Power system decision optimization method and system based on flexible climbing demand price curve

Abstract

The invention relates to the technical field of power systems, in particular to a power system decision optimization method and system based on a flexible climbing demand price curve, and the method comprises the steps: obtaining load prediction error data of a power system; constructing a flexible climbing demand model according to the load prediction error data; according to the flexible climbing demand model, constructing a flexible climbing demand price curve calculation model; a market equilibrium model is constructed according to the flexible climbing demand price curve calculation model, wherein the market equilibrium model comprises an upper-layer profit maximization model and a lower-layer clearing optimization model; and solving a decision optimization result of the power system according to the market equilibrium model. The objective of the invention is to solve the problem of insufficient flexibility of a power system and realize optimal configuration and economical and efficient operation of power system resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a power system decision optimization method and system based on a flexible ramping demand price curve. Background Art

[0002] As the global energy structure accelerates its transformation towards cleaner and lower-carbon energy, large-scale access to the power grid for renewable energy has become an inevitable trend in the development of the future power system. With the increase in the proportion of non-fossil energy in the energy consumption structure, the power system has to face the two severe challenges of renewable energy consumption and safe and stable power supply in the process of adapting to large-scale and high-proportion renewable energy. In order to effectively respond to these challenges, the power system needs to have a more flexible operation mode, and enhance the reliability and operational flexibility of the system by reserving sufficient flexible ramping capacity, so as to better adapt to the intermittent and volatile characteristics of renewable energy.

[0003] The essence of system operation flexibility refers to the use of controllable resources within the system to achieve accurate tracking and rapid response to load changes. With the continuous deepening of energy supply-side reform, the system's demand for flexible ramping capabilities will increase dramatically. In this context, how to use market-based means to reasonably incentivize resource entities that can provide flexible ramping capabilities, thereby improving the flexibility of the entire power system, has become one of the key issues that need to be urgently addressed in my country's power system reform. In terms of the construction of a flexible market mechanism, the California power market in the United States has launched a flexible ramping product (FRP) trading market, while the Midwest power market in the United States has built a ramp capability product (RCP) market to meet the auxiliary service needs of net load fluctuations in adjacent time periods during the real-time dispatch phase of the power system.

[0004] At present, the research of existing technologies usually focuses on the construction of flexible ramping market models, flexible resource modeling, and flexible ramping pricing mechanisms. Due to the lack of in-depth analysis of the role of flexible ramping in the market, it is difficult to accurately reflect the value changes of newly added flexible ramping capacity. On the other hand, due to the uncertainty of flexible ramping demand, it is impossible to fully tap the flexible ramping potential of the power system, and it is difficult to achieve the optimal balance between the economy and flexibility of the power system. Summary of the invention

[0005] In order to solve the problem of insufficient flexibility of the power system and realize the optimal configuration of power system resources and economical and efficient operation, the present invention provides a power system decision optimization method and system based on a flexible ramp demand price curve. The technical scheme adopted is as follows:

[0006] The technical solution of the first aspect of the present invention provides a decision-making optimization method for a power system based on a flexible ramping requirement price curve, and the method includes:

[0007] Obtain the load forecasting error data of the power system;

[0008] Construct a flexible ramping requirement model according to the load forecasting error data;

[0009] Construct a flexible ramping requirement price curve calculation model according to the flexible ramping requirement model;

[0010] Construct a market equilibrium model according to the flexible ramping requirement price curve calculation model, including an upper-layer profit maximization model and a lower-layer clearing optimization model;

[0011] Solve the decision-making optimization result of the power system according to the market equilibrium model.

[0012] Further, obtaining the load forecasting error data of the power system includes:

[0013] Obtain the predicted values and actual values of the system load, wind power, and photovoltaic power on historical operating days, and calculate the forecasting errors of the system load, wind power, and photovoltaic power for each time period;

[0014] Use the quantile quadratic regression fitting method to process the forecasting errors of the system load, wind power, and photovoltaic power, and extract the fluctuation ranges of the forecasting errors of the system load, wind power, and photovoltaic power;

[0015] Extract the net load forecasting error of the target time period based on the fluctuation range.

[0016] Further, constructing a flexible ramping requirement model according to the load forecasting error data includes:

[0017] According to the net load forecasting error of the target time period, combined with the predicted value of the system load in the adjacent time period, calculate the uncertain flexible ramping requirement of the system;

[0018] According to the uncertain flexible ramping requirement of the system, combined with the load difference in the adjacent time period, calculate the total flexible ramping requirement of the system.

[0019] Further, constructing a flexible ramping requirement price curve calculation model according to the flexible ramping requirement model includes:

[0020] Determine the flexible ramping requirement price curve based on the net load forecasting error and the preset price limit;

[0021] Calculate the flexible ramping clearing price and the clearing volume according to the flexible ramping requirement price curve.

[0022] Further, determining the flexible ramping requirement price curve based on the net load forecasting error and the preset price limit includes:

[0023] According to different ranges of unscheduled flexible ramping capacity, calculate the flexible ramping demand price curve based on the probability density distribution function of the net load forecasting error and in combination with the preset price bounds of the electricity energy market and flexible ramping products.

[0024] Furthermore, according to different ranges of unscheduled flexible ramping capacity, the expression for calculating the flexible ramping demand price curve based on the probability density distribution function of the net load forecasting error and in combination with the preset price bounds of the electricity energy market and flexible ramping products is:

[0025]

[0026] In the formula, represents the upward flexible ramping demand price curve for the unscheduled upward flexible ramping capacity at time t; represents the unscheduled upward flexible ramping capacity at time t; represents the downward flexible ramping demand price curve for the unscheduled downward flexible ramping capacity at time t; represents the unscheduled downward flexible ramping capacity at time t; EP U represents the upper limit of the electricity energy market bidding price; EP D represents the lower limit of the electricity energy market bidding price; FP U represents the upper limit of the upward flexible ramping product price; FP D represents the upper limit of the downward flexible ramping product price; p t (e) represents the probability density distribution function of the net load forecasting error, constructed based on the historical observation data of the system net load forecasting error, and used to reflect the probability distribution of the net load forecasting error; represents the positive error of the system net load forecasting at time t; represents the negative error of the system net load forecasting at time t; represents the uncertain upward flexible ramping demand of the system at time t; represents the uncertain downward flexible ramping demand of the system at time t.

[0027] Furthermore, construct the upper-layer profit maximization model according to the flexible ramping demand price curve calculation model, including:

[0028] Construct the profit maximization model of the new energy unit according to the flexible ramping clearing price and the power generation of the new energy unit;

[0029] Construct the profit maximization model of the thermal power unit according to the flexible ramping clearing price and the cleared volume, the power generation of the thermal power unit, and the flexible ramping capacity;

[0030] Construct a profit maximization model for thermal power units based on the flexible ramping clearing price, clearing volume, power generation of hydropower units, and flexible ramping capacity.

[0031] Furthermore, construct a lower-layer clearing optimization model according to the flexible ramping demand price curve calculation model, including:

[0032] Construct a lower-layer market clearing optimization model with the goal of minimizing the total electricity cost of the whole society. The expression of the objective function is:

[0033]

[0034] In the formula, represents the bid curve of the i-th type of unit; represents the power generation of the i-th type of unit at time t; represents the clearing price of the upward flexible ramping product at time t; represents the total upward flexible ramping demand of the system at time t; represents the clearing price of the downward flexible ramping product at time t; represents the total downward flexible ramping demand of the system at time t; N represents the set of units.

[0035] Furthermore, the constraint conditions for constructing the lower-layer clearing optimization model according to the flexible ramping demand price curve calculation model include:

[0036] Power balance constraint, and its expression is:

[0037]

[0038] In the formula, represents the power generation of thermal power units at time t; represents the power generation of hydropower units at time t; represents the power generation of new energy at time t; represents the power generation of independent energy storage at time t, represents the total load demand at time t;

[0039] Flexible ramping demand constraint, and its expression is:

[0040]

[0041] In the formula, represents the upward flexible ramping capacity of thermal power units at time t; represents the upward flexible ramping capacity of hydropower units at time t; represents the upward flexible ramping capacity of independent energy storage at time t; represents the total upward flexible ramping demand of the system at time t; represents the downward flexible ramping capacity of thermal power units at time t; represents the downward flexible ramping capacity of the hydropower unit in period t; represents the downward flexible ramping capacity of the independent energy storage in period t; represents the total downward flexible ramping demand of the system in period t; N T represents the number of thermal power units; N H represents the number of hydropower units; N S represents the number of independent energy storages.

[0042] The technical solution of the second aspect of the present invention provides a power system decision optimization system based on a flexible ramping demand price curve, which adopts the power system decision optimization method based on the flexible ramping demand price curve described in the technical solution of the first aspect of the present invention. The system includes:

[0043] A data acquisition module configured to acquire load prediction error data of the power system;

[0044] A flexible ramping demand module configured to construct a flexible ramping demand model according to the load prediction error data;

[0045] A flexible ramping demand price curve calculation module configured to construct a flexible ramping demand price curve calculation model according to the flexible ramping demand model;

[0046] A market equilibrium module configured to construct a market equilibrium model according to the flexible ramping demand price curve calculation model, including an upper-layer market member profit maximization model and a lower-layer market clearing optimization model;

[0047] A decision optimization module configured to solve the power system decision optimization result according to the market equilibrium model.

[0048] The present invention has the following beneficial effects:

[0049] The power system decision optimization method based on the flexible ramping demand price curve provided by the present invention takes into account the uncertainty of the flexible ramping demand price curve, can accurately reflect the actual situation that the value of the newly added flexible ramping capacity decreases as the capacity increases, and helps to improve the market economy of the power system; based on the flexible ramping demand price curve model, it fully considers the coupling effect of various power generation resources when providing electrical energy and flexible ramping products. The established market equilibrium model can provide effective support for reasonable capacity allocation of various resource entities in different markets, promote the optimal allocation of power system resources, improve the flexibility, reliability and economy of power system operation, and better cope with the challenges brought by the large-scale access of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] 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, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 It is a flowchart of a method for optimizing power system decision-making based on a flexible ramping requirement price curve provided by an embodiment of the present invention;

[0052] Figure 2 It is a schematic structural diagram of a power system decision-making optimization system based on a flexible ramping requirement price curve provided by an embodiment of the present invention. Detailed implementation manners

[0053] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a power system decision-making optimization method and system based on a flexible ramping requirement price 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.

[0054] 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.

[0055] The following specifically describes the specific solutions of a power system decision-making optimization method and system based on a flexible ramping requirement price curve provided by the present invention in combination with the drawings.

[0056] Please refer to Figure 1 , which shows a flowchart of a method for optimizing power system decision-making based on a flexible ramping requirement price curve provided by an embodiment of the present invention. The method includes:

[0057] Step S100: Obtain the load prediction error data of the power system;

[0058] Step S100 specifically includes:

[0059] Step S110: Obtain the predicted values and actual values of the system load, wind power and photovoltaic power on historical operating days, and calculate the prediction errors of the system load, wind power and photovoltaic power for each time period. These prediction error data reflect the deviation between the actual power generation situation of each energy source and the prediction, and can be expressed as:

[0060]

[0061] In the formula, e L,t represents the system load prediction error in the t period; represents the actual value of the system load in the t period; represents the predicted value of the system load in the t period; e W,t represents the wind power prediction error in the t period; represents the actual value of the wind power in the t period; represents the predicted value of the wind power in the t period; e S,t represents the photovoltaic power prediction error in the t period; represents the actual value of the photovoltaic power in the t period; represents the predicted value of the photovoltaic power in the t period;

[0062] Step S120: Use the quantile quadratic regression fitting method to process the system load, wind power, and photovoltaic power prediction errors, and extract the fluctuation ranges of the system load, wind power, and photovoltaic power prediction errors; specifically, use the quantile quadratic regression fitting method to process the system load prediction error data to determine the upper curve equation and lower curve equation of the system load prediction error, which can be expressed as:

[0063]

[0064] In the formula, represents the upper curve equation of the system load prediction error, which is used to represent the upper limit that the prediction error may reach when the given system load prediction value is ; a represents the first quadratic term coefficient; b represents the first linear term coefficient; c represents the first constant term coefficient; represents the lower curve equation of the system load prediction error, which is used to represent the lower limit that the prediction error may reach when the given system load prediction value is ; d represents the second quadratic term coefficient; e represents the second linear term coefficient; f represents the second constant term coefficient; the equation in this formula is used to describe the fluctuation range of the system prediction error and provides the upper and lower limit references for the calculation of the net load prediction error; a, b, c, d, e, and f can be obtained through fitting calculations of historical data;

[0065] Similarly, the upper / lower curve equations of the wind power and photovoltaic power prediction errors can be calculated, which can be expressed as: represents the upper curve equation of the wind power prediction error, represents the lower curve equation of the wind power prediction error, represents the upper curve equation of the photovoltaic power prediction error, represents the lower curve equation of the photovoltaic power prediction error;

[0066] Step S130: Extract the net load prediction error for the target period based on the fluctuation range; specifically, based on the obtained fluctuation ranges of the system load, wind power, and photovoltaic power prediction errors, for the target period, by adding the upper limit of the system load prediction error to the upper limits of the wind power and photovoltaic power prediction errors, the positive net load prediction error of the system is obtained; by adding the lower limit of the system load prediction error to the lower limits of the wind power and photovoltaic power prediction errors, the negative net load prediction error of the system is obtained, which can be expressed as:

[0067]

[0068] In the formula, represents the positive net load prediction error of the system at time t; represents the negative net load prediction error of the system at time t; in this embodiment, the positive and negative net load prediction errors comprehensively consider the uncertainties of various energy sources and can more comprehensively reflect the prediction deviation of the system net load in the target period; in this embodiment, the net load prediction error for the target period is extracted based on the fluctuation range, comprehensively considering the uncertainties of various energy sources, which can more comprehensively reflect the fluctuation of the system net load, contribute to subsequent work such as constructing a flexible ramping requirement model, and provide more accurate load prediction error data for the decision-making optimization of the power system, thereby improving the reliability and economy of the power system operation.

[0069] Step S200: Construct a flexible ramping requirement model based on the load prediction error data;

[0070] Step S200 specifically includes:

[0071] Step S210: Calculate the uncertainty flexible ramping requirement of the system according to the net load prediction error of the target period in combination with the system load prediction values of adjacent periods; specifically, for calculating the uncertainty flexible ramping requirement of the system, the change in the system load prediction values of adjacent periods needs to be considered, which can be expressed as:

[0072]

[0073] In the formula, represents the upper uncertainty flexible ramping requirement of the system at time t; represents the lower uncertainty flexible ramping requirement of the system at time t; represents the maximum value of the difference in the system load prediction values of adjacent periods, considering the influence of the natural change of the load on the ramping requirement; represents the maximum load reduction of the system load prediction values of adjacent periods; represents the system net load prediction value at time t; represents the system net load prediction value at time t + 1; The system load forecasting result after considering the comprehensive impacts of traditional energy and new energy sources (such as wind power, photovoltaic power, etc.) is the net load forecasting value of the system at time t obtained after comprehensively considering the power generation situations and forecasting errors of various energy sources, and is used for the calculation of net load forecasting errors and uncertainty flexible ramping requirements; this formula takes into account the additional upward and downward ramping requirements caused by uncertain factors such as load forecasting errors.

[0074] Step S220: According to the uncertainty flexible ramping requirements of the system, calculate the total flexible ramping demand of the system in combination with the load difference between adjacent time periods.

[0075]

[0076] In the formula, represents the total upward flexible ramping demand of the system at time t; represents the total downward flexible ramping demand of the system at time t; L t+1 represents the basic load forecasting value of the system at time t - 1; L t represents the basic load forecasting value of the system at time t; L t+1 and L t are for the load forecasting situation of only considering the traditional power system, without including the comprehensive impacts of multiple energy sources such as renewable energy on the system net load, and are mainly used for calculating the basic load change between adjacent time periods and serving as the basic data for calculating the total flexible ramping demand of the system.

[0077] In this embodiment, based on the net load forecasting error data, a flexible ramping demand model is constructed, fully considering the system load forecasting values and their differences between adjacent time periods, and integrating the uncertainty of load forecasting errors into the calculation of flexible ramping requirements. The calculated system uncertainty flexible ramping requirements and total flexible ramping demand of the system help to quantify the flexible ramping capacity required for the power system to cope with load fluctuations at different time periods. This quantification can provide a basis for subsequent flexible ramping resource allocation and scheduling, ensure that the power system can make preparations in advance when facing load fluctuations, guarantee the stable operation of the system, improve the adaptability of the system to the access of renewable energy, and at the same time provide key inputs for subsequent steps such as further constructing a flexible ramping demand price curve calculation model.

[0078] Step S300: Construct a flexible ramping demand price curve calculation model according to the flexible ramping demand model.

[0079] Step S300 specifically includes:

[0080] Step S310: Determine the flexible ramping demand price curve based on the net load prediction error and the preset price limit; specifically, according to different ranges of unprocured flexible ramping capacity, calculate the expression of the flexible ramping demand price curve based on the probability density distribution function of the net load prediction error and in combination with the preset price limits of the electricity energy market and the flexible ramping product as follows:

[0081]

[0082] In the formula, represents the upward flexible ramping demand price curve for the unprocured upward flexible ramping capacity at time t; represents the unprocured upward flexible ramping capacity at time t; represents the downward flexible ramping demand price curve for the unprocured downward flexible ramping capacity at time t; represents the unprocured downward flexible ramping capacity at time t; EP U represents the upper limit of the electricity energy market bid price; EP D represents the lower limit of the electricity energy market bid price; FP U represents the upper limit of the upward flexible ramping product price; FP D represents the upper limit of the downward flexible ramping product price; p t (e) represents the probability density distribution function of the net load prediction error, which is constructed based on the historical observation data of the system net load prediction error and is used to reflect the probability distribution of the net load prediction error; represents the positive error of the system net load prediction at time t; represents the negative error of the system net load prediction at time t; represents the uncertain upward flexible ramping demand of the system at time t; represents the uncertain downward flexible ramping demand of the system at time t;

[0083] Specifically, for the upward flexible ramping demand price curve when the unprocured upward flexible ramping capacity is in the range calculate the integral and multiply it by the upper limit of the electricity energy market bid price EP U , and then take the minimum value with the upper limit of the upward flexible ramping product price FP U to determine the price; this is because the price is affected by the comprehensive influence of the upper limit of the electricity energy market bid price and the integral of the probability density distribution function p t (e); for the downward flexible ramping demand price curve the principle is similar. When the unprocured downward flexible ramping capacity is in the range calculate the integral and multiply it by the lower limit of the bidding price EP in the electricity energy market D , and then take the minimum value with the upper limit of the price FP of the downward flexible ramping product D ; The integral calculation reflects the impact of the uncertainty of the net load prediction error on the price. Through the integral, the probability of the net load prediction error within a certain range can be comprehensively considered, multiplied by the bidding price in the electricity energy market, and then combined with the upper limit of the product price to obtain a reasonable price curve, ensuring that the price will not be too high exceeding the upper limit of the product price;

[0084] In this embodiment, when the unprocured upward flexible ramping capacity exceeds the range , the upward flexible ramping demand price curve directly takes the upper limit of the price FP of the upward flexible ramping product U ; For the downward flexible ramping demand price curve when the unprocured downward flexible ramping capacity exceeds the range , directly take the upper limit of the price FP of the downward flexible ramping product D ; This is because when the unprocured flexible ramping capacity exceeds a certain range, it is considered that the price reaches the upper limit, which conforms to the general law of the market mechanism, that is, when resources are scarce or in a special range, the price will approach the set upper limit;

[0085] The flexible ramping demand price curve calculation model constructed in this embodiment combines the uncertainty of the net load prediction error with the price boundaries of the electricity energy market and flexible ramping products through the probability density distribution function, realizing the construction of the flexible ramping demand price curve. Furthermore, it realizes that the price of the flexible ramping product can reflect the upper and lower limits of the market price, while fully considering the uncertainty brought by the net load prediction error. For different ranges of unprocured flexible ramping capacity, the price curve is determined through different calculation logics, making the price mechanism more reasonable and flexible, taking into account both the market price limit and the impact of the scarcity of flexible ramping resources on the price. The price curve of this embodiment can provide important price information for the subsequent market equilibrium model, helping market participants better evaluate the value of flexible ramping resources, promoting the rational allocation of resources and the effective operation of the market. At the same time, it also provides a reasonable price basis for system operators and market participants when considering flexible ramping services, which is conducive to balancing the interests of all parties in the market, improving the economy and reliability of the electricity market, and better coping with the uncertainty brought by load fluctuations, providing an important economic incentive mechanism for the decision-making optimization of the entire power system.

[0086] Step S320: Calculate the clearing price and clearing volume of flexible ramping according to the flexible ramping demand price curve; specifically, the clearing price of flexible ramping products determined according to the flexible ramping demand price curve can be expressed as:

[0087]

[0088] In the formula represents the clearing price of upward flexible ramping products in period t, which is calculated from the upward flexible ramping demand price curve and reflects the transaction price of upward flexible ramping products in the market during this period; represents the clearing price of downward flexible ramping products in period t, which is calculated from the downward flexible ramping demand price curve and reflects the transaction price of downward flexible ramping products in the market during this period; that is, the flexible ramping demand price curve is calculated through step S310, and the clearing price is the price curve value at the corresponding unprocured capacity, providing a price basis for subsequent market clearing;

[0089] The procurement volume of flexible ramping products corresponding to the clearing price can be expressed as:

[0090]

[0091] where represents the clearing volume of upward flexible ramping products in period t; represents the clearing volume of downward flexible ramping products in period t; represents the total upward flexible ramping demand of the system in period t; represents the total downward flexible ramping demand of the system in period t;

[0092] In this embodiment, by determining the clearing price and clearing volume according to the flexible ramping demand price curve, the price of flexible ramping products is linked to the procurement volume in market transactions. The determination of the clearing price provides a clear trading price signal for market participants, reflecting the supply-demand balance and price formation mechanism of the market; on the other hand, the calculation of the clearing volume clarifies the actual trading volume of flexible ramping products in the market, helping market participants adjust their strategies according to price and procurement volume information and realizing the effective allocation of resources. This embodiment incorporates price and trading information into the decision-making optimization process of the power system, providing more accurate quantitative information for the resource optimization configuration and market balance of the power system, enabling the entire power system to operate more economically and efficiently while meeting the system flexibility requirements.

[0093] Step S400: Construct a market equilibrium model according to the flexible ramping demand price curve calculation model, including an upper-layer profit maximization model and a lower-layer clearing optimization model;

[0094] Step S400 specifically includes:

[0095] Step S410: Construct an upper-layer profit maximization model according to the flexible ramping requirement price curve calculation model, including:

[0096] Step S411: Construct a profit maximization model for new energy units according to the flexible ramping clearing price and the power generation of new energy units. Its objective function is:

[0097]

[0098] In the formula, ρ Ri represents the profit of new energy unit Ri; represents the clearing price of the energy market at time t; represents the power generation of new energy unit Ri at time t, which is a decision variable of the upper-layer model. The unit can affect its profit by adjusting its power generation. This objective function aims to maximize the profit of new energy unit Ri. By multiplying the clearing price of the energy market at T = 96 time periods by the power generation of the new energy unit at that time period and summing over all time periods, the total profit is obtained;

[0099] Step S412: Construct a profit maximization model for thermal power units according to the flexible ramping clearing price and the cleared volume, the power generation of thermal power units, and the flexible ramping capacity. Its objective function is:

[0100]

[0101] In the formula, ρ Ti represents the profit of thermal power unit Ti; represents the power generation of thermal power unit Ti at time t; represents the clearing price of the upward flexible ramping product at time t; represents the clearing price of the downward flexible ramping product at time t; represents the fuel cost of thermal power unit Ti at time t; represents the start-up cost of thermal power unit Ti at time t;

[0102] Step S413: Construct a profit maximization model for hydropower units according to the flexible ramping clearing price and the cleared volume, the power generation of hydropower units, and the flexible ramping capacity. Its objective function is:

[0103]

[0104] Among them, ρ Hi represents the profit of hydropower unit Hi; represents the power generation of hydropower unit Hi at time t;

[0105] Among them, the constraint conditions of the upper-layer profit maximization model include:

[0106] The capacity constraint of the flexible ramping product of thermal power units is expressed as:

[0107]

[0108] In the formula, represents the power generation of thermal power unit Ti at time t; represents the upward flexible ramping capacity of thermal power unit Ti at time t; represents the downward flexible ramping capacity of thermal power unit Ti at time t; Z Ti,t represents the power generation state variable of thermal power unit Ti at time t, Z Ti,t = 0 means that thermal power unit Ti is in the shutdown state at time t, Z Ti,t = 1 means that thermal power unit Ti is in the startup state at time t; q Timax represents the maximum output of thermal power unit Ti; q Timin represents the minimum output of thermal power unit Ti;

[0109] The ramping constraint of the flexible ramping product of thermal power units is expressed as:

[0110]

[0111] In the formula, represents the power generation of thermal power unit Ti at time t + 1; represents the upward flexible ramping capacity of thermal power unit Ti at time t + 1; represents the maximum value of the upward regulation rate of thermal power unit Ti; represents the downward flexible ramping capacity of thermal power unit Ti at time t + 1; represents the maximum value of the downward regulation rate of thermal power unit Ti;

[0112] The capacity constraint of the flexible ramping product of hydroelectric units is expressed as:

[0113]

[0114] In the formula, represents the power generation of hydroelectric unit Hi at time t; represents the upward flexible ramping capacity of hydroelectric unit Hi at time t; q Himax represents the maximum output of hydroelectric unit Hi; represents the downward flexible ramping capacity of hydroelectric unit Hi at time t; q Himin represents the minimum output of hydroelectric unit Hi;

[0115] The ramping constraint of the flexible ramping product of hydroelectric units is expressed as:

[0116]

[0117] In the formula, represents the generated power of the hydropower unit Hi in the (t + 1)th period; represents the upward flexible ramping capacity of the hydropower unit Hi in the (t + 1)th period; represents the downward flexible ramping capacity of the hydropower unit Hi in the (t + 1)th period; represents the maximum value of the upward regulation rate of the hydropower unit Hi; represents the minimum value of the upward regulation rate of the hydropower unit Hi;

[0118] The all-day generated power constraint of the hydropower unit, and its expression is:

[0119]

[0120] In the formula, E Himax represents the upper limit of the all-day available generated power of the hydropower unit Hi.

[0121] In this embodiment, by constructing an upper-layer profit maximization model, the profits of different types of units (new energy, thermal power, hydropower), the clearing price of the energy market, the clearing price of the flexible ramping product, and the operating costs and constraint conditions of the units are combined to provide an optimization decision-making framework for various units. The construction of this model helps different types of units, based on their own technical characteristics and cost structures, to pursue profit maximization by adjusting the generated power and flexible ramping capacity under the premise of meeting the constraint conditions. It not only considers the revenue of the units in the traditional energy market but also takes into account the revenue of the flexible ramping product, enabling the units to participate in the electricity market competition more comprehensively. At the same time, the setting of various constraint conditions ensures the operating safety and technical feasibility of the units, avoiding the units operating beyond their physical and technical limits.

[0122] Step S420: Calculating according to the flexible ramping demand price curve, the construction of the lower-layer clearing optimization model includes:

[0123] Step S421: Constructing a lower-layer market clearing optimization model with the goal of minimizing the total electricity consumption cost of the whole society, and the expression of the objective function is:

[0124]

[0125] In the formula, represents the bidding curve of the i-th type of unit; represents the generated power of the i-th type of unit in the t-th period; represents the clearing price of the upward flexible ramping product in the t-th period; represents the total upward flexible ramping demand of the system in the t-th period; represents the clearing price of the downward flexible ramping product in the t-th period; denotes the total flexible upward ramping requirement of the system in period t; N denotes the set of units;

[0126] Among them, the constraint conditions of the lower-level clearing optimization model include:

[0127] Power balance constraint, and its expression is:

[0128]

[0129] In the formula, denotes the power generation of thermal power units in period t; denotes the power generation of hydro power units in period t; denotes the power generation of new energy in period t; denotes the power generation of independent energy storage in period t, denotes the total load demand in period t;

[0130] Flexible ramping requirement constraint, and its expression is:

[0131]

[0132] In the formula, denotes the upward flexible ramping capacity of thermal power units in period t; denotes the upward flexible ramping capacity of hydro power units in period t; denotes the upward flexible ramping capacity of independent energy storage in period t; denotes the total flexible upward ramping requirement of the system in period t; denotes the downward flexible ramping capacity of thermal power units in period t; denotes the downward flexible ramping capacity of hydro power units in period t; denotes the downward flexible ramping capacity of independent energy storage in period t; denotes the total flexible downward ramping requirement of the system in period t; N T denotes the number of thermal power units; N H denotes the number of hydro power units; C S denotes the number of independent energy storage;

[0133] The lower-layer clearing optimization model constructed in this embodiment takes the minimization of the electricity cost of the whole society as the goal and comprehensively considers the costs of the energy market and the flexible ramping market. This model takes into account the bid curves of various types of units, the generated electricity, the clearing prices of flexible ramping products, and the total demand, and sets the power balance and flexible ramping demand constraints, providing an optimization framework for the market clearing of the power system. It helps to reasonably allocate the generated electricity of various types of units and flexible ramping resources on the premise of meeting the system load demand and flexible ramping demand, ensuring the stable and economic operation of the power system. By optimizing and solving this model, an optimal resource allocation scheme can be found, enabling the entire power system to meet the system's demand for electric energy and flexible ramping capacity while minimizing costs, promoting the efficient operation of the power market, considering the flexibility requirements of the system, and providing strong support for the overall optimization and sustainable development of the power system.

[0134] Step S500: Solve the decision optimization result of the power system according to the market equilibrium model; specifically, use the double-layer multi-objective particle swarm optimization algorithm to solve. The electricity price, flexible ramping price, and the winning output of various types of units obtained by solving the lower-layer clearing model are transmitted to the upper-layer profit maximization model to optimize the bidding strategies of each unit, and market equilibrium is achieved through continuous transmission and iteration.

[0135] First, it is necessary to initialize the double-layer multi-objective particle swarm optimization algorithm. For each variable in the upper-layer profit maximization model and the lower-layer clearing optimization model, including the decision variables of various types of units (the generated electricity of new energy units, the generated electricity of thermal power units, the upward flexible ramping capacity, the downward flexible ramping capacity, the relevant variables of hydropower units, etc.), and the variables related to prices (such as the clearing price of the energy market, the clearing price of the upward flexible ramping product, the clearing price of the downward flexible ramping product, etc.), set the initial particle swarm. Each particle represents a possible combination of decision variables, its position represents the value of the variable, and its velocity represents the change trend of the variable.

[0136] For the lower-layer clearing optimization model, substitute the variable values in the initialized particle swarm into the objective function, power balance constraint, and flexible ramping requirement constraint of the lower-layer clearing optimization model, and use an optimization algorithm to solve the lower-layer model. On the premise of meeting the constraint conditions, find a set of solutions that minimize the total electricity consumption cost S of the whole society, including the optimal electricity energy price, flexible ramping price, and the winning bid output of various types of units; then transmit the electricity energy price, flexible ramping price, winning bid output of various types of units, etc. obtained by solving the lower-layer clearing model to the upper-layer profit maximization model. For the upper-layer new energy unit profit maximization model, thermal power unit profit maximization model, and hydropower unit profit maximization model, update the corresponding price and unit output and other information. Based on these updated information, the upper-layer model recalculates the profit and finds a better combination of unit decision variables under its own constraint conditions to achieve profit maximization.

[0137] Repeat the above process of solving the lower layer and updating the upper layer by information transmission, and iterate continuously. In each iteration, update the position and velocity of the particles according to the update rules of the particle swarm algorithm, and adjust the values of the decision variables; calculate the fitness functions of the upper-layer and lower-layer models (profit for the upper layer and cost for the lower layer), and evaluate the quality of the particles according to the fitness. Then check whether the preset convergence condition is met. The convergence condition can be reaching a certain number of iterations, or the change in the objective functions of the upper and lower layers is less than the preset threshold, or meeting a certain Pareto optimality, etc.; when the convergence condition is met, the finally obtained combination of decision variables (including the power generation of various types of units, flexible ramping capacity, etc.) is the optimization result of the power system decision-making, and the obtained price information (electricity energy price, flexible ramping price, etc.) reflects the price level under the market equilibrium state.

[0138] In this embodiment, by using the double-layer multi-objective particle swarm algorithm to solve the market equilibrium model, the optimization of the power system decision-making is realized. The upper and lower layer models are closely combined. The price and output information determined by the lower-layer clearing model provides an optimization basis for the upper-layer profit maximization model, and the adjustment of the upper-layer model acts on the lower-layer model in turn. Through iterative solution, it gradually approaches the market equilibrium state. This method fully considers the interests of different market participants in the power system and system constraints, and can find the optimal resource allocation scheme and price level under the premise of meeting the system operation constraints, realizing the overall optimization of the power system. It helps to improve the economic operation efficiency of the power system, enables various types of units to make reasonable bidding and output decisions according to their own costs and market conditions, while ensuring that the system has sufficient flexibility to cope with load fluctuations, promotes the optimal allocation of market resources, realizes the supply-demand balance and economic stability of the power market, provides strong decision-making support for the sustainable development of the power system, and can find more equilibrium solutions that conform to the actual market situation through continuous iterative solution, providing an effective optimization means for the stable operation and economic operation of the power system in a complex environment.

[0139] In summary, the power system decision optimization method based on the flexible ramping requirement price curve provided by the present invention takes into account the uncertainty of the flexible ramping requirement price curve, can accurately reflect the actual situation that the value of the newly added flexible ramping capacity decreases as the capacity increases, and helps to improve the market economy of the power system. Based on the flexible ramping requirement price curve model, the coupling effect of various power generation resources when providing electric energy and flexible ramping products is fully considered. The established market equilibrium model can provide effective support for reasonable capacity allocation of various resource entities in different markets, promote the optimal allocation of power system resources, improve the flexibility, reliability and economy of power system operation, and better cope with the challenges brought by the large-scale access of renewable energy.

[0140] Please refer to Figure 2 , which shows a schematic structural diagram of a power system decision optimization system based on a flexible ramping requirement price curve provided by an embodiment of the present invention. The system includes:

[0141] A data acquisition module configured to acquire load prediction error data of the power system;

[0142] A flexible ramping requirement module configured to construct a flexible ramping requirement model according to the load prediction error data;

[0143] A flexible ramping requirement price curve calculation module configured to construct a flexible ramping requirement price curve calculation model according to the flexible ramping requirement model;

[0144] A market equilibrium module configured to construct a market equilibrium model according to the flexible ramping requirement price curve calculation model, including an upper-layer market member profit maximization model and a lower-layer market clearing optimization model;

[0145] A decision optimization module configured to solve the power system decision optimization result according to the market equilibrium model.

[0146] 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 drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A power system decision optimization method based on a flexible ramping demand price curve, characterized in that: The method comprises: Obtain load forecast error data of the power system; Build a flexible ramp demand model based on load forecast error data; According to the flexible ramping demand model, a flexible ramping demand price curve calculation model is constructed; Construct a market equilibrium model based on the flexible ramping demand price curve calculation model, including an upper-level profit maximization model and a lower-level clearing optimization model; The optimization results of power system decision-making are solved based on the market equilibrium model.

2. The power system decision optimization method according to claim 1, characterized in that: Obtaining load forecast error data of the power system includes: Obtain the predicted and actual values ​​of system load, wind power and photovoltaic power generation on historical operation days, and calculate the prediction errors of system load, wind power and photovoltaic power in each period; The quantile quadratic regression fitting method is used to process the system load, wind power and photovoltaic power prediction errors, and the fluctuation range of the system load, wind power and photovoltaic power prediction errors is extracted; The net load forecast error of the target period is extracted based on the fluctuation range.

3. The power system decision optimization method according to claim 2, characterized in that: The flexible ramp demand model constructed based on load forecast error data includes: According to the net load forecast error in the target period, combined with the system load forecast value in the adjacent period, the system's uncertain flexible ramping demand is calculated; According to the uncertain flexible ramping demand of the system, the total flexible ramping demand of the system is calculated by combining the load difference between adjacent time periods.

4. The power system decision optimization method according to claim 3, characterized in that: According to the flexible ramping demand model, the flexible ramping demand price curve calculation model is constructed, including: Determine a flexible ramping demand price curve based on the net load forecast error and a preset price limit; Calculate the flexible ramping clearing price and clearing quantity based on the flexible ramping demand price curve.

5. The power system decision optimization method according to claim 4, characterized in that: Determining the flexible ramping demand price curve based on the net load forecast error and the preset price limit includes: According to different ranges of unpurchased flexible ramping capacity, the flexible ramping demand price curve is calculated based on the probability density distribution function of the net load forecast error and combined with the preset price limits of the electric energy market and flexible ramping products.

6. The power system decision optimization method according to claim 5, characterized in that: According to different ranges of unpurchased flexible ramping capacity, the expression for calculating the flexible ramping demand price curve based on the probability density distribution function of the net load forecast error and combined with the preset price limits of the electric energy market and flexible ramping products is: In the formula, represents the upward flexible ramping demand price curve for the unpurchased upward flexible ramping capacity in period t; represents the upward flexible ramping capacity that was not purchased during period t; represents the downward flexible ramping demand price curve for unpurchased downward flexible ramping capacity in period t; represents the downward flexible ramping capacity that was not purchased during period t; EP U Indicates the upper limit of the bidding price in the electricity energy market; EP D Indicates the lower limit of the bidding price in the electricity market; FP U Indicates the upper limit of the product price that can be climbed flexibly upward; FP D Indicates the upper limit of the product price that can be climbed flexibly downward; p t (e) a probability density distribution function representing the net load forecast error, which is constructed based on the historical observation data of the system net load forecast error and is used to reflect the probability distribution of the net load forecast error; It represents the positive error of the system net load forecast during period t; It represents the negative error of the system net load forecast during period t; It represents the flexible ramping demand on the uncertainty of the system during period t; It represents the flexible ramping demand under the uncertainty of the system during period t.

7. The power system decision optimization method according to any one of claims 1 to 6, characterized in that: The upper-level profit maximization model constructed based on the flexible ramping demand price curve calculation model includes: Construct a profit maximization model for new energy units based on the flexible ramp-clearing price and the power generation of new energy units; A profit maximization model for thermal power units is constructed based on the flexible ramp clearing price and clearing volume, thermal power unit power generation and flexible ramping capacity; A profit maximization model for thermal power units is constructed based on the flexible ramping clearing price and clearing volume, the power generation of hydropower units and the flexible ramping capacity.

8. The power system decision optimization method according to claim 7, characterized in that: The lower-level clearing optimization model is constructed based on the flexible ramping demand price curve calculation model, including: The lower-level market clearing optimization model is constructed with the goal of minimizing the electricity cost of the whole society. The expression of the objective function is: In the formula, represents the quotation curve of the i-th type unit; represents the power generation of the i-th type unit during period t; It represents the clearing price of the upward flexible ramping product in period t; It represents the total amount of flexible ramping demand of the system during period t; It represents the clearing price of the downward flexible ramping product in period t; It represents the total amount of flexible ramp demand of the system in period t; N represents the set of units.

9. The power system decision optimization method according to claim 8, characterized in that: The constraints for constructing the lower-level clearing optimization model based on the flexible ramping demand price curve calculation model include: The power balance constraint is expressed as: In the formula, It represents the power generation of thermal power units during period t; represents the power generation of the hydropower unit during period t; represents the power generation of renewable energy in period t; represents the power generation of independent energy storage during period t, represents the total load demand during period t; Flexible climbing demand constraint, its expression is: In the formula, It represents the upward flexible ramping capacity of thermal power units during period t; It represents the upward flexible ramping capacity of the hydropower unit during period t; represents the upward flexible ramping capacity of the independent energy storage during period t; It represents the total amount of flexible ramping demand of the system during period t; It represents the downward flexible ramping capacity of thermal power units during period t; It represents the downward flexible ramping capacity of the hydropower unit during period t; represents the downward flexible ramping capacity of the independent energy storage during period t; represents the total amount of flexible ramping demand of the system during period t; N T Indicates the number of thermal power units; N H Indicates the number of hydropower units; N S Indicates the number of independent energy storage.

10. The power system decision optimization system based on the flexible ramp demand price curve is characterized by: The power system decision optimization method based on the flexible ramp demand price curve according to any one of claims 1 to 9 is adopted, and the system comprises: A data acquisition module configured to acquire load forecast error data of the power system; a flexible ramping demand module configured to construct a flexible ramping demand model based on load forecast error data; A flexible ramping demand price curve calculation module is configured to construct a flexible ramping demand price curve calculation model according to the flexible ramping demand model; A market equilibrium module, configured to construct a market equilibrium model based on a flexible ramping demand price curve calculation model, including an upper-level market member profit maximization model and a lower-level market clearing optimization model; The decision optimization module is configured to solve the power system decision optimization result according to the market equilibrium model.

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