CN111860966A - 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 PDF

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CN111860966A
CN111860966A CN202010590503.4A CN202010590503A CN111860966A CN 111860966 A CN111860966 A CN 111860966A CN 202010590503 A CN202010590503 A CN 202010590503A CN 111860966 A CN111860966 A CN 111860966A
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徐青山
丁逸行
杨斌
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Southeast University
State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a scheduling method of an energy-storage-containing comprehensive energy system based on fuzzy related opportunity planning, which belongs to the technical field of power system scheduling and specifically comprises the following steps: performing mathematical modeling on energy storage equipment in the comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge; establishing fuzzy representation of wind power and load prediction uncertainty, and relaxing a power balance equation containing the uncertainty; converting a power balance equation into a maximum opportunity function, taking the maximum opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model taking the maximum possibility of fuzzy events and the lowest operation cost as targets; the method specifically comprises the steps of constraining the output of other equipment in the comprehensive energy system, and specifically comprising the constraint of combined cooling heating and power equipment, the constraint of a gas boiler, the constraint of an electric refrigerator and the constraint of power grid exchange power; and solving the established multi-target day-ahead scheduling model to obtain an optimal day-ahead scheduling result. The invention applies the relevant opportunity planning method in the fuzzy environment to the comprehensive energy system scheduling, and realizes the economic operation of the system while ensuring the safe and reliable energy supply.

Description

Energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning
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 related opportunity planning.
Background
The development of renewable energy and smart power grids makes the world enter the era of energy interconnection. Compared with the traditional single electric energy system, the comprehensive energy system becomes an important technical way for improving the energy efficiency and reducing the operation cost through the coupling of various energy sources such as electric energy, heat energy, natural gas and the like. In a multi-energy system, an energy storage power station is used as a buffer between an energy supply side and a user side, so that Combined Heat and Power (CHP) or Combined Cooling and Heating and Power (CCHP) equipment does not need to operate in a conventional mode of 'fixing heat by electricity' or 'fixing electricity by heat', thermoelectric decoupling is realized, and the efficiency of system scheduling is improved.
In addition, the problem of optimizing and scheduling the comprehensive energy system considering the uncertainty of renewable energy and load has attracted wide attention of scholars at home and abroad. However, the existing scheduling strategy focuses on random optimization and robust optimization, which not only has high requirements on original data, but also often sacrifices economy due to over conservation.
Disclosure of Invention
The invention provides an energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning aiming at the defects of the background art, which can reduce the calculation complexity while ensuring the calculation precision; and fuzzy related opportunity planning is applied to the scheduling of the comprehensive energy system, deterministic power balance is converted into maximum balance probability, a day-ahead economic scheduling model of the comprehensive energy system is established, and economic and reliable energy supply is achieved.
The invention adopts the following technical scheme for solving the technical problems:
the energy storage-containing comprehensive energy system scheduling method based on fuzzy related opportunity planning specifically comprises the following steps:
step S1, performing mathematical modeling on energy storage equipment in the comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge;
step S2, establishing fuzzy representation of wind power and load prediction uncertainty, and relaxing a power balance equation containing the uncertainty;
step S3, converting the power balance equation into a maximization opportunity function according to the step S2, taking the maximization opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model taking the maximum possibility of the fuzzy event and the lowest operation cost as the objectives;
Step S4, restraining the output of other devices in the comprehensive energy system, specifically comprising combined cooling heating and power generation device restraint, gas boiler restraint, electric refrigerator restraint and power grid exchange power restraint;
and S5, solving the multi-target day-ahead scheduling model established in the step S3 to obtain an optimal day-ahead scheduling result.
As a further preferable scheme of the energy storage-containing integrated energy system scheduling method based on fuzzy correlation opportunity planning of the present invention, in step S1, a mathematical model description of the energy storage device in the integrated energy system is specifically as follows:
Figure BDA0002555313170000021
in the formula, PEES,tStoring energy power for the moment t;
Figure BDA0002555313170000022
and
Figure BDA0002555313170000023
respectively represent charging and discharging;
Figure BDA0002555313170000024
and
Figure BDA0002555313170000025
respectively represent the 0-1 variable of the charging and discharging state of the stored energy, when the stored energy is in the charging state,
Figure BDA0002555313170000026
otherwise, the value is 0;
Figure BDA0002555313170000027
for maximum charge-discharge power of energy storage power stations, EratedRated capacity for energy storage; SOCtThe state of charge of the stored energy at the time t; etachAnd ηdisRespectively the efficiency of energy storage charging and discharging; SOCminAnd SOCmaxRespectively the minimum value and the maximum value of the energy storage charge state, delta t is a scheduling time interval, NTT is 1,2, … NT；
The linear loss cost process of single energy storage charging and discharging specifically comprises the following steps: setting total throughput electric quantity E of energy storage battery throughoutThe total charge and discharge times are related to the charge and discharge depth, namely:
Ethroughout＝2NEESDODErated
wherein DOD is depth of discharge, NEESThe number of charge and discharge times of the whole life cycle;
wherein, the loss cost YEESThe loss cost F of charging and discharging from any SOC to another SOC in single energy storage can be accurately calculated through integration of the exponential function of the SOCEESNamely:
Figure BDA0002555313170000031
to make FEESAs a linear function, an exponential function YEESApproximation as a piecewise function Y'EESAnd each segment is constant:
Figure BDA0002555313170000032
in the formula (I), the compound is shown in the specification,m belongs to M and is defined as a set of sections from 1 to M of SOC; if the SOC is in the segment m,
Figure BDA0002555313170000033
taking 1, otherwise, taking 0;
Figure BDA0002555313170000034
is the degradation cost coefficient when the SOC is in segment m; in each segment
Figure BDA0002555313170000035
The value of (b) satisfies the following formula:
Figure BDA0002555313170000036
in the formula, SOCmax.mAnd SOCmin.mMaximum and minimum values of SOC in section m;
Figure BDA0002555313170000037
in the formula, C2、C3…CMIs a constant, which takes a value of
Figure BDA0002555313170000038
For a continuous function, the energy storage loss function for a single charge or discharge is rewritten as:
Figure BDA0002555313170000039
as a further preferable scheme of the energy storage-containing integrated energy system scheduling method based on fuzzy correlation opportunity planning, in step S2, considering that prediction accuracies of different time scales are different, a fuzzy representation of wind power and load prediction uncertainty is established, specifically as follows: respectively representing the prediction error and the prediction value of wind power, electric load and cold and hot load by using a triangular fuzzy number and certainty;
Prediction error of wind power and load:
WT,t＝(-kwPWT,t,0,kwPWT,t)
Ele,t＝(-kElePEle,t,0,kElePEle,t)
Heat,t＝(-kHeatQHeat,t,0,kHeatQHeat,t)
Cool,t＝(-kCoolQCool,t,0,kCoolQCool,t)
in the formula, PWT,t、PEle,t、QHeat,tAnd QCool,tThe predicted values of the wind power, the electrical load, the thermal load and the cold load, kw、kEle、kHeatAnd kCoolRespectively, maximum prediction error proportionality coefficients;
the specific process of relaxing the power balance equation with the uncertain quantity is as follows:
the strict electricity, heat and cold power balance equation under consideration of the prediction error is as follows:
PGT,t+PWT,t+WT,t+PGrid,t＝PEle,t+Ele,t-ΔPWT,t+PEC,t+PEES,t
QGB,t+QHX,t＝QHeat,t+Heat,t
QAC,t+QEC,t＝QCool,t+Cool,t
in the formula, PGT,tIs the power of the gas turbine at time t; pGrid,tFor the power exchanged with the large grid at time t, Δ PWT,tThe air quantity is discarded; pEC,tThe power of the electric refrigerator at the moment t; pEES,tThe charging and discharging power of the energy storage power station at the moment t is obtained; qGB,tAnd QHX,tRespectively are the thermal powers of the gas boiler and the heat exchange device at the moment t; qAC,tAnd QEC,tThe cold power of the absorption refrigerator and the cold power of the electric refrigerator at the moment t are respectively;
relaxation is an inequality constraint to construct an uncertainty set:
|PGT,t+(PWT,t+WT,t)+PGrid,t-(PEle,t+Ele,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t
|QGB,t+QHX,t-QHeat,t-Heat,t|≤σHeat,t
|QAC,t+QEC,t-QCool,t-Cool,t|≤σCool,t
in the formula, σEle,t、σHeat,tAnd σCool,tAre constants whose size determines the feasible domain of the defined uncertainty set in the fuzzy environment.
As a further preferable scheme of the energy storage integrated energy system scheduling method based on fuzzy correlation opportunity planning of the present invention, in step S3, the power balance constraint is converted into a problem of possibility of maximizing establishment of fuzzy events in a fuzzy environment, that is:
F1,t＝max Pos{|PGT,t+(PWT,t+WT,t)+PGrid,t-(PEle,t+Ele,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t}
F2,t＝maxPos{|QGB,t+QHX,t-QHeat,t-Heat,t|≤σHeat,t}
F3,t＝maxPos{|QAC,t+QEC,t-QCool,t-Cool,t|≤σCool,t
The objective function of the lowest day-ahead operation cost of the integrated energy system is
Figure BDA0002555313170000041
Figure BDA0002555313170000051
Fgrid＝λ(t)Pbuy,tΔt
Figure BDA0002555313170000052
In the formula, Ffuel、FgridAnd FO&MRespectively representing the fuel cost, the electricity purchasing and selling cost from the power grid and the equipment operation and maintenance cost; c. CgasIs the price of natural gas, yuan/m3；ηGTAnd ηGBEfficiency of the gas turbine and the gas boiler, respectively; l isNGTaking 9.7kWh/m as the heat value of the fuel gas3(ii) a Lambda (t) is the time-of-use electricity price; electrical power purchased from the grid for time t; k is a radical ofGT、kGB、kHX、kEC、kACAnd kWTThe unit operation and maintenance costs of the gas turbine, the gas boiler, the heat exchange device, the electric refrigerator, the absorption refrigerator and the fan are respectively.
As a further preferable scheme of the energy storage-containing integrated energy system scheduling method based on fuzzy correlation opportunity planning, in step S4, the output constraints of other devices in the integrated energy system are as follows:
1) combined Cooling Heating and Power (CCHP) constraints:
QHX,t/ηHX+QAC,t/COPAC＝PGT,tγGTηWH
Figure BDA0002555313170000053
Figure BDA0002555313170000054
Figure BDA0002555313170000055
in the formula, gammaGTIs the gas turbine heat to power ratio; etaHXAnd ηWHThe efficiency of the heat exchange device and the efficiency of the waste heat boiler are respectively; COPACIs the energy efficiency ratio of the absorption refrigerator;
Figure BDA0002555313170000056
and
Figure BDA0002555313170000057
minimum and maximum values of gas turbine power, respectively;
Figure BDA0002555313170000058
and
Figure BDA0002555313170000059
the minimum value and the maximum value of the power of the heat exchange device are respectively;
Figure BDA00025553131700000510
and
Figure BDA00025553131700000511
the minimum and maximum values of the power of the absorption type refrigerating machine are respectively;
2) and (3) gas boiler restraint:
Figure BDA00025553131700000512
In the formula (I), the compound is shown in the specification,
Figure BDA00025553131700000513
and
Figure BDA00025553131700000514
maximum and minimum power values for the gas boiler;
3) the electric refrigerator restrains:
QEC,t＝PEC,tCOPEC
Figure BDA0002555313170000061
in the formula, COPECThe energy efficiency ratio of the electric refrigerator;
Figure BDA0002555313170000062
and
Figure BDA0002555313170000063
maximum and minimum power values of the electric refrigerator respectively;
4) and power grid exchange power constraint:
0≤PGrid,t≤Pgmax
in the formula, PgmaxThe maximum power purchased from the power grid.
As a further preferable scheme of the scheduling method of the energy-storage-containing integrated energy system based on fuzzy correlation opportunity planning, in step S5, the multi-target day-ahead scheduling model established in step S3 is solved, and then an optimal day-ahead scheduling result is obtained.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1) the invention provides a new energy storage loss model calculation method, which quantifies the relationship between each charge-discharge action and the loss cost, and can reduce the calculation complexity without losing too much accuracy.
2) The invention applies the related opportunity planning under the fuzzy environment to the scheduling of the comprehensive energy system, the established day-ahead scheduling model converts the equality constraint containing the uncertain variable into the objective function, and the safety and the economic operation of the system can be realized by reasonably setting the weights of different objectives when a scheduling plan is formulated.
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Fig. 1 is a flowchart of a scheduling method of an energy storage-containing integrated energy system based on fuzzy correlation programming according to the present invention;
FIG. 2 is a graph of a day ahead forecast of wind power generation, electrical load, thermal load, and cold load for an integrated energy system;
FIG. 3 is a graph showing the output results of the electric energy generation apparatus according to the present invention;
FIG. 4 is a graph of the results of the thermal energy production apparatus of the present invention;
FIG. 5 is a graph showing the results of the cold energy production apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a scheduling method of an energy storage-containing integrated energy system based on fuzzy correlation opportunity planning mainly includes the following steps:
step S1, performing mathematical modeling on energy storage equipment in the comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge;
step S2, establishing fuzzy representation of wind power and load prediction uncertainty, and taking into account that prediction accuracy at different moments is different, relaxing a power balance equation containing uncertainty;
Step S3, converting the power balance equation into a maximization opportunity function according to the step S2, taking the maximization opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model taking the maximum possibility of the fuzzy event and the lowest operation cost as the objectives;
step S4, restraining the output of other devices in the comprehensive energy system, specifically comprising combined cooling heating and power generation device restraint, gas boiler restraint, electric refrigerator restraint and power grid exchange power restraint;
and S5, solving the multi-target day-ahead scheduling model established in the step S3 to obtain an optimal day-ahead scheduling result.
In step S1, the mathematical model of the energy storage device in the integrated energy system is described as follows:
Figure BDA0002555313170000071
in the formula, PEES,tStoring energy power for the moment t;
Figure BDA0002555313170000072
and
Figure BDA0002555313170000073
respectively represent charging and discharging;
Figure BDA0002555313170000074
and
Figure BDA0002555313170000075
respectively represent the 0-1 variable of the charging and discharging state of the stored energy, when the stored energy is in the charging state,
Figure BDA0002555313170000076
otherwise, the value is 0;
Figure BDA0002555313170000077
for maximum charge-discharge power of energy storage power stations, EratedRated capacity for energy storage; SOCtA State of charge (State of charge) for storing energy at time t; etachAnd ηdisRespectively the efficiency of energy storage charging and discharging; SOCminAnd SOCmaxRespectively the minimum and maximum values of the energy storage state of charge. Δ t is the scheduling time interval, N TT is 1,2, … NT。
The loss cost process of linear single energy storage charging and discharging is as follows, and the total throughput electric quantity E of the energy storage batterythroughoutThe total charge and discharge times are related to the charge and discharge depth, namely:
Ethroughout＝2NEESDODErated
wherein DOD is depth of discharge, NEESThe number of charge and discharge times of the whole life cycle.
According to data provided by the manufacturer, loss cost YEESThe loss cost F of charging and discharging from any SOC to another SOC in single energy storage can be accurately calculated through integration of the exponential function of the SOCEESNamely:
Figure BDA0002555313170000081
to make FEESFor a linear function, we will use an exponential function YEESApproximation as a piecewise function Y'EESAnd each segment is constant:
Figure BDA0002555313170000082
wherein M belongs to M and is defined as a set of sections from 1 to M of SOC; if the SOC is in the segment m,
Figure BDA0002555313170000083
taking 1, otherwise, taking 0;
Figure BDA0002555313170000084
is the degradation cost coefficient when the SOC is in segment m. In each segment
Figure BDA0002555313170000085
The value of (b) satisfies the following formula:
Figure BDA0002555313170000086
in the formula, SOCmax.mAnd SOCmin.mThe maximum and minimum values of the SOC in segment m.
Figure BDA0002555313170000087
In the formula, C2、C3…CMIs a constant, which takes a value of
Figure BDA0002555313170000088
For a continuous function, the energy storage loss function for a single charge or discharge is then rewritten as:
Figure BDA0002555313170000089
in step S2, the fuzzy representation of the wind power and load prediction uncertainty is established in consideration of the difference in prediction accuracy at different time scales. The advantage of fuzzy numbers compared to random numbers is that their membership functions do not depend on a large amount of data. Thus, in situations where information is insufficient or difficult to collect, fuzzy numbers are a better way to describe uncertainty. In the invention, the prediction error and the prediction value of wind power, electric load and cold and hot load are respectively represented by triangular fuzzy number and certainty.
Prediction error of wind power and load:
WT,t＝(-kwPWT,t,0,kwPWT,t)
Ele,t＝(-kElePEle,t,0,kElePEle,t)
Heat,t＝(-kHeatQHeat,t,0,kHeatQHeat,t)
Cool,t＝(-kCoolQCool,t,0,kCoolQCool,t)
in the formula, PWT,t、PEle,t、QHeat,tAnd QCool,tThe predicted values of the wind power, the electrical load, the thermal load and the cold load, kw、kEle、kHeatAnd kCoolRespectively, the maximum prediction error scaling factor.
The specific procedure for relaxing the power balance equation with an indeterminate amount is as follows.
The strict electricity, heat and cold power balance equation under consideration of the prediction error is as follows:
PGT,t+PWT,t+WT,t+PGrid,t＝PEle,t+Ele,t-ΔPWT,t+PEC,t+PEES,t
QGB,t+QHX,t＝QHeat,t+Heat,t
QAC,t+QEC,t＝QCool,t+Cool,t
in the formula, PGT,tIs the power of the gas turbine at time t; pGrid,tFor the power exchanged with the large grid at time t, Δ PWT,tThe air quantity is discarded; pEC,tThe power of the electric refrigerator at the moment t; pEES,tThe charging and discharging power of the energy storage power station at the moment t is obtained. QGB,tAnd QHX,tRespectively the thermal power of the gas boiler and the heat exchange device at the moment t. QAC,tAnd QEC,tThe cold power of the absorption refrigerator and the electric refrigerator at the moment t respectively.
Relaxation is an inequality constraint to construct an uncertainty set:
|PGT,t+(PWT,t+WT,t)+PGrid,t-(PEle,t+Ele,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t
|QGB,t+QHX,t-QHeat,t-Heat,t|≤σHeat,t
|QAC,t+QEC,t-QCool,t-Cool,t|≤σCool,t
in the formula, σEle,t、σHeat,tAnd σCool,tIs a small constant whose size determines the feasible domain of the defined uncertainty set in the fuzzy environment.
In step S3, the power balance constraint is converted into a probability problem of the maximum occurrence of the fuzzy event in the fuzzy environment, that is:
F1,t＝max Pos{|PGT,t+(PWT,t+WT,t)+PGrid,t-
(PEle,t+Ele,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t}
F2,t＝max Pos{|QGB,t+QHX,t-QHeat,t-Heat,t|≤σHeat,t}
\*MERGEFORMAT(80)
F3,t＝max Pos{|QAC,t+QEC,t-QCool,t-Cool,t|≤σCool,t
the objective function of the lowest day-ahead operation cost of the integrated energy system is
Figure BDA0002555313170000101
Figure BDA0002555313170000102
Fgrid＝λ(t)Pbuy,tΔt
Figure BDA0002555313170000103
In the formula, Ffuel、FgridAnd FO&MRespectively representing the fuel cost, the electricity purchasing and selling cost from the power grid and the equipment operation and maintenance cost; c. CgasIs the price of natural gas, yuan/m3；ηGTAnd ηGBEfficiency of the gas turbine and the gas boiler, respectively; l isNGTaking 9.7kWh/m as the heat value of the fuel gas3(ii) a Lambda (t) is the time-of-use electricity price; electrical power purchased from the grid for time t; k is a radical ofGT、kGB、kHX、kEC、kACAnd kWTThe unit operation and maintenance costs of the gas turbine, the gas boiler, the heat exchange device, the electric refrigerator, the absorption refrigerator and the fan are respectively.
In step S4, the output of other devices in the integrated energy system is constrained as follows:
1) combined cooling, heating and power (CCHP) constraints
QHX,t/ηHX+QAC,t/COPAC＝PGT,tγGTηWH
Figure BDA0002555313170000104
Figure BDA0002555313170000105
Figure BDA0002555313170000106
In the formula, gammaGTIs the gas turbine heat to power ratio; etaHXAnd ηWHThe efficiency of the heat exchange device and the efficiency of the waste heat boiler are respectively; COPACIs the energy efficiency ratio of the absorption refrigerator;
Figure BDA0002555313170000107
and
Figure BDA0002555313170000108
minimum and maximum values of gas turbine power, respectively;
Figure BDA0002555313170000109
and
Figure BDA00025553131700001010
the minimum value and the maximum value of the power of the heat exchange device are respectively;
Figure BDA00025553131700001011
and
Figure BDA00025553131700001012
respectively the minimum and maximum values of the absorption refrigerator power.
2) Gas boiler restraint
Figure BDA0002555313170000111
In the formula (I), the compound is shown in the specification,
Figure BDA0002555313170000112
and
Figure BDA0002555313170000113
maximum and minimum power values for the gas boiler.
3) Electric refrigerator restraint
QEC,t＝PEC,tCOPEC
Figure BDA0002555313170000114
In the formula, COPECThe energy efficiency ratio of the electric refrigerator;
Figure BDA0002555313170000115
and
Figure BDA0002555313170000116
the maximum and minimum power values of the electric refrigerator are respectively.
4) Grid exchange power constraint
0≤PGrid,t≤Pgmax
In the formula, PgmaxThe maximum power purchased from the power grid.
In step S5, the model proposed by the present invention is a multi-objective problem, and each objective function and constraint condition are linear. Each objective function can be converted into a single-objective Mixed Integer Linear Programming (MILP) problem by giving weight to the objective function, the existing commercial software can be used for solving the problem quickly and effectively, and the built day-ahead scheduling model can be solved by programming a solver Cplex in Matlab through yalmap.
Taking a certain comprehensive energy system for combined supply of cooling, heating and power as an example, the model is analyzed. The natural gas price is 2.2 yuan/m3The peak-to-valley electricity rates are shown in table 1. Regardless of the consumer selling electricity to the grid. The parameters of each device in the garden are shown in a table 2, and the fuzzy membership degree parameters of the load and wind power prediction error are shown in a table 3. SigmaEle,t＝σHeat,t＝σCool,t＝50kW。
The transformed objective function is:
Figure BDA0002555313170000117
in the formula (I), the compound is shown in the specification,
Figure BDA0002555313170000119
TABLE 1
Figure BDA0002555313170000118
Figure BDA0002555313170000121
TABLE 2
Figure BDA0002555313170000122
TABLE 3
Figure BDA0002555313170000123
The predicted values of the wind power generation, the electrical load, the thermal load and the cold load of the integrated energy system before day are shown in fig. 2. FIG. 3 is a graph of the results of the power generation equipment. Fig. 4 is a graph of the thermal energy production equipment output results. FIG. 5 is a graph of the results of cold energy production equipment. Table 4 compares the optimization results in the three modes. The first mode is as follows: the invention provides a day-ahead economic dispatching model; and a second mode: an integrated energy system containing stored energy, but with power balance being deterministic; and a third mode: fuzzy correlation opportunity planning is adopted, but energy storage is not configured in the system. Therefore, the day-ahead scheduling scheme provided by the invention can realize economic operation of the system and consumption of renewable energy sources while ensuring reliable energy supply.
TABLE 4
Figure BDA0002555313170000131

Claims (6)

1. The energy storage-containing comprehensive energy system scheduling method based on fuzzy correlation opportunity planning is characterized by comprising the following steps:
step S1, performing mathematical modeling on energy storage equipment in the comprehensive energy system, and linearizing the loss cost of single energy storage charge and discharge;
step S2, establishing fuzzy representation of wind power and load prediction uncertainty, and relaxing a power balance equation containing the uncertainty;
step S3, converting the power balance equation into a maximization opportunity function according to the step S2, taking the maximization opportunity function as one of objective functions, and establishing a multi-objective day-ahead optimization scheduling model taking the maximum possibility of the fuzzy event and the lowest operation cost as the objectives;
step S4, restraining the output of other devices in the comprehensive energy system, specifically comprising combined cooling heating and power generation device restraint, gas boiler restraint, electric refrigerator restraint and power grid exchange power restraint;
and S5, solving the multi-target day-ahead scheduling model established in the step S3 to obtain an optimal day-ahead scheduling result.
2. The fuzzy correlation opportunity programming-based energy storage-containing integrated energy system scheduling method of claim 1, wherein: in step S1, the mathematical model of the energy storage device in the integrated energy system is described as follows:
Figure FDA0002555313160000011
In the formula, PEES,tStoring energy power for the moment t;
Figure FDA0002555313160000012
and
Figure FDA0002555313160000013
respectively represent charging and discharging;
Figure FDA0002555313160000014
and
Figure FDA0002555313160000015
respectively represent the 0-1 variable of the charging and discharging state of the stored energy, when the stored energy is in the charging state,
Figure FDA0002555313160000016
otherwise, the value is 0;
Figure FDA0002555313160000017
for maximum charge-discharge power of energy storage power stations, EratedRated capacity for energy storage; SOCtThe state of charge of the stored energy at the time t; etachAnd ηdisRespectively the efficiency of energy storage charging and discharging; SOCminAnd SOCmaxRespectively energy storage state of chargeAt is the scheduling time interval, NTT is 1,2, … NT；
The linear loss cost process of single energy storage charging and discharging specifically comprises the following steps: setting total throughput electric quantity E of energy storage batterythroughoutThe total charge and discharge times are related to the charge and discharge depth, namely:
Ethroughout＝2NEESDODErated
wherein DOD is depth of discharge, NEESThe number of charge and discharge times of the whole life cycle;
wherein, the loss cost YEESThe loss cost F of charging and discharging from any SOC to another SOC in single energy storage can be accurately calculated through integration of the exponential function of the SOCEESNamely:
Figure FDA0002555313160000021
to make FEESAs a linear function, an exponential function YEESApproximation as a piecewise function YE'ESAnd each segment is constant:
Figure FDA0002555313160000022
wherein M belongs to M and is defined as a set of sections from 1 to M of SOC; if the SOC is in the segment m,
Figure FDA0002555313160000023
Taking 1, otherwise, taking 0;
Figure FDA0002555313160000024
is the degradation cost coefficient when the SOC is in segment m; in each segment
Figure FDA0002555313160000025
The value of (b) satisfies the following formula:
Figure FDA0002555313160000026
in the formula, SOCmax.mAnd SOCmin.mMaximum and minimum values of SOC in section m;
Figure FDA0002555313160000027
in the formula, C2、C3…CMIs a constant, which takes a value of
Figure FDA0002555313160000028
For a continuous function, the energy storage loss function for a single charge or discharge is rewritten as:
Figure FDA0002555313160000029
3. the fuzzy correlation opportunity programming-based energy storage-containing integrated energy system scheduling method of claim 1, wherein: in step S2, considering that the prediction accuracy is different for different time scales, a fuzzy representation of the wind power and load prediction uncertainty is established, which is specifically as follows: respectively representing the prediction error and the prediction value of wind power, electric load and cold and hot load by using a triangular fuzzy number and certainty;
prediction error of wind power and load:
WT,t＝(-kwPWT,t,0,kwPWT,t)
Ele,t＝(-kElePEle,t,0,kElePEle,t)
Heat,t＝(-kHeatQHeat,t,0,kHeatQHeat,t)
Cool,t＝(-kCoolQCool,t,0,kCoolQCool,t)
in the formula, PWT,t、PEle,t、QHeat,tAnd QCool,tRespectively wind power, electrical load, thermal load and coldPredicted value of load, k, before dayw、kEle、kHeatAnd kCoolRespectively, maximum prediction error proportionality coefficients;
the specific process of relaxing the power balance equation with the uncertain quantity is as follows:
the strict electricity, heat and cold power balance equation under consideration of the prediction error is as follows:
PGT,t+PWT,t+WT,t+PGrid,t＝PEle,t+Ele,t-ΔPWT,t+PEC,t+PEES,t
QGB,t+QHX,t＝QHeat,t+Heat,t
QAC,t+QEC,t＝QCool,t+Cool,t
in the formula, PGT,tIs the power of the gas turbine at time t; pGrid,tFor the power exchanged with the large grid at time t, Δ P WT,tThe air quantity is discarded; pEC,tThe power of the electric refrigerator at the moment t; pEES,tThe charging and discharging power of the energy storage power station at the moment t is obtained; qGB,tAnd QHX,tRespectively are the thermal powers of the gas boiler and the heat exchange device at the moment t; qAC,tAnd QEC,tThe cold power of the absorption refrigerator and the cold power of the electric refrigerator at the moment t are respectively;
relaxation is an inequality constraint to construct an uncertainty set:
|PGT,t+(PWT,t+WT,t)+PGrid,t-(PEle,t+Ele,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t
|QGB,t+QHX,t-QHeat,t-Heat,t|≤σHeat,t
|QAC,t+QEC,t-QCool,t-Cool,t|≤σCool,t
in the formula, σEle,t、σHeat,tAnd σCool,tAre constants whose size determines the feasible domain of the defined uncertainty set in the fuzzy environment.
4. The comprehensive energy system economic dispatching method based on fuzzy correlation planning of claim 1, characterized in that: in step S3, the power balance constraint translates into a probability problem that the maximum ambiguity event holds under the ambiguity environment, namely:
F1,t＝maxPos{|PGT,t+(PWT,t+WT,t)+PGrid,t-
(PEle,t+Ele,t-ΔPWT,t)-PEC,t-PEES,t|≤σEle,t}
F2,t＝maxPos{|QGB,t+QHX,t-QHeat,t-Heat,t|≤σHeat,t}
F3,t＝maxPos{|QAC,t+QEC,t-QCool,t-Cool,t|≤σCool,t
the objective function of the lowest day-ahead operation cost of the integrated energy system is
Figure FDA0002555313160000041
Figure FDA0002555313160000042
Fgrid＝λ(t)Pbuy,tΔt
Figure FDA0002555313160000043
In the formula, Ffuel、FgridAnd FO&MRespectively representing the fuel cost, the electricity purchasing and selling cost from the power grid and the equipment operation and maintenance cost; c. CgasIs the price of natural gas, yuan/m3；ηGTAnd ηGBEfficiency of the gas turbine and the gas boiler, respectively; l isNGTaking 9.7kWh/m as the heat value of the fuel gas3(ii) a Lambda (t) is the time-of-use electricity price; electrical power purchased from the grid for time t; k is a radical ofGT、kGB、kHX、kEC、kACAnd kWTThe unit operation and maintenance costs of the gas turbine, the gas boiler, the heat exchange device, the electric refrigerator, the absorption refrigerator and the fan are respectively.
5. The fuzzy correlation opportunity programming-based energy storage-containing integrated energy system scheduling method of claim 1, wherein: in step S4, the output constraints of other devices in the integrated energy system are as follows:
1) combined Cooling Heating and Power (CCHP) constraints:
QHX,t/ηHX+QAC,t/COPAC＝PGT,tγGTηWH
Figure FDA0002555313160000044
Figure FDA0002555313160000045
Figure FDA0002555313160000046
in the formula, gammaGTIs the gas turbine heat to power ratio; etaHXAnd ηWHThe efficiency of the heat exchange device and the efficiency of the waste heat boiler are respectively; COPACIs the energy efficiency ratio of the absorption refrigerator;
Figure FDA0002555313160000051
and
Figure FDA0002555313160000052
minimum and maximum values of gas turbine power, respectively;
Figure FDA0002555313160000053
and
Figure FDA0002555313160000054
the minimum value and the maximum value of the power of the heat exchange device are respectively;
Figure FDA0002555313160000055
and
Figure FDA0002555313160000056
the minimum and maximum values of the power of the absorption type refrigerating machine are respectively;
2) and (3) gas boiler restraint:
Figure FDA0002555313160000057
in the formula (I), the compound is shown in the specification,
Figure FDA0002555313160000058
and
Figure FDA0002555313160000059
maximum and minimum power values for the gas boiler;
3) the electric refrigerator restrains:
QEC,t＝PEC,tCOPEC
Figure FDA00025553131600000510
in the formula, COPECThe energy efficiency ratio of the electric refrigerator;
Figure FDA00025553131600000511
and
Figure FDA00025553131600000512
maximum and minimum power values of the electric refrigerator respectively;
4) and power grid exchange power constraint:
0≤PGrid,t≤Pgmax
in the formula, PgmaxThe maximum power purchased from the power grid.
6. The fuzzy correlation opportunity programming-based energy storage-containing integrated energy system scheduling method of claim 1, wherein: in step S5, the multi-target day-ahead scheduling model established in step 3 is solved, and an optimal day-ahead scheduling result is obtained.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113255198A (en) * 2021-03-25 2021-08-13 上海电机学院 Multi-objective optimization method for combined cooling, heating and power supply micro-grid with virtual energy storage
CN113779783A (en) * 2021-08-30 2021-12-10 国网上海市电力公司 Multi-uncertainty-considered planning and operation joint optimization method for regional comprehensive energy system
CN113824137A (en) * 2021-10-09 2021-12-21 深圳供电局有限公司 Scheduling method and device of hybrid energy storage system and storage medium
CN115081867A (en) * 2022-06-20 2022-09-20 国网浙江省电力有限公司经济技术研究院 Energy storage planning method, device and equipment for comprehensive energy system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN110661254A (en) * 2019-09-26 2020-01-07 广东电网有限责任公司 Method for quantifying cooling, heating and power complementary benefits of regional comprehensive energy system
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

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
CN110661254A (en) * 2019-09-26 2020-01-07 广东电网有限责任公司 Method for quantifying cooling, heating and power complementary benefits of regional comprehensive energy system

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113255198A (en) * 2021-03-25 2021-08-13 上海电机学院 Multi-objective optimization method for combined cooling, heating and power supply micro-grid with virtual energy storage
CN113779783A (en) * 2021-08-30 2021-12-10 国网上海市电力公司 Multi-uncertainty-considered planning and operation joint optimization method for regional comprehensive energy system
CN113824137A (en) * 2021-10-09 2021-12-21 深圳供电局有限公司 Scheduling method and device of hybrid energy storage system and storage medium
CN113824137B (en) * 2021-10-09 2024-02-27 深圳供电局有限公司 Scheduling method, device and storage medium of hybrid energy storage system
CN115081867A (en) * 2022-06-20 2022-09-20 国网浙江省电力有限公司经济技术研究院 Energy storage planning method, device and equipment for comprehensive energy system

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