CHPS system optimization scheduling system and method considering thermal power generating unit modeling

Abstract

The invention relates to the technical field of multi-source power system optimization operation, and discloses a CHPS system optimization scheduling system and method considering thermal power generating unit modeling. The method comprises the following steps: firstly, according to the actual operation characteristics of the thermal power generating unit, performing refined modeling on the start-up burnup and gradeability of the thermal power generating unit Further, establishing an electric power system operation model which mainly comprises an electric power direct-current power flow model and a unit operation model and specifically comprises a thermal power output model, a thermal power start-stop model, a cascade hydropower model, an energy storage battery model and a photovoltaic operation model; on the basis, modeling is carried out on system frequency response considering energy storage battery emergency power support, including energy storage battery emergency power support, a frequency lowest point and a frequency change rate model. The power supply demand of the power system is ensured, and a more flexible power generation strategy is provided for the sending-end power grid, so that the operation stability of the novel power system is improved.

Description

Technical Field

[0001] This invention relates to the field of multi-source power system optimization operation technology, specifically to a CHPS (Cascade Hydropower-Photovoltaic-Storage) system optimization scheduling system and method that considers thermal power unit modeling. Background Technology

[0002] In recent years, clean energy sources such as photovoltaics and hydropower have become the main force in energy transformation, and their installed capacity has continued to expand, making multi-energy complementary optimal dispatching incorporating new energy sources a research hotspot. The increasing penetration rate of new energy units with relatively poor regulation capabilities has led to increased demand on the regulation capabilities of thermal power units in the power system. Furthermore, with the increasing proportion of installed capacity from new energy sources such as photovoltaics, system inertia decreases, exacerbating the risk of system frequency instability, making frequency security an issue that cannot be ignored in power system optimal dispatching. Energy storage batteries, with their high control precision and fast response speed, can immediately adjust the charging and discharging state to reduce system power imbalance when there is a source-load imbalance, making them an important frequency regulation method.

[0003] In the area of ​​hydro-solar complementarity, existing research focuses on the analysis of energy complementarity characteristics and the capacity configuration of complementary systems, neglecting the seasonal variations in hydropower resources. Hydropower resources are highly dependent on the natural environment; however, short-term optimal scheduling cannot account for the seasonal mismatch between power supply and demand. In existing thermal power unit modeling, the start-up fuel consumption and ramp-up capability are typically considered as constants or represented by step functions for cold / hot starts and cold / warm / hot starts. While simplifying thermal power unit modeling is feasible for short-term power system scheduling, for medium- to long-term scheduling, the differences in start-up fuel consumption under different downtime periods are significant, requiring a more accurate modeling method for thermal power unit start-up fuel consumption. With the increasing proportion of photovoltaic installations, the proportion of traditional synchronous generators connected to the grid decreases, leading to reduced system inertia. Furthermore, the intermittent and unschedulable nature of photovoltaic output makes power fluctuations more pronounced, thus reducing system frequency stability. Energy storage batteries have excellent regulation performance, but related research rarely considers their supporting role in frequency stability.

[0004] Against this backdrop, it is necessary to conduct refined modeling of thermal power units based on their actual operating characteristics, incorporate frequency security into the research, and consider the supporting role of energy storage batteries in frequency stability in order to seek the most economical dispatch strategy with the lowest operating cost.

[0005] Therefore, based on the traditional multi-energy complementary operation model, further research on the long-term optimization scheduling method of the cascade hydro-solar-storage system, which considers refined modeling of thermal power units and frequency stability support by energy storage batteries, is of great significance for promoting the development of new power systems. Summary of the Invention

[0006] To address the aforementioned problems, the present invention aims to provide a CHPS system optimization scheduling system and method that considers thermal power unit modeling. This system refines the start-up fuel consumption and ramp-up capability of thermal power units by combining their actual operating characteristics, and constructs a frequency stability constraint model that considers the emergency power support capability of energy storage batteries. This not only ensures the power supply demand of the power system but also provides a more flexible power generation strategy for the sending-end grid, thereby improving the stability of the new power system operation. The technical solution is as follows:

[0007] A CHPS system optimization scheduling method considering thermal power unit modeling includes the following steps:

[0008] Based on the actual operating characteristics of thermal power units, we obtained the start-up fuel consumption model and the ramp-up capability model of thermal power units.

[0009] Establish power branch flow models, DC transmission models, and unit operation models; the unit operation models include thermal power unit models, cascade hydropower unit models, energy storage battery models, and photovoltaic unit models;

[0010] Considering the emergency power support of energy storage batteries, a system frequency response model is established, including an emergency power support model for energy storage batteries, a maximum frequency change rate model, a minimum frequency point model, and a frequency reserve capacity model.

[0011] Based on all the above models, with the objective function of minimizing the total annual cost of the system, and considering the nodal power balance constraints (various operating constraints of the power system, thermal power unit constraints, and frequency stability constraints), a medium- and long-term optimal operation model for the cascade hydro-solar-storage system is constructed.

[0012] Input the system data, operating parameters, and equipment parameters of the cascade hydro-solar-storage system, and use a commercial solver to solve the long-term optimization operation model of the cascade hydro-solar-storage system to obtain the optimization scheduling results.

[0013] A CHPS system optimization scheduling system considering thermal power unit modeling, comprising:

[0014] Refined modeling unit: Based on the actual operating characteristics of thermal power units, the start-up fuel consumption model and the ramp-up capability model of thermal power units are obtained;

[0015] System operation modeling unit: Establishes power branch flow model, DC transmission model and unit operation model; the unit operation model includes thermal power unit model, cascade hydropower unit model, energy storage battery model and photovoltaic unit model;

[0016] System frequency response modeling unit: Considering the emergency power support of energy storage batteries, establish a system frequency response model, including an emergency power support model for energy storage batteries, a maximum rate of change of frequency model, a minimum frequency point model, and a frequency reserve capacity model;

[0017] Optimized Operation Modeling Unit: Combining all the above models, with minimizing the total annual cost of the system as the objective function, and considering node power balance constraints (various operating constraints of the power system, thermal power unit constraints, and frequency stability constraints), a medium- and long-term optimized operation model for the cascade hydro-solar-storage system is constructed.

[0018] Solving Unit: Input the system data, operating parameters and equipment parameters of the cascade hydro-solar-storage system, and use a commercial solver to solve the long-term optimization operation model of the cascade hydro-solar-storage system to obtain the optimization scheduling results.

[0019] The beneficial effects of this invention are:

[0020] 1) When constructing the operating model of thermal power units, the actual operating characteristics of the units are fully considered. This can improve the accuracy of thermal power unit modeling and reduce operating costs while ensuring that the computational workload is acceptable, and at the same time, enhance the flexibility of thermal power units. This refined modeling not only ensures the power supply demand of the power system, but also provides more flexible power generation strategies for the sending-end grid, thereby improving the stability of the new power system operation.

[0021] 2) Taking into account the supporting role of energy storage batteries in system frequency stability and comprehensively considering system frequency security, it is possible to make the most economical scheduling decisions while avoiding potential frequency security problems caused by accidents. Attached Figure Description

[0022] Figure 1 This is a flowchart of the steps of the method described in this invention;

[0023] Figure 2 This is a piecewise linearized diagram of the actual start-up fuel consumption function of a thermal power unit;

[0024] Figure 3 This is a schematic diagram showing the relationship between the ramp rate and power output of a thermal power unit.

[0025] Figure 4 The improved 6-node system example structure diagram;

[0026] Figure 5 This is a comparison diagram of unit start-up and shutdown in Scheme 1 and Scheme 2 of the embodiments;

[0027] Figure 6 This is a comparison chart of the lowest system frequency points in schemes 3 and 4 in the embodiments;

[0028] Figure 7 This is a comparison chart of the maximum rate of change of system frequency for schemes 3 and 4 in the embodiments. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0030] This invention discloses a long-term optimization scheduling method for a cascade hydro-solar-storage system that considers refined modeling of thermal power units and frequency security constraints. The specific implementation steps are as follows: Figure 1 As shown, the technical solution of the present invention includes the following steps:

[0031] Step 1: Based on the actual operating characteristics of thermal power units, obtain the start-up fuel consumption model and the ramp-up capability model of thermal power units.

[0032] (1.1) Start-up fuel consumption model for thermal power units: A refined model of the start-up fuel consumption of thermal power units can accurately consider the medium- and long-term operating costs of the units and analyze unit start-up and shutdown, making the system scheduling results more consistent with actual conditions. In actual operation, there is an exponential relationship between shutdown time and start-up fuel consumption:

[0033] ;

[0034] In the formula, g represents the thermal power unit designation; a represents the typical day designation; and t represents the time period designation. This represents the actual fuel consumption of thermal power unit g during the t-hour period of a typical day; This represents the cold start consumption of thermal power unit g. For thermal power unit g, the heat start-up consumption is ; Let g be the cooling time constant of the thermal power unit. This refers to the number of hours that thermal power unit g is continuously shut down before time period t in a typical day.

[0035] Since the above equation is a nonlinear function of downtime, this invention performs piecewise linearization on this nonlinear function, without equal division, as follows: Figure 2 As shown. During medium- and long-term scheduling, the start-up and shutdown times of the generator sets have a relatively long range, and the rate of change in fuel consumption during startup gradually decreases with increasing shutdown time until it approaches 0. Therefore, a piecewise linearization method with unequal division of the independent variable's range is more suitable. Here, the selection of the segmentation points, the slope of the piecewise function, and the intercept are considered as an optimization problem, with the objective function being to minimize the difference between the actual fuel consumption value and the piecewise linear approximation value.

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[0045] In the formula, This refers to the number of downtime hours. For thermal power unit g during shutdown Actual fuel consumption after starting the engine an hour later; This represents the approximate fuel consumption of thermal power unit g after it has been shut down for r hours and then restarted. Maximum allowable shutdown time; For shutdown The hour corresponds to the intercept of the straight line segment; For shutdown The slope of the straight line segment corresponding to the hour; , They are respectively The state variables representing the changes in intercept and slope at hour are set to 1 when the intercept or slope changes, and 0 otherwise.

[0046] By solving the above optimization problem, the linearized parameters corresponding to unit g can be obtained, and the non-equally divided start-up fuel consumption curve of thermal power unit g is linearized into 3 line segments. Let l be the line segment number. If each line segment corresponds to a start-up type, then l can also be used as the start-up type number. To facilitate calculation, an auxiliary constant is introduced. and Let these be the intercept and slope of line segment l, respectively. Thus, the start-up fuel consumption can be expressed as a constant. and Constructed linear expression:

[0047] ;

[0048] In the formula, This represents the start-up fuel consumption of thermal power unit g during the t period within a typical day a; NL is the total number of segments; For thermal power unit g, when it is started with start-up type l during time period t on a typical day, the number of continuous downtime hours before time period t (excluding time period t). Let g be a binary variable representing the start-up decision of thermal power unit g.

[0049] (1.2) Thermal Power Unit Climbing Capacity Model: Using deep peak-shaving thermal power units, considering the relationship between the unit's climbing rate and power generation, helps to fully utilize the unit's regulation capacity, improve the stability of the hydro-solar hybrid system, and enhance the power system's ability to absorb photovoltaic power. For example... Figure 3 As shown, based on the generating capacity of the unit, the deep peak-shaving thermal power unit is divided into three peak-shaving stages.

[0050] ;

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[0056] ;

[0057] ;

[0058] In the formula, denoted as , where is the power generation of thermal power unit g during typical day period t; k represents the peak-shaving phase number. , and These correspond to the maximum ramp rates for peak shaving stages PR1, PR2, and PR3, respectively. To meet PR k Peak shaving phase is when the unit load is at its minimum; As a binary variable, when thermal power unit g satisfies PR during the t-hour period of a typical day a. k The value is 1 when the minimum unit load requirement is met during the peak shaving phase; otherwise, it is 0. This represents the maximum ramp rate that thermal power unit g can achieve during the t period of a typical day a; This represents the upper limit of the output of thermal power unit g.

[0059] Step 2: Establish power branch flow model, DC transmission model and unit operation model. The unit operation model includes thermal power unit model, cascade hydropower unit model, energy storage battery model and photovoltaic unit model.

[0060] (2.1) Power flow model of branch circuits: The branch circuit power flow model can reduce the computational pressure while ensuring acceptable computational accuracy and ensuring that the branch circuit power flow is within an acceptable range.

[0061] ;

[0062] ;

[0063] In the formula, Let i be the phase angle of node i during time period t on a typical day a; Let (i,j) be the reactance between branches. This represents the maximum power flow allowed through branch (i,j).

[0064] (2.2) DC power transmission model: Model the DC power transmission mode that can be optimized and adjusted.

[0065] ;

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[0071] ;

[0072] In the formula, This represents the transmission power of the external transmission line c during a typical day's time period t; , Indicates the upper and lower limits of the power of the external transmission line c; , These represent the maximum upward and downward adjustment limits for power transmission of the external transmission line C in adjacent time periods, respectively. , These represent the upward and downward adjustment status of the power of the transmission line c during the time period t within a typical day a. The value is "1" when the power is adjusted and "0" otherwise. , These represent the maximum number of times the power of the external transmission line c is adjusted upwards and downwards.

[0073] (2.3) Thermal power unit model: Thermal power is the "ballast" of the power system and the basic guarantee for the energy supply of the cascade hydro-solar-storage system. To ensure that the computational workload of the refined thermal power unit model is acceptable, relaxed inequality constraints are adopted.

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[0085] In the formula, The spinning reserve provided by thermal power unit g during typical day a time period t; , For the maximum and minimum output values ​​of thermal power unit g; and These represent the storage space used when the device is powered on and off, respectively. For thermal power units that are type I start-up, the minimum downtime required before startup is specified. This refers to the number of consecutive downtime hours of thermal power unit g during typical day a period t-1 and before; This refers to the number of consecutive operating hours of thermal power unit g during the typical day's a period t-1 and before; , This represents the minimum number of start-up and shutdown hours for thermal power unit g. A binary variable representing the start-up and shutdown status of thermal power unit g during a typical day's time period a; Let g be a binary variable representing the shutdown decision of thermal power unit g during a typical day's time period t; A binary variable representing whether thermal power unit g is started up with type l during a typical day's time period t; To represent thermal power unit g during a typical day a period Whether it is a binary variable that starts with type l.

[0086] (2.4) Cascade hydropower unit model: The cascade hydropower model includes hydropower station output, climbing ability, hydropower conversion, water balance, reservoir capacity limit, and power generation flow constraint.

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[0090] ;

[0091] ;

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[0096] In the formula, h represents the designation of the cascade hydropower station; The power generation of the cascade hydropower station h during a typical day's time period t; The hydropower conversion efficiency of the cascade hydropower station is h. The power generation flow of the cascade hydropower station h during the t period of a typical day a; The generating head of the cascade hydropower station h during a typical day at time t; and These are the upper and lower limits of the allowable output of the hydropower station (h), respectively. A binary variable representing the operating status of a cascade hydropower station h during a typical day's time period t; The reservoir capacity of the cascade hydropower station h during a typical day, time period t; The initial head for scheduling of the cascade hydropower station h on a typical day a; The head coefficient h is the head coefficient of the cascade hydropower station; The spinning reserve provided by the cascade hydropower station h during the t period of a typical day a; and These represent the maximum and minimum allowable power generation flow rates (h) for a cascade hydropower station, respectively. The inflow rate of the cascade hydropower station h during a typical day at time t. The discharge flow of the cascade hydropower station h during the t period of a typical day a; and These are the direct upstream power stations of the cascade hydropower stations during the t-hour period of a typical day after considering the flow retention time. The power generation flow and the water discharge flow; For the directly upstream power station The time it takes for water to flow directly downstream; The natural inflow of water to the cascade hydropower station h during the t period of a typical day a; The maximum ramping power of the cascade hydropower station in adjacent time periods; and These represent the upper and lower limits of the reservoir capacity h for the cascade hydropower stations; and These represent the reservoir capacity of the cascade hydropower stations at the beginning and end of the dispatching process, respectively.

[0097] (2.5) Energy storage battery model: Energy storage batteries have two modes: charging and discharging. The charging and discharging power, state of charge, and amount of energy storage batteries need to be constrained.

[0098] ;

[0099] ;

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[0104] In the formula, b represents the energy storage battery designation; Let be a binary variable representing the charging state of energy storage battery b during a typical day a. , The upper and lower limits of charging power for energy storage battery b; , The upper and lower limits of the discharge power of energy storage battery b; Let be the charge of energy storage battery b during typical day period a t; Let be the energy dissipation rate of energy storage battery b; , These represent the battery charge and discharge efficiencies, respectively. The state of charge of energy storage battery b during time period t in a typical day a; , These are the upper and lower limits of the state of charge of energy storage battery b, respectively. , These represent the battery's charge level at the beginning and end of the dispatch process, respectively. This is the rated capacity of energy storage battery b.

[0105] (2.6) Photovoltaic unit model: The dispatched power generation of photovoltaic units cannot exceed their predicted value.

[0106] ;

[0107] In the formula, s represents the photovoltaic power station designation; Let be the power generation of photovoltaic power plant s during a typical day's time period t; The predicted output of photovoltaic power plant s during a typical day's time period t.

[0108] Step 3: Model the system frequency response considering emergency power support from energy storage batteries, including an emergency power support model for energy storage batteries, a maximum rate of change model for frequency, a minimum frequency point model, and a frequency reserve capacity model.

[0109] (3.1) Emergency Power Support Model for Energy Storage Battery: Considering the emergency power support model for energy storage battery when a power of [value missing] occurs... When there is a power deficit, it can immediately discharge at maximum power to provide a size of For emergency power support, the emergency power support model for energy storage batteries is as follows:

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[0113] In the formula, NB represents the power support that energy storage battery b can provide during a typical day's time period t; NB represents the total number of energy storage batteries. This represents the maximum power deficit that the power system may experience during time period t in a typical day a; This represents the power deficit in the power system after the energy storage battery momentarily adjusts its output when an accident occurs during a typical day's time period t.

[0114] (3.2) Maximum frequency change rate model: The greater the power shortage, the greater the maximum value of the frequency change rate; the greater the system inertia, the smaller the maximum value of the frequency change rate. Therefore, to prevent the system frequency from dropping too much after an accident, it is necessary to ensure that the system has sufficient inertia.

[0115] ;

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[0119] In the formula, , These represent the total number of thermal power units and cascade hydropower stations, respectively. , , respectively, are the inertial constants of the thermal power unit g and the cascade hydropower station h; , These represent the aggregate inertia of thermal power units and hydropower units during a typical day at time t; The system's rated frequency; This represents the total inertia of the power system during time period t in a typical day a; This represents the maximum allowable rate of change of frequency. This represents the system probability deficit for a typical day during time period t.

[0120] (3.3) Frequency minimum point model: After a disturbance occurs in the power system (such as unit tripping, line fault or sudden increase in load), the minimum value that the system frequency reaches instantaneously under the action of inertia and primary frequency regulation is the frequency minimum point.

[0121] ;

[0122] ;

[0123] In the formula, This is the dead zone of the speed controller; This is the lowest frequency allowed by the system.

[0124] (3.4) Frequency reserve capacity model: While meeting the normal output requirements, the generator set needs to reserve a portion of capacity to respond quickly when the system frequency deviates, and support frequency stability.

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] In the formula, , This indicates the upper limit of the standby power that thermal power unit g and hydropower unit h can provide; This represents the maximum total reserve power that the system can provide during a typical day's time period t.

[0130] Step 4: Combining all the above models, with the objective function of minimizing the total annual cost of the system, and considering the power balance constraints at the nodes, construct a medium- to long-term optimized operation model for the cascade hydro-solar-storage system.

[0131] (4.1) Objective function: The long-term optimization operation model of the cascade hydro-solar-storage system takes minimizing the total annual cost of the system as the optimization objective, including the operating costs of thermal power units, energy storage batteries and the standby costs of thermal power units.

[0132] ;

[0133] In the formula, This represents the total number of typical days in a year; This represents the total number of time periods in a typical day; This represents the number of days that a typical day in a year can represent; This represents the fuel consumption coefficient of the qth segment of the thermal power unit g; For the active power output of thermal power unit g during the qth segment of a typical day's time period t; This represents the no-load cost of thermal power unit g; This represents the fuel consumption of thermal power unit g during the typical day's time period t at shutdown. , These are the unit fuel cost of thermal power units and the unit standby power cost of unit g, respectively. This represents the unit power operation and maintenance cost of energy storage battery b.

[0134] (4.2) Node power balance constraint: The active and reactive power of the system must meet the node power balance constraint in each time period of a typical scheduling day.

[0135] ;

[0136] In the formula, j and v are node numbers; For node j, the set of equipment is denoted as ; g, h, s, b, c, and d represent thermal power units, cascade hydropower stations, photovoltaic power stations, battery energy storage devices, DC transmission lines, and load numbers, respectively. , These represent the discharge power and charging power of energy storage battery b during time period t in a typical day a; This represents the DC power transmitted during the t-hour period of a typical day. This represents the active power demand of load d during period t in a typical day a; , This represents the active power flow on transmission lines (i,j) and (j,v) during time period t in a typical day a.

[0137] (4.3) Compact cascade hydro-solar-storage system medium- and long-term optimization operation model: The medium- and long-term optimization operation model of the cascade hydro-solar-storage system includes the objective function, node power balance constraints, refined model of thermal power unit and power system operation model.

[0138] ;

[0139] In the formula, The objective function value; vector Represents binary variables such as the start-up and shutdown of the generator unit and the charging and discharging of the energy storage battery; vector Represent continuous variables in the model; , , , , These are abstract matrices and vectors, representing the coefficients in the objective function and constraints.

[0140] Step 5: Input the system data, operating parameters, and equipment parameters of the cascade hydro-solar-storage system, and use the commercial solver Gurobi to solve the optimized operation model of the cascade hydro-solar-storage system to obtain the optimized scheduling results, thus verifying the effectiveness of the proposed method.

[0141] This invention also proposes a CHPS system optimization scheduling system considering thermal power unit modeling, comprising:

[0142] Refined modeling unit: Based on the actual operating characteristics of thermal power units, the start-up fuel consumption model and the ramp-up capability model of thermal power units are obtained;

[0143] System operation modeling unit: Establishes power branch flow model, DC transmission model and unit operation model; the unit operation model includes thermal power unit model, cascade hydropower unit model, energy storage battery model and photovoltaic unit model;

[0144] System frequency response modeling unit: Models the system frequency response considering emergency power support of energy storage batteries, including emergency power support model of energy storage batteries, maximum rate of change of frequency model, minimum frequency point model and frequency reserve capacity model;

[0145] Optimized Operation Modeling Unit: All the above models, with minimizing the total annual cost of the system as the objective function, and considering the node power balance constraints, construct a medium- and long-term optimized operation model for the cascade hydro-photovoltaic-storage system;

[0146] Solving Unit: Input the system data, operating parameters and equipment parameters of the cascade hydro-solar-storage system, and use a commercial solver to solve the long-term optimization operation model of the cascade hydro-solar-storage system to obtain the optimization scheduling results.

[0147] For details on the modeling and solution process, please refer to the preceding text.

[0148] The effects of the present invention will be described in detail below through specific embodiments.

[0149] (1) Example introduction.

[0150] like Figure 4 As shown, taking the improved 6-node simulation system as an example, the effectiveness of the proposed long-term optimal scheduling model for a cascade hydro-solar-storage system, which considers refined modeling of thermal power units and frequency security constraints, is verified. The unit thermal power frequency regulation reserve is 30 yuan / (MWh), and the power disturbance event is considered to be a sudden increase of 5% in the total power load of the sending-end grid. The frequency requirement is set as follows: 50Hz It is 49.5Hz. 15mHz It is 0.2 Hz / s.

[0151] (2) Scenario description of the embodiment.

[0152] From the perspective of system operation economy and safety, this study investigates the impact of refined modeling of thermal power units, frequency stability constraints, and the influence of energy storage battery-supported frequency stability on the scheduling results of the proposed model. The following six schemes are set up for comparative analysis.

[0153] Option 1: Long-term scheduling of cascade hydropower-solar-storage system based on traditional thermal power unit modeling;

[0154] Option 2: Based on Option 1, consider refined modeling of thermal power units;

[0155] Option 3: Based on Option 2, further consider frequency stability constraints;

[0156] Option 4: Based on Option 3, remove the frequency constraint and retain only the spare capacity constraint;

[0157] Option 5: Based on Option 4, consider the frequency stability supported by the energy storage battery;

[0158] (3) Analysis of the results of the examples.

[0159] Table 1 compares the total system costs of the five solutions above. Figure 5 This is a comparison of the start-up and shutdown states of the generating units in Scheme 1 and Scheme 2. It can be concluded that fully utilizing the regulation capacity of deep peak-shaving thermal power units is beneficial for improving the absorption of clean energy and achieving economic benefits. When constructing the operating model of thermal power units, fully considering the actual operating characteristics of the units can improve the accuracy of the modeling, reduce operating costs, and enhance the flexibility of the units while ensuring acceptable computational load. This refined modeling not only ensures the power supply demand of the power system but also provides the sending-end grid with more flexible power generation strategies, thereby improving the hydropower and solar power absorption capacity of the new power system.

[0160] Table 1. System cost comparison of schemes 1-6 (Unit: 10) 5 Yuan .

[0161] Figure 6 and Figure 7 The hourly frequency minimum and ROCOF (The Maximum Rate of Change of Frequency) were compared between Schemes 3 and 4 after power disturbances on typical days with varying seasons. Although the economics of the model considering frequency security constraints decreased, from... Figure 6 and Figure 7It can be seen that Scheme 3 satisfies the frequency requirements for the minimum frequency point and the maximum rate of frequency change in all time periods of a typical day. However, for Scheme 4, which has no frequency constraints, the minimum frequency point and the maximum rate of frequency change exceed the limits in some time periods. This result indicates that scheduling models that only consider reserve capacity constraints can lead to potential frequency security issues after an unexpected incident.

[0162] As shown in Table 2, compared with Option 4, Option 5 reduces the total cost by 15.07 × 10⁻⁶. 5 Yuan. Energy storage batteries can complete power response on the order of milliseconds to seconds. This ultra-short-term output capability can replace part or all of the frequency regulation tasks of thermal power and hydropower units in the short term, thereby improving the economy and safety of the system.

[0163] Table 2 Cost Comparison of Options 4 and 5 (Unit: 10) 5 Yuan .

[0164] The above description is merely a specific embodiment of the present invention, but it does not limit the scope of patent protection of the present invention. Any equivalent changes or substitutions made using the content of the present invention specification and drawings, and any direct or indirect application to other related technical fields, should be included within the scope of protection of the present invention.

[0165] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0166] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0170] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A CHPS system optimization scheduling method considering thermal power unit modeling, characterized in that, The application relates to a long-term optimal operation modeling method for a cascade water-light-storage system. According to actual operation characteristics of a thermal power unit, a start-up fuel consumption model of the thermal power unit and a climbing ability model of the thermal power unit are obtained; A power branch power flow model, a DC transmission model and a unit operation model are established; the unit operation model comprises a thermal power unit model, a cascade hydropower unit model, an energy storage battery model and a photovoltaic unit model; Considering emergency power support of the energy storage battery, a system frequency response model is established, which comprises an emergency power support model of the energy storage battery, a maximum frequency change rate model, a minimum frequency point model and a frequency reserve capacity model; All the above models are integrated to construct a long-term optimal operation model for the cascade water-light-storage system, with a minimum annual total cost of the system as an objective function and node power balance constraints considered; System data, operation parameters and equipment parameters of the cascade water-light-storage system are inputted, and a commercial solver is used to solve the long-term optimal operation model of the cascade water-light-storage system to obtain an optimal scheduling result.

2. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 1, characterized in that, The start-up fuel consumption model of the thermal power unit is as follows: In actual operation, there is an exponential relationship between shutdown time of the thermal power unit and start-up fuel consumption: ； wherein subscript g denotes the thermal power unit index, a denotes the typical day index, and t denotes the time period index; denotes the actual start-up heat rate of thermal power unit g at time period t of typical day a; denotes the cold start-up consumption of thermal power unit g; denotes the number of hours of continuous shutdown of thermal power unit g prior to time period t of typical day a; denotes the cooling time constant of thermal power unit g; denotes the hot start-up consumption of thermal power unit g.

3. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 2, characterized in that, Non-uniform piecewise linearization processing is performed on the nonlinear function about the shutdown time, as follows: ； ； ； ； ； ； ； ； ； In the formula, This refers to the number of downtime hours. For thermal power unit g during shutdown Actual fuel consumption after starting the engine an hour later; This represents the approximate fuel consumption of thermal power unit g after it has been shut down for r hours and then restarted. Maximum allowable shutdown time; For shutdown The hour corresponds to the intercept of the straight line segment; For shutdown The slope of the straight line segment corresponding to the hour; , They are respectively The state variables representing the changes in intercept and slope at hour are set to 1 when the intercept or slope changes, and 0 otherwise. By solving the optimization problem, the non-equally linearization of the start-up fuel consumption curve of thermal power unit g is divided into three line segments, and l is the line segment number. Each line segment l has a corresponding slope and intercept value ; if each line segment corresponds to a start-up type, then l is also used as the start-up type number; thus the start-up fuel consumption table is expressed as a linear expression constructed by constants and . ； In the formula, represents the start-up fuel consumption of the thermal power unit g at the time period t in the typical day a; NL is the total number of segments; is the continuous shutdown hours before the time period t when the thermal power unit g is started up at the time period t in the typical day a with the start-up type l; is the start-up decision variable of the thermal power unit g, which is 1 if the thermal power unit g is turned from the shutdown state to the start-up state at the time period t in the typical day a and the start-up type is l, and 0 otherwise.

4. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 3, characterized in that, The climbing ability model of the thermal power unit is as follows: ； ； ； ； ； ； ； ； In the formula, is the power generation of the thermal power unit g in the time period t of the typical day a; k represents the number of the peak regulation stage; , and respectively correspond to the maximum ramp rate of the peak regulation PR1, PR2 and PR3 stages; is the minimum unit load of the peak regulation PRk stage; k is the minimum unit load of the peak regulation PRk stage; is a binary variable, which is 1 when the thermal power unit g meets the PRk stage in the time period t of the typical day a, and 0 otherwise; k is a binary variable, which is 1 when the thermal power unit g meets the PRk stage in the time period t of the typical day a, and 0 otherwise; is the maximum ramp rate that can be reached by the thermal power unit g in the time period t of the typical day a; is the upper limit of the output of the thermal power unit g.

5. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 4, characterized in that, The power branch power flow model is as follows: ； ； wherein is the power flow through branch (i,j) at time period t of typical day a; is the phase angle of node i at time period t of typical day a; is the phase angle of node j at time period t of typical day a; is the reactance between branches (i,j); is the maximum power flow allowed through branch (i,j).

6. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 5, characterized in that, The DC transmission model is as follows: ； ； ； ； ； ； ； wherein Pc(t) represents the transmission power of the outgoing line c at time period t of typical day a; and Pc(t) represents the transmission power of the outgoing line c at time period t of typical day a; and Pc(t) represents the transmission power of the outgoing line c at time period t of typical day a; and Pc(t) represents the transmission power of the outgoing line c at time period t of typical day a; and Pc(t) represents the transmission power of the outgoing line c at time period t of typical day a; Pc(t) represents the transmission power of the outgoing line c at time period t of typical day a; 7. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 6, characterized in that, The thermal power unit model is as follows: ； ； ； ； ； ； ； ； ； ； ； In the formula, The spinning reserve provided by thermal power unit g during typical day a time period t; , For the maximum and minimum output values ​​of thermal power unit g; and These represent the storage space used when the device is powered on and off, respectively. For thermal power units that are type I start-up, the minimum downtime required before startup is specified. This refers to the number of consecutive downtime hours of thermal power unit g during typical day a period t-1 and before; For thermal power unit g, the number of consecutive operating hours during the typical day a period t-1 and before; , This represents the minimum number of start-up and shutdown hours for thermal power unit g. A binary variable representing the start-up and shutdown status of thermal power unit g during a typical day's time period a; Let g be a binary variable representing the shutdown decision of thermal power unit g during a typical day's time period t; A binary variable representing whether thermal power unit g is started up with type l during a typical day's time period t; To represent the thermal power unit g during a typical day a period Whether it is a binary variable that starts with type l.

8. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 7, characterized in that, The cascade hydropower unit model is as follows: ； ； ； ； ； ； ； ； ； In the formula, h represents the designation of the cascade hydropower station; The power generation of the cascade hydropower station h during a typical day's time period t; The hydropower conversion efficiency of the cascade hydropower station is h. The power generation flow of the cascade hydropower station h during the t period of a typical day a; The generating head of the cascade hydropower station h during a typical day at time t; and These represent the upper and lower limits of the allowable output of the hydropower station (h). A binary variable representing the operating status of a cascade hydropower station h during a typical day's time period t; The reservoir capacity of the cascade hydropower station h during a typical day's time period t; The initial head for scheduling of the cascade hydropower station h on a typical day a; The head coefficient h is the head coefficient of the cascade hydropower station; The spinning reserve provided by the cascade hydropower station h during the t period of a typical day a; and These represent the maximum and minimum allowable power generation flow rates (h) for a cascade hydropower station, respectively. The inflow rate of the cascade hydropower station h during a typical day at time t. The discharge flow of the cascade hydropower station h during the t period of a typical day a; and These are the direct upstream power stations of the cascade hydropower stations during the t-hour period of a typical day after considering the flow retention time. The power generation flow and the water discharge flow; For the directly upstream power station The time it takes for water to flow directly downstream; The natural inflow of water to the cascade hydropower station h during the t period of a typical day a; The maximum ramping power of the cascade hydropower station in adjacent time periods; and These represent the upper and lower limits of the reservoir capacity h for the cascade hydropower stations; and These represent the reservoir capacity of the cascade hydropower stations at the beginning and end of the dispatching process, respectively.

9. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 8, characterized in that, The energy storage battery model is as follows: ； ； ； ； ； ； where b denotes the energy storage battery label; is a binary variable for the state of charge of the energy storage battery b at time period t in typical day a; and are the upper and lower limits of the charging power of the energy storage battery b; is the charging power of the energy storage battery b at time period t in typical day a; and are the upper and lower limits of the discharging power of the energy storage battery b; is the discharging power of the energy storage battery b at time period t in typical day a; is the energy of the energy storage battery b at time period t in typical day a; is the energy dissipation rate of the energy storage battery b; and denote the charging and discharging efficiencies of the battery, respectively; is the state of charge of the energy storage battery b at time period t in typical day a; and are the upper and lower limits of the state of charge of the energy storage battery b, respectively; and are the energies of the energy storage battery b at the beginning and end of the dispatch, respectively; is the rated capacity of the energy storage battery b.

10. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 9, characterized in that, The photovoltaic unit model is as follows: ； In the formula, s represents the photovoltaic power station label; is the predicted power of the photovoltaic power station s in the time period t of the typical day a. is the predicted power of the photovoltaic power station s in the time period t of the typical day a.

11. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 10, characterized in that, The emergency power support model of the energy storage battery is as follows: ； ； ； wherein Pb is the power support that the energy storage batteries b can provide in a typical day a at a time period t; NB is the total number of energy storage batteries; Pmax is the maximum power deficit that can occur in the power system in a typical day a at a time period t; Pb is the power support that the energy storage batteries b can provide in a typical day a at a time period t; NB is the total number of energy storage batteries; 12. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 11, characterized in that, The maximum frequency change rate model is as follows: ； ； ； ； In the formula, and respectively represent the total number of thermal power units and cascade hydropower stations; and respectively are the inertia constants of the thermal power units g and the cascade hydropower stations h; and respectively are the aggregate inertia of the thermal power units and the cascade hydropower stations at the time period t of the typical day a; is the rated frequency of the system; is the total inertia of the power system at the time period t of the typical day a; is the maximum value of the frequency change rate allowed; is the system probability shortage at the time period t of the typical day a.

13. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 12, characterized in that, The minimum frequency point model is as follows: ； ； wherein is the governor dead band; is the system minimum frequency.

14. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 13, characterized in that, The frequency reserve capacity model is as follows: ； ； ； ； In the formula, and represents the upper limit of the standby power that the thermal power unit g and the cascade hydropower station h can provide; is the upper limit of the total standby power that the system can provide in the typical day a time period t.

15. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 14, characterized in that, The optimal scheduling objective function of the long-term optimal operation model of the cascade water-light-storage system is as follows: ； In the formula, represents the total number of typical days in a year; represents the total number of time periods in a typical day; is the number of dates that a typical day a can represent in a year; represents the fuel consumption coefficient of the thermal power unit g in the qth period; is the active power output of the thermal power unit g in the qth period in the time period t of the typical day a; represents the no-load cost of the thermal power unit g; represents the shutdown fuel consumption of the thermal power unit g in the time period t of the typical day a; , are the unit fuel cost of the thermal power unit and the unit standby power cost of the thermal power unit g, respectively; represents the unit power operation and maintenance cost of the energy storage battery b.

16. The CHPS system optimal scheduling method considering modeling of thermal power generating units according to claim 15, characterized in that, The node power balance constraint of the long-term optimal operation model of the cascade water-light-storage system is as follows: ； In the formula, j and v are node numbers; is a set of series devices on node j; g, h, s, b, c, and d represent a thermal power unit, a cascade hydropower station, a photovoltaic power station, an energy storage battery, a direct-current transmission line, and a load number, respectively; is the active power demand of the load d at time t in the typical day a; , represents the active power flow on the transmission lines (i, j) and (j, v) at time t in the typical day a.

17. A CHPS system optimal scheduling system considering thermal power unit modeling, characterized in that, The application relates to a long-term optimal operation modeling method for a cascade water-light-storage system. The application relates to a long-term optimal operation modeling method for a cascade water-light-storage system. The application relates to a long-term optimal operation modeling method for a cascade water-light-storage system. The application relates to a long-term optimal operation modeling method for a cascade water-light-storage system. The application relates to a long-term optimal operation modeling method for a cascade water-light-storage system. A commercial solver is used to solve the long-term optimal operation model of the cascade water-light-storage system to obtain an optimal scheduling result.

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