Carbon emission control method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a carbon emission control method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring carbon emission in a previous time period; adjusting the power supply mode of the power grid side in the next time period according to the carbon emission in the previous time period; the power supply mode is used for adjusting the carbon emission of the power supply side; according to the carbon emission corresponding to the adjusted power supply mode, the power utilization mode of the user side in the next time period is adjusted; the power utilization mode is used for adjusting the carbon emission of the user side. By adopting the method, the carbon emission of the power grid system can be accurately controlled.

Description

Technical Field

[0001] The present application relates to the technical field of electric power, and particularly to a carbon emission control method, device, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] With the increasing urgency of the global demand for climate change response, a low-carbon and intelligent energy management model has gradually become the core direction of the energy system reform.

[0003] In the related art, the power grid dispatching technology mainly involves the interactive dispatching technology between the power grid and the user side based on demand response. This method usually realizes the dynamic balance of the load by price signals, incentive mechanisms, or directly controlling the load of users. For example, users increase electricity consumption when the electricity price is low and reduce electricity consumption when the electricity price is high, and the power grid realizes the dynamic balance of the load through this method.

[0004] However, this method relies on a simple price mechanism and cannot accurately control carbon emissions. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a carbon emission control method, device, computer device, computer-readable storage medium, and computer program product that can accurately control carbon emissions.

[0006] In a first aspect, the present application provides a carbon emission control method applied to a power grid system, where the power grid system includes a power grid side and a user side; the method includes:

[0007] Obtain the carbon emissions of the previous time period;

[0008] According to the carbon emissions of the previous time period, adjust the power supply mode of the power grid side in the next time period; the power supply mode is used to adjust the carbon emissions of the power grid side;

[0009] According to the carbon emissions corresponding to the adjusted power supply mode, adjust the power consumption mode of the user side in the next time period; the power consumption mode is used to adjust the carbon emissions of the user side.

[0010] In one embodiment, the step of adjusting the power supply mode of the power grid side in the next time period according to the carbon emissions of the previous time period includes:

[0011] Obtain a first optimization objective and a first constraint condition; the first optimization objective is used to minimize the carbon emissions of the power grid system; the first constraint condition includes one or more of power grid demand, generator capacity limit, and power grid stability;

[0012] Based on the carbon emissions in the previous time period, under the first constraint condition, solve the first optimization objective to obtain a power grid scheduling strategy;

[0013] According to the power grid scheduling strategy, adjust the power supply mode on the power grid side in the next time period.

[0014] In one embodiment, the step of based on the carbon emissions in the previous time period, under the first constraint condition, solve the first optimization objective to obtain a power grid scheduling strategy includes:

[0015] Initialize the first population;

[0016] According to the first constraint condition and the first optimization objective, optimize the first population to obtain a global scheduling strategy; the carbon emissions in the previous time period are used to adjust the optimization direction of the first population;

[0017] Take the global scheduling strategy as the second population, and under the first constraint condition and the first optimization objective, optimize the second population to obtain a local scheduling strategy; the carbon emissions in the previous time period are used to adjust the optimization direction of the second population;

[0018] Repeat the steps of generating the global scheduling strategy and generating the local scheduling strategy until a first preset termination condition is reached, and take the finally generated local scheduling strategy as the power grid scheduling strategy.

[0019] In one embodiment, the step of adjusting the power consumption mode on the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply mode includes:

[0020] Obtain a second optimization objective and second constraint conditions; the second optimization objective is used to minimize the carbon emissions on the user side; the second constraint conditions include one or more of the user load regulation ability and the user carbon emission limit;

[0021] Based on the carbon emissions corresponding to the adjusted power supply mode, solve the obtained second optimization objective under the second constraint condition to obtain a user load distribution strategy;

[0022] According to the user load distribution strategy, adjust the power consumption mode.

[0023] In one embodiment, the step of based on the carbon emissions corresponding to the power supply mode, solve the obtained second optimization objective under the second constraint condition to obtain a user load distribution strategy includes:

[0024] Initialize the third population;

[0025] Optimize the third population according to the second constraint condition and the second optimization objective to obtain a global load distribution strategy; the carbon emissions corresponding to the adjusted power supply method are used to adjust the optimization direction of the third population;

[0026] Use the global load distribution strategy as the fourth population, and optimize the fourth population according to the second constraint condition and the second optimization objective to obtain a local load distribution strategy; the carbon emissions corresponding to the adjusted power supply method are used to adjust the optimization direction of the fourth population;

[0027] Repeat the steps of generating the global load distribution strategy and generating the local load distribution strategy until the second preset termination condition is reached, and use the finally generated local load distribution strategy as the user load distribution strategy.

[0028] In one embodiment, the method further includes:

[0029] Use the carbon emissions corresponding to the adjusted power consumption method in the next time period as the new carbon emissions in the previous time period.

[0030] In a second aspect, the present application provides a carbon emissions control device, the device includes:

[0031] An acquisition module, configured to acquire the carbon emissions in the previous time period;

[0032] A power grid adjustment module, configured to adjust the power supply method on the power grid side in the next time period according to the carbon emissions in the previous time period; the power supply method is used to adjust the carbon emissions of the power grid system;

[0033] A user adjustment module, configured to adjust the power consumption method on the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply method; the power consumption method is used to adjust the carbon emissions on the user side.

[0034] In a third aspect, the present application provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0036] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0037] The above carbon emission control method, device, computer device, computer-readable storage medium and computer program product set the grid side as the master control end and the user side as the slave control end. Based on the fact that the power supply mode of the grid side is adjusted in real time according to the carbon emissions in the previous time period, the user side responds in a timely manner to the carbon emission intensity information of the grid side and correspondingly adjusts the carbon emissions of the user side to form an interaction between the user side and the grid side, thereby precisely controlling the carbon emissions. Brief Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is an application environment diagram of the carbon emission control method in an embodiment;

[0040] Figure 2 It is a flowchart of the steps of the carbon emission control method in an embodiment;

[0041] Figure 3 It is a structural block diagram of the carbon emission control device in an embodiment;

[0042] Figure 4 It is an internal structure diagram of a computer device in an embodiment. Detailed Embodiments

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further details the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In an exemplary embodiment, as Figure 2 shown, applied to the Figure 1 power supply system, a carbon emission control method is provided, including the following steps 202 to step 206. Among them:

[0045] Step 202, obtain the carbon emissions in the previous time period.

[0046] Among them, the previous time period can be the carbon emissions in the past hour or the past day. The specific granularity of the previous time period can be set according to the specific usage scenario.

[0047] Further, the carbon emissions in this step refer to the carbon emissions calculated by the user side according to the power consumption pattern in the previous time period. Among them, the power consumption pattern refers to the specific mode of the user during the process of consuming electric energy, including the power consumption period, equipment type, power source, etc.

[0048] Optionally, after obtaining the carbon emissions in the previous time period, it is necessary to judge them. Only when the carbon emissions exceed the set adjustment threshold, will the adjustment of the power supply mode be triggered. This strategy can effectively avoid excessive interference with the power supply system and ensure the stability and rationality of the regulation. Among them, the adjustment threshold can be adaptively set according to historical carbon emissions. For example, first set a basic threshold and calculate the dynamic adjustment coefficient in combination with historical carbon emissions. Exemplarily, the threshold can vary with the grid load P(t), for example , where α and β are adjustment coefficients.

[0049] Step 204, adjust the power supply mode on the grid side in the next time period according to the carbon emissions in the previous time period; the power supply mode is used to adjust the carbon emissions on the grid side. Among them, the power supply mode can be the power supply mode with the minimum carbon emissions to minimize the carbon emissions on the grid side.

[0050] The power supply system dynamically adjusts the power supply mode on the grid side in the next time period according to the carbon emissions in the previous time period to optimize the overall carbon emission level on the grid side.

[0051] Optionally, the grid dispatching strategy for the next time period can be formulated according to the carbon emissions in the previous time period, and the corresponding power supply mode can be adjusted through the grid dispatching strategy, thereby adjusting the carbon emissions of the grid system in the next time period. Among them, the grid dispatching strategy refers to the operation mode of regulating each generating unit on the grid side, such as the generating power and operation time of each generating unit.

[0052] Optionally, if the carbon emissions in the previous time period are high, the power supply system will give priority to increasing the power supply proportion of clean energy, such as wind power and photovoltaic power, and appropriately reduce the use of high-carbon-emission energy, such as coal-fired and oil-fired thermal power, so as to reduce the carbon emissions in the next time period.

[0053] Through this dynamic regulation mechanism, the grid can gradually reduce the overall carbon emissions while ensuring stable power supply, improve the new energy consumption rate, and enhance the green sustainability of the power system.

[0054] Step 206, adjust the power consumption mode on the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply mode; the power consumption mode is used to adjust the carbon emissions on the user side.

[0055] When the power supply mode on the grid side changes, the corresponding carbon emission intensity will also be adjusted accordingly. At this time, the user side can adjust the power consumption mode of the user side in the next time period according to the real-time obtained carbon emission information, so as to correspondingly adjust the carbon emission amount of the user side. Exemplarily, for example, increase power usage during low-carbon periods, and reduce or postpone some adjustable loads during high-carbon periods, thereby reducing the overall carbon emission amount.

[0056] Optionally, the user load distribution strategy on the user side in the next time period can be adjusted according to the carbon emission amount corresponding to the adjusted power supply mode, and then the carbon emission amount of the user side can be adjusted. Among them, the user load distribution strategy refers to intelligently adjusting the power consumption requirements of various electrical equipment or production equipment on the user side according to the change of the carbon emission intensity on the grid side in different time periods.

[0057] Exemplarily, during low-carbon periods, give priority to running high-energy-consuming equipment, such as electric vehicle charging, heat pump heating, washing machines, etc., to make full use of clean energy. During high-carbon periods, reduce unnecessary loads, such as postponing laundry, reducing the air conditioner power, or adjusting industrial production schedules, to reduce carbon emissions.

[0058] In this way, the user side can not only reduce its own carbon emissions, but also better cooperate with the grid dispatching, improving the overall energy utilization efficiency.

[0059] In the above carbon emission amount control method, the grid side is set as the master control end, and the user side is set as the slave control end. Based on the fact that the grid side adjusts the power supply mode on the grid side in the next time period according to the carbon emission amount in the previous time period in real time, the user side responds in a timely manner to the carbon emission intensity information on the grid side and correspondingly adjusts the carbon emission amount of the user side to form an interaction between the user side and the grid side, so as to precisely control the carbon emission amount.

[0060] In the process of adjusting the power supply mode on the grid side in the next time period and adjusting the power consumption mode of the user side in the next time period, corresponding optimization objectives and constraint conditions are established, and under the corresponding constraint conditions, the optimization objectives are solved to obtain the grid dispatching strategy and the user load distribution strategy. Then, according to the obtained grid dispatching strategy and user load distribution strategy, the carbon emission amounts on the grid side and the user side are adjusted.

[0061] Optionally, in one embodiment, the grid side calculates the real-time carbon emission intensity according to the current power generation structure, energy consumption situation and load demand, and feeds this information back to the user side through the power grid system. Dynamic carbon emission intensity As shown in formula (1).

[0062] Formula (1)

[0063] Among them, Represents the carbon emission cap for each user, Represents the output power of the i-th power generation unit, Represents the total load of the power grid.

[0064] Further, in one embodiment, adjusting the power supply mode on the grid side in the next time period according to the carbon emissions in the previous time period includes: obtaining a first optimization objective and a first constraint condition; the first optimization objective is used to minimize the carbon emissions of the power grid system; the first constraint condition includes one or more of grid demand, generator capacity limit, and grid stability to ensure that the solution obtained conforms to the actual operation plan; based on the carbon emissions in the previous time period, solving the first optimization objective under the first constraint condition to obtain a power grid dispatching strategy; and adjusting the power supply mode on the grid side in the next time period according to the power grid dispatching strategy.

[0065] Among them, the first optimization objective and the first constraint condition respectively refer to the optimization objective on the grid side and the constraint condition on the grid side.

[0066] The optimization objective on the grid side is to minimize the carbon emissions of the system. Therefore, the first optimization objective is as shown in formula (2).

[0067] Formula (2)

[0068] Wherein, Represents the carbon emission intensity of the i-th power generation unit in the power grid (unit: gCO2 / kWh); Represents the power generation power of the i-th power generation unit at time t (unit: kW); Represents the penalty coefficient for constraining the grid stability; z represents the demand power of the grid at time t; Represents the maximum carrying capacity of the power grid.

[0069] The grid side needs to satisfy the following first constraint condition during the dispatching process:

[0070] Among them, the grid demand needs to ensure the balance between the grid load demand and the power generation, as shown in formula (3).

[0071] Formula (3)

[0072] Among them, the generator capacity limit is that the power of each power generation unit cannot exceed its maximum capacity, as shown in formula (4) specifically.

[0073] Formula (4)

[0074] Wherein, and Respectively represent the minimum and maximum output powers of the i-th power generation unit.

[0075] Among them, grid stability means that the total power generation of the grid cannot exceed the maximum carrying capacity of the grid, as shown in the specific formula (5).

[0076] Formula (5)

[0077] After obtaining the first optimization objective and the first constraint condition, based on the carbon emissions in the previous time period, under the first constraint condition, the first optimization objective is solved to obtain the grid dispatching strategy.

[0078] Optionally, through a mathematical model, combined with the carbon emissions in the previous time period, under the given constraint conditions, the first optimization objective is solved to obtain the grid dispatching strategy.

[0079] Furthermore, in one embodiment, adjusting the power consumption mode on the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply mode includes: obtaining a second optimization objective and a second constraint condition; the second optimization objective is used to minimize the carbon emissions on the user side; the second constraint condition includes one or more of the user load regulation ability and the user carbon emission limit; based on the carbon emissions corresponding to the adjusted power supply mode, the second optimization objective is solved under the second constraint condition to obtain the user load distribution strategy; according to the user load distribution strategy, the power consumption mode is adjusted.

[0080] Among them, the second optimization objective and the second constraint condition respectively refer to the optimization objective on the user side and the constraint condition on the user side.

[0081] The optimization objective on the user side is to minimize its own carbon emissions, and the first optimization objective is as shown in formula (6).

[0082] Formula (6)

[0083] Among them, represents the power consumption of the user at time t, with the unit of kW; represents the carbon emission intensity of the electricity used by the user at time t, with the unit of gCO2 / kWh, which is dynamically adjusted by the carbon emission intensity on the grid side.

[0084] The user side needs to meet the following second constraint conditions during the dispatching process:

[0085] Among them, the user load regulation ability is expressed as the load adjustment range of each user is limited by the response ability of its equipment, as shown in the specific formula (7).

[0086] Formula (7)

[0087] Among them, represents the actual power consumption of the user at time t; represents the minimum acceptable power consumption of the user at time t; represents the maximum acceptable power consumption of the user at time t.

[0088] Among them, the user carbon emission limit means that the total carbon emissions of the user within a certain period of time should be lower than a predetermined limit value, as shown in the specific formula (8).

[0089] Formula (8)

[0090] Among them, represents the upper limit of carbon emissions for each user.

[0091] After obtaining the second optimization objective and the second constraint condition, adjust the carbon emissions corresponding to the adjusted power supply mode, and solve the second optimization objective under the second constraint condition to obtain the user load distribution strategy.

[0092] Optionally, a mathematical model can be used to solve the second optimization objective under the given constraint conditions by combining the carbon emissions corresponding to the adjusted power supply mode to obtain the user load distribution strategy.

[0093] Since the solution logics of the power grid dispatching strategy and the user load distribution strategy are the same during the solution process, first, the solution method will be described using the same embodiment, and then the solution processes of the power grid dispatching strategy and the user load distribution strategy will be further described.

[0094] First, the individuals in the initial population can be generated through historical operation data, dispatching experience, or random perturbations to ensure the diversity of optimization. The initial population includes multiple individuals, where each individual represents a power grid dispatching and user load distribution scheme. The gene encoding of each individual is the power generation of each power generation unit of the power grid, the load demand on each user side, etc.

[0095] During the iteration process, adjust the parameters of the individuals to search for the global optimal solution, and then calculate the carbon emissions of each individual. After obtaining the carbon emissions of each individual, select the individuals entering the next iteration from the initial population according to the optimization objective and the constraint conditions, and select the first target individual among the individuals entering the next iteration for reproduction to obtain the first reference population.

[0096] Among them, the first target individual for reproduction can be selected from the individuals entering the next iteration by calculating the first fitness of each individual.

[0097] Optionally, the first fitness function can be as shown in formula (9).

[0098] Formula (9)

[0099] wherein represents the carbon emissions on the grid side represents the carbon emissions on the user side

[0100] After obtaining the reference population, in the search space, the local optimal solution is searched by adjusting the parameters of individuals. For example, users can increase the load during low-carbon periods and reduce the load during high-carbon periods. Similarly, the carbon emissions of each individual are calculated. After obtaining the carbon emissions of each individual, according to the optimization objective and constraints, individuals entering the next iteration are selected from the reference population, and the second target individual is selected from the individuals entering the next iteration for reproduction to obtain the second reference population.

[0101] Among them, the second target individual can be selected from the individuals entering the next iteration for reproduction by calculating the second fitness of each individual.

[0102] Optionally, the first fitness function can be defined as the sum of the carbon emissions of the grid and the user, as specifically shown in Formula (10).

[0103] Formula (10)

[0104] wherein represents the step size represents the gradient of the fitness function

[0105] Among them, the local optimal solution can be used as the solution obtained in the first round. For accurate solution, multiple iterations will be carried out. After that, when entering the next round of iteration, the second reference population is used as the initial population in the first round of iteration process, and then the global optimal solution and the local optimal solution are solved respectively. If the difference between the local optimal solution and the optimization objective is greater than the preset condition, enter the next round of iteration again until the difference between the local optimal solution and the optimization objective is less than the preset condition.

[0106] Optionally, the preset condition can be the preset number of iterations or the change of the fitness function is less than the set threshold.

[0107] Exemplarily, the global optimal solution can be solved by the BWO algorithm (Binary Water Optimization Algorithm).

[0108] Among them, the BWO algorithm simulates the behavior of water flow and water droplets for optimization, and it searches for the optimal solution globally.

[0109] The BWO algorithm first generates an initial population of water droplets, where each water droplet represents a feasible scheduling scheme. The position of the water droplet represents the allocation of the power grid and user load, that is, the coordinates of each water droplet are shown in formula (11).

[0110] Formula (11)

[0111] The water droplets flow according to the evaluation value of the fitness function and spread to the possible optimal region. During this process, the positions of the water droplets are adjusted according to factors such as the power grid load demand, the user load adjustment ability, and the carbon emission intensity. The flow direction and distance of the water droplets are determined by the current fitness function and the carbon emission intensity. The calculation can be combined with formula (9). During the optimization process, the water droplets will select the optimal path to reduce carbon emissions.

[0112] According to the fitness function value, the better water droplets, that is, the first target individuals in the above embodiments, will be selected for "reproduction" to form new water droplets and continue to be optimized in the search space.

[0113] The BWO algorithm performs global optimization by simulating the diffusion and flow process of water droplets, iteratively updating the positions of the water droplets until the optimal power grid and user load scheduling strategy is found. The termination condition is usually to reach the maximum number of iterations or the change in the fitness value is less than the preset threshold.

[0114] Exemplarily, the local optimal solution can be solved by the Ivy Algorithm. The Ivy Algorithm first generates an initial population, where each individual represents a power grid scheduling and user load allocation scheme, as shown in formula (12).

[0115] Formula (12)

[0116] Among them, represents the power of the i-th power generation unit of the power grid, represents the power consumption of the user at time t.

[0117] The core of the Ivy Algorithm is to simulate the growth of ivy. The genes of each individual represent different power grid scheduling schemes and user load allocation strategies. In the search space, the algorithm searches for the local optimal solution by adjusting the parameters of the individuals (such as the power grid generation power and user load). For example, users can increase the load during low-carbon periods and reduce the load during high-carbon periods. By simulating the growth process of ivy in the environment, individuals can communicate with each other to find a better scheduling strategy. By selecting individuals with higher fitness for reproduction, the system is promoted to evolve towards the global optimal solution.

[0118] Furthermore, the process of solving by the BWO algorithm and the Ivy Algorithm is as follows.

[0119] 1) Initialize the optimization parameters and population: First, initialize the initial parameters such as the load demands and generation powers on the grid side and the user side, and calculate the initial carbon emission intensity. At this time, generate an optimization population for the subsequent solution of the algorithm.

[0120] 2) Global search of the BWO algorithm: The BWO algorithm explores the global optimal solution of the grid and user load scheduling by simulating the diffusion of water flow and water droplets, calculates the fitness function, and finds a globally better grid scheduling scheme and user load management scheme.

[0121] 3) Local search of the ivy algorithm: Based on the preliminary solution obtained by the BWO algorithm, the ivy algorithm performs local search by simulating the growth process of ivy plants to further optimize the accuracy of the solution. The ivy algorithm optimizes the carbon emission intensity and further improves the energy consumption efficiency by continuously adjusting the grid generation power and user load.

[0122] 4) Judgment of termination conditions: After each alternating optimization, the system will judge whether the stop condition is reached. Common stop conditions include the maximum number of iterations or the change in the fitness function being less than the set threshold. If the stop condition is met, output the optimal solution; if not, return to the BWO algorithm to continue the global search until the optimal solution is found.

[0123] In this way, through the combination of the ivy algorithm and the BWO algorithm, it is beneficial to the complementary advantages of global and local search. The BWO algorithm effectively explores the global space and avoids falling into local optima, while the ivy algorithm improves the accuracy of the solution through local refinement, making the entire optimization process more comprehensive and efficient. Moreover, the BWO provides a preliminary global search framework, and the ivy algorithm accelerates convergence through local optimization, which is beneficial to improving the convergence speed of the overall optimization. Through the alternating optimization of the ivy algorithm and the BWO algorithm, the global and local optimal balance can be achieved in the scheduling of the grid and the user side. The BWO algorithm first provides a global search framework to find a suitable solution, and the ivy algorithm then performs local optimization on this basis to refine the quality of the solution. This method can effectively reduce carbon emissions, improve the consumption capacity of distributed new energy, and maintain the stable operation of the grid.

[0124] In one embodiment, based on the carbon emissions in the previous time period, under the first constraint condition, the first optimization objective is solved to obtain a power grid scheduling strategy, including: initializing the first population; optimizing the first population according to the first constraint condition and the first optimization objective to obtain a global scheduling strategy; the carbon emissions in the previous time period are used to adjust the optimization direction of the first population; taking the global scheduling strategy as the second population, and under the first constraint condition and the first optimization objective, optimizing the second population to obtain a local scheduling strategy; the carbon emissions in the previous time period are used to adjust the optimization direction of the second population; repeating the steps of generating the global scheduling strategy and generating the local scheduling strategy until the first preset termination condition is reached, and taking the finally generated local scheduling strategy as the power grid scheduling strategy.

[0125] Among them, the first population refers to the population used when generating the power grid scheduling strategy; the second population refers to the population obtained after global optimization based on the first population.

[0126] In one embodiment, based on the carbon emissions corresponding to the power supply mode, under the second constraint condition, the second optimization objective is solved to obtain a user load distribution strategy, including: initializing the third population; optimizing the third population according to the second constraint condition and the second optimization objective to obtain a global load distribution strategy; the adjusted carbon emissions corresponding to the power supply mode are used to adjust the optimization direction of the third population; taking the global load distribution strategy as the fourth population, and according to the second constraint condition and the second optimization objective, optimizing the fourth population to obtain a local load distribution strategy; the adjusted carbon emissions corresponding to the power supply mode are used to adjust the optimization direction of the fourth population; repeating the steps of generating the global load distribution strategy and generating the local load distribution strategy until the second preset termination condition is reached, and taking the finally generated local load distribution strategy as the user load distribution strategy.

[0127] Among them, the third population refers to the population used when generating the user load distribution strategy; the fourth population refers to the population obtained after global optimization based on the third population.

[0128] Optionally, optimization is performed according to the method mentioned in the above embodiment, and specific details are not repeated in this embodiment.

[0129] In one embodiment, the above method further includes: taking the adjusted carbon emissions corresponding to the power supply mode in the next time period as the new carbon emissions in the previous time period.

[0130] After completing one round of regulation on the power grid side and the user side, the adjusted carbon emissions corresponding to the power consumption mode on the user side in the next time period will be taken as the new carbon emissions in the previous time period, and enter the next round of regulation.

[0131] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0132] Based on the same inventive concept, an embodiment of the present application also provides a carbon emission control device for implementing the carbon emission control method described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the carbon emission control device provided below can refer to the limitations on the carbon emission control method in the above text, and will not be repeated here.

[0133] In an exemplary embodiment, as Figure 3 shown, a carbon emission control device is provided, including: an acquisition module, a power grid adjustment module, and a user adjustment module, where:

[0134] The acquisition module is used to acquire the carbon emissions in the previous time period.

[0135] The power grid adjustment module is used to adjust the power supply mode on the power grid side in the next time period according to the carbon emissions in the previous time period; the power supply mode is used to adjust the carbon emissions on the power grid side.

[0136] The user adjustment module is used to adjust the power consumption mode on the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply mode; the power consumption mode is used to adjust the carbon emissions on the user side.

[0137] In one embodiment, the above power grid adjustment module includes:

[0138] The first condition acquisition unit is used to acquire the first optimization target and the first constraint condition; the first optimization target is used to minimize the carbon emissions of the power grid system; the first constraint condition includes one or more of power grid demand, generator capacity limit, and power grid stability.

[0139] The first solution unit is used to solve the first optimization target under the first constraint condition based on the carbon emissions in the previous time period to obtain a power grid dispatching strategy.

[0140] The first adjustment unit is used to adjust the power supply mode on the grid side in the next time period according to the grid dispatching strategy.

[0141] In one embodiment, the above-mentioned first solving unit includes:

[0142] The first initialization subunit is used to initialize the first population.

[0143] The first population optimization subunit is used to optimize the first population according to the first constraint condition and the first optimization goal to obtain the global dispatching strategy; the carbon emission in the previous time period is used to adjust the optimization direction of the first population.

[0144] The second population optimization subunit is used to use the global dispatching strategy as the second population and optimize the second population under the first constraint condition and the first optimization goal to obtain the local dispatching strategy; the carbon emission in the previous time period is used to adjust the optimization direction of the second population.

[0145] The first iteration subunit is used to repeat the steps of generating the global dispatching strategy and generating the local dispatching strategy until the first preset termination condition is reached, and use the finally generated local dispatching strategy as the grid dispatching strategy.

[0146] In one embodiment, the above-mentioned user adjustment module includes:

[0147] The second condition acquisition unit is used to acquire the second optimization goal and the second constraint condition; the second optimization goal is used to minimize the carbon emission on the user side; the second constraint condition includes one or more of the user load adjustment ability and the user carbon emission limit.

[0148] The second solving unit solves the second optimization goal under the second constraint condition based on the carbon emission corresponding to the adjusted power supply mode to obtain the user load distribution strategy.

[0149] The second adjustment unit adjusts the power consumption mode according to the user load distribution strategy.

[0150] In one embodiment, the above-mentioned second solving unit includes:

[0151] The second initialization subunit is used to initialize the third population.

[0152] The third population optimization subunit is used to optimize the third population according to the second constraint condition and the second optimization goal to obtain the global load distribution strategy; the carbon emission corresponding to the adjusted power supply mode is used to adjust the optimization direction of the third population.

[0153] The fourth population optimization subunit is used to take the global load distribution strategy as the fourth population, and optimize the fourth population according to the second constraint condition and the second optimization objective to obtain the local load distribution strategy; the carbon emission corresponding to the adjusted power supply mode is used to adjust the optimization direction of the fourth population.

[0154] The second iteration subunit is used to repeat the steps of generating the global load distribution strategy and generating the local load distribution strategy until the second preset termination condition is reached, and take the finally generated local load distribution strategy as the user load distribution strategy.

[0155] In one embodiment, the above device further includes:

[0156] The loop module is used to take the carbon emission corresponding to the adjusted power consumption mode in the next time period as the new carbon emission in the previous time period.

[0157] Each module in the above carbon emission control device can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0158] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 4 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store carbon emissions. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. The computer program, when executed by the processor, implements a carbon emission control method.

[0159] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0160] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method in any of the above embodiments are implemented.

[0161] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in any of the above embodiments are implemented.

[0162] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the method in any of the above embodiments are implemented.

[0163] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0164] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0165] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for controlling carbon emissions, characterized in that: Applied to a power grid system, the power grid system includes a power grid side and a user side; the method includes: Get the carbon emissions for the previous period; According to the carbon emissions in the previous time period, adjusting the power supply mode of the power grid side in the next time period; the power supply mode is used to adjust the carbon emissions on the power grid side; According to the carbon emissions corresponding to the adjusted power supply mode, the power consumption mode of the user side is adjusted in the next time period; the power consumption mode is used to adjust the carbon emissions of the user side.

2. The method according to claim 1, characterized in that The step of adjusting the power supply mode of the grid side in the next time period according to the carbon emissions in the previous time period includes: Obtaining a first optimization objective and a first constraint; the first optimization objective is used to minimize the carbon emissions of the power grid system; the first constraint includes one or more of power grid demand, generator capacity limitation, and power grid stability; Based on the carbon emissions in the previous time period, under the first constraint condition, solving the first optimization objective to obtain a power grid dispatching strategy; According to the grid dispatching strategy, the power supply mode on the grid side is adjusted in the next time period.

3. The method according to claim 2, characterized in that The step of solving the first optimization objective based on the carbon emissions in the previous time period under the first constraint condition to obtain a power grid dispatching strategy includes: Initialize the first population; According to the first constraint condition and the first optimization target, the first population is optimized to obtain a global scheduling strategy; the carbon emissions in the previous time period are used to adjust the optimization direction of the first population; The global scheduling strategy is used as the second population, and the second population is optimized under the first constraint and the first optimization target to obtain a local scheduling strategy; the carbon emissions in the previous time period are used to adjust the optimization direction of the second population; The steps of generating the global dispatch strategy and generating the local dispatch strategy are repeated until a first preset termination condition is reached, and the local dispatch strategy finally generated is used as the power grid dispatch strategy.

4. The method according to claim 1, characterized in that: The adjusting the electricity usage mode of the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply mode includes: Obtaining a second optimization objective and a second constraint; the second optimization objective is used to minimize the carbon emissions on the user side; the second constraint includes one or more of the user load regulation capability and the user carbon emission limit; Based on the carbon emissions corresponding to the adjusted power supply mode, solving the second optimization objective under the second constraint condition to obtain a user load distribution strategy; The electricity usage mode is adjusted according to the user load distribution strategy.

5. The method according to claim 4, characterized in that The step of solving the second optimization objective based on the carbon emissions corresponding to the power supply mode under the second constraint condition to obtain a user load distribution strategy includes: Initialize the third population; According to the second constraint condition and the second optimization goal, the third population is optimized to obtain a global load distribution strategy; the carbon emissions corresponding to the adjusted power supply mode are used to adjust the optimization direction of the third population; The global load distribution strategy is used as a fourth population, and the fourth population is optimized according to the second constraint condition and the second optimization goal to obtain a local load distribution strategy; the carbon emissions corresponding to the adjusted power supply mode are used to adjust the optimization direction of the fourth population; The steps of generating the global load distribution strategy and generating the local load distribution strategy are repeated until a second preset termination condition is reached, and the local load distribution strategy finally generated is used as the user load distribution strategy.

6. The method according to claim 1, characterized in that The method further comprises: The adjusted carbon emissions corresponding to the electricity usage mode in the next time period are used as the new carbon emissions in the previous time period.

7. A carbon emission control device, characterized in that: Applicable to a power grid system; the power grid system includes a power grid side and a user side; the device includes: The acquisition module is used to obtain the carbon emissions in the previous period; A power grid adjustment module, used to adjust the power supply mode of the power grid side in the next time period according to the carbon emissions in the previous time period; the power supply mode is used to adjust the carbon emissions on the power grid side; The user adjustment module is used to adjust the power usage mode of the user side in the next time period according to the carbon emissions corresponding to the adjusted power supply mode; the power usage mode is used to adjust the carbon emissions on the user side.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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