An evaluation and verification method for autonomous peak shaving of newly added planning units in a power system

Abstract

This invention discloses an evaluation and verification method for the autonomous peak shaving of newly added planning units in a power system. The method includes acquiring system data of the power system; constructing a daily optimized scheduling model based on the system data, including compensation for paid peak shaving of thermal power units; solving and obtaining annual operation simulation results based on the annual typical output curves of new energy power generation and the annual typical power curves of load, according to the daily optimized scheduling model; obtaining annual peak shaving data based on the annual operation simulation results; and establishing autonomous peak shaving principles for newly added planning units based on the annual peak shaving data. The newly added planning units in the power system complete their planning based on these autonomous peak shaving principles, and their annual power curves and annual output curves are obtained. An annual peak shaving evaluation and verification model is constructed to evaluate and verify the autonomous peak shaving of newly added planning units in the power system. This method solves the problem that current peak shaving models established at the thermal power unit level mainly consider the paid peak shaving portion. The peak shaving demand assessed from the net power fluctuation level actually includes both paid and unpaid peak shaving, but it cannot distinguish between the paid and unpaid peak shaving portions, thus failing to ensure that newly added planning units do not increase the system's peak shaving burden and consequently affect the absorption of existing new energy power generation.

Description

Technical Field

[0001] This invention relates to the field of power system operation technology for power system expansion planning, and in particular to an evaluation and verification method for autonomous peak shaving of newly added planning units in a power system. Background Technology

[0002] As the proportion of renewable energy generation in the power system increases, the operational burden it places on the system is growing. Therefore, the planning stage needs to address the flexible resource allocation issues related to frequency regulation and peak shaving. To prevent new planning units from increasing the system's peak shaving burden, a development model of self-loaded renewable energy allocation has emerged. Self-loaded renewable energy projects refer to signing medium- and long-term contracts with large electricity consumers during construction and operation to ensure reliable consumption of renewable energy generation and to maximize the utilization of flexible load-side adjustment resources.

[0003] New energy power generation is characterized by uncertainty, volatility, and intermittency, necessitating consideration of its output uncertainty during the planning phase. As the amount of new energy sources in the power system increases, achieving full absorption of all new energy power generation becomes increasingly difficult. Therefore, exploring new energy planning methods that meet certain absorption rates is of greater practical significance. To reduce the operational burden on the power grid caused by new energy planning, considerable research has explored the joint planning of new energy power generation with various flexible resources such as energy storage and thermal power units.

[0004] Current planning methods utilize both newly added flexible resources and existing system flexible resources to address the fluctuations and absorption of new energy sources. This leads to new energy power generation consuming existing system flexible resources. Insufficient peak-shaving resources are one of the main reasons for the current curtailment of new energy power in my country. The peak-shaving demand of the power system can be quantitatively assessed from two aspects: First, assessing the system's peak-shaving demand based on net load fluctuations. Second, assessing the system's paid peak-shaving demand using the cumulative deep peak-shaving volume of thermal power units. In summary, the peak-shaving model established at the thermal power unit level mainly considers the paid peak-shaving portion. The peak-shaving demand assessed from the net power fluctuation level actually includes both paid and unpaid peak-shaving, but it cannot distinguish between the paid and unpaid peak-shaving portions.

[0005] Therefore, there is an urgent need to propose an assessment and verification method that can comprehensively consider both paid and unpaid peak shaving of thermal power, and ensure that newly added planning units do not increase the system's peak shaving burden or affect the absorption of existing renewable energy power generation. Summary of the Invention

[0006] This invention provides an evaluation and verification method for the autonomous peak shaving of newly added planning units in a power system, in order to overcome the above-mentioned technical problems.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] An evaluation and verification method for the autonomous peak-shaving of newly added planning units in a power system includes the following steps:

[0009] S1: Obtain system data from the power system;

[0010] The system data includes at least system topology parameters, power supply parameters, annual typical output curves of new energy power generation, and annual typical power curves of load.

[0011] S2: Construct a daily optimized scheduling model based on system data, including the paid peak-shaving compensation fee for thermal power units;

[0012] The daily optimization scheduling model includes a daily optimization scheduling objective function and scheduling constraints.

[0013] S3: Based on the annual typical output curve of new energy power generation and the annual typical power curve of load, the annual operation simulation results are obtained by solving the daily optimization scheduling model.

[0014] Furthermore, the annual operation simulation results include at least the annual peak load period, annual valley load period, and wind curtailment period of the power system;

[0015] And obtain annual peak-shaving data based on the results of annual operational simulations;

[0016] Furthermore, the annual peak-shaving data includes paid peak-shaving volume, unpaid peak-shaving volume, and wind power consumption volume;

[0017] S4: Establish autonomous peak-shaving principles for newly added planning units based on annual peak-shaving data:

[0018] During paid peak shaving periods, no new planning units are allowed to send electricity to the power system; during unpaid peak shaving periods, new planning units are allowed to send electricity to the power system, provided that the new planning units do not increase the total annual unpaid peak shaving demand of the power system; during wind curtailment periods, the power output of new planning units sent to the power system is restricted.

[0019] S5: New planning units in the power system complete the planning based on the principle of autonomous peak shaving, and obtain their annual power curves and annual output curves;

[0020] S6: Based on the daily optimized scheduling model, the annual power curve and annual output curve are used to perform daily optimized operation simulation to obtain new annual peak shaving data, and the newly constructed annual peak shaving assessment and verification model is used to assess and verify the autonomous peak shaving of newly planned units connected to the power system.

[0021] Furthermore, the expression for the daily optimal scheduling objective function of the daily optimal scheduling model constructed in S2, which includes the paid peak-shaving compensation fee for thermal power units, is as follows:

[0022]

[0023] In the formula: These represent the output of the thermal power unit, the unit's start-up status variables, and the paid peak-shaving capacity of the thermal power unit in the h-th stage, respectively. This indicates the fuel cost of thermal power units; This refers to the paid peak-shaving compensation fee for thermal power units. This indicates the cost of starting up a thermal power unit once. Indicates the unit's start-up status variable; and This represents the paid peak-shaving capacity and compensation price for the h-th level of thermal power units; t represents the time period index; T represents the total number of time periods for daily scheduling; h represents the paid peak-shaving level index; H represents the total number of paid peak-shaving levels; α i β i γ i All of these represent the quadratic function coefficients of the power generation cost of thermal power units.

[0024] Furthermore, the scheduling constraints of the daily optimized scheduling model constructed in S2, which includes the paid peak-shaving compensation fee for thermal power units, are as follows:

[0025] These include power system power balance constraints, power system upward and downward rotation reserve constraints, paid peak-shaving capacity constraints of thermal power units, reserve capacity constraints of thermal power units, output constraints of thermal power units, and minimum start-up and shutdown time constraints of thermal power units.

[0026] The power system power balance constraint is

[0027]

[0028] In the formula: L t L represents the sum of the load power of all nodes in the power system; M represents the number of nodes in the power system; L represents the sum of the load power of all nodes in the power system. m,t n represents the load power of the m-th node; g Indicates the number of thermal power units in the power system; Indicates the output of the thermal power unit; This indicates the dispatch output of existing new energy power generation;

[0029] The power system's upward and downward rotational reserve constraints are...

[0030]

[0031] In the formula: and These represent the upward and downward reserve capacities of thermal power units, respectively. and These represent the upward and downward reserve requirements of the power system, respectively.

[0032] The paid peak-shaving capacity constraint of the thermal power units is

[0033]

[0034] In the formula: Indicates the output of the thermal power unit; This indicates the starting point for the paid peak-shaving capacity of thermal power units; This indicates the starting point of the paid peak shaving for the h-th gear of the thermal power unit;

[0035] The reserve capacity constraint of the thermal power unit is

[0036]

[0037] In the formula: and This indicates the maximum upward and downward reserve capacity of the thermal power unit; and This indicates the maximum and minimum technical output of the thermal power unit;

[0038] The output constraint of the thermal power unit is

[0039]

[0040] In the formula: Represents the operating state variables of thermal power units;

[0041] The minimum start-stop time constraint for the thermal power unit is:

[0042]

[0043] In the formula: and U represents the start-up and stop status variables of thermal power units; i With D i This indicates the minimum start-up and shutdown time of a thermal power unit.

[0044] Furthermore, S3 specifically includes the following steps:

[0045] S31: Divide the annual load and renewable energy output curves of the power system into daily datasets according to the date;

[0046] S32: Based on the CPLEX optimization solver, the daily optimization scheduling simulation is performed on the annual load and new energy output curves of the power system in the daily dataset, and the daily operation simulation classification results are output.

[0047] The daily operation simulation classification results include peak load periods, valley load periods, and wind curtailment periods within each day;

[0048] Furthermore, the valley load period refers to the paid peak-shaving compensation fee for thermal power units after the optimized scheduling simulation is conducted. The time period during which the value is not zero;

[0049] The peak load period refers to the period during which the power system's upward rotation reserve constraint is a tight constraint;

[0050] S33: According to the preset time sequence, the daily operation simulation classification results are spliced ​​together to obtain the annual operation simulation results;

[0051] Furthermore, the annual operation simulation results include the annual peak load period, annual valley load period, and wind curtailment period of the power system;

[0052] S34: Obtain annual peak-shaving data based on annual operational simulation results;

[0053] Furthermore, the annual peak-shaving data includes paid peak-shaving volume, unpaid peak-shaving volume, and wind power consumption volume;

[0054] The paid peak shaving capacity daily optimization scheduling model includes paid peak shaving compensation fees. The sum of optimization results; the free peak-shaving amount is the sum of the output adjustment of thermal power units caused by the fluctuation of net load in the power system.

[0055] Furthermore, the newly added planning units in S4 do not increase the annual total free peak-shaving demand constraint of the power system, including the power consumption constraint during peak load periods and the power transmission constraint during valley load periods, the expression of which is:

[0056]

[0057] In the formula: T P T represents the set of peak load periods in the power system when the total output of thermal power units reaches its upper limit after considering reserve and other ancillary service capacities; V P represents the set of off-peak periods in the power system; s1,t This indicates the power purchased from the grid by the newly added wind-thermal-storage bundled unit; P s2,t This indicates the power output of the bundled unit to the power grid.

[0058] Furthermore, the principle of autonomous peak shaving for newly added planning entities in S4 also includes transmission capacity constraints based on the DC power flow model and power balance constraints of the power system after the newly added planning units are connected to the grid.

[0059] The power transmission capacity constraint is as follows:

[0060] P l =M PTDF ×P

[0061]

[0062] MPTDF =B l AB -1

[0063] In the formula: P l P represents the power flow of a newly added planning unit branch l in the power system; P represents the node injection power vector of the power system. m,t This represents the element corresponding to node m in the power system, specifically the difference between the power output of the source connected to node m and the load power. Indicates the generator power at node m; This represents the new energy output of node m; P represents the power flow transmission limit of a newly added planning unit branch l in the power system; PTDF A represents the power transfer matrix; B represents the network topology correlation matrix of the power system; C represents the branch admittance matrix of the power system ignoring branch conductance.

[0064] The power balance constraint of the power system after the newly added planning unit is connected to the grid is:

[0065]

[0066] Furthermore, the annual peak-shaving assessment and verification model constructed in S6 has the following expression:

[0067]

[0068] In the formula: and This indicates the output of thermal power units before and after grid connection of newly added planning units in the power system; n g Indicates the number of thermal power units in the power system; T N This represents the set of time periods in the power system excluding peak and off-peak periods, i.e., the set of normal periods. This indicates the annual consumption of existing new energy sources; This indicates the dispatch output of existing new energy power generation.

[0069] Beneficial Effects: This invention provides an evaluation and verification method for the autonomous peak shaving of newly planned units connected to the grid in a power system. By comprehensively considering paid and unpaid peak shaving of thermal power, an autonomous peak shaving principle for newly planned entities is established to realize the planning of grid connection of newly planned units in the power system and obtain their annual power curves and annual output curves. Based on a daily optimized scheduling model, annual daily optimized operation simulation is performed according to the annual power curves and annual output curves to obtain new annual peak shaving data. The evaluation and verification of autonomous peak shaving of newly planned units connected to the grid in the power system is realized according to the constructed annual peak shaving evaluation and verification model. This effectively ensures that when newly planned units are connected to the grid, the peak shaving burden of the power grid system is not increased and the absorption of existing new energy power generation is not affected. This enables the joint planning of new energy power generation with various flexible resources such as energy storage and thermal power units. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 A flowchart illustrating the evaluation and verification method for autonomous peak shaving of newly added planning units in the power system according to the present invention;

[0072] Figure 2 This is a diagram showing the load curve and wind power curve in this embodiment;

[0073] Figure 3 This embodiment adds upward and downward ramp curves of the system before and after internet access;

[0074] Figure 4 This is a graph showing the power system's primary paid peak-shaving demand before and after the addition of the new planning unit in this embodiment.

[0075] Figure 5 This is a graph showing the secondary paid peak-shaving demand curves of the power system before and after the addition of the new planning unit in this embodiment;

[0076] Figure 6 This is the core flowchart of the evaluation and verification method for the autonomous peak shaving of newly added planning units in the power system in this embodiment. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] This embodiment provides an evaluation and verification method for the autonomous peak-shaving of newly added planning units in a power system, such as... Figure 1 As shown, it includes the following steps:

[0079] S1: Obtain system data from the power system;

[0080] The system data includes at least system topology parameters, power supply parameters, annual typical output curves of new energy power generation, and annual typical power curves of load.

[0081] Specifically, the system topology parameters include grid node numbers and types, branch connection relationships, transmission line parameters, and branch power constraints;

[0082] The power parameters include rated power, minimum start-stop time, operating cost, and ramp rate;

[0083] S2: Construct a daily optimized scheduling model based on system data, including the paid peak-shaving compensation fee for thermal power units;

[0084] The daily optimization scheduling model includes a daily optimization scheduling objective function and scheduling constraints.

[0085] The expression for the daily optimal scheduling objective function of the constructed daily optimal scheduling model, which includes the paid peak-shaving compensation fee for thermal power units, is as follows:

[0086]

[0087] In the formula: These represent the output of the thermal power unit, the unit's start-up status variables, and the paid peak-shaving capacity of the thermal power unit in the h-th stage, respectively. This indicates the fuel cost of thermal power units; This refers to the paid peak-shaving compensation fee for thermal power units. This indicates the cost of starting up a thermal power unit once. Indicates the unit's start-up status variable; and This indicates the paid peak-shaving capacity and compensation price for the h-th tier of thermal power units;

[0088] t represents the time period index; T represents the total number of time periods for daily scheduling; h represents the index of paid peak shaving levels; H represents the total number of paid peak shaving levels; αi β i γ i All of these represent the quadratic function coefficients of the power generation cost of thermal power units;

[0089] The scheduling constraints of the daily optimized scheduling model, which includes the paid peak-shaving compensation fee for thermal power units, include power system power balance constraints, power system upward and downward rotation reserve constraints, paid peak-shaving capacity constraints of thermal power units, reserve capacity constraints of thermal power units, output constraints of thermal power units, and minimum start-up and shutdown time constraints of thermal power units.

[0090] The power system power balance constraint is

[0091]

[0092] In the formula: L t L represents the sum of the load power of all nodes in the power system; M represents the number of nodes in the power system; L represents the sum of the load power of all nodes in the power system. m,t n represents the load power of the m-th node; g Indicates the number of thermal power units in the power system; Indicates the output of the thermal power unit; This indicates the dispatch output of existing new energy power generation;

[0093] The power system's upward and downward rotational reserve constraints are...

[0094]

[0095] In the formula: and These represent the upward and downward reserve capacities of thermal power units, respectively. and These represent the upward and downward reserve requirements of the power system, respectively; and The period of tight constraints, i.e., the peak load period;

[0096] The paid peak-shaving capacity constraint of the thermal power units is

[0097]

[0098] In the formula: Indicates the output of the thermal power unit; This indicates the starting point for the paid peak-shaving capacity of thermal power units; This indicates the starting point of the paid peak shaving for the h-th gear of the thermal power unit;

[0099] The reserve capacity constraint of the thermal power unit is

[0100]

[0101] In the formula: and This indicates the maximum upward and downward reserve capacity of the thermal power unit; and This indicates the maximum and minimum technical output of the thermal power unit;

[0102] The output constraint of the thermal power unit is

[0103]

[0104] In the formula: Represents the operating state variables of thermal power units;

[0105] The minimum start-stop time constraint for the thermal power unit is:

[0106]

[0107] In the formula: and U represents the start-up and stop status variables of thermal power units; i With D i Indicates the minimum start-up and shutdown time of a thermal power unit;

[0108] S3: Based on the typical annual output curves of new energy power generation and the typical annual power curves of load, the annual operation simulation results are obtained by solving the daily optimized scheduling model, such as... Figure 6 As shown;

[0109] Furthermore, the annual operation simulation results include at least the annual peak load period, annual valley load period, and wind curtailment period of the power system;

[0110] And obtain annual peak-shaving data based on the results of annual operational simulations;

[0111] Furthermore, the annual peak-shaving data includes paid peak-shaving volume, unpaid peak-shaving volume, and wind power consumption volume;

[0112] Specifically, the following steps are included:

[0113] S31: Divide the annual load and renewable energy output curves of the power system into daily datasets according to the date;

[0114] S32: Based on the CPLEX optimization solver, the daily optimization scheduling simulation is performed on the annual load and new energy output curves of the power system in the daily dataset, and the daily operation simulation classification results are output.

[0115] The daily operation simulation classification results include peak load periods, valley load periods, and wind curtailment periods within each day;

[0116] Furthermore, the valley load period refers to the paid peak-shaving compensation fee for thermal power units after the optimized scheduling simulation is conducted. The time period during which the value is not zero;

[0117] The peak load period refers to the period during which the power system's upward rotation reserve constraint is a tight constraint;

[0118] S33: According to the preset time sequence, the daily operation simulation classification results are spliced ​​together to obtain the annual operation simulation results;

[0119] Furthermore, the annual operation simulation results include the annual peak load period, annual valley load period, and wind curtailment period of the power system;

[0120] S34: Obtain annual peak-shaving data based on annual operational simulation results;

[0121] Furthermore, the annual peak-shaving data includes paid peak-shaving volume, unpaid peak-shaving volume, and wind power consumption volume;

[0122] The paid peak shaving capacity daily optimization scheduling model includes paid peak shaving compensation fees. The sum of optimization results; the free peak-shaving amount is the sum of the output adjustment of thermal power units caused by the fluctuation of net load in the power system;

[0123] In this embodiment, a comprehensive analysis of the annual data is used to verify whether the proposed method has universality throughout the year, ensuring that the conclusions are not affected by the bias of short-term samples. The annual data may show changes in load patterns at different time periods (such as seasonal differences, differences between weekdays and holidays), in order to improve the applicability of the results.

[0124] S4: Establish autonomous peak-shaving principles for newly added planning entities based on annual peak-shaving data:

[0125] During paid peak shaving periods, no new planning units are allowed to send electricity to the power system; during unpaid peak shaving periods, new planning units are allowed to send electricity to the power system, provided that the new planning units do not increase the total annual unpaid peak shaving demand of the power system; during wind curtailment periods, the power output of new planning units sent to the power system is restricted.

[0126] Specifically, to ensure that the addition of new planning units does not increase the system's paid peak-shaving burden, the following two constraints must be met regarding the electricity consumption and transmission power of the new planning units during peak and valley periods: the new planning units must not increase the annual total free peak-shaving demand of the power system, including constraints on electricity consumption during peak periods and transmission power during valley periods, expressed as follows:

[0127]

[0128] In the formula: T P T represents the set of peak load periods in the power system when the total output of thermal power units reaches its upper limit after considering reserve and other ancillary service capacities; VP represents the set of off-peak load periods in the power system (deep peak shaving by thermal power units); s1,t This indicates the power purchased from the grid by the newly added wind-thermal-storage bundled unit; P s2,t This indicates the power output of the bundled unit to the power grid;

[0129] Specifically, the principle of autonomous peak shaving for newly added planning entities (new planning units) in S4 also includes the transmission capacity constraints based on the DC power flow model and the power balance constraints of the power system after the new planning units are connected to the grid. The new planning units are the newly added "wind-thermal-load-storage bundled units".

[0130] In this embodiment, the impact of the newly added planning unit on the consumption of existing renewable energy is reflected in two aspects: the occupation of system peak-shaving resources and the existing renewable energy transmission channels. In order not to affect the consumption of existing renewable energy in the system, the proposed autonomous peak-shaving principle model also needs to consider transmission capacity constraints. That is, based on the DC power flow model, the matrix form of the transmission capacity constraint is expressed as follows:

[0131] P l =M PTDF ×P

[0132]

[0133] M PTDF =B l AB -1

[0134] In the formula: P l P represents the power flow of a newly added planning unit branch l in the power system; P represents the node injection power vector of the power system. m,t This represents the element corresponding to node m in the power system, specifically the difference between the power output of the source connected to node m and the load power. Indicates the generator power at node m; This represents the new energy output of node m; P represents the power flow transmission limit of a newly added planning unit branch l in the power system; PTDF A represents the power transfer matrix; B represents the network topology correlation matrix of the power system; C represents the branch admittance matrix of the power system ignoring branch conductance.

[0135] The power balance constraint of the power system after the newly added planning unit is connected to the grid is:

[0136]

[0137] S5: New planning units in the power system complete the planning based on the principle of autonomous peak shaving, and obtain their annual power curves and annual output curves;

[0138] S6: Based on the daily optimized scheduling model, the annual power curve and annual output curve are used to perform daily optimized operation simulation to obtain new annual peak shaving data, and the newly constructed annual peak shaving assessment and verification model is used to assess and verify the autonomous peak shaving of newly planned units connected to the power system.

[0139] Specifically, this embodiment addresses the issue of renewable energy curtailment caused by insufficient peak-shaving resources. It comprehensively considers both paid and unpaid peak-shaving by thermal power, proposing the concept and operational principles of autonomous peak-shaving as the grid connection boundary condition for newly added planning units. Furthermore, it constructs an annual demand assessment model for both paid and unpaid peak-shaving in the power system, i.e., the constructed annual peak-shaving assessment and verification model, whose expression is:

[0140]

[0141] In the formula: and This indicates the output of thermal power units before and after grid connection of newly added planning units in the power system; n g Indicates the number of thermal power units in the power system; T N This represents the set of time periods in the power system excluding peak and off-peak periods, i.e., the set of normal periods. This indicates the annual consumption of existing new energy sources; This represents the dispatch output of existing renewable energy generation. In this embodiment, free peak shaving refers to the process of generator units changing their output to adapt to changes in electricity load. In this embodiment, the change in output of thermal power units outside the paid peak shaving period is defined as the system's free peak shaving demand. Therefore, the fact that adding a new planning unit does not increase the free peak shaving demand constraint can be expressed as the fact that adding a new planning unit does not increase the total output adjustment of the system's thermal power units. Furthermore, the system's wind curtailment situation can be obtained through annual operation simulation.

[0142] The specific implementation example of this embodiment is as follows:

[0143] A case study analysis was conducted based on the IEEE 118-bus system. This case study includes 66 thermal power units, 7 hydropower units, and 3 wind farms connected to nodes 36, 69, and 77 respectively. The power system load and wind power curves are shown below. Figure 2 Furthermore, by conducting annual power system operation simulations, the original paid peak-shaving periods of the system were obtained.

[0144] The evaluation indicators of the autonomous peak-shaving principle proposed in this embodiment include: paid peak-shaving amount, unpaid peak-shaving amount (i.e., power ramp-up amount of thermal power units), and wind curtailment amount. For this purpose, the evaluation indicators of autonomous peak-shaving before and after the addition of wind-thermal-storage planning units to the system under the 10% wind curtailment scenario were obtained, as shown in Table 1.

[0145] Table 1. Peak and trough times

[0146]

[0147]

[0148] In this embodiment, after the verification convergence, none of the three indicators corresponding to the planning scheme exceeded the standard, which means that the autonomous peak shaving requirement is met. Since the termination condition of the evaluation and verification is that none of the indicators exceed the standard, the evaluation and verification is stopped when the worst indicator is close to the limit boundary. In the example, when the autonomous peak shaving requirement is met and converged, the wind curtailment is closest to the limit indicator (reduced by 0.02%), and the other two indicators are slightly better than before planning, as shown in Table 2.

[0149] Table 2. Results of Autonomous Peak Shaving Verification

[0150]

[0151] like Figure 3 This refers to the upward and downward climbing situation of thermal power units before and after the newly added planning units are connected to the grid within a certain period (one week). Figures 4 to 5 This paper demonstrates the system's primary and secondary paid peak-shaving demand before and after the addition of new planned units to the grid. Since the net power of the newly bundled units is allowed to be connected to the grid during non-paid peak-shaving periods, the system's upward / downward ramp power changes slightly, but the annual total target does not increase. Because the grid connection power of the newly bundled units alters the original system's load curve, the system's primary paid peak-shaving demand also decreases slightly. Overall, the connection of new bundled units affects the system's peak-shaving demand at different times, but it ensures that the annual peak-shaving demand does not increase, thus meeting the autonomous peak-shaving requirements. A comparison is made between the new paid peak-shaving demand, unpaid peak-shaving demand, and wind power consumption obtained from the planning and the existing paid peak-shaving demand, unpaid peak-shaving demand, and wind power consumption, thus illustrating the effectiveness of the method in this embodiment. Figure 4 This indicates that the power system primarily has a primary demand for paid peak shaving, and that the peak shaving demand remained unchanged before and after the planning for most of the period. From... Figure 5It can be seen that deep (secondary) peak shaving demand exists only in certain periods. This is because the minimum output of thermal power units in the IEEE 118-node system is relatively small, and the starting point for paid peak shaving is relatively low. This embodiment establishes the principle of autonomous peak shaving for newly planned entities by comprehensively considering both paid and unpaid peak shaving of thermal power. This is to realize the planning of grid connection for newly planned units in the power system and obtain their annual power curves and annual output curves. Based on the daily optimized scheduling model, annual daily optimized operation simulation is performed according to the annual power curves and annual output curves to obtain new annual peak shaving data. The constructed annual peak shaving assessment and verification model is used to assess and verify the autonomous peak shaving of newly planned units in the power system. This effectively ensures that when newly planned units in the power system are connected to the grid, the peak shaving burden of the power grid system is not increased, and the absorption of existing new energy power generation is not affected. This enables the joint planning of new energy power generation with various flexible resources such as energy storage and thermal power units.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating and verifying the autonomous peak-shaving capabilities of newly added planning units in a power system, characterized in that, Specifically, the following steps are included: S1: Obtain system data from the power system; The system data includes at least system topology parameters, power supply parameters, annual typical output curves of new energy power generation, and annual typical power curves of load. S2: Construct a daily optimized scheduling model based on system data, including the paid peak-shaving compensation fee for thermal power units; The daily optimization scheduling model includes a daily optimization scheduling objective function and scheduling constraints. The expression for the objective function of the S2 China-Japan optimal scheduling is as follows: In the formula: These represent the output of the thermal power unit, the unit's start-up status variables, and the paid peak-shaving capacity of the thermal power unit in the h-th stage, respectively. This indicates the fuel cost of thermal power units; This refers to the paid peak-shaving compensation fee for thermal power units. This indicates the cost of starting up a thermal power unit once. Indicates the unit's start-up status variable; and Indicates the thermal power unit number The paid peak-shaving capacity and compensation price; Indicates a time period index; This indicates the total number of time periods scheduled daily. This indicates a paid peak-shaving level index; This indicates the total number of paid peak-shaving levels; 、 、 All of these represent the quadratic function coefficients of the power generation cost of thermal power units; S3: Based on the annual typical output curve of new energy power generation and the annual typical power curve of load, the annual operation simulation results are obtained by solving the daily optimization scheduling model. Furthermore, the annual operation simulation results include at least the annual peak load period, annual valley load period, and wind curtailment period of the power system; And obtain annual peak-shaving data based on the results of annual operational simulations; Furthermore, the annual peak-shaving data includes paid peak-shaving volume, unpaid peak-shaving volume, and wind power consumption volume; S4: Establish autonomous peak-shaving principles for newly added planning units based on annual peak-shaving data: During paid peak shaving periods, no new planning units are allowed to send electricity to the power system; during unpaid peak shaving periods, new planning units are allowed to send electricity to the power system, provided that the new planning units do not increase the total annual unpaid peak shaving demand of the power system; during wind curtailment periods, the power output of new planning units sent to the power system is restricted. S5: Enables newly added planning units in the power system to complete planning based on the principle of autonomous peak shaving, and obtain their annual power curves and annual output curves; S6: Based on the daily optimization scheduling model, the annual power curve and annual output curve are used to perform daily optimization operation simulation to obtain new annual peak shaving data, and the evaluation and verification of the autonomous peak shaving of newly planned units connected to the power system is realized based on the constructed annual peak shaving evaluation and verification model. The constructed annual peak-shaving assessment and verification model is expressed as follows: In the formula: and This indicates the output of thermal power units before and after the newly added planning unit of the power system is connected to the grid; Indicates the number of thermal power units in the power system; This represents the set of time periods in the power system excluding peak and off-peak periods, i.e., the set of normal periods. This indicates the annual consumption of existing new energy sources; This indicates the dispatch output of existing new energy power generation.

2. The evaluation and verification method for autonomous peak shaving of newly added planning units in a power system according to claim 1, characterized in that, The scheduling constraints of the daily optimized scheduling model constructed in S2, which includes the paid peak-shaving compensation fee for thermal power units. These include power system power balance constraints, power system upward and downward rotation reserve constraints, paid peak-shaving capacity constraints of thermal power units, reserve capacity constraints of thermal power units, output constraints of thermal power units, and minimum start-up and shutdown time constraints of thermal power units. The power system power balance constraint is In the formula: It represents the sum of the load power of all nodes in the power system; Indicates the number of nodes in the power system; Indicates the first The load power of each node; Indicates the number of thermal power units in the power system; express This indicates the dispatch output of existing new energy power generation; The power system's upward and downward rotational reserve constraints are... In the formula: and These represent the upward and downward reserve capacities of thermal power units, respectively. and These represent the upward and downward reserve requirements of the power system, respectively. The paid peak-shaving capacity constraint of the thermal power units is In the formula: Indicates the output of the thermal power unit; This indicates the starting point for the paid peak-shaving capacity of thermal power units; Indicates the thermal power unit number The starting point for paid peak shaving; The reserve capacity constraint of the thermal power unit is In the formula: and This indicates the maximum upward and downward reserve capacity of the thermal power unit; and This indicates the maximum and minimum technical output of the thermal power unit; The output constraint of the thermal power unit is In the formula: Represents the operating state variables of thermal power units; The minimum start-stop time constraint for the thermal power unit is: In the formula: and Indicates the start-up and shutdown status variables of thermal power units; and This indicates the minimum start-up and shutdown time of a thermal power unit.

3. The evaluation and verification method for autonomous peak shaving of newly added planning units in a power system according to claim 2, characterized in that, S3 specifically includes the following steps: S31: Divide the annual load and renewable energy output curves of the power system into daily datasets according to the date; S32: Based on the CPLEX optimization solver, the daily optimization scheduling simulation is performed on the annual load and new energy output curves of the power system in the daily dataset, and the daily operation simulation classification results are output. The daily operation simulation classification results include peak load periods, valley load periods, and wind curtailment periods within each day; Furthermore, the valley load period refers to the paid peak-shaving compensation fee for thermal power units after the optimized scheduling simulation is conducted. The time period during which the value is not zero; The peak load period refers to the period during which the power system's upward rotation reserve constraint is a tight constraint; S33: According to the preset time sequence, the daily operation simulation classification results are spliced ​​together to obtain the annual operation simulation results; Furthermore, the annual operation simulation results include the annual peak load period, annual valley load period, and wind curtailment period of the power system; S34: Obtain annual peak-shaving data based on annual operational simulation results; Furthermore, the annual peak-shaving data includes paid peak-shaving volume, unpaid peak-shaving volume, and wind power consumption volume; The paid peak shaving capacity daily optimization scheduling model includes paid peak shaving compensation fees. The sum of optimization results; the free peak-shaving amount is the sum of the output adjustment of thermal power units caused by the fluctuation of net load in the power system.

4. The evaluation and verification method for autonomous peak shaving of newly added planning units in a power system according to claim 3, characterized in that, The newly added planning unit in S4 does not increase the annual total free peak-shaving demand constraint of the power system, including the power consumption constraint during peak load periods and the power transmission constraint during valley load periods. Its expression is: In the formula: This represents the set of peak load periods in the power system when the total output of thermal power units reaches its upper limit after considering the capacity of backup and other ancillary services; This represents the set of off-peak periods in the power system; This indicates the power purchased from the grid by the newly added wind-thermal-storage bundled unit; This indicates the power output of the bundled unit to the power grid.

5. The evaluation and verification method for autonomous peak shaving of newly added planning units in a power system according to claim 4, characterized in that, The S4 establishes the principle of autonomous peak shaving for newly added planning entities, and also includes transmission capacity constraints based on DC power flow models and power balance constraints of the power system after the newly added planning units are connected to the grid. The power transmission capacity constraint is as follows: In the formula: Indicates newly added planning unit branches in the power system The trend; This represents the node-injected power vector of the power system. Represents the corresponding node in the power system The elements, i.e., nodes The difference between the output power of the connected power source and the load power, and + ; Represents a node Generator power; Represents a node The output of new energy sources; Indicates newly added planning unit branches in the power system The limits of current transmission; Represents the power transfer matrix; An incidence matrix representing the network topology of a power system; This represents the branch admittance matrix of a power system, ignoring branch conductance. The power balance constraint of the power system after the newly added planning unit is connected to the grid is: 。

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