Introducing newly developed Nomadic People Optimizer (NPO) algorithm to find optimal sizing of a hybrid renewable energy

In this work, the main objective the provision of electric supply to a residential complex located in a remote area in Iraq (Thi -Qar) that has no access to electricity grid. This study relied on the Nomadic People Optimizer (NPO) for the Multi-objective design of a grid independent PV/Wind/Battery hybrid energy system. The hybrid systems considered in this study consist of a photovoltaic array, wind turbine, and battery storage. The hybrid system optimized the electricity supply of a residential complex with 30 houses in Thi- Qar which is located in southern Iraq on latitude 31.060 and longitude 46.260. The major purpose of this optimization is to find optimal sizing of renewable energy with battery storage to minimizing Total Life Cycle Cost (TLCC), this is an economic aspect, which in turn reduces the cost of energy (COE), Second objective is minimizing Total Dump Energy (TDE) with continuous provide the load by electricity (Reliability as constrained) through life cycle of project for a 25 years. The data used in this study, such as solar radiation, wind speed, and temperature was collected from weather forecast in Thi- Qar for every hour over the course of a full year; the electrical demand was collected from Thi- Qar Electricity Distribution Directorate for the same housing complex and the same number of houses in an area equipped with electricity. Also, the prices of the system components, cost of maintenance, and cost of fuel were collected from Thi- Qar Iraq market.


Introduction
Population increase, industrial and suburban developments are contributing to daily increase in energy demand beyond the level that can be effectively compensated by the available energy sources, thereby leading to energy shortage in some areas [1], [2]. Conventional energy sources are used in the largecentralized power generation systems and these sources are not just limited, they are also scarcely available on earth's crust. Furthermore, these conventional energy sources, such as fossil fuels are associated with negative environmental impact due to the high level of CO2 emission which contributes to global warming [3]. Consequently, many countries have over the years began the exploration of renewable energy sources as a source of clean energy that is inexhaustible and environmentally-friendly. These clean energy sources also help to minimize global warming, reduce over dependence on the fossil fuels, and minimize air pollution [4]. Conventional generators are used in the remote areas for electricity generation and these generators depend on fossil fuels to operate and are most times relatively more 2nd International Scientific Conference of Al-Ayen University (ISCAU-2020) IOP Conf. Series: Materials Science and Engineering 928 (2020) 022052 IOP Publishing doi: 10.1088/1757-899X/928/2/022052 2 expensive; they also release an appreciable amount of CO2. Other sources of challenges are the storage and transportation of such fuels to the rural areas where they are used to generate electricity. Hybrid renewable energy (HRE) systems are emission-less energy systems that consist of wind and solar which serves as new solutions to electricity-related problems in the remote areas that are not connected to the power grid [5], [6]. The Earth daily receives much energy from the sun [7], [8]; the amount of energy emitted by the sun per minute is enough to address global energy needs for one year, meaning that in one day, the amount of energy released by the sun can serve the current global population for 27 years. In fact, "the amount of solar energy released by the sun in 3 days is equivalent to the energy stored in all fossil energy sources." Considering this huge amount of energy from the sun, scientists have been devising ways of trapping this energy since the 18th century by building several forms of solar thermal collectors. Horace de Saussure, a Swiss scientist, invented the first ever solar thermal collector [9]. This was followed by the work of Alexander Edmond Becquerel, a French physicist who in the year 1839 invented the technology for direct energy production from solar energy. This marked the beginning of the present-day solar cell technology [10], [11]. One of the renewable energy projects that is attracting global attention is wind energy projects as they are currently attracting more interest globally compared to other renewable and conventional energy sources [12]. The increase in the number of wind energy projects is attributed to its low production cost and technology advancement when compared to the conventional and other renewable energy sources. Iraq is currently facing energy crisis which has driven the search for alternative and successful energy sources. As the country strives towards harnessing alternative energy sources, wind energy potential is seriously being considered; however, appropriate environmental assessments are necessary to ensure appropriate selection of the sites for wind turbines installation. The wind energy potential of any specific site is normally assessed using wind statistical models [5]. Over the past few years, wind energy projects have increased tremendously; Since 1995, the global installed wind energy capacity has increased from1.29 GW in 1995 to about 370 GW by 2015 [13], [14]. According to the U.S. Energy Information Administration's statistics of the U.S. renewable energy supply, the total energy generated by renewable energy resources are in the trend of increasing. Meanwhile, wind and solar energy are rapidly providing a greater percent of the total renewable energy supply each year [15]. This study employs a novel swarm-based metaheuristic called ''Nomadic People Optimizer (NPO)'' which relies on the pattern of life of nomads. The NPO simulates the life pattern of the nomads during their search for sources of life (such as grass and water for their animals); the algorithm also captures how the nomads have lived for several years and how they have been continuously migrating from place to place in search of comfort. This algorithm has a peculiar ability of achieving the right balance between exploration and exploitation and does not rely on any control parameters to control the search process. [16].

System construction and modeling
In this research, the proposed HES (Hybrid Energy System) consists of four components, i.e., the PV system, wind turbine system, (Bidirectional inverter &MPPT) (Maximum power Point Tracking) and batteries. The block diagram of the hybrid system considered in this paper is as shown in Fig. 1. The DC bus combines the output/ input of the PV panels and the battery bank while the AC bus combines the output/input of the wind turbine and the AC load.

Preference of energy sources
Hybrid renewable energy sources may feed the load when it is greater or equal to load & the loss in inverter. The excess of energy (if any) may be stored in batteries as charge operation. If the renewable energy sources less than load &loss in inverter and state of charge of battery is greater of minimum, the battery storage will discharge to cover shortage energy that provide load. The load data collected from Thi-Qar Electricity Distribution Directorate for the same housing complex and the same number of houses in an area equipped with electricity for one year for all hours.
To clarify more, the average load for 12 days of year (everyone day represent one month) was taken as shown in fig 2.

PV System
The performance of solar panels is highly influenced by weather conditions, panel temperature, and solar radiation. The output power of a PV system at time t can be calculated using [17].
is Cell temperature of a solar panel at standard test condition [°C],(‫)ܤ‬ is temperature coefficient of (ܲ ெ௫ )and ‫)ܨܴ(‬ is module derating factor. cell temperature can determine by following equation [18]: ‫ܶܥܱܰ‬ is the normal operating cell temperature of a solar panel [°C], ܶ is air temperature The datasheet of solar cell that used in this studying shown in Table (1).

Wind Turbine System
The datasheet of wind turbine that used in this study shown in Table (   The power output of the WT at time (t) can be calculated using [19]- [22]: Where represents the rated power of single WT, ) (t V is the speed of wind at time (t),(ܸ ) (ܸ ௨௧ ) ܽ݊݀ ‫)ݎܸ(‬ are the cut-in, cut-off, and rated wind speeds of the WT.
The hub height of the WT is 15 m; so, the wind speed at height 10 m is converted to wind speed at height 15 m using [23]:

Battery Storage
The datasheet of Battery that used in this study shown in Table (3)

State of charge of battery()
The ‫ܥܱܵ‬ is the charge quantity of the battery storage. ‫)ݐ(ܥܱܵ‬ is the state of charge at time (t); it is bounded by ‫ܥܱܵ‬ and ‫ܥܱܵ‬ ெ௫ ‫ܥܱܵ:‬ ≤ ‫)ݐ(ܥܱܵ‬ ≤ ‫ܥܱܵ‬ ெ௫ ; Where ‫ܥܱܵ(‬ ) is the minimum SOC of battery based on the depth of battery and is equal to ((1 − ‫)ܦܱܦ‬ × ‫ܥ‬ ெ௫ ); ‫ܥܱܵ(‬ ெ௫ ) is the maximum SOC of battery and is equal to ‫ܥ(‬ ெ௫ ). ‫ܥ(‬ ெ௫ ) is the nominal battery capacity (W).

Inverter
The inverter that used in this study is (Bidirectional inverter &MPPT) that connect between DC bus and AC bus to convert DC power to AC power to provide load and convert AC power to DC power to charge where ܲ ீିெ௫ represents the maximum power generated of the hybrid system and ܲ ூ௩ି௫ is the power maximum of the inverter. The data sheet of inverter that used in this study as shown in Table (4).

3-The proposed algorithm
In this study used is a new swarm-based metaheuristic (Nomadic People Optimizer(NPO)) that mimics the life pattern of nomads as they move around in search for sources of the life such as water and the grass for their animal; the NPO also captures the way the nomads have live d and existed for several years and keeps migrating continuously in search of comfort. The design of the NPO primarily based on the multiswarm approach as it is comprised of different clans and each clan has a clan leader (the best member of the clan). Nomads are herders who spend the whole of their life moving from place to place with their animals in search of natural life sources. The animals graze nearby water sources and in return serve as a major source of food and other necessities (such as skin and wool) to their owners. The herders also rely on the milk from the animals serve them their protein and calcium needs. Nomads does not live in a particular environment for long and does not cultivate crops within their settlement since they normally settle in an area for a short while. It could be nomads categorized to different kinds, such as Berbers, Gypsy, and Bedouins. Development of the NPO adopted in this study was inspired by the Bedouins and their lifestyle. The Bedouins comprise of the Sheikh family and the rest are considered normal families. The position of a sheikh is often hereditary but in cases where there is conflict, a normal family can contest for the position and it succeed, will become the new Sheikh. It is the duty of the sheikh as the clan leader to determine where and when the families will move to ensure their survival; the sheik also determines the pattern of positioning the normal families around the sheik's family. The family tents are normally distributed in a semicircular form around the tent of the Sheikh. The sheikh selects the families that will go in search of a new suitable position; selected families are required to move randomly in different directions and distances in search of the best location for resettlement. During the time of conflict, it is the duty of the sheikh to settle the dispute among the clans; the differences can among the clans can either be resolved in a peaceful way or through fights. The Bedouins spend their whole life travelling with their animals in search of better location that will sustain their existence. The migrate mainly during the summer and winter periods as slight territorial and climatic variations are exploited using periodic or seasonal migrations between the winter and summer grazing zones [16]. Where flow chat of Nomadic People Optimizer (NPO) shown in fig (4)

Fig. 4 Flowchart of NPO
The proposed algorithm in this work is aimed at achieving optimal sizing of the number of each of the needed energy subsystems in the HES such that the total life cycle cost ) (TLCC and total dump energy ) (DET of the system are minimized over a service life of 25 years. A typical system configuration X is a row vector of positive integers of four elements ‫ݔ(‬ ଵ ‫ݐ‬ ‫ݔ‬ ଷ ) where each element represents the required number of energy subsystems in the HES. The row vector ܺis represented thus: Where ‫ݔ‬ ଵ = number of PV modules required, ‫ݔ‬ ଶ = number of WT required, ‫ݔ‬ ଷ = number of battery modules required. Where ‫ݔ‬ ଵ ‫ݔ,‬ ଶ ‫ݔ,‬ ଷ 0 t .
Objective function and constraints of the optimization function are given by.
Constraints subject to : Where ‫ܧ‬ ்௧ ‫)ݐ(‬ is the system's total energy output at time t, and is mathematically given as: Where(‫ܧ‬ ‫)ݐ(‬ is the PV output energy at time t (W h), ‫ܧ(‬ ௐ் ‫)ݐ(‬ is the WT output energy at time t (W h), ‫ܧ(‬ ௧௧ ) is battery storage output energy at time t (W h), From equation (7) it is found that units not equal where life cycle cost total unit in dollar, while dump energy unit in watt or kW, hence, the units must be converted to the same unit to achieve optimization and the chosen unit for easy analysis is cost ($). For the dump total energy (W h), it was only considered for the RES. The conversion of the dump energy to a monetary value was done by computing the hourly contributions (%) of the PV and WT to the dump using their related energy costs for 25 years.

Minimizing Life Cycle Cost Total ()
‫ܥܥܮܶ‬ is one of the objectives of the (ܱܰܲ)it is total cost of the system through 25 years , . The implemented ‫ܶܥܥܮ‬ in this study considered the total initial capital cost of the components of the system (PV system ,wind turbines, batteries and inverter),and also include Total Replacement Cost of system component through 25 years and also include Total Maintenance Cost of system component through 25 years and is calculated by equation.
2nd International Scientific Conference of Al-Ayen University (ISCAU-2020) IOP Conf. Series: Materials Science and Engineering 928 (2020) 022052 IOP Publishing doi:10.1088/1757-899X/928/2/022052 10 The Capital Cost of subsystem components is including Purchase cost, connection cost, cost of work, cost of operation, and all it takes to work in order for the system to work well. The Total Replacement Cost that used in this studying include Replacement Cost of components that used in the system through 25 years. The Total Maintenance Cost that used in this study include Maintenance Cost of components that used in the system through 25 years. Minimizing Life Cycle Cost Total lead to Minimizing Cost of Energy ‫)ܧܱܥ(‬ that given by following equation [25]

Minimizing Total Dump Energy ()
Dump Energy is one of the objectives of the (NPO). Dump Energy occur when there are Excess of Renewable Energy and ‫)ݐ(ܥܱܵ‬ = ‫ܥܱܵ‬ ெ௫ . This condition is not desirable as there is energy wastage. The dump energy ‫)ݐ(ܧܦ‬ can be calculated at any time t within the system's lifecycle using: The Total Dump Energy through life cycle of studying (25 years) is given by equation: Where (n) is Life Cycle of project in hours and equal (219000) h Also continuous provide the load by electricity through Life Cycle of Project (25 years) as constrained.

Energy flow and balance
Renewable energy (RE) sources considered the primary source of energy in meeting the energy demand of users. The total available RE at time t is given as: Since these sources depend on solar radiation and wind speed, there is a chance of energy deficit or excess ‫ܧ(‬ ா௫/ ‫))ݐ(‬ and can be calculated using: Charging of Battery is initialized when ‫ܧ(‬ ா௫/ ‫)ݐ(‬ > 0) and ‫))ݐ(ܥܱܵ(‬ is less than the maximum ‫)ݐ(ܥܱܵ(‬ < ‫ܥܱܵ‬ ெ௫ ). During charging, the SOC of the battery at time t (SOC (t)) is calculated using equation [20], [26]: In case of deficit RE and the battery storage is above the battery's minimum SOC ‫ܧ(‬ ா௫/ ‫)ݐ(‬ < 0) and SOC > minimum ‫)ݐ(ܥܱܵ(‬ > ‫ܥܱܵ‬ , the battery power will be discharged to compensate for the deficit as follows: [20,26]: Fig . 5 presents the flowchart of the operation of the proposed algorithm in this study. The block for energy source selection in Fig. 6 has been expanded in Fig. 6 for clarity.

Results and discussion
In this study a New algorithm is used (Nomadic People Optimizer ) (ܱܰܲ)to find optimal sizing of system consist of energy source (PV system, wind turbine system ,Battery storage ) with number of inverter dependent on power maximum generated by the hybrid system and the power maximum of the inverter to minimizing Total Life Cycle Cost(ܶ‫,)ܥܥܮ‬and Total Dump Energy(ܶ‫)ܧܦ‬ where used data of load solar radiation ,wind speed and temperature for a period of a year. This is studying for 25 years. The results shown for 12 day of year (every one day represent of one month) for clarify.

Optimal configuration
The proposed system in this study was implemented for a period of 25 years considering all energy sources to arrive at the optimal configuration that would match the load demand of the users while

Conclusions
This study presented the use of a new multi-objective optimization model for optimal sizing of a PV cell/wind turbine/battery HES. In this system, PV cells and wind turbines were adopted as the primary energy sources while a battery was adopted as an intermediary source of energy. The algorithm was based on the multi-objective of minimizing ‫ܥܥܮܶ‬ and ‫.ܶܧܦ‬ Based on the results of this study, it is concluded as follows: 1-The optimal HES configuration was comprised of a PV/wind/battery system. Where number of inverter dependent on maximum power generated of the hybrid system and power maximum of the inverter, where optimum configuration that give minimum (ܶ‫)ܥܥܮ‬and minimum ‫)ܧܦܶ(‬ is (2022) number of PV Cell, (52) number of wind turbine, (1553) number of battery and (64) number of inverter. 2-The NPO could accept different energy inputs, such as wind speed and solar irradiation data; a user load profile for developing an optimally sized HES was developed in this study. The NPO has a peculiar attribute of finding a balance between exploration and exploitation. Furthermore, the NPO requires no control parameters whose values can have influence on the algorithmic search process. 3-The ability of the algorithm to reduce Total Life Cycle Cost (ܶ‫)ܥܥܮ‬and Total Dump Energy ‫)ܧܦܶ(‬ together.