Optimized scheduling method based on peak regulation compensation cost allocation and related device

Abstract

The invention belongs to a peak regulation optimization scheduling method, and provides an optimization scheduling method based on peak regulation compensation cost allocation and a related device aiming at the technical problems of large loss, high cost and imperfect deep peak regulation compensation system of the traditional thermal power generating unit deep peak regulation. A wind power prediction curve, a photovoltaic power generation prediction curve, a load prediction curve, the optimized load curve, the output of each generator set and the total peak regulation compensation cost of each generator set are used as input to be correspondingly input to an upper layer model, a middle layer model and a lower layer model, and the optimization target of the upper layer model is that the net load fluctuation is minimum; the optimization target of the middle-layer model is that the total cost of the power generation system is minimum, the new energy abandoned electricity is minimum, and the net profit of each power generator is maximum, and the optimization target of the lower-layer model is that the benefit of the wind-solar-thermal power generator alliance is maximized. The method can effectively reduce the wind and light abandoning power, reasonably share the peak regulation compensation cost, and relieve the problem that the peak regulation pressure of the power grid is large.

Description

Technical Field

[0001] This application pertains to a peak-shaving optimization scheduling method, specifically involving an optimization scheduling method and related apparatus based on the sharing of peak-shaving compensation costs. Background Technology

[0002] As the proportion of renewable energy connected to the grid continues to increase, the fluctuating, intermittent, and random nature of wind and solar power output, along with its anti-peak-shaving characteristics, is causing increasing pressure on the power grid for peak shaving, placing higher demands on the grid's peak-shaving flexibility. Currently, peak-shaving services provided by thermal power plants are the main source of grid peak-shaving flexibility, but traditional peak-shaving methods for controlling thermal power output are insufficient to meet system peak-shaving needs. Traditional deep peak-shaving by thermal power units suffers from high losses, high costs, and an imperfect compensation system, resulting in low enthusiasm among thermal power units to participate in deep peak-shaving. Therefore, improving the enthusiasm of thermal power units for deep peak-shaving has become a major problem facing the power grid. Summary of the Invention

[0003] This application addresses the technical problems of high losses, high costs, and imperfect compensation systems in traditional thermal power units' deep peak shaving, and provides an optimized scheduling method and related devices based on the sharing of peak shaving compensation costs.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application proposes an optimized scheduling method based on the sharing of peak-shaving compensation costs, including: The wind power forecast curve, photovoltaic power forecast curve, and load forecast curve are input into the upper-level model, and the daily load is time-shifted to obtain the optimized load curve; the optimization objective of the upper-level model is to minimize net load fluctuation. The optimized load curve is input into the intermediate-level model to obtain the output of each generator unit and the total peak-shaving compensation cost of each generator unit; the optimization objective of the intermediate-level model is to minimize the total cost of the power generation system, minimize the curtailment of new energy power, and maximize the net profit of each power generator. The output of each generator set and the total peak-shaving compensation cost of each generator set are input into the lower-level model to obtain the output of each generator and the allocated peak-shaving compensation cost; the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal generator alliance.

[0005] Furthermore, the objective function expression of the upper-level model includes:

[0006] in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, This represents the average net load.

[0007]

[0008] in, For time period Load after demand response For time period Forecast values ​​for wind power, For time period Forecast values ​​for photovoltaic power generation.

[0009] Furthermore, the objective function expression of the middle-level model includes:

[0010]

[0011]

[0012] in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The operating costs of wind-solar-thermal-storage combined operation participating in deep peak shaving. The operating costs of thermal power units during different peak-shaving phases. For the operating costs of energy storage power stations, For wind-solar operating costs, For the abandonment of renewable energy, This is the sum of the net profits of all generators. For the net profit of deep peak shaving of thermal power, Net profit from wind and solar power generation For net profit from energy storage peak shaving, For time period Wind power generation capacity For time period Photovoltaic power generation capacity.

[0013] Furthermore, the operating costs of thermal power units during different peak-shaving phases The calculation methods include:

[0014] in, For fuel costs, Cost of lifespan depletion, To cover oil production costs, In addition to environmental costs, This is the rated minimum output of the thermal power unit. To provide power to thermal power units, This is the maximum output of the thermal power unit during operation. This refers to the minimum output power required for stable operation of a thermal power unit without deep oil injection for peak shaving. The minimum output required for stable operation of the unit during deep peak shaving of thermal power plants using oil injection; Operating costs of energy storage power stations The calculation methods include:

[0015] in, The charging and discharging operating costs of energy storage power stations, Costs related to the lifespan of energy storage power stations. Add environmental costs to energy storage power stations.

[0016] Furthermore, the objective function expression of the lower-level model includes:

[0017] in, For the total profit of deep peak shaving of thermal power, For the total profit of wind power, For the total profit of photovoltaics, This refers to the cost sharing for peak shaving compensation of non-deep peak-shaving thermal power units. To share the cost of photovoltaic peak shaving compensation. This is for the cost sharing of wind power peak shaving compensation.

[0018] Furthermore, the constraints of the upper-level model, the middle-level model, and the lower-level model include: Total electricity consumption remains unchanged before and after demand response:

[0019] in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, For time period Load after demand response For time period The transferred load power, For time period The transferred load power; The cost of electricity purchased after demand response is less than or equal to the cost of electricity purchased before demand response:

[0020] in, For time period Pre-demand response electricity price For time period Changes in electricity prices following demand response; The maximum load that can be transferred at any given time period must meet the following requirements:

[0021] in, For time period Maximum allowable load increase rate For time period Electricity load prior to demand response For time period Changes in electricity load following demand response; Thermal power, energy storage, wind power, and photovoltaic power generation can meet the following requirements at any given time:

[0022] in, For thermal power units During the period of efforts, For time period Energy storage power station power, For time period Wind power grid connection capacity, For time period Photovoltaic power generation output For wind-solar operating costs, This refers to the serial number of the thermal power unit; The upper and lower limits of the output power of non-deep peak-shaving thermal power units are satisfied:

[0023] The upper and lower limits of the output power of deep peak-shaving thermal power units are satisfied:

[0024] in, For thermal power units During the period The lower limit of output, For thermal power units During the period The output power, For thermal power units During the period The upper limit of output, For thermal power units During the period Load rate; Thermal power units must meet their own ramping constraints at any given time:

[0025] in, This refers to the ramp-up rate of thermal power units. For thermal power units During the period of efforts, For thermal power units During the period contribution; At any given time, the output of wind power and photovoltaic power generation is less than their rated output:

[0026]

[0027] in, For time period Maximum wind power output For time period Maximum output of photovoltaic power generation; Rotational spare constraint:

[0028] in, For thermal power units The maximum change in output over any given time period. The spinning reserve capacity of the power generation system; Line transmission capacity constraints:

[0029] in, For nodes and nodes The maximum transmission capacity of the line between them For nodes and nodes Admittance between For time period node voltage phase angle, For time period node The voltage phase angle; Charging and discharging logic state constraints:

[0030] in, For time period The energy storage is in a charging state. For time period The energy storage is in a discharging state; Charging and discharging power constraints:

[0031]

[0032] in, This represents the maximum charging power of the energy storage power station. This represents the maximum discharge power of the energy storage power station. For time period The discharge power of the energy storage power station For time period The charging power of the energy storage power station; State of charge constraints:

[0033]

[0034] in, This is the state-of-charge limit for energy storage power stations. For time period The state of charge of an energy storage power station This represents the upper limit of the state of charge of an energy storage power station. For time period The state of charge of an energy storage power station The length of the scheduling period. To improve the charging efficiency of energy storage power stations. For the capacity of energy storage power stations, This refers to the discharge efficiency of the energy storage power station.

[0035] Secondly, this application proposes an optimized scheduling system based on peak-shaving compensation cost sharing, comprising: The upper-level module is used to input wind power forecast curves, photovoltaic power forecast curves, and load forecast curves into the upper-level model, and to time-shift the daily load to obtain an optimized load curve; the optimization objective of the upper-level model is to minimize net load fluctuation. The intermediate module is used to input the optimized load curve into the intermediate model to obtain the output of each generator unit and the total peak-shaving compensation cost of each generator unit; the optimization objective of the intermediate model is to minimize the total cost of the power generation system, minimize the curtailment of new energy power, and maximize the net profit of each power generator. The lower-level module is used to input the output of each generator set and the total peak-shaving compensation cost of each generator set into the lower-level model to obtain the output of each generator and the allocated peak-shaving compensation cost; the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal generator alliance.

[0036] Furthermore, the objective function expression of the upper-level model includes:

[0037] in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, This represents the average net load. The objective function expression of the intermediate-level model includes:

[0038]

[0039]

[0040] in, The operating costs of wind-solar-thermal-storage combined operation participating in deep peak shaving. The operating costs of thermal power units during different peak-shaving phases. For the operating costs of energy storage power stations, For wind-solar operating costs, For the abandonment of renewable energy, This is the sum of the net profits of all generators. For time period Forecast values ​​for wind power, For time period Forecast value of photovoltaic power generation, For the net profit of deep peak shaving of thermal power, Net profit from wind and solar power generation Net profit for energy storage peak shaving; The objective function expression of the lower-level model includes:

[0041] in, For the total profit of deep peak shaving of thermal power, For the total profit of wind power, For the total profit of photovoltaics, This refers to the cost sharing for peak shaving compensation of non-deep peak-shaving thermal power units. To share the cost of photovoltaic peak shaving compensation. Costs to be shared for wind power peak shaving compensation; Thirdly, this application proposes an electronic device, including: a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the above-mentioned optimized scheduling method based on peak shaving compensation cost allocation.

[0042] Fourthly, this application proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described optimized scheduling method based on peak shaving compensation cost allocation.

[0043] Compared with the prior art, this application has the following beneficial effects: This application proposes an optimized scheduling method based on the sharing of peak-shaving compensation costs. The method uses wind power forecast curves, photovoltaic power forecast curves, and load forecast curves, along with the optimized load curve, the output of each generating unit, and the total peak-shaving compensation cost for each generating unit as inputs. These inputs are fed into an upper-level model, a middle-level model, and a lower-level model. The optimization objective of the upper-level model is to minimize net load fluctuations; the optimization objective of the middle-level model is to minimize the total cost of the power generation system, minimize renewable energy curtailment, and maximize the net profit of each power generator; and the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal power generator alliance. This application aims at renewable energy consumption. The upper-level, middle-level, and lower-level models together form a thermal-storage coupled economic optimization scheduling model, which can effectively reduce wind and solar power curtailment, reasonably share peak-shaving compensation costs, improve the effectiveness of thermal power units participating in deep peak shaving, increase the renewable energy consumption rate, and alleviate the problem of high grid peak-shaving pressure.

[0044] This application also proposes an optimized scheduling system based on peak shaving compensation cost sharing, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned optimized scheduling method based on peak shaving compensation cost sharing. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of an optimized scheduling method based on peak-shaving compensation cost allocation in this application; Figure 2 The original load forecast curve, wind power forecast curve, and photovoltaic power generation forecast curve are shown in the embodiments of this application. Figure 3 This is a schematic diagram of the predicted load values ​​before and after PBDR. Figure 4 This is a schematic diagram of the output of the G1-G10 thermal power units and energy storage units in the embodiments of this application before the deep peak shaving of the PBDR and the energy storage auxiliary thermal power units. Figure 5This is a schematic diagram of the output of the G1-G10 thermal power units and energy storage units after deep peak shaving by PBDR and energy storage-assisted thermal power units in the embodiments of this application. Figure 6 This is a schematic diagram illustrating the calculation of peak-shaving cost allocation using the traditional allocation method in an embodiment of this application; Figure 7 This is a schematic diagram illustrating the calculation of peak shaving cost allocation based on a cooperative game-theoretic peak shaving compensation cost allocation strategy in the mid-level model of this application embodiment; Figure 8 This is a schematic diagram of an optimized scheduling system based on peak shaving compensation cost sharing in this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0049] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0050] In the description of the embodiments of this application, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0051] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0052] In the description of the embodiments of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0053] First, it should be noted that for the sake of convenience, some content in this application uses descriptions such as "wind-solar-thermal-storage", where "wind" refers to wind power, "solar" refers to photovoltaic power generation, "thermal" refers to thermal power, and "storage" refers to energy storage.

[0054] Utilizing energy storage to assist thermal power units in deep peak shaving is an effective method to improve the flexibility of power grid peak shaving. Energy storage technology can effectively realize the time shifting, storage, and utilization of electricity, which helps promote the consumption of renewable energy in the power system and alleviate the pressure on power grid peak shaving. Adopting a price-based demand-side response for loads, optimizing load curves through peak shaving and valley filling to assist power grid peak shaving, can alleviate the pressure on power grid peak shaving. Cooperative game theory can solve the problem of unreasonable cost sharing for deep peak shaving compensation and increase the enthusiasm of thermal power units to participate in deep peak shaving. Based on the above analysis, this application conducts an in-depth study on the costs and benefits of thermal power units participating in deep peak shaving, establishes a cost-benefit model of wind-solar-thermal-storage participation in peak shaving and a coupled economic optimization scheduling model of thermal-storage, and rationally allocates peak shaving compensation costs to alleviate the pressure on power grid peak shaving, which is of great significance for the construction of a new type of power system.

[0055] This application proposes an optimized scheduling method and related apparatus based on the sharing of peak shaving compensation costs. The following is a detailed description of this application in conjunction with embodiments and accompanying drawings.

[0056] like Figure 1 The diagram shown is a schematic representation of an optimized scheduling method based on peak-shaving compensation cost sharing, which may include: S101, input the wind power forecast curve, photovoltaic power forecast curve and load forecast curve into the upper-level model, time-shift the daily load to obtain the optimized load curve; the optimization objective of the upper-level model is to minimize the net load fluctuation.

[0057] The upper-level model smooths the net load curve and reduces system peak-shaving pressure by adjusting time-shiftable loads. During time-shifting, some load can be transferred from periods of low renewable energy output to peak periods, such as delaying overnight electric vehicle charging to midday when photovoltaic power generation efficiency is high. The optimized load curve has a smaller peak-to-valley difference and better matches the renewable energy output curve. This reduces the system's need for frequent start-ups and shutdowns of thermal power units for peak shaving, thus lowering peak-shaving costs.

[0058] S102, input the optimized load curve into the intermediate model to obtain the output of each generator unit and the total compensation cost for peak shaving of each generator unit; the optimization objective of the intermediate model is to minimize the total cost of the power generation system, minimize the curtailment of new energy power, and maximize the net profit of each power generator.

[0059] The goal of the mid-level model is to achieve a balance between the economic efficiency of the power generation system, the integration of renewable energy, and the revenue of power generators, while satisfying the optimized load curve. Regarding cost minimization, priority can be given to scheduling renewable energy sources with low marginal costs and essential baseload units, with thermal power units started and shut down according to bidding order. For renewable energy integration, reserved peak-shaving capacity can be used, such as deep peak-shaving by thermal power units or energy storage systems to absorb excess renewable energy. Regarding revenue for power generators, consideration can be given to the sharing of fixed costs of units and compensation for ancillary services.

[0060] S103, input the output of each generator set and the total peak-shaving compensation cost of each generator set into the lower-level model to obtain the output of each generator and the allocated peak-shaving compensation cost; the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal generator alliance.

[0061] The goal of the lower-level model is to allocate peak-shaving compensation costs within the wind-solar-thermal energy system to incentivize members to participate in peak shaving collaboratively.

[0062] This application constructs a thermal-storage coupled economic optimization scheduling model using an upper-level model, a middle-level model, and a lower-level model. The upper-level model uses price-based demand response pairs as a means to minimize net load fluctuations, shifting daily load over time to reduce peak-to-valley differences and provide an arbitrage basis for energy storage power stations. The optimized load curve is then substituted into the middle-level model. The middle-level model aims to minimize the total cost of the power generation system, maximize the renewable energy absorption rate (minimize renewable energy curtailment), and maximize the net profit of each power generator. It obtains the output of each generating unit and the total peak-shaving compensation cost, providing data support for the lower-level peak-shaving compensation allocation strategy. The lower-level model aims to maximize the benefits of the wind-solar-thermal power generator alliance, rationally allocating the total peak-shaving compensation cost to each power generator to improve the incentive for deep peak-shaving of thermal power units.

[0063] It should be noted that deep peak shaving refers to an operating mode in which thermal power units reduce their output due to the large difference between peak and valley loads on the power grid, causing the generating units to exceed the basic peak shaving range. When the output of thermal power units is between the maximum and minimum technical output values, the units are operating in the basic peak shaving phase. When the output is below the minimum technical output value, the units are operating in the deep peak shaving phase. When the load is low or the output of renewable energy is high, in order to meet the power balance of the power generation system and to absorb as much renewable energy as possible, the output of thermal power units is reduced to between the minimum technical output value and the minimum output value for stable combustion without oil injection; this is the deep peak shaving phase without oil injection. If a further reduction in output is required, oil injection for combustion assistance is necessary. In this case, the output of thermal power units needs to be reduced to between the minimum output value for stable combustion without oil injection and the minimum output value for stable combustion with oil injection; this is the deep peak shaving phase with oil injection.

[0064] Overall, this application constructs a fire-storage coupled economic optimization scheduling model, specifically including an upper-level model, a middle-level model, and a lower-level model. The objective functions of each layer model are as follows: (1) Upper-level model On the load side, a time-of-use pricing strategy is adopted for demand response adjustment. The objective is to minimize the net load fluctuation obtained by load tracking the wind-solar output curve, resulting in the optimized load curve after demand response. The objective function of the upper-level model can be expressed as:

[0065] in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, This represents the average net load.

[0066]

[0067] in, For time period Load after demand response For time period Forecast values ​​for wind power, For time period Forecast values ​​for photovoltaic power generation.

[0068] In this embodiment, the upper-level model specifically adopts a price-based demand response model for processing, and the specific method is as follows: Price-based demand response models are primarily used to formulate time-of-use electricity prices. In higher-level models, the load curve is shifted over time using the price-based demand response model to reduce peak-to-valley differences. It is an effective measure that uses electricity prices as a signal to guide users to change their electricity consumption behavior, thereby achieving peak shaving and valley filling. Most users' responses to electricity prices can be measured using the price elasticity of demand coefficient. To describe:

[0069] in, For the period preceding priced-based demand response (PBDR) The amount of electricity, For the period before price-based demand-side response The amount of electricity. Pre-PBDR period Change in electricity level Pre-PBDR period Changes in electricity prices. and All of these are the price elasticity of demand coefficients.

[0070]

[0071]

[0072] in, , , These represent electricity consumption during peak hours, normal hours, and off-peak hours before the implementation of time-of-use pricing. , , These represent electricity consumption during peak, normal, and off-peak hours after the implementation of time-of-use pricing. , , These are the electricity prices for peak hours, normal hours, and off-peak hours before the implementation of time-of-use pricing. , , These represent the changes in electricity prices during peak, normal, and off-peak hours after the implementation of time-of-use pricing. The diagonal elements of the demand price elasticity coefficient matrix E are the self-elasticity coefficients, and the off-diagonal elements are the reciprocal elasticity coefficients. Specifically, This indicates the direct impact of peak-hour electricity prices on electricity consumption during that time period. This indicates the indirect impact of normal electricity price changes on peak-hour electricity consumption. This indicates the indirect impact of changes in electricity prices during off-peak hours on peak-peak electricity consumption. This indicates the indirect impact of peak-hour electricity price changes on normal electricity consumption. This indicates the direct impact of normal electricity prices on electricity consumption during this period. This indicates the indirect impact of off-peak electricity price changes on normal electricity consumption. This indicates the indirect impact of peak-hour electricity price changes on off-peak electricity consumption. This indicates the indirect impact of normal electricity price changes on off-peak electricity consumption. This indicates the direct impact of off-peak electricity prices on electricity consumption during that time period.

[0073] Based on the electricity consumption during peak, normal, and off-peak hours after the implementation of time-of-use pricing, as well as the demand price elasticity coefficient matrix, a satisfactory demand price for a user can be calculated. Then, time shifting can be performed based on this satisfactory demand price.

[0074] (2) Middle-level model The optimized load curve obtained from the upper-level model is optimized from the obtained... After incorporating wind and solar power outputs into the intermediate-level model for power balance, the model aims to minimize the total cost of the power generation system, minimize renewable energy curtailment, and maximize the net profit of each power generator. It solves for the optimal operating modes of various generating units and energy storage, including the output of each generating unit and the total cost of deep peak shaving compensation. The objective function of the intermediate-level model is:

[0075]

[0076]

[0077] in, The operating costs of wind-solar-thermal-storage combined operation participating in deep peak shaving. The operating costs of thermal power units during different peak-shaving phases. For the operating costs of energy storage power stations, For wind-solar operating costs, For the abandonment of renewable energy, This is the sum of the net profits of all generators. For the net profit of deep peak shaving of thermal power, Net profit from wind and solar power generation For net profit from energy storage peak shaving, For time period Wind power generation capacity For time period The power output of photovoltaic power generation.

[0078] In this embodiment, the middle-layer model uses the Shapley value method (a game theory-based method for allocating cooperative benefits). The Shapley value is used to calculate the peak-shaving compensation cost borne by each power generator based on the marginal contribution of each member to the alliance, thus completing the allocation calculation.

[0079] Currently, there is no clear and comprehensive method for allocating peak-shaving compensation costs. Typically, thermal power and new energy sources (including wind and solar power) share the cost proportionally. The traditional allocation strategy for thermal power, wind power, and solar power is as follows:

[0080]

[0081]

[0082] in, For thermal power units During the period The shared costs, For thermal power units During the period Output of thermal power units that do not perform deep peak shaving For thermal power units participating in deep peak shaving compensation benefits. To compensate for the revenue from energy storage-assisted deep peak shaving of thermal power plants. For time period The cost of photovoltaic power generation For time period The cost-sharing of wind power, This represents the number of thermal power units.

[0083] Since the installed capacity of each power generator is different, this cost-sharing strategy will result in traditional thermal power units bearing more costs, while the beneficiaries of this deep peak-shaving electricity are new energy units, which is unreasonable.

[0084] Therefore, this application proposes a peak-shaving compensation cost-sharing strategy based on cooperative game theory. The Shapley value method is the most widely used method for estimating solutions to public cost-sharing game problems. Following the idea of ​​Shapley value, power generators... The compensation value that should be received is equal to that of the generator. The average marginal contribution of each wind-solar-fire e-commerce alliance it participates in. The specific expression for the Shapley value method is:

[0085] in, The Wind-Solar-Coal Power E-commerce Alliance represents the sharing of peak-shaving compensation costs among thermal power units and new energy units that do not participate in peak shaving. This indicates the number of generator sets within the Wind-Solar-Terrestrial E-commerce Alliance. n This indicates the total number of thermal power units and new energy units that do not participate in peak shaving. The order in which thermal power units and new energy units that do not participate in peak shaving are listed in the Wind-Solar-Temperature Power E-commerce Alliance is as follows: kind, express The cost of peak shaving compensation borne by the company express The cost of peak shaving compensation borne by the company For power generation The marginal contribution is measured by the impact of joining the wind-solar-fire power generation e-commerce alliance on the alliance's revenue. To share the cost with the distributors Peak shaving compensation costs, For e-commerce.

[0086] Shapley values ​​satisfy the following three properties: Property 1, Equivalence:

[0087] in, The original sorting number, This refers to the number obtained by replacing the original sorting number. This is the result of allocating peak-shaving compensation costs after replacing the original sorting numbers. The results of the peak shaving compensation cost allocation under the original sorting number are as follows. This refers to peak-shaving compensation costs. Equivalence indicates that the allocation of peak-shaving compensation costs is independent of the order in which generating units join the alliance.

[0088] Property 2, Validity:

[0089] in, Number the members in the alliance. The number of members in the alliance. For the alliance , E-commerce peak-shaving compensation fees will be issued based on the original sorting numbers. The total cost of peak shaving compensation. Peak shaving compensation costs to be shared by each power generator.

[0090] The validity surface shows that the sum of the contributions of each participant equals the total peak shaving compensation cost, without considering the impact of individual players outside the wind-solar-thermal power e-commerce alliance.

[0091] Property 3, Additivity:

[0092] in, Original sorting number The following two game scenarios involve compensation costs. and These are the characteristic functions of two games. It is the characteristic function of two games being played simultaneously. Original sorting number To conduct a game Compensation fees, Original sorting number To conduct a game Compensation costs. Whether the two games are played simultaneously or independently has no impact on the outcome of the participants' contributions.

[0093] (3) Lower-level model Based on the optimized scheduling using the mid-level model, the total peak-shaving compensation cost for each generator unit is obtained. According to the cooperative game-theoretic peak-shaving compensation sharing strategy, the total peak-shaving compensation cost for each generator unit is reasonably allocated, aiming to maximize the interests of the wind-solar-thermal power generator alliance. This yields the output and the allocated peak-shaving compensation cost for each power generator. The objective function of the lower-level model is:

[0094] in, For the total profit of deep peak shaving of thermal power, For the total profit of wind power, For the total profit of photovoltaic power generation, This refers to the cost sharing for peak shaving compensation of non-deep peak-shaving thermal power units. The cost of peak shaving compensation for photovoltaic power generation is shared. This is for the cost sharing of wind power peak shaving compensation.

[0095] The constraints of the aforementioned upper-level model, middle-level model, and lower-level model specifically include: Total electricity consumption remains unchanged before and after demand response:

[0096] in, For time period The transferred load power, For time period The transferred load power; The cost of electricity purchased after demand response is less than or equal to the cost of electricity purchased before demand response:

[0097] in, For time period Pre-demand response electricity price For time period Changes in electricity prices following demand response; The maximum load that can be transferred at any given time period must meet the following requirements:

[0098] in, For time period Maximum allowable load increase rate For time period Electricity load prior to demand response For time period Changes in electricity load following demand response; Thermal power, energy storage, wind power, and photovoltaic power generation can meet the following requirements at any given time:

[0099] in, For thermal power units During the period of efforts, For time period Energy storage power station power, For time period Wind turbine grid connection power, For time period Photovoltaic power generation output For wind-solar operating costs, This refers to the serial number of the thermal power unit; The upper and lower limits of the output power of non-deep peak-shaving thermal power units are satisfied:

[0100] The upper and lower limits of the output power of deep peak-shaving thermal power units are satisfied:

[0101] in, For thermal power units During the period The lower limit of output, For thermal power units During the period The output power, For thermal power units During the period The upper limit of output, For thermal power units During the period Load rate; Thermal power units must meet their own ramping constraints at any given time:

[0102] in, This refers to the ramp-up rate of thermal power units. For thermal power units During the period of efforts, For thermal power units During the period contribution; At any given time, the output of wind power and photovoltaic power generation is less than their rated output:

[0103]

[0104] in, For time period Maximum wind power output For time period Maximum output of photovoltaic power generation; Rotational spare constraint:

[0105] in, For thermal power units The maximum change in output over any given time period. The spinning reserve capacity of the power generation system; Line transmission capacity constraints:

[0106] in, For nodes and nodes The maximum transmission capacity of the line between them For nodes and nodes Admittance between For time period node voltage phase angle, For time period node The voltage phase angle; Charging and discharging logic state constraints:

[0107] in, This indicates that the energy storage power station is in a charging state. The energy storage power station is in a discharging state; Charging and discharging power constraints:

[0108]

[0109] in, This represents the maximum charging power of the energy storage power station. This represents the maximum discharge power of the energy storage power station. For time period The discharge power of the energy storage power station For time period The charging power of the energy storage power station; State of charge constraints:

[0110]

[0111] in, This is the state-of-charge limit for energy storage power stations. For time period The state of charge of an energy storage power station This represents the upper limit of the state of charge of an energy storage power station. For time period The state of charge of an energy storage power station The length of the scheduling period. To improve the charging efficiency of energy storage power stations. For time period The charging power of the energy storage power station For the capacity of energy storage power stations, This refers to the discharge efficiency of the energy storage power station.

[0112] When optimizing the above, it is necessary to analyze the power generation cost and peak-shaving benefits of each generator set according to its characteristics. The power generation cost of each generator set mainly includes the operation and maintenance costs of wind and solar power generation for new energy units, system reserve capacity costs, and costs of wind and solar curtailment penalties. For thermal power units, it includes coal costs, deep peak-shaving costs, and environmental costs. For energy storage power stations, it includes charging and discharging power costs, energy storage loss costs, and operating environmental costs. The wind-solar-thermal-storage benefit refers to the deep peak-shaving compensation cost of thermal power units. New energy units enjoy the grid connection revenue brought by deep peak-shaving of thermal power units. Energy storage power stations profit from the peak-valley electricity price difference through "low storage, high generation" arbitrage, and receive peak-shaving compensation benefits. Therefore, this application also establishes a cost-benefit model for wind-solar-thermal-storage participation in peak shaving, as follows: 1. Costs of wind-solar-thermal-storage systems participating in peak shaving: (1) Costs of thermal power units participating in peak shaving Thermal power units participate in peak shaving in three phases: the conventional peak shaving phase, the deep peak shaving phase without oil injection, and the deep peak shaving phase with oil injection. During the conventional peak shaving phase, thermal power units are required to provide a portion of their peak shaving capacity free of charge, without submitting a bid. When the output of a thermal power unit is less than the minimum load rate during the conventional peak shaving phase, the unit incurs loss costs and oil injection costs, at which point it is allowed to submit a compensation bid.

[0113] 1) Fuel costs .

[0114] The fuel cost during the deep peak shaving phase is the same as that during the conventional peak shaving phase, as shown in the following formula:

[0115] in, In order to cooperate with thermal power units The first consumption coefficient related to the characteristics, In order to cooperate with thermal power units The second consumption coefficient related to the characteristics, In order to cooperate with thermal power units The third consumption coefficient related to characteristics For thermal power units During the period The output power.

[0116] 2) Cost of lifespan depletion.

[0117] In this embodiment, based on the alternating force conditions of the rotor metal material, low-cycle fatigue life loss is calculated to approximate the life loss cost. as follows:

[0118]

[0119] in, This represents the loss coefficient of the thermal power unit. For the construction cost of thermal power units, For thermal power units During the period The rotor cracked under the output power of the cycle.

[0120] 3) Oil input cost .

[0121] When the peak load of a thermal power unit is too high, oil injection measures are required to maintain the normal operation of the thermal power unit.

[0122] in, For fuel consumption, For oil prices.

[0123] 4) Additional environmental costs .

[0124] The air pollutants considered in this embodiment include SO2 and NO. x As the load rate of thermal power units decreases, pollutant emissions from these units also increase, resulting in additional environmental costs.

[0125] in, Fines will be imposed for exceeding the SO2 standard per unit volume. NO per unit volume x Fines exceeding the limit The emission standard for SO2 per unit volume NO per unit volume x emission standards For thermal power units During the period SO2 emission exceedance rate For thermal power units During the period NO x Emissions exceeding standards rate.

[0126] In summary, the operating costs of thermal power units vary depending on the peak-shaving phase. It can be represented in segments as follows:

[0127] (2) Costs of new energy units participating in peak shaving.

[0128] Wind power and photovoltaic power generation are clean energy power generation, and their production costs are almost zero compared to traditional thermal power units. The wind-solar costs considered in this embodiment mainly include operation and maintenance costs, system reserve capacity costs, and wind and solar curtailment penalty costs.

[0129] 1) Wind-solar operation and maintenance costs.

[0130]

[0131] in, For wind-solar operation and maintenance costs, This is the wind power operation and maintenance cost coefficient. This represents the operation and maintenance cost coefficient for photovoltaic power generation. For time period Wind turbine grid connection power, For time period Photovoltaic generator output.

[0132] 2) System backup capacity cost.

[0133] The power grid uses reserve capacity to cope with the increased forecasting errors caused by the volatility of wind power grid connection. The additional cost of reserve capacity is:

[0134] in, For system backup capacity costs, Here, L represents the system reserve capacity cost factor, W represents the load forecast error rate, and C represents the wind power forecast error rate. For time period The load value.

[0135] 3) Penalties for abandoning renewable energy.

[0136]

[0137] in, Penalties for curtailing renewable energy sources For time period Recently, the wind curtailment power was dispatched. For time period Recently, the power of curtailed solar power was allocated. This is the wind curtailment penalty coefficient. This represents the penalty coefficient for discarded light.

[0138] In summary, the operating costs of wind-solar power generation It can be represented as:

[0139] (3) Costs of energy storage participating in peak shaving.

[0140] The cost of energy storage participating in peak shaving in this application mainly considers the cost of charging and discharging power, loss cost, and the operating environment cost of the energy storage power station.

[0141] 1) Operating costs of energy storage power stations for charging and discharging.

[0142]

[0143] in, The charging and discharging operating costs of energy storage power stations, For time period The charging power, For time period The discharge power, The unit power charge / discharge cost of energy storage power stations, This represents the total number of time periods in the power generation system's scheduling cycle.

[0144] 2) Cost of energy storage power station lifespan loss.

[0145] Based on the total investment cost of the energy storage power station and the number of cycles in each dispatch cycle, the lifespan loss cost of the energy storage power station is calculated. Perform the calculation:

[0146] in, For the investment cost of energy storage power stations, For the cycle life of energy storage power stations, For time period Binary variables representing the charging switching state of an energy storage power station. For time period The binary variable representing the discharge switching state of an energy storage power station; when switching to the charging state... Take "1", otherwise, Take "1". Among them... This represents the number of charge-discharge cycles of an energy storage power station within each scheduling cycle, used to describe the losses incurred by the energy storage power station during the charge-discharge process.

[0147] 3) Additional environmental costs of energy storage power stations.

[0148]

[0149] in, Adding environmental costs to energy storage power stations For the discharge efficiency of energy storage power stations, The pollutant emission density of energy storage power stations The unit emission cost of pollutants from energy storage power stations.

[0150] In summary, the operating costs of energy storage power stations It can be represented as:

[0151] 2. Benefits of wind-solar-thermal-storage system participating in peak shaving.

[0152] For thermal power units, as long as the cost of deep peak shaving compensation exceeds their own peak shaving costs, they will be incentivized to actively participate in peak shaving services. For energy storage, energy storage can participate in deep peak shaving of thermal power units, improve the degree of output reduction of thermal power units, and obtain profits through the peak-valley electricity price difference of the power grid by "low storage and high generation".

[0153] (1) Fire-storage peak-shaving compensation:

[0154] in, For thermal power units participating in deep peak shaving compensation benefits. To compensate for the revenue from energy storage-assisted deep peak shaving of thermal power plants. For time period The amount of electricity generated by thermal power units participating in peak shaving. For time period The amount of electricity that energy storage power stations participate in peak shaving. This is the compensation price for the electricity generated when thermal power units participate in peak shaving. This is the compensation price for the electricity generated when an energy storage power station participates in peak shaving.

[0155] (2) Net profit from deep peak shaving of thermal power units:

[0156] in, For time period Net profit from deep peak shaving of thermal power units For time period Thermal power units benefit from deep peak shaving compensation. For time period Output of peak-shaving units The clearing price for thermal power units.

[0157] (3) Net profit from wind-solar power generation:

[0158] in, For time period Net profit from wind and solar power generation The benchmark price for wind power grid connection, The benchmark feed-in tariff for photovoltaic power. For time period Wind power grid connection electricity, For time period Electricity generated by photovoltaic power generation.

[0159] (4) Benefits of deep energy storage for peak shaving:

[0160] in, For time period Benefits of deep energy storage for peak shaving To compensate for the revenue from energy storage-assisted deep peak shaving of thermal power plants. For time period Real-time peak electricity price of the power grid For time period Real-time off-peak electricity prices for the power grid.

[0161] To verify the effectiveness of the method described in this application, historical measured data of a power grid in a certain region of China were collected. For example... Figure 2 The table shows the original load forecast curve, wind power forecast curve, and photovoltaic power generation forecast curve. Parameters for thermal power units are shown in Table 1, and relevant parameters for energy storage power stations are shown in Table 2.

[0162] Table 1 Technical parameters of thermal power units

[0163] Table 2 Technical parameters of energy storage power station

[0164] like Figure 3 The figure shows a schematic diagram of the predicted load values ​​before and after PBDR. Figure 3 The solid part in the middle is the time-shifted load value. Comparing the two curves, it can be clearly seen that the load fluctuation is smaller after passing through PBDR, the peak-to-valley difference is reduced, the maximum peak-to-valley difference is reduced from 257MW to 137MW, a reduction of 46.69%, and the net load variance is reduced by 40.94%. like Figure 4 The diagram shows the output of G1-G10 thermal power units and energy storage units before PBDR and deep peak shaving of energy storage-assisted thermal power units. Figure 5 The diagram shows the output of thermal power units G1-G10 and energy storage units after deep peak shaving by PBDR and energy storage-assisted thermal power units. It should be noted that G1-G10 correspond to the ten thermal power units 1-10 in Table 1. Comparing the output before and after deep peak shaving by PBDR and energy storage-assisted thermal power units, due to the time shift of load by PBDR and energy storage units, the off-peak load is relatively smoother than before, reducing the peak shaving pressure on thermal power, and the peak output of thermal power units has decreased.

[0165] like Figure 6 The diagram shown illustrates the calculation of peak-shaving cost allocation using the traditional allocation method. Figure 7 The diagram shown illustrates the calculation of peak-shaving cost allocation using the cooperative game-based peak-shaving compensation cost allocation strategy based on the mid-level model of this application. Figure 6 and Figure 7 As can be seen, under the traditional allocation method and the peak shaving compensation cost allocation strategy based on cooperative game theory adopted in this application, the peak shaving compensation allocation costs for each power generator are shown in Table 3.

[0166] Table 3 Comparison of Peak Shaving Compensation Allocation Costs for Various Power Generators

[0167] After using the method of this application, the sharing amount is no longer shared according to the respective power generation ratio of thermal power and new energy, but is shared according to the contribution value made by each power generator to the cooperative alliance. This reduces the relatively high sharing ratio of thermal power units and reduces the peak-shaving compensation costs they bear. The Shapley value can stimulate the enthusiasm of thermal power units for peak shaving from an economic perspective.

[0168] like Figure 8 The diagram shown is a schematic of an optimized scheduling system based on peak-shaving compensation cost sharing proposed in this application, which may include: The upper-level module is used to input wind power forecast curves, photovoltaic power forecast curves, and load forecast curves into the upper-level model, and to time-shift the daily load to obtain an optimized load curve; the optimization objective of the upper-level model is to minimize net load fluctuation. The intermediate module is used to input the optimized load curve into the intermediate model to obtain the output of each generator unit and the total peak-shaving compensation cost of each generator unit; the optimization objective of the intermediate model is to minimize the total cost of the power generation system, minimize the curtailment of new energy power, and maximize the net profit of each power generator. The lower-level module is used to input the output of each generator set and the total peak-shaving compensation cost of each generator set into the lower-level model to obtain the output of each generator and the allocated peak-shaving compensation cost; the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal generator alliance.

[0169] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of each module is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0170] Furthermore, the modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0171] This application also provides an electronic device, which may include one or more processors, memory and communication interfaces.

[0172] The memory, communication interface, and processor are coupled together. For example, the memory, communication interface, and processor can be coupled together via a bus.

[0173] The communication interface is used for data transmission with other devices. The memory stores computer program code. This computer program code includes computer instructions, which, when executed by the processor, cause the electronic device to perform the steps of the optimized scheduling method based on peak-shaving compensation cost allocation.

[0174] The processor can be a processor or controller, such as a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The processor can be used to support an electronic device in performing the method steps provided in the above embodiments.

[0175] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. These buses can be categorized as address buses, data buses, control buses, etc.

[0176] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above-described optimized scheduling method based on peak shaving compensation cost allocation.

[0177] The computer-readable storage media involved in this application include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage media known in the art.

[0178] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An optimized scheduling method based on peak-shaving compensation cost allocation, characterized in that, include: The wind power forecast curve, photovoltaic power forecast curve, and load forecast curve are input into the upper-level model, and the daily load is time-shifted to obtain the optimized load curve; the optimization objective of the upper-level model is to minimize net load fluctuation. The optimized load curve is input into the intermediate-level model to obtain the output of each generator unit and the total peak-shaving compensation cost of each generator unit; the optimization objective of the intermediate-level model is to minimize the total cost of the power generation system, minimize the curtailment of new energy power, and maximize the net profit of each power generator. The output of each generator set and the total peak-shaving compensation cost of each generator set are input into the lower-level model to obtain the output of each generator and the allocated peak-shaving compensation cost; the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal generator alliance.

2. The optimized scheduling method based on peak-shaving compensation cost allocation according to claim 1, characterized in that, The objective function expression of the upper-level model includes: in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, This represents the average net load. in, For time period Load after demand response For time period Forecast values ​​for wind power, For time period Forecast values ​​for photovoltaic power generation.

3. The optimized scheduling method based on peak-shaving compensation cost allocation according to claim 1, characterized in that, The objective function expression of the intermediate-level model includes: in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The operating costs of wind-solar-thermal-storage combined operation participating in deep peak shaving. The operating costs of thermal power units during different peak-shaving phases. For the operating costs of energy storage power stations, For wind-solar operating costs, For the abandonment of renewable energy, This is the sum of the net profits of all generators. For the net profit of deep peak shaving of thermal power, Net profit from wind and solar power generation For net profit from energy storage peak shaving, For time period Wind power generation capacity For time period The power output of photovoltaic power generation.

4. The optimized scheduling method based on peak-shaving compensation cost allocation according to claim 3, characterized in that, Operating costs of thermal power units during different peak-shaving phases The calculation methods include: in, For fuel costs, Cost of lifespan depletion, To cover oil production costs, In addition to environmental costs, This is the rated minimum output of the thermal power unit. To provide power to thermal power units, This is the maximum output of the thermal power unit during operation. This refers to the minimum output power required for stable operation of a thermal power unit without deep oil injection for peak shaving. The minimum output power required for stable operation of a thermal power unit during peak shaving by adjusting the oil injection depth. Operating costs of energy storage power stations The calculation methods include: in, The charging and discharging operating costs of energy storage power stations, Costs related to the lifespan of energy storage power stations. Add environmental costs to energy storage power stations.

5. The optimized scheduling method based on peak-shaving compensation cost allocation according to claim 1, characterized in that, The objective function expression of the lower-level model includes: in, For the total profit of deep peak shaving of thermal power, For the total profit of wind power, For the total profit of photovoltaic power generation, This refers to the cost sharing for peak shaving compensation of non-deep peak-shaving thermal power units. The cost of peak shaving compensation for photovoltaic power generation is shared. This is for the cost sharing of wind power peak shaving compensation.

6. The optimized scheduling method based on peak-shaving compensation cost allocation according to claim 5, characterized in that, The constraints of the upper-level model, the middle-level model, and the lower-level model include: Total electricity consumption remains unchanged before and after demand response: in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, For time period Load after demand response For time period The transferred load power, For time period The transferred load power; The cost of electricity purchased after demand response is less than or equal to the cost of electricity purchased before demand response: in, For time period Pre-demand response electricity price For time period Changes in electricity prices following demand response; The maximum load that can be transferred at any given time period must meet the following requirements: in, For time period Maximum allowable load increase rate For time period Electricity load prior to demand response For time period Changes in electricity load following demand response; Thermal power, energy storage, wind power, and photovoltaic power generation can meet the following requirements at any given time: in, For thermal power units During the period of efforts, For time period Energy storage power station power, For time period Wind turbine grid connection power, For time period Photovoltaic power generation output For wind-solar operating costs, This refers to the serial number of the thermal power unit; The upper and lower limits of the output power of non-deep peak-shaving thermal power units are satisfied: The upper and lower limits of the output power of deep peak-shaving thermal power units are satisfied: in, For thermal power units During the period The lower limit of output, For thermal power units During the period The output power, For thermal power units During the period The upper limit of output, For thermal power units During the period Load rate; Thermal power units must meet their own ramping constraints at any given time period: in, This refers to the ramp-up rate of thermal power units. For thermal power units During the period of efforts, For thermal power units During the period contribution; At any given time, the output of wind power and photovoltaic power generation is less than their rated output: in, For time period Maximum wind power output For time period Maximum output of photovoltaic power generation; Rotational spare constraint: in, For thermal power units The maximum change in output over any given time period. The spinning reserve capacity of the power generation system; Line transmission capacity constraints: in, For nodes and nodes The maximum transmission capacity of the line between them For nodes and nodes Admittance between For time period node voltage phase angle, For time period node The voltage phase angle; Charging and discharging logic state constraints: in, For time period The energy storage power station is in a charging state. For time period The energy storage power station is in a discharging state; Charging and discharging power constraints: in, This represents the maximum charging power of the energy storage power station. This represents the maximum discharge power of the energy storage power station. For time period The discharge power of the energy storage power station For time period The charging power of the energy storage power station; State of charge constraints: in, This is the state-of-charge limit for energy storage power stations. For time period The state of charge of an energy storage power station This represents the upper limit of the state of charge of an energy storage power station. For time period The state of charge of an energy storage power station The length of the scheduling period. To improve the charging efficiency of energy storage power stations. For the capacity of energy storage power stations, This refers to the discharge efficiency of the energy storage power station.

7. An optimized scheduling system based on peak-shaving compensation cost sharing, characterized in that, include: The upper-level module is used to input wind power forecast curves, photovoltaic power forecast curves, and load forecast curves into the upper-level model, and to time-shift the daily load to obtain an optimized load curve; the optimization objective of the upper-level model is to minimize net load fluctuation. The intermediate module is used to input the optimized load curve into the intermediate model to obtain the output of each generator unit and the total peak-shaving compensation cost of each generator unit; the optimization objective of the intermediate model is to minimize the total cost of the power generation system, minimize the curtailment of new energy power, and maximize the net profit of each power generator. The lower-level module is used to input the output of each generator set and the total peak-shaving compensation cost of each generator set into the lower-level model to obtain the output of each generator and the allocated peak-shaving compensation cost; the optimization objective of the lower-level model is to maximize the benefits of the wind-solar-thermal generator alliance.

8. The optimized scheduling system based on peak-shaving compensation cost sharing according to claim 7, characterized in that: The objective function expression of the upper-level model includes: in, This refers to the time period number within the power generation system's dispatch cycle. This represents the total number of time periods in the power generation system dispatch cycle. The demand response load curve is superimposed with the wind-solar output period. Net load, This represents the average net load. The objective function expression of the intermediate-level model includes: in, The operating costs of wind-solar-thermal-storage combined operation participating in deep peak shaving. The operating costs of thermal power units during different peak-shaving phases. For the operating costs of energy storage power stations, For wind-solar operating costs, For the abandonment of renewable energy, This is the sum of the net profits of all generators. For time period Forecast values ​​for wind power, For time period Forecast value of photovoltaic power generation, For the net profit of deep peak shaving of thermal power, Net profit from wind and solar power generation For net profit from energy storage peak shaving, For time period Wind power generation capacity For time period The power output of photovoltaic power generation; The objective function expression of the lower-level model includes: in, For the total profit of deep peak shaving of thermal power, For the total profit of wind power, For the total profit of photovoltaic power generation, This refers to the cost sharing for peak shaving compensation of non-deep peak-shaving thermal power units. The cost of peak shaving compensation for photovoltaic power generation is shared. This is for the cost sharing of wind power peak shaving compensation.

9. An electronic device, characterized in that, include: A memory, one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the optimized scheduling method based on peak shaving compensation cost sharing as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the optimized scheduling method based on peak shaving compensation cost allocation as described in any one of claims 1-6.

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