Power system clearing decision optimization method and system based on capacity demand curve

Abstract

The invention relates to the technical field of electric power systems, in particular to an electric power system clearing decision optimization method and system based on a capacity demand curve, and the method comprises the steps: obtaining the related data of an electric power system in a target region; constructing different types of capacity demand curve calculation models according to the related data of the power system; according to the capacity demand curve calculation model, constructing a double-layer market clearing model which comprises an upper-layer capacity market clearing model and a lower-layer electric energy market clearing model; and based on the double-layer market clearing model, updating different types of capacity demand curves in an interactive iteration manner, and solving an optimal power system clearing decision result. The objective of the invention is to accurately evaluate market clearing results under different capacity demand curves and improve the reliability of market clearing decisions. The relation between the capacity market and the electric energy market is coordinated, and optimal configuration of electric power resources is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and specifically to an optimization method and system for power system clearing decision-making based on a capacity demand curve. Background Art

[0002] Under the background of the global energy structure accelerating towards low-carbon transformation, the power system is undergoing a profound transformation from a traditional mode to a new mode. The new power system uses new energy as the main power source, and the roles of conventional power sources such as traditional thermal power units have gradually changed from dominant power supply to auxiliary regulation. This transformation has brought new challenges to the operation and planning of the power system, among which the issues of capacity adequacy and flexibility have become the key factors restricting the safe and stable operation of the power system. Capacity adequacy is an important indicator to measure the power system's ability to meet load demands in the long term, cope with various uncertainties, and ensure reliable power supply. It is of crucial significance for maintaining the safe and stable operation of the power system. Therefore, in-depth research on the design mechanism of the capacity market has become a key topic in the power field.

[0003] Currently, in the research and practice of the capacity market in the existing technology, although there have been many studies on the capacity demand curve, there are still obvious technical defects. On the one hand, the capacity demand curve not only reflects the market's demand scale and structure for power capacity, but also directly affects the market clearing price and resource allocation efficiency. However, most of the existing studies only focus on the basic shape of the curve and the prediction of the demand scale, lacking in-depth analysis of the impact of different-shaped capacity demand curves on the decision-making results during the market clearing process, resulting in the reliability and accuracy of market clearing decisions being difficult to meet actual needs. On the other hand, the existing technology often studies the capacity market and the electricity energy market separately, ignoring the close connection and interaction between the two markets. This isolated research method leads to the lack of systematicness and coordination in the market mechanism design, making it difficult to achieve the optimal allocation of power resources. Summary of the Invention

[0004] In order to accurately evaluate the market clearing results under different capacity demand curves and improve the reliability of market clearing decisions; to coordinate the relationship between the capacity market and the electricity energy market and achieve the optimal allocation of power resources. The present invention provides an optimization method and system for power system clearing decision-making based on a capacity demand curve, and the specific technical solutions adopted are as follows:

[0005] The technical solution of the first aspect of the present invention provides an optimization method for power system clearing decision-making based on a capacity demand curve, and the method includes:

[0006] Obtain relevant data of the power system in the target area;

[0007] Construct calculation models of different types of capacity demand curves according to the relevant data of the power system;

[0008] According to the capacity demand curve calculation model, a two-layer market clearing model is constructed, including an upper-layer capacity market clearing model and a lower-layer electric energy market clearing model;

[0009] Based on the interaction and iteration of the two-layer market clearing model, different types of capacity demand curves are updated, and the optimal power system clearing decision result is solved.

[0010] Furthermore, the relevant data of the power system in the target area are obtained, including:

[0011] Obtain the capacity demand forecast data, installed capacity reserve margin data, new entry cost data, and equivalent forced outage rate data of the reference unit of the power system in the target area.

[0012] Furthermore, different types of capacity demand curve calculation models are constructed according to the relevant data of the power system, including:

[0013] Construct a concave capacity demand curve calculation model according to the relevant data of the power system, including:

[0014] Preset the first set of concave curve adjustment coefficients and combine with the relevant data of the power system to calculate the abscissas of at least three key points of the concave curve in the capacity demand dimension;

[0015] Preset the second set of concave curve adjustment coefficients and combine with the relevant data of the power system to calculate the ordinates of at least three key points of the concave curve in the price dimension;

[0016] Fit the concave capacity demand curve according to the abscissas and ordinates of at least three key points of the concave curve.

[0017] Furthermore, the expressions of the abscissas and ordinates of at least three key points of the concave curve are:

[0018] Abscissa of the first key point:

[0019]

[0020] In the formula, C P represents the capacity demand forecast value; γ a represents the first concave curve adjustment coefficient; I RM represents the installed capacity reserve margin;

[0021] Ordinate of the first key point:

[0022]

[0023] In the formula, E represents the new entry cost; E N represents the net new entry cost; R EFORd represents the equivalent forced outage rate of the reference unit;

[0024] The abscissa of the second key point:

[0025]

[0026] In the formula, γ b represents the adjustment coefficient of the second concave curve;

[0027] The ordinate of the second key point:

[0028]

[0029] In the formula, λ b represents the adjustment coefficient of the third concave curve;

[0030] The abscissa of the third key point:

[0031]

[0032] In the formula, γ c represents the adjustment coefficient of the fourth concave curve;

[0033] The ordinate of the third key point:

[0034]

[0035] In the formula, λ c represents the adjustment coefficient of the fifth concave curve.

[0036] Furthermore, constructing calculation models for different types of capacity demand curves based on relevant data and parameters of the power system also includes:

[0037] Constructing a convex capacity demand curve calculation model based on relevant data of the power system, including:

[0038] Presetting the first set of convex curve adjustment coefficients and combining with relevant data of the power system to calculate the abscissas of at least three key points of the convex curve in the capacity demand dimension;

[0039] Presetting the second set of convex curve adjustment coefficients and combining with relevant data of the power system to calculate the ordinates of at least three key points of the convex curve in the price dimension;

[0040] Fitting a convex capacity demand curve based on the abscissas and ordinates of at least three key points of the convex curve.

[0041] Furthermore, constructing an upper-layer capacity market clearing model based on the capacity demand curve calculation model includes:

[0042] Constructing an upper-layer capacity market clearing model with the goal of minimizing the capacity purchase cost, and the expression of the objective function is:

[0043]

[0044] Wherein, C represents the capacity purchase cost; i represents the index of capacity resources, and i ∈ Ω X , Ω X represents the set of various capacity resources; c X,i represents the bid price of the i-th type of capacity resource on the capacity supply curve, that is, the price at which the resource sells its capacity; represents the awarded capacity of the i-th type of capacity resource in the capacity market; d represents the index of the capacity demand curve segment; a represents the index of the capacity demand curve region; c D,d,a represents the price of the d-th segment of the capacity demand curve in region a; represents the awarded capacity of the d-th segment of the capacity demand curve in region a.

[0045] Furthermore, the constraint conditions of the upper-layer capacity market clearing model include:

[0046] Capacity supply-demand balance constraint, configured as the sum of the awarded capacity of its own capacity resources and the net capacity transmitted across regions within each region, equal to the total awarded capacity demand of the region;

[0047] Capacity demand constraint, configured to limit the awarded capacity of each segment of the capacity demand curve in each region to be between 0 and the maximum capacity demand of that segment;

[0048] Interconnection line transmission constraint, configured such that the capacity transmitted by each region to other regions with interconnection lines is between the negative maximum transmission capacity of the interconnection line and the maximum capacity demand of the capacity demand curve of the region.

[0049] Furthermore, according to the capacity demand curve calculation model, the lower-layer electric energy market clearing model is constructed as follows:

[0050] The lower-layer electric energy market clearing model is constructed with the goal of minimizing the electricity purchase cost in the electric energy market, and the expression of the objective function is:

[0051]

[0052] Wherein, E represents the electricity purchase cost in the electric energy market; m represents the index of stock resources; represents the electricity market bid price of stock resource m at time t on day h; represents the awarded quantity of stock resource m in the electric energy market at time t on day h, that is, the actual electricity energy accepted by the market for this stock resource at this time period; n represents the index of incremental resources; represents the electricity market bid price of incremental resource n at time t on day h; represents the awarded quantity of incremental resource n in the electric energy market at time t on day h, that is, the actual electricity energy accepted by the market for this incremental resource at this time period.

[0053] Further, the constraint conditions of the lower-layer electric energy market clearing model include:

[0054] The energy balance constraint is configured such that the sum of the winning bid electricity quantities of the stock resources and incremental resources for each time period of each day in each region, and the net electric energy transmitted from other regions with tie lines, is equal to the load demand of the region at that time period;

[0055] The cleared electricity quantity constraint is configured such that the winning bid electricity quantities of the stock and incremental resources in the electric energy market for each time period are non-negative and do not exceed their installed capacities.

[0056] The technical solution of the second aspect of the present invention provides a power system clearing decision optimization system based on a capacity demand curve, which adopts the power system clearing decision optimization method described in the technical solution of the first aspect of the present invention. The system includes:

[0057] A data acquisition module configured to acquire data related to the power system of the target region;

[0058] A capacity demand curve calculation module configured to construct calculation models of different types of capacity demand curves according to the data related to the power system;

[0059] A two-layer market clearing module configured to construct a two-layer market clearing model according to the capacity demand curve calculation model, including an upper-layer capacity market clearing model and a lower-layer electric energy market clearing model;

[0060] A decision optimization module configured to interactively iterate and update different types of capacity demand curves based on the two-layer market clearing model, and solve the optimal power system clearing decision result.

[0061] The present invention has the following beneficial effects:

[0062] The power system clearing decision optimization method based on the capacity demand curve provided by the present invention constructs a two-layer market clearing model based on the capacity demand curve calculation model. The two-layer market clearing model performs interactive iteration. The capacity market resource clearing combination of the upper layer model is transmitted to the lower layer model, and the clearing quantity and price results of the electricity energy market of the lower layer model are fed back to the upper layer model, thereby affecting the capacity demand curve and the capacity resource clearing combination. Through repeated iteration, the optimal power system clearing decision result is found. By using the capacity demand curve calculation model and the two-layer market clearing model, this method can accurately evaluate the market clearing results under different capacity demand curves, avoid the problem of insufficient decision reliability caused by ignoring the influence of different forms of the capacity demand curve on the market clearing results, and improve the reliability of the market clearing decision. At the same time, through the interactive iteration of the upper and lower layer models, the relationship between the capacity market and the electricity energy market is coordinated, the optimal allocation of power resources is realized, the capacity resource decision-making scheme is made more economical, and the problems of insufficient capacity adequacy and lack of flexibility faced by the power system in the process of transformation to a new mode are effectively addressed. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0064] Figure 1 It is the method flow chart of the power system clearing decision optimization method based on the capacity demand curve provided by an embodiment of the present invention;

[0065] Figure 2 It is the structural schematic diagram of the power system clearing decision optimization system based on the capacity demand curve provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a power system clearing decision optimization method and system based on the capacity demand curve proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0068] The following specifically describes the specific solutions of a power system clearing decision optimization method and system based on a capacity demand curve provided by the present invention in conjunction with the accompanying drawings.

[0069] Please refer to Figure 1 , which shows a flowchart of a method for optimizing a power system clearing decision based on a capacity demand curve provided by an embodiment of the present invention. The method includes:

[0070] Step S100: Obtain relevant data of the power system in the target area; specifically, obtain capacity demand prediction data, installed capacity reserve margin data, new entry cost data, and equivalent forced outage rate data of reference units of the power system in the target area; among them, the capacity demand prediction value can be obtained through comprehensive analysis of factors such as economic development, population growth, and industrial structure changes in the target area. Methods such as time series analysis and regression analysis can be used to predict the power capacity demand in a future period to obtain the capacity demand prediction data of the power system in the target area; the installed capacity reserve margin data is obtained by evaluating factors such as the reliability requirements of the power system, historical power generation equipment failure conditions, and load fluctuation characteristics, and is used to measure the proportion of installed capacity reserved to ensure the stability of power supply; the new entry cost involves the cost accounting of new power generation resources (such as newly built power plants, new energy storage facilities, etc.) entering the market, including at least a series of costs such as equipment procurement, installation and commissioning, land use, and operation management; at the same time, analyze the revenue situations of the electric energy market and the ancillary service market, such as the electric energy price, the ancillary service fee standard, and the revenue distribution rules of market participants, and calculate the net new entry cost, that is, the difference between the new entry cost and the revenue of the electric energy and ancillary service markets, so as to obtain the net new entry cost; the equivalent forced outage rate of the reference unit is obtained by statistically analyzing the historical situation of the unit being forced to stop running due to faults and other reasons. Statistical methods are used to analyze these data to calculate the equivalent forced outage rate of the reference unit, which reflects the reliability of the unit's operation, so as to obtain the equivalent forced outage rate data of the reference unit.

[0071] Step S200: Construct calculation models for different types of capacity demand curves based on relevant power system data; specifically, to construct calculation models for different types of capacity demand curves, key points of different types of capacity demand curves need to be determined. In the capacity demand curve calculation model, key points specifically refer to those special points that can determine the shape and position of the curve; key points are on the two-dimensional plane formed by capacity demand (abscissa) and price (ordinate), and play a crucial role in accurately depicting concave or convex capacity demand curves. Through these points, the general outline of the curve can be outlined, and the key relationship between capacity demand and price in the power market can be reflected; due to the complexity and diversity of the power system, the relationship between its capacity demand and price is not a single pattern. Concave and convex capacity demand curves can represent different market situations respectively. The concave curve reflects that within a certain range, as the capacity demand increases, the price rises relatively slowly, and after reaching a certain stage, the price rises at an accelerated rate; the convex curve represents another market situation, that is, at the beginning, as the capacity demand increases, the price rises rapidly, and then the rising rate gradually slows down, and even the price may decrease.

[0072] In this embodiment, constructing both curves simultaneously can more comprehensively analyze different stages and states of the power market. During the planning and operation of the power system, different curve shapes may appear under different market conditions. Therefore, the two curve shapes have different significances for power market risk assessment and clearing decisions. For example, the steep part of the concave curve can remind market participants and managers of the capacity shortage risk and the potential market instability factors brought about by the rapid price increase; the latter half of the convex curve (the part where the price increase slows down or even decreases) can help analyze the overcapacity risk and the impact of intensified market competition on price. By considering both curves simultaneously, a more comprehensive reference can be provided for aspects such as capacity planning, investment decision-making, and price regulation of the power system to cope with different market changes and risks.

[0073] Step S200 specifically includes:

[0074] Step S210: Construct a concave capacity demand curve calculation model based on relevant power system data, including:

[0075] Step S211: Preset the first set of concave curve adjustment coefficients and combine with relevant power system data to calculate the abscissas of at least three key points of the concave curve in the capacity demand dimension; specifically, the abscissas of at least three key points of the concave curve in the capacity demand dimension can be expressed as:

[0076] Abscissa of the first key point:

[0077]

[0078] In the formula, C PRepresents the predicted value of capacity demand, which is an estimate of the future power capacity demand in a specific area; γ a Represents the first concave curve adjustment coefficient; I RM Represents the installed capacity reserve margin, that is, the proportion of the installed capacity reserved additionally by the system to ensure the stability of power supply; this formula adjusts the predicted value of the regional target capacity demand, combines the installed capacity reserve margin and the adjustment coefficient to obtain the abscissa value, reflecting the position of the corresponding key point in the capacity demand dimension considering market regulation factors and reserve capacity.

[0079] Abscissa of the second key point:

[0080]

[0081] In the formula, γ b Represents the second concave curve adjustment coefficient;

[0082] Abscissa of the third key point:

[0083]

[0084] In the formula, γ c Represents the fourth concave curve adjustment coefficient;

[0085] Step S212: Preset the second group of concave curve adjustment coefficients and combine the relevant data of the power system to calculate the ordinates of at least three key points of the concave curve in the price dimension; specifically, the ordinates of at least three key points of the concave curve in the price dimension can be expressed as:

[0086] Ordinate of the first key point:

[0087]

[0088] In the formula, E represents the new entry cost, that is, the cost required for new generation resources to enter the market; E N Represents the net new entry cost, that is, the difference between the new entry cost and the revenue from the electricity energy and ancillary service market; R EFORd Represents the equivalent forced outage rate of the reference unit, which represents the probability that the unit is forced to stop running due to faults and other reasons; this formula takes the maximum value by comparing the new entry cost and 1.5 times the net new entry cost, and then corrects it in combination with the equivalent forced outage rate of the reference unit to obtain the ordinate value, comprehensively considering the impact of the entry cost and the unit operation reliability on the capacity demand price dimension;

[0089] Ordinate of the second key point:

[0090]

[0091] In the formula, λ b Represents the third concave curve adjustment coefficient;

[0092] The ordinate of the third key point:

[0093]

[0094] In the formula, λ c represents the adjustment coefficient of the fifth concave curve;

[0095] It should be noted that each of the above adjustment coefficients can be set corresponding adjustment coefficients for different key points of the concave and convex curves according to the historical operation of the market; these adjustment coefficients are used to adjust the positions of the key points in the dimensions of capacity demand and price to adapt to different curve shapes and actual market changes.

[0096] Step S213: Fit a concave capacity demand curve according to the abscissas and ordinates of at least three key points of the concave curve; specifically, take the abscissas of at least three key points calculated in step S211 and the corresponding ordinates calculated in step S212 as known data points, and mathematical software such as MATLAB can be used to select curve fitting methods such as polynomial fitting and spline curve fitting to fit a concave capacity demand curve according to these data points. During the fitting process, the fitting parameters should be adjusted according to the actual situation to make the fitting curve reflect the characteristics of the key points as accurately as possible to conform to the relationship between capacity demand and price in the power market;

[0097] In summary, in this embodiment, by constructing a concave capacity demand curve, various key factors of the power system are considered, such as capacity demand prediction, installed reserve margin, and new entry cost; the coordinates of the key points are calculated by means of preset adjustment coefficients and the curve is fitted, accurately presenting the specific capacity demand-price relationship in the power market, that is, when the capacity demand increases within a certain range, the price rises slowly, and after a specific stage, it rises rapidly. It helps market participants and managers to detect in advance the capacity shortage risk and the unstable factors brought by the rapid price increase, provides a reliable basis for the capacity planning, investment decision-making, price regulation, etc. of the power system, and promotes a more reasonable allocation of power resources.

[0098] Step S220: Construct a convex capacity demand curve calculation model according to the relevant data of the power system, including:

[0099] Step S221: Preset the first set of convex curve adjustment coefficients and combine with the relevant data of the power system to calculate the abscissas of at least three key points of the convex curve in the dimension of capacity demand;

[0100] The abscissas of at least three key points of the convex curve in the dimension of capacity demand can be expressed as:

[0101] The abscissa of the fourth key point:

[0102]

[0103] In the formula, γ a ′ represents the first convex curve adjustment coefficient;

[0104] The abscissa of the fifth key point:

[0105]

[0106] In the formula, γ b ′ represents the second convex curve adjustment coefficient;

[0107] The abscissa of the sixth key point:

[0108]

[0109] In the formula, γ c ′ represents the third convex curve adjustment coefficient;

[0110] Step S222 preset the second set of convex curve adjustment coefficients and combine the relevant data of the power system to calculate the ordinates of at least three key points of the convex curve in the capacity and price dimensions;

[0111] The ordinates of at least three key points of the convex curve in the capacity and price dimensions can be expressed as:

[0112] The ordinate of the fourth key point:

[0113]

[0114] The ordinate of the fifth key point:

[0115]

[0116] In the formula, λ ′ b represents the fourth convex curve adjustment coefficient;

[0117] The ordinate of the sixth key point is 0, and the ordinate being 0 is set according to the specific shape of the convex curve and the actual market situation, and is used to determine the capacity demand corresponding to the key point when the price dimension is 0;

[0118] Step S223 fit the convex capacity demand curve according to the abscissas and ordinates of at least three key points of the convex curve;

[0119] The calculation of the convex capacity demand curve constructed in this embodiment provides another perspective for power market analysis. By considering key power system data such as capacity demand forecast, installed reserve margin, and new entry cost, and combining with a preset adjustment coefficient to determine key points, a convex curve is then fitted. This enables us to more comprehensively analyze the operating conditions of the power market at different stages and under different conditions. The convex curve reflects the complex relationship between capacity demand and price in certain market scenarios, such as during infrastructure construction or the initial stage of introducing new technologies. It helps market participants and managers identify risks of overcapacity, the impact of market competition on prices, and the capacity demand when prices reach their limits, providing important reference for capacity planning, investment decisions, and price regulation of the power system, and enhancing the adaptability of power market decisions.

[0120] Step S300: According to the capacity demand curve calculation model, construct a two-layer market clearing model, including an upper-layer capacity market clearing model and a lower-layer electricity energy market clearing model; specifically, the capacity demand curve in this embodiment can be a concave capacity demand curve or a convex capacity demand curve. The concave capacity demand curve provides information on the relationship between capacity demand and price for the upper-layer model. The key points of this curve determine the corresponding prices at different capacity demand levels. When the upper-layer model solves the problem with the goal of minimizing the capacity purchase cost, it will determine the clearing combination of various capacity resources (stock and incremental resources) based on this information. For example, when the capacity demand is low, since the price of the concave curve rises slowly, the model may tend to select some stock resources with lower costs to meet the demand; as the capacity demand increases to the steep part of the curve, the model may consider introducing incremental resources to cope with higher demand and price changes. The clearing combination of capacity resources obtained by the upper-layer model is passed to the lower-layer model. When the lower-layer model solves the electricity energy price for each partition, the capacity demand characteristics reflected by the concave curve will indirectly affect the supply-demand relationship of electricity energy. Since different combinations of capacity resources will affect the power generation capacity and thus affect the supply of electricity energy, the electricity energy price is determined in combination with the load demand. Then the electricity energy price obtained by the lower-layer model is fed back to the upper-layer model, affecting the values of the key points of the concave curve, thereby adjusting the subsequent clearing combination of capacity resources. According to the characteristics of the concave curve, the model can reasonably select stock and incremental resources at different capacity demand stages. When the demand is low, give priority to using stock resources to reduce costs; when the demand grows rapidly, introduce incremental resources in a timely manner to ensure the stability and economy of power supply and achieve the optimal allocation of capacity resources.

[0121] The convex capacity demand curve provides a different capacity demand - price relationship for the upper - layer model. In the initial stage, the curve price rises rapidly. When the model solves the capacity resource clearing portfolio, it will take into account the high cost of meeting capacity demand in the early stage. As the capacity demand increases, the curve price rises more slowly or even decreases. The model can adjust the resource portfolio according to this change, increasing resource investment to utilize the scale effect or respond to market competition. Similar to the concave curve, the capacity resource clearing portfolio of the upper - layer model is transmitted to the lower - layer model, affecting the relationship between electricity energy supply and demand and price calculation. The electricity energy price of the lower - layer model is fed back to the upper - layer model, changing the key - point values of the convex curve, and then affecting subsequent capacity resource clearing decisions. The convex curve is suitable for describing the situation of the electricity market in the initial development stage or infrastructure construction stage. By cooperating with the two - layer model, it can adjust the capacity resource allocation according to different development stages of the market, promoting the smooth transition of the market to the mature stage. The price change characteristics reflected by the convex curve prompt market participants to actively participate in competition when the capacity demand increases. The solution results of the two - layer market clearing model can guide the allocation of resources to more competitive participants, improving market efficiency;

[0122] In this embodiment, by using the concave and convex capacity demand curves in cooperation with the two - layer market clearing model, it can reflect the characteristics of the electricity market from different perspectives, provide more comprehensive information for market clearing decisions, and achieve the optimal allocation of capacity resources.

[0123] Step S300 specifically includes:

[0124] Step S310: Construct an upper - layer capacity market clearing model with the goal of minimizing the capacity purchase cost. The expression of the objective function is:

[0125]

[0126] In the formula, C represents the capacity purchase cost; i represents the index of capacity resources, i ∈ Ω X , Ω X represents the set of various capacity resources; c X,i represents the bid price of the i - th type of capacity resource on the capacity supply curve, that is, the price at which the resource sells its capacity; represents the awarded capacity of the i - th type of capacity resource in the capacity market; d represents the index of the capacity demand curve segment; a represents the index of the capacity demand curve area; c D,d,a represents the price of the d - th segment of the capacity demand curve in area a; represents the awarded capacity of the d - th segment of the capacity demand curve in area a; This formula calculates the capacity supply cost of all capacity resources The difference is used to determine the capacity allocation at market clearing with the goal of minimizing the difference. It takes into account the price-capacity relationships on both the supply and demand sides of capacity, and also considers the capacity demands and prices in different regions under cross-provincial capacity transmission.

[0127] Among them, the composition of capacity resources includes stock resources and incremental resources, and their conversion relationship with installed capacity can be expressed as:

[0128]

[0129] In the formula, represents the total credible capacity, that is, the effective capacity participating in market clearing; represents the credible capacity of stock resources, that is, the effective capacity after conversion of existing capacity resources; represents the credible capacity of incremental resources; represents the installed capacity of stock resources; represents the installed capacity of incremental resources; λ X represents the credible capacity conversion coefficient of capacity resources. Due to different reliability and other factors of different types of resources, this coefficient needs to be multiplied to convert the installed capacity into credible capacity; this formula shows that the total credible capacity is composed of the credible capacities of stock resources and incremental resources, and the installed capacity is converted into the credible capacity actually participating in market clearing through the credible capacity conversion coefficient, clarifying the composition method of capacity resources, providing a basis for the calculation of the winning capacity of capacity resources in the objective function, and enabling the model to accurately consider the actual available situation of different types of capacity resources.

[0130] Among them, the constraint conditions of the upper-layer capacity market clearing model include:

[0131] Capacity supply-demand balance constraint, configured as the sum of the winning capacity of its own capacity resources in each region and the net capacity of cross-regional transmission, equal to the total winning capacity demand of the region, and its expression is:

[0132]

[0133] In the formula, represents the capacity transmitted from region r to region a across regions. A positive value represents that region r transmits capacity to region a, and a negative value represents that region a transmits capacity to region r; Ω X,a represents the set of capacity resources in region a, representing all capacity resources participating in the market in this region; Φ a represents the set of regions with connection lines to region a; this constraint condition reflects the basic principle that capacity supply and demand in the power system must be balanced. represents the total winning capacity of its own capacity resources in region a, It represents the net capacity transmitted from other regions connected to region a (taking into account the transmission direction), and the sum of the two should be equal to the total winning bid capacity demand of region a on the right side of the equation. Through this constraint, it is ensured that the capacity demand of the region can be met when the market clears, maintaining the supply-demand balance of the power system.

[0134] The capacity demand constraint is configured to limit the winning bid capacity of each segment of the capacity demand curve of each region to be between 0 and the maximum capacity demand of that segment. Its expression is:

[0135]

[0136] Where represents the maximum capacity demand corresponding to the d-th segment of the capacity demand curve of region a; this constraint condition limits the winning bid capacity range of each segment of the region capacity demand curve. It is required that the winning bid capacity cannot be negative; at the same time, it cannot exceed the maximum capacity demand of that segment. This is based on the planning and operation requirements of the actual power system to ensure that there will be no unreasonable situation of excessive capacity demand being met when the market clears, and to ensure the rationality and feasibility of the capacity demand.

[0137] The tie-line transmission constraint is configured such that the capacity transmitted from each region to other regions with tie-lines is between the negative maximum transmission capacity of the tie-line and the maximum capacity demand of the capacity demand curve of that region. Its expression is:

[0138]

[0139] In the formula, represents the maximum transmission capacity of the tie-line between region r and region a; through this constraint, it is ensured that the cross-region capacity transmission is within the safe operation range of the tie-line, avoiding safety problems such as tie-line overload caused by excessive transmission.

[0140] In this embodiment, by constructing the upper-layer capacity market clearing model with the goal of minimizing the capacity purchase cost, various factors such as the composition of capacity resources, capacity supply-demand balance, capacity demand range, and tie-line transmission limitations are comprehensively considered. The model can accurately reflect the actual supply-demand situation and transmission limitations of capacity resources in the power market, providing a scientific and reasonable decision-making basis for market clearing. By optimizing the capacity allocation, the capacity purchase cost can be reduced, the utilization efficiency of power resources can be improved, and the supply-demand balance and safe and stable operation of the power system can be guaranteed. At the same time, the model also takes into account the capacity demand and price of different regions under cross-provincial capacity transmission, which helps to achieve the optimal allocation of resources between regions.

[0141] Step S320: Construct a lower-layer electricity energy market clearing model with the goal of minimizing the electricity energy market power purchase cost. The expression of the objective function is:

[0142]

[0143] In the formula, E represents the electricity purchase cost in the electricity energy market; m represents the index of the stock resources; represents the electricity energy market quotation of the stock resource m at the t-th period on the h-th day; represents the winning bid quantity of the stock resource m in the electricity energy market at the t-th period on the h-th day, that is, the electricity energy actually accepted by the market for this stock resource during this period; n represents the index of the incremental resources; represents the electricity energy market quotation of the incremental resource n at the t-th period on the h-th day; represents the winning bid quantity of the incremental resource n in the electricity energy market at the t-th period on the h-th day, that is, the electricity energy actually accepted by the market for this incremental resource during this period; The two summation terms in the objective function are respectively for the stock resources and the incremental resources, and they jointly constitute the total electricity purchase cost. The quotations and winning bid quantities in each summation term are interrelated. The quotation reflects the price information of the resources, and the winning bid quantity is determined according to market demand, price, and other constraints. When solving the objective function, the quotations and possible combinations of winning bid quantities of all resources will be comprehensively considered to find the plan that minimizes the total electricity purchase cost, and then determine the specific winning bid quantities of each resource in each period to achieve the optimal clearing of the electricity energy market;

[0144] Among them, the constraint conditions of the lower-layer electricity energy market clearing model include:

[0145] The energy balance constraint is configured such that the total winning bid electricity quantity of the stock resources and the incremental resources at each period of each day in each region, as well as the net electricity energy transmitted by other regions with tie lines, is equal to the load demand of this region at this period. Its expression is:

[0146]

[0147] In the formula, represents the winning bid quantity of the stock resource m in region a in the electricity energy market at the t-th period on the h-th day; The winning bid quantity of the incremental resource n in region a in the electricity energy market at the t-th period on the h-th day; r represents the index of the region with a tie line to region a, r ∈ φ a ,Φ a represents the set of regions with tie lines to region a; represents the electricity energy transmitted from region r to region a across regions. A positive value indicates that region r transmits to region a, and a negative value indicates reverse transmission; The load demand of region a at the t-th period on the h-th day;

[0148] The cleared electricity quantity constraint is configured such that the winning bid electricity quantity of the stock and incremental resources in the electricity energy market at each period is non-negative and does not exceed its installed capacity. Its expression is:

[0149]

[0150] In the formula, represents the installed capacity of the stock resource m, that is, the maximum power generation capacity of this resource; represents the installed capacity of the incremental resource n;

[0151] The lower-layer electric energy market clearing model constructed in this embodiment aims to minimize the power purchase cost, and at the same time takes into account the energy balance and the clearing power quantity constraint. By optimizing the winning bids of the stock resources and incremental resources in each time period, the power purchase cost of the electric energy market can be effectively reduced, and the use efficiency of electric power resources can be improved. It helps to achieve the optimal clearing of the electric energy market, promote the rational allocation of electric power resources, and improve the economy and stability of the electric power market.

[0152] Step S400: Based on the two-layer market clearing model, interactively iterate and update the capacity demand curves of different types, and solve the optimal power system clearing decision result; specifically, in the two-layer market clearing model, the upper-layer capacity market clearing model, the lower-layer electric energy market clearing model, and the capacity demand curve interact and cooperate with each other. The capacity demand curve provides basic market demand information for the upper-layer capacity market clearing model. Different shapes and key point values of the curve reflect different demand characteristics of the market for capacity. The upper-layer model determines the clearing combination of various capacity resources (stock resources and incremental resources) based on this information, that is, decides which capacity resources participate in the market and the scale of participation. At the same time, the clearing result of the upper-layer model, the capacity resource clearing combination, will have a feedback effect on the capacity demand curve. For example, the input or withdrawal of certain resources from the market may change the supply and demand balance of the market, and thus affect the values of the key points of the capacity demand curve; the result of the upper-layer capacity market clearing model, the capacity resource clearing combination, is one of the input conditions of the lower-layer electric energy market clearing model. The lower-layer model determines the electric energy price of each sub-region according to the combination of these capacity resources, combined with factors such as load demand. The electric energy price information obtained by the lower-layer model will be fed back to the upper-layer capacity market clearing model, because the electric energy price will affect the demand and value assessment of market participants for capacity resources, thus affecting the values of the key points of the capacity demand curve and further affecting the clearing result of the upper-layer model. Therefore, although the capacity demand curve is not directly used as the input of the lower-layer electric energy market clearing model, it indirectly affects the lower-layer model through the upper-layer model. The clearing result of the upper-layer model affects the capacity demand curve, which in turn affects the capacity resource combination passed from the upper-layer model to the lower-layer model during the next iteration, thus indirectly affecting the solution of the lower-layer model. At the same time, the electric energy price obtained by the lower-layer model will affect the upper-layer model by affecting the capacity demand curve, forming a cyclic interaction process.

[0153] The solution process of the two-layer market clearing model is specifically as follows:

[0154] Determine the initial capacity demand curve, including the key point adjustment coefficients for concave and convex curves, which can be initially set according to the historical operation of the market;

[0155] Solve the upper-layer capacity market clearing model. Use the particle swarm optimization algorithm to solve the upper-layer capacity market clearing model. The particles represent different capacity resource clearing combination schemes. Each particle moves in the search space and adjusts its position according to its own objective function value and the information of the optimal particle in the group, and continuously iterates and updates until the optimal capacity resource clearing combination is found, that is, the best combination of stock resources and incremental resources in various capacity resources;

[0156] Solve the lower-layer electricity energy market clearing model. Take the capacity resource clearing combination obtained from the upper-layer model as the input, and use the YALMIP+CPLEX method to solve the lower-layer electricity energy market clearing model. YALMIP is a MATLAB toolbox for modeling optimization problems, which can conveniently define and describe optimization problems; CPLEX is an optimization solver that can efficiently solve various optimization problems such as linear programming and integer programming; use YALMIP to model the objective function and constraints of the lower-layer model, and then call the CPLEX solver to solve it to obtain the electricity energy prices of each partition;

[0157] Interactive iteration. Feed back the electricity energy prices of each partition obtained from the lower-layer model to the upper-layer capacity market clearing model. These electricity energy prices will affect the market participants' demand for and value assessment of capacity resources, thereby changing the values of the key points of the capacity demand curve; according to the new capacity demand curve, use the particle swarm optimization algorithm again to solve the upper-layer capacity market clearing model to obtain a new capacity resource clearing combination; input the new capacity resource clearing combination into the lower-layer electricity energy market clearing model again for solution to obtain new electricity energy prices. Repeat the interactive iteration between the upper and lower layers like this until the convergence condition is met, such as the change in the objective function value is less than a certain threshold or the number of iterations reaches the preset maximum value; finally, through the corresponding clearing result analysis of capacity demand curve models with different shapes, compare the market clearing results (such as capacity resource investment and construction plans, electricity energy prices, etc.) under different curve shapes, find the capacity demand curve and capacity resource investment and construction plans suitable for the operation of different regions, so as to obtain the optimal electricity system clearing decision result;

[0158] Step S400 fully considers the mutual influence between the upper-layer capacity market and the lower-layer electric energy market, as well as the key role of the capacity demand curve in the market clearing result, through the interactive iteration of the double-layer market clearing model. This method can accurately capture the complex relationship between capacity demand and price in the power market, and realize the optimal allocation of capacity resources and electric energy resources. By continuously iteratively updating the capacity demand curve, the model can adapt to the dynamic changes of the market, improving the reliability and flexibility of market clearing decisions. Ultimately, it can find the optimal capacity demand curve and capacity resource investment and construction plan suitable for different regional operating conditions, reduce the capacity purchase cost and the electricity purchase cost in the electric energy market, and ensure the safe and stable operation of the power system.

[0159] In summary, the power system clearing decision optimization method based on the capacity demand curve provided by the present invention constructs a double-layer market clearing model based on the capacity demand curve calculation model. The double-layer market clearing model performs interactive iteration, and the capacity market resource clearing combination of the upper-layer model is transmitted to the lower-layer model. The clearing quantity and price results of the lower-layer electric energy market are then fed back to the upper-layer model, thereby affecting the capacity demand curve and the capacity resource clearing combination. Through repeated iteration until the optimal power system clearing decision result is found. This method can accurately evaluate the market clearing results under different capacity demand curves by using the capacity demand curve calculation model and the double-layer market clearing model, avoiding the problem of insufficient decision reliability caused by ignoring the influence of different forms of the capacity demand curve on the market clearing result, and improving the reliability of market clearing decisions. At the same time, through the interactive iteration of the upper and lower layer models, the relationship between the capacity market and the electric energy market is coordinated, realizing the optimal allocation of power resources, making the capacity resource decision-making plan more economical, and effectively addressing the problems of insufficient capacity adequacy and lack of flexibility faced by the power system during the transformation to a new model.

[0160] Please refer to Figure 2 , which shows a schematic structural diagram of a power system clearing decision optimization system based on the capacity demand curve provided by an embodiment of the present invention. The system includes:

[0161] A data acquisition module configured to acquire data related to the power system of the target area;

[0162] A capacity demand curve calculation module configured to construct different types of capacity demand curve calculation models according to the data related to the power system;

[0163] A double-layer market clearing module configured to construct a double-layer market clearing model according to the capacity demand curve calculation model, including an upper-layer capacity market clearing model and a lower-layer electric energy market clearing model;

[0164] The decision-making optimization module is configured to interactively and iteratively update different types of capacity demand curves based on a two-layer market clearing model, and solve the optimal power system clearing decision result.

[0165] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0166] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A power system clearing decision optimization method based on capacity demand curve, characterized in that: The method comprises: Obtain relevant data on the power system in the target area; Construct different types of capacity demand curve calculation models based on power system related data; According to the capacity demand curve calculation model, a two-layer market clearing model is constructed, including the upper capacity market clearing model and the lower electricity energy market clearing model; Based on the two-layer market clearing model, different types of capacity demand curves are interactively and iteratively updated to solve the optimal power system clearing decision result.

2. The power system clearing decision optimization method according to claim 1, characterized in that: The relevant data of the power system in the target area include: Obtain capacity demand forecast data, installed reserve margin data, new entry cost data and equivalent forced outage rate data of the target area power system.

3. The power system clearing decision optimization method according to claim 2, characterized in that: Different types of capacity demand curve calculation models are constructed based on power system related data, including: A concave capacity demand curve calculation model is constructed based on relevant data of the power system, including: Preset a first set of concave curve adjustment coefficients and calculate the horizontal coordinates of at least three key points of the concave curve in the capacity demand dimension in combination with relevant data of the power system; Preset a second set of concave curve adjustment coefficients and combine relevant data of the power system to calculate the vertical coordinates of at least three key points of the concave curve in the price dimension; Fit the concave capacity demand curve based on the horizontal and vertical coordinates of at least three key points of the concave curve.

4. The power system clearing decision optimization method according to claim 3, characterized in that: The expressions of the horizontal and vertical coordinates of at least three key points of the concave curve are: The horizontal coordinate of the first key point: Where N P represents the capacity demand forecast value; γ a I represents the adjustment coefficient of the first concave curve; RM Indicates the installed reserve margin; The first key point vertical coordinate: In the formula, E represents the new entry cost; E N represents the net new entry cost; R EFORd represents the equivalent forced outage rate of the reference unit; The horizontal coordinate of the second key point: In the formula, γ b represents the adjustment coefficient of the second concave curve; The second key point vertical coordinate: In the formula, λ b represents the adjustment coefficient of the third concave curve; The abscissa of the third key point: In the formula, γ c represents the adjustment coefficient of the fourth concave curve; The third key point vertical coordinate: In the formula, λ c Represents the fifth concave curve adjustment coefficient.

5. The power system clearing decision optimization method according to claim 3, characterized in that: Different types of capacity demand curve calculation models based on power system related data and related parameters also include: A convex capacity demand curve calculation model is constructed based on relevant data of the power system, including: Preset a first set of convex curve adjustment coefficients and calculate the horizontal coordinates of at least three key points of the convex curve in the capacity demand dimension in combination with relevant data of the power system; Preset a second set of convex curve adjustment coefficients and combine relevant data of the power system to calculate the vertical coordinates of at least three key points of the convex curve in the price dimension; Fit a convex capacity demand curve based on the horizontal and vertical coordinates of at least three key points of the convex curve.

6. The power system clearing decision optimization method according to any one of claims 1 to 5, characterized in that: According to the capacity demand curve calculation model, the upper capacity market clearing model is constructed, including: The upper capacity market clearing model is constructed with the goal of minimizing the capacity purchase cost. The expression of the objective function is: Where C represents the capacity purchase cost; i represents the index of capacity resource, i∈Ω X ,Ω X Represents a collection of various capacity resources; c X,i It represents the price of the i-th capacity resource on the capacity supply curve, that is, the price at which the capacity of the resource is sold; represents the winning bid capacity of the i-th capacity resource in the capacity market; d represents the index of the capacity demand curve segment; a represents the index of the capacity demand curve area; c D,d,a represents the price of segment d of the capacity demand curve for region a; Represents the winning capacity of segment d of the capacity demand curve of area a.

7. The power system clearing decision optimization method according to claim 6, characterized in that: The constraints of the upper capacity market clearing model include: The capacity supply and demand balance constraint is configured as the sum of the bid capacity of each region’s own capacity resources and the net capacity of cross-region transmission, which is equal to the total bid capacity demand of the region; Capacity demand constraints are configured to limit the winning capacity of each segment of the capacity demand curve of each region to be between 0 and the maximum capacity demand of the segment; The tie line transmission constraint is configured so that the capacity of each region's inter-regional transmission to other regions with tie lines is between the negative tie line's maximum transmission capacity and the maximum capacity demand of the region's capacity demand curve.

8. The power system clearing decision optimization method according to claim 6, characterized in that: According to the capacity demand curve calculation model, the lower-level electricity energy market clearing model is constructed, including: The lower-level electric energy market clearing model is constructed with the goal of minimizing the electricity purchase cost in the electric energy market. The expression of the objective function is: In the formula, E represents the electricity purchase cost in the electric energy market; m represents the stock resource index; represents the market price of electricity for stock resource m at time period t on day h; It represents the winning bid amount of stock resource m in the electric energy market in the hth day and tth period, that is, the electric energy actually accepted by the market for the stock resource in the period; n represents the incremental resource index; represents the market price of electricity energy of incremental resource n at time period t on day h; It represents the winning bid of incremental resource n in the electric energy market at time period t on day h, that is, the amount of electric energy actually accepted by the market for the incremental resource at that time period.

9. The power system clearing decision optimization method according to claim 8, characterized in that: The constraints of the lower-level electricity market clearing model include: The energy balance constraint is configured as the sum of the bid power of the stock resources and incremental resources in each area at each time period every day and the net power transmitted from other areas with tie lines, which is equal to the load demand of the area at that time period; The clearing electricity constraint is configured so that the winning bid electricity of existing and incremental resources in the electricity market in each period is non-negative and does not exceed their installed capacity.

10. The power system clearing decision optimization system based on capacity demand curve is characterized by: The power system clearing decision optimization method based on the capacity demand curve according to any one of claims 1 to 9 is adopted, and the system comprises: A data acquisition module configured to acquire data related to the power system in the target area; A capacity demand curve calculation module, configured to construct different types of capacity demand curve calculation models according to power system related data; A two-layer market clearing module is configured to construct a two-layer market clearing model based on a capacity demand curve calculation model, including an upper-layer capacity market clearing model and a lower-layer electric energy market clearing model; The decision optimization module is configured to interactively and iteratively update different types of capacity demand curves based on the two-layer market clearing model to solve the optimal power system clearing decision result.

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