Power equipment optimization scheduling method and device, equipment, program product and storage medium

Abstract

The embodiment of the invention discloses a power equipment optimization scheduling method and device, equipment, a program product and a storage medium, and the method comprises the steps: obtaining park data of a comprehensive energy park, carrying out the linear programming of the power equipment of the comprehensive energy park according to a predetermined cost formula, a predetermined constraint condition and the park data, obtaining a park-level scheduling result; performing intelligent optimization on the thermal power of each thermal power generating unit and the thermal energy storage device based on the park-level scheduling result, the park data and a predetermined intelligent optimization algorithm to obtain a plant-level scheduling result; and obtaining a target scheduling result of the integrated energy park according to the park-level scheduling result and the plant-level scheduling result, and executing the target scheduling result. According to the method provided by the invention, the load of each thermal power generating unit, energy storage equipment and purchased power can be distributed more finely and reasonably, so that the scheduling scheme of the power equipment is more flexible and accurate, and the stability and the operation efficiency of the power system of the comprehensive energy park are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of energy analysis, and in particular to a method, apparatus, device, program product, and storage medium for optimizing and scheduling power equipment. Background Art

[0002] As people's environmental awareness grows, industrial parks that used to use self-provided thermal power have established wind and solar energy bases to reduce carbon emissions. For example, in a typical integrated energy park with "source, grid, load and storage", the power sources of the integrated energy park include wind and solar energy, thermal power and power purchased from the power grid. The power grid is a local power grid in the industrial park. In order to maintain a stable energy supply in the industrial park, it is necessary to uniformly dispatch the new energy, thermal power, power off the power grid, load and energy storage in the park.

[0003] In the unified dispatch of sources, grids, loads and storage, the traditional automatic power generation control method mainly focuses on the frequency control and load balance of the power system. However, this method cannot reasonably distribute the load between the units in a multi-unit system, resulting in the failure to achieve the optimal operating cost and efficiency of each unit in actual operation, reducing the operating efficiency of the park's power system. In addition, this method cannot adapt well to the fluctuations and uncertainties of renewable energy power generation output, and it is difficult to quickly respond to changes in system load. This is particularly prominent in integrated energy parks with a high proportion of renewable energy, resulting in unstable system operation and low energy utilization. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, equipment, program product and storage medium for optimizing and scheduling power equipment, which can realize a more refined and reasonable distribution of the load of each unit, and make the scheduling plan of the power equipment more flexible and accurate through hierarchical optimization, so as to quickly adapt to changes in system load and improve the stability and operation efficiency of the power system of the comprehensive energy park.

[0005] In a first aspect, an embodiment of the present invention provides a method for optimizing and dispatching electric power equipment, comprising:

[0006] Acquire park data of the integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone;

[0007] Performing linear programming on the power equipment of the integrated energy park according to a predetermined cost formula, predetermined constraints and the park data to obtain a park-level scheduling result;

[0008] Based on the park-level dispatching result, the park data and a predetermined intelligent optimization algorithm, the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized to obtain a plant-level dispatching result;

[0009] The target scheduling result of the integrated energy park is obtained according to the park-level scheduling result and the plant-level scheduling result, and the target scheduling result is executed; wherein the target scheduling result includes a target wind power scheduling result, a target photovoltaic scheduling result, a target thermal power scheduling result and a target energy storage scheduling result.

[0010] In a second aspect, an embodiment of the present invention provides a device for optimizing and dispatching electric power equipment, the device comprising:

[0011] A data acquisition module, used to acquire park data of the integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone;

[0012] A first determination module is used to perform linear programming on the power equipment of the integrated energy park according to a predetermined cost formula, a predetermined constraint condition and the park data to obtain a park-level scheduling result;

[0013] A second determination module is used to intelligently optimize the thermal power of each thermal power unit and the thermal energy storage device based on the park-level scheduling result, the park data and a predetermined intelligent optimization algorithm to obtain a plant-level scheduling result;

[0014] A result determination module is used to obtain the target scheduling result of the comprehensive energy park according to the park-level scheduling result and the plant-level scheduling result, and execute the target scheduling result; wherein the target scheduling result includes the target wind power scheduling result, the target photovoltaic scheduling result, the target thermal power scheduling result and the target energy storage scheduling result.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an electric power equipment optimization and scheduling method as described in any one of the embodiments of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing and dispatching electric power equipment as described in any one of the embodiments of the present invention.

[0017] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements a method for optimizing and scheduling electric power equipment as described in any one of the embodiments of the present invention.

[0018] In an embodiment of the present invention, the park data of the comprehensive energy park is obtained, wherein the park data includes the historical operation data and cost data of the comprehensive energy zone; the power equipment of the comprehensive energy park is linearly planned according to a predetermined cost formula, a predetermined constraint condition and the park data to obtain a park-level scheduling result; based on the park-level scheduling result, the park data and the predetermined intelligent optimization algorithm, the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized to obtain a plant-level scheduling result; the target scheduling result of the comprehensive energy park is obtained according to the park-level scheduling result and the plant-level scheduling result, and the target scheduling result is executed; wherein the target scheduling result includes the target wind power scheduling result, the target photovoltaic scheduling result, the target thermal power scheduling result and the target energy storage scheduling result. The method of the embodiment of the present invention can more accurately allocate and utilize various energy resources, including wind power energy, photovoltaic energy, thermal power energy and energy storage energy, etc., through the double-layer scheduling optimization at the park level and the plant level, so as to realize a more refined and reasonable allocation of the load of each thermal power unit, the load of the energy storage equipment and the purchased electricity, so that the scheduling plan of the power equipment is more flexible and accurate, and the stability and operation efficiency of the power system of the comprehensive energy park are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A first flow chart of a method for optimizing and dispatching electric power equipment provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the structure of the power system of the integrated energy park provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the structure of an electric heat dispatching system for a thermal power unit provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of a thermal power unit scheduling method provided by an embodiment of the present invention;

[0024] Figure 5 A second flow chart of a method for optimizing and dispatching electric power equipment provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of the system structure for optimizing the dispatch of power equipment in a comprehensive energy park provided by an embodiment of the present invention;

[0026] Figure 7 A schematic diagram of configuration parameters of a power system provided by an embodiment of the present invention;

[0027] Figure 8 A schematic diagram of the purchased electricity cost in each time period of a certain day provided by an embodiment of the present invention;

[0028] Fig. 9 The photovoltaic output situation of a certain day provided by the embodiment of the present invention is shown;

[0029] Fig.10 A schematic diagram of a park-level scheduling result provided by an embodiment of the present invention;

[0030] Fig.11 A schematic diagram of thermal power load distribution of plant-level dispatching results provided in an embodiment of the present invention;

[0031] Fig.12 A schematic diagram of thermal power load distribution of plant-level scheduling results provided by an embodiment of the present invention;

[0032] Fig.13 A schematic diagram of the structure of a power equipment optimization scheduling device provided by an embodiment of the present invention;

[0033] Fig.14 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0035] Figure 1 The first flow chart of a method for optimizing and dispatching electric power equipment provided in an embodiment of the present invention. The method in an embodiment of the present invention can realize a more refined and reasonable distribution of the load of each unit. Through the hierarchical optimization method, the dispatching plan of the electric power equipment is more flexible and accurate, so as to quickly adapt to the changes in the system load and improve the stability and operation efficiency of the power system of the comprehensive energy park. The method can be executed by an electric power equipment optimizing and dispatching device provided in an embodiment of the present invention. The device can be implemented in software and / or hardware. The following embodiments will be described by taking the device integrated in an electronic device as an example. The electronic device can be a server or a computer device, etc., with reference to Figure 1 , the method may specifically include the following steps:

[0036] Step 101: Obtain park data of the integrated energy park.

[0037] Among them, the comprehensive energy park is a park that realizes efficient supply, distribution and use of energy in a specific geographical area by optimizing the configuration and comprehensive utilization of multiple energy sources (including wind and solar new energy, thermal power energy and power energy purchased from the power grid, etc.). The park data includes the historical operation data, cost data, equipment data and environmental data of the comprehensive energy park. The cost data includes the power generation fuel cost, heating fuel cost and purchased electricity cost of the thermal power units. The historical operation data includes the power generation efficiency, heating efficiency and efficiency changes of the thermal power units under different loads in the historical period; the operation data and load data of the thermal power units in the historical period. The park data also includes the predicted values ​​of the system electric load and thermal load, as well as the relevant parameters of the adjustable electric load and adjustable thermal load; the output prediction values ​​of wind turbine power generation and photovoltaic power generation. The equipment data includes the parameters of energy storage equipment, such as the capacity, charging and discharging power and efficiency of electric and thermal energy storage; the equipment data also includes the parameters of the power grid interconnection line, such as the upper and lower power limits of the interconnection line with the public grid. The environmental data includes the ambient temperature, ambient humidity and wind force level. Specifically, when it is necessary to optimize the scheduling of various power equipment in the integrated energy park, the park data of the integrated energy park is obtained so that the optimized scheduling can be performed later based on the park data. Figure 2 The schematic diagram of the structure of the power system of the integrated energy park provided by the embodiment of the present invention. Figure 2 As shown, the power system includes wind turbines, photovoltaic units, thermal power units, electric energy storage devices, steam extraction thermal storage devices, power grids, heat networks, and electric thermal storage devices. The server includes a dispatching platform, which is connected to the power system. The dispatching platform can obtain park data through the power system, determine the target dispatching results based on the park data, and dispatch each power equipment based on the target dispatching results.

[0038] Step 102: Perform linear programming on the power equipment of the integrated energy park according to a predetermined cost formula, predetermined constraints and park data to obtain a park-level scheduling result.

[0039] Among them, the cost formula is pre-established based on domain big data and historical data of the integrated energy park, and is used to describe the function of the thermal power fuel cost, wind power generation cost, photovoltaic power generation cost and energy storage cost of the integrated energy park. Constraints are key conditions to ensure the linear programming feasibility and system stability of the power equipment in the park (integrated energy park). In this scheme, the constraints include system power balance constraints, thermal power generation constraints, thermal power heating constraints, wind power operation power constraints, photovoltaic operation power constraints, electric energy storage operation constraints, thermal energy storage operation constraints, power grid power operation constraints, electric load adjustable constraints and thermal load adjustable constraints. The linear programming in this scheme is used to minimize the cost formula under the constraints, so as to obtain the park-level scheduling results. The park-level scheduling results include wind power scheduling results, photovoltaic scheduling results, total thermal power scheduling results and electric energy storage scheduling results. The wind power scheduling results include wind power output distribution: the output level of each wind farm in different scheduling periods. The photovoltaic scheduling results include photovoltaic power generation distribution: the power generation of each photovoltaic power station or photovoltaic array in different scheduling periods. The total thermal power dispatch results include the overall output level of thermal power units at the park level in different dispatch periods and the start and stop plans of thermal power units. The energy storage dispatch results include the energy storage charging and discharging plan: the charging and discharging power and power changes of the energy storage device in different dispatch periods.

[0040] Specifically, after obtaining the park data, the cost data required for linear programming is screened and sorted out from the park data, such as the power generation fuel cost of the thermal power unit, the heating fuel cost of the thermal power unit, and the cost of purchased electricity. The cost data is brought into the cost formula, and the server can call the linear programming tool to combine the system power balance constraints, the power generation constraints of the thermal power unit, the heating constraints of the thermal power unit, the wind power operation power constraints, the photovoltaic operation power constraints, the electric energy storage operation constraints, the thermal energy storage operation constraints, the power grid power operation constraints, the electric load adjustable constraints, the thermal load adjustable constraints and the cost formula after the input to perform linear programming on the park data to obtain wind power scheduling results, photovoltaic scheduling results, total thermal power scheduling results and electric energy storage scheduling results. For example, the linear planner is used in combination with cost data and equipment data, etc., to maximize the consumption of wind power while considering the predicted output of wind turbines and the load demand of the power system; while minimizing the operating cost of thermal power units, the stability and reliability of the park power system are ensured.

[0041] Step 103: Based on the park-level dispatching result, park data and a predetermined intelligent optimization algorithm, the thermal power of each thermal power unit and thermal energy storage device is intelligently optimized to obtain a plant-level dispatching result.

[0042] Among them, the intelligent optimization algorithm is an algorithm for intelligently optimizing the thermal power of each thermal power unit combined with thermal energy storage, such as genetic algorithm and particle swarm optimization algorithm. The plant-level scheduling results include the electric power, thermal power and thermal power distribution results of each thermal power unit and thermal energy storage device. The goal of plant-level scheduling is to ensure that the operation of each thermal power unit and thermal energy storage device meets the system power balance and stability requirements. In an optional implementation, after obtaining the park-level scheduling results, the key information used to calculate the plant-level scheduling results is extracted from the park-level scheduling results, such as the overall output level of the thermal power units in the park, the start-stop plan, the load demand, the new energy output distribution and the electric energy storage charging and discharging plan. The extracted key information is organized into an input format suitable for the intelligent optimization algorithm, such as decomposing the overall output of the thermal power unit into the preliminary allocation range of each unit, and obtaining the time series of the system load demand according to the time information of the park data. According to the park data, the electricity and heat distribution of each thermal power unit under different loads and the cost parameters such as coal consumption are determined, and the boundary conditions such as the operable domain and efficiency of each thermal power unit are determined, including the upper and lower limits of power generation, the upper and lower limits of heating power and the ramp rate.

[0043] Furthermore, the population of the particle swarm algorithm is initialized based on the integrated cost parameters, the feasible domain and efficiency boundary of each thermal power unit. The population is composed of particles, each particle represents a possible scheduling scheme, and each scheduling scheme includes the distribution of electric power and thermal power of each thermal power unit and thermal energy storage device. Initialize the position and velocity of the particle, the position represents the power distribution of each power equipment, and the velocity represents the rate of change. For each particle, the fitness value is calculated according to the pre-set fitness calculation function of the particle swarm optimization algorithm, and the pros and cons of the current scheduling scheme (current particle) are evaluated according to the fitness value. The fitness calculation function includes the fuel cost of the thermal power unit, the operating cost of the thermal energy storage device, etc. Perform iterative optimization according to the following formula to gradually approach the optimal solution: v i (t+1)=w·v i (t)+c1·r1·(p best,i -x i (t))+c2·r2·(g best -x i (t)); x i (t+1)=x i (t)+v i (t+1)x i (t+1)=x i (t)+v i (t+1). Among them, v i (t) and x i (t) represent the velocity and position of the ith particle at time t, respectively, and p best,i and g bestRepresent the individual optimal solution and the global optimal solution, w, c1 and c2 are algorithm parameters, and r1 and r2 are random numbers. During the iteration process, the global optimal solution and the individual (particle) optimal solution are continuously updated until the preset stop condition is met (such as reaching the maximum number of iterations or the fitness value converges), and the plant-level scheduling result, that is, the optimal thermal power of each thermal power unit and thermal energy storage device, is obtained.

[0044] In an optional implementation, after obtaining the park-level dispatching results, the overall processing level of the thermal power units is obtained based on the park-level dispatching results, and the cost parameters are determined according to the overall processing level of the thermal power units and the park data; the operating boundary conditions of each thermal power unit are determined based on the park data and the system power balance constraints in the constraints; wherein the operating boundary conditions include the power generation operating boundary and the heating operating boundary of each thermal power unit; the input data of the intelligent optimization is determined according to the cost parameters and the operating boundary conditions. The input data includes the cost parameters and operating boundary conditions of each thermal power unit; the cost parameters of each thermal power unit include the power generation cost, heating cost and comprehensive cost of each thermal power unit; the optimization objective function of the intelligent optimization algorithm is constructed based on the cost parameters and the operating boundary conditions; the comprehensive energy park is intelligently optimized according to the optimization objective function and the intelligent optimization algorithm to obtain the plant-level dispatching results.

[0045] Step 104: Obtain the target scheduling result of the integrated energy park according to the park-level scheduling result and the plant-level scheduling result, and execute the target scheduling result.

[0046] Among them, the target dispatching results include the target wind power dispatching results, the target photovoltaic dispatching results, the target thermal power dispatching results and the target energy storage dispatching results. Specifically, the target wind power dispatching results include the output level of each wind farm in different dispatching periods; the target photovoltaic dispatching results include the power generation of each photovoltaic power station or photovoltaic array in different dispatching periods; the target thermal power dispatching results include the electric power and thermal power distribution of each thermal power unit in different dispatching periods and the start and stop plan of the thermal power unit; the target energy storage dispatching results include the charging and discharging power and power changes of the electric energy storage and thermal energy storage devices in different dispatching periods. After obtaining the park-level dispatching results and the plant-level dispatching results, the wind power dispatching results are determined as the target wind power dispatching results, the photovoltaic dispatching results are determined as the target photovoltaic dispatching results; and the electric energy storage dispatching results are determined as the target energy storage dispatching results. The target thermal power dispatching results are determined according to the plant-level dispatching results.

[0047] After obtaining the target scheduling results, the server can use the automated scheduling system (such as a programmable logic controller or distributed control system, etc.) to automatically control the operation of each energy device according to the target scheduling results. The energy management system of the integrated energy park monitors the operating status of each energy device in real time to ensure the smooth execution of the target scheduling results. During the execution of the target scheduling results, the server can collect data from the execution of the target scheduling results in real time and perform data analysis on it. According to the data analysis results, the target scheduling plan is continuously optimized to improve the operating efficiency and economy of the park's power system, while ensuring the sustainability and adaptability of the target scheduling results. Figure 3 The schematic diagram of the structure of the electric heat dispatching system of the thermal power unit provided by the embodiment of the present invention. Figure 3 As shown, the electric heat dispatching system of the thermal power unit includes a boiler, a high-pressure cylinder, a medium-pressure cylinder, a low-pressure cylinder, a decondenser heat removal network and a de-extraction heat storage device. The heat extraction steam sources include main steam extraction, hot re-extraction and middle exhaust extraction. The heat extraction is sent to the steam extraction heat removal network or steam extraction heat storage device of the thermal power unit. In this scheme, a single thermal power unit has different extraction positions under different working conditions, and there are many different schemes for different peak-shaving conditions. The extraction position is different when the power generation capacity is different. For example, at high load, heat is supplied by extracting steam from the middle exhaust; when the load is between 40% and 70%, heat is supplied by extracting steam from the hot exhaust; when the load is between 30% and 40%, heat is supplied by extracting steam from the main steam. Figure 4 Schematic diagram of the dispatching method of thermal power units provided in the embodiment of the present invention. Figure 4 As shown, the power system includes N thermal power units, each of which can provide on-grid power, thermal power extracted into the heat network, and thermal power extracted into the heat storage device.

[0048] The technical solution of this embodiment obtains the park data of the comprehensive energy park, wherein the park data includes the historical operation data and cost data of the comprehensive energy zone; linear programming is performed on the power equipment of the comprehensive energy park according to the predetermined cost formula, the predetermined constraint conditions and the park data to obtain the park-level scheduling result; the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized based on the park-level scheduling result, the park data and the predetermined intelligent optimization algorithm to obtain the plant-level scheduling result; the target scheduling result of the comprehensive energy park is obtained according to the park-level scheduling result and the plant-level scheduling result, and the target scheduling result is executed; wherein the target scheduling result includes the target wind power scheduling result, the target photovoltaic scheduling result, the target thermal power scheduling result and the target energy storage scheduling result. The technical solution of this embodiment can more accurately allocate and utilize various energy resources, including wind power energy, photovoltaic energy, thermal power energy and energy storage energy, etc., through the double-layer scheduling optimization at the park level and the plant level, so as to realize a more refined and reasonable allocation of the load of each unit, the load of the energy storage equipment and the purchased electricity, so that the scheduling plan of the power equipment is more flexible and accurate, and the stability and operation efficiency of the power system of the comprehensive energy park are improved.

[0049] Figure 5 The second flow chart of a method for optimizing the dispatching of electric power equipment provided by an embodiment of the present invention is a refinement of the above embodiment. The specific method can be as follows Figure 5 As shown, the method may include the following steps:

[0050] Step 501: Obtain park data of the integrated energy park.

[0051] Among them, the park data includes historical operation data and cost data of the integrated energy zone.

[0052] Step 502: Substitute the power generation fuel cost, heating fuel cost and purchased electricity cost into the cost formula to obtain the cost function value of the comprehensive energy park.

[0053] Among them, the cost formula is pre-established based on domain big data and historical data of the integrated energy park, and is used to describe the function of the thermal power fuel cost, wind power generation cost, photovoltaic power generation cost and energy storage cost of the integrated energy park. The cost formula in this scheme is the total variable cost formula related to electricity. Since the scheduling result will not affect the fixed cost, the cost formula mainly includes the fuel cost caused by the change of thermal power load and the cost of purchased electricity. The cost formula is shown as follows:

[0054]

[0055] Where T is the number of scheduling periods, C th , C w , C s , Cb and C g They are thermal power generation, wind power generation, photovoltaic power generation, energy storage cost and external power purchase cost. The thermal power generation cost includes thermal power generation cost and heating cost. The thermal power generation cost is shown in the following formula: C th (t) = C th_e (t)+C th_h (t); where C th_e (t) is the cost of thermal power generation, C th_h The cost of heating from thermal power.

[0056] Step 503: Perform linear programming on the power equipment of the integrated energy park based on the cost function value, park data and constraint conditions to obtain a park-level scheduling result.

[0057] After obtaining the cost function value, linear programming is performed on the power equipment of the comprehensive energy park based on the cost function value, park data and constraints. The constraints in this scheme include system power balance constraints, thermal power generation constraints, thermal power heating constraints, wind power operation power constraints, photovoltaic operation power constraints, electric energy storage operation constraints, thermal energy storage operation constraints, power grid power operation constraints, electric load adjustable constraints and thermal load adjustable constraints.

[0058] Specifically, the system power balance constraint means that the electrical and thermal power output of the system in any period must be consistent with the electrical and thermal power requirements of the system, as shown in the following formula:

[0059]

[0060] Among them, P d,e,t is the predicted value of system electric load, P out,e,t To exchange power with the public grid (power from the public grid is positive, power to the public grid is negative), l e,t P is an adjustable electrical load. d,h,t is the predicted value of system heat load, l h,t is the adjustable heat load. The power generation constraint of the thermal power unit is: P e,min ≤P i,e,t ≤P e,max ; Among them, P e,min is the lower limit of the unit output power, P e,max is the upper limit of the unit output power, P i,e,t is the output power of thermal power unit i at time t. The power generation constraint (ramp rate constraint) of thermal power unit is: ΔP d,i,e,t ≤P i,e,t -P i,e,t-1 ≤ΔP u,i,e,t ; where ΔP d,e,i,t and ΔP u,e,i,tare the lower and upper limits of the power generation ramp of generator set i at time t, P i,e,t-1 is the output power of thermal power unit i at time t-1. N G is the number of generating units (wind turbines, photovoltaic units, thermal power units and energy storage equipment).

[0061] The heat supply constraint of thermal power unit is: P h,min ≤P i,h,t ≤P h,max ; Among them, P h,min is the lower limit of the unit’s thermal output power, P h,max is the upper limit of the unit’s thermal output power, P i,h,t is the thermal power output of thermal power unit i at time t. The thermal power unit heating ramp rate constraint is: ΔP d,h,i,t ≤P i,h,t -P i,h,t-1 ≤ΔP u,h,i,t ; where ΔP d,h,i,t and ΔP u,h,i,t are the lower and upper limits of the heating ramp of generator set i at time t, P i,h,t-1 is the output thermal power of thermal power unit i at time t-1. The wind power operation power constraint means that the maximum output of the wind power unit does not exceed the predicted value at the current time: in, is the predicted value of the wind turbine at the current moment. The photovoltaic operation constraint can mean that the maximum photovoltaic output does not exceed the predicted value at the current moment: in, is the current predicted value of photovoltaic. The operation constraint of electric energy storage is: P es,e,min ≤P i,es,e,t ≤P es,e,max ; Among them, P es,e,min is the lower limit of the output power of the electric energy storage, P es,e,max The upper limit of the energy storage output power. The energy storage ramp rate constraint is: ΔP d,es,e,i,t ≤P i,es,e,t -P i,es,e,t-1 ≤ΔP u,es,e,i,t ; where ΔP d,es,e,i,t and ΔP u,es,e,i,t are the lower and upper limits of the ramp rate of the energy storage i at time t. The energy storage energy constraint is: E t,e,s =E t-1,e,s +P cha,t-1,es -P dis,t-1,e,s ; E e,min ≤E t,e,s ≤E e,max ; Among them, E t,e,s is the energy storage capacity at time t, E t-1,e,s is the energy storage capacity at time t-1, P cha,t-1,e,s is the charging power, Pdis,t-1,e,s is the discharge power, E e,min is the minimum storage capacity, E e,max is the maximum storage capacity. The thermal energy storage operation constraint is: P es,h,min ≤P i,es,h,t ≤P es,h,max ; ΔP d,es,h,i,t ≤P i,es,h,t -P i,es,h,t-1 ≤ΔP u,es,h,i,t Among them, P es,h,min is the lower limit of thermal energy storage output power, P es,h,max is the upper limit of thermal energy storage output power. ΔP d,es,h,i,t and ΔP u,es,h,i,t are the lower and upper limits of the ramp rate of thermal energy storage i at time t. The thermal energy storage operation constraint is: E t,h,s =E t-1,h,s +P cha,t-1,hs -P dis,t-1,h,s ; E e,min ≤E t,e,s ≤E e,max Among them, E t,e,s is the thermal storage energy at time t, E t-1,e,s is the thermal storage energy at time t-1, P cha,t-1,e,s is the heat storage power, P dis,t-1,e,s is the heat release power, E e,min is the minimum heat storage capacity, E e,max is the maximum heat storage capacity. The power operation constraint under the power grid is: P tl-min ≤P tl ≤P tl-max ; Among them, P tl is the active power flow of the tie line, P tl-min and P tl-max are the lower and upper limits of the tie line power respectively. The adjustable constraints of the electric load are: Δl d,i,e,t ≤l i,e,t -l i,e,t-1 ≤Δl u,i,e,t Among them, l i,e,t is the adjustable load at time t, l i,e,t-1 is the adjustable load at time t-1, is the maximum power of the adjustable load, Δl d,i,e,t and Δl u,i,e,t are the ramp rates of the adjustable load. The heat load adjustable constraint is: Δl d,i,h,t ≤l i,h,t -l i,h,t-1 ≤Δl u,i,h,t Among them, l i,h,t is the adjustable load at time t, l i,h,t-1 is the adjustable load at time t-1, is the maximum power of the adjustable load, Δl d,i,h,t and Δl u,i,h,t are the ramp rates of the adjustable load. Furthermore, the server can call a linear programming tool to perform linear programming based on the cost formula and the above constraints to obtain wind power scheduling results, photovoltaic scheduling results, total thermal power scheduling results, and electric energy storage scheduling results.

[0062] Step 504: Determine input data for the intelligent optimization algorithm based on the park-level scheduling results and park data.

[0063] Among them, the input data includes the cost parameters and operating boundary conditions of each thermal power unit; the cost parameters of each thermal power unit include the power generation cost, heating cost and comprehensive cost of each thermal power unit. Specifically, after obtaining the park-level scheduling results, the park-level scheduling results are sorted and analyzed to obtain the input data of the intelligent optimization algorithm. In this scheme, optionally, the input data of the intelligent optimization algorithm is determined based on the park-level scheduling results and the park data, including: obtaining the overall processing level of the thermal power unit based on the park-level scheduling results, and determining the cost parameters based on the overall processing level of the thermal power unit and the park data. Determine the operating boundary conditions of each thermal power unit based on the park data and the system power balance constraints in the constraints, and determine the input data of the intelligent optimization based on the cost parameters and the operating boundary conditions.

[0064] Among them, the overall processing level of thermal power units is the total output level of all thermal power units in different scheduling periods in the park-level scheduling results, including the total power of power generation and heating. The cost parameters include the power generation cost, heating cost and comprehensive cost of the thermal power units. The cost parameters are used to describe the operating cost of the thermal power units under different loads. The operating boundary conditions include the power generation operation boundary and the heating operation boundary of each thermal power unit. Specifically, after obtaining the park-level scheduling results, the park-level scheduling results are screened and sorted, and the overall processing level of the thermal power units is obtained according to the total thermal power scheduling results. After obtaining the park data, the cost parameters are obtained according to the fuel consumption rate and fuel price of the park data. The system power balance constraint can ensure that the total power generation of the power system is equal to the total load demand at any time. According to the system power balance constraint, the power generation operation boundary and heating operation boundary of each thermal power unit can be determined. Further, the input data of intelligent optimization is obtained according to the cost parameters and the operating boundary conditions. The input data of intelligent optimization can be obtained comprehensively and accurately through the park-level scheduling results and park data, thus laying the foundation for obtaining more accurate, stable and economical scheduling results.

[0065] Step 505: construct an optimization objective function of the intelligent optimization algorithm based on cost parameters and operating boundary conditions; perform intelligent optimization on the thermal power of each thermal power unit and thermal energy storage device according to the optimization objective function and the intelligent optimization algorithm to obtain a plant-level scheduling result.

[0066] Among them, the optimization objective function is constructed based on cost parameters and operating boundary conditions, and is used to describe the operating costs and performance indicators of the integrated energy park. The objective function includes thermal power fuel costs, wind power generation costs, photovoltaic power generation costs, energy storage costs, and purchased electricity costs. Constraints include (1) real-time balance of electricity and heat, and (2) upper and lower limits and change rates of electricity and heat output. Among them, constraint (1) is implemented by adding weighted maximum values ​​in the objective function, and constraint (2) is implemented by imposing upper and lower limit constraints on variables. Intelligent optimization algorithms are used to intelligently optimize the thermal power of each thermal power unit and thermal energy storage device. Intelligent optimization algorithms include particle swarm optimization algorithms, genetic algorithms, and ant colony algorithms. Intelligent optimization algorithms have global search capabilities and faster convergence speeds, and can handle complex optimization problems with nonlinearity, multiple objectives, and multiple constraints. Exemplarily, the intelligent optimization algorithm is a particle swarm optimization algorithm, and the fitness function of the particle swarm optimization algorithm is the optimization objective function. The initial population of the particle swarm optimization algorithm is constructed based on the thermal power and other data of each thermal power unit and thermal energy storage device. The population consists of particles, and each particle represents a possible scheduling result. For each particle, the fitness value is calculated according to the fitness calculation function, and the pros and cons of the current scheduling scheme (current particle) are evaluated according to the fitness value. Iterative optimization is performed according to the fitness function and the pre-set iterative formula, and the global optimal solution and the individual (particle) optimal solution are continuously updated until the pre-set stop condition is met (such as reaching the maximum number of iterations or the fitness value converges), and the plant-level scheduling result, that is, the optimal thermal power of each thermal power unit and thermal energy storage device, is obtained.

[0067] Step 506: Obtain the target scheduling result of the integrated energy park according to the park-level scheduling result and the plant-level scheduling result, and execute the target scheduling result.

[0068] Among them, the target dispatching results include the target wind power dispatching results, the target photovoltaic dispatching results, the target thermal power dispatching results and the target energy storage dispatching results. In this scheme, the target dispatching results of the comprehensive energy park are obtained according to the park-level dispatching results and the plant-level dispatching results, including: the target thermal power dispatching results are obtained based on the distribution results of the electric power of each thermal power unit, the thermal power of each thermal power unit and the total thermal power dispatching results; the target thermal power dispatching results, the park-level dispatching results and the plant-level dispatching results are sorted to obtain the target dispatching results.

[0069] Specifically, the plant-level dispatching results include the electric power and thermal power allocation results of each thermal power unit, and the park-level dispatching results include the total thermal power dispatching results. The plant-level dispatching results and the total thermal power dispatching results in the park and dispatching results are integrated to obtain the target thermal power dispatching results. The target thermal power dispatching results not only take into account the economic operation of each thermal power unit, but also ensure the overall efficiency and stability of the thermal power system. After obtaining the target thermal power dispatching results, the target thermal power dispatching results can be integrated and verified with the park-level dispatching results and the plant-level dispatching results to ensure that in each dispatching period, the total power generation of the system (including wind turbine power generation, photovoltaic energy power generation, thermal power generation and energy storage discharge, etc.) is consistent with the total load demand (including electrical load and thermal load). Check whether the operation of each power generation equipment meets its physical constraints, such as whether the power generation of each equipment is within its upper and lower limits. Ensure that the parameters such as the power and voltage of the interconnection line of the power grid are within the allowable range to avoid affecting the stability of the power grid. If it is found during the integration process that the operation of certain periods or certain power generation equipment does not meet the constraints, local adjustments are made to them. For example, appropriately adjust the output distribution of thermal power units, or change the charging and discharging strategy of energy storage. Under the premise of meeting all constraints, further optimize the dispatch results of each energy resource to improve the economy and reliability of the overall power system. Summarize the dispatch results of each energy resource after integration and adjustment to obtain the target wind power dispatch results, target photovoltaic dispatch results, target thermal power dispatch results, and target energy storage dispatch results. In this way, the target dispatch optimization results can be accurately obtained, and the target dispatch optimization results can be used to achieve efficient management and optimization of the entire integrated energy system, meet the production needs within the park, and further reduce the production electricity cost.

[0070] Figure 6 A schematic diagram of the system structure for optimizing the dispatch of power equipment in a comprehensive energy park provided by an embodiment of the present invention. Figure 6 As shown in the figure, the system for optimizing and dispatching power equipment includes a master station, a thermal substation, a wind substation, a photovoltaic substation, an energy storage substation, a load substation, and a power trading substation. The thermal substation also includes a thermal power supply substation and a thermal power heating substation, and the energy storage substation includes an electric energy storage substation and a thermal energy storage substation. The master station is connected to each substation through a network. The master station is connected to the cloud through a firewall, and the cloud transmits the data of the power system to the database and supports users to access the database remotely.

[0071] For example, Figure 7 Schematic diagram of configuration parameters of the power system provided by the embodiment of the present invention. Figure 7As shown, the off-grid power of the main grid is less than 700MW (megawatt), the photovoltaic power generation power in the park local power grid is 1GW (gigawatt), and the energy storage discharge power is 150MW-300MW; the power generation power of the thermal power unit is 2×300MW+2×350MW. The electrical load is 1500MW, and the thermal load steam supply is 400 tons / hour, that is, the steam supply per hour is 400 tons. The steam pressure is 1 MPa and the temperature is 360 degrees Celsius (℃). The cost formula is total variable cost = purchased electricity cost + thermal power fuel cost. Figure 8 This is a schematic diagram of the cost of purchased electricity in each time period of a certain day provided by an embodiment of the present invention. Figure 8 As shown, Figure 8 The horizontal axis represents time in hours, and the vertical axis represents the time-of-use electricity price in yuan / kWh. In the calculation process of the park-level dispatch results, the cost of thermal power generation is calculated at 0.18 yuan / kWh; in the calculation process of the plant-level dispatch results, the boiler heat consumed under different power generation and different heat release powers is converted into coal consumption. Fig. 9 The following is a schematic diagram of the photovoltaic output on a certain day provided by an embodiment of the present invention. Fig. 9 As shown, Fig. 9 The horizontal axis represents time in hours; the vertical axis represents power in MW. The thermal power operation constraints include that the maximum ramp rate of the thermal power unit is 1.5% / min (minutes); the unit output range is: 40%~100%. The load constraints include that the load can be adjusted by 10%; the load remains constant within a day (24 hours). The energy storage operation constraints include the maximum charge and discharge power of 150MW; the maximum storage capacity is 300MWh; the energy storage charge and discharge efficiency is 0.86; the initial and terminal energy storage capacity states are both set to 150MWh. The total amount of purchased electricity constraints include not imposing the total amount of purchased electricity constraints, calculating the total amount of purchased electricity based on the optimal cost; the total amount of purchased electricity remains consistent with the current amount. Fig.10 This is a schematic diagram of the park-level scheduling results provided by an embodiment of the present invention. Fig.10 As shown, Fig.10 The horizontal axis represents time in hours, and the vertical axis represents power in MW. Fig.10 The upper right corner shows Fig.10 The different shapes and colors represent specific contents, including electric load, thermal power generation power (thermal power), photovoltaic power generation power (photovoltaic), grid power (grid), energy storage discharge power and energy storage charging power. After obtaining the park-level dispatching results, build a thermal system under the steam extraction heating condition of the thermal power unit, perform thermal calculations, and obtain the input power parameters required by the boiler under different power supply loads and heating loads, providing input for the calculation of plant-level dispatching results. On the basis of the park-level dispatching results, considering the characteristics of power supply and heating of thermal power units, calculate the plant-level dispatching results and obtain the plant-level dispatching results. Fig.11 A schematic diagram of thermal power load distribution of plant-level scheduling results provided in an embodiment of the present invention. Fig.12 A schematic diagram of thermal power load distribution of plant-level scheduling results provided in an embodiment of the present invention. Fig.11 The horizontal axis represents time in hours, and the vertical axis represents power in MW. Fig.11 The figure also shows the specific contents represented by different shapes and colors, including four thermal power units and thermal power load. Fig.12 The horizontal axis represents time in hours, and the vertical axis represents power in MW. Fig.12 The figure also shows the specific contents represented by different shapes and colors, including four thermal power units and thermal power load.

[0072] In the technical solution of this embodiment, the park data of the comprehensive energy park is obtained, wherein the park data includes the historical operation data and cost data of the comprehensive energy zone. The power generation fuel cost, heating fuel cost and purchased electricity cost are substituted into the cost formula to obtain the cost function value of the comprehensive energy park. Based on the cost function value, park data and constraints, linear programming is performed on the power equipment of the comprehensive energy park to obtain the park-level scheduling result. The input data of the intelligent optimization algorithm is determined based on the park-level scheduling result and park data. The optimization objective function of the intelligent optimization algorithm is constructed based on cost parameters and operating boundary conditions. The comprehensive energy park is intelligently optimized according to the optimization objective function and the intelligent optimization algorithm to obtain the plant-level scheduling result. The target scheduling result of the comprehensive energy park is obtained according to the park-level scheduling result and the plant-level scheduling result. The technical solution of this embodiment, according to the park data, the power generation fuel cost, heating fuel cost and purchased electricity cost are substituted into the cost formula, and linear programming is performed in combination with the cost formula and constraints, so that the park-level scheduling result, i.e., the wind power scheduling result, photovoltaic scheduling result, total thermal power scheduling result and electric energy storage scheduling result, can be accurately obtained. The thermal power distribution of each generator set is obtained through an intelligent optimization algorithm, which takes into account both the macro-dispatching at the park level and the micro-dispatching at the plant level, thereby achieving the optimal allocation of energy and ensuring that the operating cost is minimized while meeting the load demand. The technical solution of this embodiment comprehensively considers the scheduling issues between the various elements of source (various power generation energy sources), network (power grid), load (load), and storage (energy storage device), and can flexibly respond to changes in different energy supply and demand, improve the flexibility and reliability of the power system of the comprehensive energy park, and ensure that the stable operation of the power system can be maintained under different working conditions.

[0073] Fig.13 The schematic diagram of the structure of a power equipment optimization scheduling device provided by an embodiment of the present invention is suitable for executing the power equipment optimization scheduling method provided by an embodiment of the present invention. Fig.13 As shown, the device may specifically include:

[0074] The data acquisition module 1301 is used to acquire park data of the integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone;

[0075] The first determination module 1302 is used to perform linear programming on the power equipment of the integrated energy park according to a predetermined cost formula, a predetermined constraint condition and the park data to obtain a park-level scheduling result;

[0076] The second determination module 1303 is used to perform intelligent optimization on the thermal power of each thermal power unit and the thermal energy storage device based on the park-level scheduling result, the park data and a predetermined intelligent optimization algorithm to obtain a plant-level scheduling result;

[0077] The result determination module 1304 is used to obtain the target scheduling result of the comprehensive energy park according to the park-level scheduling result and the plant-level scheduling result, and execute the target scheduling result; wherein the target scheduling result includes the target wind power scheduling result, the target photovoltaic scheduling result, the target thermal power scheduling result and the target energy storage scheduling result.

[0078] Optionally, the constraints include system power balance constraints, thermal power generation constraints, thermal power heating constraints, wind power operation power constraints, photovoltaic operation power constraints, electric energy storage operation constraints, thermal energy storage operation constraints, power grid electric power operation constraints, electric load adjustable constraints and thermal load adjustable constraints.

[0079] Optionally, the cost data includes the power generation fuel cost, heating fuel cost and purchased electricity cost of the thermal power unit; the first determination module 1302 is specifically used to: bring the power generation fuel cost, the heating fuel cost and the purchased electricity cost into the cost formula to obtain the cost function value of the comprehensive energy park;

[0080] Based on the cost function value, the park data and the constraint conditions, linear programming is performed on the power equipment of the integrated energy park to obtain a park-level scheduling result.

[0081] Optionally, the second determination module 1303 is specifically used to: determine the input data of the intelligent optimization algorithm based on the park-level scheduling result and the park data; the input data includes the cost parameters and operating boundary conditions of each thermal power unit; the cost parameters of each thermal power unit include the power generation cost, heating cost and comprehensive cost of each thermal power unit;

[0082] Constructing an optimization objective function of the intelligent optimization algorithm based on the cost parameter and the operating boundary condition;

[0083] According to the optimization objective function and the intelligent optimization algorithm, the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized to obtain a plant-level scheduling result.

[0084] Optionally, the second determination module 1303 is further used to: obtain the overall processing level of the thermal power unit based on the park-level scheduling result, and determine the cost parameter according to the overall processing level of the thermal power unit and the park data;

[0085] Determine the operating boundary conditions of each thermal power unit based on the park data and the system power balance constraint in the constraint conditions; wherein the operating boundary conditions include the power generation operating boundary and the heat supply operating boundary of each thermal power unit;

[0086] Input data for intelligent optimization is determined according to the cost parameters and the operating boundary conditions.

[0087] Optionally, the park-level dispatching result includes a wind power dispatching result, a photovoltaic dispatching result, a total thermal power dispatching result and an electric energy storage dispatching result; the plant-level dispatching result includes the electric power, thermal power of each thermal power unit and the thermal power allocation result of the thermal energy storage device; the result determination module 1304 is specifically used to: obtain the target thermal power dispatching result based on the electric power of each thermal power unit, the allocation result of the thermal power of each thermal power unit and the total thermal power dispatching result;

[0088] The target thermal power dispatching result, the park-level dispatching result and the plant-level dispatching result are sorted out to obtain the target dispatching result.

[0089] The power equipment optimization scheduling device provided in the embodiment of the present invention can execute the power equipment optimization scheduling method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. The contents not described in detail in this embodiment can refer to the description in any method embodiment of the present invention.

[0090] An embodiment of the present invention also provides a computer program product.

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] Fig.14 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, referring to Fig.14 , Fig.14 The electronic device 12 shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Fig.14 As shown, the electronic device 12 is in the form of a general computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16). The bus 18 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures. For example, these architectures include, but are not limited to, an industrial standard architecture (ISA) bus, a microchannel architecture (MAC) bus, an enhanced ISA bus, a video electronics standard association (VESA) local bus, and a peripheral component interconnect (PCI) bus. The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0093] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Fig.14not shown, usually called a "hard drive"). Although Fig.14 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical medium) can be provided. In these cases, each driver can be connected to the bus 18 through one or more data medium interfaces. The memory 28 may include at least one program product, which has a group (such as at least one) of program modules, which are configured to perform the functions of each embodiment of the present application. A program / utility 40 with a group (such as at least one) of program modules 46 can be stored in, for example, the memory 28, such program modules 46 include but are not limited to an operating system, one or more application programs, other program modules and program data, each of these examples or some combination may include the implementation of a network environment.

[0094] The program modules 46 generally perform the functions and / or methods in the embodiments described in the present application. The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Fig.14 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0095] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing a method for optimizing and dispatching electric power equipment provided in an embodiment of the present invention: obtaining park data of an integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone; performing linear programming on the electric power equipment of the integrated energy park according to a predetermined cost formula, predetermined constraints and the park data to obtain a park-level dispatching result; performing intelligent optimization on the thermal power of each thermal power unit and thermal energy storage device based on the park-level dispatching result, the park data and a predetermined intelligent optimization algorithm to obtain a plant-level dispatching result; obtaining a target dispatching result of the integrated energy park according to the park-level dispatching result and the plant-level dispatching result, and executing the target dispatching result; wherein the target dispatching result includes a target wind power dispatching result, a target photovoltaic dispatching result, a target thermal power dispatching result and a target energy storage dispatching result.

[0096] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing and scheduling electric power equipment as provided in all the embodiments of the present invention: obtaining park data of an integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone; performing linear programming on the electric power equipment of the integrated energy park according to a predetermined cost formula, predetermined constraints and the park data, to obtain a park-level scheduling result; performing intelligent optimization on the thermal power of each thermal power unit and thermal energy storage device based on the park-level scheduling result, the park data and a predetermined intelligent optimization algorithm, to obtain a plant-level scheduling result; obtaining a target scheduling result of the integrated energy park according to the park-level scheduling result and the plant-level scheduling result, and executing the target scheduling result; wherein the target scheduling result includes a target wind power scheduling result, a target photovoltaic scheduling result, a target thermal power scheduling result and a target energy storage scheduling result.

[0097] Computer readable medium can be a computer readable signal medium or a computer readable storage medium. Computer readable storage medium can be, for example, but not limited to, an electronic device, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution electronic device, device or device or used in combination with it. A computer readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer readable program code is carried. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable media other than computer-readable storage media that can send, propagate, or transport a program for use by or in conjunction with an instruction-executing electronic device, apparatus, or device.

[0098] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages, such as Java, Smalltalk, C++, and also includes conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0099] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for optimizing the dispatching of electric power equipment, characterized in that: The method comprises: Acquire park data of the integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone; Performing linear programming on the power equipment of the integrated energy park according to a predetermined cost formula, predetermined constraints and the park data to obtain a park-level scheduling result; Based on the park-level dispatching result, the park data and a predetermined intelligent optimization algorithm, the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized to obtain a plant-level dispatching result; The target scheduling result of the integrated energy park is obtained according to the park-level scheduling result and the plant-level scheduling result, and the target scheduling result is executed; wherein the target scheduling result includes a target wind power scheduling result, a target photovoltaic scheduling result, a target thermal power scheduling result and a target energy storage scheduling result.

2. The method according to claim 1, characterized in that The constraints include system power balance constraints, thermal power generation constraints, thermal power unit heating constraints, wind power operation power constraints, photovoltaic operation power constraints, electric energy storage operation constraints, thermal energy storage operation constraints, power grid electric power operation constraints, electric load adjustable constraints and thermal load adjustable constraints.

3. The method according to claim 1, characterized in that The cost data includes the power generation fuel cost, heating fuel cost and purchased electricity cost of the thermal power unit; linear programming is performed on the power equipment of the comprehensive energy park according to the predetermined cost formula, the predetermined constraint conditions and the park data to obtain the park-level scheduling results, including: Substituting the power generation fuel cost, the heating fuel cost and the purchased electricity cost into the cost formula to obtain the cost function value of the comprehensive energy park; Based on the cost function value, the park data and the constraint conditions, linear programming is performed on the power equipment of the integrated energy park to obtain a park-level scheduling result.

4. The method according to claim 1, characterized in that: Based on the park-level dispatching result, the park data and a predetermined intelligent optimization algorithm, the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized to obtain a plant-level dispatching result, including: Determining the input data of the intelligent optimization algorithm based on the park-level scheduling result and the park data; the input data includes cost parameters and operating boundary conditions of each thermal power unit; the cost parameters of each thermal power unit include power generation cost, heating cost and comprehensive cost of each thermal power unit; Constructing an optimization objective function of the intelligent optimization algorithm based on the cost parameter and the operating boundary condition; According to the optimization objective function and the intelligent optimization algorithm, the thermal power of each thermal power unit and the thermal energy storage device is intelligently optimized to obtain a plant-level scheduling result.

5. The method according to claim 4, characterized in that Determining input data of the intelligent optimization algorithm based on the park-level scheduling result and the park data includes: Obtaining the overall processing level of the thermal power units based on the park-level dispatching result, and determining the cost parameter according to the overall processing level of the thermal power units and the park data; Determine the operating boundary conditions of each thermal power unit based on the park data and the system power balance constraint in the constraint conditions; wherein the operating boundary conditions include the power generation operating boundary and the heat supply operating boundary of each thermal power unit; Input data for intelligent optimization is determined according to the cost parameters and the operating boundary conditions.

6. The method according to claim 1, characterized in that The park-level dispatching results include wind power dispatching results, photovoltaic dispatching results, total thermal power dispatching results and electric energy storage dispatching results; the plant-level dispatching results include the electric power and thermal power of each thermal power unit and the thermal power distribution results of the thermal energy storage device; The target scheduling result of the integrated energy park is obtained according to the park-level scheduling result and the plant-level scheduling result, including: Obtaining the target thermal power dispatch result based on the distribution results of the electric power of each thermal power unit, the thermal power of each thermal power unit and the total thermal power dispatch result; The target thermal power dispatching result, the park-level dispatching result and the plant-level dispatching result are sorted out to obtain the target dispatching result.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements a method for optimizing the scheduling of electric power equipment according to any one of claims 1 to 6.

8. An electric power equipment optimization dispatching device, characterized in that: include: A data acquisition module, used to acquire park data of the integrated energy park, wherein the park data includes historical operation data and cost data of the integrated energy zone; A first determination module is used to perform linear programming on the power equipment of the integrated energy park according to a predetermined cost formula, a predetermined constraint condition and the park data to obtain a park-level scheduling result; A second determination module is used to intelligently optimize the thermal power of each thermal power unit and the thermal energy storage device based on the park-level scheduling result, the park data and a predetermined intelligent optimization algorithm to obtain a plant-level scheduling result; A result determination module is used to obtain the target scheduling result of the comprehensive energy park according to the park-level scheduling result and the plant-level scheduling result, and execute the target scheduling result; wherein the target scheduling result includes the target wind power scheduling result, the target photovoltaic scheduling result, the target thermal power scheduling result and the target energy storage scheduling result.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the power equipment optimization scheduling method as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for optimizing and dispatching electric power equipment as described in any one of claims 1 to 6 is implemented.

Images