Optimal Multi-Criteria Design of a New Hybrid Power Generation System Using Ant Lion and Grey Wolf Optimizers M. A. Algabalawy General Motors Egypt mostafa.algabalawy.eg @ieee.org
A.Y. Abdelaziz Faculty of Engineering, Ain Shams University almoataz_abdelaziz@e ng.asu.edu.eg
Abstract— The importance of the hybrid power generation systems (HPGS) increased especially in the last few years. HPGS are used to distribute the load between many different energy sources (e.g. utility grid (UG), wind turbine (WT), photovoltaic (PV), fuel cell (FC), and storage battery (SB)). These power sources are combined together in different configurations to form the HPGS. There are two common operation modes for these hybrid power systems; the first one is called as a standalone mode, in which the distributed power sources are combined together to supply the power without any supporting from the utility grid. The other mode is known as the utility grid connect mode, where the combination of these power sources is paralleled connected to the utility grid. This paper introduces a new contributions on the design of the HPGS, where the proposed hybrid system includes for the first time another energy utility such as the natural gas piping network. All operations conditions of this network are considered through the design of the HPGS. Applying modern meta-heuristic optimization techniques for the first time in the sizing of the HPGS. The applied techniques are; the ant lion optimizer (ALO) and grey wolf optimizers (GWO). MATLAB software has been used to execute the optimization process using ALO and GWO, and a detailed comparison is occurred between the results of applying the above mentioned techniques and another two modern optimizations techniques; Cuckoo search algorithm (CSA) and flower pollination algorithm (FPA) to show the effectiveness of applying both of ALO and GWO. Index Terms-- Combined power generation system, multi-criteria design, distributed generation units, renewable energy sources, Grey Wolf Optimizer (GWO), Ant Lions Optimizer (ALO), Cuckoo search algorithm (CSA), Flower Pollination algorithm (FPA).
I. INTRODUCTION Meeting the continuous increased of the power demand is the main target of all government and electric companies. This is considered a bottleneck for the development process, where the shortage in electric power is actually the main barrier to
S. F. Mekhamer Faculty of Engineering, Ain Shams University saidfouadmekhamer@y ahoo.com
M.A.L.Badr Faculty of Engineering, Ain Shams University
[email protected]
achieve the human needs and expectations. During the last two decades, there have been many radical solutions to solve not only the increasing power demand but also the power quality problems. Initiating the Hybrid power generation system is considered one of these solutions, where it is a combination of different power sources such as; WT, PV, SB, gas turbine (GT), and FC. There are many modes for the hybrid system operation such as a standalone mode or connected to utility grid mode are discussed in many searches. While in this paper the utility connected mode is studied into the winter (scenario I) and summer (scenario II) weather conditions. Y. Luo, L. Shi, and G. Tu, reference [1], study the isolated grids, which contains renewable energy sources and energy storage systems. Where, the authors try to determine the size of the hybrid system elements taking into account the reliability requirement and a bi-level control strategy of the isolated grids. The sodium–sulfur battery type is recommended for power balance control in the isolated grids based on comparative analysis of current energy storage characteristics and practicability. Finally, both of genetic algorithm and sequential simulation are applied to obtain the hybrid system design. In reference [2], L. Liqun and L. Chunxia, show the efficiency of using the standalone hybrid system, which consists of WT, PV, and SB for supplying the power for a remote village. Therefore, a simulation model is built to obtain the components sizing of the proposed hybrid system. The authors have studied the most important factors for the proposed hybrid system such as; system cost, emissions, reliability. In reference [3], Q. Jawad, K. Gasem, and M. Jawad introduce a model for different hybrid power systems, which consist of WT, PV, and diesel generator. This system has been built to feed the power to an urban area. A comparison between
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many different hybrid power systems has been occurred to select the most efficient system. The authors show that, the mixed-coupling hybrid power systems give high efficiency power generation system. In reference [4], T. Jima introduces the methodology for sizing a hybrid power generation system in order to supply the power for wood and metal products factory at Welenchity site. This system includes both of wind and solar system, where the author studies all weather conditions of the wind and the solar for this sit. The author apply the HOMER software to obtain the sizing of the PV-WT-SB-diesel generator hybrid system. A. Bhowmik, reference [5], applies the Microsoft Excel software-programming package to analyze the collected data of the wind speed and solar irradiance. This analysis has been done in order to build the proper hybrid power system. On the other side, the author implements the MATLAB software to optimize the objective function, which is defined as the cost function. Moreover, the constraints of the objective function and the parameters boundaries are described. A comparison between the results of the Excel and the MATLAB has been done. H. Farghally, F. Fahmy, and M. EL-Sayed, reference [6], study the behavior of some emergency loads such as; hospitals and schools. The energy requirements are defined and studied using the HOMER software. Hybrid power system based on the combination WT and PV has been planned, and its objective function is described. The authors apply the above mentioned software to solve the hybrid system objective function. Moreover, the coordination between power system elements is achieved through a neural network controller. In reference [7], P. Gajbhiye, and P. Suhane study the performance of some critical loads at different weather conditions, where the authors collect the ambient temperature, the solar irradiance, and the wind speed for an urban area. The authors have planned to build a combination from the renewable energy sources to supply the required power to the loads. As defined, the hybrid system is combined from WT and PV, diesel generator, and SB. However, the diesel generator and the SB have been used to cover the emergency loads under varying weather conditions. The objective function of the WTPV-SB-diesel generator is defined. Finally, the authors claim that, this study introduces the most propel power delivering system for the remote locations. H. Belmili, M. Haddadi, S. Bacha, and M. F. Almi, reference [8], analyze the techno-economic considerations of the WT-PV-SB hybrid system. The objective function of this system is defined considering the loss of power supply probability. Both of genetic algorithm and particle swarm have been used to solve the objective function. Finally, a comparison is executed to show that, the particle swarm gives results more effective than the genetic algorithm. In reference [9], L. Hu, D. Xi, S. Guo, and Y. Fu discuss the methodology to determine the hybrid power system capacity. The proposed system consists of WT, PV, and SB, where the system objective function has been designed considering all expected conditions of the hybrid power system. Two different definitions are shown according to the
system connection to the utility grid, where if the system is connected to the utility it is termed as the utility connected hybrid power system. While, if it only supplies the loads, it is termed as a standalone hybrid power system. A computational comparison between the two concepts is executed. In reference [10], A. Eltamaly and M. Mohamed introduce the simulation of the PV-WT-SB hybrid power system. The authors find the size of each component of the hybrid system considering the economic issue and the system reliability using the HOMER software. In reference [11], A. Maleki and A. Askarzadeh design the PV-WT-FC hybrid power system. A combination from the meta-heuristic techniques such as; chaotic search (CS), harmony search (HS) and simulated annealing (SA) are applied to obtain the hybrid system design. Finally, the authors compare between the applied meta-heuristic techniques as well as the combined techniques. It is claimed that, the results of the combined optimization techniques are more efficient than the results of applying the individual techniques. B. Tudu, K. Manda, and N. Chakraborty, in reference [12], present micro-hydro, PV, WT and FC hybrid power system. The authors aim to find the components sizing of this system utilizing all available renewable sources. Both of the bee optimization technique and the particle swarm technique have been used to obtain the design of the hybrid power system. The results of using the bee algorithm are compared with the particle swarm technique. Both algorithms give the global solution, but PSO is fast in reaching it as the authors claim. This paper represents the optimal design of a new HPGS consisting of WT, PV, and SB, connected to both of the utility grid and the natural gas distribution network, which fuels the gas turbine (GT) with the natural gas. Therefore, the proposed system is WT-PV-SB-GT-utility grid. New meta-heuristic techniques such as ALO and GWO have been applied to optimally design the HPGS. In addition, the obtained results are compared with the results of using CSA and FPA. MATLAB programs have been built to perform the optimization process for each technique. II. MOTIVATIONS OF USING NATURAL GAS After many natural gas discoveries at the end of last century, the Egyptian government had decided to replace the applications of the liquefied petroleum gas (LPG) by the natural gas to save the rate exchange and to improve the fuel reliability for all categories of customers. Natural gas transmission and distribution networks had been constructed, which are essential to supply the natural gas to the people. Electric power and natural gas load profiles for an urban area have been studied, where the electric power and natural gas loads have been fitted in Millions of British Thermal Unit (MBTU). Although the monthly consumption of the natural gas is increased at the winter months, the monthly electricity consumption is decreased, and vice versa for the summer months as shown in Figure 1. Considering the black line describes the monthly consumption of the natural gas, the blue
line illustrates the monthly electricity consumption, and the red line represents the average monthly energy consumption.
4 shows this regulator, which automatically regulates the output pressure through closed loop control system. The red color refers to the medium pressure natural gas and the yellow color shows the low pressure natural gas. According to the manufacture design there is a lower allowable limit for the regulator input pressure [13 and 14].
Fig.1. MBTU consumptions for the studied area
There is a high diversity between the two peaks of the daily consumptions of the natural gas and the electricity as shown in Figure 2. Fig. 3. configuration of a typical hybrid power generation system
1
2
Fig. 2. the daily consumptions of the electric poawer and natural gas loads
Where, X axis refers to the day hours (from 0 to 23), Y axix refers to the energy onsumption in Million British Thermal Unit (MBTU). Line no 1 represents the electrical load profile, and line no 2 represents the natural gas load. From previous points, it is clear that there are many motivations for using the natural gas at the distribution level to generate the required electric power to feed the power for the urban areas. III. PROBLEM FORMULATION Figure 3 shows a schematic diagram for the planned hybrid generation system, which consists of WT, PV, SB, GT, and the utility grid through the required power converters. The following sections describe the proposed system. A. Natural Gas (NG) Distribution Network Description Natural gas is transmitted to the domestic areas in medium pressure network (up to 7 bar). This pressure is reduced to lower level (up to 100 mbar) through pressure regulator. Figure
Fig. 4. The 850 VARIFLO natural gas pressure regulator
So, the value of the lowest allowable limit is considered a system constraint. The pipes flow velocity also is considered another constraint, where if it exceeds certain limit, the pipe dust will move the natural gas devices and could make big deals. Equation 1 shows the effective factors on the flow velocity ( ) such as, the upstream pressure, and the pipe diameter.
=
(1)
.
where, is the average flow velocity at the point under consideration (m/s). is the gas flow rate ( /ℎ) defined as: = 7.574
.
−
(
)
.
× 10
is the absolute pressure, and it equals to 1.0135 is the natural gas pipe line internal diameter of the in
(2)
is in the range of (0.009 to 0.015) as a friction factor is the absolute input pressure in is the absolute output pressure in is the length of the pipeline of the studied area in . is the standard temperature (288 ) is the specific gravity of natural gas is the average compressibility factor is the average temperature of the flowing gas is the pipeline length in According to Figure 4 the maximum output flow rate ( the pressure regulator could be calculated as follows: =
(
−
)
= where, is the initial cost of the WT ($/ ) is the swept area of the WT ( )
is the O&M cost of wind turbine system ($/ is the escalation factor ) of
The cubic meters of the natural gas from the main pipeline are maximized considering the flow velocity and the inlet pressure for the pressure regulators as follows: (4)
(8)
where, is the SV of wind turbine ($/ is the inflation rate is the interest rate
)
2. TAC of the PV The TAC of the PV system likes the TAC of the wind turbine, where Equations 9, 10, and 11 show the initial cost, the O&M cost, the salvage cost respectively. = (9) where, is the initial cost of the PV ($/ ) is the swept area of the PV ( ) ∑
=
(10)
is the O&M cost of PV system ($/ =
B. Design Objective Total Annual Cost (TAC) TAC considered is the first and the important objective function of this system, which consists of the initial cost, operations and maintenance cost (O&M), salvage value (SV), the annual purchased natural gas from the natural gas distribution company, and the annual purchased electricity from the utility grid [15]. All these terms could be represented as the following equations: +
+
(5)
where, indicates the WT, PV, SB, and GT is the initial cost of each power source (WT, PV, SB, and GT). is the O&M cost for each power source (WT, PV, SB, and GT). is the SV of each power source (WT, PV, and GT). is the project lifetime. is the annual fuel (natural gas) cost. is the cost of the annual purchasing electricity. 1. TAC of the WT Equation 6 shows the initial cost of the wind turbine, Equation 7 shows the operation and maintenance cost, and Equation 8 shows the salvage value of the wind turbine.
)
=
Subjected to: ≤ 20 ≤ ≤
∑
(7)
where,
where, is the flow coefficient. is the absolute output pressure of the pressure regulator in bar is the absolute input pressure of the pressure regulator. It equals .
=
∑
=
(3)
(6)
) (11)
where, is the SV of PV system ($/
)
3. TAC of the SB This power source has only the initial cost and the O&M cost, and there is no SV because the aged batteries should be disposed according to Egypt environmental regulations. Equation 12 shows the initial cost of the SB, and its lifetime is lower than the project lifetime. Equation 13 shows the operation and maintenance cost (
∑
=
)
(12)
where, is the initial cost of SB ($/ ) is the SB capacity ( ) is the SB lifetime is the number of times to purchase the batteries during the project lifespan . =
∑
(13)
is the O&M cost of storage battery system ($/kW). 4. TAC of the GT This power element has annual initial cost, annual O&M cost, annual salvage cost, and annual fuel cost as shown in Equations 14, 15, 16, and 17 respectively.
=
(14)
where,
where, is the initial cost of GT ($/kW) is the GT capacity (kW)
( )=
=
(15)
where, is the salvage value of GT ($/kW)
( )=
is the O&M cost of gas turbine system ($/kW) is the escalation factor .
.
×
(17)
is fuel rate in /ℎ, is the price of the consumed natural gas in $/
.
×
(18)
where, , is the power delivered from the utility grid at an instant is the price of the power delivered from the UG in $/ Pollutant Emissions There are some emissions resulted due to the using the fossil ) and nitrogen oxides ( ) fuel such as; sulfur dioxide ( [1]. Equation 19 represents the total annual emission of the proposed HPGS. = ,
+
×∑
(
,
+
,
( )) +
× ∑
( ))
(
,
+ (19)
where, , and are the coefficients of the generation power emission characteristics. C. Design Constraints
( )+
(21)
( )× ×
×
(22)
×
(23)
≤
≤
(24)
≤
where, is the wind turbine swept area ( ) is the PV swept area (
(25)
)
IV. META-HEURISTIC OPTIMIZATION TECHNIQUES The following section introduces the applied meta-heuristic optimization techniques. In this paper, ALO and GWO have been applied using MATLAB software to find the optimal HPGS system components size for the utility connected mode. The output results have been compared with other optimizations techniques to clarify the effectiveness and the superiority of the applied techniques. A. The Ant Lion Optimizer The ALO algorithm had been created by S. Mirjalili at 2015, where the author had simulated the nature behavior of the ant lions. Moreover, S. Mirjalili categorized his simulation to five main steps of hunting prey as; random walk of ants, building traps, entrapment of ants in traps, and Catching preys and rebuilding traps are implemented. Figure 5 shows the main steps of this optimizer [15]. B. Grey Wolf Algorithm (GWO)
The physical and operation constraints should be satisfied during the hybrid power system optimizing, and the main constraint of this system is the power balance, which is shown in Equation 20. This Equation shows at any period , the total power supplied from the HPGS should equal the total demand and the system loss P (t). ( )+
<
