A method for optimizing carbon emission scheduling of energy equipment in an integrated energy system

Abstract

The present invention discloses a method for optimizing carbon emission scheduling for energy equipment in an integrated energy system. The method comprises the following steps: determining the carbon emission factor of the energy equipment, constructing a carbon emission cost function, generating carbon trading price fluctuation scenarios using Monte Carlo simulation, calculating the carbon trading risk index of the equipment under each scenario based on the cost function, determining the start and stop times based on optimal economic benefits, constructing an integrated energy system topology network structure, updating the carbon flow path set based on the start and stop times and calculating the comprehensive carbon emission coefficient, establishing a coupled model of energy flow and carbon flow between high-emission and low-emission paths, obtaining a feasible space for scheduling strategies through a collaborative strategy, and obtaining the optimal collaborative scheduling operation strategy through a particle swarm algorithm with the goal of minimizing carbon emissions. This method can effectively reduce carbon emissions and optimize equipment start and stop times, improve the economic and environmental efficiency of system operation, and achieve the dual goals of energy conservation and emission reduction and efficiency improvement, while also being highly interpretable.

Description

Technical Field

[0001] The present invention relates to the field of energy system optimization, and in particular to a method for optimizing carbon emission scheduling of energy equipment in an integrated energy system. Background Art

[0002] With the acceleration of global industrialization and urbanization, energy demand continues to rise. The heavy reliance on fossil fuels in traditional energy structures has led to a sharp increase in greenhouse gas emissions, including carbon dioxide. This has triggered serious climate change issues, including global warming, rising sea levels, and frequent extreme weather events. This has prompted widespread international consensus that effective measures must be taken to reduce carbon emissions in order to alleviate the enormous pressure on the global ecological environment.

[0003] However, the current operation and management of integrated energy systems faces numerous challenges and problems. Regarding carbon emissions quantification and management, there is a lack of precise and systematic methods for effectively monitoring and assessing them. Energy scheduling decisions often fail to fully incorporate the uncertainties of the carbon trading market and the carbon emission characteristics of energy equipment, resulting in scheduling strategies that may not adapt to market changes and fail to achieve optimal carbon emissions control. Furthermore, there is a lack of effective means to address the complex coupling relationship between energy and carbon flows in integrated energy systems, making it difficult to tap into emission reduction potential at the system level and develop efficient collaborative optimization strategies.

[0004] A method for optimizing carbon emission scheduling of energy equipment in an integrated energy system can comprehensively and accurately quantify carbon emissions, fully consider the risks of the carbon trading market and the coordinated operation of multiple energy equipment, and minimize carbon emissions from the integrated energy system through scientific and reasonable scheduling optimization. At the same time, it ensures the reliability and economy of energy supply and promotes sustainable development transformation in the energy sector. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing carbon emission scheduling of energy equipment in an integrated energy system.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] A first aspect of the present invention provides a method for optimizing carbon emission scheduling in an integrated energy system, comprising:

[0008] Determine the carbon emission factors of energy equipment and determine the carbon emission cost function based on the carbon emission factors;

[0009] Using the Monte Carlo simulation method to generate carbon trading price fluctuation scenarios, and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function;

[0010] Determining the start and stop times of energy equipment with optimal economic benefits based on the carbon trading risk indicators;

[0011] Constructing a topological network structure for the integrated energy system, updating a set of carbon flow paths in the topological network structure according to the start and stop times of the energy equipment, and calculating a comprehensive carbon emission coefficient for each carbon flow path;

[0012] Comparing the comprehensive carbon emission coefficients of each carbon flow path, establishing a carbon flow coupling model between the high-emission path and the low-emission path, and optimizing the feasible space of the scheduling strategy with a collaborative strategy;

[0013] Taking the minimum carbon emission as the optimization goal, the particle swarm algorithm is used to find the optimal solution in the feasible space of the scheduling strategy to obtain the optimal collaborative scheduling strategy.

[0014] As a further method, the method for determining the carbon emission cost function based on the carbon emission factor includes:

[0015] The cost function considering the mutual influence of carbon emissions from multiple energy inputs of the equipment is expressed as:

[0016]

[0017] Where n is the number of energy input types, m is the number of energy output types, EF i is the i-th energy input E i The corresponding carbon emission factor, E i is the input of the i-th energy, η ij is an element in the energy conversion efficiency matrix, indicating the efficiency of converting the i-th energy into the j-th energy output, α ij is the impact of the i-th energy input on the carbon emissions of the j-th energy output, is the unit price of the i-th energy input, C tax is the carbon tax, t is the equipment operating time;

[0018] The cost function considering the uncertainty of energy input is expressed as:

[0019]

[0020] Among them, x i and y i Represents the i-th energy input E i The variables with the values ​​and the i-th energy input E i Corresponding carbon emission factor EF i Variables that take values, is the i-th energy input E i The probability density function of is the i-th energy input E i Corresponding carbon emission factor EF i The probability density function of

[0021] For the entire scheduling cycle, the two cost function values ​​considered at each moment are integrated and then added to obtain the carbon emission cost function of the equipment.

[0022] As a further method, the method of generating carbon trading price fluctuation scenarios using the Monte Carlo simulation method and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function includes:

[0023] Conducting Monte Carlo simulations involves analyzing the distribution characteristics of historical carbon trading prices to obtain a probability distribution model for carbon trading prices. The simulations are then repeated several times. For each simulation, a random number is generated from the selected probability distribution as the carbon trading price. Simulated price sequences are formed based on the carbon trading prices generated in each simulation. Using continuous price samples as scenarios, multiple different carbon trading price fluctuation scenarios are constructed.

[0024] Calculate the carbon trading risk index, the expression is:

[0025]

[0026] Among them, M is the number of time intervals into which the scheduling cycle is divided, C θ,j is the carbon emission cost function value of time interval j under scenario θ, Δt j is the length of time interval j, x θ,j,i is the input of the i-th energy in time interval j under scenario θ, is the average value of the input of the i-th energy under scenario θ, α i is the proportion of the i-th energy in the input, E θ,j,ζ is the output of the ζth energy in time interval j under scenario θ, is the average output of the ζth energy source under scenario θ, β ζ is the proportion of the ζ type of energy in the output.

[0027] As a further method, the method for determining the start and stop time of energy equipment with optimal economic benefits based on the carbon trading risk index includes:

[0028] Analyze historical data on energy demand and predict energy demand in different time periods in the future;

[0029] The objective function is defined by maximizing economic benefits, and constraints are determined based on energy demand in different time periods in the future, including upper and lower limits on equipment power and limits on equipment continuous operation time.

[0030] Convert carbon trading risk indicators into economic costs and incorporate them into the objective function. Specifically, determine the cost coefficients corresponding to different risk levels and increase the corresponding cost penalties when the carbon trading risk changes.

[0031] Under the constraints, possible start and stop time combinations are obtained. For each possible start and stop time combination, the equipment start and stop time plan with the optimal economic benefits is found according to the objective function.

[0032] As a further method, the method of updating the carbon flow path set in the topological network structure according to the start and stop time of the energy device includes:

[0033] Define each energy device in the integrated energy system as a node in the graph, assign a unique identifier to each node, and record the device type and basic attributes of the node;

[0034] Edges are defined between energy devices with carbon flow, with weights set to values ​​proportional to carbon emissions. Nodes are divided into two categories: active and inactive, based on the start and stop times of the devices.

[0035] Starting from the nodes corresponding to all energy devices as the starting point of the path, recursively visit the neighboring nodes. If there is a valid path and the adjacent nodes are active, continue searching based on the neighboring nodes. Otherwise, terminate until all neighboring nodes are traversed.

[0036] Remove duplicate paths, obtain an updated path set, and sort the paths according to carbon emissions.

[0037] As a further method, the method for calculating the comprehensive carbon emission coefficient of each carbon flow path includes:

[0038] For the kth carbon flow path, the calculation formula for the comprehensive carbon emission coefficient is:

[0039]

[0040] in, is the unit energy carbon emission of the i-th energy at the j-th node on the k-th carbon flow path, in kg carbon / joule, is the weight coefficient of the sensitivity of the i-th energy source to carbon trading price fluctuations at the j-th node on the k-th carbon flow path, S is the total number of carbon trading price fluctuation scenarios generated by the Monte Carlo simulation method, and P s is the probability of the sth carbon trading price fluctuation scenario occurring, is the change in the carbon emission factor of the i-th energy at the j-th node on the k-th carbon flow path under the s-th carbon trading price fluctuation scenario; the calculation formula of the weight coefficient is:

[0041]

[0042] in, is the energy demand price elasticity coefficient of the i-th energy at the j-th node on the k-th carbon flow path, is the energy supply stability of the i-th energy source at the j-th node on the k-th carbon flow path, γ k is the length of the kth carbon flow path, ρ k is the time period adjustment coefficient of the kth carbon flow path, is the proportion of the energy usage time of the i-th energy at the j-th node in the k-th carbon flow path to the total scheduling period, is the energy conversion efficiency of the i-th energy at the j-th node on the k-th carbon flow path, is the energy quality level of the i-th energy at the j-th node on the k-th carbon flow path.

[0043] As a further method, the method of comparing the comprehensive carbon emission coefficient of each carbon flow path and establishing a carbon flow coupling model between the high emission path and the low emission path includes:

[0044] Calculate the average value μ and standard deviation σ of the comprehensive carbon emission coefficient of all carbon flow paths, set the threshold to μ+2σ, and define paths greater than or equal to the threshold as high-emission paths, otherwise they are low-emission paths;

[0045] By introducing coupling variables, the carbon flow coupling relationship between the high emission pathway and the low emission pathway is established, and the expression is:

[0046]

[0047] Among them, H and L are the high emission path set and the low emission path set respectively, λ ij is the coupling strength from high emission path i to low emission path j, C i (t) is the carbon emission of high emission path i at time t, is the carbon quota limit of high emission path i, α is the exponential coefficient of carbon emission difference, μ ij is the coupling coefficient from high emission path i to low emission path j, P HL (t) is the carbon transfer from high emission path i to low emission path j at time t, is the maximum transfer amount, β is the exponential coefficient of carbon transfer amount, w ij is the periodic impact coefficient from high emission path i to low emission path j, γ is the frequency of periodic impact, is the attenuation coefficient from high emission path i to low emission path j, P j(t) is the required power of low emission path j at time t.

[0048] As a further method, the method of optimizing the scheduling strategy by using the collaborative strategy to obtain the feasible space includes:

[0049] Obtain the constraints for energy balance, expressed as:

[0050]

[0051] Among them, Ω G,i is the set of power generation / production equipment of type i, P g,i,t is the output power of the gth power generation / production equipment belonging to the i-th energy type at time t, Ω S,i is the set of energy storage devices of type i, E j,i,t-1 and E j,i,t are the energy storage capacity of the j-th energy storage device belonging to the i-th energy type at time t-1 and time t, respectively. and are the discharge efficiency and charging efficiency of the j-th energy storage device belonging to the i-th energy type, Ω C,i is the set of conversion devices for the i-th energy type, α m,i-l,t and are the input power, conversion power and output power of the mth conversion device that converts from the i-th energy type to the l-th energy type at time t, I i,t is the input power of the input interface belonging to the i-th energy type at time t, O i,t is the output power of the output interface belonging to the i-th energy type at time t, Ω L,i is the load set of the i-th energy type, D h,i,t is the power demand of the hth load belonging to the i-th energy type at time t;

[0052] Based on the collaborative cooperation strategy, the energy transfer mechanism and carbon trading mechanism are set, and the feasible space that meets the constraints is found through depth-first search.

[0053] As a further method, the method of using a particle swarm algorithm to find the optimal solution in the feasible space of the scheduling strategy with the minimum carbon emissions as the optimization goal includes:

[0054] Initialize the particle positions and velocities. The position of each particle represents a collaborative scheduling strategy, where the particle dimensions represent the allocation ratio of different types of energy at different demand points. Minimize carbon emissions as the optimization goal, and set the fitness function to the total carbon emissions of the system under the corresponding collaborative scheduling strategy.

[0055] The expressions for updating the velocity and position of each particle are:

[0056]

[0057] in, is the velocity of the i-th particle at time t+1 in the d-th dimension, w is the inertia weight, is the velocity of the i-th particle at time t in the d-th dimension, c1 and c2 are the learning factors that control the step length of the particle moving to its own historical optimal position and the learning factors that control the step length of the particle moving to the global optimal position, r is a random number in the interval [0,1], pbest id is the historical optimal position of the i-th particle in the d-th dimension, is the position of the i-th particle at time t in the d-th dimension, gbest d is the global optimal position of the entire particle swarm in the dth dimension, is the position of the i-th particle at time t+1 in the d-th dimension.

[0058] A second aspect of the present invention provides a carbon emission scheduling optimization system in an integrated energy system, comprising:

[0059] Carbon emission factor and cost accounting module, used to determine the carbon emission factor of energy equipment and determine the carbon emission cost function based on the carbon emission factor;

[0060] A carbon trading risk simulation module is used to generate carbon trading price fluctuation scenarios using a Monte Carlo simulation method, and calculate the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function;

[0061] An intelligent equipment start-stop decision module, configured to determine the start-stop time of energy equipment based on the carbon trading risk index with optimal economic benefits;

[0062] A topological network construction and carbon flow analysis module is used to construct a topological network structure for the integrated energy system, update the carbon flow path set in the topological network structure according to the start and stop time of the energy equipment, and calculate the comprehensive carbon emission coefficient of each carbon flow path;

[0063] A coupling collaboration and space generation module is used to compare the comprehensive carbon emission coefficients of each carbon flow path, establish a carbon flow coupling model between high-emission paths and low-emission paths, and optimize the feasible space of the scheduling strategy using a collaborative strategy;

[0064] The particle swarm optimization and strategy output module is used to minimize carbon emissions and to find the optimal collaborative scheduling strategy using a particle swarm algorithm in the feasible space of the scheduling strategy.

[0065] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0066] (1) This invention generates carbon trading price fluctuation scenarios through Monte Carlo simulation and calculates risk indicators based on the carbon emission cost function, providing a reliable risk assessment basis for decision-making. Based on the risk indicators, the equipment start and stop times are determined with optimal economic benefits, minimizing operating costs while reducing carbon emissions.

[0067] (2) The present invention constructs a topological network structure and updates the carbon flow path set, calculates the comprehensive carbon emission coefficient of each path, and updates the carbon flow path set according to the start and stop time of the energy equipment, and calculates the comprehensive carbon emission coefficient of each carbon flow path. This helps to fully understand the carbon flow situation and carbon emission distribution of the system, and can optimize the carbon emissions and energy flow of the system as a whole, thereby improving the stability and reliability of the system.

[0068] (3) By establishing a coupled model of energy and carbon flows and adopting a collaborative strategy, the present invention can effectively integrate the advantages of high-emission and low-emission pathways, thereby improving the overall efficiency of the system. Finally, a particle swarm algorithm is used to optimize the system with the goal of minimizing carbon emissions. This method can avoid the emergence of local optimal solutions, ensuring that the system achieves optimal overall performance while meeting carbon emission requirements, thereby improving the system's energy efficiency and operational management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flowchart of the steps of a carbon emission scheduling optimization method in an integrated energy system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] Reference Figure 1 As shown, the present invention provides a method for optimizing carbon emission scheduling in an integrated energy system, comprising:

[0072] S100 determines a carbon emission factor of the energy equipment, and determines a carbon emission cost function based on the carbon emission factor;

[0073] It's important to explain that by determining the carbon emission factor, we can accurately measure the carbon emissions of different energy devices during operation. The carbon emission cost function determined based on this factor converts carbon emissions into economic costs, allowing system operators to comprehensively consider energy and carbon emission costs when making scheduling decisions. This not only helps drive the system towards a more environmentally friendly direction, reducing carbon emissions to combat climate change, but also provides strong support for economic decision-making, achieving a balance between economic and environmental benefits.

[0074] In a specific implementation case, the integrated energy system studied includes the following main energy equipment: gas turbines, solar photovoltaic panels, and energy storage batteries. This integrated energy system needs to meet the energy needs of multiple companies in a small industrial park, including electricity and some thermal energy needs. The production activities of companies in the park have a certain periodicity, resulting in significant fluctuations in energy demand at different time periods.

[0075] In the actual evaluation, the carbon emission factor of natural gas combustion is determined to be 0.2 kg of carbon emissions per cubic meter of natural gas. The carbon emissions of solar photovoltaic panels during operation are relatively low, and the carbon emission factor is set to 0.01 kg of carbon emissions. The carbon emission factor of energy storage batteries is 0.7 kg of carbon emissions per kilowatt-hour of electricity stored or released. The carbon emission cost function of the equipment is obtained by considering the cost function of the mutual influence of carbon emissions in the multi-energy input of the equipment and the uncertainty of energy input.

[0076] S200 uses the Monte Carlo simulation method to generate carbon trading price fluctuation scenarios and calculates the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function;

[0077] It should be explained that the Monte Carlo simulation method is used to generate carbon trading price fluctuation scenarios, taking into account the uncertainty of the carbon trading market. The fluctuation of carbon trading prices will directly affect the carbon emission costs and economic benefits of equipment in the integrated energy system. Through Monte Carlo simulation, a large number of different carbon trading price scenarios can be randomly generated, thereby more comprehensively reflecting the various situations that may arise in the carbon trading market. The carbon trading risk index of the equipment in each scenario is calculated based on the carbon emission cost function, which provides a basis for risk assessment for the scheduling optimization of the system. Under different carbon trading price scenarios, the carbon emission cost of the equipment will change. By calculating the risk index, it is possible to understand the degree of carbon trading risk faced by the equipment under various possible situations;

[0078] It should be understood that this step helps operators of integrated energy systems make more robust scheduling decisions when facing the uncertain carbon trading market. Operators can evaluate the risk levels of different scheduling options based on risk indicators and select the optimal option that can both meet energy demand and effectively control carbon trading risks, thereby improving the stability and sustainability of the system.

[0079] In the actual assessment, statistical analysis of historical carbon trading price data determined a mean of 50 yuan per ton of carbon with a standard deviation of 10 yuan. The number of simulations was set to 500, and for each simulation, a random number was generated from a selected normal distribution as the carbon trading price. A simulated price sequence was formed based on the carbon trading prices generated in each simulation. Using the continuous price samples as scenarios, 72 different carbon trading price fluctuation scenarios were ultimately constructed. The carbon trading risk indicator for energy equipment in each scenario was calculated based on the carbon emission cost function.

[0080] S300 determines the start and stop time of the energy equipment based on the carbon trading risk indicator with optimal economic benefits;

[0081] It should be explained that its purpose is to achieve effective carbon emission scheduling and maximize economic benefits. By considering carbon trading risk indicators, it is possible to evaluate the changes in carbon costs of equipment operation at different times. Determining start and stop times accordingly can reduce carbon emissions and costs while meeting energy needs.

[0082] In the actual assessment, the energy demand for different time periods in the next day is predicted. It is predicted that from 8:00 am to 12:00 pm, the electricity demand will be 20-30 kWh and the heat demand will be 15-20 kWh; from 2:00 pm to 6:00 pm, the electricity demand will be 30-40 kWh and the heat demand will be 20-25 kWh. The constraints include the upper and lower limits of equipment power (the upper limit of the gas turbine power generation is 50 kW and the lower limit is 10 kW; the charging and discharging power of the energy storage battery also has corresponding limits) and the continuous operation time limit of the equipment (the continuous operation time of the gas turbine should not exceed 8 hours). The carbon trading risk indicator is converted into economic cost and incorporated into the objective function. In the process, the cost coefficients corresponding to different risk levels are determined (the cost coefficient corresponding to the high risk level is 0.3, the medium risk level is 0.2, and the low risk level is 0.1). When the carbon trading risk changes, the corresponding cost penalty is increased. Under the constraints, possible start-stop time combinations are obtained: for gas turbines, the start-stop time combinations include starting at 8 am and stopping after running for 4 hours; or starting at 10 am and stopping after running for 3 hours, etc. For each possible start-stop time combination, the equipment start-stop time plan with the optimal economic benefits is found according to the objective function. Through calculation, it is determined that the gas turbine starts at 10 am and stops after running for 3 hours.

[0083] S400 constructs a topological network structure for the integrated energy system, updates a carbon flow path set in the topological network structure according to the start and stop time of the energy equipment, and calculates a comprehensive carbon emission coefficient for each carbon flow path;

[0084] In the actual evaluation, each energy device in the integrated energy system is defined as a node in the graph. The gas turbine node is assigned identifier G, the solar photovoltaic panel node is assigned identifier S, and the energy storage battery node is assigned identifier B. The device type and basic attributes are recorded. The resulting path set includes 11 paths, including GB (gas turbine to energy storage battery) and SB (solar photovoltaic panel to energy storage battery). The carbon emissions of the GB path are relatively high, while the carbon emissions of the path are relatively low. The comprehensive carbon emission coefficient of the GB path is 0.12.

[0085] S500 compares the comprehensive carbon emission coefficients of each carbon flow path, establishes a carbon flow coupling model between the high emission path and the low emission path, and optimizes the scheduling strategy with a collaborative strategy to obtain a feasible space;

[0086] It needs to be explained that by establishing a coupling model of energy flow and carbon flow between high-emission paths and low-emission paths, it is possible to more comprehensively analyze the energy transmission and carbon emissions on different paths, provide a more accurate basis for optimizing scheduling, and adopt a collaborative strategy, which means coordinating and cooperating between high-emission paths and low-emission paths. For example, part of the energy on the high-emission path can be transferred to the low-emission path, or the carbon flow distribution can be optimized by adjusting the operating status of the equipment. In this way, the feasible space for energy scheduling can be obtained, that is, on the premise of meeting energy demand, the optimized scheduling of carbon emissions can be achieved, and the overall efficiency and environmental protection of the integrated energy system can be improved;

[0087] In the actual assessment, the average and standard deviation of the comprehensive carbon emission coefficients of all carbon flow paths were calculated (the average value was 0.1 kg carbon / joule, and the standard deviation was 0.05 kg carbon / joule). The threshold was set to the average value plus twice the standard deviation. Paths greater than or equal to the threshold were considered high-emission paths, and otherwise were considered low-emission paths. There were three high-emission paths and eight low-emission paths. By introducing coupling variables, a carbon flow coupling relationship between high-emission paths and low-emission paths was established. Based on the collaborative cooperation strategy, the energy transfer mechanism and carbon trading mechanism were set, and a feasible space that met the constraints was found through depth-first search.

[0088] S600 takes minimizing carbon emissions as the optimization goal, and uses a particle swarm algorithm to find the optimal solution in the feasible space of the scheduling strategy to obtain the optimal collaborative scheduling strategy.

[0089] In the actual evaluation, the particle positions and velocities were initialized, the proportion of electricity provided by the gas turbine was 0.4, the proportion of electricity provided by the solar photovoltaic panel was 0.3, and the proportion of electricity provided by the energy storage battery was 0.3. The optimization goal was to minimize carbon emissions, and the fitness function was set to the total carbon emissions of the system under the corresponding collaborative scheduling strategy. After 100 iterations, the particle position that minimized the fitness function value was finally found, that is, the optimal collaborative scheduling strategy, which is: the proportion of electricity provided by the gas turbine was adjusted to 0.26, and it was operated from 10:00 to 15:00 on weekdays. The power output of the gas turbine was based on the real-time energy demand within the rated power range. The power output is dynamically adjusted between 60% and 90% of the rated power. During the peak electricity consumption period of 11:00-13:00, the power output reaches 90% of the rated power. The proportion of electricity provided by solar photovoltaic panels is increased to 0.37. When there is sufficient sunshine, it generates electricity at full power from 9:00-16:00. When the sunlight effect is poor, it generates electricity at 80% power from 10:00-15:00. The daily power generation is expected to reach 1.4 MWh. The proportion of electricity provided by energy storage batteries is 0.37. It is charged at night from 23:00 to 6:00 during the low electricity price period. The charging power is 70% of the rated power and the stored power is 0.53 MWh. It is discharged during peak electricity consumption to balance supply and demand.

[0090] In this embodiment, the method for determining the carbon emission cost function based on the carbon emission factor includes:

[0091] The cost function considering the mutual influence of carbon emissions from multiple energy inputs of the equipment is expressed as:

[0092]

[0093] Where n is the number of energy input types, m is the number of energy output types, EF i is the i-th energy input E i The corresponding carbon emission factor, E i is the input of the i-th energy, η ij is an element in the energy conversion efficiency matrix, indicating the efficiency of converting the i-th energy into the j-th energy output, α ij is the impact of the i-th energy input on the carbon emissions of the j-th energy output, is the unit price of the i-th energy input, C tax is the carbon tax, t is the equipment operating time;

[0094] The cost function considering the uncertainty of energy input is expressed as:

[0095]

[0096] Among them, x i and y i Represents the i-th energy input Ei The variables with the values ​​and the i-th energy input E i Corresponding carbon emission factor EF i Variables that take values, is the i-th energy input E i The probability density function of is the i-th energy input E i Corresponding carbon emission factor EF i The probability density function of

[0097] For the entire scheduling cycle, the two cost function values ​​considered at each moment are integrated and then added to obtain the carbon emission cost function of the equipment.

[0098] In this embodiment, the method of generating carbon trading price fluctuation scenarios using the Monte Carlo simulation method and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function includes:

[0099] Conducting Monte Carlo simulations involves analyzing the distribution characteristics of historical carbon trading prices to obtain a probability distribution model for carbon trading prices. The simulations are then repeated several times. For each simulation, a random number is generated from the selected probability distribution as the carbon trading price. Simulated price sequences are formed based on the carbon trading prices generated in each simulation. Using continuous price samples as scenarios, multiple different carbon trading price fluctuation scenarios are constructed.

[0100] Calculate the carbon trading risk index, the expression is:

[0101]

[0102] Among them, M is the number of time intervals into which the scheduling cycle is divided, C θ,j is the carbon emission cost function value of time interval j under scenario θ, Δt j is the length of time interval j, x θ,j,i is the input of the i-th energy in time interval j under scenario θ, is the average value of the input of the i-th energy under scenario θ, α i is the proportion of the i-th energy in the input, E θ,j,ζ is the output of the ζth energy in time interval j under scenario θ, is the average output of the ζth energy source under scenario θ, β ζ is the proportion of the ζ type of energy in the output.

[0103] In this embodiment, the method for determining the start and stop times of energy equipment with optimal economic benefits based on the carbon trading risk index includes:

[0104] Analyze historical data on energy demand and predict energy demand in different time periods in the future;

[0105] The objective function is defined by maximizing economic benefits, and constraints are determined based on energy demand in different time periods in the future, including upper and lower limits on equipment power and limits on equipment continuous operation time.

[0106] Convert carbon trading risk indicators into economic costs and incorporate them into the objective function. Specifically, determine the cost coefficients corresponding to different risk levels and increase the corresponding cost penalties when the carbon trading risk changes.

[0107] Under the constraints, possible start and stop time combinations are obtained. For each possible start and stop time combination, the equipment start and stop time plan with the optimal economic benefits is found according to the objective function.

[0108] In this embodiment, the method for updating the carbon flow path set in the topological network structure according to the start and stop time of the energy device includes:

[0109] Define each energy device in the integrated energy system as a node in the graph, assign a unique identifier to each node, and record the device type and basic attributes of the node;

[0110] Edges are defined between energy devices with carbon flow, with weights set to values ​​proportional to carbon emissions. Nodes are divided into two categories: active and inactive, based on the start and stop times of the devices.

[0111] Starting from the nodes corresponding to all energy devices as the starting point of the path, recursively visit the neighboring nodes. If there is a valid path and the adjacent nodes are active, continue searching based on the neighboring nodes. Otherwise, terminate until all neighboring nodes are traversed.

[0112] Remove duplicate paths, obtain an updated path set, and sort the paths according to carbon emissions.

[0113] In this embodiment, the method for calculating the comprehensive carbon emission coefficient of each carbon flow path includes:

[0114] For the kth carbon flow path, the calculation formula for the comprehensive carbon emission coefficient is:

[0115]

[0116] in, is the unit energy carbon emission of the i-th energy at the j-th node on the k-th carbon flow path, in kg carbon / joule, is the weight coefficient of the sensitivity of the i-th energy source to carbon trading price fluctuations at the j-th node on the k-th carbon flow path, S is the total number of carbon trading price fluctuation scenarios generated by the Monte Carlo simulation method, and P s is the probability of the sth carbon trading price fluctuation scenario occurring, is the change in the carbon emission factor of the i-th energy at the j-th node on the k-th carbon flow path under the s-th carbon trading price fluctuation scenario; the calculation formula of the weight coefficient is:

[0117]

[0118] in, is the energy demand price elasticity coefficient of the i-th energy at the j-th node on the k-th carbon flow path, is the energy supply stability of the i-th energy source at the j-th node on the k-th carbon flow path, γ k is the length of the kth carbon flow path, ρ k is the time period adjustment coefficient of the kth carbon flow path, is the proportion of the energy usage time of the i-th energy at the j-th node in the k-th carbon flow path to the total scheduling period, is the energy conversion efficiency of the i-th energy at the j-th node on the k-th carbon flow path, is the energy quality level of the i-th energy at the j-th node on the k-th carbon flow path.

[0119] In this embodiment, the method of comparing the comprehensive carbon emission coefficient of each carbon flow path and establishing a carbon flow coupling model between the high emission path and the low emission path includes:

[0120] Calculate the average value μ and standard deviation σ of the comprehensive carbon emission coefficient of all carbon flow paths, set the threshold to μ+2σ, and define paths greater than or equal to the threshold as high-emission paths, otherwise they are low-emission paths;

[0121] By introducing coupling variables, the carbon flow coupling relationship between the high emission pathway and the low emission pathway is established, and the expression is:

[0122]

[0123] Among them, H and L are the high emission path set and the low emission path set respectively, λ ij is the coupling strength from high emission path i to low emission path j, C i (t) is the carbon emission of high emission path i at time t, is the carbon quota limit of high emission path i, α is the exponential coefficient of carbon emission difference, μ ijis the coupling coefficient from high emission path i to low emission path j, P HL (t) is the carbon transfer from high emission path i to low emission path j at time t, is the maximum transfer amount, β is the exponential coefficient of carbon transfer amount, w ij is the periodic impact coefficient from high emission path i to low emission path j, γ is the frequency of periodic impact, is the attenuation coefficient from high emission path i to low emission path j, P j (t) is the required power of low emission path j at time t.

[0124] In this embodiment, the method for optimizing the scheduling strategy by using the collaborative strategy to obtain the feasible space includes:

[0125] Obtain the constraints for energy balance, expressed as:

[0126]

[0127] Among them, Ω G,i is the set of power generation / production equipment of type i, P g,i,t is the output power of the gth power generation / production equipment belonging to the i-th energy type at time t, Ω S,i is the set of energy storage devices of type i, E j,i,t-1 and E j,i,t are the energy storage capacity of the j-th energy storage device belonging to the i-th energy type at time t-1 and time t, respectively. and are the discharge efficiency and charging efficiency of the j-th energy storage device belonging to the i-th energy type, Ω C,i is the set of conversion devices for the i-th energy type, α m,i-l,t and are the input power, conversion power and output power of the mth conversion device that converts from the i-th energy type to the l-th energy type at time t, I i,t is the input power of the input interface belonging to the i-th energy type at time t, O i,t is the output power of the output interface belonging to the i-th energy type at time t, Ω L,i is the load set of the i-th energy type, D h,i,t is the power demand of the hth load belonging to the i-th energy type at time t;

[0128] Based on the collaborative cooperation strategy, the energy transfer mechanism and carbon trading mechanism are set, and the feasible space that meets the constraints is found through depth-first search.

[0129] In this embodiment, the method of using the particle swarm algorithm to find the optimal solution in the feasible space of the scheduling strategy with the minimum carbon emissions as the optimization goal includes:

[0130] Initialize the particle positions and velocities. The position of each particle represents a collaborative scheduling strategy, where the particle dimensions represent the allocation ratio of different types of energy at different demand points. Minimize carbon emissions as the optimization goal, and set the fitness function to the total carbon emissions of the system under the corresponding collaborative scheduling strategy.

[0131] The expressions for updating the velocity and position of each particle are:

[0132]

[0133] in, is the velocity of the i-th particle at time t+1 in the d-th dimension, w is the inertia weight, is the velocity of the i-th particle at time t in the d-th dimension, c1 and c2 are the learning factors that control the step length of the particle moving to its own historical optimal position and the learning factors that control the step length of the particle moving to the global optimal position, r is a random number in the interval [0,1], pbest id is the historical optimal position of the i-th particle in the d-th dimension, is the position of the i-th particle at time t in the d-th dimension, gbest d is the global optimal position of the entire particle swarm in the dth dimension, is the position of the i-th particle at time t+1 in the d-th dimension.

[0134] A second aspect of the present invention provides a carbon emission scheduling optimization system in an integrated energy system, comprising:

[0135] Carbon emission factor and cost accounting module, used to determine the carbon emission factor of energy equipment and determine the carbon emission cost function based on the carbon emission factor;

[0136] A carbon trading risk simulation module is used to generate carbon trading price fluctuation scenarios using a Monte Carlo simulation method, and calculate the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function;

[0137] An intelligent equipment start-stop decision module, configured to determine the start-stop time of energy equipment based on the carbon trading risk index with optimal economic benefits;

[0138] A topological network construction and carbon flow analysis module is used to construct a topological network structure for the integrated energy system, update the carbon flow path set in the topological network structure according to the start and stop time of the energy equipment, and calculate the comprehensive carbon emission coefficient of each carbon flow path;

[0139] A coupling collaboration and space generation module is used to compare the comprehensive carbon emission coefficients of each carbon flow path, establish a carbon flow coupling model between high-emission paths and low-emission paths, and optimize the feasible space of the scheduling strategy using a collaborative strategy;

[0140] The particle swarm optimization and strategy output module is used to take the minimum carbon emission as the optimization goal, and to find the optimal collaborative scheduling strategy through the particle swarm algorithm in the feasible space of the scheduling strategy.

[0141] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A carbon emission scheduling optimization method in an integrated energy system, characterized in that: The following steps are involved: Determine the carbon emission factors of energy equipment and determine the carbon emission cost function based on the carbon emission factors; Using the Monte Carlo simulation method to generate carbon trading price fluctuation scenarios, and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function; Determining the start and stop times of energy equipment with optimal economic benefits based on the carbon trading risk indicators; Constructing a topological network structure for the integrated energy system, updating a set of carbon flow paths in the topological network structure according to the start and stop times of the energy equipment, and calculating a comprehensive carbon emission coefficient for each carbon flow path; Comparing the comprehensive carbon emission coefficients of each carbon flow path, establishing a carbon flow coupling model between the high-emission path and the low-emission path, and optimizing the feasible space of the scheduling strategy with a collaborative strategy; Taking the minimum carbon emissions as the optimization goal, the particle swarm algorithm is used to find the optimal solution in the feasible space of the scheduling strategy to obtain the optimal collaborative scheduling strategy; The method for determining the carbon emission cost function based on the carbon emission factor includes: The cost function considering the mutual influence of carbon emissions from multiple energy inputs of the equipment is expressed as: in, The number of types of energy input, is the number of energy output types, For the Energy input The corresponding carbon emission factor is For the Energy input, is an element in the energy conversion efficiency matrix, indicating the Energy conversion to The efficiency of energy output, For the Energy input to the The carbon emission impact of each energy output, For the The unit price of the energy input, For carbon tax, The equipment running time; The cost function considering the uncertainty of energy input is expressed as: in, and Respectively represent Energy input The variable and the Energy input Corresponding carbon emission factors Variables that take values, For the Energy input The probability density function of For the Energy input Corresponding carbon emission factors The probability density function of For the entire scheduling cycle, the two cost function values ​​considered at each moment are integrated and then added to obtain the carbon emission cost function of the equipment.

2. The carbon emission scheduling optimization method in an integrated energy system according to claim 1 is characterized in that: The method of generating carbon trading price fluctuation scenarios using the Monte Carlo simulation method and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function includes: Conducting Monte Carlo simulations involves analyzing the distribution characteristics of historical carbon trading prices to obtain a probability distribution model for carbon trading prices. The simulations are then repeated several times. For each simulation, a random number is generated from the selected probability distribution as the carbon trading price. Simulated price sequences are formed based on the carbon trading prices generated in each simulation. Using continuous price samples as scenarios, multiple different carbon trading price fluctuation scenarios are constructed. Calculate the carbon trading risk index, the expression is: in, is the number of time intervals into which the scheduling period is divided, For the scene Next time interval The carbon emission cost function value, For the time interval length, For the scene Next time interval Middle The amount of energy input, For the scene Next The average value of the energy input, For the The proportion of energy in the input For the scene Next time interval Middle The output of energy, For the scene Next The average output of the energy For the The proportion of energy in output.

3. The carbon emission scheduling optimization method in an integrated energy system according to claim 1 is characterized in that: The method for determining the start and stop times of energy equipment with optimal economic benefits based on the carbon trading risk index includes: Analyze historical data on energy demand and predict energy demand in different time periods in the future; The objective function is defined by maximizing economic benefits, and constraints are determined based on energy demand in different time periods in the future, including upper and lower limits on equipment power and limits on equipment continuous operation time. Convert carbon trading risk indicators into economic costs and incorporate them into the objective function. Specifically, determine the cost coefficients corresponding to different risk levels and increase the corresponding cost penalties when the carbon trading risk changes. Under the constraints, possible start and stop time combinations are obtained. For each possible start and stop time combination, the equipment start and stop time plan with the optimal economic benefits is found according to the objective function.

4. The carbon emission scheduling optimization method in a comprehensive energy system according to claim 1 is characterized in that: The method for updating the carbon flow path set in the topological network structure according to the start and stop time of the energy device includes: Define each energy device in the integrated energy system as a node in the graph, assign a unique identifier to each node, and record the device type and basic attributes of the node; Edges are defined between energy devices with carbon flow, with weights set to values ​​proportional to carbon emissions. Nodes are divided into two categories: active and inactive, based on the start and stop times of the devices. Starting from the nodes corresponding to all energy devices as the starting point of the path, recursively visit the neighboring nodes. If there is a valid path and the adjacent nodes are active, continue searching based on the neighboring nodes. Otherwise, terminate until all neighboring nodes are traversed. Remove duplicate paths, obtain an updated path set, and sort the paths according to carbon emissions.

5. The carbon emission scheduling optimization method in a comprehensive energy system according to claim 1 is characterized in that: The method for calculating the comprehensive carbon emission coefficient of each carbon flow path includes: For the The calculation formula for the comprehensive carbon emission coefficient is: in, For the On the carbon flow path, Energy in the The unit energy carbon emission at each node, in kg carbon / joule, For the On the carbon flow path, Energy in the The weight coefficient of the sensitivity of each node to carbon trading price fluctuations, is the total number of carbon trading price fluctuation scenarios generated by the Monte Carlo simulation method, For the The probability of a carbon trading price fluctuation scenario occurring, For the Under the carbon trading price fluctuation scenario, On the carbon flow path, Energy in the The change in the carbon emission factor at each node; the calculation formula for the weight coefficient is: in, For the On the carbon flow path, Energy in the The energy demand price elasticity coefficient at each node is: For the On the carbon flow path, Energy in the Energy supply stability at each node, For the The length of the carbon flow path, For the The time period adjustment coefficient of the carbon flow path, For the On the carbon flow path, Energy in the The proportion of energy usage time at each node to the total scheduling period, For the On the carbon flow path, Energy in the The energy conversion efficiency at each node is For the On the carbon flow path, Energy in the The energy quality level at each node.

6. The carbon emission scheduling optimization method in a comprehensive energy system according to claim 1 is characterized in that: The method of comparing the comprehensive carbon emission coefficients of each carbon flow path and establishing a carbon flow coupling model between the high emission path and the low emission path includes: Calculate the average of the comprehensive carbon emission coefficients of all carbon flow paths and standard deviation , set the threshold to , the path that is greater than or equal to the threshold is a high-emission path, otherwise it is a low-emission path; By introducing coupling variables, the carbon flow coupling relationship between the high emission pathway and the low emission pathway is established, and the expression is: in, and are respectively a set of high emission paths and a set of low emission paths, To move from high emission pathways To low-emission pathways The coupling strength, High emission pathway At the moment of carbon emissions, High emission pathway Carbon quota limits, is the exponential coefficient of the carbon emission difference, To move from high emission pathways To low-emission pathways The coupling coefficient, For in time Time to switch from high emission paths To low-emission pathways The amount of carbon transferred, is the maximum transfer amount, is the exponential coefficient of carbon transfer, To move from high emission pathways To low-emission pathways The periodic influence coefficient of is the frequency of the periodic impact, To move from high emission pathways To low-emission pathways The attenuation coefficient, Low emission path At the moment Required power.

7. The carbon emission scheduling optimization method in a comprehensive energy system according to claim 1 is characterized in that: The method for optimizing and obtaining a feasible space of scheduling strategies by using a collaborative strategy includes: Obtain the constraints for energy balance, expressed as: in, For the A collection of power generation / production equipment of various energy types, For the Belongs to Energy generation / production equipment of various types at all times The output power, For the A collection of energy storage devices of various energy types, and Respectively Belongs to Energy storage devices of various energy types at all times and time The amount of energy stored, and Respectively Belongs to The discharge efficiency and charging efficiency of energy storage devices of different energy types, For the A collection of energy conversion devices, 、 and Respectively From the Convert the energy type to Energy type conversion equipment at all times Input power, conversion power and output power, For the The input interface of the energy type at the moment The input power, For the Output interface of energy type at time The output power, For the A collection of loads of various energy types, For the Belongs to The load of energy type at time Required power; Based on the collaborative cooperation strategy, the energy transfer mechanism and carbon trading mechanism are set, and the feasible space that meets the constraints is found through depth-first search.

8. The carbon emission scheduling optimization method in a comprehensive energy system according to claim 1 is characterized in that: The method of using the particle swarm algorithm to find the optimal solution in the feasible space of the scheduling strategy with the minimum carbon emissions as the optimization goal includes: Initialize the particle positions and velocities. The position of each particle represents a collaborative scheduling strategy, where the particle dimensions represent the allocation ratio of different types of energy at different demand points. Minimize carbon emissions as the optimization goal, and set the fitness function to the total carbon emissions of the system under the corresponding collaborative scheduling strategy. The expressions for updating the velocity and position of each particle are: in, For the The particle in Dimensional The speed of time, is the inertia weight, For the The particle in Dimensional The speed of time, and are the learning factors that control the step length of the particle moving to its own historical optimal position and the learning factors that control the step length of the particle moving to the global optimal position, respectively. for Random numbers in the interval, For the The particle in The historical best position on the dimension, For the The particle in Dimensional The location at the moment, For the entire particle swarm The global optimal position in dimension, For the The particle in Dimensional The location at the moment.

9. A carbon emission scheduling optimization system in an integrated energy system, used to execute the carbon emission scheduling optimization method in an integrated energy system according to any one of claims 1 to 8, characterized in that: The system comprises: Carbon emission factor and cost accounting module, used to determine the carbon emission factor of energy equipment and determine the carbon emission cost function based on the carbon emission factor; A carbon trading risk simulation module is used to generate carbon trading price fluctuation scenarios using a Monte Carlo simulation method, and calculate the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function; An intelligent equipment start-stop decision module, configured to determine the start-stop time of energy equipment based on the carbon trading risk index with optimal economic benefits; A topological network construction and carbon flow analysis module is used to construct a topological network structure for the integrated energy system, update the carbon flow path set in the topological network structure according to the start and stop time of the energy equipment, and calculate the comprehensive carbon emission coefficient of each carbon flow path; A coupling collaboration and space generation module is used to compare the comprehensive carbon emission coefficients of each carbon flow path, establish a carbon flow coupling model between high-emission paths and low-emission paths, and optimize the feasible space of the scheduling strategy using a collaborative strategy; The particle swarm optimization and strategy output module is used to take the minimum carbon emission as the optimization goal, and to find the optimal collaborative scheduling strategy through the particle swarm algorithm in the feasible space of the scheduling strategy.

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