<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">JPEE</journal-id><journal-title-group><journal-title>Journal of Power and Energy Engineering</journal-title></journal-title-group><issn pub-type="epub">2327-588X</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/jpee.2023.1112005</article-id><article-id pub-id-type="publisher-id">JPEE-130177</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Engineering</subject></subj-group></article-categories><title-group><article-title>
 
 
  Optimal Design of Grid-Connected Microgrid System for Cold Chain Logistics Centre in China
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Xinsheng</surname><given-names>Qin</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Xiaoling</surname><given-names>Wu</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Digital Intelligence Supply Chain Innovation Research Centre, Guangzhou College of Commerce, Guangzhou, China</addr-line></aff><pub-date pub-type="epub"><day>11</day><month>12</month><year>2023</year></pub-date><volume>11</volume><issue>12</issue><fpage>64</fpage><lpage>81</lpage><history><date date-type="received"><day>13,</day>	<month>December</month>	<year>2023</year></date><date date-type="rev-recd"><day>25,</day>	<month>December</month>	<year>2023</year>	</date><date date-type="accepted"><day>28,</day>	<month>December</month>	<year>2023</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  The growing interest in energy conservation has inspired companies to seek alternatives to highly polluting fuel electricity generation. This study designed an optimised solar wind power generation system to fulfil the energy requirement of a cold chain logistics centre. This study first conducted a thorough analysis of the clarity indicators and daily temperature positions of the cold chain logistics centre, determined the average daily electricity demand, and proposed an effective design scheme. The energy simulation software, RETScreen 8.0, was used to determine the scale of solar photovoltaic and wind power projects that meet the expected energy needs of the cold chain logistics centre. The results indicated that the estimated annual total energy demand was 833689.2 kWh. The annual power generation of 6 kW from solar photovoltaic projects and 150 kW from wind power projects was found to be enough to meet the expected electricity demand. Solar photovoltaic power generation and wind power generation account for 2.44% and 97.56%, respectively. The hybrid energy system achieved a 96.6% reduction in carbon emissions and provides a reasonable payback period of 6.1 years and an electricity generation of 904368.674 kWh per year. The feasibility of the project and the calculated period of investment return are very reasonable. Therefore, this hybrid renewable energy system provides reliable power by combining energy sources.
 
</p></abstract><kwd-group><kwd>Solar Photovoltaic</kwd><kwd> Wind Power</kwd><kwd> Energy</kwd><kwd> Cold Storage</kwd><kwd> Microgrid</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Renewable energy is crucial for sustainable economic development. With the rapid population growth, the energy demand is projected to increase by 35% - 56% between 2010 and 2050 [<xref ref-type="bibr" rid="scirp.130177-ref1">1</xref>] . Cold chain logistics is crucial for reducing postharvest losses. Accordingly, it is a component of many fruits, vegetables, and processed products. The timely storage of highly perishable goods facilitates their regular and continuous supply for storage or processing. Notably, energy consumption has become an important consideration in the operation of material handling systems for logistics warehouses. Further, in-building energy consumption consumption contributes approximately 33 percent of global energy consumption. The significance of the exploitation of renewable energy places it at the core of national energy policy, as it has great potential in electrification, improving energy efficiency, and renewable energy generation. Therefore, decreasing fossil fuel consumption in logistics buildings has the potential to decrease carbon emissions [<xref ref-type="bibr" rid="scirp.130177-ref2">2</xref>] . A critical component of cold chains is a reliable electricity supply, as the quality of the products should be guaranteed, and energy consumption directly inﬂuences the carbon emission of the cold chain logistics [<xref ref-type="bibr" rid="scirp.130177-ref3">3</xref>] . The logistics centre is a vital part of the cold chain, which is required for all chilled and frozen foods [<xref ref-type="bibr" rid="scirp.130177-ref4">4</xref>] . It has a central role in the proper storage of food, including the storage of finished value-added products for supply to the wholesaler, retailer, and other business sectors. Warehouses are major contributors to carbon emissions in the cold chain, and low-carbon warehousing design has become a popular paradigm [<xref ref-type="bibr" rid="scirp.130177-ref5">5</xref>] . Food refrigeration is a major energy consumer worldwide because approximately 40% of foods need refrigeration [<xref ref-type="bibr" rid="scirp.130177-ref6">6</xref>] . Therefore, cold chain logistics possess huge potential for adopting renewable energy technologies.</p><p>Cold chain logistics facilities require continuous power supply, such as cold rooms or walk-in warehousing, which can be large loads. The growing demand for cooling has placed heavy pressure on the grid systems of many countries. Cold chain logistics is one of the most critical blind spots in energy management. However, meeting existing demand remains a challenge to efficient energy management in cold chain logistics centre. Cold chain logistics companies have considerable motivation to reduce energy consumption. In some markets, clean energy can provide this type of electricity at a lower lifespan cost than traditional energy. The integration of renewable energy systems in a frozen food warehouse, which replaces most of the almost free energy, can serve as a cost-effective means to balance the overall power supply and minimise the carbon footprint of the warehouses. Clean energy is a promising energy source in developing countries with sufficient wind power resource and solar radiation [<xref ref-type="bibr" rid="scirp.130177-ref7">7</xref>] .</p><p>In recent years, hybrid photovoltaic wind turbines have become a popular alternative for sustainable power generation. The most widely used renewable energy sources are wind turbines and solar photovoltaics, as prices have significantly decreased and efficiency has significantly improved. Technological progress and the development of smart microgrid control technology have enabled the effective utilisation and reliable supply of renewable energy. In addition, by integrating solar panels and wind turbines, renewable energy can lower carbon emissions. However, because wind and solar projects involve many technical and economic complexities, their feasibility depends on geographical location and resource availability. In addition, preliminary studies on warehouse-related emissions were summarised and a comprehensive classification scheme was discussed to enable researchers to systematically evaluate the carbon emission of warehouse operations [<xref ref-type="bibr" rid="scirp.130177-ref8">8</xref>] .</p><p>Extensive research has been conducted on renewable energy systems comprising solar photovoltaic and wind energy. Ahmad, M et al. investigated and compared the technical and economic feasibility of grid-tied photovoltaic power generation in the small and medium-sized enterprise sector of Pakistan’s manufacturing industry in 2022 [<xref ref-type="bibr" rid="scirp.130177-ref9">9</xref>] . Gao Haiyang proposed energy-saving methods for various facets of cold chain logistics and summarised energy conservation technologies in the cold storage industry [<xref ref-type="bibr" rid="scirp.130177-ref10">10</xref>] . Nkalo et al. conducted technical and economic analysis of a solar wind power generation system to meet the electricity needs of rural communities in Nigeria [<xref ref-type="bibr" rid="scirp.130177-ref11">11</xref>] . Zhang proposed the optimal configuration of a hybrid renewable energy system equipped with photovoltaic and wind turbines systems to fulfil the electricity needs of a workshop in Adabir, Iran [<xref ref-type="bibr" rid="scirp.130177-ref12">12</xref>] . Rehman et al. studied the feasibility of grid-tied photovoltaic systems. They identified the best location for installing a photovoltaic power generation system. Imam, Amir A. et al. conducted a technical and economic feasibility assessment of a grid-tied photovoltaic energy conversion system of a residential building in Jeddahin, Saudi Arabia. The results revealed that the capacity coefficient and performance ratio were 22% and 78%, respectively [<xref ref-type="bibr" rid="scirp.130177-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.130177-ref14">14</xref>] . The potential of China’s cold chain logistics demand is enormous, cold chain logistics consume large amounts of energy. The cost of cold chain logistics accounts for approximately 70% of the total cost, which is more than twice that of developed countries [<xref ref-type="bibr" rid="scirp.130177-ref15">15</xref>] . Therefore, the power supply structure should be transformed and the generation of renewable energy increased. China has great potential for renewable energy development, and solar photovoltaic is one of the most widely used energy in China.</p><p>As stated in above literature, most of the research has focused on renewable energy uses in manufacturing and residential communities. However, design of renewable energy systems in the cold chain logistics centres has not been performed to sufficient depth. Similarly, the current research has focused on improving the energy efficiency of warehouse processes without considering cold chain logistics centres as important nodes in the network. Nonetheless, integrating renewable energy into the supply chain, coupled with energy-saving design, can greatly enhance supply chain sustainability. Therefore, we focus on efficiency and optimisation when designing the hybrid renewable energy system. Decarbonising China’s power industry and achieving China’s carbon reduction goals is urgently required. The purpose of this study is to optimise the design of a grid-tied microgrid system for a cold chain logistics centre in Guangdong, China. This study designs a grid-tied solar photovoltaic wind system to meet the power demand of a cold chain logistics centre in Guangdong, China. To achieve its purpose, this study conducted an energy analysis to determine the electricity demand. The RETscreen software is used to design and simulate proposed integrated systems to analyse their economic, technical, and carbon emission reduction performance [<xref ref-type="bibr" rid="scirp.130177-ref16">16</xref>] [<xref ref-type="bibr" rid="scirp.130177-ref17">17</xref>] .</p></sec><sec id="s2"><title>2. Method and Site Selection</title><p>The main data include technical parameters and energy loads, which are collected directly from the cold chain logistics centre through interviews and review records. The secondary sources used in this study were accessed from online databases. They include climate data from selected locations provided by NASA, which are used in the RETScreen software program. RETScreen contains a weather statistics database, which is composed of thousands of sites globally [<xref ref-type="bibr" rid="scirp.130177-ref18">18</xref>] . The RETScreen software is used to implement numerical models and allows various parameter settings from technological, economical, and environmental perspectives to be compared. It also analyses investment, operational, and costs to determine the economic benefits of the energy system.</p><p>The economic model of RETScreen software has seven main economic indicators, which are used to measure the economic of energy projects: net present value (NPV), pre-tax internal rate of return (PIRR) and modified internal return on equity (MIRR), and modified internal return on assets, simple and equity payback period (SPP), benefit–cost ratio (BCR), and annual life cycle savings (ALCS) [<xref ref-type="bibr" rid="scirp.130177-ref19">19</xref>] [<xref ref-type="bibr" rid="scirp.130177-ref20">20</xref>] .</p><p>NPV is a key indicator for evaluating hybrid energy systems. The higher the NPV, the greater the attractiveness of the project [<xref ref-type="bibr" rid="scirp.130177-ref21">21</xref>] . The calculation of NPV is shown in Equation (1).</p><p>NPV = ∑ i = 0 N C n ( 1 + r ) n (1)</p><p>C: represents the cost of capital.</p><p>N: project lifespan.</p><p>r: interest rate.</p><p>The internal rate of return refers to the discount rate at which the net present value is zero. The higher the internal rate of return, the greater are the future returns. Therefore, the internal rate of return can be used as a measure of investment profitability [<xref ref-type="bibr" rid="scirp.130177-ref22">22</xref>] .</p><p>IRR = ∑ t = 1 1 R t ( 1 + r ) t − c (2)</p><p>R<sub>t</sub>: net cash inflow during period t.</p><p>R: the discount rate.</p><p>The SPP denotes the period during which the main costs of the proposed construction project exceed their main costs from the generated income and savings, whereas the EPP is the length of time that facility holders recover their initial investment in the form of equity from the cash flows generated by the project. It is a better time indicator of project advantages than SPP. Equation (3) is used to estimate the investment payback period (PBP) of the renewable energy project being studied.</p><p>PBP = C R 1 (3)</p><p>Net BCR is the ratio of a project’s net benefit to its cost. It refers to the present value of annual income and savings as project equity. If the ratio is greater than 1, then the project is profitable, and if the ratio is less than 1, the project is typically not profitable.</p><p>ALCS refers to the normalised nominal annual savings with the same lifespan and net present value as the actual project [<xref ref-type="bibr" rid="scirp.130177-ref23">23</xref>] [<xref ref-type="bibr" rid="scirp.130177-ref24">24</xref>] . It is calculated by considering NPV, discount rate, and project lifespan:</p><p>ALCS = NPV 1 r ( 1 − 1 ( 1 + r ) N ) (4)</p><p>To determine the electricity demand, a load assessment survey was conducted to analysis the load of the cold chain logistics centre. The survey inquired about the number, rated power, variety of electric appliances, and the operation time of electric devices. The electricity demand of the cold chain logistics Centre is assumed for both desired temperatures in cooling mode. The energy demand is calculated using Equations (5) and (6).</p><p>EnergyDemand ( kW ⋅ h ) = Loaddemand ( kW ) &#215; Duration ( h ) (5)</p><p>TotalEnergyDemand ( kW ⋅ h ) = ∑ i = 1 n EnergyDemand ( kW ⋅ h ) (6)</p><p>The selected site is a cold chain logistics centre located in Jiazi Town, Shanwei City, Guangdong province, which is located approximately 220 km away from Shenzhen City, China. The facility has a north–south orientation, with a longitude of 116.1˚E, and latitude of 22.9˚N. The cold chain logistics Centre was established on September, 1999 and provides refrigeration and transportation services. This study was conducted on its commercial refrigeration and refrigeration warehouse, which is used to preserve vegetables, dairy products, meat, and fish. Meat and fish are frozen and stored in extremely low temperature refrigerators (from −15˚C to 40˚C), whereas other products are stored at relatively high temperatures of 3˚C - 10˚C. The cold chain logistics centre has some cold storage rooms and freezers for storing fruits and vegetables after harvest. It has been working 24 h a day for several consecutive months, thus requiring continuous power supply. The cold chain logistics centre uses the power grid provided by China Electric Power Group.</p></sec><sec id="s3"><title>3. Design Modelling of Hybrid Power Systems</title><sec id="s3_1"><title>3.1. Meteorological Data Evaluation</title><p>The selected location is characterised by hot summers, which stretch from May to November. When the weather is clear, the value of the sunshine index is high. The maximum external temperature of the ambient air used to optimise the load was 40˚C. The average humidity was 85% per year. The solar data were obtained from surface meteorological and solar data from NASA, which are satellite data. Wind energy resources were highest in the winter months, whereas solar energy resources were highest in the summer months. Therefore, the cold chain logistics centre was located in an area with significant potential for wind and solar sources. The solar radiation in this area was available throughout the year, with a daily average of 4.36 kWh/m<sup>2</sup>. Regarding wind power, the region experiences moderate wind speed. The average wind speed is the most important parameter for planning wind systems. The average wind speed in this site is 5.5 m/s at a 10 m anemometer height, with the highest wind speeds in November and December being 6.8 and 6.9 m/s, respectively. The minimum wind speeds in August and July are 4.4 and 4.5 m/s, respectively. <xref ref-type="table" rid="table1">Table 1</xref> lists the monthly average wind speed at the selected site.</p></sec><sec id="s3_2"><title>3.2. Overview of Power Load</title><p>The national Grid cannot ensure reliable power supply to the cold chain facility. The average power outage time at this site was approximately 450 h per year, with the duration of each power outage varying. The load profile of the cold chain logistics centre was divided into two parts. The first part included all the different electrical equipment except for coolers, whereas the second part included an overview of the load of space cooling. The main load in this facility is vegetable refrigerators and freezers, and refrigeration is one of the main energy consumption components, as well as lighting equipment, material handling</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Solar radiation and wind speed values for the cold chain logistics centre</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Month</th><th align="center" valign="middle" >Wind Speed (m/s)</th><th align="center" valign="middle" >Horizontal Solar Radiation (kWh/m<sup>2</sup>/Day)</th></tr></thead><tr><td align="center" valign="middle" >January</td><td align="center" valign="middle" >6.4</td><td align="center" valign="middle" >3.16</td></tr><tr><td align="center" valign="middle" >February</td><td align="center" valign="middle" >6.1</td><td align="center" valign="middle" >3.30</td></tr><tr><td align="center" valign="middle" >March</td><td align="center" valign="middle" >5.5</td><td align="center" valign="middle" >3.77</td></tr><tr><td align="center" valign="middle" >April</td><td align="center" valign="middle" >4.8</td><td align="center" valign="middle" >4.38</td></tr><tr><td align="center" valign="middle" >May</td><td align="center" valign="middle" >4.5</td><td align="center" valign="middle" >4.86</td></tr><tr><td align="center" valign="middle" >June</td><td align="center" valign="middle" >4.9</td><td align="center" valign="middle" >5.30</td></tr><tr><td align="center" valign="middle" >July</td><td align="center" valign="middle" >4.5</td><td align="center" valign="middle" >5.88</td></tr><tr><td align="center" valign="middle" >August</td><td align="center" valign="middle" >4.4</td><td align="center" valign="middle" >5.28</td></tr><tr><td align="center" valign="middle" >September</td><td align="center" valign="middle" >4.9</td><td align="center" valign="middle" >4.91</td></tr><tr><td align="center" valign="middle" >October</td><td align="center" valign="middle" >6.5</td><td align="center" valign="middle" >4.56</td></tr><tr><td align="center" valign="middle" >November</td><td align="center" valign="middle" >6.8</td><td align="center" valign="middle" >3.70</td></tr><tr><td align="center" valign="middle" >December</td><td align="center" valign="middle" >6.9</td><td align="center" valign="middle" >3.26</td></tr><tr><td align="center" valign="middle" >Average</td><td align="center" valign="middle" >5.5</td><td align="center" valign="middle" >4.36</td></tr></tbody></table></table-wrap><p>equipment, forklifts, electric vehicles, and office equipment [<xref ref-type="bibr" rid="scirp.130177-ref25">25</xref>] [<xref ref-type="bibr" rid="scirp.130177-ref26">26</xref>] . <xref ref-type="table" rid="table2">Table 2</xref> lists the daily load on the site.</p><p>The average hourly electrical demand for a day is shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>, which shows the daily load when the ambient temperature is 35˚C during the day and 29˚C at night. The maximum load was 94 kW, the minimum load was 65 kW, and the average load was 79.83 kW. In addition, the random load change rate was 3%, so the peak load of the cold chain logistics centre could reach 96.82 kW.</p><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Loads per day in the cold chain logistics centre</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Load</th><th align="center" valign="middle" >Rated Power (W)</th><th align="center" valign="middle" >No. of Units</th><th align="center" valign="middle" >Operating Hours</th><th align="center" valign="middle" >Load (kWh)</th></tr></thead><tr><td align="center" valign="middle" >Refrigeration room</td><td align="center" valign="middle" >4000</td><td align="center" valign="middle" >8</td><td align="center" valign="middle" >24</td><td align="center" valign="middle" >768</td></tr><tr><td align="center" valign="middle" >Freezing room</td><td align="center" valign="middle" >4500</td><td align="center" valign="middle" >6</td><td align="center" valign="middle" >24</td><td align="center" valign="middle" >648</td></tr><tr><td align="center" valign="middle" >Refrigerator</td><td align="center" valign="middle" >200</td><td align="center" valign="middle" >2</td><td align="center" valign="middle" >24</td><td align="center" valign="middle" >9.6</td></tr><tr><td align="center" valign="middle" >Exhaust fan</td><td align="center" valign="middle" >100</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >6</td><td align="center" valign="middle" >3.0</td></tr><tr><td align="center" valign="middle" >Air conditioning</td><td align="center" valign="middle" >3000</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >6</td><td align="center" valign="middle" >90</td></tr><tr><td align="center" valign="middle" >Laptops</td><td align="center" valign="middle" >50</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >6</td><td align="center" valign="middle" >1.5</td></tr><tr><td align="center" valign="middle" >Down lamp</td><td align="center" valign="middle" >50</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >8</td><td align="center" valign="middle" >0.9</td></tr><tr><td align="center" valign="middle" >Printer</td><td align="center" valign="middle" >1000</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >8</td><td align="center" valign="middle" >2.0</td></tr><tr><td align="center" valign="middle" >Compact fluorescent light bulbs</td><td align="center" valign="middle" >20</td><td align="center" valign="middle" >3</td><td align="center" valign="middle" >8</td><td align="center" valign="middle" >0.48</td></tr><tr><td align="center" valign="middle" >Desktop computer</td><td align="center" valign="middle" >300</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >36</td></tr><tr><td align="center" valign="middle" >Kettle</td><td align="center" valign="middle" >100</td><td align="center" valign="middle" >4</td><td align="center" valign="middle" >3</td><td align="center" valign="middle" >1.2</td></tr><tr><td align="center" valign="middle" >Electric vehicles</td><td align="center" valign="middle" >2000</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >120</td></tr><tr><td align="center" valign="middle" >Forklifts</td><td align="center" valign="middle" >4000</td><td align="center" valign="middle" >6</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >240</td></tr><tr><td align="center" valign="middle" >Automatic guidance vehicles</td><td align="center" valign="middle" >3000</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >360</td></tr><tr><td align="center" valign="middle" >Wi-Fi router</td><td align="center" valign="middle" >20</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" >24</td><td align="center" valign="middle" >2.4</td></tr><tr><td align="center" valign="middle"  colspan="4"  >Total</td><td align="center" valign="middle" >2284.08</td></tr></tbody></table></table-wrap><p>The cold logistics centre requires an average of 1915.92 kWh of electricity every day.</p><p><xref ref-type="fig" rid="fig2">Figure 2</xref> shows an overview of the monthly load with peak demand starting in July and decreasing from August to the end of the year. The growth is attributable to the high temperature in summer, which imposes heavy loads on the cold chain logistics centre. The average daily consumption of scaled power was 92.67 kWh/day, with a peak of 2780 kWh in July. The increasing consumption is attributable to the June harvest, during which the owner stores the harvested food in the refrigerator warehouse. During the period from storage to sales, as all refrigerators are in operation, electricity consumption increases. Further, electricity consumption is low from August to November.</p><p>In addition, the load increase was due to the impact of changes in surrounding environment and evaporation temperature, resulting in an increase in temperature in the cold chain logistics centre [<xref ref-type="bibr" rid="scirp.130177-ref27">27</xref>] . According to Star Refrigeration (Pearson, 2019), the specific energy consumption for a well-managed 100,000 m<sup>3</sup> refrigeration warehouse should be 10 kWh/m<sup>3</sup>/year, whereas for a 500,000 m<sup>3</sup> warehouse, it may be less than 5 kWh/m<sup>3</sup>/year [<xref ref-type="bibr" rid="scirp.130177-ref28">28</xref>] .</p></sec><sec id="s3_3"><title>3.3. Physical Modelling and Specification</title><p>This study focused on the renewable energy system optimisation design of a cold chain logistics centre in China because refrigeration energy consumption in cold storage accounts for the majority of the total energy consumption. Additionally, to ensure refrigeration efficiency and reduce the total energy consumption, it can effectively reduce operating costs. The optimal configuration of an integrated renewable energy system depends on various modelling parameters, such as technical and economic parameters.</p><p>The proposed hybrid system was designed based on energy requirements. The hybrid system consists of a wind turbine, solar photovoltaic array, a grid-tied inverter, and a national home appliance grid. The grid-tied system requires an inverter to adapt to the DC current generated by the photovoltaic array and feed it to the AC bus connected to the load [<xref ref-type="bibr" rid="scirp.130177-ref29">29</xref>] [<xref ref-type="bibr" rid="scirp.130177-ref11">11</xref>] . Solar photovoltaic/wind energy</p><p>systems rely solely on renewable energy. However, if solar photovoltaic and wind power meet the load requirements, the redundant electricity will be exported to the grid at a certain price. <xref ref-type="fig" rid="fig3">Figure 3</xref> shows a schematic of the proposed system.</p><p>The renewable power capacity; specifications of the photovoltaic modules, wind turbines, and inverters for the proposed system; system economic indicators; and search criteria for each component of the cold chain logistics centre are described below. The simulation software RETScreen for renewable energy projects was used to determine the capacity of solar photovoltaic or wind turbines to meet the estimated total energy demand. We proposed a hybrid system consisting of a 6 kW photovoltaic system and a 150 kW wind power system based on the electricity demand.</p><p>The first specification of the RETScreen wind power system is wind speed, which was 3.2 m/s at a height of 10 m. Atmospheric pressure and air temperature are important considerations in wind electricity projects, as they can be used to estimate the air density on site. This study estimated the atmospheric pressure and air temperature of RETScreen as 100.7 kPa and 26˚C, respectively. This resulted in a pressure coefficient of 0.0998 and a temperature coefficient of 0.961. The capacity factor was calculated based on all these specified inputs. Generally, the capacity factor of wind power system ranges from 20% to 40%. The RETScreen input for this part is presented in <xref ref-type="table" rid="table3">Table 3</xref>.</p><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> RETscreen input for 150 kW wind power system</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Parameter</th><th align="center" valign="middle" >Values</th></tr></thead><tr><td align="center" valign="middle" >Electricity price</td><td align="center" valign="middle" >0.1$</td></tr><tr><td align="center" valign="middle" >Array loss</td><td align="center" valign="middle" >0%</td></tr><tr><td align="center" valign="middle" >Miscellaneous losses</td><td align="center" valign="middle" >6%</td></tr><tr><td align="center" valign="middle" >Wing loss</td><td align="center" valign="middle" >2%</td></tr><tr><td align="center" valign="middle" >Availability</td><td align="center" valign="middle" >98%</td></tr><tr><td align="center" valign="middle" >Initial cost ($/Kwh)</td><td align="center" valign="middle" >1500</td></tr><tr><td align="center" valign="middle" >Q&amp;M cost ($/Kwh)</td><td align="center" valign="middle" >40</td></tr></tbody></table></table-wrap><p>This section determines the type, quantity, miscellaneous losses, tracking mode azimuth, slope of the solar panel, and efficiency of solar panels, and the miscellaneous losses, capacity, and efficiency of the inverter are specified. Miscellaneous loss refers to the array loss (%) caused by miscellaneous sources not considered elsewhere in the model. For example, this includes losses caused by mismatches or dirt on the module and wiring losses. <xref ref-type="table" rid="table4">Table 4</xref> provides a summary of the inputs used for the energy modelling of 6 kW solar facilities.</p><p>The recommended hybrid system includes AC and DC power sources. The inverter is used to transmit power between two bus bars, and it has a service life of 15 years. The peak electricity demand of the system determines the size of the inverters used in the hybrid system. Ideally, the inverter should be large enough to always meet the system demand, which means it must fully meet the total peak power requirements. Therefore, an inverter that is around 30% larger than the total load is recommended [<xref ref-type="bibr" rid="scirp.130177-ref30">30</xref>] . The hybrid system model established in RETScreen is shown in <xref ref-type="fig" rid="fig4">Figure 4</xref>, which consists of a 150 kW wind power generation system and a 6 kW photovoltaic facility.</p></sec></sec><sec id="s4"><title>4. Simulation Results and Discussion</title><p>RETscreen Pro simulates each feasible power configuration as well as the technical, economic, and environmental analysis of power generation systems configured as cold chain logistics centre loads based on available energy and load</p><table-wrap id="table4" ><label><xref ref-type="table" rid="table4">Table 4</xref></label><caption><title> RETscreen inputs for 6 kW photovoltaic equipment</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Parameter</th><th align="center" valign="middle" >Values</th></tr></thead><tr><td align="center" valign="middle" >PV type</td><td align="center" valign="middle" >Amorphous silicon</td></tr><tr><td align="center" valign="middle" >Solar tracking mode</td><td align="center" valign="middle" >Two-axis</td></tr><tr><td align="center" valign="middle" >Electricity price</td><td align="center" valign="middle" >0.1$</td></tr><tr><td align="center" valign="middle" >Photovoltaic power generation</td><td align="center" valign="middle" >6 kW</td></tr><tr><td align="center" valign="middle" >Inverter efficiency</td><td align="center" valign="middle" >98%</td></tr><tr><td align="center" valign="middle" >PV miscellaneous losses</td><td align="center" valign="middle" >5%</td></tr><tr><td align="center" valign="middle" >Inverter capacity</td><td align="center" valign="middle" >10 kW</td></tr><tr><td align="center" valign="middle" >Initial cost ($/kWh)</td><td align="center" valign="middle" >$1300</td></tr><tr><td align="center" valign="middle" >Miscellaneous losses of inverters</td><td align="center" valign="middle" >0%</td></tr><tr><td align="center" valign="middle" >Q&amp;M cost ($/kWh)</td><td align="center" valign="middle" >11$</td></tr></tbody></table></table-wrap><p>requirements. The results indicate that compared to the electricity prices of the national grid, this system not only meets the energy needs of the cold chain logistics centre but also yields economic benefits. We compared the technical and economic details of different scenarios, as well as electricity production, and then considered the carbon dioxide emissions reduced under the defined scenario [<xref ref-type="bibr" rid="scirp.130177-ref31">31</xref>] .</p><sec id="s4_1"><title>4.1. Technical Feasibility</title><p>The technical feasibility of integrated system depends on the solar irradiance accepted and the electricity generated. To a large extent, the efficiency and effective utilisation of the system depend on the specifications of the products used. The annual power generation of wind turbines is 882.238 MWh, and the energy generated by wind power modules mainly depends on the local wind speed, which ultimately affects the annual export electricity of the system. The increase in total electricity exports has had a positive impact on the revenue of the cold chain logistics centre. The analysis using RETScreen reveals the annual wind speed of the module. The energy output in January was the highest (86663.533 kWh), whereas the energy output was the lowest (59933.6 kWh). <xref ref-type="table" rid="table5">Table 5</xref> summarises the wind speed and electricity export from the RETScreen software.</p><p>The annual power generation of photovoltaic generators was 22.131 MWh, and the power generation of solar photovoltaic modules depends on the solar irradiance, sunny days, and sunny hours of the cold chain logistics centre, ultimately affecting the total annual export electricity of the system. The increase in</p><table-wrap id="table5" ><label><xref ref-type="table" rid="table5">Table 5</xref></label><caption><title> Wind speed and electricity export summary</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Month</th><th align="center" valign="middle" >Solar radiation Horizontal (kWh/m<sup>2</sup>/d)</th><th align="center" valign="middle" >Solar radiation titled (KWh/m<sup>2</sup>/d)</th><th align="center" valign="middle" >Electricity rate annual ($/kWh)</th><th align="center" valign="middle" >Electricity production (kWh)</th></tr></thead><tr><td align="center" valign="middle" >January</td><td align="center" valign="middle" >3.16</td><td align="center" valign="middle" >4.49</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1531.929</td></tr><tr><td align="center" valign="middle" >February</td><td align="center" valign="middle" >3.30</td><td align="center" valign="middle" >4.20</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1292.794</td></tr><tr><td align="center" valign="middle" >March</td><td align="center" valign="middle" >3.77</td><td align="center" valign="middle" >4.47</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1520.652</td></tr><tr><td align="center" valign="middle" >April</td><td align="center" valign="middle" >4.38</td><td align="center" valign="middle" >5.03</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1650.843</td></tr><tr><td align="center" valign="middle" >May</td><td align="center" valign="middle" >4.86</td><td align="center" valign="middle" >5.77</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1947.020</td></tr><tr><td align="center" valign="middle" >June</td><td align="center" valign="middle" >5.30</td><td align="center" valign="middle" >6.40</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >2085.319</td></tr><tr><td align="center" valign="middle" >July</td><td align="center" valign="middle" >5.88</td><td align="center" valign="middle" >7.37</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >2474.628</td></tr><tr><td align="center" valign="middle" >August</td><td align="center" valign="middle" >5.28</td><td align="center" valign="middle" >6.27</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >2108.000</td></tr><tr><td align="center" valign="middle" >September</td><td align="center" valign="middle" >4.91</td><td align="center" valign="middle" >6.12</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1992.442</td></tr><tr><td align="center" valign="middle" >October</td><td align="center" valign="middle" >4.56</td><td align="center" valign="middle" >6.34</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >2134.676</td></tr><tr><td align="center" valign="middle" >November</td><td align="center" valign="middle" >3.70</td><td align="center" valign="middle" >5.22</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1710.244</td></tr><tr><td align="center" valign="middle" >December</td><td align="center" valign="middle" >3.26</td><td align="center" valign="middle" >4.95</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >1682.127</td></tr><tr><td align="center" valign="middle" >Annual</td><td align="center" valign="middle" >4.37</td><td align="center" valign="middle" >5.56</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >22130.674</td></tr></tbody></table></table-wrap><p>total electricity exports has a positive impact on the revenue of this facility, whereas a decrease in exports leads to a decrease in revenue.</p><p>RETScreen analysis reveals that July had the highest energy export volume (2474.628 kWh), whereas December had the lowest energy export volume (1292.794 kWh) when considering the inherent high irradiance. This distribution was consistent with the weather conditions in Guangzhou, where July is the peak season of the high temperature season. <xref ref-type="table" rid="table6">Table 6</xref> summarises the irradiance and power exports from the RET-Screen software.</p></sec><sec id="s4_2"><title>4.2. Economic Viability</title><p>Mehmood et al. described the economic feasibility of a project as a measure of its NPV, IRR, annual lifecycle savings, and payback period, all of which are important determining factors for the financial feasibility of the project [<xref ref-type="bibr" rid="scirp.130177-ref32">32</xref>] . RET-Screen software calculates annual living savings and other financial indicators. The output of financial parameters is shown in <xref ref-type="fig" rid="fig5">Figure 5</xref>, which presents the annual cash flow of the proposed hybrid system. Considering the payback period, the cash flow of the hybrid system was positive, indicating a positive income.</p><p><xref ref-type="fig" rid="fig6">Figure 6</xref> shows the cumulative cash flow of the hybrid system. The equity recovery period of this project was the 11th month of the 3rd year. These findings have attracted renewable energy investors. <xref ref-type="fig" rid="fig7">Figure 7</xref> shows the time required to recover the investment in years, and it may be the most widely used financial indicator in investment. Noticeably, the ALCS value of this project is high. <xref ref-type="fig" rid="fig7">Figure 7</xref> also shows the financial feasibility of the proposed system.</p><table-wrap id="table6" ><label><xref ref-type="table" rid="table6">Table 6</xref></label><caption><title> Irradiance and electricity export summary</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Month</th><th align="center" valign="middle" >Wind speed m/s</th><th align="center" valign="middle" >Atmospheric Pressure (kPa)</th><th align="center" valign="middle" >Air temperature (˚C)</th><th align="center" valign="middle" >Electricity rate annual ($/kWh)</th><th align="center" valign="middle" >Electricity production (kWh)</th></tr></thead><tr><td align="center" valign="middle" >January</td><td align="center" valign="middle" >6.4</td><td align="center" valign="middle" >101.7</td><td align="center" valign="middle" >16.4</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >86663.533</td></tr><tr><td align="center" valign="middle" >February</td><td align="center" valign="middle" >6.1</td><td align="center" valign="middle" >101.5</td><td align="center" valign="middle" >17.2</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >77537.896</td></tr><tr><td align="center" valign="middle" >March</td><td align="center" valign="middle" >5.5</td><td align="center" valign="middle" >101.3</td><td align="center" valign="middle" >19.4</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >80466.243</td></tr><tr><td align="center" valign="middle" >April</td><td align="center" valign="middle" >4.8</td><td align="center" valign="middle" >101.0</td><td align="center" valign="middle" >22.4</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >66942.470</td></tr><tr><td align="center" valign="middle" >May</td><td align="center" valign="middle" >4.5</td><td align="center" valign="middle" >101.6</td><td align="center" valign="middle" >25.2</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >62872.399</td></tr><tr><td align="center" valign="middle" >June</td><td align="center" valign="middle" >4.9</td><td align="center" valign="middle" >101.3</td><td align="center" valign="middle" >27.2</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >67153.699</td></tr><tr><td align="center" valign="middle" >July</td><td align="center" valign="middle" >4.5</td><td align="center" valign="middle" >101.3</td><td align="center" valign="middle" >28.1</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >62056.387</td></tr><tr><td align="center" valign="middle" >August</td><td align="center" valign="middle" >4.4</td><td align="center" valign="middle" >101.2</td><td align="center" valign="middle" >28.0</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >59933.666</td></tr><tr><td align="center" valign="middle" >September</td><td align="center" valign="middle" >4.9</td><td align="center" valign="middle" >101.5</td><td align="center" valign="middle" >27.1</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >67350.246</td></tr><tr><td align="center" valign="middle" >October</td><td align="center" valign="middle" >6.5</td><td align="center" valign="middle" >101.1</td><td align="center" valign="middle" >24.5</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >83821.822</td></tr><tr><td align="center" valign="middle" >November</td><td align="center" valign="middle" >6.8</td><td align="center" valign="middle" >101.4</td><td align="center" valign="middle" >21.3</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >81951.158</td></tr><tr><td align="center" valign="middle" >December</td><td align="center" valign="middle" >6.9</td><td align="center" valign="middle" >101.7</td><td align="center" valign="middle" >17.6</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >85488.037</td></tr><tr><td align="center" valign="middle" >Annual</td><td align="center" valign="middle" >5.5</td><td align="center" valign="middle" >101.0</td><td align="center" valign="middle" >22.9</td><td align="center" valign="middle" >0.1</td><td align="center" valign="middle" >882,238</td></tr></tbody></table></table-wrap></sec><sec id="s4_3"><title>4.3. Emission Reduction</title><p><xref ref-type="fig" rid="fig8">Figure 8</xref> shows the total emission reduction, initial baseline GHG emissions, and proposed case carbon emissions of the hybrid system. The basic GHG emissions were 714 t CO<sub>2</sub>, whereas those of the proposed hybrid system were 24.1 t CO<sub>2</sub>. If the proposed hybrid system is implemented, the total greenhouse gas emissions can be reduced by 690 tons of carbon dioxide. This supports the argument that the proposed design is environmentally friendly and, therefore, acceptable.</p><p>The results indicate that the proposed wind/photovoltaic project has good economic benefits in the cold chain logistics centre. The analysis results indicate that the hybrid system is a more economical than wind power projects, as the values of NPV and ALCS are higher and the simple payback is shorter. In addition, <xref ref-type="fig" rid="fig8">Figure 8</xref> shows that this hybrid energy system helps to reduce greenhouse gas emissions.</p><p>This article proposes an integrated solar photovoltaic and wind energy system that with the power grid to support existing power generation capabilities. In addition, the NPV was $339.014, and the pretax IRR was 29.2%, indicating that the hybrid system is economically feasible. The environmental assessment in this article shows that the greenhouse gas emissions from solar, photovoltaic, and wind hybrid power generation have decreased by 96.6%. If China had a greenhouse gas reduction credit rate, such as $25 per ton of carbon dioxide emissions, the proposed system would generate an annual greenhouse gas reduction income of $2415.00. This greenhouse gas policy will encourage businesses to make direct efforts and adopt procedures to reduce their greenhouse gas emissions. This will gradually accelerate the national goal of increasing the proportion of renewable energy.</p><p>The total power generation of this system indicates that the renewable energy system can effectively fulfil the annual electricity demand of the cold chain</p><p>logistics centre. Compared with a single energy system, this system can provide loads more effectively. It is observed that renewable energy systems can conveniently provide loads. Although renewable energy systems are usually technically easy to implement, their economic feasibility faces many major obstacles. In the past year, the price of solar photovoltaic cells has been very high. Wind energy saving technology is limited to developed countries, which makes it very expensive in developing countries with these resources. China’s solar power generation technology is not as mature as wave energy. These barriers make renewable energy system too expensive and shorten their lifespan.</p></sec></sec><sec id="s5"><title>5. Conclusions</title><p>Renewable energy systems can be economical and feasible, especially in developing countries with abundant renewable energies. This study investigates the technological and economic feasibility of solar photovoltaic and wind energy systems in a cold chain logistics centre in China. The main conclusion that can be drawn is that an integrated system with a wind power installed capacity of 150 kW and a solar photovoltaic installed capacity of 6 kW is feasible. The feasibility of the designed hybrid system indicates that it can meet the energy needs of the cold chain logistics centre.</p><p>The load distribution of a 156 kW grid-connected solar photovoltaic wind power hybrid system was simulated using RETScreen software. Solar photovoltaic power generation reaches 22130.674 kWh, whereas wind power generation reaches 882,238 kWh. In addition, the lifespan of the project is 20 years, and the simple payback period is approximately 6 years and 1 month, which confirms the economic feasibility of the system.</p><p>The limitation of this research was by the use of secondary data to design the hybrid energy system. The data used in the technical feasibility analyses were based on the historical climate data of China. Future research directions that may improve the economic performance should be further investigated in dynamical values. In addition, land availability was not taken into account considered in this study. Thus, measuring land area required for grid-tied renewable energy system will be future research. The feasibility of renewable energy projects could be better assessed by using cost of power grid limitations and labor. Therefore, further studies would be recommended to confirm the actual energy required by cold chain logistics Centre operations and the requirements for fulfilling the renewable energy project of choice.</p><p>In general, the implementation of solar PV (PV) wind systems in this cold chain logistics could be economically feasible and environmentally. An integrated solar PV wind system will help to reduce carbon emissions and save energy in China because of the high potential in the renewable energy industry. Therefore, the finding of this study could be helpful for the government and the concerned stakeholders to extend these systems into the similar environmental conditions for applied to other industry.</p></sec><sec id="s6"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest regarding the publication of this paper.</p></sec><sec id="s7"><title>Cite this paper</title><p>Qin, X.S. and Wu, X.L. (2023) Optimal Design of Grid-Connected Microgrid System for Cold Chain Logistics Centre in China. Journal of Power and Energy Engineering, 11, 64-81. https://doi.org/10.4236/jpee.2023.1112005</p></sec></body><back><ref-list><title>References</title><ref id="scirp.130177-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Amjad, W., Munir, A., Akram, F., Parmar, A., Precoppe, M., Asghar, F. and Mahmood, F. (2023) Decentralized Solar-Powered Cooling Systems for Fresh Fruit and Vegetables to Reduce Post-Harvest Losses in Developing Regions: A Review. Clean Energy, 7, 635-653. https://doi.org/10.1093/ce/zkad015</mixed-citation></ref><ref id="scirp.130177-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">González-Torres, M., Pérez-Lombard, L., Coronel, J.F., Maestre I.R. and Yan, D. (2022) A Review on Buildings Energy Information: Trends, End-Uses, Fuels and Drivers. Energy Report, 8, 626-637. https://doi.org/10.1016/j.egyr.2021.11.280</mixed-citation></ref><ref id="scirp.130177-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Marchi, B. and Zanoni, S. (2022). Cold Chain Energy Analysis for Sustainable Food and Beverage Supply. Sustainability, 14, Article 11137. https://doi.org/10.3390/su141811137</mixed-citation></ref><ref id="scirp.130177-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">Shi, Y., Lin, Y., Lim, M.K., Tseng, M., Tan, C. and Li, Y. (2022) An Intelligent Green Scheduling System for Sustainable Cold Chain Logistics. Expert System with Application, 209, Article ID: 118378. https://doi.org/10.1016/j.eswa.2022.118378</mixed-citation></ref><ref id="scirp.130177-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">Meneghetti, A., Magro, F.D. and Simeoni, P. (2018) Fostering Renewables into the Cold Chain: How Photovoltaics Affect Design and Performance of Refrigerated Automated Warehouses. Energies, 11, Article 1029. https://doi.org/10.3390/en11051029</mixed-citation></ref><ref id="scirp.130177-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">De, R.K. and Ganguly, A. (2021) Modelling and Analysis of a Solar Thermal-Pho-tovoltaic-Hydrogen-Based Hybrid Power System for Running a Standalone Cold Storage. Journal of Cleaner Production, 293, Article ID: 126202. https://doi.org/10.1016/j.jclepro.2021.126202</mixed-citation></ref><ref id="scirp.130177-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">Lewczuk, K., K&amp;#322;odawski, M. and Gepner, P. (2021) Energy Consumption in Distributed Warehouses: A Practical Case Study of Different Warehouse Technologies. Energy, 14, Article 2709. https://doi.org/10.3390/en14092709</mixed-citation></ref><ref id="scirp.130177-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">Cannava, L., Perotti, S., Najafi, B. and Rinaldi, F. (2023) Assessing the Impact of Smart Lighting Systems and On-Site Renewable Generation in a Distribution Warehouse: A Simulation-Based Approach. 25th International Conference on Harbor, Maritime and Multimodal Logistic Modeling &amp; Simulation, Athens, 18-20 September 2023, 2724-0339.</mixed-citation></ref><ref id="scirp.130177-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">Ahmad, M., Khattak, A., Kashif Janjua, A., Alahmadi, A.A., Salman Khan, M. and Ullah, N. (2022) Techno-Economic Feasibility Analyses of Grid-Connected Solar Photovoltaic Power Plants for Small Scale Industries of Punjab, Pakistan. Frontiers in Energy Research, 10, Article 1028310. https://doi.org/10.3389/fenrg.2022.1028310</mixed-citation></ref><ref id="scirp.130177-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">Gao, H.Y. (2019) Analysis of New Energy-Saving Technology for Cold Chain Logistics. IOP Conference Series: Earth and Environmental Science, 252, Article ID: 032007. https://doi.org/10.1088/1755-1315/252/3/032007</mixed-citation></ref><ref id="scirp.130177-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">Ukoima, K.N., Owolabi, A.B., Yakub, A.O., Same, N.N., Suh, D. and Huh, J.S. (2023) Analysis of a Solar Hybrid Electricity Generation System for a Rural Community in River State, Nigeria. Energies, 16, Article 3431. https://doi.org/10.3390/en16083431</mixed-citation></ref><ref id="scirp.130177-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">Zhang, G., Xiao, C. and Razmjooy, N. (2020) Optimal Operational Strategy of Hybrid PV/Wind Renewable Energy System Using Homer: A Case Study. International Journal of Ambient Energy, 43, 3953-3966. https://doi.org/10.1080/01430750.2020.1861087</mixed-citation></ref><ref id="scirp.130177-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Rehman, S., Ahmed, M.A., Mohamed, M.H. and Al-Sulaiman, F.A. (2017) Feasibility Study of the Grid Connected 10 MW Installed Capacity PV Power Plants in Saudi Arabia. Renew &amp; Sustainable Energy Review, 80, 319-329. https://doi.org/10.1016/j.rser.2017.05.218</mixed-citation></ref><ref id="scirp.130177-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">Imam, A.A., Al-Turki, Y.A. and Sreerama, K.R. (2019) Techno-Economic Feasibility Assessment of Grid-Connected PV Systems for Residential Buildings in Saudi Arabia—A Case Study. Sustainability, 12, Article 262. https://doi.org/10.3390/su12010262</mixed-citation></ref><ref id="scirp.130177-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">Wei, P.B. and Chen, W.D. (2019) Microgrid in China: A Review in the Perspective of Application. Energy Procedia, 158, 6601-6606. https://doi.org/10.1016/j.egypro.2019.01.059</mixed-citation></ref><ref id="scirp.130177-ref16"><label>16</label><mixed-citation publication-type="other" xlink:type="simple">Jamel, F.C. and Nascimento, M.H. (2022) Methodology for Photovoltaic Plant Modelling with RETScreen Software application. International Journal for Innovation Education and Research, 10, 133-153. https://doi.org/10.31686/ijier.vol10.iss11.3998</mixed-citation></ref><ref id="scirp.130177-ref17"><label>17</label><mixed-citation publication-type="other" xlink:type="simple">Sulley, M., Acakpovi, A., Adjei, P., Sackey, D.M., Offei, F., Afonope, M., Kofi, D. and Tay, G. (2022) Techno-Economic Assessment of Grid Connected Solar PV/Wind Hybrid System. IOP Conference Series: Earth and Environment Science, 1042, Article ID: 012016. https://doi.org/10.1088/1755-1315/1042/1/012016</mixed-citation></ref><ref id="scirp.130177-ref18"><label>18</label><mixed-citation publication-type="other" xlink:type="simple">Spiru, P. and Lizica-Simona, P. (2018) Technical and Economic Analysis of a PV/Wind/Diesel Hybrid Power System for a Remote Area. Energy Procedia, 147, 343-350. https://doi.org/10.1016/j.egypro.2018.07.102</mixed-citation></ref><ref id="scirp.130177-ref19"><label>19</label><mixed-citation publication-type="other" xlink:type="simple">Bebi, E., Malka, L., Konomi, I. and Alcani, M. (2021) An Analysis towards a Sustainable Energy System in Albania Highly Supported by Large Scale Integration of Wind Resources: A Case Study of Mamaj Wind Farm. International Journal of Energy Economics and Policy, 11, 355-372. https://doi.org/10.32479/ijeep.10622</mixed-citation></ref><ref id="scirp.130177-ref20"><label>20</label><mixed-citation publication-type="other" xlink:type="simple">Kassem, Y., &amp;#199;amur, H. and Aateg, R.A.F. (2020) Exploring Solar and Wind Energy as a Power Generation Source for Solving the Electricity Crisis in Libya. Energies, 13, Article 3708. https://doi.org/10.3390/en13143708</mixed-citation></ref><ref id="scirp.130177-ref21"><label>21</label><mixed-citation publication-type="other" xlink:type="simple">Al-Rawashdeh, H., Al Bdour, J.T., Gomaa, M.R., Al-Khashman, O.A., Rezk, H., Marashli, A., Arrfou, L.M. and Louzazni, M. (2023) Performance Analysis of a Hybrid Renewable-Energy System for Green Buildings to Improve Efficiency and Reduce GHG Emissions with Multiple Scenarios. Sustainability, 15, Article ID: 7529. https://doi.org/10.3390/su15097529</mixed-citation></ref><ref id="scirp.130177-ref22"><label>22</label><mixed-citation publication-type="other" xlink:type="simple">Sakina Zaidi, S., Haider Zaidi, S.S., Khan, B.M. and Moin, L. (2023) Optimal Designing of Grid-Connected Microgrid Systems for Residential and Commercial Applications in Pakistan. Heliyon, 9, E17990. https://doi.org/10.1016/j.heliyon.2023.e17990</mixed-citation></ref><ref id="scirp.130177-ref23"><label>23</label><mixed-citation publication-type="other" xlink:type="simple">Delfín-Portela, E., Sandoval-Herazo, L.C., Reyes-González, D., Mata-Alejandro, H., López-Méndez, M.C., Fernández-Lambert, G. and Betanzo-Torres, E.A. (2023) Grid-Connected Solar Photovoltaic System for Nile Tilapia Farms in Southern Mexico: Techno-Economic and Environmental Evaluation. Applied Science, 13, Article ID: 570. https://doi.org/10.3390/app13010570</mixed-citation></ref><ref id="scirp.130177-ref24"><label>24</label><mixed-citation publication-type="other" xlink:type="simple">Kassem, Y., G&amp;#246;k&amp;#231;ekus, H. and Güvensoy, A. (2021) Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus. Energies, 14, Article ID: 7627. https://doi.org/10.3390/en14227627</mixed-citation></ref><ref id="scirp.130177-ref25"><label>25</label><mixed-citation publication-type="other" xlink:type="simple">Marchi, B. and Zanoni, S. (2022) Energy Efficiency in Cold Supply Chains of the Food and Beverage Sector. Transportation Research Procedia, 67, 56-62. https://doi.org/10.1016/j.trpro.2022.12.035</mixed-citation></ref><ref id="scirp.130177-ref26"><label>26</label><mixed-citation publication-type="other" xlink:type="simple">Mokhtara, C., Negrou, B., Bouferrouk, A., Yao, Y., Settou, N. and Ramadan, M.R. (2020) Integrated Supply-Demand Energy Management for Optimal Design of Off-Grid Hybrid Renewable Energy Systems for Residential Electrification in Arid Climates. Energy Conversion and Management, 221, Article ID: 113192. https://doi.org/10.1016/j.enconman.2020.113192</mixed-citation></ref><ref id="scirp.130177-ref27"><label>27</label><mixed-citation publication-type="other" xlink:type="simple">Thalib, H., Maarif, S. and Adhi Setiawan, E. (2022) Optimization of Solar PV System for Fishery Cold Storage Based on Ownership Model and Regulation Barrier in Indonesia. Journal of Physics: Conference Series, 2022, Article ID: 012035. https://doi.org/10.1088/1742-6596/2022/1/012035</mixed-citation></ref><ref id="scirp.130177-ref28"><label>28</label><mixed-citation publication-type="other" xlink:type="simple">Purnell, G., Swainson, M., Dwyer, S., Hammond, E. and James, C. (2021) Frozen Cold Storage Capacity in Grimsby Final Report. The Grimsby Institute, Grimsby.</mixed-citation></ref><ref id="scirp.130177-ref29"><label>29</label><mixed-citation publication-type="other" xlink:type="simple">Chief, M., Martino, A. and Begay, S.K. (2018) Analysis of Refrigeration Units in off Grid PV/Wind Hybrid Systems on Tribal Land. USDOE National Nuclear Security Administration (NNSA). https://doi.org/10.2172/1481512</mixed-citation></ref><ref id="scirp.130177-ref30"><label>30</label><mixed-citation publication-type="other" xlink:type="simple">Elegeonye, H.I., Owolabi, A.B., Ohunakin, O.S., Yakub, A.O., Yahaya, A., Same, N.N., Suh, D. and Huh, J.S. (2023) Techno-Economic Optimization of Mini-Grid Systems in Nigeria: A Case Study of a PV-Battery-Diesel Hybrid System. Energies, 16, Article ID: 4645. https://doi.org/10.3390/en16124645</mixed-citation></ref><ref id="scirp.130177-ref31"><label>31</label><mixed-citation publication-type="other" xlink:type="simple">Kofi, D., Acakpovi, A., Adjei, P., Sowah, R.A., Aggrey, G.K., Tay, G., Sulley, M. and Afonope, M. (2022) Innovative Design of Grid Connected Solar/Diesel Hybrid System Using RETScreen Software. IOP Conference Series: Earth and Environmental Science, 1042, Article ID: 012018. https://doi.org/10.1088/1755-1315/1042/1/012018</mixed-citation></ref><ref id="scirp.130177-ref32"><label>32</label><mixed-citation publication-type="other" xlink:type="simple">Mehmood, A., Shaikh, F.A. and Waqas, A. (2014) Modeling of the Solar Photovoltaic Systems to Fulfill the Energy Demand of the Domestic Sector of Pakistan Using RETSCREEN Software. 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), Pattaya, 19-21 March 2014, 1-7.</mixed-citation></ref></ref-list></back></article>