Multi-time scale optimal scheduling method for integrated energy system considering device variable working conditions

Abstract

The application discloses a kind of comprehensive energy system multi-time scale optimization scheduling method considering equipment variable working condition, it is related to energy optimization scheduling technical field, the method includes considering the energy input link in regional comprehensive energy system, production link, conversion link, storage link and consumption link, and considering equipment variable working condition characteristics, constructs dynamic energy hub model containing electric gas conversion equipment;According to dynamic energy hub model and different load prediction scales, a comprehensive energy system multi-time scale rolling optimization scheduling model is established;According to the comprehensive demand response of day-ahead scheduling stage and intraday rolling scheduling stage, a double demand response model is established, and a mixed integer nonlinear programming model is constructed according to the comprehensive energy system multi-time scale rolling optimization scheduling model and double demand response model and is solved.The application can exert the advantage in different time dimensions, while making the system operation scheduling more accurate.

Description

Technical Field

[0001] This invention relates to the field of energy optimization scheduling technology, and in particular to a multi-timescale optimization scheduling method for integrated energy systems that takes into account varying operating conditions of equipment. Background Technology

[0002] With the gradual depletion of global fossil fuels and the intensifying contradiction between energy supply and demand, integrated energy systems (IES) can interconnect various energy networks to achieve coordinated planning and optimized operation among multiple heterogeneous energy subsystems, efficiently realize the cascade utilization of energy, and promote the consumption of renewable energy. This is currently a hot topic in the field of energy research. However, in order to simplify the model, the efficiency of energy conversion devices is usually assumed to be constant during the optimization process, which may lead to unreasonable operation schemes for integrated energy systems. At the same time, due to the different response times of heterogeneous energy sources such as cold, heat, electricity, and gas, coordination and optimization need to be carried out at multiple time scales. This results in inaccuracies in the equipment output results at different time scales under constant operating conditions, which brings considerable difficulties to the actual operation and management of integrated energy systems. Summary of the Invention

[0003] Therefore, the purpose of this invention is to provide a multi-timescale optimization scheduling method for integrated energy systems that takes into account the changing operating conditions of equipment.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] This invention provides a multi-timescale optimization scheduling method for an integrated energy system that takes into account the variable operating conditions of equipment, including: (1) considering the energy input link, production link, conversion link, storage link and consumption link in the regional integrated energy system, and considering the variable operating conditions of equipment, constructing a dynamic energy hub model including electricity-to-gas equipment; the dynamic energy hub model is used to coordinate the supply and demand balance of multiple energy flows; (2) based on the dynamic energy hub model and different load forecasting scales, establishing a multi-timescale rolling optimization scheduling model for an integrated energy system that combines the dynamic energy hub model with multi-timescale optimization; (3) based on the integrated demand response of the day-ahead scheduling stage and the intraday rolling scheduling stage, establishing a dual demand response model, and constructing a mixed integer nonlinear programming model based on the multi-timescale rolling optimization scheduling model and the dual demand response model of the integrated energy system; (4) using the incremental linearization method to transform the mixed integer nonlinear programming model into a mixed integer linear programming model, and solving the mixed integer linear programming model.

[0006] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0007] 1. This invention can fully explore the scheduling characteristics of multiple energy flows at different time scales, better adapting to changes in the accuracy of photovoltaic power generation units, wind power generation units, and load forecasting. 2. This invention constructs an iteratively corrected dynamic energy hub model and applies it to the multi-time-scale scheduling model for real-time adjustments, resulting in more accurate system operation scheduling and providing practical reference for the operation and maintenance management of integrated energy operators. 3. This invention considers the impact of dual demand response in the development of a new optimized scheduling model, reducing pressure on the energy supply side and the risk of system fluctuations, improving the economic efficiency of both the user and energy supply sides, and truly realizing the interaction between the source, load, and storage of the integrated energy system. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This embodiment provides a flowchart illustrating a multi-timescale optimization scheduling method for an integrated energy system that takes into account varying equipment operating conditions.

[0010] Figure 2 This embodiment provides an overall flowchart of a multi-timescale optimization scheduling method for a comprehensive energy system that takes into account varying operating conditions of equipment.

[0011] Figure 3 This is a diagram of the regional integrated energy system structure provided in this embodiment;

[0012] Figure 4 This embodiment provides a regional energy load and wind and solar power output prediction curve.

[0013] Figure 5 The efficiency fitting curve of the selected device provided in this embodiment;

[0014] Figure 6 shows the power, heat, cooling, and gas energy supply and demand scheduling results of the system under constant operating conditions during the day-ahead scheduling phase provided in this embodiment; Figure 6(a) shows the power energy supply and demand scheduling results of the system under constant operating conditions during the day-ahead scheduling phase; Figure 6(b) shows the heat energy supply and demand scheduling results of the system under constant operating conditions during the day-ahead scheduling phase; Figure 6(c) shows the cooling energy supply and demand scheduling results of the system under constant operating conditions during the day-ahead scheduling phase; Figure 6(d) shows the gas energy supply and demand scheduling results of the system under constant operating conditions during the day-ahead scheduling phase.

[0015] Figure 7 shows the power, heat, cooling, and gas energy supply and demand scheduling results of the system under the day-ahead scheduling phase under varying operating conditions provided in this embodiment; Figure 7(a) shows the power energy supply and demand scheduling results of the system under the day-ahead scheduling phase under varying operating conditions; Figure 7(b) shows the heat energy supply and demand scheduling results of the system under the day-ahead scheduling phase under varying operating conditions; Figure 7(c) shows the cooling energy supply and demand scheduling results of the system under the day-ahead scheduling phase under varying operating conditions; Figure 7(d) shows the gas energy supply and demand scheduling results of the system under the day-ahead scheduling phase under varying operating conditions.

[0016] Figure 8 shows the power, heat, cooling, and gas energy supply and demand scheduling results of the system during the intraday scheduling phase under varying operating conditions provided in this embodiment; Figure 8(a) shows the power energy supply and demand scheduling results of the system during the intraday scheduling phase under varying operating conditions; Figure 8(b) shows the heat energy supply and demand scheduling results of the system during the intraday scheduling phase under varying operating conditions; Figure 8(c) shows the cooling energy supply and demand scheduling results of the system during the intraday scheduling phase under varying operating conditions; Figure 8(d) shows the gas energy supply and demand scheduling results of the system during the intraday scheduling phase under varying operating conditions.

[0017] Figure 9 This embodiment provides a comparison chart of grid power purchase results during the intraday rolling and real-time optimization phases;

[0018] Figure 10 is a comparison chart of the effects of different demand response strategies adopted in this embodiment; Figure 10(a) is a comparison chart of electrical load and demand response adjustment amount for different demand response strategies; Figure 10(b) is a comparison chart of heat load and demand response adjustment amount for different demand response strategies; Figure 10(c) is a comparison chart of cooling load and demand response adjustment amount for different demand response strategies; Figure 10(d) is a comparison chart of gas load and demand response adjustment amount for different demand response strategies. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 and Figure 2As shown in the figure, this embodiment provides a multi-timescale optimization scheduling method for an integrated energy system that considers varying equipment operating conditions, including the following steps: Step 100: Considering the energy input, production, conversion, storage, and consumption links in the regional integrated energy system, and considering the varying operating conditions of equipment, a dynamic energy hub model including electricity-to-gas conversion equipment is constructed. This dynamic energy hub model is used to coordinate the supply and demand balance of multiple energy flows. Step 200: Based on the dynamic energy hub model and different load forecasting scales, a multi-timescale rolling optimization scheduling model for the integrated energy system combining the dynamic energy hub model and multi-timescale optimization is established. This multi-timescale rolling optimization scheduling model for the integrated energy system can reduce the impact of power fluctuations. Step 300: Based on the integrated demand response of the day-ahead scheduling stage and the intraday rolling scheduling stage, a dual demand response model is established, and a mixed integer nonlinear programming model is constructed based on the multi-timescale rolling optimization scheduling model and the dual demand response model for the integrated energy system. Step 400: The mixed-integer nonlinear programming model is transformed into a mixed-integer linear programming model using the incremental linearization method, and the mixed-integer linear programming model is solved. Specifically, the mixed-integer linear programming model is solved in MATLAB by calling the Gurobi solver through the Yalmip toolbox, and the day-ahead scheduling scheme, intraday rolling optimization operation scheme, and intraday real-time optimization operation scheme of the integrated energy system are obtained, including the comparison operation schemes of constant operating conditions and variable operating conditions.

[0021] The traditional energy hub model describes the functional relationship between the input and output of a multi-energy flow system using a coupling matrix C, as shown in the following model:

[0022]

[0023] In the formula: α, β, ..., ω are elements in the set of energy forms ψ, such as electricity, heat, cold, gas, etc.; L is the output power vector; c is the coupling matrix. αβ denoted as , where is the efficiency of the corresponding energy conversion equipment; P is the input power vector; s is the coefficient matrix of the energy storage equipment; and E is the actual charge / discharge vector of the energy storage equipment, with charging the equipment being positive and discharging the equipment being negative.

[0024] Traditional energy hub models treat the efficiency of energy conversion equipment as a constant, simplifying the relationship between energy input and output into a linear model. However, as mentioned earlier, for most energy conversion equipment, the energy conversion efficiency varies with the load rate, i.e.: η αβ =f(R) αβ Where η is the efficiency of the energy conversion equipment; R is the load rate of the energy conversion equipment, i.e., the ratio of output to capacity.

[0025] For different energy conversion devices, the specific expression of the functional relationship between their efficiency and load rate can be obtained through polynomial fitting, which is called the energy conversion device efficiency model. For each energy conversion process, the corresponding conversion efficiency in the traditional energy hub model is iteratively corrected in real time by using the corresponding energy conversion device efficiency model. The conversion efficiency at each time point is recalculated and the value at that time point is replaced to construct a dynamic energy hub model. The dynamic energy hub model is shown below, where f() is a function of the energy conversion device efficiency.

[0026]

[0027] In integrated energy systems, micro gas turbines are the core equipment for achieving combined cooling, heating, and power (CCHP). Their output power is greatly affected by the load factor, and the power generation efficiency exhibits a non-linear relationship with the load factor. This relationship can be fitted by a fourth-order polynomial, namely: in, Let n be the electrical efficiency of the gas turbine unit at time t; n is the fitting order. The coefficients are nth-order fitting coefficients; Let t be the electrical load rate of the gas turbine unit at time t.

[0028] This embodiment considers expressing the thermoelectric ratio of the gas turbine as a quadratic function of the output electrical load rate, that is: in, The coefficients are nth-order fitting coefficients; Let t be the heat-to-electric ratio of the gas turbine unit.

[0029] Gas-fired boilers provide heat by consuming natural gas, compensating for the insufficient heating provided by gas turbines. Considering the characteristics of gas-fired boilers under variable operating conditions, their thermal efficiency is fitted as a function of a quadratic polynomial of the load rate, which can be expressed as: in, The coefficients are nth-order fitting coefficients; Let be the thermal efficiency of the gas-fired boiler unit at time t. The heat load rate of the gas-fired boiler unit at time t is the ratio of output heat power to capacity.

[0030] A heat pump is a device that generates heat using electrical energy. When gas turbines and gas boilers provide insufficient heating or when electricity prices are low, a heat pump can supply the required amount of heat. Its energy efficiency ratio can be fitted as a quadratic polynomial function of the output heat power, i.e.: in, The coefficients are nth-order fitting coefficients; Let be the coefficient of performance (COP) of the heat pump unit at time t. The heat load rate of the heat pump unit at time t is the ratio of output heat power to capacity.

[0031] Electricity-to-gas (EPG) technology is one of the new technologies considered for coupling between natural gas and electricity networks, which can promote the consumption of renewable energy and enhance system stability. In this technology, water molecules are electrolyzed into hydrogen and oxygen, and the hydrogen molecules produced from water decomposition are then stored. Typically, the stored hydrogen molecules react with carbon dioxide molecules to produce methane or natural gas and water. The resulting methane can be used to supplement the natural gas load. The chemical reaction equation is as follows:

[0032]

[0033] This embodiment simplifies the chemical process, considering only the conversion between electricity and natural gas. The mathematical model is as follows: in, These represent the output natural gas power and input electrical power of the electro-gas converter at time t, respectively; η P2G This refers to the efficiency of the electro-gas conversion.

[0034] Absorption chillers mainly consist of components such as a generator, evaporator, absorber, condenser, and solution exchanger. They primarily generate cooling power by absorbing waste heat or low-grade heat energy from gas turbines. Although their coefficient of performance (COP) is not high, they are significant for the cascade utilization of energy. Regarding the part-load characteristics of absorption chillers, their COP can be fitted as a quadratic polynomial function of the output cooling power, i.e.: in, The coefficients are nth-order fitting coefficients; The coefficient of performance (COP) of the absorption chiller at time t. Let t be the cooling load rate of the absorption chiller unit at time t.

[0035] An electric chiller is a device that uses electrical energy to drive refrigeration. Its energy efficiency coefficient (EEC) is higher than that of an absorption chiller. Considering the part-load characteristics of an electric chiller, its EEC can be expressed by a quadratic polynomial, namely: in, The coefficients are nth-order fitting coefficients; Let be the cooling efficiency coefficient of the electric refrigeration unit at time t. The cooling load rate of the electric refrigeration unit at time t is the ratio of output cooling power to capacity.

[0036] The strategy of the multi-timescale rolling optimization scheduling model for integrated energy systems mainly utilizes the progressively increasing accuracy of source and load forecasts as the time scale decreases, which can reduce the impact of source and load uncertainties on the optimal scheduling of integrated energy systems. Within the framework of this model, the upper-level layer performs scheduling of the entire integrated energy system one day in advance at 24:00 each day, with a time scale of 1 hour; the middle layer performs rolling scheduling of the integrated energy system for the next 4 hours, with a time scale of 15 minutes; and the lower-level layer formulates real-time adjustment plans for the power component of the integrated energy system, with a time scale of 5 minutes. Through the coordination and cooperation of these three levels, the integrated energy system achieves coordinated operation across multiple time scales. This novel multi-timescale optimization operation model for integrated energy systems is a scheduling model constructed with the objective function of minimizing the day-ahead operating cost of the regional integrated energy system, and with constraints such as energy supply and demand balance, energy purchase, energy equipment, energy interaction, and demand response at multiple time scales.

[0037] The day-ahead scheduling phase is the phase that formulates the scheduling plan for the next day with the objective function of minimizing the day-ahead scheduling cost. During the day-ahead scheduling phase, the constraints at the upper time scale mainly include system energy balance constraints, energy purchase constraints, equipment operation constraints, and energy interaction constraints.

[0038] The objective function is:

[0039] Among them, F a This represents the day-ahead dispatch cost of an integrated energy system, including the cost of purchasing electricity. (Difference between electricity purchase cost and electricity sales profit), cost of purchasing natural gas Equipment operation and maintenance costs and day-to-day demand response costs and These are the community's electricity and natural gas purchases during period t. and These are the corresponding energy prices for each time period, and and These represent electricity sales volume and electricity prices for different time periods; R represents the set of equipment maintenance units for the integrated energy system; p o,r and These are the maintenance cost and output power of unit r, respectively; and These are the adjustments to the day-ahead stimulus-driven demand response for heat and cooling loads, p ζ,h and p ζ,c The corresponding subsidy cost for the current stage; T is the scheduling period, which is 24 hours, with a time step of 1 hour.

[0040] 1. The supply and demand balance constraints of the integrated energy system's electrical load are:

[0041]

[0042] 2. The supply and demand balance constraints for the heat load of the integrated energy system are:

[0043]

[0044] 3. The supply and demand balance constraints for the cooling load of the integrated energy system are:

[0045]

[0046] 4. The supply and demand balance constraints for the gas load of the integrated energy system are as follows:

[0047]

[0048] 5. The operating constraints of the integrated energy system equipment are as follows:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] 6. The interaction constraints between the integrated energy system and the electricity and natural gas networks are as follows:

[0056]

[0057] 7. The constraints on energy storage equipment in integrated energy systems are as follows:

[0058]

[0059] in, This indicates the upper limit of the power of the corresponding energy equipment. Indicates the power ramp-up limit of the corresponding energy equipment. and These are the upper limits for the community's interaction with the electricity and gas networks.

[0060] The intraday rolling scheduling phase involves using a rolling optimization interleaved scheduling model, taking the latest weather conditions and system-wide cold, heat, and electricity load forecasts as inputs to generate a new system scheduling plan for the corresponding time scale. The objective function of the intraday rolling scheduling phase is the operating cost of each community within the rolling cycle, which includes energy purchase costs, equipment maintenance costs, and intraday demand response costs. During the intraday rolling scheduling phase, all equipment in the system can respond within 15 minutes. Constraints primarily include energy balance constraints, energy purchase constraints, equipment operation constraints, and energy interaction constraints at intermediate time scales; that is, the constraints of the intraday rolling scheduling phase model are the same as those of the day-ahead scheduling phase model.

[0061] The objective function is:

[0062]

[0063] Where ts is the start time of the rolling optimization; NT is the total number of rolling cycles; and These are adjustments to the intraday stimulus demand response for electricity and gas loads, p ζ,e and p ζ,g This refers to the corresponding subsidy expenses incurred during the recent scheduling phase.

[0064] By further making dynamic real-time adjustments within extremely short time scales based on the intraday rolling scheduling phase, the integrated energy system can effectively achieve optimal operation between system economy and stability, and flexibly meet energy demand in real time.

[0065]

[0066] Where t is the time corresponding to each interval Δt; μ b and These represent the adjusted price and adjusted power for purchasing electricity, respectively; V is the set of devices available to the community during the real-time dispatch phase; μ v and These represent the adjusted price and adjusted power of the v unit, respectively.

[0067] During the real-time dispatch phase, only the power sector participates in the integrated energy system optimization to achieve real-time regulation of power imbalances. The constraints at extremely short timescales include power purchase constraints and power equipment operation constraints, which are identical to those in the intraday rolling dispatch phase model and the day-ahead dispatch phase model.

[0068] Integrated demand response is a supply-demand interaction model that allows consumers to modify their energy consumption patterns based on price or incentive signals from the energy market, thereby gaining some benefits. Simultaneously, operators can improve the shape of their load curves and reduce costs. Due to the varying responses of different energy loads to price changes and external environmental factors, this embodiment establishes a dual demand response model at both the day-ahead and intraday time scales, as shown in Table 1. This fully leverages the dispatchability and responsiveness of demand-side resources.

[0069] Table 1. Types of Dual Demand Response Models

[0070]

[0071] Since electricity load is significantly affected by electricity prices, time-of-use pricing is introduced to guide users to rationally adjust their energy consumption patterns. Demand response modeling is performed using the electricity price elasticity matrix method, yielding the following elasticity coefficients for electricity consumption and prices: Where Δq and Δp are the relative increments of electricity q and electricity price p, respectively.

[0072] For time periods 1-n, an elasticity matrix is ​​established based on the ratio of fixed electricity price to time-of-use electricity price: in, E e It is the elasticity matrix of electricity consumption and electricity price, η aa η is the cross-elasticity coefficient, representing the response of electricity consumption to changes in electricity prices during other time periods. ab The elasticity coefficient represents the response of electricity consumption to changes in electricity price at that time.

[0073] The changes in electricity demand over different time periods can be correlated with changes in prices over those time periods using an elasticity matrix.

[0074] Combining the above formula, the electrical load after comprehensive demand response is obtained as follows:

[0075]

[0076] In the formula, q n Δq represents the load power in the n time periods prior to the demand-side response; n This represents the change in load power consumption over a period of n after the demand-side response.

[0077] Considering that natural gas has the same commodity attributes as electricity, and analogous to the time-of-use pricing method for electricity load mentioned above, the method for calculating natural gas load is as follows:

[0078]

[0079] Finally, to prevent peak-valley reversal, the ratio of peak-valley electricity price to gas price should be limited to a certain range, namely:

[0080] The incentive-based demand response (IBDR) discussed in this embodiment mainly targets incentive modes for transferable and reduceable loads. The IBDR model is characterized by changing only the load structure, keeping the electricity price unchanged, adding compensation costs to users to the economic indicators, and not considering price factors for cooling and heating loads. Therefore, the expression for load changes before and after the response is as follows:

[0081]

[0082]

[0083] Where, ω TR,t and H TR,t These are the transferable load indicator and the transferable load amount, respectively. ω CD,t and H CD,t These are the load reduction indicator and the load reduction amount, respectively.

[0084] During peak energy consumption periods, energy supply pressure can be alleviated by reducing unnecessary loads. However, to avoid frequent load reductions affecting user comfort, the load that can be reduced is limited by the maximum amount that can be reduced and the maximum number of reductions. It should also be subject to the maximum and minimum duration of reductions, as follows:

[0085]

[0086]

[0087]

[0088]

[0089] in, and These represent the upper and lower limits of the reduction, respectively; That's the largest reduction; and These are the maximum and minimum reduction durations, respectively.

[0090] Transferable loads can be shifted from peak price periods to lower price periods, subject to the following constraints:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] in, and These represent the load's entry and exit states at time t, respectively. and These are the upper and lower limits for load transfer; and These are the upper and lower limits for load transfer. and These represent the input and output power of the load at time t, respectively. This represents the maximum number of transfers allowed. This is the minimum transfer time.

[0098] Finally, the load factor in the overall demand response should also comply with the peak-to-valley difference constraint:

[0099]

[0100] The dual demand response strategy refers to setting different incentive subsidies on top of the day-ahead demand response to encourage users to further participate in demand-side response during the intraday phase and reduce peak energy consumption. Since electricity and gas loads adopt a price-based IDR strategy during the day-ahead phase, a compensation mechanism based on day-ahead time-of-use electricity and gas prices is used during the intraday phase to incentivize users to make further load adjustments.

[0101]

[0102] Adjustments to heating and cooling loads affect user comfort. Because users' comfort requirements for hot water and room temperature are somewhat ambiguous—meaning smaller load fluctuations have a smaller impact on user comfort, and larger load fluctuations have a greater impact—the impact of heating and cooling load adjustments on user comfort is non-linear. Therefore, a tiered compensation approach is adopted to provide incentive subsidies for adjustments to user heating and cooling loads, namely:

[0103]

[0104] In this embodiment, a typical integrated energy system case from a community in Xiong'an New Area was selected for analysis and research. Its structural diagram is shown below. Figure 3 As shown. A typical day during the transition season was selected as a sample, and the forecast curves for photovoltaic, wind turbine, and various loads on that day are shown below. Figure 4 As shown in the figure. Both PV and WT are in maximum power point tracking mode to promote the consumption of renewable energy. The energy prices in the case are shown in Table 2. The efficiency curves fitted to each device are shown in the figure. Figure 5 As shown.

[0105] Table 2 Energy Price List

[0106]

[0107] In the current stage, this example establishes a model to solve the problem based on minimizing economic costs and achieves a multi-dimensional energy supply and demand balance under constant operating conditions. The output results of the energy equipment are shown in Figure 6. The analysis is based on the scheduling results and energy prices as follows. For the supply and demand of the electrical load in Figure 6(a), when the electricity price is low, the system chooses to purchase most of the electricity from the grid. When the electricity price is high, the electricity demand is mainly met by gas turbines, renewable energy, and battery discharge. Excess electricity is sold back to the main grid to generate revenue. For the heat and cold loads in Figures 6(b) and 6(d), most of the heat is supplied by the excess heat generated by the gas turbine generator. When the electricity price is low, heat pumps are used extensively to supplement the heat demand, and electric chillers are used to meet the cold demand. At other times, absorption chillers and cold storage tanks are used to supplement the cooling. When the natural gas price is low, the main heat supply is provided by gas boilers, and the remaining heat is supplied by thermal storage devices. For the natural gas load in Figure 6(c), the power-to-gas equipment uses off-peak electricity and wind power at night to produce a large amount of natural gas, realizing peak-valley power transfer.

[0108] Figure 7 shows the day-ahead optimal scheduling scheme considering varying equipment operating conditions. It can be seen that this scheme differs significantly from the day-ahead optimal scheduling scheme under constant operating conditions. Firstly, in the electricity load scheme, the gas turbine output is higher during off-peak hours compared to Figure 6. This is because the gas turbine's load rate and efficiency are very low during this period, resulting in higher electricity consumption costs. Therefore, the system chooses to increase power generation while reducing some purchased electricity. During peak electricity price periods, the cost of purchasing electricity from the grid is higher, so the gas turbine output is increased to supply part of the electricity load. However, due to the heat-driven power supply strategy, it cannot supply the entire electricity load, and a small amount of electricity still needs to be purchased from the grid. In the heat load scheme, the gas turbine output increases from 1:00 to 7:00, and the waste heat from the gas turbine is used to increase heating. The shortfall is supplemented by heating from the more efficient heat pump. Similarly, from 15:00 to 24:00, the heat pump supplements the heating. Compared to gas boilers, the heat pump has a higher load rate during this period and can supply more heat at a higher efficiency. When the electricity price is high, the heat storage tank releases heat. In the cooling load scheme, since most of the waste heat from the gas turbine is used for heating, electric chillers are used more frequently. When electricity prices are high, absorption chillers are used more often. Between 8:00 PM and 11:00 PM, the absorption chiller load rate is very low, resulting in low cooling efficiency, yet it consumes a large amount of waste heat, making it economically unfeasible. Therefore, electric chillers are used for cooling throughout the period. In the gas load scheme, the P2G equipment operates at full power. Due to variations in the output of the gas turbine and gas boiler, the output is higher during the night compared to Figure 6, but the natural gas supply decreases slightly after 3:00 PM.

[0109] The results of the day-ahead scheduling need to be used as constraints for the intraday rolling scheduling. During the intraday phase, the integrated energy system needs to formulate the daily scheduling plan 4 hours in advance to allow time for each unit to output power. Figure 8 shows the energy supply and demand scheduling results during the intraday rolling phase under varying equipment operating conditions. It can be seen that the model in the intraday scheduling phase also achieves a balance between the supply and demand of multiple energy sources, but the scheduling results in the intraday rolling phase and the day-ahead phase are slightly different.

[0110] Based on real-time scheduling plans, the system can change the operating status of power units through rapid response, achieving real-time correction of power imbalances between energy supply and demand on a very short timescale. The power imbalance in the real-time phase is mainly caused by fluctuations in PV, WT, and power load, and can be suppressed by adjusting the power components (electricity purchase, power supply, and energy storage). Figure 9 The comparison of power dispatch results during the intraday rolling and real-time phases shows slight changes in purchased electricity, but at this point the predicted values ​​for wind and solar power output and electrical load are very close to the actual values, enabling coordinated dispatch and real-time correction of power components in the integrated energy system.

[0111] In energy management, the participation of demand-side resources in scheduling operations is crucial, and adopting a source-load interactive control strategy can comprehensively improve optimization results. Taking the system constructed in this invention as an example, the response capabilities of various loads are analyzed. The peak and valley periods of the original load are corrected. According to the types in Table 1, as shown in Figure 10, price-based demand response can guide part of the user's energy consumption curve from the peak period to the valley period, realizing the horizontal migration of user load. Furthermore, according to the incentive protocol, the power of part of the load during the corresponding period can be interrupted, realizing the vertical reduction of user load. Under the stimulation of day-ahead and intraday dual demand response, users can be further incentivized to transfer and reduce load. In summary, under the stimulation of price signals and incentive signals, the demand side can change its energy consumption curve, fully utilizing the peak-shaving capacity of the system operation.

[0112] The above descriptions of the embodiments are merely for the purpose of helping to understand the method and core ideas of the present invention. Based on the ideas of the present invention, there may be changes in specific implementation methods and application scope. The content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-time-scale optimization scheduling method for a comprehensive energy system considering varying equipment operating conditions, characterized in that, include: Considering the energy input, production, conversion, storage, and consumption stages in a regional integrated energy system, and taking into account the variable operating conditions of equipment, a dynamic energy hub model including electricity-to-gas conversion equipment is constructed. This dynamic energy hub model is used to coordinate the supply and demand balance of multiple energy flows. The dynamic energy hub model is as follows: ； In the formula, , , ···, The elements are the collection of energy forms, namely electricity, heat, cold, and gas. This is the output power vector; For the input power vector; This is the matrix of attribution coefficients for energy storage devices; This represents the actual charge / discharge vector of the energy storage device, with charging the device being positive and discharging the device being negative. f ( ) is a function of the efficiency of the energy conversion equipment. The load rate of energy conversion equipment; Based on the dynamic energy hub model and different load forecasting scales, a multi-time-scale rolling optimization scheduling model for integrated energy systems is established, which combines the dynamic energy hub model with multi-time-scale optimization. Within the framework of the multi-timescale rolling optimization scheduling model for the integrated energy system, the upper-level layer performs scheduling of the entire integrated energy system one day in advance at 24:00 every day, with a time scale of 1 hour; the middle layer performs rolling scheduling of the integrated energy system for the next 4 hours, with a time scale of 15 minutes; and the lower-level layer formulates real-time adjustment plans for the power component of the integrated energy system, with a time scale of 5 minutes. Based on the comprehensive demand response of the day-ahead scheduling phase and the intraday rolling scheduling phase, a dual demand response model is established, and a mixed integer nonlinear programming model is constructed based on the multi-timescale rolling optimization scheduling model of the integrated energy system and the dual demand response model. The mixed-integer nonlinear programming model is transformed into a mixed-integer linear programming model using the incremental linearization method, and then the mixed-integer linear programming model is solved. Solving the mixed-integer linear programming model specifically includes: By using the Gurobi solver in MATLAB and calling the Yalmip toolbox, a mixed-integer linear programming model is solved to obtain the day-ahead scheduling scheme, intraday rolling optimization operation scheme, and intraday real-time optimization operation scheme of the integrated energy system. It also includes a comparison operation scheme between constant operating conditions and variable operating conditions.

2. The multi-time-scale optimization scheduling method for a comprehensive energy system considering varying equipment operating conditions as described in claim 1, characterized in that, For each energy conversion process, the dynamic energy hub model can iteratively correct the corresponding conversion efficiency in the traditional energy hub model in real time through the corresponding energy conversion equipment efficiency model.

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