Comprehensive energy system optimization scheduling method based on multiple time scales

Abstract

The invention provides an integrated energy system optimal scheduling method based on multiple time scales, which comprises the following steps of: constructing a radix uncertainty set, constructing a day-ahead robust optimization model based on the radix uncertainty set, inputting day-ahead one-hour-level power prediction data of an integrated energy system into the day-ahead robust optimization model, and performing optimal scheduling on the day-ahead robust optimization model. Obtaining a day-ahead scheduling scheme of the integrated energy system; generating an intra-day random scene set, constructing an intra-day stochastic optimization model based on the intra-day stochastic scene set, acquiring intra-day power prediction data of the integrated energy system by taking 15 minutes as a period, inputting the intra-day stochastic optimization model to obtain an intra-day optimization scheduling scheme, and repeating the steps until optimization scheduling of all periods in the day is completed; and generating a real-time random scene set, constructing a real-time random optimization model based on the real-time random scene set, and inputting the real-time 5-minute-level power prediction data of the integrated energy system into the real-time random optimization model to obtain a real-time scheduling scheme of the integrated energy system.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy system optimization and scheduling, and in particular to an integrated energy system optimization and scheduling method based on multiple time scales. Background Art

[0002] An integrated energy system is an efficient energy utilization solution that achieves efficient energy utilization and minimizes environmental impact by integrating multiple energy forms such as electricity, heat, hydrogen and cold energy. In recent years, this system has attracted widespread attention due to its reliability and environmental friendliness. In particular, electric-hydrogen hybrid energy storage technology, as an innovative energy storage method in an integrated energy system, combines the advantages of short-cycle energy storage of batteries and long-cycle energy storage of hydrogen storage tanks. It uses water electrolysis to produce hydrogen and hydrogen fuel cells to generate electricity, which not only balances load demand and energy supply, but also enhances the flexibility and stability of the system, effectively promoting the consumption of renewable energy.

[0003] Multi-time scale optimization divides the dispatching process into multiple stages such as day-ahead, intraday and real-time, realizing dispatching optimization and adjustment at each level and level, which is an effective measure to deal with the volatility of wind power and photovoltaic power. In addition, since different energy storage devices have different power response characteristics, multi-time scale optimization dispatching can give full play to the advantages of more refined dispatching and control of each energy storage device in the process of optimized dispatching at different time scales.

[0004] However, with the continuous access of a high proportion of renewable energy, the intermittency and uncertainty of wind power and photovoltaics pose great challenges to the optimal scheduling of integrated energy systems. In the existing multi-time scale optimization scheduling technology of integrated energy systems, the characterization of wind power and photovoltaic uncertainties is mainly based on static power prediction errors, resulting in low system robustness, which may have serious consequences for the operation of integrated energy systems in some extreme scenarios. In terms of uncertainty optimization, robust optimization and stochastic optimization are two common methods for dealing with such problems. Robust optimization uses uncertainty sets to describe the fluctuation range of uncertain parameters, with the worst-case optimization as the core, thereby improving the robustness of the system and often obtaining conservative solutions. Stochastic optimization usually assumes that random variables precisely obey a certain probability distribution, and generates a large number of random scenarios to counter the interference of uncertain factors in the system, often obtaining optimistic solutions. However, the existing technology rarely considers the characteristics that the wind power and photovoltaic power prediction errors decrease as the prediction time scale shortens, resulting in a lack of distinction between uncertainty optimization methods at different time scales. Therefore, there is still a lot of room for improvement in the existing technology in improving the robustness and adaptability of the system. Summary of the invention

[0005] The present invention proposes an optimization scheduling method for an integrated energy system based on multiple time scales, which solves the problem that the prior art does not consider the uncertainty optimization of the integrated energy system under multiple time scales.

[0006] In order to solve the above technical problems, the present invention provides a method for optimizing and scheduling an integrated energy system based on multiple time scales, comprising the following steps:

[0007] Step S1: construct a cardinality uncertainty set for the integrated energy system, construct a day-ahead robust optimization model based on the cardinality uncertainty set, input the day-ahead 1-hour power forecast data of the integrated energy system into the day-ahead robust optimization model, and obtain a day-ahead scheduling plan for the integrated energy system;

[0008] Step S2: Generate a set of intra-day random scenarios, build an intra-day random optimization model based on the intra-day random scenario set, obtain intra-day 15-minute power forecast data based on the day-ahead scheduling plan with a period of 15 minutes, input the intra-day 15-minute power forecast data into the intra-day random optimization model to obtain the intra-day 15-minute scheduling plan, repeat the above steps until the optimization scheduling of all cycles within the day is completed, and obtain the intra-day scheduling plan of the integrated energy system;

[0009] Step S3: Generate a real-time random scenario set, build a real-time random optimization model based on the real-time random scenario set, obtain real-time 5-minute power forecast data based on the intraday scheduling plan, input the real-time 5-minute power forecast data into the real-time random optimization model, and obtain a real-time scheduling plan for the integrated energy system.

[0010] Preferably, the expression of the power forecast data of the integrated energy system at the one-hour level the day before in step S1 is:

[0011]

[0012] In the formula, are the predicted values ​​of wind power and photovoltaic power of the integrated energy system in period t, respectively; are random variables of wind power and photovoltaic power prediction errors respectively.

[0013] Preferably, step S1 constrains the fluctuation range of the random variable of the power prediction error by a cardinality uncertainty set, and the expression of the cardinality uncertainty set is:

[0014]

[0015] In the formula, are the empirical values ​​of wind power and photovoltaic power prediction errors of the integrated energy system, respectively; e WT are the upper and lower limits of wind power prediction error respectively; e PV are the upper and lower limits of photovoltaic power prediction error respectively; Γ WT , Γ PV are the uncertainty coefficients of wind power and photovoltaic power prediction errors, respectively, and satisfy 0≤Γ≤1.

[0016] Preferably, the expression of the day-ahead robust optimization model is:

[0017]

[0018] In the formula, x d is the decision variable in the day-ahead robust optimization model; ξ d include and are random variables of wind power and photovoltaic power prediction errors respectively; U is the cardinality uncertain set.

[0019] Preferably, the objective function of the day-ahead robust optimization model is expressed as:

[0020] F da =C buy +C om +C cut -R CO2 ;

[0021]

[0022] In the formula, F da is the objective function of the day-ahead robust optimization model; C buy C is the energy purchase cost; om is the operation and maintenance cost; C cut is the cost of power abandonment; R CO2 for carbon tax revenue; p gas are electricity price and gas price respectively; P t buy , are the power of electricity and gas purchased in period t respectively; is the operation and maintenance cost of device r; is the operating power of device r in period t; T is the total number of periods; R is the total number of devices in the integrated energy system; are the penalty costs for abandoning wind and solar power respectively; are the wind power and solar power abandonment in period t respectively; P t WT , P t PV are wind power and photovoltaic power in period t respectively; c CO2 is the carbon tax price; is the amount of natural gas consumed by the gas turbine during period t; is the amount of natural gas consumed by the gas boiler during period t; re ,δ q ,δ g They are the carbon emission coefficients of renewable energy, main gas grid and main power grid respectively.

[0023] Preferably, the constraints of the day-ahead robust optimization model include equipment operation constraints, power balance constraints, power abandonment constraints and energy purchase constraints;

[0024] The equipment operation constraints include:

[0025] 1) Energy storage equipment operation constraints:

[0026]

[0027]

[0028] In the formula, X represents energy storage equipment, including batteries BES and hydrogen storage tanks HES; E represents energy, including electricity P and hydrogen H; is the energy stored in energy storage device X during period t; are the power of charging and discharging energy E of energy storage device X during period t respectively; are the charging and discharging efficiencies of energy storage device X respectively; are the upper and lower limits of the energy stored in the energy storage device X; They are the charging and discharging states of the energy storage device X in period t, with a value of 1 indicating charging / discharging and a value of 0 indicating no charging / discharging; They are the upper and lower limits of the charging power of energy storage device X respectively; are the upper and lower limits of the energy storage device X’s discharge power respectively; Δt represents the scheduling period interval; represents the energy stored in energy storage device X at the end of the scheduling period; represents the energy stored in energy storage device X at the beginning of the scheduling period;

[0029] 2) Electrolyzer operation constraints:

[0030]

[0031] In the formula, is the hydrogen production power of the electrolyzer during period t; P t EL is the power consumption of the electrolytic cell during period t; is the hydrogen production efficiency of the electrolyzer; They are the upper and lower limits of the power consumption of the electrolytic cell respectively;

[0032] 3) Hydrogen fuel cell operation constraints:

[0033]

[0034] Where P t HFC is the power generation of the hydrogen fuel cell during period t; is the hydrogen consumption power of the hydrogen fuel cell during period t; The power generation efficiency of hydrogen fuel cells; They are the upper and lower limits of hydrogen power consumption of hydrogen fuel cells respectively;

[0035] 4) Gas turbine operation constraints:

[0036]

[0037] In the formula, is the amount of natural gas consumed by the gas turbine during period t; P t GT , are the power generation power and heating power of the gas turbine during period t respectively; are the power generation efficiency and heating efficiency of the gas turbine respectively; is the on / off state of the gas turbine during period t, with a value of 1 indicating on and a value of 0 indicating off; are the upper and lower limits of the gas turbine power generation respectively; are the upper and lower limits of the gas turbine ramp rate, respectively;

[0038] 5) Gas boiler operation constraints:

[0039]

[0040] In the formula, is the amount of natural gas consumed by the gas boiler during period t; is the heating power of the gas boiler during period t; is the heating efficiency of the gas boiler; They are the upper and lower limits of the heating power of the gas boiler respectively;

[0041] 6) Electric refrigeration machine operation constraints:

[0042]

[0043] In the formula, P is the cooling power of the electric refrigerator during period t; t EC is the power consumption of the electric refrigerator during period t; is the cooling efficiency of the electric refrigerator; They are the upper and lower limits of the refrigeration power of the electric refrigerator respectively;

[0044] 7) Absorption refrigeration machine operation constraints:

[0045]

[0046] Where: is the cooling power of the absorption chiller during period t; is the heat consumption power of the absorption refrigerator during period t; is the refrigeration efficiency of the absorption chiller; They are the upper and lower limits of the refrigeration power of the absorption chiller respectively;

[0047] The power balance constraints include:

[0048] 1) Electric power balance constraints:

[0049]

[0050] 2) Thermal power balance constraints:

[0051]

[0052] 3) Hydrogen power balance constraints:

[0053]

[0054] 4) Cold power balance constraints:

[0055]

[0056] 5) Natural gas power balance constraints:

[0057]

[0058] In the above formula, are the charging and discharging powers of the battery during period t respectively; are the power of charging and discharging hydrogen in the hydrogen storage tank during period t; P t load , are the electric, heating and cooling load powers in period t respectively;

[0059] The expression of the power curtailment constraint is:

[0060]

[0061] In the above formula, are the wind power and solar power abandonment in period t respectively; P t WT , P t PV are wind power and photovoltaic power in period t respectively;

[0062] The expression of the energy purchase constraint is:

[0063]

[0064] In the above formula, P t buy , are the power of electricity and gas purchased in period t respectively; They are the upper limits of electricity and gas purchase power respectively.

[0065] Preferably, in step S2 and step S3, an initial scene is obtained by Latin hypercube sampling, and a K-means clustering algorithm is used to perform scene reduction on the initial scene to obtain several typical scenes, and the several typical scenes are used as a random scene set.

[0066] Preferably, the expression of the intraday stochastic optimization model in step S2 is:

[0067]

[0068] In the formula, x i is the decision variable in the intraday stochastic optimization model; ξ i is an uncertain random variable generated based on a set of random scenarios.

[0069] Preferably, the objective function of the intraday stochastic optimization model in step S2 is expressed as:

[0070] F id =C buy +C om +C adj +C cut -R CO2 ;

[0071]

[0072] In the formula, F id is the objective function of the intraday stochastic optimization model; is the power adjustment cost of device r; is the power adjustment amount of device r during period t.

[0073] Preferably, the expression of the real-time stochastic optimization model in step S3 is:

[0074]

[0075] In the formula, x r is the decision variable in the real-time stochastic optimization model; r is an uncertain random variable generated based on a set of random scenarios.

[0076] The benefits of the present invention include at least:

[0077] 1. By constructing optimization models at different time scales, the system is gradually optimized and iterated, from day-ahead to intraday and then to real-time, the scheduling scheme is continuously adjusted and optimized to adapt to the scheduling needs of the integrated energy system at different time scales and improve the flexibility and accuracy of scheduling;

[0078] 2. The day-ahead robust optimization model constructed can take into account the system operation under the worst-case scenario, thereby improving the robustness of the system and ensuring stable operation under extreme conditions; the stochastic optimization model constructed uses a set of random scenarios to simulate uncertainty, combats uncertainties through the simulation of a large number of scenarios, and improves the adaptability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0080] Figure 2 is a structural block diagram of a comprehensive energy system according to an embodiment of the present invention;

[0081] Figure 3 Graph showing the total day-ahead scheduling cost under different uncertainty coefficients in an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0083] Embodiment 1

[0084] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing and scheduling an integrated energy system based on multiple time scales, comprising the following steps:

[0085] Step S1: Establish a mathematical model of an integrated energy system including electric-hydrogen hybrid energy storage, including the energy conversion relationship of each device and the operation constraints of each device.

[0086] Among them, electric-hydrogen hybrid energy storage refers to the combination of batteries, electrolyzers, hydrogen storage tanks, and hydrogen fuel cells. Batteries and hydrogen storage tanks are used to store surplus electricity and hydrogen energy in the integrated energy system, respectively. Electrolyzers convert electricity into hydrogen energy through the process of water electrolysis, and hydrogen fuel cells convert hydrogen energy into electricity to supplement the demand of electrical load.

[0087] The structure of the comprehensive energy system of this embodiment is as follows Figure 2As shown in the figure, the main energy flows in the integrated energy system are electricity, heat, hydrogen, cold and natural gas. Among them, wind turbines, photovoltaic generators, main power grids and main gas grids play the role of energy production and input; electrolyzers, hydrogen fuel cells, gas turbines, gas boilers, electric refrigerators and absorption refrigerators are the intermediate links of energy conversion; energy storage equipment includes batteries and hydrogen storage tanks; and electricity, heat and cold loads play the role of energy consumption.

[0088] First, a mathematical model of electric-hydrogen hybrid energy storage is established, which includes the following parts:

[0089] 1) Mathematical model of battery and hydrogen storage tank

[0090] The battery and hydrogen storage tank both reflect their stored energy through SOC. During operation, they should meet energy constraints, power constraints and charge / discharge mutual exclusion constraints, and they should also meet the charge / discharge balance within a scheduling cycle. The mathematical model can be expressed as:

[0091]

[0092] In the formula, the superscript X represents the name of the energy storage equipment, including the battery BES and the hydrogen storage tank HES; E represents the power of different energy flows, including electricity P and hydrogen H; is the energy stored in energy storage device X during period t; are the power of charging and discharging energy E of energy storage device X during period t respectively; are the charging and discharging efficiencies of energy storage device X respectively; are the upper and lower limits of the energy stored in the energy storage device X; They are the charging and discharging states of the energy storage device X in period t, with a value of 1 indicating charging / discharging and a value of 0 indicating no charging / discharging; They are the upper and lower limits of the charging power of energy storage device X respectively; are the upper and lower limits of the energy storage device X’s discharge power, respectively; Δt represents a scheduling period interval, the subscript T represents the final value of a scheduling cycle, and 0 represents the initial value of a scheduling cycle, so represents the energy stored in energy storage device X at the end of the scheduling period; It represents the energy stored in energy storage device X at the beginning of the scheduling period.

[0093] 2) Electrolyzer mathematical model

[0094] The electrolyzer converts electrical energy into hydrogen energy through the electrolysis of water. It is a reliable hydrogen production equipment and should meet the power constraint during operation. Its mathematical model can be expressed as:

[0095]

[0096] In the formula, is the hydrogen production power of the electrolyzer during period t; P t EL is the power consumption of the electrolytic cell during period t; is the hydrogen production efficiency of the electrolyzer; They are the upper and lower limits of the power consumption of the electrolytic cell respectively.

[0097] 3) Hydrogen fuel cell mathematical model

[0098] Hydrogen fuel cells can convert hydrogen energy into electrical energy and should meet power constraints during operation. Their mathematical model can be expressed as:

[0099]

[0100] Where P t HFC is the power generation of the hydrogen fuel cell during period t; is the hydrogen consumption power of the hydrogen fuel cell during period t; The power generation efficiency of hydrogen fuel cells; They are respectively the upper and lower limits of hydrogen consumption power of hydrogen fuel cells.

[0101] Secondly, mathematical models of the remaining equipment in the integrated energy system, including gas turbines, gas boilers, electric chillers and absorption chillers, are established as follows:

[0102] 1) Mathematical model of gas turbine

[0103] The gas turbine converts natural gas into electricity and heat through cogeneration technology. During operation, it should meet the power constraint and ramp rate constraint. Its mathematical model can be expressed as:

[0104]

[0105]

[0106] Where P t GT , are the power generation power and heating power of the gas turbine during period t respectively; is the amount of natural gas consumed by the gas turbine during period t; are the power generation efficiency and heating efficiency of the gas turbine respectively; is the on / off state of the gas turbine during period t, with a value of 1 indicating on and a value of 0 indicating off; are the upper and lower limits of the gas turbine power generation respectively; They are the upper and lower limits of the gas turbine ramp rate respectively.

[0107] 2) Mathematical model of gas boiler

[0108] Gas boiler is a reliable heating equipment that produces heat energy by consuming natural gas. It should meet the power constraint during operation. Its mathematical model can be expressed as:

[0109]

[0110] In the formula, is the amount of natural gas consumed by the gas boiler during period t; is the heating power of the gas boiler during period t; is the heating efficiency of the gas boiler; They are the upper and lower limits of the heating power of the gas boiler respectively.

[0111] 3) Mathematical model of electric refrigerator

[0112] The electric refrigerator is a reliable cold energy production equipment that can convert electrical energy into cold energy. It should meet the power constraint during operation. Its mathematical model can be expressed as:

[0113]

[0114] In the formula, P is the cooling power of the electric refrigerator during period t; t EC is the power consumption of the electric refrigerator during period t; is the cooling efficiency of the electric refrigerator; They are the upper and lower limits of the cooling power of the electric refrigerator respectively.

[0115] 4) Mathematical model of absorption refrigerator

[0116] The absorption refrigeration machine can convert heat energy into cold energy and should meet the power constraint during operation. Its mathematical model can be expressed as:

[0117]

[0118]

[0119] Where: is the cooling power of the absorption chiller during period t; is the heat consumption power of the absorption refrigerator during period t; is the refrigeration efficiency of the absorption chiller; They are the upper and lower limits of the refrigeration power of the absorption chiller respectively.

[0120] Step S2: Obtain the forecast data of various loads and wind power and photovoltaic power at the 1h level the day before, use the cardinality uncertainty set to characterize the uncertainty of the wind and photovoltaic power forecast error on a long time scale the day before, build a day-ahead robust optimization model, and solve the day-ahead robust optimization model to obtain the optimal scheduling plan of all equipment at the 1h level the day before.

[0121] Specifically, the cardinality uncertainty set is constructed based on the relative value of the offset of the random variable, which can accurately and simply describe the fluctuation of the random variable, where the random variable includes the forecast error of the day-ahead wind power and photovoltaic power. The constraint form of the cardinality uncertainty set is expressed as:

[0122]

[0123] In the formula, are random variables of wind power and photovoltaic power prediction errors respectively; are the empirical values ​​of wind power and photovoltaic power prediction errors, i.e., the historical data of wind power and photovoltaic power prediction errors; e WT are the upper and lower limits of wind power prediction error respectively; e PV are the upper and lower limits of photovoltaic power prediction error respectively; Γ WT , Γ PV are the uncertainty coefficients of wind power and photovoltaic power prediction errors, respectively, and satisfy 0≤Γ≤1.

[0124] On this basis, the power of wind power and photovoltaic power can be expressed as:

[0125]

[0126] In the formula, are the predicted values ​​of wind power and photovoltaic power in period t respectively.

[0127] The day-ahead robust optimization model characterizes the uncertainty of wind and solar power on a long time scale of the day-ahead through a cardinality uncertainty set, with the goal of minimizing the day-ahead energy purchase cost, operation and maintenance cost, power abandonment cost and carbon emission cost. The operation constraints, power balance constraints, power abandonment constraints and energy purchase constraints of each equipment in the integrated energy system are used as constraints, and the optimal scheduling plan of all equipment at the 1h level on the day-ahead is obtained with a time scale of 1h.

[0128] The day-ahead robust optimization model is based on the total cost F of the integrated energy system. da Minimum is the optimization goal. The objective function includes four parts: energy purchase cost C buy , Operation and maintenance cost C om , power abandonment cost C cut and carbon tax revenue R CO2 , whose expression is:

[0129] F da =C buy +C om +C cut -R CO2 ;

[0130]

[0131] In the formula, F da is the objective function of the day-ahead robust optimization model; p gas are electricity price and gas price respectively; P t buy , are the power of electricity and gas purchased in period t respectively; is the operation and maintenance cost of equipment r, r∈{GT,GB,EC,AC,EL,HFC,BES,HES}, where GT is gas turbine, GB is gas boiler, EC is electric chiller, AC is absorption chiller, EL is electrolyzer, HFC is hydrogen fuel cell, BES is battery, and HES is hydrogen storage tank; is the operating power of device r in period t; T is the total number of periods; R is the total number of devices in the integrated energy system; are the penalty costs for abandoning wind and solar power respectively; are the wind power and solar power abandonment in period t respectively; P t WT , P t PV are wind power and photovoltaic power in period t respectively; c CO2 is the carbon tax price; is the amount of natural gas consumed by the gas turbine during period t; is the amount of natural gas consumed by the gas boiler during period t; re ,δ q ,δ g They are the carbon emission coefficients of renewable energy, main gas grid and main power grid respectively.

[0132] The constraints of the robust optimization model today include not only the operating constraints of each device, but also the power balance constraints of electricity, heat, hydrogen, cold, and natural gas, as well as the constraints on power abandonment and energy purchase.

[0133] 1) Power balance constraints

[0134] Electrical power balance:

[0135]

[0136] Thermal power balance:

[0137]

[0138] Hydrogen power balance:

[0139]

[0140] Cold Power Balance:

[0141]

[0142] Natural Gas Power Balance:

[0143]

[0144] In the above formula, are the charging and discharging powers of the battery during period t respectively; are the power of charging and discharging hydrogen in the hydrogen storage tank during period t; P t load , They are the electric, heating and cooling load powers in period t respectively.

[0145] 2) Power curtailment constraints

[0146]

[0147] 3) Constraints on purchasing capacity

[0148] Power purchase constraints:

[0149]

[0150] Gas purchasing constraints:

[0151]

[0152] In the above formula, They are the upper limits of electricity and gas purchase power respectively.

[0153] In summary, the day-ahead robust optimization model can be summarized as follows:

[0154]

[0155] In the formula, x d is the decision variable in the day-ahead robust optimization model, including a continuous variable representing the operating power of each device and a binary variable representing the start / stop status of the device; ξ d is a random variable representing the uncertainty of wind power, including and U is the cardinality uncertainty set of the day-ahead robust optimization model.

[0156] Step S3: Based on the results of the day-ahead dispatch optimization, the power forecast data of various loads, wind power, and photovoltaic power at the 15-minute level within the day are updated. Latin hypercube sampling and K-means clustering are used to generate a set of random scenarios within the day to characterize the uncertainty of the wind and solar power forecast error at a short time scale within the day. The optimal dispatch plan for all equipment at the 15-minute level within the day is obtained by solving the intraday random optimization model.

[0157] Specifically, in step S3, an initial scenario is generated by Latin hypercube sampling, and the initial scenario is reduced by using the K-means clustering algorithm to obtain multiple typical scenarios, and all typical scenarios are used as a set of intra-day random scenarios. The Latin hypercube sampling assumes that the wind power and photovoltaic power prediction errors satisfy the normal distribution, that is, the following form:

[0158]

[0159] In the formula, μ WT , σ WT are the expected value and standard deviation of wind power prediction error respectively; μ PV , σ PV are the expected value and standard deviation of PV power prediction error, respectively.

[0160] The intraday stochastic optimization model characterizes the uncertainty of wind and solar power on a short time scale within the day through a set of intraday random scenarios, with the goal of minimizing the intraday energy purchase cost, operation and maintenance cost, power adjustment cost, power abandonment cost and carbon emission cost. It takes the operation constraints, power balance constraints, power abandonment constraints and energy purchase constraints of each equipment in the integrated energy system as constraints, solves the scheduling plan of each equipment with 4h as the control time domain and 15min as the time scale, and moves the time domain to the next time period. Repeat this process to obtain the optimal scheduling plan of all equipment at the 15min level within the day.

[0161] Intraday stochastic optimization model based on the total cost F of the integrated energy system id The optimization goal is to minimize the power adjustment of each device as much as possible. The objective function of the intraday stochastic optimization model adds the device power adjustment cost C on the basis of the day-ahead robust optimization model. adj ,Right now:

[0162] F id =C buy +C om +C adj +C cut -R CO2 ;

[0163]

[0164] In the formula, is the power adjustment cost of device r; is the power adjustment of device r during period t, including ΔP t GT , ΔP t HFC ,

[0165] The constraints of the intraday stochastic optimization model include equipment operation constraints, power balance constraints, power abandonment constraints and energy purchase constraints. The results of solving the day-ahead robust optimization model should also be followed.

[0166] In summary, the intraday stochastic optimization model can be summarized as follows:

[0167]

[0168] In the formula, x i is the decision variable in the intraday rolling random optimization scheduling model, including a continuous variable representing the operating power of each device and a binary variable representing the start and stop status of the device; i is a random variable representing the uncertainty of wind power, including and It satisfies a set of random scenarios within the day.

[0169] Step S4: Update the real-time 5-minute electric load and wind power and photovoltaic power forecast data, use Latin hypercube sampling and K-means clustering to generate a real-time random scenario set to characterize the uncertainty of wind and solar power forecast errors in real-time short time scales, and solve the random optimization model to obtain the optimal scheduling plan for electric energy equipment at the real-time 5-minute level to respond to the unbalanced electric power caused by the wind power, photovoltaic and electric load power forecast errors in the real-time stage.

[0170] Specifically, the real-time stochastic optimization model characterizes the uncertainty of wind and solar power in real-time short time scales through a set of real-time random scenarios, with the goal of minimizing the real-time energy purchase cost, operation and maintenance cost, power adjustment cost, power abandonment cost and carbon emission cost. The battery operation constraints, power balance constraints, power abandonment constraints and power purchase constraints of the integrated energy system are used as constraints. The optimal scheduling plan of real-time electric energy equipment at the 5-minute level is optimized with a time scale of 5 minutes.

[0171] The objective function and constraints of the real-time stochastic optimization model are the part of the objective function and constraints of the intraday stochastic optimization model that only contains the power-related equipment variables. The real-time stochastic optimization model can be summarized as follows:

[0172]

[0173] In the formula, x ris the decision variable in the real-time stochastic optimization model, including continuous variables representing the battery operating power, power abandonment, and power purchase, and binary variables representing the battery charging and discharging status; ξ r is a random variable representing the uncertainty of wind power, including and It satisfies the real-time random scene collection.

[0174] So far, a multi-time scale optimization scheduling model for an integrated energy system considering the uncertainty of wind and solar power and electric-hydrogen hybrid energy storage has been established. The modeling can be completed using the RSOME toolbox in the MATLAB environment and the Gurobi commercial solver can be called to solve the problem and obtain the optimal scheduling plan.

[0175] The technical solution of the present invention is described in detail below in conjunction with specific application examples.

[0176] An integrated energy system was established with a hydrogen industrial park in a southern province of my country as the research object. The uncertainty coefficient of wind and solar in the cardinality uncertainty set is taken as 0.2. In the random scenario set, it is assumed that the wind power prediction error satisfies the normal distribution with an expectation of 0, a daily standard deviation of 3%, and a real-time standard deviation of 2%; the photovoltaic prediction error satisfies the normal distribution with an expectation of 0, a daily standard deviation of 2%, and a real-time standard deviation of 1%; the number of initial scenarios generated by Latin hypercube sampling is set to 1000, and the number of typical scenarios generated by K-means clustering is set to 5. The RSOME toolbox is used in the MATLAB environment to complete the modeling and the Gurobi commercial solver is called to solve it, in order to verify the advantages of introducing a multi-time scale optimization scheduling method that considers wind and solar uncertainties in the integrated energy system. The costs of different scheduling stages obtained are shown in Table 1, where the unit of cost is 10 3 Yuan.

[0177] Table 1 Costs of different scheduling stages

[0178]

[0179] Table 1 shows the dispatching costs of the three stages. It can be seen that the dispatching costs from the day-ahead stage to the intraday stage and then to the real-time stage are gradually decreasing. The total dispatching cost of the day-ahead stage is reduced by 3.88% compared with the day-ahead stage, and the total dispatching cost of the real-time stage is reduced by 4.64% compared with the day-ahead stage. In addition, during the optimization dispatching process, the main power grid and the main gas grid undertake the main task of combating the uncertainty of wind and solar forecast errors, and provide margin for dealing with uncertain factors by purchasing electricity and gas. Compared with the results of the day-ahead robust optimization dispatching, the intraday stage increases the operation and maintenance costs and the adjustment costs of equipment power. Through the rolling random optimization dispatching model, the energy purchase cost is greatly reduced, and the system's dependence on the main power grid and the main gas grid is reduced. At the same time, the carbon tax revenue is increased by 7.55%, and the power abandonment cost is reduced by 31.74%. In the real-time stage, the electric power equipment is adjusted relative to the intraday stage, and other equipment maintains the intraday dispatching plan. Through the rapid response of battery energy storage, the power fluctuations caused by wind power, photovoltaic power and electric load are smoothed. Although the power abandonment cost is increased compared with the intraday, the energy purchase cost is reduced, and the total cost is lower. In summary, the three-stage multi-time scale optimization scheduling method of day-ahead, intraday and real-time considering the uncertainty of wind and solar power can effectively deal with the uncertainty of wind and solar power prediction errors at different time scales in the integrated energy system, provide strong operation stability and improve the economic and environmental benefits of the system.

[0180] In order to analyze the influence of the uncertainty coefficient of wind power on the day-ahead dispatch in the day-ahead robust optimization model, the wind power uncertainty coefficient Γ WT and the photovoltaic uncertainty coefficient Γ PV The values ​​are 0.2 to 1.0 respectively, and the results of solving the total scheduling costs of multiple groups of days ahead are as follows Figure 3 As shown in the figure, with the increase of the uncertainty coefficient of the wind and solar power in the base uncertainty concentration, the total cost of the day-ahead robust optimization scheduling gradually increases. The scheduling decision maker can flexibly choose the value of the uncertainty coefficient and weigh the decision between optimistic and risky decisions to obtain the best scheduling solution.

[0181] In order to verify the advantages of applying electric-hydrogen hybrid energy storage in the integrated energy system, three scenarios are set for optimization scheduling, all of which adopt the multi-time scale optimization scheduling method considering the uncertainty of wind and solar. The specific settings of the three scenarios are as follows:

[0182] Scenario 1: No energy storage equipment is used for optimal scheduling;

[0183] Scenario 2: Only battery energy storage is used for optimal dispatch;

[0184] Scenario 3, the application of electric-hydrogen hybrid energy storage for optimized scheduling, which is the technical solution of the present invention.

[0185] The intraday scheduling costs under different scenarios are shown in Table 2, where the unit of cost is 10 3 Yuan.

[0186] Table 2 Intraday scheduling costs under different scenarios

[0187]

[0188] Comparing Scenario 1 and Scenario 2, it can be seen that after adding battery energy storage, the system's energy purchase cost and power abandonment cost have been reduced, while carbon tax revenue has been increased, and the economic efficiency of system dispatch has been greatly improved. Because battery energy storage can provide a certain degree of dispatch flexibility, reduce the system's dependence on the main power grid and the main gas grid, and improve carbon emissions at the same time, but compared with Scenario 3, the wind and solar abandonment phenomenon is still obvious, and the power abandonment cost is high. Comparing Scenario 2 and Scenario 3, it can be seen that after adding electrolyzers, hydrogen fuel cells and hydrogen storage tanks to form electric-hydrogen hybrid energy storage, the system's energy purchase cost and power abandonment cost are further reduced, and carbon tax revenue is further increased. Although more equipment brings higher operation and maintenance costs and power adjustment costs, the total dispatch cost of the system is lower, and the power abandonment phenomenon is significantly improved, reflecting the advantages of electric-hydrogen hybrid energy storage.

[0189] Embodiment 2

[0190] An embodiment of the present invention provides an integrated energy system optimization and scheduling system based on multiple time scales, which is implemented based on the above-mentioned integrated energy system optimization and scheduling method based on multiple time scales, and includes an integrated energy system modeling module, a day-ahead robust optimization module, an intraday random optimization module and a real-time random optimization module.

[0191] The integrated energy system modeling module is used to establish a mathematical model of an integrated energy system that includes electric-hydrogen hybrid energy storage, including the energy conversion relationship of each device and the operating constraints of each device.

[0192] The day-ahead robust optimization module is used to obtain the forecast data of various loads and wind power and photovoltaic power at the 1h level the day before. The cardinality uncertainty set is used to characterize the uncertainty of the wind and photovoltaic power forecast error on a long time scale the day before, and a day-ahead robust optimization model is constructed. The day-ahead robust optimization model is used to solve the optimal scheduling plan of all equipment at the 1h level the day before.

[0193] The intraday random optimization module is used to update the 15-minute intraday forecast data of various loads and wind power and photovoltaic power. Latin hypercube sampling and K-means clustering are used to generate a set of intraday random scenarios to characterize the uncertainty of wind and solar power forecast errors on a short time scale within the day. The intraday random optimization module is used to solve the optimal scheduling plan for all equipment at the 15-minute level within the day.

[0194] The real-time random optimization module is used to update the real-time 5-minute electric load and wind power and photovoltaic power forecast data. Latin hypercube sampling and K-means clustering are used to generate a real-time random scenario set to characterize the uncertainty of wind and solar power forecast errors in real-time short time scales. The real-time random optimization model is used to solve the optimal scheduling plan for real-time electric energy equipment at the 5-minute level to respond to the unbalanced electric power caused by the real-time wind power, photovoltaic and electric load power forecast errors.

[0195] The present invention provides an optimization and scheduling method for an integrated energy system based on multiple time scales. A conservative robust optimization model is used for the strong uncertainty of wind and solar power prediction errors on a long time scale the day before, and an optimistic stochastic optimization model is used for the weak uncertainty of wind and solar power prediction errors on a short time scale within the day and in real time. On this basis, a multi-time scale optimization and scheduling method is established, which includes three stages: day-ahead robust optimization, intraday stochastic optimization, and real-time stochastic optimization, thereby improving the stability and flexibility of system operation and adapting to the consumption of high-penetration renewable energy in the integrated energy system.

[0196] The comprehensive energy system including electric-hydrogen hybrid energy storage constructed by the present invention combines electric-hydrogen hybrid energy storage with multi-time scale optimization scheduling technology, giving full play to the advantages of multi-time scale optimization scheduling technology for more precise scheduling and control of energy storage equipment, effectively improving the economy and environmental efficiency of system operation, and promoting the absorption of high-penetration renewable energy.

[0197] In summary, the present invention takes into account the characteristic that the prediction error of wind power and photovoltaic power decreases as the prediction time scale shortens, applies different uncertainty optimization models for wind and solar power uncertainties at different time scales, and establishes a multi-time scale optimization scheduling method including three stages: day-ahead robust optimization, intra-day random optimization and real-time random optimization. It can effectively cope with the impact of uncertainty brought by a high proportion of renewable energy in the integrated energy system, and help to improve the stability and flexibility of the system operation.

[0198] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0199] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing and dispatching an integrated energy system based on multiple time scales, characterized in that: The following steps are involved: Step S1: construct a cardinality uncertainty set for the integrated energy system, construct a day-ahead robust optimization model based on the cardinality uncertainty set, input the day-ahead 1-hour power forecast data of the integrated energy system into the day-ahead robust optimization model, and obtain a day-ahead scheduling plan for the integrated energy system; Step S2: Generate a set of intra-day random scenarios, build an intra-day random optimization model based on the intra-day random scenario set, obtain intra-day 15-minute power forecast data based on the day-ahead scheduling plan with a period of 15 minutes, input the intra-day 15-minute power forecast data into the intra-day random optimization model to obtain the intra-day 15-minute scheduling plan, repeat the above steps until the optimization scheduling of all cycles within the day is completed, and obtain the intra-day scheduling plan of the integrated energy system; Step S3: Generate a real-time random scenario set, build a real-time random optimization model based on the real-time random scenario set, obtain real-time 5-minute power forecast data based on the intraday scheduling plan, input the real-time 5-minute power forecast data into the real-time random optimization model, and obtain a real-time scheduling plan for the integrated energy system.

2. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 1, characterized in that: The expression of the power forecast data of the integrated energy system at the one-hour level the day before in step S1 is: In the formula, are the predicted values ​​of wind power and photovoltaic power of the integrated energy system in period t, respectively; are random variables of wind power and photovoltaic power prediction errors respectively.

3. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 2 is characterized in that: Step S1 constrains the fluctuation range of the random variable of the power prediction error by a cardinality uncertainty set, and the expression of the cardinality uncertainty set is: In the formula, are the empirical values ​​of wind power and photovoltaic power prediction errors of the integrated energy system, respectively; e WT are the upper and lower limits of wind power prediction error respectively; e PV are the upper and lower limits of photovoltaic power prediction error respectively; Γ WT , Γ PV are the uncertainty coefficients of wind power and photovoltaic power prediction errors, respectively, and satisfy 0≤Γ≤1.

4. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 1 is characterized in that: The expression of the day-ahead robust optimization model is: In the formula, x d is the decision variable in the day-ahead robust optimization model; ξ d include and are random variables of wind power and photovoltaic power prediction errors respectively; U is the cardinality uncertain set.

5. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 1 is characterized in that: The objective function of the day-ahead robust optimization model is expressed as: F da =C buy +C om +C cut -R CO2 ; In the formula, F da is the objective function of the day-ahead robust optimization model; C buy C is the energy purchase cost; om C is the operation and maintenance cost; cut is the cost of power abandonment; R CO2 for carbon tax revenue; p gas are electricity price and gas price respectively; P t buy , are the power of electricity and gas purchased in period t respectively; is the operation and maintenance cost of device r; is the operating power of device r in period t; T is the total number of periods; R is the total number of devices in the integrated energy system; are the penalty costs for abandoning wind and solar power respectively; are the wind power and solar power abandonment in period t respectively; P t WT , P t PV are wind power and photovoltaic power in period t respectively; c CO2 is the carbon tax price; is the amount of natural gas consumed by the gas turbine during period t; is the amount of natural gas consumed by the gas boiler during period t; re , δ q , δ g They are the carbon emission coefficients of renewable energy, main gas grid and main power grid respectively.

6. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 5 is characterized in that: The constraints of the day-ahead robust optimization model include equipment operation constraints, power balance constraints, power abandonment constraints and energy purchase constraints; The equipment operation constraints include: 1) Energy storage equipment operation constraints: In the formula, X represents energy storage equipment, including batteries BES and hydrogen storage tanks HES; E represents energy, including electricity P and hydrogen H; is the energy stored in energy storage device X during period t; are the power of charging and discharging energy E of energy storage device X during period t respectively; are the charging and discharging efficiencies of energy storage device X respectively; are the upper and lower limits of the energy stored in the energy storage device X; They are the charging and discharging states of the energy storage device X in period t, with a value of 1 indicating charging / discharging and a value of 0 indicating no charging / discharging; They are the upper and lower limits of the charging power of energy storage device X respectively; are the upper and lower limits of the energy storage device X’s discharge power respectively; Δt represents the scheduling period interval; represents the energy stored in energy storage device X at the end of the scheduling period; represents the energy stored in energy storage device X at the beginning of the scheduling period; 2) Electrolyzer operation constraints: In the formula, is the hydrogen production power of the electrolyzer during period t; P t EL is the power consumption of the electrolytic cell during period t; is the hydrogen production efficiency of the electrolyzer; They are the upper and lower limits of the power consumption of the electrolytic cell respectively; 3) Hydrogen fuel cell operation constraints: Where P t HFC is the power generation of the hydrogen fuel cell during period t; is the hydrogen consumption power of the hydrogen fuel cell during period t; The power generation efficiency of hydrogen fuel cells; They are the upper and lower limits of hydrogen power consumption of hydrogen fuel cells respectively; 4) Gas turbine operation constraints: In the formula, is the amount of natural gas consumed by the gas turbine during period t; P t GT , are the power generation power and heating power of the gas turbine during period t respectively; are the power generation efficiency and heating efficiency of the gas turbine respectively; is the on / off state of the gas turbine during period t, with a value of 1 indicating on and a value of 0 indicating off; are the upper and lower limits of the gas turbine power generation respectively; are the upper and lower limits of the gas turbine ramp rate, respectively; 5) Gas boiler operation constraints: In the formula, is the amount of natural gas consumed by the gas boiler during period t; is the heating power of the gas boiler during period t; is the heating efficiency of the gas boiler; They are the upper and lower limits of the heating power of the gas boiler respectively; 6) Electric refrigeration machine operation constraints: In the formula, P is the cooling power of the electric refrigerator during period t; t EC is the power consumption of the electric refrigerator during period t; is the cooling efficiency of the electric refrigerator; They are the upper and lower limits of the refrigeration power of the electric refrigerator respectively; 7) Absorption chiller operation constraints: Where: is the cooling power of the absorption chiller during period t; is the heat consumption power of the absorption refrigerator during period t; is the refrigeration efficiency of the absorption chiller; They are the upper and lower limits of the refrigeration power of the absorption chiller respectively; The power balance constraints include: 1) Electric power balance constraints: 2) Thermal power balance constraints: 3) Hydrogen power balance constraints: 4) Cold power balance constraints: 5) Natural gas power balance constraints: In the above formula, are the charging and discharging powers of the battery during period t respectively; are the power of charging and discharging hydrogen in the hydrogen storage tank during period t; P t load , are the electric, heating and cooling load powers in period t respectively; The expression of the power curtailment constraint is: In the above formula, are the wind power and solar power abandonment in period t respectively; P t WT , P t PV are wind power and photovoltaic power in period t respectively; The expression of the energy purchase constraint is: In the above formula, P t buy , are the power of electricity and gas purchased in period t respectively; They are the upper limits of electricity and gas purchase power respectively.

7. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 1 is characterized in that: In step S2 and step S3, an initial scene is obtained by Latin hypercube sampling, and a K-means clustering algorithm is used to perform scene reduction on the initial scene to obtain several typical scenes, and the several typical scenes are used as a random scene set.

8. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 1 is characterized in that: The expression of the intraday stochastic optimization model in step S2 is: In the formula, x i is the decision variable in the intraday stochastic optimization model; ξ i is an uncertain random variable generated based on a set of random scenarios.

9. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 5 is characterized in that: The objective function of the intraday stochastic optimization model in step S2 is expressed as: F id =C buy +C om +C adj +C cut -R CO2 ; In the formula, F id is the objective function of the intraday stochastic optimization model; is the power adjustment cost of device r; is the power adjustment amount of device r during period t.

10. The method for optimizing and dispatching an integrated energy system based on multiple time scales according to claim 1, characterized in that: The expression of the real-time stochastic optimization model in step S3 is: In the formula, x r is the decision variable in the real-time stochastic optimization model; r is an uncertain random variable generated based on a set of random scenarios.

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