CN117332997A - Low-carbon optimal scheduling method, device and equipment for comprehensive energy system - Google Patents
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Abstract
The invention discloses a low-carbon optimal scheduling method, a device and equipment for a comprehensive energy system, wherein the method comprises the following steps: constructing a comprehensive energy equipment operation model; constructing a carbon emission transaction model of the comprehensive energy system; introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model; sampling the photovoltaic output and load of the comprehensive energy system and the deviation probability distribution of the load, and generating a photovoltaic load initial scene set and occurrence probability; performing scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load; constructing a two-stage optimization scheduling model; and setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
Description
Technical Field
The invention relates to the technical field of comprehensive energy, in particular to a low-carbon optimal scheduling method, device and equipment for a comprehensive energy system.
Background
The comprehensive energy system (Integrated Energy System, IES) integrates multiple energy sources such as coal, natural gas, electricity, heat and the like in a certain area by utilizing advanced physical information technology and innovative management mode, realizes coordinated planning and interactive response among multiple energy subsystems, and effectively improves the energy utilization rate and reduces the energy consumption on the premise of meeting the multi-element energy demand in the system. For example, park-level Integrated Energy System (PIES) is a miniature integrated energy system that is directed towards end-users. With the proposal of the concepts of carbon neutralization and carbon standard reaching, compared with the traditional centralized energy supply system, the park comprehensive energy system is more and more concerned as a novel energy supply mode with wide distribution, has the advantages of high energy utilization rate, low investment cost, short construction period, various energy selections, flexible system, environmental protection, approaching to the user side and the like, and is the development direction of efficient clean energy in the future.
There is a great deal of uncertainty in the whole flow of the park comprehensive energy system source-net-load-storage, for example, patent document CN115640902a proposes a low-carbon optimal scheduling method of the park comprehensive energy system considering carbon price uncertainty. The method comprises the steps of constructing a comprehensive energy system model of energy conversion equipment such as an electric conversion gas, a gas cogeneration unit, a gas boiler and the like, introducing a time-sharing ladder-shaped carbon transaction mechanism to restrict carbon emission of a comprehensive energy system in a park, adopting an information gap decision theory to treat uncertainty of carbon price, and constructing a low-carbon optimal scheduling model which takes the minimum of purchase energy cost, operation and maintenance cost and carbon transaction cost as a target, and can bear fluctuation by maximizing and minimizing the system under a certain expected target. However, in the integrated energy system, the source (wind power, photovoltaic) -charge (electric load, thermal load, gas load and cold load) has uncertainty, and the uncertainty can bring influence to the dispatching and planning of the integrated energy system of the park, so that the integrated operation cost of the integrated energy system of the park is optimized under the background of double carbon, the energy utilization rate is improved, the carbon emission is reduced, and the reliable operation of the system is a problem to be solved urgently.
Disclosure of Invention
The invention provides a low-carbon optimal scheduling method, device and equipment for a comprehensive energy system, which can effectively optimize the operation cost of the comprehensive energy system, improve the energy utilization rate and reduce the carbon emission.
A low-carbon optimal scheduling method of a comprehensive energy system comprises the following steps:
constructing a carbon emission transaction model of the comprehensive energy system according to a pre-constructed comprehensive energy equipment operation model;
introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
sampling the photovoltaic output and load of the comprehensive energy system and the deviation probability distribution of the load, and generating a photovoltaic load initial scene set and occurrence probability;
performing scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load;
based on a typical photovoltaic load scene set and occurrence probability thereof, constructing a two-stage optimization scheduling model with minimum running cost and minimum carbon emission as targets under a worst wind power output scene and a typical photovoltaic load scene according to the carbon emission transaction model and the wind power uncertainty model;
and setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
Further, the integrated energy device operation model comprises an energy input end, an energy conversion device and an energy output end.
Further, the energy input end comprises an upper power grid, an upper air grid, a wind turbine generator and a photovoltaic unit; the energy conversion equipment comprises a gas cogeneration unit, a gas boiler, an electric refrigerator, an absorption refrigerator, an electrolytic tank, a methane reactor and a hydrogen fuel cell; the energy output end comprises a cold load, a hot load, an electric load and a gas load.
Further, according to the pre-constructed integrated energy equipment operation model, constructing a carbon emission transaction model of the integrated energy system, including:
determining a carbon emission source in the comprehensive energy equipment operation model;
setting carbon emission coefficients of each carbon emission source, and determining a carbon emission amount calculation scheme based on the output power of each carbon emission source and the corresponding carbon emission coefficient;
and determining a carbon trade cost calculation scheme according to the market carbon trade base price and the carbon emission of different intervals.
Further, a robust uncertainty adjustment parameter is introduced to construct a wind power uncertainty model, which comprises the following steps:
determining a scheduling period;
determining a value range of the robust uncertainty adjustment parameter according to the scheduling period and a wind power output fluctuation interval range;
Determining the actual wind power output according to the wind power output prediction, the wind power output prediction deviation and the wind power output fluctuation interval range;
and establishing a wind power output uncertainty set according to the actual wind power output and the value range of the robust uncertainty adjustment parameter and the wind power output fluctuation interval range to obtain the wind power uncertainty model.
Further, performing scene cut on the initial scene set and the occurrence probability of the photovoltaic load based on an improved cut-down method to obtain a typical photovoltaic load scene set and the occurrence probability thereof, including:
extracting samples from the photovoltaic load initial scene set and the occurrence probability, and setting a reduced target scene number k;
iteratively performing the following steps until the current scene number k 1 Reaching a preset target scene number k:
calculating the current k 1 The Euclidean distance of any two scenes in the plurality of scenes;
determining a scene s closest to the Euclidean distance of the scene i, multiplying the occurrence probability of the scene s by the Euclidean distance of the scene i to obtain a probability distance;
searching for a scene pair (i, s) that minimizes the probability distance;
let p s =p s +p i Wherein p is s For the probability of occurrence of scene s, p i Updating the occurrence probability of the scene s for the occurrence probability of the scene i and simultaneously reducing the scene i;
Let k 1 =k 1 -k i Updating the scene number k in the clipping process 1 Wherein k is i The number of scene pairs with the smallest probability distance value.
Further, in the two-stage optimization scheduling model, the first stage comprises an objective function with the lowest running cost and the lowest carbon emission in a typical photovoltaic load scene, and takes an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan as optimization variables; the second stage is to find the worst wind power output scene and the optimal scheme with the lowest energy purchasing cost under the worst wind power output scene.
Further, the two-stage optimization scheduling model is as follows:
where N is the typical scene number, ρ s For the occurrence probability of the scene s, lambda is the target optimization satisfaction, G buy,s Is the purchase cost under a typical scene s, Y op,s Maintenance costs for operation under typical scenario s, H co2,s N is the cost of carbon trade in a typical scenario s su,s C is the energy supply rate s For the carbon emission under a typical scene s, x is a first-stage optimization variable comprising an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan, and y is a second-stage optimization variable comprising unit output and power grid interaction quantity; u is the wind power output uncertainty set.
Further, the objective function with the lowest running cost is specifically expressed as:
minQ=min（G buy +Y op +H co2 ）
wherein Q represents the running cost of the system, G buy Representing the cost of purchasing energy, Y op Representing the running maintenance cost, H co2 Representing the cost of carbon trade.
Further, the objective function of minimum carbon emission is specifically expressed as:
minC=C buy +C G +C GL -C M
wherein C represents carbon dioxide emission, C buy Representing the actual carbon emission of the electricity purchasing of the upper power grid, C G Representing the total actual carbon emission of the gas cogeneration unit and the gas boiler, G GL Actual carbon emission as gas load, C M The amount of carbon dioxide absorbed for the system hydrogen to natural gas.
Further, solving the two-stage optimization scheduling model includes:
decomposing the two-stage optimal scheduling model into a main problem and a sub problem;
wind power predicted value is used as initial severe scene u 1 ；
In severe scene u i Solving the main problem to obtain a first-stage energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan x i Taking the objective function value of the main problem as a lower bound, wherein i is the iteration number;
will x i Carrying in the sub problem, obtaining the optimal scheme y with lowest purchase energy cost i Wind power output scene u corresponding to the same i+1 Setting the objective function value of the sub-problem as an upper bound;
And (4) giving a convergence threshold, stopping iteration when the difference between the upper bound and the lower bound is smaller than or equal to the convergence threshold, and returning to the optimal solution.
Further, the two-stage optimal scheduling model also comprises target optimal satisfaction;
the method further comprises the steps of: and taking the continuous and micro anti-Sigmoid function on the definition domain as a membership function of each target, and solving the minimum value of the membership function to obtain the maximum target optimization satisfaction.
Further, the constraint conditions include wind power output constraint, photovoltaic output constraint, gas-heat cogeneration unit operation constraint, gas boiler operation constraint, electric refrigerator operation constraint, methane reactor operation constraint, hydrogen fuel cell operation constraint, electrolyzer operation constraint, energy storage operation constraint, electric balance constraint, heat balance constraint, natural gas balance constraint, hydrogen balance constraint and cold balance constraint.
A low-carbon optimized dispatching device for an integrated energy system, comprising:
the first construction module is used for constructing a comprehensive energy equipment operation model;
the second construction module is used for constructing a carbon emission transaction model of the comprehensive energy system according to the comprehensive energy equipment operation model;
the third construction module is used for introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
The scene generation module is used for sampling the photovoltaic output, load and deviation probability distribution of the photovoltaic output and load of the comprehensive energy system to generate an initial scene set of the photovoltaic load and occurrence probability;
the reduction module is used for carrying out scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load;
the model generation module is used for constructing a two-stage optimization scheduling model with the lowest running cost and the minimum carbon emission as targets under a worst wind power output scene and a typical photovoltaic load scene according to the carbon emission transaction model and the wind power uncertainty model based on the typical photovoltaic load scene set and the occurrence probability thereof;
and the calculation module is used for setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
Further, the comprehensive energy equipment operation model constructed by the first construction module comprises an energy input end, energy conversion equipment and an energy output end; the energy input end comprises an upper power grid, an upper air grid, a wind turbine generator and a photovoltaic unit; the energy conversion equipment comprises a gas cogeneration unit, a gas boiler, an electric refrigerator, an absorption refrigerator, an electrolytic tank, a methane reactor and a hydrogen fuel cell; the energy output end comprises a cold load, a hot load, an electric load and a gas load.
Further, the second construction module constructs a carbon emission transaction model of the integrated energy system according to a pre-constructed integrated energy device operation model, including:
determining a carbon emission source in the comprehensive energy equipment operation model;
setting carbon emission coefficients of each carbon emission source, and determining a carbon emission amount calculation scheme based on the output power of each carbon emission source and the corresponding carbon emission coefficient;
and determining a carbon trade cost calculation scheme according to the market carbon trade base price and the carbon emission of different intervals.
Further, the third construction module introduces a robust uncertainty adjustment parameter to construct a wind power uncertainty model, including:
determining a scheduling period;
determining a value range of the robust uncertainty adjustment parameter according to the scheduling period and a wind power output fluctuation interval range;
determining the actual wind power output according to the wind power output prediction, the wind power output prediction deviation and the wind power output fluctuation interval range;
and establishing a wind power output uncertainty set according to the actual wind power output and the value range of the robust uncertainty adjustment parameter and the wind power output fluctuation interval range to obtain the wind power uncertainty model.
Further, the curtailing module performs scene curtailing on the initial scene set and the occurrence probability of the photovoltaic load based on an improved curtailing method to obtain a typical photovoltaic load scene set and the occurrence probability thereof, including:
Extracting samples from the photovoltaic load initial scene set and the occurrence probability, and setting a reduced target scene number k;
iteratively performing the following steps until the current scene number k 1 Reaching a preset target scene number k:
calculating the current k 1 The Euclidean distance of any two scenes in the plurality of scenes;
determining a scene s closest to the Euclidean distance of the scene i, multiplying the occurrence probability of the scene s by the Euclidean distance of the scene i to obtain a probability distance;
searching for a scene pair (i, s) that minimizes the probability distance;
let p s =p s +p i Wherein p is s For the probability of occurrence of scene s, p i Updating the occurrence probability of the scene s for the occurrence probability of the scene i and simultaneously reducing the scene i;
let k 1 =k 1 -k i Updating the scene number k in the clipping process 1 Wherein k is i The number of scene pairs with the smallest probability distance value.
Further, in the two-stage optimization scheduling model, the first stage comprises an objective function with the lowest running cost and the lowest carbon emission in a typical photovoltaic load scene, and takes an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan as optimization variables; the second stage is to find the worst wind power output scene and the optimal scheme with the lowest energy purchasing cost under the worst wind power output scene.
Further, the computing module solves the two-stage optimization scheduling model, including:
decomposing the two-stage optimal scheduling model into a main problem and a sub problem;
wind power predicted value is used as initial severe scene u 1 ；
In severe scene u i Solving the main problem to obtain a first-stage energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan x i Taking the objective function value of the main problem as a lower bound, wherein i is the iteration number;
will x i Carrying in the sub problem, obtaining the optimal scheme y with lowest purchase energy cost i Wind power output scene u corresponding to the same i+1 Setting the objective function value of the sub-problem as an upper bound;
and (4) giving a convergence threshold, stopping iteration when the difference between the upper bound and the lower bound is smaller than or equal to the convergence threshold, and returning to the optimal solution.
Further, the two-stage optimal scheduling model also comprises target optimal satisfaction;
the computing module is further for: and taking the continuous and micro anti-Sigmoid function on the definition domain as a membership function of each target, and solving the minimum value of the membership function to obtain the maximum target optimization satisfaction.
Further, the constraint conditions include wind power output constraint, photovoltaic output constraint, gas-heat cogeneration unit operation constraint, gas boiler operation constraint, electric refrigerator operation constraint, methane reactor operation constraint, hydrogen fuel cell operation constraint, electrolyzer operation constraint, energy storage operation constraint, electric balance constraint, heat balance constraint, natural gas balance constraint, hydrogen balance constraint and cold balance constraint.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of the preceding claims when executing the computer program.
The low-carbon optimal scheduling method, device and equipment for the comprehensive energy system provided by the invention at least comprise the following beneficial effects:
(1) The uncertainty of wind power, photovoltaic and load of the comprehensive energy system is fully considered, low-carbon technologies such as a carbon transaction mechanism and the like are introduced to perform optimal scheduling on the operation of the comprehensive energy system, the carbon emission of the system is reduced, grid-connected consumption of wind power and photovoltaic is promoted, the low-carbon optimization technology of the comprehensive energy system in the wind power, photovoltaic and load uncertainty environment is obtained, the operation cost of the comprehensive energy system can be effectively optimized, the energy utilization rate is improved, and the carbon emission is reduced.
(2) The wind power uncertainty model is built by introducing the robust uncertainty adjustment parameters, so that the model is more similar to the actual application, and the accuracy of the follow-up optimization scheduling is improved;
(3) The method has the advantages that the typical photovoltaic load scene set and the occurrence probability thereof are generated based on the improved reduction method, the influence of the length of the range of the value of each uncertain variable on the reduction process is greatly reduced, the dimension of each uncertain variable can be eliminated, the influence of each uncertain variable on the reduction process is more uniformly considered, the comprehensive probability distance of photovoltaic, thermal load, gas load and the like is increased, the relevance of the reduction process is improved, and the situation that the reduction result falls into single relevance is avoided.
Drawings
FIG. 1 is a flowchart of an embodiment of a low-carbon optimized scheduling method for an integrated energy system.
Fig. 2 is a flowchart of an embodiment of a carbon emission trading model for constructing a campus integrated energy system in the integrated energy system low-carbon optimization scheduling method provided by the invention.
FIG. 3 is a flowchart of an embodiment of constructing a wind power uncertainty model in the low-carbon optimized scheduling method of the integrated energy system.
Fig. 4 is a schematic diagram of comprehensive probability distances under three reduction modes in the low-carbon optimized scheduling method of the comprehensive energy system.
FIG. 5 is a flowchart of an embodiment of solving a two-stage optimal scheduling model in the low-carbon optimal scheduling method of the integrated energy system.
Fig. 6 is a schematic structural diagram of an embodiment of a low-carbon optimized dispatching device for an integrated energy system.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, in some embodiments, a low-carbon optimized scheduling method for an integrated energy system is provided, including:
s1, constructing a comprehensive energy equipment operation model;
S2, constructing a carbon emission transaction model of the comprehensive energy system according to the comprehensive energy equipment operation model;
s3, introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
s4, sampling the photovoltaic output and load of the comprehensive energy system and the deviation probability distribution of the load, and generating a photovoltaic load initial scene set and occurrence probability;
s5, carrying out scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load;
s6, based on a typical photovoltaic load scene set and occurrence probability thereof, constructing a two-stage optimization scheduling model with minimum running cost and minimum carbon emission as targets under a worst wind power output scene and a typical photovoltaic load scene according to the carbon emission transaction model and the wind power uncertainty model;
and S7, setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
Further, in step S1, the integrated energy device operation model includes an energy input end, an energy conversion device, and an energy output end; the energy input end comprises an upper power grid, an upper air grid, a wind turbine generator and a photovoltaic unit; the energy conversion equipment comprises a gas cogeneration unit, a gas boiler, an electric refrigerator, an absorption refrigerator, an electrolytic tank, a methane reactor and a hydrogen fuel cell; the energy output end comprises a cold load, a hot load, an electric load and a gas load.
The carbon emission generated in the integrated energy system finally participates in the carbon trade market for trade.
The electrolyzer firstly converts electric energy into hydrogen energy, and part of the hydrogen energy is input into the methane reactor and CO 2 The natural gas is synthesized and supplied to a gas load, a gas boiler and a gas cogeneration unit, one part of the natural gas is directly transmitted to a hydrogen fuel cell to be converted into electricity and heat energy, and the other part of the natural gas is stored through a hydrogen storage tank.
Further, referring to fig. 2, in step S2, a carbon emission trading model of the integrated energy system is constructed according to the integrated energy device operation model, including:
s21, determining a carbon emission source in the comprehensive energy equipment operation model;
s22, setting carbon emission coefficients of all carbon emission sources, and determining a carbon emission amount calculation scheme based on the output power of all the carbon emission sources and the corresponding carbon emission coefficients;
s23, determining a carbon transaction cost calculation scheme according to market carbon transaction basic prices and carbon emission in different intervals.
Specifically, in step S21, the carbon emission source in the integrated energy device operation model mainly includes electricity purchasing of the upper power grid, a gas cogeneration unit, a gas boiler and a gas load, the electricity purchasing power of the system from the upper power grid is regarded as electricity generation by the coal-fired unit, and the actual carbon emission amount is calculated according to the coal-fired unit.
Further, in step S22, the process of converting hydrogen into natural gas may absorb a part of CO 2 The actual gas load is mostly gas mainly consumed by combustion, such as coal gas and natural gas, and carbon emissions are generated during combustion, so that consideration is required. The carbon emission amount calculation scheme is as follows:
C all =C buy +C G +C GL -C M ；（1）
；（2）
；（3）
P all =P G1 +P G2 ；（4）
；（5）
；（6）
wherein C is all Representing actual carbon emission of integrated energy system of certain park, C buy Representing the actual carbon emission of the electricity purchasing of the upper power grid, C G Representing the total actual carbon emission of the gas cogeneration unit and the gas boiler, G GL Actual carbon emission as gas load, C M For the absorption of system hydrogen into natural gasCarbon dioxide amount, T is run time, alpha 1 、β 1 、γ 1 Is the carbon emission coefficient, P of the coal-fired unit buy Alpha is the output power of the upper power grid purchase 2 、β 2 、γ 2 Carbon emission coefficient, P of cogeneration unit and gas boiler G1 And P G2 Output power of the cogeneration unit and output power of the gas boiler, P all Is the sum of the output power of the cogeneration unit and the gas boiler, P GL For output natural gas power, C M For the output power of the system hydrogen to natural gas, delta GL Carbon emission coefficient, delta, for gas load M The coefficient of carbon dioxide absorption for the conversion of system hydrogen to natural gas.
The carbon emission coefficient refers to the amount of carbon emissions generated by unit energy during combustion or use of each energy source. In general, the carbon emission coefficient of a certain energy source can be considered to be unchanged during the use.
Further, in step S23, the carbon transaction cost calculation scheme is as follows:
C leave =C all -C free ；（7）
（8）
wherein C is all Representing actual carbon emission of integrated energy system of certain park, C free Representing carbon emission amount not participating in carbon transaction in park comprehensive energy system, C leave Representing the carbon transaction amount of the integrated energy system actually participating in the transaction in a park, H co2 Represents the carbon trade cost, μ represents the carbon trade base price of the market, τ represents the increase in the carbon trade price, and S represents the carbon trade volume interval length.
Further, referring to fig. 3, in step 3, a wind power uncertainty model is constructed by introducing robust uncertainty adjustment parameters, including:
s31, determining a scheduling period;
s32, determining a value range of the robust uncertainty adjustment parameter and a wind power output fluctuation interval range according to the scheduling period;
s33, determining the actual wind power output according to wind power output prediction, wind power output prediction deviation and wind power output fluctuation interval range;
and S34, establishing a wind power output uncertainty set according to the actual wind power output and the value range of the robust uncertainty adjustment parameter and the wind power output fluctuation interval range to obtain the wind power uncertainty model.
Specifically, in the embodiment, uncertainty of wind power output is described in a box type interval set form u, a robust uncertainty adjustment parameter is introduced to adjust conservation of a model, and a scheme obtained by larger values is more conservative, otherwise, the scheme is more risky.
In steps S31 and S32, a dispatching cycle T is determined, the value range of the robust uncertainty adjustment parameter is an integer within 0-T, the range of the wind power output fluctuation interval is that the sum of the state quantities of the wind power output fluctuation upwards and the wind power output fluctuation downwards in the dispatching cycle T is smaller than or equal to the robust uncertainty adjustment parameter.
Further, in step S33, the actual wind power output is the product of the wind power output predicted value minus the state quantity of the wind power output fluctuation and the predicted deviation, and the product of the state quantity of the wind power output fluctuation and the predicted deviation is added.
Specifically, the wind power uncertainty model is as follows:
；（9）
wherein u represents a wind power output uncertainty set, and P WT,t The actual output value of the wind power in the period t is represented,the predicted value of the wind power output is expressed,the predicted deviation of the wind power output is represented,representing the robust uncertainty adjustment parameter,the state quantity of the upward fluctuation of the wind power output is represented,and the state quantity of the downward fluctuation of the wind power output is represented.
In this embodiment, the wind power output is predicted by using conditional probability prediction, and the specific flow is as follows:
each predicted value in the test set and each predicted value input again in the training set are subtracted in turn, and a difference value is calculated;
forming a sample group by the predicted value and the corresponding predicted error, selecting the predicted value with a certain proportion of total samples and the highest similarity and the corresponding error into an optimal interval set, adopting a training optimizing method for the sample set to adjust the interval width, carrying out error selection analysis on all test points of the training set, selecting the sample set corresponding to the optimal reliability and the acuity, and optimizing the proportion of error sample selection;
selecting a predicted value and an error value corresponding to the new data set according to the optimized sample proportion and the similarity condition of the predicted values, and constructing an error analysis model;
calculating the variance and standard deviation of the new data set, and calculating the upper limit value and the lower limit value of the period to be predicted, namely the upper limit value and the lower limit value of the interval of the point;
and calculating the upper limit value and the lower limit value of each point in the test set, and correspondingly connecting all the points in sequence to form an upper envelope line and a lower envelope line of the whole prediction interval. So far, wind power output prediction based on the optimization error sample set is completed.
Further, in step S4, the photovoltaic output, load and deviation probability distribution thereof of the integrated energy system are sampled, and an initial scene set and occurrence probability of the photovoltaic load are generated. Compared with wind power output, the photovoltaic output and load prediction accuracy is higher, the method has obvious fluctuation rules, and the uncertainty problems can be better solved by using a random programming method.
The photovoltaic output deviates from the predicted values of the cold, hot, electric and gas loads, and in the embodiment, the actual value of the photovoltaic output and the load is regarded as the sum of the predicted values and the predicted deviation, and the expression is as follows:
；（10）
wherein,for the actual output of the photovoltaic at time t in the ith scene,is the predicted value of the photovoltaic output at the moment t in the ith scene,the deviation is predicted for the photovoltaic output,is the actual value of the load at time t in the ith scenario,is the predicted value of the load at the time t in the ith scene,is the predicted deviation of the load.
For uncertainty in photovoltaic output and load, corresponding samples can be generated by random sampling according to the deviation probability distribution. The present embodiment uses Latin hypercube sampling for scene generation to ensure that the samples cover the entire sample space of random variables.
In view of the above-described large initial scene set, in step S5 of this embodiment, the generated scenes are reduced by using an improved reduction method to extract typical scenes and their probabilities, and the reduction target scene number k is set on the premise of the sampling scene number n, so that the scene number k in the process of reduction is reduced 1 The following steps are iteratively executed until the current scene number k 1 Reaching a preset target scene number k:
calculating the current k 1 The Euclidean distance of any two scenes in the plurality of scenes;
determining a scene s closest to the Euclidean distance of the scene i, multiplying the occurrence probability of the scene s by the Euclidean distance of the scene i to obtain a probability distance;
searching for a scene pair (i, s) that minimizes the probability distance;
let p s =p s +p i Wherein p is s For the probability of occurrence of scene s, p i Updating the occurrence probability of the scene s for the occurrence probability of the scene i and simultaneously reducing the scene i;
let k 1 =k 1 -k i Updating the scene number k in the clipping process 1 Wherein k is i The number of scene pairs with the smallest probability distance value.
The Euclidean distance of any two scenes is calculated by the following formula:
；（11）
wherein D is ij Representing Euclidean distance of scene i and scene j, n representing the number of sampled scenes, P ik Representing the probability of occurrence of scene i, P jk Representing the probability of scene j occurrence.
The probability distance is calculated by the following formula:
；（12）
wherein p is is Representing the probability distance of scene i and scene s, D is Representing Euclidean distance, p, of scene i and scene s s Representing the probability of occurrence of scene s.
In order to verify the effectiveness of the modified clipping scheme proposed in the present embodiment, the sample scene set was processed with separate clipping, conventional centralized clipping and modified clipping in the present embodiment, and the three clipping schemes were evaluated by the comprehensive probability distance evaluation index, and the results are shown in table 1 and fig. 4. In this example, it is assumed that the photovoltaic output and load follow a normal distribution.
Table 1 evaluation results in different reduction modes
From table 1 and fig. 4, it is clear that, when the comprehensive probability distances of the uncertain variables in three clipping modes are compared, the comprehensive probability distances of the uncertain variables in individual clipping are larger than those of the centralized clipping and improved clipping methods, because the core idea of the improved clipping method is to calculate the probability distances of each scene in the generated scene set and the rest scenes, clip the scene with the smallest probability distance among the scene pairs while superimposing the probability thereof on another scene, and as the number of scenes is clipped, the comprehensive probability distance of the scene pair consisting of the rest scenes is the maximum value of the comprehensive probability distances of the scene pair consisting of the same number of scenes in the original scene set, and the result of the individual clipping of the uncertain variables is the maximum value of the comprehensive probability distances of the uncertain variables. Compared with the traditional centralized reduction and improved reduction, the comprehensive probability distance under the traditional centralized reduction mode of the photovoltaic load, the thermal load and the gas load is smaller than that under the traditional centralized reduction mode of the improved reduction, and the comprehensive probability distance under the traditional centralized reduction mode of the electric load and the gas load is smaller than that under the traditional centralized reduction mode of the improved reduction.
Further, in step S6, in the two-stage optimization scheduling model, the first stage includes an objective function with the lowest running cost and the lowest carbon emission in a typical photovoltaic load scenario, and takes an energy storage charging and discharging plan, a power grid interaction plan and an energy storage device output plan as optimization variables; the second stage is to find the worst wind power output scene and the optimal scheme with the lowest energy purchasing cost under the worst wind power output scene.
The two-stage optimization scheduling model is as follows:
；（13）
where N is the typical scene number, ρ s For the occurrence probability of the scene s, lambda is the target optimization satisfaction, G buy,s Is the purchase cost under a typical scene s, Y op,s Maintenance costs for operation under typical scenario s, H co2,s N is the cost of carbon trade in a typical scenario s su,s C is the energy supply rate s For the carbon emission under a typical scene s, x is a first-stage optimization variable comprising an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan, and y is a second-stage optimization variable comprising unit output and power grid interaction quantity; u is the wind power output uncertainty set.
Further, under a certain typical photovoltaic and load scene, the objective function of the two-stage optimization scheduling model comprises: the system operation cost is minimum, the carbon dioxide emission is minimum and the energy supply rate is minimum in the scheduling period.
The system running cost is minimum in the scheduling period, and the expression is as follows:
minQ=min（G buy +Y op +H co2 ）；（14）
wherein Q represents the running cost of the system, G buy Representing the cost of purchasing energy, Y op Representing the running maintenance cost, H co2 Representing the cost of carbon trade.
The carbon dioxide emission is minimum, and the expression is:
minC=C buy +C G +C GL -C M ；（15）
wherein C represents carbon dioxide emission, C buy Representing the actual carbon emission of the electricity purchasing of the upper power grid, C G Representing the total actual carbon emission of the gas cogeneration unit and the gas boiler, G GL Actual carbon emission as gas load, C M The amount of carbon dioxide absorbed for the system hydrogen to natural gas.
The energy supply rate is minimum, and the expression is:
；（16）
wherein N is su Indicating the energy supply rate and indicating the sudden increase of the load to the original value（) When the time is multiplied, the scheduling standby condition of outsourcing energy sources is P buy,e,t Representing the electricity purchasing quantity of the upper power grid at t moment, P buy,g,t Output power expressed as power purchase of upper power grid, P load,e,t Representing the electrical load at time t, P load,h,t Representing the thermal load at time t, P load,c,t Indicating the cold load at time t, P load,g,t The gas load at time t is shown.
Further, referring to fig. 5, in step S7, solving the two-stage optimization scheduling model includes:
s71, decomposing the two-stage optimal scheduling model into a main problem and a sub problem;
S72, taking wind power predicted value as initial severe scene u 1 ；
S73, in severe scene u i Lower solutionThe main problem is that a first-stage energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan x are obtained i Taking the objective function value of the main problem as a lower bound, wherein i is the iteration number;
s74, x i Carrying in the sub problem, obtaining the optimal scheme y with lowest purchase energy cost i Wind power output scene u corresponding to the same i+1 Setting the objective function value of the sub-problem as an upper bound;
and S75, giving a convergence threshold, stopping iteration when the difference between the upper bound and the lower bound is smaller than or equal to the convergence threshold, and returning to the optimal solution.
Specifically, the two-stage optimization scheduling model is in the following brief form:
；（17）
decomposing the formula (17) into a main problem formula (18) and a sub problem formula (19) and solving alternately:
；（18）
wherein:is the objective function value of the sub-problem.
；（19）
According to strong dual theory, the min of max-min in formula (17) can be converted into max form and combined with max of the outer layer to obtain
；(20)
Wherein:、、in order to have a dual variable, the two variables,as an auxiliary variable, a control signal is provided,is a bilinear term, which is converted to formula (21), and adds the constraint of formula (22) to formula (20).
；（21）
；(22)
Wherein:、is thatIs the positive and negative value of (a).
Through the above derivation and conversion, the formula (17) is converted into a mixed integer linear form formula (18) and formulas (19) -22), and the solution is carried out by the above method.
Further, the two-stage optimal scheduling model also comprises target optimal satisfaction;
the method further comprises the steps of: and taking the continuous and micro anti-Sigmoid function on the definition domain as a membership function of each target, and solving the minimum value of the membership function to obtain the maximum target optimization satisfaction.
The membership function is as follows:
；（23）
to make the shape of the anti-Sigmoid function sufficiently approximate to the original half-dipLinear function, required parametersAndreasonable setting is carried out by firstly keeping two curves atThe points at the same time are coincident, and in the present embodimentThe point at the point is used as the coincident point, and the curve characteristic parameter value of the anti-Sigmoid function can be obtained according to the coordinate of the coincident point. According to the maximum and minimum satisfaction method, the satisfaction degreeThe minimum value in membership functions for all objective functions:
；（24）
thus, the original multi-objective problem can be converted into the maximum satisfaction degree for meeting all constraintsThe problems of (1), i.eIs a problem of (a).
In some embodiments, the constraint conditions include a wind power output constraint, a photovoltaic output constraint, a gas cogeneration unit operation constraint, a gas boiler operation constraint, an electric refrigerator operation constraint, a methane reactor operation constraint, a hydrogen fuel cell operation constraint, an electrolyzer operation constraint, an energy storage operation constraint, an electrical balance constraint, a thermal balance constraint, a natural gas balance constraint, a hydrogen balance constraint, and a cold balance constraint.
Specifically, the wind power output constraint comprises that the predicted wind power output power is smaller than the rated power of the wind turbine generator; the photovoltaic output constraint is that the predicted power of the photovoltaic output is smaller than the rated power of the photovoltaic unit; the operation constraint of the gas cogeneration unit comprises that the electric power of the gas cogeneration unit is equal to the product of the conversion efficiency and the input natural gas power, the thermal power of the gas cogeneration unit is equal to the product of the conversion efficiency and the input natural gas power, the input natural gas power of the gas cogeneration unit is between the upper limit and the lower limit, and the increase of the input natural gas of the gas cogeneration unit in unit time is between the upper limit and the lower limit of the climbing slope; the gas boiler operation constraint comprises that the power of the gas boiler is the product of the input natural gas power and the conversion efficiency of the gas boiler, and the increase of the input natural gas of the gas boiler in unit time is between the upper limit and the lower limit of the climbing; the operation constraint of the electrolytic cell comprises that the hydrogen energy output by the electrolytic cell is equal to the product of the energy conversion efficiency and the input electric energy, the electric energy input by the electrolytic cell is between the upper limit and the lower limit of the input electric energy, and the increase of the electric energy input by the electrolytic cell in unit time is between the upper limit and the lower limit of the climbing; the operation constraint of the methane reactor comprises that the output natural gas power is the product of the energy conversion efficiency and the input hydrogen energy, the hydrogen energy input by the methane reactor is between the upper limit and the lower limit of the input hydrogen energy, and the increase of the input hydrogen energy in unit time is between the upper limit and the lower limit of the climbing; the hydrogen fuel cell operation constraint comprises that the output natural gas power is equal to the product of the energy conversion efficiency and the input hydrogen energy, the input hydrogen energy is between the upper limit and the lower limit, and the increase of the input hydrogen energy in unit time is between the upper limit and the lower limit of the climbing slope; the electric refrigerator operation constraints include: the cold power output by the electric refrigerator is equal to the product of the energy conversion efficiency and the input electric power, the input electric power of the electric refrigerator is between the upper limit and the lower limit, and the input electric power is increased between the upper limit and the lower limit of the climbing slope in unit time; the operational constraints of an absorption chiller include: the output cold power is equal to the product of the input heat power and the energy conversion efficiency, the input heat power is between the upper limit and the lower limit, and the input heat power is increased between the upper limit and the lower limit of the climbing in unit time; the energy storage operation constraint comprises that the current capacity of the energy storage device is equal to the product of the last short-time capacity plus the maximum power of single charge and the charge conversion efficiency, and the quotient of the maximum power of discharge and the discharge conversion efficiency is subtracted, wherein the maximum power of single charge of the energy storage device is more than or equal to 0 and less than or equal to the product of a state variable and the maximum charge power, the maximum power of single discharge of the energy storage device is more than or equal to 0 and less than or equal to the product of the state variable and the maximum discharge power, the sum of the state variable of charge and the state variable of discharge is equal to 1, and the current capacity of the energy storage device is between the upper limit and the lower limit of the capacity of the energy storage device; the electric balance constraint comprises adding the sum of the electric energy output by the corresponding equipment to the difference between the electricity purchase quantity and the electricity sales quantity of the upper-level power grid, and the sum is equal to the electric load of the system; the thermal balance constraint includes that the thermal energy output by the system is equal to the thermal load thereof; the natural gas balance constraint comprises natural gas purchased to an upper power grid and the sum of the natural gas input by each device, which is equal to the gas load of the system; the hydrogen balance constraint includes that the system inputs hydrogen energy equal to the hydrogen energy consumed by the corresponding device.
Referring to fig. 6, in some embodiments, there is also provided a low-carbon optimized scheduling apparatus for an integrated energy system, including:
a first construction module 201, configured to construct a comprehensive energy device operation model;
a second construction module 202, configured to construct a carbon emission trading model of the integrated energy system according to the integrated energy device operation model;
the third construction module 203 is configured to introduce a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
the scene generating module 204 is configured to sample photovoltaic output, load and deviation probability distribution thereof in the park, and generate an initial scene set and occurrence probability of the photovoltaic load;
the reduction module 205 performs scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability thereof;
the model generating module 206 is used for constructing a two-stage optimal scheduling model with the lowest running cost and the minimum carbon emission as targets under the worst wind power output scene and the typical photovoltaic load scene according to the carbon emission transaction model and the wind power uncertain model based on the typical photovoltaic load scene set and the occurrence probability thereof;
and the calculation module 207 is used for setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
Further, the integrated energy device operation model constructed by the first construction module 201 includes an energy input end, an energy conversion device and an energy output end; the energy input end comprises an upper power grid, an upper air grid, a wind turbine generator and a photovoltaic unit; the energy conversion equipment comprises a gas cogeneration unit, a gas boiler, an electric refrigerator, an absorption refrigerator, an electrolytic tank, a methane reactor and a hydrogen fuel cell; the energy output end comprises a cold load, a hot load, an electric load and a gas load.
Further, the second construction module 202 constructs a carbon emission trading model of the integrated energy system according to the integrated energy device operation model, including:
determining a carbon emission source in the comprehensive energy equipment operation model;
setting carbon emission coefficients of each carbon emission source, and determining a carbon emission amount calculation scheme based on the output power of each carbon emission source and the corresponding carbon emission coefficient;
and determining a carbon trade cost calculation scheme according to the market carbon trade base price and the carbon emission of different intervals.
Further, the third building module 203 introduces a robust uncertainty adjustment parameter to build a wind power uncertainty model, including:
Determining a scheduling period;
determining a value range of the robust uncertainty adjustment parameter according to the scheduling period and a wind power output fluctuation interval range;
determining the actual wind power output according to the wind power output prediction, the wind power output prediction deviation and the wind power output fluctuation interval range;
and establishing a wind power output uncertainty set according to the actual wind power output and the value range of the robust uncertainty adjustment parameter and the wind power output fluctuation interval range to obtain the wind power uncertainty model.
Further, the curtailing module 205 performs scene curtailing on the initial scene set and the occurrence probability of the photovoltaic load based on an improved curtailing method to obtain a typical photovoltaic load scene set and the occurrence probability thereof, including:
extracting samples from the photovoltaic load initial scene set and the occurrence probability, and setting a reduced target scene number k;
iteratively performing the following steps until the current scene number k 1 Reaching a preset target scene number k:
calculating the current k 1 The Euclidean distance of any two scenes in the plurality of scenes;
determining a scene s closest to the Euclidean distance of the scene i, multiplying the occurrence probability of the scene s by the Euclidean distance of the scene i to obtain a probability distance;
searching for a scene pair (i, s) that minimizes the probability distance;
Let p s =p s +p i Wherein p is s For the probability of occurrence of scene s, p i Updating the occurrence probability of the scene s for the occurrence probability of the scene i and simultaneously reducing the scene i;
let k 1 =k 1 -k i Updating the scene number k in the clipping process 1 Wherein k is i The number of scene pairs with the smallest probability distance value.
Further, in the two-stage optimization scheduling model, the first stage comprises an objective function with the lowest running cost and the lowest carbon emission in a typical photovoltaic load scene, and takes an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan as optimization variables; the second stage is to find the worst wind power output scene and the optimal scheme with the lowest energy purchasing cost under the worst wind power output scene.
Further, the computing module 207 solves the two-phase optimized scheduling model, including:
decomposing the two-stage optimal scheduling model into a main problem and a sub problem;
wind power predicted value is used as initial severe scene u 1 ；
In severe scene u i Solving the main problem to obtain a first-stage energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan x i Taking the objective function value of the main problem as a lower bound, wherein i is the iteration number;
Will x i Carrying in the sub problem, obtaining the optimal scheme y with lowest purchase energy cost i Wind power output scene u corresponding to the same i+1 Setting the objective function value of the sub-problem as an upper bound;
and (4) giving a convergence threshold, stopping iteration when the difference between the upper bound and the lower bound is smaller than or equal to the convergence threshold, and returning to the optimal solution.
Further, the two-stage optimal scheduling model also comprises target optimal satisfaction;
the computing module 207 is further configured to: and taking the continuous and micro anti-Sigmoid function on the definition domain as a membership function of each target, and solving the minimum value of the membership function to obtain the maximum target optimization satisfaction.
Further, the constraint conditions include wind power output constraint, photovoltaic output constraint, gas-heat cogeneration unit operation constraint, gas boiler operation constraint, electric refrigerator operation constraint, methane reactor operation constraint, hydrogen fuel cell operation constraint, electrolyzer operation constraint, energy storage operation constraint, electric balance constraint, heat balance constraint, natural gas balance constraint, hydrogen balance constraint and cold balance constraint.
The specific principle is to refer to the method embodiment, and will not be described herein.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the computer program.
Wherein when the processor executes the computer program and implements the method, the method comprises:
constructing a comprehensive energy equipment operation model;
constructing a carbon emission transaction model of the comprehensive energy system according to the comprehensive energy equipment operation model;
introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
sampling the photovoltaic output and load of the comprehensive energy system and the deviation probability distribution of the load, and generating a photovoltaic load initial scene set and occurrence probability;
performing scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load;
based on a typical photovoltaic load scene set and occurrence probability thereof, constructing a two-stage optimization scheduling model with minimum running cost and minimum carbon emission as targets under a worst wind power output scene and a typical photovoltaic load scene according to the carbon emission transaction model and the wind power uncertainty model;
and setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
According to the comprehensive energy equipment operation model, constructing a carbon emission transaction model of a comprehensive energy system, comprising:
Determining a carbon emission source in the comprehensive energy equipment operation model;
setting carbon emission coefficients of each carbon emission source, and determining a carbon emission amount calculation scheme based on the output power of each carbon emission source and the corresponding carbon emission coefficient;
and determining a carbon trade cost calculation scheme according to the market carbon trade base price and the carbon emission of different intervals.
The carbon emission amount calculation scheme is as follows:
C all =C buy +C G +C GL -C M ；（1）
；（2）
；（3）
P all =P G1 +P G2 ；（4）
；（5）
；（6）
wherein C is all Representing actual carbon emission of integrated energy system of certain park, C buy Representing the actual carbon emission of the electricity purchasing of the upper power grid, C G Representing the total actual carbon emission of the gas cogeneration unit and the gas boiler, G GL Actual carbon emission as gas load, C M For the conversion of system hydrogen to carbon dioxide absorbed by natural gas, T is the run time, α 1 、β 1 、γ 1 Is the carbon emission coefficient, P of the coal-fired unit buy Alpha is the output power of the upper power grid purchase 2 、β 2 、γ 2 Carbon emission coefficient, P of cogeneration unit and gas boiler G1 And P G2 Output power of the cogeneration unit and output power of the gas boiler, P all Is the sum of the output power of the cogeneration unit and the gas boiler, P GL For output natural gas power, C M For the output power of the system hydrogen to natural gas, delta GL Carbon emission coefficient, delta, for gas load M The coefficient of carbon dioxide absorption for the conversion of system hydrogen to natural gas.
The carbon trade cost calculation scheme is as follows:
C leave =C all -C free ；（7）
（8）
wherein C is all Representing actual carbon emission of integrated energy system of certain park, C free Representing carbon emission amount not participating in carbon transaction in park comprehensive energy system, C leave Representing the carbon transaction amount of the integrated energy system actually participating in the transaction in a park, H co2 Represents the carbon trade cost, μ represents the carbon trade base price of the market, τ represents the increase in the carbon trade price, and S represents the carbon trade volume interval length.
Introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model, comprising:
determining a scheduling period;
determining a value range of the robust uncertainty adjustment parameter according to the scheduling period and a wind power output fluctuation interval range;
determining the actual wind power output according to the wind power output prediction, the wind power output prediction deviation and the wind power output fluctuation interval range;
and establishing a wind power output uncertainty set according to the actual wind power output and the value range of the robust uncertainty adjustment parameter and the wind power output fluctuation interval range to obtain the wind power uncertainty model.
And determining a dispatching cycle T, wherein the value range of the robust uncertainty adjustment parameter is an integer in 0-T, and the range of the wind power output fluctuation interval is that the sum of the state quantities of the wind power output fluctuation upwards and the wind power output fluctuation downwards in the dispatching cycle T is smaller than or equal to the robust uncertainty adjustment parameter.
The actual wind power output is obtained by subtracting the product of the state quantity of the downward fluctuation of the wind power output and the predicted deviation from the predicted value of the wind power output and adding the product of the state quantity of the upward fluctuation of the wind power output and the predicted deviation.
Specifically, the wind power uncertainty model is as follows:
；（9）
wherein u represents a wind power output uncertainty set, and P WT,t The actual output value of the wind power in the period t is represented,the predicted value of the wind power output is expressed,the predicted deviation of the wind power output is represented,representing the robust uncertainty adjustment parameter,the state quantity of the upward fluctuation of the wind power output is represented,and the state quantity of the downward fluctuation of the wind power output is represented.
In this embodiment, the wind power output is predicted by using conditional probability prediction, and the specific flow is as follows:
each predicted value in the test set and each predicted value input again in the training set are subtracted in turn, and a difference value is calculated;
forming a sample group by the predicted value and the corresponding predicted error, selecting the predicted value with a certain proportion of total samples and the highest similarity and the corresponding error into an optimal interval set, adopting a training optimizing method for the sample set to adjust the interval width, carrying out error selection analysis on all test points of the training set, selecting the sample set corresponding to the optimal reliability and the acuity, and optimizing the proportion of error sample selection;
Selecting a predicted value and an error value corresponding to the new data set according to the optimized sample proportion and the similarity condition of the predicted values, and constructing an error analysis model;
calculating the variance and standard deviation of the new data set, and calculating the upper limit value and the lower limit value of the period to be predicted, namely the upper limit value and the lower limit value of the interval of the point;
and calculating the upper limit value and the lower limit value of each point in the test set, and correspondingly connecting all the points in sequence to form an upper envelope line and a lower envelope line of the whole prediction interval. So far, wind power output prediction based on the optimization error sample set is completed.
The photovoltaic output deviates from the predicted values of the cold, hot, electric and gas loads, and in the embodiment, the actual value of the photovoltaic output and the load is regarded as the sum of the predicted values and the predicted deviation, and the expression is as follows:
；（10）
wherein,for the actual output of the photovoltaic at time t in the ith scene,is the predicted value of the photovoltaic output at the moment t in the ith scene,the deviation is predicted for the photovoltaic output,is the actual value of the load at time t in the ith scenario,is the predicted value of the load at the time t in the ith scene,is the predicted deviation of the load.
In view of the large scale of the initial scene set generated as described above, the present embodiment adopts an improved clipping method to clip the generated scenes, refines the typical scenes and their probabilities, sets the clipping target scene number k on the premise of the sampling scene number n, and clips the scene number k in the process 1 The following steps are iteratively executed until the current scene number k 1 Reaching a preset target scene number k:
calculating the current k 1 The Euclidean distance of any two scenes in the plurality of scenes;
determining a scene s closest to the Euclidean distance of the scene i, multiplying the occurrence probability of the scene s by the Euclidean distance of the scene i to obtain a probability distance;
searching for a scene pair (i, s) that minimizes the probability distance;
let p s =p s +p i Wherein p is s For the probability of occurrence of scene s, p i Updating the occurrence probability of the scene s for the occurrence probability of the scene i and simultaneously reducing the scene i;
let k 1 =k 1 -k i Updating the scene number k in the clipping process 1 Wherein k is i The number of scene pairs with the smallest probability distance value.
The Euclidean distance of any two scenes is calculated by the following formula:
；（11）
wherein D is ij Representing Euclidean distance of scene i and scene j, n representing the number of sampled scenes, P ik Representing the probability of occurrence of scene i, P jk Representing the probability of scene j occurrence.
The probability distance is calculated by the following formula:
；（12）
wherein p is is Representing the probability distance of scene i and scene s, D is Representing Euclidean distance, p, of scene i and scene s s Representing the probability of occurrence of scene s.
In the two-stage optimization scheduling model, the first stage comprises an objective function with the lowest running cost and the lowest carbon emission in a typical photovoltaic load scene, and takes an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan as optimization variables; the second stage is to find the worst wind power output scene and the optimal scheme with the lowest energy purchasing cost under the worst wind power output scene.
The two-stage optimization scheduling model is as follows:
；（13）
where N is the typical scene number, ρ s For the occurrence probability of the scene s, lambda is the target optimization satisfaction, G buy,s Is the purchase cost under a typical scene s, Y op,s Maintenance costs for operation under typical scenario s, H co2,s N is the cost of carbon trade in a typical scenario s su,s C is the energy supply rate s For the carbon emission under a typical scene s, x is a first-stage optimization variable comprising an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan, and y is a second-stage optimization variable comprising unit output and power grid interaction quantity; u is windThe electrical force is not a deterministic set.
Further, under a certain typical photovoltaic and load scene, the objective function of the two-stage optimization scheduling model comprises: the system operation cost is minimum, the carbon dioxide emission is minimum and the energy supply rate is minimum in the scheduling period.
The system running cost is minimum in the scheduling period, and the expression is as follows:
minQ=min（G buy +Y op +H co2 ）；（14）
wherein Q represents the running cost of the system, G buy Representing the cost of purchasing energy, Y op Representing the running maintenance cost, H co2 Representing the cost of carbon trade.
The carbon dioxide emission is minimum, and the expression is:
minC=C buy +C G +C GL -C M ；（15）
wherein C represents carbon dioxide emission, C buy Representing the actual carbon emission of the electricity purchasing of the upper power grid, C G Representing the total actual carbon emission of the gas cogeneration unit and the gas boiler, G GL Actual carbon emission as gas load, C M The amount of carbon dioxide absorbed for the system hydrogen to natural gas.
The energy supply rate is minimum, and the expression is:
；（16）
wherein N is su Indicating the energy supply rate and indicating the sudden increase of the load to the original value（) When the time is multiplied, the scheduling standby condition of outsourcing energy sources is P buy,e,t Representing the electricity purchasing quantity of the upper power grid at t moment, P buy,g,t Output power expressed as power purchase of upper power grid, P load,e,t Representing the electrical load at time t, P load,h,t Representing the thermal load at time t, P load,c,t Indicating the cold load at time t, P load,g,t The gas load at time t is shown.
Further, solving the two-stage optimization scheduling model includes:
decomposing the two-stage optimal scheduling model into a main problem and a sub problem;
wind power predicted value is used as initial severe scene u 1 ；
In severe scene u i Solving the main problem to obtain a first-stage energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan x i Taking the objective function value of the main problem as a lower bound, wherein i is the iteration number;
will x i Carrying in the sub problem, obtaining the optimal scheme y with lowest purchase energy cost i Wind power output scene u corresponding to the same i+1 Setting the objective function value of the sub-problem as an upper bound;
and (4) giving a convergence threshold, stopping iteration when the difference between the upper bound and the lower bound is smaller than or equal to the convergence threshold, and returning to the optimal solution.
Specifically, the two-stage optimization scheduling model is in the following brief form:
；（17）
decomposing the formula (17) into a main problem formula (18) and a sub problem formula (19) and solving alternately:
；（18）
wherein:is the objective function value of the sub-problem.
；（19）
According to strong dual theory, the min of max-min in formula (17) can be converted into max form and combined with max of the outer layer to obtain
；(20)
Wherein:、、in order to have a dual variable, the two variables,as an auxiliary variable, a control signal is provided,is a bilinear term, which is converted to formula (21), and adds the constraint of formula (22) to formula (20).
；（21）
；(22)
Wherein:、is thatIs the positive and negative value of (a).
Through the above derivation and conversion, the formula (17) is converted into a mixed integer linear form formula (18) and formulas (19) -22), and the solution is carried out by the above method.
Further, the two-stage optimal scheduling model also comprises target optimal satisfaction;
the method further comprises the steps of: and taking the continuous and micro anti-Sigmoid function on the definition domain as a membership function of each target, and solving the minimum value of the membership function to obtain the maximum target optimization satisfaction.
The membership function is as follows:
；（23）
in order to make the shape of the anti-Sigmoid function sufficiently approximate to the original half-decreasing linear function, the parameters need to be matchedAndreasonable setting is carried out by firstly keeping two curves atThe points at the same time are coincident, and in the present embodimentThe point at the point is used as the coincident point, and the curve characteristic parameter value of the anti-Sigmoid function can be obtained according to the coordinate of the coincident point. According to the maximum and minimum satisfaction method, the satisfaction degreeThe minimum value in membership functions for all objective functions:
；（24）
thus, the original multi-objective problem can be converted into the maximum satisfaction degree for meeting all constraintsThe problems of (1), i.eIs a problem of (a).
The low-carbon optimal scheduling method, device and equipment for the park comprehensive energy system provided by the embodiment at least comprise the following beneficial effects:
(1) The uncertainty of wind power, photovoltaic and load of the comprehensive energy system is fully considered, low-carbon technologies such as a carbon transaction mechanism and the like are introduced to perform optimal scheduling on the operation of the comprehensive energy system, the carbon emission of the system is reduced, grid-connected consumption of wind power and photovoltaic is promoted, the low-carbon optimization technology of the comprehensive energy system in the wind power, photovoltaic and load uncertainty environment is obtained, the operation cost of the comprehensive energy system can be effectively optimized, the energy utilization rate is improved, and the carbon emission is reduced.
(2) The wind power uncertainty model is built by introducing the robust uncertainty adjustment parameters, so that the model is more similar to the actual application, and the accuracy of the follow-up optimization scheduling is improved;
(3) The method has the advantages that the typical photovoltaic load scene set and the occurrence probability thereof are generated based on the improved reduction method, the influence of the length of the range of the value of each uncertain variable on the reduction process is greatly reduced, the dimension of each uncertain variable can be eliminated, the influence of each uncertain variable on the reduction process is more uniformly considered, the comprehensive probability distance of photovoltaic, thermal load, gas load and the like is increased, the relevance of the reduction process is improved, and the situation that the reduction result falls into single relevance is avoided.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (15)
1. The low-carbon optimal scheduling method for the comprehensive energy system is characterized by comprising the following steps of:
constructing a carbon emission transaction model of the comprehensive energy system according to a pre-constructed comprehensive energy equipment operation model;
introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
sampling the photovoltaic output and load of the comprehensive energy system and the deviation probability distribution of the load, and generating a photovoltaic load initial scene set and occurrence probability;
performing scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load;
based on a typical photovoltaic load scene set and occurrence probability thereof, constructing a two-stage optimization scheduling model according to the carbon emission transaction model and the wind power uncertainty model;
and setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
2. The method of claim 1, wherein the integrated energy device operational model includes an energy input, an energy conversion device, and an energy output.
3. The method of claim 2, wherein the energy input comprises a wind turbine and a photovoltaic turbine; the energy conversion equipment comprises a gas cogeneration unit; the energy output comprises an electrical load.
4. The method of claim 1, wherein constructing a carbon emissions trading model of the integrated energy system from the pre-constructed integrated energy device operational model comprises:
determining a carbon emission source in the comprehensive energy equipment operation model;
setting carbon emission coefficients of each carbon emission source, and determining a carbon emission amount calculation scheme based on the output power of each carbon emission source and the corresponding carbon emission coefficient;
and determining a carbon trade cost calculation scheme according to the market carbon trade base price and the carbon emission of different intervals.
5. The method of claim 1, wherein introducing robust uncertainty adjustment parameters to construct a wind power uncertainty model comprises:
determining a scheduling period;
determining a value range of the robust uncertainty adjustment parameter according to the scheduling period and a wind power output fluctuation interval range;
determining the actual wind power output according to the wind power output prediction, the wind power output prediction deviation and the wind power output fluctuation interval range;
and establishing a wind power output uncertainty set according to the actual wind power output and the value range of the robust uncertainty adjustment parameter and the wind power output fluctuation interval range to obtain the wind power uncertainty model.
6. The method of claim 1, wherein scene cuts are performed on the initial scene set and the occurrence probability of the photovoltaic load based on a modified cut-down method to obtain a typical photovoltaic load scene set and the occurrence probability thereof, comprising:
extracting samples from the photovoltaic load initial scene set and the occurrence probability, and setting a reduced target scene number k;
iteratively performing the following steps until the current scene number k 1 Reaching a preset target scene number k:
calculating the current k 1 The Euclidean distance of any two scenes in the plurality of scenes;
determining a scene s closest to the Euclidean distance of the scene i, multiplying the occurrence probability of the scene s by the Euclidean distance of the scene i to obtain a probability distance;
searching for a scene pair (i, s) that minimizes the probability distance;
let p s =p s +p i Wherein p is s For the probability of occurrence of scene s, p i Updating the occurrence probability of the scene s for the occurrence probability of the scene i and simultaneously reducing the scene i;
let k 1 =k 1 -k i Updating the scene number k in the clipping process 1 Wherein k is i For the smallest probability distance valueNumber of scene pairs.
7. The method according to claim 1, wherein the two-stage optimization scheduling model is a two-stage optimization scheduling model with the lowest running cost and the minimum carbon emission as targets in a worst wind power output scenario and a typical photovoltaic load scenario, and the first stage of the two-stage optimization scheduling model comprises an objective function with the lowest running cost and the minimum carbon emission in the typical photovoltaic load scenario, and takes an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan as optimization variables; the second stage is to find the worst wind power output scene and the optimal scheme with the lowest energy purchasing cost under the worst wind power output scene.
8. The method of claim 7, wherein the two-phase optimized scheduling model is as follows:
；
where N is the typical scene number, ρ s For the occurrence probability of the scene s, lambda is the target optimization satisfaction, G buy,s Is the purchase cost under a typical scene s, Y op,s Maintenance costs for operation under typical scenario s, H co2,s N is the cost of carbon trade in a typical scenario s su,s C is the energy supply rate s For the carbon emission under a typical scene s, x is a first-stage optimization variable comprising an energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan, and y is a second-stage optimization variable comprising unit output and power grid interaction quantity; u is the wind power output uncertainty set.
9. The method according to claim 7, characterized in that the lowest running cost objective function is expressed in particular as:
minQ=min（G buy +Y op +H co2 ）
wherein Q represents the running cost of the system, G buy Representing the cost of purchasing energy, Y op Representing the running maintenance cost, H co2 Representing the cost of carbon trade.
10. The method according to claim 7, characterized in that the objective function of minimum carbon emissions is expressed in particular as:
minC=C buy +C G +C GL -C M
wherein C represents carbon dioxide emission, C buy Representing the actual carbon emission of the electricity purchasing of the upper power grid, C G Representing the total actual carbon emission of the gas cogeneration unit and the gas boiler, G GL Actual carbon emission as gas load, C M The amount of carbon dioxide absorbed for the system hydrogen to natural gas.
11. The method of claim 7, wherein solving the two-stage optimization scheduling model comprises:
decomposing the two-stage optimal scheduling model into a main problem and a sub problem;
wind power predicted value is used as initial severe scene u 1 ；
In severe scene u i Solving the main problem to obtain a first-stage energy storage charging and discharging plan, a power grid interaction plan and an energy storage equipment output plan x i Taking the objective function value of the main problem as a lower bound, wherein i is the iteration number;
will x i Carrying in the sub problem, obtaining the optimal scheme y with lowest purchase energy cost i Wind power output scene u corresponding to the same i+1 Setting the objective function value of the sub-problem as an upper bound;
and (4) giving a convergence threshold, stopping iteration when the difference between the upper bound and the lower bound is smaller than or equal to the convergence threshold, and returning to the optimal solution.
12. The method of claim 11, wherein the two-stage optimization scheduling model further includes a target optimization satisfaction;
the method further comprises the steps of: and taking the continuous and micro anti-Sigmoid function on the definition domain as a membership function of each target, and solving the minimum value of the membership function to obtain the maximum target optimization satisfaction.
13. A method according to claim 3, wherein the constraints include wind power output constraints, photovoltaic output constraints, gas cogeneration unit operation constraints, energy storage operation constraints and electric balance constraints.
14. The utility model provides a comprehensive energy system low carbon optimizes dispatch device which characterized in that includes:
the first construction module is used for constructing a comprehensive energy equipment operation model;
the second construction module is used for constructing a carbon emission transaction model of the comprehensive energy system according to the comprehensive energy equipment operation model;
the third construction module is used for introducing a robust uncertainty adjustment parameter to construct a wind power uncertainty model;
the scene generation module is used for sampling the photovoltaic output, load and deviation probability distribution of the photovoltaic output and load of the comprehensive energy system to generate an initial scene set of the photovoltaic load and occurrence probability;
the reduction module is used for carrying out scene reduction on the initial scene set and the occurrence probability of the photovoltaic load based on an improved reduction method to obtain a typical photovoltaic load scene set and the occurrence probability of the photovoltaic load;
the model generation module is used for constructing a two-stage optimization scheduling model with the lowest running cost and the minimum carbon emission as targets under a worst wind power output scene and a typical photovoltaic load scene according to the carbon emission transaction model and the wind power uncertainty model based on the typical photovoltaic load scene set and the occurrence probability thereof;
And the calculation module is used for setting constraint conditions and solving the two-stage optimal scheduling model to obtain an optimal scheduling scheme under a corresponding scene.
15. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 13 when executing the computer program.
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