Comprehensive energy optimization scheduling method of hybrid adaptive quantum particle swarm

Abstract

The invention relates to an integrated energy optimization scheduling method of a hybrid adaptive quantum particle swarm. The invention provides an optimal scheduling scheme for a comprehensive energy system containing carbon capture and power-to-gas conversion. The method comprises the following steps: improving a quantum particle swarm algorithm: combining a contraction and expansion coefficient and a fitness value in the quantum particle swarm algorithm to improve the search capability of the algorithm; using a difference strategy to update the random position of the particle, so that the particle approaches the optimal position of the population; the Levy flight strategy is added into the particle position updating process, so that the population diversity in the later convergence period of the algorithm is improved. And scheduling optimization is performed on the integrated energy system by using the improved algorithm, so that the flexibility and the economical efficiency of the operation of the integrated energy system are improved.

Description

Technical Field

[0001] The invention relates to the field of energy management and heuristic algorithms, in particular to a comprehensive energy optimization scheduling method of a hybrid adaptive quantum particle swarm. Background Art

[0002] In the field of integrated energy system scheduling optimization, traditional optimization algorithms such as genetic algorithms and particle swarm optimization have been widely used. However, when dealing with complex optimization problems, these algorithms often have problems such as slow convergence and easy to fall into local optimal solutions. In order to solve these problems, many improved algorithms have been proposed, and integrating various optimization algorithms has become a research hotspot, such as the patent "Improved Particle Swarm Optimization Method for Nonlinear Hydraulic Unit Control Parameters", which is used to solve the unit control parameter problem; the integrated model based on genetic algorithm and particle swarm optimization is used to solve the scheduling optimization problem of integrated energy systems. These methods give full play to the advantages of their respective algorithms and effectively improve the optimization effect.

[0003] However, in view of the characteristics of the integrated energy system, there is no unified standard for how to select and design appropriate optimization algorithms, and it is often necessary to try them out according to specific problems. In addition, due to the complexity of the integrated energy system, even if the optimized solution is obtained, further research is needed to verify its feasibility, economy, safety, etc.

[0004] Therefore, a hybrid adaptive quantum particle swarm algorithm is proposed to optimize the operation of the integrated energy system and improve its economy, safety, reliability and stability. It has important theoretical and practical significance and may promote the development of the field of integrated energy system scheduling optimization. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is: how to select and design a suitable optimization algorithm to optimize the economic dispatch of the integrated energy system.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: combining the shrinkage and expansion coefficients in the quantum particle swarm algorithm with the fitness value to improve the algorithm's search capability; using a differential strategy to update the random positions of particles so that the particles are close to the optimal position of the population; adding the Levy flight strategy to the particle position update process to increase the population diversity in the late convergence of the algorithm. The improved algorithm is used to schedule and optimize the integrated energy system, calculate the objective function value of the integrated energy system, and construct constraint conditions, thereby improving the flexibility and economy of the operation of the integrated energy system and completing the optimization of the integrated energy system.

[0008] The particle swarm optimization (PSO) algorithm abstracts the birds in a flock into a mathematical model. Each bird is a particle. The location of the food is the optimal solution to the problem. The distance between the bird and the food is the objective function value of the current particle. Particles have only two properties: speed and position. The speed represents the speed of movement, and the position represents the direction of movement. Each particle searches for the optimal solution in the search space separately, and records it as the current individual extreme value. The individual extreme value is shared with other particles in the entire particle swarm. The optimal individual extreme value is found as the current global optimal solution of the entire particle swarm. All particles in the particle swarm adjust their speed and position according to the current individual extreme value they find and the current global optimal solution shared by the entire particle swarm. It is a common heuristic algorithm. The quantum particle swarm optimization (QPSO) algorithm applies quantum theory to particle behavior and mainly improves the PSO algorithm in two aspects. First, the particle's moving speed is no longer considered, and the particle's position is updated according to the average optimal position of the quantum particle group. Therefore, the update of the particle's position has nothing to do with the particle's previous movement, which increases the randomness of the particle's movement and avoids the problem that the PSO algorithm is prone to fall into the local optimal solution, so that the particle can search for the global optimal solution more efficiently. Secondly, the contraction and expansion coefficient is used to control the update of the particle's position, which enhances the global convergence ability. The optimization ability of the QPSO algorithm is better than that of the PSO algorithm.

[0009] The quantum particle swarm algorithm is as follows.

[0010] In a quantum space, it is impossible to determine the speed and position of a particle at the same time, so the state of the particle must be described by the wave function ψ. The particle continuously moves toward the particle attraction point, which is the optimal position of the particle, based on the δ potential well. i (t) is close, and the probability distribution of the particle's position is determined by the Schrödinger equation. In order to obtain the precise position of the particle, the quantum state must be collapsed to the classical state. At this time, the Monte Carlo random simulation method can be used to obtain the particle's motion trajectory. The formula is as follows:

[0011]

[0012] In the formula, x id (t+1) is the position of particle i in the dth dimension at the t+1th iteration, and ± is determined by the size of u. When u>0.5, it takes a plus sign, and in other cases, it takes a minus sign; p i (t) is the random position of the particle at the tth iteration, and the calculation formula is as follows:

[0013]

[0014] In the formula, pid is the optimal position of the individual, p gd is the optimal position of the population, is a random number uniformly distributed between (0,1).

[0015] L id (t) is the potential well length, calculated as follows:

[0016] L id (t)=2α(t)|C d (t)-x id (t)| (3)

[0017] In L id The average best position is introduced into (t), denoted as C(t), that is:

[0018]

[0019] Substituting equation (3) into equation (1) we can get the new particle evolution equation:

[0020] x id (t+1)=p i (t)±α(t)|C d (t)-x id (t)|×ln[1 / u id (t)] (5)

[0021] The α in equations (3) and (5) is called the shrinkage-expansion coefficient, which is the only control parameter besides the group size and the number of iterations.

[0022] The improved hybrid adaptive quantum particle swarm algorithm is as follows.

[0023] Since the change of the shrinkage and expansion coefficient α will directly affect the behavior of the particles, the control of α is usually carried out by fixing the value and linearly reducing it. Inspired by the adaptive weight PSO algorithm, a parameter adaptive control method is proposed to adjust α. The formula is as follows:

[0024]

[0025] In the formula, α max and α min are the maximum and minimum values ​​of α, respectively. According to the literature, the maximum and minimum values ​​of α are selected to be 1 and 0.5 respectively. f is the current optimal individual fitness value of the particle, and f avg and f min They are the average fitness value and the minimum fitness value of all particles at present. Since the parameter α will be adaptively adjusted as the fitness value of the particle changes, it is called parameter adaptation.

[0026] When the fitness values ​​of each particle tend to be consistent or locally optimal, the parameter α increases; when the fitness values ​​of each particle are relatively dispersed, the parameter α decreases. At the same time, for particles whose current fitness values ​​are better than the average fitness values, the corresponding parameter α is smaller, so that the particle is retained. On the contrary, for particles whose current fitness values ​​are worse than the average fitness values, the corresponding parameter α is larger, so that the particle moves closer to a better search range.

[0027] The second is the improvement of the particle random position update strategy. The update of the optimal position of the particle is improved by combining the differential strategy. The improved formula is as follows:

[0028] P i (t+1)=P (r0)gd (t)+F(P (r1)gd (t)-P (r2)gd (t)) (7)

[0029] In the formula, F is the scaling factor, and the same adaptive change method as the parameter α control method is adopted, and the formula is as follows:

[0030]

[0031] Finally, the particle position update is improved. For the particle position update, the Levy flight strategy is adopted. The step length and time t of the Levy flight obey the Levy distribution. After simplification and Fourier transformation, the probability density function in the form of power exponential can be obtained:

[0032] Levy~μ=t -λ ; 1<λ<3 (9)

[0033] Where λ is the power exponent. Equation (9) is a probability distribution with a wide tail. In actual use, it is difficult to implement it through simple programming languages. Therefore, the Levy flight path expression proposed by Mantegna is usually used:

[0034] Levy(λ)=μ / |v| 1 / β (10)

[0035] Where, the range of parameter β is (0, 2), usually β = 1.5; parameters μ and ν are random numbers that obey the normal distribution, that is, μ~N(0,σ 2μ ), ν~N(0,σ 2ν ) ; the standard deviation σ of the normal distribution corresponding to the parameters μ and ν μ and σ ν The value satisfies formula (11):

[0036]

[0037] Substituting equation (11) into equation (10) can obtain the Levy flight path Levy(λ). The QPSO algorithm based on the Levy flight strategy needs to update the particle evolution equation in the original algorithm to:

[0038]

[0039] The specific steps of the adaptive hybrid quantum particle swarm algorithm are as follows:

[0040] (1) Initialize the number of particle swarms, the maximum number of iterations, and the position of each particle in the particle swarm, and initialize the best position of the individual particle as the current position;

[0041] (2) Calculate the average best position of the particle swarm;

[0042] (3) Calculate the current fitness value of the particle and compare it with the fitness value of the previous iteration according to the minimum fitness principle. If the current fitness value is less than the fitness value of the previous iteration, update the best position of the particle to the current position of the particle; otherwise, the best position of the particle remains unchanged.

[0043] (4) Calculate the current global best position of the population. If the fitness value corresponding to the best position of the particle is less than the fitness value of the global best position of the previous iteration, the global optimal position is updated to the best position of the particle, otherwise the global best position of the population remains unchanged;

[0044] (5) Use equation (2) and equation (12) to calculate the position of the random point and the new position of the particle respectively;

[0045] (6) Repeat (2) to (5) until the set end condition is met or the maximum number of iterations is reached.

[0046] The electric-thermal integrated energy system model considering P2G and carbon capture is as follows.

[0047] The comprehensive energy system model including P2G and carbon capture equipment proposed in the present invention is:

[0048]

[0049] In the formula, C P2G The amount of CO2 used by the P2G equipment; C buy Amount of carbon source purchased for P2G equipment; X CO2 is the P2G conversion efficiency; P P2G (t) is the active power output by P2G during period t; C FCO2 The amount of CO2 captured by the CCS device; θ B is the CCS capture rate; W P is the amount of CO2 flowing from CHP to CCS; Pgas (t) is the output power of the gas turbine in period t; η P2G The efficiency of gas turbine power generation.

[0050] The comprehensive energy system scheduling model constructed by the present invention is as follows.

[0051] The objective function of the integrated energy system scheduling model is:

[0052]

[0053] Where α, β, σ are the fuel cost coefficients of CHP units; m1 is the number of CHP units; P MTj (t) is the power generation of the jth gas turbine in period t; m2 is the number of thermal power units; c wt is the operation and maintenance cost per unit power of the wind turbine; m3 is the number of wind turbines; c vi is the operation and maintenance cost per unit power of the photovoltaic unit; m4 is the number of photovoltaic units; P WTj (t) is the actual grid power of the jth wind turbine in period t; P AWTj (t) is the wind power abandoned by the jth wind turbine in period t; c bess is the cost of battery charging and discharging power per unit time; m5 is the number of battery units; P CHAj (t) is the charging power of the jth battery unit in period t; P DISj (t) is the discharge power of the jth battery cell in time period t.

[0054] The constraints are as follows.

[0055] (1) Power balance constraints

[0056] When the system is running, it is necessary to ensure the most basic supply and demand balance, namely:

[0057]

[0058] (2) CHP unit output constraints

[0059]

[0060] Where, X CHP (t) is the operating status of the CHP unit during period t; P CHP,max is the upper limit of the power generation capacity of the CHP unit; P CHP,min is the lower limit of the power generation capacity of the CHP unit; L CHP,up is the upper limit of the ramp rate of the CHP unit; L CHP,down is the ramp rate of the CHP unit

[0061] Lower limit.

[0062] (3) P2G output constraints

[0063] 0≤P P2G (t)≤P CHP (t)X1 (17)

[0064] (4) Carbon capture CO2 constraints

[0065] 0≤P ccus (t)≤P cHP (t)X2 (18)

[0066] (5) Gas turbine output constraints

[0067]

[0068] Where, X MT (t) is the operating state of the gas turbine during period t; P MT,max is the upper limit of the power generation capacity of the gas turbine; P MT,min is the lower limit of the power generation capacity of the gas turbine; L MT,up is the upper limit of the ramp rate of the gas turbine; L MT,down It is the lower limit of the ramp rate of the gas turbine.

[0069] Since the constraints cannot give a definite feasible set due to fuzzy parameters, it is hoped that the constraints exist at a certain confidence level a, expressed as:

[0070] Cr{g(x,ζ)≤0}≥α (20)

[0071] Where α is the confidence level; Cr{·} is the credibility of the event in {·}.

[0072] The credibility measure is obtained from the possibility measure. For the possibility space (Q, P(Q), Pos), the credibility measure of event A is expressed as:

[0073]

[0074] Where Pos{A} is the possibility measure of event A; A c is the opposite event of A.

[0075] The uncertainty model of wind power, photovoltaic output and electric load demand is established as follows:

[0076]

[0077] In the formula, is the value of wind power connected to the grid at time t; is the value of photovoltaic power connected to the grid at time t; is the load size at time t; P V(t) is the value of photovoltaic power connected to the grid at time t; P WT (t) is the wind power forecast value at time t; P PV (t) is the photovoltaic prediction value at time t; P Load (t) is the load forecast value at time t.

[0078] By averaging the uncertainty models of wind turbine, photovoltaic output and electric load demand, we can obtain:

[0079]

[0080] In the formula, Find the average value of wind power connected to the grid at time t; Find the average value of photovoltaic power grid connection at time t; Find the mean value of the load at time t. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0082] Figure 1 A flowchart of an algorithm implementation of a hybrid adaptive quantum particle swarm comprehensive energy optimization scheduling method provided by an embodiment of the present invention.

[0083] Figure 2 A schematic diagram of an integrated energy system of an integrated energy optimization scheduling method of a hybrid adaptive quantum particle swarm provided by an embodiment of the present invention.

[0084] Figure 3 A system solution flow chart of a comprehensive energy optimization scheduling method of a hybrid adaptive quantum particle swarm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0085] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0086] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0087] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0088] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0089] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0090] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0091] Example 1

[0092] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides a hybrid adaptive quantum particle swarm comprehensive energy optimization scheduling method. It includes:

[0093] The motion trajectory of the particle can be obtained by Monte Carlo random simulation. The formula is as follows:

[0094]

[0095] In the formula, x id(t+1) is the position of particle i in the dth dimension at the t+1th iteration, and ± is determined by the size of u. When u>0.5, it takes a plus sign, and in other cases, it takes a minus sign; p i (t) is the random position of the particle at the tth iteration, and the calculation formula is as follows:

[0096]

[0097] In the formula, p id is the optimal position of the individual, p gd is the optimal position of the population, is a random number uniformly distributed between (0,1).

[0098] L id (t) is the potential well length, calculated as follows:

[0099] L id (t)=2α(t)|C d (t)-x id (t)| (3)

[0100] In L id The average best position is introduced into (t), denoted as C(t), that is:

[0101]

[0102] Substituting equation (3) into equation (1) we can get the new particle evolution equation:

[0103] x id (t+1)=p i (t)±α(t)|C d (t)-x id (t)|×ln[1 / u id (t)] (5)

[0104] The α in equations (3) and (5) is called the shrinkage-expansion coefficient, which is the only control parameter besides the group size and the number of iterations.

[0105] The improved hybrid adaptive quantum particle swarm algorithm is as follows.

[0106] Since the change of the shrinkage and expansion coefficient α will directly affect the behavior of the particles, the control of α is usually carried out by fixing the value and linearly reducing it. Inspired by the adaptive weight PSO algorithm, a parameter adaptive control method is proposed to adjust α. The formula is as follows:

[0107]

[0108] In the formula, α max and α minare the maximum and minimum values ​​of α, respectively. According to the literature, the maximum and minimum values ​​of α are selected to be 1 and 0.5 respectively. f is the current optimal individual fitness value of the particle, and f avg and f min They are the average fitness value and the minimum fitness value of all particles at present. Since the parameter α will be adaptively adjusted as the fitness value of the particle changes, it is called parameter adaptation.

[0109] When the fitness values ​​of each particle tend to be consistent or locally optimal, the parameter α increases; when the fitness values ​​of each particle are relatively dispersed, the parameter α decreases. At the same time, for particles whose current fitness values ​​are better than the average fitness values, the corresponding parameter α is smaller, so that the particle is retained. On the contrary, for particles whose current fitness values ​​are worse than the average fitness values, the corresponding parameter α is larger, so that the particle moves closer to a better search range.

[0110] The second is the improvement of the particle random position update strategy. The update of the optimal position of the particle is improved by combining the differential strategy. The improved formula is as follows:

[0111] P i (t+1)=P (r0)gd (t)+F(P (r1)gd (t)-P (r2)gd (t)) (7)

[0112] In the formula, F is the scaling factor, and the same adaptive change method as the parameter α control method is adopted, and the formula is as follows:

[0113]

[0114] Finally, the particle position update is improved. For the particle position update, the Levy flight strategy is adopted. The step length and time t of the Levy flight obey the Levy distribution. After simplification and Fourier transformation, the probability density function in the form of power exponential can be obtained:

[0115] Levy~μ=t -λ ; 1<λ<3 (9)

[0116] Where λ is the power exponent. Equation (9) is a probability distribution with a wide tail. In actual use, it is difficult to implement it through simple programming languages. Therefore, the Levy flight path expression proposed by Mantegna is usually used:

[0117] Levy(λ)=μ / |v| 1 / β (10)

[0118] Where, the range of parameter β is (0, 2), usually β = 1.5; parameters μ and ν are random numbers that obey the normal distribution, that is, μ~N(0,σ2μ ), ν~N(0,σ 2ν ) ; the standard deviation σ of the normal distribution corresponding to the parameters μ and ν μ and σ ν The value satisfies formula (11):

[0119]

[0120] Substituting equation (11) into equation (10) can obtain the Levy flight path Levy(λ). The QPSO algorithm based on the Levy flight strategy needs to update the particle evolution equation in the original algorithm to:

[0121]

[0122] The specific steps of the adaptive hybrid quantum particle swarm algorithm are as follows:

[0123] (1) Initialize the number of particle swarms, the maximum number of iterations, and the position of each particle in the particle swarm, and initialize the best position of the individual particle as the current position;

[0124] (2) Calculate the average best position of the particle swarm;

[0125] (3) Calculate the current fitness value of the particle and compare it with the fitness value of the previous iteration according to the minimum fitness principle. If the current fitness value is less than the fitness value of the previous iteration, update the best position of the particle to the current position of the particle; otherwise, the best position of the particle remains unchanged.

[0126] (4) Calculate the current global best position of the population. If the fitness value corresponding to the best position of the particle is less than the fitness value of the global best position of the previous iteration, the global optimal position is updated to the best position of the particle, otherwise the global best position of the population remains unchanged;

[0127] (5) Use equation (2) and equation (12) to calculate the position of the random point and the new position of the particle respectively;

[0128] (6) Repeat (2) to (5) until the set end condition is met or the maximum number of iterations is reached.

[0129] The electric-thermal integrated energy system model considering P2G and carbon capture is as follows.

[0130] The comprehensive energy system model including P2G and carbon capture equipment proposed in the present invention is:

[0131]

[0132] In the formula, C P2G The amount of CO2 used by the P2G equipment; C buyAmount of carbon source purchased for P2G equipment; X CO2 is the P2G conversion efficiency; P P2G (t) is the active power output by P2G during period t; C FCO2 The amount of CO2 captured by the CCS device; θ B is the CCS capture rate; W P is the amount of CO2 flowing from CHP to CCS; P gas (t) is the output power of the gas turbine in period t; η P2G The efficiency of gas turbine power generation.

[0133] The comprehensive energy system scheduling model constructed by the present invention is as follows.

[0134] The objective function of the integrated energy system scheduling model is:

[0135]

[0136] Where α, β, σ are the fuel cost coefficients of CHP units; m1 is the number of CHP units; P MTj (t) is the power generation of the jth gas turbine in period t; m2 is the number of thermal power units; c wt is the operation and maintenance cost per unit power of the wind turbine; m3 is the number of wind turbines; c vi is the operation and maintenance cost per unit power of the photovoltaic unit; m4 is the number of photovoltaic units; P WTj (t) is the actual grid power of the jth wind turbine in period t; P AWTj (t) is the wind power abandoned by the jth wind turbine in period t; c bess is the cost of battery charging and discharging power per unit time; m5 is the number of battery units; P CHAj (t) is the charging power of the jth battery unit in period t; P DISj (t) is the discharge power of the jth battery cell in time period t.

[0137] The constraints are as follows.

[0138] (1) Power balance constraints

[0139] When the system is running, it is necessary to ensure the most basic supply and demand balance, namely:

[0140]

[0141] (2) CHP unit output constraints

[0142]

[0143] Where, X CHP (t) is the operating status of the CHP unit during period t; P CHP,maxis the upper limit of the power generation capacity of the CHP unit; P CHP,min is the lower limit of the power generation capacity of the CHP unit; L CHP,up is the upper limit of the ramp rate of the CHP unit; L CHP,down is the ramp rate of the CHP unit

[0144] Lower limit.

[0145] (3) P2G output constraints

[0146] 0≤P P2G (t)≤P CHP (t)X1 (17)

[0147] (4) Carbon capture CO2 constraints

[0148] 0≤P ccus (t)≤P cHP (t)X2 (18)

[0149] (5) Gas turbine output constraints

[0150]

[0151] Where, X MT (t) is the operating state of the gas turbine during period t; P MT,max is the upper limit of the power generation capacity of the gas turbine; P MT,min is the lower limit of the power generation capacity of the gas turbine; L MT,up is the upper limit of the ramp rate of the gas turbine; L MT,down It is the lower limit of the ramp rate of the gas turbine.

[0152] Since the constraints cannot give a definite feasible set due to fuzzy parameters, it is hoped that the constraints exist at a certain confidence level a, expressed as:

[0153] Cr{G(x,ζ)≤0}≥α (20)

[0154] Where α is the confidence level; Cr{·} is the credibility of the event in {·}.

[0155] The credibility measure is obtained from the possibility measure. For the possibility space (Q, P(Q), Pos), the credibility measure of event A is expressed as:

[0156]

[0157] Where Pos{A} is the possibility measure of event A; A c is the opposite event of A.

[0158] The uncertainty model of wind power, photovoltaic output and electric load demand is established as follows:

[0159]

[0160] In the formula, is the value of wind power connected to the grid at time t; is the value of photovoltaic power connected to the grid at time t; is the load size at time t; P V (t) is the value of photovoltaic power connected to the grid at time t; P WT (t) is the wind power forecast value at time t; P PV (t) is the photovoltaic prediction value at time t; P Load (t) is the load forecast value at time t.

[0161] By averaging the uncertainty models of wind turbine, photovoltaic output and electric load demand, we can obtain:

[0162]

[0163] In the formula, Find the average value of wind power connected to the grid at time t; Find the average value of photovoltaic power grid connection at time t; Find the mean value of the load at time t.

[0164] Example 2

[0165] The second embodiment of the present invention is different from the previous embodiment in that:

[0166] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0167] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0168] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0169] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A hybrid adaptive quantum particle swarm comprehensive energy optimization scheduling method, characterized in that: Specifically include: The shrinkage and expansion coefficient in the quantum particle swarm algorithm is combined with the fitness value to improve the algorithm's search ability; the differential strategy is used to update the random position of the particles so that the particles move closer to the optimal position of the population; the Levy flight strategy is added to the process of particle position update to increase the population diversity in the later stage of algorithm convergence, and the improved algorithm is used to schedule and optimize the integrated energy system, calculate the objective function value of the integrated energy system, and construct constraint conditions to improve the flexibility and economy of the operation of the integrated energy system and complete the optimization of the integrated energy system.

2. According to claim 1, the shrinkage and expansion coefficient in the quantum particle swarm algorithm is combined with the fitness value to improve the algorithm search capability, characterized in that: The motion trajectory of the particles is obtained by Monte Carlo random simulation, and the formula is as follows: In the formula, x id (t+1) is the position of particle i in the dth dimension at the t+1th iteration, and ± is determined by the size of u. When u>0.5, it takes a plus sign, and in other cases, it takes a minus sign; p i (t) is the random position of the particle at the tth iteration, and the calculation formula is as follows: In the formula, p id is the optimal position of the individual, p gd is the optimal position of the population, is a random number uniformly distributed between (0,1); L id (t) is the potential well length, calculated as follows: L id (t)=2α(t)|C d (t)-x id (t)| In L id The average best position is introduced into (t), denoted as C(t), that is: Substituting equation (3) into equation (1) we can get the new particle evolution equation: x id (t+1)=p i (t)±α(t)|C d (t)-x id (t)|×ln[1 / u id (t)] The α in equations (3) and (5) is called the shrinkage-expansion coefficient, which is the only control parameter besides the group size and the number of iterations; Since the change of the shrinkage and expansion coefficient α will directly affect the behavior of the particles, and the control of α usually adopts the method of fixed value and linear reduction; inspired by the adaptive weight PSO algorithm, a parameter adaptive control method is proposed to adjust α, the formula is as follows: In the formula, α max and α min are the maximum and minimum values ​​of α, respectively. According to the literature, the maximum and minimum values ​​of α are selected to be 1 and 0.5 respectively. f is the current optimal individual fitness value of the particle, and f avg and f min They are the average fitness value and the minimum fitness value of all particles at present. Since the parameter α will be adjusted adaptively as the fitness value of the particle changes, it is called parameter adaptation; When the fitness values ​​of each particle tend to be consistent or locally optimal, the parameter α increases; when the fitness values ​​of each particle are relatively dispersed, the parameter α decreases. At the same time, for particles whose current fitness values ​​are better than the average fitness values, the corresponding parameter α is smaller, so that the particle is retained. On the contrary, for particles whose current fitness values ​​are worse than the average fitness values, the corresponding parameter α is larger, so that the particle moves closer to a better search range.

3. The method of claim 1, wherein the random position of particles is updated using a differential strategy so that the particles are close to the optimal position of the population, characterized in that: The update of the optimal position of particles is improved by combining the difference strategy. The improved formula is as follows: P i (t+1)=P (r0)gd (t)+F(P (r1)gd (t)-P (r2)gd (t)) In the formula, F is the scaling factor, and the same adaptive change method as the parameter α control method is adopted, and the formula is as follows:

4. According to claim 1, a hybrid adaptive quantum particle swarm comprehensive energy optimization scheduling method is characterized by: For the update of particle positions, the Levy flight strategy is adopted; the step size and time t of the Levy flight obey the Levy distribution. After simplification and Fourier transformation, the probability density function in power exponential form can be obtained: Lions~μ=t -λ ;1<λ<3 Where λ is the power exponent, and formula (9) is a probability distribution with a wide tail. In actual use, it is difficult to implement it through a simple programming language. Therefore, the Levy flight path expression proposed by Mantegna is usually used: Levy(λ)=μ / |v| 1 / β Where, the range of parameter β is (0, 2), usually β = 1.5; parameters μ and ν are random numbers that obey the normal distribution, that is, μ~N(0,σ 2μ ), ν~N(0,σ 2ν ) ; the standard deviation σ of the normal distribution corresponding to the parameters μ and ν μ and σ ν The value satisfies formula (11): Substituting equation (11) into equation (10) can obtain the Levy flight path Levy(λ). The QPSO algorithm based on the Levy flight strategy needs to update the particle evolution equation in the original algorithm to:

5. According to claim 1, the improved algorithm is used to optimize the scheduling of the integrated energy system, characterized in that: The specific steps of the adaptive hybrid quantum particle swarm algorithm are as follows: (1) Initialize the number of particle swarms, the maximum number of iterations, and the position of each particle in the particle swarm, and initialize the best position of the individual particle as the current position; (2) Calculate the average best position of the particle swarm; (3) Calculate the current fitness value of the particle and compare it with the fitness value of the previous iteration according to the minimum fitness principle. If the current fitness value is less than the fitness value of the previous iteration, update the best position of the particle to the current position of the particle; otherwise, the best position of the particle remains unchanged. (4) Calculate the current global best position of the population. If the fitness value corresponding to the best position of the particle is less than the fitness value of the global best position of the previous iteration, update the global optimal position to the best position of the particle. Otherwise, the global best position of the population remains unchanged. (5) Use equation (2) and equation (12) to calculate the position of the random point and the new position of the particle respectively; (6) Repeat (2) to (5) until the set end condition is met or the maximum number of iterations is reached.

6. The integrated energy system model according to claim 1, characterized in that: The proposed integrated energy system model including P2G and carbon capture equipment is: In the formula, C P2G The amount of CO2 used by the P2G equipment; C buy Amount of carbon source purchased for P2G equipment; X CO2 is the P2G conversion efficiency; P P2G (t) is the active power output by P2G during period t; C FCO2 The amount of CO2 captured by the CCS device; θ B is the CCS capture rate; W P is the amount of CO2 flowing from CHP to CCS; P gas (t) is the output power of the gas turbine in period t; η P2G For gas turbine power generation efficiency; The objective function of the integrated energy system dispatch model is: Where α, β, σ are the fuel cost coefficients of CHP units; m1 is the number of CHP units; P MTj (t) is the power generation of the jth gas turbine in period t; m2 is the number of thermal power units; c wt is the operation and maintenance cost per unit power of the wind turbine; m3 is the number of wind turbines; c vi is the operation and maintenance cost per unit power of the photovoltaic unit; m4 is the number of photovoltaic units; P WTj (t) is the actual grid power of the jth wind turbine in period t; P AWTj (t) is the wind power abandoned by the jth wind turbine in period t; c bess is the cost of battery charging and discharging power per unit time; m5 is the number of battery units; P CHAj (t) is the charging power of the jth battery unit in period t; P DISj (t) is the discharge power of the jth battery unit in time period t; The constraints are as follows: (1) Power balance constraints When the system is running, it is necessary to ensure the most basic supply and demand balance, namely: (2) CHP unit output constraints Where, X CHP (t) is the operating status of the CHP unit during period t; P CHP,max is the upper limit of the power generation capacity of the CHP unit; P CHP,min is the lower limit of the power generation capacity of the CHP unit; L CHP,up is the upper limit of the ramp rate of the CHP unit; L CHP,down is the lower limit of the ramp rate of the CHP unit; (3) P2G output constraints 0≤P P2G (t)≤P CHP (t)X1 (17) (4) Carbon capture CO2 constraints 0≤P ccus (t)≤P cHP (t)X2 (18) (5) Gas turbine output constraints Where, X MT (t) is the operating state of the gas turbine during period t; P MT,max is the upper limit of the power generation capacity of the gas turbine; P MT,min is the lower limit of the power generation capacity of the gas turbine; L MT,up is the upper limit of the ramp rate of the gas turbine; L MT,down is the lower limit of the ramp rate of the gas turbine; Since the constraints cannot give a definite feasible set due to fuzzy parameters, it is hoped that the constraints exist at a certain confidence level a, expressed as: Cr{g(x,ζ)≤0}≥α (20) Where α is the confidence level; Cr{·} is the credibility of the event in {·}; The credibility measure is obtained from the possibility measure. For the possibility space (Q, P(Q), Pos), the credibility measure of event A is expressed as: Where Pos{A} is the possibility measure of event A; A c is the opposite event of A; The uncertainty model of wind power, photovoltaic output and electric load demand is established as follows: In the formula, is the value of wind power connected to the grid at time t; is the value of photovoltaic power connected to the grid at time t; is the load size at time t; P V (t) is the value of photovoltaic power connected to the grid at time t; P WT (t) is the wind power forecast value at time t; P PV (t) is the photovoltaic prediction value at time t; P Load (t) is the load forecast value at time t; By averaging the uncertainty models of wind turbine, photovoltaic output and electric load demand, we can obtain: In the formula, Find the average value of wind power connected to the grid at time t; Find the average value of photovoltaic power grid connection at time t; Find the mean value of the load at time t.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for comprehensive energy optimization scheduling of a hybrid adaptive quantum particle swarm according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for comprehensive energy optimization scheduling of a hybrid adaptive quantum particle swarm according to any one of claims 1 to 6 are implemented.

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