Robust optimization scheduling method for comprehensive energy system containing electricity-to-hydrogen-heat system

Abstract

The invention discloses a robust optimization scheduling method for a comprehensive energy system containing an electricity-to-hydrogen-heat system. The method comprises the following steps: S1, establishing an electricity-heat-gas-hydrogen comprehensive energy system model; s2, according to the operation mode of the electricity-to-hydrogen-heat system, a steady-state model of an electrolytic cell is established, an electricity-to-hydrogen-heat system operation model is established based on a thermodynamic equation and a physical reaction principle, and the voltage relation and the energy relation of the electricity-to-hydrogen-heat system are analyzed; s3, an electricity-heat-gas-hydrogen comprehensive energy system two-order robust optimization operation model considering source load uncertainty is established, the source load uncertainty comprises double uncertainty of wind power and load, and Camp is adopted; according to the method, the uncertainty of wind power output and load requirements is considered, the distribution of multiple energy flows in the electricity-heat-gas-hydrogen comprehensive energy system is optimized, and it is ensured that the system can still keep high safety and reliability when facing uncertainty.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system scheduling optimization equipment, and specifically to a robust optimization scheduling method for an integrated energy system including an electricity-to-hydrogen-heat system. Background Art

[0002] In recent years, due to the depletion of fossil fuels and the environmental challenges they bring, renewable energy sources such as wind power and solar energy are gradually becoming the mainstay of my country's energy supply. Due to the temporal and spatial differences in energy demand and the uncertainty of wind and solar resources, there is a lack of flexible management of wind power and photovoltaics, resulting in the phenomenon of wind and solar abandonment. As a comprehensive application system of electricity, fuel, heat and refrigeration, the Integrated Energy System (IES) has effectively promoted the widespread application of renewable energy and significantly improved the comprehensive utilization efficiency of energy. However, due to the characteristics of "rich in coal, poor in oil and little gas" in my country's energy resources, the scarcity of natural gas resources has restricted the development of IES, which uses natural gas as its main fuel. In addition, the access of a high proportion of renewable energy, coupled with the limitations of existing energy storage technologies, especially the lack of long-term energy storage capacity and the lack of short-term adjustment flexibility, has brought long-term and short-term dual pressures to the safety and economic operation of IES.

[0003] In the face of the above bottlenecks, the increasing maturity of hydrogen production technology has provided new ideas for the development of IES. Hydrogen energy, with its advantages of large capacity and long-term storage, has shown great potential in the field of energy storage. In addition, hydrogen energy can be converted into various forms of energy to meet the needs of different energy systems. At the same time, it further deepens the coupling relationship between energy systems such as electricity, heat, transportation, and chemical industry, which indicates that hydrogen energy will play a more important role in the future energy system. For the optimized operation of the Inactive Distribution Networks (ADN) and the District Heat Networks (DHN), combining the electric heating and electric hydrogen production processes can improve the economy and safety of the system.

[0004] Unlike solar photovoltaic power generation, the output of wind energy mainly depends on wind speed, wind direction and turbine efficiency, which are affected by multiple factors such as terrain, season and weather, and have strong randomness. In addition, the load characteristics, energy demand, load response and other factors on the load side of the system are unpredictable and variable in time and space, making the load also have strong uncertainty. Therefore, it is of great practical significance to realize the optimized scheduling and operation of the Power-to-Hydrogen-and-heat (P2HH) system and the Electricity-Heat-Gas-Hydrogen Integrated Energy System (EHGH-IES) that considers the dual uncertainty of source and load.

[0005] At present, there are many methods for dealing with uncertainty problems, such as stochastic programming, scenario analysis, and robust optimization

[16] . The uncertainty requirements in the stochastic programming method use a probability model to meet a certain confidence interval, but it needs to rely on a large amount of uncertainty information to construct the probability distribution function of the uncertain variables; the scenario analysis method converts the uncertainty problem into a deterministic problem through sampling and scenario simplification, but when the number of scenarios is too large, it will not only increase the complexity of the calculation, but also lack effective quantitative analysis methods. Compared with the above two methods, the robust optimization method is based on the interval number theory. Through the maximum and minimum decision problems, it realizes the optimal decision of uncertain variables in the worst scenario, and requires less information on uncertainty parameters. Therefore, it is particularly important to propose a two-stage EHGH-IES robust optimization scheduling method that considers the dual uncertainty of P2HH and source and load. Summary of the invention

[0006] In order to solve the problems existing in the prior art in the above-mentioned background technology, the present invention provides the following technical solution to provide a robust optimization scheduling method for an integrated energy system including an electricity-to-hydrogen-heat system, which realizes multi-level utilization of energy and improves energy utilization efficiency, which not only helps to absorb wind power, but also enhances the flexibility of the entire energy system and ensures the stability and reliability of energy supply. The technical solution is:

[0007] A robust optimization scheduling method for an integrated energy system including an electricity-to-hydrogen-heat system comprises the following steps:

[0008] S1: Establish an electricity-heat-gas-hydrogen integrated energy system model, analyze the multi-energy complementary characteristics of the electricity-heat-gas-hydrogen integrated energy system and the conversion relationship and coupling mode between various energy forms,

[0009] S2: According to the operation mode of the power-to-hydrogen-heat system, a steady-state model of the electrolyzer is established, and the operation model of the power-to-hydrogen-heat system is established based on thermodynamic equations and physical reaction principles, and the voltage relationship and energy relationship of the power-to-hydrogen-heat system are analyzed;

[0010] The electrolyzer is a common device for the electricity-heat-gas-hydrogen integrated energy system and the electricity-to-hydrogen-heat system;

[0011] S3: Establish a two-order robust optimization operation model of the electricity-heat-gas-hydrogen integrated energy system considering source-load uncertainty. The source-load uncertainty includes the dual uncertainty of wind power and load. The C&CG algorithm and KKT conditions are used to transform and solve the proposed model to optimize the distribution of multi-energy flows in the electricity-heat-gas-hydrogen integrated energy system.

[0012] Preferably, the electricity-heat-gas-hydrogen integrated energy system includes wind power, thermal power units, gas turbines, cogeneration units, electrolyzers and methanation devices, and the electricity-to-hydrogen-heat system includes electrolyzers, hydrogen storage tanks, heat pumps and heat exchanges.

[0013] Preferably, when there is excess electricity in the integrated energy system, power is provided to the electrolyzer. The power-to-hydrogen-heat system can collect and utilize the heat energy released during the electrolysis process of the electrolyzer, and transfer it to the thermal network through heat pumps and heat exchangers. After the hydrogen generated by the power-to-hydrogen-heat system meets the hydrogen load, the remaining part will be converted into natural gas through a methanation device and injected into the natural gas network.

[0014] Preferably, in S2, a steady-state model of the electrolyzer is established to solve the relationship between the power consumption, hydrogen production rate and heat generated of the electrolyzer, and according to the energy distribution relationship between hydrogen and thermal energy in the electric-to-hydrogen-heat system, the operating state of the electric-to-hydrogen-heat system is divided into the following three conditions: thermal equilibrium state, heat output state and heat input state; the hydrogen load is calculated according to the capacity of the hydrogen storage tank and the instantaneous amount of hydrogen consumed, and the methanation device is started according to the hydrogen load condition.

[0015] Preferably, in S3, based on the above-mentioned electric-heat-gas-hydrogen integrated energy system model and the electric-to-hydrogen-heat system operation model, a two-order robust optimization operation model of the electric-heat-gas-hydrogen integrated energy system considering source-load uncertainty is established. The two-order robust optimization operation model of the electric-heat-gas-hydrogen integrated energy system is divided into two stages:

[0016] The first stage determines the preliminary operation plan of each energy supply device in the system, the power trading strategy with the upper-level power grid, and optimizes the distribution of multiple energy flows in the system to meet the system safety and economic requirements;

[0017] In the second stage, the results of the first stage are fine-tuned for the "worst scenario" with the largest prediction error. The output and load demand of thermal power units, cogeneration units, and gas turbines are adjusted to optimize the multi-energy flow distribution to enhance the robustness of the system. The adjustment results are fed back to the first stage. Through iterative optimization, it is ensured that the electric-heat-gas-hydrogen integrated energy system can maintain safe and stable operation in all possible extreme scenarios.

[0018] The beneficial effects of the technical solution provided by the present invention are:

[0019] 1. The present invention proposes a method for optimizing the scheduling of various equipment in an integrated energy system including a power-to-hydrogen-heat system. The power-to-hydrogen-heat system can adjust the output of hydrogen and thermal energy as needed, which makes it more flexible in dealing with different energy demands and load changes; the power-to-hydrogen-heat system can also recycle the heat energy generated in the electrolysis process, reducing heat energy loss.

[0020] 2. The present invention takes into account the uncertainty of wind power output and load demand, and proposes a two-stage robust scheduling method, which takes into account a variety of extreme scenarios and ensures that the system can maintain a high degree of safety and reliability when facing uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is the basic structure diagram of EHGH-IES;

[0022] Figure 2 It is a schematic diagram of the P2HH system;

[0023] Figure 3 The schematic diagram of the example system;

[0024] Figure 4 It is the load forecast diagram for each system;

[0025] Figure 5 Optimize the result graph for EHGH-IES;

[0026] Figure 6 This is a comparison chart of wind power output between scenario 1 and scenario 2;

[0027] Figure 7 This is a comparison chart of the thermal output results of CHP units in scenario 1 and scenario 2;

[0028] Figure 8 This is a comparison chart of wind power of various energy storage methods. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0031] Example 1

[0032] The present invention provides a robust optimization scheduling method for an integrated energy system including P2HH, comprising the following steps:

[0033] S1: Establish an electricity-heat-gas-hydrogen integrated energy system (EHGH-IES) model, analyze the multi-energy complementary characteristics of EHGH-IES and the conversion relationship and coupling mode between various energy forms; analyze that in addition to conventional energy equipment, EHGH-IES is also equipped with a power-to-hydrogen-heat system (P2HH) including an electrolyzer, and the electrolyzer is a common equipment for EHGH-IES and P2HH; when there is excess electricity in EHGH-IES, the electrolyzer converts electricity into hydrogen, and the heat energy generated in the conversion process can be transported to the heat network. When the energy point in EHGH-IES is insufficient, the hydrogen stored in EHGH-IES can supply energy to EHGH-IES.

[0034] S2: According to the operation mode of P2HH, a steady-state model of the electrolyzer is established, and a P2HH operation model is established based on thermodynamic equations and physical reaction principles to analyze the voltage relationship and energy relationship of the P2HH system; the energy flow relationship of P2HH is analyzed to solve the hydrogen storage capacity, hydrogen load and conversion efficiency of electricity, hydrogen, heat and gas in P2HH.

[0035] S3: Establish a two-order robust optimization operation model of EHGH-IES considering source-load uncertainty. The source-load uncertainty includes the dual uncertainty of wind power and load. The C&CG algorithm and KKT condition are used to transform and solve the proposed model, optimize the distribution of multi-energy flows in EHGH-IES, realize multi-level utilization of energy, improve energy utilization efficiency, enhance the flexibility of the entire EHGH-IES, and ensure the stability and reliability of EHGH-IES energy supply.

[0036] Example 2

[0037] The basic structure of the EHGH-IES in this embodiment is as follows Figure 1 As shown, it includes wind power (WP), thermal power units, gas turbines (GT), combined heat and power units (CHP), electrolyzers (EL), methanation reactors (MR) and other various energy loads (mainly including electrical load, thermal load, gas load and hydrogen load); P2HH is as follows Figure 2The figure shows EL, Hydrogen Storage Tank (HST), heat pump and heat exchanger. P2HH can efficiently convert excess wind energy into hydrogen energy and store it in HST. At the same time, the P2HH system can collect and utilize the heat energy released during the electrolysis process of the electrolyzer, and transfer it to the thermal network through heat pumps and heat exchangers. After the hydrogen generated meets the hydrogen load, the remaining part will be converted into natural gas through MR and injected into the natural gas network, which not only enriches the diversity of energy supply, but also ensures that the fuel requirements of CHP units and gas turbines are met. In addition, EHGH-IES realizes the two-way flow and transaction of energy through interconnection with the external natural gas grid and the upper power grid, further improving the flexibility and economy of the system.

[0038] The P2HH system optimizes the shortcomings of low efficiency of hydrogen production from electricity, balances the mismatch between thermal energy supply and demand, and effectively reduces the thermal output of the CHP unit, thereby alleviating the problem of insufficient operating flexibility of the CHP unit in the "heat-based electricity" operating mode.

[0039] In S2, a steady-state model of the electrolyzer is established to solve the relationship between the power consumption, hydrogen production rate and heat generated by the electrolyzer. According to the energy distribution relationship between hydrogen and thermal energy in P2HH, the operating state of P2HH is divided into the following three situations: thermal equilibrium state, heat output state and heat input state;

[0040] The specific process of S2 is as follows: During the electrolysis process, the electrical energy is converted into hydrogen and thermal energy. The voltage relationship and energy relationship are as shown in equations (1) and (2), which represent the energy distribution relationship between hydrogen and thermal energy.

[0041] P H2 (t) = η tn P DC (t) = U cell (t,T e )i cell (t) (1)

[0042]

[0043] The steady-state model of EL is:

[0044]

[0045] In formula (3): P h (t) represents the power consumption of EL at time t; Q h (t) represents the EL heat generation power at time t; HHV represents the higher heating value of hydrogen; η t represents the hydrogen production rate of EL at time t; η EL Indicates the efficiency of EL; Pheat (t) represents the heat generated by EL at time t.

[0046]

[0047] In formula (4), E(t) represents the capacity of the hydrogen storage tank at time t; DH(t) is the hydrogen load at time t; V H2 (t) is the hydrogen consumed by MR at time t. The P2HH system generates hydrogen and oxygen by electrolyzing water, and also generates a large amount of heat. In order to effectively utilize this heat, a closed water circulation system is adopted, which mainly includes a heat pump and a heat exchanger. Among them, the heat pump is used to increase the temperature of hot water, and the heat exchanger is used to transfer the heat in the hot water to the thermal network. In the heat recovery process, part of the heat energy H rec (t) is collected and utilized through cooling circulating water, while the other part of the heat energy P loss (t) will be lost to the environment in the form of heat conduction. Considering the efficiency η of the heat exchanger and heat pump 2 , the thermal power actually injected into the thermal network η 2 H rec (t) will be less than the recoverable heat. This efficiency coefficient η 2 Depends on many factors, such as equipment performance, working conditions, etc.

[0048] To describe the heat transfer process of the P2HH system, a model such as equations (5)-(7) can be established:

[0049] P loss =(T EL (t)-T a ) / R (6)

[0050] P heat =a 2 P H2 +b 2 T H2 (7)

[0051] Where: C w is the specific heat capacity of water, P loss (t) is the heat loss at time t; H rec (t) is the heat exchanged between EL and the heat exchanger at time t.

[0052] From the analysis of equations (1)-(7), it can be seen that the operating status of P2HH can be divided into the following three situations:

[0053] 1. Thermal equilibrium state: The heat generated by P2HH is balanced with the heat transferred to the thermal network through the heat pump and heat exchanger, that is, P heat (t)≈H rec (t);

[0054] 2. Heat output state: The heat generated by P2HH is greater than the heat transferred to the thermal network through the heat pump and heat exchanger, that is, P heat (t)>H rec (t);

[0055] 3. Heat input state: The heat generated by P2HH is less than the heat transferred to the thermal network through the heat pump and heat exchanger, that is, P heat (t) <H rec (t).

[0056] Through the above analysis, the energy flow relationship of P2HH is obtained, and the hydrogen storage capacity, hydrogen load and the conversion efficiency of electricity, hydrogen, heat and gas in P2HH are clarified; through the energy flow relationship of P2HH, two-way scheduling with EHGH-IES is realized, and efficient and stable flow of the system in EHGH-IES is achieved.

[0057] Methanation reaction is usually a process of converting carbon dioxide and hydrogen into methane and water under the action of a catalyst. Its thermochemical reaction equation is as follows:

[0058] CO 2 +4H 2 →CH 4 +2H 2 O (8)

[0059]

[0060] In formulas (8)-(10): is the H consumed in the reaction at time t 2 Volume; is the CH generated in the reaction at time t 4 Volume; m MR (t) is the CO consumed by the reaction at time t 2 The quality of MR is the loss coefficient of the reaction; ρ CO2 For CO 2 The above process calculates the hydrogen load based on the capacity of the hydrogen storage tank and the amount of hydrogen consumed immediately, and starts the methanogenization device based on the hydrogen load.

[0061] In S3, based on the above EHGH-IES model and P2HH operation model, an EHGH-IES two-order robust optimization operation model considering source-load uncertainty is established. The EHGH-IES two-order robust optimization operation model is divided into two stages:

[0062] The first stage determines the preliminary operation plan of each energy supply device in the system, the power trading strategy with the upper-level power grid, and optimizes the distribution of multiple energy flows in the system to meet the system safety and economic requirements;

[0063] In the second stage, the results of the first stage are fine-tuned for the "worst scenario" with the largest prediction error. The output and load demand of thermal power units, cogeneration units, and gas turbines are adjusted to optimize the multi-energy flow distribution to enhance the robustness of the system. The adjustment results are fed back to the first stage. Through iterative optimization, it is ensured that EHGH-IES can maintain safe and stable operation in all possible extreme scenarios.

[0064] The present invention uses the inherent coupling and complementarity of multi-energy networks to construct a flexible scheduling mechanism to cope with fluctuations in uncertain factors such as wind power and load. Without affecting the stability of the power grid's day-ahead scheduling plan (power purchase), the present invention achieves full absorption of wind power and reduction of the operating cost of the integrated energy system through real-time adjustment and optimization. At the same time, the method takes into account multiple extreme scenarios to ensure that the system can maintain a high degree of safety and reliability when facing uncertainty.

[0065] The objective function of the optimization model is shown in formula (11):

[0066]

[0067] In formula (11): c e (t) is the electricity price purchased from the superior power grid at time t; c gas F is the natural gas price purchased from the superior gas network; gas (t) is the gas purchase volume at time t; E(t) is the hydrogen storage volume at time t; c EL is the unit operating cost of EL; P EL (t) is the electric power consumed by EL at time t; P w (t) is the wind power at time t. w，cur Reduce the cost of unit wind power; ΔP w，cur (t) is the wind power reduction at time t; ΔP i (t) is the adjusted power of the i-th thermal power unit at time t; ΔP CHP (t) is the adjusted power of the CHP unit at time t; ΔP GT (t) is the adjusted power of GT at time t.

[0068] (1) Power system constraints:

[0069]

[0070] In formulas (12)-(14): Q G，i is the reactive power of the ith node; Q D，i is the reactive load of the ith node; u i 、u j is the voltage of the i-th and j-th nodes; rij is the impedance of the line; x ij is the reactance of the line; P ij is the active power flowing between node i and node j; Q ij is the reactive power flowing between node i and node j; U i，min and U i，max is the lower and upper limits of the node voltage at node i; P ij (t) / Q ij (t) is the active / reactive power at the beginning of branch ij; P ij，min， P ij，max / Q ij，min , Q ij，max P is the lower limit and upper limit of active / reactive power at the beginning of branch ij; w (t) is the predicted wind power at time t.

[0071] (2) Thermal system constraints

[0072] Temperature constraints of water supply network nodes and return network:

[0073]

[0074] In formula (15)-(16): represents the supply water temperature and return water temperature at node n at time t; is the lower and upper limits of the water supply temperature for node n; are the lower and upper limits of the return water temperature at node n.

[0075] The node heat load can be expressed as:

[0076]

[0077] In formula (17): represents the heat load at node n; Represents the mass of water flowing into the node n connected to the heat load.

[0078] The mixing node temperature can be expressed as:

[0079]

[0080] In formula (18): m out 、m in Represents the water mass flow in the supply network and return network; Indicates the supply water temperature and return water temperature at the pipe outlet n.

[0081] The network temperature loss can be expressed as:

[0082]

[0083] In formula (19): represents the end temperature and the head temperature of pipeline l at time t; T a (t) is the ambient temperature at time t.

[0084] (3) Natural gas system constraints

[0085] Upper and lower limits of gas source output:

[0086] S r,min ≤S r (t)≤S r,max (20)

[0087] In formula (20): S r，min and S r，max are the lower and upper limits of the gas supply from the natural gas source; S r (t) is the supply of natural gas source at time t.

[0088] The upper and lower limit constraints of gas pressure at natural gas network nodes:

[0089] α k,min ≤α k (t)≤α k,max (twenty one)

[0090] In formula (21): α k，min / α k，max is the lower and upper limits of the air pressure at node k; α k (t) is the air pressure at node k at time t.

[0091] Natural gas system flow constraints:

[0092]

[0093] In formula (21): q km，t is the volume of gas stored in the pipeline; C km is a constant that depends on the characteristics of the pipe.

[0094] (4) P2HH system constraints: Equations (1)-(7)

[0095] (5) Uncertainty Modeling

[0096] V W = {P w (t)(1-θ)P w (t)≤P w (t)≤(1+θ)P w (t)}(23)

[0097] V D = {P load (t)(1-δ)Pload (t)≤P load (t)≤(1+δ)P load (t)}(24) In formula (23)-(24): V W 、V D are the uncertainty sets of wind power and load respectively; θ and δ are the fluctuation coefficients of wind power and load respectively; P load (t) is the load value at time t; P load (t) is the load forecast value at time t.

[0098] The complexity of the established two-stage robust optimization model lies in the fact that it is a two-stage, three-layer min-max-min problem, which is difficult to solve directly. To this end, the column and constraint generation algorithm (C&CG) is used to solve it. The C&CG algorithm decomposes the problem into a main problem and sub-problems, and solves them alternately in an iterative manner to gradually approach the global optimal solution. The main problem is responsible for optimizing the overall operation strategy of the system under given conditions, while the sub-problems focus on identifying and handling extreme scenarios that may affect the stability of the system. The compact form of the main problem and sub-problems is:

[0099] (1) Main question

[0100]

[0101] in

[0102] Ψ(x,d)=max min b T y k (26)

[0103]

[0104] In Equations 25)-(27), Ψ(x, d) represents the objective function value of the subproblem; d represents the worst scenario in the uncertainty set determined from the subproblem in the lth iteration. The master problem is a mixed integer linear programming model, which is used to determine the decision variable x in the first stage.

[0105] (2) Sub-questions

[0106]

[0107] The main problem relaxes some constraints, and the objective function value obtained from the main problem provides a lower bound for the optimal solution of the original problem. The subproblem is solved given the current solution of the main problem, and the solution of the subproblem provides an upper bound for the optimal solution of the original problem. The main problem and the subproblem are solved by alternating iterations until the objective function values ​​of the main problem and the subproblem are equal.

[0108] After the original problem is decoupled by the C&CG algorithm, the resulting MP is a mixed integer linear programming problem, while the subproblem is still a max-min dual optimization problem, which still needs to be further transformed to speed up the solution and reduce the difficulty of solution. In order to simplify the solution process, the KKT condition and the big-M method are used to transform it, taking equations (30)-(31) as an example:

[0109]

[0110] 0≤μ G1 (t)≤M G1 g G1 (t) (31)

[0111] The technical solution provided by the present invention combines the IEEE 33-node power distribution system, the 7-node thermal system and the 6-node natural gas system to form EHGH-IES. Figure 3 As shown, see the following description:

[0112] The power grid E2-E33, gas grid N1-N4, and heat grid H1-H2 and H5-H7 are load nodes. The distribution system E1 is connected to the upper power grid; E22, E25, and E33 are each connected to a 700kW wind turbine; GT1 is connected to E6 and N5; GT2 is connected to E25 and N6; CHP is connected to E18, N6 and H1; P2HH and methanation device; connect E2, N1 and H1. In the natural gas network, nodes N1 and N2 are connected to the natural gas source. Assume that the wind power prediction error is 20%, the power load error is 10%, and the penalty cost for wind abandonment is 0.3 yuan / kWh. The EHGH-IES natural gas price is set at 3.5 yuan / Nm 3 The electricity price purchased from the upper power grid adopts the time-of-use electricity price, as shown in Table 1. The operating parameters of each device are shown in Table 2, and the system load and wind power forecast values ​​are shown in Table 2. Figure 4 shown.

[0113] Table 1 Time-of-use electricity price table

[0114]

[0115] Table 2 Operating parameters of each device

[0116]

[0117] from Figure 5The obtained EHGH-IES optimization results show that at 0-6 and 23-24, the system's wind power output is high, while the power load is relatively low. The main power supply of the system comes from wind power generation, and the excess wind energy is converted into hydrogen by EL for storage. Due to the high cost of natural gas purchase, the stored hydrogen is converted into natural gas through methanation reaction equipment in addition to meeting the hydrogen load demand, further meeting the natural gas load demand, demonstrating the system's internal flexible conversion and efficient utilization capabilities; at 7-22, the power load is high, while the wind power output is relatively low. The system timely increases the output of thermal power units, gas turbines and CHP units to ensure the stability and sufficiency of power supply. During the entire EL working process, the heat generated in the P2HH electrolysis process will be directly supplied to the thermal system through the heat exchanger to meet the heat load demand, significantly reducing the heating burden of the CHP unit. According to the operating characteristics and feasible domain analysis of the CHP unit, when the heat output of the CHP unit is reduced due to the supplement of external heat sources, its power output range is expanded, providing the system with greater flexibility and adjustment space. This flexibility enables the CHP unit to effectively respond to fluctuations in wind power output and electrical load, which not only improves the system's overall energy utilization efficiency, but also significantly enhances the system's economy and stability.

[0118] In summary, EHGH-IES has achieved maximum utilization of clean energy and optimal system operating costs through refined energy management and scheduling strategies.

[0119] In order to study the impact of the P2HH system on the operating status of EHGH-IES, two comparison scenarios were set up to conduct comparative analysis on the example system.

[0120] Scenario 1: Consider the Power-to-Hydrogen (P2H) system;

[0121] Scenario 2: Consider the P2HH system;

[0122] The comparison of wind power output results and CHP unit output results in scenario 1 and scenario 2 is as follows: Figure 6 , Figure 7 As shown. Figure 6 and Figure 7It can be seen that the wind power output of scenario 2 at 0-9, 18-21, and 23-24 is greater than that of scenario 1, while the CHP thermal output is less than that of scenario 1. The reason is that the CHP works in the "heat-to-power" mode. In order to meet the heat load demand during this period, the adjustment range of its power generation is limited. The wind power output during this period is large, resulting in excess system power generation capacity and compressing the space for wind power to be connected to the grid. However, since the P2HH system takes into account the heat recovery mechanism, the heat output pressure of the CHP unit is reduced, thereby effectively increasing the adjustment range of the CHP unit power generation, providing space for wind power consumption.

[0123] The amount of hydrogen used in each link under different scenarios is shown in Table 3. It can be seen from Table 3 that the P2HH system considering heat recovery has a significant impact on the hydrogen production and usage in each link. In terms of hydrogen production, after considering heat recovery, the hydrogen production of the hydrogen energy system has increased. The main reason is that heat recovery increases the system's ability to coordinate electricity, heat, gas and hydrogen, providing more flexibility for the system. When wind power is sufficient but grid demand is low, excess electricity can be converted into hydrogen, reducing the wind abandonment rate and improving the overall energy efficiency of the system. In terms of hydrogen utilization, after considering heat recovery, the reduction in the thermal output of the CHP unit can effectively reduce the system's electricity and gas purchase costs.

[0124] Table 3 Hydrogen utilization under scenario 1 and scenario 2

[0125]

[0126] In order to further verify the advantages of hydrogen energy storage in smoothing wind power fluctuations and improving wind power consumption rate, a comparative analysis of P2H, P2HH and battery energy storage was conducted based on the proposed two-stage robust optimization model. The comparison results are as follows: Figure 8 shown.

[0127] from Figure 8It can be seen that the wind power consumption of battery energy storage is less than that of P2H and P2HH systems. The reason is that although battery energy storage has a faster response speed, it is limited in energy storage scale and cycle. When wind power is generated, if the wind power output exceeds the acceptance capacity of the power grid, the battery energy storage cannot completely absorb the excess electricity, resulting in wind abandonment, and cannot meet the large-scale and long-term wind power storage needs. In contrast, hydrogen energy storage (including P2H and P2HH systems) has significant advantages in energy storage scale and cycle. Since the energy density of hydrogen is much higher than that of battery energy storage, hydrogen energy storage systems can store more energy in the same volume or weight. In addition, hydrogen can be stored and transported in a variety of ways such as compression and liquefaction, which enables hydrogen energy storage systems to adapt to energy storage needs in different scenarios. During the period of wind power generation, hydrogen energy storage systems can make full use of their large capacity and long-term energy storage characteristics to effectively absorb wind power that the power grid cannot absorb in time, thereby reducing the occurrence of wind abandonment. P2HH further considers the utilization of heat generated during the water electrolysis process, improving energy utilization efficiency. It not only helps to absorb wind power, but also promotes the diversified utilization of renewable energy and the flexibility of the energy system.

[0128] The system operation cost, calculation time and comprehensive energy utilization efficiency of the above three systems are shown in Table 4. As can be seen from the table, the comprehensive energy utilization efficiency of P2HH is 7.4% and 2.8% higher than that of battery energy storage and P2H system, respectively, while the total system operation cost is reduced by 8,700 yuan and 6,300 yuan, respectively, and the wind abandonment penalty cost is reduced by 649.29 yuan and 459 yuan. The main reason is that after considering the heat recovery in the electrolyzer process, this part of the heat energy can be transferred to the thermal system through the heat exchanger in the system for heating, which reduces the thermal output of the CHP unit, increases the adjustment range of the CHP unit's electrical output, and expands the space for wind power access to the grid, thereby significantly reducing the operating cost of the generator set in the system and further improving the comprehensive utilization rate of the system. At the same time, after the generated hydrogen meets the heat load, the remaining part will be converted into natural gas through the MR device to supplement the natural gas, which reduces the gas purchase cost of the natural gas system, realizes the multi-level utilization of energy, enhances the flexibility of the entire energy system, and ensures the stability and reliability of energy supply.

[0129] Table 4 System economic comparison

[0130]

[0131] Those skilled in the art will appreciate that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A robust optimization scheduling method for an integrated energy system including a power-to-hydrogen-heat system, characterized in that , S1: Establish an electricity-heat-gas-hydrogen integrated energy system model, analyze the multi-energy complementary characteristics of the electricity-heat-gas-hydrogen integrated energy system and the conversion relationship and coupling mode between various energy forms; S2: According to the operation mode of the power-to-hydrogen-heat system, a steady-state model of the electrolyzer is established, and the operation model of the power-to-hydrogen-heat system is established based on thermodynamic equations and physical reaction principles, and the voltage relationship and energy relationship of the power-to-hydrogen-heat system are analyzed; The electrolyzer is a common device for the electricity-heat-gas-hydrogen integrated energy system and the electricity-to-hydrogen-heat system; S3: Establish a two-order robust optimization operation model of the electricity-heat-gas-hydrogen integrated energy system considering source-load uncertainty. The source-load uncertainty includes the dual uncertainty of wind power and load. The C&CG algorithm and KKT conditions are used to transform and solve the proposed model to optimize the distribution of multi-energy flows in the electricity-heat-gas-hydrogen integrated energy system.

2. A robust optimization scheduling method for an integrated energy system including an electricity-to-hydrogen-heat system according to claim 1, characterized in that The electricity-heat-gas-hydrogen integrated energy system includes wind power, thermal power units, gas turbines, cogeneration units, electrolyzers and methanation devices, and the electricity-to-hydrogen-heat system includes electrolyzers, hydrogen storage tanks, heat pumps and heat exchanges.

3. The method for robust optimization scheduling of an integrated energy system including a power-to-hydrogen-heat system according to claim 2, characterized in that: When there is excess electricity in the integrated energy system, power is provided to the electrolyzer. The power-to-hydrogen-heat system can collect and utilize the heat energy released during the electrolysis process of the electrolyzer, and transfer it to the thermal network through heat pumps and heat exchangers. After the hydrogen generated by the power-to-hydrogen-heat system meets the hydrogen load, the remaining part will be converted into natural gas through a methanation device and injected into the natural gas network.

4. The method for robust optimization scheduling of an integrated energy system including a power-to-hydrogen-heat system according to claim 1, characterized in that: In S2, a steady-state model of the electrolyzer is established to solve the relationship between the power consumption, hydrogen production rate and heat generated by the electrolyzer. According to the energy distribution relationship between hydrogen and thermal energy in the power-to-hydrogen-heat system, the operating state of the power-to-hydrogen-heat system is divided into the following three conditions: thermal equilibrium state, heat output state and heat input state. The hydrogen load is calculated according to the capacity of the hydrogen storage tank and the instantaneous consumption of hydrogen, and the methanation device is started according to the hydrogen load conditions.

5. The method for robust optimization scheduling of an integrated energy system including a power-to-hydrogen-heat system according to claim 1, characterized in that: In S3, based on the above-mentioned electric-heat-gas-hydrogen integrated energy system model and the electric-to-hydrogen-heat system operation model, a two-order robust optimization operation model of the electric-heat-gas-hydrogen integrated energy system considering source and load uncertainty is established. The two-order robust optimization operation model of the electric-heat-gas-hydrogen integrated energy system is divided into two stages: The first stage determines the preliminary operation plan of each energy supply device in the system, the power trading strategy with the upper-level power grid, and optimizes the distribution of multiple energy flows in the system to meet the system safety and economic requirements; In the second stage, the results of the first stage are fine-tuned for the "worst scenario" with the largest prediction error. The output and load demand of thermal power units, cogeneration units, and gas turbines are adjusted to optimize the multi-energy flow distribution to enhance the robustness of the system. The adjustment results are fed back to the first stage. Through iterative optimization, it is ensured that the electric-heat-gas-hydrogen integrated energy system can maintain safe and stable operation in all possible extreme scenarios.

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