CN117494909A - Electricity purchasing optimization method, device and medium based on entropy weight self-adaptive IGDT - Google Patents
Electricity purchasing optimization method, device and medium based on entropy weight self-adaptive IGDT Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Abstract
The invention provides a purchase electricity optimizing method, a device and a medium based on entropy weight self-adaptive IGDT, which belong to the field of decision optimizing of electric selling companies, and aim at the problem of inaccurate evaluation result caused by unreasonable construction of the existing model, and provide the purchase electricity optimizing method based on entropy weight self-adaptive IGDT, comprising the following steps: the electricity purchasing cost of the electricity selling company is minimized to be an objective function, constraint conditions of the objective function are set, and an electricity purchasing strategy confirmation type optimization model of the electricity selling company is built; solving a power purchase strategy fixed optimization model of an electricity selling company to obtain an optimal solution; an entropy weight self-adaptive IGDT model is built, and an optimal solution obtained by an electricity purchasing strategy determination type optimization model of an electricity selling company is used as an initial parameter; and solving the entropy weight self-adaptive IGDT model to obtain the electricity purchasing cost of the electricity selling company and the electricity purchasing quantity of the electricity selling company. The method and the device can reduce the influence of uncertain factors on the electricity purchasing strategy of the electricity selling company, and optimize the economical efficiency and the risk resistance of the electricity purchasing mode of the electricity selling company.
Description
Technical Field
The invention belongs to the field of decision optimization of electricity selling companies, and particularly relates to an electricity purchasing optimization method, device and medium based on entropy weight self-adaptive IGDT.
Background
How to make an order strategy for electricity selling companies to reduce electricity purchasing cost and enhance competitive advantage becomes a problem to be solved by the electricity selling companies. The price of the electric power spot market needs to be predicted when the electric power selling company makes an order electric power decision, and the influence factors of the price of the electric power spot market in different time dimensions are different, so that the price of the electric power has stronger volatility due to the difference of market space and time, and difficulty is brought to the establishment of the electric power purchasing strategy of the electric power selling company. In addition, the user power load is influenced by factors such as power utilization time, power utilization preference, power price and the like, and the risk of the power purchasing strategy of the power selling company is closely connected with the uncertainty of the power demand. The uncertainty of electricity price and load causes the actual cost of electricity purchasing by electricity selling companies to be greatly different from the expected cost of electricity purchasing by decision making. In the strategy optimization technology considering uncertainty, more inventions currently select a stochastic programming method, a fuzzy optimization method, a robust optimization method and other processing parameter uncertainty. However, the random programming cannot solve the problem of optimization when the probability distribution of the parameters is uncertain, the fuzzy optimization ignores the properties of the uncertain parameters, the scheme obtained by robust optimization may be too conservative, and the method is difficult to reasonably quantify the size of the uncertain parameters. Therefore, a new electricity purchasing mode is needed to reduce the influence of uncertain factors on the electricity purchasing strategy of the electricity selling company so as to solve the problems caused by the existing model.
Disclosure of Invention
Aiming at the problem of inaccurate evaluation results caused by unreasonable construction of the existing model, the invention provides the electricity purchasing optimization method, device and medium based on the entropy weight self-adaptive IGDT, which can reduce risks caused by uncertain factors and optimize the economical efficiency and the risk resistance of electricity purchasing modes of electricity selling companies.
The invention adopts the following technical scheme: an electricity purchasing optimization method based on entropy weight self-adaptive IGDT comprises the following steps:
s1, constructing a power purchase strategy deterministic optimization model of an electric company by taking the minimum power purchase cost of the electric company as an objective function and setting constraint conditions of the objective function;
s2, solving a power purchase strategy fixed optimization model of the power selling company to obtain an optimal solution;
s3, constructing an entropy weight self-adaptive IGDT model, and taking an optimal solution obtained by a power purchase strategy determination type optimization model of an electricity selling company as an initial parameter;
and S4, solving an entropy weight self-adaptive IGDT model to obtain electricity purchasing cost of the electricity selling company and electricity purchasing quantity of the electricity selling company.
The invention provides an optimal electricity purchasing method of an electricity selling company based on an entropy weight self-adaptive information gap decision method (entropy weight adaptive information gap decision theory method, EAIGDT). The EAIGDT method can eliminate subjective factors under the condition of processing multiple uncertainty factors, retain the non-probability characteristics, reduce the influence of the uncertainty factors on the electricity purchasing strategy of the electricity-selling company, and optimize the economical efficiency and the risk resistance of the electricity purchasing mode of the electricity-selling company.
Further, the electricity purchasing cost of the electricity selling company comprises spot market electricity purchasing cost, deviation checking cost and running cost of an internal unit; the internal unit comprises a fuel oil unit, a gas unit, a wind turbine unit and a photovoltaic unit, the running cost of the internal unit is the running cost of the fuel oil unit and the running cost of the gas unit, and the output of the photovoltaic unit and the wind turbine unit does not account for the power generation cost;
the constraint conditions comprise electric quantity balance constraint, renewable energy source electric power constraint, decision variable non-negative constraint and internal unit output constraint of an electricity selling company, wherein the electric quantity balance constraint enables electric power generated by a generator and supplied user load to be balanced in real time, the renewable energy source electric power constraint ensures output of the renewable energy source unit, the decision variable non-negative constraint enables electric quantity purchased by a load and the electricity selling company to meet practical significance, and the internal unit output constraint of the electricity selling company enables a fuel unit, a gas unit, a wind power unit and a photovoltaic unit in the internal unit to reach proper working states.
Further, a Pymoo solver is adopted to solve a power purchase strategy fixed optimization model of the power company, and an optimal solution is obtained.
Further, the specific process of constructing the entropy weight self-adaptive IGDT model is as follows:
s3.1, under the constraint of uncertain parameter electricity price and uncertain parameter load, constructing an IGDT model containing uncertain variables;
and S3.2, setting the weight of the uncertainty variable in the IGDT model by adopting an entropy weight method, and taking the target function and the uncertainty variable of the established optimization model for the electricity purchasing strategy of the electricity selling company as the optimization targets to obtain the entropy weight self-adaptive IGDT model.
Further, the expression of the IGDT model containing uncertainty variables is:
（1）；
in the method, in the process of the invention,α 1 andα 2 an uncertainty set radius for electricity price and load, respectively;fis an objective function;xis a decision variable;u 1 an actual value of the 1 st uncertain parameter, i.e. an actual value of electricity price;u 2 an actual value for the 2 nd uncertainty parameter, i.e. the load;f cr a threshold value that is an objective function;βis a deviation factor, also known as a risk aversion coefficient, representing the degree to which the expected objective function (total cost) is above the baseline value;f 0 is the baseline value of the objective function;is a set of uncertain parameters, +.>Is a predicted value of electricity price; />Is a predicted value of the load;handgbalance and unbalance constraints, respectively.
Further, the adaptive IGDT model expression of the entropy weight is:
（2）
wherein:αradius for the uncertainty set;the weight occupied by the electricity price is the electricity price entropy weight; />The weight of the load is the load entropy weight.
Further, a heuristic algorithm in the Pymoo solver is adopted for solving, so that electricity purchasing cost of an electricity selling company and electricity purchasing quantity of the electricity selling company are obtained.
The electricity purchasing optimization device based on the entropy weight self-adaptive IGDT comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the electricity purchasing optimization method based on the entropy weight self-adaptive IGDT when executing the executable codes.
A computer readable storage medium having stored thereon a program which, when executed by a processor, implements a power purchase optimization method for implementing the entropy weight based adaptive IGDT described above.
The invention has the beneficial effects that: the invention relates to a power purchase optimization method, a device and a medium based on entropy weight self-adaptive IGDT, which can eliminate subjective factors under the condition of processing multiple uncertainty factors, retain the non-probability characteristic, reasonably quantify the influence of price and load fluctuation on the power purchase strategy of an electricity selling company, reduce the risk caused by the uncertainty factors, optimize the economical efficiency and the risk resistance of the power purchase mode of the electricity selling company and provide a guarantee support for the optimized operation of the electricity selling company.
Drawings
FIG. 1 is a schematic flow chart of the present application;
FIG. 2 is a flow chart of an adaptive IGDT model solution based on entropy weights;
FIG. 3 is a graph comparing wind and light output and load predictions;
FIG. 4 is a graph of the time-of-use electricity price of an electricity selling company;
FIG. 5 is a graph of total cost of electricity companies in three models for each time period;
FIG. 6 is a graph comparing various unit outputs and optimal electricity purchasing amounts under an adaptive IGDT model based on entropy weight;
fig. 7 is a graph comparing load curves.
Detailed Description
The technical solutions of the embodiments of the present invention will be explained and illustrated below with reference to the drawings of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all the embodiments. Based on the examples in the implementation manner, other examples obtained by a person skilled in the art without making creative efforts fall within the protection scope of the present invention.
Example 1
The embodiment is a power purchase optimization method based on entropy weight self-adaptive IGDT, as shown in FIG. 1, comprising the following steps:
s1, constructing a power purchase strategy deterministic optimization model of an electric company by taking the minimum power purchase cost of the electric company as an objective function and setting constraint conditions of the objective function; the electricity purchasing cost of the electricity selling company comprises spot market electricity purchasing cost, deviation checking cost and internal unit operation cost; the internal unit comprises a fuel oil unit, a gas unit, a wind turbine unit and a photovoltaic unit, the running cost of the internal unit is the running cost of the fuel oil unit and the running cost of the gas unit, and the output of the photovoltaic unit and the wind turbine unit does not account for the power generation cost; determining that the electricity purchasing cost of an electricity selling company is minimized as an objective function:
（3）；
in the method, in the process of the invention,C b the electricity purchasing cost is the spot market;C p checking cost for deviation;C Fuel the running cost of the fuel unit is;C Gas the operation cost of the gas unit;fis an objective function;
the spot market electricity purchasing cost of the electricity selling company is as follows:
（4）；
wherein:for the electricity price of electricity purchase of electricity-selling companies, +.>The utility model is used for purchasing electricity quantity for electricity-selling companies.
The deviation electricity quantity is generated due to the fact that the electricity quantity purchased by an electricity selling company in the day ahead is not matched with the real-time electricity consumption, and the electricity selling company needs to bear the assessment cost of the deviation electricity quantity. The deviation electric quantity assessment has a certain examination-free range, the invention considers that the assessment is carried out once every 24 hours, the assessment cost unit price is twice the day-ahead electricity purchase price, and the deviation assessment cost can be expressed as:
（5）；
wherein:the electricity quantity is purchased for the electricity selling company day before;θtaking 2% of the core-free coefficients; delta t is the time interval, and 1h is taken; />Is an electricity selling companytChecking the electric quantity by time deviation;L t is thattThe power load at the moment.
In this application, electricity-selling companies include 4 types of units, such as fuel-gas units, photovoltaic power generation units, and wind power generation units. The photovoltaic and the wind turbine output do not calculate the power generation cost, and only the output of the photovoltaic and the wind turbine is restrained. The running cost functions of the fuel oil unit and the gas unit are respectively usedC Fuel 、C Gas To express:
（6）；
in the method, in the process of the invention,N Fuel the number of the fuel units;N Gas the number of the gas units;、/>、/>is the firstiThe running cost coefficient of each fuel unit; />、/>Is the firstiThe operation cost coefficient of each gas unit;P i,t,f is the firstiThe individual fuel oil units are arranged intThe output at moment;tthe power purchase time is the power purchase time;P m,t,g is the firstmThe gas units are arranged intThe force is exerted at the moment.
The constraint conditions comprise electric quantity balance constraint, renewable energy source electric power constraint, decision variable non-negative constraint and internal unit output constraint of an electricity selling company, wherein the electric quantity balance constraint enables electric power generated by a generator and supplied user load to be balanced in real time, the renewable energy source electric power constraint ensures output of the renewable energy source unit, the decision variable non-negative constraint enables electric quantity purchased by a load and the electricity selling company to meet practical significance, and the internal unit output constraint of the electricity selling company enables a fuel unit, a gas unit, a wind power unit and a photovoltaic unit in the internal unit to reach proper working states.
Taking the periodicity of the power market into account, 24 hours a day was chosen as the scope of the study, and the data of the power market and the power system were kept updated every hour. The predicted output curves of the wind power station and the photovoltaic power station are shown in fig. 3, and the electricity purchasing price curve of the electricity selling company is shown in fig. 4. The values of the parameters of the gas and fuel unit are shown in table 1.
Table 1 gas and oil unit parameters
In an electric power system, electric energy cannot be stored in a large scale; the power generated by the generator and the supplied user load are balanced in real time; the electric quantity balance constraint is as follows:
（7）；
wherein:N w the number of the wind turbine generators is the number of the wind turbine generators;N v the number of the photovoltaic power stations is the number;P r,,t,wmax is thattMaximum output power of the wind turbine generator set at any moment;P r,t,w is the firstrThe wind turbine generator is arranged intOutput power at time;P s,t,vmax is thattMaximum output power of the photovoltaic unit at any moment;P s,t,v is the firstsThe photovoltaic unit is arranged attOutput at time, i.e. firstsThe photovoltaic unit is arranged attThe force is exerted at the moment.
When the electricity selling company formulates the proportion of renewable energy power in retail packages, the internal renewable energy unit is ensured to have enough output; renewable energy power constraints are:
（8）；
in the method, in the process of the invention,δthe ratio of renewable energy power is used;
the decision variable non-negative constraint is:
（9）。
the internal unit output constraint of the electricity selling company comprises a fuel unit, a gas unit, a wind power unit and a photovoltaic unit, wherein:
fuel oil machine set:
（10）；
wherein:P i,t,fmin is the firstiMinimum power generation capacity of the individual fuel units;P i,t,fmax is the firstiMaximum power generation capacity of the individual fuel units;is the firstiMaximum increase force of each fuel unit;P i,t-1,f is the firstiThe individual fuel oil units are arranged int-an output at time 1; />Is the firstiMaximum drop out force of individual fuel units.
Gas turbine unit:
（11）；
wherein:P m,t,gmin is the firstmMinimum power generation capacity of the individual gas units;P m,t,gmax is the firstmMaximum power generation capacity of the individual gas units;is the firstmMaximum increase force of each gas unit;P m,t-1,g is the firstmThe individual fuel oil units are arranged int-an output at time 1; />First, themMaximum drop out force of each gas unit.
Wind turbine generator system:
（12）；
wherein:P r,,t,wmax is thattTime of day (time)rMaximum output power of each wind turbine unit;P r,t,w is thattTime of day (time)rAnd the output power of each wind turbine generator.
Photovoltaic unit:
（13）；
in the method, in the process of the invention,P s,t,vmax is thattAnd the maximum output power of the photovoltaic unit at the moment.
S2, solving a power purchase strategy fixed optimization model of the power selling company to obtain an optimal solution; and solving the electricity purchasing strategy determination optimization model of the electricity selling company by adopting a Pymoo solver. The specific process is as follows: setting the electricity price and the load as initial values of individual fitness, and generating offspring through an electricity purchasing strategy determining optimization model of an electricity selling company to generate a population; and secondly, calculating individual fitness, updating the population, and if the fitness does not meet the convergence threshold, performing cross mutation to generate new offspring. And finally, after multiple iterations, if the convergence threshold is met, stopping the iteration, and outputting a solving result to obtain the electricity purchasing cost of the electricity selling company and the optimal value of the electricity purchasing quantity of the electricity selling company. The solution flow is shown in fig. 2.
S3, constructing an entropy weight self-adaptive IGDT model, and taking an optimal solution obtained by a power purchase strategy determination type optimization model of an electricity selling company as an initial parameter; the specific process is as follows:
s3.1, under the constraint of uncertain parameter electricity price and uncertain parameter load, constructing an IGDT model containing uncertain variables; under the uncertain parameter electricity price and uncertain parameter load constraint, an IGDT model containing uncertain variables is obtained:
（1）；
in the method, in the process of the invention,α 1 andα 2 an uncertainty set radius for electricity price and load, respectively;fis an objective function;xis a decision variable;u 1 an actual value of the uncertain parameter electricity price;u 2 an actual value for the uncertain parameter load;f cr a threshold value that is an objective function;βis a deviation factor, also known as a risk aversion coefficient, representing the degree to which the expected objective function (total cost) is above the baseline value;f 0 is the baseline value of the objective function;is a set of uncertain parameters, +.>Is a predicted value of electricity price; />Is a predicted value of the load;handgbalance and unbalance constraints, respectively.
The derivation process of formula (1) is as follows:
the expression of the IGDT model containing uncertainty variables is:
（14）；
in the method, in the process of the invention,is a set of uncertain parameters, +.>And->Respectively an actual value and a predicted value of the uncertain parameter;αis the radius of the uncertainty set;
the uncertainty parameter may be expressed as:
（15）；
s3.2, setting the weight of an uncertainty variable in the IGDT model by adopting an entropy weight method, and taking an objective function and the uncertainty variable of a deterministic optimization model for constructing an electricity purchasing strategy of an electricity selling company as an optimization target to obtain an entropy weight self-adaptive IGDT model:
（2）；
wherein:αradius for the uncertainty set;the weight occupied by the electricity price is the electricity price entropy weight; />The weight of the load is the load entropy weight.
The derivation process of formula (2) is as follows:
（16）；
wherein:is thattTime of day (time)kThe numerical value of the uncertain parameters is normalized; />Is thattTime of day (time)kAn uncertainty parameter original value; />To->Respectively from time 1 to time TkAn uncertainty parameter original value; />{. from time 1 to time TkA maximum value of the original value of the uncertain parameter; />{. from time 1 to time TkA minimum value of the original value of the uncertain parameter; />Is thattTime of day (time)kThe specific gravity of the uncertain parameters accounting for the sum of the uncertain parameters at each moment;H k is processed by weighting and logarithmtTime of day (time)kA number of uncertain parameter values; />Is the firstkThe weight occupied by the uncertain parameters is entropy weight;Kthe total number of variables is not determined.
Each uncertainty variable is regarded as an index of system uncertainty, and the corresponding entropy weight is calculated by using the predicted value of the uncertainty variable. According to the entropy weight, the part of the corresponding uncertainty variable occupied in the total uncertainty is obtained:
（17）；
wherein:αis the radius of the uncertainty set;α k is the firstkUncertainty of the individual uncertainty parameters.
In order to eliminate subjectivity caused by target deviation factors in the model, an objective function of an electricity purchasing strategy determination type optimization model of an electricity selling company is added as an optimization target, and an entropy weight-based self-adaptive IGDT model is obtained.
And S4, solving an entropy weight self-adaptive IGDT model by adopting a heuristic algorithm in a Pymoo solver to obtain the radius of the uncertain electricity price and load set, the electricity purchasing cost of the electricity selling company and the electricity purchasing quantity of the electricity selling company. The specific solving process is as follows: setting the electricity purchasing cost, the output of each unit and the electricity purchasing quantity as the initial value of individual fitness, and generating offspring through an entropy weight-based self-adaptive IGDT model to generate a population; and secondly, calculating individual fitness, updating the population, and if the fitness does not meet the convergence threshold, performing cross mutation to generate new offspring. And finally, after multiple iterations, if the convergence threshold is met, stopping the iteration, and outputting a solving result to obtain the electricity purchasing cost of the electricity selling company and the optimal value of the electricity purchasing quantity of the electricity selling company. The solution flow is shown in fig. 2.
The invention provides an electricity selling company optimized electricity purchasing method based on an entropy weight self-adaptive information gap decision method (entropy weight adaptive information gap decision theory method, EAIGDT). The EAIGDT method can eliminate subjective factors under the condition of processing multiple uncertainty factors, retain the non-probability characteristics, reduce the influence of the uncertainty factors on the electricity purchasing strategy of the electricity-selling company, and optimize the economical efficiency and the risk resistance of the electricity purchasing mode of the electricity-selling company.
To measure the influence of uncertain factors on the electricity purchasing strategy of an electricity selling company, an information gap decision theory (info-gap decision theory, IGDT) is introduced. The IGDT model can solve the decision problem with an indefinite amount. Different decision makers have different preferences for the risk created by the decision. Some decision makers have a greater propensity for greater revenue and therefore they are also at greater risk; while some decision makers take a conservative attitude on risks, avoiding risks as much as possible, so their possible gains are not high. Aiming at the risk preference of different decision makers in reality, the entropy weighting method is an objective weighting method, information entropy is calculated on the normalized reference data sequences of all indexes, and then the weight of the corresponding index is calculated through the information entropy value. IGDT can be classified into opportunistic models and robust models. In order to meet the electricity purchasing and selling balance, risk avoidance is usually selected by an electricity selling company in reality, so that the invention adopts a robust model. However, the conventional IGDT method requires that a deviation factor of the objective function is predetermined, and an optimal value of the objective function under a deterministic model is calculated in advance. In order to eliminate subjectivity caused by the weight of the uncertainty, the invention provides an entropy weight method for calculating the weight of each uncertainty, namely, carrying out importance evaluation on each uncertainty factor. The method eliminates the subjectivity of the weight and the subjectivity of the target deviation factor, and retains the non-probability characteristic of the IGDT method. After canceling the bias factor, the entropy weight adaptive IGDT model no longer seeks to maximize the uncertainty range through the worst objective to avoid risk.
And respectively solving the electricity purchase quantity of the electricity selling company by using a definite model, an IGDT model and an EAIGDT model, wherein the total cost and various cost values of the electricity selling company in the three models are shown in a table 2. Of the three models, the total cost of the electric company in the EAIGDT model is minimum, and is reduced by 18.13% compared with the definite model. The reason is that EAIGDT considers the uncertainty of electricity price and load, can flexibly adjust the electricity purchasing strategy according to the change of parameters at different moments, and reduces the total cost of an electricity selling company. Whereas the total cost of EAIGDT was reduced by 17.71% compared to IGDT model. The reason is that the EAIGDT model optimizes the influence weight of electricity price and load uncertainty on the total cost, so that the electricity purchasing strategy is more reasonable. In the process from 8 to 21 shown in fig. 5, the total cost of the EAIGDT model is minimum, the optimal output and the optimal electricity purchasing quantity of each unit are shown in fig. 6, the load curve after uncertainty is considered is shown in fig. 7, and the electricity selling company can flexibly purchase electricity according to the load quantity and the output of each unit, so that the electricity purchasing cost is reduced. In conclusion, the electricity purchasing strategy optimization model of the electricity selling company based on the entropy weight self-adaptive information gap decision has a better effect, and the rationality of the electricity purchasing strategy of the electricity selling company can be improved.
Table 2 costs of Electricity purchasing from Electricity-selling companies under three models
While the invention has been described in terms of specific embodiments, it will be appreciated by those skilled in the art that the invention is not limited thereto but includes, but is not limited to, those shown in the drawings and described in the foregoing detailed description. Any modifications which do not depart from the functional and structural principles of the present invention are intended to be included within the scope of the appended claims.
Claims (10)
1. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT is characterized by comprising the following steps of:
s1, constructing a power purchase strategy deterministic optimization model of an electric company by taking the minimum power purchase cost of the electric company as an objective function and setting constraint conditions of the objective function;
s2, solving a power purchase strategy fixed optimization model of the power selling company to obtain an optimal solution;
s3, constructing an entropy weight self-adaptive IGDT model, and taking an optimal solution obtained by a power purchase strategy determination type optimization model of an electricity selling company as an initial parameter;
and S4, solving an entropy weight self-adaptive IGDT model to obtain electricity purchasing cost of the electricity selling company and electricity purchasing quantity of the electricity selling company.
2. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 1, wherein the electricity purchasing costs of the electricity selling company include spot market electricity purchasing costs, deviation checking costs and internal unit operation costs; the internal unit comprises a fuel unit, a gas unit, a wind turbine and a photovoltaic unit, the running cost of the internal unit is the running cost of the fuel unit and the running cost of the gas unit, and the output of the photovoltaic unit and the wind turbine unit does not account for the power generation cost.
3. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 1, wherein the constraint conditions include an electric quantity balance constraint, a renewable energy source electric power constraint, a decision variable non-negative constraint and an electricity selling company internal unit output constraint, wherein the electric quantity balance constraint enables electric power generated by a generator to be balanced with supplied user loads in real time, the renewable energy source electric power constraint ensures the output of a renewable energy source unit, the decision variable non-negative constraint enables the load and the electricity purchasing amount of the electricity selling company to meet practical significance, and the electricity selling company internal unit output constraint enables the internal unit to reach a proper working state.
4. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 1, wherein step S2 adopts a Pymoo solver to solve an electricity purchasing strategy deterministic optimization model of an electricity selling company.
5. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 1, wherein the specific process of constructing the entropy weight self-adaptive IGDT model is as follows:
s3.1, under the constraint of uncertain parameter electricity price and uncertain parameter load, constructing an IGDT model containing uncertain variables;
and S3.2, setting the weight of the uncertainty variable in the IGDT model by adopting an entropy weight method, and taking the target function and the uncertainty variable of the established optimization model for the electricity purchasing strategy of the electricity selling company as the optimization targets to obtain the entropy weight self-adaptive IGDT model.
6. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 5, wherein the IGDT model expression containing the uncertainty variable is:
（1）；
in the method, in the process of the invention,α 1 andα 2 an uncertainty set radius for electricity price and load, respectively;fis an objective function;xis a decision variable;u 1 actual value for the 1 st uncertainty parameterI.e. the actual value of the electricity price;u 2 an actual value for the 2 nd uncertainty parameter, i.e. the load;f cr a threshold value that is an objective function;βis a deviation factor;f 0 is the baseline value of the objective function;is a set of uncertain parameters, +.>Is a predicted value of electricity price; />Is a predicted value of the load;handgbalance and unbalance constraints, respectively.
7. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 6, wherein the expression of the entropy weight self-adaptive IGDT model is:
（2）；
wherein:αradius for the uncertainty set;the weight occupied by the electricity price is the electricity price entropy weight; />The weight of the load is the load entropy weight.
8. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 1, wherein in step S4, a heuristic algorithm in a Pymoo solver is adopted for solving, so as to obtain electricity purchasing cost of an electricity selling company and electricity purchasing quantity of the electricity selling company.
9. An electricity purchasing optimization device based on an entropy weight self-adaptive IGDT, comprising a memory and one or more processors, wherein executable codes are stored in the memory, and the electricity purchasing optimization device based on the entropy weight self-adaptive IGDT is characterized in that the one or more processors are used for realizing the electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to any one of claims 1 to 8 when executing the executable codes.
10. A computer readable storage medium having stored thereon a program which, when executed by a processor, is adapted to carry out the electricity purchasing optimization method based on the entropy weight adaptive IGDT according to any one of claims 1 to 8.
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