CN115860224A - Multi-constraint optimization method and system for power data - Google Patents
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Abstract
The invention relates to the technical field of water-fire power dispatching, and provides a multi-constraint optimization method for power data, which comprises the following steps: s1: establishing a reverse sine-cosine algorithm model, wherein the reverse sine-cosine algorithm model comprises a reverse solution algorithm and a sine-cosine algorithm; s2: generating N j-dimensional initial individuals, wherein the initial individuals comprise the power generation flow of a hydropower station and the output of a thermal power generating unit; s3 the method comprises the following steps: establishing an initial population according to initial individuals, and defining a position definition formula of the initial individuals at different iteration times; s4: inputting the initial population and the target function into a reverse sine and cosine algorithm model for iterative computation, screening out an optimal individual, substituting the optimal individual into the target function, computing an optimal function value, and outputting the optimal individual and the optimal function value. The method and the device improve the calculation precision and the calculation efficiency of the optimal scheduling problem of the water-fire power system.
Description
Technical Field
The invention relates to the technical field of water-fire power dispatching, in particular to a multi-constraint optimization method and system for power data.
Background
The short-term optimized dispatching of the water-fire power system plays a crucial role in the operation of the power system, and the dispatching goal is to minimize the total operation cost of the water-fire power system in the dispatching period by optimizing the power generation amount and load distribution of each time period of a power station in the water-fire power system under the condition of meeting the operation constraint of the water-fire power system. The operation constraint of the water-fire power system mainly comprises the following steps: the system comprises a total generated energy balance constraint, a hydroelectric generation balance constraint, a reservoir station volume constraint, a hydroelectric generation power constraint, a water balance constraint, a reservoir boundary constraint, a hydroelectric conversion relation, a thermal power output power constraint and the like. Therefore, the short-term power generation scheduling of the water-fire power system is a nonlinear optimization problem containing equality constraints and inequality constraints.
The current methods for solving the problem mainly comprise a linear programming method (LP), a nonlinear programming method (NLP), a quadratic programming method (QP) and a dynamic programming method (DP); the LP, the NLP and the QP cannot directly process complex constraints, an objective function needs to be linearized by the LP, the NLP and the QP need to be continuous and micro, and the processing is to properly simplify an original model, so that an inaccurate scheduling result is easily caused; and the DP possibly faces the difficulty of 'dimension disaster' in the operation process, so that the calculation time of the short-term optimization scheduling problem of the large-scale water-fire power system is long. Therefore, a power data multi-constraint optimization method and system are needed to improve the calculation accuracy and the calculation efficiency of the optimal scheduling problem of the water and fire power system.
The power generation flow of each time period of the hydropower station and the load distribution of each time period of the thermal power station relate to personal information and power utilization conditions of power utilization areas, and the information belongs to important confidential data. Therefore, the transmission security of the power data is receiving a great deal of attention. For the power grid data security and privacy protection technology, it is urgently needed to exert the value of the power data to the maximum extent on the premise that the service data is necessarily protected.
Disclosure of Invention
The invention aims to at least solve one of the problems in the prior art, provides a multi-constraint optimization method and a multi-constraint optimization system for electric power data, and improves the calculation precision and the calculation efficiency of the optimal scheduling problem of a water-fire electric power system.
In order to achieve the above object of the present invention, according to a first aspect of the present invention, there is provided a power data multi-constraint optimization method, including the steps of: s1: establishing a reverse sine-cosine algorithm model, wherein the reverse sine-cosine algorithm model comprises a reverse solution algorithm and a sine-cosine algorithm; s2: generating N j-dimensional initial individuals, wherein the initial individuals comprise the power generation flow of a hydropower station and the output of a thermal power generating unit; s3: establishing an initial population according to initial individuals, and defining a position definition formula of the initial individuals at different iteration times; s4: inputting the initial population and the target function into a reverse sine and cosine algorithm model for iterative calculation, screening out the optimal individual, substituting the optimal individual into the target function, calculating the optimal function value, and outputting the optimal individual and the optimal function value.
Further, step S4 specifically includes the following steps:
s4-1: the reverse sine and cosine algorithm model generates a reverse individual according to the initial individual and a reverse resolving method;
s4-2: carrying out constraint processing on the initial individuals and the reverse individuals, and selecting one of each pair of the initial individuals and the reverse individuals according to a quality criterion to generate an iterative population;
s4-3: calculating an iteration population according to a sine and cosine algorithm, if the actual iteration number is smaller than the maximum iteration number, updating key parameters of the sine and cosine algorithm and the position of each individual in the iteration population to generate a new individual, selecting the individual before updating or the new individual according to a quality criterion to generate a new iteration population, and re-executing the step S3-3; if the actual iteration times reach the maximum iteration times, outputting the optimal individuals in the current iteration population;
s4-4: and calculating an optimal function value according to the optimal individual and the objective function, and outputting the optimal function value.
Further, the sine and cosine algorithm is specifically as follows:
wherein t is the actual iteration number; x i，j (t) is the original position of the ith initial individual on the dimension j when the iteration number t is reached; x i，j (t + 1) is the updated position of the initial individual i on the dimension j when the iteration number t is reached; x gb，j The position of the optimal individual of the current iteration on the dimension j is obtained; r is a radical of hydrogen 1 In order to be the amplitude conversion factor,
T mmax a is a first constant value for the maximum iteration number; r is 2 Is a second random quantity, r 3 Is a third random quantity, r 4 Is a fourth random quantity, r 2 ∈[0，2π]，r 3 ∈[-2，2]，r 4 ∈[0，1]，r 2 、r 3 、r 4 Are subject to uniform distribution.
Further, the key parameters include a position of the optimal individual of the current iteration in the dimension j, an amplitude conversion factor, and a fourth random value.
Further, the inverse solution method specifically includes:
wherein, X i，j (t) is the original position of the ith initial individual in the dimension j when the iteration number t is carried out, namely the ith solution of the initial population in the dimension j,
is X i，j (t) reverse solution, m is type of reverse learning, a j (t) represents the lower limit of the search range of dimension j for the number of iterations t, b j (t) represents the upper search range limit for dimension j for the number of iterations t.
Further, the objective function is specifically:
wherein N is s Representing the total number of water-fire electric power systems, i representing the initial individual, i.e. the ith water-fire electric power system, E 2 Represents the total cost of operation of the water-fire power system; t denotes the total number of periods of the scheduling period, k denotes the kth period, h k A weight coefficient representing the time period k,
indicates a waste discharge of the i-th water fire power during a period k, based on the measured value of the water fire>
Representing the cost of the ith water fire power in the k period.
Further, step S3 specifically includes:
s3-1: generating an initial population according to a population generation formula, wherein the population generation formula specifically comprises the following steps:
X i，j ＝X min，j +r 5 ×(X max，j -X min，j )
wherein, X i，j Is the ith initial individual of j dimension in the initial population, X max，j Is the upper bound of dimension j, X min，j Is the lower individual of dimension j; r is 5 Is a fifth random quantity, r 5 ∈(0，1)；
S3-2: defining a position definition formula of the initial individual at different iteration times, wherein the position definition formula is as follows:
X i (t)＝(X i，1 ，X i，2 ，X i，3 ，......，X i，j )(i＝1，2，3，......，N)，
wherein X i (t) represents the position of the ith initial individual at the number of iterations t; n is the total number of initial individuals; j represents the dimension of the initial individual; x i，j Representing the position of the ith initial individual in the initial population in dimension j.
Further, step S4-2 specifically includes: and carrying out constraint processing on the initial individuals and the reverse individuals, respectively calculating the fitness and the constraint violation quantity of the initial individuals and the fitness and the constraint violation quantity of the reverse individuals, and selecting one of each pair of the initial individuals and the reverse individuals according to the quality criterion, the fitness and the constraint violation quantity to generate an iterative population.
Further, the constraint processing includes equality constraint processing and inequality constraint processing.
In order to achieve the above object, according to the same inventive concept, a second aspect of the present invention provides a power data multi-constraint optimization system, which uses any one of the above power data multi-constraint optimization methods during operation; the system comprises a creating module, a generating module, an establishing module and a calculating module;
the creating module is used for creating an inverse sine and cosine algorithm model; the generating module is used for generating N j-dimensional initial individuals, and the initial individuals comprise the power generation flow of a hydropower station and the output of a thermal power generating unit; the establishing module is used for establishing an initial population and a position definition formula; the calculation module is used for putting the initial individual and the target function into a reverse sine-cosine algorithm model for iterative calculation, screening out the optimal individual, substituting the optimal individual into the target function, calculating the optimal function value, and outputting the optimal individual and the optimal function value.
The technical principle and the beneficial effects of the invention are as follows: according to the scheme, the reverse sine and cosine model is provided, reverse learning is carried out on the current solution in the process of calculating the optimal solution, the search range of the optimal solution is expanded, the search efficiency of the reverse sine and cosine model is improved, and the reverse solution has the probability of being closer to the optimal solution of the problem than the current solution by 50% according to probability theorem. After the current solution and the reverse solution are determined, iterative operation is completed by adopting a sine function and a cosine function, an optimal solution is found from the current solution and the reverse solution, and the calculation precision is improved. In the scheme, the hydropower station generating capacity and the thermal power unit output which meet the constraint are determined to be current solutions, the optimal solutions, namely the output optimal individuals, namely the optimal hydropower station generating flow and the optimal thermal power unit output in the dispatching period are found in the current solutions through the calculation of a reverse sine-cosine model, and the optimal function value calculated according to the optimal individual calculation and the objective function is the lowest comprehensive economic cost in the dispatching period. Compared with the prior art, the method has the advantages that the iterative computation process follows the principle of simplicity and rapidness, additional parameter adjustment is not needed, and the application convenience and the computation efficiency of the reverse sine and cosine model are improved; the target function does not need to be simplified, the calculation precision is improved, the problem of dimension disaster does not exist in the calculation process, and the stability is better.
Drawings
FIG. 1 is a flow chart of a power data multi-constraint optimization method according to the present invention;
FIG. 2 is a flowchart of the algorithm of the reverse sine and cosine model of the present invention;
FIG. 3 is a schematic structural diagram of a power data multi-constraint optimization system according to the present invention;
fig. 4 is a schematic diagram of the power data encryption process of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are merely for convenience of description and simplicity of description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
Example 1
In order to realize accurate optimal scheduling of a water-fire power system, the embodiment discloses a new algorithm model: the model can be used for accurately calculating the hydropower station generated flow and thermal power output of a water-fire power system in a dispatching period, and the dispatching cost is reduced to the minimum. As shown in fig. 2, which is an algorithm flowchart of the reverse sine-cosine model, the specific calculation process of the reverse sine-cosine model is as follows:
assuming an existing objective function and N individuals, wherein the dimension of each individual is j; generating an initial population from the N individuals according to a population formula, wherein the population formula specifically comprises the following steps:
X i，j ＝X min，j +r 5 ×(X max，j -X min，j )
wherein, X i，j Is the ith initial individual of j dimension in the initial population, X max，j Is the upper bound of dimension j, X min，j Is the lower individual of dimension j; r is 5 Is a fifth random quantity, r 5 ∈(0，1)；
Establishing a position formula of the initial individual at different iteration times according to a population formula:
X i (t)＝(X i，1 ，X i，2 ，X i，3 ，......，X i，j )(i＝1，2，3，......，N)，
wherein X i (t) represents the position of the ith initial individual at the number of iterations t; n is the total number of initial individuals; x i，j Representing the position of the ith initial individual in the initial population in dimension j.
Calculating a reverse individual of each initial individual based on a reverse solution algorithm, calculating the fitness of each reverse individual and a target function and the fitness of the initial individual and the target function, and screening out one group with high fitness from each pair of initial individual and reverse individual to form an iterative population;
the inverse solution method is as follows:
wherein, X i，j (t) is the original position of the ith initial individual in the dimension j when the iteration number t is carried out, namely the ith solution of the initial population in the dimension j,
is X i，j (t) reverse solution, m is type of reverse learning, a j (t) represents the lower limit of the search range of dimension j for the number of iterations t, b j (t) represents the upper limit of the search range of the dimension j when the iteration number t is carried out;
iterative computation is carried out on the iterative population by using a sine and cosine algorithm, wherein the sine and cosine algorithm specifically comprises the following steps:
wherein t is the actual iteration number; x i，j (t) is the original position of the ith initial individual on the dimension j when the iteration number t is reached; x i，j (t + 1) is the updated position of the initial individual i on the dimension j when the iteration number t is reached; x gb，j The position of the optimal individual of the current iteration on the dimension j is obtained; r is 1 In order to be the amplitude conversion factor,
T max a is a first constant value for the maximum iteration number; r is 2 Is a second random quantity, r 3 Is a third random quantity, r 4 Is a fourth random quantity, r 2 ∈[0，2π]，r 3 ∈[-2，2]，r 4 ∈[0，1]，r 2 、r 3 、r 4 Are subject to uniform distribution. In this embodiment, the value of the first constant is 2, and in other embodiments, the first constant may take other values according to requirements.
In the iterative calculation process, after each iteration, each individual in the iterative population updates the position of the individual and generates a new iterative population, so that the position of the optimal individual is changed continuously; meanwhile, after each iteration, the amplitude conversion factor is changed, and the numerical value of the fourth random quantity is redefined; and when the actual iteration times reach the maximum iteration number, the reverse sine and cosine model finishes the iterative computation and outputs the optimal individual and the optimal function value.
As shown in fig. 1, the present embodiment discloses a power data multi-constraint optimization method, which includes the following steps:
s1: establishing the reverse sine and cosine algorithm model;
s2: generating N j-dimensional initial individuals within the feasible range of the constraint condition, wherein the initial individuals comprise the power generation flow of a hydropower station and the output of a thermal power generating unit;
s3: establishing an initial population according to initial individuals, and defining a position definition formula of the initial individuals at different iteration times;
s4: inputting the initial population and the target function into a reverse sine and cosine algorithm model for iterative calculation, screening out the optimal individual, substituting the optimal individual into the target function, calculating the optimal function value, and outputting the optimal individual and the optimal function value.
Step S4 specifically includes the following steps:
s4-1: the reverse sine and cosine algorithm model generates a reverse individual according to the initial individual and a reverse resolving method;
s4-2: carrying out constraint processing on the initial individuals and the reverse individuals, and selecting one of each pair of the initial individuals and the reverse individuals according to a quality criterion to generate an iterative population;
s4-3: calculating an iteration population according to a sine and cosine algorithm, if the actual iteration number is smaller than the maximum iteration number, updating key parameters of the sine and cosine algorithm and the position of each individual in the iteration population to generate a new individual, selecting the individual before updating or the new individual according to a quality criterion to generate a new iteration population, and re-executing the step S3-3; if the actual iteration times reach the maximum iteration times, outputting the optimal individuals in the current iteration population;
s4-4: and calculating an optimal function value according to the optimal individual and the objective function, and outputting the optimal function value.
The constraint conditions of the embodiment comprise a total generated energy balance constraint, a hydroelectric power generation balance constraint, a reservoir station volume constraint, a hydroelectric power generation power constraint, a water quantity balance constraint, a reservoir boundary constraint, a hydroelectric conversion relation and a thermal power output power constraint; the method comprises the following specific steps:
and (3) system generated energy balance constraint of the water-fire power system:
P t ＝P 1t +P 2t
wherein the parameter P t Represents the load (kwh) of the t period; parameter P 1t Generating capacity (kwh) of the hydropower station in a t period; parameter P 2t Representing the power generation (kwh) of the thermal power plant during time t.
And (3) restriction of hydroelectric generation flow:
Q Hgt，min ≤Q Hgt ≤Q Hgt，max (g＝1，2，3，......N h ，t＝1，2，3，......T)
in the formula, Q Hgt The generating flow of the hydropower station g in the time period t is shown; q Hgt，min 、Q Hgt，max Respectively the minimum and maximum generating flow of the hydropower station g in the time period t; n is a radical of hydrogen h Is the number of cascade hydroelectric stations.
And (3) reservoir capacity constraint of the hydropower station:
V Hgt，min ≤V Hgt ≤V Hgt，max (g＝1，2，3，......N h ，t＝1，2，3，......T)
in the formula, V Hgt The reservoir capacity of the hydropower station g in the t period is shown; v Hgt，min 、V Hgt，max Respectively the minimum and maximum reservoir capacities of the hydropower station g in the time period t.
And (3) restriction of hydroelectric power generation power:
P Hgt，min ≤P Hgt ≤P Hgt，max (g＝1，2，3，......N h ，t＝1，2，3，......T)
in the formula: p Hgt Output power (kw) for the hydropower station g during the period t; p Hgt，min 、P Hgt，max Respectively the minimum and maximum output power (kw) of the hydropower station g in the time period t;
5) And (3) water balance constraint:
in the formula: q. q.s Hgt Natural water inflow for the g-th hydropower station in the t period
The number of hours (h) of the t-th period; taking an initial flow Q Hg0 =0, initial storage capacity->
Q Hg-1，t-τ The flow rate of the upper hydropower station during the time period of t-tau is taken out.
6) Reservoir boundary constraint:
in the formula:
the initial storage capacity of the hydropower station g; />
The terminal storage capacity of the hydropower station g.
7) The water-electricity conversion relation is as follows:
in the formula: c. C 1 、c 2 、c 3 、c 4 、c 5 、c 6 The output of the hydropower station, the storage capacity and the flow coefficient are shown.
8) Thermal power output power constraint:
P Git，min ≤P Git ≤P Git，max (i＝1，2，3，......N g ，t＝1，2，3，......T)
wherein the parameter P Git Representing the output power (kw) of the ith thermal power plant during a time period t; parameter P Git，min 、P Git，max Respectively representing the minimum value and the maximum value (kw) of the output power of the ith thermal power plant in the t period; parameter N g Representing the total number of thermal power plants in the water-fire power system.
In this embodiment, the constraint processing includes equality constraint processing and inequality constraint processing.
The inequality constraints mainly comprise flow, storage capacity and output constraints of the hydropower station and output constraints of the thermal power generating unit. For these inequality constraints, when an individual variable is out of bounds, its boundary value is taken. Taking the generated power flow constraint of the hydropower station as an example, the processing method is as follows:
other inequality constraints are processed by the same method;
compared with an inequality constraint processing method, the equality constraint processing is more complex. In the embodiment, the initial and final reservoir capacity equality constraint and the water quantity balance equality constraint of the hydropower station are met by adjusting the generating flow of the hydropower station, so that the reservoir capacity and the output of the hydropower station are determined. On the premise of ensuring that the output of the hydropower station is unchanged, the load balance of the system is met by adjusting the output of the thermal power generating unit. In the equality constraint processing process, the adjustment method of the hydropower station generating flow is the same as the adjustment method of the thermal power unit output.
In this embodiment, the modeling process of the objective function is as follows:
the short-term optimization scheduling problem of the water-gas power system comprehensively considering economic benefits and environmental protection is a multi-objective optimization problem. The optimization objective of the problem is to optimize the output of the hydro-thermal power in the dispatching period under the condition of meeting various constraint conditions of the system, so that the fuel consumption and the exhaust emission of the hydro-thermal power station in the system are minimized.
In order to minimize the water, fire and electricity running cost, maximize the economic benefit and minimize the coal consumption, the short-term multi-objective optimized scheduling of the water, fire and electricity is realized. The cost objective function is set to:
wherein F represents the total cost of the water-fire power system, k represents the kth time period, N s Represents the total number of the fire and water power systems, i represents the initial individual, i.e., the ith fire and water power system, T represents the total number of periods of the schedule period,
indicating the cost of the ith fire power in the k period.
The calculation formula of the fuel cost of the thermal power plant considering the valve effect is as follows:
wherein, a si 、b si 、c si 、e si 、f si Coefficients are calculated for the costs of the ith thermal power plant,
is the minimum output limit of the ith thermal power plant.
From the viewpoint of environmental protection, the objective of the optimal scheduling problem of minimizing exhaust emissions is to minimize the amount of harmful exhaust emissions of the thermal power plant during the scheduling period. The exhaust gas objective function of the thermal power plant in the whole scheduling period is as follows:
wherein E 1 The total waste discharge during the water-fire power system scheduling period,
for the exhaust emission of the ith hydraulic power plant in the k period, the variable satisfies:
wherein alpha is si 、β si 、γ si 、η si 、δ si The coefficients are calculated for the exhaust emissions of the thermal power plant i, respectively.
The short-term scheduling problem of the water-fire-electricity system, which considers economic benefits and environmental protection, aims to minimize fuel cost and exhaust emission at the same time. By introducing variable weight based on time period, the multi-objective optimization problem can be converted into a single objective to be solved; the final objective function is specifically:
wherein N is s Representing the total number of fire and water electric power systems, i representing the initial individual, i.e. the ith fire and water electric power system, E 2 Represents the total cost of operation of the water-fire power system; t denotes the total number of periods of the scheduling period, k denotes the kth period, h k A weight coefficient representing the time period k,
indicates a waste discharge of the i-th water fire power during a period k, based on the measured value of the water fire>
Indicating the cost of the ith fire power in the k period.
In the exhaust emission of a thermal power plant, a variable weight h k The method is obtained by calculation according to the fuel cost and the exhaust emission when the thermal power generating unit is fully loaded. Based on the parameter h k The exhaust emission of the thermal power plant can be converted into a part of the system operation cost, and two different optimization targets can be converted into a single target, so that the total operation cost of the system is minimum.
The specific implementation process is as follows:
initializing parameters of a reverse sine and cosine model, and aiming at the problem of power generation scheduling of a water-fire power system in a short period by using the power generation flow Q of a hydropower station h (g, t) and the output P of the thermal power generating unit s (g, t) is decision variable, and N is randomly generated within the feasible range of the constraint condition p An initial individual in j dimensions, which may be expressed as:
generating an initial population, and generating a reverse solution of each initial individual by using a reverse solution method after all the initial individuals update the positions of the initial individuals; executing constraint processing on the initial individuals and the reverse individuals, and calculating the fitness value and the constraint violation quantity of the initial individuals and the reverse individuals; and selecting a composition iteration population with higher fitness from each pair of initial individuals and reverse individuals to enter iteration.
And updating the optimal individual position in the sine and cosine algorithm, calculating the speed of each individual according to an improved speed updating formula, and obtaining new individuals after moving to form a new iterative population. Executing constraint processing on each individual in the new iteration population, and calculating a corresponding fitness value and a constraint violation quantity of each individual; comparing the individuals before updating with new individuals according to the quality comparison criterion, then selecting a better individual to generate a new iteration population, and entering the next iteration;
and when the actual iteration times reach the maximum iteration times of the reverse sine and cosine model, stopping iteration and outputting the optimal individuals in the current iteration population as the optimal solution of the problem, namely the power generation flow of the hydropower station and the output of the thermal power generating unit. And (4) bringing the optimal individual into the objective function and outputting a value which is the lowest comprehensive economic cost.
Example 2
Based on the same inventive concept, the embodiment provides a power data multi-constraint optimization system, and a power data multi-constraint optimization method is used in the operation process; as shown in fig. 3, the water-fire power system according to the present embodiment includes a creating module, a generating module, an establishing module, and a calculating module;
the creating module is used for creating an inverse sine and cosine algorithm model; the generating module is used for generating N j-dimensional initial individuals, and the initial individuals comprise the generating flow of a hydropower station and the output of a thermal power generating unit; the establishing module is used for establishing an initial population and a position definition formula; the calculation module is used for putting the initial individual and the target function into a reverse sine-cosine algorithm model for iterative calculation, screening out the optimal individual, substituting the optimal individual into the target function, calculating the optimal function value, and outputting the optimal individual and the optimal function value.
In a real-time process, the power data multi-constraint optimization system is stored in the cloud server, the thermal power generating unit and the hydropower station send power data to the cloud server, and the cloud server calculates the optimal individual and the optimal function value through the steps of the embodiment 1 and transmits the optimal individual and the optimal function value to the water power station and the thermal power generating unit. In order to ensure the safety of the electric power data in the transmission process, when the electric power data are uploaded to the cloud server and transmitted back to the water and fire power system from the cloud server, the electric power data are encrypted firstly. In this embodiment, the encryption means uses a semi-homomorphic encryption technology, and a semi-homomorphic encryption algorithm is used, so that the usability of the ciphertext is maintained on the premise that the semi-homomorphic characteristic is satisfied. Semi-homomorphic encryption supports only certain algorithms, such as only one addition or multiplication. The semi-homomorphic encryption process is shown in the attached figure 4, and the specific implementation steps are as follows:
the trusted authority first randomly selects two prime numbers p and q, satisfying gcd (pd, (p-1) (q-1)) =1; wherein gcd represents the least common divisor of two numbers; calculating N = pq and λ = lcm (p-1,q-1), where lcm represents the least common multiple; a random integer r is selected which is,
and u = (L (g) λ modn 2 )) -1 (ii) a Wherein L (u) = (u-1)/N; the trusted authority defines the public key as (n, r) and the private key as (λ, u). The credible structure is mainly used for guiding the water-fire power system to initialize and sending necessary public and private keys to the participating water-fire power system, and the private keys are sent to the water power station and the fire power station through a safety channel;
taking a hydropower station as an example, the generating flow Q of the hydropower station h And after the encryption is carried out through a semi-homomorphic encryption algorithm, sending the ciphertext data to the cloud server. On the premise of ensuring the safety and privacy protection of the generating capacity data, the generating capacity of the hydropower station is in the optimal state through the optimization of the cloud server algorithm. The encryption process of the hydropower station is as follows: the hydropower station collects the generated flow and records the generated flow as Q h (ii) a Random number of arbitrary selection
Calculate ciphertext->
Ciphertext data C calculated by hydropower station through communication link Q Sending the data to a cloud server; similarly, the ciphertext C available to the thermal power station P And calculating the ciphertext data C P Sending the data to a cloud server for calculation;
and after the cloud server completes the calculation, the calculated optimal ciphertext data is sent to the water supply power station and the thermal power generating unit through the communication link. After receiving the ciphertext data, the hydropower station and the thermal power generating unit firstly carry out decryption operation, wherein the decryption operation is as follows:
according to the calculated optimal data Q h Local internal regulation is performed to minimize the lowest overall economic cost. In the embodiment, a semi-homomorphic encryption algorithm is adopted, so that the usability of the ciphertext of the electric power data is kept on the premise of meeting the semi-homomorphic characteristic, the electric power data multi-constraint optimization system is operated on the premise of being based on the ciphertext data, and the result is consistent with the result obtained by operating on the plaintext data.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A multi-constraint optimization method for power data is characterized by comprising the following steps:
s1: establishing a reverse sine-cosine algorithm model, wherein the reverse sine-cosine algorithm model comprises a reverse solution algorithm and a sine-cosine algorithm;
s2: generating N j-dimensional initial individuals, wherein the initial individuals comprise the power generation flow of a hydropower station and the output of a thermal power generating unit;
s3: establishing an initial population according to initial individuals, and defining a position definition formula of the initial individuals at different iteration times;
s4: inputting the initial population and the target function into a reverse sine and cosine algorithm model for iterative calculation, screening out the optimal individual, substituting the optimal individual into the target function, calculating the optimal function value, and outputting the optimal individual and the optimal function value.
2. The power data multi-constraint optimization method as claimed in claim 1, wherein the step S4 specifically includes the steps of:
s4-1: the reverse sine and cosine algorithm model generates a reverse individual according to the initial individual and a reverse resolving method;
s4-2: carrying out constraint processing on the initial individuals and the reverse individuals, and selecting one of each pair of the initial individuals and the reverse individuals according to a quality criterion to generate an iterative population;
s4-3: calculating an iteration population according to a sine and cosine algorithm, if the actual iteration number is smaller than the maximum iteration number, updating key parameters of the sine and cosine algorithm and the position of each individual in the iteration population to generate a new individual, selecting the individual before updating or the new individual according to a quality criterion to generate a new iteration population, and re-executing the step S3-3; if the actual iteration times reach the maximum iteration times, outputting the optimal individuals in the current iteration population;
s4-4: and calculating an optimal function value according to the optimal individual and the objective function, and outputting the optimal function value.
3. The multi-constraint optimization method for power data as claimed in claim 2, wherein the sine and cosine algorithm is specifically as follows:
wherein t is the actual iteration number; x i，j (t) is the original position of the ith initial individual on the dimension j when the iteration number t is reached; x i，j (t + 1) is the updated position of the initial individual i on the dimension j when the iteration number t is reached; x gb，j The position of the optimal individual of the current iteration on the dimension j is obtained; r is a radical of hydrogen 1 In order to be the amplitude conversion factor,
T max a is a first constant value for the maximum iteration number; r is 2 Is a second random quantity, r 3 Is a third random quantity, r 4 Is a fourth random quantity, r 2 ∈[0，2π]，r 3 ∈[-2，2]，r 4 ∈[0，1]，r 2 、r 3 、r 4 Are subject to uniform distribution.
4. A power data multi-constraint optimization method according to claim 2 or 3, characterized in that the key parameters comprise the position of the optimal individual of the current iteration on the dimension j, the amplitude conversion factor and the fourth random value.
5. The power data multi-constraint optimization method according to claim 1, 2 or 3, wherein the inverse solution algorithm is specifically:
wherein X i，j (t) is the original position of the ith initial individual in the dimension j when the iteration number t is carried out, namely the ith solution of the initial population in the dimension j,
is X i，j (t) reverse solution, m is type of reverse learning, a j (t) number of iterationsLower limit of search range of dimension j at number t, b j (t) represents the upper search range limit for dimension j for the number of iterations t.
6. The power data multi-constraint optimization method according to claim 1, 2 or 3, wherein the objective function is specifically:
wherein N is s Representing the total number of water-fire electric power systems, i representing the initial individual, i.e. the ith water-fire electric power system, E 2 Represents the total cost of operation of the water-fire power system; t denotes the total number of periods of the scheduling period, k denotes the kth period, h k A weight coefficient representing the time period k is,
indicates a waste discharge of the i-th water fire power during a period k, based on the measured value of the water fire>
Representing the cost of the ith water fire power in the k period.
7. The power data multi-constraint optimization method according to claim 1, 2 or 3, wherein the step S3 specifically comprises:
s3-1: generating an initial population according to a population generation formula, wherein the population generation formula specifically comprises the following steps:
X i，j ＝X min，j +r 5 ×(X max，j -X min，j )
wherein X i，j Is the ith initial individual of j dimension in the initial population, X max，j Is the upper bound of dimension j, X min，j Is the lower individual of dimension j; r is a radical of hydrogen 5 Is a fifth random quantity, r 5 ∈(0，1)；
S3-2: defining a position definition formula of the initial individual at different iteration times, wherein the position definition formula is as follows:
X i (t)＝(X i，1 ，X i，2 ，X i，3 ，......，X i，j )(i＝1，2，3，......，N)，
wherein X i (t) represents the position of the ith initial individual at the number of iterations t; n is the total number of the initial individuals; j represents the dimension of the initial individual; x i，j Representing the position of the ith initial individual in the initial population in dimension j.
8. The multi-constraint optimization method for power data according to claim 2, wherein the step S4-2 is specifically as follows: and carrying out constraint processing on the initial individuals and the reverse individuals, respectively calculating the fitness and the constraint violation quantity of the initial individuals and the fitness and the constraint violation quantity of the reverse individuals, and selecting one of each pair of the initial individuals and the reverse individuals according to the quality criterion, the fitness and the constraint violation quantity to generate an iterative population.
9. A power data multi-constraint optimization method according to claim 2 or 8, characterized in that the constraint processing comprises equality constraint processing and inequality constraint processing.
10. A power data multi-constraint optimization system is characterized in that: the power data multi-constraint optimization method of any one of claims 1-9 is used in the operation process; the system comprises a creating module, a generating module, an establishing module and a calculating module;
the creating module is used for creating an inverse sine and cosine algorithm model; the generating module is used for generating N j-dimensional initial individuals, and the initial individuals comprise the power generation flow of a hydropower station and the output of a thermal power generating unit; the establishing module is used for establishing an initial population and a position definition formula; the calculation module is used for putting the initial individual and the target function into a reverse sine-cosine algorithm model for iterative calculation, screening out the optimal individual, substituting the optimal individual into the target function, calculating the optimal function value, and outputting the optimal individual and the optimal function value.
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