Energy equipment carbon emission scheduling optimization method in integrated energy system

Abstract

The invention discloses an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

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, and the combustion of fossil fuels, which is heavily relied upon in the traditional energy structure, has led to a sharp increase in greenhouse gas emissions such as carbon dioxide, triggering serious climate change issues such as global warming, rising sea levels, and frequent extreme climate events. This has prompted the international community to reach a broad consensus that effective measures must be taken to reduce carbon emissions in order to alleviate the huge pressure on the global ecological environment.

[0003] However, there are many challenges and problems in the operation and management of integrated energy systems. In terms of carbon emission quantification and management, there is a lack of accurate and systematic methods to effectively monitor and evaluate carbon emissions. In the energy dispatch decision-making process, the uncertainty of the carbon trading market and the carbon emission characteristics of energy equipment are often not fully considered, resulting in the dispatch strategy being unable to adapt to market changes and unable to achieve optimal control of carbon emissions. At the same time, there is a lack of effective means to deal with the complex coupling relationship between energy flow and carbon flow in the integrated energy system, making it difficult to tap the emission reduction potential from the system level and formulate 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 various energy equipment, and minimize carbon emissions in the integrated energy system through scientific and reasonable scheduling optimization, while ensuring the reliability and economy of energy supply and promoting sustainable development transformation in the energy field. 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 factor of energy equipment, and determine the carbon emission cost function based on the carbon emission factor;

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

[0010] Determine the start and stop time of energy equipment with the best economic benefits based on the carbon trading risk index;

[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 time of the energy equipment, and calculating a comprehensive carbon emission coefficient of each carbon flow path;

[0012] Compare the comprehensive carbon emission coefficients of each carbon flow path, establish a carbon flow coupling model between the high emission path and the low emission path, and optimize the feasible space of the scheduling strategy through 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 in the multi-energy input 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 source, 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 operation time;

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

[0019]

[0020] Among them, x i and i Respectively represent the i-th energy input E i The variables and the i-th energy input E i The 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 The 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] Conduct Monte Carlo simulation, specifically: analyze the historical carbon trading price distribution characteristics, obtain the probability distribution model of carbon trading prices, set the number of simulations, generate a random number from the selected probability distribution as the carbon trading price for each simulation, form a simulated price sequence based on the carbon trading price generated in each simulation, use continuous price samples as scenarios, and construct multiple different carbon trading price fluctuation scenarios;

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

[0025]

[0026] Where 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 amount of the i-th energy in time interval j under scenario θ, is the average value of the input of the i-th energy source 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-th energy source in the output.

[0027] As a further method, the method for determining the start and stop time of energy equipment with the best economic benefit 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 the constraints are determined based on the energy demand in different time periods in the future, including the upper and lower limits of equipment power and the limit on the continuous operation time of the equipment;

[0030] The carbon trading risk index is converted into economic cost and incorporated into the objective function. Specifically, the cost coefficient corresponding to different risk levels is determined, and when the carbon trading risk changes, the corresponding cost penalty is increased;

[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 best economic benefits is found according to the objective function.

[0032] As a further method, 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:

[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, and the weights are set to values ​​proportional to carbon emissions. Nodes are divided into two categories, including active and inactive states, according to the start and stop time of the equipment.

[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 total scheduling period on the k-th carbon flow path, 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 μ and standard deviation σ of the comprehensive carbon emission coefficient of all carbon flow paths, set the threshold to μ+2σ, and the path greater than or equal to the threshold is a high emission path, otherwise it is a low emission path;

[0045] By introducing coupling variables, the carbon flow coupling relationship between the high emission path and the low emission path 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 feasible space of the scheduling strategy by using the collaborative strategy 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 the i-th energy type, P g,i,t is the output power of the g-th power generation / production equipment belonging to the i-th energy type at time t, Ω S,i is the energy storage device set of the i-th energy type, 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, respectively, 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, respectively. 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] The energy transfer mechanism and carbon trading mechanism are set based on the collaborative cooperation strategy, and the feasible space that meets the constraints is found through depth-first search.

[0053] As a further method, the method of taking the minimum carbon emission as the optimization goal and searching for the optimal solution in the feasible space of the scheduling strategy by using a particle swarm algorithm includes:

[0054] Initialize the particle position and velocity. The position of each particle represents a collaborative scheduling strategy, where the particle dimension represents the allocation ratio of different types of energy at different demand points. The optimization goal is to minimize carbon emissions, and the fitness function is set to the total carbon emissions of the system under the corresponding collaborative scheduling strategy.

[0055] The expression for updating the velocity and position of each particle is:

[0056]

[0057] in, is the velocity of the ith particle at time t+1 in the dth dimension, w is the inertia weight, is the velocity of the ith particle at time t in the dth dimension, c1 and c2 are the learning factors for controlling the step length of the particle moving to its own historical optimal position and the learning factors for controlling 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 ith particle in the dth dimension, is the position of the ith particle at time t in the dth dimension, gbest d is the global optimal position of the entire particle swarm in the dth dimension, is the position of the ith particle at time t+1 in the dth 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, for generating carbon trading price fluctuation scenarios using a Monte Carlo simulation method, and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function;

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

[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 coordination and space generation module is used to compare the comprehensive carbon emission coefficient of each carbon flow path, establish a carbon flow coupling model between the high emission path and the low emission path, and optimize the feasible space of the scheduling strategy with a collaborative strategy;

[0064] The particle swarm optimization and strategy output module is used to minimize carbon emissions 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.

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

[0066] (1) The present invention generates carbon trading price fluctuation scenarios through Monte Carlo simulation and calculates risk indicators in combination with the carbon emission cost function, providing a reliable risk assessment basis for decision-making. The equipment start and stop time is determined based on the risk indicators with the best economic benefits, thereby 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) The present invention can effectively integrate the advantages of high-emission and low-emission paths and improve the overall efficiency of the system by establishing a coupling model of energy flow and carbon flow and adopting a collaborative strategy. Finally, the 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, ensure that the system achieves the best overall performance while meeting carbon emission requirements, and improve the energy efficiency and operation management level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It 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 a comprehensive 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 should be explained that by determining the carbon emission factor, the carbon emissions of different energy equipment during operation can be accurately measured. The carbon emission cost function determined based on this converts carbon emissions into economic costs, allowing system operators to comprehensively consider energy costs and carbon emission costs when making scheduling decisions. It not only helps to promote the system to develop in a more environmentally friendly direction and reduce carbon emissions to cope with climate change, but also provides strong support for economic decision-making and achieves 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 enterprises in a small industrial park, including electricity and part of the thermal energy needs. The production activities of enterprises in the park have a certain periodicity, resulting in significant fluctuations in energy demand in different time periods;

[0075] In the actual evaluation, the carbon emission factor of natural gas combustion was 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 mutual influence of carbon emissions in the multi-energy input of the equipment and the cost function of the uncertainty factors of energy input.

[0076] S200 uses 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, you can 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 an uncertain carbon trading market. Operators can evaluate the risk levels of different scheduling plans based on risk indicators and choose the best plan 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, the historical carbon trading price data was statistically analyzed to determine that its mean was 50 yuan per ton of carbon, the standard deviation was 10 yuan, and the number of simulations was set to 500. For each simulation, a random number was generated from the selected normal distribution as the carbon trading price. Based on the carbon trading price generated in each simulation, a simulated price sequence was formed, and continuous price samples were used as scenarios. Finally, 72 different carbon trading price fluctuation scenarios were constructed, and the carbon trading risk indicators of energy equipment in each scenario were calculated based on the carbon emission cost function.

[0080] S300 determines the start and stop time of the energy equipment with the best economic benefit based on the carbon trading risk index;

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

[0082] In the actual evaluation, the energy demand at different time periods in the next day is predicted. It is predicted that during the period from 8:00 to 12:00 in the morning, the electricity demand is 20-30 kWh and the heat demand is 15-20 kWh; during the period from 2:00 to 6:00 in the afternoon, the electricity demand is 30-40 kWh and the heat demand is 20-25 kWh. The constraints include the upper and lower limits of equipment power (the upper limit of the power generation of the gas turbine is 50 kW and the lower limit is 10 kW; the charging and discharging power of the energy storage battery also has corresponding restrictions) 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 index 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 and stop time combinations are obtained: for gas turbines, the start and 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 and stop time combination, the equipment start and stop time plan with the best 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 an identifier G, the solar photovoltaic panel node is assigned an identifier S, and the energy storage battery node is assigned an identifier B. The device type and basic attributes are recorded, and 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, and 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 coefficient of each carbon flow path, establishes a carbon flow coupling model between the high emission path and the low emission path, and optimizes the feasible space of the scheduling strategy by a collaborative strategy;

[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 optimal 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 evaluation, the average and standard deviation of the comprehensive carbon emission coefficient of all carbon flow paths are calculated (the average value is 0.1 kg carbon / joule, and the standard deviation is 0.05 kg carbon / joule), and the threshold is set to the average value plus two times the standard deviation. The path greater than or equal to the threshold is a high-emission path, otherwise it is a low-emission path. There are 3 high-emission paths and 8 low-emission paths. By introducing coupling variables, the carbon flow coupling relationship between high-emission paths and low-emission paths is established, and the energy transfer mechanism and carbon trading mechanism are set based on the collaborative cooperation strategy. The feasible space that meets the constraints is found through depth-first search.

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

[0089] In the actual evaluation, the particle position and speed are initialized, the proportion of electricity provided by the gas turbine is 0.4, the proportion of electricity provided by the solar photovoltaic panel is 0.3, and the proportion of electricity provided by the energy storage battery is 0.3. The minimum carbon emission is taken as the optimization goal, and the fitness function is set to the total carbon emission of the system under the corresponding collaborative scheduling strategy. After 100 iterations, the particle position that minimizes the fitness function value is finally found, that is, the optimal collaborative scheduling strategy, which is: the proportion of electricity provided by the gas turbine is adjusted to 0.26, and it runs from 10:00 to 15:00 on weekdays. The power output of the gas turbine is based on the real-time energy demand within the rated power. The power output is 90% of the rated power during the peak electricity consumption period of 11:00-13:00; 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 to 16:00, and generates electricity at 80% power from 10:00 to 15:00 when the lighting effect is poor. 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 during the low electricity price period from 23:00 to 6:00 at night. The charging power is 70% of the rated power and the storage 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 in the multi-energy input 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 source, 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 operation time;

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

[0095]

[0096] Among them, x i and i Respectively represent the i-th energy input Ei The variables and the i-th energy input E i The 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 The 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] Conduct Monte Carlo simulation, specifically: analyze the historical carbon trading price distribution characteristics, obtain the probability distribution model of carbon trading prices, set the number of simulations, generate a random number from the selected probability distribution as the carbon trading price for each simulation, form a simulated price sequence based on the carbon trading price generated in each simulation, use continuous price samples as scenarios, and construct multiple different carbon trading price fluctuation scenarios;

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

[0101]

[0102] Where 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 amount of the i-th energy in time interval j under scenario θ, is the average value of the input of the i-th energy source 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-th energy source in the output.

[0103] In this embodiment, the method for determining the start and stop time of energy equipment with the best economic benefit 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 the constraints are determined based on the energy demand in different time periods in the future, including the upper and lower limits of equipment power and the limit on the continuous operation time of the equipment;

[0106] The carbon trading risk index is converted into economic cost and incorporated into the objective function. Specifically, the cost coefficient corresponding to different risk levels is determined, and when the carbon trading risk changes, the corresponding cost penalty is increased;

[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 best 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, and the weights are set to values ​​proportional to carbon emissions. Nodes are divided into two categories, including active and inactive states, according to the start and stop time of the equipment.

[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 total scheduling period on the k-th carbon flow path, 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 μ and standard deviation σ of the comprehensive carbon emission coefficient of all carbon flow paths, set the threshold to μ+2σ, and the path greater than or equal to the threshold is a high emission path, otherwise it is a low emission path;

[0121] By introducing coupling variables, the carbon flow coupling relationship between the high emission path and the low emission path 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 of optimizing the feasible space of the scheduling strategy by using the collaborative strategy 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 the i-th energy type, P g,i,t is the output power of the g-th power generation / production equipment belonging to the i-th energy type at time t, Ω S,i is the energy storage device set of the i-th energy type, 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, respectively, 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, respectively. 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] The energy transfer mechanism and carbon trading mechanism are set based on the collaborative cooperation strategy, and the feasible space that meets the constraints is found through depth-first search.

[0129] In this embodiment, the method of taking the minimum carbon emission as the optimization goal and searching for the optimal solution in the feasible space of the scheduling strategy by using the particle swarm algorithm includes:

[0130] Initialize the particle position and velocity. The position of each particle represents a collaborative scheduling strategy, where the particle dimension represents the allocation ratio of different types of energy at different demand points. The optimization goal is to minimize carbon emissions, and the fitness function is set to the total carbon emissions of the system under the corresponding collaborative scheduling strategy.

[0131] The expression for updating the velocity and position of each particle is:

[0132]

[0133] in, is the velocity of the ith particle at time t+1 in the dth dimension, w is the inertia weight, is the velocity of the ith particle at time t in the dth dimension, c1 and c2 are the learning factors for controlling the step length of the particle moving to its own historical optimal position and the learning factors for controlling 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 ith particle in the dth dimension, is the position of the ith particle at time t in the dth dimension, gbest d is the global optimal position of the entire particle swarm in the dth dimension, is the position of the ith particle at time t+1 in the dth 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, for generating carbon trading price fluctuation scenarios using a Monte Carlo simulation method, and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function;

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

[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 coordination and space generation module is used to compare the comprehensive carbon emission coefficient of each carbon flow path, establish a carbon flow coupling model between the high emission path and the low emission path, and optimize the feasible space of the scheduling strategy with 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 best collaborative scheduling strategy through the particle swarm algorithm in the feasible space of the scheduling strategy.

[0141] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described 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 protection scope of the present invention.

Claims

1. A method for optimizing carbon emission scheduling in an integrated energy system, characterized in that: The following steps are involved: Determine the carbon emission factor of energy equipment, and determine the carbon emission cost function based on the carbon emission factor; A Monte Carlo simulation method is used to generate carbon trading price fluctuation scenarios, and the carbon trading risk index of energy equipment in each scenario is calculated based on the carbon emission cost function; Determine the start and stop time of energy equipment with the best economic benefits based on the carbon trading risk index; 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 time of the energy equipment, and calculating a comprehensive carbon emission coefficient of each carbon flow path; Compare the comprehensive carbon emission coefficients of each carbon flow path, establish a carbon flow coupling model between the high emission path and the low emission path, and optimize the feasible space of the scheduling strategy through collaborative strategy; 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.

2. The carbon emission scheduling optimization method in a comprehensive energy system according to claim 1 is characterized in that: 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 in the multi-energy input of the equipment is expressed as: 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 operation time; The cost function considering the uncertainty of energy input is expressed as: Among them, x i and i Represents the i-th energy input E i The variables and the i-th energy input E i The 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 The corresponding carbon emission factor EF i 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.

3. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, characterized in that: The method of 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 includes: Conduct Monte Carlo simulation, specifically: analyze the historical carbon trading price distribution characteristics, obtain the probability distribution model of carbon trading prices, set the number of simulations, generate a random number from the selected probability distribution as the carbon trading price for each simulation, form a simulated price sequence based on the carbon trading price generated in each simulation, use continuous price samples as scenarios, and construct multiple different carbon trading price fluctuation scenarios; Calculate the carbon trading risk index, the expression is: Where 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 amount of the i-th energy in time interval j under scenario θ, is the average value of the input of the i-th energy source 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-th energy source in the output.

4. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, characterized in that: The method for determining the start and stop time 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 the constraints are determined based on the energy demand in different time periods in the future, including the upper and lower limits of equipment power and the limit on the continuous operation time of the equipment; The carbon trading risk index is converted into economic cost and incorporated into the objective function. Specifically, the cost coefficient corresponding to different risk levels is determined, and when the carbon trading risk changes, the corresponding cost penalty is increased; 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 best economic benefits is found according to the objective function.

5. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, 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 comprises: 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, and the weights are set to values ​​proportional to carbon emissions. Nodes are divided into two categories, including active and inactive states, according to the start and stop time of the equipment. 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.

6. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, characterized in that: The method for calculating the comprehensive carbon emission coefficient of each carbon flow path includes: For the kth carbon flow path, the calculation formula for the comprehensive carbon emission coefficient is: 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: 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 total scheduling period on the k-th carbon flow path, 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.

7. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, characterized in that: The method of comparing the comprehensive carbon emission coefficient of each carbon flow path to establish a carbon flow coupling model between a high emission path and a low emission path includes: Calculate the average μ and standard deviation σ of the comprehensive carbon emission coefficient of all carbon flow paths, set the threshold to μ+2σ, and the path 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 path and the low emission path is established, and the expression is: 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.

8. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, characterized in that: The method of optimizing the feasible space of the scheduling strategy by using the collaborative strategy includes: Obtain the constraints for energy balance, expressed as: Among them, Ω G,i is the set of power generation / production equipment of the i-th energy type, P g,i,t is the output power of the g-th power generation / production equipment belonging to the i-th energy type at time t, Ω S,i is the energy storage device set of the i-th energy type, 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, respectively, 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, respectively. 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; The energy transfer mechanism and carbon trading mechanism are set based on the collaborative strategy, and the feasible space that meets the constraints is found through depth-first search.

9. The method for optimizing carbon emission scheduling in a comprehensive energy system according to claim 1, characterized in that: The method of taking the minimum carbon emission as the optimization goal and searching for the optimal solution in the feasible space of the scheduling strategy by using a particle swarm algorithm includes: Initialize the particle position and velocity. The position of each particle represents a collaborative scheduling strategy, where the particle dimension represents the allocation ratio of different types of energy at different demand points. The optimization goal is to minimize carbon emissions, and the fitness function is set to the total carbon emissions of the system under the corresponding collaborative scheduling strategy. The expression for updating the velocity and position of each particle is: in, is the velocity of the ith particle at time t+1 in the dth dimension, w is the inertia weight, is the velocity of the ith particle at time t in the dth dimension, c1 and c2 are the learning factors for controlling the step length of the particle moving to its own historical optimal position and the learning factors for controlling 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 ith particle in the dth dimension, is the position of the ith particle at time t in the dth dimension, gbest d is the global optimal position of the entire particle swarm in the dth dimension, is the position of the ith particle at time t+1 in the dth dimension.

10. A carbon emission scheduling optimization system in an integrated energy system, used to execute a carbon emission scheduling optimization method in an integrated energy system according to any one of claims 1 to 9, 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, for generating carbon trading price fluctuation scenarios using a Monte Carlo simulation method, and calculating the carbon trading risk index of energy equipment in each scenario based on the carbon emission cost function; An equipment start-stop intelligent decision module is used to determine the start-stop time of energy equipment with the best economic benefits based on the carbon trading risk index; 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 coordination and space generation module is used to compare the comprehensive carbon emission coefficient of each carbon flow path, establish a carbon flow coupling model between the high emission path and the low emission path, and optimize the feasible space of the scheduling strategy with 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 best collaborative scheduling strategy through the particle swarm algorithm in the feasible space of the scheduling strategy.

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