Peak regulation and frequency modulation cooperative scheduling decision-making method for electric power system

Abstract

The invention provides a peak regulation and frequency modulation cooperative scheduling decision-making method for a power system. The method comprises the following steps: carrying out regulation characteristic modeling on thermal power resources; the operation cost of the thermal power generating unit is segmented according to the output condition of the thermal power generating unit, and the operation cost is at least divided into a deep peak regulation stage and a conventional output stage; the method comprises the following steps: establishing a power system scheduling model which comprehensively considers peak regulation and frequency modulation demands by taking minimum system operation cost as an objective function and taking cooperative coping with the peak regulation and frequency modulation demands of a power grid as operation constraints, and carrying out coordinated distribution on thermal power resource regulation capacity between the peak regulation demands and the frequency modulation demands; wherein the power system scheduling model introduces a peak regulation capacity demand obtained based on system prediction error distribution. According to the invention, overall distribution of the adjustment resources between the peak regulation demand and the frequency modulation demand can be realized, so that the peak regulation and frequency modulation demands of the power grid are met more effectively, and the operation safety and economy of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems and automation technologies thereof, and in particular to a method for coordinated dispatching decision of peak load and frequency regulation of power systems. Background Art

[0002] In order to further reduce carbon emissions, it is necessary to build a new power system with new energy as the main body, which also means that the proportion of new energy utilization in my country's power grid will further increase in the future. In recent years, my country's new energy has developed rapidly: as of the end of December 2022, my country's wind power installed capacity is about 370 million kilowatts, an increase of 11.2% year-on-year; solar power generation installed capacity is about 390 million kilowatts, an increase of 28.1% year-on-year. However, the continuous access of new energy has led to increasing pressure on grid peak and frequency regulation, which has brought severe challenges to the dispatching and operation of the power system. Taking Chongqing power grid as an example, in recent years, the maximum peak-to-valley difference rate of Chongqing power grid during the peak load period in summer has exceeded 50%, and key indicators such as the intensity and frequency of frequency fluctuations in Chongqing power grid have also increased by more than 30%. In the future, with the construction of new power systems, the peak and frequency regulation pressure of power grids will further increase, which is specifically manifested as follows: on the one hand, the power generation space of conventional controllable power sources will be squeezed by new energy, resulting in a decrease in the peak and frequency regulation capacity of the system; on the other hand, new energy has random fluctuation characteristics and anti-peak characteristics, and its large-scale access will also increase the peak and frequency regulation pressure of the power grid. It can be seen that in the future, the contradiction between the decline in the system's peak-shaving and frequency-regulating capabilities and the increase in peak-shaving and frequency-regulating demands will become an important factor threatening the safe and stable operation of the power grid.

[0003] In order to cope with the random fluctuation characteristics of renewable energy, researchers have paid extensive attention to uncertain scheduling methods such as multi-scenario, chance constraint, and robust optimization. However, such methods have heavy computational burden and limited application scenarios, and their effects in large-scale actual power grids have not yet been verified. Therefore, such methods have not yet been applied in industrial practice. The industry still generally adopts deterministic scheduling optimization methods with high computational efficiency, which adjust the scheduling boundaries such as peak regulation and frequency regulation and reserve sufficient operating margin to cope with the random fluctuation characteristics of the system in the real-time operation stage. In the day-ahead scheduling decision-making link, at present, the power dispatching department mainly adopts the sequential scheduling method of frequency regulation first and peak regulation later to determine the regulation resources participating in frequency regulation and peak regulation, and the resources participating in frequency regulation will no longer participate in the subsequent peak regulation scheduling. This sequential scheduling method may cause the regulation resources with good peak regulation performance to be unable to participate in peak regulation scheduling because they provide frequency regulation services in the frequency regulation scheduling link, which ultimately makes it difficult for the system to meet the peak regulation demand, resulting in a series of problems such as wind abandonment and solar abandonment, which seriously threatens the safe, stable and economic operation of the power grid. In this regard, a study proposed a peak-shaving and frequency-regulation coordinated dispatch decision method, which achieved the coordinated allocation of peak-shaving and frequency-regulation resources in the same dispatch model, which can effectively alleviate the above problems. However, this method does not consider the impact of the randomness of new energy forecasts on the system's peak-shaving demand, and the dispatch decision plan formulated may not be able to effectively respond to the actual net load demand of the system. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0005] To this end, the present invention provides a method for coordinated dispatching decision of peak load and frequency regulation in a power system.

[0006] The power system peak load and frequency regulation coordinated dispatch decision method proposed by the present invention comprises:

[0007] Modeling the regulation characteristics of thermal power resources; wherein the operation cost of thermal power units is segmented according to the output of thermal power units, at least into a deep peak regulation stage and a conventional output stage;

[0008] Taking the minimization of system operating cost as the objective function and the coordinated response to the peak-shaving and frequency regulation needs of the power grid as the operating constraint, a power system dispatching model that comprehensively considers the peak-shaving and frequency regulation needs is established, and the regulation capacity of thermal power resources is coordinated and allocated between the peak-shaving and frequency regulation needs; wherein, the power system dispatching model introduces the peak-shaving capacity demand obtained based on the system prediction error distribution.

[0009] The power system peak-shaving and frequency-regulating coordinated dispatch decision method according to the above technical solution of the present invention may also have the following additional technical features:

[0010] In the above technical solution, the step of modeling the regulation characteristics of thermal power resources includes:

[0011] Setting constraints on thermal power resources; the constraints include output constraints on thermal power units, frequency regulation capacity constraints, peak regulation capacity constraints and ramp constraints;

[0012] The output constraints of the thermal power units include:

[0013] The output of thermal power units has two operating ranges, including energy service and deep peak load regulation service. In each operating range, the output of thermal power units at least meets the following conditions:

[0014]

[0015] in, represents the output of the i-th thermal power unit in energy service, represents the output of the i-th thermal power unit in deep peak load regulation service; represents the actual upward peak-shaving capacity of the i-th thermal power unit at time t, represents the actual downward peak-shaving capacity of the i-th thermal power unit at time t; represents the frequency regulation capacity provided by the i-th thermal power unit at time t, represents the frequency regulation capacity provided by the i-th thermal power unit at time t; The maximum output of the i-th thermal power unit in the energy service range, represents the minimum output of the i-th thermal power unit in the energy service interval; represents the maximum output of the i-th thermal power unit in the deep peak load regulation service interval, represents the minimum output of the i-th thermal power unit in the deep peak load regulation service interval; P i t represents the output of the i-th thermal power unit at time t;

[0016] The frequency modulation capacity constraints include:

[0017]

[0018] Among them, R Pi represents the upper frequency regulation capacity limit of the i-th thermal power unit; R Di represents the down-regulation capacity limit of the i-th thermal power unit;

[0019] The peak load capacity constraints include:

[0020]

[0021] Among them, FR Pi represents the upward peak capacity limit of the i-th thermal power unit, FR Di represents the downward peak capacity limit of the i-th thermal power unit;

[0022] The hill climbing constraints include:

[0023] -FR Pi ≤P i t -P i t-1 ≤FR Pi

[0024] -FR Di ≤P i t-1 -P i t ≤FR Di

[0025] Among them, P i t-1 Represents the output of the i-th thermal power unit at time t-1.

[0026] In the above technical solution, the deep peak regulation stage includes an oil injection peak regulation stage and a non-oil injection peak regulation stage;

[0027] The operating cost of the thermal power unit is divided into sections according to the output of the thermal power unit, including:

[0028] According to the operating cost composition of thermal power units at different operating stages, the peak load regulation cost of thermal power units is expressed as:

[0029]

[0030] Where C(P) represents the peak load regulation cost of thermal power units; f(P) represents the operating coal consumption cost; w cost (P) represents the unit loss cost under deep peak regulation; C oil represents the oil investment cost under deep peak regulation; P represents the output of thermal power units, P a , P b , P c , P d are the endpoints of the intervals divided according to the output of thermal power units, among which, P c ≤P≤P d It is the normal output stage, P a ≤P≤P c For the peak load regulation stage without oil injection, P b ≤P≤P a This is the peak load regulation stage.

[0031] In the above technical solution, the modeling of the regulation characteristics of thermal power resources also includes:

[0032] Divide the output of thermal power units into sections:

[0033]

[0034] Among them, x m It indicates the winning bid power of the unit in each section after the electric energy interval is segmented. M indicates the number of sections, and its value is 3.

[0035] The winning bid power of the units in each section meets the following constraints:

[0036] 0≤x 1 ≤P i,e,1

[0037] 0≤x 2 ≤P i,e,2 -P i,e,1

[0038] 0≤x 3 ≤P i,e,3 -P i,e,2

[0039]

[0040] Among them, P i,e,m represents the segment value of the i-th thermal power unit in the m-th segment of the spot market; P i min Indicates the minimum value of the electric energy output range.

[0041] In the above technical solution, the modeling of the regulation characteristics of thermal power resources also includes:

[0042] In the deep peak regulation stage, the power winning bid for deep peak regulation of thermal power units is divided into sections:

[0043]

[0044] Among them, y m It indicates the winning bid power of the unit in each section after the peak load regulation interval is segmented. M indicates the number of segments and its value is 3. Z indicates an integer variable, which is used to mark the deep peak load regulation requirement of the system.

[0045] The winning bid power of the units in each section meets the following constraints:

[0046] 0≤y m ≤P i,p,m-1 -P i,p,m

[0047] P i,p,0 =P i min

[0048]

[0049]

[0050] Among them, Pi,p,m It represents the segment value of the i-th thermal power unit in the m-th segment of the peak load regulation market.

[0051] In the above technical solution, the objective function of the power system dispatching model is:

[0052] min[F e (t)+F p (t)+F r (t)]

[0053] Among them, F e (t) represents the cost of the i-th thermal power unit participating in the energy service in period t, and the calculation method includes:

[0054]

[0055] Among them, C m,e It represents the electricity price of the i-th thermal power unit in the m-th thermal power generation output segment;

[0056] F p (t) represents the additional cost of the ith thermal power unit participating in the deep peak load regulation service in period t, and the calculation method includes:

[0057]

[0058] Among them, C m,p It represents the price of the i-th thermal power unit participating in the deep peak load regulation service in the m-th deep peak load regulation winning power segment;

[0059] F r (t) represents the frequency regulation capacity cost of thermal power units, and the calculation method includes:

[0060]

[0061] in, represents the upward frequency regulation capacity quotation of the i-th thermal power unit at time t, It represents the downward frequency regulation capacity quotation of the ith thermal power unit at time t, T represents the calculation period, and N represents the total number of thermal power units.

[0062] In the above technical solution, the operation constraints of the power system dispatching model include operation constraints of thermal power units, system power balance constraints, system frequency regulation capacity constraints, system peak regulation capacity constraints and line transmission constraints;

[0063] Wherein, the thermal power unit operation constraints at least include the thermal power unit operation state constraints and the thermal power unit peak and frequency regulation service mutually exclusive constraints;

[0064] The operating state constraints of the thermal power unit include:

[0065] Determine whether the output of the thermal power unit is less than the minimum value of the electric energy output range, introduce an integer variable Z, and satisfy the first constraint. If Z=0, the thermal power unit participates in deep peak regulation. If Z=1, the thermal power unit generates electricity normally without additional operation. The first constraint includes:

[0066]

[0067] Where G represents the energy constant, which can be 10 10 ;

[0068] Determine the relationship between the unit output and the minimum output after reserving the peak load capacity, and introduce the integer variable Z 1 , satisfying the second constraint, i.e., determining whether the thermal power unit is likely to participate in peak load regulation under the influence of the prediction error. The second constraint includes:

[0069]

[0070] in, It represents the reserved downward peak-shaving capacity of the i-th thermal power unit at time t; the thermal power units that enter the deep peak-shaving interval after reserving the downward peak-shaving capacity cannot participate in the frequency regulation service;

[0071] Among them, when Z = 1 and Z 1 = 1, the thermal power unit will not participate in deep peak regulation even if it reserves a margin for downward peak regulation.

[0072] When Z=1 and Z 1 = 0, the thermal power unit can generate electricity normally before reserving the margin for downward peak regulation, but it may adjust the output downward to participate in deep peak regulation due to uncertainty. and Optimize the output of thermal power units;

[0073] When Z=0 and Z 1 = 0, the thermal power unit will participate in deep peak regulation from the beginning.

[0074] The mutually exclusive constraints for peak and frequency regulation services of thermal power units include:

[0075]

[0076] The units participating in peak load regulation no longer participate in frequency regulation, and the units participating in frequency regulation do not participate in peak load regulation; when Z = 0, is limited to 0, To meet the upper and lower output constraints, thermal power units participate in deep peak load regulation services; when Z = 1, Limited to 0, thermal power units participate in energy services.

[0077] In the above technical solution, the operation constraints of the thermal power units also include the peak-shaving capacity constraints, the ramp constraints, the output segmentation constraints of the thermal power units, and the power segmentation constraints for the deep peak-shaving bid of the thermal power units.

[0078] In the above technical solution, the system power balance constraint includes:

[0079]

[0080] Among them, FH data Indicates the value of the load, FD data Indicates the value of wind power;

[0081] The system frequency modulation capacity constraints include:

[0082] The capacity that the system adjusts up or down at time t should be greater than or equal to the capacity demand that the system adjusts up or down during period t. The expression is:

[0083]

[0084] in, It represents the upward frequency regulation capacity demand of the system in period t; It represents the system's downward frequency regulation capacity demand during period t;

[0085] The line transmission constraints include:

[0086] -P lmax ≤P l ≤P lmax

[0087]

[0088] Among them, P l represents the active power transmitted by the lth line; P lmax Indicates the maximum value of active power transmitted by the lth line; K l,i K represents the power transfer factor of the thermal power unit to the lth line; l,d represents the power transfer factor of the load to the lth line; d represents the number of the load node; N D Represents the total number of load nodes; P d,t represents the load of the dth node in period t.

[0089] In the above technical solution, the system peak load capacity constraints include:

[0090]

[0091] in, It represents the system's upward peak-shaving capacity demand during period t; It represents the system's downward peak-shaving capacity demand during period t;

[0092] The calculation method of the system's peak load capacity requirement includes:

[0093]

[0094] FH bzc =0.01*FH data

[0095] FD bzc =0.1*FD data +0.01*FD zj

[0096] Among them, FR data Indicates the peak load capacity requirement of the system, FH bzc represents the standard deviation of load forecast error generation, FD bzc represents the standard deviation of the new energy error generation, FH data Indicates the value of the load, FD data Indicates the value of wind power, FD zj Represents the total installed capacity of wind power.

[0097] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are:

[0098] In order to cope with the peak-shaving and frequency-regulating pressure on the power grid brought about by the large-scale access of new energy, this paper proposes a coordinated dispatching decision method for peak-shaving and frequency-regulating power systems that takes into account the randomness of prediction. Taking into account the influence of system prediction errors, it can achieve refined coordinated allocation of power grid regulation resources between peak-shaving demand and frequency-regulating demand, ensuring that under the formulated resource scheduling plan, the system has the ability to cope with the actual peak-shaving and frequency-regulating demand of the power grid. A related case analysis was carried out based on the actual data of a provincial power grid in my country to verify the effectiveness of the proposed method. The main conclusions are as follows:

[0099] Compared with sequential dispatching, coordinated peak-shaving and frequency regulation dispatching can realize the coordinated allocation of regulation resources between peak-shaving demand and frequency regulation demand, thereby more effectively meeting the peak-shaving and frequency regulation needs of the power grid, and to a certain extent improving the safety and economy of power grid operation.

[0100] Introducing peak-shaving capacity demand for system prediction randomness can enable the power grid's day-ahead dispatching decision to effectively respond to the impact of load and renewable energy forecast errors. The unit operating status determined by the day-ahead dispatching decision plan can effectively follow the power changes of load and renewable energy curves during intraday dispatching, greatly improving the level of renewable energy consumption and effectively ensuring the safety and economy of system operation.

[0101] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0103] Figure 1 is a flow chart of a method for coordinated dispatching decision of peak load and frequency regulation of a power system according to an embodiment of the present invention;

[0104] Figure 2 It is a schematic diagram of the output operation range of thermal power units in a power system peak-shaving and frequency-regulating coordinated dispatch decision method according to an embodiment of the present invention;

[0105] Figure 3 It is a schematic diagram of peak-shaving cost characteristics of thermal power units in a method for coordinated dispatching decision of peak-shaving and frequency-shaving of a power system according to an embodiment of the present invention;

[0106] Figure 4 is a schematic diagram of wind power output and load curves for collecting data according to an embodiment of the present invention;

[0107] Figure 5 It is a comparison diagram of the day-ahead predicted net load curve of the system collecting data and the actual net load curve during the day according to an embodiment of the present invention;

[0108] Figure 6 It is a schematic diagram comparing the abandoned air volume in each period in methods M1, M2 and M3 in a calculation example of the present invention. DETAILED DESCRIPTION

[0109] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0110] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0111] Refer to the following Figures 1 to 6 To describe the power system peak-shaving and frequency-regulating coordinated dispatch decision method provided according to some embodiments of the present invention.

[0112] Some embodiments of the present application provide a method for coordinated dispatching decision of peak load and frequency regulation in a power system.

[0113] like Figure 1 As shown, the first embodiment of the present invention proposes a method for coordinated dispatching decision of peak load and frequency regulation of a power system, which mainly includes the following steps S1-S2.

[0114] S1. Model the regulation characteristics of thermal power resources; wherein the operation cost of the thermal power units is segmented according to the output of the thermal power units, at least into a deep peak regulation stage and a conventional output stage.

[0115] S2. Taking the minimization of system operating cost as the objective function and the coordinated response to the peak-shaving and frequency-regulation demands of the power grid as the operating constraint, a power system dispatching model that comprehensively considers the peak-shaving and frequency-regulation demands is established, and the regulation capacity of thermal power resources is coordinated and allocated between the peak-shaving and frequency-regulation demands; wherein, the power system dispatching model introduces the peak-shaving capacity demand obtained based on the system prediction error distribution.

[0116] It should be noted that the peak-shaving service mainly includes the start-stop peak-shaving and deep peak-shaving of thermal power. Among them, the start-stop peak-shaving can be considered in the day-ahead unit combination. The research object of this disclosure is the economic dispatch problem after the start-stop state of the unit is determined. The peak-shaving service mainly refers to the deep peak-shaving of thermal power.

[0117] In terms of regulating resource operation characteristic modeling, thermal power units, as traditional regulating resources, have the characteristics of large response delay and slow ramp speed. The operation of thermal power units is subject to many constraints. In some embodiments, regulating characteristic modeling of thermal power resources includes:

[0118] The constraints of thermal power resources are set; the constraints include output constraints of thermal power units, frequency regulation capacity constraints, peak regulation capacity constraints and ramp constraints.

[0119] The output constraints of the thermal power units include:

[0120] The output of thermal power units has two operating ranges, such as Figure 2 As shown, including energy services and deep peak load services, higher than The operating range represents the output of thermal power units in energy services, which is lower than The operating range represents the output in the deep peak load regulation service. In each operating range, the output of the thermal power unit at least meets the following conditions:

[0121]

[0122] in, represents the output of the i-th thermal power unit in energy service, represents the output of the i-th thermal power unit in deep peak load regulation service; represents the actual upward peak-shaving capacity of the i-th thermal power unit at time t, represents the actual downward peak-shaving capacity of the i-th thermal power unit at time t; represents the frequency regulation capacity provided by the i-th thermal power unit at time t, represents the frequency regulation capacity provided by the i-th thermal power unit at time t; The maximum output of the i-th thermal power unit in the energy service range, represents the minimum output of the i-th thermal power unit in the energy service interval; represents the maximum output of the i-th thermal power unit in the deep peak load regulation service interval, represents the minimum output of the i-th thermal power unit in the deep peak load regulation service interval; Represents the output of the i-th thermal power unit at time t.

[0123] It should be noted that since the units participating in peak load regulation do not participate in frequency regulation, the peak loads of the same thermal power unit and These two variables will never have values ​​at the same time.

[0124] The frequency modulation capacity constraints include:

[0125]

[0126] Among them, R Pi represents the upper frequency regulation capacity limit of the i-th thermal power unit; R Di represents the down-regulation capacity limit of the i-th thermal power unit;

[0127] The peak load capacity constraints include:

[0128]

[0129] Among them, FR Pi represents the upward peak capacity limit of the i-th thermal power unit, FR Di represents the downward peak capacity limit of the i-th thermal power unit;

[0130] The hill climbing constraints include:

[0131] -FR Pi ≤P i t -P i t-1 ≤FR Pi

[0132] -FR Di ≤P i t-1 -P i t ≤FR Di

[0133] Among them, P i t-1 Represents the output of the i-th thermal power unit at time t-1.

[0134] In some embodiments, a segmented quotation is made for the thermal power unit, that is, the operating cost of the thermal power unit is segmented according to the output of the thermal power unit, mainly divided into a deep peak-shaving stage and a conventional output stage, wherein the deep peak-shaving stage includes an oil-added peak-shaving stage and a non-oil-added peak-shaving stage; the segmented situation is as follows: Figure 3 shown.

[0135] The composition of the operating cost of thermal power units is different under different operating conditions, including the operating coal consumption cost, the unit loss cost under deep peak regulation, the oil investment cost under deep peak regulation, and the start-up and shutdown cost. The process of day-ahead dispatch is to first execute the unit combination, and then execute the economic dispatch considering peak and frequency regulation. Among them, the start-up and shutdown cost can be considered in the unit combination. The peak regulation cost of the thermal power unit can be expressed as follows according to the corresponding operating cost composition of the thermal power unit in different operating stages:

[0136]

[0137] Where C(P) represents the peak load regulation cost of thermal power units; f(P) represents the operating coal consumption cost; w cost (P) represents the unit loss cost under deep peak regulation; C oil represents the oil investment cost under deep peak regulation; P represents the output of thermal power units, P a , P b , P c , P d are the endpoints of the intervals divided according to the output of thermal power units, among which, P c ≤P≤P d It is the normal output stage, P a ≤P≤P c For the peak load regulation stage without oil injection, P b ≤P≤P a This is the peak load regulation stage.

[0138] Depend on Figure 3 It can be seen that in the deep peak load regulation stage, the operating cost of the thermal power unit increases significantly with the increase of deep peak load regulation, and in the conventional output stage, the operating cost of the thermal power unit increases significantly with the increase of the unit output. Therefore, in some embodiments, the output of the thermal power unit is further divided into sections:

[0139]

[0140] Among them, x m It indicates the winning bid power of the unit in each section after the electric energy interval is segmented. M indicates the number of sections, and its value is 3.

[0141] The winning bid power of the units in each section meets the following constraints:

[0142] 0≤x 1 ≤P i,e,1

[0143] 0≤x 2 ≤P i,e,2 -P i,e,1

[0144] 0≤x 3 ≤P i,e,3 -P i,e,2

[0145]

[0146]

[0147] Among them, P i,e,m represents the segment value of the i-th thermal power unit in the m-th segment of the spot market; P i min Indicates the minimum value of the electric energy output range.

[0148] At the same time, in the deep peak regulation stage, the power winning bid for deep peak regulation of thermal power units will be divided into sections:

[0149]

[0150] Among them, y m It indicates the winning bid power of the unit in each section after the peak load regulation interval is segmented. M indicates the number of segments and its value is 3. Z indicates an integer variable, which is used to mark the deep peak load regulation requirement of the system.

[0151] The winning bid power of the units in each section meets the following constraints:

[0152] 0≤y m ≤P i,p,m-1 -P i,p,m

[0153] P i,p,0 =P i min

[0154]

[0155] Among them, P i,p,m It represents the segment value of the i-th thermal power unit in the m-th segment of the peak load regulation market.

[0156] In step S2, the peak-shaving capacity demand obtained based on the system prediction error distribution is introduced, the total system operating cost is minimized as the objective function, and the peak-shaving and frequency regulation demands are coordinated as the operating constraints. A power system peak-shaving and frequency regulation coordinated dispatching model considering the prediction randomness is constructed, and the refined coordinated allocation of regulation resources between frequency regulation services and peak-shaving services is achieved by solving the dispatching model.

[0157] In some embodiments, the objective function of the power system dispatch model is:

[0158] min[F e (t)+F p (t)+F r (t)]

[0159] Among them, F e (t) represents the cost of the i-th thermal power unit participating in the energy service in period t, and the calculation method includes:

[0160]

[0161] Among them, C m,e It represents the electricity price of the i-th thermal power unit in the m-th thermal power generation output segment;

[0162] F p (t) represents the additional cost of the ith thermal power unit participating in the deep peak load regulation service in period t, and the calculation method includes:

[0163]

[0164] Among them, C m,p It represents the price of the i-th thermal power unit participating in the deep peak load regulation service in the m-th deep peak load regulation winning power segment;

[0165] F r (t) represents the frequency regulation capacity cost of thermal power units, and the calculation method includes:

[0166]

[0167] in, represents the upward frequency regulation capacity quotation of the i-th thermal power unit at time t, It represents the downward frequency regulation capacity quotation of the ith thermal power unit at time t, T represents the calculation period, and N represents the total number of thermal power units.

[0168] The operating constraints of the power system dispatching model are further set. In some embodiments, the operating constraints of the power system dispatching model include operating constraints of thermal power units, system power balance constraints, system frequency regulation capacity constraints, system peak regulation capacity constraints and line transmission constraints.

[0169] The thermal power unit operation constraints include at least the thermal power unit operation status constraints and the thermal power unit peak and frequency regulation service mutually exclusive constraints.

[0170] Thermal power units participate in deep peak-shaving services mainly by adjusting their own load rates, that is, significantly reducing their own output power to make room for new energy sources such as wind power to access the grid, thereby improving the system's new energy consumption level.

[0171] The operating state constraints of the thermal power unit include:

[0172] Determine whether the output of the thermal power unit is less than the minimum value of the electric energy output range, introduce an integer variable Z, and satisfy the first constraint. If Z=0, the thermal power unit participates in deep peak regulation. If Z=1, the thermal power unit generates electricity normally without additional operation. The first constraint includes:

[0173]

[0174] Where G represents the energy constant, which can be 10 10 ;

[0175] Determine the relationship between the unit output and the minimum output after reserving the peak load capacity, and introduce the integer variable Z 1 , satisfying the second constraint, i.e., determining whether the thermal power unit is likely to participate in peak load regulation under the influence of the prediction error. The second constraint includes:

[0176]

[0177] in, It represents the reserved downward peak-shaving capacity of the i-th thermal power unit at time t; the thermal power units that enter the deep peak-shaving interval after reserving the downward peak-shaving capacity cannot participate in the frequency regulation service;

[0178] Among them, when Z = 1 and Z 1 = 1, the thermal power unit will not participate in deep peak regulation even if it reserves a margin for downward peak regulation.

[0179] When Z=1 and Z 1 = 0, the thermal power unit can generate electricity normally before reserving the margin for downward peak regulation, but it may adjust the output downward to participate in deep peak regulation due to uncertainty. and Optimize the output of thermal power units;

[0180] When Z=0 and Z 1 = 0, the thermal power unit will participate in deep peak regulation from the beginning.

[0181] The mutually exclusive constraints for peak and frequency regulation services of thermal power units include:

[0182] Power constraints of thermal power units:

[0183]

[0184] Frequency regulation capacity constraints of thermal power units:

[0185]

[0186] The units participating in peak load regulation no longer participate in frequency regulation, and the units participating in frequency regulation do not participate in peak load regulation; when Z = 0, is limited to 0, To meet the upper and lower output constraints, thermal power units participate in deep peak load regulation services; when Z = 1, Limited to 0, thermal power units participate in energy services.

[0187] In some embodiments, the thermal power unit operation constraints also include the peak-shaving capacity constraints, the ramp constraints, the output segmentation constraints of the thermal power unit, and the deep peak-shaving winning power segmentation constraints of the thermal power unit set in step S1.

[0188] In some embodiments, regardless of system network losses, the sum of the outputs of all units should be equal to the load, and the system power balance constraints include:

[0189]

[0190] Among them, FH data Indicates the value of the load, FD data Indicates the value of wind power;

[0191] The system frequency modulation capacity constraints include:

[0192] The capacity that the system adjusts up or down at time t should be greater than or equal to the capacity demand that the system adjusts up or down during period t. The expression is:

[0193]

[0194] in, It represents the upward frequency regulation capacity demand of the system in period t; It represents the system's downward frequency regulation capacity demand during period t;

[0195] The line transmission constraints include:

[0196] -P lmax ≤P l ≤P lmax

[0197]

[0198] Among them, P l represents the active power transmitted by the lth line; P lmax Indicates the maximum value of active power transmitted by the lth line; K l,i K represents the power transfer factor of the thermal power unit to the lth line; l,d represents the power transfer factor of the load to the lth line; d represents the number of the load node; N D Represents the total number of load nodes; Pd,t represents the load of the dth node in period t.

[0199] In some embodiments, the system peak load capacity constraint includes:

[0200]

[0201] in, It represents the system's upward peak-shaving capacity demand during period t; It represents the system's downward peak-shaving capacity demand during period t;

[0202] The calculation method of the system's peak load capacity requirement includes:

[0203]

[0204] FH bzc =0.01*FH data

[0205] FD bzc =0.1*FD data +0.01*FD zj

[0206] Among them, FR data Indicates the peak load capacity requirement of the system, FH bzc represents the standard deviation of load forecast error generation, FD bzc represents the standard deviation of the new energy error generation, FH data Indicates the value of the load, FD data Indicates the value of wind power, FD zj Represents the total installed capacity of wind power.

[0207] In this embodiment, it is assumed that the forecast errors of new energy and loads obey the normal distribution, thereby forming the system forecast error distribution, and the system peak-shaving capacity demand is obtained based on this. However, the method proposed in the present invention is not limited to the assumption that the system forecast error distribution obeys the normal distribution, and the system forecast error distribution can also be obtained by using methods such as probability forecasting, and then the peak-shaving capacity demand under a given confidence level is obtained.

[0208] The power system dispatching model established in the above embodiment is a mixed integer linear programming (MILP) model, which can be solved by commercial solvers such as CPLEX. By solving the power system dispatching model, the refined coordinated allocation of regulation resources between frequency regulation services and peak regulation services can be achieved.

[0209] The second embodiment of the present invention proposes an implementation case of a method for coordinated dispatching decision-making of peak load and frequency regulation in a power system, and the specific process is as follows:

[0210] (1) Collect data

[0211] The wind power forecast output data comes from the official website of the PJM power market in the United States, and the load data is collected from the actual data of a provincial power grid in my country. The present invention scales the collected data to adapt to the test system. The load data and wind power generation data for 24 hours on the test day are as follows: Figure 4 As shown, the wind power penetration rate is 41.26%. In addition, the system frequency regulation capacity requirement is set to 5% of the load data in the present invention.

[0212] (2) Model building

[0213] With reference to the above embodiment, the objective function is to minimize the total operating cost of the system, and the operating constraints are to collaboratively consider the peak-shaving and frequency-regulation requirements, so as to construct a coordinated dispatching model for peak-shaving and frequency-regulation of the power system that takes into account the predictive randomness.

[0214] (3) Algorithm solution

[0215] Commercial solvers such as CPLEX are used to solve the proposed power system peak-shaving and frequency-regulation coordinated dispatch model considering prediction randomness, and then the day-ahead dispatch decision plan of each unit is obtained, and the unit's participation status in peak-shaving and frequency-regulation in intraday dispatch decisions is determined (units participating in frequency regulation the day before will participate in frequency regulation but not in peak-shaving in intraday dispatch decisions; units participating in peak-shaving the day before will decide whether to actually participate in peak-shaving based on the latest load and new energy forecast results in intraday dispatch decisions, and such units will no longer participate in frequency regulation).

[0216] The specific simulation results are as follows:

[0217] 1) Case description

[0218] This embodiment uses a modified IEEE 30-node system to verify the effectiveness of the proposed scheduling method for 24h scheduling problems. The wind power forecast output data used comes from the official website of the PJM power market in the United States, and the load data is collected from the actual data of a provincial grid in my country. The test system contains 6 thermal power units (G1-G6) as regulation resources. The main operating parameters include the minimum and maximum output values ​​of the unit's electric energy interval, the minimum and maximum output values ​​of the unit's peak-shaving interval, the unit's frequency regulation capacity limit, and the unit's climbing ability. Refer to Table 1. The frequency regulation capacity cost of thermal power units refers to Table 2, and the unit is RMB / MW.

[0219] Table 1 Thermal power unit operating parameters

[0220]

[0221]

[0222] Table 2 Frequency regulation capacity cost parameters of thermal power units

[0223]

[0224] The three methods are compared in the case analysis, as follows:

[0225] M1: The frequency and peak regulation sequential scheduling optimization method currently used in the industry. The day-ahead model does not introduce ramping to determine the reservation of peak regulation capacity.

[0226] M2: A dispatch optimization method for coordinating peak and frequency regulation needs. The day-ahead model does not introduce ramping to determine the reservation of peak-shaving capacity.

[0227] M3: A dispatch optimization method for coordinating peak-shaving and frequency-regulation needs. The day-ahead model introduces ramping to determine the reservation of peak-shaving capacity. M3 is the coordinated dispatch method for peak-shaving and frequency-regulation of the power system proposed by the present invention that takes into account the prediction randomness.

[0228] Table 3 Method comparison settings and their purpose

[0229]

[0230] It should be emphasized that the power system peak-shaving and frequency-regulation coordinated dispatching model proposed in the present invention that takes into account the prediction randomness is a day-ahead dispatching decision model, the purpose of which is to determine the peak-shaving and frequency-regulating operating status of the unit. The effectiveness of the unit operating status determined by different dispatching decision methods will be verified through intraday dispatching decisions based on load and actual prediction curves of new energy sources (comparing system operating costs, wind and solar power abandonment, etc.) to take into account the degree of adaptability of different day-ahead dispatching decision methods to the system prediction randomness. The system day-ahead predicted net load curve used in the simulation is different from the intraday actual net load curve as shown in Figure 2. Figure 5 shown.

[0231] 2) Validity Verification

[0232] In order to verify the effectiveness of the proposed method, a specific scenario of 6 thermal power units was measured and analyzed, and the final cost comparison of M1, M2 and M3 is shown in Table 4.

[0233] As can be seen from Table 4, compared with M1, the total cost of M2 is reduced by 15.9%. The cost of wind abandonment is reduced by 42.47%. However, compared with M1, the frequency regulation cost of M2 has increased by 4.32%. The reason is that M1 takes the minimum frequency regulation cost as the objective function in the frequency regulation scheduling link of sequential scheduling, so M1 can formulate the most economical frequency regulation scheduling plan. However, the units participating in the frequency regulation will no longer be able to participate in the subsequent

[0234] Peak-shaving scheduling: The thermal power units with better peak-shaving performance in M1 are unable to provide peak-shaving services because they have participated in frequency regulation first, which ultimately leads to poor peak-shaving effects in M1, which is reflected in higher wind curtailment costs.

[0235] Table 4 Comparison of operating costs of three methods

[0236]

[0237] In contrast, the peak-shaving and frequency-regulating services are coordinated and considered in M2, and the overall operation economy is improved. The thermal power units with strong peak-shaving capabilities provide peak-shaving services in M2, thereby reducing the amount of wind curtailment, such as Figure 6 As shown in the figure. It can be seen from the figure that at 23h, the wind abandonment of M1 is 27.99MW, while the wind abandonment of M2 is only 6.74MW. This is because during this period, the units 1 and 4 with strong peak-shaving capabilities in M1 both participated in frequency regulation, resulting in insufficient peak-shaving capacity of the system, and the load can only be balanced by abandoning wind. The status of the units during this period is shown in Table 5, 0 means that the unit participates in peak regulation, and 1 means that the unit participates in frequency regulation. Therefore, the total operating cost of M2, especially the wind abandonment cost, is much smaller than the total operating cost of M1, which verifies the effectiveness of coordinated scheduling.

[0238] Table 5 Unit operation status in 23h period of M1 and M2

[0239]

[0240] Comparing the operating costs of M2 and M3, the system operating cost of M3 is only 3.92575 million yuan, which is 45.57% lower than that of M2. The reason is that the reserved peak-shaving capacity can reduce wind curtailment and significantly improve the system's wind power absorption level. In M3, wind power is fully absorbed, which greatly reduces the cost of wind curtailment.

[0241] To further illustrate the effectiveness of reserved peak-shaving capacity, a detailed analysis is given below.

[0242] from Figure 6 It can be seen that in the 4-6 and 17-19 periods, the abandoned wind volume of M1 and M2 is the same, indicating that the peak-shaving demand in these periods is relatively small, and the units with better peak-shaving performance can also meet the system's demand in the day before. However, the prediction error is considered in the real-time scheduling model, and some parameters of M2 and M3 in the 5h period are shown in Table 6.

[0243] Table 6 Comparison of 5h period parameters between M2 and M3

[0244]

[0245]

[0246] Table 7 Operation status of 5h units in M1, M2 and M3

[0247]

[0248] As shown in Table 7, in the 5h period, both M1 and M2 are operated by units 1 and 4 with strong peak-shaving capability and high frequency regulation economy, and the remaining units participate in peak-shaving, while in M3, units 1 and 2 participate in peak-shaving. It is obvious that the first two methods only meet the needs of the day-ahead plan, without considering the impact of the forecast randomness, resulting in insufficient regulation speed and regulation margin to deal with the forecast randomness at this time. Therefore, in order to maintain the power balance in the system, wind abandonment occurs. In contrast, in M3, due to the reserved peak-shaving capacity, the unit operation state determined by the day-ahead scheduling decision plan can effectively follow the power changes of the load and new energy curves in the intraday scheduling, and can maintain power balance without abandoning wind, greatly reducing wind abandonment, significantly reducing the total system cost, and effectively improving the economic efficiency of power grid operation, which verifies the effectiveness of considering the response to forecast randomness.

[0249] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0250] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for coordinated dispatching decision of peak load and frequency regulation in power system, characterized in that: include: Modeling the regulation characteristics of thermal power resources; wherein the operation cost of thermal power units is segmented according to the output of thermal power units, at least into a deep peak regulation stage and a conventional output stage; Taking the minimization of system operating cost as the objective function and the coordinated response to the peak-shaving and frequency regulation needs of the power grid as the operating constraint, a power system dispatching model that comprehensively considers the peak-shaving and frequency regulation needs is established, and the regulation capacity of thermal power resources is coordinated and allocated between the peak-shaving and frequency regulation needs; wherein, the power system dispatching model introduces the peak-shaving capacity demand obtained based on the system prediction error distribution.

2. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 1, characterized in that: The regulating characteristic modeling of thermal power resources includes: Setting constraints on thermal power resources; the constraints include output constraints of thermal power units, frequency regulation capacity constraints, peak regulation capacity constraints and ramp constraints; The output constraints of the thermal power units include: The output of thermal power units has two operating ranges, including energy service and deep peak load regulation service. In each operating range, the output of thermal power units at least meets the following conditions: in, represents the output of the i-th thermal power unit in energy service, represents the output of the i-th thermal power unit in deep peak load regulation service; represents the actual upward peak-shaving capacity of the i-th thermal power unit at time t, represents the actual downward peak-shaving capacity of the i-th thermal power unit at time t; represents the frequency regulation capacity provided by the i-th thermal power unit at time t, represents the frequency regulation capacity provided by the i-th thermal power unit at time t; The maximum output of the i-th thermal power unit in the energy service range, represents the minimum output of the i-th thermal power unit in the energy service interval; represents the maximum output of the i-th thermal power unit in the deep peak load regulation service interval, represents the minimum output of the i-th thermal power unit in the deep peak load regulation service interval; P i t represents the output of the i-th thermal power unit at time t; The frequency modulation capacity constraints include: Among them, R Pi represents the upper frequency regulation capacity limit of the i-th thermal power unit; R Di represents the down-regulation capacity limit of the i-th thermal power unit; The peak load capacity constraints include: Among them, FR Pi represents the upward peak capacity limit of the i-th thermal power unit, FR Di represents the downward peak capacity limit of the i-th thermal power unit; The hill climbing constraints include: -FR Pi ≤P i t -P i t-1 ≤FR Pi -FR Di ≤P i t-1 -P i t ≤FR Di Among them, P i t-1 Represents the output of the i-th thermal power unit at time t-1.

3. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 2, characterized in that: The deep peak regulation stage includes an oil injection peak regulation stage and a non-oil injection peak regulation stage; The operating cost of the thermal power unit is divided into sections according to the output of the thermal power unit, including: According to the operating cost composition of thermal power units at different operating stages, the peak load regulation cost of thermal power units is expressed as: Where C(P) represents the peak load regulation cost of thermal power units; f(P) represents the operating coal consumption cost; w cost (P) represents the unit loss cost under deep peak regulation; C oil represents the oil investment cost under deep peak regulation; P represents the output of thermal power units, P a , P b , P c , P d are the endpoints of the intervals divided according to the output of thermal power units, among which, P c ≤P≤P d It is the normal output stage, P a ≤P≤P c For the peak load regulation stage without oil injection, P b ≤P≤P a This is the peak load regulation stage.

4. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 3 is characterized in that: The regulating characteristic modeling of thermal power resources also includes: Divide the output of thermal power units into sections: Among them, x m It indicates the winning bid power of the unit in each section after the electric energy interval is segmented. M indicates the number of sections, and its value is 3. The winning power of the units in each section meets the following constraints: 0≤x1≤P i,e,1 0≤x2≤P i,e,2 -P i,e,1 0≤x3≤P i,e,3 -P i,e,2 Among them, P i,e,m represents the segment value of the i-th thermal power unit in the m-th segment of the spot market; P i min Indicates the minimum value of the electric energy output range.

5. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 4, characterized in that: The regulating characteristic modeling of thermal power resources also includes: In the deep peak regulation stage, the power winning bid for deep peak regulation of thermal power units is divided into sections: Among them, y m It indicates the winning bid power of the unit in each section after the peak load regulation interval is segmented. M indicates the number of segments and its value is 3. Z indicates an integer variable, which is used to mark the deep peak load regulation requirement of the system. The winning power of the units in each section meets the following constraints: 0≤y m ≤P i,p,m-1 -P i,p,m P i,p,0 =P i min Among them, P i,p,m It represents the segment value of the i-th thermal power unit in the m-th segment of the peak load regulation market.

6. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 5, characterized in that: The objective function of the power system dispatch model is: min[F e (t)+F p (t)+F r (t)] Among them, F e (t) represents the cost of the i-th thermal power unit participating in the energy service in period t, and the calculation method includes: Among them, C m,e It represents the electricity price of the i-th thermal power unit in the m-th thermal power generation output segment; F p (t) represents the additional cost of the ith thermal power unit participating in the deep peak load regulation service in period t, and the calculation method includes: Among them, C m,p It represents the price of the i-th thermal power unit participating in the deep peak load regulation service in the m-th deep peak load regulation winning power segment; F r (t) represents the frequency regulation capacity cost of thermal power units, and the calculation method includes: in, represents the upward frequency regulation capacity quotation of the i-th thermal power unit at time t, It represents the downward frequency regulation capacity quotation of the ith thermal power unit at time t, T represents the calculation period, and N represents the total number of thermal power units.

7. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 6, characterized in that: The operation constraints of the power system dispatching model include operation constraints of thermal power units, system power balance constraints, system frequency regulation capacity constraints, system peak regulation capacity constraints and line transmission constraints; Wherein, the thermal power unit operation constraints at least include the thermal power unit operation state constraints and the thermal power unit peak and frequency regulation service mutually exclusive constraints; The operating state constraints of the thermal power unit include: Determine whether the output of the thermal power unit is less than the minimum value of the electric energy output range, introduce an integer variable Z, and satisfy the first constraint. If Z=0, the thermal power unit participates in deep peak regulation. If Z=1, the thermal power unit generates electricity normally without additional operation. The first constraint includes: Where G represents the energy constant; After determining the relationship between the unit output and the minimum output after reserving the peak-shaving capacity, an integer variable Z1 is introduced to satisfy the second constraint, that is, to determine whether the thermal power unit is likely to participate in the peak-shaving under the influence of the prediction error. The second constraint includes: in, It represents the reserved downward peak-shaving capacity of the i-th thermal power unit at time t; the thermal power units that enter the deep peak-shaving interval after reserving the downward peak-shaving capacity cannot participate in the frequency regulation service; Among them, when Z = 1 and Z1 = 1, the thermal power unit does not participate in deep peak regulation even if it reserves a margin for downward peak regulation. When Z = 1 and Z1 = 0, the thermal power unit can generate electricity normally before reserving the margin for downward peak regulation, but it may adjust the output downward to participate in deep peak regulation due to uncertainty. and Optimize the output of thermal power units; When Z=0 and Z1=0, the thermal power unit participates in deep peak regulation from the beginning. The mutually exclusive constraints for peak and frequency regulation services of thermal power units include: The units participating in peak load regulation no longer participate in frequency regulation, and the units participating in frequency regulation do not participate in peak load regulation; when Z = 0, is limited to 0, To meet the upper and lower output constraints, thermal power units participate in deep peak load regulation services; when Z = 1, Limited to 0, thermal power units participate in energy services.

8. The method for coordinated dispatching decision of peak load and frequency regulation of electric power system according to claim 7, characterized in that: The thermal power unit operation constraints also include the peak load capacity constraints, the ramp constraints, the output segmentation constraints of the thermal power unit, and the power segmentation constraints for the deep peak load bidding of the thermal power unit.

9. The method for coordinated dispatching decision of peak load and frequency regulation of electric power system according to claim 7, characterized in that: The system power balance constraints include: Among them, FH data Indicates the value of the load, FD data Indicates the value of wind power; The system frequency modulation capacity constraints include: The capacity that the system adjusts up or down at time t should be greater than or equal to the capacity demand that the system adjusts up or down during period t. The expression is: in, It represents the upward frequency regulation capacity demand of the system in period t; It represents the system's downward frequency regulation capacity demand during period t; The line transmission constraints include: Among them, P l represents the active power transmitted by the lth line; P lmax Indicates the maximum value of active power transmitted by the lth line; K l,i K represents the power transfer factor of the thermal power unit to the lth line; l,d represents the power transfer factor of the load to the lth line; d represents the number of the load node; N D Represents the total number of load nodes; P d,t represents the load of the dth node in period t.

10. The method for coordinated dispatching decision of peak load and frequency regulation of power system according to claim 7, characterized in that: The system peak load capacity constraints include: in, It represents the system's upward peak-shaving capacity demand during period t; It represents the system's downward peak-shaving capacity demand during period t; The calculation method of the system's peak load capacity requirement includes: FH bzc =0.01*FH data FD bzc =0.1*FD data +0.01*FD zj Among them, FR data Indicates the peak load capacity requirement of the system, FH bzc represents the standard deviation of load forecast error generation, FD bzc represents the standard deviation of the new energy error generation, FH data Indicates the value of the load, FD data Indicates the value of wind power, FD zj Represents the total installed capacity of wind power.

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