17th International Middle East Power Systems Conference, Mansoura University, Egypt, December 15-17, 2015
Optimal Multi-Criteria Design of Hybrid Power Generation Systems Using Cuckoo Search and Firefly Algorithms S. F. Mekhamer, A. Y. Abdelaziz, and M.A.L.Badr Department of Electrical Power and Machine,
M. A. Algabalawy Electrical Maintenance Engineer
Ain Shams University, Abbassia, Cairo { said_fouad, almoataz_abdelaziz& mohamed_badr }@ eng.asu.edu.eg
Egypt General Motors Egypt
Abstract - Hybrid power generation system is an important research area as a result of continuous increasing of power demand. A lot of researchers have been interested in solving the problems of power demand increase, and power quality especially in distribution levels. Distributed generation source is considered one of the used ways to reduce or avoid the above mentioned problems. Hybrid power generation system is a combination of different distributed generation sources. In this paper, techniques based on Cuckoo Search (CS) and Firefly Algorithm (FA) are used to obtain the optimal design of the hybrid power generation system. To show the superiority of the proposed methods, the results using CS and FA have been compared with recent techniques applied to the same case study.
Index Terms - Hybrid generation system, Renewable energy sources, Distributed generation, Cuckoo search, Firefly algorithm.
I. INTRODUCTION Increasing of power demand, power quality problems, and air pollution are the main reasons driving the research in hybrid generation systems. As the effect of these problems increased, as the need to study this point increased. A lot of researchers are interested in solving the above mentioned problems. Hybrid power generation system (HPGS) is one of the suggested solutions to increase the peak power capability and reliability of the power system. Also, the HPGS is considered a way to reduce the transmission power losses and the power generation pollution, because it includes renewable energy sources. The HGPS consists of many power sources such as; photovoltaic system (PV), wind turbine (WT), fuel cell (FC), and micro-turbine (MT). These sources are considered the prominent elements in the HGPS. A. El-Aal, reference [1], presents the modeling and simulation of a stand-alone hybrid power system, which consists of hydrogen Photovoltaic-Fuel Cell (PVFC) hybrid system. He tries to maximize the use of a renewable energy source, where
[email protected] the power is produced by a PV generator to meet the requirements of a user load in case of there is enough solar radiation. During periods of low solar radiation, auxiliary electricity is required. An alkaline high pressure water electrolyser is powered by the excess energy from the PV generator to produce hydrogen and oxygen at a pressure of maximum 30bar. Gases are stored without compression for short-daily and long-seasonal term. L. Zhang, G. Barakat and A. Yassine, reference, [2], present a development of a hybrid power generation system, which contains; PV, wind turbine, battery system, and diesel generator using dividing rectangles (DIRECT) algorithm. They state that, this algorithm can attain the optimum values of commercially available system devices ensuring that the system total investment cost is minimized. They give the values of the system components during a period of 20-year including the number of PV modules, the PV module surface area, the number of wind turbines, the wind turbine installation height, the battery bank number and the diesel generator operating hours with their system total investment costs. S. Kumar and V. Garg, reference [3], simulate a hybrid model of a solar/wind and fuel cell. They show a comparison between the above mentioned system and the other one. Another system includes the battery system instead of fuel cells has been used. They provide a simulation for all mentioned components of the systems. In reference [4], O. Penangsang, M. Abdillah, R. S. Wibowo, and A. Soeprijanto propose a hybrid method to optimize Photovoltaic (PV)-Battery systems. Their proposed method is named Interval type-2 where a fuzzy adaptive genetic algorithm (IT2FAGA) is used to optimize the annual cost of system (ACS). ACS consists of the annual capital cost (ACC), annual replacement cost (ARC), and annual operation cost maintenance (AOM). They compare the results of their method with the fuzzy adaptive genetic algorithm (FGA) and the standard genetic algorithm (SGA). They show that, their method (IT2FAGA) provides a better performance in minimizing the objective function than the other two methods ((FGA) and (SGA)).
17th International Middle East Power Systems Conference, Mansoura University, Egypt, December 15-17, 2015 In reference [5], T. Tahri, A. Bettahar, and M. Douani introduce a design of wind/PV/diesel hybrid power system for a village of Ain Merane, Chlef, Algeria, where the wind speed and solar radiation measurements were taken. They use the HOMER program to find the capacity of the hybrid system elements in term of technical and economic feasibility. They compare between the performance of wind/PV/diesel system and the classic connecting system. In reference [6], S. L. Trazouei, F. L. Tarazouei, and M. Ghiamy present a design for a stand-alone hybrid solar- winddiesel power generation system using imperialist competitive algorithm, particle swarm optimization and ant colony optimization. They try to minimize the net present cost of hybrid system for lifetime of the stand-alone hybrid considering the loss of power system probability (LPSP). Finally, they present the results (number of PV panels, number of wind turbines, number of battery storages, system total cost, and power diagram of hybrid power system components and reliability diagram) for solar-wind -diesel systems. In reference [7], J.S. Uprit, and A.M. Shandilya introduce the design of the hybrid energy system for a certain technical college/remote rural area for a particular site in central India (Bhopal) using the meteorological data of solar isolation and the load consumption. They use the genetic algorithm (GA) to minimize the total capital cost, subject to the constraint of the Loss of Power Supply Probability (LPSP). Y. Maklad, in reference [8], designs a stand-alone hybrid system consists of wind-PV hybrid system to cover the electricity consumption of typical residential buildings of various occupancy rates and relevant various average of electrical daily consumption. He studies the monthly average solar irradiance and the monthly average wind speed over the year. He uses MATLAB software to simulate the solar photovoltaic panels and wind turbines to obtain the hybrid system sizing with the lowest cost. He checks the correlation between solar and wind power data on an hourly, daily, and monthly basis. M. Alsayed, M. Cacciato, G. Scarcella, and G. Scelba, in reference [9], determine the design of grid connected PV–WT, by using a procedure based on a multi criteria decision analysis (MCDA) optimization approach. They take into consideration all issues, trying to reduce the grid emissions and hybrid system total costs, besides increasing the social acceptability. They apply their approach to different case studies accompanied with uncertainty analysis related to solar radiation, wind speed and load demand profiles variations. J. Weniger, T. Tjaden, and V. Quaschning, in reference [10], analyze a residential PV-battery system in order to gain insights into their sizing. They develop a simulation model on a time scale to find the PV system and battery size to identify appropriate system configurations. Based on the simulation results, an economic assessment of PV battery systems is carried out and the cost-optimal configurations for various cost scenarios are determined. There are many classifications for the HPGS based on transmission level or distribution level. In this paper we concern the distribution level. We apply the Cuckoo search
(CS) and Firefly (FA) meta-heuristic optimization techniques to find the optimal design of the hybrid power generation system. The design is compared with recent work that apply particle swarm optimization technique (PSO) to prove the superiority of the modern proposed optimization techniques. CS, FA, and PSO MATLAB codes are built as explained in [11] to obtain the optimal hybrid power generation system. II. PROBLEM FORMULATION [12] As shown in Figure 1, a typical hybrid generation system consists of different power sources including wind turbine generators, PV panels (PVs), and storage batteries (SBs). These power sources have different impacts on cost, environment, and reliability. In a hybrid generation system, they are integrated together and complement one another in order to serve the load while satisfying certain economic, environmental, and reliability criteria. The hybrid system can operate autonomously or utility-connected, which its power from conventional fossil fuel fired generators (FFGs). The only grid-linked system will be discussed.
Fig.1 configuration of a typical hybrid power generation system
Some of the calculations are to be formulated in this section. They are applicable to the hybrid system design incorporating adequacy evaluation including the uncertainties that are to be discussed in the following sub-sections. A. Design Objectives 1) Cost: Total Annual Cost (TAC) estimation has been incorporated into the hybrid generation system design total cost ($/year) that includes initial cost, operational and maintenance (OM) cost for each type of power source, and the salvage value of each equipment that should be deducted . This can be expressed as: ∑
where,
(
)
(10)
17th International Middle East Power Systems Conference, Mansoura University, Egypt, December 15-17, 2015 indicate the wind power, solar power, and battery storage, respectively, are the initial cost, present worth of salvage value, and present worth of operation and maintenance cost (OM) for equipment i, respectively. is the lifespan of the project. Cg is the annual cost for purchasing power from the utility grid. Here, it is assumed that the lifetime of the project does not exceed those of both WTGs and PV arrays. TAC calculations: For wind turbine Initial cost
)
(3) where, is the salvage value of wind turbine ($/ is the inflation rate is the interest rate Operation and Maintenance cost ∑
(
)
)
(4) where, is the operation and maintenance cost of wind turbine system ($/ ) is the escalation factor For PV panels Initial cost (5) where, is the initial cost of PV system ($/ is the swept area ( ) Salvage value
)
)
(6) where, is the salvage value of PV system ($/ Operation and Maintenance cost ∑ (7)
∑
(
)
(
)
(
)
(8) where, is the initial cost of storage battery system ($/ ) is the storage battery capacity ( ) is the storage battery lifespan is the number of times to purchase the batteries during the project lifespan . Salvage value There is no salvage value for the storage battery system Operation and Maintenance (
)
(9) where, is the operation and maintenance cost of storage battery system ($/kW).
)
(
is the operation and maintenance cost of PV system ($/ ) For Storage Battery system Initial cost
∑
(2) where, is the initial cost of wind turbine ($/ is the swept area ( ) Salvage value (
where,
)
The annual cost for purchasing power from the utility grid It can be can be calculated as follows: ∑ ( ) (10) where, ( ) is the power purchased from the utility at hour is the grid power price. 2) Reliability: Reliability is used to assess the quality of load supply. Here, the energy index of reliability (EIR) is used to measure the reliability of each candidate hybrid system design. EIR can be calculated from the expected energy not served (EENS) as follows: (11) where, is the yearly energy demand. The EENS (kWh/year) for the duration under consideration T (8760 h) can be calculated as follows: ∑ ( ( ) ( )) ( ) (12) where, ( ) is a step function that is zero when the supply exceeds or equals to the demand, and equals one if there is insufficient power in period t; ( ) ( ) ( ) (13) where, ( ) ( ) ( ) ( ) (14) ( ) is the battery charge level during hour , and Here, is the minimum permitted storage level, the term ( ) ( ) indicates the available power supply from
17th International Middle East Power Systems Conference, Mansoura University, Egypt, December 15-17, 2015 batteries during hour and provided that there is insufficient power in hour . ( ) ( ) ( ) ( )) ( ( ) (15) Where indicate the portion of the purchased power with respect to the hourly insufficient power; or else ( ) . Note that no equipment failures and unexpected load deviations are considered in calculating the EENS, which in this design is totally contributed by the fluctuations of renewable power generation. 3) Pollutant Emissions: With the increasing concerns on environment protection, there are stricter regulations on pollutant emissions. The most important emissions considered in the power generation industry due to their highly damaging effects on the ecological environment are sulfur dioxide ( ) and nitrogen oxides ( ). These emissions can be modeled through functions that associate emissions with power production for generating units. They are dependent on fuel consumption and take the quadratic form ∑ (∑ ) (16) where, , , and are the coefficients approximating the generator emission characteristics. B. Design Constraints Due to the physical or operational limits of the target system, there is a set of constraints that should be satisfied throughout system operations for any feasible solution [12]. These constraints can be summarized as follows: 1) Power Balance constraint: For any period t, the total power supply from the hybrid generation system must supply the total demand ( ) with a certain reliability criterion. This relation can be represented by: ( ) ( ) ( ) ( ) ( ) (17) Assuming that, the system loss is included into the ( ). where, ( )=
(18) {
( ) (19) ( ) (20)
( )
The state of charge (SOC) of storage batteries should not exceed the capacity of storage batteries and should be larger than the minimum permissible storage level . The total SB capacity should not exceed the allowed storage capacity and the hourly charge or discharge power should not exceed the hourly inverter capacity . As a result, we have: (23) (24) (25) The coefficient indicates the portion of purchased power from utility grid with respect to the insufficient power (26) III. META-HEURISTIC OPTIMIZATION TECHNIQUES A. Cuckoo Search Algorithm [11, 13, and 14] Cuckoo search (CS) is inspired by some species of a bird family called cuckoo because of their special lifestyle and aggressive reproduction strategy. This algorithm was proposed by Yang and Deb [11]. The CS is an optimization algorithm based on the brood parasitism of cuckoo species by laying their eggs in the communal nests of other host birds, though they may remove others’ eggs to increase the hatching probability of their own eggs. Some host birds do not behave friendly against intruders and engage in direct conflict with them. If a host bird discovers the eggs are not their own, it will either throw these foreign eggs away or simply abandon its nest and build a new nest elsewhere [13 and 14]. The CS algorithm contains a population of nests or eggs. Each egg in a nest represents a solution and a cuckoo egg represents a new solution. If the cuckoo egg is very similar to the host’s, then this cuckoo egg is less likely to be discovered. Thus, the fitness should be related to the difference in solutions. The better new solution (cuckoo) is replaced with a solution which is not so good in the nest. In the simplest form, each nest has one egg. When generating new solutions ( ) for, say cuckoo i, a Lévy flight is performed: ( ) ( ) Lévy( ) (27) where, means entry-wise multiplications
2) Bounds of Design Variables: (21) (22)
is the step size which should be related to the scales of the problem of interest, and it is more than 0. = ( ) (28) where, is the characteristic scale of the interested problem.
17th International Middle East Power Systems Conference, Mansoura University, Egypt, December 15-17, 2015 The product. Lévy flights essentially provide a random walk while their random steps are drawn from a Lévy distribution for large steps which has an infinite variance with an infinite mean. Here the consecutive jumps/steps of a cuckoo essentially form a random walk process which obeys a powerlaw step-length distribution with a heavy tail. The rules for CS are described as follows: Lévy ,(1
