CN111860966B - Energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning - Google Patents
Energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning Download PDFInfo
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Abstract
The invention discloses a fuzzy-correlated-opportunity-planning-based energy storage-containing comprehensive energy system scheduling method, which belongs to the technical field of power system scheduling and specifically comprises the following steps of: carrying out mathematical modeling on energy storage equipment in the comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge; establishing a fuzzy representation of wind power and uncertain load prediction quantity, and relaxing a power balance equation containing uncertain quantity; converting a power balance equation into a maximized opportunity function, taking the maximized opportunity function as one of objective functions, and establishing a multi-objective daily optimization scheduling model with the maximum possibility of fuzzy events and the minimum running cost as targets; the method comprises the steps of restraining the output of other equipment in the comprehensive energy system, wherein the constraint of the combined heat and power equipment, the constraint of a gas boiler, the constraint of an electric refrigerator and the constraint of the exchange power of a power grid are included; and solving the established multi-target day-ahead scheduling model to obtain an optimal day-ahead scheduling result. The invention applies the related opportunity planning method in the fuzzy environment to the comprehensive energy system scheduling, and realizes the economic operation of the system while ensuring the energy supply safety and reliability.
Description
Technical Field
The invention belongs to the technical field of power system scheduling, and particularly relates to a scheduling method of an energy storage-containing comprehensive energy system based on fuzzy relevant opportunity planning.
Background
The development of renewable energy sources and smart grids has led the world to the era of energy interconnection. Compared with the traditional single electric power energy system, the comprehensive energy system becomes an important technical approach for improving energy efficiency and reducing operation cost through the coupling of multiple energy sources such as electric energy, heat energy and natural gas. In the multi-energy system, the energy storage power station is used as a buffer between the energy supply side and the user side, so that combined HEATING AND power (CHP) or combined cooling HEATING AND power (CCHP) does not need to operate in a conventional mode of electric heating or electric heating, thermal decoupling is realized, and the dispatching efficiency of the system is improved.
In addition, the problem of optimizing and scheduling the comprehensive energy system by considering the uncertainty of renewable energy and load has attracted extensive attention from students at home and abroad. However, the existing scheduling strategy focuses on random optimization and robust optimization, so that the requirement on the original data is high, and the economy is often sacrificed due to over conservation.
Disclosure of Invention
Aiming at the defects of the background technology, the invention provides the energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning, which can reduce the calculation complexity while ensuring the calculation precision; and the fuzzy correlation opportunity planning is applied to comprehensive energy system scheduling, deterministic power balance is converted into maximum balance probability, a daily economic scheduling model of the comprehensive energy system is built, and economic and reliable energy supply is realized.
The invention adopts the following technical scheme for solving the technical problems:
The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning specifically comprises the following steps:
step S1, carrying out mathematical modeling on energy storage equipment in a comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge;
s2, establishing fuzzy representation of wind power and uncertain load prediction, and relaxing a power balance equation containing uncertain load;
Step S3, converting a power balance equation into a maximized opportunity function according to the step S2, taking the maximized opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model with the maximum possibility of fuzzy events and the minimum running cost as targets;
S4, restraining the output of other equipment in the comprehensive energy system, wherein the constraint comprises a cogeneration equipment constraint, a gas boiler constraint, an electric refrigerator constraint and a power grid exchange power constraint;
And S5, solving the multi-target day-ahead scheduling model established in the step S3, and further obtaining an optimal day-ahead scheduling result.
As a further preferable scheme of the scheduling method of the comprehensive energy system containing energy storage based on fuzzy correlation opportunity planning, in the step S1, the mathematical model description of the energy storage equipment in the comprehensive energy system is specifically as follows:
Wherein P EES,t is the energy storage power at time t; and/> Respectively representing charge and discharge; /(I)And/>Respectively 0-1 variable representing the charge and discharge states of the energy storage, when the energy storage is in a charge state,/>Otherwise 0; /(I)E rated is the maximum charge and discharge power of the energy storage power station, and E rated is the energy storage rated capacity; SOC t is the state of charge of the stored energy at time t; η ch and η dis are the efficiencies of energy storage charging and discharging respectively; SOC min and SOC max are respectively a minimum value and a maximum value of the stored-energy state of charge, Δt is a scheduled time interval, N T is a total number of time intervals, t=1, 2, … N T;
The loss cost flow of linearization single energy storage charge and discharge is specifically as follows: let the total throughput capacity E throughout of the energy storage battery be related to the total charge and discharge times and the charge and discharge depth, namely:
Ethroughout＝2NEESDODErated
wherein DOD is the depth of discharge, and N EES is the number of charge and discharge times in the whole life cycle;
The loss cost Y EES is an exponential function of the SOC, and the loss cost F EES when the single energy storage is charged or discharged from any SOC to another SOC can be accurately calculated by integrating the exponential function, that is:
To make F EES a linear function, the exponential function Y EES is approximated as a piecewise function Y' EES, and each segment is constant:
wherein, M is M, which is defined as a set of segments from 1 to M in SOC; if the SOC is in the segment m, Taking 1, otherwise taking 0; /(I)Is a degradation cost coefficient when the SOC is in segment m; in each section/>The value of (2) satisfies the following formula:
where SOC max.m and SOC min.m are the maximum and minimum values of SOC in segment m;
Wherein C 2、C3…CM is a constant, which is given such that As a continuous function, the energy storage loss function of single charge or discharge is rewritten as: /(I)
As a further preferable scheme of the scheduling method of the comprehensive energy system containing energy storage based on fuzzy correlation opportunity planning, in the step S2, the fuzzy representation of wind power and uncertain amount of load prediction is established by considering different time scale prediction precision, and the method specifically comprises the following steps: respectively using the triangle fuzzy number and the deterministic degree to represent the prediction error and the prediction value of wind power, electric load and cold load;
prediction error of wind power and load:
εWT,t＝(-kwPWT,t,0,kwPWT,t)
εEle,t＝(-kElePEle,t,0,kElePEle,t)
εHeat,t＝(-kHeatQHeat,t,0,kHeatQHeat,t)
εCool,t＝(-kCoolQCool,t,0,kCoolQCool,t)
Wherein P WT,t、PEle,t、QHeat,t and Q Cool,t are predicted values before the day for wind power, electric load, thermal load and cold load respectively, and k w、kEle、kHeat and k Cool are maximum predicted error proportionality coefficients respectively;
the specific procedure for relaxing the power balance equation containing an indefinite amount is as follows:
the strict electric, thermal and cold power balance equations under the consideration of the prediction error are:
PGT,t+PWT,t+εWT,t+PGrid,t＝PEle,t+εEle,t-ΔPWT,t+PEC,t+PEES,t
QGB,t+QHX,t＝QHeat,t+εHeat,t
QAC,t+QEC,t＝QCool,t+εCool,t
Wherein P GT,t is the power of the gas turbine at the time t; p Grid,t is the power exchanged with the large power grid at the time t, and DeltaP WT,t is the air discarding quantity; p EC,t is the power of the electric refrigerator at the time t; p EES,t is the charge and discharge power of the energy storage power station at the time t; q GB,t and Q HX,t are respectively the thermal power of the gas boiler and the heat exchange device at the time t; q AC,t and Q EC,t are the cold powers of the absorption refrigerator and the electric refrigerator at time t, respectively;
relaxation is an inequality constraint to construct an uncertainty set:
|PGT,t+(PWT,t+εWT,t)+PGrid,t-(PEle,t+εEle,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t
|QGB,t+QHX,t-QHeat,t-εHeat,t|≤σHeat,t
|QAC,t+QEC,t-QCool,t-εCool,t|≤σCool,t
where σ Ele,t、σHeat,t and σ Cool,t are constants, the size of which determines the feasible region of the defined uncertainty set in the blurred environment.
As a further preferable scheme of the energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning, in step S3, the power balance constraint is converted into the problem of maximizing the possibility of establishment of fuzzy events in a fuzzy environment, namely:
F1,t＝max Pos{|PGT,t+(PWT,t+εWT,t)+PGrid,t-(PEle,t+εEle,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t}
F2,t＝maxPos{|QGB,t+QHX,t-QHeat,t-εHeat,t|≤σHeat,t}
F3,t＝maxPos{|QAC,t+QEC,t-QCool,t-εCool,t|≤σCool,t
the objective function with the lowest day-ahead running cost of the comprehensive energy system is
Fgrid＝λ(t)Pbuy,tΔt
Wherein F fuel、Fgrid and F O&M respectively represent fuel cost, electricity purchase and selling cost from a power grid and equipment operation and maintenance cost; c gas is the price of natural gas, and Yuan/m 3;ηGT and eta GB are the efficiencies of the gas turbine and the gas boiler respectively; l NG is the heat value of the fuel gas, and 9.7kWh/m 3 is taken; lambda (t) is the time-of-use electricity price; electric power purchased from the grid for time t; k GT、kGB、kHX、kEC、kAC and k WT are the unit operation and maintenance costs of the gas turbine, the gas boiler, the heat exchange device, the electric refrigerator, the absorption refrigerator and the blower, respectively.
As a further preferable scheme of the scheduling method of the comprehensive energy system containing energy storage based on fuzzy correlation opportunity planning, in the step S4, the output constraint of other equipment in the comprehensive energy system is specifically as follows:
1) Combined cooling, heating and power (CCHP) constraint:
QHX,t/ηHX+QAC,t/COPAC＝PGT,tγGTηWH
Wherein, gamma GT is the heat-electricity ratio of the gas turbine; η HX and η WH are the efficiencies of the heat exchange device and the waste heat boiler respectively; COP AC is the energy efficiency ratio of an absorption chiller; and/> Respectively minimum and maximum values of the power of the gas turbine; /(I)And/>Respectively the minimum and maximum values of the power of the heat exchange device; /(I)And/>Respectively the minimum and maximum values of the power of the absorption refrigerator;
2) Gas boiler constraint:
In the method, in the process of the invention, And/>Maximum and minimum power values for the gas boiler;
3) Electric refrigerator restraint:
QEC,t＝PEC,tCOPEC
Wherein COP EC is the energy efficiency ratio of the electric refrigerator; and/> Maximum and minimum power values of the electric refrigerator respectively;
4) Grid exchange power constraint:
0≤PGrid,t≤Pgmax
Where P gmax is the maximum power purchased from the grid.
As a further preferable scheme of the energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning, in the step S5, the multi-target day-ahead scheduling model established in the step 3 is solved, and then an optimal day-ahead scheduling result is obtained.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1) The invention provides a new energy storage loss model calculation method, which quantifies the relation between each charge and discharge action and loss cost, and can reduce the calculation complexity without losing much accuracy.
2) The invention applies the related opportunity planning in the fuzzy environment to the comprehensive energy system scheduling, the established day-ahead scheduling model converts the equation constraint containing uncertain variables into an objective function, and the safety and economic operation of the system can be realized by reasonably setting the weights of different targets when the scheduling plan is prepared.
Drawings
FIG. 1 is a flow chart of a scheduling method of an energy storage-containing comprehensive energy system based on fuzzy correlation planning;
FIG. 2 is a graph of a daily forecast of wind power generation, electrical load, thermal load, and cold load for the integrated energy system;
FIG. 3 is a graph showing the output of the power generation device according to the present invention;
FIG. 4 is a graph showing the result of the heat energy production device of the present invention;
FIG. 5 is a graph showing the result of the cold energy production equipment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, a scheduling method of an energy storage-containing comprehensive energy system based on fuzzy correlation opportunity planning mainly comprises the following specific steps:
step S1, carrying out mathematical modeling on energy storage equipment in a comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge;
S2, establishing fuzzy representation of wind power and uncertain load prediction quantity, and considering different prediction precision at different moments, loosening a power balance equation containing uncertain quantity;
Step S3, converting a power balance equation into a maximized opportunity function according to the step S2, taking the maximized opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model with the maximum possibility of fuzzy events and the minimum running cost as targets;
S4, restraining the output of other equipment in the comprehensive energy system, wherein the constraint comprises a cogeneration equipment constraint, a gas boiler constraint, an electric refrigerator constraint and a power grid exchange power constraint;
And S5, solving the multi-target day-ahead scheduling model established in the step S3, and further obtaining an optimal day-ahead scheduling result.
In step S1 of the present invention, the mathematical model of the energy storage device in the integrated energy system is described as follows:
Wherein P EES,t is the energy storage power at time t; and/> Respectively representing charge and discharge; /(I)And/>Respectively 0-1 variable representing the charge and discharge states of the energy storage, when the energy storage is in a charge state,/>Otherwise 0; /(I)E rated is the maximum charge and discharge power of the energy storage power station, and E rated is the energy storage rated capacity; SOC t is the State of Charge (State of Charge) of the stored energy at time t; η ch and η dis are the efficiencies of energy storage charging and discharging respectively; SOC min and SOC max are the minimum and maximum values, respectively, of the stored state of charge. Δt is the scheduling time interval, N T is the total number of time intervals, t=1, 2, … N T.
The loss cost flow of linearization single energy storage charge and discharge is as follows, the total throughput capacity E throughout of the energy storage battery is related to the total charge and discharge times and the charge and discharge depth, namely:
Ethroughout＝2NEESDODErated
Wherein DOD is the depth of discharge and N EES is the number of charge and discharge times in the whole life cycle.
According to the data provided by the manufacturer, the loss cost Y EES is an exponential function of the SOC, and the loss cost F EES when the single energy storage is charged and discharged from any SOC to another SOC can be accurately calculated by integrating the exponential function, namely:
To make F EES a linear function, we approximate the exponential function Y EES as a piecewise function Y' EES, with each segment being constant:
wherein, M is M, which is defined as a set of segments from 1 to M in SOC; if the SOC is in the segment m, Taking 1, otherwise taking 0; /(I)Is a degradation cost coefficient when the SOC is in segment m. In each section/>The value of (2) satisfies the following formula:
where SOC max.m and SOC min.m are the maximum and minimum values of SOC in segment m.
Wherein C 2、C3…CM is a constant, which is given such thatThe energy storage loss function of single charge or discharge is rewritten as a continuous function: /(I)
In the step S2, fuzzy representation of wind power and uncertain load prediction quantity is established by considering different time scale prediction precision. The advantage of fuzzy numbers over random numbers is that their membership functions are not dependent on a large amount of data. Thus, in cases where the information is insufficient or difficult to collect, the fuzzy number is a better way to describe the uncertainty. In the invention, the prediction errors and the prediction values of wind power, electric load and cold load are respectively represented by triangle fuzzy numbers and determinacy.
Prediction error of wind power and load:
εWT,t＝(-kwPWT,t,0,kwPWT,t)
εEle,t＝(-kElePEle,t,0,kElePEle,t)
εHeat,t＝(-kHeatQHeat,t,0,kHeatQHeat,t)
εCool,t＝(-kCoolQCool,t,0,kCoolQCool,t)
wherein P WT,t、PEle,t、QHeat,t and Q Cool,t are predicted values before the day for wind power, electric load, thermal load and cold load respectively, and k w、kEle、kHeat and k Cool are maximum predicted error proportionality coefficients respectively.
The specific procedure for relaxing the power balance equation containing an indefinite amount is as follows.
The strict electric, thermal and cold power balance equations under the consideration of the prediction error are:
PGT,t+PWT,t+εWT,t+PGrid,t＝PEle,t+εEle,t-ΔPWT,t+PEC,t+PEES,t
QGB,t+QHX,t＝QHeat,t+εHeat,t
QAC,t+QEC,t＝QCool,t+εCool,t
Wherein P GT,t is the power of the gas turbine at the time t; p Grid,t is the power exchanged with the large power grid at the time t, and DeltaP WT,t is the air discarding quantity; p EC,t is the power of the electric refrigerator at the time t; and P EES,t is the charge and discharge power of the energy storage power station at the time t. Q GB,t and Q HX,t are respectively the thermal power of the gas boiler and the heat exchange device at the time t. Q AC,t and Q EC,t are the cold powers of the absorption refrigerator and the electric refrigerator, respectively, at time t.
Relaxation is an inequality constraint to construct an uncertainty set:
|PGT,t+(PWT,t+εWT,t)+PGrid,t-(PEle,t+εEle,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t
|QGB,t+QHX,t-QHeat,t-εHeat,t|≤σHeat,t
|QAC,t+QEC,t-QCool,t-εCool,t|≤σCool,t
Where σ Ele,t、σHeat,t and σ Cool,t are each a small constant whose size determines the feasible region of the defined uncertainty set in the blurred environment.
In step S3 of the present invention, the power balance constraint is converted into a problem of maximizing the possibility of establishment of a fuzzy event in a fuzzy environment, namely:
F1,t＝max Pos{|PGT,t+(PWT,t+εWT,t)+PGrid,t-
(PEle,t+εEle,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t}
F2,t＝max Pos{|QGB,t+QHX,t-QHeat,t-εHeat,t|≤σHeat,t}
\*MERGEFORMAT(80)
F3,t＝max Pos{|QAC,t+QEC,t-QCool,t-εCool,t|≤σCool,t
the objective function with the lowest day-ahead running cost of the comprehensive energy system is
Fgrid＝λ(t)Pbuy,tΔt
Wherein F fuel、Fgrid and F O&M respectively represent fuel cost, electricity purchase and selling cost from a power grid and equipment operation and maintenance cost; c gas is the price of natural gas, and Yuan/m 3;ηGT and eta GB are the efficiencies of the gas turbine and the gas boiler respectively; l NG is the heat value of the fuel gas, and 9.7kWh/m 3 is taken; lambda (t) is the time-of-use electricity price; electric power purchased from the grid for time t; k GT、kGB、kHX、kEC、kAC and k WT are the unit operation and maintenance costs of the gas turbine, the gas boiler, the heat exchange device, the electric refrigerator, the absorption refrigerator and the blower, respectively.
In the step S4 of the present invention, the output constraints of other devices in the integrated energy system are as follows:
1) Combined cooling, heating and power (CCHP) constraint
QHX,t/ηHX+QAC,t/COPAC＝PGT,tγGTηWH
Wherein, gamma GT is the heat-electricity ratio of the gas turbine; η HX and η WH are the efficiencies of the heat exchange device and the waste heat boiler respectively; COP AC is the energy efficiency ratio of an absorption chiller; and/> Respectively minimum and maximum values of the power of the gas turbine; /(I)And/>Respectively the minimum and maximum values of the power of the heat exchange device; /(I)And/>Respectively, the minimum and maximum values of the absorption chiller power.
2) Gas boiler restraint
In the method, in the process of the invention,And/>Is the maximum and minimum power values of the gas boiler.
3) Electric refrigerator restraint
QEC,t＝PEC,tCOPEC
Wherein COP EC is the energy efficiency ratio of the electric refrigerator; and/> Maximum and minimum power values for the electric refrigerator, respectively.
4) Grid exchange power constraint
0≤PGrid,t≤Pgmax
Where P gmax is the maximum power purchased from the grid.
In step S5 of the present invention, the model proposed by the present invention is a multi-objective problem, and each objective function and constraint condition are linear. The objective functions can be converted into a single-objective Mixed Integer Linear Programming (MILP) problem by giving weight to the objective functions, which can be quickly and effectively solved by using the existing commercial software, and the established day-ahead scheduling model can be solved by programming in Matlab by calling solver Cplex through YALMIP.
Taking a certain combined cooling heating and power integrated energy system as an example, the model is analyzed. The price of natural gas is 2.2 yuan/m 3, and the peak-to-valley electricity price is shown in table 1. The selling of electricity to the grid by the user is not considered. The parameters of each device in the park are shown in table 2, and the fuzzy membership parameters of the load and wind power prediction errors are shown in table 3.σ Ele,t＝σHeat,t＝σCool,t =50 kW.
The objective function after transformation is:
In the method, in the process of the invention,
TABLE 1
TABLE 2
TABLE 3 Table 3
The predicted daily values of wind power generation, electric load, heat load and cold load of the integrated energy system are shown in fig. 2. Fig. 3 is a graph of the output result of the electric energy production device. Fig. 4 is a graph of the output results of a thermal energy production facility. Fig. 5 is a graph of the output result of the cold energy production equipment. Table 4 shows comparison of the optimized results in three modes. Mode one: the invention provides a day-ahead economic dispatch model; mode two: the comprehensive energy system containing energy storage, but the power balance is deterministic balance; mode three: fuzzy correlation opportunity planning is adopted, but no energy storage is configured in the system. It can be seen that the day-ahead scheduling scheme provided by the invention can ensure reliable energy supply and realize economic operation of the system and renewable energy consumption.
TABLE 4 Table 4
Claims (6)
1. The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning is characterized by comprising the following steps of:
step S1, carrying out mathematical modeling on energy storage equipment in a comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge;
s2, establishing fuzzy representation of wind power and uncertain load prediction, and relaxing a power balance equation containing uncertain load;
Step S3, converting a power balance equation into a maximized opportunity function according to the step S2, taking the maximized opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model with the maximum possibility of fuzzy events and the minimum running cost as targets;
S4, restraining the output of other equipment in the comprehensive energy system, wherein the constraint comprises a cogeneration equipment constraint, a gas boiler constraint, an electric refrigerator constraint and a power grid exchange power constraint;
And S5, solving the multi-target day-ahead scheduling model established in the step S3, and further obtaining an optimal day-ahead scheduling result.
2. The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning of claim 1, wherein the method comprises the following steps: in step S1, the description of the mathematical model of the energy storage device in the integrated energy system is specifically as follows:
Wherein P EES,t is the energy storage power at time t; and/> Respectively representing charge and discharge; /(I)And/>Respectively 0-1 variable representing the charge and discharge states of the energy storage, when the energy storage is in a charge state,/>Otherwise 0; /(I)E rated is the maximum charge and discharge power of the energy storage power station, and E rated is the energy storage rated capacity; SOC t is the state of charge of the stored energy at time t; η ch and η dis are the efficiencies of energy storage charging and discharging respectively; SOC min and SOC max are respectively a minimum value and a maximum value of the stored-energy state of charge, Δt is a scheduled time interval, N T is a total number of time intervals, t=1, 2, … N T;
The loss cost flow of linearization single energy storage charge and discharge is specifically as follows: let the total throughput capacity E throughout of the energy storage battery be related to the total charge and discharge times and the charge and discharge depth, namely:
Ethroughout＝2NEESDODErated
wherein DOD is the depth of discharge, and N EES is the number of charge and discharge times in the whole life cycle;
The loss cost Y EES is an exponential function of the SOC, and the loss cost F EES when the single energy storage is charged or discharged from any SOC to another SOC can be accurately calculated by integrating the exponential function, that is:
To make F EES a linear function, the exponential function Y EES is approximated as a piecewise function Y E'ES, and each segment is constant:
wherein, M is M, which is defined as a set of segments from 1 to M in SOC; if the SOC is in the segment m, Taking 1, otherwise taking 0; /(I)Is a degradation cost coefficient when the SOC is in segment m; in each section/>The value of (2) satisfies the following formula:
where SOC max.m and SOC min.m are the maximum and minimum values of SOC in segment m;
Wherein C 2、C3…CM is a constant, which is given such that As a continuous function, the energy storage loss function of single charge or discharge is rewritten as: /(I)
3. The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning of claim 1, wherein the method comprises the following steps: in step S2, a fuzzy representation of wind power and the uncertainty of load prediction is established in consideration of different time scale prediction accuracy, specifically as follows: respectively using the triangle fuzzy number and the deterministic degree to represent the prediction error and the prediction value of wind power, electric load and cold load;
prediction error of wind power and load:
εWT,t＝(-kwPWT,t,0,kwPWT,t)
εEle,t＝(-kElePEle,t,0,kElePEle,t)
εHeat,t＝(-kHeatQHeat,t,0,kHeatQHeat,t)
εCool,t＝(-kCoolQCool,t,0,kCoolQCool,t)
Wherein P WT,t、PEle,t、QHeat,t and Q Cool,t are predicted values before the day for wind power, electric load, thermal load and cold load respectively, and k w、kEle、kHeat and k Cool are maximum predicted error proportionality coefficients respectively;
the specific procedure for relaxing the power balance equation containing an indefinite amount is as follows:
the strict electric, thermal and cold power balance equations under the consideration of the prediction error are:
PGT,t+PWT,t+εWT,t+PGrid,t＝PEle,t+εEle,t-ΔPWT,t+PEC,t+PEES,t
QGB,t+QHX,t＝QHeat,t+εHeat,t
QAC,t+QEC,t＝QCool,t+εCool,t
Wherein P GT,t is the power of the gas turbine at the time t; p Grid,t is the power exchanged with the large power grid at the time t, and DeltaP WT,t is the air discarding quantity; p EC,t is the power of the electric refrigerator at the time t; p EES,t is the charge and discharge power of the energy storage power station at the time t; q GB,t and Q HX,t are respectively the thermal power of the gas boiler and the heat exchange device at the time t; q AC,t and Q EC,t are the cold powers of the absorption refrigerator and the electric refrigerator at time t, respectively;
relaxation is an inequality constraint to construct an uncertainty set:
|PGT,t+(PWT,t+εWT,t)+PGrid,t-(PEle,t+εEle,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t
|QGB,t+QHX,t-QHeat,t-εHeat,t|≤σHeat,t
|QAC,t+QEC,t-QCool,t-εCool,t|≤σCool,t
where σ Ele,t、σHeat,t and σ Cool,t are constants, the size of which determines the feasible region of the defined uncertainty set in the blurred environment.
4. The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning of claim 1, wherein the method comprises the following steps: in step S3, the power balance constraint is converted into a problem of maximizing the probability of establishment of a fuzzy event in a fuzzy environment, namely:
F1,t＝maxPos{|PGT,t+(PWT,t+εWT,t)+PGrid,t-(PEle,t+εEle,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t}
F2,t＝maxPos{|QGB,t+QHX,t-QHeat,t-εHeat,t|≤σHeat,t}
F3,t＝maxPos{|QAC,t+QEC,t-QCool,t-εCool,t|≤σCool,t
the objective function with the lowest day-ahead running cost of the comprehensive energy system is
Fgrid＝λ(t)Pbuy,tΔt
Wherein F fuel、Fgrid and F O&M respectively represent fuel cost, electricity purchase and selling cost from a power grid and equipment operation and maintenance cost; c gas is the price of natural gas, and Yuan/m 3;ηGT and eta GB are the efficiencies of the gas turbine and the gas boiler respectively; l NG is the heat value of the fuel gas, and 9.7kWh/m 3 is taken; lambda (t) is the time-of-use electricity price; electric power purchased from the grid for time t; k GT、kGB、kHX、kEC、kAC and k WT are the unit operation and maintenance costs of the gas turbine, the gas boiler, the heat exchange device, the electric refrigerator, the absorption refrigerator and the blower, respectively.
5. The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning of claim 1, wherein the method comprises the following steps: in step S4, the output constraints of other devices in the energy system are integrated, specifically as follows:
1) CCHP constraint of combined heat and power generation equipment:
QHX,t/ηHX+QAC,t/COPAC＝PGT,tγGTηWH
Wherein, gamma GT is the heat-electricity ratio of the gas turbine; η HX and η WH are the efficiencies of the heat exchange device and the waste heat boiler respectively; COP AC is the energy efficiency ratio of an absorption chiller; and/> Respectively minimum and maximum values of the power of the gas turbine; /(I)And/>Respectively the minimum and maximum values of the power of the heat exchange device; /(I)And/>Respectively the minimum and maximum values of the power of the absorption refrigerator;
2) Gas boiler constraint:
In the method, in the process of the invention, And/>Maximum and minimum power values for the gas boiler;
3) Electric refrigerator restraint:
QEC,t＝PEC,tCOPEC
Wherein COP EC is the energy efficiency ratio of the electric refrigerator; and/> Maximum and minimum power values of the electric refrigerator respectively;
4) Grid exchange power constraint:
0≤PGrid,t≤Pgmax
Where P gmax is the maximum power purchased from the grid.
6. The energy storage-containing comprehensive energy system scheduling method based on fuzzy relevant opportunity planning of claim 1, wherein the method comprises the following steps: in step S5, the multi-objective day-ahead scheduling model established in step 3 is solved, so as to obtain an optimal day-ahead scheduling result.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111313475A (en) * | 2018-12-11 | 2020-06-19 | 华北电力大学（保定） | Power system scheduling method considering prediction error uncertain variable through power balance constraint |
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| CN110417006A (en) * | 2019-07-24 | 2019-11-05 | 三峡大学 | Consider the integrated energy system Multiple Time Scales energy dispatching method of multipotency collaboration optimization |
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