A method for optimizing the scheduling of an integrated energy system containing reversible solid oxide batteries

Abstract

The invention discloses an optimization scheduling method for an integrated energy system containing a reversible solid oxide battery, comprising the following steps: constructing a linear model of a heating network taking into account heat loss and actual heat demand of users, connecting a regional heating network with a traditional power supply network through a combined heat and power (CHP) unit to form an integrated energy system; establishing an electric-hydrogen bidirectional production and storage model containing a reversible solid oxide battery; establishing a heat demand response model with multiple response levels; collecting day-ahead scheduling data and system equipment parameters, and based on the model established in steps S1 to S3, establishing a day-ahead scheduling model for an integrated energy system containing a reversible solid oxide battery with the operator obtaining a maximum net profit as an objective function; building the day-ahead scheduling model of an integrated energy system containing a reversible solid oxide battery in step S4 based on a matlab platform and a yalmip toolbox, and calling a solver gurobi for solving the model; the invention realizes optimization of an integrated energy system with electric-hydrogen bidirectional production and storage and integrated demand response.

Description

Technical Field

[0001] The invention relates to the field of power system optimization and dispatching, and in particular to a method for optimizing and dispatching a comprehensive energy system containing a reversible solid oxide battery. Background Art

[0002] Under the "dual carbon" goal, the installed capacity of clean energy represented by wind power and photovoltaic power has increased rapidly. Under the dual influence of clean energy installed capacity and output characteristics, the pressure on clean energy consumption in the power system has become increasingly prominent. Energy storage is an important part of the clean energy consumption mechanism and an effective way to ensure the safety of the power system and improve the economy of clean energy power generation. It is of great significance to study the application of new energy storage technologies in power systems. Hydrogen energy storage can be directly coupled with clean energy through water electrolysis and hydrogen power generation, fully realizing the synergistic complementarity of electric energy and hydrogen energy, which is an ideal way to consume clean energy; in addition, it can also adjust the electric load and thermal load through demand response to adapt to the output of renewable energy. It changes the user's inherent habitual electricity consumption mode to achieve the effect of reducing or shifting the electricity load in a certain period of time, thereby responding to power supply and ensuring the stability of the power grid. And now demand response has been extended to the integrated energy system, and there is an integrated need to consider different energy substitution and load transfer.

[0003] At present, there are certain achievements in the research on the optimization scheduling strategy of the integrated energy system. However, most of the research only considers one-way energy conversion equipment, such as relatively mature proton exchange membrane electrolyzers and alkaline fuel cells, but their utilization efficiency of clean energy is not high, which affects the overall benefit of the system. The reversible solid oxide fuel cell used in the present invention can realize the bidirectional and efficient conversion between electricity and hydrogen, and is a new type of hydrogen energy storage technology route with great development potential; on the other hand, the existing scheduling technology method does not consider the actual heating supply of the heat network and the actual heat demand of users, and thus cannot meet the actual heat demand of users in actual scheduling. Therefore, it is urgent to further study the optimization scheduling strategy of the integrated energy system from the two aspects of bidirectional coupled conversion of energy and actual heat demand of users to improve the effectiveness and rationality of the existing strategy. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide an optimization scheduling method for an integrated energy system containing a reversible solid oxide battery, so as to realize the optimization of an integrated energy system with bidirectional production and storage of electricity and hydrogen energy and comprehensive demand response.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for optimizing the scheduling of an integrated energy system containing a reversible solid oxide battery, comprising the following steps:

[0006] Step S1: construct a linear model of the heating network that takes into account heat loss and actual heat demand of users, and connect the district heating network with the traditional power supply network through CHP units to form an integrated energy system;

[0007] Step S2: establishing an electricity-hydrogen bidirectional production and storage model containing a reversible solid oxide battery;

[0008] Step S3: Using demand elasticity theory and user temperature preference evaluation, a multi-response level heat demand response model is established;

[0009] Step S4: collecting day-ahead dispatching data and system equipment parameters, and based on the models established in steps S1 to S3, establishing a day-ahead dispatching model for an integrated energy system containing reversible solid oxide batteries with the objective function of obtaining maximum net profit for the operator;

[0010] Step S5: Based on the MATLAB platform and the YALMIP toolbox, the day-ahead dispatch model of the integrated energy system containing reversible solid oxide batteries in step S4 is built, and the solver GUROBI is called to solve it.

[0011] Furthermore, the model construction of the heating network considering heat loss and actual user heat demand in step S1 includes the following steps:

[0012] The model of the district heating network consists of five parts: the energy supply model of the combined heat and power unit, the main heating network model, the secondary heating network model, the heat exchange station model and the user heat load model;

[0013] 1) The energy supply model of the cogeneration unit is established as follows:

[0014] The electrical and thermal output power of a CHP unit can be represented by a linear combination of the outputs at the vertices of the operating domain.

[0015]

[0016]

[0017] In the formula, represents the power supplied by the i-th CHP unit at time t, Indicates the heat energy supplied by the i-th CHP unit at time t; NV i Represents the number of vertices of the convex polygon, P i,n represents the power supply at the vertex of the nth operating area of ​​the i-th CHP unit, H i,n represents the heating power of the nth operating region vertex of the i-th CHP unit, ξ i,n,t Represents the linear combination of the i-th CHP unit at time t and the n-th vertex in its operating domain.

[0018] 2) The model of the main heating network is established as follows:

[0019] The main heating network scheduling cycle is as follows:

[0020]

[0021] In the formula, ρ w represents the density of hot water, represents the mass flow rate of the jth main heating pipeline, represents the cross-sectional area of ​​the jth pipeline, L j represents the length of the jth pipeline, Δt represents the time required for a scheduling cycle, Indicates that the scheduling period is rounded down. 1,j represents the number of scheduling cycles of hot water flowing through the jth pipeline, rounded down to the nearest integer. 2,j represents the number of scheduling cycles of hot water flowing through the jth pipeline, rounded upwards, Φ p,Ι Represents the main heating pipeline collection;

[0022] The water temperature expression at the outlet of the main heating network is as follows:

[0023]

[0024] Where M 1,j represents the jth pipeline l 1,j The total mass of the fluid in each cycle, M 2,j represents the jth pipeline l 1,j +1 cycle fluid mass sum, represents the jth main heating pipeline tl 1,j The water temperature in each cycle, represents the jth heating pipe tl 2,j The water temperature in each cycle, represents the outlet temperature of the jth main heating pipe when it is completely insulated at time t;

[0025] The outlet temperature of the main heating network considering heat loss is as follows:

[0026]

[0027] In the formula, represents the outlet temperature of the jth pipe at time t considering heat loss, represents the thermal conductivity of the jth pipeline, c w represents the specific heat capacity of water, T t cw represents the predicted ambient temperature at time t;

[0028] 3) The specific heat exchange station model is as follows:

[0029]

[0030] In the formula, represents the total heat exchanged at the ith heat exchange station at time t, represents the supply temperature at the outlet of pipe j at time t, represents the supply temperature at the inlet of pipe j at time t, represents the reflux temperature at the outlet of pipe j at time t, represents the reflux temperature at the inlet of pipe j at time t, m j represents the mass flow rate through the secondary heating pipe j, represents the set of pipes that flow into the main heating pipes of node i, represents the set of pipes flowing out of the secondary heating pipe node i;

[0031] 4) The secondary heating network model is as follows:

[0032]

[0033] In the formula, Φ p represents the secondary heating pipe set, represents the set of pipes flowing into the secondary heating pipe node i, represents the supply temperature of node i at time t;

[0034] The equivalent equation of the cooling equation of the secondary heating network is as follows:

[0035]

[0036] The relationship between the heat load demand at node q and the supply temperature at the outlet of the heat exchange station is shown below:

[0037]

[0038] In the formula, represents the heat demand of the heating node q at time t, represents the mass flow rate through the secondary heating node q at time t, Represents the inlet supply temperature of the qth node in the heating network at time t.

[0039] The heat power provided by the heat exchange station is shown in the following formula:

[0040]

[0041] Where D i represents the set of secondary heating nodes connected to the i-th heating station, It represents the sum of the heat required by the qth heating node at time t and its pipeline transportation heat loss.

[0042] 5) The user heat load model is as follows:

[0043]

[0044] In the formula, represents the relationship coefficient between the heat demand and temperature of node i, and represents the user temperature of the i-th node at time t and t-1, respectively, c a represents the specific heat capacity of air, ρ a represents the density of air, A jh represents the heat exchange area between the user's building unit and the air, μ represents the compensation constant, S wa The average area of ​​the wall per unit heating area, S wi The average area of ​​windows per unit heating area, TR wa is the thermal resistance of the wall, TR wi is the thermal resistance of the window, Table 1 Heating area of ​​the i-th node user, N e Indicates the number of times the user's building exchanges heat with the air per hour.

[0045] Furthermore, the reversible solid oxide electrothermal-hydrogen bidirectional conversion hydrogen storage model in step S2 includes the following parts:

[0046] 1) Reversible solid oxide battery model;

[0047] The energy conversion relationship in the reversible solid oxide battery fuel cell mode is as follows:

[0048]

[0049] In the formula, are respectively the power generation power, heat generation power and hydrogen consumption rate of the fuel cell at time t; L hv is the lower heating value of hydrogen; is the electrical efficiency of the fuel cell at time t; is the thermal efficiency of the fuel cell at time t;

[0050] The simplified expressions of the electrical and thermal power consumed by the reversible solid oxide battery in electrolyzer mode are as follows:

[0051]

[0052] In the formula, They are respectively the power consumption, heat consumption and hydrogen production rate of the electrolytic cell at time t; is the heat consumption coefficient of the electrolytic cell at time t; is the electrolysis efficiency of the electrolytic cell at time t;

[0053] 2) The compressor model is established as follows:

[0054]

[0055] In the formula, p c is the compressor power consumption; R is the common gas constant; T in is the inlet temperature of hydrogen; γ is the isotropy index; η c is the mechanical efficiency; P in and P out are the inlet and outlet pressures, Indicates the mass flow rate of hydrogen;

[0056] 3) The hydrogen storage model is established as follows:

[0057]

[0058] In the formula, represents the mass of hydrogen stored in the tank at time t; M ini is the initial hydrogen storage capacity of the hydrogen storage tank; and represents the inlet and outlet hydrogen mass flow rate at time t, η EC Indicates the efficiency of hydrogen storage and use.

[0059] Furthermore, in step S3, the heat demand response model with multiple response levels is specifically as follows:

[0060]

[0061] In the formula, P represents the heating area of ​​users participating in the demand response program; t H,N represents the heat price for users who do not participate in demand response at time t, P t H,DR represents the heat price of the demand response user at time t, r k HD，P represents the heat demand response rate during the kth heat load peak period, represents the heat price increase or decrease rate during the kth heat load peak period; t,k is a binary variable, indicating that the kth heat load can only have one electricity price at time t. represents the heat load participating in the demand response program, represents the heat load that does not participate in the demand response program, represents the relationship coefficient between the heat demand and temperature of the user participating in the demand response at node i; represents the relationship coefficient between the heat demand and temperature of the user at node i who does not participate in demand response, Represents the temperature of the i-th demand response user at time t.

[0062] Further, in step S4, the day-ahead dispatching data of the integrated energy system includes the electric load forecast, the output forecast of the new energy unit and the ambient temperature forecast, and the system equipment parameters include the rated power and conversion efficiency of the output equipment, the energy storage and discharge power, efficiency and loss rate of the energy storage equipment; the objective function of the day-ahead dispatching model is as follows:

[0063]

[0064] In the formula, Φ TU represents the set of thermal power units, Φ CHP represents the set of CHP units, Φ bus represents the set of nodes that require electrical energy, Φ hd represents the set of nodes that require thermal energy, represents the operating cost of the i-th thermal power generating unit at time t, represents the operating cost of the i-th CHP unit at time t, represents the power load demand of node i at time t, P t E represents the electricity selling price at time t; the operating cost of the thermal power generator and the operating costs of CHP units The specific expression is as follows:

[0065]

[0066] In the formula, represents the unit power cost coefficient of thermal power generator, represents the unit power cost coefficient of the CHP unit, represents the power output of the i-th thermal generator at time t, represents the power output of the i-th CHP unit at time t, Represents the thermal power output of the i-th CHP unit at time t.

[0067] Furthermore, step S5 is specifically as follows:

[0068] The corresponding models in steps S1 to S3 are built based on the MATLAB platform and the YALMIP toolbox, and the day-ahead scheduling data and system equipment parameters in step S4 are substituted into them. After determining the maximum profit obtained by the operator as the objective function, the gurobi solver is used to solve the problem.

[0069] Beneficial effects of the present invention:

[0070] 1) The present invention utilizes the equivalent infinitesimal substitution theorem and the assumption that the power supply temperature of load nodes is approximately equal to construct a heating network with a linear energy flow model, so that the heating network can be analyzed and calculated in actual energy scheduling work, which promotes the development of the heating network in actual comprehensive energy scheduling.

[0071] 2) The present invention establishes a heat demand response model through demand elasticity theory and user temperature preference evaluation. The model can be more conveniently embedded in the comprehensive energy day-ahead scheduling model, and can improve the absorption capacity of renewable energy with uncertainty, thereby promoting the absorption of renewable energy.

[0072] 3) The present invention proposes to establish the relationship between electric-thermal energy and hydrogen energy through a reversible solid oxide battery, thereby realizing high-efficiency bidirectional energy flow energy conversion. The reversible solid oxide battery can be introduced into the integrated energy system as an energy conversion device, which can reduce production capacity costs and further improve the operating efficiency of the energy Internet.

[0073] 4) The present invention realizes the optimization of the comprehensive energy system with bidirectional production and storage of electricity and hydrogen energy and comprehensive demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is an example diagram of the district heating network structure;

[0075] Figure 2 Model structure of hydrogen production and storage based on reversible solid oxide battery;

[0076] Figure 3 It is the structure of an integrated energy system based on reversible solid oxide hydrogen production and storage;

[0077] Figure 4 It is a schematic diagram of the operation domain of the CHP unit;

[0078] Figure 5 This is a schematic diagram of day-ahead scheduling data;

[0079] Figure 6 Schematic diagram of renewable energy curtailment under k-fold predicted renewable energy. DETAILED DESCRIPTION

[0080] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0081] Embodiment: A method for optimizing the scheduling of an integrated energy system containing a reversible solid oxide battery comprises the following steps:

[0082] Step S1: A linear model of the heating network considering heat loss and actual heat demand of users is proposed, and the district heating network is connected with the traditional power supply network through CHP units to form an integrated energy system.

[0083] Step S2: Establish an electricity-hydrogen bidirectional production and storage model containing a reversible solid oxide battery.

[0084] Step S3: Using demand elasticity theory and user temperature preference evaluation, a multi-response level thermal demand response model is established.

[0085] Step S4: Collect day-ahead dispatch data and system equipment parameters, and based on the model established in steps S1 to S3, establish a day-ahead dispatch model for an integrated energy system containing reversible solid oxide batteries with the operator obtaining maximum net profit as the objective function.

[0086] Step S5: Based on the MATLAB platform and the YALMIP toolbox, the day-ahead dispatch model of the integrated energy system containing reversible solid oxide batteries in step S4 is built, and the solver GUROBI is called to solve it.

[0087] Further, the heating network structure diagram considering heat loss and actual heat demand of users in step S1 is shown in Figure 1 , it can be seen from the figure that the heat source of the heating network is the CHP unit, which is connected to the heat exchange station through the main heating pipeline; the heat exchange station is connected to the user's heat demand through the secondary heating pipeline; and there is heat delay and heat loss in the main heating pipeline, and heat loss in the secondary heating pipeline; the power supply network is a common power supply network, including generator sets and transmission lines; the establishment of the heating model includes the following steps:

[0088] 1) In the district heating network, the main heat source of the main heating network is the CHP unit, and the CHP unit is the hub connecting the power network and the district heating network. The output power of the electric energy and thermal energy of the CHP unit can be expressed by a linear combination of the outputs of the vertices of the operating domain.

[0089]

[0090]

[0091] 2) The hot water of the main heating network will have a certain transmission delay and heat loss. For the outlet temperature of the main heating network transmission delay and heat loss, we can first consider the temperature of the water at the outlet when the network transmission delay occurs, and then consider the outlet temperature of the heat loss based on this temperature, as shown below:

[0092]

[0093] Since the main heating network is long, the water in the pipe is supplied in multiple cycles, and the water supply temperature in each cycle is not necessarily the same, and the ratio of the mass of hot water in the entire pipe to the mass of water in unit time is not necessarily an integer value, resulting in the hot water flowing out of the main heating pipe in unit time not being supplied in one cycle. Therefore, we use the following model to represent the temperature of the water flowing out of the pipe at the outlet based on unit time:

[0094]

[0095] Based on the above consideration of network transmission delay, the outlet temperature considering heat loss is as follows:

[0096]

[0097] 3) For the heat exchange station, we can regard the main heating network as the heat source and the secondary heating network as the heat load. The heat provided by the heat source is equal to the heat required by the heat load. The specific heat exchange station model is as follows:

[0098]

[0099] 4) In the secondary heating network model, each pipeline in the heating network should satisfy the mass balance, heat balance and pipeline inlet and outlet temperature drop equations as shown below:

[0100]

[0101] because We can use the concept of equivalent infinitesimals in mathematics to Replace with Therefore, the cooling equation of this heating network can be equivalent to:

[0102]

[0103] According to the formula before and after equivalent infinitesimal substitution and the secondary heating network, the supply temperature relationship between node q and the heat exchange station outlet can be calculated as follows:

[0104]

[0105] The heat load demand of node q can be calculated from the supply temperature flowing into node q and the return temperature flowing out of node q as shown in the following formula:

[0106]

[0107] Substituting the node q heat load demand formula into the formula for the relationship between node q and the supply temperature at the heat exchange station outlet yields:

[0108]

[0109] The heat power provided by the heat exchange station is shown in the following formula:

[0110]

[0111] 5) The user heat load model is as follows:

[0112] The heat load model that meets the user's heat demand can be described as a first-order equivalent thermal parameter model as follows:

[0113]

[0114] At the same time, the structure of the hydrogen storage model based on the reversible solid oxidation battery in step S2 is shown in FIG. Figure 2 , the structure of the integrated energy system based on reversible solid oxide hydrogen storage is as follows Figure 3 As shown; reversible solid oxide batteries can enable bidirectional conversion of electricity, heat and hydrogen, and can convert excess energy from renewable energy into hydrogen for storage. When the load is large, hydrogen can be converted into electric heat energy to relieve load pressure; the model of bidirectional conversion of electricity and hydrogen storage containing a reversible solid oxide battery power supply network is shown below:

[0115] 1) Reversible solid oxide battery model

[0116] Reversible solid oxide cells can be divided into fuel cell mode or electrolyzer mode operating at high temperature, which can realize the mutual conversion between electricity, heat and hydrogen. In the fuel cell mode, electricity and heat are mainly generated by releasing hydrogen from the hydrogen storage tank. The energy conversion relationship is as follows:

[0117]

[0118] The simplified expressions of the electrical and thermal power consumed by the reversible solid oxide battery in electrolyzer mode are as follows:

[0119]

[0120] 2) Compressor model

[0121] Compressors are necessary for hydrogen utilization because hydrogen is usually stored, consumed, and transported at high pressure. Assuming the compression process is adiabatic, the power consumption of the compressor is estimated as:

[0122]

[0123] 3) Hydrogen storage model

[0124] The general approach is to use a storage tank to store compressed hydrogen. This study uses a simple hydrogen storage model and assumes that the charging and discharging efficiencies are unique. The mass balance equation for the storage tank is shown below:

[0125]

[0126] Further, according to the demand elasticity theory and user temperature preference evaluation in step S3, an easily tractable heat demand response model is established. The heat load demand and user temperature preference are shown in Table 1. The establishment of the user heat demand response model is as follows:

[0127]

[0128] Table 1 User heat load demand parameters

[0129]

[0130] Further, based on the model of steps S1 to S3, the day-ahead dispatching data and system equipment parameters collected in step 4 are substituted into the integrated energy system, and a day-ahead dispatching model of the integrated energy system with the operator obtaining maximum profit as the objective function is established. The day-ahead dispatching data includes the electric load, the output of renewable energy and the ambient temperature. Figure 4 The system equipment parameters, including the heating pipe parameters, are shown in Table 2. The system other heating network parameters are shown in Table 3. The electric heating operation limit of the CHP unit is shown in Figure 5 The unit cost coefficients of the units in the day-ahead dispatch are shown in Table 4. The establishment of the objective function includes the following steps:

[0131] The specific expression of the power generation cost of traditional generators and CHP units is as follows:

[0132]

[0133] The objective function of the day-ahead scheduling model is as follows:

[0134]

[0135] Each term in the formula represents the cost of generating electricity from the thermal generator, the cost of the CHP unit, the income from selling electricity, and the income from selling heat.

[0136] Table 2 Heating pipeline parameters

[0137]

[0138] Table 3 Other parameters of system heating network

[0139]

[0140] Table 4 Unit operating cost

[0141]

[0142] Further, according to the step S5, the corresponding models in steps S1 to S3 are built based on the matlab platform and the yalmip toolbox, and the day-ahead scheduling data and system equipment parameters in step S4 are substituted therein, and after determining the maximum profit obtained by the operator as the objective function, the gurobi solver is used to solve the problem.

[0143] This embodiment selects the following four scenarios for comparative analysis.

[0144] Scenario 1: The reversible solid oxide battery electricity-hydrogen bidirectional storage module is not added, and the integrated energy system under thermal demand response is not considered.

[0145] Scenario 2: Using the traditional electrolyzer-fuel cell electricity-hydrogen bidirectional storage module, but not considering the integrated energy system under thermal demand response.

[0146] Scenario 3: Using a reversible solid oxide battery electricity-hydrogen bidirectional storage module, but not considering the integrated energy system under thermal demand response.

[0147] Scenario 4: The reversible solid oxide battery electric heat-hydrogen bidirectional hydrogen production module is not added, but the integrated energy system under heat demand response is considered.

[0148] Scenario 5: Add a reversible solid oxide battery electricity-hydrogen bidirectional storage module and consider the integrated energy system under thermal demand response.

[0149] Table 5 is the analysis of scheduling results. Figure 6 The amount of renewable energy curtailment under k-fold predicted renewable energy in each scenario is obtained; from the comparison results in Table 5, it can be seen that scenario 2 and scenario 3 can effectively improve the operator's profit and improve the wind power absorption capacity. The total profit of scenario 3 is higher than that of scenario 2. The reason is that scenario 3 uses reversible solid oxide batteries, which improves the efficiency of electricity-hydrogen bidirectional storage, and improves the utilization rate of renewable energy. Therefore, the total amount of renewable energy used in scenario 3 is higher than that in scenario 2, so the final total profit is higher; from scenario 4, it can be seen that after the implementation of thermal demand response, the user's energy expenditure cost has been significantly reduced, the operator's total profit has also been significantly improved, and the wind power absorption capacity has also been improved. The reason is that after the implementation of thermal demand response in scenario 4, the user's energy consumption habits have changed. Users who participate in demand response will use energy at low energy cost times, which reduces the user's energy expenditure; at the same time, due to the change in users' energy consumption habits, the absorption of renewable energy has been improved, so the operator's profit has also been improved; from Figure 6 It can be seen that after using reversible solid oxide batteries and implementing thermal demand response, the wind power absorption capacity has been significantly improved.

[0150] After coupling the electricity-hydrogen bidirectional production and storage module containing reversible solid oxide batteries with the original integrated energy system, the renewable energy absorption capacity and energy utilization efficiency can be improved compared with traditional hydrogen production and storage equipment; the introduction of a practical thermal network model and the application of thermal demand response can effectively reduce users' energy expenses and increase operators' profits.

[0151] In summary, coupling the reversible solid oxide battery electricity-hydrogen bidirectional storage module with the integrated energy system and applying the heat load demand response in the integrated energy system can improve the absorption capacity of renewable energy, reduce users' energy expenditure costs and increase the operator's total profit.

[0152] Table 5 Optimization scheduling results under different scenarios

[0153]

[0154] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of an integrated energy system containing a reversible solid oxide battery, characterized in that: The following steps are involved: Step S1: construct a linear model of the heating network that takes into account heat loss and actual heat demand of users, and connect the district heating network with the traditional power supply network through CHP units to form an integrated energy system; Step S2: establishing an electricity-hydrogen bidirectional production and storage model containing a reversible solid oxide battery; Step S3: Using demand elasticity theory and user temperature preference evaluation, a multi-response level heat demand response model is established; Step S4: collecting day-ahead dispatching data and system equipment parameters, and based on the models established in steps S1 to S3, establishing a day-ahead dispatching model for an integrated energy system containing reversible solid oxide batteries with the objective function of obtaining maximum net profit for the operator; Step S5: Based on the MATLAB platform and the YALMIP toolbox, the day-ahead dispatch model of the integrated energy system containing the reversible solid oxide battery in Step S4 is constructed, and the solver GUROBI is called to solve the model; The construction of a linear model of a heating network taking into account heat loss and actual heat demand of users in step S1 includes the following steps: The model of the district heating network consists of five parts: the energy supply model of the combined heat and power unit, the main heating network model, the secondary heating network model, the heat exchange station model and the user heat load model; 1) The energy supply model of the cogeneration unit is established as follows: The electrical and thermal output power of a CHP unit can be expressed as a linear combination of the outputs of the vertices of the operating domain: In the formula, represents the power supply of the q1th CHP unit at time t, represents the heating power of the q1th CHP unit at time t; represents the number of vertices of the convex polygon operating area of ​​the q1th CHP unit, represents the power supply at the vertex of the nth operating area of ​​the q1th CHP unit, represents the heating power of the nth operating region vertex of the q1th CHP unit, represents the linear combination of the q1th CHP unit at time t with the nth vertex of its operating domain; 2) The model of the main heating network is established as follows: The main heating network scheduling cycle is as follows: In the formula, ρ w represents the density of hot water, represents the mass flow rate of the j1th main heating pipeline, represents the cross-sectional area of ​​the j1th main heating pipeline, represents the length of the j1th main heating pipeline, Δt represents the time required for a scheduling cycle, Indicates that the scheduling period is rounded down. It represents the number of scheduling cycles of hot water flowing through the j1th main heating pipeline, rounded down to the nearest integer. The number of scheduling cycles for hot water flowing through the j1th main heating pipeline is rounded up, Φ p,Ι Represents the main heating pipeline collection; The water temperature expression at the outlet of the main heating network is as follows: In the formula, Indicates the j1th main heating pipeline The total mass of the fluid in each cycle is Indicates the j1th main heating pipeline The total mass of the fluid in each cycle is Indicates the j1th main heating pipeline The water temperature in each cycle, Indicates the j1th main heating pipeline The water temperature in each cycle, represents the outlet temperature of the j1th main heating pipe when it is completely insulated at time t; The outlet temperature of the main heating network considering heat loss is as follows: In the formula, represents the outlet temperature of the j1th main heating pipeline at time t considering heat loss, represents the thermal conductivity of the j1th main heating pipeline, c w represents the specific heat capacity of water, T t cw represents the predicted ambient temperature at time t; 3) The heat exchange station model is as follows: In the formula, It represents the total heat exchanged by the heat exchange station at node i at time t, represents the supply temperature at the outlet of the main heating pipe j1 at time t, represents the supply temperature at the inlet of the heating pipe j2 at time t, represents the return temperature at the outlet of the heating pipe j2 at time t, represents the return temperature at the inlet of the main heating pipe j1 at time t, represents the mass flow rate through the secondary heating pipe j2, represents the set of main heating pipes flowing into node i, represents the set of secondary heating pipes flowing out of node i; 4) The secondary heating network model is as follows: In the formula, Φ p represents the secondary heating pipe set, represents the set of secondary heating pipes flowing into heating network node i, represents the supply temperature of node i in the heating network at time t; The equivalent equation of the cooling equation of the secondary heating network is as follows: The formula for the relationship between the user heat load demand at heating node i and the supply temperature at the outlet of the heat exchange station is as follows: In the formula, represents the heat demand of heating node i at time t, represents the mass flow rate through the secondary heating node i at time t, represents the inlet supply temperature of the i-th node of the heating network at time t; The heat power provided by the heat exchange station is shown in the following formula: In the formula, represents the set of secondary heating nodes connected to the heat exchange station at node i, It represents the sum of the heat required by the ith secondary heating node at time t and the heat loss in its pipeline transportation; 5) The user heat load model is as follows: In the formula, represents the relationship coefficient between the heat demand and temperature of node i, and represents the user temperature of the i-th node at time t and t-1, c a represents the specific heat capacity of air, ρ a represents the density of air, A jh represents the heat exchange area between the user's building unit and the air, μ represents the compensation constant, S wa The average area of ​​the wall per unit heating area, S wi The average area of ​​windows per unit heating area, TR wa is the thermal resistance of the wall, TR wi is the thermal resistance of the window, Table 1 Heating area of ​​the i-th node user, N e Indicates the number of times the user's building exchanges heat with the air per hour; The establishment of the electricity-hydrogen bidirectional storage model containing a reversible solid oxide battery in step S2 includes the following parts: 1) Reversible solid oxide battery model; The energy conversion relationship of the reversible solid oxide battery in fuel cell mode is as follows: In the formula, are respectively the power generation power, heat generation power and hydrogen consumption rate of the fuel cell at time t; L hv is the lower heating value of hydrogen; is the electrical efficiency of the fuel cell at time t; is the thermal efficiency of the fuel cell at time t; The simplified expressions of the electrical and thermal power consumed by the reversible solid oxide battery in electrolyzer mode are as follows: In the formula, They are respectively the power consumption, heat consumption and hydrogen production rate of the electrolytic cell at time t; is the heat consumption coefficient of the electrolytic cell at time t; is the electrolysis efficiency of the electrolytic cell at time t; 2) The compressor model is established as follows: In the formula, p c is the compressor power consumption; R is the common gas constant; T in is the inlet temperature of hydrogen; γ is the isotropy index; η c is the mechanical efficiency; P in and P out are the inlet and outlet pressures, represents the mass flow rate of hydrogen; 3) The hydrogen storage model is established as follows: In the formula, represents the mass of hydrogen stored in the tank at time t; M ini The initial storage volume of hydrogen in the hydrogen storage tank; and represents the inlet and outlet hydrogen mass flow rate at time t, η EC Indicates the efficiency of hydrogen in storage and use; In step S3, the heat demand response model with multiple response levels is specifically: In the formula, represents the heating area of ​​users participating in the demand response plan at heating network node i; P t H,N represents the heat price for users who do not participate in demand response at time t, P t H,DR represents the heat price of the demand response user at time t, r k HD,P represents the heat demand response rate during the kth heat load peak period, r k HP,P represents the heat price increase or decrease rate during the kth heat load peak period; t,k is a binary variable, indicating that the heat load can only have one electricity price at time t. represents the heat load of the user participating in the demand response program at the i-th node, represents the heat load of the user at the i-th node who does not participate in the demand response program, represents the relationship coefficient between the heat demand and temperature of the user participating in the demand response at node i; represents the relationship coefficient between the heat demand and temperature of the user at node i who does not participate in demand response, represents the user temperature of the ith node participating in the demand response at time t; In step S4, the day-ahead dispatching data of the integrated energy system includes the electric load forecast, the output forecast of the new energy unit and the ambient temperature forecast. The system equipment parameters include the rated power and conversion efficiency of the output equipment, the energy storage and discharge power, efficiency and loss rate of the energy storage equipment. The objective function of the day-ahead dispatching model is as follows: In the formula, Φ TU represents the set of thermal power units, Φ CHP represents the set of CHP units, Φ bus represents the set of nodes that require electrical energy, Φ hd represents the set of nodes that require thermal energy, represents the operating cost of the q2th thermal power generating unit at time t, represents the operating cost of the q1th CHP unit at time t, represents the i-th power network at time t e The power load demand of each node, P t E represents the electricity selling price at time t; the cost of thermal power generator and the power generation cost of CHP units The specific expression is as follows: In the formula, represents the unit power cost coefficient of thermal power generator, represents the unit power cost coefficient of the CHP unit, represents the power output of the q2th thermal generator at time t, represents the power output of the q1th CHP unit at time t, Represents the thermal power output of the q1th CHP unit at time t.

2. The method for optimizing the scheduling of an integrated energy system containing a reversible solid oxide battery according to claim 1, characterized in that: Step S5 is specifically as follows: The corresponding models in steps S1 to S3 are built based on the MATLAB platform and the YALMIP toolbox, and the day-ahead scheduling data and system equipment parameters in step S4 are substituted into them. After determining the maximum profit obtained by the operator as the objective function, the gurobi solver is used to solve the problem.

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