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 Economic Assessment of Photovoltaic-Based Microgrid Incorporated with Solar Tracking System Angel Xin Yee Mah, Wai Shin Ho*, Mimi Haryani Hassim, Haslenda Hashim School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia hwshin@utm.my Solar energy is a major renewable energy source as it is clean and abundant. The solar radiation yield of a PV system can be enhanced by fixing the solar panel at optimal orientation or coupling with a solar tracking system. This study aims to evaluate the cost-effectiveness of incorporating solar trackers into microgrid to improve the energy gain. A mathematical model is formulated for the optimal sizing of microgrid and the model is implemented on a case study with 500 households. The study outcome suggests the feasibility of solar tracking system is greatly dependent on its cost. When the cost of solar tracker is low, east-west single-axis tracking system is the most feasible option, but the fixed-tilt system becomes more feasible when the cost of solar tracker is high. The threshold for east-west single-axis tracking system investment is found to be <240 USD/kWp. 1. Introduction The concerns on energy security and climate change have been driving the exploration of sustainable energy supply. Solar energy is one of the most important renewable energy sources as it is clean and readily available (Huang et al., 2019). It has the potential to fulfil Malaysia energy requirement with its abundance in Malaysia (Khaliludin et al., 2020). The yield of solar energy is greatly influenced by the tilt and azimuth angle of the solar collector, which is usually oriented towards the equator with an optimal slope (Bahrami et al., 2017). Solar tracker increases the energy harvested by tracking the path of the sun from time to time (Bahrami et al., 2016) to keep the surface of PV module perpendicular to the incoming solar radiation (Alkaff et al., 2019). The solar tracking systems can be classified into single-axis tracking system and dual-axis tracking system depending on their movement degree of freedom (Awasthi et al., 2020). A single-axis system has only one rotation axis, therefore it usually consumes less energy and is less complex compared to a multi-axis system (Sumathi et al., 2017). There are several configurations of single-axis trackers including horizontal, horizontal with tilted modules, vertical, tilted and polar aligned (Nsengiyumva et al., 2018). A dual-axis solar tracking system (DAT) tracks the sun in two different axes using two pivot points to rotate. It is superior in tracking the yearly sun movement such as the altitude of sun from season to season, making it more efficient and has higher solar energy gain (Hafez et al., 2018). Several studies have been done to evaluate the techno-economic performance of solar trackers. Bahrami et al. (2016) investigated the effect of latitude on the energy yield in Europe and Africa. Alkaff et al. (2019) compared the energy gain of three tracking systems to the fixed south-oriented tracking system at six locations with latitude 0° to 55°. Bahrami et al. (2017) compared the energy gain and levelized cost of electricity (LCOE) resulted from fixed, single and dual-axis solar trackers in low latitude countries using nine selected locations in Nigeria as case study. The work is then extended to evaluate the performance of solar tracking system in northern hemisphere (Bahrami and Okoye, 2018). Although some previous studies have evaluated the LCOE of different PV systems, the intermittency of solar resources has been neglected when determining the energy system cost. For a solar-based renewable energy system, energy storage plays a vital role in balancing the intermittent energy supply and demand profiles by storing the excess energy generated by renewables and discharge at later time to compensate the energy deficits. DOI: 10.3303/CET2183076 Paper Received: 30/06/2020; Revised: 28/08/2020; Accepted: 29/08/2020 Please cite this article as: Mah A.X.Y., Ho W.S., Hassim M.H., Hashim H., 2021, Economic Assessment of Photovoltaic-Based Microgrid Incorporated with Solar Tracking System, Chemical Engineering Transactions, 83, 451-456 DOI:10.3303/CET2183076 451 A microgrid is a small-scale power system with local power generation, energy storage and load demands, which can operate as a standalone system or connected to the main grid (Li et al., 2017). Potential energy storage options include batteries, hydrogen, pumped-hydro, flywheel, compressed air storage, supercapacitor, and superconducting magnetic energy storage. The optimization of microgrid capacity is crucial to prevent system oversize and reduce the cost of investment. Jacob et al. (2018) proposed a generic sizing methodology using pinch analysis and design space to optimize the hybrid energy storage in a standalone PV- based power system. Zhang et al. (2018) adopted simulated annealing algorithm for optimizing a wind and solar-based energy system with hybrid battery-hydrogen energy storage. Huang et al. (2019) conducted multi- objective optimization on an isolated PV energy system with hydrogen and retired electric vehicle battery as energy storage. The use of solar tracking system could reduce the solar panel requirement by increasing the energy gain per unit area. However, the solar tracker installation incurs additional cost despite the reduction of solar panel investment. Up to date, the cost-effectiveness of installing solar tracking system in a microgrid is yet to be explored. This study aims to evaluate the economic performance of PV systems with and without solar trackers in a standalone microgrid, where the PV systems being studied include system with fixed-tilt and optimal slope (FO), north-south single-axis tracking (NSSAT), east-west single-axis tracking (EWSAT), vertical single-axis tracking (VSAT) and dual-axis tracking (DA) as illustrated in Figure 1. Figure 1: PV system with (a) fixed tilt (b) EWSAT (c) NSSAT (d) VSAT (e) DAT 2. Methodology This section describes the configuration of microgrid and the mathematical model used for the sizing of microgrid. Figure 2 shows the configuration of a standalone microgrid that uses solar photovoltaic (PV) to convert sunlight into direct current (DC). The DC can be converted to alternating current (AC) through an inverter to satisfy the load demand. When the electricity generated from solar radiation is greater than the load demand, excess electricity will be charged into the battery. The stored electricity will be discharged when the electricity produced from solar PV is unable to satisfy the demand. Figure 2: Configuration of microgrid 2.1 Problem statement Given the annual average hourly solar radiation collected in different types of PV systems, the capacity of the energy system is optimized for minimum operation cost while satisfying the energy demand. In the mathematical model, t represents the time in a day on hourly basis. 2.2 Mathematical model The electricity generated from solar radiation in DC form, 𝐸𝐸𝑡𝑡 PV,DC is given by Eq(1), where SR𝑡𝑡 is the solar radiation at time t and 𝑃𝑃𝑃𝑃Cap is the capacity of solar panel. N S W E ꞵ Vertical axis (a) N S W E Vertical axis (b) N S W E Vertical axis (c) N S W E Vertical axis (e) N S W E ꞵ Vertical axis (d) PV System DC bus AC bus Battery Inverter AC Loads 452 𝐸𝐸𝑡𝑡 PV,DC = SR𝑡𝑡 𝑃𝑃𝑃𝑃Cap ∀𝑡𝑡 (1) The overall energy balance is shown in Eq(2), where 𝐵𝐵𝑡𝑡Out is the amount of electricity discharged from battery at time t, 𝐵𝐵𝑡𝑡In is the amount of electricity charged into battery at time t, INVEff is inverter efficiency and E𝑡𝑡Demand is the electricity demand in AC form at time t. The efficiency of inverter represents how much DC is converted to AC power. As the electricity demand is in AC form, the term E𝑡𝑡 Demand INVEff in Eq(2) computes the corresponding DC requirement. 𝐸𝐸𝑡𝑡 PV,DC + 𝐵𝐵𝑡𝑡Out = 𝐵𝐵𝑡𝑡In − E𝑡𝑡 Demand INVEff ∀𝑡𝑡 (2) The state of charge of the battery, 𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡Bat can be determined using Eq(3), where σ is the hourly self-discharge rate, BCEff is battery charging efficiency and BDEff is the discharging efficiency of battery. As some electricity would be lost during charging and discharging of battery, the battery charging efficiency represents the net amount of electricity charged into the battery divided by the total amount of electricity input to the battery. The discharging efficiency is the ratio of net amount of electricity discharged from the battery to the total amount of electricity withdrawn from the battery. 𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡Bat = 𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡−1 Bat(1 − σ) + 𝐵𝐵𝑡𝑡InBCEff − 𝐵𝐵𝑡𝑡Out BDEff⁄ ∀𝑡𝑡 (3) The amount of battery required, 𝑁𝑁Bat can be estimated using Eqs (4) and (5), where BATRating is the capacity of a single battery, 𝐵𝐵𝐵𝐵𝑇𝑇Cap is the total battery capacity required and DOD is the maximum depth of discharge. 𝑁𝑁Bat = 𝐵𝐵𝐵𝐵𝑇𝑇 Cap BATRating (4) 𝐵𝐵𝐵𝐵𝑇𝑇Cap ≥ 𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡 Bat DOD ∀𝑡𝑡 (5) The number of inverter required, 𝑁𝑁Inv can be calculated via Eqs (6) and (7), where INVRating is the capacity of a single inverter and 𝐼𝐼𝑁𝑁𝑃𝑃Cap is the total inverter capacity required. 𝑁𝑁Inv = 𝐼𝐼𝐼𝐼𝐼𝐼 Cap INVRating (6) 𝐼𝐼𝑁𝑁𝑃𝑃Cap ≥ E𝑡𝑡 Demand INVEff ∀𝑡𝑡 (7) The objective function of this model is to minimize the annualized cost of PV-based standalone energy system ( 𝑆𝑆annual ), which comprises annualized capital cost ( 𝑆𝑆acapex ), operating cost ( 𝑆𝑆opex ), and annualized replacement cost (𝑆𝑆arep) as illustrated in Eq(8). 𝑆𝑆annual = 𝑆𝑆acapex + 𝑆𝑆opex + 𝑆𝑆arep (8) The general formulas for annualized capital and replacement cost are extracted from Huang et al. (2019). The annualized capital cost can be calculated using Eq(9), where 𝑆𝑆capex is the total capital cost, i is the interest rate, and YSL is the system life. Eq(10) shows the annualised replacement cost calculation, where 𝑆𝑆rep is the total replacement cost and YCL is the lifespan of system components. 𝑆𝑆acapex = 𝑆𝑆capex i (i+1)Y SL (i+1)YSL−1 (9) 𝑆𝑆arep = 𝑆𝑆rep i (i+1)YCL−1 (10) For this study, the capital investment includes solar panel, inverter, solar tracking system and battery, therefore Eq(9) is extended to Eq(11), where PVCapex is the unit capital cost of solar panel, STCapex is the unit capital cost of solar tracker, INVCapex is the unit capital cost of inverter, and BATCapex is the unit capital cost of battery. 𝑆𝑆acapex = �𝑃𝑃𝑃𝑃CapPVCapex + 𝑃𝑃𝑃𝑃CapSTCapex + 𝑁𝑁InvINVCapex + 𝑁𝑁BatBATCapex� i (i+1)Y SL (i+1)YSL−1 (11) The components to be replaced at regular interval are battery and inverter, thus the annualized replacement cost is computed using Eq(12), where YINV is the lifespan of inverter and YBAT is the lifespan of battery. 453 𝑆𝑆arep = 𝑁𝑁InvINVCapex i (i+1)YINV−1 + 𝑁𝑁BatBATCapex i (i+1)YBAT−1 (12) The operating cost is given by Eq(13), where PVOpex is the unit operating cost of solar panel, STOpex is the unit operating cost of solar tracker, INVOpex is the unit operating cost of inverter, and BATOpex is the unit operating cost of battery. 𝑆𝑆opex = 𝑃𝑃𝑃𝑃CapPVOpex + 𝑃𝑃𝑃𝑃CapSTOpex + 𝑁𝑁InvINVOpex + 𝑁𝑁BatBATOpex (13) 3. Case study Figure 3 shows the annual average hourly solar radiation of the PV systems. It is observed that the solar radiation yield is higher when a solar tracking system is used. With the solar trackers, the energy harvested improves significantly in the morning and evening. In this study, the mathematical model discussed in section 2 will be employed to analyze whether the solar tracking system is worth investing despite the yield improvement. Figure 4 presents the electricity demand for a community with 500 households, where the domestic load profile is extracted from Ponniran et al. (2012). This data will be used in the optimization model for the targeting of microgrid capacity. Figure 3: Average hourly solar radiation collected in each PV system Figure 4: Electricity load profile Table 1 summarizes the parameters used in the case study. As the cost of solar trackers is given as a range, several scenarios will be studied to evaluate the least-cost PV system when the price of solar tracker is at (i) lower end (ii) middle point and (iii) higher end. - 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 1: 00 A M 2: 00 A M 3: 00 A M 4: 00 A M 5: 00 A M 6: 00 A M 7: 00 A M 8: 00 A M 9: 00 A M 10 :0 0 A M 11 :0 0 A M 12 :0 0 P M 1: 00 P M 2: 00 P M 3: 00 P M 4: 00 P M 5: 00 P M 6: 00 P M 7: 00 P M 8: 00 P M 9: 00 P M 10 :0 0 P M 11 :0 0 P M 12 :0 0 A M E le ct ric ity D em an d (k W h) Time (h) 454 Table 1: Case study parameters Parameter Value Unit Ref Project lifetime 20 y Interest rate 5 % Solar Panel Capital cost of solar panel 1806 USD/kWp (Maleki, 2018) Operating cost of solar panel 54 USD/kWp/y (Maleki, 2018) Solar panel lifespan 20 y (Maleki, 2018) Inverter Nominal inverter power 3 kW (Maleki, 2018) Efficiency of inverter 95 % (Maleki, 2018) Capital cost of inverter 1583 USD/kWp (Maleki, 2018) Operating cost inverter 15 USD/kWp/y (Maleki, 2018) Inverter lifespan 10 y (Maleki, 2018) Solar Tracker EWSAT/NSSAT capital cost 135-700 USD/kWp (Bahrami et al., 2017) EWSAT/NSSAT operating cost 13.5-70 USD/kWp (Bahrami et al., 2017) VSAT capital cost 350-930 USD/kWp (Bahrami et al., 2017) VSAT operating cost 35-93 USD/kWp (Bahrami et al., 2017) DAT capital cost 600-1900 USD/kWp (Bahrami et al., 2017) DAT operating cost 60-190 USD/kWp (Bahrami et al., 2017) Solar tracker lifespan 20 y (Bahrami et al., 2017) Battery Nominal capacity of battery bank 2.1 kWh (Maleki, 2018) Capital cost of battery 310 USD (Maleki, 2018) Operating cost of battery 10 USD/y (Maleki, 2018) Battery lifespan 5 y (Maleki, 2018) Hourly self-discharged rate 0.02 % (Maleki, 2018) Charging efficiency 85 % (Maleki, 2018) Discharging efficiency 100 % (Maleki, 2018) Maximum depth of discharge 80 % (Maleki, 2018) 4. Result and discussion The mixed-integer linear programming (MILP) model is solved using the commercial optimization software GAMS, in HP Elitebook 850 G5 with Intel Core i5-8250U (1.60 GHz) processor and 8 GB RAM. The average Central Processing Unit (CPU) time for solution generation is 0.06 s with zero optimality gap. Table 2 shows the optimization results where the PV system capacity decreases with increasing solar radiation yield. The inverter capacity remains the same regardless of the type of PV system. With higher energy gain in the PV system, the required battery capacity is reduced. In scenario 1 where the cost of solar tracking system is low, EWSAT is found to be more economical than FO system. However, with increasing solar tracker cost EWSAT becomes less economical than FO (scenario 2 and 3). Table 2: Case study results FO NSSAT EWSAT VSAT DAT Solar panel capacity (kWp) 7,187 6,859 6,122 6,065 5,850 Inverter capacity (kW) 4,158 4,158 4,158 4,158 4,158 Battery capacity (kWh) 27,413 27,077 26,077 26,400 26,060 Annualized cost (USD/y) Scenario 1 2,988,462 3,075,639 2,867,668 3,104,100 3,296,683 Scenario 2 2,988,462 3,424,897 3,179,380 3,421,132 3,982,087 Scenario 3 2,988,462 3,774,156 3,491,092 3,738,164 4,667,491 To identify the minimum cost of EWSAT to be economically more feasible than the fixed-tilt system, a sensitivity analysis is conducted by interpolating the solar tracker cost within the range of 135 USD/kWp (scenario 1) and 417.5 USD/kWp (scenario 2). Figure 5 illustrates the sensitivity result, where 240 USD/kWp is identified as the threshold for EWSAT to be worth the investment. 455 Figure 5: Sensitivity result on the cost of EWSAT 5. Conclusions In this study, a MILP model has been formulated for the sizing of PV-based microgrid with battery as energy storage. 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