CN113887803A - Optimization method of gas-electricity complementary energy system - Google Patents
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Abstract
The invention provides an optimization method of a gas-electricity complementary energy system, which comprises the steps of obtaining current load scene information and current electricity utilization time; obtaining a predicted value of the current load power consumption by inquiring a pre-trained load scene database according to the current load scene information and the current power utilization time; inputting the predicted value of the current load power consumption as an input item into a preset object demand response model for calculation to obtain a plurality of groups of corresponding optimal schemes; the optimal scheme comprises the steps of configuring the current electricity utilization load and the current natural gas load according to different proportions; and selecting one scheme which meets preset constraint conditions, has the maximum benefit of the dispatching system and the minimum carbon emission from the multiple groups of optimized schemes as a final optimization scheme, and optimizing the gas-electricity complementary energy system according to the final optimization scheme. The invention selects the optimal scheme to optimize the system, realizes the enhancement of the adjustment capability of the system by considering the time dimension, and has high economic benefit and good environmental protection.
Description
Technical Field
The invention relates to the technical field of multi-energy complementation, in particular to an optimization method of a gas-electricity complementary energy system.
Background
The main appeal of the current energy system construction is that the multi-energy complementation, stable energy supply, safety, high efficiency, green and cleanness are achieved. The natural gas energy has the advantages of high efficiency, environmental protection, cleanness, energy conservation and the like, is used for low-carbon power generation, and can further improve the utilization efficiency of the natural gas energy. The gas-electricity complementary comprehensive energy system is produced by running, and can realize efficient energy utilization and ideal economic benefit only by matching with a scientific and reasonable coordination optimization method and a system operation strategy.
The advantage of gas-electricity complementation is mainly embodied in solving the problem of mismatching of energy utilization space and time dimension, the existing scheme only solves the problem of the former, namely energy utilization on site and multiple energy complementation, but the problem of the time dimension does not have a good solution, does not have economic benefit and does not have environmental protection property. In the aspect of modeling, the prior art has the problems of complex modeling, difficult algorithm solving and the like, is lack of imagination and is weaker in implementability.
Disclosure of Invention
The invention aims to provide an optimization method of a gas-electricity complementary energy system, which solves the technical problems of low economic benefit and poor environmental protection property of the existing method due to the fact that the time dimension is neglected.
In one aspect, a method for optimizing a gas-electricity complementary energy system is provided, which includes:
acquiring current load scene information and current power utilization time;
inquiring a pre-trained load scene database according to the current load scene information and the current power utilization time to obtain a predicted value of the current load power consumption;
inputting the predicted value of the current load power consumption as an input item into a preset object demand response model for calculation to obtain a plurality of groups of corresponding optimal schemes; the optimal scheme comprises the steps of configuring the current electricity utilization load and the current natural gas load according to different proportions;
and selecting one scheme which meets preset constraint conditions, has the maximum benefit of the dispatching system and the minimum carbon emission from the multiple groups of optimized schemes as a final optimization scheme, and optimizing the gas-electricity complementary energy system according to the final optimization scheme.
Preferably, the pre-trained load scenario database is obtained by:
acquiring historical power load data, power consumption price data, natural gas power generation price data, power consumption time data and weather data; forming a training characteristic data set by the electricity price data, the gas price data, the electricity load data, the electricity time data and the weather data;
and inputting the training characteristic data set as an input item into a preset power load prediction model to train by adopting a neural network clustering algorithm to obtain a load scene database under a typical scene associated with time.
Preferably, the querying a pre-trained load scenario database according to the current load scenario information and the current power utilization time specifically includes:
and inquiring the load power consumption value under the same condition with the current load scene in the load scene database under the power consumption time by taking the current load scene and the power consumption time as inquiry conditions, and outputting the load power consumption value as a predicted value of the current load power consumption.
Preferably, the preset object demand response model specifically includes:
calculating the electricity load according to the following formula:
fDR，P(αt)＝λPd|αtPmax|
wherein, Pd，tThe electric load at the time t;
the predicted value of the power load at the time t is obtained; alpha is alpha2，tThe regulation rate of the electric load at the time t is obtained; pmaxThe maximum schedulable of the power utilization load; f. ofDR，P(αt) To relate to alphatA function of (a); lambda [ alpha ]PdAnd (3) an excitation compensation coefficient for participating in demand response of the electrical load.
Preferably, the preset object demand response model further includes:
the natural gas load is calculated according to the following formula:
fDR，Q(βt)＝λQ|βtQmax|
wherein Q istThe natural gas load at the moment t;
the predicted value of the natural gas load at the time t is obtained; beta is atThe regulation rate of the natural gas at the time t; qmaxThe maximum dispatchable natural gas load; f. ofDR，Q(βt) Is to turn offIn betatA function of (a); lambda [ alpha ]QAnd (4) an excitation compensation coefficient participating in demand response for the natural gas load.
Preferably, the preset object demand response model further includes:
when the natural gas load is determined, taking the natural gas load as the output power of the gas turbine and acquiring a preset natural gas heat value and the operating efficiency of the gas turbine;
calculating the natural gas consumption of the gas turbine according to the following formula:
wherein, gGT，tThe output power of the gas turbine at the moment t; qGT，tThe consumption of natural gas;
is the heat value of natural gas; etaGTThe operating efficiency of the gas turbine.
Preferably, the obtaining of the corresponding multiple groups of preferred schemes specifically includes:
dividing the obtained predicted value of the current load power consumption into a predicted value of the power load and a predicted value of the natural gas load according to different proportions to obtain a plurality of groups of predicted values of the power load and the natural gas load in different combination modes; the sum of the predicted value of the power load and the predicted value of the natural gas load is equal to the predicted value of the current load power consumption;
respectively calculating the current power utilization load quantity corresponding to the predicted value of the power utilization load and the current natural gas load quantity corresponding to the predicted value of the natural gas load through the object demand response model;
and outputting the current power utilization load quantity corresponding to the predicted value of the power utilization load and the current natural gas load quantity corresponding to the predicted value of the natural gas load as a plurality of groups of optimized schemes.
Preferably, the preset constraint condition specifically includes:
the price constraint item is used for constraining the peak-to-valley electricity price of electricity utilization and the distributed generation network electricity price of natural gas and the price of project natural gas;
the cost constraint item is used for constraining the start-stop cost of the energy router in the gas-electricity complementary energy system;
and the generator set output constraint item is used for constraining the output of the generator and limiting the minimum value and the maximum value of the output of the generator.
Preferably, the scheduling system profit maximization specifically includes:
determining electricity cost according to the current electricity load and a preset electricity price, determining gas cost according to the current natural gas load and a preset natural gas price, determining operation time according to the natural gas consumption of the gas turbine, and determining operation maintenance cost, equipment depreciation cost and income and output of a scheduling system according to the operation time;
the maximum benefit of the dispatching system is defined by the following formula:
maxf1＝Fin-Fom-Fel-Fga-Fde-Fve
wherein, FinFor revenue production of the scheduling system, FomFor operating maintenance costs, FelFor cost of electricity, FgaFor gas cost, FdeFor depreciation costs of the apparatus, FveWhich is an investment cost.
Preferably, the minimum carbon emission amount specifically includes:
respectively determining the carbon dioxide emission converted from power grid purchase and the carbon dioxide emission converted from natural gas power generation according to the current power utilization load and the current natural gas load;
minimizing carbon emissions is defined according to the following equation:
minf2＝C1+C2+C3
wherein, C1Carbon dioxide emission converted for power grid purchasing2Carbon dioxide emissions, C, for conversion of natural gas power generation3Other carbon dioxide emissions.
In summary, the embodiment of the invention has the following beneficial effects:
the optimization method of the gas-electricity complementary energy system provided by the invention is based on a gas-electricity coordination strategy of gas and electricity on the basis of gas and electricity complementation, and achieves the effect of efficient and economic operation of the system by considering the installation scheme and the operation strategy of gas-electricity complementation. The method comprises the steps of determining a predicted value of corresponding load power consumption through screening of corresponding load scenes and power utilization time, further determining a plurality of different gas-electricity cooperative schemes according to the predicted value of the load power consumption, and selecting an optimal scheme meeting conditions to optimize a system as an optimal scheme, so that the regulation capability of a comprehensive energy system is enhanced, and the regulation capabilities of an electric power system and a natural gas system are effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a schematic diagram of a gas-electric complementary energy system according to an embodiment of the invention.
Fig. 2 is a main flow chart of an optimization method of a gas-electricity complementary energy system according to an embodiment of the present invention.
Fig. 3 is a logic diagram of an optimization method of a gas-electric complementary energy system according to an embodiment 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 will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the gas-electricity complementary energy system provided by the present application generates electricity by two power generation methods in a cooperative manner, the power grid end is directly connected to the energy router, the gas grid end generates electricity by natural gas and is connected to the energy router, and the energy router combines electric power output by the two methods to supply power to the power-on end. Wherein the gas network end converts natural gas into electricity through a gas turbine.
Fig. 2 and fig. 3 are schematic diagrams illustrating an embodiment of an optimization method of a gas-electric complementary energy system according to the present invention. In this embodiment, the method comprises the steps of:
acquiring current load scene information and current power utilization time; that is, the time and load scenario for which a time period needs to be predicted or optimized is determined first, so as to be the basis for the subsequent optimization scheme determination.
Inquiring a pre-trained load scene database according to the current load scene information and the current power utilization time to obtain a predicted value of the current load power consumption; namely, an energy supply object power consumption load prediction model and a local power and gas consumption price model are established, and then the current load power consumption is predicted according to a typical scene to obtain a predicted value of the current load power consumption.
In a specific embodiment, the pre-trained load scenario database is obtained by the following steps:
acquiring historical power load data, power consumption price data, natural gas power generation price data, power consumption time data and weather data; forming a training characteristic data set by the electricity price data, the gas price data, the electricity load data, the electricity time data and the weather data; that is, historical load data of an energy supply object is obtained, data such as historical power load, electricity price, natural gas power generation price, time and date, weather and the like are subjected to normalization processing, and a training feature data set of the electricity price, the gas price, the electricity load, the time and date and the weather data is determined;
and inputting the training characteristic data set as an input item into a preset power load prediction model, and training by adopting a neural network clustering algorithm to obtain a load scene database under a typical scene associated with time, namely training the load scene obtained by the clustering method by adopting a neural network to obtain an energy consumption curve of an energy supply object and the load scene under the typical scene.
Further, the current load scene and the power consumption time are used as query conditions, the load power consumption value under the condition that the power consumption time corresponding to the current load scene is the same as that of the current load scene in the load scene database is queried, and the load power consumption value is output as a predicted value of the current load power consumption. The load scene database in the typical scene associated with time is a database representing time, scene and load power consumption values, and other corresponding values can be searched as long as one or more values are known.
Inputting the predicted value of the current load power consumption as an input item into a preset object demand response model for calculation to obtain a plurality of groups of corresponding optimal schemes; the optimal scheme comprises the steps of configuring the current electricity utilization load and the current natural gas load according to different proportions; that is, the predicted value of the current load power consumption is used as a standard to predict through the object demand response model, and then a plurality of different electric coordination power generation schemes are combined, however, not all the schemes are favorable for power generation efficiency, and the optimal scheme needs to be selected through subsequent screening conditions.
In a specific embodiment, the preset object demand response model specifically includes:
calculating the electricity load according to the following formula:
fDR，P(αt)＝λPd|αtPmax|
wherein, Pd，tThe electric load at the time t;
the predicted value of the power load at the time t is obtained; alpha is alpha2，tThe regulation rate of the electric load at the time t is obtained; pmaxThe maximum schedulable of the power utilization load; f. ofDR，P(αt) To relate to alphatA function of (a); lambda [ alpha ]PdIncentive compensation for participating in demand response for electrical loadsAnd (4) compensating the coefficient.
The natural gas load is calculated according to the following formula:
fDR，Q(βt)＝λQ|βtQmax|
wherein Q istThe natural gas load at the moment t;
the predicted value of the natural gas load at the time t is obtained; beta is atThe regulation rate of the natural gas at the time t; qmaxThe maximum dispatchable natural gas load; f. ofDR，Q(βt) To relate to betatA function of (a); lambda [ alpha ]QAnd (4) an excitation compensation coefficient participating in demand response for the natural gas load.
When the natural gas load is determined, taking the natural gas load as the output power of the gas turbine and acquiring a preset natural gas heat value and the operating efficiency of the gas turbine;
calculating the natural gas consumption of the gas turbine according to the following formula:
wherein, gGT，tThe output power of the gas turbine at the moment t; qGT，tThe consumption of natural gas;
is the heat value of natural gas; etaGTThe operating efficiency of the gas turbine.
Specifically, in the process of determining the corresponding multiple groups of preferred schemes, dividing the obtained predicted value of the current load power consumption into a predicted value of the power load and a predicted value of the natural gas load according to different proportions, and obtaining the predicted values of the power load and the natural gas load in multiple groups of different combination modes; the sum of the predicted value of the power load and the predicted value of the natural gas load is equal to the predicted value of the current load power consumption;
respectively calculating the current power utilization load quantity corresponding to the predicted value of the power utilization load and the current natural gas load quantity corresponding to the predicted value of the natural gas load through the object demand response model;
and outputting the current power utilization load quantity corresponding to the predicted value of the power utilization load and the current natural gas load quantity corresponding to the predicted value of the natural gas load as a plurality of groups of optimized schemes.
And selecting one scheme which meets preset constraint conditions, has the maximum benefit of the dispatching system and the minimum carbon emission from the multiple groups of optimized schemes as a final optimization scheme, and optimizing the gas-electricity complementary energy system according to the final optimization scheme.
In a specific embodiment, the preset constraint condition specifically includes:
the price constraint item is used for constraining the peak-to-valley electricity price of electricity utilization and the distributed generation network electricity price of natural gas and the price of project natural gas;
the cost constraint item is used for constraining the start-stop cost of the energy router in the gas-electricity complementary energy system;
and the generator set output constraint item is used for constraining the output of the generator and limiting the minimum value and the maximum value of the output of the generator. It can be expressed by a formula which is,
in the formula (I), the compound is shown in the specification,
the generated output at the moment k is the generated output of the generator set at the moment t;
and
respectively, minimum output and maximum generated output.
Specifically, the scheduling system with the maximum profit specifically includes:
determining electricity cost according to the current electricity load and a preset electricity price, determining gas cost according to the current natural gas load and a preset natural gas price, determining operation time according to the natural gas consumption of the gas turbine, and determining operation maintenance cost, equipment depreciation cost and income and output of a scheduling system according to the operation time;
the maximum benefit of the dispatching system is defined by the following formula:
maxf1＝Fin-Fom-Fel-Fga-Fde-Fve
wherein, FinFor revenue production of the scheduling system, FomFor operating maintenance costs, FelFor cost of electricity, FgaFor gas cost, FdeFor depreciation costs of the apparatus, FveWhich is an investment cost.
Still more specifically, the carbon emission minimum specifically includes:
respectively determining the carbon dioxide emission converted from power grid purchase and the carbon dioxide emission converted from natural gas power generation according to the current power utilization load and the current natural gas load;
minimizing carbon emissions is defined according to the following equation:
minf2＝C1+C2+C3
wherein, C1Carbon dioxide emission converted for power grid purchasing2Carbon dioxide emissions, C, for conversion of natural gas power generation3Other carbon dioxide emissions.
In summary, the embodiment of the invention has the following beneficial effects:
the optimization method of the gas-electricity complementary energy system provided by the invention is based on a gas-electricity coordination strategy of gas and electricity on the basis of gas and electricity complementation, and achieves the effect of efficient and economic operation of the system by considering the installation scheme and the operation strategy of gas-electricity complementation. The method comprises the steps of determining a predicted value of corresponding load power consumption through screening of corresponding load scenes and power utilization time, further determining a plurality of different gas-electricity cooperative schemes according to the predicted value of the load power consumption, and selecting an optimal scheme meeting conditions to optimize a system as an optimal scheme, so that the regulation capability of a comprehensive energy system is enhanced, and the regulation capabilities of an electric power system and a natural gas system are effectively improved.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A method for optimizing a gas-electric complementary energy system is characterized by comprising the following steps:
acquiring current load scene information and current power utilization time;
inquiring a pre-trained load scene database according to the current load scene information and the current power utilization time to obtain a predicted value of the current load power consumption;
inputting the predicted value of the current load power consumption as an input item into a preset object demand response model for calculation to obtain a plurality of groups of corresponding optimal schemes; the optimal scheme comprises the steps of configuring the current electricity utilization load and the current natural gas load according to different proportions;
and selecting one scheme which meets preset constraint conditions, has the maximum benefit of the dispatching system and the minimum carbon emission from the multiple groups of optimized schemes as a final optimization scheme, and optimizing the gas-electricity complementary energy system according to the final optimization scheme.
2. The method of claim 1, wherein the pre-trained load scenario database is obtained by:
acquiring historical power load data, power consumption price data, natural gas power generation price data, power consumption time data and weather data; forming a training characteristic data set by the electricity price data, the gas price data, the electricity load data, the electricity time data and the weather data;
and inputting the training characteristic data set as an input item into a preset power load prediction model, and training by adopting a neural network clustering algorithm to obtain a load scene database under a typical scene associated with time.
3. The method of claim 2, wherein querying a pre-trained load scenario database according to the current load scenario information and the current power usage time specifically comprises:
and inquiring the load power consumption value under the same condition with the current load scene in the load scene database under the power consumption time by taking the current load scene and the power consumption time as inquiry conditions, and outputting the load power consumption value as a predicted value of the current load power consumption.
4. The method according to claim 3, wherein the preset object demand response model specifically comprises:
calculating the electricity load according to the following formula:
fDF,P(αt)＝λPd|αtPmax|
wherein, Pd,tThe electric load at the time t;
the predicted value of the power load at the time t is obtained; alpha is alpha2,tThe regulation rate of the electric load at the time t is obtained; pmaxThe maximum schedulable of the power utilization load; f. ofDR,P(αt) To relate to alphatA function of (a); lambda [ alpha ]PdAnd (3) an excitation compensation coefficient for participating in demand response of the electrical load.
5. The method of claim 4, wherein the preset object demand response model further comprises:
the natural gas load is calculated according to the following formula:
fDR,Q(βt)＝λQ|βtQmax|
wherein Q istThe natural gas load at the moment t;
the predicted value of the natural gas load at the time t is obtained; beta is atThe regulation rate of the natural gas at the time t; qmaxThe maximum dispatchable natural gas load; f. ofDR,Q(βt) To relate to betatA function of (a); lambda [ alpha ]QAnd (4) an excitation compensation coefficient participating in demand response for the natural gas load.
6. The method of claim 5, wherein the preset object demand response model further comprises:
when the natural gas load is determined, taking the natural gas load as the output power of the gas turbine and acquiring a preset natural gas heat value and the operating efficiency of the gas turbine;
calculating the natural gas consumption of the gas turbine according to the following formula:
wherein, gGT,tThe output power of the gas turbine at the moment t; qGT,tThe consumption of natural gas;
is the heat value of natural gas; etaGTThe operating efficiency of the gas turbine.
7. The method of claim 6, wherein the obtaining of the corresponding sets of preferred solutions specifically comprises:
dividing the obtained predicted value of the current load power consumption into a predicted value of the power load and a predicted value of the natural gas load according to different proportions to obtain a plurality of groups of predicted values of the power load and the natural gas load in different combination modes; the sum of the predicted value of the power load and the predicted value of the natural gas load is equal to the predicted value of the current load power consumption;
respectively calculating the current power utilization load quantity corresponding to the predicted value of the power utilization load and the current natural gas load quantity corresponding to the predicted value of the natural gas load through the object demand response model;
and outputting the current power utilization load quantity corresponding to the predicted value of the power utilization load and the current natural gas load quantity corresponding to the predicted value of the natural gas load as a plurality of groups of optimized schemes.
8. The method according to claim 1, wherein the preset constraints specifically include:
the price constraint item is used for constraining the peak-to-valley electricity price of electricity utilization and the distributed generation network electricity price of natural gas and the price of project natural gas;
the cost constraint item is used for constraining the start-stop cost of the energy router in the gas-electricity complementary energy system;
and the generator set output constraint item is used for constraining the output of the generator and limiting the minimum value and the maximum value of the output of the generator.
9. The method of claim 7, wherein the scheduling system revenue maximizing entity comprises:
determining electricity cost according to the current electricity load and a preset electricity price, determining gas cost according to the current natural gas load and a preset natural gas price, determining operation time according to the natural gas consumption of the gas turbine, and determining operation maintenance cost, equipment depreciation cost and income and output of a scheduling system according to the operation time;
the maximum benefit of the dispatching system is defined by the following formula:
maxf1＝Fin-Fom-Fel-Fga-Fde-Fve
wherein, FinFor revenue production of the scheduling system, FomFor operating maintenance costs, FelFor cost of electricity, FgaFor gas cost, FdeFor depreciation costs of the apparatus, FveWhich is an investment cost.
10. The method of claim 9, wherein the minimizing carbon emissions specifically comprises:
respectively determining the carbon dioxide emission converted from power grid purchase and the carbon dioxide emission converted from natural gas power generation according to the current power utilization load and the current natural gas load;
minimizing carbon emissions is defined according to the following equation:
minf2＝C1+C2+C3
wherein, C1Carbon dioxide emission converted for power grid purchasing2Carbon dioxide emissions, C, for conversion of natural gas power generation3Other carbon dioxide emissions.
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