An air conditioner load day-ahead scheduling optimization method considering dynamic carbon emission factor

Abstract

The application discloses an air conditioner load day-ahead scheduling optimization method considering a dynamic carbon emission factor, and the method comprises the following steps: determining the carbon emission factor according to the power flow, the power generation and the type of the power plant of a power grid; obtaining the indoor temperature of an air conditioner, the operation state of the air conditioner and the duration of the air conditioner being turned off and turned on; considering the comfort of the indoor temperature of a consumer, and establishing an air conditioner load day-ahead scheduling optimization model with the aim of minimizing the power cost and the carbon emission cost; using an improved multi-population evolutionary algorithm to solve the air conditioner load day-ahead scheduling optimization model, and outputting the optimal scheduling result of the air conditioner load day-ahead scheduling optimization model for different air conditioner loads. The application can accurately calculate the carbon emission, effectively control the air conditioner load in space, reduce the power consumption and further reduce the carbon emission.

Description

Technical Field

[0001] This invention belongs to the technical field of day-ahead scheduling of air conditioning load, and specifically relates to an optimization method for day-ahead scheduling of air conditioning load that considers dynamic carbon emission factors. Background Technology

[0002] As global climate issues intensify, calls for mitigating climate change and reducing carbon emissions are growing louder worldwide. The power industry, as the largest source of carbon emissions, will play a crucial role in this process, as will electricity consumers. To encourage electricity consumers to participate in carbon reduction, various countries have introduced carbon financing policies, such as carbon trading markets, tradable green certificates, and carbon taxes. Therefore, electricity users should adjust their daily load distribution to maximize their benefits.

[0003] Extensive research has been conducted on the optimal operation of electricity consumers in electricity and carbon trading markets. In 2018, scholars designed a Nash bargaining method to provide data center operators and tenants with a Pareto-efficient and equitable solution for obtaining emergency demand response, thereby effectively reducing carbon emissions from data centers. From the perspective of energy hub consumers, the "Opinions" proposed a consumption-based carbon pricing method to reduce carbon emissions by incorporating carbon prices into energy prices. In March 2021, considering the carbon emission reduction impact of demand response aggregators, the National Twelfth Five-Year Plan proposed a new smart grid resource allocation scheme. To balance economic and environmental benefits, two pecking order-based daily load transfer methods were compared. The results showed that the load transfer method based on marginal emission factors significantly reduced carbon emissions compared to the pricing-based load transfer method. In October 2021, a team of professors from Tsinghua University proposed a robust energy system scheduling method based on carbon emission flows, which can bring considerable economic benefits to electricity users. Simultaneously, based on demand response-based carbon emission intensity control, the National Carbon Emission Statistics and Accounting Working Group constructed a new low-carbon optimal scheduling system, achieving carbon emission reduction. In summary, these studies primarily focus on the optimal coordination between power generation and demand. In May 2022, scholars proposed a hybrid heuristic optimization method to generate optimal appliance operation schemes for residential users, reducing carbon emissions while saving energy costs. However, this carbon emission factor is assumed to be constant, which is inconsistent with actual carbon emissions. In fact, the carbon emission responsibility of the demand side can be identified through a virtual carbon emission flow from power generation to demand. Therefore, each electricity consumer will be assigned a carbon emission coefficient to assess the carbon emissions of each electricity consumption in each time period. In other words, electricity consumers can flexibly reduce carbon emissions by adjusting their load curves.

[0004] As one of the most commonly used household appliances, air conditioning load is well-suited for load distribution regulation due to its flexible control, large adjustment capacity, and minimal impact on consumer comfort. Generally, air conditioning load accounts for over 40% in summer. Like other temperature-controlled loads, air conditioning load is suitable for adjusting load distribution while meeting consumer comfort requirements. However, traditional day-ahead air conditioning load scheduling primarily attempts to minimize electricity costs while considering consumer comfort, neglecting the important factor of carbon emissions from electricity consumers. The model is relatively simplistic and not conducive to green energy conservation and emission reduction. Furthermore, the control strategy requires continuous adjustment, which is costly for regulating millions of households and targets a limited range of applications. Existing air conditioning load scheduling optimization methods are slow, inefficient, and computationally expensive. Moreover, air conditioning optimization scheduling typically only considers the total load reduction, without considering how the load is reduced spatially. This could lead to a single node shedding a large amount of load, affecting the stable operation of the system. Summary of the Invention

[0005] The main objective of this invention is to overcome the shortcomings and deficiencies of the prior art and provide a day-ahead scheduling optimization method for air conditioning load that considers dynamic carbon emission factors. By determining the carbon emission factors, and considering the indoor temperature requirements of different consumers and their comfort levels, the method aims to minimize electricity costs and carbon emission costs. An optimized day-ahead scheduling model for air conditioning load is established and solved using an improved multi-population evolutionary algorithm, which has better global optimization capabilities, effectively improves the quality of the optimal solution of the model, and further enhances the operational efficiency of electricity users.

[0006] The present invention adopts the following technical solution:

[0007] The primary objective is to provide a day-ahead scheduling optimization method for air conditioning loads that takes into account dynamic carbon emission factors, comprising the following steps:

[0008] S1. Determine the carbon emission factor based on the power flow of the power grid, the power generation capacity, and the type of power plant;

[0009] S2. Obtain the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on;

[0010] S3. Considering consumers' comfort with indoor temperature, and with the goal of minimizing electricity costs and carbon emission costs, establish a day-ahead scheduling optimization model for air conditioning load;

[0011] S4. Use an improved multi-population evolutionary algorithm to solve the day-ahead scheduling optimization model for air conditioning load, and output the optimal scheduling result of the day-ahead scheduling optimization model for different air conditioning loads.

[0012] As a preferred technical solution, the carbon emission factor is expressed as:

[0013]

[0014] Among them, P g For the active power output of the power plant connected to the i-th consumer node, δ g For the carbon emission factor of grid-connected power plants, P ij Let δ be the active power input from the j-th consumer node to the i-th consumer node. j Let Ω be the carbon emission factor of the j-th consumption node. i Let i be the set of branch connection nodes of the i-th consumer node.

[0015] As a preferred technical solution, the indoor temperature of the air conditioner is related to the outdoor temperature, indoor thermal parameters, operating status, and rated power of the air conditioner, as expressed as:

[0016]

[0017] Among them, T in (t+1) represents the indoor temperature in the (t+1)th time window, T out (t+1) represents the outdoor temperature in the (t+1)th time window, T in (t) represents the indoor temperature at the t-th time window, R represents the equivalent thermal resistance of the room, C represents the equivalent heat capacity of the room, Δt represents the duration of the air conditioner being turned off or on, P represents the rated power of the air conditioner, and η represents the rated power of the air conditioner. cop This refers to the air conditioner's cooling energy efficiency coefficient. "Turn off" indicates that the air conditioner is in the off state, and "turn on" indicates that the air conditioner is in the on state.

[0018] The operating status of the air conditioner is determined based on the current indoor temperature and the operating status of the previous time window, and is expressed as follows:

[0019]

[0020] Where s0(t+1) represents the operating state of the air conditioner at the start of the (t+1)th time window; a value of 0 indicates the air conditioner is off, and a value of 1 indicates it is on. AC (t+1) represents the operating state of the air conditioner within the (t+1)th time window; 0 indicates the air conditioner is off, and 1 indicates it is on. set The air conditioner is set to the indoor temperature, θ is the allowable temperature variation of the air conditioner, and u AC s0(t) represents the operating state of the air conditioner in the t-th time window. 0 indicates that the air conditioner is off and 1 indicates that the air conditioner is on. s0(t) represents the operating state of the air conditioner at the beginning of the t-th time window. 0 indicates that the air conditioner is off and 1 indicates that the air conditioner is on.

[0021] The duration of the air conditioner's on / off cycles is derived from the principle of indoor temperature change and is expressed as follows:

[0022]

[0023] Where, τ off τ is the duration for which the air conditioner is off. on This refers to the duration the air conditioner is on.

[0024] As a preferred technical solution, in step S3, based on the indoor temperature needs of different consumers and considering their comfort level with the goal of minimizing electricity and carbon emission costs, an optimization model for day-ahead air conditioning load scheduling is established:

[0025] minf = C T +C E +C C (5)

[0026]

[0027]

[0028] Among them, c T For temperature comfort deviation, c E For electricity costs, C C Let n be the carbon emission cost, n be the number of consumers, and ω be the carbon emission cost. j (t) represents the weight of the indoor temperature requirement of the j-th consumer in the t-th time window. Let be the average indoor temperature of the j-th consumer during the t-th time window. Set the indoor temperature for the j-th consumer in the t-th time window. The air conditioning operation status of the j-th consumer within the t-th time window. Let P be the duration of air conditioning operation for the j-th consumer during the t-th time window. j Let λ be the rated power of the air conditioner for the j-th consumer. E (t) represents the electricity price in the t-th time window, δ j (t) represents the carbon emission factor of the j-th consumer in the t-th time window, λ C (t) represents the carbon emission price for the t-th time window.

[0029] As a preferred technical solution, the method of using an improved multi-population evolutionary algorithm to solve the day-ahead scheduling optimization model for air conditioning load specifically includes:

[0030] S41. The objective function of the day-ahead scheduling optimization model for air conditioning load is used as the fitness function, and real number encoding is adopted.

[0031] S42. Initialize the total population Q and individual Q using an improved multi-population evolution algorithm. l And set h=1; the total population includes 3 subpopulations;

[0032] S43. Update subpopulation 1 using an elite-preserving genetic algorithm;

[0033] S44. Update subpopulation 2 using particle swarm optimization algorithm;

[0034] S45. Update subpopulation 3 using the Cuckoo algorithm;

[0035] S46. Perform migration operations among the three subpopulations;

[0036] S47. Calculate the total fitness of the total population, and replace the first individual in subpopulation 1 with the individual with the best fitness.

[0037] S48. Let h = h + 1, and iteratively execute steps S43 to S48 until the maximum number of iterations h is reached. max The optimal scheduling result of the day-ahead scheduling optimization model for air conditioning load is obtained.

[0038] As a preferred technical solution, in step S43, the subpopulation 1 is updated using a genetic algorithm based on elite preservation, specifically as follows:

[0039] Simulated binary crossover and polynomial mutation were performed on subpopulation 1;

[0040] Consumers' comfort level with indoor temperature is used as the fitness value for subpopulation 1;

[0041] Calculate the fitness value of each individual in subpopulation 1 and sort them from largest to smallest fitness value. Select the last n individuals as parents and save the individual with the smallest fitness value as the best individual as the first individual in subpopulation 1.

[0042] As a preferred technical solution, in step S44, the particle swarm optimization algorithm is used to update each individual in subpopulation 2, including velocity update and position update. The update formula is:

[0043] Q l (h+1)=Q l (h)+V l (h+1) (8)

[0044] V l (h+1)=w×V l (h)+c1×G1×(p l，best (h)-Q l (h))+c2×G2×(p g，best (h)-Q l (h)) (9)

[0045]

[0046] In the formula, Q l (h+1) represents the l-th individual in subpopulation 2 during the (h+1)-th iteration; Q l (h) represents the l-th individual in subpopulation 2 during the h-th iteration, V l (h+1) represents the update rate of the l-th individual in subpopulation 2 during the (h+1)-th iteration, w is the dynamic inertia factor, and V l (h) represents the update rate of the l-th individual in subpopulation 2 at the h-th iteration, c1 and c2 are learning factors, G1 and G2 are random variables in the interval [0, 1], and p l，best (h) represents the optimal position of the l-th individual in subpopulation 2 during the h-th iteration, p g，best (h) represents the global optimal position of subpopulation 2 at the h-th iteration, w max For the largest inertia factor, w min h is the smallest inertia factor max This represents the maximum number of iterations.

[0047] As a preferred technical solution, in step S45, the cuckoo algorithm is used to update each individual in subpopulation 3, including updating the nest and discarding the eggs.

[0048] The "updating nests" step is used to update the position of each individual in subpopulation 3 to obtain a new population. The update formula is as follows:

[0049] Q l (h+1)=Q l (h)+α×Y(λ) (11)

[0050] In the formula, Q l (h+1) represents the l-th individual in subpopulation 3 during the (h+1)-th iteration, Q. l (h) represents the l-th individual in subpopulation 3 during the h-th iteration, α is the step size factor, and Y(λ) is the Levy flight step size update function;

[0051] If the fitness function value of an individual in the new population is less than the fitness function value of the corresponding individual in subpopulation 3, then the individual in the new population replaces the corresponding individual in subpopulation 3.

[0052] The discarded bird eggs are used to iterate through each individual in the new population, with p a The probability of discarding individuals from the new population is given, and new individuals are generated to replace the discarded individuals, with the new population being designated as subpopulation 3.

[0053] The second objective is to provide a day-ahead scheduling optimization system for air conditioning load that considers dynamic carbon emission factors, applied to the aforementioned day-ahead scheduling optimization method for air conditioning load that considers dynamic carbon emission factors, including a factor determination module, an information acquisition module, a model building module, and a model solving module;

[0054] The factor determination module is used to determine carbon emission factors based on the power flow of the power grid, the power generation capacity, and the type of power plant.

[0055] The information acquisition module is used to acquire the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on.

[0056] The model building module is used to consider consumers' comfort with indoor temperature and to establish an optimization model for day-ahead scheduling of air conditioning load with the goal of minimizing electricity costs and carbon emission costs.

[0057] The model solving module is used to solve the day-ahead scheduling optimization model of air conditioning load using an improved multi-population evolution algorithm, and outputs the optimal scheduling result of the day-ahead scheduling optimization model of air conditioning load for different air conditioning loads.

[0058] The third objective is to provide a computer-readable storage medium storing a program that, when executed by a processor, implements the aforementioned method for optimizing day-ahead air conditioning load scheduling considering dynamic carbon emission factors.

[0059] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0060] 1. The technical problem solved by this invention is to propose a novel day-ahead optimization scheduling method for air conditioning load under dynamic carbon emission factors. The proposed carbon emission factor establishes a dynamic carbon emission model based on grid power flow, power generation, and power plant type. It further considers that dynamic carbon emission is not simply a constant, which is closer to actual carbon emission and can more accurately calculate carbon emission, thereby achieving effective spatial control of air conditioning load. At the same time, it establishes an operating state model of air conditioning load under different time windows. By controlling the start and stop of air conditioning at different time nodes, it optimizes the reduction of power consumption while ensuring the comfort experience of consumers, thereby further reducing carbon emissions.

[0061] 2. This invention considers the indoor temperature needs of different consumers and their comfort level, aiming to minimize electricity and carbon emission costs. It integrates electricity cost, carbon emission, and comfort functions into the day-ahead scheduling optimization model for air conditioning load. This model can control electricity costs to the greatest extent while reducing carbon emissions, and also keep consumers' indoor temperature comfort at a high level. Compared with traditional day-ahead scheduling of air conditioning load, the model is more complete and considers more aspects.

[0062] 3. The improved multi-population evolutionary algorithm GA-PSO-CS proposed in this invention can save a lot of time and cost compared with traditional optimization algorithms when solving the dynamic carbon emission factor air conditioning load scheduling problem. At the same time, the solution quality is higher, it can properly balance development and exploration, and has better global optimization ability. It effectively improves the quality of the optimal solution of the day-ahead scheduling optimization model for air conditioning load, and further improves the operational efficiency of power users. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart of a day-ahead scheduling optimization method for air conditioning load that considers dynamic carbon emission factors, as described in an embodiment of the present invention.

[0065] Figure 2 The figures shown are indoor temperature change curves of the air conditioner under different control modes in the embodiments of the present invention, wherein (a) is an indoor temperature change curve under automatic control mode; and (b) is an indoor temperature change curve under direct control mode.

[0066] Figure 3 This is a flowchart of the improved multi-population evolution algorithm GA-PSO-CS in an embodiment of the present invention.

[0067] Figure 4 This is a structural diagram of a day-ahead scheduling optimization system for air conditioning load considering dynamic carbon emission factors, as described in an embodiment of the present invention.

[0068] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention. Detailed Implementation

[0069] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0070] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0071] Example 1

[0072] Traditional day-ahead load optimization scheduling for air conditioning primarily considers consumer comfort while minimizing electricity costs, often neglecting the crucial factor of carbon emissions. This is detrimental to green energy conservation and emission reduction. Therefore, this example establishes a day-ahead load optimization method for air conditioning that considers dynamic carbon emission factors. This method can modify load distribution to significantly reduce carbon emissions and lower electricity costs while satisfying consumer comfort. Figure 1 As shown, it includes the following steps:

[0073] S1. Determine the carbon emission factor based on the power flow of the power grid, the power generation capacity, and the type of power plant;

[0074] S2. Obtain the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on;

[0075] S3. Considering consumers' comfort with indoor temperature, and with the goal of minimizing electricity costs and carbon emission costs, establish a day-ahead scheduling optimization model for air conditioning load;

[0076] S4. Use an improved multi-population evolutionary algorithm to solve the day-ahead scheduling optimization model for air conditioning load, and output the optimal scheduling result of the day-ahead scheduling optimization model for different air conditioning loads.

[0077] Furthermore, in step S1, if the carbon emission factor is determined to be a constant, it will be inconsistent with the actual carbon emissions. Therefore, this invention first determines the carbon emission factor, which is mainly determined by the power flow of the power grid, the power meter, and the type of power plant, as shown below:

[0078]

[0079] Among them, Pg For the active power output of the power plant connected to the i-th consumer node, δ g For the carbon emission factor of grid-connected power plants, P ij Let δ be the active power input from the j-th consumer node to the i-th consumer node. j Let Ω be the carbon emission factor of the j-th consumption node. i Let i be the set of branch connection nodes of the i-th consumer node.

[0080] Furthermore, in step S2, the indoor temperature of the air conditioner is mainly related to the outdoor temperature, indoor thermal parameters, operating status, and rated power of the air conditioner load, as expressed as:

[0081]

[0082] Among them, T in (t+1) represents the indoor temperature in the (t+1)th time window, T out (t+1) represents the outdoor temperature in the (t+1)th time window, T in (t) represents the indoor temperature at the t-th time window, R represents the equivalent thermal resistance of the room, C represents the equivalent heat capacity of the room, Δt represents the duration of the air conditioner being turned off or on, P represents the rated power of the air conditioner, and η represents the rated power of the air conditioner. cop This refers to the air conditioner's cooling energy efficiency coefficient. "Turn off" indicates that the air conditioner is in the off state, and "turn on" indicates that the air conditioner is in the on state.

[0083] The operating status of an air conditioner (i.e., on or off) is determined not only by its on / off state but also by the set temperature. Generally, the air conditioner will repeatedly switch between on and off states based on the dynamic indoor temperature. Figure 2 As shown in (a), in automatic control mode, after setting the temperature T, if the indoor temperature is lower than the minimum allowable temperature T... set If -θ is not specified, the air conditioner will operate in the off state; as the indoor temperature gradually rises in the off state, when the indoor temperature exceeds the maximum allowable temperature T... set At time +θ, the air conditioner will be running in the on state. Therefore, the operating state of the air conditioner at the beginning of the (t+1)th time window can be determined by the current indoor temperature and the operating state of the previous time window, expressed as:

[0084]

[0085] Where s0(t+1) represents the operating state of the air conditioner at the start of the (t+1)th time window; a value of 0 indicates the air conditioner is off, and a value of 1 indicates it is on. AC (t+1) represents the operating state of the air conditioner within the (t+1)th time window; 0 indicates the air conditioner is off, and 1 indicates it is on. setThe air conditioner is set to the indoor temperature, θ is the allowable temperature variation of the air conditioner, and u AC s0(t) represents the operating state of the air conditioner in the t-th time window. 0 indicates that the air conditioner is off and 1 indicates that the air conditioner is on. s0(t) represents the operating state of the air conditioner at the beginning of the t-th time window. 0 indicates that the air conditioner is off and 1 indicates that the air conditioner is on.

[0086] Based on the principle of indoor temperature change, the calculation formula for the duration of air conditioner off and on is as follows:

[0087]

[0088] Where, τ off τ is the duration for which the air conditioner is off. on This refers to the duration the air conditioner is on.

[0089] On the other hand, due to the direct control mode, Figure 2 The indoor temperature change curve in (a) will change, such as Figure 2 As shown in (b), when the air conditioning load is disconnected from the on state, the indoor temperature will gradually rise until it is turned on again.

[0090] Furthermore, an optimization model for advance air conditioning load scheduling is established. From the perspective of the load aggregator, the control variable is the switching state of different air conditioning loads. The optimization objective not only considers consumers' comfort level for indoor temperature but also aims to minimize electricity and carbon emission costs. Therefore, based on the indoor temperature requirements of different consumers, the day-ahead air conditioning load scheduling optimization model ODAD can be described as follows:

[0091] minf = C T +C E +C C (5)

[0092]

[0093]

[0094] Among them, C T For temperature comfort deviation, C E For electricity costs, C C Let n be the carbon emission cost, n be the number of consumers, and ω be the carbon emission cost. j (t) represents the weight of the indoor temperature requirement of the j-th consumer in the t-th time window. Let be the average indoor temperature of the j-th consumer during the t-th time window. Set the indoor temperature for the j-th consumer in the t-th time window. The air conditioning operation status of the j-th consumer within the t-th time window. Let P be the duration of air conditioner operation for the j-th consumer during the t-th time window. j Let λ be the rated power of the air conditioner for the j-th consumer. E (t) represents the electricity price in the t-th time window, δ j (t) represents the carbon emission factor of the j-th consumer in the t-th time window, λ C (t) represents the carbon emission price for the t-th time window.

[0095] Furthermore, to solve the aforementioned ODAD, this invention proposes an improved multi-population evolutionary algorithm (GA-PSO-CS algorithm). By optimizing the on / off states of air conditioners under different loads, the overall optimization objective is made smaller, achieving a better comfort experience with minimal cost while reducing carbon emissions. The solution process is as follows: Figure 3 As shown, specifically:

[0096] S41. The objective function of the day-ahead scheduling optimization model for air conditioning load is used as the fitness function, and real number encoding is adopted.

[0097] Since the constraints of ODAD need to be satisfied, the objective function of ODAD can be taken as the fitness function of the GA-PSO-CS algorithm, i.e., F = f = C. T +C E +C C For problems requiring high precision and large dimensionality, binary encoding would consume a large amount of storage space. In order to better solve the proposed problem, this invention adopts a real number encoding method.

[0098] S42. Initialize the total population Q and individual Q using an improved multi-population evolution algorithm. l And set h=1; the total population includes 3 subpopulations, namely subpopulation 1, subpopulation 2 and subpopulation 3;

[0099] S43. Update subpopulation 1 using an elite-preserving genetic algorithm;

[0100] Genetic Algorithm (GA) is a method for exploring optimal solutions by simulating the natural evolutionary process. Elitism Genetic Algorithm (EGA) adds an elite preservation mechanism to GA. The population update operations of the EGA algorithm include crossover, mutation, selection, and elite preservation. This invention uses an elite preservation-based genetic algorithm to update subpopulation 1, specifically:

[0101] 1) Since the simulated binary crossover operator has good performance for continuous optimization problems, simulated binary crossover and polynomial mutation are used to update each individual in subpopulation 1;

[0102] 2) Consumers' comfort level with indoor temperature is used as the fitness value for subpopulation 1;

[0103] 3) Calculate the fitness value of each individual in subpopulation 1 and sort them from largest to smallest fitness value. Select the last n individuals as parents and save the individual with the smallest fitness value as the best individual as the first individual in subpopulation 1. This individual does not participate in the population update process and is directly retained into the next generation of parent population.

[0104] S44. Update subpopulation 2 using particle swarm optimization algorithm;

[0105] Particle Swarm Optimization (PSO) is a heuristic algorithm with mechanisms for individual improvement, population cooperation, and competition, inspired by the predation behavior of flocks of birds or schools of fish in nature. This invention uses the PSO algorithm to update each individual in subpopulation 2. The update operations mainly include velocity update and position update, and the update formula is as follows:

[0106] Q l (h+1)=Q l (h)+V l (h+1) (8)

[0107] V l (h+1)=w×V l (h)+c1×G1×(p l，best (h)-Q l (h))+c2×G2×(p g，best (h)-Q l (h)) (9)

[0108]

[0109] In the formula, Q l (h+1) represents the l-th individual in subpopulation 2 during the (h+1)-th iteration; Q l (h) represents the l-th individual in subpopulation 2 during the h-th iteration, V l (h+1) represents the update rate of the l-th individual in subpopulation 2 during the (h+1)-th iteration, w is the dynamic inertia factor, and V l (h) represents the update rate of the l-th individual in subpopulation 2 at the h-th iteration, c1 and c2 are learning factors, G1 and G2 are random variables in the interval [0, 1], and p l，best (h) represents the optimal position of the l-th individual in subpopulation 2 during the h-th iteration, p g，best (h) represents the global optimal position of subpopulation 2 at the h-th iteration, w max For the largest inertia factor, wmin h is the smallest inertia factor max This represents the maximum number of iterations.

[0110] S45. Update subpopulation 3 using the Cuckoo algorithm;

[0111] The Cuckoo Search (CS) algorithm is a metaheuristic algorithm used to solve optimization problems. It simulates the unique nest-finding and egg-laying behavior of the cuckoo and incorporates the levy flight mechanism of some birds and fruit flies in nature. This invention uses the Cuckoo Search algorithm to update each individual in subpopulation 3, including updating nests and discarding eggs.

[0112] The "updating nests" step is used to update the position of each individual in subpopulation 3 to obtain a new population. The update formula is as follows:

[0113] Q l (h+1)=Q l (h)+α×Y(λ) (11)

[0114] In the formula, Q l (h+1) represents the l-th individual in subpopulation 3 during the (h+1)-th iteration, Q. l (h) represents the l-th individual in subpopulation 3 during the h-th iteration, α is the step size factor, and Y(λ) is the Levy flight step size update function;

[0115] If the fitness function value of an individual in the new population is less than the fitness function value of the corresponding individual in subpopulation 3, then the individual in the new population replaces the corresponding individual in subpopulation 3.

[0116] Discarding bird eggs is used to iterate through each individual in the new population, with p a The probability of discarding individuals from the new population is given, and new individuals are generated to replace the discarded individuals, with the new population being designated as subpopulation 3.

[0117] S46. Perform migration operations among the three subpopulations. Migration operations enable information exchange between subpopulations, enhance the algorithm's global optimization ability, and thus prevent the algorithm from converging too early.

[0118] S47. Calculate the total fitness of the total population, and replace the first individual in subpopulation 1 with the individual with the best fitness.

[0119] S48. Let h = h + 1, and iteratively execute steps S43 to S48 until the maximum number of iterations h is reached. max This yields the optimal scheduling result for OADO.

[0120] Example 2

[0121] like Figure 4As shown, another embodiment of the present invention provides an air conditioning load day-ahead scheduling optimization system that considers dynamic carbon emission factors, including a factor determination module, an information acquisition module, a model building module, and a model solving module;

[0122] Among them, the factor determination module is used to determine the carbon emission factor based on the power flow of the power grid, the power generation capacity, and the type of power plant;

[0123] The information acquisition module is used to acquire the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on.

[0124] The model building module is used to consider consumers' comfort with indoor temperature and to establish an optimization model for day-ahead scheduling of air conditioning load with the goal of minimizing electricity costs and carbon emission costs.

[0125] The model solving module is used to solve the day-ahead scheduling optimization model of air conditioning load using an improved multi-population evolution algorithm, and outputs the optimal scheduling result of the day-ahead scheduling optimization model of air conditioning load for different air conditioning loads.

[0126] Example 3

[0127] like Figure 5 As shown, in one embodiment, a computer-readable storage medium is provided, storing a program in a memory. When the program is executed by a processor, it implements a day-ahead scheduling optimization method for air conditioning load that considers dynamic carbon emission factors, specifically:

[0128] S1. Determine the carbon emission factor based on the power flow of the power grid, the power generation capacity, and the type of power plant;

[0129] S2. Obtain the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on;

[0130] S3. Considering consumers' comfort with indoor temperature, and with the goal of minimizing electricity costs and carbon emission costs, establish a day-ahead scheduling optimization model for air conditioning load;

[0131] S4. Use an improved multi-population evolutionary algorithm to solve the day-ahead scheduling optimization model for air conditioning load, and output the optimal scheduling result of the day-ahead scheduling optimization model for different air conditioning loads.

[0132] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0133] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing day-ahead air conditioning load scheduling considering dynamic carbon emission factors, characterized in that, Includes the following steps: S1. Determine the carbon emission factor based on the power flow of the power grid, the power generation capacity, and the type of power plant; S2. Obtain the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on; The indoor temperature of the air conditioner is related to the outdoor temperature, indoor thermal parameters, operating status, and rated power of the air conditioner; the operating status of the air conditioner is determined based on the current indoor temperature and the operating status of the previous time window; the duration of the air conditioner's on / off state is obtained based on the principle of indoor temperature change. S3. Considering consumers' comfort level regarding indoor temperature, and aiming to minimize electricity and carbon emission costs, establish a day-ahead scheduling optimization model for air conditioning load: ， ， ， Among them, C T For temperature comfort deviation, C E For electricity costs, C C Let n be the carbon emission cost, n be the number of consumers, and ω be the carbon emission cost. j (t) represents the weight of the indoor temperature requirement of the j-th consumer in the t-th time window. Let be the average indoor temperature of the j-th consumer during the t-th time window. Set the indoor temperature for the j-th consumer in the t-th time window. The air conditioning operation status of the j-th consumer within the t-th time window. Let P be the duration of air conditioning operation for the j-th consumer during the t-th time window. j Let λ be the rated power of the air conditioner for the j-th consumer. E (t) represents the electricity price in the t-th time window, δ j (t) represents the carbon emission factor of the j-th consumer in the t-th time window, λ C (t) represents the carbon emission price for the t-th time window; S4. Use an improved multi-population evolutionary algorithm to solve the day-ahead scheduling optimization model for air conditioning load, and output the optimal scheduling result of the day-ahead scheduling optimization model for different air conditioning loads.

2. The day-ahead scheduling optimization method for air conditioning load considering dynamic carbon emission factors according to claim 1, characterized in that, The carbon emission factor is expressed as: ， Among them, P g For the active power output of the power plant connected to the i-th consumer node, δ g For the carbon emission factor of grid-connected power plants, P ij Let δ be the active power input from the j-th consumer node to the i-th consumer node. j Let Ω be the carbon emission factor of the j-th consumption node. i Let i be the set of branch connection nodes of the i-th consumer node.

3. The day-ahead scheduling optimization method for air conditioning load considering dynamic carbon emission factors according to claim 1, characterized in that, The indoor temperature of the air conditioner is expressed as: ， Among them, T in (t+1) represents the indoor temperature in the (t+1)th time window, T out (t+1) represents the outdoor temperature in the (t+1)th time window, T in (t) represents the indoor temperature at the t-th time window, R represents the equivalent thermal resistance of the room, and C represents the equivalent heat capacity of the room. η represents the duration of the air conditioner being turned off or on, P represents the rated power of the air conditioner, and η represents the operating time of the air conditioner. cop This refers to the air conditioner's cooling energy efficiency coefficient. "Turn off" indicates that the air conditioner is off, and "turn on" indicates that the air conditioner is on. The operating status of the air conditioner is indicated as follows: ， Where s0(t+1) represents the operating state of the air conditioner at the start of the (t+1)th time window; a value of 0 indicates the air conditioner is off, and a value of 1 indicates it is on. AC (t+1) represents the operating state of the air conditioner within the (t+1)th time window; 0 indicates the air conditioner is off, and 1 indicates it is on. set The air conditioner is set to the indoor temperature, θ is the allowable temperature variation of the air conditioner, and u AC s0(t) represents the operating state of the air conditioner in the t-th time window. 0 indicates that the air conditioner is off and 1 indicates that the air conditioner is on. s0(t) represents the operating state of the air conditioner at the beginning of the t-th time window. 0 indicates that the air conditioner is off and 1 indicates that the air conditioner is on. The duration of the air conditioner being turned off and on is expressed as follows: ， Where, τ off τ is the duration for which the air conditioner is off. on This refers to the duration the air conditioner is on.

4. The day-ahead scheduling optimization method for air conditioning load considering dynamic carbon emission factors according to claim 1, characterized in that, The method of using an improved multi-population evolutionary algorithm to solve the day-ahead scheduling optimization model for air conditioning load is as follows: S41. The objective function of the day-ahead scheduling optimization model for air conditioning load is used as the fitness function, and real number encoding is adopted. S42. Initialize the total population Q and individual Q using an improved multi-population evolution algorithm. l And set h=1; the total population includes 3 subpopulations; S43. Update subpopulation 1 using an elite-preserving genetic algorithm; S44. Update subpopulation 2 using particle swarm optimization algorithm; S45. Update subpopulation 3 using the Cuckoo algorithm; S46. Perform migration operations among the three subpopulations; S47. Calculate the total fitness of the total population, and replace the first individual in subpopulation 1 with the individual with the best fitness. S48. Let h = h + 1, and iteratively execute steps S43 to S48 until the maximum number of iterations h is reached. max The optimal scheduling result of the day-ahead scheduling optimization model for air conditioning load is obtained.

5. The day-ahead scheduling optimization method for air conditioning load considering dynamic carbon emission factors according to claim 4, characterized in that, In step S43, subpopulation 1 is updated using an elite-preservation-based genetic algorithm, specifically as follows: Simulated binary crossover and polynomial mutation were performed on subpopulation 1; Consumers' comfort level with indoor temperature is used as the fitness value for subpopulation 1; Calculate the fitness value of each individual in subpopulation 1 and sort them from largest to smallest fitness value. Select the last n individuals as parents and save the individual with the smallest fitness value as the best individual as the first individual in subpopulation 1.

6. The day-ahead scheduling optimization method for air conditioning load considering dynamic carbon emission factors according to claim 4, characterized in that, In step S44, the particle swarm optimization algorithm is used to update each individual in subpopulation 2, including velocity and position updates. The update formula is as follows: ， ， ， In the formula, Q l (h+1) represents the l-th individual in subpopulation 2 during the (h+1)-th iteration; Q l (h) represents the l-th individual in subpopulation 2 during the h-th iteration, V l (h+1) represents the update rate of the l-th individual in subpopulation 2 during the (h+1)-th iteration, w is the dynamic inertia factor, and V l (h) represents the update rate of the l-th individual in subpopulation 2 at the h-th iteration, c1 and c2 are learning factors, G1 and G2 are random variables in the interval [0,1], and p l,best (h) represents the optimal position of the l-th individual in subpopulation 2 during the h-th iteration, p g,best (h) represents the global optimal position of subpopulation 2 at the h-th iteration, w max For the largest inertia factor, w min h is the smallest inertia factor max This represents the maximum number of iterations.

7. The day-ahead scheduling optimization method for air conditioning load considering dynamic carbon emission factors according to claim 4, characterized in that, In step S45, the cuckoo algorithm is used to update each individual in subpopulation 3, including updating the nest and discarding the eggs; The "updating nests" step is used to update the position of each individual in subpopulation 3 to obtain a new population. The update formula is as follows: ， In the formula, Q l (h+1) represents the l-th individual in subpopulation 3 during the (h+1)-th iteration, Q. l (h) represents the l-th individual in subpopulation 3 during the h-th iteration, α is the step size factor, and Y(λ) is the Levy flight step size update function; If the fitness function value of an individual in the new population is less than the fitness function value of the corresponding individual in subpopulation 3, then the individual in the new population replaces the corresponding individual in subpopulation 3. The discarded bird eggs are used to iterate through each individual in the new population, with p a The probability of discarding individuals from the new population is given, and new individuals are generated to replace the discarded individuals, with the new population being designated as subpopulation 3.

8. A day-ahead scheduling optimization system for air conditioning load considering dynamic carbon emission factors, characterized in that, An air conditioning load day-ahead scheduling optimization method considering dynamic carbon emission factors, applicable to any one of claims 1-7, includes a factor determination module, an information acquisition module, a model building module, and a model solving module; The factor determination module is used to determine carbon emission factors based on the power flow of the power grid, the power generation capacity, and the type of power plant. The information acquisition module is used to acquire the indoor temperature of the air conditioner, the operating status of the air conditioner, and the duration of the air conditioner being turned off and on. The indoor temperature of the air conditioner is related to the outdoor temperature, indoor thermal parameters, operating status, and rated power of the air conditioner; the operating status of the air conditioner is determined based on the current indoor temperature and the operating status of the previous time window; the duration of the air conditioner's on / off state is obtained based on the principle of indoor temperature change. The model building module is used to consider consumers' comfort level with indoor temperature and aims to minimize electricity and carbon emission costs by establishing a day-ahead scheduling optimization model for air conditioning load. ， ， ， Among them, C T For temperature comfort deviation, C E For electricity costs, C C Let n be the carbon emission cost, n be the number of consumers, and ω be the carbon emission cost. j (t) represents the weight of the indoor temperature requirement of the j-th consumer in the t-th time window. Let be the average indoor temperature of the j-th consumer during the t-th time window. Set the indoor temperature for the j-th consumer in the t-th time window. The air conditioning operation status of the j-th consumer within the t-th time window. Let P be the duration of air conditioning operation for the j-th consumer during the t-th time window. j Let λ be the rated power of the air conditioner for the j-th consumer. E (t) represents the electricity price in the t-th time window, δ j (t) represents the carbon emission factor of the j-th consumer in the t-th time window, λ C (t) represents the carbon emission price for the t-th time window; The model solving module is used to solve the day-ahead scheduling optimization model of air conditioning load using an improved multi-population evolution algorithm, and outputs the optimal scheduling result of the day-ahead scheduling optimization model of air conditioning load for different air conditioning loads.

9. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the day-ahead scheduling optimization method for air conditioning load that considers dynamic carbon emission factors as described in any one of claims 1-7.

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