Multi-source energy collaborative scheduling method and system with participation of self-contained power plant

Abstract

According to the multi-source energy collaborative scheduling method and system with participation of the self-contained power plant, the objective function with the minimum energy consumption cost and the maximum market income is constructed, and the objective function is solved to complete source-grid-load-storage collaborative scheduling with the energy balance constraint, the equipment operation constraint and the electricity price driving as constraint conditions. In the electricity price driving constraint, the time-of-use electricity price division time period and waste heat energy storage coordinated charging and discharging forced strategy charging and discharging time period constraint and the electric boiler steam power consumption constraint are introduced into objective function solution, so that a scheduling decision can respond to an electricity market price signal in real time; meanwhile, collaborative application of energy storage and waste heat utilization is concerned, and electricity price arbitrage benefits are achieved through discharging of the energy storage system in a high-price period to participate in production.

Description

Technical Field

[0001] The present invention belongs to the technical field related to electricity, and in particular relates to a method and system for coordinated dispatching of multi-source energy involving self-owned power plants. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Existing technologies primarily focus on day-ahead / day-intraday static dispatch models, optimizing the power generation and energy storage output of photovoltaic, wind, and coal-fired units using mixed integer linear programming or traditional unit commitment methods. Scheduling decisions must be made before the day of operation, making it difficult to respond to fluctuations in production load and renewable energy output in real time. Some studies incorporate wind-solar hybrid systems and energy storage systems into the same MILP framework to minimize operating costs, but these typically fail to account for enterprise production load characteristics. Furthermore, energy storage charging and discharging often rely on static electricity price signals, limiting utilization and economic efficiency. Solutions to address price and output uncertainty often employ robust optimization or stochastic programming, modeling market fluctuations within large-scale power grids and virtual power plant scenarios. However, these approaches generally ignore the real-time demand and operating conditions of enterprise-level production. Furthermore, research on virtual power plants and multi-energy hybrid systems has primarily focused on the grid level, lacking in-depth optimization of production load characteristics, waste heat utilization, and coordinated control of energy storage systems within captive power plants in continuous production industries such as chemical and metallurgical industries. In addition, the scheduling of renewable energy communities and microgrids is mainly based on time arbitrage, lacking a precise matching strategy for industrial production continuity and load fluctuations. It is difficult to obtain optimal economic and environmental benefits amidst electricity price fluctuations and carbon market incentives, and it is also unable to meet the current comprehensive requirements of low-carbon transformation and efficient energy use.

[0004] In summary, the following problems still exist in the current low-carbon transformation of self-owned power plants: 1) Using a day-ahead / intraday static scheduling model, scheduling decisions are made in advance, making it difficult to respond to production load and new energy output fluctuations in real time, and the energy collaborative optimization capability is insufficient; 2) The characteristics of enterprise production loads are not taken into account. Energy storage charging and discharging mostly rely on static electricity price signals and lack dynamic coordinated control with production conditions, which limits energy storage utilization and economic efficiency. 3) Only taking energy consumption cost as the optimization target, ignoring the dynamic coupling of peak / flat / valley time-of-use electricity prices and carbon market costs, lacking a multi-objective coordinated optimization mechanism for economic benefits and carbon emission reduction Summary of the Invention To overcome the deficiencies of the above-mentioned prior art, the present invention provides a multi-source energy collaborative scheduling method and system involving self-owned power plants, which effectively improves the self-owned power plants' capacity to absorb new energy, the utilization rate of energy storage systems, and the comprehensive energy management benefits, and provides an innovative technical path for low-carbon and intelligent energy use in the industrial field.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for coordinated dispatching of multi-source energy with the participation of a self-owned power plant, comprising: Establish a multi-source energy model to characterize the energy supply costs of captive power plants and the power grid; Based on the established multi-source energy model, an objective function is established with the goal of minimizing the energy consumption cost and maximizing the market profit of the multi-source energy system. The objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints, and the coordinated operation of the self-contained power plant and the power grid is achieved; wherein, the electricity price drive constraints include the time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with the waste heat storage, the charging and discharging period constraints, and the steam consumption constraints of the electric boiler.

[0006] In a second aspect, the present invention provides a multi-source energy coordinated dispatching system involving self-owned power plants, comprising: A model building module is configured to: establish a multi-source energy model that characterizes the energy supply costs of various energy sources from a self-owned power plant and a power grid; The solution module is configured as follows: based on the established multi-source energy model, an objective function is established with the goal of minimizing the energy consumption cost and maximizing the market profit of the multi-source energy system, and the objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints, so as to achieve the coordinated operation of the self-contained power plant and the power grid; wherein, the electricity price drive constraint includes the time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with the waste heat storage, the charging and discharging period constraint and the steam consumption constraint of the electric boiler.

[0007] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0009] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0010] One or more of the above technical solutions have the following beneficial effects: In the present invention, multi-source energy including self-owned power plants is modeled, and an objective function of minimizing energy consumption cost and maximizing market benefits is constructed. The objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints to complete the coordinated dispatch of source, grid, load and storage. The time-of-use electricity price division period in the electricity price drive constraint is coordinated with the charging and discharging mandatory strategy of waste heat storage, the charging and discharging period constraint and the steam consumption constraint of electric boilers. By introducing time-of-use electricity prices into the objective function solution, the dispatching decision can respond to the price signal of the electricity market in real time. At the same time, attention is paid to the coordinated application of energy storage and waste heat utilization, and the energy storage system is discharged during high-price periods to participate in production to achieve electricity price arbitrage benefits.

[0011] The solution of the present invention effectively improves the self-contained power plant's ability to absorb new energy, the utilization rate of the energy storage system and the comprehensive energy management benefits, and provides an innovative technical path for low-carbon and intelligent energy use in the industrial field.

[0012] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0014] Figure 1 This is a flow chart of the multi-source energy collaborative scheduling method involving a self-owned power plant in the first embodiment of the present invention. DETAILED DESCRIPTION

[0015] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0016] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0017] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0018] Example 1 This embodiment discloses a multi-source energy collaborative scheduling method involving a self-owned power plant, including: Establish a multi-source energy model to characterize the energy supply costs of captive power plants and the power grid; Based on the established multi-source energy model, an objective function is established with the goal of minimizing the energy consumption cost and maximizing the market profit of the multi-source energy system. The objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints, and the coordinated operation of the self-contained power plant and the power grid is achieved; wherein, the electricity price drive constraints include the time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with the waste heat storage, the charging and discharging period constraints, and the steam consumption constraints of the electric boiler.

[0019] The following is a detailed description of the multi-source energy coordinated scheduling method proposed in this embodiment involving a self-owned power plant: Construct a multi-source energy supply modeling system: establish power generation and energy supply models for various energy forms such as self-contained coal-fired units, photovoltaics, wind power, electric boilers and grid-purchased electricity, and characterize the energy supply costs of each type of energy respectively.

[0020] 1. Power generation cost of coal-fired units :

[0021] in, For the period Power generation of coal-fired units (kWh); is the benchmark coal-fired power generation cost (yuan / kWh).

[0022] 2. Cost of purchasing electricity from the power grid / connecting to the grid / .

[0023] Constraints on electricity purchase and sales:

[0024] in, , Time The amount of electricity purchased from the grid / connected to the grid (kWh).

[0025]

[0026] in, and Time-of-use electricity purchase / sale price (yuan / kWh) (if there is no grid-connected income, ).

[0027] 3. Cost of steam generation by electric boiler :

[0028] in, For the period Electric boiler power consumption (kWh); For the period Enterprise steam demand (t / h); is the thermal efficiency of the electric boiler.

[0029] 4. Photovoltaic and wind power have no additional operating costs and directly “zero” marginal costs:

[0030] in, , For the period Available output of photovoltaic and wind power (kWh) (given or calculated by output factor).

[0031] 5. Energy storage system.

[0032] Charge and discharge power constraints:

[0033] in, , For the period Energy storage system charge / discharge capacity (kWh).

[0034] SOC dynamic balance:

[0035] in, For the period Energy storage system SOC (kWh); , It is the energy storage charging and discharging efficiency.

[0036] SOC Boundary:

[0037] Establish a production load characteristic model: collect and model enterprise production load data such as steam demand and power load. Power load balancing for each period ,satisfy:

[0038] The steam load has been incorporated into the power balance, through accomplish.

[0039] 6. Waste heat recovery system The waste heat from the flue gas of the coal-fired unit is converted into steam through the waste heat boiler to drive power generation. satisfy:

[0040] in, is the waste heat boiler efficiency, is the waste heat conversion coefficient of the coal-fired unit Constraints:

[0041] Introduce a time-of-use electricity price mechanism and constraints: divide electricity prices into three periods: peak, flat, and valley, and establish an economic model for each energy supply mode, especially constraining the energy storage system to charge during valley periods and discharge during peak periods.

[0042] The definition of peak / off-peak / valley periods directly determines the following: 1. The charging and discharging period constraints of the energy storage system (valley charging and peak discharging); 2. The calculation of cost items that depend on electricity prices, such as power purchases from the grid and electric boilers; Electricity price time-of-use mechanism and constraints: Time-of-use electricity price Definition of time periods:

[0043] Energy storage charging and discharging mandatory strategy, and charging and discharging period constraints for the coordinated use of waste heat and energy storage:

[0044] That is, waste heat is charged first during off-peak hours, and at least 80% of waste heat power generation during off-peak hours is used for energy storage charging, reducing power purchases from the grid; waste heat is supplemented for discharge during peak hours, and when the energy storage SOC is lower than 50,000kWh, waste heat power generation supplements 50% of the discharge gap.

[0045] Combined with capacity constraints .

[0046] This example constructs a collaborative optimization objective function: This function comprehensively considers energy costs, market returns, and fixed equipment costs, centered around profit maximization. By integrating energy costs and market returns, this objective function minimizes the equation "total cost minus total return" to achieve the dual optimization of "minimizing energy costs and maximizing market returns." The solution must satisfy the aforementioned production load characteristic model.

[0047] The specific expression of the objective function is:

[0048] Among them, the cost items include: coal-fired power generation cost , the cost of purchasing electricity from the power grid , electricity cost of electric boiler ; Income items are: Grid electricity sales revenue ; Fixed costs are: New energy / energy storage daily fixed costs .

[0049] The objective function must satisfy the following constraints: 1. Energy balance constraints: (1) Power load balance, (2) SOC dynamic balance.

[0050] 2. Equipment operation constraints: (1) Power purchase and sales constraints, (2) SOC boundaries.

[0051] 3. Electricity price driving constraints: (1) Time-of-use electricity price division and charging and discharging mandatory strategy coordinated with waste heat storage, charging and discharging period constraints, (2) Electric boiler steam generation power consumption.

[0052] Data acquisition and control execution: For data collection, the system connects to photovoltaic inverters, energy storage BMSs, and grid metering devices via the Modbus protocol for real-time data acquisition. For computational optimization, a multi-objective optimization engine supports online algorithm switching. For control execution, a PLC controller regulates coal-fired unit output, energy storage charging and discharging switches, and electric boiler power, with command response latency less than 100ms.

[0053] Method for solving the constructed objective function: The objective function is solved using a linear programming (LP) algorithm. By constructing a single-objective optimization model under linear constraints and using an industrial-grade solver to achieve efficient scheduling decisions, the specific steps are as follows: 1. Data preprocessing and model input Basic parameter configuration: Input energy costs (such as benchmark cost of coal power = 0.3 yuan / kWh, peak / flat / valley electricity prices (1.0 yuan / kWh during peak hours, 0.2 yuan / kWh during valley hours), equipment parameters (30MW coal-fired unit capacity, 120,000kWh energy storage capacity, 0.9 charge and discharge efficiency), load data (power load ), steam demand 110 tons / h) and new energy output (photovoltaic 10MW, wind power 2MW).

[0054] Variable and constraint initialization: Define the continuous decision variable: coal-fired power generation , Power grid purchase / sale of electricity 、 Energy storage charging / discharging power 、 , energy storage SOC , waste heat power generation , and initialize the initial state of energy storage (50% capacity), waste heat boiler efficiency =0.3kWh / yuan.

[0055] 2. Linear programming model construction Objective function (profit maximization, equivalent to minimizing "total cost − total benefit"):

[0056] Constraints: Energy balance constraints: Power load balancing:

[0057] SOC dynamic balance:

[0058] Equipment operation constraints: Equipment output limit:

[0059] It represents the actual output (kWh) of photovoltaic or wind power in time period t, that is, the actual amount of electricity generated by the photovoltaic / wind turbine in this time period, which is affected by factors such as weather and equipment operating status.

[0060] It represents the available output (kWh) of photovoltaic or wind power in time period t, that is, the maximum possible power generation of the photovoltaic / wind turbine under ideal conditions (such as full power operation) during this period, which is usually determined by natural conditions such as light intensity and wind speed as well as equipment capacity.

[0061] Energy storage operation constraints:

[0062] Constraints on electricity purchase and sales: ( ) Waste heat power generation:

[0063] SOC Boundary:

[0064] Electricity price driven constraints: (1) Define the electricity price signals and time ranges during peak, flat and valley periods, which directly affect the electricity purchase cost, energy storage charging and discharging strategy and waste heat utilization priority. Valley period (11:00-15:00): Energy storage charging capacity , ensuring that waste heat is used for energy storage charging first; Peak period (18:00-22:00): when energy storage When waste heat power generation , filling the gap in energy storage discharge. (2) Electric boiler steam consumption constraint: Electric boiler power consumption is determined by the rigidity of steam demand, that is, it is directly included in the power load balance calculation.

[0065] 3. Solver configuration and optimization execution Calling a commercial solver: Input the constructed linear programming model into a dedicated optimization program (such as energy_optimization_full.py) and solve it by calling an industrial-grade solver. The configuration parameters are as follows: Solution parameters: Set the solution time limit to 300 seconds and the optimal solution accuracy to 10 -6, ensuring that 24-hour optimization is completed within 10 seconds; Program execution: Run the optimization program to automatically generate variables, constraints, and objective functions, and call the solver to complete the 24-period scheduling optimization; Result output: The program outputs energy supply strategies, grid interaction plans, and equipment operating parameters for each time period.

[0066] 4. Scheduling result output and execution Generate an optimization plan: Output energy supply strategies for each time period (e.g., 30,000kWh of energy storage charging during off-peak hours and 108,000kWh of energy storage discharging during peak hours), grid interaction plans (20,000kWh of electricity purchased during off-peak hours and 4,000kWh of electricity purchased during peak hours), and equipment operating parameters.

[0067] Control instructions are issued: The PLC system executes the dispatch plan L: 1. Adjust the output of the coal-fired unit to the optimal value; 2. Control the energy storage system to coordinate the charging of waste heat during valley hours and discharge during peak hours; 3. The electric boiler operates rigidly according to steam demand ( ).

[0068] In this embodiment, the objective function is solved by executing an optimization program (e.g., energy_optimization_full.py), which models the energy scheduling problem as a linear programming model and solves it using a commercial solver. The optimization program ensures 24-hour scheduling optimization within 10 seconds, meeting industrial-grade real-time control requirements. The program's core logic includes variable definition, constraint addition, and result parsing. The specific algorithm flow is described in the objective function solving method above. Through this optimized scheduling strategy, the embodiment achieves coordinated operation of the captive power plant and the power grid, with energy storage charging during off-peak hours and discharging during peak hours, effectively reducing the company's electricity costs on a typical day, validating the effectiveness of the method described in this paper.

[0069] Assume a company operates a coal-fired, self-contained power plant with stable production. The unit capacity is 30MW, and the steam output is 110 tons / h. The steam and electricity generated are all used for production, and 30MWh of electricity is drawn from the grid every hour. Without coordinated dispatch of power sources, grids, loads, and storage, and without the construction of renewable energy generation and energy storage, the company's daily energy consumption cost is: Coal-fired power generation costs:

[0070]

[0071] Grid electricity cost:

[0072] =432,000 yuan Total energy cost:

[0073] In the above calculation, the coal price is 1,200 yuan / ton, the coal consumption per kilowatt-hour is 300 grams of coal / kilowatt-hour, and the average electricity price is 0.6 yuan / kilowatt-hour.

[0074] When the power plant builds a wind-solar complementary and energy storage system and adopts the method of this embodiment to optimize the multi-system operation scheduling by combining production energy consumption characteristics, peak / flat / valley time-of-use electricity prices, new energy output and energy storage status.

[0075] First, consider the differences in electricity prices during different time periods. Assuming there are three electricity price standards for peak, off-peak, and off-peak periods, the off-peak price is 0.2 yuan / kWh from 11:00 AM to 3:00 PM, the peak price is 1.0 yuan / kWh from 6:00 PM to 10:00 PM, and the remaining price remains at 0.6 yuan / kWh. If the plant site covers 100 mu (approximately 16 acres) and the plant area is 100,000 square meters, the total installed capacity of solar photovoltaic power generation on the plant can reach 10 MW. If a 2 MW wind turbine is also installed, with an effective utilization time of 6 hours per day, the daily power generation is 12,000 kWh, for a total of 42,000 kWh of renewable energy generation.

[0076] For this enterprise, if the energy storage system has a capacity of 120,000 kWh, energy storage is performed when the electricity price is at its lowest point (0.2 yuan / kWh), the output of the coal-fired units is reduced, and all steam is generated by electric boilers to provide normal steam for the enterprise's production. When the electricity price is at its peak, the coal-fired units are operated normally, and the energy storage system is used to provide electricity for production. The enterprise's electricity and gas demand remains the same as before, and the energy consumption cost is calculated as: (1) Valley period (11:00 - 15:00, 4 hours) Energy storage charging capacity:

[0077]

[0078] Energy storage charging cost:

[0079] Electricity cost of electric boiler:

[0080]

[0081] Wind power provides electricity: Waste heat power generation:

[0082] Of this, 7200kWh / h is used for energy storage charging, reducing the grid's electricity purchase costs:

[0083] During the off-peak period, the cost of electricity purchased from the power grid for the enterprise is:

[0084] (2) Peak hours (18:00 - 22:00, 4 hours) Power generation of coal-fired units:

[0085]

[0086] Energy storage discharge capacity:

[0087]

[0088] Wind power provides electricity:

[0089] Waste heat power generation: When the energy storage SOC is lower than 50,000 kWh, waste heat power generation can supplement the discharge gap, reducing the high-priced electricity purchase by 2,500 kWh / h and saving costs:

[0090] The cost of electricity purchased from the power grid for corporate electricity consumption is:

[0091] (3) At-the-money period (the remaining 16 hours) Power generation of coal-fired units:

[0092]

[0093] Wind power provides electricity:

[0094] The cost of electricity purchased from the power grid for corporate electricity consumption is:

[0095] Solar energy is calculated at 100% output during the off-peak period and 50% output between 8:00-11:00 and 11:00-18:00. The total daily power generation is 70MWh. Taking into account the different electricity prices in different periods, solar power generation can save 26,000 yuan per day.

[0096] The total construction cost of 10MW solar photovoltaic power generation is 40 million yuan, and the total construction cost of 2MW wind power generation is 16 million yuan. If the service life is converted to 25 years, the average daily cost is 6,136 yuan.

[0097] Total production energy cost:

[0098] The self-contained power plant built a wind-solar complementary and energy storage system and adopted this method to combine production energy consumption characteristics, peak / flat / valley time-of-use electricity prices, new energy output and energy storage status to optimize multi-system operation and scheduling, which can increase daily benefits by 52,544 yuan.

[0099] This embodiment aims to address core issues facing traditional captive power plants during the low-carbon transition, including inefficient multi-energy synergy, insufficient time-of-use electricity price responsiveness, and poor energy storage economics. This embodiment's technical solution closely addresses captive power plant scenarios in continuous production industries such as chemical, metallurgical, and building materials. By constructing a dynamic coupling model encompassing "production energy characteristics - multi-source energy supply - time-of-use electricity price signals - energy storage status," it achieves fully integrated and optimized scheduling of coal-fired units, photovoltaic power, wind power, energy storage devices, and grid power purchases. This invention transcends the limitations of traditional single-coal power supply and static scheduling models by incorporating peak, flat, and valley electricity prices into a multi-objective optimization function, enabling scheduling decisions to respond to electricity market price signals in real time. It also focuses on the coordinated application of energy storage and waste heat utilization, enabling energy storage systems to discharge during high-price periods to participate in production and achieve electricity price arbitrage benefits. Technological innovation is reflected in the collaborative modeling of multi-source energy, dynamic scheduling strategies driven by time-of-use electricity prices, the construction of multi-objective optimization systems, and the collaborative control mechanism of waste heat and energy storage. Through the integration of the above technologies, the self-contained power plant's ability to absorb new energy, the utilization rate of the energy storage system, and the comprehensive energy management benefits are effectively improved, providing an innovative technical path for low-carbon and intelligent energy use in the industrial field.

[0100] Example 2 The purpose of this embodiment is to provide a multi-source energy coordinated dispatching system involving self-owned power plants, including: A model building module is configured to: establish a multi-source energy model that characterizes the energy supply costs of various energy sources from a self-owned power plant and a power grid; The solution module is configured as follows: based on the established multi-source energy model, an objective function is established with the goal of minimizing the energy consumption cost and maximizing the market profit of the multi-source energy system, and the objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints, so as to achieve the coordinated operation of the self-contained power plant and the power grid; wherein, the electricity price drive constraint includes the time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with the waste heat storage, the charging and discharging period constraint and the steam consumption constraint of the electric boiler.

[0101] In further embodiments, there is also provided: An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed by the processor. When the computer instructions are executed by the processor, the method described in Example 1 is performed. For the sake of brevity, no further details are given here.

[0102] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0103] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0104] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in embodiment 1 is performed.

[0105] The method in Example 1 can be directly implemented as being executed by a hardware processor, or by a combination of hardware and software modules within the processor. The software module can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not given here.

[0106] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0107] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions contained in program modules, which are executed in a device on a real or virtual processor of a target to perform the process / method described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0108] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0109] In the context of the present invention, computer program code or related data can be carried by any appropriate carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0110] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A multi-source energy collaborative dispatching method involving self-owned power plants, characterized in that: include: Establish a multi-source energy model to characterize the energy supply costs of captive power plants and the power grid; Based on the established multi-source energy model, an objective function is established with the goal of minimizing the energy consumption cost and maximizing the market profit of the multi-source energy system. The objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints, and the coordinated operation of the self-contained power plant and the power grid is achieved; wherein, the electricity price drive constraints include the time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with the waste heat storage, the charging and discharging period constraints, and the steam consumption constraints of the electric boiler.

2. The multi-source energy coordinated scheduling method involving a self-owned power plant according to claim 1, characterized in that: The energy balance constraint includes a power load balance constraint and an SOC dynamic balance constraint.

3. The multi-source energy coordinated scheduling method involving a self-owned power plant according to claim 1, characterized in that: The equipment operation constraints include: equipment output limit constraints, energy storage operation constraints, power purchase and sales constraints, waste heat power generation constraints and SOC boundary constraints.

4. The multi-source energy coordinated scheduling method involving a self-owned power plant according to claim 1, characterized in that: The objective function is: ， in, The cost of coal-fired power generation, The cost of purchasing electricity from the grid, The electricity cost of the electric boiler is The revenue from electricity sales to the power grid, It is the daily fixed cost of new energy / energy storage.

5. The multi-source energy coordinated dispatching method involving a self-owned power plant according to claim 1, characterized in that: The time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with waste heat energy storage charging and discharging period constraints include: if it is in the valley period, then ,in, for The amount of energy storage system charged during the period, for Waste heat power generation during the period; if ,but , for The discharge amount of the energy storage system during the period.

6. The multi-source energy coordinated dispatching system involving self-owned power plants is characterized by: include: A model building module is configured to: establish a multi-source energy model that characterizes the energy supply costs of various energy sources from a self-owned power plant and a power grid; The solution module is configured as follows: based on the established multi-source energy model, an objective function is established with the goal of minimizing the energy consumption cost and maximizing the market profit of the multi-source energy system, and the objective function is solved with energy balance constraints, equipment operation constraints and electricity price drive as constraints, so as to achieve the coordinated operation of the self-contained power plant and the power grid; wherein, the electricity price drive constraint includes the time-of-use electricity price division period and the charging and discharging mandatory strategy coordinated with the waste heat storage, the charging and discharging period constraint and the steam consumption constraint of the electric boiler.

7. The multi-source energy coordinated dispatching system involving a self-owned power plant according to claim 6, characterized in that: In the solution module, the energy balance constraints include power load balance constraints and SOC dynamic balance constraints; the equipment operation constraints include: equipment output limit constraints, energy storage operation constraints, power purchase and sales constraints, waste heat power generation constraints and SOC boundary constraints.

8. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 5 is completed.

9. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 5.

10. A computer program product, characterized in that The invention comprises a computer program, which is used to implement the method according to any one of claims 1 to 5 when the computer program is executed by a processor.

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