Photo-thermal power station heat storage capacity optimal configuration method considering fuel gas hydrogen doping economy

Abstract

A photo-thermal power station heat storage capacity optimal configuration method considering fuel gas hydrogen doping economy comprises the steps that firstly, a fuel gas hydrogen doping technology is used for coupling a traditional combined heat and power supply system and a photo-thermal power station, a mathematical model of a fuel gas hydrogen doping combined heat and power generation unit is constructed, and a large-capacity heat storage system is introduced into the photo-thermal power station; the heat energy is transmitted to the heat storage system through photo-thermal conversion; secondly, scheduling economic factors such as system power generation cost, system operation and maintenance cost, carbon transaction cost and heat transmission loss are comprehensively considered, and a comprehensive energy system economic scheduling model is established; on the basis, on the premise that the daily operation cost of the system is minimum, the balance point of the optimal configuration of the heat storage capacity and the fuel gas hydrogen-doped income is explored, and the optimal heat storage capacity configuration method of the photo-thermal power station is determined; and finally, the optimal heat storage capacity is selected by calculating the overall benefit of the synergistic effect of the fuel gas hydrogen doping technology and the photo-thermal power station. The heat storage capacity configuration of the photo-thermal power station is optimized, so that the thermoelectric matching degree of the system is higher, and the operation economic benefit of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized scheduling of integrated energy systems, and in particular to a method for optimizing the configuration of heat storage capacity of a solar thermal power station taking into account the economic benefits of hydrogen blending with natural gas. Background Art

[0002] The environmental pollution caused by the use of traditional fossil energy is becoming increasingly serious. Vigorously developing renewable energy to replace fossil energy and reduce carbon dioxide emissions are important ways to achieve energy conservation. Among them, the status of new energy is gradually transforming from alternative energy to dominant energy. How to establish a more stable, flexible and low-carbon new energy structure system and improve the efficiency of new energy utilization is a technical problem that urgently needs to be overcome.

[0003] The diversified utilization of hydrogen energy is an ideal carrier for realizing large-scale development and utilization of new energy. However, the current immature hydrogen transportation technology restricts the development of hydrogen energy. The use of hydrogen enriched compressed natural gas (HCNG) technology to inject hydrogen produced by electric hydrogen production into natural gas pipelines provides the possibility for large-scale consumption of hydrogen energy and long-distance economic transportation.

[0004] On the other hand, solar thermal power generation technology is a new form of power generation that can achieve "light-heat-electricity" energy conversion. Concentrating solar power (CSP) power stations have the ability to operate continuously for a long time and can achieve continuous and stable power generation. Compared with conventional thermal power units, they have better regulation characteristics and have advantages such as fast ramp rate and short start-stop time. Among them, the thermal energy storage (TES) system not only has the advantages of long life and low cost, but also can effectively guarantee the output stability and operation continuity of the CSP power station. It is the basis for the CSP power station to participate in the system peak regulation; if the operation needs can be fully considered in the planning stage, and the thermal storage capacity can be reasonably configured, it can effectively alleviate the system peak regulation pressure and improve the economic efficiency of the integrated energy system (IES).

[0005] Existing research rarely uses gas-hydrogen blending technology in comprehensive energy, and there is no in-depth research on the problem of synergistic output between hydrogen-blended gas units and CSP plants in actual operation. Therefore, it is of great significance to study the operating economy of gas-hydrogen blending units and the configuration of heat storage capacity of CSP plants. Summary of the invention

[0006] In order to effectively promote the consumption of new energy and improve the economic benefits of system operation, the present invention provides a method for optimizing the configuration of the heat storage capacity of a solar thermal power station taking into account the economic benefits of hydrogen blending of gas. After considering the economic benefits of hydrogen blending of gas, the system heat storage capacity optimization configuration adopted by the present invention is more reasonable, which has significant advantages in improving the flexibility of the operation of the comprehensive energy system, promoting the consumption of new energy and reducing carbon emissions, and improving the economic benefits of the system.

[0007] The technical solution adopted by the present invention is:

[0008] A method for optimizing the configuration of heat storage capacity of a solar thermal power station considering the economic benefits of hydrogen blending with natural gas comprises the following steps:

[0009] Step 1: Combine the gas-hydrogen blending technology in the thermal power integrated energy system containing the CSP power station to build a mathematical model of the gas-hydrogen blending cogeneration unit;

[0010] Step 2: According to the heat transfer characteristics of the CSP thermal storage system, a mathematical model of heat transfer and loss between the gas-hydrogen blended unit and the CSP power station is established;

[0011] Step 3: Comprehensively consider dispatch economic factors such as system power generation cost, operation and maintenance cost, carbon trading cost and heat transmission loss, and establish an economic dispatch model for the integrated energy system;

[0012] Step 4: Taking into account the dispatch economy, solve the thermal storage capacity of the CSP plant when the operating cost is the lowest, and establish a thermal storage capacity configuration model for the CSP plant;

[0013] Step 5: Under the condition of satisfying system constraints, the system operating cost and gas-hydrogen blending unit revenue under different heat storage capacities are calculated through double-layer optimization iterations to ultimately determine the optimal heat storage capacity of the system.

[0014] The thermal power integrated energy system including a solar thermal power station specifically includes: a gas-hydrogen cogeneration unit, a solar thermal power station, a photovoltaic power station, a wind farm, and electric energy storage; the relevant formulas of the gas-hydrogen cogeneration unit are derived from formulas (1) to (5) in step 1, the relevant formulas of the solar thermal power station are derived from formulas (6) to (8) in step 2, and the relevant formulas of the electric energy storage are:

[0015]

[0016] Where: E EES (t) is the energy storage capacity in time period t; τ is the dissipation coefficient of the energy storage; P EES_ch (t), P EES_dis (t) are the charging and discharging power of the energy storage in time period t; η sch , η sdis are the charging and discharging efficiency of electrical energy storage, respectively.

[0017] The mathematical model of the gas-hydrogen mixed heat and power cogeneration unit is established, including:

[0018] 1) Hydrogen-blended gas turbine model:

[0019]

[0020] Where: R GT (t) is the hydrogen blending ratio of the gas turbine during period t; P GT,g (t) P GT,HCNG (t) are the natural gas, hydrogen and hydrogen-mixed gas power input to the gas turbine during the period t; H HCNG are the calorific values ​​of hydrogen, natural gas and mixed hydrogen gas respectively; P GT,e (t), P GT,h (t) are the electrical and thermal power output of the gas turbine during the period t; η GT,e , η GT,h are the electrical and thermal conversion efficiencies of the gas turbine, respectively.

[0021] 2) Electrolyzer model:

[0022]

[0023] Where: is the hydrogen production power of the electrolyzer in time period t; is the hydrogen production of the electrolyzer in time period t; is the molar mass of hydrogen; is the hydrogen density; △t represents the scheduling period;

[0024] 3) Carbon capture equipment model:

[0025]

[0026] Where: P CCS,e (t) is the electrical power supplied to the carbon capture equipment; P GT,e (t) represents the electric power output of the gas unit in the period t; P CCS,e1 (t), P CCS,e2 (t) are the fixed power loss and the power consumed by the CO2 treatment operation; is the CO2 emission of the gas generator set in period t; e GT is the unit carbon emission intensity; CCS The energy required to capture a unit of CO2 for the carbon capture equipment; is the total amount of CO2 captured by the carbon capture equipment; μ CCS is the carbon capture efficiency. 4) Electric heater model:

[0027] Q EH(t) = P EH (t)η ah (5);

[0028] Where: Q EH (t) and P EH (t) are the heating power and power consumption of the electric heater in time period t; η ah It is the electric-heat conversion efficiency of the electric heater.

[0029] In step 2, the heat transfer characteristics of the thermal storage system of the CSP power station are specifically as follows: when the light intensity is high during the day, the CSP power station converts the light energy reflected by the solar field into heat energy through a concentrating and collecting device, and stores the excess heat in the heat storage system through a heat transfer medium; when the heat load is at its peak at night, the CSP power station transfers the heat energy in the heat storage system to users to meet the heat load demand.

[0030] The established mathematical model of heat transfer and loss of gas-hydrogen-blended units and CSP power plants includes:

[0031] 1) Concentrating solar collector system model of solar thermal power station:

[0032] Q S-H (t) = η SF S SF D solar (t)-Q cur (t) (6);

[0033] Where: Q S-H (t) is the heat power transmitted from the mirror field to the heat transfer medium at time t; η SF Represents light-to-heat conversion efficiency; S SF Indicates the mirror area of ​​the CSP power station; D solar (t) is the solar radiation per unit area at time t; Q cur (t) is the heat lost at time t.

[0034] 2) Thermal energy storage system model of CSP power station:

[0035]

[0036] Where: Q TES_ch (t) and Q TES_dis (t) represents the heat storage and heat release power of the heat storage system in time period t; η TES_ch and η TES_dis represents the heat storage and heat release efficiency of the heat storage system; Q H-T (t), Q T-H (t) are the thermal power of the heat transfer medium flowing to the heat storage system and the thermal power of the heat storage system flowing to the heat transfer medium at time t; Q T-HL (t) is the thermal energy supplied by the heat storage device to the heat load at time t.

[0037] 3) Solar thermal power station power generation system model:

[0038] P CSP (t) = Q H-P (t)η PB (8);

[0039] Where: P CSP (t) represents the power generation of the CSP plant at time t; Q H-P (t) is the thermal power flowing into the power generation system at time t; η PB is the heat-to-electricity conversion efficiency.

[0040] 4) Heat energy transmission loss model:

[0041]

[0042] Where: N M represents the set of all heat output equipment; △t represents the operating time of the heat output equipment; represents the heat output of unit i in time period t; c represents the specific heat capacity of water; m(t) represents the flow rate of water heated by the pipeline in time period t; T s (t) represents the temperature of the heating water supply pipeline at time t; T r (t) represents the return water pipe temperature at time t; P GT,h (t) represents the thermal power output of the gas turbine during the period t; T s,max 、T s,min 、T r,max and T r,min Respectively represent the maximum and minimum water supply temperature, the maximum and minimum return water temperature; T end Indicates the pipe outlet temperature; T start Indicates the pipe inlet temperature; T out represents the external environment temperature; k represents the heat transfer coefficient per unit length of the pipeline; L represents the length of the pipeline.

[0043] In step 3, the economic dispatch model of the integrated energy system is established, and the objective function and constraints are as follows:

[0044] a. Objective function:

[0045]

[0046] Where: F represents the total cost of system operation; N T represents the scheduling cycle, t represents the scheduling period, t=1,2,3,…,24; C GT (t), C W (t), C EX (t), C ME (t) and C TES (t) represent the fuel cost, wind and solar power curtailment penalty cost, electricity interaction cost, equipment operation and maintenance cost, carbon trading cost and thermal storage system construction cost of the system in time period t.

[0047] (1) Fuel cost:

[0048]

[0049] Where: C GT (t) is the fuel cost of the gas turbine in period t; C CH4 is the unit price of natural gas.

[0050] (2) Penalty costs for curtailing wind and solar power:

[0051]

[0052] Where: η W Indicates the penalty coefficient for abandoning wind and solar power, They represent the wind power and photovoltaic power input into the system in time period t respectively; P WT,i (t), P PV,i (t) represent the wind power and photovoltaic power absorbed by the system in time period t.

[0053] (3) Electricity interaction cost:

[0054]

[0055] Where: P ex (t) represents the interaction power between the power grid and the external power grid in time period t; C rb (t) and C rs (t) are the electricity purchase and sales prices in period t respectively.

[0056] (4) Equipment operation and maintenance costs:

[0057]

[0058] Where: N M is the total number of generating units in the grid; C mi is the operation and maintenance cost coefficient of unit i; P i CG (t) is the output of unit i in time period t.

[0059] (5) Carbon trading costs:

[0060]

[0061] Where: E p Represents the initial carbon emission quota of the system; Allocate a quota for regional carbon emissions per unit of electricity; E is the conversion factor of electricity; ac (t) represents the actual carbon emissions of the system during period t; represents the system carbon trading cost; k Ca is the carbon trading market price.

[0062] (6) Thermal storage system construction cost:

[0063]

[0064] Where: C a is the average annual cost of the heat storage system; C0 is the unit investment price; E h,max is the capacity of the heat storage system; r is the discount rate; Y is the service life of the heat storage system; C TES (t) is the average daily construction cost of the heat storage system.

[0065] b. Constraints:

[0066] ①. Electric and thermal power balance constraints:

[0067]

[0068] P GT,h (t)+Q T-HL (t)+Q EH (t) = Q load (t)+Q s (t)(18);

[0069] Where: P load (t) is the power load in the grid during time period t; Q load (t) is the heat load in the network during period t, Q s (t) is the heat loss in the network during period t.

[0070] ②. Fuel gas mixed with hydrogen can balance constraints:

[0071]

[0072] Where: V GT,mix (t) are the amount of hydrogen produced by the electrolyzer and injected into the pipeline, the amount of natural gas purchased, and the amount of hydrogen-blended fuel gas consumed by the gas turbine during time period t.

[0073] ③. Hydrogen-blended fuel gas transmission and operation constraints:

[0074]

[0075] Where: q ij (t) is the pipeline flow between gas network nodes i and j at time t; σ ij is the pipeline transmission parameter between gas grid nodes i and j; εij (t) is the gas network pipeline direction parameter at time t, which can be -1, 0, or 1; p i (t) is the pressure value of gas network node i at time t; p j (t) represents the pressure value of gas network node j at time t; q ij,max is the upper limit of pipeline flow between gas network nodes i and j; p i,max 、p i,min are the upper and lower limits of the pressure value of gas network node i respectively; is the gas supply flow rate of the gas source at time t; They are the upper and lower limits of the gas supply flow rate of the gas source respectively.

[0076] ④. Grid interaction power constraints:

[0077]

[0078] Where: P buy (t) and P sell (t) represents the power purchase and sales of the system in time period t; x buy (t) and x sell (t) respectively represent the electricity purchasing and selling status in time period t; P ex，max Indicates the upper limit of the interactive power.

[0079] ⑤. Unit output constraints:

[0080]

[0081] Where: and are the maximum and minimum output of unit i respectively; and are the up and down ramp rate limits of unit i respectively.

[0082] ⑥. Constraints of thermal energy storage system in CSP power plants:

[0083]

[0084] Where: and represents the maximum power of heat storage and heat release of the heat storage system in time period t; x TES_ch (t) and x TES_dis (t) are the charging and discharging states of the heat storage device at time t; E TES (t) is the heat storage amount in period t; and are the minimum and maximum heat storage capacities of the heat storage system respectively; E TES (0) represents the heat storage capacity of the heat storage device at the initial time of the scheduling period; E TES (N T△t) represents the heat storage capacity of the heat storage device at the end of the scheduling period;

[0085] ⑦.Heat network temperature constraints:

[0086]

[0087] Where: T SH,g 、T SH,h are the heat medium temperatures of the water supply pipe and the return pipe of the first heating station respectively; T g,max and T h,min T are the upper temperature limit of the water supply pipe and the lower temperature limit of the return pipe at the first heating station respectively; in,max and T in,min Respectively represent the upper and lower limits of the indoor temperature in the heating area. in (t) represents the indoor temperature of the heating area at time t.

[0088] In step 4, the established CSP power station heat storage capacity configuration model specifically includes:

[0089]

[0090] C = min[C1 + C2 - C3] (26);

[0091] Where: C1 is the operation and maintenance cost of CSP; C m,CSP is the operation and maintenance cost coefficient of the CSP power station; C2 is the construction cost of the heat storage system; C3 is the operating income of the hydrogen-blended gas turbine; C e The on-grid electricity price for gas turbine power generation; C h P is the heat unit price of gas turbine; CSP (t) represents the power generation of the CSP plant in time period t; P GT,e (t) represents the electric power output of the gas turbine during the period t; P GT,h (t) represents the thermal power output of the gas turbine during the period t.

[0092] In step 5, the optimization scheduling model of the combined heat and power supply integrated energy system of the hydrogen-blended gas unit and the CSP power station includes: a hydrogen-blended gas unit profit model, a CSP power station heat storage capacity configuration model, and an integrated energy system economic scheduling model;

[0093] The profit model of hydrogen-blended gas generator sets is derived from step 4, as follows:

[0094] C3=C e P GT,e (t)+C h P GT,h (t)

[0095] Where: C3 is the operating income of hydrogen-blended gas turbine; C eThe on-grid electricity price for gas turbine power generation; C h P is the heat unit price of gas turbine; GT,e (t), P GT,h (t) are the electrical and thermal power output of the gas turbine during period t.

[0096] The thermal storage capacity configuration model of the CSP power station includes equations (25) and (26);

[0097] The economic dispatch model of the integrated energy system is derived from step 3, which specifically includes equation (10);

[0098] The decision variable of the upper-level planning model is the heat storage capacity of the CSP power station heat storage system.

[0099] The upper-level planning model mainly determines the thermal storage capacity of the CSP power station through the CSP operation and maintenance cost C1, the thermal storage system construction cost C2, and the operating income of the hydrogen-blended gas turbine C3. The formula comes from step 4, including formula (25) and formula (26);

[0100] The heat storage system is the basis of heating in a CSP plant. The size of the heat storage capacity directly affects the construction cost of the heat storage system. Reasonable configuration of the heat storage system capacity can improve the economic efficiency of CSP operation. Taking into account the economic efficiency of hydrogen blending of natural gas, the heat storage capacity of the CSP plant when the dispatching cost is the lowest is solved, which is defined as the optimal heat storage capacity.

[0101] The decision variables of the lower-level planning model are the output of each coupled device in each period, the actual output of wind and solar power, the purchased electricity power, the purchased gas power, etc.

[0102] The lower-level planning model mainly minimizes the daily operating cost of the system by reasonably planning various operating costs of the system. The formula comes from step 3, including formula (10).

[0103] The system's electrical load is borne by gas turbine power generation, CSP power generation system, wind power and photovoltaic grid connection, power purchase from external power grid, and discharge of electric energy storage equipment. The thermal load is borne by gas turbine heat generation, CSP power station heat storage system, and electric heaters.

[0104] The output of each coupled device includes: gas turbine electric heat output, solar thermal power station electric heat output, electric heater heat output, and electric energy storage device electric output;

[0105] The actual output of wind and solar power is the power that is finally put into use by users after photovoltaic power stations and wind farms integrate the power generated by photovoltaic power and wind power into the system, excluding the abandoned wind and solar power.

[0106] The purchased power is part of the power purchased by the system from the external power grid to meet the system load demand;

[0107] The purchased gas power is the natural gas power required for the gas turbine to perform work.

[0108] In step 5, the system operating cost and gas-hydrogen blending unit benefits under different heat storage capacities are calculated through double-layer optimization iterations; the details are as follows:

[0109] Two-level planning includes two levels of optimization problems. The upper level first makes a decision and passes it to the lower level. On this basis, the lower level determines its own feasible domain, optimizes the lower level problem, and passes the optimization result to the upper level. After multiple iterations, the optimal solution is obtained.

[0110] In the present invention, the upper layer first configures the heat storage capacity of the heat storage system of the CSP power station. The formula is derived from step 4, specifically including formula (25) and formula (26);

[0111] After configuring the heat storage capacity of the CSP thermal storage system, it is passed to the lower layer, which determines the operating range of the CSP thermal storage system and optimizes the operation of the integrated energy system. The formula is derived from step 3, specifically including formula (10). After obtaining the operation optimization result, the result is passed to the upper layer. The flow chart is shown in the instruction manual. Figure 4 shown.

[0112] The present invention provides a method for optimizing the configuration of heat storage capacity of a solar thermal power station considering the economic benefits of hydrogen blending with natural gas, and the technical effects are as follows:

[0113] (1) In the method of the present invention, a concentrating solar power station (CSP) cooperates with the gas hydrogen blending technology to participate in energy supply. Compared with the independent operation of traditional gas units, it can effectively improve the system's energy utilization efficiency, renewable energy consumption capacity and scheduling economy, while reducing carbon emissions.

[0114] (2) In the method of the present invention, the heat storage capacity of the CSP power station has a great influence on the operation of the integrated energy system. When the heat storage capacity is too low, the CSP power station cannot store excess heat for power generation when there is no sunlight, and the system needs to purchase electricity from outside, thereby increasing the operating cost of the system. When the heat storage capacity is too large, it exceeds the heat storage demand of the system, resulting in a waste of resources and an increase in investment costs.

[0115] (3) After considering the economic benefits of hydrogen blending of natural gas, the optimized configuration of the system heat storage capacity adopted by the present invention is more reasonable, which has significant advantages in improving the operational flexibility of the integrated energy system, promoting the consumption of new energy and reducing carbon emissions, thereby improving the economic benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0117] Figure 1 This is a structural diagram of the electric and thermal integrated energy system of the present invention.

[0118] Figure 2 It is a schematic diagram of the internal energy flow of the solar thermal power station of the present invention.

[0119] Figure 3 This is the electric heat load and wind power photovoltaic prediction output diagram of the present invention.

[0120] Figure 4 It is a flow chart of the dual-layer optimization configuration of the present invention.

[0121] Figure 5 This is a diagram showing the impact of the heat storage capacity of the present invention on the system operating cost and the benefits of hydrogen blending with fuel gas.

[0122] Figure 6 This is a diagram of the electric power optimization results of the present invention.

[0123] Figure 7 This is a diagram of the thermal power optimization results of the present invention.

[0124] Figure 8 This is a diagram showing the changes in heat storage power and heat storage amount of the heat storage device of the present invention. DETAILED DESCRIPTION

[0125] A method for optimizing the configuration of the heat storage capacity of a CSP power station considering the economic benefits of gas-hydrogen blending, firstly, the conventional cogeneration system and the CSP power station are coupled by using the gas-hydrogen blending technology, a mathematical model of the gas-hydrogen blending CSP unit is constructed, and a large-capacity heat storage system is introduced for the CSP power station, and heat energy is transferred to the heat storage system through photothermal conversion; secondly, the dispatching economic factors such as the system power generation cost, the system operation and maintenance cost, the carbon trading cost and the heat transmission loss are comprehensively considered, and an economic dispatching model of the comprehensive energy system is established; on this basis, the balance point between the optimal configuration of the heat storage capacity and the benefits of gas-hydrogen blending is explored on the premise of minimizing the daily operation cost of the system, and the optimal heat storage capacity configuration method of the CSP power station is determined; finally, the optimal heat storage capacity is selected by calculating the overall benefits of the synergistic effect of the gas-hydrogen blending technology and the CSP power station. The present invention optimizes the configuration of the heat storage capacity of the CSP power station, makes the thermal-electric matching degree of the system higher, and improves the economic benefits of the system operation.

[0126] In order to verify the feasibility of the proposed heat storage capacity configuration model, the optimal heat storage capacity is obtained through the model proposed by the present invention and the results are analyzed. The following three typical scenarios are given for comparative analysis. The structure of the electric-thermal integrated energy system of the present invention is as follows: Figure 1 The schematic diagram of energy flow inside a CSP power station is shown in Figure 2 As shown in the figure, the system electric heat load and wind power photovoltaic forecast output diagram is as follows Figure 3 shown.

[0127] Scenario 1: The electric-thermal integrated energy system uses a common heat storage device, without considering the economic benefits of hydrogen blending with gas;

[0128] Scenario 2: The electric-thermal integrated energy system adopts a CSP power station heat storage system, without considering the economic benefits of mixing hydrogen with gas;

[0129] Scenario 3: The electric-thermal integrated energy system adopts a solar thermal power station heat storage system and considers the economic feasibility of hydrogen blending with natural gas.

[0130] The scheduling results corresponding to the above three scenarios are shown in Table 1.

[0131] Table 1 Comparison of scheduling results in different scenarios

[0132]

[0133] As shown in Table 1, the total cost of scenario 1 is the highest among the three scenarios. From the analysis of various costs, the equipment operation and maintenance costs, gas costs, wind and solar power abandonment costs and carbon emission costs in the three scenarios are similar. The main reason is that the cost of purchasing and selling electricity from the power grid is higher in scenario 1, that is, scenario 1 needs to purchase more electricity from the external power grid. The system of scenario 2 contains a solar thermal power station. The special feature of the solar thermal power station is that it not only has a heat storage system, but also has a power generation function. This feature alleviates the power supply pressure of the gas unit during peak loads, thereby reducing the system's purchase of electricity from the external power grid. The system of scenario 1 uses ordinary heat storage devices, so the system heat storage device investment and construction costs of scenario 1 are higher and it is more dependent on the external power grid during peak power consumption.

[0134] In scenario 3, the economy of hydrogen-blended gas is taken into consideration, and the heat storage system mainly stores heat during the noon period, while the heat release is mainly concentrated at night. During the noon period, electricity consumption increases and heating demand decreases. The solar thermal power station operates at the minimum power generation capacity, and the system uses more economical hydrogen-blended gas for power generation. At the same time, solar energy can enter the heat storage system through photothermal conversion, and the heat storage system can store more heat for use at night. Therefore, it is more reasonable to use hydrogen-blended gas at noon. Although the fuel cost is partially increased, the economy of hydrogen-blended gas is improved and the investment and construction cost of the heat storage system is reduced.

[0135] In order to explore the balance point between the optimal configuration of the thermal storage capacity of the CSP power station and the benefits of hydrogen blending in gas, a two-layer optimization configuration model is established. Its structure is as follows: Figure 4 As shown in Figure 2, by solving with CPLEX, we can obtain the process of system operating cost and gas hydrogen blending benefits changing with the capacity of the CSP thermal storage device, as shown in Figure 2. Figure 5 As shown. Figure 5It can be seen that with the increase of heat storage capacity, the total cost of peak-shaving of the power system first decreases and then increases. When the heat storage capacity reaches 685MW·h, the optimal comprehensive cost is 1.4788 million yuan; when the heat storage capacity is 0, the output of the solar thermal power station cannot be flexibly adjusted, and the peak-shaving task is only undertaken by the gas-fired units. The wind and solar power consumption is small and the peak-shaving cost is high; as the heat storage capacity gradually increases, the two participate in the system peak-shaving together, which reduces the peak-shaving pressure of the gas-fired units and reduces the system operating costs; when the heat storage capacity is higher than 685MW·h, the peak-shaving demand has been basically met, but the heat storage cost is high, and continuing to increase the heat storage capacity will increase the cost.

[0136] However, the benefits of blending gas with hydrogen are different. As the heat storage capacity increases, it first increases and then decreases. When the heat storage capacity reaches 590MW·h, the maximum benefit is 1.5184 million yuan. When the heat storage capacity is 0, the CSP station outputs naturally, the peak-shaving pressure of the power system is large, and more peak-shaving costs need to be shared during the noon period, and the net benefit decreases. As the heat storage capacity gradually increases, the adjustable range of the CSP station increases, and auxiliary gas units participate in the peak-shaving of the power grid during some periods, obtain compensation while reducing the shared costs, and the net benefit increases. When the heat storage capacity is higher than 590MW·h, the increased unit heat storage system construction cost is higher than the reduced shared costs and the total peak-shaving benefits obtained, and the net benefit shows a decreasing trend.

[0137] Table 2 Comparison of operation results of different heat storage capacities

[0138]

[0139] Taking into account the economic benefits of hydrogen blending in gas and the system operating cost, the optimal capacity of the heat storage system is calculated to be 656MW·h. From the analysis of Table 2, it can be seen that when the heat storage device is built considering only the benefits of the gas unit, in order to increase the net benefit, the configuration of the heat storage capacity is reduced due to the high construction investment cost. However, this leads to a decrease in the output flexibility of the CSP power station, and the required peak-shaving capacity cannot be provided during periods of high peak-shaving demand, resulting in wind and solar abandonment. The system operating cost increased by 4.23% compared with the optimal configuration.

[0140] When only the optimal operating cost is considered, in order to meet the system peak-shaving needs and reduce the amount of gas input by thermal power units, solar thermal power generation is given priority in peak-shaving, so a larger heat storage capacity is required; although this configuration scheme can minimize operating costs, the income of the gas unit is 4.74% lower than that of the optimal configuration.

[0141] The system operation results at the optimal heat storage capacity are as follows Figure 6As shown in the figure, the wind power and photovoltaic grid-connected curves are basically consistent with the predicted curves, and the wind and solar power abandonment are relatively small. During the period of 1:00-7:00, the wind power generation is large, and the CSP station converts the remaining heat in the thermal storage system after the end of the previous scheduling day into electrical energy to maintain continuous power generation. During the period of 18:00-24:00, the wind power grid-connected power is small and photovoltaic power generation stops, and the power demand of the power system increases. At this time, the CSP station converts the heat in the thermal storage system into electrical energy to make up for the output shortage on the power generation side, while reducing the output of the gas unit and reducing carbon emissions; during 8:00-17:00, due to the increase in photovoltaic power generation, the CSP station reduces the output to assist the gas unit in participating in peak regulation, freeing up space for photovoltaic power generation to be connected to the grid, and improving the output flexibility of the CSP station and the stability of the power system.

[0142] Under the optimal heat storage capacity, the thermal power optimization curve of the system is as follows: Figure 7 As shown by Figure 7 It can be seen that between 8:00 pm and 7:00 am, the heat load is high, and heating is provided by electric heaters, gas turbines and the thermal storage system of the CSP power station. The electricity price is lower at night, and the electric heater converts electrical energy into thermal energy to supply heat to the system. The thermal storage system of the CSP power station releases the heat stored during the day at night to meet the system demand. From 10:00 to 18:00, it is the best time for the CSP power station to store heat. At this time, the system heat load demand is entirely borne by the gas turbine, and the CSP power station stores heat through the concentrating solar collector system in preparation for nighttime heating.

[0143] Among them, the heat storage power and heat storage change diagram of the thermal storage device of the solar thermal power station are as follows: Figure 8 As shown. Figure 8 It can be seen that the heat storage device cannot store and release heat at the same time, and the heat storage and release in a scheduling cycle are the same, ensuring that the heat storage capacity is consistent throughout the scheduling cycle. Heat storage is mainly concentrated in the noon period, and heat release is mainly concentrated in the night period, which realizes the transfer of power generation of the solar thermal power station, has strong scheduling economy, and can effectively reduce the comprehensive operating cost of the system.

Claims

1. A method for optimizing the thermal storage capacity of a solar thermal power plant considering the economic benefits of hydrogen blending with natural gas, characterized in that The following steps are involved: Step 1: Combine the gas-hydrogen blending technology in the thermal power integrated energy system containing the CSP power station to build a mathematical model of the gas-hydrogen blending cogeneration unit; Step 2: According to the heat transfer characteristics of the CSP thermal storage system, a mathematical model of heat transfer and loss between the gas-hydrogen blended unit and the CSP power station is established; Step 3: Comprehensively consider dispatch economic factors such as system power generation cost, operation and maintenance cost, carbon trading cost and heat transmission loss, and establish an economic dispatch model for the integrated energy system; Step 4: Taking into account the dispatch economy, solve the thermal storage capacity of the CSP plant when the operating cost is the lowest, and establish a thermal storage capacity configuration model for the CSP plant; Step 5: Under the condition of satisfying system constraints, the system operating cost and gas-hydrogen blending unit revenue under different heat storage capacities are calculated through double-layer optimization iterations to ultimately determine the optimal heat storage capacity of the system.

2. According to claim 1, a method for optimizing the configuration of heat storage capacity of a CSP power station considering the economic benefits of hydrogen blending with fuel gas, characterized in that: The mathematical model of the gas-hydrogen mixed heat and power cogeneration unit is established, including: 1) Hydrogen-blended gas turbine model: Where: R GT (t) is the hydrogen blending ratio of the gas turbine during period t; P GT,g (t) P GT,HCNG (t) are the natural gas, hydrogen and hydrogen-mixed gas power input to the gas turbine during the period t; H HCNG are the calorific values ​​of hydrogen, natural gas and mixed hydrogen gas respectively; P GT,e (t), P GT,h (t) are the electrical and thermal power output of the gas turbine during the period t; η GT,e , η GT,h are the electricity and heat conversion efficiency of the gas turbine respectively; 2) The electrolytic cell model is: Where: is the hydrogen production power of the electrolyzer in time period t; is the hydrogen production of the electrolyzer in time period t; is the molar mass of hydrogen; is the hydrogen density; △t represents the scheduling period; 3) The carbon capture equipment model is: Where: P CCS,e (t) is the electrical power supplied to the carbon capture equipment; P GT,e (t) represents the electric power output of the gas unit in the period t; P CCS,e1 (t), P CCS,e2 (t) are the fixed power loss and the power consumed by the CO2 treatment operation; is the CO2 emission of the gas generator set in period t; e GT is the unit carbon emission intensity; CCS The energy required to capture a unit of CO2 for the carbon capture equipment; is the total amount of CO2 captured by the carbon capture equipment; μ CCS for carbon capture efficiency; 4) The electric heater model is: Q EH (t)=P EH (t)η ah (5); Where: Q EH (t) and P EH (t) are the heating power and power consumption of the electric heater in time period t; η ah It is the electric-heat conversion efficiency of the electric heater.

3. According to claim 1, a method for optimizing the thermal storage capacity of a CSP power station considering the economic efficiency of hydrogen blending with fuel gas, characterized in that: In step 2, the heat transfer and loss mathematical model of the gas-hydrogen blending unit and the solar thermal power station is established, specifically including: 1) Concentrating solar collector system model of solar thermal power station: Q S-H (t)=η SF S SF D solar (t)-Q cur (t)(6); Where: Q S-H (t) is the heat power transmitted from the mirror field to the heat transfer medium at time t; η SF Represents light-to-heat conversion efficiency; S SF Indicates the mirror area of ​​the CSP power station; D solar (t) is the solar radiation per unit area at time t; Q cur (t) is the heat loss at time t; 2) Thermal energy storage system model of CSP power station: Where: Q TES_ch (t) and Q TES_dis (t) represents the heat storage and heat release power of the heat storage system in time period t; η TES_ch and η TES_dis It represents the heat storage and heat release efficiency of the heat storage system; Q H-T (t), Q T-H (t) are the thermal power of the heat transfer medium flowing to the heat storage system and the thermal power of the heat storage system flowing to the heat transfer medium at time t; Q T-HL (t) is the heat energy supplied by the heat storage device to the heat load at time t; 3) Solar thermal power station power generation system model: P CSP (t)=Q H-P (t)η PB (8); Where: P CSP (t) represents the power generation of the CSP plant at time t; Q H-P (t) is the thermal power flowing into the power generation system at time t; η PB is the heat-to-electricity conversion efficiency; 4) Heat energy transmission loss model: Where: N M represents the set of all heat output equipment; △t represents the operating time of the heat output equipment; represents the heat output of unit i in time period t; c represents the specific heat capacity of water; m(t) represents the flow rate of water heated by the pipeline in time period t; T s (t) represents the temperature of the heating water supply pipeline at time t; T r (t) represents the return water pipe temperature at time t; P GT,h (t) represents the thermal power output of the gas turbine during the period t; T s,max 、T s,min 、T r,max and T r,min Respectively represent the maximum and minimum water supply temperature, the maximum and minimum return water temperature; T end Indicates the pipe outlet temperature; T start Indicates the pipe inlet temperature; T out represents the external environment temperature; k represents the heat transfer coefficient per unit length of the pipeline; L represents the length of the pipeline.

4. According to claim 1, a method for optimizing the configuration of heat storage capacity of a CSP power station considering the economic efficiency of hydrogen blending with fuel gas, characterized in that: In step 3, the comprehensive energy system economic dispatch model established includes: Objective function: Where: F represents the total cost of system operation; N T represents the scheduling cycle, t represents the scheduling period, t=1,2,3,…,24; C GT (t), C W (t), C EX (t), C ME (t) and C TES (t) represent the fuel cost, wind and solar curtailment penalty cost, power exchange cost, equipment operation and maintenance cost, carbon trading cost and thermal storage system construction cost of the system in time period t; (1) Fuel cost: Where: C GT (t) is the fuel cost of the gas turbine in period t; C CH4 is the unit price of natural gas; (2) Penalty costs for curtailing wind and solar power: Where: η W Indicates the penalty coefficient for abandoning wind and solar power, Respectively represent the wind power and photovoltaic power input into the system during time period t; P WT,i (t), P PV,i (t) represent the wind power and photovoltaic power absorbed by the system in time period t respectively; (3) Electricity interaction cost: Where: P ex (t) represents the interaction power between the power grid and the external power grid in time period t; C rb (t) and C rs (t) are the electricity purchase and sales prices for time period t; (4) Equipment operation and maintenance costs: Where: N M is the total number of generating units in the grid; C mi is the operation and maintenance cost coefficient of unit i; P i CG (t) is the output of unit i in period t; (5) Carbon trading costs: Where: E p Represents the initial carbon emission quota of the system; Allocate a quota for regional carbon emissions per unit of electricity; E is the conversion factor of electricity; ac (t) represents the actual carbon emissions of the system during period t; represents the system carbon trading cost; k Ca is the carbon trading market price; (6) Heat storage system construction cost: Where: C a is the average annual cost of the heat storage system; C0 is the unit investment price; E h,max is the capacity of the heat storage system; r is the discount rate; Y is the service life of the heat storage system; C TES (t) is the average daily construction cost of the heat storage system.

5. According to claim 4, a method for optimizing the configuration of heat storage capacity of a CSP power station taking into account the economic benefits of hydrogen blending with fuel gas, characterized in that: The constraints of the objective function include: ①. Electric and thermal power balance constraints: P GT,h (t)+Q T-HL (t)+Q EH (t)=Q load (t)+Q s (t)(18); Where: P load (t) is the power load in the grid during time period t; Q load (t) is the heat load in the network during period t, Q s (t) is the heat loss in the network during period t; ②. Fuel gas mixed with hydrogen can balance constraints: Where: V GT,mix (t) are the amount of hydrogen produced by the electrolyzer and injected into the pipeline, the amount of natural gas purchased, and the amount of hydrogen-blended fuel gas consumed by the gas turbine during time period t; ③. Hydrogen-blended fuel gas transmission and operation constraints: Where: q ij (t) is the pipeline flow between gas network nodes i and j at time t; σ ij is the pipeline transmission parameter between gas grid nodes i and j; ε ij (t) is the gas network pipeline direction parameter at time t, which can be -1, 0, or 1; p i (t) is the pressure value of gas network node i at time t; p j (t) represents the pressure value of gas network node j at time t; q ij,max is the upper limit of pipeline flow between gas network nodes i and j; p i,max 、p i,min are the upper and lower limits of the pressure value of gas network node i respectively; is the gas supply flow rate of the gas source at time t; They are the upper and lower limits of the gas supply flow rate of the gas source respectively; ④. Grid interaction power constraints: Where: P buy (t) and P sell (t) represents the power purchase and sales of the system in time period t; x buy (t) and x sell (t) respectively represent the electricity purchasing and selling status in time period t; P ex，max Indicates the upper limit of the interaction power; ⑤. Unit output constraints: Where: and are the maximum and minimum output of unit i respectively; and are the up and down ramp rate limits of unit i respectively; ⑥. Constraints of thermal energy storage system in CSP power plants: Where: and represents the maximum power of heat storage and heat release of the heat storage system in time period t; x TES_ch (t) and x TES_dis (t) are the charging and discharging states of the heat storage device at time t; E TES (t) is the heat storage amount in period t; and are the minimum and maximum heat storage capacities of the heat storage system respectively; E TES (0) represents the heat storage capacity of the heat storage device at the initial time of the scheduling period; E TES (N T △t) represents the heat storage capacity of the heat storage device at the end of the scheduling period; ⑦.Heat network temperature constraints: Where: T SH,g 、T SH,h are the heat medium temperatures of the water supply pipe and the return pipe of the first heating station respectively; T g,max and T h,min T are the upper temperature limit of the water supply pipe and the lower temperature limit of the return pipe at the first heating station respectively; in,max and T in,min Respectively represent the upper and lower limits of the indoor temperature in the heating area; T in (t) represents the indoor temperature of the heating area at time t.

6. According to claim 5, a method for optimizing the configuration of heat storage capacity of a CSP power station considering the economic efficiency of hydrogen blending with fuel gas, characterized in that: In step 4, the established CSP power station heat storage capacity configuration model specifically includes: C = min[C1 + C2 - C3] (26); Where: C1 is the operation and maintenance cost of CSP; C m,CSP is the operation and maintenance cost coefficient of the CSP power station; C2 is the construction cost of the heat storage system; C3 is the operating income of the hydrogen-blended gas turbine; C e The on-grid electricity price for gas turbine power generation; C h P is the heat unit price of gas turbine; CSP (t) represents the power generation of the CSP plant in time period t; P GT,e (t) represents the electric power output of the gas turbine during the period t; P GT,h (t) represents the thermal power output of the gas turbine during the period t.

7. According to claim 6, a method for optimizing the configuration of heat storage capacity of a CSP power station considering the economic efficiency of hydrogen blending with fuel gas, characterized in that: In step 5, the optimization scheduling model of the combined heat and power supply integrated energy system of the hydrogen-blended gas unit and the CSP power station includes: a hydrogen-blended gas unit profit model, a CSP power station heat storage capacity configuration model, and an integrated energy system economic scheduling model; The profit model of hydrogen-blended gas generator sets is as follows: C3=C e P GT,e (t)+C h P GT,h (t) Where: C3 is the operating income of hydrogen-blended gas turbine; C e C is the on-grid electricity price for gas turbine power generation; h P is the heat unit price of gas turbine; GT,e (t), P GT,h (t) are the electrical and thermal power output of the gas turbine during the period t; The thermal storage capacity configuration model of the CSP power station includes equations (25) and (26); The economic dispatch model of the integrated energy system includes formula (10).

8. According to claim 7, a method for optimizing the configuration of heat storage capacity of a CSP power station taking into account the economic benefits of hydrogen blending with fuel gas, characterized in that: The decision variable of the upper-level planning model is the heat storage capacity of the CSP power station heat storage system. The upper-level planning model determines the thermal storage capacity of the CSP power station through the CSP operation and maintenance cost C1, the thermal storage system construction cost C2, and the operating income of the hydrogen-blended gas turbine C3, which specifically includes equations (25) and (26); The decision variables of the lower-level planning model are the output of each coupled device in each period, the actual output of wind and solar power, the purchased electricity power, the purchased gas power, etc. The lower-level planning model minimizes the daily operating cost of the system by rationally planning various operating costs of the system, including formula (10).

9. A method for optimizing the thermal storage capacity of a CSP power station considering the economic benefits of hydrogen blending with fuel gas according to claim 8, characterized in that: In step 5, the system operating cost and gas-hydrogen blending unit benefits under different heat storage capacities are calculated through double-layer optimization iterations; the details are as follows: Bi-level planning involves two levels of optimization problems. The upper level first makes a decision and passes it to the lower level. The lower level determines its own feasible domain based on this, optimizes the lower level problem, and passes the optimization result to the upper level. After multiple iterations, the optimal solution is obtained. The upper layer first configures the heat storage capacity of the CSP power station heat storage system. After configuring the heat storage capacity of the CSP power station heat storage system, it is passed to the lower layer. The lower layer determines the operating range of the CSP power station heat storage system and optimizes the operation of the integrated energy system.

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