CN116128127A - Source-load coordination multi-objective optimization scheduling method under load aggregator mode - Google Patents
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Abstract
The invention discloses a source-load coordination multi-objective optimization scheduling method under a load aggregator mode, which comprises the following steps: acquiring power system source load side monitoring data and constructing a data set; performing clustering analysis on the data by using a Fuzzy C-Means clustering algorithm (FCM) to obtain typical data types; and matching the power generated by the source side with the power required by the load side through an SDO intelligent optimization algorithm to realize multi-objective optimization scheduling. The advantages are as follows: (1) The typical data types are obtained through the clustering analysis of the power system source-load side data, so that subjectivity of manually making rules is avoided, and the error probability of manual classification is reduced; (2) By utilizing the intelligent optimization algorithm, the energy scheduling method additionally considers the economic benefit of system operation on the basis of energy conservation and high efficiency. (3) And the theory is combined with the practice, and the parameter updating of the physical equipment is realized according to the algorithm model result, so that the practical power system dispatching is completed.
Description
Technical Field
The invention relates to the technical field of power markets, in particular to a source-load coordination multi-objective optimization scheduling method under a load aggregator mode.
Background
In the power market, load Aggregators (LA) aggregate load resources in a contractual manner and act as responsive resource suppliers to guide users to actively participate in grid scheduling. The literature [ load aggregator business based demand response resource integration method and operation mechanism [ J ]. Power system automation, 2013,37 (17): 78-86.] details the operation mechanism, scheduling and control strategy of LA and introduces its differences from other participants in the power system. The literature [ DLC-based air conditioner load double-layer optimized scheduling and control strategy [ J ]. Chinese motor engineering journal, 2014 (10): 1546-1555.DOI:10.13334/j.0258-8013.PCsee.2014.10.005 ] [ load control and distribution network reconstruction under the participation of an air conditioner aggregator [ J ]. Electric power system automation, 2018,42 (2): 42-49.DOI:10.7500/AEPS20170614023] is to aggregate air conditioner equipment with heat storage capacity and then participate in electric power system scheduling, and a scheduling model considering electricity price factors is established. The literature [ electric vehicle charge and discharge scheduling research based on demand side discharge bidding [ J ]. Electric network technology, 2016,40 (4): 1140-1146.DOI:10.13335/j.1000-3673.Pst.2016.04.024 ] provides a framework for aggregating electric vehicles to participate in electric network scheduling, and a double-layer scheduling model is established according to a day-ahead plan and a real-time plan. Document [7] classifies the types of demand response contracts on the basis of load reduction, establishes an LA optimization model considering price influence, and provides a basis for making an LA power generation plan in the power market. The problem of load diversity was not considered in the above study, making LA modulation function single.
Based on the automatic demand response technology, a demand response operation mode with a load aggregator as an intermediate is presented. The mode mainly realizes interaction between the power grid and the user by means of a third party of the load aggregator, and a series of research works have been carried out on the mode, so that a lot of achievements are achieved. The national laboratory of Lorentberkeley, U.S. develops a communication information architecture supporting automatic demand response-Open automatic demand response communication protocol (Open automated demand response communications specification, open ADR) for enabling information communication between entities in automatic demand response under an intelligent power network. The advanced measurement system can collect, store and process the power consumption information in the intelligent power grid, collect response data of the user DR and the like, feed the information back to the power grid and the load aggregator in real time, and provide data support for realizing real-time interaction of the users of the power grid load aggregator. The Honival corporation has developed an automatic demand response server and load management equipment based on the OpenADR, supports a user to realize the management of the load equipment through a terminal, and builds a set of automatic demand response paradigm system applying the OpenADR on the basis.
Disclosure of Invention
The invention aims to solve the technical problems that: 1) The large-scale renewable energy source access power grid has various economic benefits, but the output has the characteristics of fluctuation and the like, and brings difficulty to the work of a dispatching department; 2) The traditional demand response signals mainly depend on manual transmission, and a person manually shuts down equipment or adjusts the running power of the equipment, so that the user side cannot acquire DR information of the power grid side in time, the power grid side cannot adjust the DR signals according to the latest electric energy consumption information of the user in real time, and the reliability and efficiency of realizing peak clipping and valley filling of DR are reduced; 3) In the existing research, how the load aggregator chooses users to participate in the automatic demand response in the implementation process of the automatic demand response is rarely researched.
In order to overcome the defects in the prior art, the invention discloses a source-load coordination multi-objective optimization scheduling method in a load aggregator mode. According to the invention, a genetic algorithm is adopted to solve the optimal combination of user participation demand response selected by a load aggregator under multiple objectives, and a multi-objective optimal scheduling model is constructed by taking the maximum new energy consumption, the maximum system operation benefit and the minimum power generation fluctuation as optimization objectives.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a source-load coordination multi-objective optimal scheduling method in a load aggregator mode, the method comprising:
step one: acquiring power system source load side monitoring data and constructing a data set;
step two: performing clustering analysis on the data by using a Fuzzy C-Means clustering algorithm (FCM) to obtain typical data types;
step three: and matching the power generated by the source side with the power required by the load side through an SDO intelligent optimization algorithm to realize multi-objective optimization scheduling.
In the first step: data acquisition and construction data set
(1) Scheme without intelligent ammeter
And the intelligent electric meters are uniformly installed, data reading is realized through a 485 communication interface, and uploading of energy consumption data is realized through an Ethernet transmission mode.
(2) Scheme for equipped ammeter but unable to communicate
Some meters are long in installation time, no 485 interface is provided, or no authority for reading meter data is provided, in this case, energy consumption data can be acquired by installing an infrared reader, and then uploading is performed in a GPRS wireless mode
(3) Scheme for accessing energy consumption data into factory system
For example, the utility company has perfect data collection scheme, can collect data from the database of the original system or adopting OPC communication mode, and can upload data according to data collection and transmission protocol after processing through system configuration
(4) And finally, manufacturing the data and the label into a data set for subsequent normalization processing.
In the second step,: cluster analysis
The medium voltage distribution network has a plurality of feeder lines, the positions and the running conditions of the feeder lines are different, and the feeder line classification has no clear standard.
(1) FCM algorithm principle
When the number of data classifications is C, the data center point is C z Each sample d j (j is more than 0 and less than or equal to n) belonging to a certain class L i (0 < i.ltoreq.C) with a membership degree of U ij The constraint of the objective function is:
in the formula (1), m is a membership factor, and is generally 2. D j -C z The value of the target value J is smaller, which indicates that the similarity between the data and the center point is higher.
Equation (2) specifies that the sum of membership of each data is 1.
(2) Numerical iteration
U ij And C z The iterative formula of (2) is as follows:
U ij the initialization value of (2) is randomly given by the system.
In the third step: SDO intelligent optimization algorithm
The supply and demand optimization algorithm aims to combine market prices with different heat energy, and adopts the supply and demand optimization algorithm to seek an optimal solution meeting the requirements of taste, quantity and price.
(1) Algorithm initialization
Assuming that the power grid has n markets for selling, each market can sell d energy sources with different taste levels, and each energy source has a certain output quantity and market pricing. D heat prices in the market represent a set of candidate solutions for the d-dimensional variable of the optimization problem, and meanwhile, the optimality evaluation is started by taking the quantity of d heat in the market as a set of feasible solutions, and if the feasible solutions are superior to the current candidate solutions, the candidate solutions are replaced by the current feasible solutions. The thermal energy pricing and thermal energy quantity for these n markets are represented by two matrices X, Y:
in which x is i And y i Pricing and remaining amount of the ith thermal energy, respectively; x is x ij And y ij The pricing and quantity of the jth thermal energy in the ith market, respectively.
The energy price and the energy quantity in each market are respectively subjected to optimality evaluation by adopting a fitness function, and the fitness functions of the energy price and the energy quantity are as shown in the formula (7) for n markets:
(2) Calculating balance quantity and balance price of energy commodity
Assuming an average price x for each energy source 0 And average number y 0 The process of each iteration is variable, and one energy quantity is selected from the set of energy quantities of each market as an average vector of the quantity, and the larger the fitness value of the energy quantity in the market is, the larger the probability of selecting the heat quantity of each market is. Meanwhile, each market can also select one price from the energy price set according to the probability of the market or adopt the average value of all market energy prices as the balance price. Balance quantity y of energy commodities 0 The expression is as follows:
y 0 ＝y k ，k＝R(Q) (8)
wherein:
wherein: f (y) i ) For the energy quantity y i Is a fitness value of (a); r () is a comparison operator.
Balance price x of energy commodity 0 The expression is as follows:
wherein:
wherein: f (x) i ) Pricing energy x i Is a fitness value of (a); r, r 1 Is [0,1]Random numbers in (a) and (b).
A supply function and a demand function. According to the average number y 0 Average price x 0 The supply function and the demand function are given separately as follows:
y i，t+1 ＝y 0 -α(x i，t -x 0 ) (10)
x i，t+1 ＝x 0 +β(y i，t -y 0 ) (11)
wherein: x is x i，t And y i，t The price and the quantity of the ith energy commodity are respectively the nth iteration; alpha and beta are the demand weight and the supply weight respectively, and the balance price and the balance quantity are updated by adjusting alpha and beta.
Combining the formula (10) with the formula (11), the required formula can be rewritten to obtain:
x i，t+1 ＝x 0 -αβ(x i，t -x 0 ) (12)
the supply weight α and the demand weight β are respectively:
wherein: t is the maximum number of iterations. The product of the supply weight α and the demand weight β is represented by a variable L, which can be obtained:
the variable L helps the algorithm make a smooth transition between exploration and development. When L is less than 1, the balance price x is obtained by belonging to a stable mode and adjusting the supply weight alpha and the demand weight beta 0 Different energy prices around, which can beThe steady mode mechanism focuses on "development" to improve the local exploration ability of the algorithm by randomly varying the random number r between the current price and the equilibrium price. L (L)>1 belongs to an unstable mode which allows the price of energy commodities in any market to be far from equilibrium, and an unstable mode mechanism forces each market to strengthen the "explored" unknown region in the search space to improve the global search capability of the algorithm.
(3) Algorithm step
step1: setting the number N of energy markets, the maximum iteration number T, the problem dimension and the search space. Initializing energy pricing x according to past experience i Initializing the energy quantity y according to the actual capacity condition i Let the current iteration number t=0;
step2: begin to calculate energy price x i And energy quantity y i Is adapted to the degree value F xi And F yi If F yi Is superior to F xi Then use y i Instead of x i Preserve x best The optimal solution is the optimal solution under the current market condition;
step3: determining a supply weight alpha and a demand weight beta;
step4: for each market, the average quantity y is determined using equation (8) 0 The method comprises the steps of carrying out a first treatment on the surface of the Determining an average price x using (9) 0 ；
step5: starting to update the quantity y of energy sources by using the method (10) i The method comprises the steps of carrying out a first treatment on the surface of the Updating the price x of energy using (11) i . Then, the energy price x is calculated again based on the equation (8) and the equation (9) i And energy quantity y i Adaptation value F of (2) xi And F yi If F yi Is superior to F xi Then use y i Instead of x i Preserve x best Is the current optimal solution;
step6: let t=t+1. Judging whether the algorithm reaches the set iteration times or termination conditions, and if so, outputting an optimal solution x best Ending the algorithm; otherwise, continuing to iterate step 2-step 6.
The method also comprises the following steps: updating plant operating parameters
(1) And the intelligent edge gateway, the intelligent ammeter and other networking equipment are adopted to realize the downloading of parameters in an Ethernet transmission mode, and the physical equipment for dispatching the electric energy acquires the parameters through a 485 communication interface and updates the data, so that the dispatching of the actual equipment is completed.
The invention has the technical effects and advantages that:
(1) The typical data types are obtained through the clustering analysis of the power system source-load side data, so that subjectivity of manually making rules is avoided, and the error probability of manual classification is reduced;
(2) By utilizing the intelligent optimization algorithm, the energy scheduling method additionally considers the economic benefit of system operation on the basis of energy conservation and high efficiency.
(3) And the theory is combined with the practice, and the parameter updating of the physical equipment is realized according to the algorithm model result, so that the practical power system dispatching is completed.
Drawings
Fig. 1 is a flowchart of a source-load coordination multi-objective optimization scheduling method in a load aggregator mode.
Detailed Description
The following description of the technical solutions in the embodiments of the present invention will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. 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.
As shown in fig. 1, the invention discloses a source-load coordination multi-objective optimization scheduling method in a load aggregator mode, which comprises the following steps: acquiring power system source load side monitoring data and constructing a data set; performing clustering analysis on the data by using a Fuzzy C-Means clustering algorithm (FCM) to obtain typical data types; and matching the power generated by the source side with the power required by the load side through an SDO intelligent optimization algorithm to realize multi-objective optimization scheduling. The specific flow chart is as follows:
step one: data acquisition and construction data set
(1) Scheme without intelligent ammeter
And the intelligent electric meters are uniformly installed, data reading is realized through a 485 communication interface, and uploading of energy consumption data is realized through an Ethernet transmission mode.
(2) Scheme for equipped ammeter but unable to communicate
Some meters are long in installation time, no 485 interface is provided, or no authority for reading meter data is provided, in this case, energy consumption data can be acquired by installing an infrared reader, and then uploading is performed in a GPRS wireless mode
(3) Scheme for accessing energy consumption data into factory system
For example, the utility company has perfect data collection scheme, can collect data from the database of the original system or adopting OPC communication mode, and can upload data according to data collection and transmission protocol after processing through system configuration
(4) And finally, manufacturing the data and the label into a data set for subsequent normalization processing.
Step two: cluster analysis
The medium voltage distribution network has a plurality of feeder lines, the positions and the running conditions of the feeder lines are different, and the feeder line classification has no clear standard.
(1) FCM algorithm principle
When the number of data classifications is C, the data center point is C z Each sample d j (j is more than 0 and less than or equal to n) belonging to a certain class L i (0 < i.ltoreq.C) with a membership degree of U ij The constraint of the objective function is:
in the formula (1), m is a membership factor, and is generally 2. D j -C z Representation ofThe smaller the Euclidean distance between the data point and the data center, the higher the similarity between the data and the center point is indicated by the smaller the value of the target value J.
Equation (2) specifies that the sum of membership of each data is 1.
(2) Numerical iteration
U ij And C z The iterative formula of (2) is as follows:
U ij the initialization value of (2) is randomly given by the system.
Step three: SDO intelligent optimization algorithm
The supply and demand optimization algorithm aims to combine market prices with different heat energy, and adopts the supply and demand optimization algorithm to seek an optimal solution meeting the requirements of taste, quantity and price.
(1) Algorithm initialization
Assuming that the power grid has n markets for selling, each market can sell d energy sources with different taste levels, and each energy source has a certain output quantity and market pricing. D heat prices in the market represent a set of candidate solutions for the d-dimensional variable of the optimization problem, and meanwhile, the optimality evaluation is started by taking the quantity of d heat in the market as a set of feasible solutions, and if the feasible solutions are superior to the current candidate solutions, the candidate solutions are replaced by the current feasible solutions. The thermal energy pricing and thermal energy quantity for these n markets are represented by two matrices X, Y:
in which x is i And y i Pricing and remaining amount of the ith thermal energy, respectively; x is x ij And y ij The pricing and quantity of the jth thermal energy in the ith market, respectively.
The energy price and the energy quantity in each market are respectively subjected to optimality evaluation by adopting a fitness function, and the fitness functions of the energy price and the energy quantity are as shown in the formula (7) for n markets:
(2) Calculating balance quantity and balance price of energy commodity
Assuming an average price x for each energy source 0 And average number y 0 The process of each iteration is variable, and one energy quantity is selected from the set of energy quantities of each market as an average vector of the quantity, and the larger the fitness value of the energy quantity in the market is, the larger the probability of selecting the heat quantity of each market is. Meanwhile, each market can also select one price from the energy price set according to the probability of the market or adopt the average value of all market energy prices as the balance price. Balance quantity y of energy commodities 0 The expression is as follows:
y 0 ＝y k ，k＝R(Q) (8)
wherein:
wherein: f (y) i ) For the energy quantity y i Is a fitness value of (a); r () is a comparison operator.
Balance price x of energy commodity 0 The expression is as follows:
wherein:
wherein: f (x) i ) Pricing energy x i Is a fitness value of (a); r, r 1 Is [0,1]Random numbers in (a) and (b).
A supply function and a demand function. According to the average number y 0 Average price x 0 The supply function and the demand function are given separately as follows:
y i，t+1 ＝y 0 -α(x i，t -x 0 ) (10)
x i，t+1 ＝x 0 +β(y i，t -y 0 ) (11)
wherein: x is x i，t And y i，t The price and the quantity of the ith energy commodity are respectively the nth iteration; alpha and beta are the demand weight and the supply weight respectively, and the balance price and the balance quantity are updated by adjusting alpha and beta.
Combining the formula (10) with the formula (11), the required formula can be rewritten to obtain:
x i，t+1 ＝x 0 -αβ(x i，t -x 0 ) (12)
the supply weight α and the demand weight β are respectively:
wherein: t is the maximum number of iterations. The product of the supply weight α and the demand weight β is represented by a variable L, which can be obtained:
the variable L helps the algorithm make a smooth transition between exploration and development. When L is less than 1, the balance price x is obtained by belonging to a stable mode and adjusting the supply weight alpha and the demand weight beta 0 Surrounding different energy prices, which can be randomly varied between the current price and the equilibrium price by a random number r, the steady-mode mechanism focuses on "development" to improve the local exploration ability of the algorithm. L (L)>1 belongs to an unstable mode which allows the price of energy commodities in any market to be far from equilibrium, and an unstable mode mechanism forces each market to strengthen the "explored" unknown region in the search space to improve the global search capability of the algorithm.
(3) Algorithm step
step1: setting the number N of energy markets, the maximum iteration number T, the problem dimension and the search space. Initializing energy pricing x according to past experience i Initializing the energy quantity y according to the actual capacity condition i Let the current iteration number t=0;
step2: begin to calculate energy price x i And energy quantity y i Is adapted to the degree value F xi And F yi If F yi Is superior to F xi Then use y i Instead of x i Preserve x best The optimal solution is the optimal solution under the current market condition;
step3: determining a supply weight alpha and a demand weight beta;
step4: for each market, the average quantity y is determined using equation (8) 0 The method comprises the steps of carrying out a first treatment on the surface of the Determining an average price x using (9) 0 ；
step5: starting to update the quantity y of energy sources by using the method (10) i The method comprises the steps of carrying out a first treatment on the surface of the Updating the price x of energy using (11) i . Then, the energy price x is calculated again based on the equation (8) and the equation (9) i And energy quantity y i Adaptation value F of (2) xi And F yi If F yi Is superior to F xi Then use y i Instead of x i Preserve x best Is the current optimal solution;
step6: let t=t+1. Judging whether the algorithm reaches the set iteration times or termination conditions, and if so, outputting an optimal solution x best Ending the algorithm; otherwise, continuing to iterate step 2-step 6.
Step four: updating plant operating parameters
(1) And the intelligent edge gateway, the intelligent ammeter and other networking equipment are adopted to realize the downloading of parameters in an Ethernet transmission mode, and the physical equipment for dispatching the electric energy acquires the parameters through a 485 communication interface and updates the data, so that the dispatching of the actual equipment is completed.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A source-load coordination multi-objective optimal scheduling method in a load aggregator mode, the method comprising:
step one: acquiring power system source load side monitoring data and constructing a data set;
step two: performing clustering analysis on the data by using a Fuzzy C-Means clustering algorithm (FCM) to obtain typical data types;
step three: and matching the power generated by the source side with the power required by the load side through an SDO intelligent optimization algorithm to realize multi-objective optimization scheduling.
2. The method for source load coordination multi-objective optimization scheduling in a load aggregator mode according to claim 1, wherein the method comprises the following steps: in the first step: data acquisition and construction data set
(1) Scheme without intelligent ammeter
The intelligent electric meters are uniformly installed, data reading is realized through a 485 communication interface, and uploading of energy consumption data is realized through an Ethernet transmission mode;
(2) Scheme for equipped ammeter but unable to communicate
Some meters are long in installation time, no 485 interface is provided, or no authority for reading meter data is provided, in this case, energy consumption data can be acquired by installing an infrared reader, and then uploading is performed in a GPRS wireless mode
(3) Scheme for accessing energy consumption data into factory system
For example, the utility company has perfect data collection scheme, can collect data from the database of the original system or adopting OPC communication mode, and can upload data according to data collection and transmission protocol after processing through system configuration
(4) And finally, manufacturing the data and the label into a data set for subsequent normalization processing.
3. The method for source load coordination multi-objective optimization scheduling in a load aggregator mode according to claim 1, wherein the method comprises the following steps: in the second step,: cluster analysis
The medium voltage distribution network has a plurality of feeder lines, the positions and the running conditions of the feeder lines are different, and the feeder line classification has no definite standard;
(1) FCM algorithm principle
When the number of data classifications is c, the data center point is that the membership degree of each sample belonging to a certain class is c, the constraint condition of the objective function is:
in the formula (1), m is a membership factor, and is generally 2; the Euclidean distance between the data point and the data center is represented, and the smaller the value of the target value J is, the higher the similarity between the data and the center point is;
equation (2) specifies that the sum of membership of each data is 1.
(2) Numerical iteration
The iterative formula of the sum is as follows:
the initialization value of (2) is randomly given by the system.
4. The method for source load coordination multi-objective optimization scheduling in a load aggregator mode according to claim 1, wherein the method comprises the following steps: in the third step: SDO intelligent optimization algorithm
The supply and demand optimization algorithm aims to combine market prices with different heat energy, and adopts the supply and demand optimization algorithm to seek an optimal solution meeting the requirements of taste, quantity and price;
(1) Algorithm initialization
Assuming that the power grid heat energy is sold in n markets, each market can sell d energy sources with different taste levels, and each energy source has a certain output quantity and market pricing; d heat energy prices in the market represent a group of candidate solutions of d-dimensional variables of the optimization problem, meanwhile, the quantity of d heat energy in the market is used as a group of feasible solutions to start optimizing evaluation, and if the feasible solutions are superior to the current candidate solutions, the candidate solutions are replaced by the current feasible solutions; the thermal energy pricing and thermal energy quantity for these n markets are represented by two matrices X, Y:
in which x is i And y i Pricing and remaining amount of the ith thermal energy, respectively; x is x ij And y ij Pricing and quantity of j-th thermal energy in i-th market, respectively;
the energy price and the energy quantity in each market are respectively subjected to optimality evaluation by adopting a fitness function, and the fitness functions of the energy price and the energy quantity are as shown in the formula (7) for n markets:
(2) Calculating balance quantity and balance price of energy commodity
Assuming an average price x for each energy source 0 And average number y 0 The energy quantity is selected from the collection of energy quantity of each market as the average vector of the quantity, and the larger the fitness value in the market is, the larger the probability of selecting the heat quantity of each market is; meanwhile, each market can also select one price from the energy price set according to the probability of the market or adopt the average value of all market energy prices as the balance price; balance quantity y of energy commodities 0 The expression is as follows:
y 0 ＝y k ，k＝R(Q) (8)
wherein:
wherein: f (y) i ) For the energy quantity y i Is a fitness value of (a); r () is a comparison operator.
Balance price x of energy commodity 0 The expression is as follows:
wherein:
wherein: f (x) i ) Pricing energy x i Is a fitness value of (a); r, r 1 Is [0,1]Random numbers in (a);
a supply function and a demand function; according to the average number y 0 Average price x 0 The supply function and the demand function are given separately as follows:
y i，t+1 ＝y 0 -α(x i，t -x 0 ) (10)
x i，t+1 ＝x 0 +β(y i，t -y 0 ) (11)
wherein: x is x i，t And y i，t The price and the quantity of the ith energy commodity are respectively the nth iteration; alpha and beta are the demand weight and the supply weight respectively, and the balance price and the balance quantity are updated by adjusting alpha and beta;
combining the formula (10) with the formula (11), the required formula can be rewritten to obtain:
x i，t+1 ＝x 0 -αβ(x i，t -x 0 ) (12)
the supply weight α and the demand weight β are respectively:
wherein: t is the maximum iteration number; the product of the supply weight α and the demand weight β is represented by a variable L, which can be obtained:
the variable L facilitates the algorithm to make a smooth transition between exploration and development; when L is less than 1, the balance price x is obtained by belonging to a stable mode and adjusting the supply weight alpha and the demand weight beta 0 Different energy prices around, which can randomly change between the current price and the equilibrium price through a random number r, a steady mode mechanism focuses on "development" to improve the local exploration ability of the algorithm; l > 1 belongs to an unstable mode, which allows the price of energy commodities in any market to be far away from the equilibrium price, and an unstable mode mechanism forces each market to strengthen the 'exploration' of unknown areas in the search space to improve the global search capability of the algorithm;
(3) Algorithm step
step1: setting the number N of energy markets, the maximum iteration number T, the problem dimension and the search space; initializing energy pricing x according to past experience i Initializing the energy quantity y according to the actual capacity condition i Let the current iteration number t=0;
step2: begin to calculate energy price x i And energy quantity y i Is adapted to the degree value F xi And F yi If F yi Is superior to F xi Then use y i Instead of x i Preserve x best The optimal solution is the optimal solution under the current market condition;
step3: determining a supply weight alpha and a demand weight beta;
step4: for each market, the average quantity y is determined using equation (8) 0 The method comprises the steps of carrying out a first treatment on the surface of the Determining an average price x using (9) 0 ；
step5: starting to update the quantity y of energy sources by using the method (10) i The method comprises the steps of carrying out a first treatment on the surface of the Updating the price x of energy using (11) i The method comprises the steps of carrying out a first treatment on the surface of the Then, the energy price is calculated again based on the formula (8) and the formula (9)Grid x i And energy quantity y i Adaptation value F of (2) xi And F yi If F yi Is superior to F xi Then use y i Instead of x i Preserve x best Is the current optimal solution;
step6: let t=t+1; judging whether the algorithm reaches the set iteration times or termination conditions, and if so, outputting an optimal solution x best Ending the algorithm; otherwise, continuing to iterate step 2-step 6.
5. The method for source load coordination multi-objective optimization scheduling in a load aggregator mode according to claim 1, wherein the method comprises the following steps: the method also comprises the following steps: updating plant operating parameters
(1) And the intelligent edge gateway, the intelligent ammeter and other networking equipment are adopted to realize the downloading of parameters in an Ethernet transmission mode, and the physical equipment for dispatching the electric energy acquires the parameters through a 485 communication interface and updates the data, so that the dispatching of the actual equipment is completed.
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