Cloud-edge collaborative-based power system balanced optimization regulation method and device

Abstract

The application discloses a cloud-edge collaborative-based power system balance optimization regulation method and device, wherein the method comprises the following steps: acquiring a target point set of a market residual demand curve; solving a strategy bidding model of a generator set which is established in advance according to the target point set of the market residual demand curve to obtain an optimal strategy bidding coefficient of the generator set; judging whether the current market is balanced according to the optimal strategy bidding coefficient of the generator set and a preset balance condition, wherein when the current market is balanced, generating an energy balance optimization regulation scheme of the market according to the optimal strategy bidding coefficient of the generator set. Thus, the problems that key information of the market cannot be timely and fairly disclosed in the power market optimization scheduling, and the efficiency of the power market is reduced are solved.

Description

Technical Field

[0001] This application relates to the field of power system optimization operation technology, and in particular to a power system equilibrium optimization control method and device based on cloud-edge collaboration. Background Technology

[0002] Promoting the sharing, mutual assistance, and optimal allocation of power resources on a larger scale nationwide through market-based mechanisms, and advancing the construction of a unified electricity market, is imperative. In a larger-scale electricity market with a wider audience, each generating unit can only determine its optimal bid by possessing key market information. When information asymmetry exists among participants, rational decision-making by generating units and market efficiency cannot be guaranteed. To improve market efficiency and orderliness, the disclosure of key market information is necessary.

[0003] Current electricity market economic dispatch and clearing methods rarely consider the timely disclosure of market information by market operators. Related technologies are mostly based on mathematical programming with equilibrium constraints (MPEC) problems, characterizing the process by which market participants reach equilibrium through bidding. Limited by the single-bid clearing model of existing electricity markets, MPEC problems are usually based on hypothetical scenarios or predicted data, failing to fully leverage the guiding role of information in generator decision-making. This leads to tentative or irrational bidding behavior by participants, resulting in low market efficiency.

[0004] To ensure the disclosure of market information and leverage its guiding role in market equilibrium, related technologies have explored the market information that should be disclosed in the electricity market and its interaction with the power grid. Furthermore, research exists on optimal information dissemination strategies among power generation companies, electricity retailers, electricity users, the power grid, and dispatching agencies.

[0005] However, existing power market optimization dispatching methods cannot disclose key market information in a timely and fair manner, and cannot fully leverage the rationality and competitiveness of generator unit strategic bidding behavior through information exchange, resulting in reduced power market efficiency. There is currently no relevant literature on energy balance optimization control with real-time information exchange. Summary of the Invention

[0006] This application provides a cloud-edge collaborative power system equilibrium optimization control method and device to solve the problems of the inability to disclose key market information in a timely and fair manner in the existing power market optimization dispatch, which leads to the reduction of power market efficiency.

[0007] The first aspect of this application provides a power system equilibrium optimization control method based on cloud-edge collaboration, comprising the following steps: obtaining a target point set of the market surplus demand curve; solving a pre-established strategic bidding model of generator units based on the target point set of the market surplus demand curve to obtain the optimal strategic bidding coefficient of the generator units; determining whether the current market is in equilibrium based on the optimal strategic bidding coefficient of the generator units and preset equilibrium conditions, wherein, when the current market is in equilibrium, generating an energy equilibrium optimization control scheme for the market based on the optimal strategic bidding coefficient of the generator units.

[0008] Optionally, in one embodiment of this application, before obtaining the set of vertices of the market residual demand curve, the method further includes: obtaining basic market operation data; calculating the overall market residual demand curve based on the basic market operation data; calculating the clearing price coordinates of the vertices of the residual demand curve and the power coordinates of the vertices of the residual demand curve according to the overall market residual demand curve; and generating a set of target points for the market residual demand curve based on the clearing price coordinates of the vertices of the residual demand curve and the power coordinates of the vertices of the residual demand curve.

[0009] Optionally, in one embodiment of this application, obtaining basic market operation data includes: obtaining data on generator sets, including: the upper limit of technical output of each generator set, the primary term coefficient and the secondary term coefficient of each generator set's bid; and obtaining the day-ahead active load forecast parameters within the market scope.

[0010] Optionally, in one embodiment of this application, the overall market residual demand curve is calculated based on the basic market operation data, wherein the formula for calculating the overall market residual demand curve is:

[0011]

[0012]

[0013]

[0014] Wherein, equation (1) represents the maximum and minimum marginal cost constraints; a i b is the coefficient of the linear term of the marginal cost declared by generator unit i. i P is the constant coefficient of the marginal cost declared by generator unit i. i max For the maximum technical output of generator set i, λ i min Let λ be the minimum marginal cost of generator set i. i max Let g be the maximum marginal cost of generator set i; Equation (2) represents the production capacity constraint of generator set i; λ is the market clearing price, and g is the production capacity constraint of generator set i.i (λ,a i ,b i Equation (3) is the function of the winning bid power of generator set i with respect to the market clearing price and its bidding parameters; Equation (4) is the expression for the market surplus demand curve, r m (λ) is a function of the market surplus demand as a function of the clearing price, P d These are the active power load forecast parameters for the day-ahead period within the market scope.

[0015] Optionally, in one embodiment of this application, calculating the clearing price coordinates of the apex of the remaining demand curve and the power coordinates of the apex of the remaining demand curve based on the overall market remaining demand curve, and generating a target point set of the market remaining demand curve based on the clearing price coordinates of the apex of the remaining demand curve and the power coordinates of the apex of the remaining demand curve, includes: setting λ0 = 0, and calculating the clearing price coordinates of the apex of the remaining demand curve:

[0016] λ m =min{λ|λ∈λ min ∪λ max ,λ>λ m-1} (4)

[0017] Where, λ m Let be the clearing price at the m-th vertex of the market surplus demand curve. The set of minimum marginal costs. The set of maximum marginal costs; the calculated N R The clearing price values ​​of each vertex constitute a set.

[0018] Calculate the power coordinates of the vertex of the remaining demand curve:

[0019]

[0020] Among them, P i R Let N be the power coordinate of the i-th vertex of the market surplus demand curve; calculate N... R The power values ​​of each vertex constitute a set. make The set of vertices of the market surplus demand curve is taken as the set of target points of the market surplus demand curve.

[0021] Optionally, in one embodiment of this application, before solving the pre-established strategic pricing model of the generator set based on the target point set of the market residual demand curve, the method further includes: establishing the strategic pricing model of the generator set:

[0022]

[0023]

[0024] Equation (6) is the expression for the residual demand curve of generator set s; r s (λ) Residual demand curve function of generator set s, r m (λ) is determined by the set of vertices of the market surplus demand curve. P represents the coefficients of the linear and constant terms of the marginal cost declared by generator set s in the previous round of bidding; Equation (7) is the optimization model expression for generator set s to optimize its own profit; s The rated power of generator set s.

[0025] Optionally, in one embodiment of this application, solving the pre-established strategic pricing model of the generator sets based on the target point set of the market residual demand curve to obtain the optimal strategic pricing coefficient of the generator sets includes: solving the strategic pricing model of each generator set:

[0026]

[0027]

[0028] Equation (8) is the equivalent form of the optimization problem of equation (7); q s (P s ) is r s The inverse function of (λ), Let be the clearing power that maximizes the profit of generator set s; Equation (9) is the constraint condition for the strategy bidding coefficient; Based on the clearing power that maximizes the profit of generator set s and the constraint condition for the strategy bidding coefficient, the optimal strategy bidding coefficient a of the generator set is obtained. s and b s .

[0029] Optionally, in one embodiment of this application, the preset equilibrium condition includes: the change between the optimal strategy bidding coefficient of each generator set and the optimal strategy bidding coefficient of the previous round is less than a preset error threshold.

[0030] Optionally, in one embodiment of this application, generating a market energy balance optimization control scheme based on the optimal strategy bidding coefficient of the generator set includes: calculating the market clearing price and the winning bid power of each generator set based on the optimal strategy bidding coefficient of the generator set, and obtaining the market energy balance optimization control result.

[0031] A second aspect of this application provides a power system equilibrium optimization and control device based on cloud-edge collaboration, comprising: an acquisition module for acquiring a set of target points of the market surplus demand curve; a processing module for solving a pre-established strategic bidding model of generator units based on the set of target points of the market surplus demand curve to obtain the optimal strategic bidding coefficient of the generator units; and a control module for determining whether the current market is in equilibrium based on the optimal strategic bidding coefficient of the generator units and preset equilibrium conditions, wherein, when the current market is in equilibrium, an energy equilibrium optimization and control scheme for the market is generated based on the optimal strategic bidding coefficient of the generator units.

[0032] Optionally, in one embodiment of this application, the acquisition module includes: a calculation unit, configured to acquire basic market operation data and calculate the overall market residual demand curve based on the basic market operation data; and a generation unit, configured to calculate the clearing price coordinates of the vertices of the residual demand curve and the power coordinates of the vertices of the residual demand curve based on the overall market residual demand curve, and generate a set of target points for the market residual demand curve based on the clearing price coordinates of the vertices of the residual demand curve and the power coordinates of the vertices of the residual demand curve.

[0033] Optionally, in one embodiment of this application, the calculation unit is further configured to: acquire data of the generator sets, including: the upper limit of the technical output of each generator set, the first-order coefficient and the second-order coefficient of the bid price of each generator set; acquire the day-ahead active power load forecast parameters within the market scope; and calculate the overall market residual demand curve based on the basic market operation data, wherein the formula for calculating the overall market residual demand curve is:

[0034]

[0035]

[0036]

[0037] Wherein, equation (10) represents the maximum and minimum marginal cost constraints; a i b is the coefficient of the linear term of the marginal cost declared by generator unit i. i P is the constant coefficient of the marginal cost declared by generator unit i. i max For the maximum technical output of generator set i, λ i min Let λ be the minimum marginal cost of generator set i. i max Let g be the maximum marginal cost of generator set i; Equation (11) is the production capacity constraint of generator set i; λ is the market clearing price, and g is the production capacity constraint of generator set i. i (λ,a i ,bi Equation (12) is the function of the winning bid power of generator set i with respect to the market clearing price and its bidding parameters; Equation (12) is the expression for the market surplus demand curve, r m (λ) is a function of the market surplus demand as a function of the clearing price, P d These are the active power load forecast parameters for the day-ahead period within the market scope;

[0038] The generation unit is further configured to, setting λ0 = 0, calculate the clearing price coordinates of the vertex of the residual demand curve:

[0039] λ m =min{λ|λ∈λ min ∪λ max ,λ>λ m-1} (13)

[0040] Where, λ m Let be the clearing price at the m-th vertex of the market surplus demand curve. The set of minimum marginal costs. The set of minimum marginal costs; the calculated N R The clearing price values ​​of each vertex constitute a set. Calculate the power coordinates of the vertex of the remaining demand curve:

[0041]

[0042] Among them, P i R Let N be the power coordinate of the i-th vertex of the market surplus demand curve; calculate N... R The power values ​​of each vertex constitute a set. make The set of vertices of the market surplus demand curve is taken as the set of target points of the market surplus demand curve.

[0043] Optionally, in one embodiment of this application, the processing module includes: a modeling unit, used to model a strategy pricing model for the generator set.

[0044]

[0045]

[0046] Equation (15) is the expression for the residual demand curve of generator set s; r s (λ) Residual demand curve function of generator set s, r m (λ) is determined by the set of vertices of the market surplus demand curve. Let P represent the coefficients of the linear and constant terms of the marginal cost declared by generator set s in the previous round of bidding; Equation (16) is the optimization model expression for generator set s to optimize its own profit; s Let be the rated power of generator set s;

[0047] The solver unit is used to solve the strategy pricing model for each generator set.

[0048]

[0049]

[0050] Equation (17) is the equivalent form of the optimization problem of equation (16); q s (P s ) is r s The inverse function of (λ), Let be the clearing power that maximizes the profit of generator set s; Equation (18) is the constraint condition for the strategy bidding coefficient; Based on the clearing power that maximizes the profit of generator set s and the constraint condition for the strategy bidding coefficient, the optimal strategy bidding coefficient a of the generator set is obtained. s and b s .

[0051] Optionally, in one embodiment of this application, the control module is further configured to calculate the market clearing price and the winning bid power of each generator set based on the optimal strategy bidding coefficient of the generator set when the change between the optimal strategy bidding coefficient of each generator set and the optimal strategy bidding coefficient of the previous round is less than a preset error threshold, thereby obtaining the energy balance optimization control result of the market.

[0052] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the cloud-edge collaborative power system balancing optimization control method as described in the above embodiments.

[0053] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the cloud-edge collaborative power system balance optimization control method as described in the above embodiments.

[0054] Therefore, this application has at least the following beneficial effects:

[0055] The pre-established strategic bidding model for generating units is solved by analyzing the target point set of the market residual demand curve to obtain the optimal strategic bidding coefficients for the generating units. Based on these coefficients and pre-defined equilibrium conditions, the current market equilibrium is determined, and an optimized energy balance control scheme is generated according to the optimal bidding coefficients. Through refined information interaction and iterative decision-making processes between the cloud and the edge, the privacy of generating units is protected while key information is disclosed in the cloud. This allows each generating unit at the edge to make full decisions and form rational bids under complete information, improving the efficiency of the electricity market equilibrium outcome. This solves the problem of reduced efficiency in electricity market efficiency caused by the inability to disclose key market information in a timely and fair manner during optimal dispatch.

[0056] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0057] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0058] Figure 1 A flowchart illustrating a power system equilibrium optimization control method based on cloud-edge collaboration, provided for an embodiment of this application;

[0059] Figure 2 A schematic diagram of a power system equilibrium optimization control method based on cloud-edge collaboration provided in an embodiment of this application;

[0060] Figure 3 A block diagram illustrating a cloud-edge collaborative power system balancing optimization and control device provided in this application embodiment;

[0061] Figure 4 A schematic diagram of the structure of the electronic device provided in the application embodiment.

[0062] Explanation of reference numerals in the attached diagram: Acquisition module-100, Processing module-200, Control module-300, Memory-401, Processor-402, Communication interface-403. Detailed Implementation

[0063] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0064] The following description, with reference to the accompanying drawings, outlines a cloud-edge collaborative power system equilibrium optimization control method, apparatus, electronic device, and storage medium based on embodiments of this application. Addressing the problems mentioned in the background section, this application provides a cloud-edge collaborative power system equilibrium optimization control method. In this method, a target point set of the market surplus demand curve is obtained; a pre-established strategic bidding model for generating units is solved based on the target point set of the market surplus demand curve to obtain the optimal strategic bidding coefficients for the generating units; and the market equilibrium is determined based on the optimal strategic bidding coefficients of the generating units and preset equilibrium conditions. If the market is in equilibrium, an energy equilibrium optimization control scheme is generated based on the optimal strategic bidding coefficients of the generating units. This solves the problem of the inability to disclose key market information in a timely and fair manner during power market optimization scheduling, which leads to reduced efficiency in the power market.

[0065] Specifically, Figure 1 This is a flowchart illustrating a cloud-edge collaborative power system equilibrium optimization control method provided in an embodiment of this application.

[0066] like Figure 1 As shown, the power system equilibrium optimization control method based on cloud-edge collaboration includes the following steps:

[0067] In step S101, the target point set of the market remaining demand curve is obtained.

[0068] To improve the efficiency of electricity market equilibrium outcomes and enable generating units in the electricity market to make full decisions with complete information, this application utilizes cloud-based and generator-side collaboration. The cloud platform compiles real-time bidding information from generator units at the edge and discloses key information to market participants. Generating units then perform power system equilibrium optimization and control based on the disclosed information. Firstly, an embodiment of this application obtains the set of target points in the market residual demand curve calculated by the cloud.

[0069] Information disclosed in the cloud directly impacts the decision-making and privacy of market participants. The embodiments of this application, through a refined information disclosure method in the cloud, protect the privacy of market participants while ensuring they have sufficient information for decision-making. For example... Figure 2 As shown, the process includes three steps: obtaining basic market operation data, calculating the overall remaining demand curve of the market, and disclosing the resulting remaining demand curve. The specific steps are as follows:

[0070] 1-1) Obtain basic market operation data, specifically including:

[0071] 1-1-1) Generator set related data: the upper limit of technical output of each generator set, the linear and quadratic coefficients of the bid price of each generator set, where the bid price refers to the quadratic function of the cost of the generator set with respect to the bid power;

[0072] 1-1-2) Forecast parameters: Day-ahead active power load forecast within the market scope;

[0073] 1-2) Calculate the overall market residual demand curve, as shown in the following expression:

[0074]

[0075]

[0076]

[0077] Wherein, equation (1) represents the maximum and minimum marginal cost constraints; a i b is the coefficient of the linear term of the marginal cost declared by generator unit i. i P is the constant coefficient of the marginal cost declared by generator unit i. i max For the maximum technical output of generator set i, λ i min Let λ be the minimum marginal cost of generator set i. i max Let $\mathbf{i}$ be the maximum marginal cost of generator set $i$. The marginal cost of generator set $i$ is the first derivative of its bid function.

[0078] Equation (2) represents the production capacity constraint of generator unit i; λ is the market clearing price, and g i (λ,a i ,b i Let be the winning bid power of generator set i as a function of the market clearing price and its bidding parameters;

[0079] Equation (3) is the expression for the market surplus demand curve, r m (λ) is a function of the market surplus demand as a function of the clearing price, P d These are the active power load forecast parameters for the current day within the market scope.

[0080] In one embodiment of this application, the clearing price coordinates and power coordinates of the apex of the remaining demand curve are calculated based on the overall market remaining demand curve, and a set of target points for the market remaining demand curve is generated based on the clearing price coordinates and power coordinates of the apex of the remaining demand curve.

[0081] 1-3) After calculating the overall remaining demand curve of the market, the remaining demand curve of the market is disclosed in the cloud, specifically including:

[0082] 1-3-1) Let λ0 = 0, calculate the clearing price coordinates of the vertex of the residual demand curve, as shown in the following expression:

[0083] λm =min{λ|λ∈λ min ∪λ max ,λ>λ m-1} (4)

[0084] Where, λ m Let be the clearing price at the m-th vertex of the market surplus demand curve. The set of minimum marginal costs. The set of values ​​with maximum marginal cost;

[0085] The calculated N R The clearing price values ​​of each vertex constitute a set.

[0086] 1-3-2) Calculate the power coordinates of the peak of the residual demand curve, as shown in the following expression:

[0087]

[0088] Among them, P i R The power coordinates of the i-th vertex of the market surplus demand curve;

[0089] The calculated N R The power values ​​of each vertex constitute a set.

[0090] 1-3-3) Order Real-time cloud-based announcement (λ) R ,P R This yields the set of vertices of the market surplus demand curve, which is the set of target points of the market surplus demand curve.

[0091] In step S102, the pre-established strategic pricing model of the generator set is solved based on the target point set of the market residual demand curve to obtain the optimal strategic pricing coefficient of the generator set.

[0092] like Figure 2 As shown, the embodiments of this application obtain strategic pricing information for each edge generator unit through an optimized decision-making method. This includes three steps: obtaining supply and demand information from the cloud market, a strategic pricing decision-making method for each edge generator unit, and uploading strategic pricing information by each edge generator unit. The specific steps are as follows:

[0093] 2-1) Obtain supply and demand information in the cloud market, including the set of vertices of the market remaining demand curve obtained in step S101;

[0094] 2-2) Strategic Bidding Decision Method for Each Edge Generator Unit: The strategic bidding process for generator units is not unique. In order to achieve the purpose of subsequent equilibrium analysis and control, this application proposes a strategic bidding decision method for each edge generator unit based on the principles of information disclosure and profit maximization. The specific steps are as follows:

[0095] 2-2-1) Establish the strategy bidding model for each end generator unit, with the following expression:

[0096]

[0097]

[0098] Equation (6) is the expression for the residual demand curve of generator set s; r s (λ) Residual demand curve function of generator set s, r m (λ) is determined by the set of vertices of the market surplus demand curve. These represent the coefficients of the linear and constant terms of the marginal cost declared by generator set s in the previous round of bidding, respectively.

[0099] Equation (7) is the optimization model expression for the generator set s to optimize its own profit; P s Let be the rated power of generator set s;

[0100] It should be noted that in practical applications, in order to control market behavior and deal with supply and demand tensions, regulatory agencies can set an upper limit constraint on the strategy pricing coefficient to avoid excessively high clearing prices. In response to the above policy constraints, additional constraints need to be considered in (7).

[0101] 2-2-2) Solve the strategic pricing model for generator sets established in the above embodiment based on the target point set of the market surplus demand curve to obtain the optimal strategic pricing coefficient for generator sets. This specifically includes the following steps:

[0102] The strategy pricing model for each generator unit is solved as follows:

[0103]

[0104]

[0105] Equation (8) is the equivalent form of the optimization problem of equation (7); q s (P s ) is r s The inverse function of (λ), Let be the clearing power that maximizes the profit of generator set s; Equation (9) is the constraint condition for the strategy bidding coefficient; Solving, we get Then, generator set s can determine the optimal strategy bidding coefficient a according to equation (9). s and b s ;

[0106] 2-3) The strategic quotation information of each generator unit at the edge is submitted to the cloud, along with the coefficients a of the linear and constant terms of its marginal cost. s b s .

[0107] In step S103, the market is determined to be in equilibrium based on the optimal strategy bidding coefficient of the generator set and the preset equilibrium conditions. If the market is in equilibrium, an energy equilibrium optimization and control scheme for the market is generated based on the optimal strategy bidding coefficient of the generator set.

[0108] It is understood that, in the embodiments of this application, after the generator set submits the strategy quotation information to the cloud, the embodiments of this application can determine whether the current strategy quotation information can achieve power system equilibrium based on the strategy quotation information and the pre-set equilibrium conditions.

[0109] As one possible implementation, the equilibrium condition can be that the change between the optimal strategy bidding coefficient of each generator set and the optimal strategy bidding coefficient of the previous round is less than a preset error threshold. Specifically, it is determined whether the change in the strategy bidding coefficient of each generator set compared to its previous bid is less than the preset error upper limit. If so, the equilibrium result of the cloud-edge collaborative market is obtained, and the bidding information submitted by each edge generator set at this time is the market equilibrium bid. The preset error upper limit can be taken between 0 and 0.1 according to the accuracy requirements, with 0 indicating an exact solution. If not, the cloud recalculates and discloses the remaining market demand curve, and each edge generator set simulates and updates its bidding coefficient according to the newly calculated remaining market demand curve.

[0110] Optionally, in one embodiment of this application, generating a market energy balance optimization control scheme based on the optimal strategy bidding coefficient of the generator sets includes: calculating the market clearing price and the winning bid power of each generator set based on the optimal strategy bidding coefficient of the generator sets, and obtaining the market energy balance optimization control result.

[0111] In the embodiments of this application, the cloud computing energy balance optimization and control results are used to determine and publish the clearing price and the winning bid power of each edge generator unit; the winning bid power of generator unit s is the edge generator unit s in the last iteration process. Accordingly, the market clearing price is calculated using the following formula:

[0112]

[0113] Since the market reaches equilibrium after the last iteration, the clearing price calculated for any generator set s should be the same, and only one calculation corresponding to (10) needs to be performed.

[0114] The cloud-edge collaborative power system equilibrium optimization control method discloses overall market supply and demand information to edge generator units in real time through the cloud and allows edge generator units to change their declared cost information in real time. This replaces the traditional economic dispatch method, which only allows single bids and relies on market operators to directly clear the market. The following is a detailed description of the cloud-edge collaborative power system equilibrium optimization control method of this application using a specific embodiment. The specific steps are as follows:

[0115] 3-1) Initialize the price of the generator sets at the edge; use the price of each generator set equalized the previous day as the initial value of the price of the generator sets for the current day;

[0116] 3-2) Cloud computing and disclosure of the remaining market demand curve;

[0117] 3-3) Simulate and update the bidding coefficients of each generator set at the edge;

[0118] 3-4) The cloud determines whether the market has reached equilibrium. If the change in the strategy bidding coefficient of each generator set compared to its previous bid is less than the preset error limit, then the equilibrium result of the cloud-edge collaborative market is obtained, and the bidding information submitted by each edge generator set at this time is the market equilibrium bid; otherwise, return to step 3-2) and iterate the cloud disclosure of the remaining market demand curve information and the edge update bidding results again.

[0119] 3-5) Calculate the market clearing price and the winning bid power of each generator set based on the optimal strategy bidding coefficient of the generator set, and obtain the energy balance optimization and control results of the market.

[0120] The proposed cloud-edge collaborative power system equilibrium optimization and control method, as described in this application, discloses information in real time through the cloud. At the edge, each generating unit optimizes its bidding based on the disclosed information. This method protects the privacy of generating units while ensuring the disclosure of key information in the cloud, enabling them to make informed decisions and form rational bids. This improves the efficiency of power market equilibrium outcomes. It addresses the problems in existing power market optimization and dispatching methods, such as the inability to disclose key market information in a timely and fair manner, which leads to reduced power market efficiency.

[0121] Next, referring to the accompanying drawings, a power system balance optimization and control device based on cloud-edge collaboration is described according to an embodiment of this application.

[0122] Figure 3This is a block diagram of a power system balancing optimization and control device based on cloud-edge collaboration, according to an embodiment of this application.

[0123] like Figure 3 As shown, the cloud-edge collaborative power system balancing optimization and control device 10 includes: an acquisition module 100, a processing module 200, and a control module 300.

[0124] The module 100 is used to acquire the set of target points of the market surplus demand curve. The processing module 200 solves the pre-established strategic pricing model of the generator sets based on the set of target points of the market surplus demand curve to obtain the optimal strategic pricing coefficients of the generator sets. The control module 300 is used to determine whether the current market is in equilibrium based on the optimal strategic pricing coefficients of the generator sets and preset equilibrium conditions. If the current market is in equilibrium, it generates an energy equilibrium optimization control scheme for the market based on the optimal strategic pricing coefficients of the generator sets.

[0125] Optionally, in one embodiment of this application, the acquisition module includes: a calculation unit, used to acquire basic market operation data and calculate the overall market residual demand curve based on the basic market operation data; and a generation unit, used to calculate the clearing price coordinates of the apex of the residual demand curve and the power coordinates of the apex of the residual demand curve based on the overall market residual demand curve, and generate a set of target points for the market residual demand curve based on the clearing price coordinates of the apex of the residual demand curve and the power coordinates of the apex of the residual demand curve.

[0126] Optionally, in one embodiment of this application, the calculation unit is further configured to: acquire data on generator sets, including: the upper limit of technical output of each generator set, the primary and secondary coefficients of the bid price of each generator set; acquire the day-ahead active power load forecast parameters within the market scope; and calculate the overall market residual demand curve based on basic market operation data, wherein the formula for calculating the overall market residual demand curve is:

[0127]

[0128]

[0129]

[0130] Wherein, equation (11) represents the maximum and minimum marginal cost constraints; a i b is the coefficient of the linear term of the marginal cost declared by generator unit i. i P is the constant coefficient of the marginal cost declared by generator unit i. i max For the maximum technical output of generator set i, λ i min Let λ be the minimum marginal cost of generator set i.i max Let g be the maximum marginal cost of generator set i; Equation (12) is the production capacity constraint of generator set i; λ is the market clearing price, and g is the production capacity constraint of generator set i. i (λ,a i ,b i ) is a function of the winning bid power of generator set i with respect to the market clearing price and its bidding parameters; Equation (13) is the expression for the market surplus demand curve, r m (λ) is a function of the market surplus demand as a function of the clearing price, P d These are the active power load forecast parameters for the day-ahead period within the market scope;

[0131] The generation unit is further used to calculate the clearing price coordinates of the vertex of the residual demand curve, setting λ0 = 0:

[0132] λ m =min{λ|λ∈λ min ∪λ max ,λ>λ m-1} (14)

[0133] Where, λ m Let be the clearing price at the m-th vertex of the market surplus demand curve. The set of minimum marginal costs. The set of maximum marginal costs; the calculated N R The clearing price values ​​of each vertex constitute a set. Calculate the power coordinates of the peak of the residual demand curve:

[0134]

[0135] Among them, P i R Let N be the power coordinate of the i-th vertex of the market surplus demand curve; calculate N... R The power values ​​of each vertex constitute a set. make The set of vertices of the market surplus demand curve is taken as the set of target points of the market surplus demand curve.

[0136] Optionally, in one embodiment of this application, the processing module includes: a building unit, used to build a strategy pricing model for the generator set.

[0137]

[0138]

[0139] Equation (16) is the expression for the residual demand curve of generator set s; r s(λ) Residual demand curve function of generator set s, r m (λ) is determined by the set of vertices of the market surplus demand curve. P represents the coefficients of the linear and constant terms of the marginal cost declared by generator set s in the previous round of bidding; Equation (17) is the optimization model expression for generator set s to optimize its own profit; s Let be the rated power of generator set s;

[0140] The solver unit is used to solve the strategy pricing model for each generator set.

[0141]

[0142]

[0143] Equation (18) is the equivalent form of the optimization problem of equation (17); q s (P s ) is r s The inverse function of (λ), Let be the clearing power that maximizes the profit of generator set s; Equation (19) is the constraint condition for the strategy bidding coefficient; Based on the clearing power that maximizes the profit of generator set s and the constraint condition for the strategy bidding coefficient, the optimal strategy bidding coefficient a of the generator set is obtained. s and b s .

[0144] Optionally, in one embodiment of this application, the control module is further configured to calculate the market clearing price and the winning bid power of each generator set based on the optimal strategy bidding coefficient of the generator set when the change between the optimal strategy bidding coefficient of each generator set and the optimal strategy bidding coefficient of the previous round is less than a preset error threshold, thereby obtaining the energy balance optimization control result of the market.

[0145] It should be noted that the foregoing explanation of an embodiment of a power system equilibrium optimization control method based on cloud-edge collaboration also applies to a power system equilibrium optimization control device based on cloud-edge collaboration in this embodiment, and will not be repeated here.

[0146] The cloud-edge collaborative power system equilibrium optimization and control device proposed in this application, through a refined information interaction and iterative decision-making process between the cloud and the edge, ensures that the cloud discloses key information while protecting the privacy of generating units. This enables each generating unit at the edge to make full decisions and form rational bids under complete information, which is conducive to improving the efficiency of power market equilibrium results. Thus, it solves the problem of the inability to disclose key market information in a timely and fair manner in power market optimization and dispatch, which leads to reduced efficiency of the power market.

[0147] Figure 4A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0148] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0149] When the processor 402 executes the program, it implements the power system balance optimization and control method based on cloud-edge collaboration provided in the above embodiments.

[0150] Furthermore, electronic devices also include:

[0151] Communication interface 403 is used for communication between memory 401 and processor 402.

[0152] The memory 401 is used to store computer programs that can run on the processor 402.

[0153] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0154] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0155] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0156] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0157] This embodiment also provides a computer-readable storage medium storing a computer program thereon, characterized in that the program, when executed by a processor, implements the above-described cloud-edge collaborative power system balance optimization and control method.

[0158] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0159] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0160] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0161] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0162] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A power system equilibrium optimization control method based on cloud-edge collaboration, characterized in that, The cloud-edge collaboration refers to collaboration between the cloud and the edge, where the edge refers to the generator set. The steps include: Obtain basic market operation data, calculate the overall market residual demand curve based on the basic market operation data, calculate the clearing price coordinates and power coordinates of the peak of the residual demand curve based on the overall market residual demand curve, generate the target point set of the market residual demand curve based on the clearing price coordinates and power coordinates of the peak of the residual demand curve, and obtain the target point set of the market residual demand curve. The optimal strategic pricing coefficient for the generator set is obtained by solving the pre-established strategic pricing model of the generator set based on the target point set of the market residual demand curve. The market is determined to be in equilibrium based on the optimal strategy bidding coefficient of the generator set and the preset equilibrium conditions. When the market is in equilibrium, an energy equilibrium optimization and control scheme for the market is generated based on the optimal strategy bidding coefficient of the generator set.

2. The method according to claim 1, characterized in that, The acquisition of basic market operation data includes: Obtain data on generator sets, including: the technical output limit of each generator set, and the primary and secondary coefficients of the price quote for each generator set; Obtain the day-ahead active load forecast parameters within the market scope.

3. The method according to claim 2, characterized in that, The overall market residual demand curve is calculated based on the aforementioned basic market operation data. The formula for calculating the overall market residual demand curve is as follows: （1） （2） （3） Equation (1) represents the maximum and minimum marginal cost constraints. Let the coefficient of the first-order term of the marginal cost declared by generator unit i be . For the constant term coefficient of the marginal cost declared by generator unit i, To maximize the technical output of generator set i, Let be the minimum marginal cost of generator set i. Let $i$ be the maximum marginal cost of generator set $i$. Equation (2) represents the production capacity constraint of generator set i; The market clearing price. Let be the winning bid power of generator set i as a function of the market clearing price and its bidding parameters; Equation (3) is the expression for the market surplus demand curve. Let be a function of market surplus demand as a function of clearing price. These are the active power load forecast parameters for the current day within the market scope.

4. The method according to claim 3, characterized in that, Calculate the clearing price coordinates and power coordinates of the apex of the remaining demand curve based on the overall market remaining demand curve. Generate a target point set for the market remaining demand curve based on the clearing price coordinates and power coordinates of the apex of the remaining demand curve, including: make Calculate the clearing price coordinates of the vertex of the remaining demand curve: （4） in, Let be the clearing price at the m-th vertex of the market surplus demand curve. The set of minimum marginal costs. The set of values ​​with maximum marginal cost; The calculated The clearing price values ​​of each vertex constitute a set. ; Calculate the power coordinates of the vertex of the remaining demand curve: （5） in, The power coordinates of the i-th vertex of the market surplus demand curve; The calculated The power values ​​of each vertex constitute a set. ; make The set of vertices of the market surplus demand curve is taken as the set of target points of the market surplus demand curve.

5. The method according to claim 4, characterized in that, Before solving the pre-established strategic pricing model for generator sets based on the target point set of the market residual demand curve, the following steps are also included: Establish a strategic pricing model for generator sets: （6） （7） Equation (6) is the expression for the residual demand curve of generator set s; The residual demand curve function of generator set s, Determined by the set of vertices of the market surplus demand curve, , These represent the coefficients of the linear and constant terms of the marginal cost declared by generator set s in the previous round of bidding, respectively. Equation (7) is the optimization model expression for the generator set s to optimize its own profit; The rated power of generator set s.

6. The method according to claim 5, characterized in that, Based on the target point set of the market residual demand curve, the pre-established strategic pricing model for generator sets is solved to obtain the optimal strategic pricing coefficients for generator sets, including: Solve the strategy pricing model for each generator set: （8） （9） Equation (8) is the equivalent form of the optimization problem of equation (7); for inverse function, Let be the clearing power that maximizes the profit of generator set s; Equation (9) is the constraint condition for the strategy bidding coefficient; The optimal strategy bidding coefficient for the generator set is obtained based on the clearing power that maximizes the profit of generator set s and the aforementioned strategy bidding coefficient constraints. and .

7. The method according to claim 6, characterized in that, The preset equilibrium condition includes: the change between the optimal strategy bidding coefficient of each generator set and the optimal strategy bidding coefficient of the previous round is less than a preset error threshold.

8. The method according to claim 6, characterized in that, The process of generating a market-based energy balance optimization control scheme based on the optimal strategy bidding coefficient of the generator set includes: The market clearing price and the winning bid power of each generator set are calculated based on the optimal strategy bidding coefficient of the generator set, and the energy balance optimization and control results of the market are obtained.

9. A power system balancing and optimization control device based on cloud-edge collaboration, characterized in that, include: The acquisition module is used to acquire basic market operation data, calculate the overall market residual demand curve based on the basic market operation data, calculate the clearing price coordinates and power coordinates of the peaks of the residual demand curve based on the overall market residual demand curve, generate a target point set for the market residual demand curve based on the clearing price coordinates and power coordinates of the peaks of the residual demand curve, and acquire the target point set for the market residual demand curve. The processing module solves the pre-established strategic pricing model of the generator set based on the target point set of the market residual demand curve to obtain the optimal strategic pricing coefficient of the generator set. The control module is used to determine whether the current market is in equilibrium based on the optimal strategy bidding coefficient of the generator set and preset equilibrium conditions. When the current market is in equilibrium, an energy equilibrium optimization control scheme for the market is generated based on the optimal strategy bidding coefficient of the generator set.

10. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the cloud-edge collaborative power system balance optimization control method as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the cloud-edge collaborative power system balance optimization control method as described in any one of claims 1-8.

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