Double-layer optimization scheduling method and system based on complementary characteristics of multiple types of energy, and medium
By constructing a two-layer optimization scheduling model and coordinating the complementary characteristics of wind, solar, water, fire and storage, the problem of resource characteristic coordination in traditional scheduling methods is solved, the efficient consumption of clean energy and economic operation of the system are achieved, and the regulation burden of thermal power units is reduced.
Patent Information
- Application Number
- CN202510957274.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-11
- Publication Date
- 2025-10-21
AI Technical Summary
Traditional optimization scheduling methods for multiple types of power sources are difficult to fully coordinate the physical characteristics of different types of resources, and rely on expert experience, making it difficult to objectively reflect the complex relationship between different goals, increasing the regulation burden of thermal power units.
A two-layer optimization scheduling model based on the complementary characteristics of multiple energy types is constructed, including an upper model and a lower model. The upper model aims to minimize the net load variance, minimize the amount of wind and solar power curtailment, and maximize hydropower generation. The lower model aims to minimize the operating cost of thermal power units. Gurobi is called by Python for optimization and solution, realizing master-slave collaborative optimization.
It has significantly improved the level of clean energy consumption, optimized the system operation economy, improved the system net load characteristics, reduced the regulation burden of thermal power units, and supported the safe and economical operation of the power system under a high proportion of new energy access.
Smart Images
Figure CN120824847A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of energy scheduling technology, and in particular to a double-layer optimization scheduling method, system and medium based on the complementary characteristics of multiple energy types. Background Art
[0002] The inevitable trend of large-scale clean energy integration into the power grid, represented by wind power and photovoltaics, presents significant intermittency, volatility, and uncertainty in wind and photovoltaic output, posing significant challenges to the safe, stable, and economical operation of the power system. As important flexible regulation resources, the complementary and coordinated operation of hydropower, thermal power, and energy storage systems is key to improving the system's absorption capacity, ensuring grid stability, and reducing operating costs.
[0003] Currently, there is a foundation for research on optimal scheduling involving multiple power sources. Some methods use a single objective, such as minimizing total cost or maximizing clean energy consumption, for overall optimization. However, these methods struggle to fully coordinate the physical characteristics of different resource types, such as the reservoir scheduling constraints of hydropower stations, the startup, shutdown, and ramping constraints of thermal power units, and the charge-discharge state transition constraints of energy storage. Furthermore, these methods offer complementary benefits, such as the rapid regulation capabilities of hydropower to mitigate wind and solar fluctuations, the spatiotemporal shifting capabilities of energy storage, and the baseload guarantee role of thermal power units. Other methods attempt to transform multiple objectives into a single objective through weighted summation. However, the weighting often relies on experience, making it difficult to objectively reflect the complex relationships between different objectives and ensuring the stability of the system's net load. This increases the regulation burden on thermal power units, potentially leading to frequent adjustments and shortening their service life. Summary of the Invention
[0004] The technical problem to be solved by the present invention is that the traditional optimization and scheduling method of multiple types of power sources is difficult to fully coordinate the physical characteristics of different types of resources, and relies on expert experience, making it difficult to objectively reflect the complex relationship between different goals, which increases the regulation burden of thermal power units; the purpose of the present invention is to provide a two-layer optimization and scheduling method, system and medium based on the complementary characteristics of multiple types of energy, improve on the traditional multi-type power source optimization and scheduling technology, construct a two-layer optimization and scheduling model that deeply integrates the complementary characteristics of multiple types of energy, and design an efficient solution method, which significantly improves the level of clean energy consumption, optimizes the system operation economy, and effectively improves the system net load characteristics, providing strong support for the safe and economical operation of the power system under a high proportion of new energy access.
[0005] The present invention is achieved through the following technical solutions: This solution provides a two-tiered optimization scheduling method based on the complementary characteristics of multiple energy sources, including: Collect basic data on multiple energy sources and build operational models for these energy sources; the basic data includes historical output data, forecasted load data, wind power forecast output data, and photovoltaic forecast output data for wind power, photovoltaic power, hydropower, and thermal power; Based on basic data and operating models, a two-tier optimization scheduling model that considers the complementary characteristics of multiple energy sources is constructed; the two-tier optimization scheduling model includes an upper-tier model and a lower-tier model; the upper-tier model considers wind, solar, hydropower, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation; the lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs; the lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition; Solve the two-level optimization scheduling model to obtain the scheduling strategy.
[0006] A further optimization solution is that the basic data takes into account both temporal and spatial correlation and uncertainty.
[0007] A further optimization scheme is that the objective function of the upper model includes: Net load variance minimization objective function: ; in, represents the variance of the net load; Indicates the scheduling period; express Net load of the system at any moment; Indicates the average value of the system net load; express The total active load of the system at any moment; 、 、 、 Respectively Wind power output, photovoltaic output, hydropower output and energy storage output at all times; Minimum objective function for curtailing wind and solar power: ; in, Indicates the total amount of wind and solar power curtailment; 、 Respectively The amount of wind and solar power curtailment at each moment; Objective function for maximizing hydropower generation: ; in, Represents the total hydropower generation.
[0008] A further optimization solution is that the constraints of the upper model include: wind, solar, hydro and energy storage combined output constraints, wind and solar curtailment constraints, hydropower station constraints and energy storage operation constraints; The wind, solar, hydro and storage combined output constraint includes: the minimum output of the wind, solar, hydro and storage combined is greater than or equal to zero, and the maximum output of the wind, solar, hydro and storage combined is less than or equal to the total active load; The curtailment constraints include: the difference between the predicted wind power output and the wind power output is greater than or equal to zero, and less than or equal to the predicted wind power output; the difference between the predicted photovoltaic output and the photovoltaic output is greater than or equal to zero, and less than or equal to the predicted photovoltaic output; The energy storage operation constraints include: energy storage system power constraints, energy storage system charge state constraints and energy storage charging and discharging constraints.
[0009] A further optimization solution is that the energy storage system power constraint is: ; The state of charge constraint of the energy storage system is: ; The energy storage charge and discharge constraints: ; Where: for Energy storage capacity at all times; 、 They are Energy storage charging and discharging power at all times; 、 are charging efficiency and discharging efficiency respectively; For energy storage systems State of charge at the moment; 、 are the lower and upper limits of energy storage SOC respectively; 、 are the maximum values of energy storage charging and discharging power respectively; 、 are all 0-1 variables, respectively Energy storage charging and discharging indicators at all times, Indicates energy storage charging, Indicates energy storage discharge.
[0010] A further optimization scheme is that the objective function of the lower layer model includes:
[0011] Where: The operating cost of thermal power units; is the coal consumption cost of thermal power units; The start-up and shutdown costs of thermal power units; is a 0-1 variable, Indicates thermal power unit exist Always on, Indicates thermal power unit exist Always turn off the phone; For thermal power units exist Always make contributions; 、 、 For thermal power units Coal consumption coefficient; For thermal power units exist Startup costs at the moment; is the total number of thermal power units.
[0012] A further optimization scheme is that the constraints of the lower model include: Power balance constraint: The sum of the total output of thermal power units and the combined output of wind, solar, hydro and storage is equal to the total active load; Thermal power unit operation constraints: ; ; Where: 、 Thermal power units The minimum and maximum active output; 、 Thermal power units Maximum upward and downward climbing rates; Line transmission capacity constraints: ; Where: For nodes 、 The admittance between For nodes exist The voltage phase angle at the moment; For nodes 、 The maximum transmission capacity between.
[0013] A further optimization scheme is to solve the two-layer optimization scheduling model to obtain a scheduling strategy, including the following methods: The two-level optimization scheduling model is simplified into a mixed integer linear programming model, and then the commercial solver Gurobi is called through Python for optimization and solution: Initialize the decision variables of the upper model; Under the conditions of satisfying all constraints, the upper model is optimized to obtain the optimal wind, solar, hydro and storage output plan, and the corresponding net load curve is calculated; The net load curve is input into the lower model as a hard constraint condition, and the lower model is optimized to obtain the thermal power start-stop plan and output plan; The key information is transmitted between the optimization operation process of the upper model and the optimization operation of the lower model, and the optimization operation process of the upper model is adjusted according to the key information; The final solution is obtained when the convergence conditions are met.
[0014] This solution also provides a two-tier optimization scheduling system based on the complementary characteristics of multiple energy sources, which is used to implement the above-mentioned two-tier optimization scheduling method based on the complementary characteristics of multiple energy sources; the system includes: An acquisition module is used to collect basic data of various energy types and build operation models for various energy types; the basic data includes historical output data of wind power, photovoltaic power, hydropower and thermal power, predicted load data, wind power predicted output data and photovoltaic predicted output data; A model building module is used to construct a two-tier optimization scheduling model based on basic data and an operating model that considers the complementary characteristics of multiple energy sources. The two-tier optimization scheduling model includes an upper-tier model and a lower-tier model. The upper-tier model considers wind, solar, hydro, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation. The lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs. The lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition. The solution module is used to solve the two-layer optimization scheduling model to obtain the scheduling strategy.
[0015] This solution also provides a computer-readable medium having a computer program stored thereon, and the computer program is executed by a processor to implement the above-mentioned two-layer optimization scheduling method based on the complementary characteristics of multiple types of energy.
[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. The present invention provides a two-layer optimization scheduling method, system and medium based on the complementary characteristics of multiple types of energy. It improves on the traditional multi-type power supply optimization scheduling technology, constructs a two-layer optimization scheduling model that deeply integrates the complementary characteristics of multiple types of energy, and designs an efficient solution method, which significantly improves the level of clean energy consumption, optimizes the system operation economy, and effectively improves the system net load characteristics, providing strong support for the safe and economical operation of the power system under a high proportion of new energy access.
[0017] 2. The present invention provides a two-layer optimization scheduling method, system and medium based on the complementary characteristics of multiple energy types; in the process of solving the model, a master-slave collaborative optimization strategy is proposed, and Python is used to call Gurobi for optimization and solution, so that the upper and lower optimizations can transmit key information to each other during the iteration process, and continuously adjust the upper-level plan to seek a better overall solution.
[0018] 3. The present invention provides a two-layer optimization scheduling method, system and medium based on the complementary characteristics of multiple energy types; it can solve the problems of low multi-energy coordination efficiency, high energy abandonment rate, and rising thermal power regulation costs, and better coordinate the differentiated regulation characteristics of wind, solar, water, fire and storage, such as hydropower flexibility, energy storage time and space translation capability and thermal power base load guarantee role, to achieve precise utilization of complementary characteristics to smooth net load fluctuations, layered collaborative optimization of clean energy consumption and thermal power economy, and reduce system operating costs and energy abandonment losses, to support the safe and economic operation of a high proportion of new energy power grids. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings: Figure 1 This is a flow chart of a two-layer optimization scheduling method based on the complementary characteristics of multiple energy sources; Figure 2 Schematic diagram of the double-layer optimization scheduling principle based on the complementary characteristics of multiple energy types; Figure 3 Schematic diagram of the solution process of the two-level optimization scheduling model. DETAILED DESCRIPTION
[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.
[0021] Traditional optimization scheduling methods for multiple power sources have difficulty fully coordinating the physical characteristics of different types of resources. Furthermore, they rely on expert experience and are unable to objectively reflect the complex relationships between different objectives, increasing the regulation burden on thermal power units. Therefore, this solution provides the following embodiments to address the aforementioned technical issues: Example 1 This embodiment provides a two-tier optimization scheduling method based on the complementary characteristics of multiple energy sources, such as Figure 1 and Figure 2As shown, it includes: Step 1: Collect basic data of multiple energy types and construct operation models for multiple energy types; the basic data includes historical output data, forecast load data, wind power forecast output data, and photovoltaic forecast output data of wind power, photovoltaic power, hydropower, and thermal power; the basic data takes into account temporal and spatial correlation and uncertainty; the operation models of multiple energy types include a hydropower station model, which includes upper and lower limits on the active power output of the hydropower station, water storage flow constraints, and water release flow constraints of the hydropower station; a thermal power unit model, which includes upper and lower limits on the output of the thermal power unit, up and down ramp constraints, and operation constraints; an energy storage system operation model, which includes energy storage charging and discharging power constraints, power constraints, and upper and lower limits on the energy storage SOC; in this solution, the system net load refers to the residual load that the thermal power unit needs to bear in the power grid after deducting the output of wind, solar, hydropower, and storage. To reduce the frequent adjustment of the output plan of the thermal power unit, it is necessary to minimize the net load fluctuation as much as possible by optimizing the output of wind, solar, hydropower, and storage; at the same time, it is necessary to minimize the curtailment of wind and solar power and maximize the hydropower generation to reduce the environmental pollution caused by the power generation of the thermal power unit.
[0022] Step 2: Based on the basic data and operation model, a two-tier optimization scheduling model is constructed that considers the complementary characteristics of multiple energy sources. The two-tier optimization scheduling model includes an upper-tier model and a lower-tier model. The upper-tier model considers wind, solar, hydro, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation. The lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs. The lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition. The objective function of the upper model includes: Net load variance minimization objective function: ; in, represents the variance of the net load; Indicates the scheduling period; express Net load of the system at any moment; Indicates the average value of the system net load; express The total active load of the system at any moment; 、 、 、 Respectively Wind power output, photovoltaic output, hydropower output and energy storage output at all times; In order to reduce the frequent adjustment of the planned output of thermal power units, the output of wind, solar, and hydropower is adjusted to make the fluctuation of the net load curve as small as possible, and the minimum net load variance is used as the first objective function of the upper model.
[0023] Minimum objective function for curtailing wind and solar power: ; in, Indicates the total amount of wind and solar power curtailment; 、 Respectively The amount of wind and solar power curtailed at each moment; In order to better absorb wind power and photovoltaic power and realize large-scale absorption of new energy, under the premise of meeting relevant constraints, as much wind power and photovoltaic power as possible should be absorbed to reduce wind and solar power abandonment. Therefore, minimizing wind and solar power abandonment is used as the second objective function of the upper model. Objective function for maximizing hydropower generation: ; in, Represents the total hydropower generation.
[0024] In order to make more use of clean energy, and under the premise of meeting the relevant output constraints of the hydropower station, we should make use of hydropower as much as possible to achieve cleanness and environmental protection. Therefore, the maximum hydropower generation is used as the third objective function of the upper model.
[0025] The constraints of the upper model include: wind, solar, hydro and energy storage combined output constraints, wind and solar curtailment constraints, hydropower station constraints and energy storage operation constraints; The combined output constraints of wind, solar, hydro and storage include: the minimum combined output of wind, solar, hydro and storage is greater than or equal to zero, and the maximum combined output of wind, solar, hydro and storage is less than or equal to the total active load; that is: ; in, express The minimum output of wind, solar, and hydropower at any given moment is temporarily set to 0 in this article; express The maximum total output of wind, solar and water storage at any moment, this embodiment sets for ; Since thermal power units need to bear the base load and peak load of the system, the net load is greater than 0. The situation where the total load of the system is less than the output of wind power and photovoltaic power is not considered for the time being.
[0026] The curtailment constraints include: the difference between the predicted wind power output and the wind power output is greater than or equal to zero, and less than or equal to the predicted wind power output; the difference between the predicted photovoltaic output and the photovoltaic output is greater than or equal to zero, and less than or equal to the predicted photovoltaic output; that is: ; ; Where: for The predicted output of wind power at each moment, ; for The predicted output of photovoltaic power at any moment, .
[0027] Hydropower plant constraints include: Output constraints: ; Storage flow constraints: ; Water release flow constraints: ; in, 、 They represent the minimum and maximum technical output of the hydropower station respectively; express The water storage flow of the hydropower station at each moment; 、 Respectively The minimum and maximum water storage flow of the hydropower station at each moment; express The water discharge flow of the hydropower station at each moment; 、 Respectively The minimum and maximum water discharge flow of the hydropower station at each moment.
[0028] Energy storage operation constraints include: energy storage system power constraints, energy storage system charge state constraints, and energy storage charging and discharging constraints.
[0029] The energy storage system power constraint is: ; The state of charge constraint of the energy storage system is: ; Energy storage charging and discharging constraints: ; Where: for Energy storage capacity at all times; 、 They are Energy storage charging and discharging power at all times; 、 are charging efficiency and discharging efficiency respectively; For energy storage systems State of charge at the moment; 、 are the lower and upper limits of energy storage SOC respectively; 、 are the maximum values of energy storage charging and discharging power respectively; 、 are all 0-1 variables, respectively Energy storage charging and discharging indicators at all times, Indicates energy storage charging, Indicates energy storage discharge.
[0030] The lower model is based on the wind, solar, hydropower and storage power and net load curves obtained from the upper model. The lower model establishes an optimal dispatch model for thermal power units with the goal of minimizing the operating costs of thermal power units. The operating costs of thermal power units include coal consumption costs and start-up and shutdown costs. The specific objective function of the lower model is:
[0031] Where: The operating cost of thermal power units; is the coal consumption cost of thermal power units; The start-up and shutdown costs of thermal power units; is a 0-1 variable, Indicates thermal power unit exist Always on, Indicates thermal power unit exist Always turn off the phone; For thermal power units exist Always make contributions; 、 、 For thermal power units Coal consumption coefficient; For thermal power units exist Startup costs at the moment; is the total number of thermal power units.
[0032] The constraints of the underlying model include: Power balance constraint: The sum of the total output of thermal power units and the combined output of wind, solar, hydro and storage is equal to the total active load; Right now: ; Thermal power units exist Total output at the moment:
[0033] Thermal power unit operation constraints: Active power output constraints of thermal power units: ; Thermal power unit ramp constraints: ; Where: 、 Thermal power units The minimum and maximum active output; 、 Thermal power units Maximum upward and downward climbing rates; Line transmission capacity constraints: ; Where: For nodes 、 The admittance between For nodes exist The voltage phase angle at the moment; For nodes 、 The maximum transmission capacity between.
[0034] To address the challenges posed by the growing installed capacity of wind and photovoltaic power to the optimal dispatch of power systems and to flexibly utilize these resources, a two-tiered optimal dispatch model was constructed that considers the complementary nature of multiple resource types: wind, solar, hydro, thermal, and storage. The upper tier of this model considers wind, solar, hydro, and storage, aiming to minimize system net load variance, minimize wind and solar curtailment, and maximize total hydropower generation. The lower tier considers thermal power units, aiming to minimize their operating costs. The net load curves obtained from the upper tier are applied as constraints to the optimal dispatch of thermal power units in the lower tier, leveraging the complementary nature of these resources.
[0035] Step 3: Solve the two-level optimization scheduling model to obtain the scheduling strategy; Figure 3 As shown, this step specifically includes the following method: The two-level optimization scheduling model is simplified into a mixed integer linear programming model, and then the commercial solver Gurobi is called through Python for optimization and solution: Initialize the decision variables of the upper model; specific decision variables include: planned wind and solar power output, planned hydropower output, and energy storage charging and discharging plan; (Linearize the upper-level model into a mixed-integer linear programming model) Under all constraints, optimize the upper-level model (mixed-integer linear programming model) to obtain the optimal wind, solar, hydropower and storage output plan, and calculate the corresponding net load curve; The net load curve is input into the lower model as a hard constraint condition, and the lower model is optimized to obtain the thermal power start-stop plan and output plan; Key information is transmitted between the upper-level model optimization process and the lower-level model optimization process, and the upper-level model optimization process is adjusted based on the key information; key information includes the acceptable fluctuation range of the net load curve and the thermal power regulation capacity margin. (Specifically, the thermal power start-stop plan and output plan obtained from the lower-level model optimization process are fed back to the upper-level model to provide the thermal power unit regulation margin.) If it does not converge, the net load fluctuation range is updated and the upper model is solved again, and the final solution is obtained when the convergence conditions are met.
[0036] Example 2 This embodiment provides a two-tier optimization scheduling system based on the complementary characteristics of multiple energy types, which is used to implement the two-tier optimization scheduling method based on the complementary characteristics of multiple energy types described in Example 1. The system includes: An acquisition module is used to collect basic data of various energy types and build operation models for various energy types; the basic data includes historical output data of wind power, photovoltaic power, hydropower and thermal power, predicted load data, wind power predicted output data and photovoltaic predicted output data; A model building module is used to construct a two-tier optimization scheduling model based on basic data and an operating model that considers the complementary characteristics of multiple energy sources. The two-tier optimization scheduling model includes an upper-tier model and a lower-tier model. The upper-tier model considers wind, solar, hydro, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation. The lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs. The lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition. The solution module is used to solve the two-layer optimization scheduling model to obtain the scheduling strategy.
[0037] Example 3 This embodiment provides a computer-readable medium having a computer program stored thereon. The computer program is executed by a processor to implement the two-tier optimization scheduling method based on the complementary characteristics of multiple energy sources as described in Example 1. Specifically, the following steps are performed: Step 1: Collect basic data of multiple energy types and build operation models for multiple energy types; the basic data includes historical output data, forecast load data, wind power forecast output data, and photovoltaic forecast output data of wind power, photovoltaic power, hydropower, and thermal power; The basic data take into account both temporal and spatial correlations and uncertainties.
[0038] Step 2: Based on the basic data and operation model, a two-tier optimization scheduling model is constructed that considers the complementary characteristics of multiple energy sources. The two-tier optimization scheduling model includes an upper-tier model and a lower-tier model. The upper-tier model considers wind, solar, hydro, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation. The lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs. The lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition. Step 3: Solve the two-layer optimization scheduling model to obtain the scheduling strategy.
[0039] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A two-layer optimization scheduling method based on the complementary characteristics of multiple energy sources, characterized by: include: Collect basic data on multiple energy sources and build operational models for these energy sources; the basic data includes historical output data, forecasted load data, wind power forecast output data, and photovoltaic forecast output data for wind power, photovoltaic power, hydropower, and thermal power; Based on basic data and operating models, a two-tier optimization scheduling model that considers the complementary characteristics of multiple energy sources is constructed; the two-tier optimization scheduling model includes an upper-tier model and a lower-tier model; the upper-tier model considers wind, solar, hydropower, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation; the lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs; the lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition; Solve the two-level optimization scheduling model to obtain the scheduling strategy.
2. The two-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 1 is characterized in that: The basic data take into account both temporal and spatial correlations and uncertainties.
3. The double-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 1 is characterized in that: The objective function of the upper model includes: Net load variance minimization objective function: ； in, represents the variance of the net load; Indicates the scheduling period; express Net load of the system at any moment; Indicates the average value of the system net load; express Total active load of the system at any moment; 、 、 、 Respectively Wind power output, photovoltaic output, hydropower output and energy storage output at all times; Minimum objective function for curtailing wind and solar power: ； in, Indicates the total amount of wind and solar power curtailment; 、 Respectively The amount of wind and solar power curtailment at each moment; Objective function for maximizing hydropower generation: ； in, Represents the total hydropower generation.
4. The two-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 3 is characterized in that: The constraints of the upper model include: wind, solar, hydro and energy storage combined output constraints, wind and solar curtailment constraints, hydropower station constraints and energy storage operation constraints; The wind, solar, hydro and storage combined output constraint includes: the minimum output of the wind, solar, hydro and storage combined is greater than or equal to zero, and the maximum output of the wind, solar, hydro and storage combined is less than or equal to the total active load; The curtailment constraints include: the difference between the predicted wind power output and the wind power output is greater than or equal to zero, and less than or equal to the predicted wind power output; the difference between the predicted photovoltaic output and the photovoltaic output is greater than or equal to zero, and less than or equal to the predicted photovoltaic output; The energy storage operation constraints include: energy storage system power constraints, energy storage system charge state constraints and energy storage charging and discharging constraints.
5. The double-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 4 is characterized in that: The energy storage system power constraint is: ； The state of charge constraint of the energy storage system is: ； The energy storage charge and discharge constraints: ； Where: for Energy storage capacity at all times; 、 They are Energy storage charging and discharging power at all times; 、 are charging efficiency and discharging efficiency respectively; For energy storage systems State of charge at the moment; 、 are the lower and upper limits of energy storage SOC respectively; 、 are the maximum values of energy storage charging and discharging power respectively; 、 are all 0-1 variables, respectively Energy storage charging and discharging indicators at all times, Indicates energy storage charging, Indicates energy storage discharge.
6. The double-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 1 is characterized in that: The objective function of the lower model includes: Where: The operating cost of thermal power units; is the coal consumption cost of thermal power units; The start-up and shutdown costs of thermal power units; is a 0-1 variable, Indicates thermal power unit exist Always on, Indicates thermal power unit exist Always turn off the phone; For thermal power units exist Always make contributions; 、 、 For thermal power units Coal consumption coefficient; For thermal power units exist Startup costs at the moment; is the total number of thermal power units.
7. The double-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 6 is characterized in that: The constraints of the lower model include: Power balance constraint: The sum of the total output of thermal power units and the combined output of wind, solar, hydro and storage is equal to the total active load; Thermal power unit operation constraints: ； ； Where: 、 Thermal power units The minimum and maximum active output; 、 Thermal power units Maximum upward and downward climbing rates; Line transmission capacity constraints: ； Where: For nodes 、 The admittance between For nodes exist The voltage phase angle at the moment; For nodes 、 The maximum transmission capacity between.
8. The double-layer optimization scheduling method based on the complementary characteristics of multiple energy sources according to claim 1 is characterized in that: The method of solving the two-layer optimization scheduling model to obtain the scheduling strategy includes: The two-level optimization scheduling model is simplified into a mixed integer linear programming model, and then the commercial solver Gurobi is called through Python for optimization and solution: Initialize the decision variables of the upper model; Under the conditions of satisfying all constraints, the upper model is optimized to obtain the optimal wind, solar, hydro and storage output plan, and the corresponding net load curve is calculated; The net load curve is input into the lower model as a hard constraint condition, and the lower model is optimized to obtain the thermal power start-stop plan and output plan; The key information is transmitted between the optimization operation process of the upper model and the optimization operation of the lower model, and the optimization operation process of the upper model is adjusted according to the key information; The final solution is obtained when the convergence conditions are met.
9. A two-layer optimization scheduling system based on the complementary characteristics of multiple energy sources is characterized by: A system for implementing a two-tier optimization scheduling method based on the complementary characteristics of multiple energy sources as described in any one of claims 1 to 8; the system comprises: An acquisition module is used to collect basic data of various energy types and build operation models for various energy types; the basic data includes historical output data of wind power, photovoltaic power, hydropower and thermal power, predicted load data, wind power predicted output data and photovoltaic predicted output data; A model building module is used to construct a two-tier optimization scheduling model based on basic data and an operating model that considers the complementary characteristics of multiple energy sources. The two-tier optimization scheduling model includes an upper-tier model and a lower-tier model. The upper-tier model considers wind, solar, hydro, and storage, with the goal of minimizing net load variance, minimizing wind and solar curtailment, and maximizing hydropower generation. The lower-tier model considers thermal power units, with the goal of minimizing thermal power unit operating costs. The lower-tier model uses the net load curve obtained by solving the upper-tier model as a hard constraint condition. The solution module is used to solve the two-layer optimization scheduling model to obtain the scheduling strategy.
10. A computer-readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the two-layer optimization scheduling method based on the complementary characteristics of multiple energy types as described in any one of claims 1 to 8.