CN117691615A - Micro-grid client side flexible load resource interactive operation optimization method and equipment - Google Patents
Micro-grid client side flexible load resource interactive operation optimization method and equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a micro-grid client side flexible load resource interactive operation optimization method and equipment. The method comprises the steps of constructing a client-side flexible load model; constructing an objective function and constraint conditions of the client-side flexible load model; determining the weight value of each objective function by adopting an analytic hierarchy process; and solving the client-side flexible load model by using an improved gray wolf optimization algorithm to obtain an optimized wind power output value, an optimized photovoltaic power output value and an optimized power value of the pumped storage unit. In this way, the problems of load fluctuation, time variability, randomness, insufficient power grid depth peak regulation resources and deficient interactive operation of adjustable load resources caused by the access of flexible loads can be solved, so that the client-side flexible load resources are mutually interacted, cooperatively optimized and effectively complemented, and the peak clipping and valley filling purposes are achieved.
Description
Technical Field
The invention relates to the technical field of client-side flexible load optimization, in particular to a micro-grid client-side flexible load resource interactive operation optimization method and equipment.
Background
In order to provide deep excavation demand response potential on the load side, the regulating capability of the load side to new energy sources is improved, and flexible loads such as photovoltaic, wind power, pumped storage, residential air conditioning load, industrial load, commercial load and the like are connected. And when the flexible loads are accessed, larger load fluctuation, time variability, randomness and insufficient power grid depth peak regulation resources are brought, and the interactive operation of the load resources can be adjusted to be deficient. The electricity consumption behavior of the client side is more complex, a multidimensional coupling relation is formed in the aspects of environment, society, time, economy and the like, and accurate client side load characteristics and distribution rules are difficult to obtain.
Disclosure of Invention
According to the embodiment of the invention, an interactive operation optimization scheme for flexible load resources at a micro-grid client side is provided. The scheme solves the problems of insufficient load fluctuation, time variability and randomness, insufficient power grid depth peak regulation resources and deficient adjustable load resource interactive operation caused by the access of flexible load.
In a first aspect of the invention, a method for optimizing flexible load resource interactive operation of a micro-grid client side is provided. The method comprises the following steps:
constructing a client side flexible load model;
Constructing an objective function and constraint conditions of the client-side flexible load model;
determining the weight value of each objective function by adopting an analytic hierarchy process;
and solving the client-side flexible load model by using an improved gray wolf optimization algorithm to obtain an optimized wind power output value, an optimized photovoltaic power output value and an optimized power value of the pumped storage unit.
Further, the client-side flexible load model includes an industrial load model and a non-industrial load model; the industrial load model comprises a steel rolling process load model and a refining furnace load model; the non-industrial load model comprises an air conditioning load model and an electric heating load model;
the steel rolling process load model comprises the following components:
wherein,the power required by the rolling process is completed for n k-type billets; />The power required by the rough rolling process is completed for the 1 st k-type billet; />The power required by the rough rolling process is completed for the i-th k-type billet; />The time interval between the i-th k-type billet entering the rough rolling process and the 1-th k-type billet entering the rough rolling process; />The power required by finishing the finish rolling process for the 1 st k-type billet; />The power required by finishing the finish rolling process for the ith k-type billet; />The time interval between the i-th k-type billet entering the finish rolling process and the 1-th k-type billet entering the finish rolling process; t is t roll The time of rolling steel;
the refining furnace load model comprises:
wherein P is eaf (t eaf ) Is the electric arc furnace power; t is t eaf The working time of the arc furnace is; t is t on The starting time of the arc furnace is; Δt (delta t) up The temperature rise time is the temperature rise time; Δt (delta t) down The temperature is the time of temperature decrease; t is t off The closing time of the arc furnace is; p (P) rated The total power consumed by the arc furnace; epsilon (t) eaf ) Is the power change rate of the arc furnace;
the air conditioner load model includes:
wherein,the electric power of the air conditioner; />The energy efficiency ratio of the air conditioner is; />Cooling capacity/heating capacity of an air conditioner in a room; n (N) ac The number of the air conditioning units participating in micro-grid regulation is the number of the air conditioning units participating in micro-grid regulation; zeta type i (t ac ) When zeta is the running state of the ith air conditioning unit i (t ac ) When=1, it indicates that the i-th air conditioner is turned on, and ζ is i (t ac ) =0, indicating that the i-th air conditioner is turned off; p (P) ac,all (t) represents the total load of all air conditioners;
the electric heating load model includes:
wherein P is dcn,l Load power of the first electric heating cluster; lambda (lambda) l (t dcn ) The cluster running state is; n (N) dcn The number of the electric heating clusters; p (P) dcn (t dcn ) Is N dcn And the total load power of the electric heating clusters.
Further, the objective function of the client-side flexible load model comprises an economic benefit objective function, a carbon emission reduction objective function and a load loss rate objective function;
the economic benefit objective function includes:
Wherein,the power of the wind power at the ith moment is the internet power of the wind power; />The power of the photovoltaic network is the photovoltaic network power at the ith moment; />The internet surfing power for pumping energy for the ith moment; />Pumping power for pumping energy for the ith moment; />The power of the pump storage unit at the ith moment; />The air quantity is discarded; />Is the amount of light discarded; c (C) i The online electricity price is obtained; />The electricity price for pumping water; c (C) r Punishment of electricity price for wind and light discarding; mu (mu) gy The time-sharing electricity price is the industrial load; mu (mu) fgy Time-of-use electricity prices for non-industrial loads; f (F) 1 Is economic benefit; t (T) 1 Is the total number of hours per day;
the carbon emission reduction objective function includes:
wherein,CS, the amount of carbon dioxide produced for combustion of coal coal Coal consumption converted into thermal power generation for clean energy power generation; />Total electric quantity for combined system power generation grid connection, t 2 Is the length of time; maxF 2 Maximum carbon emission reduction, F 2 Reducing the discharge amount of carbon; t (T) 2 The number of the wind power generation sets, the photovoltaic sets and the pumped storage sets is the number of the wind power generation sets, the photovoltaic sets and the pumped storage sets;
the load loss rate objective function includes:
wherein Z represents load type, LPSP Z,t P is the loss rate of Z load at t time Z,LS,t For the actual supply of the micro-grid Z-load at time t, P Z,Load,t The demand of the load of the micro-grid at the moment Z; lambda is the load loss rate.
Further, constraint conditions of the client-side flexible load model comprise micro-grid reliability constraint, output power balance constraint, grid receiving power constraint, positive and negative rotation standby constraint, pumped storage output power constraint and reservoir capacity constraint;
The microgrid reliability constraints include:
wherein,and->Respectively the minimum value and the maximum value of the load loss rate;
the output power balancing constraint includes:
wherein,the predicted force of wind power at the ith moment is represented; />Representing the predicted output of the photovoltaic at the ith moment;
the grid received power constraint includes:
wherein,the predicted load value at the i-th moment; omega is the maximum allowable deviation between the internet power and the load;
the positive and negative rotation reserve constraint includes:
wherein,representing the maximum output power of the pumped storage at the ith moment; />Representing the minimum output power of pumped storage at the ith moment; />The positive rotation standby requirement at the i-th moment; />The demand is the negative rotation standby at the i-th moment;
the pumped storage output power constraint comprises:
wherein P is pmax Is the maximum value of pumping power; p (P) homax Is the maximum value of the generated power; η (eta) p Pumping efficiency of the water pump; η (eta) h The power generation efficiency of the water turbine unit is;is the lower reservoir capacity; t is t d For the duration time of power generation of the lower reservoir turbine unit, t u Pumping water for the upper reservoir water pump for a long time; />Is the upper reservoir capacity;
the reservoir capacity constraint includes:
wherein,is the minimum value of the upper reservoir capacity; />Is the maximum value of the upper reservoir capacity; / >Is the minimum value of the lower reservoir capacity; />Is the maximum value of the reservoir capacity; t is t u Pumping water for the upper reservoir for a duration; t is t d The power generation duration time is the lower reservoir turbine unit.
Further, the solving the client-side flexible load model with the improved wolf optimization algorithm includes:
s201, carrying out initial assignment on a gray wolf population, a nonlinear update factor, an A coefficient vector and a C coefficient vector, and importing wind power generation power, photovoltaic power generation power, load power and maximum iteration times; carrying out initial assignment on the wind power output value, the photovoltaic power output value and the power of the four pumped storage units;
s202, judging whether assignment of wind power output values, photovoltaic power output values and power of four pumped storage units meets constraint conditions, and if yes, normalizing an objective function of the client side flexible load model to obtain a multi-objective function normalized value; otherwise, the wind power output value, the photovoltaic power output value and the assignment of the power of the four pumped storage units are adjusted to meet constraint conditions, and S202 is executed again;
s203, calculating optimal values, suboptimal values and general values of the multi-objective function normalization values searched by all the wolf individuals, corresponding to three wolf individuals, and storing the final positions of the three wolf individuals;
S204, updating the nonlinear updating factor, the A coefficient vector and the C coefficient vector;
and S205, updating the positions of other wolf individuals except the three wolf individuals, returning to the execution S203, and updating the fitness and the positions of the three wolf individuals until the maximum iteration times are reached, and outputting the optimized wind power output value, the photovoltaic power output value and the power of the four pumped storage units.
Further, the initial assignment of the sirius population through random variable Tent chaotic mapping comprises the following steps:
wherein alpha is 1 As a demarcation point parameter, alpha 1 ＝0.499；x i Is a randomly generated number.
Further, the calculating the optimal value, the suboptimal value and the general value of the multi-objective function normalization value searched by all the wolf individuals corresponds to three wolf individuals, and the storing the final positions of the three wolf individuals includes:
wherein,representing the distance between other individuals in the wolf group and alpha wolves; />Representing the distance between other individuals in the wolf group and beta wolves; />Representing the distance between other individuals in the wolf group and delta wolves; />Representing the current position of alpha wolf; />Representing the current position of beta wolf; />Representing the current position of delta wolf; />The position of the current gray wolf individual; / >Representing the positions of other individuals of the population, which are influenced by alpha wolves and need to be adjusted; />Representing the positions of other individuals of the population, which are influenced by beta wolves and need to be adjusted; />Representing the positions of other individuals of the population, which are affected by delta wolf and need to be adjusted; />Representing final position vectors of three wolf individuals; />C coefficient vector for alpha wolf; />C coefficient vector for βwolf; />A C coefficient vector that is δwolf; />A coefficient vector of alpha wolf; />An a coefficient vector being beta wolf; />An a coefficient vector that is δwolf; />Is->Maximum value of>Is->Minimum value->Representing final position vectors of three wolf individuals; θ 1 Representing the confidence of alpha wolf; θ 2 Representing the confidence of beta wolf; θ 3 Representing the confidence of δwolf.
Further, the updating the nonlinear update factor, the a coefficient vector and the C coefficient vector includes:
wherein,is a coefficient vector A; />Is a C coefficient vector; />Is a nonlinear update factor; />For the first random parameter, +.>For the second random parameter, +.>Q is the current iteration number, Q max Representing the maximum number of iterations of the gray wolf population.
In a second aspect of the invention, a micro-grid client side flexible load resource interactive operation optimization device is provided. The device comprises:
The first construction module is used for constructing a client-side flexible load model;
the second construction module is used for constructing an objective function and constraint conditions of the client-side flexible load model;
the weight value determining module is used for determining the weight value of each objective function by adopting an analytic hierarchy process;
and the calculation module is used for solving the client-side flexible load model by utilizing an improved gray wolf optimization algorithm to obtain an optimized wind power output value, an optimized photovoltaic power output value and an optimized power value of the pumped storage unit.
In a third aspect of the invention, an electronic device is provided. At least one processor of the electronic device; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the invention.
Through the scheme, the client side flexible load resources can be interconnected and interacted, cooperatively optimized and effectively complemented, so that the peak clipping and valley filling purposes are achieved.
It should be understood that the description in this summary is not intended to limit the critical or essential features of the embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the present invention will become apparent from the description that follows.
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The above and other features, advantages and aspects of embodiments of the present invention will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
FIG. 1 shows a flow chart of a micro grid client side flexible load resource interactive operation optimization method according to an embodiment of the invention;
FIG. 2 shows a flow chart for solving the client-side flexible load model using a modified gray wolf optimization algorithm in accordance with an embodiment of the present invention;
FIG. 3 shows a block diagram of a micro grid client side flexible load resource interactive operation optimization device according to an embodiment of the invention;
FIG. 4 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the invention;
wherein 400 is an electronic device, 401 is a computing unit, 402 is a ROM, 403 is a RAM, 404 is a bus, 405 is an I/O interface, 406 is an input unit, 407 is an output unit, 408 is a storage unit, 409 is a communication unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Fig. 1 shows a flowchart of a micro-grid client side flexible load resource interactive operation optimization method according to an embodiment of the invention.
The method comprises the following steps:
s101, constructing a client side flexible load model.
In this embodiment, the client-side flexible load model includes an industrial load model and a non-industrial load model; the industrial load model comprises a steel rolling process load model and a refining furnace load model.
Specifically, the steel rolling process load model includes:
wherein,the power required by the rolling process is completed for n k-type billets; />The power required by the rough rolling process is completed for the 1 st k-type billet; />The power required by the rough rolling process is completed for the i-th k-type billet; />The time interval between the i-th k-type billet entering the rough rolling process and the 1-th k-type billet entering the rough rolling process; />The power required by finishing the finish rolling process for the 1 st k-type billet; / >The power required by finishing the finish rolling process for the ith k-type billet; />The time interval between the i-th k-type billet entering the finish rolling process and the 1-th k-type billet entering the finish rolling process; t is t roll The time of rolling steel;
specifically, a refining furnace load model includes:
wherein P is eaf (t eaf ) Is the electric arc furnace power; t is t eaf The working time of the arc furnace is; t is t on The starting time of the arc furnace is; Δt (delta t) up The temperature rise time is the temperature rise time; Δt (delta t) down The temperature is the time of temperature decrease; t is t off The closing time of the arc furnace is; p (P) rated The total power consumed by the arc furnace; epsilon (t) eaf ) For the rate of change of the power of the electric arc furnace, for the same electric arc furnace,Δt in the formula (3) can be determined according to practical circumstances up ,Δt down ,P rated ,ε(t eaf ) When the temperature is set to be constant, for an electric arc furnace, only a time sequence t of starting and stopping in a certain time period is needed to be provided on And t off The preparation method is finished;
in the present embodiment, the non-industrial load generally refers to residential air conditioning load and commercial electric heating load; the non-industrial load model includes an air conditioning load model and an electric heating load model.
The specific gravity of the air conditioner load is larger in resident load, the load reduction capability of a single air conditioner in a micro-grid is limited, and the resident air conditioner load has larger dispersivity and higher uncertainty. Therefore, the load aggregation business is required to perform regional integration centralized control on the residential air conditioning load, so that the air conditioning unit clusters participate in the allocation of the micro-grid, and the air conditioning load control capacity is enlarged.
Specifically, the air conditioning load model includes:
wherein,the electric power of the air conditioner; />The energy efficiency ratio of the air conditioner is; />Cooling capacity/heating capacity of an air conditioner in a room; n (N) ac Is prepared from ginsengThe number of air conditioning units regulated and controlled by the micro-grid; zeta type i (t ac ) When zeta is the running state of the ith air conditioning unit i (t ac ) When=1, it indicates that the i-th air conditioner is turned on, and ζ is i (t ac ) =0, indicating that the i-th air conditioner is turned off; p (P) ac,all (t) represents the total load of all air conditioners;
when a large number of commercial district electric heating users with different capacities and powers are connected to the micro-grid, in order to ensure that the micro-grid operators perform cluster unified control on the electric heating load groups, the electric heating load groups are subjected to centralized processing, so that the electric heating specification parameters after the centralized processing are similar as much as possible.
The electric heating load model includes:
wherein P is dcn,l Load power of the first electric heating cluster; lambda (lambda) l (t dcn ) The cluster running state is; n (N) dcn The number of the electric heating clusters; p (P) dcn (t dcn ) Is N dcn And the total load power of the electric heating clusters.
S102, constructing an objective function and constraint conditions of the client-side flexible load model.
In this embodiment, the objective functions of the client-side flexible load model include an economic benefit objective function, a carbon reduction displacement objective function, and a load loss rate objective function.
The economic benefit objective function considers the maximization of the economic benefit of the grid-connected operation of the combined system, and also considers the punishment cost of wind and light discarding, but does not consider the start-stop cost of the pumped storage unit, and specifically comprises the following steps:
wherein,the power of the wind power at the ith moment is the internet power of the wind power; />The power of the photovoltaic network is the photovoltaic network power at the ith moment; />The internet surfing power for pumping energy for the ith moment; />Pumping power for pumping energy for the ith moment; />The power of the pump storage unit at the ith moment; />The air quantity is discarded; />Is the amount of light discarded; c (C) i The online electricity price is obtained; />The electricity price for pumping water; c (C) r Punishment of electricity price for wind and light discarding; mu (mu) gy The time-sharing electricity price is the industrial load; mu (mu) fgy Time-of-use electricity prices for non-industrial loads; f (F) 1 Is economic benefit; t (T) 1 For the total hours of a day, i.e. T 1 ＝24。
The carbon emission reduction objective function considers a carbon emission reduction maximization objective of replacing a thermal generator set with a wind-light-pumped storage combined power generation system, and comprises the following steps:
wherein,CS, the amount of carbon dioxide produced for combustion of coal coal Coal consumption converted into thermal power generation for clean energy power generation; />Total electric quantity for combined system power generation grid connection, t 2 For a length of time, for example 1 hour; max F 2 Maximum carbon emission reduction, F 2 Reducing the discharge amount of carbon; t (T) 2 The number of the wind power generation sets, the photovoltaic sets and the pumped storage sets is the number of the wind power generation sets, the photovoltaic sets and the pumped storage sets; the number of the wind power units, the photovoltaic units and the pumped storage units is equal.
The electric power is used as a main carrier of the micro-grid, and the power supply reliability is an important index for measuring the reliability of the micro-grid. In order to improve the reliability of the micro-grid, the load loss rate (Loss ofpower supply probability, LPSP) is used as a standard for measuring the reliability of the micro-grid, and the smaller the LPSP is, the higher the reliability of the micro-grid is, so that the minimization of the load loss rate is targeted. The load loss rate objective function includes:
wherein Z represents load type, LPSP Z,t P is the loss rate of Z load at t time Z,LS,t For the actual supply of the micro-grid Z-load at time t, P Z,Load,t The demand of the load of the micro-grid at the moment Z; lambda is the load loss rate.
As an embodiment of the present invention, the method further comprises: normalizing the objective function of the client-side flexible load model, including:
minF＝ω 1 (F 1 /F 1 wp )+ω 2 (F 2 /F 2 wp )+ω 3 (λ/λ′ wp )
wherein F is multi-targetNormalizing the function;the total income of the wind-solar power generation system during joint operation is obtained; />Carbon emission reduction standard quantity when the wind-solar power generation system is operated in a combined mode; lambda's' wp The load loss rate reference value is used for the joint operation of the wind-solar power generation system; omega 1 ,ω 2 ,ω 3 Weight values assigned to the three sub-targets.
In this embodiment, the constraint conditions of the client-side flexible load model include a micro-grid reliability constraint, an output power balance constraint, a grid receiving power constraint, a positive and negative rotation standby constraint, a pumped storage output power constraint and a reservoir capacity constraint.
The microgrid reliability constraints include:
wherein,and->Respectively the minimum value and the maximum value of the load loss rate; the load loss rate of the power grid is an important index for representing the power failure probability of equipment in a period of time and reflecting the reliability of the power grid.
The output power balancing constraint includes:
wherein,the predicted force of wind power at the ith moment is represented; />Representing the predicted output of the photovoltaic at the ith moment;
the grid received power constraint includes:
wherein,the predicted load value at the i-th moment; ω is the maximum allowable deviation between the internet power and the load, and the maximum allowable deviation is ω=5%;
the positive and negative rotation reserve constraint includes:
wherein,representing the maximum output power of the pumped storage at the ith moment; />Representing the minimum output power of pumped storage at the ith moment; />The positive rotation standby requirement at the i-th moment; />The demand is the negative rotation standby at the i-th moment; the technical specification of the configuration of the emergency capacity of the electric power system indicates that, The size of the load backup capacity is generally 2% -5% of the maximum load, the large power system adopts a smaller value, the small power system adopts a larger value, and in the embodiment, the maximum load is 5% according to the standard of the small power system.
The pumped storage output power constraint comprises:
wherein P is pmax Is the maximum value of pumping power; p (P) homax Is the maximum value of the generated power; η (eta) p Pumping efficiency of the water pump; η (eta) h The power generation efficiency of the water turbine unit is;is the lower reservoir capacity; t is t d For the duration time of power generation of the lower reservoir turbine unit, t u Pumping water for the upper reservoir water pump for a long time; />Is the upper reservoir capacity;
the reservoir capacity constraint includes:
wherein,is the minimum value of the upper reservoir capacity; />Is the maximum value of the upper reservoir capacity; />Is the minimum value of the lower reservoir capacity; />Is the maximum value of the reservoir capacity; t is t u Pumping water for the upper reservoir for a duration; t is t d The power generation duration time is the lower reservoir turbine unit.
S103, determining the weight value of each objective function by adopting an analytic hierarchy process.
ω 1 ,ω 2 ,ω 3 The values of (2) are determined by analytic hierarchy process (Analytical Hierarchy Process, AHP), and the main steps are as follows:
firstly, dividing an objective function of a client side flexible load model into an objective class and a criterion class;
And secondly, weighting the elements according to the importance relation of the elements, and obtaining a judgment matrix by quantization according to the scale division and the weight of the elements.
Specifically, the elements are compared with each other, and then assigned according to the importance of each element using a scale of 1-9.
The 1-9 scale is defined as follows: 1 represents that two elements have the same importance; 3 represents that one element is slightly less important than the other element than two elements; 5 represents that one element is more important than the other element than two elements; 7 represents that two elements are less important than one another; 9 represents that one element is very important compared to the other element; 2. 4, 6, 8 represent the median values of the above-mentioned adjacent judgments.
According to the scale definition, the expert performs scoring, and the judgment matrix obtained after quantization is represented by the following formula:
/>
wherein b 11 ～b 33 On a 1-9 scale.
Thirdly, calculating square roots of all element products of each row of the judgment matrix to obtainThe following are provided:
finally, toAnd normalizing to obtain weight values corresponding to the three objective functions.
Obtaining omega 1 ,ω 2 ,ω 3 Is a value of (2).
And S104, solving the client-side flexible load model by using an improved gray wolf optimization algorithm to obtain an optimized wind power output value, an optimized photovoltaic power output value and an optimized power value of the pumped storage unit.
In this embodiment, the improved gray wolf optimization algorithm comprises:
a) Wolf group grading
The gray wolves have different grades in the population, alpha wolves are decision makers of the wolves, and represent the optimal solution of the algorithm; the beta wolf is responsible for assisting the alpha wolf in making decisions and represents the suboptimal solution of the algorithm; delta wolf listens to alpha wolf and beta wolf commands, representing a general solution to the algorithm; omega wolf is the bottom wolf and represents a candidate solution for the algorithm. The adaptability of the upper wolves is not good, and the upper wolves are replaced by the lower wolves in turn.
In the traditional gray wolf optimization algorithm, an initial population is randomly generated, and the quality of the generated population cannot be guaranteed. The Tent chaotic map is suitable for population initialization, has the characteristics of strong regularity, randomness and ergodic property, can provide more diversified initial solutions for solving the traditional gray wolf optimization algorithm, and is favorable for solving the gray wolf algorithm optimization. Therefore, the invention adopts Tent chaotic mapping to generate the initial population of the wolf, and adopts random variable Tent chaotic mapping as shown in the following expression because of the unstable condition of basic Tent chaotic mapping:
wherein alpha is 1 As a demarcation point parameter, alpha 1 ＝0.499；x i Is a randomly generated number.
b) Surrounding prey
During the prey of wolves, their behavior is defined as follows:
where Q represents the number of iterations of the current population,and->Representing the position of the prey and the position of the wolf, respectively.
The a coefficient vector and the C coefficient vector are as follows:
wherein,is a coefficient vector A; />Is a C coefficient vector; />Is a nonlinear update factor; />For the first random parameter, +.>For the second random parameter, +.>Q is the current iteration number, Q max Representing the maximum number of iterations of the gray wolf population.
In the traditional wolf algorithm, the convergence factorThe method is linearly decreasing, and controls the wolves to search or attack the prey, and the method has the defects that the key point of the early search of the algorithm is not clear, and the later stage is easy to sink into the local search, so that the method is not beneficial to searching the globally optimal solution. The invention introduces nonlinear update factors to improve the traditional gray wolf optimization algorithm, and the nonlinear function is in the initial stage of iteration +.>The range of the value of the number is wider, and the range of the number which can be used for searching is wider; whereas in the late stage of the iteration, +.>The method is small, is favorable for local search of the algorithm, and accelerates the convergence rate of the algorithm. Convergence factor is determined by the following method>Nonlinear adjustment is performed.
When (when)When the candidate Jie Yuan is the best solution, a global search is performed. When- >And when the candidate solution approaches the optimal solution, carrying out local search. In addition, a->Indicating that the position of the wolf has a large influence weight on the prey, otherwise, indicating that the influence weight is small. />The method has the function of providing global searching of the population in the decision space in the whole iterative process and avoiding the optimization process from being trapped into local optimum.
c) Prey
Wolves are continually approaching the prey under the alpha, beta and delta wolves bands, during which their position is permanently dynamically changing until the hunting is successful. The main difference between GWO and other SI algorithms is their social leadership hierarchy, which can be of considerable value for the improvement of GWO search capability. The higher the grade of the wolf, the deeper the understanding of the prey and the stronger the leader ability, which plays a vital role in group hunting during the search. However, in the original GWO hunting (see formula below), the weight coefficients of the three top wolves are the same, which is clearly contradictory to the ranking system of the real wolves. The process of the traditional wolf algorithm can be expressed by the following formula:
/>
wherein,representing the distance between other individuals in the wolf group and alpha wolves; />Representing the distance between other individuals in the wolf group and beta wolves; / >Representing the distance between other individuals in the wolf group and delta wolves; />Representing the current position of alpha wolf; />Representing the current position of beta wolf; />Representing the current position of delta wolf; />The position of the current gray wolf individual; />Representing the positions of other individuals of the population, which are influenced by alpha wolves and need to be adjusted; />Representing the positions of other individuals of the population, which are influenced by beta wolves and need to be adjusted; />Representing the positions of other individuals of the population, which are affected by delta wolf and need to be adjusted; />Representing final position vectors of three wolf individuals; />C coefficient vector for alpha wolf; />C coefficient vector for βwolf; />A C coefficient vector that is δwolf; />A coefficient vector of alpha wolf; />An a coefficient vector being beta wolf; />Is the a coefficient vector of δwolf.
The embodiment introduces dynamic confidence parameters to further improve the traditional wolf optimization algorithm, and enhances the different importance of the algorithm to different grades of wolf individuals. The position update of the basic wolf algorithm is to place alpha, beta and delta wolves equally in the process of searching for hunting, and update the positions of other wolves by means of evenly distributing weights, which does not show the different importance of different grades of wolf individuals. In order to solve the problem, a dynamic confidence adjustment strategy based on the position of the high-grade wolf is provided, and the specific expression is as follows:
Wherein,is->Maximum value of>Is->Minimum value +.>Representing final position vectors of three wolf individuals; θ 1 Representing the confidence of alpha wolf; θ 2 Representing the confidence of beta wolf; θ 3 Representing the confidence of δwolf. The positions of the three wolf individuals are the optimal value, the suboptimal value and the general value of the normalized value of the multi-objective function.
In this embodiment, as shown in fig. 2, the solving the client-side flexible load model by using the improved gray wolf optimization algorithm includes:
s201, carrying out initial assignment on the gray wolf population, the nonlinear update factor, the A coefficient vector and the C coefficient vector, and introducing wind power generation power P w, photovoltaic power generation P v Load power P load And a maximum number of iterations; for wind power output value P wo Photovoltaic power output value P vo Performing initial assignment on the power of the four pumped storage units;
s202, judging the output value P of the wind power wo Photovoltaic power output value P vo And whether the assignment of the power of the four pumped storage units meets constraint conditions or not, if so, normalizing the objective function of the client-side flexible load model to obtain a multiple objective function normalized value; otherwise, for wind power output value P wo Photovoltaic power output value P vo And the assignment of the power of the four pumped storage units is adjusted to meet the constraint condition, and S202 is executed again;
s203, calculating optimal values, suboptimal values and general values of the multi-objective function normalization values searched by all the wolf individuals, corresponding to three wolf individuals, and storing the final positions of the three wolf individuals;
s204, performing non-linear updating on the non-linear updating factorUpdating the A coefficient vector and the C coefficient vector;
s205, updating the positions of other wolf individuals except the three wolf individuals, returning to S203, updating the fitness and the positions of the three wolf individuals, judging whether the maximum iteration number is met, judging whether the values of alpha, beta and delta meet the model constraint conditions if the maximum iteration number is not met, and if the values of alpha, beta and delta meet the model constraint conditions, directly jumping to S204; if not, resetting the value according to the constraint condition and jumping to S204; if the maximum iteration number is reached, outputting the optimized wind power output value P wo Photovoltaic power output value P vo And the power of four pumped storage units.
In a specific embodiment 1, a micro-grid including a wind farm, a photovoltaic power station and a pumped storage power station is taken as a research object, an hour is taken as a time node, the power generation condition of the system for 24 hours a day is optimized, and a particle swarm optimization algorithm and an improved gray wolf optimization algorithm result are compared and analyzed. The four pumped storage units work simultaneously, and the working states of the four pumped storage units are set to be consistent, namely, pumping water or generating power simultaneously.
Table 1 comparison analysis of model objective function values before and after pumped storage of pumped storage power station parameters:
TABLE 2 comparison analysis of objective function values of models before and after pumped storage
The concrete examples of the wind-light-pumped storage system prove that the economic benefit and the carbon emission reduction of the system are increased, the average fluctuation rate of the output power of the system is reduced, and the feasibility of the flexible load resource interactive operation optimization method at the client side of the micro-grid is proved.
In specific embodiment 2, a micro-grid including a wind farm, a photovoltaic power station, a pumped storage power station, a residential air conditioning cluster load, a high energy consumption industrial cluster load and a commercial electric heating load is taken as a research object in China, and according to the existing prediction model, the prediction errors of the load and the wind power are gradually reduced along with the shortening of the time and meet the normal distribution, so that the daily and real-time prediction errors of the wind power are respectively 20% and 5%, and the daily and real-time prediction errors of the load are respectively 3% and 1%. The load electricity consumption is divided into 3 periods of peak, flat and valley, the peak periods are 11:00-12:00 and 17:00-21:00, the flat periods are 09:00-10:00 and 13:00-16:00, and the valley periods are 00:00-08:00 and 22:00-24:00. In order to verify the effectiveness of the invention, the operation condition of the system for 24 hours a day is optimized by taking the hours as a time node, and 4 scenes are set for comparison analysis.
Scene 1: the system does not contain a pumped storage power station, only considers photovoltaic power generation and wind power generation, and loads are residential area air conditioning cluster loads, high energy consumption industrial cluster loads and commercial area electric heating loads;
scene 2: the system comprises a pumped storage power station, and takes wind power generation into consideration, wherein the load is residential area air conditioning cluster load, high energy consumption industrial cluster load and commercial area electric heating load;
scene 3: the system comprises a pumped storage power station, and the load is an air conditioning cluster load of a residential area, an industrial cluster load with high energy consumption and an electric heating load of a commercial area in consideration of photovoltaic power generation;
scene 4: the energy storage power station comprises a pumped storage power station, and wind power generation and photovoltaic power generation are considered, wherein the load is a residential area air conditioning cluster load, a high energy consumption industrial cluster load and a commercial area electric heating load.
Table 3 electric heating parameters
Table 4 various load electricity prices
Table 5 comprehensive revenue for microgrid operators
As can be seen from table 4, after the algorithm optimization, the average cost ratio is slightly lower before the optimization; the residential load, the commercial load and the industrial load have small electricity price rising in peak time, and the influence on the residential load, the commercial load and the industrial load is almost negligible; the electricity prices of the three parts are not very different at ordinary times; the resident load, commercial load and industrial load drop more in the valley period.
Analyzing the comprehensive benefits of the micro grid operators of table 5, it is known that scenario 1 of the pumped storage power station is the least comprehensive benefit without consideration; when wind power or photovoltaic is combined with a pumped storage unit to be used as a flexible power supply (scene 2/scene 3), the income of a micro-grid operator is increased compared with that of scene 1, and 8026 yuan and 19645 yuan are respectively increased; in scenario 4, revenue 40040 is increased over scenario 1, and 32014 and 20395 are increased over scenario 2 and scenario 3.
Analytical simulations showed that: the energy rejection of the micro-grid mainly occurs at 9:00-16:00 and 22:00-5:00 the next day, and the electricity purchase mainly occurs at 6:00-8:00 and 17:00-21:00. After analysis of wind-solar energy power generation capacity and load power consumption of different scenes, the following conclusion can be obtained:
1) In the scene 1, the system has a large amount of energy abandoning and simultaneously needs to purchase electricity from the outside, the energy abandoning and energy purchasing coexist in one scheduling period of the system, the energy scheduling flexibility is poor, and the energy waste is caused;
2) With the continuous introduction of equipment, the new energy consumption capacity of scenes 2, 3 and 4 is gradually improved, the period of energy discarding is gradually reduced, but scene 2 still needs a large amount of electricity purchasing from the outside;
3) The energy waste of the scene 3 is reduced compared with the energy waste of the scene 2 due to the existence of photovoltaic power generation;
4) The combined pumped storage-wind-light effect in scenario 4 will use peak load transfer, with energy purchasing occurring only at 18:00. The analysis shows that the problem of the asynchronism of the system energy and the load peak valley is solved, the utilization rate of the energy in the system is improved, and the energy waste is reduced.
The effectiveness of the micro-grid client side flexible load resource interactive operation optimization method is also proved by the specific embodiment 2.
According to the embodiment of the invention, the client side flexible load resources are interconnected and interacted, cooperatively optimized and effectively complemented, so that the peak clipping and valley filling purposes are achieved.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
The above description of the method embodiments will be further described with reference to the following embodiments of the apparatus having the same inventive concept as the method in the previous embodiments.
As shown in fig. 3, the apparatus 300 includes:
a first building module 310 for building a client-side flexible load model;
a second construction module 320, configured to construct an objective function and a constraint condition of the client-side flexible load model;
a weight value determining module 330, configured to determine a weight value of each objective function by using an analytic hierarchy process;
and the calculation module 340 is used for solving the client-side flexible load model by utilizing an improved gray wolf optimization algorithm to obtain an optimized wind power output value, a photovoltaic power output value and a power value of the pumped storage unit.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
In the technical scheme of the invention, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to the embodiment of the invention, the invention further provides electronic equipment.
Fig. 4 shows a schematic block diagram of an electronic device 400 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
The electronic device 400 comprises a computing unit 401 that may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 may also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Various components in electronic device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, etc.; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408, such as a magnetic disk, optical disk, etc.; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the respective methods and processes described above, for example, the methods S101 to S104. For example, in some embodiments, methods S101-S104 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 400 via the ROM 402 and/or the communication unit 409. When the computer program is loaded into RAM 403 and executed by computing unit 401, one or more steps of methods S101 to S104 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the methods S101-S104 by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The utility model provides a micro-grid client side flexible load resource interactive operation optimization method which is characterized by comprising the following steps:
constructing a client side flexible load model;
constructing an objective function and constraint conditions of the client-side flexible load model;
determining the weight value of each objective function by adopting an analytic hierarchy process;
and solving the client-side flexible load model by using an improved gray wolf optimization algorithm to obtain an optimized wind power output value, an optimized photovoltaic power output value and an optimized power value of the pumped storage unit.
2. The method of claim 1, wherein the client side flexible load model comprises an industrial load model and a non-industrial load model; the industrial load model comprises a steel rolling process load model and a refining furnace load model; the non-industrial load model comprises an air conditioning load model and an electric heating load model;
the steel rolling process load model comprises the following components:
wherein,the power required by the rolling process is completed for n k-type billets; />The power required by the rough rolling process is completed for the 1 st k-type billet; />The power required by the rough rolling process is completed for the i-th k-type billet; />The time interval between the i-th k-type billet entering the rough rolling process and the 1-th k-type billet entering the rough rolling process; / >The power required by finishing the finish rolling process for the 1 st k-type billet; />The power required by finishing the finish rolling process for the ith k-type billet; />The time interval between the i-th k-type billet entering the finish rolling process and the 1-th k-type billet entering the finish rolling process; t is t roll The time of rolling steel;
the refining furnace load model comprises:
wherein P is eaf (t eaf ) Is the electric arc furnace power; t is t eaf The working time of the arc furnace is; t is t on The starting time of the arc furnace is; Δt (delta t) up The temperature rise time is the temperature rise time; Δt (delta t) down The temperature is the time of temperature decrease; t is t off The closing time of the arc furnace is; p (P) rated The total power consumed by the arc furnace; epsilon (t) eaf ) Is the power change rate of the arc furnace;
the air conditioner load model includes:
wherein,the electric power of the air conditioner; />The energy efficiency ratio of the air conditioner is; />Cooling capacity/heating capacity of an air conditioner in a room; n (N) ac The number of the air conditioning units participating in micro-grid regulation is the number of the air conditioning units participating in micro-grid regulation; zeta type i (t ac ) When zeta is the running state of the ith air conditioning unit i (t ac ) When=1, it indicates that the i-th air conditioner is turned on, and ζ is i (t ac ) =0, indicating that the i-th air conditioner is turned off; p (P) ac,all (t) represents the total load of all air conditioners;
the electric heating load model includes:
wherein P is dcn,l Load power of the first electric heating cluster; lambda (lambda) l (t dcn ) The cluster running state is; n (N) dcn The number of the electric heating clusters; p (P) dcn (t dcn ) Is N dcn And the total load power of the electric heating clusters.
3. The method of claim 2, wherein the objective function of the client side flexible load model comprises an economic objective function, a carbon reduction objective function, a load loss rate objective function;
the economic benefit objective function includes:
wherein,the power of the wind power at the ith moment is the internet power of the wind power; />The power of the photovoltaic network is the photovoltaic network power at the ith moment; />The internet surfing power for pumping energy for the ith moment; />Pumping power for pumping energy for the ith moment; />The power of the pump storage unit at the ith moment; />The air quantity is discarded; />Is the amount of light discarded; c (C) i The online electricity price is obtained; />The electricity price for pumping water; c (C) r Punishment of electricity price for wind and light discarding; mu (mu) gy The time-sharing electricity price is the industrial load; mu (mu) fgy Time-of-use electricity prices for non-industrial loads; f (F) 1 Is economic benefit; t (T) 1 Is the total number of hours per day;
the carbon emission reduction objective function includes:
wherein,CS, the amount of carbon dioxide produced for combustion of coal coal Coal consumption converted into thermal power generation for clean energy power generation; />Total electric quantity for combined system power generation grid connection, t 2 Is the length of time; max F 2 Maximum carbon emission reduction, F 2 Reducing the discharge amount of carbon; t (T) 2 The number of the wind power generation sets, the photovoltaic sets and the pumped storage sets is the number of the wind power generation sets, the photovoltaic sets and the pumped storage sets;
The load loss rate objective function includes:
wherein Z represents load type, LPSP Z,t P is the loss rate of Z load at t time Z,LS,t For the actual supply of the micro-grid Z-load at time t, P Z,Load,t The demand of the load of the micro-grid at the moment Z; lambda is the load loss rate.
4. A method according to claim 3, wherein the constraints of the customer side flexible load model include microgrid reliability constraints, output power balance constraints, grid reception power constraints, positive and negative rotation reserve constraints, pumped storage output power constraints and reservoir capacity constraints;
the microgrid reliability constraints include:
wherein,and->Respectively the minimum value and the maximum value of the load loss rate;
the output power balancing constraint includes:
wherein,the predicted force of wind power at the ith moment is represented; />Representing the predicted output of the photovoltaic at the ith moment;
the grid received power constraint includes:
wherein,the predicted load value at the i-th moment; omega is the maximum allowable deviation between the internet power and the load;
the positive and negative rotation reserve constraint includes:
wherein,representing the maximum output power of the pumped storage at the ith moment; />Representing the minimum output power of pumped storage at the ith moment; / >The positive rotation standby requirement at the i-th moment; />The demand is the negative rotation standby at the i-th moment;
the pumped storage output power constraint comprises:
wherein P is pmax Is the maximum value of pumping power; p (P) homax Is the maximum value of the generated power; η (eta) p Pumping efficiency of the water pump; η (eta) h The power generation efficiency of the water turbine unit is;is the lower reservoir capacity; t is t d For the duration time of power generation of the lower reservoir turbine unit, t u Pumping water for the upper reservoir water pump for a long time; />Is the upper reservoir capacity;
the reservoir capacity constraint includes:
wherein,is the minimum value of the upper reservoir capacity; />Is the maximum value of the upper reservoir capacity; />Is the minimum value of the lower reservoir capacity; />Is the maximum value of the reservoir capacity; t is t u Pumping water for the upper reservoir for a duration; t is t d The power generation duration time is the lower reservoir turbine unit.
5. The method of claim 1, wherein solving the client side flexible load model using a modified wolf optimization algorithm comprises:
s201, carrying out initial assignment on a gray wolf population, a nonlinear update factor, an A coefficient vector and a C coefficient vector, and importing wind power generation power, photovoltaic power generation power, load power and maximum iteration times; carrying out initial assignment on the wind power output value, the photovoltaic power output value and the power of the four pumped storage units;
S202, judging whether assignment of wind power output values, photovoltaic power output values and power of four pumped storage units meets constraint conditions, and if yes, normalizing an objective function of the client side flexible load model to obtain a multi-objective function normalized value; otherwise, the wind power output value, the photovoltaic power output value and the assignment of the power of the four pumped storage units are adjusted to meet constraint conditions, and S202 is executed again;
s203, calculating optimal values, suboptimal values and general values of the multi-objective function normalization values searched by all the wolf individuals, corresponding to three wolf individuals, and storing the final positions of the three wolf individuals;
s204, updating the nonlinear updating factor, the A coefficient vector and the C coefficient vector;
and S205, updating the positions of other wolf individuals except the three wolf individuals, returning to the execution S203, and updating the fitness and the positions of the three wolf individuals until the maximum iteration times are reached, and outputting the optimized wind power output value, the photovoltaic power output value and the power of the four pumped storage units.
6. The method of claim 5, wherein the initial assignment of the wolf population by random variable Tent chaotic map comprises:
Wherein alpha is 1 As a demarcation point parameter, alpha 1 ＝0.499；x i Is a randomly generated number.
7. The method of claim 5, wherein calculating the optimal, sub-optimal, and general values of the multi-objective function normalization values searched for by all the wolf individuals corresponds to three wolf individuals, and storing the final positions of the three wolf individuals comprises:
wherein,representing the distance between other individuals in the wolf group and alpha wolves; />Representing the distance between other individuals in the wolf group and beta wolves; />Representing the distance between other individuals in the wolf group and delta wolves; />Representing the current position of alpha wolf; />Representing the current of beta wolfA location; />Representing the current position of delta wolf; />The position of the current gray wolf individual; />Representing the positions of other individuals of the population, which are influenced by alpha wolves and need to be adjusted; />Representing the positions of other individuals of the population, which are influenced by beta wolves and need to be adjusted; />Representing the positions of other individuals of the population, which are affected by delta wolf and need to be adjusted; />Representing final position vectors of three wolf individuals; />C coefficient vector for alpha wolf; />C coefficient vector for βwolf; />A C coefficient vector that is δwolf; />A coefficient vector of alpha wolf;an a coefficient vector being beta wolf; />An a coefficient vector that is δwolf; / >Is->Maximum value of>Is->Minimum value->Representing final position vectors of three wolf individuals; θ 1 Representing the confidence of alpha wolf; θ 2 Representing the confidence of beta wolf; θ 3 Representing the confidence of δwolf.
8. The method of claim 5, wherein updating the nonlinear update factor, the a-coefficient vector, and the C-coefficient vector comprises:
wherein,is a coefficient vector A; />Is a C coefficient vector; />Is a nonlinear update factor; />As a first random parameter, for the second random parameter, +.>Q is the current iteration number, Q max Representing the maximum number of iterations of the gray wolf population.
9. The utility model provides a little electric wire netting customer side flexible load resource interdynamic operation optimizing device which characterized in that includes:
the first construction module is used for constructing a client-side flexible load model;
the second construction module is used for constructing an objective function and constraint conditions of the client-side flexible load model;
the weight value determining module is used for determining the weight value of each objective function by adopting an analytic hierarchy process;
and the calculation module is used for solving the client-side flexible load model by utilizing an improved gray wolf optimization algorithm to obtain an optimized wind power output value, an optimized photovoltaic power output value and an optimized power value of the pumped storage unit.
10. An electronic device comprising at least one processor; and
a memory communicatively coupled to the at least one processor; it is characterized in that the method comprises the steps of,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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