Carbon energy collaborative optimization control method and device for regional integrated energy system

Abstract

The invention discloses a carbon energy collaborative optimization control method and device for a regional integrated energy system, and belongs to the technical field of low-carbon operation optimization of the regional integrated energy system.The method comprises the steps that a regional integrated energy system optimization control model, a node marginal price model, a carbon flow model and a producer and consumer low-carbon optimization control model are established by obtaining basic data; and iteratively solving to obtain an optimal operation scheme. The node marginal price and node carbon potential dual guide signals are innovatively constructed, and the carbon cost difference is accurately conducted; establishing a producer and consumer carbon energy collaborative hub model, and quantifying differential carbon energy external characteristics; and designing a double-layer optimization iteration solving framework, and solving the carbon energy interaction strong coupling problem. Example verification shows that the total carbon emission amount of the system can be reduced, meanwhile, the operation cost is reduced, economical efficiency and low-carbon performance are both considered, and an effective solution is provided for low-carbon economical operation of the regional comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of regional integrated energy system operation optimization technology, specifically relating to a method and device for carbon energy synergistic optimization control of regional integrated energy systems. Background Technology

[0002] Regional Integrated Energy Systems (RIES), through the coordinated planning, operation, and optimization of multiple energy sources such as electricity, gas, heat, and cooling, can effectively improve energy utilization efficiency and promote the large-scale consumption of renewable energy, becoming an important technological path for driving the decarbonization of energy systems. Among these, the user side, especially multi-energy users, is not only the end point of energy consumption but is also gradually becoming a flexible resource with energy production and regulation capabilities, and their carbon reduction potential is receiving increasing attention. In recent years, with the rapid development and continuous cost reduction of technologies such as distributed photovoltaics, small gas turbines, and energy storage, the role of users is gradually shifting from traditional energy consumers to "prosumers" with both energy production and consumption capabilities. These users can actively participate in system operation and regulation, providing new solutions for the low-carbon, economical, and safe operation of RIES.

[0003] Prosumers typically possess resources such as distributed renewable energy generation equipment, energy storage systems, gas turbines, and flexible loads. By optimizing the scheduling of their own resources, prosumers can not only reduce their own energy costs and carbon emissions, but also alleviate the power supply pressure on the distribution network during peak hours and improve system operational flexibility by participating in demand response and engaging in bidirectional energy interaction with the distribution network. It is worth noting that numerous studies have indicated that the transfer and conversion of energy flows are often accompanied by the transfer of carbon emission responsibility, i.e., the accompanying "carbon flow." Therefore, the bidirectional energy interaction between prosumers and the distribution network is essentially a process of carbon emission transfer and interaction between the two parties. Because prosumers are equipped with clean power generation units such as photovoltaics or relatively low-carbon power generation equipment such as gas turbines, the carbon emission intensity corresponding to their energy interaction with the distribution network differs significantly from that of simply relying on the upstream grid for power supply. This makes it highly likely that prosumers can deeply participate in the low-carbon operation of the system through bidirectional carbon-energy interaction with the distribution network. However, this bidirectional energy-carbon interaction also significantly increases the complexity of low-carbon economic scheduling for both parties. Currently, the main factors restricting the full realization of the energy conservation and carbon reduction potential of prosumers and consumers include the following aspects:

[0004] Firstly, in terms of guidance signals from the distribution network to producers and consumers, existing methods mainly rely on traditional electricity price signals, such as the Distribution Locational Marginal Price (DLMP). DLMP can effectively reflect the differences in marginal energy supply costs at different times and locations, thereby guiding producers and consumers to use more energy when electricity prices are low and to use less or sell energy when prices are high. However, a single DLMP signal cannot effectively transmit the carbon cost differences resulting from energy consumption behavior at different times and nodes. In fact, due to the strong temporal volatility of renewable energy output such as wind and solar power, and the constantly changing combination of traditional units and clean energy output within RIES, the carbon emissions generated per unit of energy obtained from different nodes of the grid vary dynamically at different times. This spatiotemporal difference in carbon emissions cannot be directly reflected through electricity price signals. Therefore, relying solely on DLMP is insufficient to accurately guide producers and consumers to optimize their energy consumption behavior from the perspective of reducing carbon emissions. There is an urgent need for guidance signals that can simultaneously reflect changes in the carbon cost of energy consumption, forming a dual incentive of "price" and "carbon potential".

[0005] Secondly, regarding the characterization of the interaction between prosumers and the distribution network, while DLMP can reflect the economic value of prosumers selling electricity, it is difficult to quantify the impact of their energy sales behavior on the distribution network's carbon emissions. Different prosumers, due to differences in their internal resource endowments, such as configured photovoltaic capacity, gas turbine efficiency, and energy storage charging and discharging strategies, exhibit significant differences in the carbon emission intensity (i.e., "carbon potential") per unit of electricity they feed back to the distribution network. For example, a prosumer primarily generating electricity from photovoltaics and equipped with energy storage typically feeds back electricity with a much higher cleanliness level than a prosumer mainly relying on gas turbine power generation. Currently, there is a lack of effective methods and models to quantify these differentiated "carbon energy external characteristics" of prosumers. This makes it difficult for the distribution network to distinguish the quality of a prosumer's carbon emission attributes when receiving electricity, hindering the development of differentiated incentive strategies and preventing prosumers from prioritizing the consumption of their internal low-carbon energy and providing cleaner electricity to the grid.

[0006] Furthermore, the complex interplay and coupling between carbon and energy interactions between prosumers and the distribution network present challenges to coordinated optimization and control. Adjustments to the distribution network's operating scheme not only alter the energy flow distribution and nodal marginal prices within the system but also induce dynamic changes in carbon flow distribution and nodal carbon potential. Upon receiving these changed price and carbon potential signals, prosumers adjust their operating strategies accordingly, which in turn alters the energy interaction power and carbon emission interaction between them and the distribution network. This feedback mechanism—distribution network control – signal update – prosumer response – interaction change – distribution network re-control—tightly couples the operating states of both the distribution network and prosumers, meaning that decisions by one directly impact the optimization results of the other. This strong coupling makes coordinated optimization and control between the distribution network and prosumers difficult, hindering the achievement of overall regional carbon and economic optimization.

[0007] Although the aforementioned issues have gradually attracted attention from academia and industry, most current research on RIES optimization control involving prosumers and consumers still focuses on guiding bidirectional energy interaction between the two parties through price signals to reduce operating costs. While some studies have begun to notice the carbon emission transfer associated with energy interaction between prosumers and the distribution network, the quantification of this carbon flow is often rather crude. For example, some studies still use regional or system-level average carbon emission factors for calculation, which ignores the dynamic changes of carbon emission factors over time and space, as well as the differences in carbon emission characteristics among different prosumers. This simplified carbon accounting method may lead to scheduling results deviating from the true carbon emission situation, failing to accurately tap the carbon reduction potential of prosumers, and resulting in a final optimization scheme that is not optimal in terms of both economy and low carbon emissions. Summary of the Invention

[0008] To address this, the present invention provides a method and apparatus for coordinated optimization control of carbon energy in a regional integrated energy system, which solves the problems in existing regional integrated energy systems where a single electricity price signal cannot transmit carbon cost differences, the external characteristics of carbon energy of producers and consumers are difficult to quantify, and the interaction and coupling of carbon energy between the distribution network and producers and consumers leads to difficulties in coordinated optimization, making it impossible to balance economic efficiency and low carbon emissions.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for coordinated optimization control of carbon energy in a regional integrated energy system, comprising the following steps:

[0010] S1. Obtain basic data for the RIES carbon energy collaborative optimization control of the regional integrated energy system. The basic data includes RIES network topology parameters, distribution network node load forecast values, gas distribution network node load forecast values, producer-consumer user equipment technical parameters, producer-consumer internal electricity / heat / cooling multi-energy load forecast values, photovoltaic output forecast values, upstream grid electricity price, distribution network root node carbon potential and natural gas carbon emission factor.

[0011] S2. Based on the aforementioned basic data and combined with the two-way carbon energy interaction relationship between prosumers and the distribution network and gas distribution network, a RIES optimization control model is established with the minimum total operating cost of the distribution network as the optimization objective. The total operating cost of the distribution network includes the cost of purchasing electricity from the upper-level grid, the cost of natural gas supply, the energy trading cost with prosumers, and the carbon emission cost.

[0012] S3. Based on the RIES optimization control model, construct the RIES nodal marginal price model and carbon flow model. Solve the nodal marginal price model and carbon flow model of RIES to determine the nodal marginal price and dynamic carbon potential, forming a dual guiding signal of nodal marginal price and nodal carbon potential.

[0013] S4. Under the coordination of the dual guidance signals, and in conjunction with the basic data, a low-carbon optimization control model for producers and consumers is established with the goal of minimizing the total operating cost of the producers and consumers themselves. The total operating cost of the producers and consumers themselves includes the energy purchase and sale cost with the distribution network / gas distribution network, the maintenance cost of their own equipment, the cost of flexible load adjustment, and the cost of carbon emissions.

[0014] S5. By iteratively solving the RIES optimization control model and the producer-consumer low-carbon optimization control model, the operating status of the power distribution network, gas distribution network and producer-consumer users is dynamically adapted to achieve coordinated optimization of the operating schemes of the power distribution network, gas distribution network and producer-consumers, and finally output the optimal operating scheme that takes into account both economy and low carbon emissions.

[0015] As a preferred scheme for the carbon energy synergistic optimization control method of regional integrated energy system, the objective function of the RIES optimization control model is to minimize the total operating cost of the distribution network and gas distribution network. The total operating cost is calculated by accumulating the cost at a set time within the optimization control period.

[0016] The cost of purchasing electricity from the upstream power grid is determined by multiplying the purchase price, purchased power, and time step at a set time. The carbon emission cost is determined by multiplying the carbon potential of the root node of the distribution network and the purchased power, the carbon potential of the producer-consumer electricity sales and the electricity sales power, and the carbon potential of the gas distribution network source node and the gas supply power at a set time by the carbon price and time step.

[0017] As a preferred scheme for the carbon energy synergistic optimization control method of regional integrated energy system, the constraints of the RIES optimization control model include distribution network operation constraints and gas distribution network operation constraints;

[0018] In the distribution network operation constraints, the active power balance constraint limits the node load, the power purchased and sold by producers and consumers and the power transmitted by the line to meet the balance of income and expenditure. The reactive power balance constraint constrains the reactive power. The upper limit constraint of power flow limits the sum of the squares of the active power and reactive power of the line to not exceed the square of the line capacity. The voltage constraint limits the node voltage to within the preset upper and lower limits.

[0019] In the gas distribution network operation constraints, the power balance constraint limits the node gas load, gas source output, producer and consumer gas purchase power and pipeline transmission flow to meet the balance of income and expenditure. The gas pressure-pipeline flow constraint is determined according to the correlation between the gas pressure difference at both ends of the pipeline and the flow rate. The pipeline flow upper limit constraint limits the pipeline flow rate to the allowable range. The node gas pressure constraint limits the node gas pressure to the preset upper and lower limits. The gas source power constraint limits the gas source output to not exceed the upper limit.

[0020] As a preferred scheme for the carbon energy collaborative optimization control method of regional integrated energy system, in the node marginal price model, the marginal electricity price of the distribution network node is equal to the dual multiplier of the active power balance constraint of that node, and the marginal gas price of the gas distribution network node is equal to the dual multiplier of the power balance equation constraint of that node; in the electricity transaction between integrated energy operators and producers and consumers, the purchase price of electricity is directly adopted from the corresponding node marginal electricity price, the sales price of electricity is the product of the node marginal electricity price and the preset discount factor, and the natural gas transaction price is directly adopted from the corresponding node marginal gas price of the gas distribution network.

[0021] The carbon flow model is divided into a distribution network carbon flow model and a gas distribution network carbon flow model. In the distribution network carbon flow model, the carbon potential of the root node is a preset value. The carbon potential of nodes other than the root node is determined by the sum of the products of the line carbon flow density and the corresponding transmission power, and the sum of the products of the carbon potential of the electricity sold by producers and consumers and the electricity sold, divided by the sum of the transmission power of the lines and the electricity sold by producers and consumers. The line carbon flow density is equal to the carbon potential of the first node of the line. In the gas distribution network carbon flow model, the node carbon potential is determined by the sum of the products of the carbon potential of the gas source unit and the gas supply power, and the sum of the products of the pipeline carbon flow density and the pipeline flow rate, divided by the sum of the gas supply power and the pipeline flow rate. The pipeline carbon flow density is equal to the carbon potential of the first node of the pipeline. The carbon potential of electricity purchased by producers and consumers is equal to the carbon potential of the node connected to the distribution network, and the carbon potential of gas purchased is equal to the carbon potential of the node connected to the gas distribution network.

[0022] As a preferred scheme for the carbon energy synergistic optimization control method of regional integrated energy systems, before establishing the producer-consumer low-carbon optimization control model, the method further includes constructing a carbon energy synergistic hub model. The carbon energy synergistic hub model describes the input-output relationship between energy flow and carbon flow in matrix form. Energy flow input includes basic energy flow input, renewable energy power generation output, and energy storage charging and discharging power. Energy flow output includes internal multi-energy loads and power sold to the distribution network. Carbon flow input includes the carbon flow density corresponding to each energy flow input. Carbon flow output includes the carbon flow density corresponding to each load and the carbon potential of electricity sold. The matrix incorporates energy allocation coefficients, equipment conversion efficiency, and the coupling characteristics of energy flow and carbon flow of renewable energy power generation equipment and energy storage equipment.

[0023] The objective function of the producer-consumer low-carbon optimization control model is to minimize the total daily operating cost, which is calculated by accumulating the cost at each moment within the optimization control cycle; the energy purchase and sale cost of the distribution network / gas distribution network is determined by multiplying the electricity purchase price and the electricity purchase power, the electricity sale price and the electricity sale power, and the gas purchase price and the gas purchase power, combined with the time step.

[0024] The self-maintenance cost of the equipment is determined by multiplying the maintenance cost coefficient of each piece of equipment by the output, the maintenance cost coefficient of the energy storage equipment by the absolute value of the charging and discharging power, and then multiplying by the time step.

[0025] The flexible load adjustment cost is determined by the sum of the product of the unit adjustment cost of transferable, reduceable, and replaceable loads and the corresponding adjustment power; the carbon emission cost is determined by the sum of the product of the carbon flux density of electrical, heat, and cooling loads and the corresponding load power, multiplied by the carbon price and the time step.

[0026] As a preferred scheme for the carbon energy synergistic optimization control method of regional integrated energy system, the constraints of the producer-consumer low-carbon optimization control model include energy flow-carbon flow balance constraints, energy purchase constraints, equipment power constraints, energy storage constraints, and flexible load adjustment constraints.

[0027] The energy flow-carbon flow balance constraint ensures that the input and output of energy flow and carbon flow satisfy a matrix relationship, and that the electrical, thermal, and cooling loads are the superposition values ​​of the base load and the transfer, reduction, and substitution adjustment power;

[0028] The energy purchase constraints limit the power of electricity purchase, electricity sales, and gas purchase to no more than their respective upper limits; the equipment power constraints limit the operating power of various equipment to no more than the rated capacity; in the energy storage constraints, the cumulative power is updated according to the correlation between the initial power, self-discharge rate, charge and discharge power, charge and discharge efficiency and time step, the charge and discharge power is limited to the maximum charge and discharge power range, the cumulative power is limited to the upper and lower limits range, and the cumulative power at the beginning and end of the control cycle remains consistent.

[0029] In the flexible load adjustment constraint, the adjustment amount of the transferable load is within the preset ratio range of the reference load, and the total adjustment amount within the cycle is zero. The adjustment amount of the load that can be reduced does not exceed the preset ratio of the reference load. The sum of the electric, heat, and cold adjustment amounts of the replaceable load is zero, and their respective adjustment amounts are within the preset ratio range of the reference load.

[0030] As a preferred scheme for the carbon energy synergistic optimization control method of regional integrated energy system, the iterative solution process includes initialization, upper-level model solution, signal update, lower-level model solution, and convergence judgment steps;

[0031] In the initialization phase, the power consumption of electricity purchases, sales, and gas purchases by prosumers is set to zero, and the number of iterations is the initial value. In the upper-level model solution phase, the operation schemes of the distribution network and gas distribution network are obtained. In the signal update phase, the marginal price and carbon potential of the nodes are calculated based on the results of the upper-level model, and the transaction price and carbon potential signals with prosumers are updated. In the lower-level model solution phase, the optimization models of each prosumer are solved in sequence, and the energy interaction power and the carbon potential of electricity sales are updated. In the convergence judgment phase, convergence is determined by whether the relative error of the power consumption of electricity purchases, sales, and gas purchases in the current iteration and the previous iteration does not exceed the preset threshold. If convergence is not achieved, the number of iterations is updated and the iterative solution process is repeated.

[0032] The present invention also provides a carbon energy synergistic optimization control device for a regional integrated energy system, comprising:

[0033] The data acquisition module is used to acquire basic data for the RIES carbon energy collaborative optimization control of the regional integrated energy system. The basic data includes RIES network topology parameters, distribution network node load forecasts, gas distribution network node load forecasts, producer-consumer user equipment technical parameters, producer-consumer internal electricity / heat / cooling multi-energy load forecasts, photovoltaic output forecasts, upstream grid electricity price, distribution network root node carbon potential, and natural gas carbon emission factor.

[0034] The RIES optimization model construction module is used to establish an RIES optimization control model based on the aforementioned basic data and the bidirectional carbon energy interaction relationship between prosumers and users and the distribution network and gas distribution network, with the goal of minimizing the total operating cost of the distribution network. The total operating cost of the distribution network includes the cost of purchasing electricity from the upper-level grid, the cost of natural gas supply, the cost of energy trading with prosumers, and the cost of carbon emissions.

[0035] The price and carbon potential model construction module is used to construct the node marginal price model and carbon flow model of RIES based on the RIES optimization control model. The node marginal price and dynamic carbon potential are determined by solving the node marginal price and dynamic carbon potential through the node marginal price model and carbon flow model, forming a dual guiding signal of node marginal price and node carbon potential.

[0036] The producer-consumer optimization model construction module is used to establish a producer-consumer low-carbon optimization control model under the coordination of the dual guidance signals and in combination with the basic data, with the optimization objective of minimizing the producer's own total operating cost. The producer's own total operating cost includes the energy purchase and sale cost with the distribution network / gas distribution network, its own equipment maintenance cost, flexible load adjustment cost, and carbon emission cost.

[0037] The iterative solution module is used to iteratively solve the RIES optimized control model and the producer-consumer low-carbon optimized control model, dynamically adapt to the operating status of the power distribution network, gas distribution network and producer-consumer users, realize the coordinated optimization of the operating schemes of the power distribution network, gas distribution network and producer-consumers, and finally output the optimal operating scheme that takes into account both economy and low carbon.

[0038] As a preferred scheme for the carbon energy synergistic optimization control device of the regional integrated energy system, in the RIES optimization model construction module, the objective function of the RIES optimization control model is to minimize the total operating cost of the distribution network and the gas distribution network. The total operating cost is calculated by accumulating the cost at a set time within the optimization control cycle.

[0039] The cost of purchasing electricity from the upstream power grid is determined by multiplying the purchase price, purchased power, and time step at a set time. The carbon emission cost is determined by multiplying the carbon potential of the root node of the distribution network and the purchased power, the carbon potential of the producer-consumer electricity sales and the electricity sales power, and the carbon potential of the gas distribution network source node and the gas supply power at a set time by the carbon price and time step.

[0040] As a preferred scheme for the carbon energy collaborative optimization control device of the regional integrated energy system, the constraints of the RIES optimization control model in the RIES optimization model construction module include distribution network operation constraints and gas distribution network operation constraints.

[0041] In the distribution network operation constraints, the active power balance constraint limits the node load, the power purchased and sold by producers and consumers and the power transmitted by the line to meet the balance of income and expenditure. The reactive power balance constraint constrains the reactive power. The upper limit constraint of power flow limits the sum of the squares of the active power and reactive power of the line to not exceed the square of the line capacity. The voltage constraint limits the node voltage to within the preset upper and lower limits.

[0042] In the gas distribution network operation constraints, the power balance constraint limits the node gas load, gas source output, producer and consumer gas purchase power and pipeline transmission flow to meet the balance of income and expenditure. The gas pressure-pipeline flow constraint is determined according to the correlation between the gas pressure difference at both ends of the pipeline and the flow rate. The pipeline flow upper limit constraint limits the pipeline flow rate to the allowable range. The node gas pressure constraint limits the node gas pressure to the preset upper and lower limits. The gas source power constraint limits the gas source output to not exceed the upper limit.

[0043] As a preferred scheme for the carbon energy collaborative optimization control device of the regional integrated energy system, in the node marginal price model of the price and carbon potential model construction module, the node marginal electricity price of the distribution network is equal to the dual multiplier of the active power balance constraint of that node, and the node marginal gas price of the gas distribution network is equal to the dual multiplier of the power balance equation constraint of that node; in the electricity transaction between the integrated energy operator and the producer and consumer, the purchase price of electricity is directly adopted from the corresponding node marginal electricity price, the sales price of electricity is the product of the node marginal electricity price and the preset discount factor, and the natural gas transaction price is directly adopted from the corresponding node marginal gas price of the gas distribution network;

[0044] The carbon flow model in the price and carbon potential model construction module is divided into a distribution network carbon flow model and a gas distribution network carbon flow model. In the distribution network carbon flow model, the carbon potential of the root node is a preset value. The carbon potential of nodes other than the root node is determined by the sum of the products of line carbon flow density and corresponding transmission power, and the sum of the products of producer-consumer electricity sales carbon potential and electricity sales power, divided by the sum of line transmission power and producer-consumer electricity sales power. The line carbon flow density is equal to the carbon potential of the first node of the line. In the gas distribution network carbon flow model, the node carbon potential is determined by the sum of the products of gas source unit carbon potential and gas supply power, and the sum of the products of pipeline carbon flow density and pipeline flow rate, divided by the sum of gas supply power and pipeline flow rate. The pipeline carbon flow density is equal to the carbon potential of the first node of the pipeline. The producer-consumer electricity purchase carbon potential is equal to the carbon potential of the node connected to the distribution network, and the gas purchase carbon potential is equal to the carbon potential of the node connected to the gas distribution network.

[0045] As a preferred option for carbon energy synergistic optimization control devices in regional integrated energy systems, it also includes:

[0046] The carbon energy synergy hub model construction module is used to construct a carbon energy synergy hub model. The carbon energy synergy hub model describes the input-output relationship of energy flow and carbon flow in matrix form. Energy flow input includes basic energy flow input, renewable energy power generation output, and energy storage charging and discharging power. Energy flow output includes internal multi-energy loads and power sold to the distribution network. Carbon flow input includes the carbon flow density corresponding to each energy flow input. Carbon flow output includes the carbon flow density corresponding to each load and the carbon potential of electricity sold. The matrix incorporates energy allocation coefficients, equipment conversion efficiency, and the coupling characteristics of energy flow and carbon flow of renewable energy power generation equipment and energy storage equipment.

[0047] As a preferred scheme for the carbon energy collaborative optimization control device of the regional integrated energy system, in the producer-consumer optimization model construction module, the objective function of the producer-consumer low-carbon optimization control model is to minimize the total daily operating cost, which is calculated by accumulating the cost at each moment within the optimization control cycle; the energy purchase and sale cost of the distribution network / gas distribution network is determined by multiplying the electricity purchase price and the electricity purchase power, the electricity sale price and the electricity sale power, and the gas purchase price and the gas purchase power, combined with the time step.

[0048] The self-maintenance cost of the equipment is determined by multiplying the maintenance cost coefficient of each piece of equipment by the output, the maintenance cost coefficient of the energy storage equipment by the absolute value of the charging and discharging power, and then multiplying by the time step.

[0049] The flexible load adjustment cost is determined by the sum of the product of the unit adjustment cost of transferable, reduceable, and replaceable loads and the corresponding adjustment power; the carbon emission cost is determined by the sum of the product of the carbon flux density of electrical, heat, and cooling loads and the corresponding load power, multiplied by the carbon price and the time step.

[0050] As a preferred scheme for carbon energy collaborative optimization control device in regional integrated energy system, the constraints of the producer-consumer optimization model construction module include energy flow-carbon flow balance constraints, energy purchase constraints, equipment power constraints, energy storage constraints, and flexible load adjustment constraints.

[0051] The energy flow-carbon flow balance constraint ensures that the input and output of energy flow and carbon flow satisfy a matrix relationship, and that the electrical, thermal, and cooling loads are the superposition values ​​of the base load and the transfer, reduction, and substitution adjustment power;

[0052] The energy purchase constraints limit the power of electricity purchase, electricity sales, and gas purchase to no more than their respective upper limits; the equipment power constraints limit the operating power of various equipment to no more than the rated capacity; in the energy storage constraints, the cumulative power is updated according to the correlation between the initial power, self-discharge rate, charge and discharge power, charge and discharge efficiency and time step, the charge and discharge power is limited to the maximum charge and discharge power range, the cumulative power is limited to the upper and lower limits range, and the cumulative power at the beginning and end of the control cycle remains consistent.

[0053] In the flexible load adjustment constraint, the adjustment amount of the transferable load is within the preset ratio range of the reference load, and the total adjustment amount within the cycle is zero. The adjustment amount of the load that can be reduced does not exceed the preset ratio of the reference load. The sum of the electric, heat, and cold adjustment amounts of the replaceable load is zero, and their respective adjustment amounts are within the preset ratio range of the reference load.

[0054] As a preferred scheme for the carbon energy collaborative optimization control device of the regional integrated energy system, the iterative solution module includes the following steps: initialization, upper-level model solution, signal update, lower-level model solution, and convergence judgment. In the initialization phase, the electricity purchase, electricity sale, and gas purchase power of producers and consumers are set to zero, and the number of iterations is set to the initial value. In the upper-level model solution phase, the operation schemes of the distribution network and gas distribution network are obtained. In the signal update phase, the marginal price and carbon potential of nodes are calculated based on the results of the upper-level model, and the transaction price and carbon potential signals with producers and consumers are updated. In the lower-level model solution phase, the optimization models of each producer and consumer are solved sequentially, and the energy interaction power and electricity sale carbon potential are updated. In the convergence judgment phase, convergence is determined by whether the relative error between the electricity purchase, electricity sale, and gas purchase power of the current iteration and the previous iteration does not exceed a preset threshold. If convergence is not achieved, the number of iterations is updated and the iterative solution process is repeated.

[0055] The present invention has the following advantages:

[0056] First, this invention addresses the problem that traditional single electricity price signals cannot reflect the spatiotemporal dynamics of carbon emissions, guiding producers and consumers to optimize their energy consumption behavior from both economic and low-carbon perspectives, and contributing to the low-carbon transformation of regional integrated energy systems.

[0057] Second, this invention constructs a producer-consumer carbon energy collaborative hub model to characterize the coupling relationship between internal multi-energy conversion and carbon emissions, quantifies the differentiated carbon energy external characteristics of different producers and consumers, provides a quantitative basis for the distribution network to formulate differentiated incentive strategies, and promotes producers and consumers to prioritize the consumption of internal low-carbon energy.

[0058] Third, a two-layer optimization iterative solution framework is designed to effectively solve the problem of strong coupling between distribution network and carbon energy interaction between producers and consumers, realize dynamic collaborative optimization of the operation schemes of both parties, and ensure the overall optimal operation of RIES.

[0059] Fourth, it significantly reduces system carbon emissions and operating costs. Case studies have shown that it can reduce total RIES carbon emissions by about 14% while reducing operating costs for producers and consumers, thus balancing environmental and economic benefits.

[0060] Fifth, improve energy efficiency and system flexibility by optimizing energy interaction between producers and consumers and distribution and gas distribution networks, promote the large-scale consumption of renewable energy, alleviate peak power supply pressure on distribution networks, and enhance system operation stability and anti-interference capabilities.

[0061] Sixth, the model and method are highly adaptable and scalable, and can be adapted to different types of prosumers and complex network topologies, providing a standardized and scalable technical solution for the low-carbon economic operation of RIES with multiple prosumers. Attached Figure Description

[0062] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0063] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0064] Figure 1 This is a schematic diagram of the carbon energy synergistic optimization control method for a regional integrated energy system provided in an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of a RIES structure containing multiple consumer users provided in an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of a typical producer-consumer structure provided in an embodiment of the present invention;

[0067] Figure 4 This is a flowchart of the iterative solution process for the RIES and producer-consumer optimization control model provided in this embodiment of the invention;

[0068] Figure 5 This is a schematic diagram of the IEEE 33-node distribution network structure provided in an embodiment of the present invention;

[0069] Figure 6 This is a schematic diagram of a 20-node natural gas network structure provided in an embodiment of the present invention;

[0070] Figure 7 The load curve and photovoltaic output prediction value of producer-consumer 1 provided in this embodiment of the invention;

[0071] Figure 8 This is the producer-consumer peak load distribution provided in this embodiment of the invention;

[0072] Figure 9 The upper-level grid LMP and node 1 carbon potential are provided in the embodiments of the present invention;

[0073] Figure 10 This invention provides a comparison of the DLMP, nodal carbon potential, and electrical balance of progenitors and consumers in different scenarios.

[0074] Figure 11 This invention provides a comparison of the electricity purchased and sold by producer-consumer 1 and producer-consumer 2 under different scenarios in this embodiment.

[0075] Figure 12 This invention provides a comparison of the carbon potential of electricity sales by prosumer 1 and prosumer 2 under different scenarios in this embodiment of the invention.

[0076] Figure 13 This invention provides a comparison of electricity sales prices for prosumers 1 and 2 under different scenarios in this embodiment.

[0077] Figure 14 This is a schematic diagram of the architecture of the regional integrated energy system carbon energy collaborative optimization control device provided in an embodiment of the present invention. Detailed Implementation

[0078] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] Example 1

[0080] See Figure 1 This invention provides a method for coordinated optimization and control of carbon energy in a regional integrated energy system, comprising the following steps:

[0081] S1. Obtain basic data for the RIES carbon energy collaborative optimization control of the regional integrated energy system. The basic data includes RIES network topology parameters, distribution network node load forecast values, gas distribution network node load forecast values, producer-consumer user equipment technical parameters, producer-consumer internal electricity / heat / cooling multi-energy load forecast values, photovoltaic output forecast values, upstream grid electricity price, distribution network root node carbon potential and natural gas carbon emission factor.

[0082] S2. Based on the aforementioned basic data and combined with the two-way carbon energy interaction relationship between prosumers and the distribution network and gas distribution network, a RIES optimization control model is established with the minimum total operating cost of the distribution network as the optimization objective. The total operating cost of the distribution network includes the cost of purchasing electricity from the upper-level grid, the cost of natural gas supply, the energy trading cost with prosumers, and the carbon emission cost.

[0083] S3. Based on the RIES optimization control model, construct the RIES nodal marginal price model and carbon flow model. Solve the nodal marginal price model and carbon flow model of RIES to determine the nodal marginal price and dynamic carbon potential, forming a dual guiding signal of nodal marginal price and nodal carbon potential.

[0084] S4. Under the coordination of the dual guidance signals, and in conjunction with the basic data, a low-carbon optimization control model for producers and consumers is established with the goal of minimizing the total operating cost of the producers and consumers themselves. The total operating cost of the producers and consumers themselves includes the energy purchase and sale cost with the distribution network / gas distribution network, the maintenance cost of their own equipment, the cost of flexible load adjustment, and the cost of carbon emissions.

[0085] S5. By iteratively solving the RIES optimization control model and the producer-consumer low-carbon optimization control model, the operating status of the power distribution network, gas distribution network and producer-consumer users is dynamically adapted to achieve coordinated optimization of the operating schemes of the power distribution network, gas distribution network and producer-consumers, and finally output the optimal operating scheme that takes into account both economy and low carbon emissions.

[0086] In this embodiment, in step S2, the objective function of the RIES optimization control model is to minimize the total operating cost of the distribution network and the gas distribution network. The total operating cost is calculated by accumulating the costs at set times within the optimization control cycle. The cost of purchasing electricity from the upstream grid is determined by multiplying the electricity purchase price, the purchased power, and the time step at the set time. The carbon emission cost is determined by multiplying the carbon potential of the distribution network root node and the purchased power, the carbon potential of the producer-consumer electricity sales and the sold power, and the carbon potential of the gas distribution network source node and the gas supply power at the set time by the carbon price and the time step.

[0087] Specifically, in the RIES optimal control model, the Integrated Energy Service Operator (IESO) aims to minimize the total operating costs of the distribution network and gas distribution network, including the cost of purchasing electricity from the upstream grid, the cost of natural gas supply, the cost of transactions with producers and consumers, and the cost of carbon emissions. Therefore, the objective function of the established RIES optimal control model is:

[0088] (1)

[0089] (2)

[0090] (3)

[0091] (4)

[0092] (5)

[0093] In the formula, f IESO This represents the total operating cost of the IESO. These represent the costs of purchasing electricity from the upstream power grid, the cost of supplying natural gas, the cost of purchasing electricity from producers and consumers, and the cost of carbon emissions for the IEO at time t. The power and price purchased from the upper-level power grid at time t; The time step is for optimizing control; T is the optimization control period; Let be the gas source cost coefficient at node s and the gas supply power at time t; For time t, the electricity and gas prices that producer-consumer n purchases and sells to ISO; For time t, the active power of electricity purchased and sold by consumer n to IESO and the power of gas purchased. The carbon potential of the root node of the distribution network and the source node of the gas distribution network; For the potential of producers and consumers to sell carbon dioxide to IESO; For carbon price; For the set of gas distribution network nodes; A gathering of producers and consumers.

[0094] In this embodiment, in step S2, the constraints of the RIES optimized control model include distribution network operation constraints and gas distribution network operation constraints. Among the distribution network operation constraints, the active power balance constraint limits the node load, the power purchased and sold by producers and consumers, and the transmission power of the lines to meet the balance of income and expenditure. The reactive power balance constraint constrains reactive power. The upper limit constraint of power flow limits the sum of the squares of the active power and reactive power of the lines to not exceed the square of the line capacity. The voltage constraint limits the node voltage to be within the preset upper and lower limits. Among the gas distribution network operation constraints, the power balance constraint limits the node gas load, gas source output, the power purchased by producers and consumers, and the pipeline transmission flow to meet the balance of income and expenditure. The gas pressure-pipeline flow constraint is determined according to the correlation between the gas pressure difference at both ends of the pipeline and the flow rate. The upper limit constraint of pipeline flow limits the pipeline flow rate to be within the allowable range. The node gas pressure constraint limits the node gas pressure to be within the preset upper and lower limits. The gas source power constraint limits the gas source output to not exceed the upper limit.

[0095] Specifically, distribution network operation constraints include active power balance constraints, reactive power balance constraints, power flow upper limit constraints, and voltage constraints, etc., and the specific formulas are as follows:

[0096] (6)

[0097] (7)

[0098] (8)

[0099] (9)

[0100] (10)

[0101] (11)

[0102] (12)

[0103] (13)

[0104] (14)

[0105] In the formula, , Let t be the active and reactive loads of distribution network node b at time t; , For time t, the reactive power that consumer n purchases or sells to IESO; , Let t represent the active and reactive power of line l in the distribution network at time t. The square of the current in line l; , Let L be the resistance and reactance of line l; , Let be the set of the first and last nodes of line l; For the active power balance constraint at node b of the distribution network; Let L be the capacity of line l; , Let be the voltage at the end node and the beginning node of line l at time t; , , Let be the voltage, upper voltage limit, and lower voltage limit at time t of node b; Let be the power coefficient of node b; Let n be the power sold by producer-consumer n to IESO at time t during the k-th iteration, and let t be the power sold by producer-consumer n to IESO during the (k+1)-th iteration. The upper limit.

[0106] Specifically, gas distribution network operation constraints include power balance constraints, gas pressure-pipeline flow constraints, upper limit constraints on pipeline flow constraints, node voltage constraints, and gas source power constraints.

[0107] (15)

[0108] (16)

[0109] (17)

[0110] (18)

[0111] (19)

[0112] (20)

[0113] In the formula, Let t be the gas load at node s of the gas distribution network at time t; The output power of the gas source at node s at time t; The flow rate of gas distribution network pipeline p; , Let be the set of the first and last nodes of pipe p; For the dual multiplier of the power balance equation constraint at node s of the gas distribution network; , Let be the air pressure at the end and beginning nodes of pipe p; sign(·) is the sign function; For pipeline transmission parameters; For pipeline capacity; , , Let t be the air pressure, upper limit air pressure, and lower limit air pressure at node s; This refers to the compression ratio of the compressor. This is the upper limit of the gas source output.

[0114] In this embodiment, in step S3, in the node marginal price model, the marginal electricity price of the distribution network node is equal to the dual multiplier of the active power balance constraint of that node, and the marginal gas price of the gas distribution network node is equal to the dual multiplier of the power balance equation constraint of that node; in the integrated power transaction between energy operators and producers and consumers, the purchase price of electricity is directly adopted from the corresponding node marginal electricity price, the sales price of electricity is the product of the node marginal electricity price and the preset discount factor, and the natural gas transaction price is directly adopted from the corresponding node marginal gas price of the gas distribution network.

[0115] The carbon flow model is divided into a distribution network carbon flow model and a gas distribution network carbon flow model. In the distribution network carbon flow model, the carbon potential of the root node is a preset value. The carbon potential of nodes other than the root node is determined by the sum of the products of the line carbon flow density and the corresponding transmission power, and the sum of the products of the carbon potential of the electricity sold by producers and consumers and the electricity sold, divided by the sum of the transmission power of the lines and the electricity sold by producers and consumers. The line carbon flow density is equal to the carbon potential of the first node of the line. In the gas distribution network carbon flow model, the node carbon potential is determined by the sum of the products of the carbon potential of the gas source unit and the gas supply power, and the sum of the products of the pipeline carbon flow density and the pipeline flow rate, divided by the sum of the gas supply power and the pipeline flow rate. The pipeline carbon flow density is equal to the carbon potential of the first node of the pipeline. The carbon potential of electricity purchased by producers and consumers is equal to the carbon potential of the node connected to the distribution network, and the carbon potential of gas purchased is equal to the carbon potential of the node connected to the gas distribution network.

[0116] Specifically, the model formulas for the nodal marginal electricity price of each node in the distribution network and the nodal marginal gas price of each node in the gas distribution network are as follows:

[0117] (twenty one)

[0118] (twenty two)

[0119] In the formula, Let $b$ be the marginal electricity price at node $b$. Let be the marginal gas price at node s.

[0120] The electricity price for IESO to trade electricity and natural gas with prosumers is as follows:

[0121] (twenty three)

[0122] (twenty four)

[0123] In the formula, This is the discount factor.

[0124] Among them, there is a two-way energy interaction between prosumers and the distribution network. According to carbon flow theory, while there is a two-way energy interaction, prosumers also extract or inject indirect carbon emissions from the distribution network. Therefore, the formula for the RIES carbon flow model with multiple prosumers is:

[0125] (25)

[0126] (26)

[0127] (27)

[0128] (28)

[0129] (29)

[0130] (30)

[0131] (31)

[0132] In the formula, Let be the carbon potential of node b in the distribution network at time t; Let be the carbon flux density of line l at time t; For the set of distribution network nodes; Let be the carbon potential of the first node of line l at time t; Let be the carbon potential of node s in the gas distribution network at time t; The carbon potential of the unit supplying gas at node s; Let be the carbon flux density in pipe p at time t; Let be the carbon potential at the first node of pipe p at time t; The carbon potential of electricity purchases by producers and consumers at node b; For the carbon purchasing potential of producers and consumers at node s

[0133] In this embodiment, before establishing the producer-consumer low-carbon optimization control model in step S4, a carbon energy synergy hub model is constructed. The carbon energy synergy hub model describes the input-output relationship between energy flow and carbon flow in matrix form. Energy flow input includes basic energy flow input, renewable energy power generation output, and energy storage charging and discharging power. Energy flow output includes internal multi-energy loads and power sold to the distribution network. Carbon flow input includes the carbon flow density corresponding to each energy flow input. Carbon flow output includes the carbon flow density corresponding to each load and the carbon potential of electricity sold. The matrix incorporates energy allocation coefficients, equipment conversion efficiency, and the coupling characteristics of energy flow and carbon flow between renewable energy power generation equipment and energy storage equipment.

[0134] The objective function of the producer-consumer low-carbon optimization control model is to minimize the total daily operating cost, which is calculated by accumulating the cost at each moment within the optimization control cycle; the energy purchase and sale cost of the distribution network / gas distribution network is determined by multiplying the electricity purchase price and the electricity purchase power, the electricity sale price and the electricity sale power, and the gas purchase price and the gas purchase power, combined with the time step.

[0135] The self-maintenance cost of the equipment is determined by multiplying the maintenance cost coefficient of each piece of equipment by the output, the maintenance cost coefficient of the energy storage equipment by the absolute value of the charging and discharging power, and then multiplying by the time step.

[0136] The flexible load adjustment cost is determined by the sum of the product of the unit adjustment cost of transferable, reduceable, and replaceable loads and the corresponding adjustment power; the carbon emission cost is determined by the sum of the product of the carbon flux density of electrical, heat, and cooling loads and the corresponding load power, multiplied by the carbon price and the time step.

[0137] Specifically, producer-consumer operation optimization aims to minimize total operating costs while also satisfying equipment operating power constraints and energy and carbon balance constraints in internal supply, conversion, distribution, storage, and consumption. Therefore, step 4 first constructs a carbon energy coordination hub model for producers-consumers to characterize the carbon energy coupling relationships within producers-consumers, providing equation balance constraints for the producer-consumer optimization control model, and then constructing the optimization control model.

[0138] (1) Carbon energy co-hub model of producers and consumers

[0139] Specifically, the carbon energy synergy hub model for prosumers established in this invention is based on the traditional Energy Hub (EH) model. Further considering the bidirectional energy interaction between prosumers and RIES on the basis of the EH, the mathematical relationship between the energy flow input and output within the prosumer can be obtained:

[0140] (32)

[0141] In the formula, ,…, For the first, ..., of the prosumers Each energy flow output corresponds to the multi-energy load within the producer-consumer; The power output sold by producers to the distribution network; ,…, For the first, ..., of the prosumers One energy flow input; The coupling coefficient is composed of the energy distribution coefficient and the conversion efficiency.

[0142] By further introducing carbon flow based on equation (32), the input-output relationship between energy flow and carbon flow can be established:

[0143] (33)

[0144] In the formula, ,…, For the first, ..., of the prosumers Each carbon flow output corresponds to the carbon flow density of the multi-energy load within the producer-consumer; The carbon potential corresponding to producers selling electricity to the distribution network; ,…, For the first, ..., of the prosumers The carbon flux density corresponding to each energy input; X in , Y out Let C and D be the energy-carbon flow input and output vectors of the progenitor and consumer, respectively; let D be the energy flow coupling matrix and carbon flow coupling matrix of the progenitor and consumer, respectively; and let H be the carbon-energy co-coupling matrix of the progenitor and consumer. Therefore, equation (32) can be written in the following matrix form:

[0145] (34)

[0146] Based on equation (34), and further considering renewable energy and energy storage devices, the carbon energy synergy hub model for producers and consumers can be obtained as follows:

[0147] (35)

[0148] In the formula, and The number of renewable energy power generation and energy storage devices; and These are the energy flow coupling matrices for renewable energy power generation and energy storage devices, respectively. and Carbon flow coupling matrix for renewable energy power generation and energy storage devices.

[0149] by Figure 2 Taking the typical producer-consumer structure shown as an example, its carbon energy co-hub model is as follows:

[0150] (35)

[0151] In the formula, , Let n be the photovoltaic output and carbon potential of the photovoltaic unit at time t; , For the charge and discharge power of energy storage and the corresponding carbon potential, when When the value is greater than 0, it indicates charging. <0 indicates discharge; For time t, the gas purchase power of consumer n The corresponding carbon flux density; , , Let n be the electrical, heating, and cooling load power of the generator n at time t; , , Let t be the carbon potential of the electricity, heat, and cooling loads of the consumer n at time t.

[0152] To further illustrate the carbon energy co-hub model for producers and consumers, Figure 3 Taking a typical prosumer structure as an example, this prosumer obtains electricity and natural gas from the distribution network and is internally equipped with photovoltaic generators, gas turbines, gas boilers, electric refrigeration, absorption chillers, and energy storage devices. The prosumer has multiple energy loads (electric, cooling, and heating) and can sell excess electricity back to the distribution network. For Figure 2 The typical producer-consumer structure shown has the following energy flow coupling matrix and carbon flow coupling matrix:

[0153] (35)

[0154] In the formula, The proportion of the total electrical energy within the producers and consumers at time t that is allocated to the electrical load; The proportion of purchased natural gas allocated to the gas turbine at time t; For gas turbine power generation efficiency; The proportion of the heat power output by the gas-fired boiler at time t that is allocated to the heat load; The proportion of purchased natural gas allocated to the gas-fired boiler at time t; The conversion efficiency of the gas-fired boiler; The proportion of the total electrical energy within the producer-consumer at time t that is allocated to the electric chiller unit; For the conversion efficiency of the electric refrigeration unit; The proportion of the heat power output from the gas-fired boiler at time t that is allocated to the absorption chiller unit; The conversion efficiency of the absorption chiller unit; The proportion of electrical energy generated and consumed by consumers at time t that is sold to the distribution network. It is a 0-1 variable, where a value of 0 indicates energy storage charging and a value of 1 indicates energy storage discharging; , These are intermediate variables used to simplify matrix representations.

[0155] In this embodiment, in step S4, the constraints of the producer-consumer low-carbon optimization control model include energy flow-carbon flow balance constraints, energy purchase constraints, equipment power constraints, energy storage constraints, and flexible load adjustment constraints.

[0156] The energy flow-carbon flow balance constraint ensures that the input and output of energy flow and carbon flow satisfy a matrix relationship, and that the electrical, thermal, and cooling loads are the superposition values ​​of the base load and the transfer, reduction, and substitution adjustment power;

[0157] The energy purchase constraints limit the power of electricity purchase, electricity sales, and gas purchase to no more than their respective upper limits; the equipment power constraints limit the operating power of various equipment to no more than the rated capacity; in the energy storage constraints, the cumulative power is updated according to the correlation between the initial power, self-discharge rate, charge and discharge power, charge and discharge efficiency and time step, the charge and discharge power is limited to the maximum charge and discharge power range, the cumulative power is limited to the upper and lower limits range, and the cumulative power at the beginning and end of the control cycle remains consistent.

[0158] In the flexible load adjustment constraint, the adjustment amount of the transferable load is within the preset ratio range of the reference load, and the total adjustment amount within the cycle is zero. The adjustment amount of the load that can be reduced does not exceed the preset ratio of the reference load. The sum of the electric, heat, and cold adjustment amounts of the replaceable load is zero, and their respective adjustment amounts are within the preset ratio range of the reference load.

[0159] Specifically, upon receiving the nodal marginal price and nodal carbon potential signals from the IESO, the prosumer aims to minimize its total operating costs, including energy purchase and sale costs, equipment maintenance costs, flexible load adjustment costs, and carbon emission costs. Based on the optimization control results, the prosumer's external power purchase and sale and its carbon potential for electricity sales are determined. Therefore, taking prosumer n as an example, the objective function of the established prosumer low-carbon optimization control model is:

[0160] (36)

[0161] (37)

[0162] (38)

[0163] (39)

[0164] (40)

[0165] In the formula, The daily operating cost of producer-consumer n; These are the energy purchase and sales costs, equipment maintenance costs, flexible load adjustment costs, and carbon emission costs for producer-consumer n at time t, respectively. For each of the n devices, there are prosumers and consumers. Maintenance cost coefficient and output; A collection of devices; , These represent the maintenance cost coefficient and operating power of the energy storage device at time t, respectively. , , These are the unit power regulation costs for transferable, reduceable, and replaceable loads, respectively; , , These represent the adjustable power of the transferable, reduceable, and replaceable loads at time t, respectively. , , , respectively, represent the carbon flux density of the electrothermal cooling load at time t; This refers to the carbon price.

[0166] The constraints for optimal control of producer-consumer n are as follows:

[0167] 1) Energy flow-carbon flow balance constraint

[0168] The producer-consumer n must satisfy the energy flow-carbon flow balance constraint shown in equation (35), and the multi-energy load at time t is the sum of the baseline value and the load regulation power:

[0169] (41)

[0170] (42)

[0171] (43)

[0172] In the formula, , , This is the baseline value of the electrical heating cooling load at time t; , , The amount of electrical load transferred in, reduced, and replaced at time t; , , The amount of heat load transferred, reduced, and replaced at time t; , , The amount of cooling load transferred, reduced, and replaced at time t.

[0173] 2) Energy purchase constraints. Because producers and consumers are connected to the power distribution network and gas distribution network, the power exchange between these networks is constrained by the transmission capacity of the lines:

[0174] (44)

[0175] (45)

[0176] (46)

[0177] In the formula, , , These are the upper limits of the electricity purchase, electricity sales, and gas purchase capacity for producer-consumer n, respectively.

[0178] 3) Equipment power constraints. The operating power of the equipment is limited by its capacity:

[0179] (44)

[0180] In the formula, For equipment Maximum operating power.

[0181] 4) Energy storage constraints. The cumulative energy capacity and charging / discharging power of the energy storage device must be limited within a certain range, and the cumulative energy capacity should remain equal at the beginning and end of the control cycle to maintain the energy balance of a complete control cycle.

[0182] (45)

[0183] (46)

[0184] (47)

[0185] (48)

[0186] In the formula, , The cumulative energy storage capacity at time t and time t+1; The self-discharge rate of the energy storage; The charging and discharging power for energy storage; , The charging and discharging efficiency of energy storage; , This represents the maximum charging and discharging power of the energy storage. , These are the upper and lower limits of the cumulative energy storage capacity; , The cumulative energy storage capacity at time 0 and time T.

[0187] 5) Flexible load adjustment constraints. Taking electrical load as an example, transferable loads are transferred only in time, and the total load within the entire control cycle must remain unchanged due to load transfer, while also being subject to limitations on the total transferable load. Reduceable loads are subject to limitations on the total reduceable load. Substituteable loads can only be substituted for different types of loads within the same time period, and it must be ensured that the sum of electrical, heating, and cooling loads within the same time period does not change due to energy substitution.

[0188] (49)

[0189] (50)

[0190] (51)

[0191] (52)

[0192] (53)

[0193] In the formula, , , The proportion of transferable, reduceable, and substitutable loads for producer-consumer n.

[0194] In this embodiment, in step S5, the iterative solution process includes initialization, upper-level model solution, signal update, lower-level model solution, and convergence judgment steps;

[0195] In the initialization phase, the power consumption of electricity purchases, sales, and gas purchases by prosumers is set to zero, and the number of iterations is the initial value. In the upper-level model solution phase, the operation schemes of the distribution network and gas distribution network are obtained. In the signal update phase, the marginal price and carbon potential of the nodes are calculated based on the results of the upper-level model, and the transaction price and carbon potential signals with prosumers are updated. In the lower-level model solution phase, the optimization models of each prosumer are solved in sequence, and the energy interaction power and the carbon potential of electricity sales are updated. In the convergence judgment phase, convergence is determined by whether the relative error of the power consumption of electricity purchases, sales, and gas purchases in the current iteration and the previous iteration does not exceed the preset threshold. If convergence is not achieved, the number of iterations is updated and the iterative solution process is repeated.

[0196] Specifically, Figure 4 The iterative solution process for the RIES and producer-consumer optimization control models is demonstrated, specifically including the following steps:

[0197] 1) Initialize the electricity purchase capacity of producers and consumers =0, electricity sales power =0, gas purchasing power =0; Set the iteration count k=1;

[0198] 2) Solve the IES optimization control model shown in equations (1)-(20) to obtain the IES operation scheme, including the power purchased by the distribution network from the main network, the node voltage distribution, the line power flow distribution, and the gas source output, node gas pressure distribution and pipeline flow distribution of the gas distribution network;

[0199] 3) Based on the IES nodal marginal price model and carbon flow model shown in equations (21)-(31), solve for the nodal marginal price and nodal carbon potential of the distribution network and gas distribution network; and update the electricity price of the IES transaction with producers and consumers at the k-th iteration. , gas price and node carbon potential , ;

[0200] 4) Set n=1;

[0201] 5) Solve the optimal control model of producer-consumer n shown in equations (36)-(53) to obtain the operating scheme of producer-consumer n, including electricity purchase, gas purchase, equipment operating power, load adjustment power, etc., and update the energy flow interaction power between producer-consumer and IES at the kth iteration. , , And the carbon potential of electricity sales ;

[0202] 6) n = n + 1; and determine whether n > N, that is, the solution for all producer-consumer optimization control schemes is complete. If it is true, proceed to step 7; otherwise, return to step 5.

[0203] 7) Determine if the following convergence conditions are met:

[0204] (54)

[0205] In the formula, The pre-set convergence threshold; , , Let k be the electricity purchased, sold, and gas purchased by consumer n from IES at time t during the (k-1)th iteration. If the convergence condition is met, proceed to step 8; otherwise, let k = k + 1. , , And return to step 2;

[0206] 8) Output the IES and the operating plans of all producers and consumers.

[0207] For embodiments of the present invention, select Figure 5 The IEEE 33-node distribution network shown is Figure 6 Taking a 20-node natural gas grid coupled integrated energy system as an example, the types and access locations of each producer-consumer are shown in Table 1. Type 1 producers-consumers are equipped with larger-capacity photovoltaic generators and energy storage devices, while Type 2 producers-consumers are equipped with larger-capacity gas turbines. The equipment parameters for different types of producers-consumers are shown in Table 2. Taking producer-consumer 1 as an example, its load curve and predicted photovoltaic output are shown in Table 2. Figure 7 As shown. The peak loads of each producer and consumer are as follows: Figure 8 As shown. The carbon potential of the root node of the distribution network and the marginal electricity price at the node of the upstream grid are as follows. Figure 9 As shown.

[0208] Table 1. Types and access locations of each producer and consumer.

[0209] Table 2 Equipment parameters for different types of prosumers

[0210] To analyze the effectiveness of the proposed RIES carbon energy collaborative optimization control method involving multiple prosumer users, the following three scenarios were set up:

[0211] Scenario 1: Only the bidirectional energy interaction between prosumers and IES is considered, and the carbon emission costs are not considered in the upper and lower optimization models;

[0212] Scenario 2: Considering the two-way interaction of carbon energy, the carbon emissions corresponding to the two-way interaction of energy between producers and consumers and the distribution network are all calculated using the average carbon emission factor;

[0213] Scenario 3: Considering the two-way interaction of carbon energy, the carbon emissions of producers and consumers purchasing energy from the distribution network are calculated using nodal carbon potential accounting, while the carbon emissions of producers and consumers selling electricity to the distribution network are calculated using the CESH model.

[0214] Table 3 shows the economic costs and carbon emissions of IES and prosumer users under different scenarios. Comparing Scenario I and Scenario II, the economic costs for prosumers increased by approximately 24% in Scenario II, while the economic costs for IES decreased by approximately 3%. The total carbon emissions of the entire RIES (IES + all prosumers) decreased by approximately 7%, indicating that considering only the energy interaction between prosumers and IES is insufficient to unleash the carbon reduction potential of prosumers. Specifically, because the optimal control of prosumers in Scenario II takes carbon emission costs into account, they choose to purchase cleaner natural gas, resulting in an increase in energy purchase costs of approximately 17%. Meanwhile, in order to reduce their own carbon emissions, IESOs reduced their external electricity purchases, lowering their external electricity purchase costs by approximately 36%, and chose to purchase more electricity from prosumer users, resulting in an increase in their transaction costs with prosumers of approximately 20%.

[0215] Comparing Scenario I and Scenario III, the economic costs of IES are roughly the same, but in Scenario III, the economic costs for prosumers are reduced by approximately 6%, and total carbon emissions are reduced by 14%, indicating that the proposed method can improve the operating economics of prosumers and reduce the carbon emissions of RIES. Specifically, in Scenario III, the energy purchase and sale costs for prosumer users are reduced by approximately 14%, while the load adjustment costs are increased by 126%, indicating that Scenario III mainly achieves carbon emission reduction by adjusting internal flexible loads, avoiding the cost increase caused by purchasing natural gas for carbon reduction.

[0216] Table 3. Economic costs and carbon emissions for IES and prosumer users in different scenarios.

[0217] Taking consumer 1 as an example, Figure 10 This demonstrates the DLMP, nodal carbon potential, and electrical balance of prosumer 1 under different scenarios. Figure 10 (a) and Figure 10 As shown in (d) of the diagram, in Scenario I, the prosumer mainly adjusts its internal equipment and flexible loads based on the DLMP of its node. During the period from 22:00 to 2:00, when the DLMP electricity price is high, the prosumer meets its electricity demand by generating electricity through gas turbines. During the period from 3:00 to 6:00, when the electricity price is low, the prosumer obtains electricity by purchasing electricity from the distribution network, and the energy storage is charged during this period. During the period from 8:00 to 15:00, the photovoltaic generator output is high, and the prosumer relies on photovoltaic power supply, selling excess electricity to the distribution network and storing it in energy storage. During the period from 17:00 to 22:00, when the electricity price is high, the prosumer meets its electricity demand by generating electricity through gas turbines and sells the electricity to the distribution network to alleviate the evening peak power supply pressure on the distribution network.

[0218] Depend on Figure 10 (b) and Figure 10 As shown in (e), since Scenario II uses the average carbon emission factor to calculate carbon emissions, the carbon emission factor issued by the distribution network to the producers and consumers is a fixed value (0.6730 kgCO2 / kWh). Therefore, in order to reduce carbon emissions, producers and consumers increase the power generation of gas turbines and the power sold to the distribution network during the periods of 0:00-2:00, 6:00-8:00, and 17:00-22:00. In other words, they choose to purchase and use cleaner natural gas to meet their electricity demand.

[0219] Depend on Figure 10 (b) and Figure 10 As shown in (e), in Scenario III, prosumers adjust their internal equipment and flexible loads by considering both the DLMP (Dynamic Dynamics Per Minute) and the node carbon potential signal. Regarding DLMP response, the prosumer's adjustment mechanism is similar to that in Scenario I: increasing electricity purchases and charging of energy storage devices during periods of low electricity prices, and generating electricity from gas turbines and discharging energy storage devices during periods of high electricity prices. Unlike Scenario I, in Scenario III, when the node carbon potential is high between 0:00-7:00 and 17:00-22:00, prosumers reduce their electricity demand by shifting load out and replacing electrical load with other forms of load demand; when the node carbon potential is low between 10:00-14:00, they increase their electricity demand by shifting load in, thereby reducing their own carbon emissions.

[0220] To further verify the effectiveness of the proposed producer-consumer carbon energy co-hub model in IES and producer-consumer optimal control. Figure 11 , Figure 12 and Figure 13 The electricity purchase and sale power, carbon potential of electricity sale, and electricity sale price of different prosumers were compared under Scenario II and Scenario III (taking prosumers 1 and 2 as examples). Figure 11As shown in (a), 11(a), and 12(a), since Scenario II uses the average carbon emission factor to calculate carbon emissions, there is no difference in the external carbon emission characteristics of prosumers for the distribution network. That is, the carbon emission factors corresponding to different prosumers buying and selling electricity to the distribution network are completely consistent at the same time. Therefore, in order to reduce operating costs, the distribution network in Scenario II mainly considers the interaction between the electricity sales price of prosumers and the prosumers themselves. For example, during the period from 16:00 to 21:00, the electricity sales price of prosumer 1 is higher than that of prosumer 2, so the electricity sales power of prosumer 1 is lower than that of prosumer 2. Scenario III, on the other hand, quantifies the differences in the carbon emission characteristics of prosumers through the proposed prosumer carbon energy coordination hub model. At the same time, the carbon potential of different prosumers selling electricity to the distribution network is different, such as... Figure 12 In (b), during the period from 18:00 to 21:00, the carbon potential of electricity sold by producer-consumer 1 is lower than that of producer-consumer 2, meaning that during this period, the carbon emissions generated by the distribution network purchasing the same amount of electricity from producer-consumer 1 are lower than those from producer-consumer 2. Therefore, by Figure 11 (b) and Figure 12 As shown in (b), although the electricity price sold by producer-consumer 1 was higher than that of producer-consumer 2 during this period, producer-consumer 1 sold more electricity than producer-consumer 2 during this period in order to reduce carbon emissions. The above analysis results indicate that the proposed method can quantify the differences in carbon emission extrinsic characteristics exhibited by different producer-consumers on the distribution network, thereby formulating a more accurate carbon potential signal to guide the bidirectional energy interaction between producer-consumers and IES, fully leveraging the carbon emission reduction potential of producer-consumer users, and reducing the overall carbon emissions of the system.

[0221] Example 2

[0222] See Figure 14 Embodiment 2 of the present invention also provides a regional integrated energy system carbon energy synergistic optimization control device, comprising:

[0223] The data acquisition module 100 is used to acquire basic data for the RIES carbon energy collaborative optimization control of the regional integrated energy system. The basic data includes RIES network topology parameters, distribution network node load forecast values, gas distribution network node load forecast values, producer-consumer user equipment technical parameters, internal electricity / heat / cooling multi-energy load forecast values ​​for producers and consumers, photovoltaic output forecast values, upstream grid electricity price, distribution network root node carbon potential and natural gas carbon emission factor.

[0224] The RIES optimization model construction module 200 is used to establish an RIES optimization control model based on the basic data and combined with the two-way carbon energy interaction relationship between prosumers and users and the distribution network and gas distribution network, with the goal of minimizing the total operating cost of the distribution network. The total operating cost of the distribution network includes the cost of purchasing electricity from the upper-level grid, the cost of natural gas supply, the cost of energy trading with prosumers, and the cost of carbon emissions.

[0225] The price and carbon potential model construction module 300 is used to construct the node marginal price model and carbon flow model of RIES based on the RIES optimization control model. The node marginal price and dynamic carbon potential are determined by solving the node marginal price and dynamic carbon potential through the node marginal price model and carbon flow model, forming a dual guiding signal of node marginal price and node carbon potential.

[0226] The producer-consumer optimization model construction module 400 is used to establish a producer-consumer low-carbon optimization control model under the coordination of the dual guidance signals and in combination with the basic data, with the optimization objective of minimizing the producer's own total operating cost. The producer's own total operating cost includes the energy purchase and sale cost with the distribution network / gas distribution network, its own equipment maintenance cost, flexible load adjustment cost, and carbon emission cost.

[0227] The iterative solution module 500 is used to iteratively solve the RIES optimized control model and the producer-consumer low-carbon optimized control model, dynamically adapt to the operating status of the power distribution network, gas distribution network and producer-consumer users, realize the coordinated optimization of the operating schemes of the power distribution network, gas distribution network and producer-consumer users, and finally output the optimal operating scheme that takes into account both economy and low carbon.

[0228] In this embodiment, in the RIES optimization model construction module 200, the objective function of the RIES optimization control model is to minimize the total operating cost of the distribution network and the gas distribution network. The total operating cost is calculated by accumulating the cost at a set time within the optimization control cycle.

[0229] The cost of purchasing electricity from the upstream power grid is determined by multiplying the purchase price, purchased power, and time step at a set time. The carbon emission cost is determined by multiplying the carbon potential of the root node of the distribution network and the purchased power, the carbon potential of the producer-consumer electricity sales and the electricity sales power, and the carbon potential of the gas distribution network source node and the gas supply power at a set time by the carbon price and time step.

[0230] In this embodiment, the constraints of the RIES optimization control model in the RIES optimization model construction module 200 include distribution network operation constraints and gas distribution network operation constraints.

[0231] In the distribution network operation constraints, the active power balance constraint limits the node load, the power purchased and sold by producers and consumers and the power transmitted by the line to meet the balance of income and expenditure. The reactive power balance constraint constrains the reactive power. The upper limit constraint of power flow limits the sum of the squares of the active power and reactive power of the line to not exceed the square of the line capacity. The voltage constraint limits the node voltage to within the preset upper and lower limits.

[0232] In the gas distribution network operation constraints, the power balance constraint limits the node gas load, gas source output, producer and consumer gas purchase power and pipeline transmission flow to meet the balance of income and expenditure. The gas pressure-pipeline flow constraint is determined according to the correlation between the gas pressure difference at both ends of the pipeline and the flow rate. The pipeline flow upper limit constraint limits the pipeline flow rate to the allowable range. The node gas pressure constraint limits the node gas pressure to the preset upper and lower limits. The gas source power constraint limits the gas source output to not exceed the upper limit.

[0233] In this embodiment, in the nodal marginal price model of the price and carbon potential model construction module 300, the marginal electricity price of the distribution network node is equal to the dual multiplier of the active power balance constraint of that node, and the marginal gas price of the gas distribution network node is equal to the dual multiplier of the power balance equation constraint of that node; in the integrated power transaction between energy operators and producers and consumers, the purchase price of electricity is directly adopted from the corresponding nodal marginal electricity price, the sales price of electricity is the product of the nodal marginal electricity price and the preset discount factor, and the natural gas transaction price is directly adopted from the corresponding nodal marginal gas price of the gas distribution network.

[0234] The carbon flow model of the price and carbon potential model construction module 300 is divided into a distribution network carbon flow model and a gas distribution network carbon flow model. In the distribution network carbon flow model, the carbon potential of the root node is a preset value. The carbon potential of nodes other than the root node is determined by the sum of the products of line carbon flow density and corresponding transmission power, and the sum of the products of producer-consumer electricity sales carbon potential and electricity sales power, divided by the sum of line transmission power and producer-consumer electricity sales power. The line carbon flow density is equal to the carbon potential of the first node of the line. In the gas distribution network carbon flow model, the node carbon potential is determined by the sum of the products of gas source unit carbon potential and gas supply power, and the sum of the products of pipeline carbon flow density and pipeline flow rate, divided by the sum of gas supply power and pipeline flow rate. The pipeline carbon flow density is equal to the carbon potential of the first node of the pipeline. The producer-consumer electricity purchase carbon potential is equal to the carbon potential of the node connected to the distribution network, and the gas purchase carbon potential is equal to the carbon potential of the node connected to the gas distribution network.

[0235] This embodiment also includes:

[0236] The carbon energy synergy hub model construction module 600 is used to construct a carbon energy synergy hub model. The carbon energy synergy hub model describes the input-output relationship between energy flow and carbon flow in matrix form. Energy flow input includes basic energy flow input, renewable energy power generation output, and energy storage charging and discharging power. Energy flow output includes internal multi-energy loads and power sold to the distribution network. Carbon flow input includes the carbon flow density corresponding to each energy flow input. Carbon flow output includes the carbon flow density corresponding to each load and the carbon potential of electricity sold. The matrix incorporates energy allocation coefficients, equipment conversion efficiency, and the coupling characteristics of energy flow and carbon flow between renewable energy power generation equipment and energy storage equipment.

[0237] In this embodiment, in the producer-consumer optimization model construction module 400, the objective function of the producer-consumer low-carbon optimization control model is to minimize the total daily operating cost, which is calculated by accumulating the cost at each moment within the optimization control cycle; the energy purchase and sale cost of the distribution network / gas distribution network is determined by multiplying the electricity purchase price and the electricity purchase power, the electricity sales price and the electricity sales power, and the gas purchase price and the gas purchase power, combined with the time step.

[0238] The self-maintenance cost of the equipment is determined by multiplying the maintenance cost coefficient of each piece of equipment by the output, the maintenance cost coefficient of the energy storage equipment by the absolute value of the charging and discharging power, and then multiplying by the time step.

[0239] The flexible load adjustment cost is determined by the sum of the product of the unit adjustment cost of transferable, reduceable, and replaceable loads and the corresponding adjustment power; the carbon emission cost is determined by the sum of the product of the carbon flux density of electrical, heat, and cooling loads and the corresponding load power, multiplied by the carbon price and the time step.

[0240] In this embodiment, the constraints of the producer-consumer optimization model construction module 400 include energy flow-carbon flow balance constraints, energy purchase constraints, equipment power constraints, energy storage constraints, and flexible load adjustment constraints.

[0241] The energy flow-carbon flow balance constraint ensures that the input and output of energy flow and carbon flow satisfy a matrix relationship, and that the electrical, thermal, and cooling loads are the superposition values ​​of the base load and the transfer, reduction, and substitution adjustment power;

[0242] The energy purchase constraints limit the power of electricity purchase, electricity sales, and gas purchase to no more than their respective upper limits; the equipment power constraints limit the operating power of various equipment to no more than the rated capacity; in the energy storage constraints, the cumulative power is updated according to the correlation between the initial power, self-discharge rate, charge and discharge power, charge and discharge efficiency and time step, the charge and discharge power is limited to the maximum charge and discharge power range, the cumulative power is limited to the upper and lower limits range, and the cumulative power at the beginning and end of the control cycle remains consistent.

[0243] In the flexible load adjustment constraint, the adjustment amount of the transferable load is within the preset ratio range of the reference load, and the total adjustment amount within the cycle is zero. The adjustment amount of the load that can be reduced does not exceed the preset ratio of the reference load. The sum of the electric, heat, and cold adjustment amounts of the replaceable load is zero, and their respective adjustment amounts are within the preset ratio range of the reference load.

[0244] In this embodiment, the iterative solution module 500 includes the following steps: initialization, upper-level model solving, signal updating, lower-level model solving, and convergence judgment. In the initialization phase, the electricity purchase, electricity sale, and gas purchase power of prosumers are set to zero, and the number of iterations is set to an initial value. In the upper-level model solving phase, the operation schemes of the distribution network and gas distribution network are obtained. In the signal updating phase, the marginal price and carbon potential of nodes are calculated based on the upper-level model results, and the transaction price and carbon potential signals with prosumers are updated. In the lower-level model solving phase, the optimization models of each prosumer are solved sequentially, and the energy interaction power and electricity sale carbon potential are updated. In the convergence judgment phase, convergence is determined by whether the relative error between the electricity purchase, electricity sale, and gas purchase power of the current iteration and the previous iteration does not exceed a preset threshold. If convergence is not achieved, the number of iterations is updated, and the iterative solution process is repeated.

[0245] It should be noted that the information interaction and execution process between the modules of the above-mentioned device are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0246] Example 3

[0247] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores program code of a regional integrated energy system carbon energy synergistic optimization control method, the program code including instructions for executing the regional integrated energy system carbon energy synergistic optimization control method of Embodiment 1 or any possible implementation thereof.

[0248] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0249] Example 4

[0250] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0251] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can execute the regional integrated energy system carbon energy synergistic optimization control method of Embodiment 1 or any possible implementation thereof by calling the program instructions.

[0252] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0253] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0254] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0255] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for coordinated optimization control of carbon energy in a regional integrated energy system, characterized in that, Includes the following steps: Acquire basic data for the coordinated optimization and control of carbon energy in the regional integrated energy system RIES. The basic data includes RIES network topology parameters, distribution network node load forecasts, gas distribution network node load forecasts, producer-consumer user equipment technical parameters, internal electricity / heat / cooling multi-energy load forecasts for producers and consumers, photovoltaic output forecasts, upstream grid electricity prices, distribution network root node carbon potential, and natural gas carbon emission factors. Based on the aforementioned basic data and combined with the two-way carbon energy interaction between prosumers and the distribution network and gas distribution network, a RIES optimization control model is established with the goal of minimizing the total operating cost of the distribution network. The total operating cost of the distribution network includes the cost of purchasing electricity from the upper-level grid, the cost of natural gas supply, the cost of energy trading with prosumers, and the cost of carbon emissions. Based on the aforementioned RIES optimization control model, a node marginal price model and a carbon flow model of RIES are constructed. The node marginal price and dynamic carbon potential are determined by solving the node marginal price and dynamic carbon potential through the RIES node marginal price model and carbon flow model, forming a dual guiding signal of node marginal price and node carbon potential. Under the coordination of the dual guidance signals and combined with the basic data, a low-carbon optimization control model for producers and consumers is established with the goal of minimizing the total operating cost of the producers and consumers themselves. The total operating cost of the producers and consumers themselves includes the energy purchase and sale cost with the distribution network / gas distribution network, the maintenance cost of their own equipment, the cost of flexible load adjustment, and the cost of carbon emissions. By iteratively solving the RIES optimal control model and the producer-consumer low-carbon optimal control model, the operating status of the power distribution network, gas distribution network and producer-consumer users is dynamically adapted to achieve coordinated optimization of the operating schemes of the power distribution network, gas distribution network and producer-consumers, and finally output the optimal operating scheme that takes into account both economy and low carbon emissions.

2. The regional integrated energy system carbon energy synergistic optimization control method according to claim 1, characterized in that, The objective function of the RIES optimization control model is to minimize the total operating cost of the distribution network and gas distribution network. The total operating cost is calculated by accumulating the cost at set times within the optimization control period. The cost of purchasing electricity from the upstream power grid is determined by multiplying the purchase price, purchased power, and time step at a set time. The carbon emission cost is determined by multiplying the carbon potential of the root node of the distribution network and the purchased power, the carbon potential of the producer-consumer electricity sales and the electricity sales power, and the carbon potential of the gas distribution network source node and the gas supply power at a set time by the carbon price and time step.

3. The regional integrated energy system carbon energy synergistic optimization control method according to claim 2, characterized in that, The constraints of the RIES optimization control model include distribution network operation constraints and gas distribution network operation constraints; In the distribution network operation constraints, the active power balance constraint limits the node load, the power purchased and sold by producers and consumers and the power transmitted by the line to meet the balance of income and expenditure. The reactive power balance constraint constrains the reactive power. The upper limit constraint of power flow limits the sum of the squares of the active power and reactive power of the line to not exceed the square of the line capacity. The voltage constraint limits the node voltage to within the preset upper and lower limits. In the gas distribution network operation constraints, the power balance constraint limits the node gas load, gas source output, producer and consumer gas purchase power and pipeline transmission flow to meet the balance of income and expenditure. The gas pressure-pipeline flow constraint is determined according to the correlation between the gas pressure difference at both ends of the pipeline and the flow rate. The pipeline flow upper limit constraint limits the pipeline flow rate to the allowable range. The node gas pressure constraint limits the node gas pressure to the preset upper and lower limits. The gas source power constraint limits the gas source output to not exceed the upper limit.

4. The regional integrated energy system carbon energy synergistic optimization control method according to claim 1, characterized in that, In the aforementioned node marginal price model, the marginal electricity price of a distribution network node is equal to the dual multiplier of the active power balance constraint of that node, and the marginal gas price of a gas distribution network node is equal to the dual multiplier of the power balance equation constraint of that node; in the electricity transactions between integrated energy operators and producers and consumers, the purchase price of electricity is directly adopted from the corresponding node marginal electricity price, the sales price of electricity is the product of the node marginal electricity price and the preset discount factor, and the natural gas transaction price is directly adopted from the corresponding node marginal gas price of the gas distribution network. The carbon flow model is divided into a distribution network carbon flow model and a gas distribution network carbon flow model. In the distribution network carbon flow model, the carbon potential of the root node is a preset value. The carbon potential of nodes other than the root node is determined by the sum of the products of the line carbon flow density and the corresponding transmission power, and the sum of the products of the carbon potential of the producer-consumer electricity sales and the electricity sales power, divided by the sum of the line transmission power and the producer-consumer electricity sales power. The line carbon flow density is equal to the carbon potential of the first node of the line. In the gas distribution network carbon flow model, the node carbon potential is determined by the sum of the products of the carbon potential of the gas source unit and the gas supply power, and the sum of the products of the pipeline carbon flow density and the pipeline flow rate, divided by the sum of the gas supply power and the pipeline flow rate. The pipeline carbon flow density is equal to the carbon potential of the first node of the pipeline. The carbon potential of producers purchasing electricity is equal to the carbon potential of nodes connected to the distribution network, and the carbon potential of purchasing gas is equal to the carbon potential of nodes connected to the gas distribution network.

5. The regional integrated energy system carbon energy synergistic optimization control method according to claim 1, characterized in that, Before establishing the producer-consumer low-carbon optimization control model, a carbon energy synergy hub model is also constructed. This model characterizes the input-output relationship between energy flow and carbon flow in matrix form. Energy flow input includes basic energy flow input, renewable energy power generation output, and energy storage charging and discharging power. Energy flow output includes internal multi-energy loads and power sold to the distribution network. Carbon flow input includes the carbon flow density corresponding to each energy flow input. Carbon flow output includes the carbon flow density corresponding to each load and the carbon potential of electricity sold. The matrix incorporates energy allocation coefficients, equipment conversion efficiency, and the coupling characteristics of energy flow and carbon flow between renewable energy power generation equipment and energy storage equipment. The objective function of the producer-consumer low-carbon optimization control model is to minimize the total daily operating cost, which is calculated by accumulating the cost at each moment within the optimization control cycle; the energy purchase and sale cost of the distribution network / gas distribution network is determined by multiplying the electricity purchase price and the electricity purchase power, the electricity sale price and the electricity sale power, and the gas purchase price and the gas purchase power, combined with the time step. The self-maintenance cost of the equipment is determined by multiplying the maintenance cost coefficient of each piece of equipment by the output, the maintenance cost coefficient of the energy storage equipment by the absolute value of the charging and discharging power, and then multiplying by the time step. The flexible load adjustment cost is determined by the sum of the product of the unit adjustment cost of transferable, reduceable, and replaceable loads and the corresponding adjustment power; the carbon emission cost is determined by the sum of the product of the carbon flux density of electrical, heat, and cooling loads and the corresponding load power, multiplied by the carbon price and the time step.

6. The regional integrated energy system carbon energy synergistic optimization control method according to claim 5, characterized in that, The constraints of the producer-consumer low-carbon optimization control model include energy flow-carbon flow balance constraints, energy purchase constraints, equipment power constraints, energy storage constraints, and flexible load adjustment constraints. The energy flow-carbon flow balance constraint ensures that the input and output of energy flow and carbon flow satisfy a matrix relationship, and that the electrical, thermal, and cooling loads are the superposition values ​​of the base load and the transfer, reduction, and substitution adjustment power; The energy purchase constraints limit the power of electricity purchase, electricity sales, and gas purchase to no more than their respective upper limits; the equipment power constraints limit the operating power of various types of equipment to no more than their rated capacity. In the energy storage constraints, the cumulative energy is updated according to the correlation between the initial energy, self-discharge rate, charge and discharge power, charge and discharge efficiency and time step. The charge and discharge power is limited to the maximum charge and discharge power range, the cumulative energy is limited to the upper and lower limits, and the cumulative energy at the beginning and end of the control cycle remains consistent. In the flexible load adjustment constraint, the adjustment amount of the transferable load is within the preset ratio range of the reference load, and the total adjustment amount within the cycle is zero. The adjustment amount of the load that can be reduced does not exceed the preset ratio of the reference load. The sum of the electric, heat, and cold adjustment amounts of the replaceable load is zero, and their respective adjustment amounts are within the preset ratio range of the reference load.

7. The regional integrated energy system carbon energy synergistic optimization control method according to claim 1, characterized in that, The iterative solution process includes initialization, upper-level model solving, signal updating, lower-level model solving, and convergence judgment steps; In the initialization phase, the power consumption of electricity purchase, electricity sales, and gas purchase by prosumers is set to zero, and the number of iterations is the initial value. In the upper-level model solution phase, the operation schemes of the distribution network and gas distribution network are obtained. In the signal update phase, the marginal price and carbon potential of the nodes are calculated based on the results of the upper-level model, and the transaction price and carbon potential signals with prosumers are updated. In the lower-level model solution phase, the optimization models of each prosumer are solved in sequence, and the energy interaction power and electricity sales carbon potential are updated. The convergence judgment phase determines whether convergence has occurred based on whether the relative errors of the power purchase, power sale, and gas purchase power of the current iteration and the previous iteration do not exceed the preset threshold. If convergence has not occurred, the iteration count is updated and the iterative solution process is repeated.

8. A carbon energy synergistic optimization control device for a regional integrated energy system, characterized in that, include: The data acquisition module is used to acquire basic data for the RIES carbon energy collaborative optimization control of the regional integrated energy system. The basic data includes RIES network topology parameters, distribution network node load forecasts, gas distribution network node load forecasts, producer-consumer user equipment technical parameters, producer-consumer internal electricity / heat / cooling multi-energy load forecasts, photovoltaic output forecasts, upstream grid electricity price, distribution network root node carbon potential, and natural gas carbon emission factor. The RIES optimization model construction module is used to establish an RIES optimization control model based on the aforementioned basic data and the bidirectional carbon energy interaction relationship between prosumers and users and the distribution network and gas distribution network, with the goal of minimizing the total operating cost of the distribution network. The total operating cost of the distribution network includes the cost of purchasing electricity from the upper-level grid, the cost of natural gas supply, the cost of energy trading with prosumers, and the cost of carbon emissions. The price and carbon potential model construction module is used to construct the node marginal price model and carbon flow model of RIES based on the RIES optimization control model. The node marginal price and dynamic carbon potential are determined by solving the node marginal price and dynamic carbon potential through the node marginal price model and carbon flow model, forming a dual guiding signal of node marginal price and node carbon potential. The producer-consumer optimization model construction module is used to establish a producer-consumer low-carbon optimization control model under the coordination of the dual guidance signals and in combination with the basic data, with the optimization objective of minimizing the producer's own total operating cost. The producer's own total operating cost includes the energy purchase and sale cost with the distribution network / gas distribution network, its own equipment maintenance cost, flexible load adjustment cost, and carbon emission cost. The iterative solution module is used to iteratively solve the RIES optimized control model and the producer-consumer low-carbon optimized control model, dynamically adapt to the operating status of the power distribution network, gas distribution network and producer-consumer users, realize the coordinated optimization of the operating schemes of the power distribution network, gas distribution network and producer-consumers, and finally output the optimal operating scheme that takes into account both economy and low carbon.

9. A regional integrated energy system carbon energy synergistic optimization control device according to claim 8, characterized in that, In the RIES optimization model construction module, the objective function of the RIES optimization control model is to minimize the total operating cost of the distribution network and the gas distribution network. The total operating cost is calculated by accumulating the cost at a set time within the optimization control cycle. The cost of purchasing electricity from the upstream power grid is determined by multiplying the purchase price, the purchased power, and the time step at a set time. The carbon emission cost is determined by multiplying the carbon potential of the distribution network root node and the purchased power, the carbon potential of the producer-consumer electricity sales and the electricity sales power, and the carbon potential of the gas distribution network source node and the gas supply power at a set time by the carbon price and the time step. In the RIES optimization model construction module, the constraints of the RIES optimization control model include distribution network operation constraints and gas distribution network operation constraints; In the distribution network operation constraints, the active power balance constraint limits the node load, the power purchased and sold by producers and consumers and the power transmitted by the line to meet the balance of income and expenditure. The reactive power balance constraint constrains the reactive power. The upper limit constraint of power flow limits the sum of the squares of the active power and reactive power of the line to not exceed the square of the line capacity. The voltage constraint limits the node voltage to within the preset upper and lower limits. In the gas distribution network operation constraints, the power balance constraint limits the node gas load, gas source output, producer gas purchase power and pipeline transmission flow to meet the balance of income and expenditure. The gas pressure-pipeline flow constraint is determined according to the correlation between the gas pressure difference at both ends of the pipeline and the flow rate. The pipeline flow upper limit constraint limits the pipeline flow to the allowable range. The node gas pressure constraint limits the node gas pressure to the preset upper and lower limits. The gas source power constraint limits the gas source output to not exceed the upper limit. In the nodal marginal price model of the price and carbon potential model construction module, the marginal electricity price of a distribution network node is equal to the dual multiplier of the active power balance constraint of that node, and the marginal gas price of a gas distribution network node is equal to the dual multiplier of the power balance equation constraint of that node; in the electricity trading between integrated energy operators and producers and consumers, the purchase price of electricity is directly adopted from the corresponding nodal marginal electricity price, the sales price of electricity is the product of the nodal marginal electricity price and the preset discount factor, and the natural gas trading price is directly adopted from the corresponding nodal marginal gas price of the gas distribution network. The carbon flow model of the price and carbon potential model construction module is divided into a distribution network carbon flow model and a gas distribution network carbon flow model. In the distribution network carbon flow model, the carbon potential of the root node is a preset value. The carbon potential of nodes other than the root node is determined by the sum of the products of the line carbon flow density and the corresponding transmission power, and the sum of the products of the producer-consumer electricity sales carbon potential and the electricity sales power, divided by the sum of the line transmission power and the producer-consumer electricity sales power. The line carbon flow density is equal to the carbon potential of the first node of the line. In the gas distribution network carbon flow model, the node carbon potential is determined by the sum of the products of the gas source unit carbon potential and the gas supply power, and the sum of the products of the pipeline carbon flow density and the pipeline flow rate, divided by the sum of the gas supply power and the pipeline flow rate. The pipeline carbon flow density is equal to the carbon potential of the first node of the pipeline. The carbon potential of producers purchasing electricity is equal to the carbon potential of nodes connected to the distribution network, and the carbon potential of purchasing gas is equal to the carbon potential of nodes connected to the gas distribution network.

10. A regional integrated energy system carbon energy synergistic optimization control device according to claim 8, characterized in that, Also includes: A carbon energy synergy hub model construction module is used to construct a carbon energy synergy hub model. The carbon energy synergy hub model describes the input-output relationship between energy flow and carbon flow in matrix form. Energy flow input includes basic energy flow input, renewable energy power generation output, and energy storage charging and discharging power. Energy flow output includes internal multi-energy loads and power sold to the distribution network. Carbon flow input includes the carbon flow density corresponding to each energy flow input. Carbon flow output includes the carbon flow density corresponding to each load and the carbon potential of electricity sold. The matrix incorporates energy allocation coefficients, equipment conversion efficiency, and the coupling characteristics of energy flow and carbon flow between renewable energy power generation equipment and energy storage equipment. In the producer-consumer optimization model construction module, the objective function of the producer-consumer low-carbon optimization control model is to minimize the total daily operating cost, which is calculated by accumulating the cost at each moment within the optimization control cycle; the energy purchase and sale cost of the distribution network / gas distribution network is determined by multiplying the electricity purchase price and the electricity purchase power, the electricity sale price and the electricity sale power, and the gas purchase price and the gas purchase power, combined with the time step. The self-maintenance cost of the equipment is determined by multiplying the maintenance cost coefficient of each piece of equipment by the output, the maintenance cost coefficient of the energy storage equipment by the absolute value of the charging and discharging power, and then multiplying by the time step. The flexible load adjustment cost is determined by the sum of the product of the unit adjustment cost of transferable, reduceable, and replaceable loads and the corresponding adjustment power; the carbon emission cost is determined by the sum of the product of the carbon flux density of electrical, heat, and cooling loads and the corresponding load power, multiplied by the carbon price and the time step. In the producer-consumer optimization model construction module, the constraints of the producer-consumer low-carbon optimization control model include energy flow-carbon flow balance constraints, energy purchase constraints, equipment power constraints, energy storage constraints, and flexible load adjustment constraints. The energy flow-carbon flow balance constraint ensures that the input and output of energy flow and carbon flow satisfy a matrix relationship, and that the electrical, thermal, and cooling loads are the superposition values ​​of the base load and the transfer, reduction, and substitution adjustment power; The energy purchase constraints limit the power of electricity purchase, electricity sales, and gas purchase to no more than their respective upper limits; the equipment power constraints limit the operating power of various types of equipment to no more than their rated capacity. In the energy storage constraints, the cumulative energy is updated according to the correlation between the initial energy, self-discharge rate, charge and discharge power, charge and discharge efficiency and time step. The charge and discharge power is limited to the maximum charge and discharge power range, the cumulative energy is limited to the upper and lower limits, and the cumulative energy at the beginning and end of the control cycle remains consistent. In the flexible load adjustment constraint, the adjustment amount of the transferable load is within the preset ratio range of the reference load, and the total adjustment amount within the cycle is zero; the adjustment amount of the load that can be reduced does not exceed the preset ratio of the reference load; the sum of the electric, heating, and cooling adjustment amounts of the replaceable load is zero, and each adjustment amount is within the preset ratio range of the reference load. The iterative solution module includes the following steps: initialization, upper-level model solving, signal updating, lower-level model solving, and convergence judgment. In the initialization phase, the power consumption of electricity purchased, sold, and gas purchased by prosumers is set to zero, and the number of iterations is set to the initial value. In the upper-level model solving phase, the operation schemes of the distribution network and gas distribution network are obtained. In the signal updating phase, the marginal price and carbon potential of nodes are calculated based on the upper-level model results, and the transaction price and carbon potential signals with prosumers are updated. In the lower-level model solving phase, the optimization models of each prosumer are solved sequentially, and the energy interaction power and electricity sales carbon potential are updated. The convergence judgment phase determines whether convergence has occurred based on whether the relative errors of the power purchase, power sale, and gas purchase power of the current iteration and the previous iteration do not exceed the preset threshold. If convergence has not occurred, the iteration count is updated and the iterative solution process is repeated.

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