CN114219121A - Regional comprehensive energy multi-objective optimization method based on source-load prediction - Google Patents

Regional comprehensive energy multi-objective optimization method based on source-load prediction Download PDF

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CN114219121A
CN114219121A CN202111267335.6A CN202111267335A CN114219121A CN 114219121 A CN114219121 A CN 114219121A CN 202111267335 A CN202111267335 A CN 202111267335A CN 114219121 A CN114219121 A CN 114219121A
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肖龙海
金海�
施海峰
袁国珍
樊卡
高忠旭
成佳斌
张扬
王晓明
赵龙安
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State Grid Zhejiang Electric Power Co Ltd Haining Power Supply Co
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Abstract

The invention discloses a regional comprehensive energy multi-objective optimization method based on source-load prediction. The method comprises the following steps: forecasting the photovoltaic, wind power output, power load, heat consumption load and cold consumption load in unit optimization time according to the photovoltaic, wind power generation operation characteristics, weather and other factors in the region; setting a safety threshold of the difference between the predicted data and the actual sampling data in consideration of the system voltage supply and demand balance limit; and substituting the predicted values of the photoelectric output, the wind power output, the power load, the heat consumption load and the cold consumption load into the optimization function to solve by taking the lowest carbon emission and the lowest energy supply cost as a multi-objective optimization function, obtaining the output reference of the optimization time of the energy supply unit, judging whether the difference between the actual sampling value and the predicted value exceeds a safety threshold value by the system, and adjusting the predicted value on the basis. The invention improves the response speed, reduces the overshoot in the control process, effectively reduces the carbon emission and the operation cost, and avoids the imbalance of supply and demand voltages caused by inaccurate prediction.

Description

Regional comprehensive energy multi-objective optimization method based on source-load prediction
Technical Field
The invention relates to the field of energy optimization, in particular to a regional comprehensive energy multi-objective optimization method based on source-load prediction.
Background
In recent years, environmental issues such as carbon emissions have been receiving increasing attention in many fields. Electric energy is in various fields of life, industry and traffic, and energy supply in a low-carbon environment-friendly way is one of key tasks of carbon peak reaching and carbon neutralization work.
The comprehensive energy system is an important form of a new generation of electric energy system, combines wind energy, light energy and heat energy complementation on the basis of traditional electric energy supply, can perform intelligent control and cooperative control, is effectively applied to the aspects of economic benefit, non-cross-network consumption of renewable energy and the like, and draws more and more attention in the low-carbon field.
At present, the optimization of the comprehensive energy system is mostly single target optimization on factors such as economic benefit or new energy consumption, optimization is carried out while a plurality of factors are ignored, an optimization function is usually based on real-time sampling data, a large promotion space exists in response speed, overshoot is easier to occur in the control process, system adjustment shock can be caused even in serious conditions, social energy and economic unnecessary waste can be caused by the series of factors, and the advantages of the comprehensive energy system in the fields of environmental protection, energy conservation and the like can not be fully exerted.
For example, a method and an apparatus for optimizing the operation of a regional integrated energy system disclosed in chinese patent literature, which is disclosed in CN112381335A, includes: establishing an operation economic model of the comprehensive energy system based on the Stackelberg game; and solving by adopting a particle swarm algorithm according to the running economic model of the comprehensive energy system, and optimizing the running parameters of the energy system according to the solving result, so that the running optimization control of each device is facilitated.
The optimization of the comprehensive energy system by the scheme is the single target optimization of economic benefits, and the simultaneous optimization of a plurality of factors is omitted.
Disclosure of Invention
The invention mainly solves the problem of optimizing a single target in the prior art; the regional comprehensive energy multi-target optimization method based on the source-load prediction is provided, optimization is carried out based on source-load prediction data and by combining carbon emission and economic benefit multi-target factors, and meanwhile appropriate adjustment is carried out according to real-time sampling data.
The technical problem of the invention is mainly solved by the following technical scheme:
a regional comprehensive energy multi-objective optimization method based on source-load prediction is characterized by comprising the following steps:
step 1: according to the operation characteristics of the photovoltaic power generation unit and the wind power generation unit in the region, work output in unit optimization time of the region and power load, heat consumption load and cold consumption load in unit optimization time are predicted;
step 2: judging whether the difference between the actual sampling data and the prediction data exceeds a safety threshold generated according to supply and demand voltage balance limit; if yes, entering step 3; if not, entering the step 4;
and step 3: adjusting the predicted value according to the difference between the actual sampling data and the predicted data;
and 4, step 4: solving to obtain a work output reference of unit optimization time by taking the lowest carbon emission and the lowest energy supply cost as optimization target optimization constraints;
and 5: the energy supply unit performs work extraction according to the work extraction reference of the unit optimization time.
According to the scheme, based on the data of source-load prediction, the response speed is improved, overshoot in the control process is reduced, system regulation oscillation is avoided, optimization is performed through multi-objective factors, carbon emission and operation cost are effectively reduced, meanwhile, appropriate adjustment is made according to real-time sampling data, and imbalance of supply and demand voltages caused by inaccurate prediction is avoided.
Preferably, the step 1 specifically comprises the following steps:
collecting and transmitting data, wherein the data comprises solar radiation intensity, wind power and wind speed, rainfall, highest air temperature and lowest air temperature;
building a deep learning prediction model, taking historical meteorological information and historical power generation data of the area where the photovoltaic power generation unit and the wind power generation unit are located as an input sequence of the prediction model, carrying out normalization processing on the collected data, carrying out DBN (direct binary bus) unsupervised training, and carrying out BP (back propagation) supervised training;
and inputting the parameters into the trained prediction model to obtain a prediction result.
The prediction method is power/load prediction based on a deep belief network.
Preferably, the safety threshold generated according to the supply and demand voltage balance limit is:
Figure BDA0003326102890000021
Figure BDA0003326102890000022
Figure BDA0003326102890000023
Figure BDA0003326102890000025
Figure BDA0003326102890000024
wherein, KpowerPurchasing an electricity sag coefficient for the outside in sag control;
Kpvthe droop coefficient of the photovoltaic power generation in the droop control is obtained;
Kwtthe droop coefficient of the wind power generation in the droop control is;
Keloadthe droop coefficient of the electrical load in droop control;
Khloadthe load droop coefficient of the electric heating machine in droop control is shown;
Kcloadthe droop coefficient is the load droop coefficient of the refrigerating machine in droop control;
Peloadthe method comprises the following steps of (1) providing power load for users of the comprehensive energy system;
Figure BDA0003326102890000031
outputting work within unit optimization time for the photovoltaic power generation unit of the comprehensive energy system obtained through prediction;
Figure BDA0003326102890000032
outputting work within unit optimization time for the predicted wind power generation unit of the comprehensive energy system;
Figure BDA0003326102890000033
the user electricity load in unit optimization time is obtained by predicting the comprehensive energy system;
Figure BDA0003326102890000034
for predicting the load of the electric heating furnace in the unit optimization time of the obtained comprehensive energy system,
Figure BDA0003326102890000035
to obtain for predictionThe load of the refrigerating machine in the unit optimization time of the comprehensive energy system is obtained.
Generating the safety threshold in accordance with supply and demand voltage balance limits based on safety considerations.
Preferably, when the difference between the actual sampled data and the predicted data exceeds a safety threshold:
Figure BDA0003326102890000036
Figure BDA0003326102890000037
Figure BDA0003326102890000038
Figure BDA0003326102890000039
Figure BDA00033261028900000310
Figure BDA00033261028900000311
Figure BDA00033261028900000312
if not, then,
ΔPpv.r＝ΔPwt.r＝ΔPeload.r＝ΔQcload.r＝ΔQhload.r＝ΔPcload.r＝ΔPhload.r＝0
wherein the content of the first and second substances,
Figure BDA00033261028900000313
for the photovoltaic power generation system of the comprehensive energy systemActual sampling values;
Figure BDA00033261028900000314
outputting an actual sampling value for a wind power generation system of the comprehensive energy system;
Figure BDA00033261028900000315
an actual sampling value of the electrical load for a user of the comprehensive energy system is obtained;
Figure BDA00033261028900000316
the actual sampling value of the cold consumption load of the comprehensive energy system is obtained;
Figure BDA00033261028900000317
the actual sampling value is the heat consumption load actual sampling value of the comprehensive energy system;
ΔPpv.rpredicting a correction value for the work output of the photovoltaic power generation within the unit correction time of the comprehensive energy system;
ΔPwt.rpredicting a corrected value for the wind power generation work output within the unit correction time of the comprehensive energy system;
ΔPeload.rpredicting a correction value for the user electric load within the unit correction time of the comprehensive energy system;
ΔPcload.rpredicting a correction value for the load of the refrigerating machine within the unit correction time of the comprehensive energy system;
ΔPhload.rpredicting a correction value for the load of the electric heating furnace in the unit correction time of the comprehensive energy system;
ΔQcload.rcorrecting the cold consumption load of the comprehensive energy system;
ΔQhload.rcorrecting the heat consumption load of the comprehensive energy system;
Figure BDA0003326102890000041
cooling load in unit optimization time obtained by prediction of the comprehensive energy system;
Figure BDA0003326102890000042
the heat consumption load in unit optimization time is obtained by predicting the comprehensive energy system;
ηcthe electric-cold conversion rate of the refrigeration machine of the comprehensive energy system;
ηloss，Cthe electric-cold conversion loss rate of the refrigerator of the comprehensive energy system is improved;
Figure BDA0003326102890000043
optimizing the gas furnace consumption flow in time for a unit of the comprehensive energy system;
ηboilerthe gas-heat conversion rate of the gas of the comprehensive energy system is obtained;
ηloss，boilerthe loss is the conversion of gas-heat energy of the gas of the comprehensive energy system;
ηpothe electric-heat conversion rate of the electric heating furnace of the comprehensive energy system is obtained;
ηloss，pothe electric-heat conversion loss rate of the electric heating furnace of the comprehensive energy system;
Figure BDA0003326102890000044
the heat release power of the heat storage device of the comprehensive energy system is released;
Figure BDA0003326102890000045
the heat storage device of the comprehensive energy system stores heat power.
And proper adjustment is made according to the real-time sampling data, so that the imbalance of supply and demand voltages caused by inaccurate prediction is avoided.
Preferably, the constraints include:
electric energy balance constraint:
Figure BDA0003326102890000046
wherein the content of the first and second substances,
Figure BDA0003326102890000047
the power purchasing power of the external power grid in unit optimization time is optimized for the comprehensive energy system;
Figure BDA0003326102890000048
the work is output in unit optimization time for the storage battery of the comprehensive energy system;
Figure BDA0003326102890000049
the power is output in unit optimization time for the micro gas engine of the comprehensive energy system;
Figure BDA00033261028900000410
the storage battery stores power in unit time of the comprehensive energy system;
cold energy balance constraint:
Figure BDA00033261028900000411
and (3) heat energy balance constraint:
Figure BDA0003326102890000051
external power purchase restraint:
Figure BDA0003326102890000052
wherein the content of the first and second substances,
Figure BDA0003326102890000053
the lower limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
Figure BDA0003326102890000054
for integrating energy system with external electricityAn upper limit of the on-line electric power;
external gas purchase restraint:
Figure BDA0003326102890000055
wherein the content of the first and second substances,
Figure BDA0003326102890000056
optimizing the fuel consumption of the micro gas engine in unit time;
mminthe lower limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
mmaxthe upper limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
battery charge and discharge power constraint:
Figure BDA0003326102890000057
Figure BDA0003326102890000058
wherein the content of the first and second substances,
Figure BDA0003326102890000059
the upper limit of battery discharge for the comprehensive energy system;
Figure BDA00033261028900000510
charging an upper limit for the battery of the comprehensive energy system;
and (3) constraint of battery storage capacity:
SOCmin≤SOCT≤SOCmax
Figure BDA00033261028900000511
therein, SOCTThe storage capacity of the battery in the Tth optimization time period is obtained;
SOCminthe lower limit of the stored electric quantity of the battery of the comprehensive energy system;
SOCmaxthe upper limit of the battery storage capacity of the comprehensive energy system;
c is the equivalent capacitance of the battery;
q is the battery capacity;
v is the voltage of the battery in the Tth optimization time period;
ηbatthe charge-discharge efficiency of the battery;
t is unit optimization time;
the charge and discharge relationship is as follows:
Figure BDA0003326102890000061
the micro gas engine is restrained:
Figure BDA0003326102890000062
Figure BDA0003326102890000063
wherein eta ismtThe gas-heat conversion rate of the micro gas engine is obtained;
ηloss，mtgas-heat conversion loss rate of the micro gas engine;
Figure BDA0003326102890000064
optimizing the lower power limit in time for the micro gas engine unit;
Figure BDA0003326102890000065
optimizing the upper limit of power in unit time for the micro gas engine;
energy storage device thermal energy capacity constraint:
Figure BDA0003326102890000066
wherein the content of the first and second substances,
Figure BDA0003326102890000067
an upper limit of the battery storage capacity of the comprehensive energy system;
Figure BDA0003326102890000068
and the lower limit of the battery storage capacity of the comprehensive energy system.
Preferably, the carbon emissions of the units in the integrated energy system are as follows:
external power purchase carbon emission function:
Figure BDA0003326102890000069
wherein, CpowerThe total amount of externally purchased electric carbon emission;
αpowerthe carbon emission coefficient of external electricity purchase;
function of carbon emission of micro gas engine:
Figure BDA00033261028900000610
wherein, CmtThe total carbon emission of the micro gas engine;
αmtthe carbon emission coefficient of the micro gas engine is shown;
function of carbon emission from gas boiler:
Figure BDA00033261028900000611
wherein, CboilerThe total amount of the carbon emission of the gas furnace;
αboileris the carbon block coefficient of the gas furnace.
Considering the cost of environmental protection.
Preferably, optimization constraints are optimized by taking the lowest carbon emission and the lowest energy supply cost as optimization targets;
considering the objective optimization function with the lowest energy cost:
Figure BDA0003326102890000071
the objective optimization function considering the lowest carbon emission:
min F2＝Cpower+Cmt+Cboiler
wherein S ispowerA cost factor for purchasing electricity for the outside of the integrated energy system;
Swtthe wind power generation cost coefficient of the comprehensive energy system is obtained;
Spvthe photovoltaic power generation cost coefficient of the comprehensive energy system is obtained;
Smtthe cost coefficient of the micro gas engine of the comprehensive energy system is obtained;
Sbat，disGa battery discharge cost factor for the integrated energy system;
Sbat，chara battery charging cost factor for the integrated energy system;
Sboileris the gas furnace cost coefficient of the comprehensive energy system.
And the carbon emission and the operation cost are effectively reduced by optimizing the multi-objective factors.
Preferably, the multi-objective optimization function is solved by adopting an alternative direction multiplier method, and the specific process is as follows:
establishing a multi-objective optimization function;
carrying out fuzzy processing on the target function by adopting a membership function:
Figure BDA0003326102890000072
wherein, FiRepresents the ith target;
μiis FiDegree of membership of;
Figure BDA0003326102890000073
is FiMaximum value of (d);
Figure BDA0003326102890000074
is FiMinimum value of (d);
fuzzy optimization model for maximizing satisfaction:
Figure BDA0003326102890000075
Figure BDA0003326102890000076
Figure BDA0003326102890000077
representing the satisfaction degree of multi-target decision for the minimum value of all target membership degrees;
g (x) is a constraint function vector;
and substituting the work parameters to construct an augmented Lagrange function, and solving by utilizing the gradient to obtain work reference values of all units of the comprehensive energy system.
The invention has the beneficial effects that:
1. based on the data of source-load prediction, the response speed is improved, the overshoot in the control process is reduced, and the system regulation oscillation is avoided.
2. And the carbon emission and the operation cost are effectively reduced by optimizing the multi-objective factors.
3. And proper adjustment is made according to the real-time sampling data, so that the imbalance of supply and demand voltages caused by inaccurate prediction is avoided.
Drawings
FIG. 1 is a flow chart of the regional comprehensive energy multi-objective optimization method of the invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
the regional comprehensive energy multi-objective optimization method based on source-load prediction in the embodiment is shown in fig. 1, and includes the following steps:
step 1: according to the operation characteristics of the photovoltaic power generation unit and the wind power generation unit in the region, work output in unit optimization time of the region and power load, heat consumption load and cold consumption load in unit optimization time are predicted.
The integrated energy system adopted by the embodiment comprises: external power grid, photovoltaic power generation unit, wind power generation unit, battery, external gas grid, micro gas engine, gas furnace, heat storage device, electric heater, refrigerator, electricity load, cold load, and heat load (.
The electric load of the present embodiment does not include a refrigerator load and an electric heater load.
The prediction data includes the following processes:
and acquiring and transmitting data, wherein the data comprises solar radiation intensity, wind power and wind speed, rainfall, highest air temperature and lowest air temperature.
And (3) building a deep learning prediction model, taking historical meteorological information and historical power generation data of the areas where the photovoltaic power generation units and the wind power generation units are as input sequences of the prediction model, carrying out normalization processing on the collected data, and carrying out DBN (direct binary bus) unsupervised training and BP (back propagation) supervised training.
And inputting the parameters into the trained prediction model to obtain a prediction result.
Step 2: judging whether the difference between the actual sampling data and the prediction data exceeds a safety threshold generated according to supply and demand voltage balance limit; if yes, entering step 3; if not, the step 4 is entered.
For safety purposes, a safety threshold generated according to supply and demand voltage balance limits:
Figure BDA0003326102890000081
Figure BDA0003326102890000082
Figure BDA0003326102890000083
Figure BDA0003326102890000091
Figure BDA0003326102890000092
wherein, KpowerPurchasing an electricity sag coefficient for the outside in sag control;
Kpvthe droop coefficient of the photovoltaic power generation in the droop control is obtained;
Kwtthe droop coefficient of the wind power generation in the droop control is;
Keloadthe droop coefficient of the electrical load in droop control;
Khloadthe load droop coefficient of the electric heating machine in droop control is shown;
Kcloadthe droop coefficient is the load droop coefficient of the refrigerating machine in droop control;
Peloadthe method comprises the following steps of (1) providing power load for users of the comprehensive energy system;
Figure BDA0003326102890000093
outputting work within unit optimization time for the photovoltaic power generation unit of the comprehensive energy system obtained through prediction;
Figure BDA0003326102890000094
outputting work within unit optimization time for the predicted wind power generation unit of the comprehensive energy system;
Figure BDA0003326102890000095
the user electricity load in unit optimization time is obtained by predicting the comprehensive energy system;
Figure BDA0003326102890000096
for predicting the load of the electric heating furnace in the unit optimization time of the obtained comprehensive energy system,
Figure BDA0003326102890000097
and optimizing the load of the refrigerating machine in the unit optimization time for the predicted comprehensive energy system.
And step 3: and adjusting the predicted value according to the difference between the actual sampling data and the predicted data.
When the difference between the actual sampled data and the predicted data exceeds a safety threshold:
Figure BDA0003326102890000098
Figure BDA0003326102890000099
Figure BDA00033261028900000910
Figure BDA00033261028900000911
Figure BDA00033261028900000912
Figure BDA00033261028900000913
Figure BDA00033261028900000914
if not, then,
ΔPpv.r＝ΔPwt.r＝ΔPeload.r＝ΔQcload.r＝ΔQhload.r＝ΔPcload.r＝ΔPhload.r＝0
wherein the content of the first and second substances,
Figure BDA0003326102890000101
outputting an actual sampling value for a photovoltaic power generation system of the comprehensive energy system;
Figure BDA0003326102890000102
outputting an actual sampling value for a wind power generation system of the comprehensive energy system;
Figure BDA0003326102890000103
an actual sampling value of the electrical load for a user of the comprehensive energy system is obtained;
Figure BDA0003326102890000104
the actual sampling value of the cold consumption load of the comprehensive energy system is obtained;
Figure BDA0003326102890000105
the actual sampling value is the heat consumption load actual sampling value of the comprehensive energy system;
ΔPpv.rpredicting a correction value for the work output of the photovoltaic power generation within the unit correction time of the comprehensive energy system;
ΔPwt.rpredicting a corrected value for the wind power generation work output within the unit correction time of the comprehensive energy system;
ΔPeload.rpredicting a correction value for the user electric load within the unit correction time of the comprehensive energy system;
ΔPcload.rpredicting a correction value for the load of the refrigerating machine within the unit correction time of the comprehensive energy system;
ΔPhload，rpredicting a correction value for the load of the electric heating furnace in the unit correction time of the comprehensive energy system;
ΔQcload，rfor cooling of integrated energy systemsA load consumption correction value;
ΔQhload，rcorrecting the heat consumption load of the comprehensive energy system;
Figure BDA0003326102890000106
cooling load in unit optimization time obtained by prediction of the comprehensive energy system;
Figure BDA0003326102890000107
the heat consumption load in unit optimization time is obtained by predicting the comprehensive energy system;
ηcthe electric-cold conversion rate of the refrigeration machine of the comprehensive energy system;
ηloss，cthe electric-cold conversion loss rate of the refrigerator of the comprehensive energy system is improved;
Figure BDA00033261028900001011
optimizing the gas furnace consumption flow in time for a unit of the comprehensive energy system;
ηboilerthe gas-heat conversion rate of the gas of the comprehensive energy system is obtained;
ηloss，boilerthe loss is the conversion of gas-heat energy of the gas of the comprehensive energy system;
ηpothe electric-heat conversion rate of the electric heating furnace of the comprehensive energy system is obtained;
ηloss，pothe electric-heat conversion loss rate of the electric heating furnace of the comprehensive energy system;
Figure BDA0003326102890000109
the heat release power of the heat storage device of the comprehensive energy system is released;
Figure BDA00033261028900001010
the heat storage device of the comprehensive energy system stores heat power.
And 4, step 4: and solving to obtain the work output reference of unit optimization time by taking the lowest carbon emission and the lowest energy supply cost as optimization target optimization constraints.
The constraint conditions comprise electric energy balance constraint, cold energy balance constraint, heat energy balance constraint, external electricity purchasing constraint, external gas purchasing constraint, battery charging and discharging power constraint, battery storage electric quantity constraint, micro gas engine constraint and energy storage device heat energy capacity constraint.
Electric energy balance constraint:
Figure BDA0003326102890000111
wherein the content of the first and second substances,
Figure BDA0003326102890000112
the power purchasing power of the external power grid in unit optimization time is optimized for the comprehensive energy system;
Figure BDA0003326102890000113
the work is output in unit optimization time for the storage battery of the comprehensive energy system;
Figure BDA0003326102890000114
the power is output in unit optimization time for the micro gas engine of the comprehensive energy system;
Figure BDA0003326102890000115
and the storage battery stores power in the unit time of the comprehensive energy system.
Cold energy balance constraint:
Figure BDA0003326102890000116
and (3) heat energy balance constraint:
Figure BDA0003326102890000117
external power purchase restraint:
Figure BDA0003326102890000118
wherein the content of the first and second substances,
Figure BDA0003326102890000119
the lower limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
Figure BDA00033261028900001110
the upper limit of the power purchased by the comprehensive energy system and the external power grid.
External gas purchase restraint:
Figure BDA00033261028900001111
wherein the content of the first and second substances,
Figure BDA00033261028900001112
optimizing the fuel consumption of the micro gas engine in unit time;
mminthe lower limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
mmaxthe upper limit of the power purchased by the comprehensive energy system and the external power grid.
Battery charge and discharge power constraint:
Figure BDA00033261028900001113
Figure BDA00033261028900001114
wherein the content of the first and second substances,
Figure BDA00033261028900001115
the upper limit of battery discharge for the comprehensive energy system;
Figure BDA00033261028900001116
and the upper limit of the charging of the battery of the comprehensive energy system is reached.
And (3) constraint of battery storage capacity:
SOCmin≤SOCT≤SOCmax
Figure BDA0003326102890000121
therein, SOCTThe storage capacity of the battery in the Tth optimization time period is obtained;
SOCminthe lower limit of the stored electric quantity of the battery of the comprehensive energy system;
SOCmaxthe upper limit of the battery storage capacity of the comprehensive energy system;
c is the equivalent capacitance of the battery;
q is the battery capacity;
v is the voltage of the battery in the Tth optimization time period;
ηbatthe charge-discharge efficiency of the battery;
and t is unit optimization time.
The charge and discharge relationship is as follows:
Figure BDA0003326102890000122
the micro gas engine is restrained:
Figure BDA0003326102890000123
Figure BDA0003326102890000124
wherein eta ismtThe gas-heat conversion rate of the micro gas engine is obtained;
ηloss，mtgas-heat conversion loss rate of the micro gas engine;
Figure BDA0003326102890000125
optimizing the lower power limit in time for the micro gas engine unit;
Figure BDA0003326102890000126
the upper limit of the power in the unit optimization time is optimized for the micro gas engine.
Energy storage device thermal energy capacity constraint:
Figure BDA0003326102890000127
wherein the content of the first and second substances,
Figure BDA0003326102890000128
an upper limit of the battery storage capacity of the comprehensive energy system;
Figure BDA0003326102890000129
and the lower limit of the battery storage capacity of the comprehensive energy system.
The integrated energy system comprises an external electricity purchasing carbon emission function, a micro gas engine carbon emission function and a gas boiler carbon emission function.
The carbon emissions of each unit are as follows:
external power purchase carbon emission function:
Figure BDA00033261028900001210
wherein, CpowerThe total amount of externally purchased electric carbon emission;
αpowerthe carbon emission coefficient of the external power purchase.
Function of carbon emission of micro gas engine:
Figure BDA0003326102890000131
wherein, CmtIs a micro-scaleTotal carbon emission of the gas engine;
αmtthe carbon emission coefficient of the micro gas engine.
Function of carbon emission from gas boiler:
Figure BDA0003326102890000132
wherein, CboilerThe total amount of the carbon emission of the gas furnace;
αboileris the carbon block coefficient of the gas furnace.
Optimizing constraints by taking the lowest carbon emission and the lowest energy supply cost as optimization targets;
considering the objective optimization function with the lowest energy cost:
Figure BDA0003326102890000133
the objective optimization function considering the lowest carbon emission:
min F2＝Cpower+Cmt+Cboiler
wherein S ispowerA cost factor for purchasing electricity for the outside of the integrated energy system;
Swtthe wind power generation cost coefficient of the comprehensive energy system is obtained;
Spvthe photovoltaic power generation cost coefficient of the comprehensive energy system is obtained;
Smtthe cost coefficient of the micro gas engine of the comprehensive energy system is obtained;
Sbat，disca battery discharge cost factor for the integrated energy system;
Sbat，chara battery charging cost factor for the integrated energy system;
Sboileris the gas furnace cost coefficient of the comprehensive energy system.
The multi-objective optimization function is solved by adopting an alternative direction multiplier method, and the specific process is as follows:
establishing a multi-objective optimization function;
carrying out fuzzy processing on the target function by adopting a membership function:
Figure BDA0003326102890000134
wherein, FiRepresents the ith target;
μiis FiDegree of membership of;
Figure BDA0003326102890000141
is FiMaximum value of (d);
Figure BDA0003326102890000142
is FiMinimum value of (d);
fuzzy optimization model for maximizing satisfaction:
Figure BDA0003326102890000143
Figure BDA0003326102890000144
Figure BDA0003326102890000145
representing the satisfaction degree of multi-target decision for the minimum value of all target membership degrees;
g (x) is a constraint function vector;
and substituting the work output parameters to construct an augmented Lagrange function, and solving by utilizing a gradient to obtain the work output reference value of each unit of the comprehensive energy system.
Substituting the work output parameter into:
Figure BDA0003326102890000146
constructing an augmented Lagrangian function:
L(x，z，λ)＝f(x)+g(z)+λT(Ax+Bz-c)+ρ/2*||Ax+Bz-c||2
the augmented Lagrange function solving steps are as follows:
Step1：
Figure BDA0003326102890000147
Step2：
Figure BDA0003326102890000148
Step3：
λk+1＝λk+ρ(Ax+Bz-c)
and solving by utilizing the gradient to obtain the work output reference value of each unit of the comprehensive energy system.
And 5: the energy supply unit performs work extraction according to the work extraction reference of the unit optimization time.
The scheme of this embodiment can be based on the data of source-load prediction, has promoted response speed, has reduced the overshoot among the control process, has avoided the system control to vibrate, optimizes through the multi-objective factor, has effectively reduced carbon emission and running cost, makes suitable adjustment according to real-time sampling data simultaneously, has avoided because the inaccurate supply and demand voltage unbalance that causes of prediction.
It should be understood that the examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.

Claims (8)

1. A regional comprehensive energy multi-objective optimization method based on source-load prediction is characterized by comprising the following steps:
step 1: according to the operation characteristics of the photovoltaic power generation unit and the wind power generation unit in the region, work output in unit optimization time of the region and power load, heat consumption load and cold consumption load in unit optimization time are predicted;
step 2: judging whether the difference between the actual sampling data and the prediction data exceeds a safety threshold generated according to supply and demand voltage balance limit; if yes, entering step 3; if not, entering the step 4;
and step 3: adjusting the predicted value according to the difference between the actual sampling data and the predicted data;
and 4, step 4: solving to obtain a work output reference of unit optimization time by taking the lowest carbon emission and the lowest energy supply cost as optimization target optimization constraints;
and 5: the energy supply unit performs work extraction according to the work extraction reference of the unit optimization time.
2. The method for multi-objective optimization of regional integrated energy based on source-load prediction as claimed in claim 1, wherein the step 1 specifically comprises the following processes:
collecting and transmitting data, wherein the data comprises solar radiation intensity, wind power and wind speed, rainfall, highest air temperature and lowest air temperature;
building a deep learning prediction model, taking historical meteorological information and historical power generation data of the area where the photovoltaic power generation unit and the wind power generation unit are located as an input sequence of the prediction model, carrying out normalization processing on the collected data, carrying out DBN (direct binary bus) unsupervised training, and carrying out BP (back propagation) supervised training;
and inputting the parameters into the trained prediction model to obtain a prediction result.
3. The method for multi-objective optimization of regional integrated energy based on source-load prediction as claimed in claim 1, wherein the safety threshold generated according to supply and demand voltage balance limitation is as follows:
Figure FDA0003326102880000011
Figure FDA0003326102880000012
Figure FDA0003326102880000013
Figure FDA0003326102880000014
Figure FDA0003326102880000015
wherein, KPowerPurchasing an electricity sag coefficient for the outside in sag control;
Kpvthe droop coefficient of the photovoltaic power generation in the droop control is obtained;
Kwtthe droop coefficient of the wind power generation in the droop control is;
Keloadthe droop coefficient of the electrical load in droop control;
Khloadthe load droop coefficient of the electric heating machine in droop control is shown;
Kcloadthe droop coefficient is the load droop coefficient of the refrigerating machine in droop control;
Peloadthe method comprises the following steps of (1) providing power load for users of the comprehensive energy system;
Figure FDA0003326102880000021
outputting work within unit optimization time for the photovoltaic power generation unit of the comprehensive energy system obtained through prediction;
Figure FDA0003326102880000022
outputting work within unit optimization time for the predicted wind power generation unit of the comprehensive energy system;
Figure FDA0003326102880000023
the user electricity load in unit optimization time is obtained by predicting the comprehensive energy system;
Figure FDA0003326102880000024
for predicting the load of the electric heating furnace in the unit optimization time of the obtained comprehensive energy system,
Figure FDA0003326102880000025
and optimizing the load of the refrigerating machine in the unit optimization time for the predicted comprehensive energy system.
4. The regional comprehensive energy multi-objective optimization method based on source-load prediction as claimed in claim I or 3, wherein when the difference between the actual sampled data and the predicted data exceeds a safety threshold:
Figure FDA0003326102880000026
Figure FDA0003326102880000027
Figure FDA0003326102880000028
Figure FDA0003326102880000031
Figure FDA0003326102880000032
Figure FDA0003326102880000033
Figure FDA0003326102880000034
if not, then,
ΔPpv，r＝ΔPwt，r＝ΔPeload，r＝ΔQcload，r＝ΔQhload，r＝ΔPcload，r＝ΔPhload，r＝0
wherein the content of the first and second substances,
Figure FDA0003326102880000035
outputting an actual sampling value for a photovoltaic power generation system of the comprehensive energy system;
Figure FDA0003326102880000036
outputting an actual sampling value for a wind power generation system of the comprehensive energy system;
Figure FDA0003326102880000037
an actual sampling value of the electrical load for a user of the comprehensive energy system is obtained;
Figure FDA0003326102880000038
the actual sampling value of the cold consumption load of the comprehensive energy system is obtained;
Figure FDA0003326102880000039
the actual sampling value is the heat consumption load actual sampling value of the comprehensive energy system;
ΔPpv.rpredicting a correction value for the work output of the photovoltaic power generation within the unit correction time of the comprehensive energy system;
ΔPwt.ras a comprehensive energy systemCalculating a wind power generation work output prediction correction value within a unit correction time;
ΔPeload.rpredicting a correction value for the user electric load within the unit correction time of the comprehensive energy system;
ΔPcload.rpredicting a correction value for the load of the refrigerating machine within the unit correction time of the comprehensive energy system;
ΔPhload.rpredicting a correction value for the load of the electric heating furnace in the unit correction time of the comprehensive energy system;
ΔQcload.rcorrecting the cold consumption load of the comprehensive energy system;
ΔQhload.rcorrecting the heat consumption load of the comprehensive energy system;
Figure FDA0003326102880000041
cooling load in unit optimization time obtained by prediction of the comprehensive energy system;
Figure FDA0003326102880000042
the heat consumption load in unit optimization time is obtained by predicting the comprehensive energy system;
ηcthe electric-cold conversion rate of the refrigeration machine of the comprehensive energy system;
ηloss，cthe electric-cold conversion loss rate of the refrigerator of the comprehensive energy system is improved;
Figure FDA0003326102880000043
optimizing the gas furnace consumption flow in time for a unit of the comprehensive energy system;
ηboilerthe gas-heat conversion rate of the gas of the comprehensive energy system is obtained;
ηloss，boilerthe loss is the conversion of gas-heat energy of the gas of the comprehensive energy system;
ηpothe electric-heat conversion rate of the electric heating furnace of the comprehensive energy system is obtained;
ηloss，pofor comprehensive energy systemThe electric-thermal conversion loss rate of the electric heating furnace;
Figure FDA0003326102880000044
the heat release power of the heat storage device of the comprehensive energy system is released;
Figure FDA0003326102880000051
the heat storage device of the comprehensive energy system stores heat power.
5. The method for multi-objective optimization of regional integrated energy based on source-load prediction as claimed in claim 1, wherein the constraint condition includes:
electric energy balance constraint:
Figure FDA0003326102880000052
wherein the content of the first and second substances,
Figure FDA0003326102880000053
the power purchasing power of the external power grid in unit optimization time is optimized for the comprehensive energy system;
Figure FDA0003326102880000054
the work is output in unit optimization time for the storage battery of the comprehensive energy system;
Figure FDA0003326102880000055
the power is output in unit optimization time for the micro gas engine of the comprehensive energy system;
Figure FDA0003326102880000056
the storage battery stores power in unit time of the comprehensive energy system;
cold energy balance constraint:
Figure FDA0003326102880000057
and (3) heat energy balance constraint:
Figure FDA0003326102880000058
external power purchase restraint:
Figure FDA0003326102880000059
wherein the content of the first and second substances,
Figure FDA00033261028800000510
the lower limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
Figure FDA0003326102880000061
the upper limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
external gas purchase restraint:
Figure FDA0003326102880000062
wherein the content of the first and second substances,
Figure FDA0003326102880000063
optimizing the fuel consumption of the micro gas engine in unit time;
mminthe lower limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
mmaxthe upper limit of the power purchasing power of the comprehensive energy system and the external power grid is set;
battery charge and discharge power constraint:
Figure FDA0003326102880000064
Figure FDA0003326102880000065
wherein the content of the first and second substances,
Figure FDA0003326102880000066
the upper limit of battery discharge for the comprehensive energy system;
Figure FDA0003326102880000067
charging an upper limit for the battery of the comprehensive energy system;
and (3) constraint of battery storage capacity:
SOCmin≤SOCT≤SOCmax
Figure FDA0003326102880000068
therein, SOCTThe storage capacity of the battery in the Tth optimization time period is obtained;
SOCminthe lower limit of the stored electric quantity of the battery of the comprehensive energy system;
SOCmaxthe upper limit of the battery storage capacity of the comprehensive energy system;
c is the equivalent capacitance of the battery;
q is the battery capacity;
v is the voltage of the battery in the Tth optimization time period;
ηbatthe charge-discharge efficiency of the battery;
t is unit optimization time;
the charge and discharge relationship is as follows:
Figure FDA0003326102880000071
the micro gas engine is restrained:
Figure FDA0003326102880000072
Figure FDA0003326102880000073
wherein eta ismtThe gas-heat conversion rate of the micro gas engine is obtained;
ηloss，mtgas-heat conversion loss rate of the micro gas engine;
Figure FDA0003326102880000074
optimizing the lower power limit in time for the micro gas engine unit;
Figure FDA0003326102880000075
optimizing the upper limit of power in unit time for the micro gas engine;
energy storage device thermal energy capacity constraint:
Figure FDA0003326102880000076
wherein the content of the first and second substances,
Figure FDA0003326102880000077
an upper limit of the battery storage capacity of the comprehensive energy system;
Figure FDA0003326102880000081
and the lower limit of the battery storage capacity of the comprehensive energy system.
6. The method for multi-objective optimization of regional integrated energy based on source-load prediction as claimed in claim 1, wherein the carbon emission function of each unit in the integrated energy system is as follows:
external power purchase carbon emission function:
Figure FDA0003326102880000082
wherein, CpowerThe total amount of externally purchased electric carbon emission;
αpowerthe carbon emission coefficient of external electricity purchase;
function of carbon emission of micro gas engine:
Figure FDA0003326102880000083
wherein, CmtThe total carbon emission of the micro gas engine;
αmtthe carbon emission coefficient of the micro gas engine is shown;
function of carbon emission from gas boiler:
Figure FDA0003326102880000084
wherein, CboilerThe total amount of the carbon emission of the gas furnace;
αboileris the carbon block coefficient of the gas furnace.
7. The regional comprehensive energy multi-objective optimization method based on source-load prediction as claimed in claim 1, wherein the optimization target optimization constraints are that carbon emission is lowest and energy supply cost is lowest;
considering the objective optimization function with the lowest energy cost:
Figure FDA0003326102880000091
the objective optimization function considering the lowest carbon emission:
min F2＝Cpower+Cmt+Cboiler
wherein S ispowerA cost factor for purchasing electricity for the outside of the integrated energy system;
Swtthe wind power generation cost coefficient of the comprehensive energy system is obtained;
Spvthe photovoltaic power generation cost coefficient of the comprehensive energy system is obtained;
Smtthe cost coefficient of the micro gas engine of the comprehensive energy system is obtained;
Sbat，disca battery discharge cost factor for the integrated energy system;
Sbat，chara battery charging cost factor for the integrated energy system;
Sboileris the gas furnace cost coefficient of the comprehensive energy system.
8. The method as claimed in claim 7, wherein the multi-objective optimization function is solved by an alternative direction multiplier method, and the specific process is as follows:
establishing a multi-objective optimization function;
carrying out fuzzy processing on the target function by adopting a membership function:
Figure FDA0003326102880000092
wherein, FiRepresents the ith target;
μiis FiDegree of membership of;
Figure FDA0003326102880000101
is FiMaximum value of (d);
Figure FDA0003326102880000102
is FiMinimum value of (d);
fuzzy optimization model for maximizing satisfaction:
Figure FDA0003326102880000103
Figure FDA0003326102880000104
Figure FDA0003326102880000105
representing the satisfaction degree of multi-target decision for the minimum value of all target membership degrees;
g (x) is a constraint function vector;
and substituting the work output parameters to construct an augmented Lagrange function, and solving by utilizing a gradient to obtain the work output reference value of each unit of the comprehensive energy system.
CN202111267335.6A 2021-10-28 2021-10-28 Regional comprehensive energy multi-objective optimization method based on source-load prediction Pending CN114219121A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115693793A (en) * 2022-10-11 2023-02-03 国网浙江省电力有限公司 Energy optimization control method for regional micro-grid
CN117111451A (en) * 2023-10-24 2023-11-24 湘江实验室 Multi-energy system intelligent regulation and control method and device based on source network charge storage topology

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115693793A (en) * 2022-10-11 2023-02-03 国网浙江省电力有限公司 Energy optimization control method for regional micro-grid
CN115693793B (en) * 2022-10-11 2024-05-17 国网浙江省电力有限公司 Regional micro-grid energy optimization control method
CN117111451A (en) * 2023-10-24 2023-11-24 湘江实验室 Multi-energy system intelligent regulation and control method and device based on source network charge storage topology
CN117111451B (en) * 2023-10-24 2024-02-02 湘江实验室 Multi-energy system intelligent regulation and control method and device based on source network charge storage topology

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