of the section flow s in the time period t, represents the shadow price information of the non-negative constraint of the negative relaxation variable of the section flow s in the time period t.

[0117] In the embodiments of the present application, the Lagrange function includes the output variable of each unit in each time period, the component power variable of each cross-province transaction, the positive relaxation variable of each section flow constraint, and the negative relaxation variable of each section flow constraint. The first correlation relationship can be obtained by taking the partial derivative of the Lagrange function with respect to the output variable of each unit in each time period. The second correlation relationship can be obtained by taking the partial derivative of the Lagrange function with respect to the component power variable of each cross-province transaction in each time period. The third correlation relationship can be obtained by taking the partial derivative of the Lagrange function with respect to the positive relaxation variable and the negative relaxation variable of each section flow constraint in each time period, respectively.

[0118] In one embodiment, according to the shadow price information and the correlation relationship, the abnormal electricity price tracing result is output, including steps S402-S404, which are specifically as follows:

[0119] Step S402, in the case that the bid of the unit is greater than the generation cost of the unit, it is determined that the abnormal electricity price tracing result is the bid abnormality of the unit.

[0120] In the embodiments of the present application, the generation cost of the unit can be estimated by historical data or technical parameters. The generation cost of some units may be false, resulting in that the bid of the unit is greater than the generation cost of the unit. Since the bid of the unit i can be represented by the linear combination of the shadow price information corresponding to the load balance constraint, the positive and negative reserve constraints of the province where the unit is located, the upper and lower limits of the unit output, the upper and lower climbing constraints of the unit in adjacent time periods, and the upper and lower limits of the section flow, when the shadow price information corresponding to the load balance constraint is abnormal, it can be traced to the bid abnormality of the unit.

[0121] Step S404, in the case that the bid of the unit is equal to the generation cost of the unit, it is determined that the abnormal electricity price tracing result is that the output of the unit satisfies the shadow price information corresponding to the corresponding constraint.

[0122] In the embodiments of the present application, when the offer of the unit is not abnormal, and the shadow price information corresponding to the load balance constraint is abnormal, it can be traced back to the abnormal shadow price information corresponding to the fact that the output of the unit meets the corresponding constraint. Specifically, the shadow price information of the upper limit constraint of the unit output is abnormal, which indicates that the unit output is limited, forcing the calling of high-priced units, so that the shadow price information corresponding to the load balance constraint is abnormal.

[0123] The embodiments of the present application can automatically and quickly trace back the cause of the abnormal electricity price through the shadow price information and the associated relationship, facilitating the subsequent optimization of the regional power spot market parameters.

[0124] It should be understood that, although each step in the flowchart involved in the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0125] Based on the same inventive concept, the embodiments of the present application also provide a full-constraint shadow price information processing device based on power spot market parameters for implementing the above-mentioned full-constraint shadow price information processing method based on power spot market parameters. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more full-constraint shadow price information processing device embodiments based on power spot market parameters provided below can refer to the limitations of the full-constraint shadow price information processing method based on power spot market parameters described above, and will not be repeated here.

[0126] In one exemplary embodiment, referring to Figure 4 , a full-constraint shadow price information processing device 4 based on power spot market parameters is provided, comprising:

[0127] The market parameter acquisition module 410 is configured to acquire regional power spot market parameters, and the regional power spot market parameters are used to record the offers of each unit in the power system, the transmission price of each cross-provincial transaction component, and the out-of-limit penalty factor of the cross-section flow constraint.

[0128] The clearing model establishing module 420 is configured to establish a regional power spot market clearing model, with an optimization target of minimizing a sum of total power generation cost, cross-province power transmission cost and penalty cost for exceeding a constraint of a branch power flow of the power system, and a constraint condition of satisfying corresponding constraints by power output of each unit, power transmission power of each cross-province transaction component, and forward power flow relaxation variable and reverse power flow relaxation variable of each branch;

[0129] The shadow price information obtaining module 430 is configured to perform a solving operation on the regional power spot market clearing model, to obtain shadow price information corresponding to the constraint condition and a correlation between the regional power spot market parameter and the shadow price information, and the correlation is used to represent that the regional power spot market parameter is a linear combination of the shadow price information.

[0130] The abnormal electricity price tracing result output module 440 is configured to output the abnormal electricity price tracing result according to the shadow price information and the correlation.

[0131] In one of the embodiments, the shadow price information obtaining module 430 is configured to construct a Lagrange function according to the regional power spot market clearing model.

[0132] The solving operation is performed on the Lagrange function according to a preset optimization solving condition, to obtain the shadow price information corresponding to the constraint condition and the correlation between the regional power spot market parameter and the shadow price information.

[0133] In one of the embodiments, the abnormal electricity price tracing result output module 440 is configured to determine that the abnormal electricity price tracing result is abnormal bidding of the unit in a case where the bidding of the unit is greater than the power generation cost of the unit.

[0134] In a case where the bidding of the unit is equal to the power generation cost of the unit, it is determined that the abnormal electricity price tracing result is abnormal shadow price information corresponding to the power output of the unit satisfying the corresponding constraint.

[0135] The above-mentioned modules in the full-constraint shadow price information processing device based on the power spot market parameter can be realized by software, hardware and a combination thereof in whole or in part. The above-mentioned modules can be embedded in or independent of a processor in a computer device in a hardware form, or can be stored in a memory in a computer device in a software form, so as to be called and executed by a processor to perform the operations corresponding to the above-mentioned modules.

[0136] In one exemplary embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram of the computer device can be as shown in Figure 5The computer device shown in the figure includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, Near Field Communication (NFC) or other technologies. The computer program is executed by the processor to realize a kind of full constraint shadow price information processing method based on power spot market parameter or hetero fiber detection method. The display unit of the computer device is used to form visually visible picture, which can be display screen, projection device or virtual reality imaging device. The display screen can be liquid crystal display screen or electronic ink display screen, and the input device of the computer device can be touch layer covered on the display screen, or key, trackball or touchpad arranged on the shell of the computer device, or external keyboard, touchpad or mouse etc.

[0137] Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In one embodiment, a computer device is provided, including a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the above-mentioned full constraint shadow price information processing method based on power spot market parameter. The steps of the full constraint shadow price information processing method based on power spot market parameter can be the steps in the full constraint shadow price information processing method based on power spot market parameter in each of the above-mentioned embodiments.

[0138] In one embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, causing the processor to perform the steps of the above-mentioned method for processing full-constraint shadow price information based on power spot market parameters. The steps of the method for processing full-constraint shadow price information based on power spot market parameters can be the steps of any of the above-mentioned embodiments of the method for processing full-constraint shadow price information based on power spot market parameters.

[0139] In one embodiment, a computer program product is provided, comprising a computer program, the computer program, when executed by a processor, causing the processor to perform the steps of the above-mentioned method for processing full-constraint shadow price information based on power spot market parameters. The steps of the method for processing full-constraint shadow price information based on power spot market parameters can be the steps of any of the above-mentioned embodiments of the method for processing full-constraint shadow price information based on power spot market parameters.

[0140] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., and is not limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., and is not limited thereto.

[0142] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered as the scope of the present application.

[0143] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for processing fully constrained shadow price information based on electricity spot market parameters, characterized in that, The method includes: Obtain regional electricity spot market parameters; the regional electricity spot market parameters are used to record the bid prices of each unit in the power system, the transmission prices of each inter-provincial transaction component, and the cross-section power flow constraint over-limit penalty factor; With the optimization objective of minimizing the sum of the total generation cost of the power system, the inter-provincial transmission cost, and the penalty cost for exceeding the limit of the cross-section power flow constraint, and with the constraints being that the output of each unit, the transmission power of each inter-provincial trading component, and the forward and reverse power flow relaxation variables of each cross section satisfy the corresponding constraints, a regional power spot market clearing model is established. The regional electricity spot market clearing model is solved to obtain the shadow price information corresponding to the constraints and the correlation between the regional electricity spot market parameters and the shadow price information; the correlation is used to characterize that the regional electricity spot market parameters are a linear combination of the shadow price information; Based on the shadow price information and the correlation, the abnormal electricity price tracing results are output.

2. The method for processing fully constrained shadow price information based on electricity spot market parameters according to claim 1, characterized in that, The expression for the optimization objective is: in, This represents the quote for unit i. This represents the output of unit i during time period t. This represents the transmission price of component k in inter-provincial electricity transactions. This represents the transmission power of inter-provincial transaction component k during time period t. This represents the penalty factor for exceeding the limit of the power flow constraint at the cross-section. This represents the positive tidal relaxation variable of the cross-sectional tidal current s over time period t. Let N represent the reverse slack variable of the cross-sectional s current in time period t, N represent the total number of units, T represent the total number of time periods, NK represent the total number of inter-provincial transaction components, and NS represent the total number of cross-sectional slack variables.

3. The method for processing fully constrained shadow price information based on electricity spot market parameters according to claim 1, characterized in that, The constraints include: Provincial load balance constraints, provincial positive reserve constraints, provincial negative reserve constraints, unit output upper and lower limit constraints, unit ramping constraints, cross-sectional power flow constraints, trading component and tie line power coupling constraints, inter-provincial priority plan power transmission constraints, inter-provincial trading component power non-negative constraints, inter-provincial DC tie line power upper and lower limit constraints, and cross-sectional power flow constraint relaxation variable non-negative constraints. The provincial load balancing constraint is as follows: in, This represents the output of unit i in province a during time period t. This represents the transmission power of DC tie line m of province a during time period t. This represents the total load power of province a. This represents the shadow price information for the load balancing constraints of province a within time period t. Indicates a collection of provinces and regions. This indicates the number of generating units in province a. This indicates the number of DC tie lines connected to province a. Indicates the total number of time periods; The provincial reserve constraint is as follows: in, This represents the maximum output of unit i in province a. This indicates the positive reserve requirement of province A. This represents the shadow price information of the positive reserve constraint of province a in time period t; The provincial negative reserve constraint is: in, This represents the minimum output of unit i in province a. This indicates the negative reserve requirement of province a. This represents the shadow price information of the negative reserve constraint of province a in time period t; The upper and lower limits of the unit's output are constrained as follows: in, This indicates the lower limit of the output of unit i. This represents the upper limit of output of group i. This represents the shadow price information indicating the lower limit of the output of unit i during time period t. The shadow price information represents the maximum output of unit i during time period t; The unit's ramp-up constraint is: in, This represents the ramp-up limit for unit i. This represents the downhill ramp limit for unit i. This represents the shadow price information for unit i during the uphill climb in time period t. This represents the shadow price information for unit i during the downhill climb in time period t; The cross-sectional power flow constraint is: in, This represents the upper limit of the cross-sectional power flow s. This represents the lower bound of the cross-sectional power flow s. This indicates the sensitivity of unit i to the cross-sectional power flow s. This indicates the sensitivity of the inter-provincial DC tie line m to the cross-sectional power flow s. This indicates the sensitivity of the load d to the cross-sectional tidal current s. This represents the transmission power of DC tie line m during time period t. This represents the transmission power of load d during time period t. This represents the shadow price information representing the lower bound constraint of the cross-sectional current s over time period t. This represents the shadow price information indicating the upper limit constraint of the cross-sectional current s over time period t. This represents the positive tidal relaxation variable of the cross-sectional tidal current s over time period t. The reverse slack variable represents the cross-sectional tidal current s over time period t; The coupling constraint between the transaction component and the tie-line power is: in, This represents the transmission power of transaction component k during time period t. This represents the shadow price information of the coupling constraints of transaction component k in time period t; The power transmission limit for the inter-provincial priority plan is as follows: in, This indicates the lower limit of the power delivery volume. The shadow price information represents the electricity delivery constraint of component k in inter-provincial transactions; The non-negativity constraint of the cross-provincial transaction component power is: in, The shadow price information represents the power non-negativity constraint of cross-provincial transaction component k in time period t; The upper and lower power limits for the inter-provincial DC interconnection line are: in, Indicates the lower limit of the power supply of the tie line. This indicates the upper limit of the power transmission capacity of the tie line. This represents the shadow price information for the lower limit of the power transmission capacity of the inter-provincial DC transmission line m during time period t. Shadow price information representing the upper limit of the power transmission capacity of inter-provincial DC transmission line m in time period t; The non-negativity constraint of the sectional power flow constraint relaxation variable is: in, This represents the shadow price information of the cross-sectional tidal current s under the positive relaxation variable non-negative constraint in time period t. This represents the shadow price information of the non-negative constraint of the negative slack variable of the cross-sectional current s over time period t.

4. The method for processing fully constrained shadow price information based on electricity spot market parameters according to claim 1, wherein the step of performing a solution operation on the regional electricity spot market clearing model to obtain the shadow price information corresponding to the constraint conditions and the correlation between the regional electricity spot market parameters and the shadow price information is characterized in that, include: Based on the regional electricity spot market clearing model, a Lagrange function is constructed; Based on the preset optimization conditions, the Lagrange function is solved to obtain the shadow price information corresponding to the constraints and the correlation between the regional electricity spot market parameters and the shadow price information.

5. The method for processing fully constrained shadow price information based on electricity spot market parameters according to claim 4, characterized in that, The expression for the Lagrange function is: in, , , , These are matrices composed of the power output of each unit in each time period, the power of each inter-provincial transaction component, and the power flow constraint relaxation variables at each cross section. It represents the transmission power of the cross-sectional power flow s over time time t. This represents the quote for unit i. This represents the output of unit i during time period t. This represents the transmission price of component k in inter-provincial electricity transactions. This represents the transmission power of inter-provincial transaction component k during time period t. This represents the penalty factor for exceeding the limit of the power flow constraint at the cross-section. This represents the positive tidal relaxation variable of the cross-sectional tidal current s over time period t. Let represent the reverse slack variable of the cross-sectional power flow s over time period t, N represent the total number of generating units, T represent the total number of time periods, NK represent the total number of inter-provincial transaction components, and NS represent the total cross-sectional power flow. This represents the output of unit i in province a during time period t. This represents the transmission power of DC tie line m of province a during time period t. This represents the total load power of province a. Indicates a collection of provinces and regions. This indicates the number of generating units in province a. ND indicates the number of DC tie lines connected to province a, and ND indicates the total number of loads. This represents the maximum output of unit i in province a. This indicates the positive reserve requirement of province A. This represents the minimum output of unit i in province a. This indicates the negative reserve requirement of province a. This indicates the lower limit of the output of unit i. This represents the upper limit of output of group i. This represents the ramp-up limit for unit i. This represents the downhill ramp limit for unit i. This represents the upper limit of the cross-sectional power flow s. This represents the lower bound of the cross-sectional power flow s. This indicates the sensitivity of unit i to the cross-sectional power flow s. This indicates the sensitivity of the inter-provincial DC tie line m to the cross-sectional power flow s. This indicates the sensitivity of the load d to the cross-sectional tidal current s. This represents the transmission power of transaction component k during time period t. This represents the transmission power of DC tie line m during time period t. This indicates the lower limit of the power delivery volume. Indicates the lower limit of the power supply of the tie line. This indicates the upper limit of the power transmission capacity of the tie line. This represents the transmission power of load d during time period t. This represents the shadow price information for the load balancing constraints of province a within time period t. This represents the shadow price information of the positive reserve constraint of province a in time period t. This represents the shadow price information of the negative reserve constraint of province a in time period t. This represents the shadow price information indicating the lower limit of the output of unit i during time period t. This represents the shadow price information for the maximum output of generator unit i during time period t. This represents the shadow price information for unit i during the uphill climb in time period t. This represents the shadow price information for unit i during the downhill climb in time period t. This represents the shadow price information representing the lower bound constraint of the cross-sectional current s over time period t. This represents the shadow price information indicating the upper limit constraint of the cross-sectional current s over time period t. This represents the shadow price information representing the coupling constraints of transaction component k within time period t. The shadow price information represents the electricity delivery constraint of component k in inter-provincial transactions. This represents the shadow price information indicating the non-negativity constraint of the power of cross-provincial transaction component k in time period t. This represents the shadow price information for the lower limit of the power transmission capacity of the inter-provincial DC transmission line m during time period t. The shadow price information represents the upper limit of the power transmission capacity of the inter-provincial DC transmission line m within the time period t. This represents the shadow price information of the cross-sectional tidal current s under the positive relaxation variable non-negative constraint in time period t. This represents the shadow price information of the non-negative constraint of the negative slack variable of the cross-sectional current s over time period t.

6. The method for processing fully constrained shadow price information based on electricity spot market parameters according to claim 1, characterized in that, The associations include: The first correlation between the price quoted by each unit in the power system and the shadow price information corresponding to the output of each unit meeting the corresponding constraints; The second correlation between the transmission price of each inter-provincial trading component and the shadow price information corresponding to the transmission power of each inter-provincial trading component satisfying the corresponding constraints; The third correlation relationship between the cross-sectional current flow constraint over-limit penalty factor and the positive current flow relaxation variables and negative current flow relaxation variables of each cross-section satisfying the shadow price information corresponding to the corresponding constraints. The first association relationship is as follows: in, This represents the quote for unit i. This represents the output of unit i during time period t. This represents the shadow price information for the load balancing constraints of province a within time period t. This represents the shadow price information of the positive reserve constraint of province a in time period t. This represents the shadow price information of the negative reserve constraint of province a in time period t. This represents the shadow price information indicating the lower limit of the output of unit i during time period t. This represents the shadow price information for the maximum output of generator unit i during time period t. This represents the shadow price information for unit i during the uphill climb in time period t. This represents the shadow price information for unit i during the downhill climb in time period t. This represents the shadow price information for unit i during the uphill climb in time period t+1. This represents the shadow price information for unit i during the downhill climb in time period t+1. This indicates the sensitivity of unit i to the cross-sectional power flow s. This represents the shadow price information indicating the upper limit constraint of the cross-sectional current s over time period t. The shadow price information represents the lower bound constraint of the cross-sectional flow s within time period t, and NS represents the total number of cross-sectional flows. The second association is: in, This represents the transmission price of component k in inter-provincial electricity transactions. This represents the shadow price information within time period t, which is constrained by the load balancing of the end province r. This represents the shadow price information for the load balancing constraints of the sending province s over time period t. This indicates the sensitivity of the inter-provincial DC tie line m to the cross-sectional power flow s. This represents the shadow price information representing the lower bound constraint of the cross-sectional current s over time period t. This represents the shadow price information indicating the upper limit constraint of the cross-sectional current s over time period t. This represents the shadow price information for the lower limit of the power transmission capacity of the inter-provincial DC transmission line m during time period t. The shadow price information represents the upper limit of the power transmission capacity of the inter-provincial DC transmission line m within the time period t. The shadow price information represents the electricity delivery constraint of component k in inter-provincial transactions. The third association is: or, in, This represents the shadow price information of the cross-sectional tidal current s under the positive relaxation variable non-negative constraint in time period t. This represents the shadow price information of the non-negative constraint of the negative slack variable of the cross-sectional current s over time period t.

7. The method for processing fully constrained shadow price information based on electricity spot market parameters according to claim 1, wherein the correlation includes a first correlation between the bid price of each generating unit in the power system and the shadow price information corresponding to the output of each generating unit satisfying the corresponding constraints, and the method for outputting abnormal electricity price tracing results based on the shadow price information and the correlation is characterized in that, include: If the unit's bid price is greater than the unit's power generation cost, the abnormal electricity price tracing result is determined to be an abnormal bid price for the unit. When the unit's quoted price equals the unit's power generation cost, the abnormal electricity price tracing result is determined to be an anomaly in the shadow price information corresponding to the unit's output meeting the relevant constraints.

8. A fully constrained shadow price information processing device based on electricity spot market parameters, characterized in that, The device includes: The market parameter acquisition module is used to acquire regional electricity spot market parameters; the regional electricity spot market parameters are used to record the bid prices of each unit in the power system, the transmission prices of each inter-provincial transaction component, and the cross-section power flow constraint over-limit penalty factor; The clearing model building module is used to establish a regional electricity spot market clearing model with the optimization objective of minimizing the sum of the total generation cost of the power system, the inter-provincial transmission cost and the cross-section power flow constraint over-limit penalty cost. The constraints are the output of each unit, the transmission power of each inter-provincial transaction component, and the forward power flow relaxation variables and reverse power flow relaxation variables of each section. The shadow price information acquisition module is used to perform a solution operation on the regional electricity spot market clearing model to obtain the shadow price information corresponding to the constraints and the correlation between the regional electricity spot market parameters and the shadow price information; the correlation is used to characterize that the regional electricity spot market parameters are a linear combination of the shadow price information; The traceability result output module is used to output the traceability result of abnormal electricity prices based on the shadow price information and the correlation relationship.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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