Method and system for optimizing low-carbon operation of power system

Abstract

The invention discloses a power system low-carbon operation optimization method and system, and the method comprises the steps: constructing a price type demand response model which comprises a reducible load and a transferable load and a replacement type demand response model which enables the mutual conversion of electric energy and heat energy based on a comprehensive energy management system architecture; establishing an energy supply side multi-energy coupling equipment operation model, introducing a green certificate and stepped carbon transaction mutual recognition mechanism, and constructing a green certificate-stepped carbon transaction mathematical model; constructing a demand response optimization scheduling model of the integrated energy system under the green certificate-stepped carbon transaction mechanism by taking the minimum total cost of system operation as a target; and carrying out optimization solution on the model to obtain a multi-target collaborative power system operation optimal scheme. According to the method, collaborative optimization of demand response, green certificate and carbon transaction mechanism is realized, peak load shifting is effectively realized, the system operation cost and carbon emission are reduced, and decision support is provided for low-carbon operation of a novel power system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system operation optimization technology, and particularly relates to a method and system for optimizing the low-carbon operation of a power system. Background Technology

[0002] Traditional power system optimization methods primarily focus on supply-demand balance and cost minimization. However, modern power systems need to consider more factors, including renewable energy integration, load-side demand response, and green certificate-carbon trading. Load-side demand response is a method for managing power system load by adjusting end-user energy consumption behavior to respond to changes in system demand and balance the gap between supply and demand. On the other hand, green certificates and carbon trading are important mechanisms for reducing greenhouse gas emissions. The green certificate system incentivizes the system to consume a certain proportion of green electricity by granting a certain number of certificates to renewable energy generators. Carbon trading is a market mechanism that achieves greenhouse gas emission reduction targets by trading carbon dioxide emission credits.

[0003] However, most existing studies consider demand response and green certificate-carbon trading mechanisms separately, failing to fully leverage their synergistic effects. Furthermore, in energy supply-side modeling, there is insufficient detailed modeling of multi-energy coupled devices such as cogeneration units, heat pumps, and Kalina cycles, making it difficult to accurately characterize the system's energy conversion relationships. Regarding energy storage constraints, traditional fixed capacity constraints cannot reflect the dynamic characteristics of energy storage devices in actual operation, affecting the accuracy of optimization results. Therefore, a low-carbon operation optimization method for power systems that comprehensively considers load-side demand response, green certificate-carbon trading mechanisms, and the dynamic characteristics of multi-energy coupled devices on the energy supply side is needed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for optimizing the low-carbon operation of power systems. By constructing a multi-type demand response behavior model on the load side and a multi-energy coupled equipment operation model on the energy supply side, and introducing a mutual recognition mechanism and collaborative constraint relationship between green certificates and tiered carbon trading, a unified optimization scheduling model is established to achieve multi-objective collaborative optimization of the low-carbon, safe and economical aspects of the power system.

[0005] In a first aspect, the present invention provides a method for optimizing the low-carbon operation of a power system, comprising: Construct a comprehensive energy management system architecture that takes into account load-side demand response; Based on the aforementioned integrated energy management system architecture, and considering the characteristics of load-side user behavior, a price-based demand response model that includes load reduction and load transfer, as well as a substitution demand response model that allows for the conversion of electricity and heat, are constructed. Based on the price-based demand response model and the substitution-based demand response model, an operation model for energy supply-side equipment is established. Based on the operation model for energy supply-side equipment, a mutual recognition mechanism between green certificates and tiered carbon trading is introduced to construct a mathematical model for green certificate-tiered carbon trading. Based on the aforementioned green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total system operating cost, and considering power balance, equipment operating upper and lower limits, ramping constraints, and dynamic feasible domain constraints of energy storage equipment, a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism is constructed. The demand response optimization scheduling model is optimized and solved to obtain the optimal operating scheme of the power system with multi-objective cooperation.

[0006] Secondly, the present invention provides a low-carbon operation optimization system for power systems, comprising: The first building module is configured to build an integrated energy management system architecture that takes into account load-side demand response; The second construction module is configured to construct a price-based demand response model that includes load reduction and load transfer, as well as an alternative demand response model that converts electricity and heat, based on the integrated energy management system architecture and considering the characteristics of load-side user behavior. The third construction module is configured to establish an energy supply-side equipment operation model based on a price-based demand response model and a substitution-based demand response model, and based on the energy supply-side equipment operation model, introduce a mutual recognition mechanism between green certificates and tiered carbon trading to construct a green certificate-tiered carbon trading mathematical model. The fourth construction module is configured to construct a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism based on the aforementioned green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total operating cost of the system, taking into account power balance, upper and lower limits of equipment operation, ramping constraints, and dynamic feasible domain constraints of energy storage equipment. The solution module is configured to optimize and solve the demand response optimization scheduling model to obtain the optimal operating scheme of the power system with multi-objective cooperation.

[0007] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the power system low-carbon operation optimization method according to any embodiment of the present invention.

[0008] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the power system low-carbon operation optimization method according to any embodiment of the present invention.

[0009] This application presents a method and system for optimizing the low-carbon operation of power systems, a demand response model, and an alternative demand response model for the mutual conversion of electrical and thermal energy. It establishes an operation model for multi-energy coupled equipment on the energy supply side and introduces a green certificate and tiered carbon trading mutual recognition mechanism to construct a green certificate-tiered carbon trading mathematical model. With the goal of minimizing the total system operating cost, it constructs a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism. The model is optimized and solved to obtain the optimal power system operation scheme with multi-objective synergy, achieving synergistic optimization of demand response, green certificates, and carbon trading mechanisms. This effectively reduces peak shaving and valley filling, lowers system operating costs and carbon emissions, and provides decision support for the low-carbon operation of new power systems. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart of a low-carbon operation optimization method for a power system provided in an embodiment of the present invention; Figure 2 A structural block diagram of a power system low-carbon operation optimization system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Please see Figure 1 The diagram shows a flowchart of a method for optimizing the low-carbon operation of a power system according to this application.

[0014] like Figure 1 As shown, the optimization method for low-carbon operation of power systems specifically includes the following steps: Step S101: Construct a comprehensive energy management system architecture that takes into account load-side demand response.

[0015] In this step, an integrated energy management system architecture is constructed, which includes the energy supply side, energy storage side, load side, and external energy network. The energy supply side includes cogeneration units, gas boilers, heat pumps, and Karina cycles. The energy storage side includes electrical energy storage and thermal energy storage. The load side includes electrical load and thermal load. The external energy network includes the upstream power grid and the upstream gas grid.

[0016] Step S102: Based on the integrated energy management system architecture and considering the characteristics of load-side user behavior, construct a price-based demand response model that includes load reduction and load transfer, as well as an alternative demand response model that converts electricity and heat into each other.

[0017] In this step, the price-based demand response model includes a load reduction model and a load transfer model. The load reduction model satisfies the load reduction capacity constraint, the cumulative reduction constraint, and the continuous reduction constraint; the load transfer model satisfies the energy conservation constraint and the transfer capacity constraint. The expression for the load reduction model is: , , In the formula, This represents the actual power consumption that can be reduced after the reduction in time period t. The baseline power consumption that can be reduced for a time period t. This represents the amount of load reduction that can be reduced over time period t. The price response coefficient for reducing load over a time period t. The real-time electricity price for time period t. The benchmark electricity price for time period t. The price sensitivity index for load reduction, This represents the maximum amount of load that can be reduced over a time period t. The expression for the transferable load model is: , , , In the formula, This represents the actual electrical power of the load that can be transferred over time period t. This represents the baseline electrical power consumption of the transferable load over a time period t. This represents the transferable load power transferred from other time periods during time period t. This represents the transferable load power transferred out from other time periods during time period t. The price response coefficient for transferable loads is used to characterize the intensity of load transfer. This represents the average electricity price during the dispatching period.

[0018] Specifically, the expressions for the capacity reduction constraint, cumulative weakening constraint, and continuous reduction constraint are as follows: , , , In the formula, This represents the maximum consecutive load reduction threshold allowed within adjacent time periods. This represents the amount of load reduction that can be reduced in time period t+1. The maximum cumulative reduction that the user can accept; The expressions for the energy conservation constraint and the transfer capacity constraint are as follows: , , , In the formula, This represents the maximum transferable load over time period t.

[0019] Furthermore, the alternative demand response model satisfies the maximum substitutable load constraint; The expression for the alternative demand response model is: , , In the formula, For alternative electrical loads, The electrothermal substitution coefficient, The amount of heat load to be replaced. The calorific value per unit of electrical energy. For the energy utilization rate of electricity, The calorific value of heat energy per unit. Energy utilization rate of thermal energy; The expression for the maximum replaceable load constraint is: , In the formula, , These are the minimum and maximum substitution amounts of the replaceable electrical load, respectively. , These represent the minimum and maximum replacement amounts of the replaceable heat load, respectively.

[0020] Step S103: Based on the price-based demand response model and the substitution-based demand response model, establish an energy supply-side equipment operation model, and based on the energy supply-side equipment operation model, introduce a mutual recognition mechanism between green certificates and tiered carbon trading to construct a green certificate-tiered carbon trading mathematical model.

[0021] In this step, the operating model of the power supply side equipment includes the electrical output power model and the thermal output power model of the gas turbine in the combined heat and power unit, and the expression is: , , In the formula, This refers to the power generation efficiency of a gas turbine under rated operating conditions. Let be the gas input power of the gas turbine during time period t. This represents the maximum gas input power of the gas turbine. This is the exhaust waste heat proportionality coefficient of the gas turbine under rated operating conditions. This refers to the electrical output power of the gas turbine. and These are the partial load correction factors for the electrical efficiency of the gas turbine. This refers to the heat output power of the gas turbine. and These are the partial load correction factors for the waste heat from gas turbine exhaust; The energy supply-side equipment operation model also includes an energy distribution model for the gas turbine after incorporating the Kalina cycle and heat pump, expressed as follows: , In the formula, for The electrical power flowing from the gas turbine into the heat pump unit at any given time. For gas turbine The electrical power constantly flowing to the electrical subsystem for The heat power input from the gas turbine to the waste heat boiler at any given time. For gas turbine The heat power is constantly input into the Karina cycle; The power supply side equipment operation model also includes an electrical output power model and a heat output power model for the cogeneration unit after introducing the Karina cycle and heat pump, with the following expressions: , In the formula, For the cogeneration unit on the power supply side The electrical and thermal output power at any given time For Karina's cycle The electrical power output at all times For the cogeneration unit on the power supply side The electrical and thermal output power at any given time Waste heat boiler The heat output power at all times, For heat pump The thermal power output at all times; The energy supply-side equipment operation model also includes energy conversion models for the Karina cycle, waste heat boiler, heat pump, and gas boiler, expressed as follows: , , , , , , , In the formula, for The low-temperature waste heat that Karina circulates and discharges at all times The waste heat ratio coefficient for the Karina cycle. For the Karina cycle thermoelectric conversion efficiency. for Waste heat constantly enters the waste heat boiler. for The steam output from the waste heat boiler at all times The heat exchange efficiency of the waste heat boiler. for The heat output power of the heat pump unit at all times. The coefficient of performance (COP) of the heat pump unit. for The electrical power consumed by the heat pump unit at all times. for The heat output of the gas boiler at all times For the thermal efficiency of gas-fired boilers, for The amount of natural gas consumed by the gas-fired boiler at any given time; The energy supply-side equipment operation model also includes electrical energy storage and thermal energy storage models, expressed as follows: , , In the formula, The energy of charging and discharging at time t. The self-discharge rate of the energy storage device. The charge / discharge energy at time t-1 To improve the charging efficiency of energy storage devices. The charging power of energy storage devices, This refers to the discharge power of the energy storage device. The discharge efficiency of the energy storage device. The stored heat energy at time t For heat storage loss rate, For the stored heat energy at time t-1, The input conversion rate of the thermal storage equipment. The input thermal power of the thermal storage equipment. The output thermal power of the thermal storage device. This refers to the output conversion rate of the thermal storage equipment.

[0022] Furthermore, the green certificate-tiered carbon trading mathematical model includes a carbon emission quota sub-model, an actual carbon emission sub-model, and a carbon emission trading sub-model under the tiered carbon trading mechanism. The expression for the carbon emission quota sub-model is as follows: , In the formula, Carbon emission allowances for integrated energy systems For the scheduling period, Carbon emission allowances for electricity purchased from higher authorities. For carbon emission allowances of combined heat and power (CHP) units, Carbon emission allowances for gas-fired boilers, Carbon emission allowances per unit of electricity consumption of coal-fired power units. For the electricity purchased by the superior during period t, To improve the efficiency of power grid purchasing, As a carbon emission factor for the power grid, This is the carbon emission correction factor for the power grid. Carbon emission allowances per unit of natural gas consumption for natural gas-fired power plants. Let be the electrical power output of the combined heat and power unit at time t. For the electrical output efficiency of a combined heat and power (CHP) unit, For the carbon emission factor of electricity from combined heat and power (CHP) units, Let be the thermal power output of the combined heat and power unit at time t. For the heat output efficiency of a combined heat and power (CHP) unit, For the carbon emission factor of natural gas in combined heat and power (CHP) units, For the rated efficiency of the combined heat and power unit, This is the carbon emission correction factor for combined heat and power (CHP) units. The heat energy output of the gas-fired boiler during time period t. For the thermal efficiency of gas-fired boilers The carbon emission factor of natural gas from gas-fired boilers, The rated efficiency of the gas-fired boiler. This is the carbon emission correction factor for gas-fired boilers; The expression for the actual carbon emission sub-model is as follows: , In the formula, The actual total carbon emissions of the integrated energy system. The actual carbon emissions from electricity purchased from higher levels. This represents the total actual carbon emissions from combined heat and power (CHP) units and gas-fired boilers. , , and , , These are the carbon emission calculation parameters for coal-fired power units and gas turbines, respectively. For the electricity purchased by the superior during period t, , These are adjustment factors that take into account the impact of temperature on carbon emissions. The temperature factor at time t. The output power of combined heat and power units and gas-fired boilers during time period t. The power-heat conversion factor for a combined heat and power (CHP) unit. Let be the electrical power output of the combined heat and power unit at time t. Let be the thermal power output of the combined heat and power unit at time t. Let t be the output power of the gas-fired boiler at time t; The expression for the carbon emissions trading sub-model is: , , In the formula, For carbon trading costs, For market carbon trading prices, For the increase in carbon trading prices, The actual amount of carbon emission trading participated in by the integrated energy system. The length of the set carbon emission range, Carbon emission allowances for integrated energy systems Carbon emissions offset by green certificates.

[0023] Step S104: Based on the green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total operating cost of the system, and considering power balance, upper and lower limits of equipment operation, ramp-up constraints, and dynamic feasible domain constraints of energy storage equipment, a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism is constructed.

[0024] In this step, the objective function expression of the demand response optimization scheduling model is: , , , In the formula, For the total cost of the comprehensive energy system, This refers to the natural gas consumption of combined heat and power (CHP) units. This refers to the natural gas consumption of the gas-fired boiler unit. The total number of energy types, This represents the total number of energy storage device types. For electricity purchase costs, For carbon trading costs, For maintenance costs, For green certificate transaction costs, For time-of-use pricing in an integrated energy management system, To purchase energy costs, This refers to the unit price of natural gas. It is a low-calorific-value natural gas. For the first The operation and maintenance cost coefficient of energy conversion equipment For the first Output power of energy conversion equipment, This is the coefficient for the operation and maintenance costs of energy storage equipment. For the first The charging power of energy storage devices For the first Energy release power of energy storage devices.

[0025] Furthermore, the constraints on wind power output and power balance are as follows: , , In the formula, Let t be the actual wind power output at time t. The predicted wind power output at time t. The discharge power of the energy storage device. The charging power for energy storage devices, For grid power, This refers to the electrical power flowing from the gas turbine to the electrical subsystem. The Karina cycle provides thermal power to the gas turbine. For the optimized electrical load, The thermal power of the gas-fired boiler. The output thermal power of the thermal storage device. The input thermal power of the thermal storage equipment. The output thermal power of the thermal storage device. The output thermal power of the thermal storage device. For the optimized heat load, This refers to the natural gas consumption of the gas turbine. This refers to the natural gas consumption of the gas-fired boiler. Total natural gas consumption; The upper and lower limits and ramping constraints for supplying combined heat and power units, gas-fired boilers, and the power grid are as follows: , , , , , , , , , In the formula, This is the lower limit of the electrical power of a combined heat and power (CHP) unit. This is the upper limit of the electrical power of a combined heat and power (CHP) unit. This represents the lower limit of the thermal power of a combined heat and power (CHP) unit. This is the upper limit of the electrical power of a combined heat and power (CHP) unit. This represents the lower limit of the thermal power of a gas-fired boiler. This is the upper limit of the thermal power of a gas-fired boiler. This represents the lower limit of gas consumption volume for combined heat and power (CHP) units. This represents the upper limit of gas consumption volume for combined heat and power (CHP) units. This represents the lower limit of the gas consumption volume for gas-fired boilers. This represents the upper limit of the gas consumption volume for a gas-fired boiler. This is the lower limit for the amount of electricity that can be purchased. The upper limit of the power purchase capacity, This represents the electrical power output of the combined heat and power unit at the previous moment. The ramp-up power of the combined heat and power unit's electrical power. The heat output of the combined heat and power unit at the previous moment. The ramp-up power of the thermal power of the combined heat and power unit. This refers to the heat output of the gas-fired boiler at the previous moment. The ramp-up power of the gas-fired boiler's thermal power; The power constraints for the Karina cycle, waste heat boiler, and heat pump are as follows: , , , In the formula, This is the lower limit of the cyclic output power of Karina. This is the upper limit of the cyclic output power of Karina. This represents the lower limit of the heat output of the waste heat boiler. This represents the upper limit of the heat output of the waste heat boiler. This represents the lower limit of the heat output power of the heat pump unit. This is the upper limit of the heat power output of the heat pump unit; The constraints on the dynamic shrinkage of the feasible domain of energy storage equipment, the safety of equipment collaborative operation, the continuity of energy storage status changes, and the recoverable operating area of ​​equipment are as follows; , , , , , , In the formula, This represents the lower limit of the energy available for energy storage devices. This represents the dynamic capacity correction of the energy storage device at time t. Let be the energy of the energy storage device at time t. This represents the upper limit of the energy available for energy storage devices. This represents the lower limit of the energy required for thermal storage equipment. This represents the dynamic capacity correction of the thermal storage equipment at time t. Let be the energy of the thermal storage device at time t. This represents the upper limit of the energy available for thermal storage equipment. This refers to the dynamic shrinkage coefficient of the energy storage device's capacity. The energy stored in the previous moment. The coefficient of dynamic shrinkage of the thermal storage equipment's capacity. The energy of the thermal storage device at the previous moment, This represents the total number of operating parameters. The total number of external factors. For the total number of devices, This refers to the safety threshold coefficient for the coordinated operation of electrical and thermal energy storage. This is a correction factor related to operating parameters, used to correct for capacity changes caused by factors such as environment and equipment aging. For the j-th external factor (climate change, load fluctuation), Let j be the impact factor on equipment capacity related to the j-th external factor (climate change, load fluctuation). This is the correction factor for the k-th device. Let be the performance coefficient of the k-th device. This is the correction factor for the k-th device. Dynamic factors affecting the capacity reduction of energy storage devices include load changes and operating cycles. Let m be the proportion coefficient of the recoverable operating range of the thermal storage equipment under external factors. Let m be the correction factor for the thermal storage equipment under external factors. Let n be the thermal conductivity factor of the thermal storage device under operating parameters. The dynamic factors affecting the capacity shrinkage of thermal storage equipment under operating parameters n. This refers to the safety threshold coefficient for the operation of thermal storage equipment. Correction factors related to the operating parameters of thermal storage equipment. For the adjustment factor related to the j-th external factor of the thermal storage equipment, This refers to the maximum allowable energy change of a thermal storage device per unit time. , , In the formula, This refers to the safety threshold coefficient for the coordinated operation of electrical energy storage and thermal energy storage. , , , , , , In the formula, This refers to the maximum permissible energy change of an energy storage device per unit time. This represents the maximum permissible energy change of a thermal storage device per unit time. This refers to the recoverable operating energy range of energy storage devices. This refers to the recoverable operating energy range of the thermal storage equipment. This is the lower limit proportional coefficient of the recoverable operating range of energy storage equipment. This represents the upper limit of energy storage devices. This is the lower limit proportional coefficient of the recoverable operating range of the thermal storage equipment. This is the upper limit proportional coefficient of the recoverable operating range of the thermal storage equipment. This represents the upper limit of energy for thermal storage equipment.

[0026] Step S105: Optimize and solve the demand response optimization scheduling model to obtain the optimal operating scheme of the power system with multi-objective cooperation.

[0027] In this step, the YALMIP toolbox is used for modeling on the MATLAB R2022a platform, and the IBMILOG CPLEX 12.10 solver is used for optimization. YALMIP is an efficient modeling language that can convert optimization models into a standard form that the solver can recognize; CPLEX is a mature commercial solver that excels at handling mixed-integer linear programming problems, with high solution efficiency and good stability.

[0028] In summary, the method of this application, firstly, at the load-side modeling level, achieves a refined characterization of load-side flexibility capacity by constructing price-based demand response models that can reduce and transfer loads, as well as substitution-based demand response models that convert electricity into heat. This enables dynamic adjustment of user energy consumption behavior based on electricity price signals, effectively smoothing load fluctuations, achieving peak shaving and valley filling, and reducing the system peak-valley difference. Secondly, at the energy supply-side modeling level, a refined operation model of multi-energy coupled equipment, including gas turbines, Kalina cycles, heat pumps, waste heat boilers, gas boilers, and electric and thermal energy storage, is established. This accurately characterizes the energy conversion relationships between various devices. By introducing partial load correction coefficients, the model more closely reflects the actual operating characteristics of the equipment, providing an accurate model basis for system optimization and scheduling. Secondly, at the carbon emission reduction mechanism level, a mutual recognition mechanism between green certificates and tiered carbon trading is introduced. By quantifying the carbon emission reduction behind green certificates and their offsetting relationship with carbon emission rights trading, a mathematical model of green certificates-tiered carbon trading is constructed. This achieves synergistic constraints between green certificate consumption and carbon emission costs, promoting the consumption of renewable energy while effectively suppressing carbon emissions through the tiered carbon pricing mechanism, forming a dual-drive of "green certificates incentivizing renewable energy and carbon trading constraining fossil energy." Furthermore, at the optimization scheduling model level, multiple dynamic constraints are considered, such as the dynamic contraction of the feasible domain of energy storage devices, collaborative operation safety, continuity of state changes, and recoverable operating areas. This breaks through the limitations of traditional fixed capacity constraints, making the operation plan of energy storage devices more consistent with actual physical characteristics and improving the accuracy and practicality of the optimization results. Ultimately, by using a unified optimization scheduling model aimed at minimizing the total system operating cost, the invention achieves synergistic optimization of three types of heterogeneous resources: demand response resources, multi-energy coupled equipment, and green certificate-carbon trading mechanisms. The results of the case studies show that the invention can effectively reduce system operating costs, reduce carbon emissions, improve the level of renewable energy consumption, and ensure the safe and stable operation of the system, providing scientific and effective decision support for the low-carbon operation of new power systems.

[0029] Please see Figure 2 The diagram shows a structural block diagram of a power system low-carbon operation optimization system according to this application.

[0030] like Figure 2As shown, the power system low-carbon operation optimization system 200 includes a first building module 210, a second building module 220, a third building module 230, a fourth building module 240, and a solution module 250.

[0031] The first construction module 210 is configured to construct an integrated energy management system architecture that considers load-side demand response; the second construction module 220 is configured to, based on the integrated energy management system architecture and considering load-side user behavior characteristics, construct a price-based demand response model that includes load reduction and load transfer, as well as a substitution demand response model that allows for the mutual conversion of electricity and heat; the third construction module 230 is configured to, based on the price-based demand response model and the substitution demand response model, establish an energy supply-side equipment operation model, and, based on the energy supply-side equipment operation model, introduce green energy... The system establishes a mutual recognition mechanism between green certificates and tiered carbon trading, constructing a mathematical model for green certificate-tiered carbon trading. A fourth construction module 240 is configured to, based on the green certificate-tiered carbon trading mathematical model, construct a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism, taking the minimization of total system operating cost as the objective, and considering power balance, equipment operating limits, ramping constraints, and dynamic feasible domain constraints of energy storage equipment. A solution module 250 is configured to optimize and solve the demand response optimization scheduling model to obtain the optimal operating scheme for the multi-objective collaborative power system.

[0032] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0033] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the power system low-carbon operation optimization method in any of the above method embodiments. In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows: Construct a comprehensive energy management system architecture that takes into account load-side demand response; Based on the aforementioned integrated energy management system architecture, and considering the characteristics of load-side user behavior, a price-based demand response model that includes load reduction and load transfer, as well as a substitution demand response model that allows for the conversion of electricity and heat, are constructed. Based on the price-based demand response model and the substitution-based demand response model, an operation model for energy supply-side equipment is established. Based on the operation model for energy supply-side equipment, a mutual recognition mechanism between green certificates and tiered carbon trading is introduced to construct a mathematical model for green certificate-tiered carbon trading. Based on the aforementioned green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total system operating cost, and considering power balance, equipment operating upper and lower limits, ramping constraints, and dynamic feasible domain constraints of energy storage equipment, a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism is constructed. The demand response optimization scheduling model is optimized and solved to obtain the optimal operating scheme of the power system with multi-objective cooperation.

[0034] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the power system low-carbon operation optimization system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to a processor, which can be connected to the power system low-carbon operation optimization system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0035] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the power system low-carbon operation optimization method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the power system low-carbon operation optimization system. The output device 340 may include a display screen or other display device.

[0036] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0037] In one implementation, the above-described electronic device is applied in a power system low-carbon operation optimization system for a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Construct a comprehensive energy management system architecture that takes into account load-side demand response; Based on the aforementioned integrated energy management system architecture, and considering the characteristics of load-side user behavior, a price-based demand response model that includes load reduction and load transfer, as well as a substitution demand response model that allows for the conversion of electricity and heat, are constructed. Based on the price-based demand response model and the substitution-based demand response model, an operation model for energy supply-side equipment is established. Based on the operation model for energy supply-side equipment, a mutual recognition mechanism between green certificates and tiered carbon trading is introduced to construct a mathematical model for green certificate-tiered carbon trading. Based on the aforementioned green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total system operating cost, and considering power balance, equipment operating upper and lower limits, ramping constraints, and dynamic feasible domain constraints of energy storage equipment, a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism is constructed. The demand response optimization scheduling model is optimized and solved to obtain the optimal operating scheme of the power system with multi-objective cooperation.

[0038] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the low-carbon operation of a power system, characterized in that, include: Construct a comprehensive energy management system architecture that takes into account load-side demand response; Based on the aforementioned integrated energy management system architecture, and considering the characteristics of load-side user behavior, a price-based demand response model that includes load reduction and load transfer, as well as a substitution demand response model that allows for the conversion of electricity and heat, are constructed. Based on the price-based demand response model and the substitution-based demand response model, an operation model for energy supply-side equipment is established. Based on the operation model for energy supply-side equipment, a mutual recognition mechanism between green certificates and tiered carbon trading is introduced to construct a mathematical model for green certificate-tiered carbon trading. Based on the aforementioned green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total system operating cost, and considering power balance, equipment operating upper and lower limits, ramping constraints, and dynamic feasible domain constraints of energy storage equipment, a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism is constructed. The demand response optimization scheduling model is optimized and solved to obtain the optimal operating scheme of the power system with multi-objective cooperation.

2. The method for optimizing low-carbon operation of a power system according to claim 1, characterized in that, The integrated energy management system architecture that considers load-side demand response includes: Construct an integrated energy management system architecture that includes the energy supply side, energy storage side, load side, and external energy network. The energy supply side includes cogeneration units, gas boilers, heat pumps, and Karina cycles. The energy storage side includes electrical energy storage and thermal energy storage. The load side includes electrical load and thermal load. The external energy network includes the upstream power grid and the upstream gas grid.

3. The method for optimizing low-carbon operation of a power system according to claim 1, characterized in that, The price-based demand response model includes a load reduction model and a load transfer model. The load reduction model satisfies load reduction constraints, cumulative reduction constraints, and continuous reduction constraints. The load transfer model satisfies energy conservation constraints and transfer capacity constraints. The expression for the load reduction model is: ， ， In the formula, This represents the actual power consumption that can be reduced after the reduction in time period t. The baseline power consumption that can be reduced for a time period t. This represents the amount of load reduction that can be reduced over time period t. The price response coefficient for reducing load over a time period t. The real-time electricity price for time period t. The benchmark electricity price for time period t. The price sensitivity index for load reduction, This represents the maximum amount of load that can be reduced over a time period t. The expression for the transferable load model is: ， ， ， In the formula, This represents the actual electrical power of the load that can be transferred over time period t. This represents the baseline electrical power consumption of the transferable load over a time period t. This represents the transferable load power transferred from other time periods during time period t. This represents the transferable load power transferred out from other time periods during time period t. The price response coefficient for transferable loads is used to characterize the intensity of load transfer. This represents the average electricity price during the dispatching period.

4. The method for optimizing low-carbon operation of a power system according to claim 1, characterized in that, The alternative demand response model satisfies the maximum substitutable load constraint. The expression for the alternative demand response model is: ， ， In the formula, For alternative electrical loads, The electrothermal substitution coefficient, The amount of heat load to be replaced. The calorific value per unit of electrical energy. For the energy utilization rate of electricity, The calorific value of heat energy per unit. Energy utilization rate of thermal energy; The expression for the maximum replaceable load constraint is: ， In the formula, , These are the minimum and maximum substitution amounts of the replaceable electrical load, respectively. , These represent the minimum and maximum replacement amounts of the replaceable heat load, respectively.

5. The method for optimizing low-carbon operation of a power system according to claim 1, characterized in that, The power supply side equipment operation model includes the electrical output power model and thermal output power model of the gas turbine in the combined heat and power unit, and the expression is: ， ， In the formula, This refers to the power generation efficiency of a gas turbine under rated operating conditions. Let be the gas input power of the gas turbine during time period t. This represents the maximum gas input power of the gas turbine. This is the exhaust waste heat proportionality coefficient of the gas turbine under rated operating conditions. This refers to the electrical output power of the gas turbine. and These are the partial load correction factors for the electrical efficiency of the gas turbine. This refers to the heat output power of the gas turbine. and These are the partial load correction factors for the waste heat from gas turbine exhaust; The energy supply-side equipment operation model also includes an energy distribution model for the gas turbine after incorporating the Karina cycle and heat pump, expressed as follows: ， In the formula, for The electrical power flowing from the gas turbine into the heat pump unit at any given time. For gas turbine The electrical power constantly flowing to the electrical subsystem for The heat power input from the gas turbine to the waste heat boiler at any given time. For gas turbine The heat power is constantly input into the Karina cycle; The power supply side equipment operation model also includes an electrical output power model and a heat output power model for the cogeneration unit after introducing the Karina cycle and heat pump, with the following expressions: ， In the formula, For the cogeneration unit on the power supply side The electrical and thermal output power at any given time For Karina's cycle The electrical power output at all times For the cogeneration unit on the power supply side The electrical and thermal output power at any given time Waste heat boiler The heat output power at all times, For heat pump The thermal power output at all times; The energy supply-side equipment operation model also includes energy conversion models for the Karina cycle, waste heat boiler, heat pump, and gas boiler, expressed as follows: ， ， ， ， ， ， ， In the formula, for The low-temperature waste heat that Karina circulates and discharges at all times The waste heat ratio coefficient for the Karina cycle. For the Karina cycle thermoelectric conversion efficiency. for Waste heat constantly enters the waste heat boiler. for The steam output from the waste heat boiler at all times The heat exchange efficiency of the waste heat boiler. for The heat output power of the heat pump unit at all times. The coefficient of performance (COP) of the heat pump unit. for The electrical power consumed by the heat pump unit at all times. for The heat output of the gas boiler at all times For the thermal efficiency of gas-fired boilers, for The amount of natural gas consumed by the gas-fired boiler at any given time; The energy supply-side equipment operation model also includes electrical energy storage and thermal energy storage models, expressed as follows: ， ， In the formula, The energy of charging and discharging at time t. The self-discharge rate of the energy storage device. The charge / discharge energy at time t-1 To improve the charging efficiency of energy storage devices. The charging power of energy storage devices, This refers to the discharge power of the energy storage device. The discharge efficiency of the energy storage device. The stored heat energy at time t For heat storage loss rate, For the stored heat energy at time t-1, The input conversion rate of the thermal storage equipment. The input thermal power of the thermal storage equipment. The output thermal power of the thermal storage device. This refers to the output conversion rate of the thermal storage equipment.

6. The method for optimizing low-carbon operation of a power system according to claim 1, characterized in that, The green certificate-tiered carbon trading mathematical model includes a carbon emission quota sub-model, an actual carbon emission sub-model, and a carbon emission trading sub-model under the tiered carbon trading mechanism. The expression for the carbon emission quota sub-model is as follows: ， In the formula, Carbon emission allowances for integrated energy systems For the scheduling period, Carbon emission allowances for electricity purchased from higher authorities. For carbon emission allowances of combined heat and power (CHP) units, Carbon emission allowances for gas-fired boilers, Carbon emission allowances per unit of electricity consumption of coal-fired power units. For the electricity purchased by the superior during period t, To improve the efficiency of power grid purchasing, As a carbon emission factor for the power grid, This is the carbon emission correction factor for the power grid. Carbon emission allowances per unit of natural gas consumption for natural gas-fired power plants. Let be the electrical power output of the combined heat and power unit at time t. For the electrical output efficiency of a combined heat and power (CHP) unit, For the carbon emission factor of electricity from combined heat and power (CHP) units, Let be the thermal power output of the combined heat and power unit at time t. For the heat output efficiency of a combined heat and power (CHP) unit, For the carbon emission factor of natural gas in combined heat and power (CHP) units, For the rated efficiency of the combined heat and power (CHP) unit, This is the carbon emission correction factor for combined heat and power (CHP) units. The heat energy output of the gas-fired boiler during time period t. For the thermal efficiency of gas-fired boilers The carbon emission factor of natural gas from gas-fired boilers, The rated efficiency of the gas-fired boiler. This is the carbon emission correction factor for gas-fired boilers; The expression for the actual carbon emission sub-model is as follows: ， In the formula, The actual total carbon emissions of the integrated energy system. The actual carbon emissions from electricity purchased from higher levels. This represents the total actual carbon emissions from combined heat and power (CHP) units and gas-fired boilers. , , and , , These are the carbon emission calculation parameters for coal-fired power units and gas turbines, respectively. For the electricity purchased by the superior during period t, , These are adjustment factors that take into account the impact of temperature on carbon emissions. The temperature factor at time t. The output power of combined heat and power units and gas-fired boilers during time period t. The power-heat conversion factor for a combined heat and power (CHP) unit. Let be the electrical power output of the combined heat and power unit at time t. Let be the thermal power output of the combined heat and power unit at time t. Let t be the output power of the gas-fired boiler at time t; The expression for the carbon emissions trading sub-model is: ， ， In the formula, For carbon trading costs, For market carbon trading prices, For the increase in carbon trading prices, The actual amount of carbon emission trading participated in by the integrated energy system. The length of the set carbon emission range, Carbon emission allowances for integrated energy systems Carbon emissions offset by green certificates.

7. The method for optimizing low-carbon operation of a power system according to claim 1, characterized in that, The objective function expression of the demand response optimization scheduling model is: ， ， ， In the formula, For the total cost of the comprehensive energy system, This refers to the natural gas consumption of combined heat and power (CHP) units. This refers to the natural gas consumption of the gas-fired boiler unit. The total number of energy types, This represents the total number of energy storage device types. For electricity purchase costs, For carbon trading costs, For maintenance costs, For green certificate transaction costs, For time-of-use pricing in an integrated energy management system, To purchase energy costs, This refers to the unit price of natural gas. It is a low-calorific-value natural gas. For the first The operation and maintenance cost coefficient of energy conversion equipment For the first Output power of energy conversion equipment, This is the coefficient for the operation and maintenance costs of energy storage equipment. For the first The charging power of energy storage devices For the first Energy release power of energy storage devices.

8. A low-carbon operation optimization system for power systems, characterized in that, include: The first building module is configured to build an integrated energy management system architecture that takes into account load-side demand response; The second construction module is configured to construct a price-based demand response model that includes load reduction and load transfer, as well as an alternative demand response model that converts electricity and heat, based on the integrated energy management system architecture and considering the characteristics of load-side user behavior. The third construction module is configured to establish an energy supply-side equipment operation model based on a price-based demand response model and a substitution-based demand response model, and based on the energy supply-side equipment operation model, introduce a mutual recognition mechanism between green certificates and tiered carbon trading to construct a green certificate-tiered carbon trading mathematical model. The fourth construction module is configured to construct a demand response optimization scheduling model for the integrated energy system under the green certificate-tiered carbon trading mechanism based on the aforementioned green certificate-tiered carbon trading mathematical model, with the goal of minimizing the total operating cost of the system, taking into account power balance, upper and lower limits of equipment operation, ramping constraints, and dynamic feasible domain constraints of energy storage equipment. The solution module is configured to optimize and solve the demand response optimization scheduling model to obtain the optimal operating scheme of the power system with multi-objective cooperation.

9. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 7.

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