Energy hub day-ahead planning and scheduling system and method based on risk constraint method

Abstract

The invention relates to the technical field of integrated energy system control, in particular to an energy hub day-ahead planning and scheduling system and method based on a risk constraint method. The planning system provided by the invention comprises a renewable energy module, a combined energy module, a battery exchange station module, a hydrogen production and refueling station module, an energy storage module and a load redistribution module, and through the load redistribution module, load planning is carried out on an energy hub to improve profits; the uncertainty and risk of wind speed, solar radiation, load, natural gas, energy market price and pull-in vehicle state are processed by using conditional value-at-risk indexes, and meanwhile, a hydrogen energy preparation and storage model is also considered in an energy hub system.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy system control technology, and in particular to a day-ahead planning and dispatching system and method for an energy hub based on a risk constraint method. Background Art

[0002] At present, with the development and popularization of renewable energy and clean energy, energy hubs including water electrolysis hydrogen production and refueling stations and battery swap stations are gradually increasing. However, with the access of renewable energy and hydrogen energy, various factors including wind and solar unit power, station vehicle status, local load and market price have brought uncertainty to the energy hub system, increasing the decision-making risk of energy hub planning in the future. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the existing technology and propose a day-ahead planning and scheduling system and method for energy hubs based on a risk constraint method, which can perform load planning for energy hubs to improve profits, and use the conditional value at risk (CVaR) indicator to process factors such as wind speed, solar radiation, load, natural gas and energy market prices, and the status of vehicles entering the station to reduce uncertainty and risk.

[0004] To achieve the above object, the present invention adopts the following specific technical solutions:

[0005] The energy hub day-ahead planning and dispatching system based on the risk constraint method provided by the present invention comprises:

[0006] Renewable Energy Module, which calculates the power of wind and solar units;

[0007] Combined energy module, including a combined heat and power unit and a chiller unit, the combined energy module is used to calculate the power supply and heating power of the combined heat and power unit and the output power of the chiller unit to meet the cooling load demand;

[0008] The battery swap station module is used to calculate the number of charging and discharging batteries at the electric vehicle battery swap station and the input and output power of the swap station;

[0009] The hydrogen production and refueling station module is used to calculate the amount of hydrogen supplied by the hydrogen production and refueling station, the demand for hydrogen by vehicles, and the amount of hydrogen produced by the hydrogen production equipment;

[0010] An energy storage module, including an electric power storage unit and a hydrogen energy storage unit, wherein the energy storage module is used to plan the storage status of hydrogen and electricity;

[0011] Load redistribution module is used to modify the load curve to achieve redistribution of electrical, heating and cooling loads.

[0012] Furthermore, the calculation formula for the power of wind and solar units in the renewable energy module is:

[0013]

[0014]

[0015] P WT (s,t)=0.5×ρ×A×η WT ×W(s,t) 3 ;

[0016] Among them, s represents the scene, t represents the time, T is the temperature of the photovoltaic panel, and T environment is the ambient temperature, T Normal is the general temperature of the photovoltaic panel, is the general temperature of the environment, SR is the amount of light radiation, SR Normal is the general light intensity, P PV is the photovoltaic output power, is the photovoltaic output under standard conditions, SR standard is the standard light radiation, PC is the number of parallel photovoltaic panels, SC is the number of series photovoltaic panels, γ is the temperature coefficient, T standard is the standard temperature, P WT is the wind turbine output, ρ is the air density, A is the blade area, wind turbine power generation efficiency, and W is the wind speed.

[0017] Furthermore, the expression of the combined heat and power unit of the combined energy module is as follows:

[0018]

[0019]

[0020]

[0021]

[0022] Among them, P CHP (s,t) is the electricity supply of the CHP unit, For electricity supply from natural gas in cogeneration units, is the power conversion efficiency of the cogeneration unit, H CHP (s,t) is the heating power of the cogeneration unit, For natural gas heating power, Heat conversion efficiency of cogeneration unit, is the maximum power generation of the cogeneration unit, The maximum heat supply for the combined heat and power unit;

[0023] The expression of the chiller unit is as follows:

[0024] CChiller (s,t)=H Chiller (s,t)×η Chiller ;

[0025]

[0026] Among them, C Chiller (s, t) is the output power of the refrigerator unit, H Chiller (s,t) is the heat input of the refrigerator unit, is the maximum output load of the chiller unit.

[0027] Furthermore, the expression of the battery swap station module is as follows:

[0028] B Charged (s,t)=B Charged (s,t-1)+B charging (s,t-1)-B Discharged (s,t)-B EV (s,t);

[0029]

[0030]

[0031] B Empty (s,t)=B EV (s,t)+B Empty (s,t-1)+B Discharged (s,t)-B Charging (s,t-1);

[0032]

[0033]

[0034] Among them, B Charged (s,t) is the number of batteries charged at time t, B Charged (s,t-1) is the number of batteries charged at time t-1, B Charging (s,t-1) is the number of batteries being charged at time t-1, B Discharged (s,t) is the number of batteries that have been discharged at time t, B EV (s,t) the number of electric vehicles at time t; B Charged (s,t1) is the number of batteries charged at time t1, Initial number of charged batteries, B Discharged (s,t1) is the number of batteries discharged at time t1, B EV (s,t1) is the number of electric vehicles at time t1; B Charged(s,t n ) is t n The number of batteries charged at any given moment, μ Charged is the charging factor, B Empty (s,t) is the number of empty batteries available at time t, B Empty (s,t-1) is the number of empty batteries available at time t-1, is the number of empty batteries initially available, B Empty (s,t n ) is t n The number of empty batteries available at any time, μ Epmty The conversion factor for available empty batteries;

[0035] B Charging (s,t)≤B Empty (s,t);

[0036] B Charging (s,t)≤N Charger ;

[0037] B Discharged (s,t)≤N Discharger ;

[0038] N EV (s,t)≤AS(s,t)≤AS max ;

[0039] B Discharged (s,t)+B Charged (s,t)+B Charging (s,t)+B Empty (s,t)≤WC;

[0040] Among them, N Charging (s,t) The number of batteries being charged at time t, B Empty (s,t) is the number of empty batteries available at time t, N Charger is the number of charging devices, N Charged (s,t) is the number of batteries discharged at time t, N Discharger is the number of discharge facilities, N EV (s, t) is the number of electric vehicles at time t, AS(s, t) is the available parking spaces at time t, and AS max is the maximum number of available parking spaces;

[0041]

[0042]

[0043]

[0044] in, is the number of trams that can enter the battery swap station at time t, is the number of trams of passive load type at time t, is the number of trams of active load type at time t, TC EV (s) is the total cost of the tram in scenario s, is the active load tram conversion coefficient, is the maximum cost of the tram in scenario s;

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] in, is the output power of the battery swap station at time t, BC is the battery capacity, η Discharged is the discharge efficiency, is the input power of the battery swap station at time t, is the maximum power of the battery swap station, Input variables for the boundary of the battery swap station at time t, is the output variable of the battery swap station boundary at time t.

[0051] Furthermore, the expression of the hydrogen production and hydrogenation station module is as follows:

[0052]

[0053] HG HV (s,h,t)=Rand(0,1)×VT;

[0054]

[0055] Among them, HG P2G (s,t) is the hydrogen production of the water electrolysis hydrogen production equipment at time t, is the amount of hydrogen released by the hydrogen production and refueling station at time t, is the gas collected by the hydrogen production and refueling station at time t, HG HV (s,ν,t) is the total amount of hydrogen supplied by the hydrogen production and refueling station to v vehicles at time t, HG HV (s,h,t) is the demand for h vehicles entering the hydrogen production and refueling station, P P2G (s,t) is the power of the hydrogen production and refueling station, η P2Gis the electricity-to-hydrogen conversion efficiency of the hydrogen production and refueling station, and HHV is the high calorific value of hydrogen.

[0056] Furthermore, the expression of the energy storage module is as follows:

[0057]

[0058] SoC min ≤SoC(s,t)≤SoC max ;

[0059]

[0060]

[0061] K Charged (s,t)+K Discharged (s,t)≤1;

[0062] Among them, SoC(s,t) is the hydrogen and electricity storage state at time t, SoC(s,t-1) is the hydrogen and electricity storage state at time t-1, and X Charged (s,t) is the charging free variable at time t, η Charged is the storage coefficient, η Discharged is the release coefficient, SoC min Minimum storage capacity for hydrogen and electricity, SoC max It is the largest storage capacity for hydrogen and electricity. is the minimum charged free variable, is the maximum charge free variable, K Charged (s,t) is the binary variable charged at time t, is the minimum exergonic free variable, is the maximum free variable, K Discharged (s,t) is the binary variable that releases energy at time t.

[0063] Furthermore, the expression of the load redistribution module is as follows:

[0064]

[0065]

[0066] ∑ t RL(s,t)=0;

[0067] in, is the load power after load redistribution at time t, is the load power before load redistribution at time t, RL(s,t) is the reconstructed load power, μ LR Reassign factors to loads.

[0068] The present invention also provides an energy hub day-ahead planning and dispatching method based on a risk constraint method, which is applied to the above-mentioned planning system, including setting the total profit as the objective function, calculating the power supply cost, power storage cost, hydrogen energy supply cost, power consumption cost, wind turbine cost, natural gas input cost, photovoltaic cost, cogeneration cost, refrigerator cost, cost of supplying natural gas and hydrogen, power storage cost and hydrogen storage cost, and obtaining the objective function;

[0069] Set the operation model of electricity, heating, cooling loads, natural gas and hydrogen to constrain the planning system;

[0070] Set up a risk strategy model and use the conditional value-at-risk indicator to deal with the uncertainty and risk of the planning system.

[0071] Furthermore, the expression of the objective function is as follows:

[0072]

[0073] Among them, f(s) is the total profit objective function, is the electricity supply cost under scenario s, TC BSS (s) is the cost of the battery swap station under scenario s, TC HFS (s) is the hydrogen supply cost under scenario s, is the electricity cost under scenario s, TC WT (s) is the wind turbine cost under scenario s, is the natural gas input cost under scenario s, TC PV (s) is the photovoltaic cost under scenario s, TC CHP (s) is the combined heat and power cost under scenario s, TC Chiller (s) is the refrigerator cost under scenario s, TC P2G (s) is the cost of supplying natural gas and hydrogen under scenario s, TC ESS (s) is the electricity storage cost under scenario s, TC HSS (s) is the hydrogen storage cost under scenario s;

[0074] Combined heat and power cost TC CHP (S), Refrigeration machine cost TC Chiller (S), the cost of natural gas and hydrogen TC P2G (S), fan cost TC WT (s), photovoltaic cost TC PV The expression of (s) is as follows:

[0075] TC CHP (s,t)=∑ t (P CHP (s,t)+H CHP(s,t))×OC CHP ;

[0076] TC Chiller (s,t)=∑ t C Chiller (s,t)×OC Chiller ;

[0077] TC P2G (s)=∑ t G P2G (s,t)×OC P2G ;

[0078] TC WT (s)=∑ t P WT (s,t)×OC WT ;

[0079] TC PV (s)=∑ t P PV (s,t)×OC PV ;

[0080] Among them, OC CHP is the operating cost of the combined heat and power system, OC Chiller is the chiller operating cost, OC P2G For the cost of transporting hydrogen and natural gas, OC WT is the wind turbine operating cost, OC PV is the photovoltaic operating cost;

[0081] Electricity cost Electricity supply costs Natural gas input cost The expression is as follows:

[0082]

[0083]

[0084]

[0085] Among them, MC Electricity (s,t) is the electricity price at time t, MC Gas (s,t) is the price of natural gas at time t;

[0086] Electricity storage cost TC ESS (s) and hydrogen storage cost TC HSS The expression of (s) is as follows:

[0087] TC ESS (s)=∑ t PESS (s,t)×OC ESS ;

[0088] TC HSS (s)=∑ t HG HSS (s,t)×OC HSS ;

[0089] Among them, OC ESS is the operating cost of the power storage device, OC HSS The operating cost of hydrogen storage equipment;

[0090] Battery Swap Station Cost TC BSS The expression of (s) is as follows:

[0091] TC BSS (s)=∑ t [B EV (s,t)×BC×MC Electricity (s,t)-B Charging (s,t)×CD];

[0092] TC HFS (s)=∑ v,t HG HV (s,v,t)×HGP;

[0093] Among them, CD is the cooling power demand and HGP is the hydrogen selling price.

[0094] Furthermore, the expressions of the operation models of electricity, heating, cooling loads, natural gas, and hydrogen are as follows:

[0095]

[0096] H CHP (s,t)=H Chiller (s,t)+HD(s,t);

[0097] C Chiller (s, t) = CD(s, t);

[0098]

[0099]

[0100]

[0101]

[0102] in, is the boundary variable of power input at time t, is the boundary variable of power output at time t;

[0103] The expression of the risk strategy model is as follows:

[0104] f Risk =(1-β)×∑ s (f(s)×π(s))+β×CVaR;

[0105]

[0106] VaR-f Risk (s)≤ψ(s);

[0107] Among them, f Risk is the risk function, parameter β is the risk parameter, changing β from 0 to 1 will enhance the robustness against the worst-case scenario, CVaR is the conditional value at risk indicator, VaR is the value at risk index, α is the confidence interval, π(s) is the probability under scenario s, and ψ(s) is the risk variable under scenario s.

[0108] The present invention can achieve the following technical effects:

[0109] The present invention provides a load redistribution module and performs load planning for energy hubs to improve profits. The conditional value at risk (CVaR) indicator is used to handle the uncertainty and risks of wind speed, solar radiation, load, natural gas and energy market prices, and the status of incoming vehicles. The production and storage model of hydrogen energy is considered in the energy hub system. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 It is a schematic diagram of the influence of the changes of parameters α and β on the total profit according to an embodiment of the present invention. DETAILED DESCRIPTION

[0111] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are represented by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, the detailed description thereof will not be repeated.

[0112] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0113] The embodiment of the present invention provides an energy hub day-ahead planning and dispatching system based on a risk constraint method, including:

[0114] Renewable Energy Module, which calculates the power of wind and solar units;

[0115] Combined energy module, including a combined heat and power unit and a chiller unit, the combined energy module is used to calculate the power supply and heating power of the combined heat and power unit and the output power of the chiller unit to meet the cooling load demand;

[0116] The battery swap station module is used to calculate the number of charging and discharging batteries at the electric vehicle battery swap station and the input and output power of the swap station;

[0117] The hydrogen production and refueling station module is used to calculate the amount of hydrogen supplied by the hydrogen production and refueling station, the demand for hydrogen by vehicles, and the amount of hydrogen produced by the hydrogen production equipment;

[0118] An energy storage module, including an electric power storage unit and a hydrogen energy storage unit, wherein the energy storage module is used to plan the storage status of hydrogen and electricity;

[0119] Load redistribution module is used to modify the load curve to achieve redistribution of electrical, heating and cooling loads.

[0120] The output of renewable resources in the renewable energy module is directly affected by climatic conditions and geographical specifications. Therefore, the power of wind and solar units can be calculated based on technical characteristics and meteorological data (such as wind speed, solar irradiance and air density). The expression is as follows:

[0121]

[0122]

[0123] P WT (s,t)=0.5×ρ×A×η WT ×W(s,t) 3 (3)

[0124] Among them, s represents the scene, t represents the time, T is the temperature of the photovoltaic panel, T environment is the ambient temperature, T Normal is the temperature of the photovoltaic panel under normal circumstances. is the general temperature of the environment. SR is the amount of light radiation. Normal is the general light intensity. PV is the photovoltaic output power, is the photovoltaic output under standard conditions, SR standard is the standard light radiation. PC is the number of parallel photovoltaic panels, SC is the number of series photovoltaic panels, and γ is the temperature coefficient. standard is the standard temperature. WT is the wind turbine output, ρ is the air density, A is the blade area, wind turbine power generation efficiency, and W is the wind speed. The temperature of the photovoltaic panel is determined by formula (1), the photovoltaic power generation is determined by formula (2), and the wind power generation is obtained by formula (3).

[0125] The combined energy module consists of a combined heat and power plant and a chiller. Due to the recovery of waste heat, the efficiency of the combined heat and power plant is higher than that of a single power generation facility. The expression is as follows:

[0126]

[0127]

[0128]

[0129]

[0130] Among them, P CHP For the electricity supply of cogeneration, For the supply of electricity from natural gas in cogeneration systems, is the power conversion efficiency of the cogeneration system, H CHP is the heating power of the cogeneration system, For natural gas heating power, Heat conversion efficiency of cogeneration system. is the maximum power generation of the cogeneration system, is the maximum heat supply of the combined heat and power system. Formulas (4) and (5) are the electricity and heat generated by the combined energy system, and formulas (6) and (7) are the maximum capacity constraints of the combined heat and power equipment. At the same time, the combined energy system is used to meet the cooling load demand, and its refrigerator model is as follows:

[0131] C Chiller (s,t)=H Chiller (s,t)×η Chiller (8)

[0132]

[0133] Among them, C Chiller is the refrigerator output power, H Chiller is the heat input of the refrigerator, is the maximum output load of the refrigerator, and formulas (8) and (9) are the output and maximum capacity constraints of the refrigerator respectively.

[0134] The battery exchange station module is an electric vehicle power supply relay station with battery exchange as the core. The present invention uses an integer linear model to simulate the battery exchange station, and its expression is as follows:

[0135] B Charged (s,t)=B Charged (s,t-1)+B charging (s,t-1)-B Charged (s,t)-B EV (s,t) (10)

[0136]

[0137]

[0138] B Empty (s,t)=B EV (s,t)+B Empty (s,t-1)+B Discharged (s,t)-B Charging (s,t-1) (13)

[0139]

[0140]

[0141] Among them, B Charged (s,t) is the number of charged batteries at time t, B Charged (s,t-1) is the number of charged batteries at time t-1, B Charging (s,t-1) is the number of batteries being charged at time t-1, B Discharged (s,t) is the number of batteries that have been discharged at time t, B EV (s,t) The number of electric vehicles at time t. B Charged (s,t1) is the number of charged batteries at time t1, Initial number of charged batteries, B Discharged (s,t1) is the number of discharged batteries at time t1, B EV (s,t1) is the number of electric vehicles at time t1. Charged (s,t n ) is the number of charged batteries at time tn, μ Charged is the charging factor, B Empty (s,t) is the number of empty batteries available at time t, B Empty (s,t-1) is the number of empty batteries available at time t-1, is the number of empty batteries available initially, B Empty (s,t n ) is t n Number of empty batteries available at any time, μ Epmty is the conversion factor of available empty batteries. Equation (10) is the available charged batteries in each time interval, equations (11) and (12) are the number of charged batteries in the first time period and the last time period, equation (13) is the available empty batteries in each time interval, and equations (14) and (15) respectively simulate the dependence of the previous day on the next day.

[0142] B Charging (s,t)≤BEmpty (s,t) (16)

[0143] B Charging (s,t)≤N Charger (17)

[0144] B Discharged (s,t)≤N Discharger (18)

[0145] N EV (s,t)≤AS(s,t)≤AS max (19)

[0146] B Discharged (s,t)+B Charged (s,t)+B Charging (s,t)+B Empty (s,t)≤WC (20)

[0147] Among them, B Charging (s, t) The number of batteries being charged at time t, B Empty (s,t) is the number of empty batteries available at time t, N Charger is the number of charging devices, V Discharged (s,t) The number of discharged batteries at time t, N Charger Number of discharge facilities, N EV (s, t) is the number of electric vehicles at time t, AS(s, t) is the available parking spaces at time t, and AS max is the maximum available parking space. Equations (16)-(18) are the constraints for charging, discharging, and empty batteries. Constraints (19) and (20) indicate the number of entering vehicles and the total number of batteries.

[0148]

[0149]

[0150]

[0151] in, is the number of trams that can enter the battery swap station at time t, The number of passive load type trams at time t, is the number of active load type trams at time t, TC EV (s) is the total cost of the tram in scenario s, is the active load tram conversion coefficient, is the maximum cost of the tram in scenario s. Equation (21) is the vehicle load constraint, equation (22) is the vehicle entry quantity constraint, and equation (23) is the vehicle total cost constraint.

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] in, is the output power of the battery swap station at time t, BC is the battery capacity, η Discharged is the discharge efficiency, is the input power of the power swap station at time t, is the maximum power of the battery swap station, Input variables for the boundary of the battery swap station at time t, is the boundary output variable of the battery swap station at time t. Equations (24) and (25) are the power constraints between the battery swap station and the grid, while equations (26)-(28) are the power constraints and operation modes between the grid and the battery swap station.

[0158] The hydrogen production and refueling station module consumes hydrogen energy in the water electrolysis hydrogen production equipment and hydrogen storage tanks. The expression is:

[0159]

[0160] HG HV (s,h,t)=Rand(0,1)×VT (30)

[0161]

[0162] Among them, HG P2G (s,t) Hydrogen production of water electrolysis equipment at time t, is the amount of hydrogen released by the hydrogen production and refueling station at time t, is the gas collected by the hydrogen production and refueling station at time t, HG HV (s,v,t) is the total amount of hydrogen supplied by the hydrogen production and refueling station to v hydrogen fuel cell vehicles at time t, HG HV (s,h,t) is the demand for h hydrogen fuel cell vehicles entering the hydrogen production and refueling station. P2G (s,t) is the power of hydrogen production and hydrogenation station, η P2G is the electricity-to-hydrogen conversion efficiency of the hydrogen production and refueling station. HHV is the higher heating value of hydrogen. Equation (29) is the hydrogen balance constraint, the hydrogen demand of fuel cell vehicles is given by Equation (30), and Equation (31) is the hydrogen production constraint.

[0163] The energy storage module includes an electric storage unit and a hydrogen energy storage unit. Electricity storage is suitable for short-term electricity supply, and hydrogen storage is suitable for long-term energy storage needs. It can be used as a seasonal energy storage medium to provide energy supply. The relevant constraints are as follows:

[0164]

[0165] SoC min ≤SoC(s,t)≤SoC max (33)

[0166]

[0167]

[0168] K Charge (s,t)+K Discharged (s,t)≤1; (36)

[0169] Among them, SoC(s,t) is the hydrogen and electricity storage state at time t, SoC(s,t-1) is the hydrogen and electricity storage state at time t-1, and X Charged (s,t) is the charging free variable at time t, η Charged is the storage coefficient, η Discharged is the release coefficient, SoC min Minimum storage capacity for hydrogen and electricity, SoC max It is the largest storage capacity for hydrogen and electricity. is the minimum charged free variable, is the maximum charge free variable, K Charged (s,t) is the binary variable charged at time t, is the minimum exergonic free variable, is the maximum free variable, K Discharged (s,t) is the binary variable that releases energy at time t.

[0170] To achieve higher flexibility, it is assumed that the energy management system is able to modify the load profile according to the available resources and forecasts. In this regard, a load redistribution module is considered, which can be applied to electrical, heating and cooling loads to achieve the stated goal.

[0171]

[0172]

[0173] ∑ t RL(s,t)=0 (35)

[0174] in, is the load power after load redistribution at time t, is the load power before load redistribution at time t, RL(s,t) is the reconstructed load power, μ LR is the load redistribution coefficient. Wherein, formula (37) is the new load curve after load redistribution, formula (38) is the lower limit / upper limit of the redistributed load, and formula (39) is the supply and demand conservation constraint.

[0175] The present invention also provides an energy hub day-ahead planning and dispatching method based on a risk constraint method. First, the total profit is used as the objective function, and its expression is as follows:

[0176]

[0177] in, is the electricity supply cost under scenario s, TC BSS (s) is the cost of the battery swap station under scenario s, TC HFS (s) is the hydrogen supply cost under scenario s, is the electricity cost under scenario s, TC WT (s) is the wind turbine cost under scenario s, is the natural gas input cost under scenario s, TC PV (s) is the photovoltaic cost under scenario s, TC CHP (s) is the combined heat and power cost under scenario s, TC Chiller (s) is the refrigerator cost under scenario s, TC P2G (s) is the cost of supplying natural gas and hydrogen under scenario s, TC ESS (s) is the electricity storage cost under scenario s, TC HST (s) is the hydrogen storage cost under scenario s. The cost functions of cogeneration, refrigerators, water electrolysis hydrogen production equipment, fans, and photovoltaics are as follows:

[0178] TC CHP (s,t)=∑ t (P CHP (S,t)+H CHP (s,t))×OC CHP (37)

[0179] TC Chiller (s,t)=∑ t C Chiller (s,t)×OC Chiller (38)

[0180] TC P2G (s)=∑ t G P2G (s,t)×OC P2G (39)

[0181] TCWT (s)=∑ t P WT (s,t)×OC WT (40)

[0182] TC PV (s)=∑ t P PV (s,t)×OC PV (41)

[0183] Among them, OC CHP is the operating cost of the combined heat and power system, OC Chiller is the chiller operating cost, OC P2G For the cost of transporting hydrogen and natural gas, OC WT is the wind turbine operating cost, OC PV The photovoltaic operating cost.

[0184] The cost of electricity exchange is as follows:

[0185]

[0186]

[0187]

[0188] Among them, MC Electricity (s,t) is the electricity price at time t, MC Gas (s,t) is the price of natural gas at time t.

[0189] The costs of the electricity storage unit and the hydrogen storage unit are as follows:

[0190] TC ESS (s)=∑ t P ESS (s,t)×OC ESS (45)

[0191] TC HSS (s)=∑ t HG HSS (s,t)×OC HSS (46)

[0192] Among them, OC ESS is the operating cost of the power storage device, OC HSS It is the operating cost of hydrogen storage equipment.

[0193] The cost function of a battery swap station is shown below:

[0194] TC BSS (s)=∑ t [B EV(s,t)×BC×MC Electricity (s,t)-B Charging (s,t)×CD] (47)

[0195] TC HFS (s)=∑ v,t HG HV (s,v,t)×HGP (48)

[0196] Among them, CD is the cooling power demand and HGP is the hydrogen selling price.

[0197] The power balance equations for electricity, heating, cooling loads and the operating modes of natural gas and hydrogen are shown below:

[0198]

[0199] H CHP (s,t)=H Chiller (s,t)+HD(s,t) (50)

[0200] C Chiller (s,t)=CD(s,t) (51)

[0201]

[0202]

[0203]

[0204]

[0205] in, is the boundary variable of power input at time t, is the boundary variable of power output at time t.

[0206] The risk strategy is used to avoid the risk between the profits of different equipment, and its expression is as follows:

[0207] f Risk =(1-β)×∑ s (f(s)×π(s))+β×CVaR (56)

[0208]

[0209] VaR-f Risk (s)≤ψ(s) (58)

[0210] The parameter β is the risk parameter. Changing β from 0 to 1 will enhance the robustness against the worst-case scenario. CVaR is the conditional value at risk indicator, VaR is the value at risk index, α is the confidence interval, π(s) is the probability under scenario s, ψ(s) is the risk variable under scenario s, and f Risk is the risk function.

[0211] The specifications of wind and solar units, cooling systems, energy storage systems, and hydrogen production and natural gas transportation are captured in the relevant information of the present invention, and the required information on site-related operating and energy costs, load curves, wind speeds, and solar radiation is collected. The sufficient time required to replace all batteries of each vehicle in the battery swap station is 15 minutes, and each parking space of the battery swap station can support 4 vehicles per hour. The proposed model is optimized for a 24-hour cycle with a time interval of 1 hour. The impact of parameters α and β in the proposed risk strategy on the total profit is shown in the figure. Figure 1 shown.

[0212] The load redistribution model used in the present invention can change the local load demand according to the available resources and the allowed range, and shows the profit changes under the redistribution of cooling, heating and power supply loads.

[0213] Table 1 Profits of load redistribution

[0214]

[0215] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0216] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0217] The above specific implementations of the present invention do not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A day-ahead planning and dispatching system for energy hubs based on a risk constraint method, characterized in that: include: Renewable Energy Module, which calculates the power of wind and solar units; A combined energy module, including a cogeneration unit and a refrigeration unit, wherein the combined energy module is used to calculate the power supply and heating power of the cogeneration unit and the output power of the refrigeration unit to meet the cooling load demand; The battery swap station module is used to calculate the number of charging and discharging batteries at the electric vehicle battery swap station and the input and output power of the swap station; The hydrogen production and refueling station module is used to calculate the amount of hydrogen supplied by the hydrogen production and refueling station, the demand for hydrogen by vehicles, and the amount of hydrogen produced by the hydrogen production equipment; An energy storage module, including an electric power storage unit and a hydrogen energy storage unit, wherein the energy storage module is used to plan the storage status of hydrogen and electricity; Load redistribution module is used to modify the load curve to achieve redistribution of electrical, heating and cooling loads.

2. The energy hub day-ahead planning and dispatching system based on the risk constraint method according to claim 1 is characterized in that: The expression of wind energy and solar energy unit power in the renewable energy module is: P WT (s,t)=0.5×ρ×A×η WT ×W(s,t) 3 ; Among them, s represents the scene, t represents the time, T is the temperature of the photovoltaic panel, and T environment is the ambient temperature, T Normal is the general temperature of the photovoltaic panel, is the general temperature of the environment, SR is the amount of light radiation, SR Normal is the general light intensity, P PV is the photovoltaic output power, is the photovoltaic output under standard conditions, SR standard is the standard light radiation, PC is the number of parallel photovoltaic panels, SC is the number of series photovoltaic panels, γ is the temperature coefficient, T standard is the standard temperature, P WT is the wind turbine output, ρ is the air density, A is the blade area, wind turbine power generation efficiency, and W is the wind speed.

3. The energy hub day-ahead planning and dispatching system based on the risk constraint method according to claim 1 is characterized in that: The expression of the combined heat and power unit of the combined energy module is as follows: Among them, P CHP (s,t) is the electricity supply of the CHP unit, For electricity supply from natural gas in cogeneration units, is the power conversion efficiency of the cogeneration unit, P CHP (s,t) is the heating power of the cogeneration unit, For natural gas heating power, Heat conversion efficiency of cogeneration unit, is the maximum power generation of the cogeneration unit, The maximum heat supply for the combined heat and power unit; The expression of the refrigerator unit is as follows: C Chiller (s,t)=H Chiller (s,t)×η Chiller ; Among them, C Chiller (s, t) is the output power of the refrigerator unit, H Chiller (s,t) is the heat input of the refrigerator unit, is the maximum output load of the chiller unit.

4. The energy hub day-ahead planning and dispatching system based on the risk constraint method according to claim 1 is characterized in that: The expression of the battery swap station module is as follows: B Charged (s,t)=B Charged (s,t-1)+B Charging (s,t-1)-B Discharged (s,t)-B EV (s,t); B Empty (s,t)=B EV (s,t)+B Empty (s,t-1)+B Discharged (s,t)-B Charging (s,t-1); Among them, B Charged (s,t) is the number of batteries charged at time t, B Charged (s,t-1) is the number of batteries charged at time t-1, B Charging (s,t-1) is the number of batteries being charged at time t-1, B Discharged (s,t) is the number of batteries that have been discharged at time t, B EV (s,t) the number of electric vehicles at time t; B Charged (s,t1) is the number of batteries charged at time t1, Initial number of charged batteries, B Discharged (s,t1) is the number of batteries discharged at time t1, B EV (s,t1) is the number of electric vehicles at time t1; B Charged (s,t n ) is t n The number of batteries charged at any given moment, μ Charged is the charging factor, B Empty (s,t) is the number of empty batteries available at time t, B Empty (s,t-1) is the number of empty batteries available at time t-1, is the number of empty batteries initially available, B Empty (s,t n ) is t n The number of empty batteries available at any time, μ Epmty The conversion factor for available empty batteries; B Charging (s,t)≤B Empty (s,t); B Charging (s,t)≤N Charger ; B Discharged (s,t)≤N Discharger ; N EV (s,t)≤AS(s,t)≤AS max ; B Discharged (s,t)+B Charged (s,t)+B Charging (s,t)+B Empty (s,t)≤WC; Among them, B Charging (s,t) The number of batteries being charged at time t, B Enpty (s,t) is the number of empty batteries available at time t, N Charger is the number of charging devices, B Discharged (s,t) is the number of batteries discharged at time t, N Discharger is the number of discharge facilities, N EV (s, t) is the number of electric vehicles at time t, AS(s, t) is the available parking spaces at time t, and AS max is the maximum number of available parking spaces; in, is the number of trams that can enter the battery swap station at time t, is the number of trams of passive load type at time t, is the number of trams of active load type at time t, TC EV (s) is the total cost of the tram in scenario s, is the active load tram conversion coefficient, is the maximum cost of the tram in scenario s; in, is the output power of the battery swap station at time t, BC is the battery capacity, η Discharged is the discharge efficiency, is the input power of the battery swap station at time t, is the maximum power of the battery swap station, Input variables for the boundary of the battery swap station at time t, is the output variable of the battery swap station boundary at time t.

5. The energy hub day-ahead planning and dispatching system based on the risk constraint method according to claim 1 is characterized in that: The expression of the hydrogen production and hydrogenation station module is as follows: HG HV (s,h,t)=Rand(0,1)×VT; Among them, HG P2G (s,t) is the hydrogen production of the water electrolysis hydrogen production equipment at time t, is the amount of hydrogen released by the hydrogen production and refueling station at time t, is the gas collected by the hydrogen production and refueling station at time t, HG HV (s,v,t) is the total amount of hydrogen supplied by the hydrogen production and refueling station to v vehicles at time t, HG HV (s,h,t) is the demand for h vehicles entering the hydrogen production and refueling station, P P2G (s,t) is the power of the hydrogen production and refueling station, η P2G is the electricity-to-hydrogen conversion efficiency of the hydrogen production and refueling station, and HHV is the high calorific value of hydrogen.

6. The energy hub day-ahead planning and dispatching system based on the risk constraint method according to claim 1 is characterized in that: The expression of the energy storage module is as follows: SoC min ≤SoC(s,t)≤SoC max ; K Charged (s,t)+K Discharged (s,t)≤1; Where SoC(s,t) is the hydrogen and electricity storage state at time t, SoC(s,t-1) is the hydrogen and electricity storage state at time t-1, and X Charged (s,t) is the charging free variable at time t, η Charged is the storage coefficient, η Discharged is the release coefficient, SoC min Minimum storage capacity for hydrogen and electricity, SoC max It is the largest storage capacity for hydrogen and electricity. is the minimum charged free variable, is the maximum charge free variable, K Charged (s,t) is the binary variable charged at time t, is the minimum exergonic free variable, is the maximum free variable, K Discharged (s,t) is the binary variable that releases energy at time t.

7. The energy hub day-ahead planning and dispatching system based on the risk constraint method according to claim 1 is characterized in that: The expression of the load redistribution module is as follows: ∑ t RL(s,t)=0; in, is the load power after load redistribution at time t, is the load power before load redistribution at time t, RL(s,t) is the reconstructed load power, μ LR Reassign factors to loads.

8. A method for day-ahead planning and dispatching of energy hubs based on a risk constraint method, applied to the day-ahead planning and dispatching system of energy hubs based on a risk constraint method as claimed in any one of claims 1 to 7, characterized in that: The method comprises: The total profit is set as the objective function, and the electricity supply cost, electricity storage cost, hydrogen supply cost, electricity consumption cost, wind turbine cost, natural gas input cost, photovoltaic cost, cogeneration cost, refrigerator cost, cost of supplying natural gas and hydrogen, electricity storage cost, and hydrogen storage cost are calculated to obtain the objective function; Setting operation models of electricity, heating, cooling loads, natural gas, and hydrogen to constrain the planning system; A risk strategy model is set up to handle the uncertainty and risk of the planning system using a conditional value-at-risk indicator.

9. The energy hub day-ahead planning and dispatching method based on the risk constraint method according to claim 8 is characterized in that: The expression of the objective function is as follows: Among them, f(s) is the total profit objective function, is the electricity supply cost under scenario s, TC BSS (S) is the cost of the battery swap station under scenario s, TC HFS (s) is the hydrogen supply cost under scenario s, is the electricity cost under scenario s, TC WT (s) is the wind turbine cost under scenario s, is the natural gas input cost under scenario s, TC PV (s) is the photovoltaic cost under scenario s, TC CHP (s) is the combined heat and power cost under scenario s, TC Chiller (s) is the refrigerator cost under scenario s, TC P2G (S) is the cost of supplying natural gas and hydrogen under scenario s, TC ESS (s) is the electricity storage cost under scenario s, TC HSS (s) is the hydrogen storage cost under scenario s; The combined heat and power cost TC CHP (s), Refrigeration machine cost TC Chiller (s), the cost of natural gas and hydrogen TC P2G (s), fan cost TC WT (s), photovoltaic cost TC PV The expression of (s) is as follows: TC CHP (s,t)=∑ t (P CHP (s,t)+H CHP (s,t))×OC CHP ; TC Chiller (s,t)=∑ t C Chiller (s,t)×OC Choller ; TC P2G (s)=∑ t G P2G (s,t)×OC P2G ; TC WT (s)=∑ t P WT (s,t)×OC WT ; TC PV (s)=∑ t P PV (s,t)×OC PV ; Among them, OC CHP is the operating cost of the combined heat and power system, OC Chiller is the chiller operating cost, OC P2G For the cost of transporting hydrogen and natural gas, OC WT is the wind turbine operating cost, OC PV is the photovoltaic operating cost; The electricity cost Electricity supply costs Natural gas input cost The expression is as follows: Among them, MC Electricity (s,t) is the electricity price at time t, MC Gas (s,t) is the price of natural gas at time t; The electricity storage cost TC ESS (s) and hydrogen storage cost TC HSS The expression of (s) is as follows: TC ESS (s)=∑ t P ESS (s,t)×OC ESS ; TC HSS (s)=∑ t HG HSS (s,t)×OC HSS ; Among them, OC ESS is the operating cost of the power storage device, OC HSS The operating cost of hydrogen storage equipment; The battery swap station costs TC BSS The expression of (S) is as follows: TC BSS (s)=∑ t [B EV (s,t)×BC×MC Electricity (s,t)-B Charging (s,t)×CD]; TC HFS (s)=∑ v,t H G HV (s,v,t)×HGP; Among them, CD is the cooling power demand and HGP is the hydrogen selling price.

10. The energy hub day-ahead planning and dispatching method based on the risk constraint method according to claim 9 is characterized in that: The expressions of the operation models of the electricity, heating, cooling loads, natural gas and hydrogen are as follows: H CHP (s,t)=H Chiller (s,t)+HD(s,t); H Chiller (s,t)=CD(s,t); in, is the boundary variable of power input at time t, is the boundary variable of power output at time t; The expression of the risk strategy model is as follows: f Risk =(1-β)×∑ s (f(s)×π(s))+β×CVaR; VaR-f Risk (s)≤ψ(s); Among them, f Risk is the risk function, parameter β is the risk parameter, changing β from 0 to 1 will enhance the robustness against the worst-case scenario, CVaR is the conditional value at risk indicator, VaR is the value at risk index, α is the confidence interval, π(s) is the probability under scenario s, and ψ(s) is the risk variable under scenario s.

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