CN109345005A - A multi-dimensional optimization method for integrated energy system based on improved whale algorithm - Google Patents

A multi-dimensional optimization method for integrated energy system based on improved whale algorithm Download PDF

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CN109345005A
CN109345005A CN201811060521.0A CN201811060521A CN109345005A CN 109345005 A CN109345005 A CN 109345005A CN 201811060521 A CN201811060521 A CN 201811060521A CN 109345005 A CN109345005 A CN 109345005A
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曲春辉
赵洋
陶思钰
王岩
徐青山
孙文文
杨硕
孙辰军
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Ducheng Weiye Group Co Ltd
State Grid Corp of China SGCC
Southeast University
China Electric Power Research Institute Co Ltd CEPRI
State Grid Hebei Electric Power Co Ltd
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China Electric Power Research Institute Co Ltd CEPRI
State Grid Hebei Electric Power Co Ltd
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Abstract

The invention discloses a kind of based on the integrated energy system multidimensional optimization method for improving whale algorithm, this method models integrated energy system, with the minimum objective function of overall cost of operation, using electricity, heat, cold equilibrium condition as constraint condition, the globally optimal solution of objective function is acquired using improvement whale algorithm.Wherein, improved whale algorithm is to be obtained on the basis of traditional whale algorithm by introducing self-adaptive weight sum Cauchy function.One aspect of the present invention improves the ability of searching optimum of whale algorithm, avoids and falls into local optimum;On the other hand local search ability is improved, improves convergence rate and precision, the stability having had.

Description

一种基于改进鲸鱼算法的综合能源系统多维寻优方法A multi-dimensional optimization method for integrated energy system based on improved whale algorithm

技术领域technical field

本发明涉及一种综合能源系统多维寻优方法,尤其涉及一种基于改进鲸鱼算 法对含分布式新能源发电的冷-热-电综合能源系统进行多维寻优的方法。The invention relates to a multi-dimensional optimization method for an integrated energy system, in particular to a multi-dimensional optimization method for a cold-heat-electric integrated energy system containing distributed new energy power generation based on an improved whale algorithm.

背景技术Background technique

近年来,随着能源危机的日趋严重和分布式电源技术的成熟完善,含可再生 能源和高能源利用率的冷-热-电综合能源系统也受到了广泛关注。综合能源系统 既充分利用了可再生能源发电的清洁、环保的特性,同时在能量梯级利用概念的 基础上,以天然气为一次能源,产生冷、热、电的联产联供。是一个具有较高能 源利用率和资源互补性的系统。In recent years, with the increasingly serious energy crisis and the maturity and improvement of distributed power technology, the integrated energy system of cold-heat-electricity with renewable energy and high energy utilization has also received extensive attention. The integrated energy system not only makes full use of the clean and environmentally friendly characteristics of renewable energy power generation, but also uses natural gas as primary energy on the basis of the concept of energy cascade utilization to generate co-generation and co-supply of cooling, heat and electricity. It is a system with high energy utilization and resource complementarity.

对于综合能源系统的优化问题,首先需要建立一个含多种能源形式的系统优 化运行模型,以整体运营成本最小为目标函数,并需要考虑不同能量形式之间的 转化效率以及多种类型发电与储能的约束条件。采用稳定、可靠的高精度的优化 算法进行求解,以实现能流综合利用与协同优化,满足最终用户的多类型负荷需 求。For the optimization problem of the integrated energy system, it is first necessary to establish a system optimization operation model with multiple energy forms. The objective function is to minimize the overall operating cost, and it is necessary to consider the conversion efficiency between different energy forms and various types of power generation and storage. energy constraints. A stable and reliable high-precision optimization algorithm is used to solve the problem, so as to realize the comprehensive utilization and collaborative optimization of energy flow and meet the multi-type load requirements of end users.

鲸鱼算法是由澳大利亚研究人员Mirjalili等人近年来最新提出的一种通过模仿座头鲸的捕食行为特征而实现的优化搜索算法。然而,传统的鲸鱼算法采用线 性惯性权重法,因而容易陷入局部最优解、收敛速度慢且收敛精度低。The whale algorithm is an optimized search algorithm recently proposed by Australian researchers Mirjalili et al. by imitating the hunting behavior characteristics of humpback whales. However, the traditional whale algorithm adopts the linear inertia weight method, so it is easy to fall into the local optimal solution, the convergence speed is slow and the convergence accuracy is low.

发明内容SUMMARY OF THE INVENTION

发明目的:提供了一种基于改进鲸鱼算法的综合能源系统多维寻优方法,以 解决对于包含了可再生能源和冷-热-电联供系统的经济运行的寻优问题。The purpose of the invention is to provide a multi-dimensional optimization method for an integrated energy system based on an improved whale algorithm, so as to solve the optimization problem of economic operation including renewable energy and a combined cooling-heating-electricity system.

技术方案:本发明所提供的综合能源系统多维寻优方法包括如下步骤:(1) 建立可再生能源及冷热电负荷的模型、系统运行模型、储能设备模型和能量转换 设备模型,确定综合能源系统中的各项运营成本的计算方式以及电、热、冷平衡 条件;其中,各项运营成本包括:可再生能源机组的总运营成本、燃气总成本、 冷热电联产系统CCHP机组的总维护成本、交易成本和储能设备的运行总成本; (2)基于确定的各项运营成本计算综合能源系统的整体运营成本,以整体运营 成本最小作为目标函数,并以电、热、冷平衡条件作为约束条件;(3)采用鲸鱼 算法基于约束条件对目标函数进行一日时间尺度内的多维度寻优。Technical solution: The multi-dimensional optimization method for an integrated energy system provided by the present invention includes the following steps: (1) establishing a model of renewable energy and cooling, heating and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining the comprehensive The calculation method of various operating costs in the energy system and the balance conditions of electricity, heat and cooling; among them, each operating cost includes: the total operating cost of renewable energy units, the total cost of gas, and the The total maintenance cost, transaction cost and the total operating cost of the energy storage equipment; (2) Calculate the overall operating cost of the integrated energy system based on the determined operating costs, take the minimum overall operating cost as the objective function, and use electricity, heat, and cooling as the objective function. The balance condition is used as the constraint condition; (3) The whale algorithm is used to carry out multi-dimensional optimization of the objective function on the one-day time scale based on the constraint condition.

进一步地,步骤(3)包括以下步骤:Further, step (3) comprises the following steps:

(31)随机产生N个满足约束条件的目标函数中各变量的解向量;将解向量 的数目N视为鲸鱼的数目;将目标函数中的变量数M视为鲸鱼搜索空间的维数; 将第i种解向量视为第i只鲸鱼在M维空间中的位置 i=1,2,…,N;将目标函数值视为适应度函数值;(31) Randomly generate N solution vectors of each variable in the objective function that satisfy the constraints; regard the number N of solution vectors as the number of whales; regard the number of variables M in the objective function as the dimension of the whale search space; The i-th solution vector is regarded as the position of the i-th whale in the M-dimensional space i=1, 2, ..., N; regard the objective function value as the fitness function value;

(32)设置最大迭代次数,将最大迭代次数记为itmax;将当前迭代次数记为 k并初始化:k=1;将当前鲸鱼的序数记为i并初始化:i=1;将第1只鲸鱼视为 最优个体;(32) Set the maximum number of iterations, record the maximum number of iterations as it max ; record the current number of iterations as k and initialize: k=1; record the ordinal number of the current whale as i and initialize: i=1; Whales are regarded as optimal individuals;

(33)更新最优个体:将第i只鲸鱼个体的适应度函数值与最优个体的适应 度函数值进行比较,将函数值小的鲸鱼个体更新为最优个体,如果两者相等则最 优个体不变;记录更新的最优个体及其适应度函数值和位置Xp(33) Update the optimal individual: Compare the fitness function value of the i-th whale individual with the fitness function value of the optimal individual, and update the whale individual with the smaller function value as the optimal individual. If the two are equal, the optimal individual The optimal individual remains unchanged; record the updated optimal individual and its fitness function value and position X p ;

(34)更新参数ω、A、C和l:(34) Update parameters ω, A, C and l:

A=2ω·r-ω,A=2ω·r-ω,

C=2·r,C=2·r,

其中,ω为线性收敛因子,t为当前迭代次数,itmax是最大迭代次数,r为[0,1] 间的随机数,l为[-1,1]之间的随机数;Among them, ω is the linear convergence factor, t is the current number of iterations, it max is the maximum number of iterations, r is a random number between [0, 1], and l is a random number between [-1, 1];

(35)利用更新的参数来更新鲸鱼的位置:(35) Utilize the updated parameters to update the whale’s position:

产生随机数p,如果p≥0.5,则利用下式更新位置:Generate a random number p, and if p ≥ 0.5, update the position using the following formula:

其中,表示第i只鲸鱼和猎物之间的距离,b为用于限定对 数螺旋形状的常数;in, represents the distance between the i-th whale and its prey, and b is a constant used to define the logarithmic spiral shape;

如果p<0.5且|A|<1,则利用下式更新位置:If p<0.5 and |A|<1, update the position with:

D=|C·Xp(t)-X(t)|,D = |C·Xp(t)-X(t)|,

X(t+1)=ω(t)·Xp(t)-A·D,X(t+1)=ω(t)·X p (t)-A·D,

t为当前迭代次数,itmax是最大迭代次数,A·D为包围步长;t is the current number of iterations, it max is the maximum number of iterations, and A·D is the bracketing step size;

如果p<0.5且|A|≥1,则利用下式更新位置:If p < 0.5 and |A| ≥ 1, then update the position using:

式中,Xij(t)为变异前的第i只鲸鱼的第j个位置点,即步骤(31)中随机产 生的第i个解向量中第j个自变量的解;In the formula, X ij (t) is the j-th position of the i-th whale before mutation, that is, the solution of the j-th independent variable in the i-th solution vector randomly generated in step (31);

(36)令i=i+1,判断i是否达到N;如果未达到N,则重复执行步骤(33) 至(35);反之则转至步骤(37);(36) make i=i+1, judge whether i reaches N; if it does not reach N, then repeat steps (33) to (35); otherwise, go to step (37);

(37)令k=k+1,判断k是否达到最大迭代次数itmax;若没有达到,则重复 执行步骤(33)至(36);反之则结束算法,得到经更新的鲸鱼的最优位置Xp即为目标函数的全局最优解。(37) make k=k+1, judge whether k reaches the maximum iteration number it max ; if not, repeat steps (33) to (36); otherwise, end the algorithm to obtain the updated optimal position of the whale X p is the global optimal solution of the objective function.

有益效果:本发明与现有技术相比,在求解综合能源系统的全局最优解时, 在传统鲸鱼算法的基础之上,引入了自适应权重和柯西变异而进行计算。自适应 惯性权重的引入改进了传统鲸鱼算法的局部搜索能力,提高了收敛精度;通过柯 西变异算子对鲸鱼的位置进行变异,提高了鲸鱼算法的全局搜索能力,避免在求 解过程中陷入局部最优。Beneficial effects: Compared with the prior art, the present invention introduces adaptive weight and Cauchy variation for calculation based on the traditional whale algorithm when solving the global optimal solution of the integrated energy system. The introduction of adaptive inertia weight improves the local search ability of the traditional whale algorithm and improves the convergence accuracy; the position of the whale is mutated by the Cauchy mutation operator, which improves the global search ability of the whale algorithm and avoids getting caught in the local area during the solution process. optimal.

附图说明Description of drawings

图1是本发明实施例提供改进鲸鱼算法的流程图。FIG. 1 is a flowchart of an improved whale algorithm provided by an embodiment of the present invention.

具体实施方式Detailed ways

以下是结合附图对本发明进行详细说明。The following is a detailed description of the present invention with reference to the accompanying drawings.

本发明的基于改进鲸鱼算法的综合能源系统多维寻优方法旨在解决冷-热- 电联供综合能源系统在满足冷、热、电平衡约束以及储能设备约束和电热、电冷 转换设备约束的条件下,达到运行总成本最低目标的优化问题。其实施步骤如下:The multi-dimensional optimization method of the integrated energy system based on the improved whale algorithm of the present invention aims to solve the problem that the integrated energy system of cold-heat-electricity combined supply meets the constraints of cooling, heating and electricity balance, energy storage equipment constraints and electric heating, electric cooling conversion equipment constraints Under the conditions of , the optimization problem that achieves the goal of minimizing the total operating cost. Its implementation steps are as follows:

步骤S1:建立可再生能源及冷热电负荷的模型、系统运行模型、储能设备 模型和能量转换设备模型,确定综合能源系统中的各项运营成本的计算方式以及 电、热、冷平衡条件;其中,各项运营成本包括:可再生能源机组的总运营成本、 燃气总成本、冷热电联产系统CCHP机组的总维护成本、交易成本和储能设备 的运行总成本;Step S1: Establish models of renewable energy and cooling, heating and power loads, system operation models, energy storage equipment models and energy conversion equipment models, and determine the calculation methods of various operating costs in the integrated energy system and the balance conditions of electricity, heat and cold ; Among them, various operating costs include: total operating cost of renewable energy units, total gas cost, total maintenance cost of CCHP units of the combined cooling, heating and power system, transaction cost and total operating cost of energy storage equipment;

(1)可再生能源及冷热电负荷的数学模型:(1) Mathematical model of renewable energy and cooling, heating and power load:

式中,km(t)为0-1变量,用于控制可再生能源是否投入。Cg为发电成本参 数,Com为运行维护成本参数,Cdm为停机维护成本。Cm(t)为可再生能源机组m 的成本。为t时段可再生能源机组m的出力,且满足功率约束:In the formula, km ( t ) is a 0-1 variable, which is used to control whether the renewable energy is input. C g is the power generation cost parameter, C om is the operation and maintenance cost parameter, and C dm is the downtime maintenance cost. C m (t) is the cost of the renewable energy unit m. is the output of the renewable energy unit m in period t, and it satisfies the power constraint:

其中,分别为可再生能源机组m出力的上、下限。in, and are the upper and lower limits of the output of the renewable energy unit m, respectively.

(2)系统运行模型:(2) System operation model:

其中,分别为CCHP机组k电输出功率的上限和下限;分别为电输出功率的向下爬坡速率和向上爬坡速率。in, and are the upper limit and lower limit of the output power of the k-electricity of the CCHP unit, respectively; and are the downward ramp rate and the upward ramp rate of the electrical output power, respectively.

CCHP机组k中的余热锅炉运行约束如下:The operating constraints of the waste heat boiler in CCHP unit k are as follows:

Hk(t)/ηHEX+Lk(t)/ηCOP≤ηFHFk(t)+ηbFk-b(t),H k (t)/η HEX +L k (t)/η COP ≤η FH F k (t)+η b F kb (t),

其中,ηFH为CCHP中内燃机余热回收系数;ηb为锅炉补燃热效率;ηHEX为 换热器效率;ηCOP为制冷机制冷系数;为CCHP机组k中余热锅炉最大运 行功率;Fk(t)为t时段CCHP机组k中内燃机消耗的天然气流量;Fk-b(t)为n 时段CCHP机组k中余热锅炉补偿消耗天然气流量;Hk(t)为CCHP机组k中余 热锅炉回热器的热输出功率;Lk(t)为CCHP机组k中制冷设备的制冷输出功率。 Fk(t)和Fk-b(t)通过热量来计量。Wherein, η FH is the waste heat recovery coefficient of the internal combustion engine in the CCHP; η b is the boiler supplementary combustion heat efficiency; η HEX is the heat exchanger efficiency; η COP is the refrigeration coefficient of the refrigerator; is the maximum operating power of the waste heat boiler in the CCHP unit k; F k (t) is the natural gas flow consumed by the internal combustion engine in the CCHP unit k in the t period; F kb (t) is the natural gas flow for the compensation consumption of the waste heat boiler in the CCHP unit k in the n period; H k (t) is the heat output power of the waste heat boiler regenerator in CCHP unit k; L k (t) is the cooling output power of the refrigeration equipment in CCHP unit k. F k (t) and F kb (t) are measured by heat.

(3)储能设备的数学模型:(3) Mathematical model of energy storage equipment:

电储能的运行约束如下:The operating constraints of electric energy storage are as follows:

0≤Pdis(t)≤Pmax0≤P dis (t)≤P max ,

0≤Pchar(t)≤Pmax0≤P char (t)≤P max ,

SOC(t)=SOC(t-1)+ηcharPchar(t)-Pdis(t)/ηdisSOC(t)=SOC(t-1)+η char P char (t)-P dis (t)/η dis ,

SOCmin≤SOC(t)≤SOCmaxSOC min ≤SOC(t)≤SOC max ,

其中,Pchar(t)和Pdis(t)分别为电储能的充、放电功率,Pmax为其最大值;SOC(t) 为t时段电储能的容量;ηchar和ηdis分别为电储能的充电和放电效率;SOCmin和 SOCmax分别为电储能的容量最小值和最大值。Among them, P char (t) and P dis (t) are the charging and discharging power of the electric energy storage, respectively, and P max is the maximum value; SOC (t) is the capacity of the electric energy storage in the t period; η char and η dis are respectively is the charging and discharging efficiency of the electric energy storage; SOC min and SOC max are the minimum and maximum capacity of the electric energy storage, respectively.

热储能的运行约束如下:The operational constraints of thermal energy storage are as follows:

HT(t)=ηTHT(t-1)+ηTDHTD(t)-HTC(t)/ηTCH T (t)=n T H T (t-1)+n TD H TD (t)-H TC (t)/n TC ,

其中,HTD(t)和HTC(t)分别为储热装置的充、放热功率,为储热装置 的充、放热功率中的最大值;HT(t)为t时段储热装置储热水平;ηTD和ηTC分别 为储热装置的充热和放热效率;1-ηT为储热装置经过单位时间后的损耗率;为储热装置的储热容量。储能设备的运行成本表述如下:Among them, H TD (t) and H TC (t) are the charging and discharging power of the heat storage device, respectively, is the maximum value of the charging and discharging power of the heat storage device; H T (t) is the heat storage level of the heat storage device in the t period; η TD and η TC are the charging and discharging efficiencies of the heat storage device, respectively; 1-η T is the loss rate of the heat storage device after unit time; is the heat storage capacity of the heat storage device. The operating costs of energy storage equipment are expressed as follows:

Cbat(t)=CEES(Pchar(t)+Pdis(t))+CTES(HTD(t)+HTC(t))+CCES(LTD(t)+LTC(t)),C bat (t)=C EES (P char (t)+P dis (t))+C TES (H TD (t)+H TC (t))+C CES (L TD (t)+L TC ( t)),

其中,CEES、CTES和CCES分别为电、热、冷储能的循环损耗成本;LTD(t)和 LTC(t)分别为冷储能的充、放冷功率。Among them, C EES , C TES and C CES are the cycle loss costs of electricity, heat and cold energy storage, respectively; L TD (t) and L TC (t) are the charging and discharging power of cold energy storage, respectively.

(4)电热、电冷能量转换设备的数学模型如下:(4) The mathematical model of the electric heating and electric cooling energy conversion equipment is as follows:

HEH(t)=ηEHPEH(t),H EH (t)=η EH P EH (t),

LEC(t)=ηECPEC(t),L EC (t) = η EC P EC (t),

其中,下标EH和EC分别表示电热和电冷转换;ηEH和ηEC分别为电热和电 冷转换效率。运行成本如下所示:Among them, the subscripts EH and EC represent the conversion of electricity to heat and electricity, respectively; η EH and η EC represent the conversion efficiencies of electricity to heat and electricity, respectively. The running costs are as follows:

Cconv(t)=CEHPEH(t)+CECPEC(t)。C conv (t) = C EH P EH (t) + C EC P EC (t).

步骤S2:基于确定的各项运营成本计算综合能源系统的整体运营成本,以 整体运营成本最小作为目标函数,并以电、热、冷平衡条件作为约束条件。Step S2: Calculate the overall operating cost of the integrated energy system based on the determined operating costs, take the minimum overall operating cost as the objective function, and take the electricity, heat, and cooling balance conditions as the constraints.

目标函数:Objective function:

其中,Cgas为单位燃气的价格;CCCHP,l为CCHP机组l的维护成本;NCCHP为 CCHP机组数量。Among them, C gas is the price of unit gas; C CCHP,l is the maintenance cost of CCHP unit l; N CCHP is the number of CCHP units.

约束条件:Restrictions:

其中,Pbuy(t)、Psell(t)分别为园区向上级电网购电和售电功率;Eload(t)、 Hload(t)、Lload(t)分别为园区总的电、热和冷负荷。Among them, P buy (t) and P sell (t) are the power purchased and sold by the park to the upper power grid, respectively; E load (t), H load (t), and L load (t) are the total electricity and heat of the park, respectively. and cooling load.

步骤S3:采用鲸鱼算法基于约束条件对目标函数进行一日时间尺度内的多 维度寻优。Step S3: The whale algorithm is used to perform multi-dimensional optimization on the objective function within a one-day time scale based on constraints.

如图1所示,本发明采用的鲸鱼算法为改进的鲸鱼算法,其大体思路为:首 先,计算每只鲸鱼个体的适应度函数值,并记录最优个体及其位置Xp。然后, 更新参数ω,A,C,l。接着,产生随机数p,如果p<0.5,则继续判断,如果|A| <1,则鲸鱼自适应惯性权重游走,否则,柯西变异游走。如果p≥0.5,则鲸鱼 螺旋游走。判断鲸鱼数量是否达到最大值,且达到最大迭代次数。若是,则结束 算法,得到鲸鱼的最优位置。As shown in FIG. 1 , the whale algorithm adopted in the present invention is an improved whale algorithm. The general idea is as follows: first, calculate the fitness function value of each individual whale, and record the optimal individual and its position X p . Then, update the parameters ω, A, C, l. Next, generate a random number p, if p < 0.5, continue to judge, if |A| < 1, then the whale swims with adaptive inertia weight, otherwise, it swims with Cauchy variation. If p ≥ 0.5, the whale swims in a spiral. Determine whether the number of whales has reached the maximum value and the maximum number of iterations has been reached. If so, end the algorithm and get the optimal position of the whale.

利用改进的鲸鱼算法基于步骤S2中的约束条件求解目标函数的最优解的具 体步骤如下:The specific steps of using the improved whale algorithm to solve the optimal solution of the objective function based on the constraints in step S2 are as follows:

(1)随机产生N个满足约束条件的目标函数中各变量的解向量;将解向量 的数目N视为鲸鱼的数目;将目标函数中的变量数M视为鲸鱼搜索空间的维数; 将第i种解向量视为第i只鲸鱼在M维空间中的位置 i=1,2,…,N;将目标函数值视为适应度函数值;(1) Randomly generate N solution vectors of each variable in the objective function that satisfy the constraints; regard the number N of solution vectors as the number of whales; regard the number of variables M in the objective function as the dimension of the whale search space; The i-th solution vector is regarded as the position of the i-th whale in the M-dimensional space i=1, 2, ..., N; regard the objective function value as the fitness function value;

(2)设置最大迭代次数,将最大迭代次数记为itmax;将当前迭代次数记为k 并初始化:k=1;将当前鲸鱼的序数记为i并初始化:i=1;将第1只鲸鱼视为最 优个体;(2) Set the maximum number of iterations, and record the maximum number of iterations as it max ; record the current number of iterations as k and initialize: k=1; record the ordinal number of the current whale as i and initialize: i=1; Whales are regarded as optimal individuals;

(3)更新最优个体:将第i只鲸鱼个体的适应度函数值与最优个体的适应度 函数值进行比较,将函数值小的鲸鱼个体更新为最优个体,如果两者相等则最优 个体不变;记录更新的最优个体及其适应度函数值和位置Xp(3) Update the optimal individual: compare the fitness function value of the i-th whale individual with the fitness function value of the optimal individual, and update the whale individual with the smaller function value as the optimal individual. The optimal individual remains unchanged; record the updated optimal individual and its fitness function value and position X p ;

(4)更新参数ω、A、C和l:(4) Update parameters ω, A, C and l:

A=2ω·r-ω,A=2ω·r-ω,

C=2·r,C=2·r,

其中,ω为线性收敛因子,随迭代次数增加从1线性减小到0;t为当前迭代 次数,itmax是最大迭代次数,r为[0,1]间的随机数,l为[-1,1]之间的随机数;Among them, ω is a linear convergence factor, which decreases linearly from 1 to 0 as the number of iterations increases; t is the current number of iterations, it max is the maximum number of iterations, r is a random number between [0, 1], and l is [-1 , a random number between 1];

(5)利用更新的参数来更新鲸鱼的位置:(5) Use the updated parameters to update the whale's position:

产生随机数p,如果p≥0.5,则鲸鱼螺旋游走,用于模拟鲸鱼以螺旋式运动 来捕获猎物。此时,利用下式更新位置:A random number p is generated. If p≥0.5, the whale swims in a spiral, which is used to simulate the whale's spiral motion to capture prey. At this point, the position is updated using the following formula:

其中,表示第i只鲸鱼和猎物之间的距离,b为用于限定对 数螺旋形状的常数;in, represents the distance between the i-th whale and its prey, and b is a constant used to define the logarithmic spiral shape;

如果p<0.5且|A|<1,则鲸鱼自适应惯性权重游走。此时,利用下式更新位置:If p<0.5 and |A|<1, the whale swims with adaptive inertial weights. At this point, the position is updated using the following formula:

D=|C·Xp(t)-X(t)|,D = |C·Xp(t)-X(t)|,

X(t+1)=ω(t)·Xp(t)-A·D,X(t+1)=ω(t)·X p (t)-A·D,

t为当前迭代次数,itmax是最大迭代次数,A·D为包围步长。鲸鱼的泡泡网 捕食行为的数学描述方法包括收缩包围机制,该机制随着收敛因子ω的减小而实 现;将收敛因子ω作为自适应惯性权重引入后,对传统鲸鱼算法的线性惯性权重 进行了改进,提高了局部搜索能力和收敛精度;t is the current number of iterations, it max is the maximum number of iterations, and A·D is the bracketing step size. The mathematical description method of the whale's bubble net prey behavior includes the shrinkage and encirclement mechanism, which is realized with the decrease of the convergence factor ω; after the convergence factor ω is introduced as the adaptive inertia weight, the linear inertia weight of the traditional whale algorithm is carried out. Improvements have been made to improve the local search ability and convergence accuracy;

如果p<0.5且|A|≥1,则鲸鱼柯西变异游走,用于模拟鲸鱼个体根据彼此位置 来随机搜索食物的行为。此时,利用下式更新位置:If p<0.5 and |A|≥1, then the Cauchy whale swims with variation, which is used to simulate the behavior of individual whales randomly searching for food according to each other's positions. At this point, the position is updated using the following formula:

式中,Xij(t)为变异前的第i只鲸鱼的第j个位置点,即步骤(1)中随机产 生的第i个解向量中第j个自变量的解;In the formula, X ij (t) is the j-th position of the i-th whale before mutation, that is, the solution of the j-th independent variable in the i-th solution vector randomly generated in step (1);

(6)令i=i+1,判断i是否达到N;如果未达到N,则重复执行步骤(3)至 (5);反之则转至步骤(7);(6) make i=i+1, judge whether i reaches N; if it does not reach N, then repeat steps (3) to (5); otherwise, go to step (7);

(7)令k=k+1,判断k是否达到最大迭代次数itmax;若没有达到,则重复执 行步骤(3)至(6);反之则结束算法,得到经更新的鲸鱼的最优位置Xp即为目 标函数的全局最优解。(7) Let k=k+1, judge whether k reaches the maximum iteration number it max ; if not, repeat steps (3) to (6); otherwise, end the algorithm to obtain the updated optimal position of the whale X p is the global optimal solution of the objective function.

Claims (7)

1. A multi-dimensional optimization method for an integrated energy system is characterized by comprising the following steps:
(1) establishing a model of renewable energy and cold, heat and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining a calculation mode of each operation cost and balanced conditions of electricity, heat and cold in a comprehensive energy system; wherein, each item of operation cost includes: the method comprises the following steps of (1) total operation cost of a renewable energy source unit, total gas cost, total maintenance cost of a combined cooling heating and power system (CCHP) unit, transaction cost and total operation cost of energy storage equipment;
(2) determining the overall operation cost of the comprehensive energy system based on each operation cost, taking the minimum overall operation cost as a target function, and taking the balance conditions of electricity, heat and cold as constraint conditions;
(3) and performing multi-dimensional optimization on the objective function within a time scale of one day by adopting a whale algorithm based on a constraint condition.
2. The multi-dimensional optimization method of the integrated energy system according to claim 1, wherein in the step (1), based on the established mathematical models of renewable energy and cooling, heating and power loads,
in the formula, Cm(t) the cost of the renewable energy unit m at the time period t; cgIs a power generation cost parameter;generating capacity of the renewable energy source unit m at a time period t; comA cost parameter for operation and maintenance; k is a radical ofm(t) is a variable of 0 to 1 for controlling whether renewable energy is input; cdmMaintenance costs for shutdown;andrespectively is the upper limit and the lower limit of the output of the renewable energy unit m.
3. The multi-dimensional optimization method of integrated energy system according to claim 1, wherein in step (1), based on the established system operation model,
wherein,andrespectively setting the upper limit and the lower limit of the k electric output power of the CCHP unit;andthe downward climbing rate and the upward climbing rate of the electrical output power are respectively;
the waste heat boiler operation constraints in the CCHP unit k are as follows:
Hk(t)/ηHEX+Lk(t)/ηCOP≤ηFHFk(t)+ηbFk-b(t),
wherein Hk(t) is the heat output power of a waste heat boiler regenerator in a CCHP unit k, ηHEXTo the heat exchanger efficiency; l isk(t) is the refrigeration output power of the refrigeration equipment in the CCHP unit k, ηCOPη being the refrigeration coefficient of the refrigeratorFHThe waste heat recovery coefficient of the internal combustion engine in CCHP; fk(t) natural gas flow consumed by the internal combustion engine in the CCHP unit k during the period t ηbThe thermal efficiency is complemented-burning for the boiler; fk-b(t) compensating the consumed natural gas flow rate of the waste heat boiler in the CCHP unit k at a time period t;the maximum operation power of a waste heat boiler in a CCHP unit k; fk(t) and Fk-b(t) is measured by heat.
4. The multi-dimensional optimization method for the integrated energy system according to claim 1, wherein in the step (1), the operation of the electrical energy storage is constrained according to the established mathematical model of the energy storage device as follows:
SOC(t)=SOC(t-1)+ηcharPchar(t)-Pdis(t)/ηdis
SOCmin≤SOC(t)≤SOCmax
0≤Pdis(t)≤Pmax
0≤Pchar(t)≤Pmax
wherein, SOC (t) is the capacity of the electric energy storage in the period t; pchar(t) and Pdis(t) charging and discharging power, P, for electrical energy storage, respectivelymaxFor maximum value of charge-discharge power of electric energy storage ηcharAnd ηdisCharging and discharging efficiencies for electrical energy storage, respectively; SOCminAnd SOCmaxThe minimum value and the maximum value of the capacity of the electric energy storage are respectively;
the operating constraints of thermal energy storage are as follows:
HT(t)=ηTHT(t-1)+ηTCHTC(t)-HTD(t)/ηTD
wherein HT(t) the heat storage level of the heat storage device is 1- ηTη is the loss rate of the heat storage device after unit timeTDAnd ηTCThe heat charging efficiency and the heat discharging efficiency of the heat storage device are respectively; hTD(t) and HTC(t) the charging and discharging powers of the heat storage device are respectively;is the heat storage capacity of the heat storage device;the maximum value of the charging power and the discharging power of the heat storage device;
the operating cost of the energy storage device is expressed as follows:
Cbat(t)=CEES(Pchar(t)+Pdis(t))+CTES(HTD(t)+HTC(t))+CCES(LTD(t)+LTC(t)),
wherein, Cbat(t) is the total operating cost of the energy storage device during the period t; cEES、CTESAnd CCESThe cycle loss costs of electricity, heat, and cold stored energy, respectively; l isTD(t) and LTCAnd (t) cold charging power and cold discharging power of cold energy storage in a time period t respectively.
5. The multi-dimensional optimization method of the integrated energy system according to claim 1, wherein in the step (1), according to the mathematical model of the electric heating and cooling energy conversion equipment,
HEH(t)=ηEHPEH(t),
LEC(t)=ηECPEC(t),
wherein HEH(t) heat quantity converted at time t, LEH(t) the cold quantity converted at the moment t; pEH(t) and PEC(t) the amount of electricity converted into heat and cold at time t, ηEHAnd ηECThe electric heating conversion efficiency and the electric cooling conversion efficiency are respectively obtained;andupper limits of the amount of electricity that can be converted into heat and cold, respectively;
the operating costs of the electric heat and electric cold energy conversion equipment are as follows:
Cconv(t)=CEHPEH(t)+CECPEC(t),
wherein, Cconv(t) is the total operating cost of the electric heating and cooling energy conversion equipment at the moment t; cEHAnd CECThe cost of unit electric heat and electric cold conversion respectively.
6. The method according to claim 1, wherein in step (2), if the objective function is denoted as f, then there is
Wherein, C (t) is the whole operation cost in the period t; cm(t) the cost of the renewable energy unit m at the time period t; n is a radical ofreThe total number of the renewable energy units; cgasIs the unit price of the gas; n is a radical ofCCHPThe number of units of a combined cooling heating and power system CCHP; fl(t) is the natural gas flow consumed by the internal combustion engine in the CCHP unit l during a period t; fl-b(t) compensating the consumption of natural gas flow by a waste heat boiler in a CCHP unit l at a time period t; cCCHP,lThe maintenance cost of the CCHP unit l in the period t; ctrade(t) transaction cost for t period;Cbat(t) is the total operating cost of the energy storage device during the period t; cconv(t) is the operating cost of the electric heating and cooling energy conversion equipment;
the constraint conditions are as follows:
wherein,generating capacity of the renewable energy source unit m at a time period t; pl CCHP(t) is the generated energy of the CCHP unit l in a period t; pbuy(t) and Psell(t) purchasing electricity and selling electric power to the upper-level power grid in the park respectively; pchar(t) and Pdis(t) charging and discharging power for electrical energy storage, respectively; pEH(t) and PEC(t) the electric quantities converted into heat and cold at time t, respectively; eload(t)、Hload(t) and Lload(t) total electrical, thermal and cold load for the park, respectively; hTI(t) and HTO(t) the heat storage absorption amount and the discharge amount of the heat storage device in the period of t are respectively; l isTI(t) and LTO(t) the cold storage absorption amount and the discharge amount of the cold storage device in the period of t are respectively; hl(t) is the heat output power of a waste heat boiler heat regenerator in the CCHP unit l; l isl(t) is the refrigeration output power of the refrigeration equipment in the CCHP unit l; hEH(t) heat quantity converted at time t, LEHAnd (t) is the cold energy converted at the moment t.
7. The multi-dimensional optimization method for the integrated energy system according to claim 1, wherein the step (3) comprises the steps of:
(31) randomly generating solution vectors of variables in N objective functions meeting constraint conditions; considering the number of solution vectors, N, as the number of whales; taking the variable number M in the objective function as the dimension of a whale search space; regarding the ith solution vector as the position of the ith whale in the M-dimensional space Taking the objective function value as a fitness function value;
(32) setting the maximum iteration times, and recording the maximum iteration times as itmax(ii) a Recording the current iteration number as k and initializing: k is 1; the ordinal number of the current whale is recorded as i and initialized: i is 1; the 1 st whale was considered as the optimal individual;
(33) updating the optimal individual: comparing the fitness function value of the ith whale individual with the fitness function value of the optimal individual, updating the whale individual with a small function value into the optimal individual, and keeping the optimal individual unchanged if the two are equal; recording updated optimal individual and fitness function value and position X thereofp
(34) Update parameters ω, A, C, and l:
A=2ω·r-ω,
C=2·r,
where ω is a linear convergence factor, t is the current iteration number, itmaxIs the maximum number of iterations, r is [0, 1 ]]Is a random number of [ -1, 1 ]]A random number in between;
(35) updating the location of the whale with the updated parameters:
generating a random number p, if p ≧ 0.5, updating the location using the following equation:
wherein,represents the distance between the ith whale and the prey, b is a constant defining the shape of a logarithmic spiral;
if p <0.5 and | A | <1, then the location is updated using:
D=|C·Xp(t)-X(t)|,
X(t+1)=ω(t)·Xp(t)-A·D,
t is the current iteration number itmaxIs the maximum iteration number, and A.D is the surrounding step length;
if p <0.5 and | A | ≧ 1, the location is updated using the following equation:
in the formula, Xij(t) is the jth position point of the ith whale before mutation, namely the solution of the jth independent variable in the ith solution vector randomly generated in the step (31);
(36) making i equal to i +1, and judging whether i reaches N; if not, repeating the steps (33) to (35); otherwise, go to step (37);
(37) let k equal to k +1, judge whether k reaches the maximum iteration number itmax(ii) a If not, repeating the steps (33) to (36); otherwise, the algorithm is ended to obtain the updated optimal position X of the whalepWhich is the global optimal solution of the objective function.
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