Comprehensive energy system energy management method considering low-carbon demand response

Abstract

The invention discloses an integrated energy system energy management method considering low-carbon demand response, and relates to the field of integrated energy system energy management. Establishing an energy-carbon flow tracking model of a heat network, an electricity network and a gas network; a carbon flow model is adopted to quantify node carbon emission intensity in a comprehensive energy system, and an energy-carbon comprehensive price model is provided according to the node carbon emission intensity and is used for low-carbon excitation of an energy service provider to user demand response; considering the energy consumption behaviors of different aggregators, dividing the load aggregators into resident load aggregators, industrial load aggregators and commercial load aggregators, and establishing a low-carbon demand response model for adjusting the energy consumption behaviors of the users under comprehensive price excitation; a double-layer coordination low-carbon energy management optimization model is established, a Steinberg game solving framework is proposed based on an energy-carbon comprehensive price model, and a comprehensive energy system service provider is taken as a leader, and a load aggregator is taken as a follower.

Description

Technical Field

[0001] The present invention relates to the field of energy management of integrated energy systems, and in particular to an energy management method for integrated energy systems that takes low-carbon demand response into consideration. Background Art

[0002] In recent years, the development of energy systems has mainly focused on efficient, clean and low-carbon strategies. In order to achieve the deep integration of new technologies in the energy field, adapt to the development of distributed energy, meet the diversified energy needs such as heat, electricity and gas, and strive to carry out energy revolution. Integrated energy systems are crucial to promoting the large-scale development of renewable energy, realizing the complementary advantages of different energy sources, and ensuring the clean, low-carbon, safe and efficient energy system, which has attracted widespread research attention.

[0003] As greenhouse gas emissions continue to rise, it is difficult to meet the system's clean and low-carbon needs by only considering the coordination of the integrated energy system. It is urgent to release the flexibility and adjustment capacity of demand-side resources. "Source-load interaction" will become a prominent feature of the future power system. Building a deep low-carbon power system on the energy production side and achieving high electrification on the energy consumption side, this way of working together on both ends is an important way to promote China's new power system dominated by renewable energy towards clean and low-carbon development. On the one hand, the energy production side can promote the consumption of large-scale renewable energy and tap the carbon reduction potential contained in the integrated energy system by utilizing the characteristics of horizontal complementarity of heat, electricity and gas and vertical integration of source-grid-load-storage in the integrated energy system. It is one of the important means to improve the low-carbon and economic operation of the energy system. On the other hand, based on the coupling characteristics of heterogeneous energy flows in the integrated energy system and the flexible response potential on the demand side, the demand response concept has been extended to the heating and gas supply systems, forming a comprehensive demand response. As the resources on the demand side continue to expand, the demand response on the energy consumption side to change the energy consumption behavior of users through incentive mechanisms is also one of the key ways to accelerate decarbonization. The low-carbon incentive mechanism requires data support from the quantification of carbon emissions on the consumer side, but both the macro-statistical method and the life cycle method only measure carbon emissions from the perspective of generating carbon emissions, and cannot measure carbon emissions on the consumer side to study the impact of load changes on system carbon emissions. Therefore, it is urgent to establish a suitable model to measure carbon emissions on the consumer side.

[0004] With the development of integrated energy systems and the reform of energy markets, the interaction between energy production and energy consumption is becoming increasingly frequent. The energy production side is operated and managed by energy service providers to provide energy services to consumers. Load aggregators integrate small and medium loads to enable users to participate in demand response with a lower threshold, reasonably optimize the allocation of energy loads on the consumer side, and help consumers make the best decision on energy demand. The energy management strategy of the integrated energy service provider will affect the energy demand of consumers managed by the load aggregator. Similarly, consumers' energy demand will also respond to the energy management strategy of the integrated energy service provider. By utilizing the horizontal complementarity of heat, electricity and gas in the integrated energy system, the integrated energy service provider mobilizes more low-carbon and zero-carbon energy utilization and promotes the absorption capacity of renewable energy. Load aggregators use the low-carbon demand response incentives provided by the integrated energy service provider to reduce consumers' dependence on high-carbon emission intensity energy by formulating demand response strategies, and help achieve carbon peak and carbon neutrality effectively. Therefore, in-depth research on carbon emission quantification methods based on the characteristics of heat, electricity and gas networks, low-carbon demand response incentives and low-carbon energy management strategies is of great significance to improving the economy and low-carbon nature of the integrated energy system, and has become one of the research hotspots in recent years.

[0005] In the prior art, the energy management of the integrated energy system has the following problems:

[0006] The first is a carbon emission quantification method based on the characteristics of heat, electricity, and gas networks. The macro-measurement of carbon emissions on the energy side is too extensive and difficult to support in-depth research on low-carbonization. The role of the consumer side in energy conservation and emission reduction is becoming increasingly prominent, and carbon trajectory tracking has become an important technology to clarify the user's emission reduction responsibilities. The integrated energy system has multiple sources and multiple energy ports. Combined with the carbon trajectory analysis method, it can effectively track the carbon intensity of multiple energy ports, provide carbon data support for the low-carbon optimization of the integrated energy system, and give play to the initiative and potential of users in energy conservation and emission reduction. The existing carbon emission quantification method has not formed an explicit expression of the source-load carbon emission relationship, and cannot distinguish the carbon emission contribution of the source to the load. It is also necessary to calculate the node carbon index of the entire system. The matrix scale is large, and some methods add virtual nodes to equivalently process energy losses, which is complex to calculate.

[0007] The second is low-carbon demand response incentives. From the perspective of participating in the integrated energy system dispatch from the energy consumption side, users can adjust their multi-energy loads through comprehensive demand response, so that the energy production side can reasonably arrange output and promote multi-energy coordination. However, most of the current incentive methods for demand response focus on time-sharing energy prices and energy comfort to adjust load demand, ignoring the "carbon emission intensity of load nodes", a key factor affecting user carbon emissions, which makes it impossible for users to perceive the carbon emission differences caused by their own energy habits and tap the low-carbon potential on the user side. Existing research has not directly used intuitive carbon signals to guide the user side to respond to demand. How to add carbon signals to better guide the demand side to respond to low carbon is a key issue.

[0008] The third is low-carbon energy management strategy. The interaction between energy service providers and energy consumers is becoming more and more frequent. The energy management strategy of energy service providers will affect the energy demand of consumers managed by load aggregators. Similarly, consumers' energy demand will also respond to the energy management strategy of energy service providers. Steinberg master-slave game is usually used to analyze the interaction between different intelligent agents in integrated energy systems. However, there are few studies analyzing the impact of introducing Steinberg master-slave game mechanism and low-carbon demand response on integrated energy systems.

[0009] Based on the above problems, it is necessary to propose a new energy management method for integrated energy systems that takes low-carbon demand response into consideration. Summary of the invention

[0010] In order to solve the problems that the existing carbon emission quantification methods proposed in the background technology fail to form an explicit expression of the source-load carbon emission relationship, cannot distinguish the carbon emission contribution of the source to the load, and must calculate the node carbon index of the entire system, the matrix scale is large, some methods add virtual nodes to equivalently process energy losses, the calculation is complex, and the existing research does not directly use intuitive carbon signals to guide the user side to respond to demand. How to add carbon signals and better guide the demand side to respond to low-carbon, etc., the present invention provides an energy management method for an integrated energy system that takes low-carbon demand response into consideration.

[0011] The present invention is implemented by the following technical solution: an energy management method for an integrated energy system considering low-carbon demand response, comprising the following three steps:

[0012] S1: Establish an energy-carbon flow tracking model based on the characteristics of heat, electricity and gas networks: Combine the direct carbon emissions generated by energy production, attach the virtual carbon emission flow to the energy flow, transfer the carbon emissions on the energy production side to the energy consumption side, and quantify the carbon emissions generated by energy use on the consumption side; the details are as follows:

[0013] S1-1: Establishing a power network energy-carbon flow tracking model:

[0014] Based on the known steady-state power flow of the power network, the current correlation matrix between the power source node and the grid node is established. ,element The expression is:

[0015]

[0016] Where n is the number of nodes in the power network; , are the current phasors flowing through the grid branch ij and the grid node j respectively; is the set of power system branches ij with terminal node j, i is also a grid node;

[0017] The current at power node k is expressed as a matrix according to the injection node I ˙ ( k ) G = [ 0 , ⋯ , 0 , I ˙ k G , 0 , ⋯ , 0 ] T , the injection current component of a single power node k to the node is obtained as:

[0018]

[0019] In the formula, is the injected current vector of all grid nodes under the action of power node k alone; is the current vector flowing out of the power node k; is the current phasor flowing out of the power node k;

[0020] Given the current component of the power node k acting on the grid node j, the expression for the active power provided by the power node k to the grid node j can be directly obtained as:

[0021]

[0022] In the formula, is the current from power node k acting on grid node j conjugation of; is the voltage phasor of grid node j;

[0023] Assumptions Starting from power node k, the current flows through path l to grid node j. The total voltage loss on the αth branch in path l is , then the total loss active power expression transmitted from power node k to grid node j is:

[0024]

[0025] In the formula, is the set of all current flow paths from power node k to grid node j; is the conjugate of the current component of the total current acting on the power supply node k and the grid node j on the path l; is the total voltage loss on the αth grid branch in path l; is the total loss voltage between power node k and grid node j; is the total loss active power transmitted from power source node k to grid node j;

[0026] The total active power provided by power node k to grid node j for and The sum is expressed as:

[0027]

[0028] In the formula, is the voltage phasor of power node k; is the total active power provided by power node k to grid node j;

[0029] Define the carbon intensity of grid node j is the carbon emission transmitted when the grid node j outputs unit active power, expressed as:

[0030]

[0031] In the formula, is the set of all power nodes; is the carbon intensity of power node k; is the injected power of grid node j; is the carbon intensity of grid node j;

[0032] S1-2: Establishing a thermal network energy-carbon flow tracking model:

[0033] The carbon flow tracking modeling of the steady-state thermal system with mass regulation is carried out using the superposition principle of thermal system. The meaning of the superposition principle of thermal system is: according to the direction of mass flow and network topology, the heat network is decoupled into sub-networks heated by a single heat source. After the heat provided by a single heat source is injected into the pipeline, it is evenly distributed according to the mass flow of the heat transfer medium in the pipeline; the temperature components of multiple heat sources acting on the same node can be superimposed;

[0034] Based on the superposition principle of thermal systems, the temperature vector of non-source nodes in a single thermal network Through the temperature conversion matrix J and the temperature vector of the heat network source node The product of can be directly obtained. A single heat network is a heat supply network or a heat recovery network, which can be expressed as:

[0035]

[0036] J = MS in S AB = [ J 11 J 12 J 21 J 22 ]

[0037]

[0038]

[0039] In the formula, is the mass flow vector flowing out of the non-source node of the heating network; is the temperature vector of the source node of the heating network; is the temperature vector of the non-source node of the heating network in the lossless heat transfer model; is the temperature conversion matrix; is the mass flow matrix of the inflow pipe of the heating network; A is the pipeline temperature loss matrix; B is the correlation matrix between the inflow node and the pipeline; cp is the subscript of matrix B; is the set of inflow nodes of pipeline p; is the set of all nodes in the heating network;

[0040] In the heating network, the total thermal power provided by the source node e to the non-source node f is expressed as follows:

[0041]

[0042] In the formula, is the temperature component of the lossless heat transfer from the source node e of the heating network to the non-source node f of the heating network; is the mass flow rate of heat medium flowing out of non-source node f of the heating network; is the specific heat capacity of the heat medium;

[0043] Under the action of multiple heat sources, the carbon intensity of the non-source node f of the heating network is expressed as:

[0044]

[0045] In the formula, is the carbon intensity of heat source node e in the heating network; is the temperature of the non-source node f of the heating network; is the carbon intensity of the non-source node f in the heating network; is the source node set of the heating network;

[0046] The heat recovery network is a reverse flow network of heat medium after the source and load of the heating network are exchanged. Using the same modeling method, the expression of the total thermal power provided by the source node e' to the non-source node f' in the heat recovery network is as follows:

[0047]

[0048] In the formula, is the temperature component of the heat recovery network source node e' acting on the heat recovery network non-source node f' by lossless heat transfer; is the mass flow rate of heat medium flowing out of the non-source node f' of the heat recovery network; is the specific heat capacity of the heat medium;

[0049] The carbon emission intensity of the non-source node f of the heat recovery network under the action of all heat sources in the lower return pipe is expressed as:

[0050]

[0051] In the formula, is the carbon intensity of the heat source node e' of the heat recovery network; is the temperature of the non-source node f' of the heat recovery network; is the carbon intensity of the non-source node f' of the heat recovery network; is the set of source nodes of the heat recovery network;

[0052] S1-3: Establishing a natural gas network energy-carbon flow tracking model

[0053] Use the reverse flow tracking method to track the amount of air each load receives from the air source:

[0054] Considering node u as the input gas source node, the net flow balance equation of this node is as follows:

[0055]

[0056] In the formula, is the node set of the gas source node u; is the gas source output of gas source node u; is the gas flow rate of pipeline uy; is the net inflow of gas source node u;

[0057] is the countercurrent distribution matrix, whose elements [ A v ] uy The expression is as follows:

[0058] [ A v ] uy = { 1 , u = y − Q yu / Q y , y ∈ Ω u − 0 , other

[0059] In the formula, is the net inflow air flow of load node y; then the net flow balance equation of load node y changes to the following:

[0060]

[0061] In the formula, is the net nodal gas flow vector; is the node gas source force vector;

[0062] The expression of the gas flow provided by the gas source node u to the load node y is as follows:

[0063] Q y = ∑ k = 1 W [ A v − 1 ] yu Q G , u

[0064] Where W is the number of nodes in the natural gas network; is the gas source output of gas source node u;

[0065] Based on the principle of proportional contribution, the gas flow rate contribution of each gas source to the load node y is expressed as follows:

[0066] Q Load , y = Q Load , y Q y Q y = Q Load , y Q y ∑ k = 1 W [ A v − 1 ] yu Q G , u

[0067] Define the carbon intensity of natural gas network load node y The carbon emissions delivered when the load node y outputs a unit gas flow rate are expressed as follows:

[0068] e y Q = Q Load , y Q y ∑ k = 1 W [ A v − 1 ] yu Q G , u e G , u Q

[0069] In the formula, is the carbon intensity of gas source node u; is the carbon intensity of load node y.

[0070] S2: Establishing an energy-carbon comprehensive cost constraint model:

[0071] The key issue in the low-carbon economic operation of the integrated energy system is how energy service providers can provide low-carbon incentives to consumers. The established energy-carbon flow tracking model is adopted. Based on the integrated energy system architecture, the energy-carbon flow relationship of each energy consumption link is obtained by tracking the embedded carbon flow, and the carbon emission intensity of each node in the integrated energy system is obtained; the carbon price of energy use is obtained by identifying the virtual carbon emissions of consumers; the carbon cost of each kilowatt-hour of electricity, heat and natural gas demand is equal to the product of the carbon tax and the corresponding carbon emission intensity, and then the carbon price is added to the energy cost to form an energy-carbon comprehensive cost, thereby introducing the carbon price signal into the low-carbon incentives of energy service providers to consumers. The mathematical formula is as follows:

[0072]

[0073]

[0074]

[0075] In the formula, , and They are the electricity-carbon integrated price, the gas-carbon integrated price, and the heat-carbon integrated price; , and They are electricity sales price, gas sales price and heat sales price respectively; is the carbon tax price.

[0076] S3: Establish a two-level coordinated low-carbon energy management optimization model based on Steinberg game:

[0077] The integrated energy system is the energy supplier for end users, connecting users with the urban energy system. The integrated energy system includes the power subsystem, natural gas subsystem and thermal subsystem. Figure 1 The figure shows the typical system structure of an integrated energy system. The power subsystem is directly connected to the power grid and end users, including cogeneration units, electric energy storage equipment and wind turbines; the thermal subsystem is an independent system that is not interconnected with the urban thermal system, including cogeneration units, thermal energy storage equipment and gas boiler equipment; the natural gas subsystem is connected to the natural gas pipeline network and supplies natural gas to the integrated energy system and users through the natural gas pipeline network and power-to-gas units; the natural gas subsystem includes natural gas energy storage equipment and power-to-gas equipment;

[0078] Figure 2 It is the energy optimization strategy framework of the integrated energy system. In the figure, the energy service provider is the leader and the load aggregators are followers. Considering the energy consumption behaviors of different aggregators, the load aggregators are divided into residential load aggregators, industrial load aggregators, and commercial load aggregators, and a two-layer coordinated low-carbon optimization model of Steinberg game with one master and multiple followers is established. The energy service provider optimizes the energy-carbon comprehensive price and operation strategy. The operation strategy includes the output of each unit, the interaction with the upper power grid and gas grid, and provides energy supply services for the load aggregators. The load aggregators optimize the energy consumption strategy according to the energy-carbon comprehensive price information of the energy service provider to guide users to respond to low-carbon demand while minimizing the energy cost, as follows:

[0079] S3-1: Establish an optimization model for upper-level energy service providers:

[0080] S3-1-1: Establish the objective function:

[0081] The objective function of the energy service provider is to maximize the profit from energy sales, which is expressed as follows:

[0082]

[0083] In the formula, , , , , and They are electricity sales revenue, gas sales revenue, heat sales revenue, electricity purchase cost, gas purchase cost and environmental cost; is the set of load aggregators; T is the scheduling time;

[0084] , and are the electricity load demand, gas load demand and heat load demand of load aggregator x at time t respectively; and The prices for electricity purchased from the main grid and gas purchased from the gas source by the energy service provider are as follows; is the main grid carbon intensity; and The energy service provider purchases electricity from the main grid and gas from the gas source respectively; and are the natural gas demand of gas boiler and the natural gas demand of cogeneration unit at time t respectively; is the calorific value of natural gas; is the carbon intensity of the gas source;

[0085] S3-1-2: Establish constraints:

[0086] The constraints include power network constraints, natural gas network constraints, thermal network constraints, gas boiler unit constraints, combined heat and power unit constraints, power-to-gas unit constraints, energy storage equipment constraints, wind turbine unit constraints, and energy sales price constraints;

[0087] ① Power network constraints:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] In the formula, is the set of generators at grid node i; is the set of power network nodes connected to the power grid node i; is the set of load nodes at grid node i; is the set of cogeneration units at grid node i; is the set of power-to-gas units at grid node i; is the active output of the generator at power node k at time t; is the active power flow of the power grid branch ij at time t; is the electric load demand of load aggregator x at time t; is the electrical output of the combined heat and power unit w at time t; is the electricity demand of the power-to-gas unit w at time t; is the reactive power output of the generator at power node k at time t; is the active power flow of the power grid branch ij at time t; is the reactive power demand of the load aggregator x at time t; is the resistance of the grid branch ij; is the reactance of the grid branch ij; is the voltage of grid node i at time t; is the phase angle of grid node i at time t; and are the minimum active output and maximum active output of the generator at power node k respectively; and are the minimum reactive power output and maximum reactive power output of the generator at power node k respectively; and are the maximum active transmission capacity and reactive transmission capacity of the power grid branch ij respectively; and are the minimum and maximum voltages of all grid nodes respectively;

[0098] ② Natural gas network constraints:

[0099]

[0100]

[0101]

[0102]

[0103] In the formula, is the gas source set of gas source node u; is the set of natural gas network nodes connected to the gas source node u; is the set of cogeneration units at the gas source node u; is the set of gas boiler units at the gas source node u; is the set of power-to-gas units at the gas source node u; is the gas output of the gas source node with gas source number d at time t; is the air flow of line uy at time t; is the natural gas demand of the cogeneration unit d1 at time t; is the gas boiler unit at time t natural gas demand; is the gas output of the electric-to-gas unit d2 at time t; is the maximum output of the gas source; L i 'It is the set of nodes of the load aggregator on the natural gas network; and represents the node pressure of the gas source node u and the load node y, and It is the minimum and maximum gas pressure of natural gas network nodes;

[0104] Since the gas distribution network is radial, that is, the direction of the natural gas flow is pre-set, the Weymouth equation is used to approximate the natural gas flow. Since the Weymouth equation is nonlinear and non-convex, it is first tapered to the second order, and the expression is as follows:

[0105]

[0106] In the formula, is the pipeline parameter of the natural gas pipeline uy;

[0107] Then the feasible domain of the optimization model is on the second-order cone surface, which is still a non-convex optimization problem. After relaxing the second-order cone Weymouth equation, the second-order cone programming inequality constraint is obtained. The feasible domain will become a second-order cone and converted into a convex optimization problem for solution. The expression is as follows:

[0108]

[0109] ③ Thermal network constraints:

[0110] The heat network modeling using the quality regulation model, the heating network mixing equation is as follows:

[0111]

[0112] In the formula, is the heating network pipeline flowing into node a; is the heating network pipeline flowing out of node a; is a collection of heating network nodes; is the terminal temperature of the heating network pipe flowing into the heating network pipe o at time t; is the heating network temperature of node a at time t; is the mass flow rate of pipe o in the heating network at time t; node a is the code of each node in the heating network;

[0113] The mixing temperature equation of the heat recovery network is as follows:

[0114]

[0115] In the formula, is the heat recovery network pipe flowing into node a'; is the heat recovery network pipe flowing out of node a'; is the mass flow rate of the heat recovery network pipe o' at time t; is the terminal temperature of the heat recovery network pipe flowing into the heat recovery network pipe o' at time t; is the temperature of the heat recovery network at node a' at time t; node a' is the code of each node in the heat recovery network;

[0116] The temperature loss equation of the heating network is as follows:

[0117]

[0118] In the formula, is the temperature at the head end of the heating network pipe flowing into node a at time t; v is the pipe heat transfer coefficient; c is the specific heat capacity of water; is the water flow density; and R are the length and thermal resistance of the pipe, respectively;

[0119] The temperature loss equation of the heat recovery network is as follows:

[0120]

[0121] In the formula, is the temperature at the head end of the heat recovery network pipe flowing into node a' at time t;

[0122] The temperature equation at the head end of the heating network is as follows:

[0123]

[0124] The temperature equation at the head end of the heat recovery network is as follows:

[0125]

[0126] The output equation of the combined heat and power unit is as follows:

[0127]

[0128] In the formula, is the number of cogeneration units; is the combined heat and power unit at node a; is the thermal output of the cogeneration unit d1 at time t; is the mass flow rate outflowing from the cogeneration unit d1 at time t;

[0129] The output equation of the gas boiler unit is as follows:

[0130] ,

[0131] In the formula, is the number of gas boiler units; is the gas boiler unit at node a; is the heat output of gas boiler y at time t; The gas boiler at time t Outflow mass flow rate;

[0132] The temperature constraint equation of the heating network node is as follows:

[0133] ,

[0134] In the formula, and are the minimum and maximum temperatures of node a in the heating network, respectively;

[0135] The heat load demand equation is as follows:

[0136]

[0137] In the formula, is the heat demand of the heat load at the heating network node a at time t; is the mass flow rate of the heating network load flowing into pipe o at time t;

[0138] The temperature constraint equation of the heat recovery network node is as follows:

[0139]

[0140] In the formula, and are the minimum and maximum temperatures of node a' in the heat recovery network respectively;

[0141] ④Cogeneration unit constraints:

[0142]

[0143]

[0144]

[0145] In the formula, and are the thermal output and electrical output of the cogeneration unit at time t respectively; and are the thermal efficiency and electrical efficiency of the cogeneration unit, respectively; and is the minimum and maximum value of the thermal output of the cogeneration unit;

[0146] ⑤ Constraints of gas boiler units:

[0147]

[0148]

[0149] In the formula, is the thermal output of the gas boiler unit at time t; is the thermal efficiency of the gas boiler unit; and It is the minimum and maximum value of the thermal output of the gas boiler unit;

[0150] ⑥ Constraints on power-to-gas units:

[0151]

[0152]

[0153] In the formula, is the gas efficiency of the power-to-gas unit; and The minimum and maximum gas output of the power-to-gas unit

[0154] ⑦ Energy storage equipment constraints:

[0155] Since the models of electric energy storage, gas energy storage and thermal energy storage are similar, a unified model is used for construction:

[0156]

[0157]

[0158]

[0159]

[0160] In the formula, and They are respectively the energy discharge and energy charging of the energy storage device at time t; Decision-making quantity for charging and discharging energy storage equipment; and They are the maximum energy storage release and maximum energy charging respectively; The storage energy of the energy storage device; and are the minimum and maximum values ​​of the energy storage capacity of the energy storage device; and They are respectively the charging efficiency and the discharging efficiency of the energy storage device;

[0161] ⑧Constraints of fan unit:

[0162]

[0163] In the formula, is the actual power output of the wind turbine unit at time t; The predicted power output of the wind turbine unit;

[0164] ⑨ Constraints on energy sales price:

[0165] The energy service provider sells energy to the load aggregator at a price that is within the following constraints, ensuring that the price of the energy service provider is no higher than an average value:

[0166]

[0167]

[0168]

[0169] In the formula, and They are the minimum and maximum prices of electricity sold by energy service providers; and They are the minimum and maximum gas selling prices of energy service providers; and They are the minimum and maximum selling prices of heat by energy service providers; , and They are the average prices of electricity sold by energy service providers, the average prices of gas sold, and the average prices of heat sold;

[0170] S3-2: Establish load aggregator optimization model:

[0171] S3-2-1: Establishment of Residential Load Aggregator Constraints:

[0172] ① Design objective function: maximize consumer surplus, that is, the difference between the utility function of residential users and energy cost. The calculation formula is as follows:

[0173]

[0174]

[0175]

[0176]

[0177]

[0178] In the formula, is the utility function; and They are the electricity utility function, heat utility function and gas utility function at time t respectively; and is the electrical preference coefficient; and is the thermal preference coefficient; and is the gas preference coefficient; and They are the electricity load demand, heat load demand and gas load demand of the residential load aggregator;

[0179] ② Establish constraints:

[0180] The transferable electric load constraints are as follows:

[0181]

[0182]

[0183]

[0184]

[0185] The transferable gas load constraints are as follows:

[0186]

[0187]

[0188]

[0189]

[0190] The heat load constraints that can be reduced are as follows:

[0191]

[0192]

[0193]

[0194] In the formula, and They are the original electric load demand and rigid electric load demand of residential load aggregators; For residential load aggregators, the electric load demand can be shifted; The maximum value of the transferable electric load demand of the residential load aggregator; It is the rigid electric load ratio coefficient of the residential load aggregator; and They are the original gas load demand and rigid gas load demand of residential load aggregators; Shiftable gas load demand for residential load aggregators; The maximum value of the transferable gas load demand of the residential load aggregator; is the rigid gas load ratio factor for residential load aggregators; and They are the original heat load demand and curtailable heat load demand of the residential load aggregator; The maximum value of heat load demand that can be reduced at a single time for residential load aggregators; The maximum value of heat load demand that can be reduced by the residential load aggregator;

[0195] S3-2-2: Establish commercial load aggregator constraints:

[0196] ① Establish the objective function and minimize the energy cost:

[0197]

[0198] ② Establish constraints:

[0199] The transferable electric load constraints are as follows:

[0200]

[0201]

[0202]

[0203]

[0204] The transferable gas load constraints are as follows:

[0205]

[0206]

[0207]

[0208] The heat load constraints that can be reduced are as follows:

[0209]

[0210]

[0211]

[0212] In the formula, and They are the original electric load demand and rigid electric load demand of commercial load aggregators; To transfer electric load demand to commercial load aggregators; The maximum value of the transferable electric load demand of commercial load aggregators; The rigid electric load ratio factor for commercial load aggregators; and They are the original gas load demand and rigid gas load demand of commercial load aggregators; Shiftable gas load demand for commercial load aggregators; The maximum value of the transferable gas load demand of the commercial load aggregator; Rigid gas load ratio factor for commercial load aggregators; and They are the original heat load demand and curtailable heat load demand of commercial load aggregators; The maximum value of heat load demand that can be reduced at a single time for commercial load aggregators; For commercial load aggregators, the maximum value of heat load demand can be reduced;

[0213] S3-2-3: Establishment of industrial load aggregator constraints:

[0214] ① Establish the objective function and minimize the energy cost:

[0215]

[0216] ② Establish constraints:

[0217] The transferable electric load constraints are as follows:

[0218]

[0219]

[0220]

[0221]

[0222] The transferable gas load constraints are as follows:

[0223]

[0224]

[0225]

[0226]

[0227] The heat load constraints that can be reduced are as follows:

[0228]

[0229]

[0230]

[0231] In the formula, and They are the original electric load demand and rigid electric load demand of industrial load aggregators; To provide industrial load aggregators with the ability to shift electrical load demand; The maximum value of the transferable electric load demand for industrial load aggregators; The rigid electric load ratio coefficient for industrial load aggregators; and They are the raw gas load demand and rigid gas load demand of industrial load aggregators; Shiftable gas load demand for industrial load aggregators; The maximum value of the transferable gas load demand of the industrial load aggregator; The rigid gas load ratio factor for industrial load aggregators; and They are the original heat load demand and curtailable heat load demand of industrial load aggregators; For industrial load aggregators, the maximum heat load demand can be reduced at a single time; For industrial load aggregators the maximum heat load demand can be reduced.

[0232] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides an integrated energy system energy management method that takes into account low-carbon demand response. The method introduces carbon prices into the pricing strategy of integrated energy service providers by tracking implicit carbon emissions during energy use. Subsequently, the carbon price is added to the energy price to form an energy-carbon comprehensive price. In the game process, load aggregators obtain energy-carbon comprehensive prices and use comprehensive demand response to optimize their energy consumption strategies, reduce their dependence on high-carbon emission intensity energy, encourage more low-carbon and zero-carbon energy utilization, promote the absorption capacity of renewable energy, and help achieve "carbon peak and carbon neutrality" effectively, which has high engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0233] Figure 1 A typical system structure diagram of an integrated energy system according to a specific embodiment of the present invention.

[0234] Figure 2 This is a framework diagram of the energy optimization strategy for the integrated energy system of the present invention. DETAILED DESCRIPTION

[0235] The present invention is further described below in conjunction with specific embodiments.

[0236] An integrated energy system energy management method considering low-carbon demand response includes the following three steps:

[0237] S1: Establish an energy-carbon flow tracking model based on the characteristics of heat, electricity and gas networks: Combine the direct carbon emissions generated by energy production, attach the virtual carbon emission flow to the energy flow, transfer the carbon emissions on the energy production side to the energy consumption side, and quantify the carbon emissions generated by energy use on the consumption side; the details are as follows:

[0238] S1-1: Establishing a power network energy-carbon flow tracking model:

[0239] Based on the known steady-state power flow of the power network, the current correlation matrix between the power source node and the grid node is established. ,element The expression is:

[0240]

[0241] Where n is the number of nodes in the power network; , are the current phasors flowing through the grid branch ij and the grid node j respectively; is the set of power system branches ij with terminal node j, i is also a grid node;

[0242] The current at power node k is expressed as a matrix according to the injection node I ˙ ( k ) G = [ 0 , ⋯ , 0 , I ˙ k G , 0 , ⋯ , 0 ] T , the injection current component of a single power node k to the node is obtained as:

[0243]

[0244] In the formula, is the injected current vector of all grid nodes under the action of power node k alone; is the current vector flowing out of the power node k; is the current phasor flowing out of the power node k;

[0245] Given the current component of the power node k acting on the grid node j, the expression for the active power provided by the power node k to the grid node j can be directly obtained as:

[0246]

[0247] In the formula, is the current from power node k acting on grid node j conjugation of; is the voltage phasor of grid node j;

[0248] Assumptions Starting from power node k, the current flows through path l to grid node j. The total voltage loss on the αth branch in path l is , then the total loss active power expression transmitted from power node k to grid node j is:

[0249]

[0250] In the formula, is the set of all current flow paths from power node k to grid node j; is the conjugate of the current component of the total current acting on the power supply node k and the grid node j on the path l; is the total voltage loss on the αth grid branch in path l; is the total loss voltage between power node k and grid node j; is the total loss active power transmitted from power source node k to grid node j;

[0251] The total active power provided by power node k to grid node j for and The sum is expressed as:

[0252]

[0253] In the formula, is the voltage phasor of power node k; is the total active power provided by power node k to grid node j;

[0254] Define the carbon intensity of grid node j is the carbon emission transmitted when the grid node j outputs unit active power, expressed as:

[0255]

[0256] In the formula, is the set of all power nodes; is the carbon intensity of power node k; is the injected power of grid node j; is the carbon intensity of grid node j;

[0257] S1-2: Establishing a thermal network energy-carbon flow tracking model:

[0258] The carbon flow tracking modeling of the steady-state thermal system with mass regulation is carried out using the superposition principle of thermal system. The meaning of the superposition principle of thermal system is: according to the direction of mass flow and network topology, the heat network is decoupled into sub-networks heated by a single heat source. After the heat provided by a single heat source is injected into the pipeline, it is evenly distributed according to the mass flow of the heat transfer medium in the pipeline; the temperature components of multiple heat sources acting on the same node can be superimposed;

[0259] Based on the superposition principle of thermal systems, the temperature vector of non-source nodes in a single thermal network Through the temperature conversion matrix J and the temperature vector of the heat network source node The product of can be directly obtained. A single heat network is a heat supply network or a heat recovery network, which can be expressed as:

[0260]

[0261] J = MS in S AB = [ J 11 J 12 J 21 J 22 ]

[0262]

[0263]

[0264] In the formula, is the mass flow vector flowing out of the non-source node of the heating network; is the temperature vector of the source node of the heating network; is the temperature vector of the non-source node of the heating network in the lossless heat transfer model; is the temperature conversion matrix; is the mass flow matrix of the inflow pipe of the heating network; A is the pipeline temperature loss matrix; B is the correlation matrix between the inflow node and the pipeline; cp is the subscript of matrix B; is the set of inflow nodes of pipeline p; is the set of all nodes in the heating network;

[0265] In the heating network, the total thermal power provided by the source node e to the non-source node f is expressed as follows:

[0266]

[0267] In the formula, is the temperature component of the lossless heat transfer from the source node e of the heating network to the non-source node f of the heating network; is the mass flow rate of heat medium flowing out of non-source node f of the heating network; is the specific heat capacity of the heat medium;

[0268] Under the action of multiple heat sources, the carbon intensity of the non-source node f of the heating network is expressed as:

[0269]

[0270] In the formula, is the carbon intensity of heat source node e in the heating network; is the temperature of the non-source node f of the heating network; is the carbon intensity of the non-source node f in the heating network; is the source node set of the heating network;

[0271] The heat recovery network is a reverse flow network of heat medium after the source and load of the heating network are exchanged. Using the same modeling method, the expression of the total thermal power provided by the source node e' to the non-source node f' in the heat recovery network is as follows:

[0272]

[0273] In the formula, is the temperature component of the heat recovery network source node e' acting on the heat recovery network non-source node f' by lossless heat transfer; is the mass flow rate of heat medium flowing out of the non-source node f' of the heat recovery network; is the specific heat capacity of the heat medium;

[0274] The carbon emission intensity of the non-source node f of the heat recovery network under the action of all heat sources in the lower return pipe is expressed as:

[0275]

[0276] In the formula, is the carbon intensity of the heat source node e' of the heat recovery network; is the temperature of the non-source node f' of the heat recovery network; is the carbon intensity of the non-source node f' of the heat recovery network; is the set of source nodes of the heat recovery network;

[0277] S1-3: Establishing a natural gas network energy-carbon flow tracking model

[0278] Use the reverse flow tracking method to track the amount of air each load receives from the air source:

[0279] Considering node u as the input gas source node, the net flow balance equation of this node is as follows:

[0280]

[0281] In the formula, is the node set of the gas source node u; is the gas source output of gas source node u; is the gas flow rate of pipeline uy; is the net inflow of gas source node u;

[0282] is the countercurrent distribution matrix, whose elements [ A v ] uy The expression is as follows:

[0283] [ A v ] uy = { 1 , u = y − Q yu / Q y , y ∈ Ω u − 0 , other

[0284] In the formula, is the net inflow air flow of load node y; then the net flow balance equation of load node y changes to the following:

[0285]

[0286] In the formula, is the net nodal gas flow vector; is the node gas source output vector;

[0287] The expression of the gas flow provided by the gas source node u to the load node y is as follows:

[0288] Q y = ∑ k = 1 W [ A v − 1 ] yu Q G , u

[0289] Where W is the number of nodes in the natural gas network; is the gas source output of gas source node u;

[0290] Based on the principle of proportional contribution, the gas flow rate contribution of each gas source to the load node y is expressed as follows:

[0291] Q Load , y = Q Load , y Q y Q y = Q Load , y Q y ∑ k = 1 W [ A v − 1 ] yu Q G , u

[0292] Define the carbon intensity of natural gas network load node y The carbon emissions delivered when the load node y outputs a unit gas flow rate are expressed as follows:

[0293] e y Q = Q Load , y Q y ∑ k = 1 W [ A v − 1 ] yu Q G , u e G , u Q

[0294] In the formula, is the carbon intensity of gas source node u; is the carbon intensity of load node y.

[0295] S2: Establishing an energy-carbon comprehensive cost constraint model:

[0296] The established energy-carbon flow tracking model is adopted. Based on the integrated energy system architecture, the energy-carbon flow relationship of each energy consumption link is obtained by tracking the embedded carbon flow, and the carbon emission intensity of each node in the integrated energy system is obtained; the carbon price of energy use is obtained by identifying the virtual carbon emissions of consumers; the carbon cost of each kilowatt-hour of electricity, heat and natural gas demand is equal to the product of carbon tax and the corresponding carbon emission intensity, and then the carbon price is added to the energy cost to form the energy-carbon comprehensive cost, so as to introduce the carbon price signal into the low-carbon incentive measures of energy service providers to consumers. The mathematical formula is as follows:

[0297]

[0298]

[0299]

[0300] In the formula, , and They are the electricity-carbon integrated price, the gas-carbon integrated price, and the heat-carbon integrated price; , and They are electricity sales price, gas sales price and heat sales price respectively; is the carbon tax price.

[0301] S3: Establish a two-level coordinated low-carbon energy management optimization model based on Steinberg game:

[0302] The integrated energy system includes an electric power subsystem, a natural gas subsystem and a thermal subsystem; the electric power subsystem is directly connected to the power grid and end users, including cogeneration units, electric energy storage equipment and wind turbines; the thermal subsystem is an independent system that is not interconnected with the urban thermal system, including cogeneration units, thermal energy storage equipment and gas boiler equipment; the natural gas subsystem is connected to the natural gas pipeline network and supplies natural gas to the integrated energy system and users through the natural gas pipeline network and power-to-gas units; the natural gas subsystem includes natural gas energy storage equipment and power-to-gas equipment;

[0303] The energy optimization strategy framework of the integrated energy system is as follows: the energy service provider is the leader and the load aggregators are followers; considering the energy consumption behaviors of different aggregators, the load aggregators are divided into residential load aggregators, industrial load aggregators, and commercial load aggregators, and a two-layer coordinated low-carbon optimization model of Steinberg game with one master and multiple followers is established; the energy service provider optimizes the energy-carbon comprehensive price and operation strategy, and the operation strategy includes the output of each unit, the interaction with the upper power grid and gas grid, and provides energy supply services for the load aggregators; the load aggregators optimize the energy consumption strategy according to the energy-carbon comprehensive price information of the energy service provider to guide users to respond to low-carbon demand while minimizing energy costs, as follows:

[0304] S3-1: Establish an optimization model for upper-level energy service providers:

[0305] S3-1-1: Establish the objective function:

[0306] The objective function of the energy service provider is to maximize the profit from energy sales, which is expressed as follows:

[0307]

[0308] In the formula, , , , , and They are electricity sales revenue, gas sales revenue, heat sales revenue, electricity purchase cost, gas purchase cost and environmental cost; is the set of load aggregators; T is the scheduling time;

[0309] , and are the electricity load demand, gas load demand and heat load demand of load aggregator x at time t respectively; and The prices for electricity purchased from the main grid and gas purchased from the gas source by the energy service provider are as follows; is the main grid carbon intensity; and The energy service provider purchases electricity from the main grid and gas from the gas source respectively; and are the natural gas demand of gas boiler and the natural gas demand of cogeneration unit at time t respectively; is the calorific value of natural gas; is the carbon intensity of the gas source;

[0310] S3-1-2: Establish constraints:

[0311] The constraints include power network constraints, natural gas network constraints, thermal network constraints, gas boiler unit constraints, combined heat and power unit constraints, power-to-gas unit constraints, energy storage equipment constraints, wind turbine unit constraints, and energy sales price constraints;

[0312] ① Power network constraints:

[0313]

[0314]

[0315]

[0316]

[0317]

[0318]

[0319]

[0320]

[0321]

[0322] In the formula, is the set of generators at grid node i; is the set of power network nodes connected to the power grid node i; is the set of load nodes at grid node i; is the set of cogeneration units at grid node i; is the set of power-to-gas units at grid node i; is the active output of the generator at power node k at time t; is the active power flow of the power grid branch ij at time t; is the electric load demand of load aggregator x at time t; is the electrical output of the combined heat and power unit w at time t; is the electricity demand of the power-to-gas unit w at time t; is the reactive power output of the generator at power node k at time t; is the active power flow of the power grid branch ij at time t; is the reactive power demand of the load aggregator x at time t; is the resistance of the grid branch ij; is the reactance of the grid branch ij; is the voltage of grid node i at time t; is the phase angle of grid node i at time t; and are the minimum active output and maximum active output of the generator at power node k respectively; and are the minimum reactive power output and maximum reactive power output of the generator at power node k respectively; and are the maximum active transmission capacity and reactive transmission capacity of the power grid branch ij respectively; and are the minimum and maximum voltages of all grid nodes respectively;

[0323] ② Natural gas network constraints:

[0324]

[0325]

[0326]

[0327]

[0328] In the formula, is the gas source set of gas source node u; is the set of natural gas network nodes connected to the gas source node u; is the set of cogeneration units at the gas source node u; is the set of gas boiler units at the gas source node u; is the set of power-to-gas units at the gas source node u; is the gas output of the gas source node with gas source number d at time t; is the air flow of line uy at time t; is the natural gas demand of the cogeneration unit d1 at time t; is the gas boiler unit at time t natural gas demand; is the gas output of the electric-to-gas unit d2 at time t; is the maximum output of the gas source; L i 'It is the set of nodes of the load aggregator on the natural gas network; and represents the node pressure of the gas source node u and the load node y, and It is the minimum and maximum gas pressure of natural gas network nodes;

[0329] Since the gas distribution network is radial, that is, the direction of the natural gas flow is pre-set, the Weymouth equation is used to approximate the natural gas flow. Since the Weymouth equation is nonlinear and non-convex, it is first tapered to the second order, and the expression is as follows:

[0330]

[0331] In the formula, is the pipeline parameter of the natural gas pipeline uy;

[0332] Then the feasible domain of the optimization model is on the second-order cone surface, which is still a non-convex optimization problem. After relaxing the second-order cone Weymouth equation, the second-order cone programming inequality constraint is obtained. The feasible domain will become a second-order cone and converted into a convex optimization problem for solution. The expression is as follows:

[0333]

[0334] ③ Thermal network constraints:

[0335] The heat network modeling using the quality regulation model, the heating network mixing equation is as follows:

[0336]

[0337] In the formula, is the heating network pipeline flowing into node a; is the heating network pipeline flowing out of node a; is a collection of heating network nodes; is the terminal temperature of the heating network pipe flowing into the heating network pipe o at time t; is the heating network temperature of node a at time t; is the mass flow rate of pipe o in the heating network at time t; node a is the code of each node in the heating network;

[0338] The mixing temperature equation of the heat recovery network is as follows:

[0339]

[0340] In the formula, is the heat recovery network pipe flowing into node a'; is the heat recovery network pipe flowing out of node a'; is the mass flow rate of the heat recovery network pipe o' at time t; is the terminal temperature of the heat recovery network pipe flowing into the heat recovery network pipe o' at time t; is the temperature of the heat recovery network at node a' at time t; node a' is the code of each node in the heat recovery network;

[0341] The temperature loss equation of the heating network is as follows:

[0342]

[0343] In the formula, is the temperature at the head end of the heating network pipe flowing into node a at time t; v is the pipe heat transfer coefficient; c is the specific heat capacity of water; is the water flow density; and R are the length and thermal resistance of the pipe, respectively;

[0344] The temperature loss equation of the heat recovery network is as follows:

[0345]

[0346] In the formula, is the temperature at the head end of the heat recovery network pipe flowing into node a' at time t;

[0347] The temperature equation at the head end of the heating network is as follows:

[0348]

[0349] The temperature equation at the head end of the heat recovery network is as follows:

[0350]

[0351] The output equation of the combined heat and power unit is as follows:

[0352]

[0353] In the formula, is the number of cogeneration units; is the combined heat and power unit at node a; is the thermal output of the cogeneration unit d1 at time t; is the mass flow rate outflowing from the cogeneration unit d1 at time t;

[0354] The output equation of the gas boiler unit is as follows:

[0355] ,

[0356] In the formula, is the number of gas boiler units; is the gas boiler unit at node a; is the heat output of gas boiler y at time t; The gas boiler at time t Outflow mass flow rate;

[0357] The temperature constraint equation of the heating network node is as follows:

[0358] ,

[0359] In the formula, and are the minimum and maximum temperatures of node a in the heating network, respectively;

[0360] The heat load demand equation is as follows:

[0361]

[0362] In the formula, is the heat demand of the heat load at the heating network node a at time t; is the mass flow rate of the heating network load flowing into pipe o at time t;

[0363] The temperature constraint equation of the heat recovery network node is as follows:

[0364]

[0365] In the formula, and are the minimum and maximum temperatures of node a' in the heat recovery network respectively;

[0366] ④Cogeneration unit constraints:

[0367]

[0368]

[0369]

[0370] In the formula, and are the thermal output and electrical output of the cogeneration unit at time t respectively; and are the thermal efficiency and electrical efficiency of the cogeneration unit, respectively; and is the minimum and maximum value of the thermal output of the cogeneration unit;

[0371] ⑤ Constraints of gas boiler units:

[0372]

[0373]

[0374] In the formula, is the thermal output of the gas boiler unit at time t; is the thermal efficiency of the gas boiler unit; and It is the minimum and maximum value of the thermal output of the gas boiler unit;

[0375] ⑥ Constraints on power-to-gas units:

[0376]

[0377]

[0378] In the formula, is the gas efficiency of the power-to-gas unit; and The minimum and maximum gas output of the power-to-gas unit

[0379] ⑦ Energy storage equipment constraints:

[0380] Since the models of electric energy storage, gas energy storage and thermal energy storage are similar, a unified model is used for construction:

[0381]

[0382]

[0383]

[0384]

[0385] In the formula, and They are respectively the energy discharge and energy charging of the energy storage device at time t; Decision-making quantity for charging and discharging energy storage equipment; and They are the maximum energy storage release and maximum energy charging respectively; The storage energy of the energy storage device; and are the minimum and maximum values ​​of the energy storage capacity of the energy storage device; and They are respectively the charging efficiency and the discharging efficiency of the energy storage device;

[0386] ⑧Constraints of fan unit:

[0387]

[0388] In the formula, is the actual power output of the wind turbine unit at time t; The predicted power output of the wind turbine unit;

[0389] ⑨ Constraints on energy sales price:

[0390] The energy service provider sells energy to the load aggregator at a price that is within the following constraints, ensuring that the price of the energy service provider is no higher than an average value:

[0391]

[0392]

[0393]

[0394] In the formula, and They are the minimum and maximum prices of electricity sold by energy service providers; and They are the minimum and maximum gas selling prices of energy service providers; and They are the minimum and maximum selling prices of heat by energy service providers; , and They are the average prices of electricity sold by energy service providers, the average prices of gas sold, and the average prices of heat sold;

[0395] S3-2: Establish load aggregator optimization model:

[0396] S3-2-1: Establishment of Residential Load Aggregator Constraints:

[0397] ① Design objective function: maximize consumer surplus, that is, the difference between the utility function of residential users and energy cost. The calculation formula is as follows:

[0398]

[0399]

[0400]

[0401]

[0402]

[0403] In the formula, is the utility function; and They are the electricity utility function, heat utility function and gas utility function at time t respectively; and is the electrical preference coefficient; and is the thermal preference coefficient; and is the gas preference coefficient; and They are the electricity load demand, heat load demand and gas load demand of the residential load aggregator;

[0404] ② Establish constraints:

[0405] The transferable electric load constraints are as follows:

[0406]

[0407]

[0408]

[0409]

[0410] The transferable gas load constraints are as follows:

[0411]

[0412]

[0413]

[0414]

[0415] The heat load constraints that can be reduced are as follows:

[0416]

[0417]

[0418]

[0419] In the formula, and They are the original electric load demand and rigid electric load demand of residential load aggregators; For residential load aggregators, the electric load demand can be shifted; The maximum value of the transferable electric load demand of the residential load aggregator; It is the rigid electric load ratio coefficient of the residential load aggregator; and They are the original gas load demand and rigid gas load demand of residential load aggregators; Shiftable gas load demand for residential load aggregators; The maximum value of the transferable gas load demand of the residential load aggregator; is the rigid gas load ratio factor for residential load aggregators; and They are the original heat load demand and curtailable heat load demand of the residential load aggregator; The maximum value of heat load demand that can be reduced at a single time for residential load aggregators; The maximum value of heat load demand that can be reduced by the residential load aggregator;

[0420] S3-2-2: Establish commercial load aggregator constraints:

[0421] ① Establish the objective function and minimize the energy cost:

[0422]

[0423] ② Establish constraints:

[0424] The transferable electric load constraints are as follows:

[0425]

[0426]

[0427]

[0428]

[0429] The transferable gas load constraints are as follows:

[0430]

[0431]

[0432]

[0433] The heat load constraints that can be reduced are as follows:

[0434]

[0435]

[0436]

[0437] In the formula, and They are the original electric load demand and rigid electric load demand of commercial load aggregators; To transfer electric load demand to commercial load aggregators; The maximum value of the electric load demand that can be transferred by commercial load aggregators; The rigid electric load ratio factor for commercial load aggregators; and They are the original gas load demand and rigid gas load demand of commercial load aggregators; Shiftable gas load demand for commercial load aggregators; The maximum value of the transferable gas load demand of the commercial load aggregator; Rigid gas load ratio factor for commercial load aggregators; and They are the original heat load demand and curtailable heat load demand of commercial load aggregators; The maximum value of heat load demand that can be reduced at a single time for commercial load aggregators; For commercial load aggregators, the maximum value of heat load demand can be reduced;

[0438] S3-2-3: Establishment of industrial load aggregator constraints:

[0439] ① Establish the objective function and minimize the energy cost:

[0440]

[0441] ② Establish constraints:

[0442] The transferable electric load constraints are as follows:

[0443]

[0444]

[0445]

[0446]

[0447] The transferable gas load constraints are as follows:

[0448]

[0449]

[0450]

[0451]

[0452] The heat load constraints that can be reduced are as follows:

[0453]

[0454]

[0455]

[0456] In the formula, and They are the original electric load demand and rigid electric load demand of industrial load aggregators; To provide industrial load aggregators with the ability to shift electrical load demand; The maximum value of the transferable electric load demand for industrial load aggregators; The rigid electric load ratio coefficient for industrial load aggregators; and They are the raw gas load demand and rigid gas load demand of industrial load aggregators; Shiftable gas load demand for industrial load aggregators; The maximum value of the transferable gas load demand of the industrial load aggregator; The rigid gas load ratio factor for industrial load aggregators; and They are the original heat load demand and curtailable heat load demand of industrial load aggregators; For industrial load aggregators, the maximum heat load demand can be reduced at a single time; For industrial load aggregators the maximum heat load demand can be reduced.

[0457] The scope of protection claimed by the present invention is not limited to the above specific embodiments. For those skilled in the art, the present invention may have various deformations and changes. Any modifications, improvements and equivalent substitutions made within the concept and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy management method for an integrated energy system considering low-carbon demand response, characterized in that: The following steps are involved: S1: Establish an energy-carbon flow tracking model based on the characteristics of heat, electricity and gas networks: Combine the direct carbon emissions generated by energy production, attach the virtual carbon emission flow to the energy flow, transfer the carbon emissions on the energy production side to the energy consumption side, and quantify the carbon emissions generated by energy use on the consumption side; S2: Establish an energy-carbon comprehensive cost constraint model: Using the established energy-carbon flow tracking model, based on the integrated energy system architecture, the energy-carbon flow relationship of each energy consumption link is obtained by tracking the embedded carbon flow, and the carbon emission intensity of each node in the integrated energy system is obtained; the carbon price of energy use is obtained by identifying the consumer's virtual carbon emissions; the carbon cost per kilowatt-hour of electricity, heat and natural gas demand is equal to the product of the carbon tax and the corresponding carbon emission intensity, and then the carbon price is added to the energy cost to form the energy-carbon comprehensive cost; S3: Establish a two-level coordinated low-carbon energy management optimization model based on Steinberg game: The integrated energy system includes an electric power subsystem, a natural gas subsystem and a thermal subsystem; the electric power subsystem is directly connected to the power grid and end users, including cogeneration units, electric energy storage equipment and wind turbines; the thermal subsystem is an independent system that is not interconnected with the urban thermal system, including cogeneration units, thermal energy storage equipment and gas boiler equipment; the natural gas subsystem is connected to the natural gas pipeline network and supplies natural gas to the integrated energy system and users through the natural gas pipeline network and power-to-gas units; the natural gas subsystem includes natural gas energy storage equipment and power-to-gas equipment; the energy optimization strategy framework of the integrated energy system is: the energy service provider is the leader The load aggregators are followers; considering the energy consumption behaviors of different load aggregators, the load aggregators are divided into residential load aggregators, industrial load aggregators, and commercial load aggregators, and a two-layer coordinated low-carbon optimization model of Steinberg game with one master and multiple followers is established; the energy service provider optimizes the energy-carbon comprehensive price and operation strategy. The operation strategy includes the output of each unit, the interaction with the upper-level power grid and gas grid, and provides energy supply services for the load aggregators; the load aggregators optimize the energy consumption strategy according to the energy-carbon comprehensive price information of the energy service provider to guide users to respond to low-carbon demand while minimizing the energy cost.

2. The energy management method of an integrated energy system considering low-carbon demand response according to claim 1, characterized in that: Step S1 is specifically as follows: S1-1: Establishing a power network energy-carbon flow tracking model: Based on the known steady-state power flow of the power network, the current correlation matrix between the power source node and the grid node is established. ,element The expression is: ， Where n is the number of nodes in the power network; , are the current phasors flowing through the grid branch ij and the grid node j respectively; is the set of power system branches ij with terminal node j, i is also a grid node; The current at power node k is expressed as a matrix according to the injection node , the injection current component of a single power node k to the node is obtained as: ， In the formula, is the injected current vector of all grid nodes under the action of power node k alone; is the current vector flowing out of the power node k; is the current phasor flowing out of the power node k; Given the current component of the power node k acting on the grid node j, the expression for the active power provided by the power node k to the grid node j can be directly obtained as: ， In the formula, is the current from power node k acting on grid node j conjugation of; is the voltage phasor of grid node j; Assumptions Starting from power node k, the voltage flows through path l to grid node j. The total voltage loss on the αth branch in path l is , then the total loss active power expression transmitted from power source node k to grid node j is: ， In the formula, is the set of all current flow paths from power node k to grid node j; is the conjugate of the current component of the total current acting on the power supply node k and the grid node j on the path l; is the total voltage loss on the αth grid branch in path l; is the total loss voltage between power node k and grid node j; is the total loss active power transmitted from power source node k to grid node j; The total active power provided by power node k to grid node j for and The sum is expressed as: ， In the formula, is the voltage phasor of power node k; is the total active power provided by power node k to grid node j; Define the carbon intensity of grid node j is the carbon emission transmitted when the grid node j outputs unit active power, expressed as: ， In the formula, is the set of all power nodes; is the carbon intensity of power node k; is the injected power of grid node j; is the carbon intensity of grid node j; S1-2: Establishing a thermal network energy-carbon flow tracking model: The carbon flow tracking modeling of the steady-state thermal system with mass regulation is carried out using the superposition principle of the thermal system. The meaning of the superposition principle of the thermal system is: according to the direction of mass flow and network topology, the heat network is decoupled into sub-networks heated by a single heat source. After the heat provided by the single heat source is injected into the pipeline, it is evenly distributed according to the mass flow of the heat transfer medium in the pipeline. The temperature components of multiple heat sources acting on the same node can be superimposed; Based on the superposition principle of thermal systems, the temperature vector of non-source nodes in a single thermal network Through the temperature conversion matrix J and the temperature vector of the heat network source node The product of can be directly obtained. A single heat network is a heat supply network or a heat recovery network, which can be expressed as: ， ， ， ， In the formula, is the mass flow vector flowing out of the non-source node of the heating network; is the temperature vector of the source node of the heating network; is the temperature vector of the non-source node of the heating network in the lossless heat transfer model; is the temperature conversion matrix; is the mass flow matrix of the inflow pipe of the heating network; A is the pipeline temperature loss matrix; B is the correlation matrix between the inflow node and the pipeline; cp is the subscript of matrix B; is the set of inflow nodes of pipeline p; is the set of all nodes in the heating network; In the heating network, the total thermal power provided by the source node e to the non-source node f is expressed as follows: ， In the formula, is the temperature component of the lossless heat transfer from the source node e of the heating network to the non-source node f of the heating network; is the mass flow rate of heat medium flowing out of non-source node f of the heating network; is the specific heat capacity of the heat medium; Under the action of multiple heat sources, the carbon intensity of the non-source node f of the heating network is expressed as: ， In the formula, is the carbon intensity of heat source node e in the heating network; is the temperature of the non-source node f of the heating network; is the carbon intensity of the non-source node f in the heating network; is the source node set of the heating network; The heat recovery network is a reverse flow network of heat medium after the source and load of the heating network are exchanged. Using the same modeling method, the expression of the total thermal power provided by the source node e' to the non-source node f' in the heat recovery network is as follows: ， In the formula, is the temperature component of the heat recovery network source node e' acting on the heat recovery network non-source node f' by lossless heat transfer; is the mass flow rate of heat medium flowing out of the non-source node f' of the heat recovery network; is the specific heat capacity of the heat medium; The carbon emission intensity of the non-source node f' of the heat recovery network under the action of all heat sources in the lower return pipe is expressed as: ， In the formula, is the carbon intensity of the heat source node e' of the heat recovery network; is the temperature of the non-source node f' of the heat recovery network; is the carbon intensity of the non-source node f' of the heat recovery network; is the set of source nodes of the heat recovery network; S1-3: Establishing a natural gas network energy-carbon flow tracking model Use the reverse flow tracking method to track the amount of air each load receives from the air source: Considering node u as the input gas source node, the net flow balance equation of this node is as follows: ， In the formula, is the node set of the gas source node u; is the gas source output of gas source node u; is the gas flow rate of pipeline uy; is the net inflow of gas source node u; is the countercurrent distribution matrix, whose elements The expression is as follows: ， In the formula, is the net inflow air flow of load node y; then the net flow balance equation of load node y changes to the following: ， In the formula, is the net nodal gas flow vector; is the node gas source force vector; The expression of the gas flow provided by the gas source node u to the load node y is as follows: ， Where W is the number of nodes in the natural gas network; is the gas source output of gas source node u; Based on the principle of proportional contribution, the gas flow rate contribution of each gas source to the load node y is expressed as follows: ， Define the carbon intensity of natural gas network load node y The carbon emissions delivered when the load node y outputs a unit gas flow rate are expressed as follows: ， In the formula, is the carbon intensity of gas source node u; is the carbon intensity of load node y.

3. The energy management method of an integrated energy system considering low-carbon demand response according to claim 2, characterized in that: Step S2 is specifically as follows: Introducing carbon price signals into the low-carbon incentives provided by energy service providers to consumers can be expressed mathematically as follows: ， ， ， In the formula, , and They are the electricity-carbon integrated price, the gas-carbon integrated price, and the heat-carbon integrated price; , and They are electricity sales price, gas sales price and heat sales price respectively; is the carbon tax price.

4. The energy management method of an integrated energy system considering low-carbon demand response according to claim 3 is characterized in that: Step S3 is as follows: S3-1: Establish an optimization model for upper-level energy service providers: S3-1-1: Establish the objective function: The objective function of the energy service provider is to maximize the profit from energy sales, which is expressed as follows: ， In the formula, , , , , and They are electricity sales revenue, gas sales revenue, heat sales revenue, electricity purchase cost, gas purchase cost and environmental cost; is the set of load aggregators; T is the scheduling time; , and are the electricity load demand, gas load demand and heat load demand of load aggregator x at time t respectively; and The prices for electricity purchased from the main grid and gas purchased from the gas source by the energy service provider are as follows; is the main grid carbon intensity; and The energy service provider purchases electricity from the main grid and gas from the gas source respectively; and are the natural gas demand of gas boiler and the natural gas demand of cogeneration unit at time t respectively; is the calorific value of natural gas; is the carbon intensity of the gas source; S3-1-2: Establish constraints: The constraints include power network constraints, natural gas network constraints, thermal network constraints, gas boiler unit constraints, combined heat and power unit constraints, power-to-gas unit constraints, energy storage equipment constraints, wind turbine unit constraints, and energy sales price constraints; ① Power network constraints: ， ， ， ， ， ， ， ， ， In the formula, is the set of generators at grid node i; is the set of power network nodes connected to the power grid node i; is the set of load nodes at grid node i; is the set of cogeneration units at grid node i; is the set of power-to-gas units at grid node i; is the active output of the generator at power node k at time t; is the active power flow of the power grid branch ij at time t; is the electric load demand of load aggregator x at time t; is the electrical output of the combined heat and power unit w at time t; is the electricity demand of the power-to-gas unit w at time t; is the reactive power output of the generator at power node k at time t; is the active power flow of the power grid branch ij at time t; is the reactive power demand of the load aggregator x at time t; is the resistance of the grid branch ij; is the reactance of the grid branch ij; is the voltage of grid node i at time t; is the phase angle of grid node i at time t; and are the minimum active output and maximum active output of the generator at power node k respectively; and are the minimum reactive power output and maximum reactive power output of the generator at power node k respectively; and are the maximum active transmission capacity and reactive transmission capacity of the power grid branch ij respectively; and are the minimum and maximum voltages of all grid nodes respectively; ② Natural gas network constraints: ， ， ， ， In the formula, is the gas source set of gas source node u; is the set of natural gas network nodes connected to the gas source node u; is the set of cogeneration units at the gas source node u; is the set of gas boiler units at the gas source node u; is the set of power-to-gas units at the gas source node u; is the gas output of the gas source node with gas source number d at time t; is the air flow of line uy at time t; is the natural gas demand of the cogeneration unit d1 at time t; is the gas boiler unit at time t natural gas demand; is the gas output of the electric-to-gas unit d2 at time t; is the maximum output of the gas source; L i 'It is the set of nodes of the load aggregator on the natural gas network; and represents the node pressure of the gas source node u and the load node y, and It is the minimum and maximum gas pressure of natural gas network nodes; Since the gas distribution network is radial, that is, the direction of the natural gas flow is pre-set, the Weymouth equation is used to approximate the natural gas flow. Since the Weymouth equation is nonlinear and non-convex, it is first tapered to the second order, and the expression is as follows: ， In the formula, is the pipeline parameter of the natural gas pipeline uy; Then the feasible domain of the optimization model is on the second-order cone surface, which is still a non-convex optimization problem. After relaxing the second-order cone Weymouth equation, the second-order cone programming inequality constraint is obtained. The feasible domain will become a second-order cone and converted into a convex optimization problem for solution. The expression is as follows: ， ③ Thermal network constraints: The heat network modeling using the quality regulation model, the heating network mixing equation is as follows: ， In the formula, is the heating network pipeline flowing into node a; is the heating network pipeline flowing out of node a; is a collection of heating network nodes; is the terminal temperature of the heating network pipe flowing into the heating network pipe o at time t; is the heating network temperature of node a at time t; is the mass flow rate of pipe o in the heating network at time t; node a is the code of each node in the heating network; The mixing temperature equation of the heat recovery network is as follows: ， In the formula, is the heat recovery network pipe flowing into node a'; is the heat recovery network pipe flowing out of node a'; is the mass flow rate of the heat recovery network pipe o' at time t; is the terminal temperature of the heat recovery network pipe flowing into the heat recovery network pipe o' at time t; is the temperature of the heat recovery network at node a' at time t; node a' is the code of each node in the heat recovery network; The temperature loss equation of the heating network is as follows: ， In the formula, is the temperature at the head end of the heating network pipe flowing into node a at time t; v is the pipe heat transfer coefficient; c is the specific heat capacity of water; is the water flow density; and R are the length and thermal resistance of the pipe, respectively; The temperature loss equation of the heat recovery network is as follows: ， In the formula, is the temperature at the head end of the heat recovery network pipe flowing into node a' at time t; The temperature equation at the head end of the heating network is as follows: ， The temperature equation at the head end of the heat recovery network is as follows: ， The output equation of the combined heat and power unit is as follows: ， In the formula, is the number of cogeneration units; is the combined heat and power unit at node a; is the thermal output of the cogeneration unit d1 at time t; is the mass flow rate outflowing from the cogeneration unit d1 at time t; The output equation of the gas boiler unit is as follows: ， In the formula, is the number of gas boiler units; is the gas boiler unit at node a; is the heat output of gas boiler y at time t; The gas boiler at time t Outflow mass flow rate; The temperature constraint equation of the heating network node is as follows: ， In the formula, and are the minimum and maximum temperatures of node a in the heating network, respectively; The heat load demand equation is as follows: ， In the formula, is the heat demand of the heat load at the heating network node a at time t; is the mass flow rate of the heating network load flowing into pipe o at time t; The temperature constraint equation of the heat recovery network node is as follows: ， In the formula, and are the minimum and maximum temperatures of node a' in the heat recovery network respectively; ④Cogeneration unit constraints: ， ， ， In the formula, and are the thermal output and electrical output of the cogeneration unit at time t respectively; and are the thermal efficiency and electrical efficiency of the cogeneration unit, respectively; and is the minimum and maximum value of the thermal output of the cogeneration unit; ⑤ Constraints of gas boiler units: ， ， In the formula, is the thermal output of the gas boiler unit at time t; is the thermal efficiency of the gas boiler unit; and It is the minimum and maximum value of the thermal output of the gas boiler unit; ⑥ Constraints on power-to-gas units: ， ， In the formula, is the gas efficiency of the power-to-gas unit; and It is the minimum and maximum value of gas output of the power-to-gas unit; ⑦ Energy storage equipment constraints: Since the models of electric energy storage, gas energy storage and thermal energy storage are similar, a unified model is used for construction: ， ， ， ， In the formula, and They are respectively the energy discharge and energy charging of the energy storage device at time t; Decision-making quantity for charging and discharging energy storage equipment; and They are the maximum energy storage release and maximum energy charging respectively; The storage energy of the energy storage device; and are the minimum and maximum values ​​of the energy storage capacity of the energy storage device; and They are respectively the charging efficiency and the discharging efficiency of the energy storage device; ⑧Constraints of fan unit: ， In the formula, is the actual power output of the wind turbine unit at time t; Forecasted power output of wind turbine units; ⑨ Constraints on energy sales price: The energy service provider sells energy to the load aggregator at a price that is within the following constraints, ensuring that the price of the energy service provider is no higher than an average value: ， ， ， In the formula, and They are the minimum and maximum prices of electricity sold by energy service providers; and They are the minimum and maximum gas selling prices of energy service providers; and They are the minimum and maximum selling prices of heat by energy service providers; , and They are the average prices of electricity sold by energy service providers, the average prices of gas sold, and the average prices of heat sold; S3-2: Establish load aggregator optimization model: S3-2-1: Establishment of Residential Load Aggregator Constraints: ① Design objective function: maximize consumer surplus, that is, the difference between the utility function of residential users and energy cost. The calculation formula is as follows: ， ， ， ， ， In the formula, is the utility function; and They are the electricity utility function, heat utility function and gas utility function at time t respectively; and is the electrical preference coefficient; and is the thermal preference coefficient; and is the gas preference coefficient; and They are the electricity load demand, heat load demand and gas load demand of the residential load aggregator; ② Establish constraints: The transferable electric load constraints are as follows: ， ， ， ， The transferable gas load constraints are as follows: ， ， ， ， The heat load constraints that can be reduced are as follows: ， ， ， In the formula, and They are the original electric load demand and rigid electric load demand of residential load aggregators; For residential load aggregators, the electric load demand can be shifted; The maximum value of the transferable electric load demand of the residential load aggregator; It is the rigid electric load ratio coefficient of the residential load aggregator; and They are the original gas load demand and rigid gas load demand of residential load aggregators; Shiftable gas load demand for residential load aggregators; The maximum value of the transferable gas load demand of the residential load aggregator; is the rigid gas load ratio factor for the residential load aggregator; and They are the original heat load demand and curtailable heat load demand of the residential load aggregator; The maximum value of heat load demand that can be reduced at a single time for residential load aggregators; The maximum value of heat load demand that can be reduced by the residential load aggregator; S3-2-2: Establish commercial load aggregator constraints: ① Establish the objective function and minimize the energy cost: ， ② Establish constraints: The transferable electric load constraints are as follows: ， ， ， ， The transferable gas load constraints are as follows: ， ， ， The heat load constraints that can be reduced are as follows: ， ， ， In the formula, and They are the original electric load demand and rigid electric load demand of commercial load aggregators; To transfer electric load demand to commercial load aggregators; The maximum value of the electric load demand that can be transferred by commercial load aggregators; The rigid electric load ratio factor for commercial load aggregators; and They are the original gas load demand and rigid gas load demand of commercial load aggregators; Shiftable gas load demand for commercial load aggregators; The maximum value of the transferable gas load demand of the commercial load aggregator; Rigid gas load ratio factor for commercial load aggregators; and They are the original heat load demand and curtailable heat load demand of commercial load aggregators; The maximum value of heat load demand that can be reduced at a single time for commercial load aggregators; For commercial load aggregators, the maximum value of heat load demand can be reduced; S3-2-3: Establishment of industrial load aggregator constraints: ① Establish the objective function and minimize the energy cost: ， ② Establish constraints: The transferable electric load constraints are as follows: ， ， ， ， The transferable gas load constraints are as follows: ， ， ， ， The heat load constraints that can be reduced are as follows: ， ， ， In the formula, and They are the original electric load demand and rigid electric load demand of industrial load aggregators; To provide industrial load aggregators with the ability to shift electrical load demand; The maximum value of the transferable electric load demand for industrial load aggregators; The rigid electric load ratio coefficient for industrial load aggregators; and They are the raw gas load demand and rigid gas load demand of industrial load aggregators; Shiftable gas load demand for industrial load aggregators; The maximum value of the transferable gas load demand of the industrial load aggregator; The rigid gas load ratio factor for industrial load aggregators; and They are the original heat load demand and curtailable heat load demand of industrial load aggregators; For industrial load aggregators, the maximum heat load demand can be reduced at a single time; For industrial load aggregators the maximum heat load demand can be reduced.

Images