Comprehensive energy system optimization method and electronic equipment considering dual uncertainty

Abstract

The present invention provides an integrated energy system optimization method and electronic equipment that considers dual uncertainties, relating to the field of power grid technology. The method comprises: considering the uncertainty of photovoltaic output, establishing an electric power balance constraint using a chance-constrained programming method; considering the uncertainty of electricity prices, establishing an integrated energy system optimization scheduling model with the minimum operating cost of the system under the worst-case electricity price scenario as the objective function; and solving the integrated energy system optimization scheduling model to obtain a target optimization scheduling scheme. The present invention simultaneously considers the impact of the uncertainty of internal photovoltaic output and external electricity prices on the system, resulting in a more reasonable optimization scheduling scheme that avoids the high cost risks of volatility on the system, can balance economic efficiency and system stability, and is more suitable for practical applications.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to an integrated energy system optimization method and electronic equipment considering dual uncertainty. Background Art

[0002] An integrated energy system (IES) organically integrates and synergistically optimizes multiple energy sources, aiming to achieve efficient energy utilization and meet diverse energy needs. This system offers significant advantages, including high energy efficiency, the ability to achieve cascaded energy utilization, and the promotion of clean energy consumption. It has become a research hotspot in the current energy transition.

[0003] Existing technologies focus on optimizing integrated energy systems in deterministic scenarios. However, because integrated energy systems include a large amount of renewable energy, their output is subject to significant uncertainty. Furthermore, electricity prices in the power market are affected by multiple factors, such as energy market fluctuations and policy adjustments, exhibiting complex fluctuations. Consequently, optimization results based on deterministic scenarios lack rationality and applicability in practical applications. Summary of the Invention

[0004] The embodiments of the present invention provide a comprehensive energy system optimization method and electronic device that consider dual uncertainty, so as to solve the problem of lack of rationality and applicability of energy system optimization in deterministic scenarios in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a method for optimizing an integrated energy system taking dual uncertainties into account, including:

[0006] Considering the uncertainty of photovoltaic output, the power balance constraint is established using the chance-constrained programming method;

[0007] Considering the uncertainty of electricity prices, an integrated energy system optimization scheduling model is established with the minimum operating cost of the system under the worst electricity price scenario as the objective function;

[0008] The optimal scheduling model of the integrated energy system is solved to obtain the target optimal scheduling plan.

[0009] In a second aspect, an embodiment of the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the integrated energy system optimization method considering dual uncertainty as provided in the first aspect of the above embodiment.

[0010] The embodiment of the present invention provides an integrated energy system optimization method and electronic device that considers dual uncertainty. The above-mentioned integrated energy system optimization method that considers dual uncertainty includes: considering the uncertainty of photovoltaic output, using the opportunity constraint programming method to establish an electric power balance constraint; considering the uncertainty of electricity price, taking the minimum operating cost of the system under the worst electricity price scenario as the objective function, and establishing an integrated energy system optimization scheduling model; solving the integrated energy system optimization scheduling model to obtain a target optimization scheduling scheme. In the embodiment of the present invention, the uncertainty of photovoltaic output is considered to establish an electric power balance constraint to ensure the normal operation of the system; at the same time, the uncertainty of electricity price is considered to establish an objective function to limit the cost by minimizing the operating cost. The embodiment of the present invention simultaneously considers the impact of the uncertainty of internal photovoltaic output and external electricity price on the system, and the obtained optimization scheduling scheme is more reasonable, avoids the high cost risk caused by volatility to the system, can take into account both economy and system stability, and is more suitable for practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a schematic structural diagram of an integrated energy system provided by an embodiment of the present invention;

[0012] Figure 2 This is a flowchart of an implementation method of an integrated energy system optimization method considering dual uncertainties provided by an embodiment of the present invention;

[0013] Figure 3 The historical data of electricity purchase prices provided by the embodiment of the present invention;

[0014] Figure 4 The historical data of electricity prices provided by the embodiment of the present invention;

[0015] Figure 5 This is a schematic diagram of the worst electricity purchase price scenario provided by an embodiment of the present invention;

[0016] Figure 6 This is a schematic diagram of the worst electricity price scenario provided by an embodiment of the present invention;

[0017] Figure 7 This is a comparison chart of the economic performance of the integrated energy system optimization method considering dual uncertainty provided by an embodiment of the present invention and the economic performance of the scheduling method in the prior art;

[0018] Figure 8 It is a structural diagram of an integrated energy system optimization device considering dual uncertainty provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Figure 1This is a schematic diagram of the structure of the integrated energy system provided by the embodiment of the present invention. Figure 1 As 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 boiler (GB), electrical energy storage (EES), thermal energy storage (TES) and other equipment.

[0021] In an integrated energy system, electricity demand is primarily met by photovoltaic generators. When photovoltaic power generation cannot meet the system's electricity needs, the system activates the combined heat and power (CHP) unit. The electric energy storage system (EES) charges and stores excess electricity when electricity prices are low, and discharges it to supplement the system's electricity needs when prices are high, thereby optimizing economic efficiency and energy efficiency. Waste heat from the CHP unit (CHP) is used to meet the system's heat needs, and excess heat can be stored in the thermal energy storage system (TES). When the CHP unit's waste heat is insufficient, supplementary heat is provided by the gas-fired boiler (GB). While meeting the heat load, the thermal energy storage system (TES) can also balance fluctuations in heat supply and demand, providing greater system flexibility. In a combined CHP-CCS-P2G system (comprising a combined heat and power (CHP) unit, carbon capture equipment, and power-to-gas (P2G) equipment), when the CHP unit experiences excess power, the excess electricity is used by the P2G unit to produce hydrogen or synthetic natural gas, achieving energy storage. The carbon dioxide (CO2) produced during CHP operation is absorbed by the CCS unit and used for a methanation reaction in the P2G unit to convert it into natural gas. This natural gas can then be re-transferred to the CHP unit, reducing gas purchases from the grid.

[0022] Due to the uncertainty of renewable energy output and the complex fluctuation characteristics of electricity prices, the optimization results based on deterministic scenarios lack rationality and applicability in practical applications.

[0023] Based on the above, the embodiment of the present invention provides a comprehensive energy system optimization method considering dual uncertainty. Figure 2 , which shows a flowchart of an implementation of a comprehensive energy system optimization method considering dual uncertainty provided by an embodiment of the present invention, and is described in detail as follows:

[0024] The above-mentioned integrated energy system optimization method considering dual uncertainty includes:

[0025] S101: Considering the uncertainty of photovoltaic output, the power balance constraint is established using the chance-constrained programming method;

[0026] Constraints are a key component in ensuring that model solutions are feasible, reasonable, and meet practical operational requirements. They define the operational boundaries of the system and balance the complex relationships between energy supply, conversion, storage, and demand. Specifically, constraints may include: electrical power balance constraints, thermal power balance constraints, upper and lower limits on equipment output, grid interaction constraints, and electrical and thermal energy storage device constraints.

[0027] Specifically, based on the energy system optimization in a deterministic scenario (without considering uncertainty), the constraints are as follows:

[0028] Electric power balance constraints may include:

[0029]

[0030] in, for The power purchased at the time, for The power generation capacity of the cogeneration unit at any given moment, for The discharge power of the energy storage at any moment, for The output power of the photovoltaic unit at any moment, for The power consumption of the power-to-gas equipment at each moment, for The power consumption of the carbon capture equipment at any given moment, for The electricity sales power at the time, for The charging power of the energy storage at all times, for The electrical load at the time; the unit of each parameter can be kW. Thermal power balance constraints can include:

[0031]

[0032] in, for The heat production power of the cogeneration unit at any moment, for The heating power of the gas boiler at all times, for The heat release power of thermal energy storage at all times, for The charging power of thermal energy storage at all times, for The heat load at the moment; the unit of each parameter can be kW. Upper and lower limit constraints can include:

[0033]

[0034] in, and are the upper and lower limits of the electric power of the cogeneration unit respectively; and are the upper and lower limits of thermal power of the cogeneration unit respectively; and They are the upper and lower limits of the thermal power of the gas boiler respectively; and These are the upper and lower limits of the power consumption of the gas boiler. The unit of each parameter can be kW.

[0035] Grid interaction constraints can include:

[0036]

[0037]

[0038]

[0039]

[0040] in, for The power purchased at the time, is the upper limit of the power purchase of the system and the grid, The power purchasing status of the system and the grid; for The electricity sales power at the time, is the upper limit of the power sold by the system and the grid, The power sales status of the system and the grid; for The interaction power between the system and the grid at any moment. 、 、 、 、 The unit can be kW. 、 Is a pure value.

[0041] Electric energy storage device constraints can include:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] in, for The capacity of the energy storage device at that moment, kWh; and They are the proportion of upper and lower limits of the state of charge of the electric energy storage equipment; is the upper limit of the capacity of the electric energy storage device, kWh; for Real-time charging state variables of energy storage devices at all times, for Real-time discharge state variables of the energy storage device; for The charging power of the energy storage device at any moment, for The discharge power of the energy storage device at that moment, kW; is the upper limit of the charging power of the energy storage device, is the upper limit of the discharge power of the electric energy storage device, kW.

[0050] Thermal storage device constraints can include:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] in, for The capacity of the thermal energy storage device at any moment, kWh; and They are the proportion of upper and lower limits of the state of charge of thermal energy storage equipment; is the upper limit of thermal energy storage equipment capacity, kWh; for Thermal power of thermal energy storage equipment charging at all times, for Thermal power of thermal energy storage equipment discharged at the moment, kW; for Real-time charging state variables of thermal energy storage equipment at all times, for Real-time heat release state variables of thermal energy storage equipment; The upper limit of thermal energy storage device charging power, The upper limit of heat release power of thermal energy storage equipment, kW.

[0059] Photovoltaic output is affected by factors such as sunlight intensity and weather conditions, and exhibits significant randomness. Actual output can deviate significantly from predicted values, directly impacting the system's power balance. Therefore, it's necessary to refine power balance constraints to account for uncertainty.

[0060] In the embodiment of the present invention, a chance-constrained programming method can be used to incorporate the uncertainty of photovoltaic output, thereby ensuring that the system can maintain power balance under a certain probability and guaranteeing stable operation of the system.

[0061] Taking into account the uncertainty of photovoltaic output (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 the prediction error is assumed to be normally distributed, then the variance is expressed as , we can get the following formula:

[0062]

[0063] in, for The predicted power of the photovoltaic unit at the moment, represents a normal distribution, The mean is 0 and the variance is Normal distribution.

[0064] The chance-constrained programming method is used to establish the power balance constraint considering the uncertainty of photovoltaic output, ensuring that the probability of photovoltaic output meets the confidence level. , that is, when considering photovoltaic uncertainty, the constraint condition becomes at least With a probability of , the system's power generation power (including photovoltaic, traditional power sources, etc.) can meet the load demand.

[0065] Based on this, the electric power balance constraint can be expressed as

[0066]

[0067] Thus, the uncertainty constraint is transformed into a deterministic constraint.

[0068] Let the cumulative distribution function of the random variable be , then using the theoretical knowledge of probability statistics, the above formula is further converted into the following:

[0069]

[0070] Assuming that the prediction obeys the normal distribution, the inverse function related to the standard normal distribution can be introduced to solve it. The converted electric power balance constraint is:

[0071]

[0072] Based on the above, it can be seen that in one possible implementation, the electric power balance constraint may include:

[0073]

[0074] in, for The power purchased at the time, for The power generation capacity of the cogeneration unit at any given moment, for The discharge power of the energy storage at any moment, for The output power of the photovoltaic unit at any moment, for The power consumption of the power-to-gas equipment at each moment, for The power consumption of the carbon capture equipment at any given moment, for The electricity sales power at the time, for The charging power of the energy storage at all times, for The electrical load at the moment, is the inverse function of the standard normal distribution, is the confidence level, is the variance of the PV output prediction error.

[0075] Based on the above analysis, in addition to the electric power balance constraint, the constraints of the integrated energy system optimization scheduling model can also include: thermal power balance constraint, equipment output upper and lower limit constraints, grid interaction constraints, electric energy storage equipment constraints, and thermal energy storage equipment constraints.

[0076] Except for the electric power balance constraint, the other constraints do not consider uncertainty. Therefore, the constraints are as shown above and are not adjusted.

[0077] S102: Considering the uncertainty of electricity prices, the objective function is to minimize the system operating cost under the worst electricity price scenario and establish an integrated energy system optimization scheduling model;

[0078] Electricity prices in the power market are influenced by a variety of factors, including supply and demand, policy adjustments, and other factors. Their uncertainty primarily manifests in their impact on system operating costs. Different electricity price scenarios can alter the costs and benefits of decisions such as power generation and purchasing. For example, when electricity prices rise, the cost of purchasing electricity from the main grid increases. The original objective function based on a fixed electricity price cannot reflect this change, potentially leading to excessively high system operating costs or poor efficiency.

[0079] Therefore, in the embodiment of the present invention, the uncertainty of electricity prices is taken into account, the deterministic system objective function is adjusted, and a penalty term considering the uncertainty of electricity prices is introduced. Various electricity price fluctuations can be comprehensively considered during the optimization process, so that the system can achieve cost minimization under different electricity price scenarios.

[0080] In a possible implementation, S102 may include:

[0081] S1021: Obtain historical electricity price data and construct an electricity price uncertainty set;

[0082] S1022: Based on a two-layer robust optimization algorithm, determine a lower-layer model according to the electricity price uncertainty set; wherein the lower-layer model is used to represent the worst-case scenario of electricity prices;

[0083] S1023: Establishing an upper-level model with the minimum operating cost as the objective function;

[0084] S1024: The upper-level model and the lower-level model form an integrated energy system optimization scheduling model.

[0085] Robust optimization is a method for solving optimization problems that aims to find solutions that perform well in the presence of uncertainty. By accounting for uncertainty in the optimization model, the resulting optimal solution maintains a certain level of performance under various possible parameter variations, resulting in strong stability and reliability.

[0086] In robust optimization algorithms, the upper-level model typically considers the overall system, while the lower-level model typically optimizes its own performance based on its own local interests, given the upper-level decisions. This application establishes the upper-level model with minimizing operating cost as the objective function, while the lower-level model considers electricity price uncertainty.

[0087] First, we need to define an uncertainty set to describe the possible range of variation of the parameter. In the embodiment of the present invention, the parameter is the electricity price. The embodiment of the present invention can construct an uncertainty set based on historical electricity price data to describe the possible range of variation of the electricity price. Figure 3 and Figure 4The upper and lower models together form an integrated energy system optimization scheduling model, which comprehensively considers the uncertainty of photovoltaic output and electricity price. This model is more comprehensive and accurate.

[0088] The worst case scenario usually refers to the electricity price fluctuation scenario that makes the system operating cost reach the highest level. Figure 5 and Figure 6 The worst electricity price scenario is shown. For electricity purchase, the purchase price may be at the upper limit of its fluctuation range in each period; for electricity sales, the sales price may be at the lower limit of its fluctuation range in each period. At the same time, the combination of electricity price fluctuations may also make the comprehensive costs related to electricity prices in the system reach the maximum. Therefore, in the embodiment of the present invention, This item is used to consider the worst electricity price scenario within the range defined by the electricity price uncertainty set, so as to maximize the value of this item in the objective function. is an auxiliary variable used to limit the price deviation penalty term to be effective only during the period of electricity price uncertainty; It is the period of uncertainty in electricity prices; is the electricity price uncertainty parameter, which is used to indicate the degree of system uncertainty.

[0089] The objective function is to minimize the operating cost of the system under the worst electricity price scenario. The objective function can be obtained as follows:

[0090]

[0091] Among them, the outer min is the minimization of the system operation cost, and the inner max is the worst case considering the uncertainty of electricity price.

[0092] Thus, in one possible implementation, the objective function It can be:

[0093]

[0094] in, The cost of electricity purchase and sale between the system and the grid, is the gas purchase cost of the system from the gas grid, is the degradation cost of the electric energy storage device, is the degradation cost of thermal energy storage equipment, is the equipment configuration cost, is the electricity price deviation coefficient, for The interaction power between the system and the grid at any moment, The period of electricity price uncertainty is is an auxiliary variable, is the electricity price uncertainty parameter.

[0095] S103: Solve the integrated energy system optimization scheduling model to obtain a target optimization scheduling plan.

[0096] By solving the above-mentioned integrated energy system optimization scheduling model considering uncertainty, we can obtain the target optimization scheduling plan that takes into account both economy and system stability.

[0097] Based on the above analysis, the integrated energy system optimization and scheduling model in the embodiments of the present invention is a robust model. It incorporates a "min-max" objective function and a non-convex robust optimization problem, which cannot be solved directly. Therefore, it is necessary to linearize the non-convex model to make it more efficient and easier to solve.

[0098] In a possible implementation, S103 may include:

[0099] S1031: Use strong duality theory to linearize the integrated energy system optimization scheduling model and obtain a linearized integrated energy system optimization scheduling model;

[0100] Strong duality theory is an important theory in convex optimization. It states that under certain conditions, the original optimization problem (called the primal problem) and its dual problem have the same optimal solution. For linear programming problems, strong duality always holds true. Based on this, the present invention uses strong duality theory to linearize the model, transforming the primal problem into its dual problem, resulting in a linearized integrated energy system optimization scheduling model.

[0101] In a possible implementation, S1031 may include:

[0102] 1. Using strong duality theory, the maximization problem in the lower-level model of the integrated energy system optimization scheduling model is transformed into a minimization problem, and the adjusted lower-level model is obtained;

[0103] set up and As dual variables, the strong duality theory is used to transform the max problem into a min problem, and auxiliary variables are introduced to avoid nonlinear problems. In one possible implementation, the adjusted lower-level model may include:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] in, and is the dual variable, is an auxiliary variable.

[0111] 2. Substitute the adjusted lower-level model into the objective function to obtain the adjusted objective function and form a new integrated energy system optimization scheduling model.

[0112] Substitute the adjusted lower layer model into the objective function. In one possible implementation, the adjusted objective function It can be:

[0113]

[0114] S1032: Solve the linearized integrated energy system optimization scheduling model to obtain the target optimization scheduling plan.

[0115] For the linearized integrated energy system optimization scheduling model, a commercial solver can be directly called to obtain the target optimization scheduling solution; for example, the GUROBI solver.

[0116] The embodiment of the present invention comprehensively considers the uncertainty of photovoltaic output and the uncertainty of electricity price, can take into account both economy and system stability, and is more suitable for practical application.

[0117] Based on the above, the integrated energy system optimization scheduling model is constructed according to the above method, and the target optimization scheduling scheme is obtained by using the GUROBI solver. At the same time, the deterministic scheme in the existing technology is used to obtain the optimized scheduling scheme. The economic efficiency of the two schemes is calculated respectively, as shown in Table 1.

[0118] Table 1 Economic comparison table

[0119]

[0120] Photovoltaic power generation is greatly affected by weather conditions, and the output is highly uncertain. The electricity price in the power market is also affected by many factors, such as market supply and demand, policy adjustments, etc., and has a large volatility. Figure 7 As can be seen, by simultaneously considering the uncertainty of photovoltaic output and electricity prices in the embodiments of the present invention, a robust and economical optimized scheduling strategy can be obtained. Furthermore, by rationally arranging energy procurement and use, fully utilizing periods of low electricity prices and avoiding excessive electricity purchases during periods of high electricity prices, the electricity purchase cost of the integrated energy system can be reduced.

[0121] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0122] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0123] Figure 8 A schematic diagram of the structure of an integrated energy system optimization device considering dual uncertainty provided by an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0124] like Figure 8 As shown in Figure 2, the integrated energy system optimization device considering dual uncertainty includes:

[0125] The constraint condition establishment module 21 is used to consider the uncertainty of photovoltaic output and establish the power balance constraint using the chance constraint programming method;

[0126] The model building module 22 is used to consider the uncertainty of electricity prices and establish an integrated energy system optimization scheduling model with the minimum operating cost of the system under the worst electricity price scenario as the objective function;

[0127] The solution module 23 is used to solve the integrated energy system optimization scheduling model to obtain a target optimization scheduling solution.

[0128] In one possible implementation, the electric power balance constraint may include:

[0129]

[0130] in, for The power purchased at the time, for The power generation capacity of the cogeneration unit at any given moment, for The discharge power of the energy storage at any moment, for The output power of the photovoltaic unit at any moment, for The power consumption of the power-to-gas equipment at each moment, for The power consumption of the carbon capture equipment at any given moment, for The electricity sales power at the time, for The charging power of the energy storage at all times, for The electrical load at the moment, is the inverse function of the standard normal distribution, is the confidence level, is the variance of the PV output prediction error.

[0131] In a possible implementation, the constraints of the integrated energy system optimization scheduling model may also include: thermal power balance constraints, equipment output upper and lower limit constraints, grid interaction constraints, electric energy storage equipment constraints, and thermal energy storage equipment constraints.

[0132] In one possible implementation, the model building module 22 may include:

[0133] A data construction unit, used to obtain historical electricity price data and construct an electricity price uncertainty set;

[0134] A lower-layer model construction unit is used to determine a lower-layer model based on a two-layer robust optimization algorithm and a set of electricity price uncertainties; wherein the lower-layer model is used to represent the worst-case scenario of electricity prices;

[0135] An upper-level model building unit is used to build an upper-level model with the minimum operating cost as the objective function;

[0136] The model output unit is used for the upper and lower models to form an integrated energy system optimization scheduling model.

[0137] In one possible implementation, the objective function It can be:

[0138]

[0139] in, The cost of electricity purchase and sale between the system and the grid, is the gas purchase cost of the system from the gas grid, is the degradation cost of the electric energy storage device, is the degradation cost of thermal energy storage equipment, is the equipment configuration cost, is the electricity price deviation coefficient, for The interaction power between the system and the grid at any moment, The period of electricity price uncertainty is is an auxiliary variable, is the electricity price uncertainty parameter.

[0140] In a possible implementation, the solution module 23 may include:

[0141] A linearization unit is used to linearize the integrated energy system optimization scheduling model using strong duality theory to obtain a linearized integrated energy system optimization scheduling model;

[0142] The solution output unit is used to solve the linearized integrated energy system optimization scheduling model and obtain the target optimization scheduling solution.

[0143] In one possible implementation, the linearization unit may include:

[0144] A lower-level model adjustment subunit is used to transform the maximization problem in the lower-level model of the integrated energy system optimization scheduling model into a minimization problem by using strong duality theory, thereby obtaining an adjusted lower-level model;

[0145] The objective function adjustment subunit is used to substitute the adjusted lower-level model into the objective function to obtain the adjusted objective function and form a new integrated energy system optimization scheduling model.

[0146] In one possible implementation, the adjusted lower-layer model may include:

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] in, and is the dual variable, is an auxiliary variable.

[0154] In one possible implementation, the adjusted objective function It can be:

[0155] .

[0156] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method in the above method embodiment when executing the computer program.

[0157] In the above embodiments, the descriptions of each embodiment have their own focus. For parts not described or recorded in detail in one embodiment, please refer to the relevant descriptions of other embodiments. Unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features of different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0158] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A comprehensive energy system optimization method considering dual uncertainty, characterized in that: include: Considering the uncertainty of photovoltaic output, the power balance constraint is established using the chance-constrained programming method; Considering the uncertainty of electricity prices, an integrated energy system optimization scheduling model is established with the minimum operating cost of the system under the worst electricity price scenario as the objective function; Solving the integrated energy system optimization scheduling model to obtain a target optimization scheduling plan; Considering the uncertainty of electricity prices, the objective function is to minimize the operating cost of the system under the worst electricity price scenario, and establish an optimal scheduling model for the integrated energy system, including: Obtain historical electricity price data and construct an electricity price uncertainty set; Based on a two-layer robust optimization algorithm, a lower-layer model is determined according to the electricity price uncertainty set; wherein the lower-layer model is used to represent the worst-case scenario of electricity prices; The upper model is established with the minimum operating cost as the objective function; The upper model and the lower model form the integrated energy system optimization scheduling model; The objective function for: in, The cost of electricity purchase and sale between the system and the grid, is the gas purchase cost of the system from the gas grid, is the degradation cost of the electric energy storage device, is the degradation cost of thermal energy storage equipment, is the equipment configuration cost, is the electricity price deviation coefficient, for The interaction power between the system and the grid at any moment, The period of electricity price uncertainty is is an auxiliary variable, is the electricity price uncertainty parameter.

2. The integrated energy system optimization method considering dual uncertainty according to claim 1 is characterized in that: The electric power balance constraints include: in, for The power purchased at the time, for The power generation capacity of the cogeneration unit at any given moment, for The discharge power of the energy storage at any moment, for The output power of the photovoltaic unit at any moment, for The power consumption of the power-to-gas equipment at each moment, for The power consumption of the carbon capture equipment at any given moment, for The electricity sales power at the time, for The charging power of the energy storage at all times, for The electrical load at the moment, is the inverse function of the standard normal distribution, is the confidence level, is the variance of the PV output prediction error.

3. The integrated energy system optimization method considering dual uncertainty according to claim 2 is characterized in that: The constraints of the integrated energy system optimization scheduling model also include: thermal power balance constraints, equipment output upper and lower limit constraints, grid interaction constraints, electric energy storage equipment constraints, and thermal energy storage equipment constraints.

4. The integrated energy system optimization method considering dual uncertainty according to claim 1 is characterized in that: Solving the integrated energy system optimization scheduling model to obtain a target optimization scheduling solution includes: The integrated energy system optimization scheduling model is linearized by using strong duality theory to obtain a linearized integrated energy system optimization scheduling model; The linearized integrated energy system optimization scheduling model is solved to obtain the target optimization scheduling solution.

5. The integrated energy system optimization method considering dual uncertainty according to claim 4 is characterized in that: The strong duality theory is used to linearize the integrated energy system optimization scheduling model to obtain a linearized integrated energy system optimization scheduling model, including: The strong duality theory is used to transform the maximization problem in the lower layer model of the integrated energy system optimization scheduling model into a minimization problem, and an adjusted lower layer model is obtained; Substituting the adjusted lower-layer model into the objective function to obtain the adjusted objective function, a new integrated energy system optimization scheduling model is formed.

6. The integrated energy system optimization method considering dual uncertainty according to claim 5 is characterized in that: The adjusted lower-level model includes: in, and is the dual variable, is an auxiliary variable.

7. The integrated energy system optimization method considering dual uncertainty according to claim 6 is characterized in that: The adjusted objective function for: 。 8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for optimizing an integrated energy system considering dual uncertainty as described in any one of claims 1 to 7 is implemented.

Images