Robust interval optimization method for hydrogen energy storage integrated energy system considering electricity and heat

Abstract

The application discloses a kind of adjustable robust interval optimization control methods of electric-thermal comprehensive energy system considering hydrogen energy storage, method includes: the wind-hydrogen hybrid system model under intermittent operation mode is established;The uncertainty of wind power is considered in interval form, and a robust interval optimization model containing wind power is constructed, which satisfies the allowable constraint condition within all wind power output allowable intervals, and the robust interval optimization model is converted into a single-layer model using duality theory;Based on the robust interval optimization model containing wind power, a time-varying participation factor is introduced, and an adjustable robust interval optimization model of electric-thermal comprehensive energy system considering wind power uncertainty is constructed, and the non-convex nonlinear model is converted into a linear model using binary expansion method;The electric-thermal comprehensive energy system composed of PJM-5 node power system and 6 node thermal system is optimized and analyzed, the wind power consumption is increased, and the system operation cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to an adjustable robust range optimization control method for an integrated electric-thermal energy system that considers hydrogen energy storage. Background Technology

[0002] In recent years, against the backdrop of ecological degradation and fossil fuel scarcity, the proportion of renewable energy sources such as wind power and solar power has risen sharply, drawing widespread attention to integrated energy systems. However, due to the high proportion of wind power integrated into the grid, the volatility and uncertainty of wind power pose a significant threat to the safe operation of the power system, especially in areas with heating needs. As an important component of integrated energy systems, the electricity-heat integrated energy system can improve the flexibility of system operation and the system's ability to absorb wind power. Therefore, coordinating and optimizing the power system and district heating system to improve energy efficiency and wind power absorption capacity is of great significance.

[0003] Furthermore, the increasing proportion of renewable energy generation has reduced the flexibility of the power system under the constraints of the existing power supply structure. To improve the flexibility of the power system in high-penetration renewable energy generation systems, the strategy of combining renewable energy generation systems with energy storage systems to form hybrid systems has been vigorously developed. However, conventional energy storage, due to its single energy conversion form, has a limited role in reducing the use of fossil fuels and lowering carbon emissions. In comparison, hydrogen energy storage, as a new type of energy storage method with zero carbon emissions and multi-energy combined storage and supply capabilities, and which has a certain substitution effect on traditional energy sources such as coal and gas, is considered a secondary energy source that perfectly complements electricity.

[0004] In practical operation, using deterministic models to solve wind power uncertainties may be infeasible. Common methods to address wind power uncertainties include stochastic programming and robust optimization. Stochastic programming requires obtaining the probability distribution function of the uncertainties and involves significant computation; robust optimization, on the other hand, only requires the range of variation of the uncertainties, not their probability distribution. However, traditional robust optimization treats the power generation of conventional turbines and wind power output as fixed values. Once the uncertainty set is given, adjustments cannot be made, resulting in large prediction errors. This can lead to unnecessary wind power reductions and make it difficult to accurately track the output power of wind farms. Robust interval optimization is an effective method for handling wind power uncertainties; therefore, it is necessary to control the integrated electric-thermal energy system from the perspective of robust interval optimization. Summary of the Invention

[0005] This invention provides an adjustable robust range optimization control method for an integrated electric-thermal energy system considering hydrogen energy storage. While ensuring the safe and economical operation of the integrated electric-thermal energy system, this invention fully utilizes the regulation capabilities of conventional units, maximizes the absorption of wind power, and effectively solves the problem of inaccurate tracking of wind farm output power caused by errors in wind power output prediction data. It exhibits strong robustness under uncertain prediction environments, as detailed below:

[0006] A method for adjustable robust range optimization control of an integrated electric-thermal energy system considering hydrogen energy storage, the method comprising:

[0007] Establish a model for a wind-hydrogen hybrid system that considers intermittent operating mode;

[0008] Considering the uncertainty of wind power in interval form, a robust interval optimization model containing wind power is constructed. The allowable constraints are satisfied in all allowable intervals of wind power output. The duality theory is used to transform the robust interval optimization model into a single-layer model.

[0009] Based on the robust interval optimization model with wind power, a time-varying participation factor is introduced to construct an adjustable robust interval optimization model for the integrated electric-thermal energy system that considers the uncertainty of wind power. The non-convex nonlinear model is then transformed into a linear model using the binary expansion method.

[0010] An optimization analysis was conducted on the integrated electric-thermal energy system consisting of the PJM-5 node power system and the 6 node thermal system to increase the absorption of wind power and reduce system operating costs.

[0011] The wind-hydrogen hybrid system model is as follows:

[0012]

[0013]

[0014]

[0015]

[0016] Among them, P h,t P fc,t Q represents the power consumption of the electrolyzer and the power generation of the fuel cell, respectively; h,t Q fc,t H represents the heat production power of the electrolyzer and the fuel cell, respectively; H2 Indicates the high calorific value of hydrogen; n t The m represents the hydrogen production rate of the electrolyzer at time t; t η is the hydrogen consumption rate of the fuel cell at time t; h η fc E represents the efficiency of the electrolyzer and the fuel cell, respectively;t λ represents the capacity of the hydrogen storage tank at time t; h,t and λ fc,t Q is a binary variable, indicating that the hydrogen energy storage system cannot be charged and discharged simultaneously at time t; H2,t η represents the thermal energy stored in the thermal storage tank at time t; ch Indicates the efficiency of the heat exchanger; Q con,t Q thermal,t These represent the thermal power consumed by the hydrogen energy storage system at time t and the thermal power provided to the regional heating network system, respectively.

[0017] The uncertainty of wind power is considered in interval form, and a robust interval optimization model incorporating wind power is constructed. This model satisfies the allowable constraints within all allowable wind power output intervals. Using duality theory, this robust interval optimization model is transformed into a single-layer model:

[0018] 1) Considering the uncertainty of wind power in interval form, a robust interval optimization model incorporating wind power is constructed. When the wind power output is within any given interval range, the corresponding optimal objective is obtained, so that the wind farm output is based on the interval:

[0019]

[0020] Where x represents the output power of a conventional unit; y represents the output power of a wind farm; Indicates the allowable output range of wind power; [y d ,y u ] represents the wind power prediction interval, and A, B, D, and V are constant coefficient matrices; denoted by , x represents the upper limit of conventional unit output, d represents the lower limit of wind power forecast interval, and u represents the upper limit of wind power forecast interval.

[0021] 2) Based on duality theory, by introducing the variable λ, it is transformed into a single-level linear programming model, as follows:

[0022]

[0023] By introducing auxiliary variables, it can be transformed into:

[0024]

[0025] Among them, A i B i D i All are matrices with constant coefficients.

[0026] Through duality theory, it can be transformed into:

[0027]

[0028] The linear model is as follows:

[0029]

[0030] The beneficial effects of the technical solution provided by this invention are:

[0031] 1. This invention treats the CHP unit as a flexible and controllable unit. Compared with the traditional "heat-driven power generation" operation mode of CHP units, treating the CHP unit as an adjustable unit not only allows for the adjustment of the CHP unit's output, thus enabling the CHP unit's thermal output to reach the ideal value at different times; but also improves the power system's optimization configuration capability, enhances the grid's ability to absorb wind curtailment, effectively utilizes the CHP unit's power generation capacity, and further reduces the system's operating costs.

[0032] 2. This invention introduces a hydrogen energy storage system to improve the flexibility of the power system in high-penetration renewable energy power generation systems, and fully considers the thermal balance requirements of the hydrogen energy storage system, so that the reaction temperature of the electrolyzer and fuel cell is coupled with the uncertainty of wind power, exhibiting dynamic thermal balance characteristics, thereby improving the working efficiency of the hydrogen energy storage system.

[0033] 3. This invention introduces a time-varying participation factor to adjust the up and down adjustment power of the adjustable unit according to the unbalanced power of wind power at different times. The adjustable unit then allocates the power adjustment amount of each unit according to its own power generation cost quotation, so as to improve the reserve capacity of the adjustable unit, better cope with the volatility and intermittency of wind power resources, and promote the grid connection and consumption of wind power.

[0034] 4. This invention employs an adjustable robust interval optimization strategy to coordinate and optimize the integrated electric-thermal energy system, which can calculate the maximum allowable output power range of wind power. This effectively solves the problem that the wind farm optimization and control scheme deviates significantly from the actual operating scenario due to large errors in wind power prediction data, and has strong robustness in environments with prediction uncertainty. Attached Figure Description

[0035] Figure 1 A flowchart for the adjustable robust range optimization of an electro-thermal integrated energy system considering hydrogen energy storage is provided by the present invention.

[0036] Figure 2 This is a schematic diagram of the integrated electric-thermal energy system provided by the present invention;

[0037] Figure 3 The topology of the integrated electric-thermal energy system consisting of the PJM-5 node power system and the 6 node thermal system is shown.

[0038] Figure 4 The electrical load curve of the integrated electric-thermal energy system over 24 hours;

[0039] Figure 5 A 24-hour heat load curve for an integrated electric-thermal energy system;

[0040] Figure 6 This is a schematic diagram of the predicted wind power range;

[0041] Figure 7 This is a schematic diagram comparing the results of thermal systems using hydrogen energy storage and those using conventional energy storage;

[0042] Figure 8 This is a comparison chart of available wind power using hydrogen energy storage and conventional energy storage.

[0043] Figure 9 This is a schematic diagram showing the changes in the participation factor s of the adjustable units (Unit 1 and Unit 5);

[0044] Figure 10 A schematic diagram comparing the upward adjustment power of adjustable units (Unit 1 and Unit 5) when the participating factor s is a variable. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0046] To reduce actual wind power prediction errors and calculate wind power output range applicable to all wind power scenarios, this invention proposes an adjustable robust range optimization control method for an integrated electric-thermal energy system considering hydrogen energy storage, which exhibits strong robustness under prediction uncertainty.

[0047] Example 1

[0048] A robust range optimization method for a hydrogen energy storage-electricity-thermal integrated energy system is proposed, see [link to relevant documentation]. Figure 1 The calculation method includes the following steps:

[0049] 101: Construct the basic structure of the integrated electric-thermal energy system, model the physical structure and node temperature of the regional heat network, and establish a wind-hydrogen hybrid system model considering intermittent operation mode;

[0050] 102: Considering the uncertainty of wind power in interval form, construct a robust interval optimization model containing wind power, so that the system satisfies the allowable constraints in all allowable intervals of wind power output. Use duality theory to transform the robust interval optimization model into a single-layer model.

[0051] 103: Based on the robust interval optimization model with wind power, a time-varying participation factor is introduced to construct an adjustable robust interval optimization model for the integrated electric-thermal energy system considering the uncertainty of wind power. The non-convex nonlinear model is transformed into a linear model by using the binary expansion method, and the CPLEX in MATLAB is called to solve the linear model.

[0052] 104: An optimization analysis was conducted on the integrated electric-thermal energy system consisting of a PJM-5 node power system and a 6 node thermal system. The control results of two sets of calculations were compared and analyzed, with hydrogen energy storage and conventional energy storage, and with and without the participation factor s as a variable. The results verified that when hydrogen energy storage is used and the participation factor is set as a variable, the system has more flexibility to adjust the uncertainty of wind power, thereby increasing the wind power absorption capacity and reducing the system operating cost.

[0053] In summary, this embodiment of the invention, based on the known wind power forecast range obtained through steps 101-104, proposes an adjustable robust range optimization control strategy for an integrated electric-thermal energy system considering hydrogen energy storage through robust range optimization control. Furthermore, the impact of adjustable robust range optimization control on wind power absorption and turbine output is analyzed and compared. By introducing the maximum allowable output power range of the wind farm as the control target, the amount of wind curtailment can be effectively reduced, the frequency of conventional turbine output power adjustment can be decreased, and the system's wind power absorption capacity can be enhanced.

[0054] Example 2

[0055] The scheme in Example 1 will be explained in detail below with specific calculation formulas and examples:

[0056] 201: Construct the basic structure of the integrated electric-thermal energy system, model the physical structure and node temperature of the regional heat network, and establish a wind-hydrogen hybrid system model considering intermittent operation mode;

[0057] The basic framework of the integrated electric-thermal energy system and the establishment of the district heating system operation model are as follows:

[0058]

[0059]

[0060]

[0061] Among them, P n,t Indicates the electrical output of the CHP unit n; Q represents the electrical power of CHP unit n at the extreme point k; n,t This indicates the thermal output of CHP unit n; This represents the thermal power of CHP unit n at the extreme point k; Indicates the electrothermal coefficient of the CHP unit; Indicates heat load; c h This indicates the specific heat capacity of water; This indicates the mass flow rate of water in the pipe; This represents the water supply temperature at node i; The return water temperature at node i; m out Indicates the mass flow rate of the outflow node; m in This represents the mass flow rate flowing into the node; Indicates the temperature at the mixed node; Indicates the mass flow rate and temperature at the end of the inlet pipe; Indicates the temperature of pipe l at the end node; T represents the temperature of pipe l at the first end node; a,t The ambient temperature at time t is represented by λ; the pipe transmission impedance by λ is represented by L; and the pipe length by m is represented by λ. t express; Indicates the upper limit of the water supply pipe temperature; Indicates the lower limit of the water supply pipeline temperature; This represents the thermal output of the CHP unit at node i; This indicates the heat load.

[0062] The specific model of the wind-hydrogen hybrid system is as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Among them, P h,t P fc,t Q represents the power consumption of the electrolyzer and the power generation of the fuel cell, respectively; h,t Q fc,t H represents the heat production power of the electrolyzer and the fuel cell, respectively; H2 Indicates the high calorific value of hydrogen; n t The m represents the hydrogen production rate of the electrolyzer at time t; t η is the hydrogen consumption rate of the fuel cell at time t; h η fc E represents the efficiency of the electrolyzer and the fuel cell, respectively; t λ represents the capacity of the hydrogen storage tank at time t;h,t and λ fc,t Q is a binary variable, indicating that the hydrogen energy storage system cannot be charged and discharged simultaneously at time t; H2,t η represents the thermal energy stored in the thermal storage tank at time t; ch Indicates the efficiency of the heat exchanger; Q con,t Q thermal,t These represent the thermal power consumed by the hydrogen energy storage system at time t and the thermal power provided to the regional heating network system, respectively. Let P represent the grid-connected power of the j-th wind farm at time t. fc P represents the output of the fuel cell. h P represents the power consumed by the electrolytic cell. w,j,t Indicates dispatchable wind power; This indicates the upper limit of the predicted wind power capacity. This indicates the upper limit of the permissible power output for wind power. P represents the lower limit of the predicted wind power capacity. w , j , t This indicates the lower limit of permissible wind power output.

[0070] 202: Considering the uncertainty of wind power in the form of intervals, construct a robust interval optimization model containing wind power so that the system satisfies the allowable constraints in all allowable intervals of wind power output.

[0071] Step 202 includes:

[0072] 1) Considering the uncertainty of wind power in interval form, a robust interval optimization model incorporating wind power is constructed. When the wind power output is within any given interval range, the corresponding optimal objective can be obtained, thus establishing the wind farm output on an interval basis:

[0073]

[0074] Where x represents the output power of a conventional unit; y represents the output power of a wind farm; Indicates the allowable output range of wind power; [y d ,y u ] represents the wind power prediction interval, and A, B, D, and V are constant coefficient matrices; denoted by , x represents the upper limit of conventional unit output, d represents the lower limit of wind power forecast interval, and u represents the upper limit of wind power forecast interval.

[0075] 2) Based on duality theory, by introducing the variable λ, the two-level model in equation (10) is transformed into a single-level linear programming model, as follows:

[0076] In robust optimization, to ensure the safety of system operation in any wind power generation scenario, the model in equation (10) is transformed into a two-layer model:

[0077]

[0078] By introducing auxiliary variables, equation (11) can be transformed into:

[0079]

[0080] Among them, A i B i D i All are matrices with constant coefficients.

[0081] The dual problem of equation (12) is:

[0082]

[0083] Using duality theory, equation (10) can be transformed into:

[0084]

[0085] 203: Based on the robust interval optimization model with wind power, an adjustable robust interval optimization model for the integrated electric-thermal energy system considering the uncertainty of wind power is constructed. The non-convex nonlinear model is transformed into a linear model by using the binary expansion method.

[0086] Step 203 includes:

[0087] 1) First, based on the adjustable robust interval optimization model including wind power, a time-varying participation factor is introduced to construct an adjustable robust interval optimization model for the integrated electricity-heat energy system that considers the uncertainty of wind power:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] In equation (15), the first term represents the power generation cost of a conventional generating unit. i P represents the electricity generation cost quote for conventional unit i. i,t N represents the output of the conventional unit at time t. i The first term represents the number of thermal power units. The second term represents the cost of wind power, c. wind P represents the cost quote for wind power generation. w,j,t N represents the available wind power at time t. j The first item indicates the number of wind farms. The third item indicates the cost of power generation and heat generation for the CHP turbines. (P) chp,t Q represents the electrical output of the CHP unit at time t. chp,t The thermal output of the CHP unit at time t is represented by N. CHP Indicates the number of CHP units. CHP The first item represents the electricity generation cost quote for the CHP unit; the fourth item represents the cost of the hydrogen energy storage system. h c fc For the unit operating cost of electrolyzers and fuel cells, c sh The unit capacity investment cost of the hydrogen storage tank; NH represents the number of hydrogen energy storage systems; the fifth item represents the adjustment cost of the adjustable unit for power adjustment based on wind power imbalance. This indicates the downward adjustment power of the adjustable unit at time t; c represents the downward adjustment power of the adjustable unit at time t. i This represents the cost of obtaining a wind power unbalanced power permit per unit, s i,t Represents time-varying participating factors. This indicates the upper limit of the predicted wind power capacity. This indicates the upper limit of the permissible power output for wind power. This indicates the lower limit of the predicted wind power output. P w,j,t This indicates the lower limit of permissible wind power output.

[0100] In equation (16), P i,t This represents the output of the thermal power unit at time t. Let P represent the grid-connected power of the j-th wind farm at time t. n,t D represents the electrical output of the CHP unit. t This indicates the total electrical load of the system.

[0101] In equation (17), Let P represent the grid-connected power of the j-th wind farm at time t. fc P represents the output of the fuel cell. h This indicates the power consumed by the electrolytic cell.

[0102] In equation (18), P i,min and P i,max Indicates the upper and lower limits of output of conventional generating units; and Indicates the upper and lower limits of predicted wind power output; P w，j，t and This indicates the upper and lower limits of the permissible wind power output.

[0103] In equation (19), and P represents the upper and lower limits of the unit's ramp rate. i,t-1 This represents the output of the thermal power unit at time t-1.

[0104] In equation (21), Lim l and Indicates the upper and lower limits of the line's output power, G l-i G l-b G l-j These represent the power generation ratio factors for conventional units, loads, and wind power, respectively.

[0105] In equations (22)-(25), α j,t ,β j,t ,μ l-j,t ,π l-j,t It is a binary variable. This indicates that the power is adjusted downwards. This indicates that the power is adjusted upwards.

[0106] Equations (16)-(21) represent the constraints under conventional wind power scenarios, and equations (22)-(25) represent the constraints under worst-case scenarios.

[0107] 2) The fourth term in equation (15) contains s i,t and P w,j,t Since these are all decision variables, the binary expansion method is used to transform this term:

[0108] First, we introduce two variables. and To replace unbalanced wind power:

[0109]

[0110] Then, the two variables above are expanded using the binary expansion method:

[0111]

[0112]

[0113] Multiply both ends by s i,t And let z ki =xki s i,t ,w ki =y ki s i,t :

[0114]

[0115]

[0116] Through the above expansion, the bilinear terms can be transformed into linear terms:

[0117]

[0118]

[0119]

[0120]

[0121] Finally, the objective function is transformed into:

[0122]

[0123] 204: The robust wind power range model uses the system's operational safety under the worst-case wind power output conditions as a constraint. It achieves the maximum safe wind power output range solution that satisfies the minimum wind curtailment requirement by dynamically adjusting the wind power output range. On one hand, it introduces a time-varying participation factor, adjusting the power of adjustable units based on wind power imbalance, which helps alleviate the uncertainty of wind power generation, especially when the reserve capacity of conventional units is insufficient. On the other hand, by introducing the maximum allowable output power range as the control objective of the wind farm, the wind farm is more likely to adhere to its power generation plan, thereby reducing wind curtailment.

[0124] In summary, the embodiments of the present invention can effectively solve the optimization control results of the integrated electric-thermal energy system through the above steps 201-204.

[0125] Example 3

[0126] The following section combines specific experiments, Figure 4 and Figure 5 To verify the feasibility of the solutions in Examples 1 and 2, please refer to the following description:

[0127] Taking the integrated electric and thermal energy system consisting of a PJM-5 node power system and a 6 node thermal system as an example, the feasibility of the schemes in Examples 1 and 2 is verified. The system structure is as follows: Figure 3 As shown.

[0128] Nodes 1, 3, and 5 of the power system are connected to thermal power units G1, G2, G3, and G4, respectively; node 5 is connected to a wind farm with an installed capacity of 450MW; assuming that the unit at node 4 is a CHP unit and is connected to the thermal system; the electrical load is evenly distributed to nodes 2, 3, and 4. The coupling element in the thermal system, the CHP unit, is connected to node 1, and the heat load is evenly distributed to nodes 4, 5, and 6.

[0129] In the power system, the lower limit of the unit's output is set at 30% of its capacity, and the unit's power generation cost is quoted at $14, $15, $30, $35, and $10 respectively. The power generation cost of the wind farm is $5 / MW. The predicted output power range of the wind farm uses actual data from a certain wind farm and is proportionally adjusted according to the wind farm capacity used in this example system. Figure 4 and Figure 5 The predicted electrical and thermal load values ​​for the system over a 24-hour period are respectively. Figure 6 The predicted output power range for wind power is 24 hours.

[0130] The embodiments of this invention present optimization results for hydrogen energy storage systems and conventional energy storage systems, as follows: Figure 7 and Figure 8 As shown, by Figure 7 It can be seen that the CHP unit's thermal output trend in Scenario 1 is completely consistent with the thermal load. However, in Scenario 2, the CHP thermal output is affected by the hydrogen energy storage thermal balance system, sometimes lower than, and sometimes higher than, the CHP unit's thermal output in Scenario 1. When the thermal output of the CHP unit in Scenario 2 is lower than that in Scenario 1, the system has sufficient wind power while meeting the electrical load demand. This excess wind power is converted into hydrogen energy and stored by the electrolyzer through a water electrolysis device. The generated heat energy meets the heat requirements of the electrolyzer, and the excess heat energy participates in the district heating system's heat network circulation in the form of hot water. When the thermal output of the CHP unit in Scenario 2 is higher than that in Scenario 1, the reaction temperature of the electrolyzer and fuel cell has not reached the rated temperature. In order to ensure maximum efficiency, the heat network needs to provide some heat energy so that the electrolyzer and fuel cell can operate in the maximum efficiency mode.

[0131] Depend on Figure 7 and Figure 8 It is known that when configuring a hydrogen energy storage system in an IEDHS (Integrated Energy Storage System), the heat released during the operation of the electrolyzer and fuel cell can provide some heat energy for the thermal system, thereby reducing the thermal output of the CHP (Continuous Power Generation) unit. As the thermal output of the CHP unit decreases, its electrical output also decreases, increasing the upper limit of the allowable wind power output range, allowing more wind power to be utilized. In contrast, with battery energy storage, the heat load of the thermal system is entirely borne by the CHP unit, and the battery maintains overall charge-discharge balance. However, it lacks multi-energy conversion capabilities, thus still resulting in significant wind curtailment.

[0132] Furthermore, the results of the participating factor s being a variable and s being a constant proposed in the embodiments of the present invention are as follows: Figure 9 As shown, the upward adjustment power of adjustable units 1 and 5 is as follows: Figure 10 As shown. By Figure 10 It can be seen that when s is a variable, the upward adjustment capacity of the adjustable unit is generally greater than that when s is a constant. This is because when wind power is high and the system electrical load is low, the participation factor adjusts the upward and downward adjustment power of the adjustable unit according to the wind power imbalance at that moment. The adjustable unit allocates the adjustable capacity according to its own power generation cost, increases the reserve capacity of the unit, maintains the system power balance, absorbs more wind power, and improves the economic efficiency of system operation.

[0133] In summary, the advantages of adjustable robust range optimization control for an integrated electric-thermal energy system considering hydrogen storage are as follows: under the premise of ensuring the safe and stable operation of the system, it can fully explore the regulation capabilities of conventional units and cogeneration units, increase wind power absorption, reduce system operating costs, and effectively solve the problem of large deviations between the day-ahead optimization scheme and the actual operating scenario caused by the predicted output power of wind power. It also has strong robustness in environments with prediction uncertainty.

[0134] References

[0135] [1] Zhang Liudong, Yuan Yubo, Sun Dayan, et al. Robust interval optimization scheduling of integrated electric-thermal energy system considering heat network transmission delay [J]. Electric Power Automation Equipment. 2018, 32(12):59-93.

[0136] [2] Luo Ping, Zhou Haobing, Xu Lin, et al. Day-ahead optimization scheduling of multi-microgrid combined cooling, heating and power based on interval optimization [J]. Automation of Electric Power Systems, 2022, 32: 1-16.

[0137] [3] Zeng Bo, Xu Fuqiang, Liu Yu, et al. Multi-objective interval optimization planning method for energy hub considering the synergistic effect of renewable energy and demand response [J]. Proceedings of the CSEE, 2021, 41(21):7212-7225.

[0138] [4] R.Zhang, T.Jiang, L.Bai. Adjustable robust power dispatch with combined wind-storage system and carbon capture power plants under low-carboneconomy. International Journal of Electrical Power&Energy Systems, 2019, Vol.113, pp.772-781.

[0139] [5]RAJabr.Adjustable Robust OPF With Renewable Energy Sources.IEEETransactions on Power Systems, 2013, Vol.28, (4) pp.4742-4751.

[0140] [6]Z.Li,W.Wu and B.Zhang.Robust Look-Ahead Power Dispatch WithAdjustable Conservativeness Accommodating Significant Wind PowerIntegration.IEEE Transaction on Sustainable Energy, 2015, Vol.6, (2), pp.357-368.

[0141] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0142] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0143] 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for adjustable robust range optimization control of an integrated electric-thermal energy system considering hydrogen energy storage, characterized in that, The method includes: Establish a model for a wind-hydrogen hybrid system that considers intermittent operating mode; Considering the uncertainty of wind power in interval form, a robust interval optimization model containing wind power is constructed. The allowable constraints are satisfied in all allowable intervals of wind power output. The duality theory is used to transform the robust interval optimization model into a single-layer model. Based on the robust interval optimization model with wind power, a time-varying participation factor is introduced to construct an adjustable robust interval optimization model for the integrated electric-thermal energy system that considers the uncertainty of wind power. This model is a non-convex nonlinear model, and the binary expansion method is used to transform the non-convex nonlinear model into a linear model. An optimization analysis was conducted on the integrated electric-thermal energy system consisting of the PJM-5 node power system and the 6 node thermal system to increase the wind power absorption capacity and reduce the system operating cost. The linear model is as follows: ； Where T represents time; This refers to the number of thermal power units. This is the electricity generation cost quote for conventional unit i; Let t be the output of the conventional unit; The number of wind farms; Quoting the cost of wind power generation; For dispatchable wind power; The number of hydrogen energy storage systems; It is hydrogen gas; c h c fc The unit operating cost of electrolyzers and fuel cells; P h,t P fc,t These represent the power consumption of the electrolyzer and the power generation of the fuel cell, respectively. The unit capacity investment cost of the hydrogen storage tank; Let t be the capacity of the hydrogen storage tank at time t; This refers to the number of CHP units; Provide a quote for the power generation cost of the CHP unit; The downward adjustment power of the adjustable unit at time t; t represents the downward adjustment power of the adjustable unit at time t; s represents the participation factor. Let t be the electrical output of the CHP unit; Two variables, WCu i,t and WPd i,t, are introduced to represent the unbalanced power of wind power: ； Wherein, Pu w,j,t and Pd w,j,t represent the upper and lower limits of the predicted wind power; and Indicates the upper and lower limits of the permissible wind power output; Let z ki = x ki s i,t , w ki = y ki s i,t : Transform bilinear terms into linear terms: ； ； ； ； in, It is a time-varying participating factor.

2. The adjustable robust range optimization control method for an electro-thermal integrated energy system considering hydrogen energy storage according to claim 1, characterized in that, The wind-hydrogen hybrid system model is as follows: ； ； ； ； Among them, P h,t P fc,t Q represents the power consumption of the electrolyzer and the power generation of the fuel cell, respectively; h,t Q fc,t H represents the heat production power of the electrolyzer and the fuel cell, respectively; H2 Indicates the high calorific value of hydrogen; n t The m represents the hydrogen production rate of the electrolyzer at time t; t η is the hydrogen consumption rate of the fuel cell at time t; h η fc E represents the efficiency of the electrolyzer and the fuel cell, respectively; t λ represents the capacity of the hydrogen storage tank at time t; h,t and λ fc,t Q is a binary variable, indicating that the hydrogen energy storage system cannot be charged and discharged simultaneously at time t; H2,t η represents the thermal energy stored in the thermal storage tank at time t; ch Indicates the efficiency of the heat exchanger; Q con,t Q thermal,t These represent the thermal power consumed by the hydrogen energy storage system at time t and the thermal power provided to the regional heating network system, respectively.

3. The adjustable robust range optimization control method for an integrated electric-thermal energy system considering hydrogen energy storage according to claim 1, characterized in that, The uncertainty of wind power is considered in interval form, and a robust interval optimization model incorporating wind power is constructed. This model satisfies the allowable constraints within all allowable wind power output intervals. Using duality theory, this robust interval optimization model is transformed into a single-layer model: 1) Considering the uncertainty of wind power in interval form, a robust interval optimization model incorporating wind power is constructed. When the wind power output is within any given interval range, the corresponding optimal objective is obtained, so that the wind farm output is based on the interval: ； Where x represents the output power of a conventional unit; y represents the output power of a wind farm; Indicates the allowable output range of wind power; [y d , y u ] represents the wind power prediction interval, and A, B, D, and V are constant coefficient matrices; This indicates the upper limit of output of a conventional unit. d represents the lower limit of conventional unit output; u represents the upper limit of wind power forecast interval; d represents the subscript of the lower limit of wind power forecast interval; u represents the superscript of the upper limit of wind power forecast interval. 2) Based on duality theory, by introducing the variable λ, it is transformed into a single-level linear programming model, as follows: ； By introducing auxiliary variables, it can be transformed into: ； Among them, A i B i D i All are matrices with constant coefficients; Through duality theory, it can be transformed into: 。

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