A pumped storage carbon reduction efficiency accounting method, device and equipment considering new energy consumption and thermal power deep peak regulation, and a storage medium

Abstract

The application discloses a pumped storage carbon reduction efficiency accounting method, device and equipment considering new energy consumption and deep peak regulation of thermal power, and a storage medium. The method provided by the application considers new energy consumption of a power grid and a carbon emission intensity target of a thermal power unit, establishes a water-wind-light-fire-pumped storage complementary optimization scheduling model considering deep peak regulation of the thermal power, and realizes fine calculation of carbon reduction efficiency of a pumped storage power station from two aspects of a thermal power replacement effect of new energy consumption and an operation condition improvement effect of the thermal power by comparing long-series water-wind-light-fire-pumped storage and water-wind-light-fire hourly fine optimization scheduling processes. The application provides an effective method for quantitative calculation of carbon reduction efficiency of the pumped storage power station, and can effectively serve planning and design of the pumped storage power station and a new-type power system.

Description

Technical Field

[0001] This invention relates to the field of optimized dispatching of new energy power generation, and in particular to a method, apparatus, equipment and storage medium for calculating the carbon reduction efficiency of pumped storage power generation that takes into account both new energy consumption and deep peak shaving of thermal power. Background Technology

[0002] With the large-scale grid connection of renewable energy sources, primarily wind and solar, the anti-peak-shaving characteristics of these renewable energy sources and the uncertainties faced by the power system are constantly increasing. The curtailment of wind and solar power is becoming increasingly prominent, posing a significant challenge to the power balance of the power system. Energy storage is a key component supporting the integration and consumption of a high proportion of renewable energy. Pumped storage is currently the most mature, safest, most reliable, and largest-scale form of energy storage. Pumped storage power stations currently account for nearly 90% of my country's total energy storage capacity, providing long-term, stable peak-shaving and frequency regulation services and promoting the consumption of renewable energy. However, current assessments or optimizations of the carbon reduction efficiency of pumped storage typically only consider the electricity substitution benefits of thermal power, without meticulously considering the impact of pumped storage peak shaving and valley filling on the carbon emission intensity of deep peak shaving by thermal power under conditions of large-scale renewable energy grid connection.

[0003] The construction and operation of pumped storage power stations are of great significance for promoting the grid integration and consumption of new energy sources, improving the flexibility of the power system, and enhancing the reliability of grid operation. Existing technologies are mostly focused on pumped storage capacity planning and dispatching, lacking refined calculation methods for the carbon reduction efficiency of pumped storage power stations. Summary of the Invention

[0004] To address the aforementioned technical problems in existing technologies, this invention provides a method, apparatus, equipment, and storage medium for calculating the carbon reduction efficiency of pumped storage power generation that takes into account both renewable energy consumption and deep peak shaving of thermal power. This solves the technical problem in existing technologies that do not meticulously consider the impact of pumped storage peak shaving and valley filling on the carbon emission intensity of deep peak shaving of thermal power under the condition of large-scale grid connection of renewable energy.

[0005] To achieve the above objectives, the technical solution of this invention is as follows:

[0006] The first aspect of this invention provides a method for calculating the carbon reduction efficiency of pumped storage hydropower, taking into account both renewable energy consumption and deep peak shaving by thermal power. The method includes:

[0007] Construct a complementary optimization scheduling model of hydro-wind-solar-thermal-pumped storage that takes into account deep peak shaving of thermal power, and construct a corresponding first objective function that minimizes the amount of abandoned renewable energy and a second objective function that minimizes the carbon emission intensity of thermal power.

[0008] The first objective function is: In the formula: T is the number of time periods in the scheduling period; L t The grid load demand during time period t; These represent the wind power output, solar power output, hydropower output, and thermal power output during time period t, respectively. The pumped-storage power station outputs power during time period t. When the pumped-storage power station releases water to generate electricity... A positive value indicates that the pumped-storage power station is storing water and energy. The value is negative; the second objective function is: In the formula: S t The carbon emission intensity of thermal power plants during period t;

[0009] The constraints for constructing the complementary optimal scheduling model of water-wind-solar-thermal-pumped storage are defined.

[0010] Solving the aforementioned hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model yields the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity.

[0011] A comparative model is constructed for the aforementioned hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, and the comparative model is solved to obtain the second renewable energy curtailment, the second thermal power generation, and the second thermal power average carbon emission intensity. The comparative model refers to a model that does not consider the scheduling and operation of pumped storage, but only considers the hydro-wind-solar-thermal complementary optimal scheduling, and the comparative model and the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model have the same objective function.

[0012] Based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity, calculate the long-term / full-life-cycle carbon reduction efficiency of the pumped storage power station: In the formula: Indicates the power generation of the first thermal power plant, S i This represents the average carbon emission intensity of the first thermal power plant. Indicates the power generation of the second thermal power plant, This indicates the average carbon emission intensity of the second thermal power plant.

[0013] This invention provides a pumped storage carbon reduction efficiency calculation device that takes into account both renewable energy consumption and deep peak shaving by thermal power. The device includes:

[0014] The module is used to construct a complementary optimal scheduling model of water-wind-solar-thermal-pumped storage that takes into account the deep peak shaving of thermal power, as well as the corresponding first objective function of minimizing the amount of abandoned renewable energy and the second objective function of minimizing the carbon emission intensity of thermal power.

[0015] The first objective function is: In the formula: T is the number of time periods in the scheduling period; L t The grid load demand during time period t; These represent the wind power output, solar power output, hydropower output, and thermal power output during time period t, respectively. The pumped-storage power station outputs power during time period t. When the pumped-storage power station releases water to generate electricity... A positive value indicates that the pumped-storage power station is storing water and energy. The value is negative; the second objective function is: In the formula: S t The carbon emission intensity of thermal power plants during period t;

[0016] The construction module is also used to construct the constraints of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model;

[0017] The solution module is used to solve the water-wind-solar-thermal-pumped storage complementary optimal scheduling model to obtain the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity.

[0018] The construction module is also used to construct a comparative model of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, and solve the comparative model to obtain the second renewable energy curtailment, the second thermal power generation, and the second thermal power average carbon emission intensity; wherein, the comparative model refers to a model that does not consider the scheduling and operation of pumped storage, but only considers the hydro-wind-solar-thermal complementary optimal scheduling, and the comparative model and the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model have the same objective function;

[0019] The calculation module is used to calculate the long-term / full-life-cycle carbon reduction efficiency of pumped storage power stations based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity. In the formula: Indicates the power generation of the first thermal power plant, This represents the average carbon emission intensity of the first thermal power plant. Indicates the power generation of the second thermal power plant, This indicates the average carbon emission intensity of the second thermal power plant.

[0020] In some embodiments, the construction module is further configured to construct water level constraints for pumped storage power stations during the scheduling period:

[0021]

[0022] In the formula, T is the water level of the pumped storage upper reservoir at the end of the scheduling period; ΔZ p The acceptable drawdown level deviation for the upper pumped storage reservoir during the daily scheduling period;

[0023] Pumped storage power station discharge flow constraints:

[0024]

[0025] In the formula, These represent the upper and lower limits of the flow rate of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the flow rate for pumped storage power stations to generate electricity during time period t;

[0026] Output and storage capacity constraints of pumped storage power stations:

[0027]

[0028] In the formula: These represent the upper and lower limits of the power output of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the output of a pumped storage power station during time period t when releasing water for power generation;

[0029] Pumped storage upper reservoir water level constraints:

[0030]

[0031] In the formula: This indicates the water level of the upper reservoir of the pumped storage hydroelectric power plant at the beginning of time period t; These represent the upper and lower limits of the water level in the upper reservoir of the pumped storage hydroelectric power plant during time period t.

[0032] Thermal power plant ramping constraints:

[0033]

[0034] Where: N thermal,ramp This represents the climbing ability of the thermal power units in the system.

[0035] Thermal power output constraints:

[0036]

[0037] In the formula: These represent the upper and lower limits of thermal power output during time period t;

[0038] Daily water consumption constraints of cascade hydropower station reservoirs:

[0039]

[0040] In the formula: W t,m This represents the water consumption of reservoir m during time period t; The daily water consumption limit for reservoir m; ΔW m The acceptable range of daily water consumption error for reservoir m;

[0041] Reservoir discharge flow constraints:

[0042]

[0043] In the formula: These represent the upper and lower limits of the allowable reservoir discharge flow for reservoir m during time period t, respectively.

[0044] Reservoir / pumped storage upper reservoir characteristic constraints:

[0045]

[0046] In the formula: This is expressed as the water level-capacity relationship in the upper reservoir (m) of a water reservoir / pumped storage facility. The relationship between tailwater level and flow rate in the upper reservoir / pumped storage system;

[0047] Hydropower station output constraints:

[0048]

[0049] This represents the output of hydropower station m during time period t; These represent the upper and lower limits of the power output of hydropower station m during time period t;

[0050] Reservoir water level constraints:

[0051]

[0052] Z t,m This indicates the initial water level of reservoir m at time t; These represent the upper and lower limits of the water level of reservoir m during time period t, respectively.

[0053] Water balance constraints:

[0054] In the formula: V t,m QI represents the initial water storage capacity of reservoir m in time period t; t,m Let QO be the inflow rate of reservoir m during time period t; t,m Let QIt be the discharge flow of reservoir m during time period t; t,m:m+1 QP represents the flow rate between reservoir m and reservoir m+1. t Let QP be the flow rate of water pumped / released from the lower reservoir m by the pumped-storage power station during time period t. When the pumped-storage power station releases water to generate electricity, QP is... t When the pumped storage power station is storing water, QP is a positive value. t It is a negative value.

[0055] In some embodiments, the apparatus further includes: an acquisition module, configured to use the hydropower output of the power grid system, the operating status of the pumped storage power station, and the output of the pumped storage power station as decision variables in a hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model; and to substitute the decision variables, namely the hydropower output, the operating status of the pumped storage power station, and the pumped storage power station's processing capacity, into the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model for solution, thereby obtaining the Pareto solution of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model.

[0056] The Pareto solution can be expressed as follows:

[0057] In some embodiments, the solution module is further configured to calculate the membership degree of each Pareto solution under each objective function based on the objective function of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model; if the objective of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model is to obtain the minimum value of any of the objective functions, the membership function of the Pareto solution is:

[0058]

[0059] If the objective of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model is to obtain the maximum value of any of the objective functions, then the membership function of the Pareto solution is:

[0060]

[0061] In the formula: k represents the number of the objective function in the multi-objective optimization model; j represents the number of the Pareto solution; η k,j Let be the normalized membership value of the j-th Pareto solution under the k-th objective function; and These are the maximum and minimum values ​​of the k-th objective in the multi-objective optimization model, respectively.

[0062] Calculate the membership degree γ of each Pareto solution. j :

[0063]

[0064] In the formula: K represents the number of objective functions in the multi-objective optimization model; J represents the number of Pareto solutions;

[0065] The membership degree γ j The maximum solution is determined as the optimal optimization scheme of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model, and the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity are determined.

[0066] This invention provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the above-mentioned method for calculating the carbon reduction efficiency of pumped storage power generation, taking into account both new energy consumption and deep peak shaving of thermal power.

[0067] This invention provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, enable the above-mentioned method for calculating the carbon reduction efficiency of pumped storage power generation, taking into account both renewable energy consumption and deep peak shaving by thermal power.

[0068] The method provided by this invention considers the grid's renewable energy consumption and the carbon emission intensity targets of thermal power units. It establishes a water-wind-solar-thermal-pumped storage complementary optimal scheduling model that takes into account the deep peak shaving of thermal power. By comparing the hourly refined optimization scheduling processes of long-series water-wind-solar-thermal-pumped storage and water-wind-solar-thermal, it realizes the refined calculation of the carbon reduction efficiency of pumped storage power stations from two aspects: the role of renewable energy consumption in replacing thermal power and the improvement of thermal power operation conditions. It provides an effective method for the quantitative calculation of the carbon emission reduction efficiency of pumped storage power stations and can effectively serve the planning and design of pumped storage power stations and new power systems. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the structure of the pumped storage carbon reduction efficiency calculation system that takes into account the consumption of new energy and the deep peak shaving of thermal power provided in the embodiment of the present invention;

[0070] Figure 2 This is a flowchart illustrating a method for calculating the carbon reduction efficiency of pumped storage power generation, taking into account both new energy consumption and deep peak shaving of thermal power, provided by an embodiment of the present invention.

[0071] Figure 3 This is a flowchart illustrating a method for calculating the carbon reduction efficiency of pumped storage power generation that considers both new energy consumption and deep peak shaving of thermal power, provided by an embodiment of the present invention.

[0072] Figure 4 This is a schematic diagram of the composition structure of the pumped storage carbon reduction efficiency calculation device that takes into account the consumption of new energy and the deep peak shaving of thermal power provided in the embodiment of the present invention.

[0073] Figure 5 This is a schematic diagram of the composition structure of the pumped storage carbon reduction efficiency calculation device that takes into account the consumption of new energy and the deep peak shaving of thermal power, provided in an embodiment of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] In the following description, references to "some embodiments" refer to a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the invention have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the invention pertain. The terminology used in the embodiments of the invention is for the purpose of describing the embodiments of the invention only and is not intended to limit the invention.

[0076] In related technologies, the carbon emission reduction effect of pumped storage power stations is mainly reflected in two aspects: (i) pumped storage promotes the consumption of wind and solar new energy sources, thereby replacing part of the thermal power generation and achieving carbon emission reduction; (ii) pumped storage power stations participate in intraday grid peak shaving, thereby reducing the time and depth of thermal power peak shaving and improving the operating efficiency of thermal power units, thus achieving carbon emission reduction.

[0077] The following describes an exemplary application of the water-wind-solar-thermal-pumped storage carbon reduction efficiency calculation device according to embodiments of the present invention. This device can be implemented as a terminal or a server. In one implementation, the device can be implemented as a laptop, tablet, desktop computer, mobile device, or other types of terminal. In another implementation, it can also be implemented as a server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present invention. The following will illustrate an exemplary application of calculating the carbon reduction efficiency of water-wind-solar-thermal-pumped storage energy as a server.

[0078] See Figure 1 , Figure 1This is a schematic diagram of the structure of the hydro-wind-solar-thermal-pumped hydro storage carbon reduction efficiency calculation system 10 provided in this embodiment of the invention. To calculate the carbon reduction efficiency of hydro-wind-solar-thermal-pumped hydro storage, this embodiment of the invention can provide a hydro-wind-solar-thermal-pumped hydro storage carbon reduction efficiency calculation platform, which can be implemented as a hydro-wind-solar-thermal-pumped hydro storage carbon reduction efficiency calculation application. The hydro-wind-solar-thermal-pumped hydro storage carbon reduction efficiency calculation system 100 provided in this embodiment of the invention includes a terminal 110, a network 120, and a server 130, wherein the server 130 is the server for the hydro-wind-solar-thermal-pumped hydro storage carbon reduction efficiency calculation application. The server 130 can constitute the hydro-wind-solar-thermal-pumped hydro storage carbon reduction efficiency calculation device of this embodiment of the invention. Terminal 110 connects to server 130 via network 120, which can be a wide area network, a local area network, or a combination of both.

[0079] In some embodiments, please refer to Figure 1 When calculating the carbon reduction efficiency of the hydro-wind-solar-thermal-pumped storage energy system in the power grid, terminal 110 sends the calculation task to server 130 via network 120. Server 130 responds to the calculation task initiated by terminal 110 by constructing a complementary optimal scheduling model of hydro-wind-solar-thermal-pumped storage that considers deep peak shaving of thermal power, and correspondingly constructs a first objective function to minimize the amount of new energy curtailment and a second objective function to minimize the carbon emission intensity of thermal power; and constructs a complementary optimal scheduling model of hydro-wind-solar-thermal-pumped storage. The constraints of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model are defined; the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity are solved; a comparative model of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model is constructed and solved to obtain the second renewable energy curtailment, the second thermal power generation, and the second thermal power average carbon emission intensity; based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity, the long-term / full-life-cycle carbon reduction efficiency of the pumped storage power station is calculated. After obtaining the calculation results of the hydro-wind-solar-thermal-pumped storage carbon reduction efficiency, server 130 sends the calculation results to terminal 110 through network 120.

[0080] This invention provides a method for calculating the carbon reduction efficiency of pumped storage hydropower, taking into account both renewable energy consumption and deep peak shaving by thermal power. See [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating a method for calculating the carbon reduction efficiency of pumped storage hydropower, taking into account both renewable energy consumption and deep peak shaving by thermal power, provided by an embodiment of the present invention. Figure 2 The steps shown are explained.

[0081] Step S210: Construct a complementary optimization scheduling model of water-wind-solar-thermal-pumped storage that takes into account deep peak shaving of thermal power, and correspondingly construct a first objective function that minimizes the amount of abandoned renewable energy and a second objective function that minimizes the carbon emission intensity of thermal power.

[0082] It should be noted that existing pumped storage power stations typically possess intraday regulation capabilities, primarily serving as peak-shaving and peak-loading points during intraday grid operation. Therefore, the operation mode of pumped storage power stations is closely related to the intraday output conditions of renewable energy sources, hydropower, and grid load changes. Furthermore, the refined calculation of the carbon reduction efficiency of pumped storage should also be based on the intraday operation of the grid. Based on this, a complementary optimal scheduling model for hydropower, wind power, solar power, thermal power, and pumped storage, considering deep peak shaving by thermal power, is constructed, denoted as Model 1.

[0083] In some embodiments, a lower curtailment rate of renewable energy in the power grid indicates a lower output of thermal power and a lower carbon emission intensity. Furthermore, a more stable output of thermal power means fewer periods of participation in deep peak shaving, resulting in lower operating costs and carbon emission intensity. Therefore, the first objective function of Model 1 is to minimize curtailment of wind and solar renewable energy; the second objective function is to minimize the carbon emission intensity of thermal power, expressed as follows:

[0084] The first objective function is:

[0085]

[0086] In the formula: T is the number of time periods in the scheduling period; L t The grid load demand during time period t; These represent the wind power output, solar power output, hydropower output, and thermal power output during time period t, respectively. The pumped-storage power station outputs power during time period t. When the pumped-storage power station releases water to generate electricity... A positive value indicates that the pumped-storage power station is storing water and energy. It is a negative value;

[0087] The second objective function is:

[0088]

[0089] In the formula: S t The carbon emission intensity of thermal power plants during period t.

[0090] Step S220: Construct the constraints of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model.

[0091] Step S230: Solve the water-wind-solar-thermal-pumped storage complementary optimal scheduling model to obtain the first new energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity.

[0092] Step S240: Construct a comparative model of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model, and solve the comparative model to obtain the second renewable energy curtailment, the second thermal power generation, and the second thermal power average carbon emission intensity.

[0093] In some embodiments, the comparative model refers to a model that does not consider the scheduling and operation of pumped storage, but only considers the complementary optimal scheduling of water-wind-solar-thermal energy, and the comparative model and the complementary optimal scheduling model of water-wind-solar-thermal-pumped storage have the same objective function.

[0094] It should be noted that the comparative model and the water-wind-solar-thermal-pumped storage complementary optimization scheduling model share the following constraints: thermal power ramp-up constraint, thermal power output constraint, daily water consumption constraint of cascade hydropower station reservoirs, reservoir outflow constraint, reservoir / pumped storage upper reservoir characteristic constraint, hydropower station output constraint, reservoir water level constraint, and water balance constraint. The water balance constraint is modified as follows:

[0095]

[0096] In the formula: V t,m QI represents the initial water storage capacity of reservoir m in time period t; t,m Let QO be the inflow rate of reservoir m during time period t; t,m Let QIt be the discharge flow of reservoir m during time period t; t,m:m+1 Let be the flow rate between reservoir m and reservoir m+1.

[0097] Step S250: Based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity, calculate the long-term / full-life-cycle carbon reduction efficiency of the pumped storage power station.

[0098] In some embodiments, the carbon reduction efficiency is:

[0099]

[0100] In the formula: Indicates the power generation of the first thermal power plant, This represents the average carbon emission intensity of the first thermal power plant. Indicates the power generation of the second thermal power plant, This indicates the average carbon emission intensity of the second thermal power plant.

[0101] In some embodiments, the constraints of step S220 may include the following constraints: pumped storage power station scheduling period water level constraints, pumped storage power station discharge flow constraints, pumped storage power station output and storage capacity constraints, pumped storage upper reservoir water level constraints, thermal power ramping constraints, thermal power output constraints, cascade hydropower station reservoir daily water consumption constraints, reservoir discharge flow constraints, reservoir / pumped storage upper reservoir characteristic constraints, hydropower output constraints, reservoir water level constraints, and water balance constraints.

[0102] The method provided by this invention considers the grid's renewable energy consumption and the carbon emission intensity targets of thermal power units. It establishes a water-wind-solar-thermal-pumped storage complementary optimal scheduling model that takes into account the deep peak shaving of thermal power. By comparing the hourly refined optimization scheduling processes of long-series water-wind-solar-thermal-pumped storage and water-wind-solar-thermal, it realizes the refined calculation of the carbon reduction efficiency of pumped storage power stations from two aspects: the role of renewable energy consumption in replacing thermal power and the improvement of thermal power operation conditions. It provides an effective method for the quantitative calculation of the carbon emission reduction efficiency of pumped storage power stations and can effectively serve the planning and design of pumped storage power stations and new power systems.

[0103] In some embodiments, the carbon emission intensity of thermal power refers to the combined carbon emission intensity of thermal power units under different deep peak shaving stages. Here, the peak shaving of thermal power units can be divided into three stages: conventional peak shaving (RPS), deep non-combustion peak shaving (DPS), and deep combustion-supporting peak shaving (DPSC), with the corresponding load rates of thermal power units being approximately 50%–100%, 40%–50%, and 30%–40%, respectively.

[0104] In the conventional peak-shaving phase of this invention: during this phase, the operation of auxiliary equipment systems and pollutant emissions of thermal power units are within the rated range, and the carbon emission intensity of thermal power units mainly comes from coal combustion.

[0105] The deep non-combustion peak shaving stage of this invention embodiment: When the load rate of thermal power unit is less than 50%, the turbine operating efficiency decreases, the boiler experiences incomplete combustion, the mechanical friction loss of the unit system increases, and the operation of auxiliary equipment (cooling equipment, gas source device, etc.) leads to an increase in the carbon emission intensity of the unit.

[0106] In the deep combustion-supporting peak-shaving stage of this invention embodiment: when the load rate of thermal power units is less than 40%, the desulfurization efficiency of the system decreases, resulting in increased consumption of desulfurizing agent; in addition, the concentration of nitrogen oxides at the denitrification device increases, the operating power of the desulfurization and denitrification device increases significantly, which in turn leads to an increase in carbon emission intensity.

[0107] In some embodiments, with the increasing penetration of new energy sources such as wind and solar power into the power grid, thermal power units need to participate in deep peak shaving, frequently entering low-load areas to promote the consumption of new energy. In the scenario of thermal power participating in deep peak shaving, the carbon emission intensity of thermal power units is not only related to the amount of coal consumed, but also to factors such as the operation of the turbine and boiler systems, and system desulfurization and denitrification. As the depth of peak shaving by thermal power increases, its operating efficiency and carbon emission intensity will also increase accordingly.

[0108] In this invention, the carbon emission intensity of thermal power plants is determined by the corresponding carbon emission intensity of thermal power plants under different deep peak shaving stages. The peak shaving of thermal power units can be divided into three stages: (1) conventional peak shaving (RPS), (2) deep non-combustion peak shaving (DPS), and (3) deep combustion-supporting peak shaving (DPSC). The load rates of thermal power units corresponding to each stage are approximately 50%–100%, 40%–50%, and 30%–40%, respectively. Based on this, the calculation method for the carbon emission intensity of thermal power units throughout the entire process is as follows:

[0109] (1) Conventional peak-shaving phase: During this phase, the auxiliary equipment systems and pollutant emissions of thermal power units are within the rated range. The carbon emission intensity of the units mainly comes from coal combustion, which can be expressed as:

[0110]

[0111] In the formula: S coal This indicates the carbon emission intensity caused by coal combustion in thermal power units, expressed in kg / MWh; O F (P) represents the corrected carbon oxidation rate of the thermal power unit, %; H represents the coal consumption for power generation of the thermal power unit, kg / MWh; C C The carbon content of coal is a statistical measure, expressed as a percentage. and M C The molar masses of CO2 and C are 44 g / mol and 12 g / mol, respectively.

[0112] (2) Deep non-combustion peak shaving stage: When the load rate of thermal power units is below 50%, the turbine operating efficiency decreases, incomplete combustion occurs in the boiler, mechanical friction losses of the unit system increase, and the operation of auxiliary equipment (desuperheating equipment, gas source devices, etc.) leads to an increase in the unit's carbon emission intensity. The increase in carbon emission intensity caused by the above effects can be calculated according to the following formula:

[0113]

[0114] In the formula: S aux This indicates the additional carbon emission intensity caused by the decline in turbine and boiler efficiency, as well as the operation of auxiliary equipment, expressed in kg / MWh; C q P represents the CO2 produced per unit of electricity. NLet be the rated power of the thermal power unit (MW); α be the proportion of energy consumption of auxiliary equipment; ξ(P) be the combustion efficiency of the boiler under load factor P (%); ε(P) be the absolute internal efficiency of the steam turbine under load factor P (%); ξ and ε are related to the load factor of the thermal power unit and can be based on typical load factor conditions (P). 30％ P 40％ P 50％ P N Piecewise linear interpolation was used to obtain the measured data under the following conditions:

[0115]

[0116] In the formula: R L P represents the load factor of a thermal power unit. DPS P represents the output value of thermal power plants during the peak-shaving phase with deep non-combustion capability; DPSC This refers to the output value of thermal power plants during the deep combustion and peak shaving phase.

[0117] (3) Deep combustion-supporting peak-shaving stage: When the load rate of thermal power units is below 40%, the system desulfurization efficiency decreases, leading to increased consumption of desulfurizing agents; in addition, the concentration of nitrogen oxides at the denitrification unit increases, and the operating power of the desulfurization and denitrification unit increases significantly, resulting in increased carbon emission intensity. The increase in carbon emission intensity caused by the above effects can be calculated according to the following formula:

[0118]

[0119] In the formula: S sn The additional carbon emission intensity caused by desulfurization and denitrification units is expressed in kg / MWh; ρ S The sulfur content of the coal; Δη S (P) represents the desulfurization efficiency of the thermal power unit, in %; M S The molar mass of sulfur is 32 g / mol; α S The energy consumption ratio for system desulfurization; γ N The energy consumption ratio for denitrification of the system.

[0120] In summary, the carbon emission intensity S of a thermal power unit throughout the entire process can be expressed as:

[0121]

[0122] In some embodiments, the above method may further include steps S261 to S262:

[0123] Step S261: The hydropower output of the power grid system, the operating status of the pumped storage power station, and the output of the pumped storage power station are used as decision variables in the hydro-wind-solar-thermal-pumped storage complementary optimization scheduling model.

[0124] Step S262: Substitute the decision variables, namely hydropower output, the operating status of the pumped storage power station and the output of the pumped storage power station, into the water-wind-solar-thermal-pumped storage complementary optimization scheduling model for solution, and obtain the Pareto solution set of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model.

[0125] In some embodiments, the Pareto solution can be expressed as

[0126] In some embodiments, the constraints of the established hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model that takes into account deep peak shaving of thermal power include:

[0127] (1) Water level constraints during the scheduling period of pumped storage power stations:

[0128]

[0129] In the formula, T is the water level of the pumped storage upper reservoir at the end of the scheduling period; ΔZ p The acceptable drawdown level deviation for the upper pumped storage reservoir during the daily scheduling period;

[0130] (2) Constraints on the discharge flow of pumped storage power stations:

[0131]

[0132] In the formula, These represent the upper and lower limits of the flow rate of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the flow rate for pumped storage power stations to generate electricity during time period t;

[0133] (3) Output and storage capacity constraints of pumped storage power stations:

[0134]

[0135] In the formula: These represent the upper and lower limits of the power output of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the output of a pumped storage power station during time period t when releasing water for power generation;

[0136] (4) Upper reservoir water level constraints for pumped storage:

[0137]

[0138] In the formula: This indicates the water level of the upper reservoir of the pumped storage hydroelectric power plant at the beginning of time period t; These represent the upper and lower limits of the water level in the upper reservoir of the pumped storage hydroelectric power plant during time period t.

[0139] (5) Thermal power plant ramping constraints:

[0140]

[0141] Where: N thermal,ramp This represents the climbing ability of the thermal power units in the system.

[0142] (6) Thermal power output constraints:

[0143]

[0144] In the formula: These represent the upper and lower limits of thermal power output during time period t;

[0145] (7) Daily water consumption constraints of reservoirs in cascade hydropower stations:

[0146]

[0147] In the formula: W t,m This represents the water consumption of reservoir m during time period t; The daily water consumption limit for reservoir m; ΔW m The acceptable range of daily water consumption error for reservoir m;

[0148] (8) Reservoir discharge flow constraints:

[0149]

[0150] In the formula: These represent the upper and lower limits of the allowable reservoir discharge flow for reservoir m during time period t, respectively.

[0151] (9) Reservoir / pumped storage upper reservoir characteristic constraints:

[0152]

[0153] In the formula: This is expressed as the water level-capacity relationship in the upper reservoir (m) of a water reservoir / pumped storage facility. The relationship between tailwater level and flow rate in the upper reservoir / pumped storage system;

[0154] (10) Output constraints of hydropower stations:

[0155]

[0156] This represents the output of hydropower station m during time period t; These represent the upper and lower limits of the power output of hydropower station m during time period t;

[0157] (11) Reservoir water level constraints:

[0158]

[0159] Z t,m This indicates the initial water level of reservoir m at time t; These represent the upper and lower limits of the water level of reservoir m during time period t, respectively.

[0160] (12) Water balance constraint:

[0161] In the formula: V t,m QI represents the initial water storage capacity of reservoir m in time period t; t,m Let QO be the inflow rate of reservoir m during time period t; t,m Let QIt be the discharge flow of reservoir m during time period t; t,m:m+1 QP represents the flow rate between reservoir m and reservoir m+1. t Let QP be the flow rate of water pumped / released from the lower reservoir m by the pumped-storage power station during time period t. When the pumped-storage power station releases water to generate electricity, QP is... t When the pumped storage power station is storing water, QP is a positive value. t It is a negative value.

[0162] Considering the operating characteristics of various power sources in the system, the decision variable of the above-mentioned hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model is set as hydropower output. The operating status δ of a pumped storage power station (δ=1 for releasing water for power generation; δ=-1 for storing water for energy; δ=0 for neither storing nor releasing water), and the output of the pumped storage power station. The solution to the complementary optimal scheduling model of water-wind-solar-thermal-pumped storage can be expressed as:

[0163]

[0164] The complementary optimal scheduling model of water-wind-solar-thermal-pumped storage can be solved using multi-objective optimization algorithms, such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II), to obtain the Pareto solution set. Then, a fuzzy multi-objective decision-making method based on normalized membership degrees is used to select the optimal compromise scheme for the model. The specific process is as follows:

[0165] First, based on the objective function of the complementary optimal scheduling model of water-wind-solar-thermal-pumped storage, the membership degree of all solutions in all Pareto solutions is calculated under a certain objective function:

[0166] When the objective of the complementary optimization scheduling model of water-wind-solar-thermal-pumped storage is to obtain the minimum value of a certain function, the membership function of the Parero solution is:

[0167]

[0168] When the objective of the complementary optimal scheduling model of water-wind-solar-thermal-pumped storage is to obtain the maximum value of a certain function, the membership function of the Parero solution is:

[0169]

[0170] In the formula: k represents the number of the objective function in the multi-objective optimization model; j represents the number of the Pareto solution of the model; η k,j Let be the normalized membership value of the j-th Pareto solution under the k-th objective function; and These are the maximum and minimum values ​​of the k-th objective in the multi-objective optimization model, respectively.

[0171] Based on this, the membership degree γ of each Pareto solution is further calculated. j :

[0172]

[0173] In the formula: K represents the number of objective functions in the model; J represents the number of Pareto solutions.

[0174] γ j The solution with the largest value is the optimal compromise solution for the model. Let Ec denote the amount of renewable energy wasted under the optimal compromise solution of water-wind-solar-thermal-pumped storage complementary operation on day i. i Thermal power generation is recorded as The average carbon emission intensity of thermal power is denoted as

[0175] A comparative verification model is established for the above-mentioned water-wind-solar-thermal-pumped storage complementary optimal scheduling model 1. That is, without considering the scheduling and operation of pumped storage, a water-wind-solar-thermal complementary optimal scheduling model is constructed, denoted as Model 2. Model 2 has the same objective function and constraints as Model 1. The objective function of Model 2 is changed to:

[0176]

[0177] The constraints of Model 2 are the constraints (1) to (8) in step two, where (1) the water balance constraint is changed to:

[0178]

[0179] The same solution algorithm and multi-objective decision-making method were used to solve Model 2 and select the optimal compromise scheme. The amount of renewable energy curtailed under the optimal compromise scheme of water-wind-solar-thermal complementary operation on day i (i.e., the second renewable energy curtailment in the above embodiment) is denoted as Ec'. i The total power generation of thermal power (i.e., the power generation of the second thermal power plant in the above embodiment) is denoted as The average carbon emission intensity of thermal power (i.e., the second average carbon emission intensity of thermal power in the above embodiment) is denoted as

[0180] In some embodiments, hourly data on wind and solar renewable energy output, reservoir inflow, and grid load are used as inputs to Model 1 and Model 2 to optimize the daily hydro-wind-solar-thermal-pumped storage and hydro-wind-solar-thermal complementary operation processes. Based on the optimization results, multi-objective decisions are made to obtain a refined multi-energy complementary optimized operation process for the long series / full life cycle, and the thermal power generation is statistically analyzed daily. and and the daily average carbon emission intensity of thermal power and Based on this, the carbon reduction efficiency W of pumped storage power stations over the long series / full life cycle is calculated from two aspects: replacing thermal power generation with pumped storage and improving the operating conditions of thermal power plants. c :

[0181]

[0182] In the formula: W c This indicates the carbon emission reduction of a pumped storage power station over a long series / full life cycle.

[0183] The technical flowchart of the method for calculating the carbon reduction efficiency of pumped storage considering both renewable energy consumption and deep peak shaving of thermal power proposed in this invention is as follows: Figure 3 As shown.

[0184] Figure 4 This is a schematic diagram of the composition and structure of the pumped storage carbon reduction efficiency calculation device that takes into account both new energy consumption and deep peak shaving of thermal power, as provided in an embodiment of the present invention. Figure 4 As shown, the pumped storage carbon reduction efficiency calculation device 400, which considers both renewable energy consumption and deep peak shaving of thermal power, includes: a construction module 401, used to construct a complementary optimization scheduling model of water-wind-solar-thermal-pumped storage considering deep peak shaving of thermal power, and correspondingly construct a first objective function that minimizes renewable energy curtailment and a second objective function that minimizes the carbon emission intensity of thermal power; wherein, the first objective function is: In the formula: T is the number of time periods in the scheduling period; L t The grid load demand during time period t; These represent the wind power output, solar power output, hydropower output, and thermal power output during time period t, respectively. The pumped-storage power station outputs power during time period t. When the pumped-storage power station releases water to generate electricity... A positive value indicates that the pumped-storage power station is storing water and energy. The value is negative; the second objective function is: In the formula: S tLet t be the carbon emission intensity of thermal power during time period t; the construction module 401 is also used to construct the constraints of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model; the solution module 402 is used to solve the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model to obtain the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity; the construction module 401 is also used to construct a comparison model of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, and solve the comparison model to obtain the second renewable energy curtailment, the first thermal power generation, and the first average carbon emission intensity of thermal power; the construction module 401 is also used to construct a comparison model of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, and solve the comparison model to obtain the second renewable energy curtailment, the first average carbon emission intensity of thermal power, the second average carbon emission intensity of thermal power, and the third average carbon emission intensity of thermal power. The comparison model refers to a model that does not consider the scheduling and operation of pumped storage power plants, but only considers the complementary optimal scheduling of hydro-wind-solar-thermal power plants, and the comparison model and the complementary optimal scheduling model of hydro-wind-solar-thermal-pumped storage power plants have the same objective function; the calculation module 403 is used to calculate the long-term / full-life-cycle carbon reduction efficiency of pumped storage power plants based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity. In the formula: Indicates the power generation of the first thermal power plant, S i This represents the average carbon emission intensity of the first thermal power plant. Indicates the power generation of the second thermal power plant, This indicates the average carbon emission intensity of the second thermal power plant.

[0185] In some embodiments, the construction module is further configured to construct water level constraints for pumped storage power stations during the scheduling period:

[0186]

[0187] In the formula, T is the water level of the pumped storage upper reservoir at the end of the scheduling period; ΔZ p The acceptable drawdown level deviation for the upper pumped storage reservoir during the daily scheduling period;

[0188] Pumped storage power station discharge flow constraints:

[0189]

[0190] In the formula, These represent the upper and lower limits of the flow rate of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the flow rate for pumped storage power stations to generate electricity during time period t;

[0191] Output and storage capacity constraints of pumped storage power stations:

[0192]

[0193] In the formula: These represent the upper and lower limits of the power output of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the output of a pumped storage power station during time period t when releasing water for power generation;

[0194] Pumped storage upper reservoir water level constraints:

[0195]

[0196] In the formula: This indicates the water level of the upper reservoir of the pumped storage hydroelectric power plant at the beginning of time period t; These represent the upper and lower limits of the water level in the upper reservoir of the pumped storage hydroelectric power plant during time period t.

[0197] Thermal power plant ramping constraints:

[0198]

[0199] Where: N thermal,ramp This represents the climbing ability of the thermal power units in the system.

[0200] Thermal power output constraints:

[0201]

[0202] In the formula: These represent the upper and lower limits of thermal power output during time period t;

[0203] Daily water consumption constraints of cascade hydropower station reservoirs:

[0204]

[0205] In the formula: W t,m This represents the water consumption of reservoir m during time period t; The daily water consumption limit for reservoir m; ΔW m The acceptable range of daily water consumption error for reservoir m;

[0206] Reservoir discharge flow constraints:

[0207]

[0208] In the formula: These represent the upper and lower limits of the allowable reservoir discharge flow for reservoir m during time period t, respectively.

[0209] Reservoir / pumped storage upper reservoir characteristic constraints:

[0210]

[0211] In the formula: This is expressed as the water level-capacity relationship in the upper reservoir (m) of a water reservoir / pumped storage facility. The relationship between tailwater level and flow rate in the upper reservoir / pumped storage system;

[0212] Hydropower station output constraints:

[0213]

[0214] This represents the output of hydropower station m during time period t; These represent the upper and lower limits of the power output of hydropower station m during time period t;

[0215] Reservoir water level constraints:

[0216]

[0217] Z t,m This indicates the initial water level of reservoir m at time t; These represent the upper and lower limits of the water level of reservoir m during time period t, respectively.

[0218] Water balance constraints:

[0219] In the formula: V t,m QI represents the initial water storage capacity of reservoir m in time period t; t,m Let QO be the inflow rate of reservoir m during time period t; t,m Let QIt be the discharge flow of reservoir m during time period t; t,m:m+1 QP represents the flow rate between reservoir m and reservoir m+1. t Let QP be the flow rate of water pumped / released from the lower reservoir m by the pumped-storage power station during time period t. When the pumped-storage power station releases water to generate electricity, QP is... t When the pumped storage power station is storing water, QP is a positive value. t It is a negative value.

[0220] In some embodiments, the apparatus further includes: an acquisition module, configured to use the hydropower output of the power grid system, the operating status of the pumped storage power station, and the output of the pumped storage power station as decision variables of a hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model; to substitute the decision variables, namely the hydropower output, the operating status of the pumped storage power station, and the output of the pumped storage power station, into the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model for solving, thereby obtaining the Pareto solution set of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, wherein the Pareto solution can be expressed as...

[0221] In some embodiments, the solution module is further configured to calculate the membership degree of each Pareto solution in each Pareto solution set under each objective function based on the objective function of the complementary optimization scheduling of water-wind-solar-thermal-pumped storage; if the objective of the complementary optimization scheduling of water-wind-solar-thermal-pumped storage is to obtain the minimum value of any of the objective functions, the membership function of the Pareto solution is:

[0222]

[0223] If the objective of the complementary optimal scheduling of water-wind-solar-thermal-pumped storage is to obtain the maximum value of any of the aforementioned objective functions, then the membership function of the Pareto solution is:

[0224]

[0225] In the formula: k represents the number of the objective function in the multi-objective optimization model; j represents the number of the Pareto solution; η k ,j Let be the normalized membership value of the j-th Pareto solution under the k-th objective function; and These are the maximum and minimum values ​​of the k-th objective in the multi-objective optimization model, respectively.

[0226] Calculate the membership degree γ of each Pareto solution. j :

[0227]

[0228] In the formula: K represents the number of objective functions in the multi-objective optimization model; J represents the number of Pareto solutions;

[0229] The membership degree γ j The maximum solution is determined as the optimal optimization scheme of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model, and the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity are determined.

[0230] It should be noted that the description of the apparatus in this embodiment is similar to the description of the method embodiment described above, and has similar beneficial effects to the same method embodiment; therefore, it will not be repeated. For technical details not disclosed in this apparatus embodiment, please refer to the description of the method embodiment of this invention for understanding.

[0231] It should be noted that, in the embodiments of the present invention, if the above-mentioned method for calculating the carbon reduction efficiency of pumped storage power generation, which takes into account both new energy consumption and deep peak shaving of thermal power, is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of the present invention are not limited to any specific hardware and software combination.

[0232] Correspondingly, embodiments of the present invention provide a pumped storage energy reduction efficiency calculation device that takes into account both new energy consumption and deep peak shaving of thermal power. Figure 5 This is a schematic diagram of the composition and structure of a pumped storage carbon reduction efficiency calculation device that takes into account both new energy consumption and deep peak shaving of thermal power, as provided in an embodiment of the present invention. Figure 5 As shown, the pumped-storage energy storage carbon reduction efficiency calculation device 500, which considers both renewable energy consumption and deep peak shaving of thermal power, includes at least: a processor 501 and a computer-readable storage medium 502 configured to store executable instructions. The processor 501 typically controls the overall operation of the pumped-storage energy storage carbon reduction efficiency calculation device. The computer-readable storage medium 502 is configured to store instructions and applications executable by the processor 501, and can also cache data to be processed or processed by the processor 501 and various modules in the pumped-storage energy storage carbon reduction efficiency calculation device 500. This can be implemented using flash memory or random access memory (RAM).

[0233] This invention provides a storage medium storing executable instructions. When these executable instructions are executed by a processor, they cause the processor to perform the method provided in this invention, for example... Figure 2 The method shown.

[0234] In some embodiments, the storage medium may be a computer-readable storage medium, such as a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic surface memory, optical disc, or a compact disk-read-only memory (CD-ROM); or it may be a device that includes one or any combination of the above-mentioned memories.

[0235] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0236] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file containing other programs or data, for example, in one or more scripts within a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files storing one or more modules, subroutines, or code sections). As an example, executable instructions may be deployed to execute on a single electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0237] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of the present invention are included within the scope of protection of the present invention.

[0238] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the invention, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention. The sequence numbers of the above-described embodiments of the invention are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0239] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not performed.

[0240] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating the carbon reduction efficiency of pumped storage hydropower, taking into account both renewable energy consumption and deep peak shaving by thermal power, characterized in that... The method includes: Construct a complementary optimization scheduling model of hydro-wind-solar-thermal-pumped storage that takes into account deep peak shaving of thermal power, and construct a corresponding first objective function that minimizes the amount of abandoned renewable energy and a second objective function that minimizes the carbon emission intensity of thermal power. The first objective function is: In the formula: T is the number of time periods in the scheduling period; L t The grid load demand during time period t; These represent the wind power output, solar power output, hydropower output, and thermal power output during time period t, respectively. The pumped-storage power station outputs power during time period t. When the pumped-storage power station releases water to generate electricity... A positive value indicates that the pumped-storage power station is storing water and energy. The value is negative; the second objective function is: In the formula: S t The carbon emission intensity of thermal power plants during period t; The constraints for constructing the complementary optimal scheduling model of water-wind-solar-thermal-pumped storage are defined. Solving the aforementioned hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model yields the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity. A comparative model is constructed for the aforementioned hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, and the comparative model is solved to obtain the second renewable energy curtailment, the second thermal power generation, and the second thermal power average carbon emission intensity. The comparative model refers to a model that does not consider the scheduling and operation of pumped storage, but only considers the hydro-wind-solar-thermal complementary optimal scheduling, and the comparative model and the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model have the same objective function. Based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity, calculate the long-term / full-life-cycle carbon reduction efficiency of the pumped storage power station: In the formula: Indicates the power generation of the first thermal power plant, S i This represents the average carbon emission intensity of the first thermal power plant. Indicates the power generation of the second thermal power plant, This indicates the average carbon emission intensity of the second-largest thermal power plant.

2. The method according to claim 1, characterized in that, The constraints for constructing the water-wind-solar-thermal-pumped storage complementary optimal scheduling model include: Constructing water level constraints for pumped storage power stations during the scheduling period: In the formula, T is the water level of the pumped storage upper reservoir at the end of the scheduling period; ΔZ p The acceptable drawdown level deviation for the upper pumped storage reservoir during the daily scheduling period; Constructing discharge flow constraints for pumped storage power stations: In the formula, These represent the upper and lower limits of the flow rate of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the flow rate for pumped storage power stations to generate electricity during time period t; Constructing the output and storage capacity constraints of pumped storage power stations: In the formula: These represent the upper and lower limits of the power output of a pumped storage power station during time period t when storing water and energy. These represent the upper and lower limits of the output of a pumped storage power station during time period t when releasing water for power generation; Constructing upper reservoir water level constraints for pumped storage: In the formula: This indicates the water level of the upper reservoir of the pumped storage hydroelectric power plant at the beginning of time period t; These represent the upper and lower limits of the water level in the upper reservoir of the pumped storage hydroelectric power plant during time period t. Constructing ramp-up constraints for thermal power plants: Where: N thermal,ramp This represents the climbing ability of the thermal power units in the system. Constructing constraints on thermal power output: In the formula: These represent the upper and lower limits of thermal power output during time period t; Constructing daily water consumption constraints for cascade hydropower station reservoirs: In the formula: W t,m This represents the water consumption of reservoir m during time period t; The daily water consumption limit for reservoir m; ΔW m The acceptable range of daily water consumption error for reservoir m; Constructing reservoir discharge flow constraints: In the formula: These represent the upper and lower limits of the allowable reservoir discharge flow for reservoir m during time period t, respectively. Constructing upper reservoir characteristic constraints for reservoirs / pumped storage: In the formula: This is expressed as the water level-capacity relationship in the upper reservoir (m) of a water reservoir / pumped storage facility. The relationship between tailwater level and flow rate in the upper reservoir / pumped storage system; Constructing power output constraints for hydropower stations: This represents the output of hydropower station m during time period t; These represent the upper and lower limits of the power output of hydropower station m during time period t; Constructing reservoir water level constraints: Z t,m This indicates the initial water level of reservoir m at time t; These represent the upper and lower limits of the water level of reservoir m during time period t, respectively. Construct water balance constraints: In the formula: V t,m QI represents the initial water storage capacity of reservoir m in time period t; t,m Let QO be the inflow rate of reservoir m during time period t; t,m Let QIt be the discharge flow of reservoir m during time period t; t,m:m+1 QP represents the flow rate between reservoir m and reservoir m+1. t Let QP be the flow rate of water pumped / released from the lower reservoir m by the pumped-storage power station during time period t. When the pumped-storage power station releases water to generate electricity, QP is... t When the pumped storage power station is storing water, QP is a positive value. t It is a negative value.

3. The method according to claim 1, characterized in that, The method further includes: The hydropower output of the power grid system, the operating status of pumped storage power stations, and the output of pumped storage power stations are used as decision variables in the optimization model. The decision variables, namely hydropower output, the operating status of the pumped storage power station, and the output of the pumped storage power station, are substituted into the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model for solution, thus obtaining the Pareto solution set of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model. The Pareto solution can be expressed as follows:

4. The method according to claim 3, characterized in that, Solving the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model yields the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity, including: Based on the objective function of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model, the membership degree of each Pareto solution in each Pareto solution set under each objective function is calculated. If the objective of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model is to obtain the minimum value of any of the aforementioned objective functions, then the membership function of the Pareto solution is: If the objective of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model is to obtain the maximum value of any of the aforementioned objective functions, then the membership function of the Pareto solution is: In the formula: k represents the number of the objective function in the multi-objective optimization model; j represents the number of the Pareto solution; η k,j Let be the normalized membership value of the j-th Pareto solution under the k-th objective function; and These are the maximum and minimum values ​​of the k-th objective in the multi-objective optimization model, respectively. Calculate the membership degree γ of each Pareto solution. j : In the formula: K represents the number of objective functions in the multi-objective optimization model; J represents the number of Pareto solutions; The membership degree γ j The maximum solution is determined as the optimal optimization scheme of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model, and the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity are determined.

5. A pumped storage carbon reduction efficiency calculation device that takes into account both new energy consumption and deep peak shaving of thermal power, characterized in that, The device includes: The module is used to construct a complementary optimal scheduling model of water-wind-solar-thermal-pumped storage that takes into account the deep peak shaving of thermal power, as well as the corresponding first objective function of minimizing the amount of abandoned renewable energy and the second objective function of minimizing the carbon emission intensity of thermal power. The first objective function is: In the formula: T is the number of time periods in the scheduling period; L t The grid load demand during time period t; These represent the wind power output, solar power output, hydropower output, and thermal power output during time period t, respectively. The pumped-storage power station outputs power during time period t. When the pumped-storage power station releases water to generate electricity... A positive value indicates that the pumped-storage power station is storing water and energy. The value is negative; the second objective function is: In the formula: S t The carbon emission intensity of thermal power plants during period t; The construction module is also used to construct the constraints of the water-wind-solar-thermal-pumped storage complementary optimization scheduling model; The solution module is used to solve the water-wind-solar-thermal-pumped storage complementary optimal scheduling model to obtain the first renewable energy curtailment, the first thermal power generation, and the first thermal power average carbon emission intensity. The construction module is also used to construct a comparative model of the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model, and solve the comparative model to obtain the second renewable energy curtailment, the second thermal power generation, and the second thermal power average carbon emission intensity; wherein, the comparative model refers to a model that does not consider the scheduling and operation of pumped storage, but only considers the hydro-wind-solar-thermal complementary optimal scheduling, and the comparative model and the hydro-wind-solar-thermal-pumped storage complementary optimal scheduling model have the same objective function; The calculation module is used to calculate the long-term / full-life-cycle carbon reduction efficiency of pumped storage power stations based on the first thermal power generation, the first thermal power average carbon emission intensity, the second thermal power generation, and the second thermal power average carbon emission intensity. In the formula: Indicates the power generation of the first thermal power plant, This represents the average carbon emission intensity of the first thermal power plant. Indicates the power generation of the second thermal power plant, This indicates the average carbon emission intensity of the second-largest thermal power plant.

6. An electronic device, characterized in that, include: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the pumped storage carbon reduction efficiency calculation method according to any one of claims 1 to 4, which takes into account the consumption of new energy sources and deep peak shaving of thermal power.

7. A computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the pumped storage carbon reduction efficiency calculation method according to any one of claims 1 to 4, which takes into account the consumption of new energy sources and deep peak shaving of thermal power.

Images