A large new energy base peak shaving power supply capacity optimization method

Abstract

This invention discloses a method for optimizing the capacity of peak-shaving power sources in large-scale new energy bases, relating to the field of power system optimization and dispatching technology. The invention includes: acquiring full-cycle power time-series data of new energy generation and power transmission; constructing and solving an upper-level optimization dispatching model to obtain the energy storage capacity sequence of energy storage devices throughout the entire cycle; setting time boundary points when the energy storage capacity of the energy storage devices reaches their operational upper limit, and dividing the time series into multiple continuous time segments based on all boundary points; constructing and solving a lower-level optimization dispatching model within each time segment to obtain the optimization results for each time segment; aggregating the optimization results of each time segment to output the dispatching plan and capacity optimization scheme for peak-shaving power sources. This invention, based on a progressive optimization strategy of dynamically segmenting energy storage capacity, effectively overcomes the shortcomings of traditional one-time solutions with large computational loads and unsatisfactory optimization effects with fixed segmentation, improving the solution efficiency and engineering practicality of medium- and long-term capacity optimization problems.

Description

Technical Field

[0001] This invention relates to a method for optimizing the peak-shaving power capacity of a large-scale new energy base, belonging to the field of power system optimization and dispatching technology. Background Technology

[0002] Currently, large-scale new energy bases, with wind and solar power as the main power sources, are developing rapidly. However, the intermittency and volatility of wind and solar resources pose significant challenges to the power balance and stable operation of the power system. Against this backdrop, configuring appropriate peak-shaving power sources and conducting scientific capacity optimization have become crucial to ensuring the efficient consumption of new energy and the safe and stable operation of the power grid.

[0003] Currently, methods for medium- to long-term capacity optimization of complex systems containing new energy sources, energy storage, and various peak-shaving power sources mainly fall into two categories. The first category involves establishing a high-precision, full-cycle optimization model that includes detailed operational constraints for all units and solving it in one go using methods such as mixed-integer linear programming. Theoretically, this method offers the highest accuracy, but due to factors such as unit start-up and shutdown constraints and nonlinear costs, the model's dimensionality increases exponentially with the optimization cycle, facing the "curse of dimensionality" problem. In practical engineering, it is often difficult to apply due to the enormous computational resource consumption and excessively long solution time. The second category, to reduce computational burden, divides the long-term optimization problem into multiple independent sub-problems with fixed time lengths (such as daily or weekly) for separate solutions. While this method alleviates computational pressure, its subjective and rigid segmentation method fails to consider the cross-period coupling characteristics of flexible resources such as energy storage and peak-shaving power sources, as well as the overall operating status of the system. This leads to optimization decisions at the segment boundaries often deviating from the actual optimal path, sacrificing the overall economic efficiency and rationality of the optimization results.

[0004] Therefore, how to strike a balance between high-precision modeling and acceptable computational costs, and design a method for optimizing the peak-shaving power capacity of new energy bases that can both reflect the long-term operating characteristics of the system and solve problems efficiently, has become a technical challenge that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to propose a method for optimizing the peak-shaving power capacity of large-scale new energy bases. This method aims to solve the problems in existing new energy base capacity optimization methods, such as the low efficiency of high-precision full-cycle one-time solution and the unreasonable optimization results caused by fixed-time segmented solution which cannot comprehensively consider system state coupling.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, this invention proposes a method for optimizing the peak-shaving power capacity of a large-scale new energy base, comprising:

[0008] Acquire full-cycle power time-series data for new energy power generation and external power transmission;

[0009] Based on the full-cycle power time series data, the pre-constructed upper-level optimization scheduling model is solved to obtain the energy storage power sequence and its corresponding time series of the energy storage device in the full cycle. The upper-level optimization scheduling model is a simplified model of the actual system including peak-shaving power units.

[0010] Based on the energy storage power sequence, a time boundary point is set when the energy storage power of the energy storage device reaches its operating upper limit value, and the time sequence is divided into multiple continuous time segments according to all the boundary points.

[0011] Within each time segment, a lower-level optimization scheduling model corresponding to that time segment is constructed and solved to obtain the optimization results for each time segment. The lower-level optimization scheduling model takes a single peak-shaving power unit as the scheduling object and includes the cost parameters and operating constraints of the peak-shaving power unit.

[0012] By aggregating the optimization results of each time segment, the scheduling plan and capacity optimization scheme of the peak-shaving power supply are output.

[0013] Furthermore, the solution to the pre-built upper-level optimization scheduling model includes:

[0014] Each peak-shaving power unit is aggregated into an equivalent peak-shaving power unit group;

[0015] The objective function of the upper-level optimization scheduling model is constructed with the goal of minimizing the sum of wind and solar curtailment costs and fuel costs.

[0016] A mixed-integer linear programming algorithm is used to solve the objective function over the entire time scale to obtain the energy storage power sequence of the energy storage device over the entire time scale.

[0017] Furthermore, the objective function of the upper-level optimized scheduling model is:

[0018]

[0019] In the formula, The objective function for optimizing the scheduling model at the upper level; Minimize operation; Let be the cost of wind curtailment for wind farm i during time period t; The cost of curtailment of solar power station i during time period t; Fuel cost of peak-shaving power unit group during time period t; , These are the numbers of wind power and solar power stations, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. The unit fuel cost of peak-shaving power units in time period t; , These represent the theoretical power generation and the actual planned power generation of wind farm i during time period t, respectively. , These represent the theoretical power generation and the actual planned power generation of photovoltaic station i during time period t, respectively. The power generation of the peak-shaving power unit group during time period t; To solve for the step size;

[0020] The constraints of the upper-level optimization scheduling model include system power balance constraints, wind and solar power output constraints, peak-shaving power unit group output constraints, energy storage capacity and charging and discharging power constraints.

[0021] The mathematical expression for the system power balance constraint is:

[0022]

[0023] In the formula, The system transmits power during time period t;

[0024] The mathematical expression for the wind and solar power output constraint is:

[0025]

[0026] The mathematical expression for the output constraint of the peak-shaving power unit group is:

[0027]

[0028] In the formula, The rated power of the peak-shaving power unit group, , These are the minimum and maximum load rates of the peak-shaving power unit group, respectively.

[0029] The mathematical expressions for the energy storage capacity and charge / discharge power constraints are as follows:

[0030]

[0031]

[0032] In the formula, and This represents the amount of electricity generated by the energy storage device during time period t and time period t-1; , These represent the charging and discharging power of the energy storage during time period t; The self-discharge rate of the stored energy; , These are the charging and discharging efficiencies of energy storage, respectively. , These represent the charging and discharging states of energy storage during time period t; , These are the upper and lower limits of the electricity required for safe operation of energy storage; , These are the minimum and maximum values ​​of the energy storage charging power, respectively. , These are the minimum and maximum values ​​of the energy storage discharge power, respectively.

[0033] Furthermore, the method employs a mixed-integer linear programming algorithm to solve the objective function over the entire time scale to obtain the energy storage capacity sequence of the energy storage device over the entire time scale, including:

[0034] The unit fuel cost of peak-shaving power units in the upper-level optimized scheduling model during time period t. The problem is simplified to a constant C greater than 0, and a linearized objective function is constructed, transforming the original optimization problem into a mixed-integer linear programming problem.

[0035] Solving the mixed-integer linear programming problem yields the energy storage power sequence over the entire time scale.

[0036] Furthermore, based on the energy storage power sequence, a time boundary point is set when the energy storage capacity of the energy storage device reaches its operating upper limit. The time sequence is then divided into multiple consecutive time segments based on all boundary points, including:

[0037] Traverse the energy storage capacity sequence, when the energy storage capacity at a certain moment satisfies Record the moment. The first time series The time series is divided into multiple consecutive time segments based on all the dividing points. , , ..., ,…,in, This represents the current energy storage capacity. This represents the maximum energy storage capacity.

[0038] Furthermore, the construction and solution of the lower-level optimization scheduling model corresponding to this time segment includes:

[0039] The objective function of the lower-level optimization scheduling model is constructed with the goal of minimizing the sum of the costs of wind and solar curtailment and the costs of each peak-shaving power unit.

[0040] A mixed-integer linear programming algorithm is used to solve the lower-level optimization scheduling model in each time segment in chronological order, so as to obtain the optimization results of each time segment.

[0041] Furthermore, the objective function of the lower-level optimized scheduling model is:

[0042]

[0043] In the formula, The objective function for optimizing the lower-level scheduling model; Minimize operation; The power generation cost of peak-shaving power unit i during time period t; This refers to the number of peak-shaving power units; Let be the cost of wind curtailment for wind farm i during time period t; The cost of curtailment of solar power station i during time period t; , These are the numbers of wind power and solar power stations, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. , These represent the theoretical power generation and the actual planned power generation of wind farm i during time period t, respectively. , These represent the theoretical power generation and the actual planned power generation of photovoltaic station i during time period t, respectively. To solve for the step size; the generation cost of peak-shaving power units. Including fuel costs Start-up and shutdown costs Unit loss cost Additional oil input costs ;

[0044] The constraints of the lower-level optimization scheduling model include system power balance constraints, wind and solar power output constraints, output constraints of each peak-shaving power unit, minimum start-up and shutdown time constraints, energy storage capacity and charging and discharging power constraints.

[0045] The mathematical expression for the system power balance constraint is:

[0046]

[0047] In the formula, The system transmits power during time period t;

[0048] The mathematical expression for the wind and solar power output constraint is:

[0049]

[0050] The mathematical expressions for the output and minimum start-up / shutdown time constraints of each peak-shaving power unit are as follows:

[0051]

[0052]

[0053] In the formula, Let be the active power of unit i during time period t; The rated power of unit i; , These represent the continuous operating time and downtime of the unit at time t-1, respectively. , These are the minimum allowed continuous operating time and downtime of the unit, respectively. and These represent the operating states of unit i during time period t and time period t-1, respectively.

[0054] The mathematical expressions for the energy storage capacity and charge / discharge power constraints are as follows:

[0055]

[0056]

[0057] In the formula, and This represents the amount of electricity generated by the energy storage device during time period t and time period t-1; , These represent the charging and discharging power of the energy storage during time period t; The self-discharge rate of the stored energy; , These are the charging and discharging efficiencies of energy storage, respectively. , These represent the charging and discharging states of energy storage during time period t; , These are the upper and lower limits of the electricity required for safe operation of energy storage; , These are the minimum and maximum values ​​of the energy storage charging power, respectively. , These are the minimum and maximum values ​​of the energy storage discharge power, respectively.

[0058] Furthermore, the mixed-integer linear programming algorithm is used to solve the lower-level optimization scheduling model sequentially in each time segment, obtaining the optimization results for each time segment, including:

[0059] Linearize the fuel cost of peak-shaving power unit i in time period t;

[0060] A mixed-integer linear programming algorithm is used to sequentially process each time segment in chronological order. , , ..., The lower-level optimization scheduling model is solved in ... to obtain the optimization results for each time segment;

[0061] Except for the first time segment, the operating status of the peak-shaving power unit at the beginning of each time segment, as well as the continuous operation or shutdown time, are all set to the corresponding values ​​at the end of the previous time segment.

[0062] Furthermore, the aggregation of optimization results for each time segment outputs a scheduling plan and capacity optimization scheme for peak-shaving power supply, including:

[0063] The optimization results of each time segment are aggregated and compared with the depreciation investment cost of peak-shaving power unit i. Add them together to obtain the total cost over the entire lifecycle, including the depreciation investment cost. The calculation formula is:

[0064]

[0065] In the formula, r is the discount rate, and n is the service life of the peak-shaving power unit. The purchase cost of peak-shaving power unit i.

[0066] Furthermore, before constructing and solving the lower-level optimal scheduling model, the following steps are also included:

[0067] Multiple peak-shaving power unit capacity configuration schemes are preset;

[0068] For each of the aforementioned capacity configuration schemes, the construction and solution of the lower-level optimization scheduling model are executed sequentially, and the optimization results of each time segment are aggregated to obtain the corresponding total system cost;

[0069] The scheduling plan and capacity optimization scheme of the output peak-shaving power supply are the scheme with the optimal total system cost among all capacity configuration schemes and its corresponding scheduling plan.

[0070] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0071] This invention achieves a balance between computational efficiency and optimization accuracy by constructing a hierarchical progressive optimization framework. First, a simplified aggregation model is used to quickly solve for the energy storage capacity sequence that indicates the long-term trend of the system's surplus capacity. Based on this sequence, time segments are dynamically divided at the objective moment when the energy storage capacity reaches its upper limit, replacing subjective and fixed segmentation rules, ensuring that each segment corresponds to a physically complete operational phase. Finally, fine-grained optimization is performed within each segment. This method reduces the computational burden from exponential to linear, significantly improving the solution speed for year-round scale optimization problems. Simultaneously, its segmentation method is adaptively coupled with the system's operating state, fundamentally avoiding the unreasonable optimization results caused by traditional fixed segmentation, resulting in a capacity configuration scheme that is both globally economical and practically feasible. Attached Figure Description

[0072] Figure 1 This is a flowchart of the method for optimizing peak-shaving power capacity in large-scale new energy bases proposed in an embodiment of the present invention;

[0073] Figure 2 This is a graph showing the annual hourly power curves of wind power, photovoltaic power, and power transmission proposed in an embodiment of the present invention;

[0074] Figure 3 This is a diagram showing the monthly average power of wind power, photovoltaic power, external power transmission, and source-load difference as proposed in an embodiment of the present invention.

[0075] Figure 4 This is a graph showing the annual change in the percentage of energy storage capacity proposed in an embodiment of the present invention.

[0076] Figure 5 This is a diagram showing the power output of each unit during periods of insufficient wind and solar power output as proposed in this embodiment of the invention. Detailed Implementation

[0077] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use.

[0078] Example 1:

[0079] The embodiment proposes a method for optimizing the peak-shaving power capacity of a large-scale new energy base, such as... Figure 1 As shown, it includes:

[0080] S1: Obtain full-cycle power time-series data for new energy power generation and external power transmission;

[0081] S2: Based on the full-cycle power time-series data, solve the pre-built upper-level optimization scheduling model to obtain the energy storage power sequence and its corresponding time sequence of the energy storage device in the full cycle. The upper-level optimization scheduling model is a simplified model of the actual system including peak-shaving power units.

[0082] S3: Based on the energy storage power sequence, set a time boundary point when the energy storage power of the energy storage device reaches its operating upper limit value, and divide the time sequence into multiple continuous time segments according to all the boundary points.

[0083] S4: Within each time segment, construct and solve the lower-level optimization scheduling model corresponding to that time segment to obtain the optimization results for each time segment. The lower-level optimization scheduling model takes a single peak-shaving power unit as the scheduling object and includes the cost parameters and operating constraints of the peak-shaving power unit.

[0084] S5: Aggregate the optimization results of each time segment and output the peak-shaving power supply scheduling plan and capacity optimization scheme.

[0085] In this embodiment, solving the pre-built upper-level optimization scheduling model includes:

[0086] Each peak-shaving power unit is aggregated into an equivalent peak-shaving power unit group;

[0087] The objective function of the upper-level optimization scheduling model is constructed with the goal of minimizing the sum of wind and solar curtailment costs and fuel costs.

[0088] A mixed-integer linear programming algorithm is used to solve the objective function over the entire time scale to obtain the energy storage power sequence of the energy storage device over the entire time scale.

[0089] In this embodiment, the objective function of the upper-layer optimized scheduling model is:

[0090] (1)

[0091] In the formula, The objective function for optimizing the scheduling model at the upper level; Minimize operation; Let be the cost of wind curtailment for wind farm i during time period t; The cost of curtailment of solar power station i during time period t; Fuel cost of peak-shaving power unit group during time period t; , These are the numbers of wind power and solar power stations, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. The unit fuel cost of peak-shaving power units in time period t; , These represent the theoretical power generation and the actual planned power generation of wind farm i during time period t, respectively. , These represent the theoretical power generation and the actual planned power generation of photovoltaic station i during time period t, respectively. The power generation of the peak-shaving power unit group during time period t; To determine the step size, we take 1h;

[0092] The constraints of the upper-level optimization scheduling model include system power balance constraints, wind and solar power output constraints, peak-shaving power unit group output constraints, energy storage capacity and charging and discharging power constraints.

[0093] The mathematical expression for the system power balance constraint is:

[0094] (2)

[0095] In the formula, The system transmits power during time period t;

[0096] The mathematical expression for the wind and solar power output constraint is:

[0097] (3)

[0098] The mathematical expression for the output constraint of the peak-shaving power unit group is:

[0099] (4)

[0100] In the formula, The rated power of the peak-shaving power unit group, , These are the minimum and maximum load rates of the peak-shaving power unit group, respectively.

[0101] The mathematical expressions for the energy storage capacity and charge / discharge power constraints are as follows:

[0102] (5)

[0103] (6)

[0104] In the formula, and This represents the amount of electricity generated by the energy storage device during time period t and time period t-1; , These represent the charging and discharging power of the energy storage during time period t; The self-discharge rate of the stored energy; , These are the charging and discharging efficiencies of energy storage, respectively. , These represent the charging and discharging states of energy storage during time period t; , These are the upper and lower limits of the electricity required for safe operation of energy storage; , These are the minimum and maximum values ​​of the energy storage charging power, respectively. , These are the minimum and maximum values ​​of the energy storage discharge power, respectively.

[0105] In this embodiment, the step of employing a mixed-integer linear programming algorithm to solve the objective function over the entire time scale to obtain the energy storage capacity sequence of the energy storage device over the entire time scale includes:

[0106] The unit fuel cost of peak-shaving power units in the upper-level optimized scheduling model during time period t. The problem is simplified to a constant C greater than 0, and a linearized objective function is constructed, transforming the original optimization problem into a mixed-integer linear programming problem.

[0107] Solving the mixed-integer linear programming problem yields the energy storage power sequence over the entire time scale.

[0108] By simplifying the unit fuel cost of peak-shaving power unit groups to a coefficient greater than 0, the upper-level model avoids the complex modeling of nonlinear cost curves. After maintaining the linear structure, a mixed-integer linear programming algorithm is used to solve the problem directly. This significantly improves the solution speed across the entire time scale while ensuring the correctness of the timing of energy storage behavior, and provides reliable boundary conditions for fine optimization at the lower level.

[0109] In this embodiment, the step of setting a time boundary point when the energy storage device reaches its upper operating limit based on the energy storage power sequence, and dividing the time sequence into multiple consecutive time segments based on all boundary points, includes:

[0110] Traverse the energy storage capacity sequence, when the energy storage capacity at a certain moment satisfies This indicates that at that moment, the wind and solar power output in the vicinity is relatively high or the demand for power transmission is relatively low. At this time, the peak-shaving power units output little or no power. Since the energy storage capacity is sufficient to support the system's power transmission for a period of time, the output status of the peak-shaving power units will not change in the short term. Record this moment. The first time series The time series is divided into multiple consecutive time segments based on all the dividing points. , , ..., ,…,in, This represents the current energy storage capacity. This represents the maximum energy storage capacity.

[0111] In this embodiment, constructing and solving the lower-level optimization scheduling model corresponding to the time segment includes:

[0112] The objective function of the lower-level optimization scheduling model is constructed with the goal of minimizing the sum of the costs of wind and solar curtailment and the costs of each peak-shaving power unit.

[0113] A mixed-integer linear programming algorithm is used to solve the lower-level optimization scheduling model in each time segment in chronological order, so as to obtain the optimization results of each time segment.

[0114] In this embodiment, the objective function of the lower-level optimization scheduling model is:

[0115] (7)

[0116] In the formula, The objective function for optimizing the lower-level scheduling model; Minimize operation; The power generation cost of peak-shaving power unit i during time period t; This refers to the number of peak-shaving power units; Let be the cost of wind curtailment for wind farm i during time period t; The cost of curtailment of solar power station i during time period t; , These are the numbers of wind power and solar power stations, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. , These represent the theoretical power generation and the actual planned power generation of wind farm i during time period t, respectively. , These represent the theoretical power generation and the actual planned power generation of photovoltaic station i during time period t, respectively. To solve for the step size.

[0117] Peak-shaving power generation cost Including fuel costs Start-up and shutdown costs Unit loss cost Additional oil input costs The power generation cost of the generating unit has different components at different load rates, specifically expressed as follows:

[0118] (8)

[0119]

[0120] (9)

[0121]

[0122] In the formula, The load factor of peak-shaving power unit i during time period t, expressed as a percentage. , , The load factor is divided according to the different component costs of unit i; Let be the active power of unit i during time period t; , , These are the coal consumption characteristic coefficients of unit i, respectively. The fuel price is set at 720 yuan / ton. This represents the operating status of unit i during time period t; , These are the start-up and shutdown costs for unit i, respectively; The rated power of unit i, , This represents the coefficient of the unit loss curve. The purchase cost of unit i.

[0123] The constraints of the lower-level optimization scheduling model include system power balance constraints, wind and solar power output constraints, output constraints of each peak-shaving power unit, minimum start-up and shutdown time constraints, energy storage capacity and charging and discharging power constraints.

[0124] The mathematical expression for the system power balance constraint is:

[0125] (10)

[0126] In the formula, The system transmits power during time period t;

[0127] The mathematical expression for the wind and solar power output constraint is:

[0128] (11)

[0129] The mathematical expressions for the output and minimum start-up / shutdown time constraints of each peak-shaving power unit are as follows:

[0130] (12)

[0131] (13)

[0132] In the formula, Let be the active power of unit i during time period t; The rated power of unit i; , These represent the continuous operating time and downtime of the unit at time t-1, respectively. , These are the minimum allowed continuous operating time and downtime of the unit, respectively. and These represent the operating states of unit i during time period t and time period t-1, respectively.

[0133] The mathematical expressions for the energy storage capacity and charge / discharge power constraints are as follows:

[0134] (14)

[0135] (15)

[0136] In the formula, and This represents the amount of electricity generated by the energy storage device during time period t and time period t-1; , These represent the charging and discharging power of the energy storage during time period t; The self-discharge rate of the stored energy; , These are the charging and discharging efficiencies of energy storage, respectively. , These represent the charging and discharging states of energy storage during time period t; , These are the upper and lower limits of the electricity required for safe operation of energy storage; , These are the minimum and maximum values ​​of the energy storage charging power, respectively. , These are the minimum and maximum values ​​of the energy storage discharge power, respectively.

[0137] In this embodiment, a mixed-integer linear programming algorithm is used to solve the lower-level optimization scheduling model sequentially in each time segment, obtaining the optimization results for each time segment, including:

[0138] Linearize the fuel cost of peak-shaving power unit i in time period t;

[0139] A mixed-integer linear programming algorithm is used to sequentially process each time segment in chronological order. , , ..., The lower-level optimization scheduling model is solved in ... to obtain the optimization results for each time segment;

[0140] Except for the first time segment, the operating status of the peak-shaving power unit at the beginning of each time segment, as well as the continuous operation or shutdown time, are all set to the corresponding values ​​at the end of the previous time segment.

[0141] In this embodiment, the aggregation of optimization results from each time segment to output a peak-shaving power supply scheduling plan and capacity optimization scheme includes:

[0142] The optimization results of each time segment are aggregated and compared with the depreciation investment cost of peak-shaving power unit i. Add them together to obtain the total cost over the entire lifecycle, including the depreciation investment cost. The calculation formula is:

[0143] (16)

[0144] In the formula, r is the discount rate, which is taken as 5%; n is the service life of the peak-shaving power unit, which is taken as 30 years. The purchase cost of peak-shaving power unit i.

[0145] In this embodiment, the wind power and photovoltaic capacity and the cost of wind and solar curtailment penalties are shown in Table 1; the relevant parameters of the selectable peak-shaving power units are shown in Table 2; and the relevant parameters of the energy storage devices are shown in Table 3.

[0146] Table 1. Relevant parameters for wind power and photovoltaic power

[0147]

[0148] Table 2 Relevant parameters of peak-shaving power units

[0149]

[0150] Table 3 Relevant parameters of energy storage devices

[0151]

[0152] The breakdown of costs for peak-shaving power units at different load rates is shown in the following formula:

[0153] (17)

[0154] Figure 2 The hourly power series of wind power, photovoltaic power and external power transmission in a large new energy base are given on an 8760-hour scale. Figure 3 The monthly average power output of wind power, photovoltaic power, external power transmission, and source-load difference within this large-scale new energy base is presented.

[0155] In this embodiment, before performing steps S4 and S5, the following steps are also included:

[0156] Based on the hourly power operation characteristics of wind power, photovoltaic power, and power transmission, this embodiment designs seven peak-shaving power unit capacity configuration schemes, specifically: one 300 MW unit, one 135 MW unit, one 200 MW unit, one 135 MW unit, one 300 MW unit, one 200 MW unit, two 135 MW units, two 200 MW units, and two 300 MW units. For each configuration scheme, the solution process S4 and S5 are executed sequentially to obtain the total system cost corresponding to each scheme; the capacity optimization scheme output by S5 is the scheme with the optimal total system cost among all configuration schemes, and its corresponding unit scheduling plan.

[0157] After completing the optimization solution for the peak-shaving power capacity of this large-scale new energy base Figure 4 The annual variation curve of the percentage of energy storage capacity obtained from the upper-level optimization model is given. Based on this energy storage capacity time series, the entire time scale of 8760 hours is divided into 185 time segments. The lower-level optimization model is solved for each time segment, and the optimization results of all peak-shaving power capacity configuration schemes are finally summarized, as shown in the table below:

[0158] Table 4 Optimization results of various capacity combination schemes

[0159]

[0160] As shown in the table above, the optimal peak-shaving power capacity configuration for the system's total cost is one 135 MW unit and one 200 MW unit. This result demonstrates that the optimization process (S2 to S5) in this embodiment can efficiently and accurately select the globally economically optimal capacity combination from numerous candidate configurations. Under this optimal scheme, the total cost of the system throughout its entire lifecycle is 397 million yuan, including 189.33 million yuan for wind and solar curtailment costs, 24.05 million yuan for depreciation and investment costs of the peak-shaving power units, and 183.62 million yuan for power generation costs. Throughout the entire lifecycle, the peak-shaving power units and renewable energy generation account for 20.8% and 79.2% of total power generation, respectively, with a renewable energy absorption rate of 87%.

[0161] To verify the actual operational characteristics of the capacity optimization results, a typical time segment that characterizes insufficient renewable energy output was selected for analysis. For example... Figure 5 As shown, during this period, wind power output remained low, resulting in a power shortage in the system. At this time, the system selected to operate 135 MW and 200 MW units under combined high load, while the energy storage device continued to discharge. After photovoltaic power generation recovered, the 135 MW unit was shut down as planned, the 200 MW unit maintained at 55% load, and the energy storage switched to charging mode. When renewable energy power became more abundant, the peak-shaving power units were shut down, and renewable energy and energy storage met the system's power transmission needs. This operation process demonstrates that the capacity scheme and scheduling plan obtained based on the method in this embodiment exhibit excellent performance in both global economy and local executability, achieving a balance between high-precision modeling and efficient solution.

[0162] Those skilled in the art should understand that the above embodiments are merely illustrative of the technical solutions of the present invention and not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing peak-shaving power capacity in a large-scale new energy base, characterized in that, include: Acquire full-cycle power time-series data for new energy power generation and external power transmission; Based on the full-cycle power time series data, the pre-constructed upper-level optimization scheduling model is solved to obtain the energy storage power sequence and its corresponding time series of the energy storage device in the full cycle. The upper-level optimization scheduling model is a simplified model of the actual system including peak-shaving power units. Based on the energy storage power sequence, a time boundary point is set when the energy storage power of the energy storage device reaches its operating upper limit value, and the time sequence is divided into multiple continuous time segments according to all the boundary points. Within each time segment, a lower-level optimization scheduling model corresponding to that time segment is constructed and solved to obtain the optimization results for each time segment. The lower-level optimization scheduling model takes a single peak-shaving power unit as the scheduling object and includes the cost parameters and operating constraints of the peak-shaving power unit. By aggregating the optimization results of each time segment, the scheduling plan and capacity optimization scheme of the peak-shaving power supply are output.

2. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 1, characterized in that, The solution to the pre-built upper-level optimization scheduling model includes: Each peak-shaving power unit is aggregated into an equivalent peak-shaving power unit group; The objective function of the upper-level optimization scheduling model is constructed with the goal of minimizing the sum of wind and solar curtailment costs and fuel costs. A mixed-integer linear programming algorithm is used to solve the objective function over the entire time scale to obtain the energy storage power sequence of the energy storage device over the entire time scale.

3. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 2, characterized in that, The objective function of the upper-level optimization scheduling model is: In the formula, The objective function for optimizing the scheduling model at the upper level; Minimize operation; Let be the cost of wind curtailment for wind farm i during time period t; The cost of curtailment of solar power station i during time period t; Fuel cost of peak-shaving power unit group during time period t; , These are the numbers of wind power and solar power stations, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. The unit fuel cost of peak-shaving power units in time period t; , These represent the theoretical power generation and the actual planned power generation of wind farm i during time period t, respectively. , These represent the theoretical power generation and the actual planned power generation of photovoltaic station i during time period t, respectively. The power generation of the peak-shaving power unit group during time period t; To solve for the step size; The constraints of the upper-level optimization scheduling model include system power balance constraints, wind and solar power output constraints, peak-shaving power unit group output constraints, energy storage capacity and charging and discharging power constraints. The mathematical expression for the system power balance constraint is: In the formula, The system transmits power during time period t; The mathematical expression for the wind and solar power output constraint is: The mathematical expression for the output constraint of the peak-shaving power unit group is: In the formula, The rated power of the peak-shaving power unit group, , These are the minimum and maximum load rates of the peak-shaving power unit group, respectively. The mathematical expressions for the energy storage capacity and charge / discharge power constraints are as follows: In the formula, and This represents the amount of electricity generated by the energy storage device during time period t and time period t-1; , These represent the charging and discharging power of the energy storage during time period t; The self-discharge rate of the stored energy; , These are the charging and discharging efficiencies of energy storage, respectively. , These represent the charging and discharging states of energy storage during time period t; , These are the upper and lower limits of the electricity required for safe operation of energy storage; , These are the minimum and maximum values ​​of the energy storage charging power, respectively. , These are the minimum and maximum values ​​of the energy storage discharge power, respectively.

4. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 3, characterized in that, The method employs a mixed-integer linear programming algorithm to solve the objective function over the entire time scale, obtaining the energy storage capacity sequence of the energy storage device over the entire time scale, including: The unit fuel cost of peak-shaving power units in the upper-level optimized scheduling model during time period t. The problem is simplified to a constant C greater than 0, and a linearized objective function is constructed, transforming the original optimization problem into a mixed-integer linear programming problem. Solving the mixed-integer linear programming problem yields the energy storage power sequence over the entire time scale.

5. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 1, characterized in that, The step involves setting time demarcation points based on the energy storage power sequence, where the energy storage capacity of the energy storage device reaches its operational upper limit. The time sequence is then divided into multiple consecutive time segments based on all demarcation points, including: Traverse the energy storage capacity sequence, when the energy storage capacity at a certain moment satisfies Record the moment. The first time series The time series is divided into multiple consecutive time segments based on all the dividing points. , , ..., ,…,in, This represents the current energy storage capacity. This represents the maximum energy storage capacity.

6. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 1, characterized in that, The construction and solution of the lower-level optimization scheduling model corresponding to the time segment includes: The objective function of the lower-level optimization scheduling model is constructed with the goal of minimizing the sum of the costs of wind and solar curtailment and the costs of each peak-shaving power unit. A mixed-integer linear programming algorithm is used to solve the lower-level optimization scheduling model in each time segment in chronological order, so as to obtain the optimization results of each time segment.

7. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 6, characterized in that, The objective function of the lower-level optimization scheduling model is: In the formula, The objective function for optimizing the lower-level scheduling model; Minimize operation; The power generation cost of peak-shaving power unit i during time period t; This refers to the number of peak-shaving power units; Let be the cost of wind curtailment for wind farm i during time period t; The cost of curtailment of solar power station i during time period t; , These are the numbers of wind power and solar power stations, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. , These represent the theoretical power generation and the actual planned power generation of wind farm i during time period t, respectively. , These represent the theoretical power generation and the actual planned power generation of photovoltaic station i during time period t, respectively. To solve for the step size; the generation cost of peak-shaving power units. Including fuel costs Start-up and shutdown costs Unit loss cost Additional oil input costs ; The constraints of the lower-level optimization scheduling model include system power balance constraints, wind and solar power output constraints, output constraints of each peak-shaving power unit, minimum start-up and shutdown time constraints, energy storage capacity and charging and discharging power constraints. The mathematical expression for the system power balance constraint is: In the formula, The system transmits power during time period t; The mathematical expression for the wind and solar power output constraint is: The mathematical expressions for the output and minimum start-up / shutdown time constraints of each peak-shaving power unit are as follows: In the formula, Let be the active power of unit i during time period t; The rated power of unit i; , These represent the continuous operating time and downtime of the unit at time t-1, respectively. , These are the minimum allowed continuous operating time and downtime of the unit, respectively. and These represent the operating states of unit i during time period t and time period t-1, respectively. The mathematical expressions for the energy storage capacity and charge / discharge power constraints are as follows: In the formula, and This represents the amount of electricity generated by the energy storage device during time period t and time period t-1; , These represent the charging and discharging power of the energy storage during time period t; The self-discharge rate of the stored energy; , These are the charging and discharging efficiencies of energy storage, respectively. , These represent the charging and discharging states of energy storage during time period t; , These are the upper and lower limits of the electricity required for safe operation of energy storage; , These are the minimum and maximum values ​​of the energy storage charging power, respectively. , These are the minimum and maximum values ​​of the energy storage discharge power, respectively.

8. The method for optimizing peak-shaving power capacity in large-scale new energy bases according to claim 7, characterized in that, The method employs a mixed-integer linear programming algorithm to solve the lower-level optimization scheduling model sequentially in each time segment, obtaining the optimization results for each time segment, including: Linearize the fuel cost of peak-shaving power unit i in time period t; A mixed-integer linear programming algorithm is used to sequentially process each time segment in chronological order. , , ..., The lower-level optimization scheduling model is solved in ... to obtain the optimization results for each time segment; Except for the first time segment, the operating status of the peak-shaving power unit at the beginning of each time segment, as well as the continuous operation or shutdown time, are all set to the corresponding values ​​at the end of the previous time segment.

9. The method for optimizing peak-shaving power capacity in a large-scale new energy base according to claim 1, characterized in that, The aggregation of optimization results for each time segment outputs a scheduling plan and capacity optimization scheme for peak-shaving power supply, including: The optimization results of each time segment are aggregated and compared with the depreciation investment cost of peak-shaving power unit i. Add them together to obtain the total cost over the entire lifecycle, including the depreciation investment cost. The calculation formula is: In the formula, r is the discount rate, and n is the service life of the peak-shaving power unit. The purchase cost of peak-shaving power unit i.

10. The method for optimizing peak-shaving power capacity in a large-scale new energy base according to claim 1, characterized in that, Before constructing and solving the lower-level optimal scheduling model, the following steps are also included: Multiple peak-shaving power unit capacity configuration schemes are preset; For each of the aforementioned capacity configuration schemes, the construction and solution of the lower-level optimization scheduling model are executed sequentially, and the optimization results of each time segment are aggregated to obtain the corresponding total system cost; The scheduling plan and capacity optimization scheme of the output peak-shaving power supply are the scheme with the optimal total system cost among all capacity configuration schemes and its corresponding scheduling plan.

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