CN117494909B - 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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Abstract
The application 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 application 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 the electricity purchasing strategy determination type optimization model of the electricity selling company, and the 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）；
Wherein, alpha 1 and alpha 2 are the uncertain set radii of electricity price and load respectively; f is an objective function; x is a decision variable; u 1 is the actual value of the 1 st uncertain parameter, i.e. the actual value of electricity price; u 2 is the actual value of the 2 nd uncertainty parameter, i.e. the load; f cr is the threshold of the objective function; beta is a deviation factor, also known as a risk aversion coefficient, representing the extent 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; /(I)Is a predicted value of the load; h and g are balance and imbalance constraints, respectively.
Further, the adaptive IGDT model expression of the entropy weight is:
（2）
wherein: alpha is the radius of the uncertainty set; the weight occupied by the electricity price is the electricity price entropy weight; /(I) The weight of the load is the load entropy weight.
Further, a heuristic algorithm in a 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）；
Wherein, C b is the spot market electricity purchasing cost; c p is the deviation checking cost; c Fuel is the running cost of the fuel unit; c Gas is the running cost of the gas unit; f is an objective function;
the spot market electricity purchasing cost of the electricity selling company is as follows:
（4）；
Wherein: For the electricity price purchased by 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; θ is a core-free coefficient, 2% is taken; delta t is the time interval, and 1h is taken; /(I) The electric quantity is checked for the deviation of the time t of the electricity selling company; l t is the power load at time t.
In the application, the electricity selling company comprises 4 types of units, namely a fuel oil unit, a gas unit, a photovoltaic generator unit and a wind power generator unit. 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 unit and the gas unit are respectively represented by C Fuel、CGas:
（6）；
Wherein N Fuel is the number of fuel units; n Gas is the number of gas units; 、/>、/> The running cost coefficient of the ith fuel unit; /(I) 、/>The operation cost coefficient of the ith gas unit; p i,t,f is the output of the ith fuel unit at the time t; t is the electricity purchasing time; and P m,t,g is the output of the mth gas unit at the time t.
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 is the number of wind turbines; n v is the number of photovoltaic power stations; p r,t,wmax is the maximum output power of the wind turbine at the moment t; p r,t,w is the output power of the r-th wind turbine at the time t; p s,t,vmax is the maximum output power of the photovoltaic unit at the moment t; p s,t,v is the output power of the s-th photovoltaic unit at the time t, namely the output power of the s-th photovoltaic unit at the time t.
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）；
Wherein delta is the power proportion of renewable energy sources;
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 minimum power generation capacity of the ith fuel unit; p i,t,fmax is the maximum power generation capacity of the ith fuel unit; is the maximum increasing force of the ith fuel unit; p i,t-1,f is the output of the ith fuel unit at the time t-1; /(I) Is the maximum drop-out force of the ith fuel unit.
Gas turbine unit:
（11）；
wherein: p m,t,gmin is the minimum power generation capacity of the mth gas unit; p m,t,gmax is the maximum power generation capacity of the mth gas turbine unit; Is the maximum increasing force of the mth gas unit; p m,t-1,g is the output of the mth fuel unit at the time t-1; /(I) Maximum drop-out force of the mth gas unit.
Wind turbine generator system:
（12）；
Wherein: p r,t,wmax is the maximum output power of the r-th wind turbine at the t moment; p r,t,w is the output power of the r-th wind turbine at the time t.
Photovoltaic unit:
（13）；
Wherein P s,t,vmax is the maximum output power of the photovoltaic unit at the moment t.
S2, solving a power purchase strategy fixed optimization model of the power selling company to obtain an optimal solution; and adopting Pymoo solver to solve the electricity purchasing strategy determination optimization model of the electricity selling company. 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）；
Wherein, alpha 1 and alpha 2 are the uncertain set radii of electricity price and load respectively; f is an objective function; x is a decision variable; u 1 is the actual value of the uncertain parameter electricity price; u 2 is the actual value of the uncertain parameter load; f cr is the threshold of the objective function; beta is a deviation factor, also known as a risk aversion coefficient, representing the extent 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; /(I)Is a predicted value of the load; h and g are balance and imbalance 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; α is 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: alpha is the radius of the uncertainty set; the weight occupied by the electricity price is the electricity price entropy weight; /(I) The weight of the load is the load entropy weight.
The derivation process of formula (2) is as follows:
（16）；
Wherein: The value of the kth uncertain parameter at the t moment is normalized; /(I) The original value of the parameter is not determined for the kth time t; /(I)To/>The original values of the k uncertain parameters are respectively from 1 st to T th time; /(I){. Is the maximum value of the original values of k uncertain parameters from 1 st to T moment; /(I){. Is the minimum value of the original values of k uncertain parameters from 1 st to T moment; /(I)The specific gravity of the kth uncertain parameter at the t moment accounting for the sum of the uncertain parameters at each moment; h k is the kth uncertainty parameter value at time t after weighted and logarithmic processing; /(I)The weight occupied by the kth uncertain parameter is entropy weight; k is the total number of uncertainty variables.
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: α is the radius of the uncertainty set; alpha k is the uncertainty of the kth uncertainty parameter.
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 uncertainty set of the electricity price and the load, 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 a 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, EAIGDT has the smallest total cost of the electric company, and compared with the definite model, the total cost is reduced by 18.13%. 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 the electricity selling company. And compared with the IGDT model, EAIGDT has the total cost reduced by 17.71 percent. 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 EAIGDT models 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 an 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 (5)
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; 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; 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 to be balanced with supplied user load 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 load and electricity purchasing quantity of the electricity selling company to meet practical significance, and the internal unit output constraint of the electricity selling company enables the internal unit to reach a proper working state;
the electricity purchasing cost of an electricity selling company is minimized as an objective function:
（3）；
Wherein, C b is the spot market electricity purchasing cost; c p is the deviation checking cost; c Fuel is the running cost of the fuel unit; c Gas is the running cost of the gas unit; f is an objective function;
the spot market electricity purchasing cost of the electricity selling company is as follows:
（4）；
Wherein: For the electricity price purchased by electricity-selling companies,/> The electricity quantity is purchased for an electricity selling company;
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; θ is a core-free coefficient, 2% is taken; delta t is the time interval, and 1h is taken; The electric quantity is checked for the deviation of the time t of the electricity selling company; l t is the power load at time t;
the running cost functions of the fuel unit and the gas unit are respectively represented by C Fuel、CGas:
（6）；
Wherein N Fuel is the number of fuel units; n Gas is the number of gas units; 、/>、/> The running cost coefficient of the ith fuel unit; /(I) 、/>The operation cost coefficient of the ith gas unit; p i,t,f is the output of the ith fuel unit at the time t; t is the electricity purchasing time; p m,t,g is the output of the mth gas unit at the time t;
The electric quantity balance constraint is as follows:
（7）；
wherein: n w is the number of wind turbines; n v is the number of photovoltaic power stations; p r,t,wmax is the maximum output power of the wind turbine at the moment t; p r,t,w is the output power of the r-th wind turbine at the time t; p s,t,vmax is the maximum output power of the photovoltaic unit at the moment t; p s,t,v is the output power of the s-th photovoltaic unit at the time t, namely the output power of the s-th photovoltaic unit at the time t;
Renewable energy power constraints are:
（8）；
Wherein delta is the power proportion of renewable energy sources;
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 minimum power generation capacity of the ith fuel unit; p i,t,fmax is the maximum power generation capacity of the ith fuel unit; is the maximum increasing force of the ith fuel unit; p i,t-1,f is the output of the ith fuel unit at the time t-1; is the maximum drop-out force of the ith fuel unit;
Gas turbine unit:
（11）；
wherein: p m,t,gmin is the minimum power generation capacity of the mth gas unit; p m,t,gmax is the maximum power generation capacity of the mth gas turbine unit; is the maximum increasing force of the mth gas unit; p m,t-1,g is the output of the mth fuel unit at the time t-1; maximum drop-out force of the mth gas unit;
wind turbine generator system:
（12）；
Wherein: p r,t,wmax is the maximum output power of the r-th wind turbine at the t moment; p r,t,w is the output power of the r-th wind turbine at the t moment;
photovoltaic unit:
（13）；
wherein P s,t,vmax is the maximum output power of the photovoltaic unit at the moment t;
S2, adopting Pymoo solver to solve the electricity purchasing strategy fixed optimization model of the electricity 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; the specific process for 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;
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 confirmed optimization model of the electricity purchasing strategy of the electricity selling company as optimization targets to obtain an entropy weight self-adaptive IGDT model;
And S4, solving an entropy weight self-adaptive IGDT model by adopting a heuristic algorithm in a Pymoo solver to obtain electricity purchasing cost of an 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 expression of the IGDT model containing the uncertainty variable is:
（1）；
Wherein, alpha 1 and alpha 2 are the uncertain set radii of electricity price and load respectively; f is an objective function; x is a decision variable; u 1 is the actual value of the 1 st uncertain parameter, i.e. the actual value of electricity price; u 2 is the actual value of the 2 nd uncertainty parameter, i.e. the load; f cr is the threshold of the objective function; beta is 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; /(I)Is a predicted value of the load; h and g are balance and imbalance constraints, respectively.
3. The electricity purchasing optimization method based on the entropy weight self-adaptive IGDT according to claim 1, wherein the expression of the entropy weight self-adaptive IGDT model is:
（2）；
Wherein: alpha is the radius of the uncertainty set; The weight occupied by the electricity price is the electricity price entropy weight; /(I) The weight of the load is the load entropy weight.
4. 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 3 when executing the executable codes.
5. 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 as claimed in any one of claims 1 to 3.
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