Integrated energy system optimization method considering dual uncertainty and electronic equipment

Abstract

The invention provides an integrated energy system optimization method considering dual uncertainty and electronic equipment, and relates to the technical field of power grids. The method comprises the steps that photovoltaic output uncertainty is considered, and an electric power balance constraint is established by adopting a chance constraint programming method; considering the uncertainty of the electricity price, taking the minimum operation cost of the system in the scene with the worst electricity price as a target function, and establishing an optimal scheduling model of the integrated energy system; and solving the integrated energy system optimization scheduling model to obtain a target optimization scheduling scheme. According to the method, the influence of the uncertainty of the internal photovoltaic output and the external electricity price on the system is considered at the same time, the obtained optimal scheduling scheme is more reasonable, the high-cost risk caused by volatility on the system is avoided, economy and system stability can be considered, and the method is more suitable for practical application.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular, to an integrated energy system optimization method and an electronic device considering double uncertainties. Background Art

[0002] An integrated energy system (IES) is an energy system that organically integrates multiple energy forms and synergistically optimizes them, aiming to achieve efficient energy utilization and meet diverse energy consumption demands. This system has significant advantages such as high energy utilization efficiency, the ability to achieve cascaded energy utilization, and the promotion of clean energy consumption, and has become a research hotspot under the current background of energy transformation.

[0003] In the prior art, the optimization of integrated energy systems focuses on the optimization of energy systems under deterministic scenarios. However, since a large amount of renewable energy is included in the integrated energy system, its output has significant uncertainties; at the same time, the electricity price in the power market is affected by multiple factors such as energy market fluctuations and policy adjustments, showing complex fluctuation characteristics; therefore, the optimization results based on deterministic scenarios lack rationality and applicability in practical applications. Summary of the Invention

[0004] Embodiments of the present invention provide an integrated energy system optimization method and an electronic device considering double uncertainties to solve the problem that the optimization of energy systems under deterministic scenarios in the prior art lacks rationality and applicability.

[0005] In a first aspect, embodiments of the present invention provide an integrated energy system optimization method considering double uncertainties, including: Considering the uncertainty of photovoltaic output, an electric power balance constraint is established by using the chance-constrained programming method; Considering the uncertainty of electricity price, with the minimum operating cost of the system in the worst electricity price scenario as the objective function, an integrated energy system optimal scheduling model is established; The integrated energy system optimal scheduling model is solved to obtain the target optimal scheduling scheme.

[0006] In a second aspect, embodiments of the present invention provide an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the integrated energy system optimization method considering double uncertainties provided in the first aspect of the above embodiments is implemented.

[0007] An embodiment of the present invention provides an optimization method and an electronic device for an integrated energy system considering double uncertainties. The above-mentioned optimization method for the integrated energy system considering double uncertainties includes: considering the uncertainty of photovoltaic output, and establishing an electric power balance constraint by using the chance-constrained programming method; considering the uncertainty of electricity price, taking the minimum operation cost of the system under the worst electricity price scenario as the objective function, and establishing an optimal scheduling model for the integrated energy system; solving the optimal scheduling model for the integrated energy system to obtain an optimal scheduling solution. In the embodiment of the present invention, the electric power balance constraint is established considering the uncertainty of photovoltaic output to ensure the normal operation of the system; at the same time, the uncertainty of electricity price is considered to establish the objective function with the minimum operation cost to limit the cost. The embodiment of the present invention simultaneously considers the influence of the uncertainties of internal photovoltaic output and external electricity price on the system, and the obtained optimal scheduling solution is more reasonable, avoiding the high-cost risk caused by volatility to the system, and can balance economy and system stability, and is more suitable for practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a schematic structural diagram of an integrated energy system provided by an embodiment of the present invention; Figure 2 is a flowchart of the implementation of an optimization method for an integrated energy system considering double uncertainties provided by an embodiment of the present invention; Figure 3 is the historical data of the purchase electricity price provided by an embodiment of the present invention; Figure 4 is the historical data of the selling electricity price provided by an embodiment of the present invention; Figure 5 is a schematic diagram of the worst-case scenario of the purchase electricity price provided by an embodiment of the present invention; Figure 6 is a schematic diagram of the worst-case scenario of the selling electricity price provided by an embodiment of the present invention; Figure 7 is a comparison diagram of the economy of the optimization method for an integrated energy system considering double uncertainties provided by an embodiment of the present invention and the economy of the scheduling method in the prior art; Figure 8 is a schematic structural diagram of an optimization device for an integrated energy system considering double uncertainties provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0010] Figure 1 is a schematic structural diagram of an integrated energy system provided by an embodiment of the present invention. As Figure 1As shown in the figure, the integrated energy system includes: photovoltaic power generation units, combined heat and power (CHP) units, power to gas (P2G) equipment, carbon capture and storage (CCS) equipment, gas boilers (GB), electrical energy storage (EES) equipment, thermal energy storage (TES) equipment, and other equipment.

[0011] In the integrated energy system, the power demand is preferentially met by the power generation of photovoltaic power generation units. When the photovoltaic power generation cannot meet the system's power demand, the combined heat and power (CHP) unit is started. The electrical energy storage (EES) equipment charges and stores the excess electrical energy when the electricity price is low, and discharges to supplement the system's power demand when the electricity price is high, thereby achieving the optimization of economy and energy efficiency. The waste heat of the combined heat and power (CHP) unit is used to meet the system's heat demand, and the excess heat can be stored in the thermal energy storage (TES) equipment; when the waste heat of the combined heat and power (CHP) unit is insufficient, the gas boiler (GB) provides supplementary heat. The thermal energy storage (TES) equipment can balance the supply and demand fluctuations of heat while meeting the heat load, providing greater flexibility for the system. In the CHP-CCS-P2G combined operation system (including: combined heat and power unit, carbon capture equipment, and power to gas equipment), when the combined heat and power (CHP) unit has excess power, the excess power is utilized by the power to gas (P2G) equipment to produce hydrogen or synthetic natural gas to achieve energy storage. The carbon dioxide (CO2) generated during the operation of the combined heat and power (CHP) unit is absorbed by the carbon capture and storage (CCS) equipment and used for the methanation reaction of the power to gas (P2G) equipment to be converted into natural gas. This part of natural gas can be transported to the combined heat and power (CHP) unit for reuse, reducing the purchase of natural gas from the gas network.

[0012] Due to the uncertainty of renewable energy output and the complex fluctuating characteristics of electricity prices, the optimization results based on deterministic scenarios lack rationality and applicability in practical applications.

[0013] Based on the above, the embodiments of the present invention provide an optimization method for an integrated energy system considering double uncertainties. Refer to Figure 2 , which shows the implementation flowchart of the optimization method for the integrated energy system considering double uncertainties provided by the embodiments of the present invention, and is described in detail as follows: The above-mentioned optimization method for the integrated energy system considering double uncertainties includes: S101: Considering the uncertainty of photovoltaic output, an electric power balance constraint is established by using the chance-constrained programming method; Constraints are a key component to ensure the feasibility, reasonableness, and compliance with actual operation requirements of the model solution. They define the boundaries of system operation and balance the complex relationships among energy supply, conversion, storage, and demand. Specifically, constraints can include: power balance constraint, heat power balance constraint, upper and lower limits of equipment output constraint, grid interaction constraint, electrical energy storage device constraint, and thermal energy storage device constraint.

[0014] Specifically, based on the optimization of the energy system under deterministic scenarios (without considering uncertainties), each constraint is as follows: The power balance constraint can include:

[0015] Among them, is the purchased power at time is the power generation of the combined heat and power unit at time is the discharging power of the electrical energy storage at time is the output power of the photovoltaic unit at time is the power consumption of the power-to-gas device at time is the power consumption of the carbon capture device at time is the sold power at time is the charging power of the electrical energy storage at time is the electrical load at time; the unit of each parameter can be kW. The heat power balance constraint can include:

[0016] Among them, is the heat production power of the combined heat and power unit at time is the heat production power of the gas boiler at time is the heat release power of the thermal energy storage at time is the heat charging power of the thermal energy storage at time is the heat load at time; the unit of each parameter can be kW. The upper and lower limit constraints can include:

[0017] Among them, and are the upper and lower limits of the electric power of the combined heat and power unit, respectively; and are the upper and lower limits of the heat power of the combined heat and power unit, respectively; and are the upper and lower limits of the heat power of the gas boiler, respectively; and are the upper and lower limits of the power consumption of the gas boiler, respectively. The unit of each parameter can be kW.

[0018] The grid interaction constraints can include:

[0019]

[0020]

[0021]

[0022] Among them, is the power purchase at time is the upper limit of the power purchase between the system and the grid, is the power purchase status bit between the system and the grid; is the power sale at time is the upper limit of the power sale between the system and the grid, is the power sale status bit between the system and the grid; is the interaction power between the system and the grid at time. 、 、 、 、 The units of can all be kW, 、 are pure numerical values.

[0023] The constraints of the electrical energy storage device can include:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] Among them, is the capacity of the electrical energy storage device at time, kWh; and are respectively the upper and lower limits of the state of charge of the electrical energy storage device as a percentage; is the upper limit of the capacity of the electrical energy storage device, kWh; is the real-time charging state variable of the electrical energy storage device at time, is the real-time discharging state variable of the electrical energy storage device at time; is the charging power of the electrical energy storage device at time, is the discharging power of the electrical energy storage device at time, kW; is the upper limit of the charging power of the electrical energy storage device, is the upper limit of the discharging power of the electrical energy storage device, kW.

[0031] The constraints of the thermal energy storage device can include:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Among them, is the capacity of the thermal energy storage device at time, kWh; and are respectively the upper and lower limits of the state of charge of the thermal energy storage device as a percentage; is the upper limit of the capacity of the thermal energy storage device, kWh; is the charging thermal power of the thermal energy storage device at time, is the discharging thermal power of the thermal energy storage device at time, kW; is the real-time heat charging state variable of the thermal energy storage device at time, is the real-time heat discharging state variable of the thermal energy storage device at time; is the upper limit of the heat charging power of the thermal energy storage device, is the upper limit of the heat discharging power of the thermal energy storage device, in kW.

[0039] The output of photovoltaic power is affected by factors such as light intensity and weather conditions, and has significant randomness. Its actual output may deviate greatly from the predicted value, directly affecting the power balance state of the system. Therefore, it is necessary to improve the electric power balance constraint and consider the uncertainty.

[0040] In the embodiments of the present invention, the chance-constrained programming method can be used to incorporate the uncertainty of photovoltaic output, ensuring that the system can maintain power balance with a certain probability and guaranteeing the stable operation of the system.

[0041] Considering the uncertainty of photovoltaic output (the output power of photovoltaic units), the actual value of photovoltaic output is regarded as the sum of the predicted value and the error value, and it is assumed that the prediction error follows a normal distribution, then the variance is expressed as Then the following formula can be obtained:

[0042] Among them, is the predicted power of the photovoltaic unit at time represents the normal distribution, represents the normal distribution with a mean of 0 and a variance of .

[0043] Using the chance-constrained programming method to establish the electric power balance constraint considering the uncertainty of photovoltaic output, ensuring that the probability of photovoltaic output should meet the confidence level , that is, when considering photovoltaic uncertainty, the constraint condition becomes that at least probability, the power generation of the system (including photovoltaic, traditional power sources, etc.) can meet the load demand.

[0044] Based on this, the electric power balance constraint can be expressed as

[0045] Thus, the uncertainty constraint is transformed into a deterministic constraint.

[0046] Let the cumulative distribution function of the random variable be , then using the relevant theoretical knowledge of probability and statistics, the above formula is further converted as follows:

[0047] Assuming that the prediction follows a normal distribution, the inverse function related to the standard normal distribution can be introduced for solution, and the converted electric power balance constraint is:

[0048] Based on the above, in a possible implementation manner, the electric power balance constraint may include:

[0049] wherein, is the purchased electric power at time , is the power generation of the combined heat and power unit at time , is the discharging power of the electric energy storage at time , is the output power of the photovoltaic unit at time , is the power consumption of the power-to-gas equipment at time , is the power consumption of the carbon capture equipment at time , is the sold electric power at time , is the charging power of the electric energy storage at time , is the electric load at time , is the inverse function of the standard normal distribution, is the confidence level, is the variance of the photovoltaic output prediction error.

[0050] Based on the above analysis, in addition to the electric power balance constraint, the constraint conditions of the integrated energy system optimization scheduling model may further include: heat power balance constraint, equipment output upper and lower limit constraint, power grid interaction constraint, electric energy storage equipment constraint, heat energy storage equipment constraint.

[0051] Except for the electric power balance constraint, other constraint conditions do not consider uncertainty. Therefore, each constraint condition is as shown above and is not adjusted.

[0052] S102: Considering the uncertainty of electricity price, with the minimum operating cost of the system under the worst electricity price scenario as the objective function, an integrated energy system optimization scheduling model is established; The electricity price is affected by various factors in the electricity market, such as supply and demand relationship, policy adjustment, etc. Its uncertainty is mainly reflected in the impact on the system operating cost. Different electricity price scenarios will change the costs and benefits of decisions such as power generation and power purchase. For example, when the electricity price rises, the cost of purchasing electricity from the main power grid increases, and the original objective function based on fixed electricity price cannot reflect this change, which may lead to too high system operating cost or poor efficiency.

[0053] Therefore, in the embodiments of the present invention, considering the uncertainty of electricity prices, the objective function of the deterministic system is adjusted, and a penalty term considering the uncertainty of electricity prices is introduced, so that various electricity price fluctuations can be comprehensively considered during the optimization process, and the system can minimize costs under different electricity price scenarios.

[0054] In a possible implementation manner, S102 may include: S1021: Obtain historical electricity price data and construct an electricity price uncertainty set; S1022: Based on the two-layer robust optimization algorithm, determine the lower-layer model according to the electricity price uncertainty set; wherein, the lower-layer model is used to represent the worst electricity price scenario; S1023: Establish an upper-layer model with the minimum operating cost as the objective function; S1024: The upper-layer model and the lower-layer model form an integrated energy system optimal scheduling model.

[0055] The robust optimization algorithm is a method for solving optimization problems. It aims to find solutions that can still perform well in the presence of uncertainty factors. By considering uncertainty factors in the optimization model, the obtained optimal solution can maintain a certain performance level under various possible parameter changes, and has strong stability and reliability.

[0056] In the robust optimization algorithm, the upper-layer model usually starts from the overall system, and the lower-layer model generally optimizes its own performance from its own local interests based on the given upper-layer decision. In this application, an upper-layer model is established with the minimum operating cost as the objective function, and at the same time, the lower-layer model considers the uncertainty of electricity prices.

[0057] First, it is necessary to define an uncertainty set to describe the possible range of parameter changes. In the embodiments of the present invention, the parameter is the electricity price. The embodiments of the present invention can construct an uncertainty set based on historical electricity price data to describe the possible range of electricity prices, refer to Figure 3 and Figure 4 . The upper-layer model and the lower-layer model together form an integrated energy system optimal scheduling model, comprehensively considering the uncertainty of photovoltaic power output and electricity prices, and this model is more comprehensive and accurate.

[0058] The worst-case scenario usually refers to the electricity price fluctuation scenario that makes the system operating cost reach the highest, Figure 5 and Figure 6 shows the worst electricity price scenario. For electricity purchase, it may be that the electricity purchase price is at the upper limit of its fluctuation range in each period; for electricity sale, it may be that the electricity sale price is at the lower limit of its fluctuation range in each period. At the same time, the fluctuation combination of electricity prices may also make the various costs related to electricity prices in the system reach the maximum comprehensively. Therefore, in the embodiments of the present invention, through This item is considered to find the worst electricity price scenario within the range defined by the electricity price uncertainty set, so that the value of this item in the objective function is maximized. Among them, is an auxiliary variable used to limit the price deviation penalty term to be effective only during the electricity price uncertainty period; is the electricity price uncertainty period; is the electricity price uncertainty parameter, which is used to represent the degree of system uncertainty.

[0059] The objective function is to minimize the operating cost of the system under the worst electricity price scenario. Thus, the objective function can be obtained as follows:

[0060] Among them, the outer min is to minimize the operating cost of the system, and the inner max is to consider the worst case of electricity price uncertainty.

[0061] Thus, in a possible implementation manner, the objective function can be:

[0062] Among them, is the cost of buying and selling electricity between the system and the power grid, is the cost of purchasing gas by the system from the gas network, is the degradation cost of the electrical energy storage device, is the degradation cost of the thermal energy storage device, is the equipment configuration cost, is the electricity price deviation coefficient, is the power interaction between the system and the power grid at time is the electricity price uncertainty period, is an auxiliary variable, is the electricity price uncertainty parameter.

[0063] S103: Solve the integrated energy system optimal scheduling model to obtain the target optimal scheduling plan.

[0064] By solving the above integrated energy system optimal scheduling model considering uncertainty, the target optimal scheduling plan that takes into account both economy and system stability can be obtained.

[0065] Based on the above analysis, it can be known that the integrated energy system optimal scheduling model in the embodiment of the present invention is a robust model, which includes a "min-max" objective function and a non-convex robust optimization problem and cannot be directly solved. Therefore, it is necessary to linearize the non-convex model to make the model more convenient for efficient solution.

[0066] In a possible implementation manner, S103 may include: S1031: Linearize the integrated energy system optimal scheduling model using the strong duality theory to obtain a linearized integrated energy system optimal scheduling model; The strong duality theory is an important theory in convex optimization, which shows that under certain conditions, the original optimization problem (referred to as the primal problem) and its dual problem have the same optimal solution. For linear programming problems, strong duality always holds. Based on this, the embodiments of the present invention use the strong duality theory to linearize the model, transform the primal problem into the dual problem, and obtain a linearized integrated energy system optimal scheduling model.

[0067] In a possible implementation manner, S1031 may include: 1. Use the strong duality theory to transform the maximization problem in the lower-layer model of the integrated energy system optimal scheduling model into a minimization problem to obtain an adjusted lower-layer model; Let and be dual variables, use the strong duality theory to transform the max problem into a min problem, and introduce auxiliary variables to avoid non-linear problems. In a possible implementation manner, the adjusted lower-layer model may include:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Among them, and are dual variables, is an auxiliary variable.

[0074] 2. Substitute the adjusted lower-layer model into the objective function to obtain an adjusted objective function, forming a new integrated energy system optimal scheduling model.

[0075] Substitute the above adjusted lower-layer model into the objective function. In a possible implementation manner, the adjusted objective function may be:

[0076] S1032: Solve the linearized integrated energy system optimal scheduling model to obtain the target optimal scheduling plan.

[0077] For the optimized scheduling model of the linearized integrated energy system, a commercial solver can be directly called to solve it and obtain the target optimized scheduling plan; for example, the GUROBI solver.

[0078] The embodiments of the present invention comprehensively consider the uncertainty of photovoltaic power output and electricity price uncertainty, can balance economy and system stability, and are more suitable for practical applications.

[0079] Based on the above, an optimized scheduling model of the integrated energy system is constructed according to the above method, and the GUROBI solver is used to solve it to obtain the target optimized scheduling plan; at the same time, an optimized scheduling plan is obtained by using the deterministic plan in the prior art; the economies of the two plans are calculated respectively, as shown in Table 1.

[0080] Table 1 Economic comparison table

[0081] Photovoltaic power generation is greatly affected by weather conditions, and its output has strong uncertainty. Moreover, the electricity price in the electricity market is also affected by various factors, such as market supply and demand relations, policy adjustments, etc., and has large fluctuations. Referring to Table 1 and Figure 7 It can be seen that after comprehensively considering the uncertainties of photovoltaic power output and electricity price in the embodiments of the present invention, an optimized scheduling strategy with both robustness and economy can be obtained. At the same time, by reasonably arranging the procurement and use of energy, making full use of the low-price electricity period and avoiding excessive power procurement during the high-price electricity period, the electricity purchase cost of the integrated energy system can be reduced.

[0082] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0083] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0084] Figure 8 The structural schematic diagram of the optimized device for the integrated energy system considering double uncertainties provided by the embodiments of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown and are described in detail as follows: As Figure 8 shown, the optimized device for the integrated energy system considering double uncertainties includes: A constraint condition establishment module 21, which is used to consider the uncertainty of photovoltaic power output and establish an electric power balance constraint by using the chance-constrained programming method; A model establishment module 22, which is used to consider the electricity price uncertainty and establish an optimized scheduling model of the integrated energy system with the minimum operating cost of the system in the worst-case scenario of the electricity price as the objective function; The solution module 23 is used to solve the integrated energy system optimal scheduling model to obtain the target optimal scheduling plan.

[0085] In a possible implementation manner, the electric power balance constraint may include:

[0086] Among them, is the purchased electric power at time is the generated power of the combined heat and power unit at time is the discharging power of the electric energy storage at time is the output power of the photovoltaic unit at time is the power consumption of the power-to-gas equipment at time is the power consumption of the carbon capture equipment at time is the sold electric power at time is the charging power of the electric energy storage at time is the electric load at time is the inverse function of the standard normal distribution, is the confidence level, is the variance of the photovoltaic output prediction error.

[0087] In a possible implementation manner, the constraint conditions of the integrated energy system optimal scheduling model may further include: heat power balance constraint, equipment output upper and lower limit constraint, grid interaction constraint, electric energy storage equipment constraint, heat energy storage equipment constraint.

[0088] In a possible implementation manner, the model establishment module 22 may include: The data construction unit is used to obtain historical electricity price data and construct an electricity price uncertainty set; The lower-layer model construction unit is used to determine the lower-layer model based on the two-layer robust optimization algorithm according to the electricity price uncertainty set; among them, the lower-layer model is used to represent the worst-case scenario of the electricity price; The upper-layer model construction unit is used to establish an upper-layer model with the minimum operating cost as the objective function; The model output unit is used to form an integrated energy system optimal scheduling model from the upper-layer model and the lower-layer model.

[0089] In a possible implementation manner, the objective function may be:

[0090] Among them, is the cost of purchasing and selling electricity between the system and the power grid, is the cost of purchasing gas by the system from the gas network, is the degradation cost of the electric energy storage device, is the degradation cost of the thermal energy storage device, is the equipment configuration cost, is the electricity price deviation coefficient, is the interactive power between the system and the power grid at time is the time period of electricity price uncertainty, is an auxiliary variable, is the electricity price uncertainty parameter.

[0091] In a possible implementation manner, the solving module 23 may include: A linearization unit, configured to linearize the integrated energy system optimal scheduling model by using the strong duality theory to obtain a linearized integrated energy system optimal scheduling model; A solution output unit, configured to solve the linearized integrated energy system optimal scheduling model to obtain an optimal scheduling solution of the target.

[0092] In a possible implementation manner, the linearization unit may include: A lower-layer model adjustment subunit, configured to convert the maximization problem in the lower-layer model of the integrated energy system optimal scheduling model into a minimization problem by using the strong duality theory to obtain an adjusted lower-layer model; A target function adjustment subunit, configured to substitute the adjusted lower-layer model into the target function to obtain an adjusted target function, and form a new integrated energy system optimal scheduling model.

[0093] In a possible implementation manner, the adjusted lower-layer model may include:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] Among them, and are dual variables, is an auxiliary variable.

[0100] In a possible implementation, the adjusted objective function may be: .

[0101] An embodiment of the present invention further provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the methods in the above method embodiments are implemented.

[0102] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. Without special instructions and logical conflicts, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0103] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An integrated energy system optimization method considering double uncertainties, characterized in that, Including: Considering the uncertainty of photovoltaic power output, an opportunity-constrained programming method is used to establish the electric power balance constraint; Considering the uncertainty of electricity price, with the minimum operating cost of the system in the worst electricity price scenario as the objective function, an integrated energy system optimal scheduling model is established; Solving the integrated energy system optimal scheduling model to obtain the target optimal scheduling scheme.

2. The integrated energy system optimization method considering double uncertainties according to claim 1, characterized in that The electric power balance constraint includes: Among them, is the purchased power at time is the power generation of the combined heat and power unit at time is the discharging power of the electrical energy storage at time is the output power of the photovoltaic unit at time is the power consumption of the power-to-gas equipment at time is the power consumption of the carbon capture equipment at time is the sold power at time is the charging power of the electrical energy storage at time is the electrical load at time is the inverse function of the standard normal distribution, is the confidence level, is the variance of the photovoltaic output prediction error.

3. The integrated energy system optimization method considering double uncertainties according to claim 2, characterized in that The constraint conditions of the integrated energy system optimal scheduling model also include: thermal power balance constraint, upper and lower limits of equipment output constraint, grid interaction constraint, electric energy storage equipment constraint, thermal energy storage equipment constraint.

4. The integrated energy system optimization method considering double uncertainties according to any one of claims 1 to 3, characterized in that The method of establishing the integrated energy system optimal scheduling model by considering the uncertainty of electricity price with the minimum operating cost of the system in the worst electricity price scenario as the objective function includes: Obtaining historical electricity price data and constructing an electricity price uncertainty set; Based on the two-layer robust optimization algorithm, determining the lower-layer model according to the electricity price uncertainty set; wherein, the lower-layer model is used to represent the worst electricity price scenario; Establishing an upper-layer model with the minimum operating cost as the objective function; The upper-layer model and the lower-layer model form the integrated energy system optimal scheduling model.

5. The integrated energy system optimization method considering dual uncertainties according to claim 4, characterized in that The objective function is as follows: Among them, is the cost of purchasing and selling electricity between the system and the power grid, is the cost of purchasing gas by the system from the gas network, is the degradation cost of the electrical energy storage device, is the degradation cost of the thermal energy storage device, is the equipment configuration cost, is the electricity price deviation coefficient, is the interactive power between the system and the power grid at time is the time period of electricity price uncertainty, is the auxiliary variable, is the electricity price uncertainty parameter.

6. The integrated energy system optimization method considering dual uncertainties according to claim 5, characterized in that The method of solving the integrated energy system optimal scheduling model to obtain the target optimal scheduling scheme includes: Using the strong duality theory to linearize the integrated energy system optimal scheduling model to obtain a linearized integrated energy system optimal scheduling model; Solving the linearized integrated energy system optimal scheduling model to obtain the target optimal scheduling scheme.

7. The integrated energy system optimization method considering double uncertainties according to claim 6, characterized in that The method of using the strong duality theory to linearize the integrated energy system optimal scheduling model to obtain a linearized integrated energy system optimal scheduling model includes: Using the strong duality theory to transform the maximization problem in the lower-layer model of the integrated energy system optimal scheduling model into a minimization problem to obtain an adjusted lower-layer model; Substituting the adjusted lower-layer model into the objective function to obtain an adjusted objective function, forming a new integrated energy system optimal scheduling model.

8. The integrated energy system optimization method considering dual uncertainties according to claim 7, characterized in that The adjusted lower-layer model includes: Among them, and are dual variables, is an auxiliary variable.

9. The integrated energy system optimization method considering double uncertainties according to claim 8, characterized in that, The adjusted objective function is as follows: 。 10. An electronic device, characterized in that, Including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the integrated energy system optimization method considering double uncertainties as described in any one of claims 1 to 9.

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