Comprehensive energy system rolling optimization scheduling method and device based on frequency domain similarity

Abstract

The invention provides an integrated energy system rolling optimization scheduling method and device based on frequency domain similarity, and belongs to the technical field of integrated energy system operation regulation and control. The method comprises the following steps: constructing an integrated energy system optimization scheduling model; generating a typical scene set, and solving the optimal scheduling model under each scene of the typical scene set to obtain a reference scheduling plan corresponding to each scene; the frequency domain similarity between an actual operation scene and each scene in the typical scene set is calculated by acquiring actual operation data of a scheduling period, and then the frequency domain similarity is utilized to perform rolling correction on the reference scheduling plan. According to the method, the similarity index is taken as the estimated value of the scene probability, and the reference scheduling plan is corrected through rolling scheduling, so that the coping capacity of the scheduling result to uncertainty can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of comprehensive energy system operation regulation, and particularly relates to a comprehensive energy system rolling optimization scheduling method and device based on frequency domain similarity. BACKGROUND

[0002] A comprehensive energy system is an energy system coupling multiple heterogeneous energy flows such as electricity, heat, cold, and gas (natural gas / hydrogen). Optimized scheduling using the multi-energy flow coordination characteristics in a comprehensive energy system is a powerful way to improve system energy efficiency, reduce energy supply costs, and accommodate new energy. However, under the uncertainty and volatility of new energy and multi-energy loads, it is difficult to obtain accurate prediction information in advance, and rolling correction of the scheduling plan is usually needed based on actual new energy output and load energy consumption to ensure the effectiveness of the scheduling strategy.

[0003] Current research on rolling optimization of comprehensive energy systems mostly only considers the direct coupling of long-time-scale and short-time-scale scheduling plans, without considering correction of the scheduling plan based on actual operation data. For example, the invention patent with application number 202310369874.3 first performs long-period source and load energy matching evaluation and optimization, then considers prediction information, and performs day-ahead stochastic optimization scheduling based on conditional risk value, without further correction of the scheduling plan based on actual operation data. For example, the invention patent with application number 202411844877.9 uses the short-time-scale hydrogen storage amount obtained by lower-level solving as the initial hydrogen storage amount of the long-time-scale energy scheduling model, but also only targets prediction data and does not involve a rolling correction method. The scheduling results of the above-mentioned existing methods lack the ability to respond to uncertainty. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art and provide a comprehensive energy system rolling optimization scheduling method and device based on frequency domain similarity. The present application considers the uncertainty of new energy output and load, corrects the baseline scheduling plan, and gradually eliminates the influence of uncertainty to improve system energy efficiency, reduce energy supply costs, and increase the ability to accommodate new energy.

[0005] The first aspect of the present application provides a comprehensive energy system rolling optimization scheduling method based on frequency domain similarity, comprising:

[0006] constructing a comprehensive energy system optimization scheduling model;

[0007] generating a set of typical scenarios, solving the optimization scheduling model under each scenario of the set of typical scenarios, and obtaining a baseline scheduling plan corresponding to each scenario;

[0008] The actual operation data of a scheduling period is acquired, a frequency domain similarity of an actual operation scene and each scene in the typical scene set is calculated, and then the reference scheduling plan is corrected by using the frequency domain similarity.

[0009] In one specific embodiment of the application, the integrated energy system optimization scheduling model is composed of an objective function and constraint conditions.

[0010] The objective function is to minimize the total operation cost of the integrated energy system, and is expressed as follows:

[0011]

[0012] In the formula, t is the number of a scheduling period; Y is a set composed of all scheduling periods, Y={1, 2,..., N T}, N T is the total number of scheduling periods; is the active power value of the tie line of the integrated energy system in the scheduling period t; Δt is the time interval between adjacent scheduling periods; i is the number of any device in the integrated energy system; S D is the set of all devices in the integrated energy system; S G is the set of all thermal power units in the integrated energy system, S W is the set of all new energy power generation units in the integrated energy system, S CHP is the set of all cogeneration units in the integrated energy system, S GB is the set of all gas boilers in the integrated energy system, S AC is the set of all absorption units in the integrated energy system, S EB is the set of all electric boilers in the integrated energy system, S EC is the set of all electric refrigerators in the integrated energy system; is the electricity purchase price in the scheduling period t, c s,i is the start-up cost of the device i, is the operation cost of the device i; is a 0-1 variable representing the start-up action of the device i in the scheduling period t, and if the device i is switched from the shutdown state to the start-up state in the scheduling period t, then is 1, otherwise is 0; is a 0-1 variable representing the operation state of the device i in the scheduling period t, and if the device i is in the start-up state in the scheduling period t, then is 1, and if the device i is in the shutdown state in the scheduling period t, then is 0; is the power generated by the device i in the scheduling period t;

[0013] when i∈S G ∪S GB ∪S AC when i∈S i is the operation cost coefficient of a thermal power unit, a gas boiler or an absorption unit;

[0014] when i∈S CHP when i∈S E,i and c H,i are the power generation cost coefficient and the heat supply cost coefficient of the cogeneration unit i respectively; is the active power and the heat power generated by the cogeneration unit i in the dispatching period t;

[0015] The constraint conditions include:

[0016] The equipment operation state constraint:

[0017]

[0018] wherein, is a 0-1 variable representing the shutdown action of the equipment i in the dispatching period t, if the equipment i is turned off from the on state to the off state in the dispatching period t, then is 1, otherwise is 0;

[0019] The equipment operation constraint:

[0020]

[0021] wherein, P i,min and P i,max are the upper limit and the lower limit of the power generated by the equipment i respectively; R i,up and R i,down are the upward ramp rate and the downward ramp rate of the equipment i respectively; are 0-1 variables representing whether the equipment i is in the process of starting up or shutting down at time t respectively;

[0022] The new energy power generation unit operation constraint:

[0023]

[0024] wherein, P t i,pre is the predicted value of the active power generated by the new energy power generation unit i in the dispatching period t;

[0025] The cogeneration unit operation constraint:

[0026]

[0027] wherein, EP iS i,k and H i,k are the active power value and the thermal power value of the kth endpoint of the feasible region of the cogeneration unit i, respectively; is the kth combination coefficient of the cogeneration unit i at the dispatching period t;

[0028] Battery operation constraints:

[0029]

[0030]

[0031] where S ES is the set of all energy storages in the integrated energy system; and are the charging power and the discharging power of the battery i at the dispatching period t, respectively; is the state of charge of the battery i at the dispatching period t;P c,i,max and P dc,i,max are the maximum value of the charging power and the maximum value of the discharging power of the battery i, respectively;E i,min and E i,max are the minimum value and the maximum value of the state of charge of the battery i, respectively;η c,i and η dc,i are the charging efficiency and the discharging efficiency of the battery i, respectively, is the state of charge of the battery i at the dispatching period t-1;s i is the self-discharge rate of the battery i; are the state of charge, the charging power and the discharging power of the battery i at the first dispatching period, respectively, is the initial state of charge of the battery i stored at the initial time before dispatching;

[0032] Cooling load and heating load constraints:

[0033]

[0034] where, is the thermal capacity of the heating load i or the cooling load i; is the indoor temperature of the heating load i or the cooling load i at the dispatching period t; is the indoor temperature of the heating load i or the cooling load i at the dispatching period t-1;U i is the thermal conductance of the heating load i or the cooling load i; is the ambient temperature at the dispatching period t;τ i,min and τ i,max are the minimum value and the maximum value of the indoor temperature of the heating load i or the cooling load i, respectively; is the thermal power consumed by the heating load i;S HL is the set of all heating loads in the integrated energy system, SCL is a set of all cooling loads in the integrated energy system;

[0035] Energy balance constraints:

[0036]

[0037] wherein, η EB,i is the heating coefficient of the electric boiler i, COP i is the refrigeration coefficient of the electric refrigerator i; P t L,i is the active power consumed by the electric load i in the dispatching period t, S L is a set of all electric loads in the integrated energy system;

[0038] Unit start-stop process state variable constraints:

[0039]

[0040] wherein, are the maximum shutdown times and the maximum startup times of the equipment i in the entire dispatching period, respectively; are the minimum single startup and shutdown times of the equipment i, respectively; is a continuous variable representing the time when the current equipment i is closed, is the maximum continuous shutdown time of the equipment i are the times when the equipment i is in the startup process and the shutdown process, respectively;

[0041] Unit start-stop process operation constraints:

[0042]

[0043] wherein, M is a positive number; R i,su and R i,sd are the startup rate and the shutdown rate of the equipment i, respectively; are the minimum and maximum power limits in the startup process of the equipment i, respectively; are the minimum and maximum power limits in the shutdown process of the equipment i, respectively.

[0044] In one specific embodiment of the present application, the obtaining of the reference dispatching plan corresponding to each scenario comprises:

[0045] the predicted power P t i,pre of the new energy in each typical scenario in the typical scenario set is normalized to obtain the normalized new energy predicted power P t L,i the electric load power P Substitute the parameters into the optimized scheduling model, and then solve the optimized scheduling model to obtain the baseline scheduling plan corresponding to this scenario. This represents the baseline scheduling plan corresponding to the m-th scenario.

[0046] In one specific embodiment of the present invention, the calculation of the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set includes...

[0047] 1) Obtain the time T from the start of the scheduling period to the current time. now Discrete-time series of actual available power of new energy sources Discrete-time series of actual operating power of electrical load Discrete time series of actual ambient temperature values

[0048] 2) The discrete time series obtained in step 1) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0049]

[0050] Where j is the imaginary unit;

[0051] 3) For each scene m in the typical scene set, extract the first T segments of that scene. now The predicted power of new energy sources P at each time point t i,pre Electrical load power P t L,i Ambient temperature The discrete-time series of predicted new energy power in this scenario were generated respectively. Discrete Time Series of Electrical Load Power Discrete time series of ambient temperature

[0052] 4) The discrete time series obtained in step 3) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0053]

[0054] in, These represent the predicted power of new energy sources, the power of electrical load, and the ambient temperature at time n under scenario m, respectively.

[0055] 5) Based on the results of steps 1) to 4), calculate the frequency domain similarity between the actual running scenario and all scenarios in the typical scenario set;

[0056] Wherein, the calculation process of the actual sequence and the frequency domain similarity of scene m is as follows:

[0057] The frequency domain distance of the new energy actual available power discrete time sequence and the new energy predicted power discrete time sequence under scene m is calculated:

[0058]

[0059] Wherein, f c is the number of Fourier series considered in the calculation, E(·) represents the energy of the time sequence, represents the frequency domain distance of the Fourier series of the discrete time sequence corresponding to the new energy actual available power and the new energy predicted power under scene m respectively, and the expression is as follows:

[0060]

[0061] Wherein, and [k] represents the kth term of the Fourier series , a k , b k are the modules of the kth term of the two Fourier series respectively, θ k , are the phase angles of the kth term of the two Fourier series respectively, and j is the imaginary unit;

[0062] The frequency domain distance of the electric load actual running power discrete time sequence and the electric load power discrete time sequence under scene m is calculated:

[0063]

[0064] Wherein, represents the frequency domain distance of the Fourier series of the discrete time sequence corresponding to the electric load actual running power and the electric load power under scene m respectively;

[0065] The frequency domain distance of the actual environment temperature value discrete time sequence and the environment temperature discrete time sequence under scene m is calculated:

[0066]

[0067] Wherein, represents the frequency domain distance of the Fourier series of the discrete time sequence corresponding to the actual environment temperature value and the environment temperature under scene m respectively;

[0068] The frequency domain similarity of the actual running scene and scene m is calculated:

[0069]

[0070] In a specific embodiment of the present application, the rolling correction of the reference scheduling plan comprises:

[0071]

[0072] In the formula, is the corrected scheduling plan, and Ω is a set of typical scenarios.

[0073] The second aspect of the present application provides a comprehensive energy system rolling optimization scheduling device based on frequency domain similarity, comprising:

[0074] A reference scheduling plan generation module is configured to generate a set of typical scenarios, solve the optimization scheduling model under each scenario of the set of typical scenarios, and obtain a reference scheduling plan corresponding to each scenario;

[0075] A rolling optimization module is configured to obtain actual operation data of a scheduling period, calculate the frequency domain similarity between an actual operation scenario and each scenario in the set of typical scenarios, and then use the frequency domain similarity to perform rolling correction on the reference scheduling plan.

[0076] In a specific embodiment of the present application, the comprehensive energy system optimization scheduling model is composed of an objective function and constraint conditions;

[0077] The objective function is to minimize the total operation cost of the comprehensive energy system, and the expression is as follows:

[0078]

[0079] In the formula, t is the number of scheduling periods; Y is a set composed of all scheduling periods, Y = {1, 2,..., N T}, N T is the total number of scheduling periods; is the active power value of the tie line of the comprehensive energy system in the scheduling period t; Δt is the time interval between adjacent scheduling periods; i is the number of any device in the comprehensive energy system; S D is the set of all devices in the comprehensive energy system; S G is the set of all thermal power units in the comprehensive energy system, S W is the set of all new energy power generation units in the comprehensive energy system, S CHP is the set of all cogeneration units in the comprehensive energy system, S GB is the set of all gas boilers in the comprehensive energy system, S AC is the set of all absorption units in the comprehensive energy system, S EB is the set of all electric boilers in the comprehensive energy system, S ECIt is a collection of electric chillers together in an integrated energy system; Let c be the electricity purchase price during the dispatch period t. s,i The startup cost of device i, The operating cost of device i; To characterize the 0-1 variables representing the power-on action of device i during scheduling period t, if device i changes from a power-off state to a power-on state during scheduling period t, then... The value is 1, otherwise The value is 0; To represent the 0-1 variables of the operating state of device i during scheduling period t, if device i is in the powered-on state during scheduling period t, then... The value is 1. If device i is in a powered-off state during the scheduling period t, then... The value of is 0; Let i be the power generated by device i during the scheduling period t;

[0080] When i∈S G ∪S GB ∪S AC At that time, c i This is the operating cost coefficient for thermal power units, gas-fired boilers, or absorption units.

[0081] When i∈S CHP At that time, c E,i and c H,i These are the power generation cost coefficient and the heating cost coefficient of cogeneration unit i, respectively. The active power and thermal power generated by cogeneration unit i during the dispatch period t;

[0082] The constraints include:

[0083] Equipment operating status constraints:

[0084]

[0085] In the formula, To characterize the 0-1 variable representing the shutdown action of device i during scheduling period t, if device i changes from an on state to a off state during scheduling period t, then... The value is 1, otherwise... The value is 0;

[0086] Equipment operating constraints:

[0087]

[0088] In the formula, P i,min and P i,max R represents the upper and lower limits of the power generated by device i, respectively; i,up and R i,downThese are the upward ramp rate and downward ramp rate of device i, respectively; These are 0-1 variables representing whether device i is in the startup or shutdown process at time t;

[0089] Operating constraints of new energy generator sets:

[0090]

[0091] In the formula, P t i,pre This is the predicted value of the active power generated by the new energy generator unit i during the dispatch period t;

[0092] Operating constraints of combined heat and power units:

[0093]

[0094] In the formula, EP i P is the set of feasible domain endpoints for cogeneration unit i; i,k and H i,k These are the active power and thermal power values ​​at the k-th endpoint of the feasible region of cogeneration unit i, respectively. Let i be the k-th combination coefficient of cogeneration unit i during the scheduling period t;

[0095] Battery operating constraints:

[0096]

[0097] In the formula, S ES It is the collection of all energy storage systems in an integrated energy system; and These represent the charging power and discharging power of battery i during the scheduling period t, respectively. P represents the charge level of battery i during the scheduling period t. c,i,max and P dc,i,max These represent the maximum charging power and the maximum discharging power of battery i, respectively; E i,min and E i,max η represents the minimum and maximum values ​​of the battery capacity i, respectively; c,i and η dc,i Here, represents the charging efficiency and discharging efficiency of battery i, respectively, and represents the charge level of battery i during the scheduling period t-1; s i Let i be the self-loss rate of battery i; These represent the battery's charge level, charging power, and discharging power during the first scheduling period, respectively. The amount of electricity stored in battery i at the initial moment before scheduling;

[0098] Cooling and heating load constraints:

[0099]

[0100] In the formula, The heat capacity of heat load i or cold load i; The indoor temperature of heat load i or cooling load i during the scheduling period t; U represents the indoor temperature of heat load i or cooling load i during the scheduling period t-1; i The thermal conductivity is the heat load i or the cooling load i. τ represents the ambient temperature during the scheduling period t; i,min and τ i,max These represent the minimum and maximum values ​​of the indoor temperature, respectively, for heat load i or cooling load i. The heat power consumed by heat load i; S HL S is the set of all heat loads in an integrated energy system. CL It is the collection of all cooling loads in an integrated energy system;

[0101] Energy balance constraints:

[0102]

[0103] In the formula, η EB,i Let COP be the coefficient of performance of electric boiler i. i P is the coefficient of performance (COP) of the electric chiller i. t L,i S represents the active power consumed by electrical load i during the dispatch period t. L It is the collection of all electrical loads within a comprehensive energy system;

[0104] State variable constraints during unit start-up and shutdown:

[0105]

[0106] In the formula, These represent the maximum number of times device i is shut down and the maximum number of times it is powered on during the entire scheduling period; These are the minimum single power-on and power-off times for device i, respectively; As a continuous variable representing the time that device i has been off, Maximum continuous shutdown time for device i These represent the time when device i is in the startup and shutdown processes, respectively.

[0107] Operating constraints during unit start-up and shutdown:

[0108]

[0109] Where M is a positive number; R i,su and R i,sdThese are the power-on rate and power-off rate of device i, respectively; These are the minimum and maximum power limits during the startup process of device i, respectively; These are the minimum and maximum power limits for device i during the stopping process, respectively.

[0110] In a specific embodiment of the present invention, obtaining the baseline scheduling plan corresponding to each scenario includes:

[0111] The predicted new energy power P in each of the typical scenarios is set together. t i,pre Electrical load power P t L,i Ambient temperature Substitute the parameters into the optimized scheduling model, and then solve the optimized scheduling model to obtain the baseline scheduling plan corresponding to this scenario. This represents the baseline scheduling plan corresponding to the m-th scenario.

[0112] In one specific embodiment of the present invention, the calculation of the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set includes...

[0113] 1) Obtain the time T from the start of the scheduling period to the current time. now Discrete-time series of actual available power of new energy sources Discrete-time series of actual operating power of electrical load Discrete time series of actual ambient temperature values

[0114] 2) The discrete time series obtained in step 1) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0115]

[0116] Where j is the imaginary unit;

[0117] 3) For each scene m in the typical scene set, extract the first T segments of that scene. now The predicted power of new energy sources P at each time point t i,pre Electrical load power P t L,i Ambient temperature The discrete-time series of predicted new energy power in this scenario were generated respectively. Discrete Time Series of Electrical Load Power Discrete time series of ambient temperature

[0118] 4) The discrete time series obtained in step 3) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0119]

[0120] in, These represent the predicted power of new energy sources, the power of electrical load, and the ambient temperature at time n under scenario m, respectively.

[0121] 5) Based on the results of steps 1) to 4), calculate the frequency domain similarity between the actual running scenario and all scenarios in the typical scenario set;

[0122] The calculation process for the frequency domain similarity between the actual sequence and scene m is as follows:

[0123] Calculate the frequency domain distance between the discrete-time series of actual available new energy power and the discrete-time series of predicted new energy power under scenario m:

[0124]

[0125] Among them, f c E(·) represents the energy of the time series, where E is the number of terms in the Fourier series considered in the calculation. The frequency domain distance between the Fourier series of the discrete time series corresponding to the actual available power of new energy and the predicted power of new energy under scenario m is expressed as follows:

[0126]

[0127] in, and [k] represents the Fourier series. The kth term, a k b k θ are the moduli of the k-th term of the two Fourier series, respectively. k , , respectively, are the phase angles of the k-th terms of the two Fourier series, where j is the imaginary unit;

[0128] Calculate the frequency domain distance between the discrete-time series of the actual operating power of the electrical load and the discrete-time series of the electrical load power under scenario m:

[0129]

[0130] in, This represents the frequency domain distance between the Fourier series of the discrete time series corresponding to the actual operating power of the electrical load and the power of the electrical load under scenario m.

[0131] Calculate the frequency domain distance between the discrete-time series of actual ambient temperature values ​​and the discrete-time series of ambient temperature values ​​in scenario m:

[0132]

[0133] in, This represents the frequency domain distance between the actual ambient temperature and the Fourier series of the discrete time series corresponding to the ambient temperature in scene m.

[0134] Calculate the frequency domain similarity between the actual running scenario and scenario m:

[0135]

[0136] In a specific embodiment of the present invention, the step of rolling correction of the baseline scheduling plan includes:

[0137]

[0138] In the formula, This is the revised scheduling plan, and Ω represents the typical scenario set.

[0139] A third aspect of the present invention provides an electronic device comprising:

[0140] At least one processor; and a memory communicatively connected to said at least one processor;

[0141] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described rolling optimization scheduling method for an integrated energy system based on frequency domain similarity.

[0142] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described rolling optimization scheduling method for an integrated energy system based on frequency domain similarity.

[0143] The features and beneficial effects of this invention are as follows:

[0144] This invention uses a similarity index as an estimate of scene probability, and then corrects the baseline scheduling plan through rolling scheduling. This addresses the challenges of traditional Euclidean distance-based time series analysis methods failing to accurately capture curve similarity, and dynamic time warping (DTW) methods struggling to distinguish time shift differences and handle noise. This invention effectively improves the scheduling results' ability to cope with uncertainty. Attached Figure Description

[0145] Figure 1This is an overall flowchart of a rolling optimization scheduling method for a comprehensive energy system based on frequency domain similarity, according to an embodiment of the present invention. Detailed Implementation

[0146] This invention proposes a rolling optimization scheduling method and apparatus for a comprehensive energy system based on frequency domain similarity, which will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0147] A first aspect of this invention proposes a rolling optimization scheduling method for a comprehensive energy system based on frequency domain similarity, comprising:

[0148] Construct an integrated energy system optimization scheduling model;

[0149] Generate a set of typical scenarios, and solve the optimized scheduling model under each scenario in the set of typical scenarios to obtain the baseline scheduling plan for each scenario;

[0150] By acquiring actual operating data of the scheduling cycle, the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set is calculated, and then the frequency domain similarity is used to perform rolling correction on the baseline scheduling plan.

[0151] In a specific embodiment of the present invention, the overall process of the integrated energy system rolling optimization scheduling method based on frequency domain similarity is as follows: Figure 1 As shown, it includes the following steps:

[0152] 1) Construct an optimized scheduling model for the integrated energy system, which consists of an objective function and constraints. The objective function minimizes the total operating cost of the integrated energy system. The constraints include start-up and shutdown related constraints such as unit start-up and shutdown processes, maximum continuous shutdown time, and minimum start-up time. The specific steps are as follows:

[0153] 1-1) Construct the objective function of the integrated energy system optimization scheduling model.

[0154] In this embodiment, the objective function minimizes the total operating cost of the integrated energy system while satisfying various constraints.

[0155]

[0156] In the formula, t is the number of the scheduling period; Υ is the set of all scheduling periods, Υ={1,2,...,N T}, N T N represents the total number of scheduling periods. For example, for daytime scheduling, each scheduling period lasts 15 minutes, then N = 10 ... T It is 96; S represents the active power value of the tie line in the integrated energy system during dispatch period t; Δt represents the time interval between adjacent dispatch periods; i represents the number of any device in the integrated energy system; S D S is the collection of all equipment in an integrated energy system. G S is the set of all thermal power units in an integrated energy system. W S is the set of all new energy generator units in an integrated energy system. CHP S is the collection of all combined heat and power units in an integrated energy system. GB S is the collection of all gas-fired boilers in an integrated energy system. AC S is the collection of all absorption chillers in an integrated energy system. EB S is the collection of all electric boilers in an integrated energy system. EC It is a collection of electric chillers together in an integrated energy system; Let c be the electricity purchase price during the dispatch period t. s,i The startup cost of device i, The operating cost of device i; To characterize the 0-1 variables representing the power-on action of device i during scheduling period t, if device i changes from a power-off state to a power-on state during scheduling period t, then... The value is 1, otherwise The value is 0; To represent the 0-1 variables of the operating state of device i during scheduling period t, if device i is in the powered-on state during scheduling period t, then... The value is 1. If device i is in a powered-off state during the scheduling period t, then... The value of is 0. Let be the power generated by device i during the scheduling period t.

[0157] When i∈S G ∪S GB ∪S AC At that time, c i This refers to the operating cost coefficient for traditional thermal power units, gas-fired boilers, or absorption turbine units; for thermal power units, For thermal power unit i, the active power generated during dispatch period t; for gas-fired boilers, Let be the thermal power generated by gas-fired boiler i during the dispatch period t; for absorption chillers, Let be the thermal power generated by absorption chiller unit i during the dispatch period t. When i∈S CHP At that time, c E,i and c H,i These are the power generation cost coefficient and the heating cost coefficient of cogeneration unit i, respectively. and These represent the active power and thermal power generated by cogeneration unit i during the dispatch period t, respectively.

[0158] 1-2) Construct constraints for the integrated energy system optimization scheduling model, including:

[0159] Equipment operating status constraints:

[0160]

[0161] In the formula, To characterize the 0-1 variable representing the shutdown action of device i during scheduling period t, if device i changes from an on state to a off state during scheduling period t, then... The value is 1, otherwise... The value is 0.

[0162] Equipment operating constraints:

[0163]

[0164] In the formula, P i,min and P i,max These represent the upper and lower limits of the power generated by device i, respectively; for thermal power units, P i,min and P i,max These represent the lower and upper limits of the active power generated by thermal power unit i, respectively; for gas-fired boilers, P... i,min and P i,max These represent the lower and upper limits of the thermal power generated by gas-fired boiler i, respectively; for absorption chillers, P... i,min and P i,max These represent the lower and upper limits of the thermal power generated by absorption chiller i, respectively; for electric boilers, P... i,min and P i,max These represent the lower and upper limits of the electrical power consumed by electric boiler i, respectively; for electric chiller, P... i,min and P i,max These represent the lower and upper limits of the electrical power consumed by the electric chiller i, respectively. For electric boilers, Let be the electrical power consumed by electric boiler i during the scheduling period t; for electric chiller, R represents the electrical power consumed by the electric chiller i during the scheduling period t. i,up and R i,down These are the upward ramp rate and downward ramp rate of device i, respectively; These are 0-1 variables representing whether device i is in the startup or shutdown process at time t.

[0165] Operating constraints of new energy generator sets:

[0166]

[0167] In the formula, P t i,preThis is the predicted value of the active power generated by the new energy generator unit i during the dispatch period t.

[0168] Operating constraints of combined heat and power units:

[0169]

[0170] In the formula, EP i P is the set of feasible domain endpoints for cogeneration unit i; i,k and H i,k These are the active power and thermal power values ​​at the k-th endpoint of the feasible region of cogeneration unit i, respectively. Let be the k-th combination coefficient of cogeneration unit i during the scheduling period t.

[0171] Battery operating constraints:

[0172]

[0173] In the formula, S ES It is the collection of all energy storage systems in an integrated energy system; and These represent the charging power and discharging power of battery i during the scheduling period t, respectively. P represents the charge level of battery i during the scheduling period t. c,i,max and P dc,i,max These represent the maximum charging power and the maximum discharging power of battery i, respectively; E i,min and E i,max η represents the minimum and maximum values ​​of the battery capacity i, respectively; c,i and η dc,i Here, represents the charging efficiency and discharging efficiency of battery i, respectively, and represents the charge level of battery i during the scheduling period t-1; s i Let i be the self-loss rate of battery i; These represent the battery's charge level, charging power, and discharging power during the first scheduling period, respectively. This refers to the amount of electricity stored in battery i at the initial moment before scheduling.

[0174] Cooling and heating load constraints:

[0175]

[0176] In the formula, The heat capacity of heat load i or cold load i; The indoor temperature of heat load i or cooling load i during the scheduling period t; U represents the indoor temperature of heat load i or cooling load i during the scheduling period t-1; i The thermal conductivity is the heat load i or the cooling load i. τ represents the ambient temperature during the scheduling period t;i,min and τ i,max These represent the minimum and maximum values ​​of the indoor temperature, respectively, for heat load i or cooling load i. The heat power consumed by heat load i; S HL S is the set of all heat loads in an integrated energy system. CL It is the collection of all cooling loads in an integrated energy system.

[0177] Energy balance constraints:

[0178]

[0179]

[0180] In the formula, η EB,i Let COP be the coefficient of performance of electric boiler i. i P is the coefficient of performance (COP) of the electric chiller i. t L,i S represents the active power consumed by electrical load i during the dispatch period t. L It is the collection of all electrical loads within a comprehensive energy system.

[0181] State variable constraints during unit start-up and shutdown:

[0182]

[0183] In the formula, These represent the maximum number of times device i is shut down and the maximum number of times it is powered on during the entire scheduling period; These are the minimum single power-on and power-off times for device i, respectively; As a continuous variable representing the time that device i has been off, Maximum continuous shutdown time for device i These represent the time that device i is in the startup and shutdown processes, respectively.

[0184] Operating constraints during unit start-up and shutdown:

[0185]

[0186]

[0187] Where M is a very large positive number, greater than the maximum value among all unit capacities; R i,su and R i,sd These are the power-on rate and power-off rate of device i, respectively; These are the minimum and maximum power limits during the startup process of device i, respectively; These are the minimum and maximum power limits for device i during the stopping process, respectively.

[0188] 2) Generate a set of typical scenarios, and solve the model established in step 1) under each scenario of the typical scenario set to obtain the baseline scheduling plan corresponding to each scenario, including the following steps:

[0189] 2-1) Generate a set of typical scenarios.

[0190] In this embodiment, a set of scenarios is selected or generated based on historical operating data, prediction data, etc. Then, based on methods such as k-means clustering and hierarchical clustering, several typical scenarios are formed through scenario clustering and reduction to form a typical scenario set Ω. Each scenario in the typical scenario set contains the predicted power P of new energy sources. t i,pre Predicted power P of electrical load t L,i Ambient temperature That is, Ω = {π1, π2, ..., π} p}, π m Let Ω be the m-th scene in the typical scene set, where m is the scene number, and m = 1, 2, ..., p. The typical scene set Ω contains a total of p scenes.

[0191] 2-2) Based on the results of step 2-1), iterate through each typical scenario in the typical scenario set and calculate the predicted power P of the new energy source in that scenario. t i,pre Electrical load power P t L,i Ambient temperature Substitute the parameters into the corresponding positions in the model established in step 1) (i.e., equations (6), (16), (17), and (21)), and then solve the model. The optimal solution obtained is the baseline scheduling plan corresponding to this scenario. This includes the operating power, heating power, charging power, and discharging power of all energy storage devices. This represents the baseline scheduling plan corresponding to the m-th scenario.

[0192] In this embodiment, the baseline scheduling plan obtained in step 2) can provide a basis for subsequent rolling adjustments.

[0193] 3) By obtaining the actual running data of the scheduling cycle, calculate the frequency domain similarity between the actual running scenario and each scenario in the typical scenario set, and then use the frequency domain similarity to perform rolling correction on the baseline scheduling plan obtained in step 2).

[0194] In this embodiment, the predicted power P of the new energy source is utilized. t i,pre Electrical load power P t L,i Ambient temperature Using actual operational data with uncertainties, the baseline scheduling plan obtained in step 2) is rolled over and revised. The specific steps are as follows:

[0195] 3-1) Obtain the time T from the start of the scheduling period to the current time. now Discrete-time series of actual available power of new energy sources Discrete-time series of actual operating power of electrical load Discrete time series of actual ambient temperature values

[0196] 3-2) The discrete time series obtained in step 3-1) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0197]

[0198] Where j is the imaginary unit.

[0199] 3-3) For each scenario m∈Ω, extract the first T segments of that scenario. now The predicted power of new energy sources P at each time point t i,pre Electrical load power P t L,i Ambient temperature The discrete-time series of predicted new energy power in this scenario were generated respectively. Discrete Time Series of Electrical Load Power Discrete time series of ambient temperature

[0200] 3-4) The discrete time series obtained in step 3-3) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0201]

[0202] in, These represent the predicted power of new energy sources, the power of electrical load, and the ambient temperature at time n under scenario m.

[0203] 3-5) Based on the results of steps 3-1) to 3-4), calculate the frequency domain similarity between the actual running scenario and all scenarios in the typical scenario set.

[0204] In this embodiment, for any scene m in the typical scene set, the specific steps for calculating the frequency domain similarity between the actual sequence and scene m are as follows:

[0205] 3-5-1) Calculate the frequency domain distance between the discrete-time series of actual available new energy power and the discrete-time series of predicted new energy power under scenario m:

[0206]

[0207] Among them, f c E(·) represents the energy of the time series, where E is the number of terms in the Fourier series considered in the calculation. The frequency domain distance between the Fourier series of the discrete time series corresponding to the actual available power of new energy and the predicted power of new energy under scenario m is expressed as follows:

[0208]

[0209] in, and [k] represents the Fourier series. The kth term, a k b k θ are the moduli of the k-th term of the two Fourier series, respectively. k , are the phase angles of the k-th terms of the two Fourier series, respectively, and j is the imaginary unit.

[0210] 3-5-2) Calculate the frequency domain distance between the discrete-time series of the actual operating power of the electrical load and the discrete-time series of the electrical load power under scenario m:

[0211]

[0212] in, This represents the frequency domain distance between the Fourier series of the discrete time series corresponding to the actual operating power of the electrical load and the power of the electrical load in scenario m.

[0213] 3-5-3) Calculate the frequency domain distance between the discrete-time series of the actual ambient temperature and the discrete-time series of the ambient temperature in scenario m:

[0214]

[0215] in, This represents the frequency domain distance between the actual ambient temperature and the Fourier series of the discrete time series corresponding to the ambient temperature in scene m.

[0216] 3-5-4) Based on the results of steps 3-5-1)-3-5-3), calculate the frequency domain similarity between the actual running scenario and scenario m:

[0217]

[0218] 3-6) Based on the results of step 3-5), revise the baseline scheduling plan obtained in step 2):

[0219]

[0220] In the formula, The revised scheduling plan combines the current actual operating information with future prediction information, which can improve the overall optimization scheduling effect and improve the overall system operating efficiency.

[0221] To implement the above embodiments, a second aspect of the present invention proposes a rolling optimization scheduling device for a comprehensive energy system based on frequency domain similarity, comprising:

[0222] A baseline scheduling plan generation module is used to generate a set of typical scenarios, solve the optimized scheduling model under each scenario in the set of typical scenarios, and obtain the baseline scheduling plan corresponding to each scenario.

[0223] The rolling optimization module is used to obtain the actual running data of the scheduling cycle, calculate the frequency domain similarity between the actual running scenario and each scenario in the typical scenario set, and then use the frequency domain similarity to perform rolling correction on the baseline scheduling plan.

[0224] In one specific embodiment of the present invention, the integrated energy system optimization scheduling model consists of an objective function and constraints;

[0225] The objective function is to minimize the total operating cost of the integrated energy system, and its expression is as follows:

[0226]

[0227] In the formula, t is the number of the scheduling period; Υ is the set of all scheduling periods, Υ={1,2,...,N T}, N T This represents the total number of scheduling periods; S represents the active power value of the tie line in the integrated energy system during dispatch period t; Δt represents the time interval between adjacent dispatch periods; i represents the number of any device in the integrated energy system; S D S is the collection of all equipment in an integrated energy system. G S is the set of all thermal power units in an integrated energy system. W S is the set of all new energy generator units in an integrated energy system. CHP S is the collection of all combined heat and power units in an integrated energy system. GB S is the collection of all gas-fired boilers in an integrated energy system. AC S is the collection of all absorption chillers in an integrated energy system.EB S is the collection of all electric boilers in an integrated energy system. EC It is a collection of electric chillers together in an integrated energy system; Let c be the electricity purchase price during the dispatch period t. s,i The startup cost of device i, The operating cost of device i; To characterize the 0-1 variables representing the power-on action of device i during scheduling period t, if device i changes from a power-off state to a power-on state during scheduling period t, then... The value is 1, otherwise The value is 0; To represent the 0-1 variables of the operating state of device i during scheduling period t, if device i is in the powered-on state during scheduling period t, then... The value is 1. If device i is in a powered-off state during the scheduling period t, then... The value of is 0; Let i be the power generated by device i during the scheduling period t;

[0228] When i∈S G ∪S GB ∪S AC At that time, c i This is the operating cost coefficient for thermal power units, gas-fired boilers, or absorption units.

[0229] When i∈S CHP At that time, c E,i and c H,i These are the power generation cost coefficient and the heating cost coefficient of cogeneration unit i, respectively. The active power and thermal power generated by cogeneration unit i during the dispatch period t;

[0230] The constraints include:

[0231] Equipment operating status constraints:

[0232]

[0233] In the formula, To characterize the 0-1 variable representing the shutdown action of device i during scheduling period t, if device i changes from an on state to a off state during scheduling period t, then... The value is 1, otherwise... The value is 0;

[0234] Equipment operating constraints:

[0235]

[0236] In the formula, P i,min and P i,maxR represents the upper and lower limits of the power generated by device i, respectively; i,up and R i,down These are the upward ramp rate and downward ramp rate of device i, respectively; These are 0-1 variables representing whether device i is in the startup or shutdown process at time t;

[0237] Operating constraints of new energy generator sets:

[0238]

[0239] In the formula, P t i,pre This is the predicted value of the active power generated by the new energy generator unit i during the dispatch period t;

[0240] Operating constraints of combined heat and power units:

[0241]

[0242] In the formula, EP i P is the set of feasible domain endpoints for cogeneration unit i; i,k and H i,k These are the active power and thermal power values ​​at the k-th endpoint of the feasible region of cogeneration unit i, respectively. Let i be the k-th combination coefficient of cogeneration unit i during the scheduling period t;

[0243] Battery operating constraints:

[0244]

[0245] In the formula, S ES It is the collection of all energy storage systems in an integrated energy system; and These represent the charging power and discharging power of battery i during the scheduling period t, respectively. P represents the charge level of battery i during the scheduling period t. c,i,max and P dc,i,max These represent the maximum charging power and the maximum discharging power of battery i, respectively; E i,min and E i,max η represents the minimum and maximum values ​​of the battery capacity i, respectively; c,i and η dc,i Here, represents the charging efficiency and discharging efficiency of battery i, respectively, and represents the charge level of battery i during the scheduling period t-1; s i Let i be the self-loss rate of battery i; These represent the battery's charge level, charging power, and discharging power during the first scheduling period, respectively. The amount of electricity stored in battery i at the initial moment before scheduling;

[0246] Cooling and heating load constraints:

[0247]

[0248] In the formula, The heat capacity of heat load i or cold load i; The indoor temperature of heat load i or cooling load i during the scheduling period t; U represents the indoor temperature of heat load i or cooling load i during the scheduling period t-1; i The thermal conductivity is the heat load i or the cooling load i. τ represents the ambient temperature during the scheduling period t; i,min and τ i,max These represent the minimum and maximum values ​​of the indoor temperature, respectively, for heat load i or cooling load i. The heat power consumed by heat load i; S HL S is the set of all heat loads in an integrated energy system. CL It is the collection of all cooling loads in an integrated energy system;

[0249] Energy balance constraints:

[0250]

[0251] In the formula, η EB,i Let COP be the coefficient of performance of electric boiler i. i P is the coefficient of performance (COP) of the electric chiller i. t L,i S represents the active power consumed by electrical load i during the dispatch period t. L It is the collection of all electrical loads within a comprehensive energy system;

[0252] State variable constraints during unit start-up and shutdown:

[0253]

[0254] In the formula, These represent the maximum number of times device i is shut down and the maximum number of times it is powered on during the entire scheduling period; These are the minimum single power-on and power-off times for device i, respectively; As a continuous variable representing the time that device i has been off, Maximum continuous shutdown time for device i These represent the time that device i is in the startup and shutdown processes, respectively.

[0255] Operating constraints during unit start-up and shutdown:

[0256]

[0257]

[0258] Where M is a positive number; R i,su and R i,sd These are the power-on rate and power-off rate of device i, respectively; These are the minimum and maximum power limits during the startup process of device i, respectively; These are the minimum and maximum power limits for device i during the stopping process, respectively.

[0259] In a specific embodiment of the present invention, obtaining the baseline scheduling plan corresponding to each scenario includes:

[0260] The predicted new energy power P in each of the typical scenarios is set together. t i,pre Electrical load power P t L,i Ambient temperature Substitute the parameters into the optimized scheduling model, and then solve the optimized scheduling model to obtain the baseline scheduling plan corresponding to this scenario. This represents the baseline scheduling plan corresponding to the m-th scenario.

[0261] In a specific embodiment of the present invention, calculating the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set includes:

[0262] 1) Obtain the time T from the start of the scheduling period to the current time. now Discrete-time series of actual available power of new energy sources Discrete-time series of actual operating power of electrical load Discrete time series of actual ambient temperature values

[0263] 2) The discrete time series obtained in step 1) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0264]

[0265] Where j is the imaginary unit;

[0266] 3) For each scene m in the typical scene set, extract the first T segments of that scene. now The predicted power of new energy sources P at each time point t i,pre Electrical load power P t L,i Ambient temperature The discrete-time series of predicted new energy power in this scenario were generated respectively. Discrete Time Series of Electrical Load Power Discrete time series of ambient temperature

[0267] 4) The discrete time series obtained in step 3) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series.

[0268]

[0269] in, These represent the predicted power of new energy sources, the power of electrical load, and the ambient temperature at time n under scenario m, respectively.

[0270] 5) Based on the results of steps 1) to 4), calculate the frequency domain similarity between the actual running scenario and all scenarios in the typical scenario set;

[0271] The calculation process for the frequency domain similarity between the actual sequence and scene m is as follows:

[0272] Calculate the frequency domain distance between the discrete-time series of actual available new energy power and the discrete-time series of predicted new energy power under scenario m:

[0273]

[0274] Among them, f c E(·) represents the energy of the time series, where E is the number of terms in the Fourier series considered in the calculation. The frequency domain distance between the Fourier series of the discrete time series corresponding to the actual available power of new energy and the predicted power of new energy under scenario m is expressed as follows:

[0275]

[0276] in, and [k] represents the Fourier series. The kth term, a k b k θ are the moduli of the k-th term of the two Fourier series, respectively. k , , respectively, are the phase angles of the k-th terms of the two Fourier series, where j is the imaginary unit;

[0277] Calculate the frequency domain distance between the discrete-time series of the actual operating power of the electrical load and the discrete-time series of the electrical load power under scenario m:

[0278]

[0279] in, This represents the frequency domain distance between the Fourier series of the discrete time series corresponding to the actual operating power of the electrical load and the power of the electrical load under scenario m.

[0280] Calculate the frequency domain distance between the discrete-time series of actual ambient temperature values ​​and the discrete-time series of ambient temperature values ​​in scenario m:

[0281]

[0282] in, This represents the frequency domain distance between the actual ambient temperature and the Fourier series of the discrete time series corresponding to the ambient temperature in scene m.

[0283] Calculate the frequency domain similarity between the actual running scenario and scenario m:

[0284]

[0285] In a specific embodiment of the present invention, the step of rolling correction of the baseline scheduling plan includes:

[0286]

[0287] In the formula, This is the revised scheduling plan, and Ω represents the typical scenario set.

[0288] This allows the use of similarity indicators as estimates of scenario probabilities, which in turn enables the correction of the baseline scheduling plan through rolling scheduling, effectively improving the scheduling results' ability to cope with uncertainties.

[0289] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0290] At least one processor; and a memory communicatively connected to said at least one processor;

[0291] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described rolling optimization scheduling method for an integrated energy system based on frequency domain similarity.

[0292] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described rolling optimization scheduling method for an integrated energy system based on frequency domain similarity.

[0293] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0294] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a frequency-domain similarity-based integrated energy system rolling optimization scheduling method according to the above embodiments.

[0295] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0296] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0297] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0298] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0299] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0300] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0301] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0302] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0303] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A rolling optimization scheduling method for a comprehensive energy system based on frequency domain similarity, characterized in that, include: Construct an integrated energy system optimization scheduling model; Generate a set of typical scenarios, and solve the optimized scheduling model under each scenario in the set of typical scenarios to obtain the baseline scheduling plan for each scenario; By acquiring actual operating data of the scheduling cycle, the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set is calculated, and then the frequency domain similarity is used to perform rolling correction on the baseline scheduling plan.

2. The method according to claim 1, characterized in that, The integrated energy system optimization scheduling model consists of an objective function and constraints. The objective function is to minimize the total operating cost of the integrated energy system, and its expression is as follows: In the formula, t is the number of the scheduling period; Υ is the set of all scheduling periods, Υ={1,2,...,N T }, N T This represents the total number of scheduling periods; S represents the active power value of the tie line in the integrated energy system during dispatch period t; Δt represents the time interval between adjacent dispatch periods; i represents the number of any device in the integrated energy system; S D S is the collection of all equipment in an integrated energy system. G S is the set of all thermal power units in an integrated energy system. W S is the set of all new energy generator units in an integrated energy system. CHP S is the collection of all combined heat and power units in an integrated energy system. GB S is the collection of all gas-fired boilers in an integrated energy system. AC S is the collection of all absorption chillers in an integrated energy system. EB S is the collection of all electric boilers in an integrated energy system. EC It is a collection of electric chillers together in an integrated energy system; Let c be the electricity purchase price during the dispatch period t. s,i The startup cost of device i, The operating cost of device i; To characterize the 0-1 variables representing the power-on action of device i during scheduling period t, if device i changes from a power-off state to a power-on state during scheduling period t, then... The value is 1, otherwise The value is 0; To represent the 0-1 variables of the operating state of device i during scheduling period t, if device i is in the powered-on state during scheduling period t, then... The value is 1. If device i is in a powered-off state during the scheduling period t, then... The value of is 0; Let i be the power generated by device i during the scheduling period t; When i∈S G ∪S GB ∪S AC At that time, c i This is the operating cost coefficient for thermal power units, gas-fired boilers, or absorption units. When i∈S CHP At that time, c E,i and c H,i These are the power generation cost coefficient and the heating cost coefficient of cogeneration unit i, respectively. The active power and thermal power generated by cogeneration unit i during the dispatch period t; The constraints include: Equipment operating status constraints: In the formula, To characterize the 0-1 variable representing the shutdown action of device i during scheduling period t, if device i changes from an on state to a off state during scheduling period t, then... The value is 1, otherwise... The value is 0; Equipment operating constraints: In the formula, P i,min and P i,max R represents the upper and lower limits of the power generated by device i, respectively; i,up and R i,down These are the upward ramp rate and downward ramp rate of device i, respectively; These are 0-1 variables representing whether device i is in the startup or shutdown process at time t; Operating constraints of new energy generator sets: In the formula, P t i,pre This is the predicted value of the active power generated by the new energy generator unit i during the dispatch period t; Operating constraints of combined heat and power units: In the formula, EP i P is the set of feasible domain endpoints for cogeneration unit i; i,k and H i,k These are the active power and thermal power values ​​at the k-th endpoint of the feasible region of cogeneration unit i, respectively. Let i be the k-th combination coefficient of cogeneration unit i during the scheduling period t; Battery operating constraints: In the formula, S ES It is the collection of all energy storage systems in an integrated energy system; and These represent the charging power and discharging power of battery i during the scheduling period t, respectively. P represents the charge level of battery i during the scheduling period t. c,i,max and P dc,i,max These represent the maximum charging power and the maximum discharging power of battery i, respectively; E i,min and E i,max η represents the minimum and maximum values ​​of the battery capacity i, respectively; c,i and η dc,i Here, represents the charging efficiency and discharging efficiency of battery i, respectively, and represents the charge level of battery i during the scheduling period t-1; s i Let i be the self-loss rate of battery i; These represent the battery's charge level, charging power, and discharging power during the first scheduling period, respectively. The amount of electricity stored in battery i at the initial moment before scheduling; Cooling and heating load constraints: In the formula, The heat capacity of heat load i or cold load i; The indoor temperature of heat load i or cooling load i during the scheduling period t; U represents the indoor temperature of heat load i or cooling load i during the scheduling period t-1; i The thermal conductivity is the heat load i or the cooling load i. τ represents the ambient temperature during the scheduling period t; i,min and τ i,max These represent the minimum and maximum values ​​of the indoor temperature, respectively, for heat load i or cooling load i. The heat power consumed by heat load i; S HL S is the set of all heat loads in an integrated energy system. CL It is the collection of all cooling loads in an integrated energy system; Energy balance constraints: In the formula, η EB,i Let COP be the coefficient of performance of electric boiler i. i Let i be the coefficient of performance (COP) of the electric chiller. S represents the active power consumed by electrical load i during the dispatch period t. L It is the collection of all electrical loads within a comprehensive energy system; State variable constraints during unit start-up and shutdown: In the formula, These represent the maximum number of times device i is shut down and the maximum number of times it is powered on during the entire scheduling period; These are the minimum single power-on and power-off times for device i, respectively; As a continuous variable representing the time that device i has been off, Maximum continuous shutdown time for device i These represent the time when device i is in the startup and shutdown processes, respectively. Operating constraints during unit start-up and shutdown: Where M is a positive number; R i,su and R i,sd These are the power-on rate and power-off rate of device i, respectively; These are the minimum and maximum power limits during the startup process of device i, respectively; These are the minimum and maximum power limits for device i during the stopping process, respectively.

3. The method according to claim 2, characterized in that, The process of obtaining the baseline scheduling plan for each scenario includes: The predicted power of new energy sources in each of the typical scenarios will be set together. Electrical load power Ambient temperature Substitute the parameters into the optimized scheduling model, and then solve the optimized scheduling model to obtain the baseline scheduling plan corresponding to this scenario. This represents the baseline scheduling plan corresponding to the m-th scenario.

4. The method according to claim 3, characterized in that, The calculation of the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set includes... 1) Obtain the time T from the start of the scheduling period to the current time. now Discrete-time series of actual available power of new energy sources Discrete-time series of actual operating power of electrical load Discrete time series of actual ambient temperature values t = 1, 2, ..., T now ; 2) The discrete time series obtained in step 1) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series. : Where j is the imaginary unit; 3) For each scene m in the typical scene set, extract the first T segments of that scene. now The predicted power of new energy sources P at each time point t i ,pre Electrical load power P t L,i Ambient temperature The discrete-time series of new energy predicted power under this scenario were generated respectively. Discrete Time Series of Electrical Load Power Discrete time series of ambient temperature 4) The discrete time series obtained in step 3) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series. : in, These represent the predicted power of new energy sources, the power of electrical load, and the ambient temperature at time n under scenario m, respectively. 5) Based on the results of steps 1) to 4), calculate the frequency domain similarity between the actual running scenario and all scenarios in the typical scenario set; The calculation process for the frequency domain similarity between the actual sequence and scene m is as follows: Calculate the frequency domain distance between the discrete-time series of actual available new energy power and the discrete-time series of predicted new energy power under scenario m: Among them, f c E(·) represents the energy of the time series, where E is the number of terms in the Fourier series considered in the calculation. The frequency domain distance between the Fourier series of the discrete time series corresponding to the actual available power of new energy and the predicted power of new energy under scenario m is expressed as follows: in, and [k] represents the Fourier series. The kth term, a k b k θ are the moduli of the k-th term of the two Fourier series, respectively. k , , respectively, are the phase angles of the k-th terms of the two Fourier series, where j is the imaginary unit; Calculate the frequency domain distance between the discrete-time series of the actual operating power of the electrical load and the discrete-time series of the electrical load power under scenario m: in, This represents the frequency domain distance between the Fourier series of the discrete time series corresponding to the actual operating power of the electrical load and the power of the electrical load under scenario m. Calculate the frequency domain distance between the discrete-time series of actual ambient temperature values ​​and the discrete-time series of ambient temperature values ​​in scenario m: in, This represents the frequency domain distance between the actual ambient temperature and the Fourier series of the discrete time series corresponding to the ambient temperature in scene m. Calculate the frequency domain similarity between the actual operating scenario and scenario m:

5. The method according to claim 4, characterized in that, The rolling revision of the baseline scheduling plan includes: In the formula, This is the revised scheduling plan, and Ω represents the typical scenario set.

6. A rolling optimization scheduling device for a comprehensive energy system based on frequency domain similarity, characterized in that, include: The model building module is used to build an integrated energy system optimization scheduling model; A baseline scheduling plan generation module is used to generate a set of typical scenarios, solve the optimized scheduling model under each scenario in the set of typical scenarios, and obtain the baseline scheduling plan corresponding to each scenario. The rolling optimization module is used to obtain the actual running data of the scheduling cycle, calculate the frequency domain similarity between the actual running scenario and each scenario in the typical scenario set, and then use the frequency domain similarity to perform rolling correction on the baseline scheduling plan.

7. The apparatus according to claim 6, characterized in that, The integrated energy system optimization scheduling model consists of an objective function and constraints. The objective function is to minimize the total operating cost of the integrated energy system, and its expression is as follows: In the formula, t is the number of the scheduling period; Υ is the set of all scheduling periods, Υ={1,2,...,N T }, N T This represents the total number of scheduling periods; S represents the active power value of the tie line in the integrated energy system during dispatch period t; Δt represents the time interval between adjacent dispatch periods; i represents the number of any device in the integrated energy system; S D S is the collection of all equipment in an integrated energy system. G S is the set of all thermal power units in an integrated energy system. W S is the set of all new energy generator units in an integrated energy system. CHP S is the collection of all combined heat and power units in an integrated energy system. GB S is the collection of all gas-fired boilers in an integrated energy system. AC S is the collection of all absorption chillers in an integrated energy system. EB S is the collection of all electric boilers in an integrated energy system. EC It is a collection of electric chillers together in an integrated energy system; Let c be the electricity purchase price during the dispatch period t. s,i The startup cost of device i, The operating cost of device i; To characterize the 0-1 variables representing the power-on action of device i during scheduling period t, if device i changes from a power-off state to a power-on state during scheduling period t, then... The value is 1, otherwise The value is 0; To represent the 0-1 variables of the operating state of device i during scheduling period t, if device i is in the powered-on state during scheduling period t, then... The value is 1. If device i is in a powered-off state during the scheduling period t, then... The value of is 0; Let i be the power generated by device i during the scheduling period t; When i∈S G ∪S GB ∪S AC At that time, c i This is the operating cost coefficient for thermal power units, gas-fired boilers, or absorption units. When i∈S CHP At that time, c E,i and c H,i These are the power generation cost coefficient and the heating cost coefficient of cogeneration unit i, respectively. The active power and thermal power generated by cogeneration unit i during the dispatch period t; The constraints include: Equipment operating status constraints: In the formula, To characterize the 0-1 variable representing the shutdown action of device i during scheduling period t, if device i changes from an on state to a off state during scheduling period t, then... The value is 1, otherwise... The value is 0; Equipment operating constraints: In the formula, P i,min and P i,max R represents the upper and lower limits of the power generated by device i, respectively; i,up and R i,down These are the upward ramp rate and downward ramp rate of device i, respectively; These are 0-1 variables representing whether device i is in the startup or shutdown process at time t; Operating constraints of new energy generator sets: In the formula, P t i,pre This is the predicted value of the active power generated by the new energy generator unit i during the dispatch period t; Operating constraints of combined heat and power units: In the formula, EP i P is the set of feasible domain endpoints for cogeneration unit i; i,k and H i,k These are the active power and thermal power values ​​at the k-th endpoint of the feasible region of cogeneration unit i, respectively. Let i be the k-th combination coefficient of cogeneration unit i during the scheduling period t; Battery operating constraints: In the formula, S ES It is the collection of all energy storage systems in an integrated energy system; and These represent the charging power and discharging power of battery i during the scheduling period t, respectively. P represents the charge level of battery i during the scheduling period t. c,i,max and P dc,i,max These represent the maximum charging power and the maximum discharging power of battery i, respectively; E i,min and E i,max η represents the minimum and maximum values ​​of the battery capacity i, respectively; c,i and η dc,i Here, represents the charging efficiency and discharging efficiency of battery i, respectively, and represents the charge level of battery i during the scheduling period t-1; s i Let i be the self-loss rate of battery i; These represent the battery's charge level, charging power, and discharging power during the first scheduling period, respectively. The amount of electricity stored in battery i at the initial moment before scheduling; Cooling and heating load constraints: In the formula, The heat capacity of heat load i or cold load i; The indoor temperature of heat load i or cooling load i during the scheduling period t; U represents the indoor temperature of heat load i or cooling load i during the scheduling period t-1; i The thermal conductivity is the heat load i or the cooling load i. τ represents the ambient temperature during the scheduling period t; i,min and τ i,max These represent the minimum and maximum values ​​of the indoor temperature, respectively, for heat load i or cooling load i. The heat power consumed by heat load i; S HL S is the set of all heat loads in an integrated energy system. CL It is the collection of all cooling loads in an integrated energy system; Energy balance constraints: In the formula, η EB,i Let COP be the coefficient of performance of electric boiler i. i Let i be the coefficient of performance (COP) of the electric chiller. S represents the active power consumed by electrical load i during the dispatch period t. L It is the collection of all electrical loads within a comprehensive energy system; State variable constraints during unit start-up and shutdown: In the formula, These represent the maximum number of times device i is shut down and the maximum number of times it is powered on during the entire scheduling period; These are the minimum single power-on and power-off times for device i, respectively; As a continuous variable representing the time that device i has been off, Maximum continuous shutdown time for device i These represent the time when device i is in the startup and shutdown processes, respectively. Operating constraints during unit start-up and shutdown: Where M is a positive number; R i,su and R i,sd These are the power-on rate and power-off rate of device i, respectively; These are the minimum and maximum power limits during the startup process of device i, respectively; These are the minimum and maximum power limits for device i during the stopping process, respectively.

8. The apparatus according to claim 7, characterized in that, The process of obtaining the baseline scheduling plan for each scenario includes: The predicted power of new energy sources in each of the typical scenarios will be set together. Electrical load power Ambient temperature Substitute the parameters into the optimized scheduling model, and then solve the optimized scheduling model to obtain the baseline scheduling plan corresponding to this scenario. This represents the baseline scheduling plan corresponding to the m-th scenario.

9. The apparatus according to claim 8, characterized in that, The calculation of the frequency domain similarity between the actual operating scenario and each scenario in the typical scenario set includes... 1) Obtain the time T from the start of the scheduling period to the current time. now Discrete-time series of actual available power of new energy sources Discrete-time series of actual operating power of electrical load Discrete time series of actual ambient temperature values t = 1, 2, ..., T now ; 2) The discrete time series obtained in step 1) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series. : Where j is the imaginary unit; 3) For each scene m in the typical scene set, extract the first T segments of that scene. now Predicted power of new energy sources at each time point Electrical load power Ambient temperature The discrete-time series of new energy predicted power under this scenario were generated respectively. Discrete Time Series of Electrical Load Power Discrete time series of ambient temperature 4) The discrete time series obtained in step 3) Perform a discrete Fourier transform to obtain the Fourier series of each discrete time series. : in, These represent the predicted power of new energy sources, the power of electrical load, and the ambient temperature at time n under scenario m, respectively. 5) Based on the results of steps 1) to 4), calculate the frequency domain similarity between the actual running scenario and all scenarios in the typical scenario set; The calculation process for the frequency domain similarity between the actual sequence and scene m is as follows: Calculate the frequency domain distance between the discrete-time series of actual available new energy power and the discrete-time series of predicted new energy power under scenario m: Among them, f c E(·) represents the energy of the time series, where E is the number of terms in the Fourier series considered in the calculation. The frequency domain distance between the Fourier series of the discrete time series corresponding to the actual available power of new energy and the predicted power of new energy under scenario m is expressed as follows: in, and [k] represents the Fourier series. The kth term, a k b k θ are the moduli of the k-th term of the two Fourier series, respectively. k , , respectively, are the phase angles of the k-th terms of the two Fourier series, where j is the imaginary unit; Calculate the frequency domain distance between the discrete-time series of the actual operating power of the electrical load and the discrete-time series of the electrical load power under scenario m: in, This represents the frequency domain distance between the Fourier series of the discrete time series corresponding to the actual operating power of the electrical load and the power of the electrical load under scenario m. Calculate the frequency domain distance between the discrete-time series of actual ambient temperature values ​​and the discrete-time series of ambient temperature values ​​in scenario m: in, This represents the frequency domain distance between the actual ambient temperature and the Fourier series of the discrete time series corresponding to the ambient temperature in scene m. Calculate the frequency domain similarity between the actual operating scenario and scenario m:

10. The apparatus according to claim 9, characterized in that, The rolling revision of the baseline scheduling plan includes: In the formula, This is the revised scheduling plan, and Ω represents the typical scenario set.

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