001.docx CHEMICAL ENGINEERING TRANSACTIONS VOL. 83, 2021 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš Copyright © 2021, AIDIC Servizi S.r.l. ISBN 978-88-95608-81-5; ISSN 2283-9216 Novel Designing Framework of Stand-alone and Grid- connected Hybrid Photovoltaic/Wind/Battery Renewable Energy System Considering Reliability, Cost and Emission Indices Amirreza Naderipoura, Zulkurnain Abdul-Maleka, Shohreh Nasrib, Saber Arabi Nowdehc, Hesam Kamyabd,*, Shreeshivadasan Chelliapane, Mohd Wazir Bin Mustafaf, Abdullah Asuhaimi Mohd Zinf aInstitute of High Voltage & High Current, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru. bDepartment of Electrical Engineering, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran. cGolestan Technical and Vocational Training Center, Gorgan, Iran. dMalaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia. eEngineering Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia fSchool of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia. hesam_kamyab@yahoo.com This paper novel multi-criteria designing framework of a grid-connected hybrid photovoltaic (PV)/wind turbine (WT) sustainable and clean energy system with battery (BA) storage (HPV/WT/BA) considering cost, reliability and emission costs are presented dependent on actual irradiance and wind speed patterns to include an annual load. The designing objective is optimal sizing of the HPV/WT/BA system to minimize the total net present cost (TNPC) as well as the loss of load and CO2 emission cost with satisfying reliability constraint as energy not supplied probability (ENSP) considering stand-alone and grid-connected modes. The results showed that purchasing the power from network reduced the TNPC and also improved the hybrid system reliability and the reliability constraint is in the allowable range. The results also showed the superiority of the moth flame optimizer than the well-known particle swarm optimization (PSO) algorithm in achieving to lower TNPC and better reliability. 1. Introduction Mandatory cessation of greenhouse gas emissions, air pollutants, industrial waste and sewage, and even the destruction of natural resources are seen as positive aspects of the spread of Covid-19 disease in the environment. Covid-19 pursued a set of limitations, including reducing travel, reducing non-essential purchases, stopping polluting industries, reducing forest and rangeland damage, and so on, all of which reduced air and nature pollution (Naderipour et al., 2020a). In the other hand in recent years, the use of sustainable energy has been growing (Kamyab et al., 2020). Different countries are trying to benefit from these resources due to their industrial applications and geographical location (Naderipour et al., 2019). Increasing the price of fossil fuels and the effects of these resources on the environment is also one of the reasons for the increase in countries' incentives to use sustainable energy (Kamyab et al., 2018). It makes a lot of sense to use distributed generation (DG) resources, especially based on sustainable energy sources, to provide the power needed by remote consumers over fossil power plants and to avoid spending cost on construction of transmission lines (Zin et al., 2016). One of the most important objectives of sustainable sources application is to reduce costs (Abdmouleh et al., 2017). The use of sustainable energy sources to DOI: 10.3303/CET2183095 Paper Received: 05/09/2020; Revised: 12/11/2020; Accepted: 13/11/2020 Please cite this article as: Naderipour A., Abdul-Malek Z., Nasri S., Arabi Nowdeh S., Kamyab H., Chelliapan S., Mustafa M.W.B., Mohd Zin A.A., 2021, Novel Designing Framework of Stand-alone and Grid-connected Hybrid Photovoltaic/Wind/Battery Renewable Energy System Considering Reliability, Cost and Emission Indices, Chemical Engineering Transactions, 83, 565-570 DOI:10.3303/CET2183095 565 supply electricity to distributed and stand-alone loads in remote areas is a good way to reduce the economic costs of expanding and transmitting network lines, reducing environmental emission and increasing energy efficiency (Jahannoosh et al., 2020). In the hybrid systems, energy production fluctuations are controlled by using two or more sources of energy production and by storage systems, thus improving the reliability of the load (Hadidian-Moghaddam et al., 2018). The hybrid systems are used as stand-alone to provide remote loads from the power grid. Also, energy hybrid systems can be used in grid-connected mode (Naderipour et al., 2020b). This means that the main priority of the system is to supply the energy demand of the system and sometimes the excess power of the system is injected into the network (Rullo et al., 2019). When a hybrid energy system is based on PV and WT energy, an auxiliary energy source is needed to compensate for the fluctuations in the production capacity of these units due to changes in irradiance and wind speed (Jahannoush et al.,, 2020). Therefore, it is necessary to decide the size of the components optimally in order to achieve stable electricity in a hybrid system, as well as a compromise between energy output and consumption (Naderipour et al., 2020b). In addition, one of the major advantages of using hybrid energy systems is the reduction of environmental emission (Moghaddam et al., 2020). Environmental emission produced in the design and optimization of hybrid energy systems must be considered as an important factor (Jafar-Nowdeh et al., 2020). Therefore, minimizing the energy generation cost in this type of system as a function of the main goal, the optimal size of the system component to supply the load should be done by considering other indices such as emission cost and load loss penalty (Zin et al., 2016). In this paper multi-criteria designing of stand-alone and grid-connected hybrid HPV/WT/BA system to minimize the total net present cost (TNPC) as well as the loss of load and CO2 emission cost with satisfying reliability, the constraint is proposed to supply the annual load using moth flame optimizer and the results are compared with particle swarm optimization (PSO) algorithm. 2. Hybrid energy system 2.1 Page layout system configuration The HPV/WT/BA sustainable energy system consists of PV, WT, battery (BA) and inverter to convert DC power to AC power according to Figure 1. Figure 1: HPV/WT/BA sustainable energy system 2.2 PV model The production power by photovoltaic ( 𝑃𝑃𝑃𝑃𝑃𝑃) is defined based on irradiance radiated to the PV surfaces as given by Eq(1) (Gharavi et al., 2015): 𝑃𝑃𝑃𝑃𝑃𝑃 = (𝑃𝑃𝑃𝑃𝑃𝑃,𝑆𝑆𝑆𝑆𝑆𝑆×E𝑃𝑃𝑃𝑃) × (𝐼𝐼𝐼𝐼𝑃𝑃(𝑡𝑡)×cos(𝜃𝜃𝑃𝑃𝑃𝑃)+𝐼𝐼𝐼𝐼𝐻𝐻(𝑡𝑡)×sin (𝜃𝜃𝑃𝑃𝑃𝑃)) 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 (1) where, 𝑆𝑆𝑃𝑃(𝑡𝑡) and 𝑆𝑆𝐻𝐻(𝑡𝑡) refer to the horizontal and vertical component of irradiance, 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 refers to standard conditions irradiance (1,000 W/m2), 𝜃𝜃𝑃𝑃𝑃𝑃 indicate PV installation angle (deg), E𝑃𝑃𝑃𝑃 is PV tracking efficiency and 𝑃𝑃𝑃𝑃𝑃𝑃,𝑆𝑆𝑆𝑆𝑆𝑆 is PV nominal power (Hadidian-Moghaddam et al., 2016). DC/DC AC/DC Inverter DC Bus AC Bus BA PV Network Load Wind Turbine 566 2.3 Wind Turbine model The wind turbine output power based on wind speed (m/s) is defined in Eq(2) (Hadidian-Moghaddam et al., 2016). 𝑃𝑃𝑊𝑊𝑆𝑆= ⎩ ⎨ ⎧ 0 ;𝑉𝑉𝑉𝑉 ≤ 𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉,𝑉𝑉𝑉𝑉 ≥ 𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉𝑡𝑡 𝑃𝑃𝑃𝑃𝑉𝑉𝑡𝑡 × � 𝑃𝑃𝑉𝑉−𝑃𝑃𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉 𝑃𝑃𝐼𝐼−𝑃𝑃𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉 � 𝑚𝑚 ; 𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉 ≤ 𝑉𝑉𝑉𝑉 ≤ 𝑉𝑉𝑉𝑉 𝑃𝑃𝑃𝑃𝑉𝑉𝑡𝑡 + 𝑃𝑃𝑉𝑉𝑉𝑉𝑡𝑡𝑃𝑃𝑉𝑉𝑡𝑡−𝑃𝑃𝑚𝑚𝑉𝑉𝑡𝑡 𝑃𝑃𝑉𝑉𝑉𝑉𝑡𝑡𝑃𝑃𝑉𝑉𝑡𝑡−𝑃𝑃𝐼𝐼 × (𝑉𝑉𝑉𝑉 − 𝑉𝑉𝑉𝑉) ; 𝑉𝑉𝑉𝑉 ≤ 𝑉𝑉𝑉𝑉 ≤ 𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉𝑡𝑡 (2) Here, 𝑃𝑃𝑊𝑊𝑆𝑆 represents the WT output power, 𝑉𝑉𝑉𝑉 represents the wind speed, Vcutin represents the cut-in speed, 𝑉𝑉𝑉𝑉 represents the nominal speed, Vcutout represents the cut-out speed, Pmwt represents the maximum power of the WT unit (kW), and 𝑃𝑃𝑉𝑉𝑉𝑉𝑡𝑡𝑉𝑉𝑉𝑉𝑡𝑡 represents the output power at the cut-out speed. At 40 m, the wind data is registered, and the height of the turbine is 15 m. In Eq(3), the wind speed at this altitude is determined (Hadidian-Moghaddam et al., 2016). 𝑣𝑣𝑊𝑊ℎ = 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 × � Hwt 𝐻𝐻𝑉𝑉𝑡𝑡𝐻𝐻𝑟𝑟𝑟𝑟𝑟𝑟 � 𝜇𝜇 (3) Here, 𝑣𝑣𝑊𝑊ℎ represents the wind speed at Hwt, 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 represents wind speed at 𝐻𝐻𝑉𝑉𝑡𝑡𝐼𝐼𝑟𝑟𝑟𝑟, and 𝜇𝜇 is exponent law coefficient that its range is from 0.14 to 0.25 (Hadidian-Moghaddam et al., 2016). 2.4 Battery model The battery is used to compensate for fluctuations in the supply of renewable energy resource and improve load reliability (Hadidian-Moghaddam et al., 2016). When the energy produced by the renewable energy sources at time t exceeds the load demand, the excess energy is transferred to the battery and stored. The energy stored in the battery at time t (𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡(𝑡𝑡)) is given by the Eq(4). 𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡(𝑡𝑡) = 𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡(𝑡𝑡 − 1) + ��𝑃𝑃𝑃𝑃𝑃𝑃(𝑡𝑡) + 𝑃𝑃𝑊𝑊𝑆𝑆(𝑡𝑡)� − 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜(𝑡𝑡) 𝜂𝜂𝐼𝐼𝐼𝐼𝐼𝐼 � .∆𝑡𝑡 (4) Here, 𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡(𝑡𝑡) represents the stored energy in the battery at time t-1, 𝑃𝑃𝑃𝑃𝐵𝐵𝑜𝑜(𝑡𝑡) represents the load demand at time t, 𝜂𝜂𝐼𝐼𝑉𝑉𝐼𝐼 represents the inverter efficiency, and ∆𝑡𝑡 represents the time step (1 h). If the load exceeds the energy generated from the renewable sources, the battery is applied to compensate for the lack of load energy. The battery energy at time t is calculated by the Eq(5). 𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡(𝑡𝑡) = 𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡(𝑡𝑡 − 1) − �𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜(𝑡𝑡) 𝜂𝜂𝐼𝐼𝐼𝐼𝐼𝐼 − �𝑃𝑃𝑃𝑃𝑃𝑃(𝑡𝑡) + 𝑃𝑃𝑊𝑊𝑆𝑆(𝑡𝑡)�� .∆𝑡𝑡 (5) 2.5 Objective function The objective function of designing the HPV/WT/BA system is defined by the Eq(6). 𝑀𝑀𝑉𝑉𝑉𝑉 𝑇𝑇𝑇𝑇𝑃𝑃𝑆𝑆 = 𝑇𝑇𝑃𝑃𝑆𝑆𝑉𝑉𝐵𝐵𝑐𝑐 + 𝑇𝑇𝑃𝑃𝑆𝑆𝑚𝑚𝐵𝐵𝑉𝑉𝑉𝑉&𝑃𝑃𝑐𝑐𝑟𝑟𝐼𝐼 + 𝑇𝑇𝑃𝑃𝑆𝑆𝐼𝐼𝑟𝑟𝑐𝑐 + 𝑇𝑇𝑃𝑃𝑆𝑆𝑙𝑙𝑃𝑃𝑙𝑙𝑙𝑙 + ∆ × [∑ 𝑆𝑆𝑃𝑃(𝑡𝑡)𝑉𝑉 + ∑ 𝑆𝑆𝐶𝐶(𝑃𝑃𝑡𝑡)𝑚𝑚 + 𝑇𝑇𝑃𝑃𝑆𝑆𝐸𝐸𝑚𝑚𝑉𝑉𝑙𝑙𝑙𝑙𝑉𝑉𝑃𝑃𝑉𝑉 + 𝑇𝑇𝑃𝑃𝑆𝑆𝐸𝐸𝑚𝑚𝑉𝑉𝑙𝑙𝑙𝑙𝑉𝑉𝑃𝑃𝑉𝑉 (6) Here, 𝑇𝑇𝑇𝑇𝑃𝑃𝑆𝑆 represents the cost of the hybrid system over its lifespan, and 𝑇𝑇𝑃𝑃𝑆𝑆𝑉𝑉𝐵𝐵𝑐𝑐, 𝑇𝑇𝑃𝑃𝑆𝑆𝑚𝑚𝐵𝐵𝑉𝑉𝑉𝑉&𝑃𝑃𝑐𝑐𝑟𝑟𝐼𝐼, and 𝑇𝑇𝑃𝑃𝑆𝑆𝐼𝐼𝑟𝑟𝑐𝑐 represent the initial capital cost, maintenance and operation cost, and cost of replacing components, respectively. Here, CP(t) represents the cost of energy purchased from the network per hour, CD(mt) represents the price paid for the maximum power purchased per month. 2.6 Energy not supplied probability constraint In this study, the energy not supplied probability (ENSP) is defined for reliability evaluation. The ENSP changes between 0 and 1 values in the Eq(7) (Ahmadi and Abedi, 2016). 𝐸𝐸𝑇𝑇𝑆𝑆𝑃𝑃 = 𝐸𝐸𝐸𝐸𝑆𝑆 ∑ [𝑃𝑃𝐿𝐿(𝑡𝑡)]𝑆𝑆 𝑡𝑡=1 = ∑ [𝑃𝑃𝐿𝐿(𝑡𝑡)−(𝑃𝑃𝑃𝑃𝑃𝑃(𝑡𝑡)+𝑃𝑃𝑊𝑊𝑆𝑆(𝑡𝑡))−𝑆𝑆𝑆𝑆𝑆𝑆𝐵𝐵𝐵𝐵(𝑡𝑡)]𝑆𝑆 𝑡𝑡=1 ∑ [𝑃𝑃𝐿𝐿(𝑡𝑡)]𝑆𝑆 𝑡𝑡=1 (7) The reliability indices as reliability constraint are presented by the Eq(8). 𝐸𝐸𝑇𝑇𝑆𝑆𝑃𝑃 ≤ 𝐸𝐸𝑇𝑇𝑆𝑆𝑃𝑃𝑚𝑚𝐵𝐵𝑚𝑚 (8) Where, 𝐸𝐸𝑇𝑇𝑆𝑆𝑃𝑃𝑚𝑚𝐵𝐵𝑚𝑚 is the maximum value of 𝐸𝐸𝑇𝑇𝑆𝑆𝑃𝑃. 567 3. Simulation results The results of HPV/WT/BA system design for stand-alone and grid-connected mode are presented aimed at minimizing the TNPC and satisfying the ENSP reliability constraint based on the MFO, and the design results are compared with PSO method. 3.1 System data The moth–flame optimization (MFO) is applied to design the HPV/WT/BA system aimed TNPC with satisfying the ENSP. The total load is 269 MWh for a year. In Figure 2-4, data of irradiance, wind speed, and load for a year are shown. The parameters of the hybrid system are presented in Table 1. Table 1: Hybrid system parameters (Naderipour et al., 2020b) Device 𝑇𝑇𝑃𝑃𝑆𝑆𝑉𝑉 (USD/unit) 𝑇𝑇𝑃𝑃𝑆𝑆𝐼𝐼 (USD/unit) 𝑇𝑇𝑃𝑃𝑆𝑆𝑚𝑚 (USD/unit) Rated Capacity Efficiency )%( Lifetime (Year) Wind 19,400 15,000 75 7.5kW - 20 PV 7,000 6,000 20 1kW - 20 Battery 750 700 7 1kW 85 10 Inverter 800 750 7 1kWh 90 15 Figure 2: The horizontal and vertical solar irradiance during a year Figure 3: The wind speed during a year 568 Figure 4: The load demand during a year 3.2 Stand-alone and Grid-connected mode results The findings indicate (Table 2) that the procurement of electricity from the network decreased the TNPC and also increased the efficiency of the hybrid system and the limitation of reliability is within the permitted limit. The findings also revealed the supremacy of the moth flame optimizer in achieving lower TNPC and higher efficiency than the well-known PSO algorithm. Table 2: Comparison the results of HPV/WT/BA system designing with previous studies Parameter 𝑇𝑇𝑊𝑊𝐻𝐻 𝑇𝑇𝑃𝑃𝑃𝑃 𝑇𝑇𝐵𝐵𝐵𝐵𝑡𝑡𝑡𝑡 𝑃𝑃𝐼𝐼𝑉𝑉𝐼𝐼 (𝑘𝑘𝑘𝑘) 𝜃𝜃𝑃𝑃𝑃𝑃 (𝑑𝑑𝑉𝑉𝑑𝑑) 𝐸𝐸𝑇𝑇𝑆𝑆𝑃𝑃 (%) 𝑇𝑇𝑇𝑇𝑃𝑃𝑆𝑆 𝑆𝑆𝑆𝑆𝐸𝐸 𝑇𝑇𝑃𝑃𝑆𝑆𝐸𝐸𝑚𝑚𝑉𝑉𝑙𝑙𝑙𝑙𝑉𝑉𝑃𝑃𝑉𝑉 𝑇𝑇𝑃𝑃𝑆𝑆𝑉𝑉𝑟𝑟𝑡𝑡 MFO (stand-alone) 8 128 144 47.84 35.92 0.0021 1.3077 0.2432 -- -- MFO (grid-connected) 8 127 144 47.91 35.44 0.0010 1.2946 0.2407 20,703 4,860 PSO (stand-alone) (Jahanbani et al., 2010) 14 115 69 47.62 37.36 0.0070 2.8025 0.5209 -- -- 4. Conclusion In this paper, the novel framework of optimal design of HPV/WT/BA Energy System is presented to minimize the TNPC costs, load loss cost, as well as emission, cost satisfying the reliability constraint as ENSP in stand- alone and grid-connected modes based on real irradiance and wind speed data. The amount of PVs, WTs, batteries, inverter power transmitted to the load and the angle of the PV panels that are optimally calculated using the MFO approach are the decision variables for the design problem. 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