CN114169800B - Energy scheduling method of comprehensive energy system - Google Patents
Energy scheduling method of comprehensive energy system Download PDFInfo
- Publication number
- CN114169800B CN114169800B CN202111616548.5A CN202111616548A CN114169800B CN 114169800 B CN114169800 B CN 114169800B CN 202111616548 A CN202111616548 A CN 202111616548A CN 114169800 B CN114169800 B CN 114169800B
- Authority
- CN
- China
- Prior art keywords
- energy
- load
- power
- time
- cold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000004044 response Effects 0.000 claims abstract description 90
- 238000005265 energy consumption Methods 0.000 claims abstract description 10
- 238000013210 evaluation model Methods 0.000 claims abstract description 9
- 238000004146 energy storage Methods 0.000 claims description 49
- 230000008859 change Effects 0.000 claims description 34
- 230000035807 sensation Effects 0.000 claims description 25
- 238000009826 distribution Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 21
- 239000007789 gas Substances 0.000 claims description 19
- 230000005611 electricity Effects 0.000 claims description 18
- 238000010521 absorption reaction Methods 0.000 claims description 17
- 238000001816 cooling Methods 0.000 claims description 15
- 230000007613 environmental effect Effects 0.000 claims description 11
- 230000007246 mechanism Effects 0.000 claims description 9
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000020169 heat generation Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 230000009194 climbing Effects 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 4
- 239000003345 natural gas Substances 0.000 claims description 4
- 239000013256 coordination polymer Substances 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 3
- 238000005338 heat storage Methods 0.000 claims description 3
- 230000004060 metabolic process Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000005855 radiation Effects 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 10
- 230000007704 transition Effects 0.000 description 7
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 3
- 238000004134 energy conservation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an energy scheduling method of a comprehensive energy system, which comprises the steps of obtaining operation parameters of a target comprehensive energy system; establishing a basic operation model of the comprehensive energy system; constructing a user participation evaluation model; simulating uncertainty in the comprehensive demand response by adopting a normal cloud model; based on the basic operation model and the comprehensive demand response model of the comprehensive energy system, the minimum input energy of the comprehensive energy system is taken as an objective function, and the final optimal scheduling result of the comprehensive energy system is obtained by solving under the set boundary condition. The method can effectively describe randomness and ambiguity of the user participation in the comprehensive demand response will, can obviously reduce equivalent input energy and energy loss of the system, and improves new energy consumption rate, overall energy efficiency and system stability of the system, and has high reliability and good stability.
Description
Technical Field
The invention belongs to the field of electric automation, and particularly relates to an energy scheduling method of a comprehensive energy system.
Background
Along with the development of economic technology and the improvement of living standard of people, electric energy becomes an indispensable secondary energy source in the production and living of people, and brings endless convenience to the production and living of people. Therefore, ensuring stable and reliable supply of electric energy becomes one of the most important tasks of the electric power system.
The comprehensive energy system is an energy integrated management system for integrating the traditional energy systems in a certain area to realize unified planning and unified scheduling; the system can comprehensively arrange multiple types of resources in the area, meets the requirements of multiple types of energy sources such as electricity, gas, heat, cold and the like of users through the production and flexible conversion of the energy sources, and has the advantages of energy source coordination complementation, energy conservation, environmental protection and the like; thus, integrated energy systems have grown as an important component of electrical power systems.
In the power system, the demand response technology is widely applied, and interaction between a source side and a load side can be realized in the dispatching process of the power system, so that the operation flexibility and the reliability of the power system are effectively improved. Under the background that the comprehensive energy system exists, the power demand response can be expanded into the comprehensive demand response, and the response capability of the system is richer and the form is more various. The comprehensive demand response can be used as an important means for excavating the load side regulation potential of the comprehensive energy system, and plays an important supporting role in optimizing the energy structure of the comprehensive energy system.
However, the current energy scheduling method for the comprehensive energy system is often the scheduling method of the reference power system, so that the application effect is poor, the user will of the comprehensive energy system is not considered, and the scheduling reliability and stability are poor.
Disclosure of Invention
The invention aims to provide an energy scheduling method of a comprehensive energy system, which is designed for the comprehensive energy system, considers the user wish and has high reliability and good stability.
The energy scheduling method of the comprehensive energy system provided by the invention comprises the following steps:
S1, acquiring operation parameters of a target comprehensive energy system;
S2, establishing a basic operation model of the comprehensive energy system according to the operation parameters obtained in the step S1;
s3, constructing a user participation evaluation model, and quantifying the comprehensive demand response proportion of each period;
s4, simulating uncertainty in the comprehensive demand response by adopting a normal cloud model, so as to establish a comprehensive demand response model;
S5, solving under a set boundary condition by taking the minimum input energy of the comprehensive energy system as an objective function based on the basic operation model of the comprehensive energy system established in the step S2 and the comprehensive demand response model established in the step S4, so as to obtain a final optimal scheduling result of the comprehensive energy system.
The step S2 of establishing a basic operation model of the comprehensive energy system specifically comprises the following steps:
A. the relationship between energy supply and demand of the integrated energy system is expressed by the following expression:
Wherein L 1～Lm represents the load amounts of m loads on the load side; s 1～Sn is energy provided by n energy sources on the energy source side; μ ij is the energy conversion coefficient of the energy source j to the load i and μ ij＝αijηij,αij is the energy distribution coefficient of the energy source j to the load i, η ij is the energy conversion efficiency in the process of providing energy to the load i by the energy source j, i=1, 2, m, j=1, 2, n;
B. the uncertainty of the wind power output of the energy source side is expressed by the following formula:
Wherein P WT is the output power of the fan; u is a wind velocity component perpendicular to the plane of rotation of the blade; u in is the cut-in wind speed of the fan; u out is the cut-out wind speed of the fan; ρ is the air density; a is the area swept by the fan blade; c P is the aerodynamic efficiency of the blower; η is the efficiency of the generator in the fan;
C. The load uncertainty on the load side is expressed by the following equation:
Wherein P L is the actual electrical load value; p L0 is the electrical load predictor; Δp L is the electrical load change value; lambda P is the amount of uncertainty in the superposition of the electrical loads; h L is the actual heat load value; h L0 is a thermal load predictor; Δh L is the thermal load change value; lambda H is the amount of uncertainty in the superposition of the thermal load; c L is the actual cold load value; c L0 is a predicted value of the cold load; Δc L is the cold load variation value; lambda C is the amount of uncertainty in the superposition of the cold loads.
The step S3 of constructing a user participation evaluation model so as to quantify the comprehensive demand response proportion of each period, specifically comprises the following steps:
a. the following formula is adopted as a human body thermal comfort evaluation model:
PMV＝(0.303e-0.036M+0.028)·TL
Wherein PMV is a heat sensation average scale prediction index, and when PMV is less than 0, human sensation cold is determined, PMV > 0 is human sensation heat is determined, and pmv=0 is determined to be human sensation comfortable; m is the metabolism rate of the human body; TL is the human body heat load, and
W is mechanical work during human body movement, P a is partial pressure of water vapor in the environment, t a is air temperature, f cl is percentage of clothing covering the surface of a human body, t cl is average temperature of the surface of the clothing of the human body, t s is average radiation temperature of the environment, and h c is convective heat transfer coefficient;
b. the percentage model of the number of people unsatisfactory for a certain environmental condition is expressed by the following formula:
Wherein PPD is a percent of dissatisfaction prediction index;
c. C, correcting the percentage model of the number of people unsatisfied for a certain environmental condition, which is obtained in the step b, to obtain a corrected percentage model of the number of people unsatisfied for a certain environmental condition:
Wherein PPD' is a modified predictive dissatisfaction percentage index;
d. The adjustable portion on the load side is expressed by the following expression:
Wherein DeltaP lim,t is the upper power limit of the load side electric load which can participate in the comprehensive demand response at the time t; p flex is the flexible load of the load side electrical load; p semi is the semi-compliant load of the load side electrical load; PPD t' is the corrected forecast dissatisfaction percentage index at the time t; ΔH lim,t is the upper power limit at which the load side thermal load can participate in the integrated demand response at time t; h flex is a flexible load of the load side thermal load; h semi is the semi-compliant load of the load side thermal load; Δc lim,t is the upper power limit at which the load side cooling load can participate in the integrated demand response at time t; c flex is a flexible load of a load side cooling load; c semi is the semi-compliant load of the load side cooling load.
The step S4 of simulating uncertainty in the comprehensive demand response by adopting a normal cloud model, thereby establishing a comprehensive demand response model, specifically comprising the following steps:
(1) The self-elasticity coefficient of the energy consumption behavior influence of the energy price change on the energy user is expressed by the following formula:
ΔLi,t＝εii·ΔCi,t
Wherein DeltaL i,t is the change of the demand of energy source side energy i at the time t; epsilon ii is the self-elasticity coefficient and is negative; Δc i,t is the price change of energy source side energy i at time t; i is P, H or C;
(2) The mutual elasticity coefficient of the energy consumption behavior influence of the energy price change on the energy user is expressed by the following formula:
ΔLi,t＝εij·ΔCj,t
Wherein DeltaL i,t is the change of the demand of energy source side energy i at the time t; epsilon ij is the coefficient of mutual elasticity and is positive; Δc j,t is the price change of energy source side energy j at time t; j takes the value P, H or C;
(3) The price elasticity mechanism of the integrated demand response is integrated into the following matrix multiplication form:
Wherein epsilon PP is the self-elasticity coefficient of the electric energy price requirement; epsilon HH is the self-elasticity coefficient of the heat energy price requirement; epsilon CC is the self-elasticity coefficient of the cold energy price requirement; epsilon PH is the mutual elasticity coefficient of the electric-thermal price requirement; epsilon PC is the electric-cold price demand mutual elasticity coefficient; epsilon HP is the thermal-electrical price demand coefficient of mutual elasticity; epsilon HC is the heat-cold price demand mutual elasticity coefficient; epsilon CP is the cold-electricity price demand mutual elasticity coefficient; epsilon CH is the coefficient of cold-hot price demand mutual elasticity; Δc P,t is the electricity price change amount; Δc H,t is the heat value variation; Δc C,t is the cold price variation;
(4) Dividing the uncertainty of the comprehensive demand response into the uncertainty of a demand response boundary and the uncertainty of a price elasticity coefficient;
(5) And processing the uncertainty of the demand response boundary by adopting a probability cloud model of mixed uncertainty:
h(x)＝f(g(x))
F () is a membership function, and corresponds to a percent unsatisfied index of prediction; g (x) is a probability density function and corresponds to a thermal sensation average scale prediction index;
(6) The percentage of heat sensation average scale prediction-prediction dissatisfaction index after introducing uncertainty is expressed by the following equation:
A percentage of dissatisfaction index of the prediction after introducing uncertainty in the formula; lambda PMV is a random quantity superimposed by a heat sensation average scale prediction index, lambda PMV -N (1, he), and He is a variance of an uncertain quantity;
(7) Consider uncertainty in the price elastic coefficient: lambda ε -N (1, en) and En-N (sigma, he), wherein lambda ε is the uncertainty of superposition of the price elastic coefficients, en is the variance of the elastic coefficients, sigma is the mean value of En, he is the super entropy of lambda ε; therefore λ ε obeys a normal cloud distribution with parameters C (1, σ, he), where ε = λ ε·ε0, ε is the final price elastic coefficient, ε 0 is the preset elastic coefficient;
(8) The following formula is adopted as a membership function expression of the elastic coefficient certainty:
Wherein μ (γ ε) is a membership function of the elastic coefficient certainty; gamma ε is the uncertainty of the elastic coefficient; en is the variance of the elastic coefficient.
The step S5 of using the minimum input energy of the integrated energy system as an objective function specifically includes the following steps:
the minimum input energy of the comprehensive energy system is used as an optimization target, and the obtained objective function is as follows:
minEin＝PBuy+GBuy·LHV
Wherein E in is the input energy of the comprehensive energy system; p Buy is the electricity purchasing quantity of the comprehensive energy system to the upper power grid; g Buy is the upward air online air purchasing amount of the comprehensive energy system; LHV is the natural gas low heating value; the scheduling step size is 1 hour, and scheduling is performed in daily units.
The set boundary conditions in step S5 specifically include the following steps:
1) The following formula is adopted as the constraint of the cogeneration unit:
Wherein S CHP,t is a variable of 0-1, which is used for indicating the start-stop state of the cogeneration unit, and S CHP,t =1 indicates the start-up of the cogeneration unit; The lower limit of the output electric power of the cogeneration unit; p CHP,t is the power of the cogeneration unit at time t; /(I) The upper limit of the output electric power of the cogeneration unit; /(I)The lower limit of the output thermal power of the cogeneration unit; h CHP,t is the heat generation power of the cogeneration unit at the moment t; /(I)The upper limit of the output thermal power of the cogeneration unit; the upper limit of the climbing of the power generation power of the cogeneration unit is set; /(I) The upper limit of the climbing of the heat production power of the cogeneration unit is set; t on is the start-up time of the cogeneration unit; /(I)The minimum start-up time of the cogeneration unit is set; t off is the shutdown time of the cogeneration unit; /(I)The minimum shutdown time of the cogeneration unit is set;
2) The following formulas are adopted as the constraint conditions of the gas boiler, the heat pump, the absorption refrigerator and the electric refrigerator:
Wherein H GB,min is the lower limit of the output force of the gas boiler; h GB,t is the output value of the gas boiler at the moment t; h GB,max is the upper limit of the gas boiler output; h HP,min is the lower limit of heat pump output; h HP,t is the output value of the heat pump at the moment t; h HP,max is the upper limit of heat pump output; c AC,min is the lower limit of the output of the absorption refrigerator; c AC,t is the output of the absorption refrigerator at the moment t; c AC,max is the upper limit of the output of the absorption refrigerator; c EC,min is the lower limit of the output of the electric refrigerator; c EC,t is the output of the electric refrigerator at the moment t; ; c EC,max is the upper limit of the output of the electric refrigerator; p WT,t is the wind power generation power at the moment t; the maximum wind power which can participate in grid connection is the wind power output power at the moment t;
3) The following equation is used as the energy storage device constraint:
Wherein DeltaP s,t is the charge and discharge power of the electric energy storage device at the moment t; Δp s,max is the upper limit of the charge and discharge power of the electrical energy storage device; ΔH s,t is the charging and discharging power of the thermal energy storage device at time t; Δh s,max is the upper limit of the charge and discharge power of the thermal energy storage device; delta C s,t is the charge-discharge cold power of the cold energy storage device at the moment t; Δc s,max is the upper limit of the charge-discharge cold power of the cold energy storage device; e P,0 is the initial energy storage state of the electrical energy storage device; t is the scheduled time step; e P,cap is the rated capacity of the electrical energy storage device; e H,0 is the initial energy storage state of the thermal energy storage device; e H,cap is the rated capacity of the thermal energy storage device; e C,0 is the initial energy storage state of the cold energy storage device; e C,cap is the rated capacity of the cold energy storage device;
4) The following equation is used as a demand response conservation constraint:
Wherein C P,min is the lower limit of the electric energy response; c P,t is the selling price of the electric energy at the time t; c P,max is the upper limit of the electrical energy response; c H,min is the lower limit of the thermal energy response; c H,t is the selling price of heat energy at the time t; c H,max is the upper limit of the thermal energy response; c C,min is the lower limit of the cold energy response; c C,t is the selling price of cold energy at the time t; c C,max is the upper limit of the cold energy response; Δp t is the variation of the electrical energy at time t; Δp lim,t is the maximum responsivity of the electrical energy at time t; ΔH t is the variation of heat energy at time t; ΔH lim,t is the maximum responsivity of heat energy at time t; Δc t is the amount of change of cold energy at time t; Δc lim,t is the maximum responsivity of the cold energy at time t;
5) The following equation is used as the power balance constraint:
Wherein P L,t is the electrical load value at time t; Δp s,t is the charge-discharge power of the power storage device at time t; p P2H,t is the electric power consumed by converting electric energy into heat energy at time t; p P2C,t is the electric power consumed by converting the electric energy into the cold energy at the moment t; p WT,t is the fan power at time t; p CHP,t is the power generated by the cogeneration unit at the moment t; p net is the external electricity purchasing and selling power at the time t; h L,t is the thermal load value at time t; ΔH s,t is the charge and discharge power of the heat storage device at time t; h H2C,t is the thermal power consumed by converting the thermal energy at the time t into the cold energy; h CHP,t is the heat power generated by the cogeneration unit at the moment t; h GB,t is the heat generating power of the gas boiler at the time t; h HP,t is the heat pump heat generation power at time t; c L,t is the cold load value at time t; delta C s,t is the cooling power of the cooling device at time t; c AC,t is the cold power generated by the absorption refrigerator at the moment t; and C EC,t is the cold power generated by the electric refrigerator at the time t.
The energy scheduling method of the comprehensive energy system provides a user participation wish evaluation model, and realizes accurate quantification of the comprehensive demand response participation proportion in each period; secondly, simulating the mixing uncertainty of the elastic coefficient by adopting a normal cloud model, and establishing a comprehensive demand response model considering the participation will of the user; on the basis, the constructed comprehensive demand response model is introduced into a comprehensive energy system dispatching operation framework, and on the basis of comprehensively considering uncertainty of various factors such as cold and hot loads, wind power output, user participation will and the like, a comprehensive energy system optimizing dispatching model is constructed with the aim of minimizing the equivalent input energy of the system; therefore, the method can effectively describe randomness and ambiguity of the user participation in the comprehensive demand response will, can obviously reduce the equivalent input energy and energy loss of the system, and improves the new energy consumption rate, the overall energy efficiency and the system stability of the system, and has high reliability and good stability.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a cloud droplet distribution of user participation integrated demand response will in the method of the present invention.
FIG. 3 is a schematic diagram of a cloud of coefficient of elasticity uncertainty distribution in the method of the present invention.
FIG. 4 is a schematic diagram of the range of demand response in the method of the present invention.
FIG. 5 is a schematic diagram of a wind power output prediction curve in the method of the present invention.
FIG. 6 is a schematic representation of a transition season load prediction curve in the method of the present invention.
FIG. 7 is a schematic diagram of summer and winter load prediction curves in the method of the present invention.
FIG. 8 is a schematic diagram of the distribution of the electric load and the electricity price in the transition season in the method of the present invention.
FIG. 9 is a schematic diagram of the load and heat supply rate, and the distribution of the cold load and heat supply rate for transition Ji Re in the method of the present invention.
FIG. 10 is a schematic diagram of the frequency distribution of the system equivalent energy input at each time in the method of the present invention.
Detailed Description
A schematic process flow diagram of the method of the present invention is shown in fig. 1: the energy scheduling method of the comprehensive energy system provided by the invention comprises the following steps:
S1, acquiring operation parameters of a target comprehensive energy system;
s2, establishing a basic operation model of the comprehensive energy system according to the operation parameters obtained in the step S1; the method specifically comprises the following steps:
A. the relationship between energy supply and demand of the integrated energy system is expressed by the following expression:
Wherein L 1～Lm represents the load amounts of m loads on the load side; s 1～Sn is energy provided by n energy sources on the energy source side; μ ij is the energy conversion coefficient of the energy source j to the load i and μ ij＝αijηij,αij is the energy distribution coefficient of the energy source j to the load i, η ij is the energy conversion efficiency in the process of providing energy to the load i by the energy source j, i=1, 2, m, j=1, 2, n;
B. The uncertainty model of the source side considers the uncertainty of the wind power output, the magnitude of the wind power output is influenced by the rotating speed of the blade, and the fan power is directly proportional to the third power of the wind speed, so the uncertainty of the wind power output of the energy source side is expressed by adopting the following formula:
Wherein P WT is the output power of the fan; u is a wind velocity component perpendicular to the plane of rotation of the blade; u in is the cut-in wind speed of the fan; u out is the cut-out wind speed of the fan; ρ is the air density; a is the area swept by the fan blade; c P is the aerodynamic efficiency of the blower; η is the efficiency of the generator in the fan;
C. The load uncertainty on the load side is expressed by the following equation:
Wherein P L is the actual electrical load value; p L0 is the electrical load predictor; Δp L is the electrical load change value; lambda P is the amount of uncertainty in the superposition of the electrical loads; h L is the actual heat load value; h L0 is a thermal load predictor; Δh L is the thermal load change value; lambda H is the amount of uncertainty in the superposition of the thermal load; c L is the actual cold load value; c L0 is a predicted value of the cold load; Δc L is the cold load variation value; lambda C is the uncertainty of the superposition of the cold load;
s3, constructing a user participation evaluation model, and quantifying the comprehensive demand response proportion of each period; the method specifically comprises the following steps:
a. the satisfaction degree of people on the environment temperature is evaluated by measuring the difference between the heat generation amount of the human body and the heat dissipation amount of the human body to the outside, namely the heat load of the human body; the following formula is therefore used as a model for evaluating human thermal comfort:
PMV＝(0.303e-0.036M+0.028)·TL
Wherein PMV is a heat sensation average scale prediction index, and when PMV is less than 0, human sensation cold is determined, PMV > 0 is human sensation heat is determined, and pmv=0 is determined to be human sensation comfortable; m is the metabolism rate of the human body; TL is the human body heat load, and
W is mechanical work during human body movement, P a is partial pressure of water vapor in the environment, t a is air temperature, f cl is percentage of clothing covering the surface of a human body, t cl is average temperature of the surface of the clothing of the human body, t s is average radiation temperature of the environment, and h c is convective heat transfer coefficient;
b. the percentage model of the number of people unsatisfactory for a certain environmental condition is expressed by the following formula:
wherein PPD is a percent of dissatisfaction prediction index; the smaller the absolute value of the heat sensation average scale prediction index is, the smaller the proportion of unsatisfied users is, otherwise, the larger the proportion of unsatisfied users is;
c. Since the predictive dissatisfaction percentage index setting is relatively conservative, 5% of users are always dissatisfied with the environment under any condition, and the users can consider that the users do not participate in the willingness of the comprehensive demand response and should be classified into a rigid load. Thereby, the improvement of the percent dissatisfaction prediction index can be further proposed; therefore, the percentage model of the number of people unsatisfied for a certain environmental condition obtained in the step b is corrected, and the percentage model of the number of people unsatisfied for a certain environmental condition after correction is obtained:
Wherein PPD' is a modified predictive dissatisfaction percentage index;
d. Abstracting the willingness of users to participate in the comprehensive demand response into the satisfied user percentage described by the predicted unsatisfied percentage index, and when the users with semi-flexible loads are satisfied with the environmental conditions, considering that the users are willing to participate in IDR, otherwise, the users are unwilling; the adjustable portion on the load side is expressed by the following expression:
Wherein DeltaP lim,t is the upper power limit of the load side electric load which can participate in the comprehensive demand response at the time t; p flex is the flexible load of the load side electrical load; p semi is the semi-compliant load of the load side electrical load; PPD t' is the corrected forecast dissatisfaction percentage index at the time t; ΔH lim,t is the upper power limit at which the load side thermal load can participate in the integrated demand response at time t; h flex is a flexible load of the load side thermal load; h semi is the semi-compliant load of the load side thermal load; Δc lim,t is the upper power limit at which the load side cooling load can participate in the integrated demand response at time t; c flex is a flexible load of a load side cooling load; c semi is a semi-flexible load of the load side cooling load;
S4, a real-time price comprehensive demand response mechanism is adopted, and the response of the user participating in the comprehensive demand response to the energy price change is described through setting a price elastic coefficient; simulating uncertainty in the comprehensive demand response by adopting a normal cloud model, so as to establish a comprehensive demand response model; the method specifically comprises the following steps:
(1) The self-elasticity coefficient of the energy consumption behavior influence of the energy price change on the energy user is expressed by the following formula:
ΔLi,t＝εii·ΔCi,t
Wherein DeltaL i,t is the change of the demand of energy source side energy i at the time t; epsilon ii is the self-elasticity coefficient and is negative; Δc i,t is the price change of energy source side energy i at time t; i is P, H or C;
(2) When the energy price changes, the energy demand type of the user may also change, namely, a coupling response is generated; therefore, the following expression is used to represent the mutual elasticity coefficient of the energy price change affecting the energy consumption behavior of the energy user:
ΔLi,t＝εij·ΔCj,t
Wherein DeltaL i,t is the change of the demand of energy source side energy i at the time t; epsilon ij is the coefficient of mutual elasticity and is positive; Δc j,t is the price change of energy source side energy j at time t; j takes the value P, H or C;
(3) The price elasticity mechanism of the integrated demand response is integrated into the following matrix multiplication form:
Wherein epsilon PP is the self-elasticity coefficient of the electric energy price requirement; epsilon HH is the self-elasticity coefficient of the heat energy price requirement; epsilon CC is the self-elasticity coefficient of the cold energy price requirement; epsilon PH is the mutual elasticity coefficient of the electric-thermal price requirement; epsilon PC is the electric-cold price demand mutual elasticity coefficient; epsilon HP is the thermal-electrical price demand coefficient of mutual elasticity; epsilon HC is the heat-cold price demand mutual elasticity coefficient; epsilon CP is the cold-electricity price demand mutual elasticity coefficient; epsilon CH is the coefficient of cold-hot price demand mutual elasticity; Δc P,t is the electricity price change amount; Δc H,t is the heat value variation; Δc C,t is the cold price variation;
In the implementation, considering the difference of grades among different energies, a user has certain preference in the action of energy substitution; according to the related theory of energy grade, the energy grade of electricity is generally considered to be highest, the cold times are the lowest, and the heat is the lowest, so when the electricity price is reduced, a user can have stronger wish to use the electric energy to replace the cold and the heat; when the electricity price is increased, fewer people can replace the electric energy by cold and heat; according to the logic, the value of the price elastic matrix is set as
(4) Because the interaction process between the demand response and the user exists, the uncertainty of the demand response comprises subjective thinking of the user, is subjective uncertainty and needs to consider randomness and ambiguity at the same time; wherein the randomness represents the random behavior of the user, and the ambiguity represents the ambiguous willingness of the user to participate in the integrated demand response; the uncertainty of the comprehensive demand response can be divided into the uncertainty of the demand response boundary and the uncertainty of the price elasticity coefficient; thus, the uncertainty of the integrated demand response is divided into the uncertainty of the demand response boundary and the uncertainty of the price elasticity coefficient;
(5) And processing the uncertainty of the demand response boundary by adopting a probability cloud model of mixed uncertainty:
h(x)＝f(g(x))
F () is a membership function, and corresponds to a percent unsatisfied index of prediction; g (x) is a probability density function and corresponds to a thermal sensation average scale prediction index; the value range of the dissatisfaction percentage index is predicted to be 0-1, and the ambiguity of the user participation in the comprehensive demand response will can be described; the heat sensation average scale prediction index only calculates the theoretical comfort degree of a human body, and a random quantity is added on the basis of the index in consideration of the ambiguity between the comfort degree of a user and the response willingness of participating in comprehensive demands;
(6) The percentage of heat sensation average scale prediction-prediction dissatisfaction index after introducing uncertainty is expressed by the following equation:
A percentage of dissatisfaction index of the prediction after introducing uncertainty in the formula; lambda PMV is a random quantity superimposed by a heat sensation average scale prediction index, lambda PMV -N (1, he), and He is a variance of an uncertain quantity; because the uncertainty of the heat sensation average scale prediction index is related to the value of the heat sensation average scale prediction index, a multiplication superposition method is more suitable; the cloud drip diagram of the user participation IDR willingness distribution is shown in figure 2;
(7) Consider uncertainty in the price elastic coefficient: lambda ε -N (1, en) and En-N (sigma, he), wherein lambda ε is the uncertainty of superposition of the price elastic coefficients, en is the variance of the elastic coefficients, sigma is the mean value of En, he is the super entropy of lambda ε; therefore λ ε obeys a normal cloud distribution with parameters C (1, σ, he), where ε = λ ε·ε0, ε is the final price elastic coefficient, ε 0 is the preset elastic coefficient; to avoid the occurrence of distant outliers, the range of variation of λ ε is defined within the interval [1-3En,1+3en ], and En is defined within the interval [ σ -3He, σ+3he ]; the cloud drip chart of the uncertainty distribution of the elastic coefficient is shown in fig. 3;
(8) The following formula is adopted as a membership function expression of the elastic coefficient certainty:
Wherein μ (γ ε) is a membership function of the elastic coefficient certainty; gamma ε is the uncertainty of the elastic coefficient; en is the variance of the elastic coefficient; the relationship between the energy price and the demand response is shown in fig. 4;
S5, solving under a set boundary condition by taking the minimum input energy of the comprehensive energy system as an objective function based on the basic operation model of the comprehensive energy system established in the step S2 and the comprehensive demand response model established in the step S4, so as to obtain a final optimal scheduling result of the comprehensive energy system;
In specific implementation, the method takes the minimum input energy of the comprehensive energy system as an objective function, and specifically comprises the following steps:
the minimum input energy of the comprehensive energy system is used as an optimization target, and the obtained objective function is as follows:
min Ein＝PBuy+GBuy·LHV
Wherein E in is the input energy of the comprehensive energy system, and the unit is kWh; p Buy is the electricity purchasing amount of the comprehensive energy system to the upper power grid, and the unit is kWh; g Buy is the upward gas online shopping amount of the comprehensive energy system, and the unit is m 3; LHV is the low heat value of natural gas, and the value is 9.7kWh/m 3; the step length of scheduling is 1 hour, and scheduling is implemented by taking a day as a unit;
in specific implementation, the set boundary conditions specifically comprise the following steps:
1) The following formula is adopted as the constraint of the cogeneration unit:
/>
Wherein S CHP,t is a variable of 0-1, which is used for indicating the start-stop state of the cogeneration unit, and S CHP,t =1 indicates the start-up of the cogeneration unit; The lower limit of the output electric power of the cogeneration unit; p CHP,t is the power of the cogeneration unit at time t; /(I) The upper limit of the output electric power of the cogeneration unit; /(I)The lower limit of the output thermal power of the cogeneration unit; h CHP,t is the heat generation power of the cogeneration unit at the moment t; /(I)The upper limit of the output thermal power of the cogeneration unit; the upper limit of the climbing of the power generation power of the cogeneration unit is set; /(I) The upper limit of the climbing of the heat production power of the cogeneration unit is set; t on is the start-up time of the cogeneration unit; /(I)The minimum start-up time of the cogeneration unit is set; t off is the shutdown time of the cogeneration unit; /(I)The minimum shutdown time of the cogeneration unit is set;
2) The following formulas are adopted as the constraint conditions of the gas boiler, the heat pump, the absorption refrigerator and the electric refrigerator:
Wherein H GB,min is the lower limit of the output force of the gas boiler; h GB,t is the output value of the gas boiler at the moment t; h GB,max is the upper limit of the gas boiler output; h HP,min is the lower limit of heat pump output; h HP,t is the output value of the heat pump at the moment t; h HP,max is the upper limit of heat pump output; c AC,min is the lower limit of the output of the absorption refrigerator; c AC,t is the output of the absorption refrigerator at the moment t; c AC,max is the upper limit of the output of the absorption refrigerator; c EC,min is the lower limit of the output of the electric refrigerator; c EC,t is the output of the electric refrigerator at the moment t; ; c EC,max is the upper limit of the output of the electric refrigerator; p WT,t is the wind power generation power at the moment t; the maximum wind power which can participate in grid connection is the wind power output power at the moment t;
3) The following equation is used as the energy storage device constraint:
/>
Wherein DeltaP s,t is the charge and discharge power of the electric energy storage device at the moment t; Δp s,max is the upper limit of the charge and discharge power of the electrical energy storage device; ΔH s,t is the charging and discharging power of the thermal energy storage device at time t; Δh s,max is the upper limit of the charge and discharge power of the thermal energy storage device; delta C s,t is the charge-discharge cold power of the cold energy storage device at the moment t; Δc s,max is the upper limit of the charge-discharge cold power of the cold energy storage device; e P,0 is the initial energy storage state of the electrical energy storage device; t is the scheduled time step; e P,cap is the rated capacity of the electrical energy storage device; e H,0 is the initial energy storage state of the thermal energy storage device; e H,cap is the rated capacity of the thermal energy storage device; e C,0 is the initial energy storage state of the cold energy storage device; e C,cap is the rated capacity of the cold energy storage device;
4) The following equation is used as a demand response conservation constraint:
Wherein C P,min is the lower limit of the electric energy response; c P,t is the selling price of the electric energy at the time t; c P,max is the upper limit of the electrical energy response; c H,min is the lower limit of the thermal energy response; c H,t is the selling price of heat energy at the time t; c H,max is the upper limit of the thermal energy response; c C,min is the lower limit of the cold energy response; c C,t is the selling price of cold energy at the time t; c C,max is the upper limit of the cold energy response; Δp t is the variation of the electrical energy at time t; Δp lim,t is the maximum responsivity of the electrical energy at time t; ΔH t is the variation of heat energy at time t; ΔH lim,t is the maximum responsivity of heat energy at time t; Δc t is the amount of change of cold energy at time t; Δc lim,t is the maximum responsivity of the cold energy at time t;
5) The following equation is used as the power balance constraint:
Wherein P L,t is the electrical load value at time t; Δp s,t is the charge-discharge power of the power storage device at time t; p P2H,t is the electric power consumed by converting electric energy into heat energy at time t; p P2C,t is the electric power consumed by converting the electric energy into the cold energy at the moment t; p WT,t is the fan power at time t; p CHP,t is the power generated by the cogeneration unit at the moment t; p net is the external electricity purchasing and selling power at the time t; h L,t is the thermal load value at time t; ΔH s,t is the charge and discharge power of the heat storage device at time t; h H2C,t is the thermal power consumed by converting the thermal energy at the time t into the cold energy; h CHP,t is the heat power generated by the cogeneration unit at the moment t; h GB,t is the heat generating power of the gas boiler at the time t; h HP,t is the heat pump heat generation power at time t; c L,t is the cold load value at time t; delta C s,t is the cooling power of the cooling device at time t; c AC,t is the cold power generated by the absorption refrigerator at the moment t; and C EC,t is the cold power generated by the electric refrigerator at the time t.
The process according to the invention is further illustrated by the following examples:
Example 1:
The parameters were set as follows:
The equipment units in IES include: fan, CHP unit, gas boiler, heat pump, absorption refrigerator, electric refrigerator, and cold, hot, and electric energy storage elements. Wherein, the prediction curve of wind power output is shown in fig. 5. The performance parameters of each device are shown in table 1. The predicted data of the energy load is derived from typical daily data of a comprehensive energy system of a certain park. In order to verify the applicability of the model under various conditions, typical daily loads in summer, winter and transitional seasons are selected for analysis, wherein the load prediction curves in the transitional seasons are shown in fig. 6, and the load prediction curves in summer and winter are shown in fig. 7. The electricity purchasing price and the gas purchasing price of operators are set to be fixed prices, which are respectively 0.7 yuan/kWh and 1.9 yuan/m 3; the natural gas has a low calorific value of 9.7kWh/m 3. The operator can sell the energy in the form of real-time price, the basic electricity price is set to be 0.7 yuan/kWh, the basic heat supply price and the basic cold supply price are set to be 0.35 yuan/kWh and 0.54 yuan/kWh respectively, the price change range is set to be 50%, and the energy price can fluctuate within the range of 50% -150% of the basic price.
TABLE 1IES Productivity apparatus parameter information schematic Table
The method comprises the steps of simulating the concrete expression of uncertainty in a model by constructing a large number of uncertain scenes, and converting a multiple mixed uncertainty problem into a certainty problem under each scene to solve. In random scene generation, sampling is carried out on single uncertainties such as load, wind power output, ambient temperature and the like by adopting a Monte Carlo method; the mixing uncertainty in the composite demand response is sampled using a forward cloud generator.
The forward cloud generator is an algorithm for generating a series of samples (cloud drops) conforming to normal cloud distribution under the condition of knowing C (Ex, sigma, he) parameters, and the working principle is as follows: first, a random number En following the N (σ, he) distribution is generated, and then, with En as a sample variance, a cloud x following the N (Ex, en) distribution is generated. The normal cloud distribution parameter to which the uncertain amount of the elastic coefficient obeys is C (1.000,0.0333,0.0100).
100 Groups of uncertainty samples are generated on the basis of three different typical days of summer, winter and transitional seasons respectively to form 300 optimal scheduling scenes in total, so that the generated samples can cover as many uncertainty scenes as possible.
To study the effect of uncertainty in IES on system input energy consumption and equipment operation and maintenance loss, and the effect of the proposed IES optimal scheduling strategy on system energy efficiency, the following example scenarios are set for comparison study, and specific scenario settings are shown in table 2.
TABLE 2 schematic setting table for comparative examples
In the set example scenario, the uncertainty is not considered in the scenarios 1, 2 and 3, and the method is mainly used for researching the influence of the comprehensive demand response mechanism on the energy consumption of the system. Comparison of the optimized results for the three scenarios is shown in table 3. The distribution of the electric load and the electricity price in the transition season is shown in fig. 8, the distribution of the electric load and the heat price in the transition Ji Re (a graph) and the distribution of the electric load and the cold price in the transition Ji Re (b graph) are shown in fig. 9.
TABLE 3 optimization results vs. (uncertainty is not considered) schematic table
The equivalent energy loss is calculated by equipment operation and maintenance cost, and the expression is as follows:
Wherein E loss is the equivalent energy loss (kWh) of the system, C O&M is the operation and maintenance cost of the system, and C ele is the unit cost of the system when purchasing electricity to the upper power grid.
The IDR mechanism is not introduced in the scenario 1, the energy price does not change with time, and an operator completely supplies energy according to the original requirement of a user, so that the equivalent energy input and energy loss of the system are the highest. In addition, in scenario 1, the load and the wind power output sometimes do not match, so that the phenomenon of wind abandoning occurs in winter when the wind power resources are sufficient, and the new energy output is not completely consumed.
An IDR mechanism is introduced in the scenario 2, but a load substitution mechanism is not introduced, an operator adjusts the energy price according to the real-time load, and a user adjusts the self energy utilization behavior according to the energy price. Because the scene 2 is used for properly adjusting the load, the demand and the supply can be matched better, the wind abandoning problem in winter is solved, and the equivalent input energy and the energy loss of the system are reduced. In scenario 2, in transition seasons and summer, the system equivalent input energy is respectively reduced by 1.75% and 2.58% compared with scenario 1; in winter, due to the improvement of the wind energy absorption rate, the equivalent input energy of the system is reduced by 35.96% compared with the situation 1.
Scenario 3 introduces an energy substitution mechanism based on scenario 2, and a user can adjust the type of energy used by the user according to own needs and energy price, so that the multi-energy complementation of the IES is realized. Scenario 3 further reduces the system equivalent energy input and energy loss under the condition of ensuring wind power consumption. In the scenario 3, the equivalent input energy of the system is respectively reduced by 43.47%, 41.78% and 59.34% in transitional seasons, summer and winter compared with the scenario 1; the equivalent energy loss of the system is reduced by 33.85%, 28.74% and 33.07% respectively. According to the theory related to energy grade, when electric energy is converted into cold and heat energy, the heat energy in the environment can be absorbed, so that the output energy is more than the input energy. Therefore, the equivalent input energy of the system is greatly reduced.
The mean and variance of the results of the optimized scheduling of the uncertain scenes in the scenes 4, 5 and 6 are shown in the table 4. When the uncertainty of IES and IDR is considered, the system equivalent energy input of scheduling results for each season of each scenario rises to different extents. In addition, because uncertainty is introduced, the equivalent input energy and energy loss of the system can fluctuate within a certain range, and the fluctuation degree can be described by standard deviation. The frequency distribution histogram of the equivalent input energy of the system is shown in fig. 10.
Table 4 comparison of optimized results (accounting for uncertainty) schematic table
Referring to table 4 and fig. 10, the comprehensive energy system optimization scheduling model provided by the invention can still effectively reduce equivalent input energy and energy loss of the system under various uncertain scenes, thereby achieving the purposes of energy conservation and emission reduction. In addition, the optimal scheduling model can effectively reduce the fluctuation degree of equivalent input energy and energy loss of the system and improve the stability of the system.
Claims (5)
1. An energy scheduling method of an integrated energy system comprises the following steps:
S1, acquiring operation parameters of a target comprehensive energy system;
s2, establishing a basic operation model of the comprehensive energy system according to the operation parameters obtained in the step S1; the method specifically comprises the following steps:
A. the relationship between energy supply and demand of the integrated energy system is expressed by the following expression:
Wherein L 1～Lm represents the load amounts of m loads on the load side; s 1～Sn is energy provided by n energy sources on the energy source side; μ ij is the energy conversion coefficient of the energy source j to the load i and μ ij＝αijηij,αij is the energy distribution coefficient of the energy source j to the load i, η ij is the energy conversion efficiency in the process of providing energy to the load i by the energy source j, i=1, 2..m, j=1, 2..n;
B. the uncertainty of the wind power output of the energy source side is expressed by the following formula:
Wherein P WT is the output power of the fan; u is a wind velocity component perpendicular to the plane of rotation of the blade; u in is the cut-in wind speed of the fan; u out is the cut-out wind speed of the fan; ρ is the air density; a is the area swept by the fan blade; c P is the aerodynamic efficiency of the blower; η is the efficiency of the generator in the fan;
C. The load uncertainty on the load side is expressed by the following equation:
Wherein P L is the actual electrical load value; p L0 is the electrical load predictor; Δp L is the electrical load change value; lambda P is the amount of uncertainty in the superposition of the electrical loads; h L is the actual heat load value; h L0 is a thermal load predictor; Δh L is the thermal load change value; lambda H is the amount of uncertainty in the superposition of the thermal load; c L is the actual cold load value; c L0 is a predicted value of the cold load; Δc L is the cold load variation value; lambda C is the uncertainty of the superposition of the cold load;
s3, constructing a user participation evaluation model, and quantifying the comprehensive demand response proportion of each period;
s4, simulating uncertainty in the comprehensive demand response by adopting a normal cloud model, so as to establish a comprehensive demand response model;
S5, solving under a set boundary condition by taking the minimum input energy of the comprehensive energy system as an objective function based on the basic operation model of the comprehensive energy system established in the step S2 and the comprehensive demand response model established in the step S4, so as to obtain a final optimal scheduling result of the comprehensive energy system.
2. The energy scheduling method of the integrated energy system according to claim 1, wherein the constructing the user participation evaluation model in step S3 quantifies the integrated demand response ratio of each period of time, specifically comprising the steps of:
a. the following formula is adopted as a human body thermal comfort evaluation model:
PMV＝(0.303e-0.036M+0.028)·TL
Wherein PMV is a heat sensation average scale prediction index, and when PMV is less than 0, human sensation cold is determined, PMV > 0 is human sensation heat is determined, and pmv=0 is determined to be human sensation comfortable; m is the metabolism rate of the human body; TL is the human body heat load, and
W is mechanical work during human body movement, P a is partial pressure of water vapor in the environment, t a is air temperature, f cl is percentage of clothing covering the surface of a human body, t cl is average temperature of the surface of the clothing of the human body, t s is average radiation temperature of the environment, and h c is convective heat transfer coefficient;
b. the percentage model of the number of people unsatisfactory for a certain environmental condition is expressed by the following formula:
Wherein PPD is a percent of dissatisfaction prediction index;
c. C, correcting the percentage model of the number of people unsatisfied for a certain environmental condition, which is obtained in the step b, to obtain a corrected percentage model of the number of people unsatisfied for a certain environmental condition:
Wherein PPD' is a modified predictive dissatisfaction percentage index;
d. The adjustable portion on the load side is expressed by the following expression:
Wherein DeltaP lim,t is the upper power limit of the load side electric load which can participate in the comprehensive demand response at the time t; p flex is the flexible load of the load side electrical load; p semi is the semi-compliant load of the load side electrical load; PPD t' is the corrected forecast dissatisfaction percentage index at the time t; ΔH lim,t is the upper power limit at which the load side thermal load can participate in the integrated demand response at time t; h flex is a flexible load of the load side thermal load; h semi is the semi-compliant load of the load side thermal load; Δc lim,t is the upper power limit at which the load side cooling load can participate in the integrated demand response at time t; c flex is a flexible load of a load side cooling load; c semi is the semi-compliant load of the load side cooling load.
3. The energy scheduling method of the integrated energy system according to claim 2, wherein the step S4 of modeling uncertainty in the integrated demand response by using a normal cloud model, thereby establishing an integrated demand response model, specifically comprises the following steps:
(1) The self-elasticity coefficient of the energy consumption behavior influence of the energy price change on the energy user is expressed by the following formula:
ΔLi,t＝εii·ΔCi,t
Wherein DeltaL i,t is the change of the demand of energy source side energy i at the time t; epsilon ii is the self-elasticity coefficient and is negative; Δc i,t is the price change of energy source side energy i at time t; i is P, H or C;
(2) The mutual elasticity coefficient of the energy consumption behavior influence of the energy price change on the energy user is expressed by the following formula:
ΔLi,t＝εij·ΔCj,t
Wherein DeltaL i,t is the change of the demand of energy source side energy i at the time t; epsilon ij is the coefficient of mutual elasticity and is positive; Δc j,t is the price change of energy source side energy j at time t; j takes the value P, H or C;
(3) The price elasticity mechanism of the integrated demand response is integrated into the following matrix multiplication form:
Wherein epsilon PP is the self-elasticity coefficient of the electric energy price requirement; epsilon HH is the self-elasticity coefficient of the heat energy price requirement; epsilon CC is the self-elasticity coefficient of the cold energy price requirement; epsilon PH is the mutual elasticity coefficient of the electric-thermal price requirement; epsilon PC is the electric-cold price demand mutual elasticity coefficient; epsilon HP is the thermal-electrical price demand coefficient of mutual elasticity; epsilon HC is the heat-cold price demand mutual elasticity coefficient; epsilon CP is the cold-electricity price demand mutual elasticity coefficient; epsilon CH is the coefficient of cold-hot price demand mutual elasticity; Δc P,t is the electricity price change amount; Δc H,t is the heat value variation; Δc C,t is the cold price variation;
(4) Dividing the uncertainty of the comprehensive demand response into the uncertainty of a demand response boundary and the uncertainty of a price elasticity coefficient;
(5) And processing the uncertainty of the demand response boundary by adopting a probability cloud model of mixed uncertainty:
h(x)＝f(g(x))
F () is a membership function, and corresponds to a percent unsatisfied index of prediction; g (x) is a probability density function and corresponds to a thermal sensation average scale prediction index;
(6) The percentage of heat sensation average scale prediction-prediction dissatisfaction index after introducing uncertainty is expressed by the following equation:
A percentage of dissatisfaction index of the prediction after introducing uncertainty in the formula; lambda PMV is a random quantity superimposed by a heat sensation average scale prediction index, lambda PMV -N (1, he), and He is a variance of an uncertain quantity;
(7) Consider uncertainty in the price elastic coefficient: lambda ε -N (1, en) and En-N (sigma, he), wherein lambda ε is the uncertainty of superposition of the price elastic coefficients, en is the variance of the elastic coefficients, sigma is the mean value of En, he is the super entropy of lambda ε; therefore λ ε obeys a normal cloud distribution with parameters C (1, σ, he), where ε = λ ε·ε0, ε is the final price elastic coefficient, ε 0 is the preset elastic coefficient;
(8) The following formula is adopted as a membership function expression of the elastic coefficient certainty:
Wherein μ (γ ε) is a membership function of the elastic coefficient certainty; gamma ε is the uncertainty of the elastic coefficient; en is the variance of the elastic coefficient.
4. The energy scheduling method of the integrated energy system according to claim 3, wherein the step S5 uses the minimum input energy of the integrated energy system as an objective function, and specifically comprises the following steps:
the minimum input energy of the comprehensive energy system is used as an optimization target, and the obtained objective function is as follows:
minEin＝PBuy+GBuy·LHV
Wherein E in is the input energy of the comprehensive energy system; p Buy is the electricity purchasing quantity of the comprehensive energy system to the upper power grid; g Buy is the upward air online air purchasing amount of the comprehensive energy system; LHV is the natural gas low heating value; the scheduling step size is 1 hour, and scheduling is performed in daily units.
5. The energy scheduling method of the integrated energy system according to claim 4, wherein the set boundary conditions in step S5 specifically include the following steps:
1) The following formula is adopted as the constraint of the cogeneration unit:
Wherein S CHP,t is a variable of 0-1, which is used for indicating the start-stop state of the cogeneration unit, and S CHP,t =1 indicates the start-up of the cogeneration unit; The lower limit of the output electric power of the cogeneration unit; p CHP,t is the power of the cogeneration unit at time t; the upper limit of the output electric power of the cogeneration unit; /(I) The lower limit of the output thermal power of the cogeneration unit; h CHP,t is the heat generation power of the cogeneration unit at the moment t; /(I)The upper limit of the output thermal power of the cogeneration unit; /(I)The upper limit of the climbing of the power generation power of the cogeneration unit is set; /(I)The upper limit of the climbing of the heat production power of the cogeneration unit is set; t on is the start-up time of the cogeneration unit; /(I)The minimum start-up time of the cogeneration unit is set; t off is the shutdown time of the cogeneration unit; /(I)The minimum shutdown time of the cogeneration unit is set;
2) The following formulas are adopted as the constraint conditions of the gas boiler, the heat pump, the absorption refrigerator and the electric refrigerator:
Wherein H GB,min is the lower limit of the output force of the gas boiler; h GB,t is the output value of the gas boiler at the moment t; h GB,max is the upper limit of the gas boiler output; h HP,min is the lower limit of heat pump output; h HP,t is the output value of the heat pump at the moment t; h HP,max is the upper limit of heat pump output; c AC,min is the lower limit of the output of the absorption refrigerator; c AC,t is the output of the absorption refrigerator at the moment t; c AC,max is the upper limit of the output of the absorption refrigerator; c EC,min is the lower limit of the output of the electric refrigerator; c EC,t is the output of the electric refrigerator at the moment t; c EC,max is the upper limit of the output of the electric refrigerator; p WT,t is the wind power generation power at the moment t; the maximum wind power which can participate in grid connection is the wind power output power at the moment t;
3) The following equation is used as the energy storage device constraint:
Wherein DeltaP s,t is the charge and discharge power of the electric energy storage device at the moment t; Δp s,max is the upper limit of the charge and discharge power of the electrical energy storage device; ΔH s,t is the charging and discharging power of the thermal energy storage device at time t; Δh s,max is the upper limit of the charge and discharge power of the thermal energy storage device; delta C s,t is the charge-discharge cold power of the cold energy storage device at the moment t; Δc s,max is the upper limit of the charge-discharge cold power of the cold energy storage device; e P,0 is the initial energy storage state of the electrical energy storage device; t is the scheduled time step; e P,cap is the rated capacity of the electrical energy storage device; e H,0 is the initial energy storage state of the thermal energy storage device; e H,cap is the rated capacity of the thermal energy storage device; e C,0 is the initial energy storage state of the cold energy storage device; e C,cap is the rated capacity of the cold energy storage device;
4) The following equation is used as a demand response conservation constraint:
Wherein C P,min is the lower limit of the electric energy response; c P,t is the selling price of the electric energy at the time t; c P,max is the upper limit of the electrical energy response; c H,min is the lower limit of the thermal energy response; c H,t is the selling price of heat energy at the time t; c H,max is the upper limit of the thermal energy response; c C,min is the lower limit of the cold energy response; c C,t is the selling price of cold energy at the time t; c C,max is the upper limit of the cold energy response; Δp t is the variation of the electrical energy at time t; Δp lim,t is the maximum responsivity of the electrical energy at time t; ΔH t is the variation of heat energy at time t; ΔH lim,t is the maximum responsivity of heat energy at time t; Δc t is the amount of change of cold energy at time t; Δc lim,t is the maximum responsivity of the cold energy at time t;
5) The following equation is used as the power balance constraint:
Wherein P L,t is the electrical load value at time t; Δp s,t is the charge-discharge power of the power storage device at time t; p P2H,t is the electric power consumed by converting electric energy into heat energy at time t; p P2C,t is the electric power consumed by converting the electric energy into the cold energy at the moment t; p WT,t is the fan power at time t; p CHP,t is the power generated by the cogeneration unit at the moment t; p net is the external electricity purchasing and selling power at the time t; h L,t is the thermal load value at time t; ΔH s,t is the charge and discharge power of the heat storage device at time t; h H2C,t is the thermal power consumed by converting the thermal energy at the time t into the cold energy; h CHP,t is the heat power generated by the cogeneration unit at the moment t; h GB,t is the heat generating power of the gas boiler at the time t; h HP,t is the heat pump heat generation power at time t; c L,t is the cold load value at time t; delta C s,t is the cooling power of the cooling device at time t; c AC,t is the cold power generated by the absorption refrigerator at the moment t; and C EC,t is the cold power generated by the electric refrigerator at the time t.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111616548.5A CN114169800B (en) | 2021-12-27 | 2021-12-27 | Energy scheduling method of comprehensive energy system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111616548.5A CN114169800B (en) | 2021-12-27 | 2021-12-27 | Energy scheduling method of comprehensive energy system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114169800A CN114169800A (en) | 2022-03-11 |
| CN114169800B true CN114169800B (en) | 2024-05-28 |
Family
ID=80488205
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111616548.5A Active CN114169800B (en) | 2021-12-27 | 2021-12-27 | Energy scheduling method of comprehensive energy system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114169800B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117455210B (en) * | 2023-12-26 | 2024-04-05 | 山东建筑大学 | Comprehensive energy system scheduling method, system, medium and equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108734350A (en) * | 2018-05-17 | 2018-11-02 | 燕山大学 | A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor |
| CN111340274A (en) * | 2020-02-17 | 2020-06-26 | 国网冀北电力有限公司 | Virtual power plant participation-based comprehensive energy system optimization method and system |
| CN112583021A (en) * | 2020-11-23 | 2021-03-30 | 国家电网有限公司 | Comprehensive energy system optimal scheduling method and device considering comprehensive demand response |
| CN112952807A (en) * | 2021-02-09 | 2021-06-11 | 西安理工大学 | Multi-objective optimization scheduling method considering wind power uncertainty and demand response |
| CN113128775A (en) * | 2021-04-26 | 2021-07-16 | 山东大学 | Comprehensive energy system scheduling method and device considering demand response and coupling degree |
-
2021
- 2021-12-27 CN CN202111616548.5A patent/CN114169800B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108734350A (en) * | 2018-05-17 | 2018-11-02 | 燕山大学 | A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor |
| CN111340274A (en) * | 2020-02-17 | 2020-06-26 | 国网冀北电力有限公司 | Virtual power plant participation-based comprehensive energy system optimization method and system |
| CN112583021A (en) * | 2020-11-23 | 2021-03-30 | 国家电网有限公司 | Comprehensive energy system optimal scheduling method and device considering comprehensive demand response |
| CN112952807A (en) * | 2021-02-09 | 2021-06-11 | 西安理工大学 | Multi-objective optimization scheduling method considering wind power uncertainty and demand response |
| CN113128775A (en) * | 2021-04-26 | 2021-07-16 | 山东大学 | Comprehensive energy system scheduling method and device considering demand response and coupling degree |
Non-Patent Citations (1)
| Title |
|---|
| 考虑需求响应与光伏不确定性的综合能源系统鲁棒优化;翟晶晶;吴晓蓓;傅质馨;朱少杰;刘皓明;;中国电力;20200805(第08期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114169800A (en) | 2022-03-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ding et al. | Integrated stochastic energy management for data center microgrid considering waste heat recovery | |
| CN109523052B (en) | Virtual power plant optimal scheduling method considering demand response and carbon transaction | |
| CN106099993B (en) | A kind of power source planning method for adapting to new energy and accessing on a large scale | |
| CN110991773B (en) | Wind power consumption-oriented two-stage source load storage optimization scheduling method | |
| CN111738502A (en) | Multi-energy complementary system demand response operation optimization method for promoting surplus wind power consumption | |
| CN106056256B (en) | Interactive micro-grid scheduling method for balancing power supply and demand relationship | |
| CN114374232B (en) | Two-stage optimization scheduling method of comprehensive energy system considering demand response | |
| CN111008739A (en) | Optimal regulation and control and income distribution method and system for cogeneration virtual power plant | |
| CN111898806B (en) | Electric-thermal coupling source storage integrated multipotency flow park operation optimization method and system | |
| CN110796373B (en) | Multi-stage scene generation electric heating system optimization scheduling method for wind power consumption | |
| CN111724045B (en) | Comprehensive energy system energy efficiency evaluation and improvement method based on data driving | |
| CN110276489B (en) | exergy -loss-lowest-based multi-energy hub distributed optimization scheduling method and system | |
| Chen et al. | Optimal scheduling strategy of a regional integrated energy system considering renewable energy uncertainty and heat network transmission characteristics | |
| CN114169800B (en) | Energy scheduling method of comprehensive energy system | |
| Yu et al. | Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source–load power interval prediction | |
| Yang et al. | Optimal dispatch for a combined cooling, heating and power microgrid considering building virtual energy storage | |
| CN109376406B (en) | Energy supply system superstructure model, modeling method, computer device and storage medium | |
| CN117611382A (en) | Rural comprehensive energy system optimization method considering multilayer cooperation and demand response | |
| CN116031954B (en) | Virtual power plant optimal scheduling method under fuzzy opportunity constraint | |
| CN115936336B (en) | Virtual power plant capacity configuration and regulation operation optimization method | |
| CN115641209A (en) | Comprehensive energy optimization model construction method considering carbon transaction and demand response | |
| CN115906401A (en) | Comprehensive energy system operation optimization method considering auxiliary service carbon transaction | |
| CN112465228B (en) | User-side comprehensive energy system optimal configuration method | |
| CN113065759A (en) | Comprehensive energy system optimal scheduling method and system considering energy quality | |
| CN112990541A (en) | Power peak clipping decision method based on family comprehensive energy demand response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |