Power grid carbon emission reduction cost optimization method considering high-energy-consumption load dynamic characteristics

Abstract

The invention provides a power grid carbon emission reduction cost optimization method considering high-energy-consumption load dynamic characteristics, and belongs to the technical field of power grid optimization, and the method comprises the steps: obtaining a power grid structure, historical power grid operation data, and historical power grid cost data; determining carbon emission based on power generation, carbon emission based on load and carbon emission based on energy storage in each specified time period; determining a load-carbon emission function and a carbon emission reduction-cost function in all specified time periods; collecting real-time power grid operation data, and determining a real-time scheduling strategy; power grid operation is adjusted based on a real-time scheduling strategy, and power grid carbon emission reduction cost optimization is achieved. The power grid dispatching strategy can be dynamically optimized, the accuracy, adaptability and low-carbon operation capacity of power grid dispatching are improved, synchronous coordination of economy, reliability and low-carbon targets of a power grid is achieved, the dispatching strategy with high operability is provided for energy conservation and emission reduction, and an efficient and stable operation mechanism is provided for an intelligent power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid optimization, and in particular to a method for optimizing the carbon emission reduction cost of a power grid by considering the dynamic characteristics of high energy consumption loads. Background Art

[0002] The power industry is an important source of carbon emissions. Traditional grid operation optimization methods mainly focus on reducing power generation costs and ensuring system reliability, but pay less attention to the comprehensive optimization of carbon emissions. With the introduction of the carbon neutrality goal, how to reduce carbon emissions while meeting load demand has become a key issue. However, high-energy-consuming loads have significant dynamic characteristics. Although the introduction of energy storage systems can alleviate fluctuations, their own energy losses also indirectly increase carbon emissions. Existing methods make it difficult to balance carbon reduction goals with economic costs.

[0003] Therefore, the present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads. Summary of the invention

[0004] The present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads. By determining the carbon emissions based on power generation, the carbon emissions based on loads, and the carbon emissions based on energy storage in each specified time period, the load-carbon emission function and the carbon emission reduction-cost function in all specified time periods are determined, and real-time power grid operation data are collected. Based on the real-time power grid operation data, the load-carbon emission function, and the carbon emission reduction-cost function, a real-time dispatching strategy is determined and the power grid operation is adjusted to achieve the optimization of the carbon emission reduction cost of the power grid. The power grid dispatching strategy can be dynamically optimized, the accuracy, adaptability, and low-carbon operation capability of the power grid dispatching are improved, and the synchronous coordination of the power grid economy, reliability, and low-carbon goals is achieved, providing a highly operable dispatching strategy for energy conservation and emission reduction, and providing an efficient and stable operation mechanism for smart grids.

[0005] The present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads, comprising:

[0006] 101: Obtain the grid structure, historical grid operation data, and historical grid cost data;

[0007] 102: Determine the carbon emissions based on generation, the carbon emissions based on load, and the carbon emissions based on energy storage in each specified time period;

[0008] 103: Determine the load-carbon emission function for all specified time periods, and determine the carbon reduction-cost function for all specified time periods;

[0009] 104: Collect real-time grid operation data, and determine a real-time dispatching strategy based on the real-time grid operation data, a load-carbon emission function, and a carbon emission reduction-cost function;

[0010] 105: Adjust grid operation based on real-time dispatch strategies to optimize grid carbon emission reduction costs.

[0011] According to the grid carbon emission reduction cost optimization method considering the dynamic characteristics of high energy consumption loads provided by the present invention, the grid structure includes all energy storage devices, all power generation sources and installed capacity;

[0012] The historical grid operation data includes historical temperature data within multiple specified time periods, historical power generation data and historical load data of each power generation source within multiple specified time periods, and historical energy storage data of each energy storage device within multiple specified time periods;

[0013] The historical power grid cost data includes dispatch cost data within multiple specified time periods, power generation cost data of each power generation source within multiple specified time periods, and energy storage cost data of each energy storage device within multiple specified time periods.

[0014] According to the method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads provided by the present invention, the carbon emissions based on power generation, the carbon emissions based on loads, and the carbon emissions based on energy storage in each specified time period are determined, including:

[0015] Determine, based on grid structure data, historical grid operation data, and historical grid cost data, carbon emissions based on power generation, carbon emissions based on load, and carbon emissions based on energy storage for each specified time period;

[0016]

[0017] Among them, C1 t represents the carbon emissions based on power generation in the tth specified time period, C2 t represents the carbon emission based on load in the tth specified time period, C3 t represents the carbon emissions based on energy storage in the tth specified time period, L t represents the actual load of the power grid in the tth specified time period, δ t represents the transmission loss rate in the tth specified time period, EF i represents the carbon emission factor of the i-th power generation source category, Pr i represents the priority of the generation source of the i-th generation source category, represents the power generation cost of the jth power source in the i-th power source category in the t-th specified time period, maxPg i represents the maximum power generation efficiency of all power generation source categories in the tth specified time period, represents the generation load of the jth generation source in the i-th generation source category in the t-th specified time period, k irepresents the efficiency reduction factor of the i-th power generation source category, represents the maximum generation load of all the generation sources in the i-th generation source category in the t-th specified time period, T t represents the ambient temperature of the tth specified time period, TC represents the preset ambient temperature, α i NF represents the temperature sensitivity factor of the i-th power source category. t represents the carbon emission value based on start-stop scheduling in the tth specified time period, L tm It represents the actual load of the power grid at the mth moment in the tth specified time period, L tm+Δm represents the actual load of the power grid at the m+Δmth moment in the tth specified time period, γ1 represents the carbon emission amplification factor based on flexible scheduling, represents the charging power of the kth energy storage device in the tth specified time period, represents the discharge power of the kth energy storage device in the tth specified time period, τ k represents the energy storage efficiency, γ2 represents the carbon emission factor based on energy storage, N1 represents the number of power generation source categories, iN2 represents the number of power generation sources in the i-th power generation source category, and N3 represents the number of energy storage devices.

[0018] According to the method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads provided by the present invention, the load-carbon emission function within all specified time periods is determined, including:

[0019] Determine a load-carbon emission function for all specified time periods based on generation-based carbon emissions, load-based carbon emissions, and energy storage-based carbon emissions for all specified time periods;

[0020]

[0021] Where LC represents the load-carbon emission function, C2 a represents the carbon emission based on load in the a-th specified time period, C3 a represents the carbon emissions based on energy storage in the ath specified time period, ∈ represents the interaction effect factor between two specified time periods, and Nu represents the number of specified time periods.

[0022] According to the method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads provided by the present invention, the carbon emission reduction-cost function within all specified time periods is determined, including:

[0023] Determine the total cost in each specified time period based on the dispatch cost data in each specified time period in the historical grid cost data, the power generation cost data of all power generation sources, and the energy storage cost data of all energy storage devices;

[0024] Determine the total carbon emissions in each specified time period based on the carbon emissions based on power generation, the carbon emissions based on loads, and the carbon emissions based on energy storage in each specified time period;

[0025] TC t =C1 t +C2 t +C3 t ;

[0026] Among them, TC t represents the total carbon emissions in the tth specified time period;

[0027] Determine a carbon reduction-cost function for all specified time periods based on the total cost value and the total carbon emissions for all specified time periods;

[0028]

[0029] Among them, RC represents the low-cost carbon reduction limit, RM represents the medium-cost carbon reduction limit, and C t represents the total cost in the tth specified time period, BC represents the baseline carbon emission, represents the unit emission reduction cost in the tth specified time period, w t represents the weight of the tth specified time period, β1 and β2 represent the first adjustment parameter and the second adjustment parameter respectively, CL represents the critical value of unit emission reduction cost in the low-cost emission reduction stage, and CU represents the critical value of unit emission reduction cost in the medium-cost emission reduction stage. The number of specified time periods indicating that the unit abatement cost is below the critical unit abatement cost in the low-cost abatement stage; It represents the specified number of time periods when the unit abatement cost is between the unit abatement cost threshold in the low-cost abatement stage and the unit abatement cost threshold in the medium-cost abatement stage.

[0030] According to the method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads provided by the present invention, the real-time power grid operation data includes real-time load demand, carbon emission benchmark, the output power of each power generation source, the operating status of each power generation source, the power generation cost of each power generation source and the carbon emission reduction cost.

[0031] According to the method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high-energy-consuming loads provided by the present invention, a real-time dispatching strategy is generated based on real-time power grid operation data, a load-carbon emission function, and a carbon emission reduction-cost function, including:

[0032] Input real-time grid operation data, load-carbon emission function, and carbon reduction-cost function into the optimization model;

[0033] Based on the model output results, a real-time scheduling strategy of low cost and high emission reduction is determined, where the scheduling strategy includes multiple scheduling instructions.

[0034] According to the method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads provided by the present invention, the operation of the power grid is adjusted based on a real-time dispatching strategy to achieve the optimization of the carbon emission reduction cost of the power grid, including:

[0035] Send each dispatch instruction in the real-time dispatch strategy to the dispatch target corresponding to the power grid, and monitor the operating status of each dispatch target after executing the dispatch instruction;

[0036] Based on the real-time grid operation data and the operating status of all dispatch targets after executing the dispatch instructions, determine whether further optimization is needed;

[0037] If necessary, the model parameters are optimized based on the operating status of all scheduling targets after executing the scheduling instructions, and the re-scheduling strategy is determined.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] By determining the carbon emissions based on power generation, load and energy storage in each specified time period, determining the load-carbon emission function and carbon reduction-cost function in all specified time periods, collecting real-time grid operation data, and based on the real-time grid operation data, load-carbon emission function and carbon reduction-cost function, determining the real-time dispatching strategy and adjusting the grid operation to achieve grid carbon reduction cost optimization. The grid dispatching strategy can be dynamically optimized, improving the accuracy, adaptability and low-carbon operation capability of grid dispatching, achieving the synchronous coordination of grid economy, reliability and low-carbon goals, providing a highly operational dispatching strategy for energy conservation and emission reduction, and providing an efficient and stable operation mechanism for smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a flow chart of a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1:

[0044] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads, such as Figure 1 As shown, including:

[0045] 101: Obtain the grid structure, historical grid operation data, and historical grid cost data;

[0046] 102: Determine the carbon emissions based on generation, the carbon emissions based on load, and the carbon emissions based on energy storage in each specified time period;

[0047] 103: Determine the load-carbon emission function for all specified time periods, and determine the carbon reduction-cost function for all specified time periods;

[0048] 104: Collect real-time grid operation data, and determine a real-time dispatching strategy based on the real-time grid operation data, a load-carbon emission function, and a carbon emission reduction-cost function;

[0049] 105: Adjust grid operation based on real-time dispatch strategies to optimize grid carbon emission reduction costs.

[0050] In this embodiment, a functional relationship between the impact of load demand on carbon emissions in all specified time periods is established.

[0051] In this embodiment, a carbon emission reduction-cost function is established within all specified time periods to describe the balance between emission reduction and economic benefits.

[0052] In this embodiment, real-time data is input into the optimization model, and the load-carbon emission function and the carbon emission reduction-cost function are combined to generate a real-time scheduling strategy with low cost and high emission reduction.

[0053] In this embodiment, based on the optimized dispatching strategy, the grid operation is dynamically adjusted, including the dispatching of power generation sources, the charging and discharging of energy storage equipment, and the load-side response, so as to achieve the carbon emission reduction target and minimize the cost while meeting the load demand.

[0054] The beneficial effects of the above technical solution are as follows: by determining the carbon emissions based on power generation, the carbon emissions based on load, and the carbon emissions based on energy storage in each specified time period, determining the load-carbon emission function and the carbon emission reduction-cost function in all specified time periods, collecting real-time grid operation data, and based on the real-time grid operation data, the load-carbon emission function and the carbon emission reduction-cost function, determining the real-time dispatching strategy and adjusting the grid operation to achieve grid carbon emission reduction cost optimization. The grid dispatching strategy can be dynamically optimized, the accuracy, adaptability and low-carbon operation capability of the grid dispatching are improved, the synchronous coordination of the grid economy, reliability and low-carbon goals is achieved, a highly operable dispatching strategy is provided for energy conservation and emission reduction, and an efficient and stable operation mechanism is provided for smart grids.

[0055] Embodiment 2:

[0056] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads, wherein the power grid structure includes all energy storage devices, all power generation sources and installed capacity;

[0057] The historical grid operation data includes historical temperature data within multiple specified time periods, historical power generation data and historical load data of each power generation source within multiple specified time periods, and historical energy storage data of each energy storage device within multiple specified time periods;

[0058] The historical power grid cost data includes dispatch cost data within multiple specified time periods, power generation cost data of each power generation source within multiple specified time periods, and energy storage cost data of each energy storage device within multiple specified time periods.

[0059] In this embodiment, the grid structure refers to the sum of all components in the grid, including power generation sources (such as thermal power, wind power, photovoltaic power, etc.), energy storage equipment (such as battery energy storage system), and installed capacity of power generation sources (referring to the rated maximum power output capacity of the power generation equipment).

[0060] In this embodiment, the historical grid operation data is key data that records the operation of the grid in multiple specified time periods in the past, including environmental factors (such as historical temperature data), power generation data of each power generation source (reflecting its power generation and operating conditions), historical load data (reflecting electricity demand), and historical energy storage data of energy storage equipment (reflecting the charging and discharging status of the energy storage system).

[0061] In this embodiment, historical grid cost data refers to the economic data incurred by the grid during operation over multiple specified time periods in the past, including dispatch costs (the cost of allocating power generation and load resources), the power generation cost of each power generation source (such as fuel costs, maintenance costs), and the energy storage cost of energy storage equipment (such as charging, discharging and loss costs).

[0062] The beneficial effects of the above technical solution: obtaining the grid structure, historical grid operation data, and historical grid cost data can provide a data basis for determining carbon emissions based on power generation, carbon emissions based on load, and carbon emissions based on energy storage, so as to achieve accurate optimization of grid carbon emissions and operating costs.

[0063] Embodiment 3:

[0064] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads, and determines the carbon emissions based on power generation, the carbon emissions based on loads, and the carbon emissions based on energy storage in each specified time period, including:

[0065] Determine, based on grid structure data, historical grid operation data, and historical grid cost data, carbon emissions based on power generation, carbon emissions based on load, and carbon emissions based on energy storage for each specified time period;

[0066]

[0067] Among them, C1 t represents the carbon emissions based on power generation in the tth specified time period, C2 t represents the carbon emission based on load in the tth specified time period, C3 t represents the carbon emissions based on energy storage in the tth specified time period, L t represents the actual load of the power grid in the tth specified time period, δ t represents the transmission loss rate in the tth specified time period, EF i represents the carbon emission factor of the i-th power generation source category, Pr i represents the priority of the generation source of the i-th generation source category, represents the power generation cost of the jth power source in the i-th power source category in the t-th specified time period, maxPg i represents the maximum power generation efficiency of all power generation source categories in the tth specified time period, represents the generation load of the jth generation source in the i-th generation source category in the t-th specified time period, k i represents the efficiency reduction factor of the i-th power generation source category, represents the maximum generation load of all the generating sources in the i-th generating source category in the t-th specified time period, T t represents the ambient temperature of the tth specified time period, TC represents the preset ambient temperature, α i NF represents the temperature sensitivity factor of the i-th generation source category. t represents the carbon emission value based on start-stop scheduling in the tth specified time period, L tm It represents the actual load of the power grid at the mth moment in the tth specified time period, Ltm+Δm represents the actual load of the power grid at the m+Δmth moment in the tth specified time period, γ1 represents the carbon emission amplification factor based on flexible scheduling, represents the charging power of the kth energy storage device in the tth specified time period, represents the discharge power of the kth energy storage device in the tth specified time period, τ k represents the energy storage efficiency, γ2 represents the carbon emission factor based on energy storage, N1 represents the number of power generation source categories, iN2 represents the number of power generation sources in the i-th power generation source category, and N3 represents the number of energy storage devices.

[0068] In this embodiment, The power allocation coefficient of the i-th power source category indicates that the power source meets the actual load L of the t-th specified time period. t proportion.

[0069] In this embodiment, It represents the power generation of the power grid after the load has undergone transmission loss in the tth specified time period.

[0070] In this embodiment, the preset ambient temperature TC i It represents the optimal operating temperature of the i-th power generation source category in the power grid.

[0071] In this embodiment, It represents the power generation efficiency of the i-th power source category in the t-th specified time period after being affected by the operating load and ambient temperature.

[0072] In this embodiment, It represents the load change rate of the power grid during the tth specified time period. If the load change rate is large, it means that the load has a sharp increase or decrease.

[0073] In this embodiment, when the load changes rapidly, flexible power generation resources need to be dispatched to maintain grid stability. These resources usually generate additional carbon emissions when responding quickly to load changes. This additional carbon emission LC2 t It is caused by the load change rate.

[0074] In this embodiment, the carbon emissions based on power generation represent the carbon emissions generated by power generation from a power generation source in a power grid during a specified time period.

[0075] In this embodiment, load-based carbon emissions refer to carbon emissions indirectly caused by electricity consumption at the load end of the power grid within a specified time period, which is related to electricity demand.

[0076] In this embodiment, the carbon emissions based on energy storage represent the carbon emissions caused by the charging and discharging behaviors and efficiency losses of the energy storage device within a specified time period.

[0077] The beneficial effects of the above technical solution are: determining the carbon emissions based on power generation, the carbon emissions based on load, and the carbon emissions based on energy storage in each specified time period can refine carbon emissions and provide a more scientific basis for carbon emission reduction optimization.

[0078] Embodiment 4:

[0079] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads, and determines the load-carbon emission function within all specified time periods, including:

[0080] Determine a load-carbon emission function for all specified time periods based on generation-based carbon emissions, load-based carbon emissions, and energy storage-based carbon emissions for all specified time periods;

[0081]

[0082] Where LC represents the load-carbon emission function, C2 a represents the carbon emission based on load in the a-th specified time period, C3 a represents the carbon emissions based on energy storage in the ath specified time period, ∈ represents the interaction effect factor between two specified time periods, and Nu represents the number of specified time periods.

[0083] In this embodiment, LC1 t ×LC2 a It represents the nonlinear coupling between carbon emissions based on power generation and load, capturing their amplifying effect on carbon emissions.

[0084] In this embodiment, -(LC3 t +LC3 a ) reflects the cross-cycle regulatory effect of carbon emissions based on energy storage, which helps to smooth out peak loads and valley loads, thereby reducing total carbon emissions.

[0085] The beneficial effects of the above technical solution are: determining the load-carbon emission function within all specified time periods, providing an accurate and scientific decision-making basis for grid dispatch optimization and demand-side management, and helping to balance electricity demand and carbon emission reduction targets.

[0086] Embodiment 5:

[0087] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads, and determines the carbon emission reduction-cost function within all specified time periods, including:

[0088] Determine the total cost in each specified time period based on the dispatch cost data in each specified time period in the historical grid cost data, the power generation cost data of all power generation sources, and the energy storage cost data of all energy storage devices;

[0089] Determine the total carbon emissions in each specified time period based on the carbon emissions based on power generation, the carbon emissions based on loads, and the carbon emissions based on energy storage in each specified time period;

[0090] TC t =C1 t +C2 t +C3 t ;

[0091] Among them, TC t represents the total carbon emissions in the tth specified time period;

[0092] Determine a carbon reduction-cost function for all specified time periods based on the total cost value and the total carbon emissions for all specified time periods;

[0093]

[0094] Among them, RC represents the low-cost carbon reduction limit, RM represents the medium-cost carbon reduction limit, and C t represents the total cost in the tth specified time period, BC represents the baseline carbon emission, represents the unit emission reduction cost in the tth specified time period, w t represents the weight of the tth specified time period, β1 and β2 represent the first adjustment parameter and the second adjustment parameter respectively, CL represents the critical value of unit emission reduction cost in the low-cost emission reduction stage, and CU represents the critical value of unit emission reduction cost in the medium-cost emission reduction stage. The number of specified time periods indicating that the unit abatement cost is below the critical unit abatement cost in the low-cost abatement stage; It represents the specified number of time periods when the unit abatement cost is between the unit abatement cost threshold in the low-cost abatement stage and the unit abatement cost threshold in the medium-cost abatement stage.

[0095] In this embodiment, the dispatch cost represents the operating cost required for the power grid to meet the load demand in each specified time period, including the cost of power generation dispatch and energy storage charging and discharging.

[0096] In this embodiment, the power generation cost represents the unit electricity production cost of various power generation sources (such as coal power and wind power) in the power system.

[0097] In this embodiment, the energy storage cost refers to the operating cost of the energy storage device (such as a battery), including the energy loss cost during the charging and discharging process and the equipment maintenance cost.

[0098] In this embodiment, the total cost represents the total economic expenditure of the power grid operation within a specified time period, including dispatching cost, power generation cost and energy storage cost.

[0099] In this embodiment, the total carbon emissions represent the total amount of carbon emissions generated by power generation, load and energy storage activities in the power grid during a specified time period.

[0100] The beneficial effects of the above technical solution are: determining the carbon emission reduction-cost function within all specified time periods can achieve a quantitative trade-off between carbon emission reduction optimization and economic cost control, and provide a scientific basis and practical tools for low-carbon power grid construction.

[0101] Embodiment 6:

[0102] An embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads, wherein the real-time power grid operation data includes real-time load demand, carbon emission benchmark, output power of each power generation source, operating status of each power generation source, power generation cost of each power generation source and carbon emission reduction cost.

[0103] In this embodiment, the real-time load demand indicates the actual power demand of the power grid at the current moment, including various types of loads such as residential, commercial, and industrial loads.

[0104] In this embodiment, the carbon emission benchmark is the carbon emission quota.

[0105] In this embodiment, the output power of the power generation source is the actual power generation power of each power generation equipment (such as coal power, gas power, wind power, solar power) in real-time operation.

[0106] In this embodiment, the operating status of the power generation source indicates the current operating status of each power generation source (such as starting, stopping, partial load operation, etc.).

[0107] In this embodiment, the power generation cost is the unit power generation cost of each power generation source, reflecting the economic expenditure required to produce electricity.

[0108] In this embodiment, the carbon emission reduction cost is the additional economic cost required to achieve unit carbon emission reduction, and is used to measure the economic efficiency of emission reduction measures.

[0109] The beneficial effects of the above technical solution are: collecting real-time grid operation data to provide a data basis for determining the dispatching strategy, thereby achieving the synchronous coordination of the grid's economy, reliability and low-carbon goals.

[0110] Embodiment 7:

[0111] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high-energy-consuming loads, and generates a real-time scheduling strategy based on real-time power grid operation data, a load-carbon emission function, and a carbon emission reduction-cost function, including:

[0112] Input real-time grid operation data, load-carbon emission function, and carbon reduction-cost function into the optimization model;

[0113] Based on the model output results, a real-time scheduling strategy of low cost and high emission reduction is determined, wherein the scheduling strategy includes multiple scheduling instructions.

[0114] In this embodiment, the optimization model takes minimizing economic costs and maximizing carbon emission reductions as dual objectives, comprehensively considers load demand, power generation costs and carbon emissions, and the input functions and data are calculated by the model to output a real-time scheduling strategy that meets multi-objective optimization.

[0115] In this embodiment, based on the output results of the optimization model, a scheduling strategy that meets the low-cost and high emission reduction requirements is determined. The scheduling strategy includes multiple specific operation instructions, such as adjusting the power output of the power generation source, instructing the energy storage device to charge or discharge, and responding to the demand on the load side.

[0116] In this embodiment, the scheduling strategy is refined into specific execution instructions and distributed to corresponding scheduling targets (such as power generation sources, energy storage devices or load devices).

[0117] The beneficial effects of the above technical solution are as follows: based on real-time grid operation data, load-carbon emission function and carbon emission reduction-cost function, real-time dispatching strategy is determined to enhance the economic adaptability of low-carbon operation of the grid.

[0118] Embodiment 8:

[0119] The embodiment of the present invention provides a method for optimizing the carbon emission reduction cost of a power grid taking into account the dynamic characteristics of high energy consumption loads, adjusting the operation of the power grid based on a real-time dispatching strategy, and realizing the optimization of the carbon emission reduction cost of the power grid, including:

[0120] Send each dispatch instruction in the real-time dispatch strategy to the dispatch target corresponding to the power grid, and monitor the operating status of each dispatch target after executing the dispatch instruction;

[0121] Based on the real-time grid operation data and the operating status of all dispatch targets after executing the dispatch instructions, determine whether further optimization is needed;

[0122] If necessary, the model parameters are optimized based on the operating status of all scheduling targets after executing the scheduling instructions, and the re-scheduling strategy is determined.

[0123] In this embodiment, the real-time generated scheduling strategy is refined into specific scheduling instructions, such as adjusting the output power of a certain generator set, instructing energy storage equipment to charge, etc.

[0124] In this embodiment, the instructions are distributed to the power generation equipment, energy storage device or load side according to the actual location and type of the scheduling target.

[0125] In this embodiment, the actual operating status of the scheduling target after executing the instruction is monitored in real time, the output changes of the power generation equipment, the charging and discharging behavior of the energy storage equipment and the response of the load are obtained, the actual operating status of the scheduling target is compared with the scheduling instruction, and the execution deviation or dynamic changes of the power grid are evaluated.

[0126] In this embodiment, based on real-time power grid data and the execution status of the dispatching objectives, it is determined whether the current dispatching strategy meets the load demand, economic objectives and carbon emission requirements. If there is a deviation or new optimization space, the next round of optimization is triggered.

[0127] In this embodiment, the parameters of the optimization model are adjusted in combination with the actual operating status of the scheduling target, the scheduling strategy is re-determined, and a new round of scheduling instructions are issued to form a closed-loop control.

[0128] The beneficial effects of the above technical solution are: adjusting the grid operation based on the real-time dispatching strategy to achieve the optimization of the grid carbon emission reduction cost, dynamically optimizing the grid dispatching strategy, improving the accuracy, adaptability and low-carbon operation capability of the grid dispatching, and providing an efficient and stable operation mechanism for the smart grid.

[0129] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads, characterized in that: include: 101: Obtain the grid structure, historical grid operation data, and historical grid cost data; 102: Determine the carbon emissions based on generation, the carbon emissions based on load, and the carbon emissions based on energy storage in each specified time period; 103: Determine the load-carbon emission function for all specified time periods, and determine the carbon reduction-cost function for all specified time periods; 104: Collect real-time grid operation data, and determine a real-time dispatching strategy based on the real-time grid operation data, a load-carbon emission function, and a carbon emission reduction-cost function; 105: Adjust grid operation based on real-time dispatch strategies to optimize grid carbon emission reduction costs.

2. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 1, characterized in that: The grid structure includes all energy storage devices, all power generation sources and installed capacity; The historical grid operation data includes historical temperature data within multiple specified time periods, historical power generation data and historical load data of each power generation source within multiple specified time periods, and historical energy storage data of each energy storage device within multiple specified time periods; The historical power grid cost data includes dispatch cost data within multiple specified time periods, power generation cost data of each power generation source within multiple specified time periods, and energy storage cost data of each energy storage device within multiple specified time periods.

3. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 1, characterized in that: Determine generation-based carbon emissions, load-based carbon emissions, and storage-based carbon emissions for each specified time period, including: Determine, for each specified time period, carbon emissions based on power generation, carbon emissions based on load, and carbon emissions based on energy storage based on power grid structure data, historical power grid operation data, and historical power grid cost data; Among them, C1 t represents the carbon emissions based on power generation in the tth specified time period, C2 t represents the carbon emission based on load in the tth specified time period, C3 t represents the carbon emissions based on energy storage in the tth specified time period, L t represents the actual load of the power grid in the tth specified time period, δ t represents the transmission loss rate in the tth specified time period, EF i represents the carbon emission factor of the i-th power generation source category, Pr i represents the priority of the generation source of the i-th generation source category, represents the power generation cost of the jth power source in the i-th power source category in the t-th specified time period, maxPg i represents the maximum power generation efficiency of all power generation source categories in the tth specified time period, represents the generation load of the jth generation source in the i-th generation source category in the t-th specified time period, k i represents the efficiency reduction factor of the i-th power generation source category, represents the maximum generation load of all the generating sources in the i-th generating source category in the t-th specified time period, T t Indicates the ambient temperature of the tth specified time period, TC i represents the preset ambient temperature of the i-th power generation source category, α i NF represents the temperature sensitivity factor of the i-th generation source category. t represents the carbon emission value based on start-stop scheduling in the tth specified time period, L tm It represents the actual load of the power grid at the mth moment in the tth specified time period, L tm+Δm represents the actual load of the power grid at the m+Δmth moment in the tth specified time period, γ1 represents the carbon emission amplification factor based on flexible scheduling, represents the charging power of the kth energy storage device in the tth specified time period, represents the discharge power of the kth energy storage device in the tth specified time period, τ k represents the energy storage efficiency, γ2 represents the carbon emission factor based on energy storage, N1 represents the number of power generation source categories, iN2 represents the number of power generation sources in the i-th power generation source category, and N3 represents the number of energy storage devices.

4. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 3 is characterized in that: Determine the load-carbon emission function for all specified time periods, including: Determine a load-carbon emission function for all specified time periods based on generation-based carbon emissions, load-based carbon emissions, and energy storage-based carbon emissions for all specified time periods; Where LC represents the load-carbon emission function, C2 a represents the carbon emission based on load in the a-th specified time period, C3 a represents the carbon emissions based on energy storage in the ath specified time period, ∈ represents the interaction effect factor between two specified time periods, and Nu represents the number of specified time periods.

5. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 4 is characterized in that: Determine the carbon reduction-cost function for all specified time periods, including: Determine the total cost in each specified time period based on the dispatch cost data in each specified time period in the historical grid cost data, the power generation cost data of all power generation sources, and the energy storage cost data of all energy storage devices; Determine the total carbon emissions in each specified time period based on the carbon emissions based on power generation, the carbon emissions based on loads, and the carbon emissions based on energy storage in each specified time period; TC t =C1 t +C2 t +C3 t ; Among them, TC t represents the total carbon emissions in the tth specified time period; Determine a carbon reduction-cost function for all specified time periods based on the total cost value and the total carbon emissions for all specified time periods; Among them, RC represents the low-cost carbon reduction limit, RM represents the medium-cost carbon reduction limit, and C t represents the total cost in the tth specified time period, BC represents the baseline carbon emission, represents the unit emission reduction cost in the tth specified time period, w t represents the weight of the tth specified time period, β1 and β2 represent the first adjustment parameter and the second adjustment parameter respectively, CL represents the critical value of unit emission reduction cost in the low-cost emission reduction stage, and CU represents the critical value of unit emission reduction cost in the medium-cost emission reduction stage. The number of specified time periods indicating that the unit abatement cost is below the critical unit abatement cost in the low-cost abatement stage; It represents the specified number of time periods when the unit abatement cost is between the unit abatement cost threshold in the low-cost abatement stage and the unit abatement cost threshold in the medium-cost abatement stage.

6. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 1, characterized in that: The real-time grid operation data includes real-time load demand, carbon emission benchmark, output power of each power generation source, operating status of each power generation source, power generation cost of each power generation source and carbon emission reduction cost.

7. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 1, characterized in that: Generate real-time dispatch strategies based on real-time grid operation data, load-carbon emission function, and carbon reduction-cost function, including: Input real-time grid operation data, load-carbon emission function, and carbon reduction-cost function into the optimization model; Based on the model output results, a real-time scheduling strategy of low cost and high emission reduction is determined, wherein the scheduling strategy includes multiple scheduling instructions.

8. The method for optimizing the carbon emission reduction cost of a power grid considering the dynamic characteristics of high energy consumption loads according to claim 7, characterized in that: Adjust grid operation based on real-time dispatch strategies to optimize grid carbon emission reduction costs, including: Send each dispatch instruction in the real-time dispatch strategy to the dispatch target corresponding to the power grid, and monitor the operating status of each dispatch target after executing the dispatch instruction; Based on the real-time grid operation data and the operating status of all dispatch targets after executing the dispatch instructions, determine whether further optimization is needed; If necessary, the model parameters are optimized based on the operating status of all scheduling targets after executing the scheduling instructions, and the re-scheduling strategy is determined.

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