CN102904289A

CN102904289A - Optimal Capacity Allocation Method for Sea Island New Energy System Based on Fruit Fly Optimization Algorithm

Info

Publication number: CN102904289A
Application number: CN2012103948863A
Authority: CN
Inventors: 包艳; 王辉; 陈向华; 袁小芳; 王北宁
Original assignee: Changsha University
Current assignee: Changsha University
Priority date: 2012-10-18
Filing date: 2012-10-18
Publication date: 2013-01-30

Abstract

The invention provides an island new energy system optimal capacity allocation method based on a drosophila optimization algorithm. A new energy system mainly comprises a photovoltaic generator, a wind power generator, a storage battery and a diesel engine and is characterized in that the capacity allocation of the new energy system is optimized by utilizing the drosophila optimization algorithm. The method comprises the following specific steps of: 1, selecting the types of the photovoltaic generator and the wind power generator according to the natural conditions and climate conditions of an island and determining the parameter values of the photovoltaic generator, the wind power generator, the storage battery and the diesel engine; 2, determining island load conditions which mainly includes a load power utilization time distribution condition, properties of loads in each time period, power values and a value of the load with the maximum peak value; 3, determining an object function of the drosophila optimization algorithm; and 4, obtaining the optimal capacity allocation parameter X={NPV,p,NWT,NBAT,p} of the island new energy system by utilizing the drosophila optimization algorithm. The method provided by the invention has the beneficial effects of greatly lowering the power generation production cost of the island new energy system, giving play to the maximal economic benefit of the electric capacity, reducing the consumption of fossil resources and the emission of environmental pollutants, promoting the island electrical energy to be more environmental-friendly and economical, and providing a sufficient electric energy guarantee for the island.

Description

Optimal Capacity Allocation Method for Sea Island New Energy System Based on Fruit Fly Optimization Algorithm

技术领域 technical field

本发明涉及一种海岛新能源系统容量配置方法，具体为一种基于果蝇优化算法的海岛新能源系统最优容量配置方法。 The invention relates to a capacity configuration method for a new energy system of an island, in particular to a method for configuring an optimal capacity of a new energy system for an island based on a fruit fly optimization algorithm. the

背景技术 Background technique

新能源指的是非炭的能源，如风能、水能、太阳能、生物质能等。最主要的优点是开发新能源有利于社会的可持续发展，有利于人类健康，有效地保护地球环境，海岛新能源可提高供电质量与清洁能源的利用效率，减少岛内化石资源的消耗量和环境污染物排放；增强发电接纳能力，满足岛内居民长期稳定的用电需求，避免因海底电缆故障引起的岛内大面积停电事故；提升电网整体抗灾能力，为电网应急提供支撑；制定海岛微电网的规程规范，为今后此类工程的设计、建设、运行和维护积累经验；并能够有效解决海岛地区的用电难问题，减少化石资源的消耗量和环境污染物排放，促进海岛电能更加绿色、经济、环保。 New energy refers to non-carbon energy, such as wind energy, water energy, solar energy, biomass energy, etc. The main advantage is that the development of new energy is conducive to the sustainable development of society and human health, and effectively protects the global environment. Island new energy can improve the quality of power supply and the utilization efficiency of clean energy, and reduce the consumption of fossil resources and Discharge of environmental pollutants; enhance power generation capacity to meet the long-term and stable electricity demand of residents on the island, and avoid large-scale power outages on the island caused by submarine cable failures; improve the overall disaster resistance of the power grid and provide support for power grid emergencies; The regulations and specifications of the power grid can accumulate experience for the design, construction, operation and maintenance of such projects in the future; and can effectively solve the problem of power consumption in island areas, reduce the consumption of fossil resources and environmental pollutant emissions, and promote greener island power , Economy and environmental protection. the

由于大陆没有电网连接的独立海岛，海岛的供电主要包括光伏发电、风电和柴油机发电，但光伏发电、风电受天气因素影响较大。因而，海岛新能源还设有蓄电池保存多余的电量，如何降低发电生产成本，提高新能源的利用率，发挥电容量的最大经济效益是当前面临的最大问题。 Since there are no independent islands connected to the grid in the mainland, the island's power supply mainly includes photovoltaic power generation, wind power and diesel engine power generation, but photovoltaic power generation and wind power are greatly affected by weather factors. Therefore, Sea Island New Energy also has a storage battery to save excess power. How to reduce the production cost of power generation, improve the utilization rate of new energy, and maximize the economic benefits of the capacity is the biggest problem currently facing. the

发明内容 Contents of the invention

本发明的目的在于提供一种基于果蝇优化算法的海岛新能源系统最优容量配置方法，确保系统在使用寿命内，系统的运行成本最低，经济效益最佳。 The purpose of the present invention is to provide a method for optimal capacity configuration of the island new energy system based on the fruit fly optimization algorithm to ensure that the operating cost of the system is the lowest and the economic benefit is the best within the service life of the system. the

为了达到上述目的，本发明采用以下技术方案： In order to achieve the above object, the present invention adopts the following technical solutions:

一种基于果蝇优化算法的海岛新能源系统最优容量配置方法，新能源系统主要包括：光伏发电、风电、蓄电池、柴油机以及充电控制器、逆变器，其特征在于，所述新能源系统容量配置采用果蝇优化算法进行优化，具体步骤如下： A method for optimal capacity configuration of an island new energy system based on the fruit fly optimization algorithm. The new energy system mainly includes: photovoltaic power generation, wind power, storage batteries, diesel engines, charge controllers, and inverters. It is characterized in that the new energy system The capacity configuration is optimized using the fruit fly optimization algorithm, and the specific steps are as follows:

1)根据海岛的自然条件、气候条件选择光伏发电、风电的类型，确认光伏发电、风电、蓄电池、柴油机的参数值； 1) Select the type of photovoltaic power generation and wind power according to the natural conditions and climate conditions of the island, and confirm the parameter values of photovoltaic power generation, wind power, storage battery and diesel engine;

2)确定海岛的负荷情况，主要包括负载用电时间分布情况、各个时段负荷的性质、功率值、最大峰值负载的值； 2) Determine the load condition of the island, mainly including the time distribution of load power consumption, the nature of the load at each time period, the power value, and the value of the maximum peak load;

3)确定果蝇优化算法的目标函数为式(1)和式(2)描述的系统运行成本函数， 3) Determine that the objective function of the fruit fly optimization algorithm is the system operation cost function described by formula (1) and formula (2),

$\underset{x x}{min min} STC STC ((x x)) = = \underset{x x}{min min} {{{C C}_{T T} ((x x)) + + {C C}_{M m} ((x x)) + + {C C}_{I I} ((x x)) + + {C C}_{T T,, D D.}}} - - - - - - ((11))$

其中x为系统优化变量组成的一维向量：x＝{N_PV,p,N_WT,N_BAT,p}，N_PV，p是并联光伏电池串数，N_WT为风力发电机组数，N_BAT，p为并联蓄电池串数； Where x is a one-dimensional vector composed of system optimization variables: x={N _PV,p ,N _WT ,N _BAT,p }, N _PV,p is the number of parallel photovoltaic cell strings, N _WT is the number of wind turbines, N _{BAT , p} is the number of battery strings connected in parallel;

$STC STC (({N N}_{PV PV,, p p},, {N N}_{WT WT},, {N N}_{BAT BAT,, p p})) = = {Σ Σ}_{i i = = 11}^{{n no}_{PV PV}} {N N}_{PV PV}^{i i} (({C C}_{PV PV}^{i i} + + n no \cdot &Center Dot; {M m}_{PV PV}^{i i})) + + {Σ Σ}_{j j = = 11}^{{n no}_{PV PV}} {N N}_{WT WT}^{j j} (({C C}_{WT WT}^{j j} + + n no \cdot \cdot {M m}_{WT WT}^{j j} + + {C C}_{h h}^{j j} + + n no \cdot &Center Dot; {C C}_{hm hm}^{j j}))$

$+ + {Σ Σ}_{k k = = 11}^{{n no}_{BAT BAT}} {N N}_{BAT BAT}^{k k} \cdot &Center Dot; (({C C}_{BAT BAT}^{k k} + + {y the y}_{BAT BAT}^{k k} \cdot &Center Dot; {C C}_{BAT BAT}^{k k} + + ((n no - - {y the y}_{BAT BAT}^{k k} - - 11)) \cdot &Center Dot; {M m}_{BAT BAT}^{k k})) + + {C C}_{I I} + + {C C}_{T T,, D D.}$

$+ + {C C}_{INV INV} (({y the y}_{INV INV} + + 11)) + + {M m}_{INV INV} ((n no - - {y the y}_{INV INV} - - 11)) - - - - - - ((22))$

其中，n为系统使用寿命年限、n_PV、n_WT、n_BAT分别为光伏电池、风力发电机组和蓄电池的类型总数；

C_INV分别为第i种类型光伏电池、第j种类型风力发电机组、第k种类型蓄电池以及逆变器的购买成本，单位为：元；

M_INV对应于光伏电池、风力发电机组、蓄电池、逆变器一年的维修成本，单位为：元/年；

为风力发电机组安装塔的购买成本；

为对应的安装塔每年的维修成本；

y_INV为蓄电池、逆变器在系统使用寿命内期望更换次数；C_I为系统的安装成本，包括系统各组成部分的安装成本，以及充电控制器的购买成本；

为第i中类型光伏电池的总数，其中

为第i种光伏电池的串联数量；而

为第i种光伏电池的并联串数，为优化设计的变量； Among them, n is the service life of the system, n _PV , n _WT , and n _BAT are the total types of photovoltaic cells, wind turbines and storage batteries;

C _INV is the purchase cost of the i-th type of photovoltaic cell, the j-th type of wind turbine, the k-th type of storage battery and the inverter, and the unit is: yuan;

M _INV corresponds to the annual maintenance cost of photovoltaic cells, wind turbines, storage batteries, and inverters, and the unit is: yuan/year;

The purchase cost of installing towers for wind turbines;

is the annual maintenance cost of the corresponding installation tower;

y _INV is the expected replacement times of battery and inverter within the service life of the system; C _I is the installation cost of the system, including the installation cost of each component of the system and the purchase cost of the charge controller;

is the total number of photovoltaic cells of the type i, where

is the number of series connection of the i-th photovoltaic cell; and

is the number of parallel strings of the i-th photovoltaic cell, and is a variable for optimal design;

为第k中类型蓄电池组的蓄电池总数；

为蓄电池的串联数量，

为蓄电池并联数，是优化设计变量；C_T，D为柴油机在使用寿命内的总成本，可用下列公式进行计算：

is the total number of storage batteries of the type k storage battery pack;

is the number of batteries connected in series,

is the number of batteries connected in parallel, which is the optimal design variable; C _{T, D} is the total cost of the diesel engine within its service life, which can be calculated by the following formula:

${C C}_{T T,, D D.} = = {C C}_{I I,, D D.} + + {M m}_{D D.} + + \frac{{C C}_{D D.}}{{Life life}_{D D.}} + + {C C}_{fuel fuel} - - - - - - ((33))$

其中C_I，D为柴油发电机的安装成本，单位为：元；M_D为柴油发电机每小时的维护费用，单位为：元/小时；C_D为柴油机的购买成本，单位为：元；Life_D为柴油机的使用寿命，单位为：小时；C_fuel为柴油机运行一小时所消耗的燃料成本； Among them _{, C I and D} are the installation cost of the diesel generator, the unit is: yuan; M _D is the maintenance cost of the diesel generator per hour, the unit is: yuan/hour; C _D is the purchase cost of the diesel engine, the unit is: yuan; Life _D is the service life of the diesel engine, the unit is: hour; C _fuel is the fuel cost consumed by the diesel engine running for one hour;

建立系统容量配置优化设计的约束条件，所述系统的约束条件主要包括以下内容： Establish the constraint conditions for the optimal design of system capacity allocation, the constraints of the system mainly include the following contents:

1)功率平衡：系统所提供的功率与负载所需功率相等 1) Power balance: the power provided by the system is equal to the power required by the load

P_p(t)＝P_L(t) (4) P _p (t) = P _L (t) (4)

其中P_p(t)为系统所提供的功率，可用下列公式计算： Among them, P _p (t) is the power provided by the system, which can be calculated by the following formula:

P_p(t)＝P_RE(t)+P_D(t)-P_B(t) (5) P _p (t) = P _RE (t) + P _D (t) - P _B (t) (5)

其中 $P_{RE} (t) = Σ_{i = 1}^{n_{PV}} N_{PV}^{i} \cdot P_{PV}^{i} (t) + Σ_{j = 1}^{n_{WT}} N_{WT}^{j} \cdot P_{WT}^{j} (t)$ 为可再生能源提供的功率。P_B(t)为蓄电池组的输入/输出功率：P_B(t)＞0时蓄电池处于充电状态，当P_B(t)＜0时蓄电池处于放电状态。 in $P_{RE} (t) = Σ_{i = 1}^{{no}_{PV}} N_{PV}^{i} \cdot P_{PV}^{i} (t) + Σ_{j = 1}^{{no}_{WT}} N_{WT}^{j} \cdot P_{WT}^{j} (t)$ Power provided by renewable energy sources. P _B (t) is the input/output power of the battery pack: when P _B (t) > 0, the battery is in a charging state, and when P _B (t) < 0, the battery is in a discharging state.

2)蓄电池的充电状态不能超过蓄电池最大荷电量与最小荷电量的限制。 2) The state of charge of the battery cannot exceed the limits of the maximum and minimum charge of the battery. the

SOC_min≤SOC(t)≤SOC_max(6) SOC _min ≤ SOC(t) ≤ SOC _max (6)

3)P_D：柴油机年度发电量占系统所提供电量的10％以内，即P_D/(P_RE+P_D)≤0.1 3) P _D : The annual power generation of the diesel engine accounts for less than 10% of the electricity provided by the system, that is, P _D /(P _RE +P _D )≤0.1

4)其他约束条件： 4) Other constraints:

$\{\begin{matrix} 11 \leq \leq {N N}_{PV PV,, p p}^{i i} \leq \leq {N N}_{PV PV,, p p max max}^{i i} \\ 11 \leq \leq {N N}_{WT WT}^{j j} \leq \leq {N N}_{WT WT max max}^{} \\ 11 \leq \leq {N N}_{BAT BAT,, p p}^{k k} \leq \leq {N N}_{BAT BAT,, p p max max}^{k k} \end{matrix} - - - - - - ((77))$

其中

是分别根据光伏电池、风力发电机组、蓄电池以及峰值负载计算的。 in

It is calculated based on photovoltaic cells, wind turbines, storage batteries and peak loads respectively.

(4)采用果蝇优化算法，不断搜索，计算系统的成本，如此往复，直到算法运行结束，输出最优解，得到海岛新能源系统的最优容量配置参数x＝{N_PV，p,N_WT,N_BAT,p}。 (4) Use the fruit fly optimization algorithm to continuously search and calculate the cost of the system, and so on, until the end of the algorithm operation, output the optimal solution, and obtain the optimal capacity configuration parameters of the island new energy system x = {N _{PV, p} , N _WT , N _{BAT, p} }.

进一步，所述的果蝇优化算法步骤为： Further, the steps of the fruit fly optimization algorithm are:

1)先随机初始化果蝇群体的位置； 1) First randomly initialize the position of the fruit fly group;

2)然后赋给每个果蝇嗅觉，即寻找食物的随机方向和随机距离； 2) Then give each fruit fly a sense of smell, that is, a random direction and a random distance to find food;

3)由于无法得知食物的具体位置，因此当单个果蝇单次飞行到达一个位置(xi，yi)时，计算与原点的距离Di，将距离的倒数作为味道浓度判定值Si，如式(8)所示： 3) Since the specific location of the food cannot be known, when a single fruit fly reaches a location (xi, yi) in a single flight, the distance Di from the origin is calculated, and the reciprocal of the distance is used as the judgment value Si of the taste concentration, as shown in the formula ( 8) as shown in:

$\{\begin{matrix} Di Di = = \sqrt{{xi xi}^{22} + + {yi yi}^{22}} \\ Si Si = = \frac{11}{Di Di} \end{matrix} - - - - - - ((88))$

4)将味道浓度判定值Si代入味道浓度判定函数，求出单个果蝇所在位置的味道浓度，其中味道浓度判定函数根据实际的问题而设定； 4) Substituting the taste concentration judgment value Si into the taste concentration judgment function to obtain the taste concentration at the position of a single fruit fly, wherein the taste concentration judgment function is set according to actual problems;

5)找出整个果蝇群体中味道浓度最大的果蝇，将此果蝇所在的坐标位置设定为最佳味道浓度值，并且果蝇群里往该方向飞行； 5) Find the fruit fly with the highest taste concentration in the whole fruit fly group, set the coordinate position of the fruit fly as the best taste concentration value, and fly in this direction in the fruit fly group;

利用迭代寻优的方法，重复执行步骤2)到步骤5)直到找到食物位置。 Using the method of iterative optimization, repeat step 2) to step 5) until the food position is found. the

进一步，所述为系统使用寿命年限n取值范围为5-20年。 Further, it is stated that the service life of the system n ranges from 5 to 20 years. the

本发明的有益效果是： The beneficial effects of the present invention are:

1、大大降低海岛新能源系统发电生产成本，发挥电容量的最大经济效益； 1. Greatly reduce the production cost of island new energy system power generation, and maximize the economic benefits of electric capacity;

2、减少化石资源的消耗量和环境污染物排放，促进海岛电能更加绿色、经济、环保； 2. Reduce the consumption of fossil resources and the discharge of environmental pollutants, and promote island power to be more green, economical and environmentally friendly;

3、为海岛提供充足电能保障。 3. Provide sufficient power guarantee for the island. the

附图说明 Description of drawings

图1果蝇优化算法流程图 Figure 1 Flow chart of fruit fly optimization algorithm

图2海岛新能源系统最优容量配置方法流程图 Figure 2 Flowchart of optimal capacity configuration method for island new energy system

图3每月负载特性图 Figure 3 monthly load characteristic diagram

图4年负载所需功率情况 Figure 4 The power required by the load in a year

图5A岛一年内每小时环境温度值 Figure 5A Island's hourly ambient temperature in a year

图6基于果蝇优化算法风光柴蓄混合发电系统总成本 Figure 6 The total cost of wind-solar-diesel-storage hybrid power generation system based on fruit fly optimization algorithm

图7HOMER软件光辐射数据输入界面 Figure 7 HOMER software optical radiation data input interface

图8HOMER软件中风速输入界面 Figure 8 Wind speed input interface in HOMER software

具体实施方式 Detailed ways

本发明主要针对的与大陆没有电网连接的独立海岛，海岛的供电主要由光伏发电、风电组成，但是考虑到光伏发电、风电受天气因素影响比较大，因而采用了蓄电池。当光伏发电、风电的功率超出用电负荷时，蓄电池充电；当光伏发电、风电的功率低于用电负荷时，蓄电池放电。柴油机的作用主要是作为备用电源，当光伏发电、风电以及蓄电池供电不足时，或者系统设备检修或者故障时，采用柴油机供电。因而，海岛新能源系统包括：光伏发电、风电、蓄电池、柴油机4个主要部分组成。 The present invention is mainly aimed at independent islands that are not connected to the mainland. The power supply of the islands is mainly composed of photovoltaic power generation and wind power. However, considering that photovoltaic power generation and wind power are greatly affected by weather factors, batteries are used. When the power of photovoltaic power generation and wind power exceeds the power load, the battery is charged; when the power of photovoltaic power generation and wind power is lower than the power load, the battery is discharged. The role of the diesel engine is mainly as a backup power supply. When the photovoltaic power generation, wind power and battery power supply are insufficient, or when the system equipment is overhauled or fails, the diesel engine is used for power supply. Therefore, the island new energy system includes four main parts: photovoltaic power generation, wind power, storage battery, and diesel engine. the

海岛新能源系统的最优容量配置是要保证系统在使用寿命内，系统的运行成本最低。因此，将系统的成本函数看作优化设计的目标函数。 The optimal capacity configuration of the island new energy system is to ensure that the operating cost of the system is the lowest within the service life of the system. Therefore, the cost function of the system is regarded as the objective function of the optimal design. the

海岛新能源系统的成本函数包括以下几个部分： The cost function of the island new energy system includes the following parts:

A.光伏电池、风力发电机组、风力发电机组安装塔、蓄电池、充电控制器、逆变器以及柴油发电机的购买成本和安装成本。 A. Purchase and installation costs of photovoltaic cells, wind turbines, wind turbine installation towers, batteries, charge controllers, inverters, and diesel generators. the

B.在系统使用寿命内蓄电池、风力发电机组、充电控制器、逆变器、柴油机的更换成本。 B. Replacement costs of batteries, wind turbines, charge controllers, inverters, and diesel engines within the service life of the system. the

C.光伏电池、风力发电机组、安装塔、蓄电池在其使用寿命内的维修成本。 C. The maintenance cost of photovoltaic cells, wind turbines, installation towers, and storage batteries within their service life. the

D.柴油发电机在系统受用寿命内的操作和维护费用。 D. Operation and maintenance costs of diesel generators during the service life of the system. the

E.柴油发电机在系统使用寿命内所消耗的燃料费。 E. Fuel cost consumed by diesel generators during the service life of the system. the

采用如下的数学方程来描述： Described by the following mathematical equation:

其中x为系统优化变量组成的一维向量：x＝{N_PV,p,N_WT,N_BAT,p}，N_PV，p是并联光伏电池串数，N_WT为风力发电机组数，N_BAT，p为并联蓄电池串数。 Where x is a one-dimensional vector composed of system optimization variables: x={N _PV,p ,N _WT ,N _BAT,p }, N _PV,p is the number of parallel photovoltaic cell strings, N _WT is the number of wind turbines, N _{BAT , p} is the number of battery strings connected in parallel.

假定系统的使用寿命年限为20年，那么： Assuming the service life of the system is 20 years, then:

$STC STC (({N N}_{PV PV,, p p},, {N N}_{WT WT},, {N N}_{BAT BAT,, p p})) = = {Σ Σ}_{i i = = 11}^{{n no}_{PV PV}} {N N}_{PV PV}^{i i} (({C C}_{PV PV}^{i i} + + 2020 \cdot &Center Dot; {M m}_{PV PV}^{i i})) + + {Σ Σ}_{j j = = 11}^{{n no}_{PV PV}} {N N}_{WT WT}^{j j} (({C C}_{WT WT}^{j j} + + 2020 \cdot &Center Dot; {M m}_{WT WT}^{j j} + + {C C}_{h h}^{j j} + + 2020 \cdot \cdot {C C}_{hm hm}^{j j}))$

$+ + {Σ Σ}_{k k = = 11}^{{n no}_{BAT BAT}} {N N}_{BAT BAT}^{k k} \cdot &Center Dot; (({C C}_{BAT BAT}^{k k} + + {y the y}_{BAT BAT}^{k k} \cdot \cdot {C C}_{BAT BAT}^{k k} + + ((2020 - - {y the y}_{BAT BAT}^{k k} - - 11)) \cdot &Center Dot; {M m}_{BAT BAT}^{k k})) + + {C C}_{I I} + + {C C}_{T T,, D D.}$

+C_INV(y_INV+1)+M_INV(20-y_INV-1)(2) +C _INV (y _INV +1)+M _INV (20-y _INV -1)(2)

其中，n_PV、n_WT、n_BAT分别为光伏电池、风力发电机组和蓄电池的类型总数；

C_INV分别为第i种类型光伏电池、第j种类型风力发电机组、第k种类型蓄电池以及逆变器的购买成本(元)；

MINV对应于光伏电池、风力发电机组、蓄电池、逆变器一年的维修成本(元/年)；

为风力发电机组安装塔的购买成本；为对应的安装塔每年的维修成本；

yINV为蓄电池、逆变器在系统使用寿命内期望更换次数；C_I为系统的安装成本，包括系统各组成部分的安装成本，以及充电控制器的购买成本。为第i中类型光伏电池的总数，其中

为第i种光伏电池的串联数量；而

为第i种光伏电池的并联串数，为优化设计的变量。 Among them, n _PV , n _WT , and n _BAT are the total types of photovoltaic cells, wind turbines, and storage batteries, respectively;

C _INV is the purchase cost (yuan) of the i-th type of photovoltaic cell, the j-th type of wind turbine, the k-th type of storage battery and the inverter;

MINV corresponds to the annual maintenance cost of photovoltaic cells, wind turbines, storage batteries, and inverters (yuan/year);

The purchase cost of installing towers for wind turbines; is the annual maintenance cost of the corresponding installation tower;

yINV is the expected replacement times of battery and inverter within the service life of the system; C _I is the installation cost of the system, including the installation cost of each component of the system and the purchase cost of the charge controller. is the total number of photovoltaic cells of the type i, where

is the number of series connection of the i-th photovoltaic cell; and

is the number of parallel strings of the i-th photovoltaic cell, and is a variable for optimal design.

为第k中类型蓄电池组的蓄电池总数。

为蓄电池的串联数量，

为蓄电池并联数，是优化设计变量。C_T，D为柴油机在使用寿命内的总成本，可用下列公式进行计算： is the total number of batteries in the k-th type battery pack.

is the number of batteries connected in series,

The number of batteries connected in parallel is the optimal design variable. C _{T, D} is the total cost of the diesel engine within its service life, which can be calculated by the following formula:

其中C_I，D为柴油发电机的安装成本(元)；M_D为柴油发电机每小时的维护费用(元/小时)；C_D为柴油机的购买成本(元)；Life_D为柴油机的使用寿命(小时)；C_fuel为柴油机运行一小时所消耗的燃料成本。 Among them _{, C I and D} are the installation cost of the diesel generator (yuan); M _D is the maintenance cost of the diesel generator per hour (yuan/hour); C _D is the purchase cost of the diesel engine (yuan); Life _D is the use of the diesel engine Life (hours); C _fuel is the cost of fuel consumed by the diesel engine running for one hour.

系统容量配置优化设计的约束条件： Constraints for optimal design of system capacity configuration:

海岛新能源系统系统目标函数的约束条件是根据用户的负载要求和元件特性建立的，用来保证系统所提供的功率满足负载需求，同时由柴油机所提供的功率在给定范围内，以减少系统对环境的污染。本系统的约束条件主要包括以下内容： The constraint conditions of the system objective function of the island new energy system are established according to the user's load requirements and component characteristics to ensure that the power provided by the system meets the load requirements, and at the same time the power provided by the diesel engine is within a given range to reduce the system load. pollution of the environment. The constraints of this system mainly include the following:

(1)功率平衡：系统所提供的功率与负载所需功率相等 (1) Power balance: the power provided by the system is equal to the power required by the load

P_p(t)＝P_L(t) (4) P _p (t) = P _L (t) (4)

其中 $P_{RE} (t) = Σ_{i = 1}^{n_{PV}} N_{PV}^{i} \cdot P_{PV}^{i} (t) + Σ_{j = 1}^{n_{WT}} N_{WT}^{j} \cdot P_{WT}^{j} (t)$ 为可再生能源提供的功率。P_B(t)为蓄电池组的输入/输出功率：P_B(t)＞0时蓄电池处于充电状态，当P_B(t)＜0时蓄电池处于放电状态。 in $P_{RE} (t) = Σ_{i = 1}^{{no}_{PV}} N_{PV}^{i} &Center Dot; P_{PV}^{i} (t) + Σ_{j = 1}^{{no}_{WT}} N_{WT}^{j} \cdot P_{WT}^{j} (t)$ Power provided by renewable energy sources. P _B (t) is the input/output power of the battery pack: when P _B (t) > 0, the battery is in a charging state, and when P _B (t) < 0, the battery is in a discharging state.

(2)蓄电池的充电状态不能超过蓄电池最大荷电量与最小荷电量的限制。 (2) The state of charge of the battery cannot exceed the limits of the maximum charge capacity and the minimum charge capacity of the battery. the

SOC_min≤SOC(t)≤SOC_max (6) SOC _min ≤ SOC(t) ≤ SOC _max (6)

(3)P_D：柴油机年度发电量占系统所提供电量的10％以内，即P_D/(P_RE+P_D)≤0.1 (3) P _D : The annual power generation of the diesel engine accounts for less than 10% of the electricity provided by the system, that is, P _D /(P _RE +P _D )≤0.1

(4)其他约束条件： (4) Other constraints:

$\{\begin{matrix} 11 \leq \leq {N N}_{PV PV,, p p}^{i i} \leq \leq {N N}_{PV PV,, p p max max}^{} \\ 11 \leq \leq {N N}_{WT WT}^{j j} \leq \leq {N N}_{WT WT max max}^{} \\ 11 \leq \leq {N N}_{BAT BAT,, p p}^{k k} \leq \leq {N N}_{BAT BAT,, p p max max}^{k k} \end{matrix} - - - - - - ((77))$

其中

果蝇优化算法： Fruit fly optimization algorithm:

果蝇优化算法是一种基于果蝇觅食行为推演出寻找全局优化的新方法，具体算法通过如下步骤实现： The fruit fly optimization algorithm is a new method based on the foraging behavior of fruit flies to deduce and find global optimization. The specific algorithm is realized through the following steps:

1)先随机初始化果蝇群体的位置； 1) Initialize the position of the fruit fly population randomly;

3)由于无法得知食物的具体位置，因此当单个果蝇单次飞行到达一个位置(xi，yi)时，计算与原点的距离Di，将距离的倒数作为味道浓度判定值Si，如式(8)； 3) Since the specific location of the food cannot be known, when a single fruit fly reaches a location (xi, yi) in a single flight, the distance Di from the origin is calculated, and the reciprocal of the distance is used as the judgment value Si of the taste concentration, as shown in the formula ( 8);

5)找出整个果蝇群体中味道浓度最大的果蝇，将此果蝇所在的坐标位置设定为最佳味道浓度值，并且果蝇群里往该方向飞行。 5) Find the fruit fly with the highest taste concentration in the entire fruit fly population, set the coordinate position of the fruit fly as the optimal taste concentration value, and fly in this direction in the fruit fly group. the

海岛新能源系统容量配置优化设计的实现： The realization of the optimal design of the capacity configuration of the island new energy system:

利用果蝇优化算法对海岛新能源系统进行优化设计的主要步骤如下： The main steps of using the fruit fly optimization algorithm to optimize the design of the island new energy system are as follows:

(1)根据海岛的自然条件、气候条件选择光伏发电、风电的类型，获取有关光伏发电、风电等的参数值，以及蓄电池、柴油机的相关参数值。 (1) Select the type of photovoltaic power generation and wind power according to the natural conditions and climate conditions of the island, and obtain the parameter values of photovoltaic power generation and wind power, as well as the relevant parameter values of batteries and diesel engines. the

(2)了解海岛的负荷情况，主要包括负载用电时间分布、各个时段负荷的性质、功率大小、最大峰值负载的大小等。 (2) Understand the load situation of the island, mainly including the time distribution of load power consumption, the nature of the load at each time period, the size of the power, the size of the maximum peak load, etc. the

(3)确定果蝇优化算法的目标函数为式(1)和(2)描述的系统运行成本函数，建立系统容量配置优化设计的约束条件； (3) Determine that the objective function of the fruit fly optimization algorithm is the system operating cost function described by formulas (1) and (2), and establish the constraint conditions for the optimal design of system capacity configuration;

(4)采用果蝇优化算法，不断搜索，计算系统的成本，如此往复，直到算法运行结束，输出最优解，得到海岛新能源系统的最优容量配置参数x＝{N_PV,p,N_WT,N_BAT,p}。 (4) Use the fruit fly optimization algorithm to continuously search and calculate the cost of the system, and so on, until the end of the algorithm operation, output the optimal solution, and obtain the optimal capacity configuration parameters of the island new energy system x={N _PV,p ,N _WT , N _{BAT, p} }.

海岛新能源系统最优容量配置方法进行如下实施： The optimal capacity configuration method of the island new energy system is implemented as follows:

A岛，将系统总仿真时间(一年)划分为若干个相等时间段(假定为1小时)，即对风能资源、太阳能资源的评估以及负荷预测都以小时为单位。 Island A divides the total system simulation time (one year) into several equal time periods (assumed to be 1 hour), that is, the evaluation of wind energy resources, solar energy resources and load forecasting are all in hours. the

发电系统是为满足用户的用电要求设计的，要为用户提供可靠的电力，就必须认真分析用户的用电负荷特征。主要是了解用户的最大用电负荷和平均日用电量。对A岛的具体用电情况进行统计可得用电的高峰期分布在每年的5～10月之间，海岛2008、2009年的最大日用电量是7800度，均分布在夏季。 The power generation system is designed to meet the user's power consumption requirements. In order to provide users with reliable power, it is necessary to carefully analyze the user's power load characteristics. The main purpose is to understand the user's maximum power load and average daily power consumption. According to the statistics of the specific electricity consumption of Island A, the peak period of electricity consumption is distributed between May and October each year. The maximum daily electricity consumption of the island in 2008 and 2009 is 7800 kWh, which are all distributed in summer. the

其具体负荷用电情况如表1所示：A岛的负荷情况可分为居民用电负荷、商业用电负荷以及大功率用电负荷。其中居民用电是由岛上常住居民的家用电器情况决定的，其用电量相对来说比较小，一般只占用电总量的8％～9％；由于A岛是一个旅游型岛屿，如图3和图4所示，在每年的5～10月是其旅游旺季，在此期间岛上的商业用电负荷会大幅增加，据统计，岛上商业用电量占其总用电量的80％；大功率用电负荷主要为岛上渔业生产用电，相对来说，大功率用电比较稳定，一般会在固定时间开启。其耗电量也较小。 The specific load and electricity consumption is shown in Table 1: the load of island A can be divided into residential electricity load, commercial electricity load and high-power electricity load. Among them, the electricity consumption of residents is determined by the household appliances of the permanent residents on the island, and its electricity consumption is relatively small, generally only occupying 8% to 9% of the total electricity; since Island A is a tourist island, such as As shown in Figure 3 and Figure 4, the peak tourist season is from May to October each year. During this period, the island’s commercial power consumption load will increase significantly. According to statistics, the island’s commercial power consumption accounts for 10% of its total power consumption 80%; the high-power electricity load is mainly used for fishery production on the island. Relatively speaking, the high-power electricity consumption is relatively stable, and it is generally turned on at a fixed time. It also consumes less power. the

表1项目实施地的负荷用电情况表 Table 1 The load and electricity consumption table of the project implementation site

A岛一年内光辐射量和风速的数据见图7和图8所示。 Figure 7 and Figure 8 show the data of light radiation and wind speed on island A in one year. the

统计A岛多年的气温变化，采用Sinusoidal函数计算出其一年内每小时的温度数据。 Calculate the temperature changes of island A over the years, and use the Sinusoidal function to calculate its hourly temperature data within a year. the

$T T ((t t)) = = 0.5 0.5 [[(({T T}_{max max} + + {T T}_{min min})) + + (({T T}_{max max} - - {T T}_{min min})) sin sin ((\frac{22 π π ((t t - - {t t}_{p p}))}{24 twenty four}))]] - - - - - - ((99))$

其中，T_max和T_min分别为该日温度的最大值和最小值；T(t)为该日任意时刻的温度值；t_p为平均温度时刻。 Among them, T _max and T _min are the maximum and minimum temperature of the day, respectively; T(t) is the temperature value at any time of the day; t _p is the average temperature time.

根据A岛气象站多年收集的数据，由上述公式离散得到一年的逐时的温度数据图5所示，在本设计中采用风力发电机组、光伏电池以及蓄电池的相关参数如表2～4所示。柴油机的额定功率为350KW，其购买价格为298000元，使用寿命为7000h，目前柴油的价格为7元/L；假定柴油机每小时的维护费用为1.5元，双向逆变器的额定功率为350Kw，其维护费用为4000元/年，逆变器的转换效率为95％。光伏电池和风力发电机组的使用寿命一般都为20年，蓄电池的使用寿命为3年，假定系统使用寿命为20年，则在系统使用寿命内蓄电池的更换次数y_BAT＝6。由于光伏阵列串联电池的个数以及系统中蓄电池组的串联个数是由系统直流总线电压决定，风光柴蓄混合发电系统的直流总线电压为标准值480V，可得光伏电池和蓄电池的串联数风别为

According to the data collected by the meteorological station of Island A for many years, the hourly temperature data of one year can be obtained by the above formula, as shown in Figure 5. In this design, the relevant parameters of wind turbines, photovoltaic cells and storage batteries are shown in Tables 2-4. Show. The rated power of the diesel engine is 350KW, the purchase price is 298,000 yuan, and the service life is 7000h. The current price of diesel is 7 yuan/L; assuming that the maintenance cost of the diesel engine is 1.5 yuan per hour, and the rated power of the bidirectional inverter is 350Kw, Its maintenance cost is 4,000 yuan/year, and the conversion efficiency of the inverter is 95%. The service life of photovoltaic cells and wind power generators is generally 20 years, and the service life of batteries is 3 years. Assuming that the service life of the system is 20 years, the battery replacement times y _BAT =6 within the service life of the system. Since the number of cells connected in series in the photovoltaic array and the number of batteries connected in series in the system are determined by the DC bus voltage of the system, the DC bus voltage of the wind-solar-diesel-storage hybrid power generation system is a standard value of 480V, and the number of series-connected photovoltaic cells and batteries can be obtained don't

表2风力发电机组参数 Table 2 Wind turbine parameters

表3光伏电池参数 Table 3 Photovoltaic cell parameters

表4蓄电池参数 Table 4 battery parameters

确定新能源混合发电系统各元件参数后，采用MATLAB编程实现用果蝇优化算法对混合系统进行优化设计，根据图2所示，在MATLAB环境下运行程序，可得混合系统成本的仿真图如图6所示。图中纵坐标为混合系统发电成本，横坐标为味道浓度判定值，由图6可见，能满足算法收敛的要求。独立新能源混合发电系统最小运行成本为2605万元。对应系统最小运行成本的最优配置个体解如表5所示。即风光柴蓄混合发电系统最优配置是由37台功率为10KW和58台功率为5KW的风力发电机组；82×20个容量为180W和135×30个容量为110W的光伏电池以及2×240个额定容量为800Ah的蓄电池组成。 After determining the parameters of each component of the new energy hybrid power generation system, use MATLAB programming to realize the optimal design of the hybrid system with the fruit fly optimization algorithm. According to Figure 2, run the program in the MATLAB environment, and the simulation diagram of the cost of the hybrid system can be obtained as shown in Figure 2. 6. The ordinate in the figure is the power generation cost of the hybrid system, and the abscissa is the judgment value of the taste concentration. It can be seen from Figure 6 that it can meet the requirements of algorithm convergence. The minimum operating cost of the independent new energy hybrid power generation system is 26.05 million yuan. The optimal configuration individual solution corresponding to the minimum operating cost of the system is shown in Table 5. That is, the optimal configuration of the solar-diesel-storage hybrid power generation system consists of 37 wind turbines with a power of 10KW and 58 wind turbines with a power of 5KW; 82×20 photovoltaic cells with a capacity of 180W and 135×30 photovoltaic cells with a capacity of 110W; and 2×240 A battery with a rated capacity of 800Ah. the

表5优化设计结果 Table 5 Optimal Design Results

Claims

1. optimum capacity collocation method of the island new energy resources system based on the fruit bat optimized algorithm, new energy resources system mainly comprises: photovoltaic generation, wind-powered electricity generation, storage battery, diesel engine, it is characterized in that, described new energy resources system capacity configuration adopts the fruit bat optimized algorithm to be optimized, and concrete steps are as follows:

1) according to the natural conditions on island, the type that weather conditions are selected photovoltaic generation, wind-powered electricity generation, confirms the parameter value of photovoltaic generation, wind-powered electricity generation, storage battery, diesel engine;

2) determine the load condition on island, mainly comprise character, the performance number of load electricity consumption time distribution situation, each period load, the value of peak-peak load;

3) target function of determining the fruit bat optimized algorithm is the system operation cost function that formula (1) and formula (2) are described,

\min_{x} STC (x) = \min_{x} {C_{T} (x) + C_{M} (x) + C_{I} (x) + C_{T, D}} - - - (1)

Wherein x is the one-dimensional vector that the system optimization variable forms: x={N _{PV, p}, N _WT, N _{BAT, p}, N _{PV, p}Parallel photovoltaic battery strings number, N _WTBe wind turbine generator number, N _{BAT, p}Be multiple-connected battery string number;

STC (N_{PV, p}, N_{WT}, N_{BAT, p}) = Σ_{i = 1}^{n_{PV}} N_{PV}^{i} (C_{PV}^{i} + n \cdot M_{PV}^{i}) + Σ_{j = 1}^{n_{PV}} N_{WT}^{j} (C_{WT}^{j} + n \cdot M_{WT}^{j} + C_{h}^{j} + n \cdot C_{hm}^{j})

+ Σ_{k = 1}^{n_{BAT}} N_{BAT}^{k} \cdot (C_{BAT}^{k} + y_{BAT}^{k} \cdot C_{BAT}^{k} + (n - y_{BAT}^{k} - 1) \cdot M_{BAT}^{k}) + C_{I} + C_{T, D}

+ C_{INV} (y_{INV} + 1) + M_{INV} (n - y_{INV} - 1) - - - (2)

Wherein, n is system's time limit in useful life, n _PV, n _WT, n _BATBe respectively the type sum of photovoltaic cell, wind turbine generator and storage battery;

C _INVBe respectively the purchase cost of i type of photovoltaic cell, j type wind turbine generator, k type storage battery and inverter, unit is: unit;

M _INVCorresponding to photovoltaic cell, wind turbine generator, storage battery, the maintenance cost in 1 year of inverter, unit is: unit/year;

The purchase cost of tower is installed for wind turbine generator;

Maintenance cost for installation tower every year of correspondence;

y _INVFor storage battery, inverter expect to change number of times in system in useful life; C _IBe the installation cost of system, comprise the installation cost of each part of system, and the purchase cost of charge controller;

Be the sum of type photovoltaic cell among the i, wherein

Be the series connection quantity of i kind photovoltaic cell; And

Be the parallel connection string number of i kind photovoltaic cell, be the variable of optimal design;

It is the storage battery sum of type batteries among the k;

Be the series connection quantity of storage battery,

For storage battery number in parallel, it is the optimal design variable; CT, D are the total cost of diesel engine within useful life, and available following formula calculates:

C_{T, D} = C_{I, D} + M_{D} + \frac{C_{D}}{{Life}_{D}} + C_{fuel} - - - (3)

C wherein _{I, D}Installation cost (unit) for diesel engine generator; M _DBe diesel engine generator maintenance cost hourly, unit is: unit/hour; C _DBe the purchase cost of diesel engine, unit is: unit; Life _DBe the useful life of diesel engine, unit is: hour; C _FuelMove the fuel cost that consumed in a hour for diesel engine;

Set up the constraints of power system capacity configuration optimization design, the constraints of described system mainly comprises following content:

One) power-balance: the power that system provides equates with the load power demand

P _p(t)＝P _L(t) (4)

P wherein _p(t) power that provides for system, available following formula calculates:

P _p(t)＝P _RE(t)+P _D(t)-P _B(t) (5)

Wherein

P_{RE} (t) = Σ_{i = 1}^{n_{PV}} N_{PV}^{i} \cdot P_{PV}^{i} (t) + Σ_{j = 1}^{n_{WT}} N_{WT}^{j} \cdot P_{WT}^{j} (t)

Be the power that regenerative resource provides, P _B(t) be the I/O power of batteries: P _B(t)＞0 an o'clock storage battery is in charged state, works as P _B(t)＜0 an o'clock storage battery is in discharge condition;

Two) charged state of storage battery can not surpass the maximum carrying capacity of storage battery and minimum charged quantitative limitation;

SOC _min≤SOC(t)≤SOC _max (6)

Three) P _D: diesel engine year energy output account for electric weight that system provides 10% in, i.e. P _D/ (P _RE+ P _D)≤0.1

Four) other constraintss:

\{\begin{matrix} 1 \leq N_{PV, p}^{i} \leq N_{PV, p \max}^{i} \\ 1 \leq N_{WT}^{j} \leq N_{WT \max}^{j} \\ 1 \leq N_{BAT, p}^{k} \leq N_{BAT, p \max}^{k} \end{matrix} - - - (7)

Wherein

To calculate according to photovoltaic cell, wind turbine generator, storage battery and peak load respectively;

Five) adopt the fruit bat optimized algorithm, constantly search, the cost of computing system, and so forth, until the algorithm end of run, the output optimal solution obtains the optimum capacity configuration parameter x of island new energy resources system={ N _{PV, p}, N _WT, N _{BAT, p}.

2. the optimum capacity collocation method of a kind of island new energy resources system based on the fruit bat optimized algorithm according to claim 1, it is characterized in that: described fruit bat optimized algorithm step is:

1) position of first random initializtion fruit bat colony;

2) then be assigned to each fruit bat sense of smell, the i.e. random direction of search of food and random distance;

3) owing to can't learn the particular location of food, therefore when single fruit bat single flight arrives a position (xi, yi), calculate the distance D i with initial point, with the inverse of distance as flavor concentration decision content Si, shown in (8):

\{\begin{matrix} Di = \sqrt{{xi}^{2} + {yi}^{2}} \\ Si = \frac{1}{Di} \end{matrix} - - - (8)

4) with flavor concentration decision content Si substitution flavor concentration decision function, obtain the flavor concentration of single fruit bat position, wherein the flavor concentration decision function is set according to the problem of reality;

5) find out the fruit bat of flavor concentration maximum in the whole fruit bat colony, the coordinate position at this fruit bat place be set as the best flavors concentration value, and in the fruit bat group toward this direction flight;

Utilize the method for iteration optimizing, repeated execution of steps 2) to step 5) until find the food position.

3. the optimum capacity collocation method of a kind of island new energy resources system based on the fruit bat optimized algorithm according to claim 2 is characterized in that: described for the system's time limit in useful life n span be 5-20.