A low-carbon scheduling method for two-stage optimization of power grid thermal system of polytropic distributed heat sources

Abstract

The application provides a kind of polymeric distributed heat source power grid heat system day two-stage optimization low carbon scheduling method and related device, including obtaining the initial parameter of power grid heat system;Through aggregator aggregation heat load resource, and determine the feasible region of heat load after aggregation;Calculate the optimal economic operation unit output of power grid heat system to meet system load demand, obtain the power distribution of power grid heat system;According to system power distribution and carbon emission flow theory, calculate the carbon potential intensity of each node of power grid and each node of heat network;Based on carbon potential intensity, the optimal low carbon operation unit output of power grid heat system is calculated using the objective function of low carbon scheduling stage, and low carbon scheduling is carried out based on the optimal low carbon operation unit output.The application determines the feasible region by aggregating heat load resource through aggregator, and considers initial parameters and carbon potential intensity in pre-scheduling and low carbon scheduling two stages respectively, realizes the optimization low carbon scheduling of power grid heat system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-carbon scheduling of power grid heat systems, and particularly relates to a two-stage optimization low-carbon scheduling method for a power grid heat system with aggregated distributed heat sources. BACKGROUND

[0002] Among various types of integrated energy systems, power grid heat systems have attracted much attention in the past decade. By complementing and cooperatively managing different types of energy systems, the overall economy of the system can be improved. With increasing concerns about climate change, it has become increasingly important to study the low-carbon operation of power grid heat systems. Power grid heat systems are divided into transmission and distribution levels, and the concept of "virtual" carbon emission flow for measuring carbon footprint provides a reasonable method for analyzing carbon emissions and promoting low-carbon demand response.

[0003] Heat sources such as heat pumps, electric boilers, or thermal storage can facilitate the conversion of electricity and heat. Considering the interconnected multi-site cogeneration system with heat pumps, the effective unit commitment method can adjust power production to adapt to more renewable energy, and the integration of electric heating devices can convert reduced wind power into heat energy for heating. To release the potential flexibility of the system, the thermal inertia of the regional heating network and the thermostatic controlled load (TCL) in the heating system can be incorporated into the scheduling strategy, and a high computational efficiency is used to manage a large number of flexible resources. The uncertainty of outdoor temperature and indoor heat load can be modeled by stochastic programming and distributed robust joint chance-constrained programming.

[0004] Existing research has made important contributions to the implementation of low-carbon scheduling strategies for power grid heat systems, but there are some deficiencies. The CEF theory mostly only considers the carbon potential of the distribution network system, but does not act on the heat system; the power-carbon two-stage active distribution network scheduling model does not consider the topology of the heat network structure; the economic dispatching model of the cogeneration system does not consider the influence of system carbon emissions; the method of analyzing the carbon responsibility of producers and consumers based on node carbon potential theory does not consider the aggregation of heat load. SUMMARY

[0005] Therefore, the application provides a two-stage optimization low-carbon scheduling method for a power grid heat system with aggregated distributed heat sources, which acts on the power grid heat system with aggregated distributed heat sources, considers the safe operation constraints of the system, uses the node carbon potential obtained after the pre-scheduling of the power grid heat system as the low-carbon scheduling signal, and also considers the aggregation and decomposition process of a large number of heat-controllable residents, thereby improving the economy and flexibility of the overall operation of the system.

[0006] In order to achieve the above purpose, the technical scheme provided by the application is as follows:

[0007] In a first aspect, the application provides a two-stage optimization low-carbon scheduling method for a power grid heat system with aggregated distributed heat sources, comprising the following steps:

[0008] Obtain the initial parameters of the power grid thermal system;

[0009] The thermal load resources are aggregated using an aggregator, and the feasible region of the aggregated thermal load is determined.

[0010] Within the feasible domain of thermal load, based on initial parameters and the objective function of the pre-scheduling stage, the optimal economic operating unit output of the power grid thermal system to meet the system load demand is calculated, and the power distribution of the power grid thermal system is obtained.

[0011] Based on the system power distribution and carbon emission flow theory, calculate the carbon potential intensity of each node in the power grid and each node in the heating network;

[0012] Based on carbon potential intensity, the optimal low-carbon operating unit output of the power grid thermal system is calculated using the objective function of the low-carbon dispatch phase, and low-carbon dispatch is carried out based on the optimal low-carbon operating unit output.

[0013] Furthermore, the pre-scheduling objective of the pre-scheduling phase objective function is to optimize the day-ahead economic operation and determine the minimum operating cost for the entire day. The pre-scheduling phase objective function is as follows:

[0014]

[0015] In the formula, It is the objective function of the pre-scheduling phase; It refers to the number of wind turbines. It is a function of the operating cost of the wind turbine. It is the operating cost coefficient of the wind turbine. Is the wind turbine in? Actual output at any given moment; It refers to the number of combined heat and power (CHP) units. It is a function of the operating cost of a combined heat and power unit. It is a combined heat and power generator in Active power at any given time It is a combined heat and power generator in Heat production capacity at any given time It is the operating cost coefficient of a combined heat and power unit; This refers to the number of conventional generators. It is a function of the operating cost of a conventional generator. Is it a conventional generator in Output power at any moment This is the operating cost coefficient for conventional generators; It refers to the number of electric boilers. It is a function of the operating cost of the electric boiler. It's an electric boiler. Input electrical power at any given time It is the operating cost coefficient of the electric boiler; It refers to the number of gas-fired boilers. It is a function of the operating cost of the gas-fired boiler. It is a gas-fired boiler. The heat power provided at all times It is the operating cost coefficient of a gas-fired boiler; It refers to the number of energy storage systems. It is a function of the operating cost of the energy storage system. It is the operating cost coefficient of the energy storage system. and Separate energy storage systems The charging power and discharging power at any given time, where the subscript i represents the node number of a certain type of equipment connected to the power grid heating system, and T is the pre-scheduled time period.

[0016] Furthermore, the objective function for the low-carbon scheduling phase is as follows:

[0017]

[0018] In the formula, It is the objective function for the low-carbon scheduling phase; It is the objective function of the pre-scheduling phase; It is the number of nodes in the power grid participating in load demand response. It is the first power grid The node load demand response cost of each node. It is the demand response cost coefficient. and These are nodes exist Adjusting load power upwards and downwards at specific times; It refers to the number of nodes in the power grid system. It is the first power grid system The node carbon emission cost of each node, It is the carbon emission cost coefficient. It is a low-carbon compensation incentive coefficient. It is the first power grid system Each node The nodal carbon intensity at time t, i.e., the carbon potential intensity at each node of the power grid. It is the first power grid system Power after each node responds to demand; It refers to the number of nodes in the heating network system. It is the first heating network system The nodal carbon intensity of each node. It is the first heating network system Each node The nodal carbon intensity at any given time, i.e., the carbon potential intensity at each node of the heating network. It is the first heating network system Power after each node responds to demand; It refers to the number of energy storage systems. It is the first The carbon emission cost of connecting each node to the energy storage system and These are nodes The connected energy storage system The charging power and discharging power at any given time; It refers to the number of distributed energy units. It is the first The carbon emission cost of connecting individual nodes to distributed energy units To access the first The carbon emission intensity of distributed energy units per node and These are nodes The connected distributed energy units are in The electrical and thermal power at any given moment.

[0019] Furthermore, the carbon potential intensity of each node in the power grid and each node in the heating network is calculated according to the following formula:

[0020] Carbon potential intensity at grid nodes:

[0021]

[0022] In the formula, It is a node The nodal carbon intensity, i.e., the carbon potential intensity at each node of the power grid. It is a generator to the node Injected active power, It is a node Generator carbon strength of generator set It is a line Transmission power, It is a line The carbon intensity of the branch, It is directed to the node A collection of transmission lines into which power is injected;

[0023] Carbon potential intensity at heating network nodes:

[0024]

[0025] In the formula, It is a water supply pipe node in the heating network. The nodal carbon strength, Based on nodes For all water supply pipes of the injection node, It is a water supply pipe carbon strength, is the mass flow rate of the water supply pipe , is the outlet water temperature of the water supply pipe , is the temperature difference between the inlet water and the outlet water of the water supply pipe ;

[0026] is the node carbon intensity of the return pipe node in the heat supply network, is all the return pipes with the node as the injection node, is the carbon intensity of the return pipe , is the mass flow rate of the return pipe , is the outlet water temperature of the return pipe , is the temperature difference between the inlet water and the outlet water of the return pipe .

[0027] Further, the inlet water temperature and the outlet water temperature of the water supply / return pipe are determined by using the heat supply pipe model, and the expression of the heat supply pipe model is as follows:

[0028]

[0029]

[0030]

[0031] In the formula, is the inlet water temperature of the water supply / return pipe at the moment , is the pipe temperature of the water supply / return pipe at the inlet node , is the pipe temperature of the return pipe at the outlet node ; is the outlet water temperature of the water supply / return pipe at the moment , is an intermediate variable of the water supply / return pipe , and are the ambient temperatures of the inlet node and the outlet node at the moment , is the heat conduction coefficient per unit length of the water supply / return pipe , c is the heat coefficient, , and are the water supply / return pipes are the mass flow rate, length and cross-sectional area at time is the density of water is the heat scheduling interval.

[0032] Further, when performing scheduling calculation according to the objective function, the constraint conditions also need to be met, as follows:

[0033]

[0034] wherein, , and are the minimum active power, the active power at time and the maximum active power of the cogeneration unit; , and are the minimum heat production power, the heat production power at time and the maximum heat production power of the cogeneration unit; is the ramping power of the cogeneration unit; , and are the feasible operating region parameters of the th cogeneration generator; is the heat power provided by the electric boiler is the maximum heat power provided by the electric boiler, and the minimum heat power is 0; is the input electric power of the boiler is the electric-heat conversion coefficient; and are the minimum and maximum input electric power of the electric boiler; is the heat power provided by the gas boiler; is the natural gas flow rate; is the low calorific value of natural gas; is the gas-heat conversion efficiency; and are the lower and upper limits of the natural gas flow rate; and are the upper and lower limits of the power output of the th conventional generator; is the ramp rate limit of the th conventional generator; is the actual output of the wind turbine is the cumulative distribution function of the standard normal distribution and are the mean and variance of the wind power prediction, respectively​ is the confidence level; and are the nodes charging and discharging power; and are the nodes charging and discharging power upper limit; and are the charging and discharging efficiency; is the capacity; is the state of charge, and are the state of charge lower and upper limits; is the node active power after implementing demand response at time; , and are the nodes original load, up-regulated load power and down-regulated load power at time; is the node upper limit of adjustable load power at time.

[0035] Further, the thermal load resource is described by using a thermal controllable resident (TCR) model, and for the node , the aggregated thermal load feasible region is as follows:

[0036]

[0037] In the formula, and are the aggregated scaling factor and translation factor, and are the scaling factor and translation factor of each individual TCR in the aggregator set belonging to the node , is the accurate aggregated feasible region of the aggregator of the node , is the thermal power consumption of the aggregator of the node , is a real number set, and are the coefficient matrixes as constraint conditions.

[0038] In a second aspect, the present application provides a device for aggregating distributed heat sources for day-ahead two-stage optimization low-carbon scheduling of a power grid thermal system, comprising:

[0039] a data acquisition module configured to acquire initial parameters of the power grid thermal system;

[0040] a polymerization module configured to aggregate heat load resources by a polymerization device and determine an aggregated heat load feasible region;

[0041] a pre-scheduling calculation module configured to calculate, within the heat load feasible region, optimal economic operation unit output of the power grid heat system to meet system load demand based on initial parameters and a pre-scheduling stage objective function, and obtain power distribution of the power grid heat system;

[0042] a carbon potential intensity calculation module configured to calculate carbon potential intensity of each node of the power grid and each node of the heat network according to the system power distribution and carbon emission flow theory;

[0043] a low-carbon scheduling calculation module configured to calculate optimal low-carbon operation unit output of the power grid heat system based on the carbon potential intensity and a low-carbon scheduling stage objective function, and perform low-carbon scheduling based on the optimal low-carbon operation unit output.

[0044] In a third aspect, the present application further provides a computer device, which comprises a processor and a memory:

[0045] The memory is configured to store a computer program and send instructions of the computer program to the processor.

[0046] The processor executes the instructions of the computer program to implement the method for two-stage optimal low-carbon scheduling of the power grid heat system of the aggregated distributed heat source according to the first aspect.

[0047] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to implement the method for two-stage optimal low-carbon scheduling of the power grid heat system of the aggregated distributed heat source according to the first aspect.

[0048] In summary, the present application provides a kind of polymeric distributed heat source power grid heat system day two-stage optimization low carbon scheduling method and related device, including obtaining the initial parameter of power grid heat system;Through aggregator aggregation heat load resource, and determine the heat load feasible region after aggregation;In the range of heat load feasible region, based on initial parameter and pre-scheduling stage objective function, the optimal economic operation unit output of power grid heat system to meet system load demand is calculated, and the power distribution of power grid heat system is obtained;According to system power distribution and carbon emission flow theory, the carbon potential intensity of each node of power grid and each node of heat network is calculated;Based on carbon potential intensity, the optimal low carbon operation unit output of power grid heat system is calculated using the objective function of low carbon scheduling stage, and low carbon scheduling is carried out based on the optimal low carbon operation unit output.The present application determines the feasible region by aggregating heat load resource using aggregator, and considers initial parameter and carbon potential intensity in pre-scheduling and low carbon scheduling two stages respectively, realizes the optimization low carbon scheduling of power grid heat system, solves the problems that CEF theory does not act on heat system in existing research, heat and power cogeneration system economic scheduling model does not consider system carbon emission influence and does not consider heat load aggregation. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 A flow chart of a kind of polymeric distributed heat source power grid heat system day two-stage optimization low carbon scheduling method provided by the embodiment of the present application;

[0051] Figure 2 A composition block diagram of a kind of polymeric distributed heat source power grid heat system day two-stage optimization low carbon scheduling device provided by the embodiment of the present application;

[0052] Figure 3 A composition block diagram of a kind of computer equipment. DETAILED DESCRIPTION

[0053] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below, combined with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Referring to Figure 1 The embodiment of the present application provides a kind of polymeric distributed heat source power grid thermodynamic system day-to-day two-stage optimization low-carbon scheduling method, comprising the following steps:

[0055] S1: the initial parameter of power grid thermodynamic system is acquired.

[0056] It needs to be explained that the purpose of this step is to provide basic data for subsequent calculation and analysis.Initial parameters include the structure information of power grid, generator parameters, load demand prediction, pipe parameters of heat network, heat load demand and the like.These parameters describe the initial state and characteristics of the system.

[0057] S2: heat load resources are aggregated by aggregator, and the feasible region of aggregated heat load is determined.

[0058] It needs to be explained that aggregator is a tool or mechanism for integrating dispersed resources;feasible region refers to the range of variable values that meet certain constraint conditions, which refers to the possible range of values of heat load under various limit conditions.

[0059] This step integrates dispersed heat load by using aggregator, considers the equipment parameters and heating demand differences of different residential users, and then determines the feasible range after aggregation.The purpose is to improve the flexibility of load aggregator, provide clear constraint conditions for subsequent scheduling, and ensure that the scheduling scheme is within the feasible heat load range.

[0060] S3: within the heat load feasible region, based on the initial parameters and the objective function of pre-scheduling stage, the optimal economic operation unit output of power grid thermodynamic system to meet system load demand is calculated, and the power distribution of power grid thermodynamic system is obtained.

[0061] It needs to be explained that the objective function of pre-scheduling stage includes generator operation cost, energy procurement cost and the like;unit output refers to the power output of each generator, heat source and the like.

[0062] This step determines the optimal power output combination of each unit in power grid thermodynamic system under the constraint of heat load feasible region and system load demand based on the objective function of pre-scheduling stage, with the goal of minimizing operation cost, by optimization algorithm.The purpose is to determine the optimal power output of each unit in power grid thermodynamic system to meet economic operation target, so as to obtain the power distribution of the system, and provide basis for subsequent analysis and scheduling.

[0063] S4: according to system power distribution and carbon emission flow theory, the carbon potential intensity of each node of power grid and each node of heat network is calculated.

[0064] It should be noted that the carbon emission flow theory is used to analyze the distribution of carbon emissions in the power system; and the carbon potential intensity is an index for measuring the degree of carbon emissions at the node.

[0065] In this step, the carbon emission flow theory is used to analyze the distribution of carbon emissions in each part of the system, and the relationship between the carbon emissions at each node and the energy flow is determined in combination with the power distribution of the system. The purpose is to calculate the carbon potential intensity of each node in the power grid and heat grid, that is, the measurement value of the carbon emissions per unit of energy at the node, to provide a basis for low-carbon dispatching.

[0066] S5: Based on the carbon potential intensity, the optimal low-carbon operation unit output of the power grid and heat system is calculated by using the target function in the low-carbon dispatching stage, and low-carbon dispatching is performed based on the optimal low-carbon operation unit output.

[0067] It should be noted that the low-carbon dispatching stage target function aims to achieve low carbon, and can include carbon emission cost, carbon emission reduction target, etc.; and the low-carbon dispatching refers to a dispatching strategy for reducing system carbon emissions by optimizing unit output, etc.

[0068] In this step, the carbon potential intensity is used as the basis for optimization calculation by using the target function in the low-carbon dispatching stage. The target is usually to minimize the system carbon emissions on the basis of considering factors such as carbon emission cost. The purpose is to determine the optimal output of each unit under the condition of meeting the low-carbon target, and to realize the low-carbon operation of the power grid and heat system.

[0069] The embodiment provides a kind of power grid and heat system day-to-day two-stage optimization low-carbon dispatching method of polymeric distributed heat source, and heat load resource is aggregated by aggregator, the influence of the aggregation of heat load on system scheduling is fully considered, and the flexibility of load aggregator and the overall performance of system are improved. Carbon potential intensity of each node of heat network is calculated, carbon emission flow theory is applied to heat system, and comprehensive carbon potential analysis of power grid and heat network is realized. In the calculation process, the parameters and structure of the heat network are considered, the heat load resource is aggregated, and the carbon potential intensity of the heat network node is calculated, so that the influence of the heat network structure topology on scheduling is fully considered. In the low-carbon dispatching stage, carbon emission factor is considered, by the design of target function and the calculation of carbon potential intensity, the system carbon emission is included in the scheduling decision, and the balance of economic operation and low-carbon target is realized.

[0070] In an embodiment of the present application, the power grid and heat system considers comprehensive distributed energy (DER), which sequentially includes a combined heat and power (CHP) unit and a boiler unit output model, a conventional power generator (CON) model, a renewable energy wind power generator model, a distributed energy storage system (ES) model and a power load demand response (DR) model. Based on the power grid and heat system, the following constraint conditions need to be met when scheduling calculation is performed:

[0071]

[0072] In the formula, , and These are the minimum active power of the combined heat and power unit, and... Real-time active power and maximum active power; , and These are the minimum heat production capacity of the combined heat and power unit, and... Real-time heat production capacity and maximum heat production capacity; For the ramp-up power of cogeneration units; , and It is the first Feasible operating area parameters for a combined heat and power generator; The heat power provided to the electric boiler The maximum thermal power provided to the electric boiler, and the minimum thermal power is 0; Input electrical power to the boiler, The electrothermal conversion coefficient; and These are the minimum and maximum input electrical power of the electric boiler; The thermal power provided to the gas-fired boiler; Natural gas flow rate; It has a low calorific value for natural gas; For gas-heat conversion efficiency; and These are the lower and upper limits for natural gas flow, respectively. and It is the first The upper and lower limits of the power output of a conventional generator; It is the first The ramp rate limit of a conventional generator; This refers to the actual output of the wind turbine. It is the cumulative distribution function of the standard normal distribution. and These are the mean and variance of wind power generation forecasts, respectively. It is the confidence level; and They are nodes Charging and discharging power; and They are nodes Maximum charging and discharging power; and It refers to charge / discharge efficiency; It refers to capacity; It is in charging state. and is the lower and upper limit of the state of charge; is the node is the node is the active power after implementing demand response at the moment , is the node is the original load, the up-regulated load power and the down-regulated load power of the node at the moment is the node at the moment is the upper limit of the adjustable load power of the node at the moment

[0073] The embodiment constructs a power grid thermal system model containing various distributed energy sources, covering a combined heat and power unit, a boiler, a conventional generator, a wind turbine, a distributed energy storage system and a power load demand response model. A series of constraint conditions that the system needs to meet during dispatch calculation are defined, related to the power, thermal power, ramping power, input and output limits of various distributed energy equipment. The safety operation constraints of the power system distributed resource output constraint, load demand response constraint, network structure topology and line flow constraint are also considered, effectively guaranteeing the safe and stable operation of the power system.

[0074] In an embodiment of the present application, the thermal load resource is described by using a thermal controllable resident TCR model. The thermal controllable resident TCR model considers the coupling relationship between the heating demand of the resident building and the comfortable temperature and the environment temperature, realizes accurate description of the heating flexibility, and adjusts the indoor temperature within the comfort range of the temperature requirement of the user. The TCR resource generally participates in system operation regulation and market transaction after being integrated by an aggregator, and the equipment parameters and heating demand of different resident users are different. Fully utilizing the complementary characteristics in the feasible domain can improve the flexibility of the load aggregator. The accurate aggregation feasible domain of multiple TCRs is equal to the Minkowski sum of the feasible domains thereof:

[0075]

[0076] wherein: , is the heat consumption of the TCR aggregator located at the node at the moment is the accurate aggregation feasible domain of the aggregator; represents the Minkowski sum calculation

[0077] The maximum internal approximate polyhedron feasible domain is to translate and scale the basic isomorphic polyhedron of each individual TCR, and then calculate the Minkowski sum of the approximate feasible region. The maximum internal approximate polyhedron feasible domain can be expressed as:

[0078] ​

[0079] In the formula, and This can be obtained by averaging the parameters of all internal TCRs within the aggregator; and These are scaling factors and translation factors; This represents a group of TCRs belonging to the aggregator; It is the first The internal approximate polyhedron of a TCR. The above planning can be transformed into an equivalent linear programming problem:

[0080]

[0081] In the formula, ; These are newly introduced auxiliary variables. Because of the uncertainty in hourly outdoor ambient temperature, the inequality constraints can be rearranged into chance constraints; It is the probability of violating the inequality constraint; It is the first One inequality constraint; and Representation matrix and The OK; Represents a matrix with uncertain parameters The first in The uncertainty of ambient temperature is modeled using the Distributed Broken Bar Chance Constraint (DRCCP), resulting in the final maximum inner approximation model of the standard linear TCR, located at node [node number missing]. The Minkowski sums of multiple TCR feasible regions under the aggregator are computable as follows:

[0082]

[0083] In the formula, and The scaling factor and translation factor after aggregation. and It is for nodes aggregator collection The scaling factor and translation factor for each individual TCR It is a node The exact aggregation feasible region of the aggregator. It is a node The heat dissipation of the aggregator, It is the set of real numbers. and It is the coefficient matrix that serves as a constraint condition.

[0084] The embodiment adopts a polyhedral-based TCR aggregation / decomposition method to obtain the inner approximation feasible region and equivalent cost parameters of the TCR aggregator, models the uncertainty of the parameters through a distributed robust chance constraint (DRCCP), obtains a final maximum inner approximation model of the standard linear TCR, and better considers the constraint conditions of the thermal load in system scheduling.

[0085] In an embodiment of the present application, the carbon emissions of the power grid thermal system are reasonably analyzed by establishing a carbon emission flow theory and its calculation model. Although carbon dioxide is directly emitted by the generator, due to the use of energy, the consumer is actually the main driving force of emission. The contribution of energy demand per megawatt hour to total carbon emissions can vary greatly at different distribution levels, depending on the location distribution of the load in the network.

[0086] According to the power grid carbon emission flow CEF (Carbon Emission Flow) model, the carbon emission flow of the power system can be obtained as follows:

[0087] 1) The branch carbon intensity BCI (Branch Carbon Intensity) of each line represents the carbon emissions per megawatt hour of energy flowing through the transmission line, and according to the proportional sharing assumption, the branch carbon intensity of all transmission lines flowing out of the bus shares the same value, which is equal to the node carbon intensity NCI (Node Carbon Intensity) of the node flowing in: , wherein: represents the direction of power flow, and the node is the inflow node of the line .

[0088] 2) The carbon emission flow rate CEFR (Carbon Emission Flow Rate) of each line is equal to the sum of the carbon emissions embedded in the power flow: , wherein: represents the transmission power of the line .

[0089] 3) The NCI of each node is determined by the active power injection of the transmission lines and generators connected to the node, and follows the principle of energy superposition. Under the proportional sharing assumption, the NCI is equal to the weighted average of the carbon intensity of all injected energy flow, which is calculated by the following formula:

[0090]

[0091] , wherein: represents the active power injected by the generator to the node , and is the node GCI of the generator set, for injecting power to the nodes a set of power transmission lines.

[0092] The heat network is composed of heat sources, heat loads and heat supply pipelines. Heat is generated by heat sources and transported to loads through water circulation in heating pipelines. The carbon emission flow of the heat supply system can be obtained as follows:

[0093] 1) Inlet CEFR: the inlet CEFR of each heating pipeline is equal to the product of its BCI and inlet energy flow:

[0094]

[0095] The similar outlet CEFR can be expressed as follows:

[0096]

[0097] 2) Carbon emissions of heat loss: due to the transmission loss of the heat network, the outlet water temperature of each pipeline is lower than the inlet water temperature:

[0098]

[0099] Where: , respectively represent the temperature difference between the inlet and outlet water of the pipeline in the water supply network and the return water network.

[0100]

[0101] 3) NCI: for each node in the heat network, the water injected from different pipelines is mixed, so the NCI of the water supply pipe and the return water pipe node can be expressed as follows:

[0102]

[0103] Where: respectively represent all water supply pipelines and all return water pipelines with the node as the injection node. According to the principle of energy scheduling, the BCI of each pipeline in the water supply network and the return water network is equal to the NCI of the pipeline inflow node.

[0104] 4) Carbon emissions of heat source nodes and heat load nodes:

[0105] Heat sources inject heat into pipelines to increase water temperature. The carbon emissions related to the injected heat should be allocated to the heat source node, so the CEFR of the water supply network heat source node is the sum of the CEFR of the return water network heat source node and the CEFR of the heat supply unit itself:

[0106]

[0107] wherein: represents the set of nodes injected thermal power, represents the set of heat supply nodes.

[0108] The present embodiment analyzes the carbon emissions of the power grid heat supply system by establishing a carbon emission flow theory and a calculation model thereof. In the power grid, branch carbon intensity (BCI), carbon emission flow rate (CEFR) and node carbon intensity (NCI) are defined to quantify the carbon emissions of each part; in the heat supply network, the inlet and outlet CEFR, heat loss carbon emissions, node NCI and carbon emissions of the heat source and heat load nodes are determined, so as to comprehensively analyze the carbon emission situation and contribution of different parts of the power grid heat supply system.

[0109] In the calculation of the carbon intensity in the foregoing embodiments, the parameters of the inlet and outlet water temperatures of the pipeline need to be determined. In an embodiment of the present application, a heat supply network pipeline model is proposed to determine the relevant parameters.

[0110] The heat supply system is an inertial system. Considering the transmission delay along the length of the pipeline and the thermal inertia of the pipeline wall, the pipeline temperature at the outlet at time t is derived from the inlet node temperature at time t-1. temperature at the outlet at time t is derived from the inlet node temperature at time t-1. is an intermediate variable related to the mass flow rate, which can be expressed as: wherein: is the density of water, taken as . , , are the mass flow rate, length and cross-sectional area of the water supply / return pipeline at time t, is the heat scheduling interval.

[0111] does not correspond to an accurate time interval, and the average weighted method is used, i.e. is the floor of , which is used as the weight of the contribution of the temperature at the inlet position of the pipeline to the temperature at the outlet position at times and . In addition, there is a temperature difference between the hot water temperature in the heat supply network pipeline and the ambient temperature outside the pipeline, so the hot water exchanges heat with the outside environment during the flow in the pipeline, which is reflected as heat loss. Considering the time delay and heat loss factors, it is assumed that is equal to 0, and the heat supply network dynamic model considering time delay and heat loss is established as follows:

[0112] ​

[0113] in: Indicates water supply / return pipes Thermal conductivity per unit length, Represents a node Ambient temperature.

[0114] This embodiment clarifies the inlet and outlet water temperature parameters in the pipeline during carbon intensity calculation and proposes a heating network pipeline model. This model considers the inertia of the thermal system, including pipeline transmission delay and thermal inertia, and introduces intermediate variables related to mass flow rate to describe pipeline characteristics. It also uses an average weighting method to handle inaccurate time intervals and determine their weight in calculating the pipeline temperature contribution. Finally, a dynamic model of the heating network is established considering heat loss factors, while also taking into account the heating system network topology, pipeline temperature constraints, and heat exchange constraints to ensure the safe and stable operation of the heating system.

[0115] In the pre-scheduling phase, baseline power flow calculations for the power grid and heating network are performed based on distributed resource output constraints and the system's original load demand. This calculation serves as the basis for formulating scheduling strategies. The objective function is to minimize energy costs over all time intervals. After pre-scheduling is completed, the overall carbon potential of the power grid is analyzed using carbon emission flow theory based on the power flow calculation results, providing a reference for the low-carbon scheduling phase. In one embodiment of the invention, a pre-scheduling phase objective function is provided, wherein energy costs are expressed in the general form of convex quadratic functions of electricity and heat, as follows:

[0116]

[0117] In the formula, It is the objective function of the pre-scheduling phase; It refers to the number of wind turbines. It is a function of the operating cost of the wind turbine. It is the operating cost coefficient of the wind turbine. Is the wind turbine in? Actual output at any given moment; It refers to the number of combined heat and power (CHP) units. It is a function of the operating cost of a combined heat and power unit. It is a combined heat and power generator in Active power at any given time It is a combined heat and power generator in Heat production capacity at any given time It is the operating cost coefficient of a combined heat and power unit; This refers to the number of conventional generators. It is a function of the operating cost of a conventional generator. Is it a conventional generator in Output power at any moment This is the operating cost coefficient for conventional generators; It refers to the number of electric boilers. It is a function of the operating cost of the electric boiler. It's an electric boiler. Input electrical power at any given time It is the operating cost coefficient of the electric boiler; It refers to the number of gas-fired boilers. It is a function of the operating cost of the gas-fired boiler. It is a gas-fired boiler. The heat power provided at all times It is the operating cost coefficient of a gas-fired boiler; It refers to the number of energy storage systems. It is a function of the operating cost of the energy storage system. It is the operating cost coefficient of the energy storage system. and Separate energy storage systems The charging power and discharging power at any given time, where the subscript i represents the node number of a certain type of equipment connected to the power grid heating system, and T is the pre-scheduled time period.

[0118] In one embodiment of the present invention, an objective function for the low-carbon scheduling stage is proposed as follows:

[0119]

[0120] The objective function in the pre-scheduling phase Based on this, the system's load demand response cost and various carbon emission costs were added, resulting in the optimization objective function for the low-carbon scheduling phase. .in: It is the number of nodes in the power grid participating in load demand response. For the power grid The node load demand response cost is used to compensate users for power inconvenience caused by adjusting their own power consumption, thereby alleviating peak load pressure on the power grid. This is the demand response cost coefficient; / For power grid / heating network The node carbon emission cost of each node, where the carbon emission cost coefficient is... and low-carbon compensation incentive coefficient This is to encourage users to choose energy sources with low carbon emission intensity and to improve the absorption rate of renewable energy in the system; For the first Power after each node responds to demand; For the first Carbon emission costs of connecting individual nodes to an energy storage system; For the first The carbon emission cost of connecting each node to a distributed energy unit, of which The carbon emission intensity of the distributed energy unit accessing the first node. The carbon emission intensity of the distributed energy unit accessing the first node.

[0121] The low-carbon scheduling method proposed in the above embodiment acts on the power grid heat system of the aggregated distributed heat source, considers system safe operation constraints and electric-thermal coupling, is more suitable for the optimal scheduling of the power grid heat system, ensures stable operation of the system, has high calculation efficiency, and can promote renewable energy and reduce overall system operation cost and total carbon emission cost. Under the guidance of low-carbon incentives, the node carbon potential obtained by pre-scheduling the power grid heat system is used as a low-carbon scheduling signal, the flexible advantages of distributed resources such as demand response and energy storage systems are fully utilized, low-carbon scheduling is coordinated, bidirectional coordination and interaction on the supply and demand sides are promoted, and the economy and flexibility of overall system operation are improved; the aggregation and disaggregation process of a large number of heat-controllable residents is considered, the polyhedral aggregation and disaggregation method fully considers the heterogeneity of heat-controllable body parameters, strictly ensures the operation range and thermal comfort of a single heat-controllable body, and ensures that the heat network pipeline temperature and the heat network node temperature remain within a safe range.

[0122] Based on the same inventive concept, the embodiments of the present application also provide an aggregated distributed heat source power grid heat system day-ahead two-stage optimization low-carbon scheduling device for implementing the above-mentioned aggregated distributed heat source power grid heat system day-ahead two-stage optimization low-carbon scheduling method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in the following aggregated distributed heat source power grid heat system day-ahead two-stage optimization low-carbon scheduling device embodiments can be referred to the limitations of the aggregated distributed heat source power grid heat system day-ahead two-stage optimization low-carbon scheduling method in the above text, which will not be repeated here.

[0123] Referring to Figure 2 , the present application provides an aggregated distributed heat source power grid heat system day-ahead two-stage optimization low-carbon scheduling device, comprising:

[0124] A data acquisition module is configured to acquire initial parameters of the power grid heat system.

[0125] An aggregation module is configured to aggregate heat load resources through an aggregator and determine a heat load feasible region after aggregation.

[0126] A pre-scheduling calculation module is configured to calculate optimal economic operation unit output of the power grid heat system to meet system load demand within the heat load feasible region after aggregation, based on the initial parameters and a pre-scheduling stage objective function, and obtain power distribution of the power grid heat system.

[0127] A carbon potential intensity calculation module is configured to calculate carbon potential intensity of each node of the power grid and each node of the heat network according to the system power distribution and carbon emission flow theory.

[0128] a low-carbon scheduling calculation module, configured to calculate optimal low-carbon operation unit output of the power grid thermal system by using a target function of a low-carbon scheduling stage based on the carbon potential intensity, and perform low-carbon scheduling based on the optimal low-carbon operation unit output.

[0129] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit or module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit or module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0130] With reference to Figure 3 The embodiment of the present application also provides a computer device, comprising a memory and a processor and a computer program stored in the memory, when the computer program is executed on the processor, the computer program realizes the power grid thermal system day-ahead two-stage optimization low-carbon scheduling method of the aggregated distributed heat source as any one of the above methods.

[0131] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The computer device is only an example and does not constitute a limitation on the computer device, and can include more or fewer components than shown, or combine certain components, or different components, for example, can also include input and output devices, network access devices and the like.

[0132] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0133] The memory can be an internal storage unit of the computer device in some embodiments, for example, a hard disk or a memory of the computer device. The memory can also be an external storage device of the computer device in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both the internal storage unit and the external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, for example, program codes of the computer program, etc. The memory can also be used to temporarily store data that has been output or will be output.

[0134] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is run by a processor to implement the method for low-carbon scheduling of an aggregated distributed heat source power grid heat system in a day-ahead two-stage optimization manner.

[0135] In this embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the computer program for instructing the relevant hardware to complete all or part of the processes in the above-described embodiment methods can be stored in a computer readable storage medium. The computer program can be executed by a processor to implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunication signal.

[0136] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0137] Those of ordinary skill in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0138] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal equipment and methods can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0139] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for low-carbon scheduling of a two-stage day-ahead optimization of a thermal system of a power grid of a polytropic distributed heat source, characterized in that, The method comprises the following steps: obtaining initial parameters of a power grid and heat system; aggregating heat load resources by an aggregator and determining a feasible region of the aggregated heat load; within the feasible region of the heat load, calculating optimal economic operation unit output of the power grid and heat system to meet system load demand based on the initial parameters and a pre-scheduling stage objective function, and obtaining power distribution of the power grid and heat system; calculating carbon potential intensity of each node of the power grid and each node of the heat grid according to system power distribution and carbon emission flow theory; calculating optimal low-carbon operation unit output of the power grid and heat system based on the carbon potential intensity and using a low-carbon scheduling stage objective function, and performing low-carbon scheduling based on the optimal low-carbon operation unit output; the pre-scheduling stage objective function is day-ahead economic operation optimization, and determines minimum operation cost for the whole day, and the pre-scheduling stage objective function is as follows: ； wherein, is the pre-scheduling stage objective function; is the number of wind turbines, is the operation cost function of wind turbines, is the operation cost coefficient of wind turbines, is the actual output of wind turbines at time ; is the number of combined heat and power units, is the operation cost function of combined heat and power units, is the active power of combined heat and power generators at time ; is the heat output of combined heat and power generators at time ; is the operation cost coefficient of combined heat and power units; is the number of conventional generators, is the operation cost function of conventional generators, is the output power of conventional generators at time ; is the operation cost coefficient of conventional generators; is the number of electric boilers, is the operation cost function of electric boilers, is the input electric power of electric boilers at time ; is the operation cost coefficient of electric boilers; is the number of gas boilers, is the operation cost function of gas boilers, is the heat output of gas boilers at time ; is the operation cost coefficient of gas boilers; is the number of energy storage systems, is the operation cost function of energy storage systems, is the operation cost coefficient of energy storage systems, and are the charging power and discharging power of energy storage systems at time , respectively, and subscript i represents the node number of a certain type of device connected to the power grid system, n represents the node number of the heat network system, and T is the pre-scheduling period. 2.The method of claim 1, wherein, the objective function of the low-carbon scheduling stage is as follows: ； In the formula, It is the objective function for the low-carbon scheduling phase; It is the objective function of the pre-scheduling phase; It is the number of nodes in the power grid participating in load demand response. It is the first power grid The node load demand response cost of each node. It is the demand response cost coefficient. and These are nodes exist Adjusting load power upwards and downwards at specific times; It refers to the number of nodes in the power grid system. It is the first power grid system The node carbon emission cost of each node, It is the carbon emission cost coefficient. It is a low-carbon compensation incentive coefficient. It is the first power grid system Each node The nodal carbon intensity at time t, i.e., the carbon potential intensity at each node of the power grid. It is the first power grid system Power after each node responds to demand; It refers to the number of nodes in the heating network system. It is the first heating network system The nodal carbon intensity of each node. It is the first heating network system Each node The nodal carbon intensity at any given time, i.e., the carbon potential intensity at each node of the heating network. It is the first heating network system Power after each node responds to demand; It refers to the number of energy storage systems. It is the first The carbon emission cost of connecting each node to the energy storage system and These are nodes The connected energy storage system The charging power and discharging power at any given time; It refers to the number of distributed energy units. It is the first The carbon emission cost of connecting individual nodes to distributed energy units To access the first The carbon emission intensity of distributed energy units per node and These are nodes The connected distributed energy units are in The electrical and thermal power at any given moment. 3.The method of claim 1, wherein, the carbon potential intensity of each node of the power grid and each node of the heat grid is calculated according to the following formula respectively: carbon potential intensity of a power grid node: ； wherein is the nodal carbon intensity, i.e. the grid's nodal carbon potential intensity, is the nodal carbon intensity, i.e. the grid's nodal carbon potential intensity, is the active power injected by the generators to the node is the active power injected by the generators to the node is the generator carbon intensity of the generator set of the node is the generator carbon intensity of the generator set of the node is the transmission power of the line is the transmission power of the line is the branch carbon intensity of the line is the branch carbon intensity of the line is the set of transmission lines injecting power to the node is the set of transmission lines injecting power to the node carbon potential intensity of a heat grid node: ； wherein is the carbon intensity of the node of a water supply pipe in the district heating network, is all water supply pipes with the node as injection node, is the carbon intensity of the water supply pipe , is the mass flow of the water supply pipe , is the outlet temperature of the water supply pipe , is the temperature difference between inlet and outlet of the water supply pipe . It is a return water pipe node in the heating network. The nodal carbon strength, Based on nodes For all return water pipes of the injection node, It is a return water pipe carbon strength, It is a return water pipe mass flow rate It is a return water pipe The outlet water temperature, It is a return water pipe The temperature difference between the inlet and outlet water.

4. The method of claim 3, wherein the method further comprises: Water supply / return pipe The water inlet temperature and the water outlet temperature are determined using a heat network pipe model, the expression of which is as follows: ； ； ； In the formula, It is a water supply / return pipe exist The inlet water temperature at any given time, It is the water supply / return pipe at the inlet node The pipe temperature, It is the return water pipe at the outlet node Pipe temperature; It is a water supply / return pipe exist The water temperature at any given time It is a water supply / return pipe intermediate variables, and These are the entry nodes. and export node exist The ambient temperature at any given time It is a water supply / return pipe Thermal conductivity per unit length, where c is the thermal conductivity. , and These are water supply / return pipes. exist Mass flow rate, length, and cross-sectional area at any given time. The density of water, This refers to the heat management interval.

5. The method of claim 1, wherein the method further comprises: when performing scheduling calculation according to the objective function, constraint conditions need to be met, as follows: ； where, , and are the minimum active power, the active power at the moment and the maximum active power of the cogeneration unit, respectively; , and are the minimum heat production power, the heat production power at the moment and the maximum heat production power of the cogeneration unit, respectively; is the ramping power of the cogeneration unit; , and are the feasible operating region parameters of the th cogeneration unit; is the heat power provided by the electric boiler, is the maximum heat power provided by the electric boiler, and the minimum heat power is 0; is the input electric power of the boiler, is the electric-heat conversion coefficient; and are the minimum and maximum input electric power of the electric boiler; is the heat power provided by the gas boiler; is the natural gas flow rate; is the low calorific value of the natural gas; is the gas-heat conversion efficiency; and are the lower and upper limits of the natural gas flow rate, respectively; and are the upper and lower limits of the power output of the th conventional generator; is the ramp rate limit of the th conventional generator; is the actual output of the wind turbine, is the cumulative distribution function of the standard normal distribution, and are the mean and variance of the wind power prediction, respectively, is the confidence level; and are the charging and discharging power of the node , respectively; and are the upper limits of the charging and discharging power of the node , respectively; and are the charging and discharging efficiencies; is the capacity; is the state of charge, and are the lower and upper limits of the state of charge; is the node exist Active power after demand response is implemented in real time; , and They are nodes exist The original load, the increased load power, and the decreased load power at any given time; For nodes exist The upper limit of the adjustable load power at any given time.

6. The method of claim 1, wherein the method further comprises: The thermal load resource is described by using a thermal controllable resident (TCR) model, and for a node , the aggregated thermal load feasible region is as follows: ； wherein and are the scaling and translation factors after aggregation, and are the scaling and translation factors for each individual TCR belonging to the aggregator set of node , is the exact aggregation feasible region of the aggregator of node , is the thermal power consumption of the aggregator of node , is the set of real numbers, and are the coefficient matrices as constraint conditions.

7. A device for low-carbon scheduling of a two-stage day-ahead optimization of a thermal system of a power grid of a polytropic distributed heat source, characterized in that, comprise: a data acquisition module for obtaining initial parameters of a power grid and heat system; an aggregation module for aggregating heat load resources by an aggregator and determining a feasible region of the aggregated heat load; a pre-scheduling calculation module for calculating optimal economic operation unit output of the power grid and heat system to meet system load demand within the feasible region of the heat load based on the initial parameters and a pre-scheduling stage objective function, and obtaining power distribution of the power grid and heat system; a carbon potential intensity calculation module for calculating carbon potential intensity of each node of the power grid and each node of the heat grid according to system power distribution and carbon emission flow theory; a low-carbon scheduling calculation module for calculating optimal low-carbon operation unit output of the power grid and heat system based on the carbon potential intensity and using a low-carbon scheduling stage objective function, and performing low-carbon scheduling based on the optimal low-carbon operation unit output; the pre-scheduling stage objective function is day-ahead economic operation optimization, and determines minimum operation cost for the whole day, and the pre-scheduling stage objective function is as follows: ； In the formula, It is the objective function of the pre-scheduling phase; It refers to the number of wind turbines. It is a function of the operating cost of the wind turbine. It is the operating cost coefficient of the wind turbine. Is the wind turbine in? Actual output at any given moment; It refers to the number of combined heat and power (CHP) units. It is a function of the operating cost of a combined heat and power unit. It is a combined heat and power generator in Active power at any given time It is a combined heat and power generator in Heat production capacity at any given time It is the operating cost coefficient of a combined heat and power unit; This refers to the number of conventional generators. It is a function of the operating cost of a conventional generator. Is it a conventional generator in Output power at any moment This is the operating cost coefficient for conventional generators; It refers to the number of electric boilers. It is a function of the operating cost of the electric boiler. It's an electric boiler. Input electrical power at any given time It is the operating cost coefficient of the electric boiler; It refers to the number of gas-fired boilers. It is a function of the operating cost of the gas-fired boiler. It is a gas-fired boiler. The heat power provided at all times It is the operating cost coefficient of a gas-fired boiler; It refers to the number of energy storage systems. It is a function of the operating cost of the energy storage system. It is the operating cost coefficient of the energy storage system. and Separate energy storage systems The charging power and discharging power at any given time, where the subscript i represents the node number of a certain type of equipment connected to the power grid system, n represents the node number of the heating network system, and T is the pre-scheduled time period.

8. A computer device, comprising: the device comprises a processor and a memory: the memory is used to store a computer program and send instructions of the computer program to the processor; the processor executes the method according to the instructions of the computer program.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method.

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