Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

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Article Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Ramakrishna S. S. Nuvvula 1, Devaraj Elangovan 2,* , Kishore Srinivasa Teegala 3, Rajvikram Madurai Elavarasan 4,* , Md. Rabiul Islam 5,* and Ravikiran Inapakurthi 6

1 School of Electrical Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India; [email protected] 2 TIFAC-CORE, Vellore Institute of Technology (VIT), Vellore 632014, India 3 Electrical & Electronics Engineering, GMR Institute of Technology, Rajam 532127, India; [email protected] or [email protected] 4 Clean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, TX 77553, USA 5 School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia 6 Electrical & Electronics Engineering, Raghu Engineering College, Dakamarri, Visakhapatnam 531162, India; [email protected] * Correspondence: [email protected] (D.E.); [email protected] (R.M.E.); [email protected] (M.R.I.) Abstract: Higher penetration of variable renewable energy sources into the grid brings down the Citation: Nuvvula, R.S.S.; plant load factor of thermal power plants. However, during sudden changes in load, the thermal Elangovan, D.; Teegala, K.S.; power plants support the grid, though at higher ramping rates and with inefﬁcient operation. Madurai Elavarasan, R.; Islam, M.R.; Hence, further renewable additions must be backed by battery energy storage systems to limit the Inapakurthi, R. Optimal Sizing of ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, Battery-Integrated Hybrid Renewable battery-integrated renewable energy systems that include ﬂoating solar, bifacial rooftop, and wind Energy Sources with Ramp Rate energy systems are evaluated for a designated smart city in India to reduce ramping support by Limitations on a Grid Using a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle ALA-QPSO. Energies 2021, 14, 5368. swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid https://doi.org/10.3390/en14175368 renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss

Academic Editor: (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with Dimitrios Katsaprakakis four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of Received: 15 July 2021 floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an Accepted: 24 August 2021 LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the Published: 28 August 2021 results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: multi-objective ALA-QPSO; renewable energy sources; floating solar PV; bifacial solar published maps and institutional affil- panels; battery energy storage system iations.

1. Introduction Copyright: © 2021 by the authors. Energy being one of the most imported commodities in developing nations, an increase Licensee MDPI, Basel, Switzerland. in crude oil and coal prices witnessed widening ﬁscal deﬁcits. In contrast, renewable This article is an open access article energy costs have drastically reduced from 2010 to 2019 owing to mass production. These distributed under the terms and favorable conditions allowed developing economies to invest in eco-friendly and cost- conditions of the Creative Commons effective renewable energy sources to comply with Sustainable Development Goals decided Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ during the Paris Agreement. India, with its crude imports pegged at 82% of its energy 4.0/). needs, witnessed an 84% fall in renewable energy costs in this period, with tenders falling

Energies 2021, 14, 5368. https://doi.org/10.3390/en14175368 https://www.mdpi.com/journal/energies Energies 2021, 14, 5368 2 of 25

and cost-effective renewable energy sources to comply with Sustainable Development Goals decided during the Paris Agreement. India, with its crude imports pegged at 82% of its energy needs, witnessed an 84% fall in renewable energy costs in this period, with tenders falling to as low as 33 USD/MWh [1]. With digital transformation creating exciting employment opportunities, urban areas in India are witnessing a mass exodus from Energies 2021rural, 14 ,areas 5368 [2]. This has resulted in significant demographic changes that have propped2 of 23 up the energy, water, and transportation needs of the cities. This has led to dented air quality mainly due to conventional transportation and outdated and inefficient industrial equipment. With allto these as low developments, as 33 USD/MWh the [1]. Withenergy digital needs transformation of urban creatingareas are exciting set to employment dou- opportunities, urban areas in India are witnessing a mass exodus from rural areas [2]. This ble by 2030. Substantialhas resulted deployment in significant of rene demographicwable energy changes systems that have proppedis essential up the to energy, evade water, coal-/gas-based powerand transportationplants to meet needs the ofadditional the cities. Thisenergy has needs. led to dented air quality mainly due to conventional transportation and outdated and inefficient industrial equipment. With all 1.1. Current Electric theseEnergy developments, Scenario in the India energy needs of urban areas are set to double by 2030. Substantial deployment of renewable energy systems is essential to evade coal-/gas-based power With the introductionplants to meetof the the Electricity additional energyAct of needs. 2003, plenty of opportunities unfolded for private players as all the electric power segments (generation, transmission, and distribution) opened up1.1. for Current investments. Electric Energy As Scenarioof February in India 2020, the total installed capacity in India was 367.28 GW, ofWith which the introduction only 25.2% of was theElectricity from the Act central of 2003, sector plenty and of opportunities the remaining unfolded for private players as all the electric power segments (generation, transmission, and distri- was shared betweenbution) state opened and private up for investments. sectors. The As ofcapacity February addition 2020, the total is at installed a compounded capacity in India annual growth ratewas of 5.19%, 367.28 GW, which of which reflects only staggering 25.2% was from economic the central growth sector in and recent the remaining years. was Besides, the share ofshared renewable between energy state and is private pegged sectors. at 23.5%, The capacity though addition it is is far at a from compounded the am- annual bitious target of 175growth GW of rate renewable of 5.19%, which additi reflectsons by staggering 2021–2022. economic The growth rising in cost recent of years.oil and Besides, the share of renewable energy is pegged at 23.5%, though it is far from the ambitious target coal mining policy ofissues 175 GW in ofcoal-exporting renewable additions countries by 2021–2022. have led The to rising a decrease cost of oilin andthe coalplant mining load factor of conventionalpolicy issues generators, in coal-exporting as shown countries in Figure have led 1 to [3]. a decrease This has inthe also plant led load to factorre- of duced growth of conventionalconventional generators, energy sources, as shown as in Figureevident1[ 3from]. This Table has also 1. ledDifficulties to reduced growthin land acquisition, especiallyof conventional in urban energy areas, sources, where as evident the actual from Table demand1. Difficulties exists, in have land acquisition,led to diminished interestespecially from private in urban players. areas, where In thethis actual context, demand municipal exists, have corporations led to diminished and interest from private players. In this context, municipal corporations and distribution companies distribution companiesare required are required to step to up step investments up investments in renewable in energyrenewable systems energy in partnership systems with in partnership withprivate private players players to exude to exude confidence confidence in private in players. private players.

Plant Load Factors of Various Sectors 90

Plant Load Factor (%) 50 2009–102010–112011–122012–132013–142014–152015–162016–172017–182018–19

Overall PLF Central State Private

Figure 1. Plant load factors of various sectors in India. Figure 1. Plant load factors of various sectors in India.

In addition, municipalIn addition, corporations municipal can corporations benefit from can such beneﬁt partnerships from such partnerships that open that up open investments in otherup needs investments of the in city, other such needs as of piped the city, gas such and as pipedfiber optics. gas and To ﬁber make optics. such To make such partnerships viable, a proper methodology has to be followed for optimally sizing partnerships viable,renewable a proper energy methodology systems. Since ha thes to energy be followed usage pattern for is subjectedoptimally to economicsizing re- activity, newable energy systems.which isSince affected the by energy factors suchusage as pattern special economic is subjected zones, to access economic to roads, activity, and the main which is affected bycity, factors site selection such as becomes special a keyeconomic factor in zones, planning access for renewable to roads, energy and systems. the main With the city, site selection becomesdata regarding a key economic factor activityin planning in hand, for land renewable cover and loadenergy data systems. can be forecasted, With and the data regarding economichence, a distribution activity systemin hand, can land be planned cover accordingly. and load data can be forecasted, and hence, a distribution system can be planned accordingly.

Energies 2021, 14, 5368 3 of 23

Table 1. Energy scenario in India, 2010–2019 [3].

Conventional Year-on-Year Year Required Available Deﬁcit Peak Demand Peak Met Energy Generation Growth (MU) (MU) (%) (MW) (MW) (MU) (%) 2009–2010 830,594 746,644 −10.1 119,166 104,009 771,551 6.6 2010–2011 861,591 788,355 −8.5 122,287 110,256 811,143 5.56 2011–2012 937,199 857,886 −8.5 130,006 116,191 876,887 8.11 2012–2013 995,557 908,652 −8.7 135,453 123,294 912,056 4.01 2013–2014 1,002,257 959,829 −4.2 135,918 129,815 967,150 6.04 2014–2015 1,068,923 1,030,785 −3.6 148,166 141,160 1,048,673 8.43 2015–2016 1,114,408 1,090,850 −2.1 153,366 148,463 1,107,822 5.64 2016–2017 1,142,929 1,135,334 −0.7 159,542 156,934 1,160,141 4.72 2017–2018 1,213,326 1,204,697 −0.7 164,066 160,752 1,206,306 3.98 2018–2019 1,274,595 1,267,526 −0.6 177,022 175,528 1,249,337 3.57

1.2. Electric Energy Scenario in the Proposed Location As of February 2020, the installed capacity of Andhra Pradesh was 24.803 GW, with renewable energy pegged at just 8.313 GW in contrast to the target of 18 GW by 2021–2022 [4]. To meet the target, involvement from all the stakeholders such as municipal corporations, cooperative societies, and private players is desired. Visakhapatnam, being the second-largest city in Andhra Pradesh, is contributing more than 10% of the state gross domestic product, strengthened by its diversified industrial segments such as steel and pharma and needs to contribute heavily in meeting the renewable energy target. With the city designated as a smart city by the central government, funds amounting to 150 million USD yearly are awarded for its development. The Greater Visakhapatnam Municipal Corporation (GVMC), with modest revenue receipts, is leveraging itself in sustainable projects in the city. Recent renewable investments of the GVMC, a 2 MW floating solar PV power plant in the Mudasarlova Reservoir, and a proposed 15 MW floating power plant over the Meghadrigedda Reservoir are evidence of its efforts for deploying clean energy systems in the city. Further, the GVMC has installed rooftop solar panels over all the government offices and schools, making use of the incentives provided by the central government. The GVMC has also been actively installing LED-based street lights all over the city and has installed 91,775 LED streetlights with a project cost of 10.5 million USD, resulting in an annual saving of 24 GWh of energy. This has been achieved through the annuity- based deemed savings model without any investment from the GVMC. These investment models by the corporation can further be leveraged to envision a DC distribution system to facilitate aggressive solar PV installations by the public as well.

2. Literature Review High penetration of variable renewable energy sources results in wide frequency ﬂuctuations due to the low inertia of the electric grid [5]. To improve the reliability of the electric grid, it is highly recommended to deploy an energy storage system (ESS). Of all ESS technologies, a battery energy storage system (BESS) is preferable owing to their modularity. Batteries are used for grid applications such as time shifting, regulation, spinning and non-spinning reserves, voltage support, demand charge management, load following, transmission/distribution line upgrade deferral, and black start. However, their capacity is limited by capital and O&M costs, and regular replacements though their costs are more competitive than ever [6]. The size of a BESS drastically changes the economics of the entire project. The optimal sizing of a BESS under different settings is widely cited in the literature [7–10]. Lithium-ion-based BESSs have been studied to act as spinning reserves when the grid capacity is maxed out, as they have advantages of a large number of cycles and speciﬁc energy compared to lead-acid batteries. However, since they suffer from degradation issues, estimation techniques are employed to assess the health of a BESS [11]. Minimizing life cycle loss is considered as one of the objectives to avoid over- exploitation of a BESS. In this context, a balanced mixture of renewable energy sources and conventional energy sources is required. Nevertheless, conventional energy sources are Energies 2021, 14, 5368 4 of 23

required to operate at their technical minimum and are regulated by their ramping limits. This necessitates the optimal sizing of a hybrid renewable energy system (HRES) by using appropriate techno-economic reliability indices. Meta-heuristic methods such as particle swarm optimization, differential evolution, evolutionary algorithms, and nature-inspired techniques such as ant-lion optimization and gray-wolf optimization are used in various research areas of electrical engineering [12–16]. Particle swarm optimization (PSO), introduced by Kennedy and Eberhart in 1995, is a nature-inspired algorithm inspired by the collective behavior of birds in search of food. The introduction of traditional PSO created a lot of interest in the research community, and it led to adaptations to it, resulting in several variants of PSO to improve the exploration and exploitation of particles. The convergence behavior of particles was analyzed in [17], which demonstrated that the particles in PSO are attracted by an attractor. By using this phenomenon, several variants of PSO were proposed. However, not all of them guarantee global convergence. Along similar lines, QPSO was introduced in 2004 inspired by both quantum mechanics and the concepts of PSO for enhanced exploration capabilities [18]. In QPSO, the velocity component in the original PSO is replaced by a delta potential well, thus enabling the particles to venture the entire search space with some probability. This allows the particles to attain a global optimum. There have been several variants of QPSO as well [19–21]. An adaptive local attractor QPSO (ALA-QPSO) was proposed in [21] to enhance the search performance by introducing the weighted mean personal best of the particles. Optimal sizing of an HRES has been widely explored in the literature, however with different combinations of technologies [16,22]. Most of the research work is focused on stand-alone systems to enhance the sustainability of remote areas [23–25]. Recently, the focus has been shifted toward grid-connected scenarios due to the fall in solar PV prices [26–30]. There have been quite a few studies on optimal sizing of large-scale grid- connected systems, mostly due to the complexities involved in renewable energy potential assessment, especially for rooftop PV systems [31]. To increase power density, bifacial PV panels were studied in [32,33]. The use of roofing materials to improve rear-side power generation results in reduced cooling needs in heat-prevalent areas. The integration of floating solar PV systems is on the rise worldwide due to low leasing costs and ease of plant availability for maintenance [34–36]. Due to the intermittent nature of renewable energy sources, energy storage systems are included in the HRES to provide stiffness to the grid. There are several studies on optimizing the size of energy storage systems, focusing on peak shifting and renewable smoothing, along with ancillary services such as voltage regulation and frequency regulation [28,37,38]. In [39], a multi-objective equilibrium optimizer was used to achieve operational and economic efficiency by integrating renewable energy sources into the grid. In [40], an optimal combination of PV systems and DSTATCOM was achieved using the multi-objective modified ant lion optimizer. With the growing acceptance of electric vehicles, Li-ion batteries are becoming more prominent in vehicle-to- grid scenarios [28,41–43]. In addition to modularity, significant research in fault diagnosis, and second-use energy storage applications, a BESS has become more acceptable compared to other technologies [44–46]. To avoid the premature convergence problem of conventional PSO, a double-exponential- function-based dynamic inertia weight technique was developed for the optimal parameter estimation of a PV cell module [47]. As a way to solve energy management problems, lower bill costs, peak-to-average ratio, and carbon emissions, efficient integration of RES with BES a load scheduling and energy storage Management controller based is discussed [48].

2.1. Key Contributions Despite a large number of articles in the area of optimal sizing of RE sources, there are few papers that address potential assessment in cities using GIS tools to arrive at the upper bounds. Most of the papers address the issue of either cost optimization or voltage and frequency issues, but they do not consider the limitations on the ramp rates of thermal power plants (TPPs) and the financial repercussions on the TPPs in the event of phasing out diesel and gas generators to improve the sustainability of cities. This paper addresses the issue of optimally sizing the HRES in Visakhapatnam by Energies 2021, 14, 5368 considering the sustainability and ramp rate limitations on conventional energy5 sources of 23 using GIS tools to assess the renewable energy potential. The process flowchart for the proposed work is shown in Figure 2. Initially, a city is selected and its potential for renewable energy sources is studied using geographic information system (GIS) tools. power plants (TPPs) and the ﬁnancial repercussions on the TPPs in the event of phasing Based on the available resources, possible land cover is assessed and optimal sizing of the out diesel and gas generators to improve the sustainability of cities. system is arrived at by applying optimization techniques to realize techno-economic re- This paper addresses the issue of optimally sizing the HRES in Visakhapatnam by consideringquirements the such sustainability as loss of andpower ramp supply rate limitations probability on (LPSP), conventional levelized energy cost sourcesof energy using(LCoE), GIS toolsand battery to assess life the cycle renewable loss (LCL). energy potential. The process ﬂowchart for the proposedThe work main is contributions shown in Figure of this2. paper Initially, are as a cityfollows: is selected and its potential for renewablei. A energy methodology sources for is studiedsite selection, using geographicresource assessment, information and system energy (GIS) management tools. Based on thefor large-scale available resources, renewable possible energy integration land cover is is developed. assessed and optimal sizing of theii. system Optimal is arrived sizing at by of applying potential optimization renewable en techniquesergy sources to realize and a techno-economic battery bank is as- requirementssessed such to asminimize loss of powergrid ramping, supply probabilitylevelized cost (LPSP), of energy, levelized and loss cost of of load energy using (LCoE), andvarious battery multi-objective life cycle loss (LCL).optimization techniques.

Figure 2. Process flowchart of the proposed research. Figure 2. Process ﬂowchart of the proposed research.

The main contributions of this paper are as follows: i. A methodology for site selection, resource assessment, and energy management for large-scale renewable energy integration is developed. ii. Optimal sizing of potential renewable energy sources and a battery bank is assessed to minimize grid ramping, levelized cost of energy, and loss of load using various multi-objective optimization techniques. Subsequent sections of this paper are organized as follows: Section2 deals with site selection and renewable resource assessment in the chosen urban areas. In Section3, a mathematical model of the presented renewable energy sources is developed. In Section4 , the adopted multi-objective optimization technique is explained, while in Section5, the results are compared with legacy optimization techniques. In addition, sensitivity analysis Energies 2021, 14, 5368 6 of 25

Subsequent sections of this paper are organized as follows: Section 2 deals with site selection and renewable resource assessment in the chosen urban areas. In Section 3, a mathematical model of the presented renewable energy sources is developed. In Section 4, the adopted multi-objective optimization technique is explained, while in Section 5, the results are compared with legacy optimization techniques. In addition, sensitivity analysis is presented to evaluate the impact of reduced battery size due to outages and price appreciation due to policy changes. Finally, concluding remarks are made in Section 6.

Energies 2021, 14, 5368 2.2. Site Selection and Resource Assessment 6 of 23 The proposed HRES with a battery bank is shown in Figure 3. The system contains decentralized rooftop PV panels, floating PV panels, and wind turbines installed over ishilltops presented and to seashores. evaluate Due the impactto their ofintermittenc reduced batteryy, suitable size converters due to outages are placed and to price inte- appreciationgrate with duethe battery-integrated to policy changes. DC Finally, bus. concluding The DC bus remarks accommodates are made EV in Section charging6. stations and street-lighting systems with which better efficiency is envisaged. A battery 2.2.bank Site is Selection also associated and Resource with Assessment the DC bus for charging and discharging during the mis- matchThe between proposed generation HRES with and a batteryload demand. bank is Fu shownrther, in the Figure DC bus3. The is integrated system contains with the decentralizedAC grid through rooftop a transformer PV panels, ﬂoatingto supply PV the panels, AC loads and windof the turbinescity. After installed matching over the hilltopssupply and and seashores. demand, Dueany toexcess their power intermittency, is wheeled suitable to other converters load centers are placed via the to integrate grid. with theAs battery-integrated mentioned in Section DC bus. 1, Visakhapatnam The DC bus accommodates has become EVan charginginvestment stations destination and street-lightingfor companies systems ever since with the which bifurcation better efﬁciency of the state is envisaged.of Andhra APradesh, battery along bank with is also the associateddesignation with of thea smart DC bus city. for The charging city is andone dischargingof the leading during pharmaceutical the mismatch hubs between in India generationthat caterand to world loaddemand. needs. In Further, addition, the electr DC busicity is demand integrated is expected with the ACto spike grid throughfurther due a transformerto announcements to supply such the ACas a loads railway of the zone city. and After industrial matching corridors, the supply leading and demand,to further anyinvestments. excess power However, is wheeled capacity to other addition load centers using thermal via the grid.power plants takes a long time.

Figure 3. Block diagram of the proposed hybrid renewable energy system. Figure 3. Block diagram of the proposed hybrid renewable energy system.

AsFurther, mentioned some in of Section the power1, Visakhapatnam purchase ag hasreements become have an investmentalready expired destination in 2019, forwhile companies some power ever since plants the in bifurcationthe vicinity of are the close state to of their Andhra end of Pradesh, life. In contrast, along with renewa- the designationble energy ofsystems a smart take city. only The months city is oneto inst ofall the and leading integrate pharmaceutical with the grid hubs and in their India ca- thatpacities cater can to world be easily needs. expandable In addition, at a faster electricity pace. demandThis study is expectedfocuses on to the spike cityfurther bounda- dueries, to where announcements there is a suchdrastic as change a railway in zoneland andusage industrial and building corridors, cover leading due toto the further special investments. However, capacity addition using thermal power plants takes a long time. Further, some of the power purchase agreements have already expired in 2019, while some power plants in the vicinity are close to their end of life. In contrast, renewable energy

systems take only months to install and integrate with the grid and their capacities can be easily expandable at a faster pace. This study focuses on the city boundaries, where there is a drastic change in land usage and building cover due to the special economic zones. The type of housing is more inclined toward multi-story buildings where rooftops are under-used or individual houses with sufficient space for solar installations. The GVMC can propose a public–private partnership model with those rooftop owners and hence reduce the leasing costs of rooftop solar power systems. Besides, water bodies are explored to enable the interested parties to invest in floating solar PV systems. This will also benefit Energies 2021, 14, 5368 7 of 25

economic zones. The type of housing is more inclined toward multi-story buildings where rooftops are under-used or individual houses with sufficient space for solar in- Energies 2021, 14, 5368 stallations. The GVMC can propose a public–private partnership model with those roof-7 of 23 top owners and hence reduce the leasing costs of rooftop solar power systems. Besides, water bodies are explored to enable the interested parties to invest in floating solar PV systems. This will also benefit the city with reduced water evaporation as the city is thestruggling city with for reduced water watersecurity evaporation for decades. as theLikewise, city is strugglingwind energy for systems water security can be for decades.mounted Likewise, on shorelines wind energyand hilltops. systems Based can beon mountedthe master on plan shorelines provided and by hilltops. the infra- Based onstructure the master firm plan AECOM provided [49], growth by the infrastructurecenters and human ﬁrm AECOMsettlements [49 in], the growth proposed centers city and humanare shown settlements in Figure in 4. the proposed city are shown in Figure4.

Figure 4. Growth centers and human settlements in Visakhapatnam. Figure 4. Growth centers and human settlements in Visakhapatnam.

ItIt is is observed observed that that the the industrial industrial activities activities in in Parawada Parawada and and Atchutapuram Atchutapuram areas areas have have enabled satellite growth centers, marked by orange stripes, to grow at a faster rate, enabled satellite growth centers, marked by orange stripes, to grow at a faster rate, in in addition to the expansion of the city core. Given this, the area marked with grid lines is addition to the expansion of the city core. Given this, the area marked with grid lines is taken for the proposed area for the HRES to be set up. Assessment of HRESs is done at taken for the proposed area for the HRES to be set up. Assessment of HRESs is done at the proposed location using the Polygon tool of Google Earth Pro, where purple and blue the proposed location using the Polygon tool of Google Earth Pro, where purple and blue patches along with yellow pins indicate potential areas for floating PV, rooftop, and wind Energies 2021, 14, 5368 patches along with yellow pins indicate potential areas for ﬂoating PV, rooftop,8 of and 25 wind energy system installations, respectively, as shown in Figure 5. energy system installations, respectively, as shown in Figure5.

Figure 5. Identification of potential areas for renewable energy plant installations using Google Figure 5. Identiﬁcation of potential areas for renewable energy plant installations using Google Earth Pro. Earth Pro. Four potential areas for floating PV systems, are identified and their areas are calculated. Similarly, six possible areas for rooftop solar power systems are recognized after taking out unused land cover and dense areas where the installation is not viable. Fur- ther, the potential of wind energy systems is assessed over hilltops with a minimum of 200 m height in the region and on the shorelines that are not part of any economic activity. The potential of each HRES is given in Table 2, along with the potential quantity of solar panels and wind turbines. These values are considered as the upper bounds for optimal sizing of the HRES. Further, accessibility of the HRES to the grid is assessed, as shown in Figure 6 [50], through which concerns regarding grid connectivity are addressed. The proposed location has a good number of substations with 132 kV, 220 kV, and 400 kV voltage levels. The entire energy in the HRES can be evacuated through these substations.

Table 2. Energy potential of various renewable energy technologies in the proposed location.

S. no. Technology Potential of Renewable Resource Identified number of areas 4 Total available area (km2) 2.933 Available area (m2) 586,630 (assuming 20% use) 1 Floating PV system Potential for installed capacity (MW) (power 105 MW density 180 W/m2) Maximum-possible number of solar panels (360 293,315 Wp) Identified number of areas 6 Rooftop bifacial Total available area (km2) 24.46 2 PV system Available area (km2) 1.223 (assuming 5% acceptance)

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Four potential areas for ﬂoating PV systems, are identiﬁed and their areas are calculated. Similarly, six possible areas for rooftop solar power systems are recognized after taking out unused land cover and dense areas where the installation is not viable. Fur- ther, the potential of wind energy systems is assessed over hilltops with a minimum of 200 m height in the region and on the shorelines that are not part of any economic activity. The potential of each HRES is given in Table2, along with the potential quantity of solar panels and wind turbines. These values are considered as the upper bounds for optimal sizing of the HRES. Further, accessibility of the HRES to the grid is assessed, as shown in Figure6[50] , through which concerns regarding grid connectivity are addressed. The proposed location has a good number of substations with 132 kV, 220 kV, and 400 kV voltage levels. The entire energy in the HRES can be evacuated through these substations.

Table 2. Energy potential of various renewable energy technologies in the proposed location.

S. no. Technology Potential of Renewable Resource Identified number of areas 4 Total available area (km2) 2.933 Available area (m2) 586,630 1 Floating PV system (assuming 20% use) Potential for installed capacity (MW) 105 MW (power density 180 W/m2) Maximum-possible number of solar 293,315 panels (360 Wp) Identified number of areas 6 Total available area (km2) 24.46 Available area (km2) 1.223 Rooftop bifacial (assuming 5% acceptance) 2 PV system Potential for installed capacity (MW) 260 MW (power density 215 W/m2) Maximum-possible number of bifacial 611,500 solar panels (430 Wp) Identified number of areas 5 Total length of hilltops and 11,140 shoreline (m) Available length (m) 3 Wind energy system 11,140 (assuming 100% use) Potential for installed capacity (MW) 62 MW Maximum-possible number of wind 30 turbines (2.1 MW)

2.3. Observations from the Load Profile of the Proposed Location The load profile is generated from five different areas in the proposed location, as shown in Figure7, that represent various load segments, viz. predominantly industrial (Gajuwaka and switching station), residential (Pendurty and dairy farm), and pharma (Parawada) sectors. It is also evident that pharma needs are high and the load profile is almost flat similar to residential loads. However, the heavy industry zone has a diversified load profile with peaks from 6 PM to 11 PM. From the load profile at the proposed location, it is observed that demand rises from the first quarter (Q1) onward and peaks in the second quarter, while Q4 exhibits weak demand. Since the peak loads correspond to the second quarter during which monsoon retreats, solar PV systems suffer from cloud cover. Hence, a BESS plays a crucial role during these days. Besides, the daily load curve indicates that Energies 2021, 14, 5368 9 of 25

Potential for installed capacity (MW) (power 260 MW density 215 W/m2) Maximum-possible number of bifacial solar 611,500 panels (430 Wp) Identified number of areas 5 Total length of hilltops and shoreline (m) 11,140 Energies 2021, 14, 5368 Available length (m) 9 of 23 11,140 3 Wind energy system (assuming 100% use) Potential for installed capacity (MW) 62 MW Maximum-possible number of wind turbines load peaks from 6 PM to 10 PM, during which time energy is drawn either30 from the grid or from the combination of a BESS and(2.1 a wind MW) energy system.

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Figure 6. Access to grid in the proposed site. Figure 6. Access to grid in the proposed site. 2.3. Observations from the Load Profile of the Proposed Location The load profile is generated from five different areas in the proposed location, as shown in Figure 7, that represent various load segments, viz. predominantly industrial (Gajuwaka and switching station), residential (Pendurty and dairy farm), and pharma (Parawada) sectors. It is also evident that pharma needs are high and the load profile is almost flat similar to residential loads. However, the heavy industry zone has a diversified load profile with peaks from 6 PM to 11 PM. From the load profile at the proposed location, it is observed that demand rises from the first quarter (Q1) onward and peaks in the second quarter, while Q4 exhibits weak demand. Since the peak loads correspond to the second quarter during which monsoon retreats, solar PV systems suffer from cloud cover. Hence, a BESS plays a crucial role during these days. Besides, the daily load curve indicates that load peaks from 6 PM to 10 PM, during which time energy is drawn either from the grid or from the combination of a BESS and a wind energy system.

Figure 7. Electric load Figurepattern 7. atElectric the proposed load pattern location. at the proposed location.

2.4. Land Cover and Shading Analysis From the land cover of Visakhapatnam, as shown in Figure 8a, it is seen that building density, indicated in red color, is on the rise in the proposed location, with a large number of industries being set up in the special economic zones. Similarly, the blue color indicates water bodies with sufficient depth for floating solar panels. The 3D map of one of the residential areas is obtained using an open CADmapper online tool and is fed to the Sketchup Pro, which is also freeware, for shading analysis. The shading time is rated from yellow to dark blue, yellow indicating less shading time and dark blue indicating more shading time. The result shows that a good number of rooftops can be used for installation as long as there is sufficient spacing between buildings when adjacent building heights differ by a large margin. A rooftop PV system is designed for a sample building in the proposed location, and shading analysis is performed at 4 PM when the maximum shading occurs, as shown in Figure 8b. The total area of the rooftop is 323 m2, including water tanks and a stairway. The illustrated PV array has an area of 183 m2 (70 panels of 2.61 m2 each) and hence amounting to a ground coverage ratio of 0.56. Though azimuth correction of 42° can be applied for the rooftop system, thus increasing the irradiance on the rear part of the panels to boost the bifacial gain, it is aesthetically not appealing. In some cases, this causes inconvenience to operating personnel during maintenance. Hence, azimuth correction is advised for those buildings constructed with azimuth correction.

Energies 2021, 14, 5368 10 of 23

2.4. Land Cover and Shading Analysis From the land cover of Visakhapatnam, as shown in Figure8a, it is seen that building density, indicated in red color, is on the rise in the proposed location, with a large number of industries being set up in the special economic zones. Similarly, the blue color indicates water bodies with sufﬁcient depth for ﬂoating solar panels. The 3D map of one of the residential areas is obtained using an open CADmapper online tool and is fed to the Sketchup Pro, which is also freeware, for shading analysis. The shading time is rated from Energies 2021, 14, 5368 11 of 25 yellow to dark blue, yellow indicating less shading time and dark blue indicating more shading time.

Figure 8.8. (a(a)) Land Land cover cover of of the the proposed proposed area. area. (b) Shading(b) Shading analysis analysis of a proposed of a proposed rooftop rooftop PV configuration PV configuration over a building. over a building. The result shows that a good number of rooftops can be used for installation as long as3. Mathematical there is sufficient Modeling spacing between buildings when adjacent building heights differ by a largeDue margin. to the Alack rooftop of sufficient PV system data isregarding designed bio-waste for a sample and building its feasibility in the in proposed the pro- location,posed location, and shading along analysiswith technology is performed developments at 4 PM when in other the maximum renewable shading energy occurs, tech- 2 asnologies, shown inonly Figure solar8b. and The wind total areaenergy of the technologies rooftop is 323 are m considered, including in water this tanksstudy. and The a 2 2 stairway.model equations The illustrated for the two PV arraytechnologies has an areaare we ofll 183 cited m in(70 the panels literature, of 2.61 and m henceeach) only and ◦ hencetheir equations amounting are to provided a ground in coverage this section ratio to of avoid 0.56. Though redundancy. azimuth However, correction modeling of 42 can of bebifacial applied PV panels for the that rooftop are used system, for thusrooftop increasing systems theis presented irradiance explicitly. on the rear part of the panels to boost the bifacial gain, it is aesthetically not appealing. In some cases, this causes inconvenience3.1. Solar PV System to operating personnel during maintenance. Hence, azimuth correction is advised for those buildings constructed with azimuth correction. In this work, bifacial solar panels are considered for rooftop PV systems, while 3.mono-facial Mathematical PV panels Modeling are considered for the floating PV system. The modeling of irradiance on the front part of the panels is the same for both cases, while only rear irradiance Due to the lack of sufficient data regarding bio-waste and its feasibility in the proposed applies for bifacial panels. As a first step, the angle of incidence on both sides of the panel location, along with technology developments in other renewable energy technologies, is calculated, as in Equation (1). Equations (1)–(6) of the irradiance of PV panels are only solar and wind energy technologies are considered in this study. The model equations adapted from [51]. for the two technologies are well cited in the literature, and hence only their equations are providedθθβθβγθ=+cos in this-1 () cos section .cos to avoid() 180 - redundancy.{} sin .sin However, .cos modeling ()() -180 -of bifacial -180 PV panels thatiFront are used for rooftop z systems is presented z explicitly.  AZ (1) θθβθβγθ=+-1 (){} ()() 3.1.iarRe Solar PVcos System() cos z .cos 180 - sin z .sin 180 - .cos -180 - Az In this work, bifacial solar panels are considered for rooftop PV systems, while mono- facialThere PV panels are a aretotal considered of three components for the floating of PVirradiance system. that The modelingfall on the of front irradiance and rear on thesides front of the part bifacial of the panelspanel, isi.e., the direct same fornormal both irradiance cases, while (DNI), only rear diff irradianceused horizontal applies irra- for diance (DHI), and an albedo, due to reflected irradiance, as shown in Figure 9. The total irradiance on the PV panels is given by Equation (2). Total = front + rear I PV I PV I PV (2)

front(rear) = front(rear) + front(rear) + front(rear) I PV I PVDN I PVDH I PVAlb

Energies 2021, 14, 5368 11 of 23

bifacial panels. As a ﬁrst step, the angle of incidence on both sides of the panel is calculated, as in Equation (1). Equations (1)–(6) of the irradiance of PV panels are adapted from [51].

−1 θiFront = cos (cos θz. cos(180 − β) + {sin θz. sin β. cos[(γ − 180) − (θAZ − 180)]}) −1 (1) θiRear = cos (cos θz. cos(180 − β) + {sin θz. sin(180 − β). cos[(γ − 180) − θAz]})

There are a total of three components of irradiance that fall on the front and rear sides of the bifacial panel, i.e., direct normal irradiance (DNI), diffused horizontal irradiance (DHI), and an albedo, due to reﬂected irradiance, as shown in Figure9. The total irradiance on the PV panels is given by Equation (2).

Energies 2021, 14, 5368 Total f ront rear 12 of 25 IPV = IPV + IPV f ront(rear) f ront(rear) f ront(rear) f ront(rear) (2) IPV = IPVDN + IPVDH + IPVAlb

FigureFigure 9. 9.Illustration Illustration of of irradiance irradiance on on the the titled titled solar solar panel. panel.

TheThe albedo albedo further further has has two two components, components, a reflected a reflected direct direct beam beam and reflectedand reflected diffused dif- irradiance,fused irradiance, as given as in given Equation in Equation (3). (3). Total Farm Farm (3) TotalI =FarmI +FarmI IPV.AlbPV .=Alb IPVAlbdirPVAlbdir+ IPVAlbdiPVAlbdif f (3) The albedo can be arrived at by integrating reflected irradiance along all the breadth The albedo can be arrived at by integrating reflected irradiance along all the breadth of the row, as given in Equation (4). of the row, as given in Equation (4). F.Panel I ()l = I ()R F − ()l η F.PanelPVAlb.dir(dif))= GndDI DHI A dl gnd( ) diff IPVAlb.dir(di f ) l IGndDI(DHI)RAFdl−gnd l ηdi f f h (4) F.Farm h 1 F. panel (4) F.FarmI (()) =()l 1=R F.panelI ( ) ()l dl IPVAlbPVAlb.dir(d.dir f ) dfl h IPV .AlbPV.dr.Alb(di.dr f )(difl )dl 0 h 0 where Fdl-gnd is the view factor given by where Fdl-gnd is the view factor given by 1 1 ψ () (()11-sin− sin ψo (ll))...... morning morning = 2 o Fdl−gnd = 21 Fdl− gnd (sin ψ2(l) − sin ψ1(l))...... a f ternoon 12 ()sinψψ()l -sin () l ...... afternoon Finally, the hourly PV power2 can21 be assessed using Equation (5). Finally, the hourly PV power can be assessedFront using EquationRe (5).ar prpv_hourly = η f ront.pr_ f ront IPV + ηRear.pr_Rear IPv (5) FrontRe ar ppIpI=+ηη.. (5) where η f rontrpv,η__Re_ReRe hourlyar—efficiencies front rof front front PV and rear ar cells r ar Pv p , pη —frontη &rear sides rating of panel wherer_ f ront r_Refrontar, Re ar —efficiencies of front and rear cells

pr _ front , pr _ Rear —front &rear sides rating of panel Similar equations are applied for mono-facial PV panels used in floating solar PV systems, however without considering rear irradiance, as given in Equation (6). = η Front p fpv _ hourly fpv .p fpv I PV (6)

3.2. Wind Energy Conversion System (WECS) The city is not a good location for installing wind power plants at the ground level. However, there are some specific locations such as hilltops with more than 200 m elevation from the ground and the shoreline where a WECS can be installed at higher altitudes. Though they can accommodate a low number of turbines, the system will support the grid during peak hours in the evening, as shown in the wind profile, obtained after processing the data generated from the NREL Geospatial wind data of the proposed lo-

Energies 2021, 14, 5368 12 of 23

Similar equations are applied for mono-facial PV panels used in ﬂoating solar PV systems, however without considering rear irradiance, as given in Equation (6).

Front p f pv_hourly = η f pv.p f pv IPV (6)

3.2. Wind Energy Conversion System (WECS) The city is not a good location for installing wind power plants at the ground level. However, there are some speciﬁc locations such as hilltops with more than 200 m elevation from the ground and the shoreline where a WECS can be installed at higher altitudes. Energies 2021, 14, 5368 13 of 25 Though they can accommodate a low number of turbines, the system will support the grid during peak hours in the evening, as shown in the wind proﬁle, obtained after processing the data generated from the NREL Geospatial wind data of the proposed location and cationgiven and in Figure given 10in. Figure This reduces 10. This the reduces need for the higher need BESSfor higher racks, BESS helping racks, in reducing helping in the reducingcost and the maintenance cost and maintenance of the system. of the system. wind speed (m/s) speed wind

FigureFigure 10. 10. WindWind profile proﬁle at at the the proposed proposed location location at at different different elevations. elevations.

TheThe wind wind speeds speeds at at the the proposed proposed location location are are obtained obtained from from weather weather stations stations and and areare extrapolated extrapolated to to the the required required height height usin usingg Equation Equation (7). (7). The The power power generated generated from from the the WECSWECS can can be be obtained obtained using using Equation Equation (8) (8) [10] [10.]. Per Per the the technical technical details details of of wind wind turbines turbines providedprovided in in Section Section 3,3 ,a a capacity capacity use use factor factor of of ~40% ~40% can can be be achieved. achieved. γ  hdes  v == vre f des (7)(7) v vref  hre f   href  where γ is the surface roughness and is chosen to be 0.18 for the proposed location. where γ is the surface roughness and is chosen to be 0.18 for the proposed location.   0 f orv<>< vciorv > vco 0 forv3 vci3 orv v co (v −vci) pwtg =  Pr ∗ 3 3 f orvci ≤ v ≤ vr (8) (v33r −vci)  ()vv- ci =∗ Pr f orvr < v ≤ vco ≤≤ pwtgPforvvv ci r (8) r ()33  vvrci- where rate power p = 0.5C .ρ .η .A .v3. r p a g w r <≤ Pforvrr v v co = ρ η 3 where rate power pr 0.5C p . a . g .Aw .vr .

3.3. Battery Energy Storage System As discussed in Section 1, a Li-ion-based BESS is considered in this study as BESSs have more specific energy and can be scaled to racks and containers. Further, they have a good round-trip efficiency of 87–94%. The size of a BESS depends on the required number of hours of support. In this study, a maximum of one daily cycle is considered to support the grid for 4 hours during peak time, i.e., 7 PM to 11 PM. The technical details of the selected BESS are presented in Table 3, which is based on the Samsung SDI E3-R099 [52] model with a life cycle performance of 6000 cycles for continuous discharge of 1C at 25 °C. In this paper, the maximum limit for the number of racks is derived from the peak load, the number of hours, and the grid capacity.

Energies 2021, 14, 5368 13 of 23

3.3. Battery Energy Storage System As discussed in Section1, a Li-ion-based BESS is considered in this study as BESSs have more speciﬁc energy and can be scaled to racks and containers. Further, they have a good round-trip efﬁciency of 87–94%. The size of a BESS depends on the required number of hours of support. In this study, a maximum of one daily cycle is considered to support the grid for 4 hours during peak time, i.e., 7 PM to 11 PM. The technical details of the selected BESS are presented in Table3, which is based on the Samsung SDI E3-R099 [ 52] model with a life cycle performance of 6000 cycles for continuous discharge of 1C at 25 ◦C. In this paper, the maximum limit for the number of racks is derived from the peak load, the number of hours, and the grid capacity.

Table 3. Technical details of components, along with costs.

S. no. Type of Parameter Bifacial Rooftop Floating PV Wind Speciﬁcation PV Panels [53] Panels [53] Turbines [54] Batteries [52] 1 Technology Cell type Polycrystalline Mono c-Si Tubular Lithium ion Voltage 52.27 33.75 690 774–1004 V (VMPP) Current (A) 9.31 9.78 1895 111 Ah 2 Electrical Power (energy) 430 W 360 W 2.1 MW (99 kWh) Temperature −0.42%/◦C −0.39%/◦C NA NA coefﬁcient Dimensions 1996 × 1310 × 40 (mm3) 1640 × 992 × 35 (mm3) 111 (m) 442 × 702 × 2124 (mm3) 3 Mechanical Weight 36.5 kg 17.5 kg NA 670 kg

Capital 796 USD 1031 USD 980 USD 350 USD/kWh 4 Financial [55] requirements O&M costs 12 USD 16 USD 25 USD 35 USD

3.4. Energy Management Strategy To regulate the power ﬂow and frequency in the power system network, the load demand must be met by the generating stations, as given in Equation (9) [10]. PL = Pf pv + Prpv + Pwt + Pgrid ± Pbat (9) The grid is constrained by the following assumptions to ensure network reliability: i. The grid is never operated below the technical minimum of 55% of the load. ii. The grid is allowed to supply only 80% of the maximum load to accommodate future demand. iii. The ramp rates of the grid are restricted to ±0.5%/min of its capacity. The quantity of power accessed from the grid follows Equation (10). The grid compen- sates the net load after the load is met by renewable energy. However, the grid is limited by ramping rates and then the maximum capacity. n o n o Pgridlim : Pgridmin, Pgridmax , Rgridlim : Rgridmin, Rgridmax

 P (t) − P (t) i f P < P − P < P   l ren gridmin l ren gridmax  Pgrid(t) = Pgrid(t − 1) + Rgridmax i f Pgrid(t) − Pgrid(t − 1) > Rgridmax (10)   Pgrid(t − 1) + Rgridmin i f Pgrid(t) − Pgrid(t − 1) > Rgridmin The power mismatch between the load, HRES, and grid is given by Equation (11). n o Pmis(t) = P1(t) − Pren(t) + Pgrid(t) (11) The ﬂowchart for the EMS is shown in Figure 11. Initially, the size of the renewable energy system is fed to the EMS and the corresponding powers are calculated. The ramp Energies 2021, 14, 5368 14 of 23

rate limitations of the thermal power plants, battery power, and energy limitations are Energies 2021, 14, 5368 considered. Using these, the net power mismatch is calculated for the given load15 at of any 25

given instant. Then, power dispatch based on the two scenarios of power mismatch, excess load and excess generation, is carried out.

Pgrid max Evaluate power from HRES E =++ bmax PrenPPP rpv fpv wt P UpdatePren based on renewable rampinglimits bmax

Input grid &battery parameters 0 Grid capacitylimitsof Pgrid lim &Ramping limits R grid lim

Battery power &energylimits Pbatlim ,E bat lim Pbmin

Read Load PL Pb (-1)t Pb ()t

Pgrid min Ebmin Evaluate Net Load Ptnl at timeinstant ' ' = Ptnl() Pt L ()-P ren () t 0

Pgrid (-1)t Ptgrid () Update Pgrid ( t ) as in eq .(10) Etb (-1) Eb ()t

Apply ramping limits to the grid as in eq. (10)

+ Pmis (t)=P l (t) -{P ren (t)+P grid (t)+Pbat (t)+P loss (t) P wheel (t)}

Pmis (t)>0

Update Pbat ( t ) as in eq .(12) Update Pbat ( t ) as in eq .(14)

Update E() t as in eq .(15) Update Ebat () t as in eq .(13) bat

P ()tPtPt=++ ()- () P () tPt () =++ loss L gridr en bat Pwheel()tPt grid () Pr en () tPtPt bat () - L ()

FigureFigure 11. 11. FlowFlow chart chart for for the the energy energy management management system. system.

3.5. Excess Load Scenario 3.5. ExcessIn case Load the Scenarioramping rates or power drawn from the grid exceeds the maximum capacity,In the case BESS the rampingprovides ratessupport or powerto the system, drawn fromthereby the matching grid exceeds the theload maximum demand. Nevertheless,capacity, the BESSthe BESS provides is also support limited by to thethe system,minimum thereby and maximum matching power the load and demand. energy constraints,Nevertheless, as thegiven BESS by Eq is alsouations limited (12) byand the (13), minimum respectively, and maximum taken from power [10]. and energy constraints, as given by Equations (12) and (13), respectively, taken from [10]. Pt() ()= mis Ptbatmin P batmax ( tP ),(t) , Et bat ( -1) - E bat min (12) P (t) = min P (t), misη , E (t − 1) − E (12) bat batmax bat, dch bat batmin ηbat,dch ()=∗Δ ( ) () EEbatbat(t)tEEtPtt= maxmax{E{batmin batmin, E ,bat( batt − 1 -1) − -Pbat bat(t) ∗ ∆t}} (13)(13)

Battery energy power limits {}{} Pbat lim : Pbat min , Pbat max , Ebat lim : Ebat min , Ebat max

Energies 2021, 14, 5368 15 of 23

Battery energy power limits

Pbatlim : {Pbatmin, Pbatmax}, Ebatlim : {Ebatmin, Ebatmax} n o Then the mismatched power is updated as Pmis(t) = P1(t) − Pren(t) + Pgrid(t) + Pbat In the case of any further supply–demand mismatch, the demand response will have to set in and curtail the non-essential loads.

3.6. Excess Generation Scenario In the case of excess generation, the grid intake is reduced until the mismatch nulliﬁes or the grid reaches its technical minimum. If the excess supply persists, then the BESS will charge. As before, the battery intake is limited by its constraints on charging power and energy, as in Equations (14) and (15), taken from [10]. Pmis(t) Pbat(t) = max Pbatmin(t), , Ebatmax − Ebat(t − 1) (14) ηbat,ch

Ebat(t) = min{Ebatmax, Ebat(t − 1) − Pbat(t) ∗ ∆t} (15) If there is any excess generation even after the battery capacity reaches a maximum, the excess energy is wheeled to other load centers.

4. Multi-Objective Adaptive-Local-Attractor-Based Quantum-Behaved Particle Swarm Optimization (ALA-QPSO) In the original PSO, the position and velocity of the i-th particle in each iteration ‘it’ are updated according to Equation (16). As discussed in Section1, the trajectory of these particles is inﬂuenced by an attractor [21].

it+1 it it it vel = ω.vel + c1.r1. pbesti − pos + c2.r2. gbest − pos i i i i (16) it+1 it it+1 posi = posi + veli Using the concept of particle trajectory, the principle of uncertainty from quantum mechanics is applied to generate a delta potential well that allows the particles to move across the entire search space. The position pos of the j-th dimension in the i-th particle is updated using the adaptive local attractor in each iteration according to Equation (17) [21]. The first term is updated based on the adaptive local attractor given in Equation (18), which is updated based on an adaptive contraction–expansion coefficient in Equation (19) and the diversity of the fitness of the population fitij from the mean fitness fitmean in Equation (20). The second term in Equation (17) is updated according to the weighted mean personal best position of all the particles, as given in Equation (21).

1 posij(it + 1) = atrij ∓ α mpbestj(it) − posij(it) ∗ ln uij(it) (17) 0 + 0 f or rand ≤ 0.5; 0 − 0 otherwise

where atrij (it) is the adaptive local attractor, as deﬁned in

div(t) div(it) atr (it) = ζ (t) ∗ p (it) + 1 − ζ ∗ 1 − ∗ gbest (it) (18) ij ij pop bestij ij pop j

where pop is the population size, pbestij(it) indicates the personal best of the i-th particle during the it-th iteration, and gbestj(it) is the global best of all the particles in the it- th iteration. The contraction–expansion coefﬁcient is given by

(α1 − αo)(itmax − it) α = αo + (19) itmax Energies 2021, 14, 5368 16 of 23

where α0 is the initial weight and itmax is the maximum number of iterations. The diversity factor in each iteration, div(it), is given by Equation (20).

(it) (it)2 pop ( it it (it) (it) f itij − f itmean max f it − f , i f max f it − f it div(it) = ∑ , F = ij mean ij mean (20) i=1 F 1, otherwise

The weighted mean personal best of each particle in the it-th iteration, mpbest(it), is given in Equation (21). pop mpbest(it) = ∑ ri(it)pbesti(it), i=1      pop   1 f iti(it)  (21)  1 − pop , i f ∑ f it (it) 6= 0  ( ) = pop−1   k whereri it ∑ f itk(it) k=1  k=1   1  pop , otherwise Then the ﬁtness of the particles is evaluated until convergence is achieved or until maximum iterations are completed. The Algorithm 1 for the same is given below.

Algorithm 1 ALA-QPSO i. Initialize the parameters and particles in the population with random position vectors. ii. Evaluate the ﬁtness of all the particles and calculate the personal best for each particle and the global best for the swarm. iii. Calculate the population diversity as in Equation (20). iv. Update the weighted mean personal-best position using Equation (21). v. Update the adaptive local attractor for each particle as in Equation (18) vi. Update the position for each particle using Equation (17). vii. If the terminating condition is not met, go to step iii.

In the case of ALA-MQPSO, a mutation strategy is introduced after step vi to enhance the exploitation capability of the algorithm. For optimal sizing of the HRES, three objective functions, LPSP, LCoE [10], and LCL [54] of the BESS, are considered and are given in Equations (22)–(24). The LPSP represents the amount of load that is cut off by the demand response control, while the LCL represents the usage of the BESS. A higher LCL means frequent cycling of the BESS, which reduces the life of the battery pack even before its rated end-of-life period.

8760h i ∑ PL(t) − Pgrid(t) + Pf pv(t) + Prpv(t) + Pwt(t) ± Pbat(t) = LPSP = t 1 (22) 8760 ∑ PL(t) t=1

4 ∑ Ni.Ci. fcr + Ni.OMi = LCoE = i 1 (23) 4 ∑ Pi i=1

r(1+r)l where fcr = is the discount factor, r is the interest rate, l is the lifetime of the project, (1+r)l −1 Ci is the capital expenditure, and OMi is the cost of operation and maintenance.

eq 8760 kp n 1 Ebat(t) − Ebat(t − 1) LCL = 100 = ∗ f ail f ail ∑ 0.5 (24) Ebatmax N100 N100 t=1

f ail where kp is the Peukert lifetime constant, N100 is the maximum number of cycles that a eq battery experiences before its rated end of life, and n100 is equivalent 100%-DOD cycle number of K half cycles. Energies 2021, 14, 5368 17 of 23

5. Simulation Results and Analysis Based on the assessed potential in Section2, optimal sizing of the HRES is achieved to minimize the LPSP, LCoE, and LCL. ALA-QPSO techniques are applied, and the results are compared with four other benchmark techniques, four variants of differential evolution (DE) algorithms. The adopted algorithm is compared with variants of DE as it has proven to be an effective optimization technique for enhancing the exploration of the population. The optimization parameters for the algorithms under comparison are presented in Table4. The same values are chosen for parameters, wherever necessary, for all the algorithms to prove the effectiveness of the proposed algorithm. The results are screened further based on the minimum LPSP, as shown in Table5. The result obtained is shown to be near optimal as the combination fared well in most of the constraints that include the least number of hours of load loss and the least number of hours of excess generation. This will result in better proﬁtability of the proposed system.

Table 4. Optimization parameters of benchmark techniques.

Optimization No of Scaling Crossover Personal Social S. no. Population Inertia Technique Iterations Factor Rate Weight Weight 1 ALA-mQPSO 20 100 - - 1 2 2 2 ALA-QPSO 20 100 - - 1 2 2 3 DE/best/1 100 100 0.8 0.9 - - - 4 DE/rand/1 100 100 0.8 0.9 - - - DE/rand-to- 5 100 100 0.8 0.9 - - - best/1 6 DE/best/2 100 100 0.8 0.9 - - -

Table 5. Results for optimal sizing with the adopted optimization technique.

Technologies Objectives Optimization Technique No of Bifacial # Floating No of Wind No of LPSP LCoE LCL Rooftop PV Panels PV Panels Turbines Batteries (%) ALA-MQPSO 611500 258756 30 3690 0.005 0.077 0.0087 ALA-QPSO 596692 275716 29 3276 0.0098 0.0789 0.0096 DE/rand/1 607125 260731 29 3361 0.0089 0.077 0.0094

DE/current-to- 418323 205534 29 2181 0.0144 0.0765 0.0091 rand/1 Mode DE/current-to- 349610 288965 29 1130 0.0103 0.0786 0.0103 best/1 DE/best/2 559014 157921 29 9690 0.0123 0.0805 0.0091

A comparison of the Pareto fronts of three optimization techniques is depicted in Figure 12. Further, the power distribution among the sources over 5 days in each quarter of a year, i.e., 21 to 26 March, June, September, and December, is shown in Figures 13–16 with the minimum LPSP conﬁguration. The same is shown with the minimum LCOE conﬁguration for June in Figure 17. The results prove that the use of the BESS is limited only for a transition from peak to off-peak hours in most of the cases, except in June since it is the monsoon season. During this time, the BESS has helped in maintaining the frequency of the grid constant by compensating for any generation loss by the PV system. It should also be noted that the participation of battery power in the proposed system is mostly during the violation of ramp rate limits, in the early morning or in the evening. Hence, we notice that at noon, ramp rates are not violated the power dispatch from the grid is at its technical minimum. This shows that the higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. Hence, if BESSs are not used during the violation of ramp rate limits, it may burden the TPPs further in EnergiesEnergies 2021 2021, ,14 14, ,5368 5368 1919 ofof 2525

AA comparisoncomparison ofof thethe ParetoPareto frontsfronts ofof thrthreeee optimizationoptimization techniquestechniques isis depicteddepicted inin FigureFigure 12. 12. Further, Further, the the power power distribution distribution am amongong the the sources sources over over 5 5 days days in in each each quarter quarter ofof aa year, year, i.e., i.e., 21 21 toto 26 26 March,March, June,June, September, September, andand December,December, isis shownshown inin FiguresFigures 13–1613–16 withwith thethe minimumminimum LPSPLPSP confconfiguration.iguration. TheThe samesame isis shownshown withwith thethe minimumminimum LCOELCOE configurationconfiguration forfor JuneJune inin FigureFigure 17.17. TheThe resultsresults proveprove thatthat thethe useuse ofof thethe BESSBESS isis limitedlimited onlyonly for for a a transition transition from from peak peak to to off-peak off-peak hours hours in in most most of of the the cases, cases, except except in in June June since since itit is is the the monsoon monsoon season. season. DuringDuring thisthis time,time, thethe BESSBESS hashas helpedhelped inin maintainingmaintaining thethe frequencyfrequency ofof thethe gridgrid con-con- stantstant byby compensatingcompensating forfor anyany generationgeneration lossloss byby thethe PVPV system.system. ItIt shouldshould alsoalso bebe notednoted thatthat thethe participationparticipation ofof batterybattery powerpower inin ththee proposedproposed systemsystem isis mostlymostly duringduring thethe vio-vio- lationlation of of ramp ramp rate rate limits, limits, in in the the early early morning morning or or in in the the evening. evening. Hence, Hence, we we notice notice that that at at noon,noon, rampramp ratesrates areare notnot violatedviolated thethe powerpower dispatchdispatch fromfrom thethe gridgrid isis atat itsits technicaltechnical Energies 2021, 14, 5368 minimum.minimum. ThisThis showsshows thatthat thethe higherhigher penetrpenetrationation ofof variablevariable renewablerenewable energyenergy sources18sources of 23 intointo thethe gridgrid bringsbrings downdown thethe plantplant loadload factfactoror ofof thermalthermal powerpower plplants.ants. Hence,Hence, ifif BESSsBESSs areare notnot usedused duringduring thethe violationviolation ofof rampramp raterate limits,limits, itit maymay burdenburden thethe TPPsTPPs furtherfurther inin thethe eventevent ofof phasing-outphasing-out ofof diesel-diesel- oror gas-bagas-basedsed plants.plants. Similarly,Similarly, thethe powerpower distributiondistribution theforfor event JuneJune with ofwith phasing-out mutation-basedmutation-based of diesel- ALA-QPSOALA-QPSO or gas-based isis plottedplotted plants. inin Similarly, FigureFigure 1818 the forfor powerthethe minimumminimum distribution LCoELCoE forconfiguration.configuration. June with mutation-based Here,Here, thethe montmont ALA-QPSOhh ofof JuneJune is isis plotted selected,selected, in Figure asas solarsolar 18 forpowerpower the minimumundergoesundergoes LCoE widewide conﬁguration.changeschanges during during Here, that that the month. month. month of June is selected, as solar power undergoes wide changes during that month.

FigureFigureFigure 12. 12. 12.Pareto Pareto Pareto fronts fronts fronts of of of proposed proposed proposed techniques. techniques. techniques.

Energies 2021, 14, 5368 20 of 25 Figure 13. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 March. FigureFigure 13. 13.Power Power distributiondistribution with with ALA-QPSO ALA-QPSO for for the the minimum minimum LPSP LPSP for for 21–26 21–26 March. March.

Figure 14. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 June. Figure 14. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 June.

Figure 15. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 September.

Figure 16. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 December.

Energies 2021, 14, 5368 20 of 25 Energies 2021, 14, 5368 20 of 25

Energies 2021, 14, 5368 19 of 23

Figure 14. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 June. Figure 14. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 June.

Figure 15. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 September. FigureFigure 15. 15.Power Power distribution distribution with with ALA-QPSO ALA-QPSO for fo ther the minimum minimum LPSP LPSP for for 21–26 21–26 September. September.

Energies 2021, 14, 5368 21 of 25 Figure 16. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 December. Figure 16. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 December. Figure 16. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 December.

Figure 17. Power distribution with ALA-QPSO for the minimum LCoE for 21–26 June. Figure 17. Power distribution with ALA-QPSO for the minimum LCoE for 21–26 June.

Figure 18. Power distribution with mutation-based ALA-QPSO for the minimum LCoE for 21–26 June.

A sensitivity analysis is performed on the results obtained through ALA-QPSO. In the first scenario, it is assumed that there is 50% damage to the BESS. The power distribution with this scenario is shown in Figure 19 for June. It can be observed that the demand response is enforced during peak hours. It should be noted that the LCoE has increased drastically to 0.1372 USD, which is 73% more. In addition, the LPSP has increased to 1.62%. We conclude that preventive maintenance is necessary to avoid any load loss due to BESS reduction. Another sensitivity analysis is performed for 20% demand appreciation and 20% BESS reduction. The results for the same are shown in Figure 20. It is observed that due to the rise in demand, ramping rates of the grid are maxed out, and hence the BESS supports the grid during sunset, leading to less backup power for the peak time. It is observed that the LCoE has increased drastically to 0.1577 USD/kW, which is twice that of the actual, and the LPSP has increased to 1.85%. Further, in the case of reduced solar power, the BESS cannot compensate for the generation, even when the ramping is maxed out. It can be concluded that for reliable operation, the size of the BESS should be increased when the LCoE configuration is considered.

Energies 2021, 14, 5368 21 of 25

Energies 2021, 14, 5368 20 of 23

Figure 17. Power distribution with ALA-QPSO for the minimum LCoE for 21–26 June.

FigureFigure 18.18. PowerPower distribution distribution with with mutation-based mutation-based ALA-QPSO ALA-QPSO for the for minimum the minimum LCoE LCoE for 21–26 for June. 21–26 June.

AA sensitivitysensitivity analysis analysis is is performed performed on on the the results results obtained obtained through through ALA-QPSO. ALA-QPSO. In the In ﬁrstthe first scenario, scenario, it is assumedit is assumed that therethat there is 50% is damage 50% damage to the to BESS. the BESS. The power The power distribution distri- withbution this with scenario this scenario is shown is inshown Figure in 19Figure for June.19 for ItJune. can beIt can observed be observed that the that demand the de- responsemand response is enforced is enforced during during peak hours. peak Ithours. should It should be noted be that noted the that LCoE the has LCoE increased has in- drasticallycreased drastically to 0.1372 to USD, 0.1372 which USD, is 73%which more. is 73% In addition,more. In addition, the LPSP hasthe LPSP increased has increased to 1.62%. Weto 1.62%. conclude We that conclude preventive that maintenancepreventive maintenance is necessary is to necessary avoid any to load avoid loss any due load to BESS loss reduction.due to BESS Another reduction. sensitivity Another analysis sensitivity is performed analysis for is 20% performed demand appreciationfor 20% demand and 20% ap- BESSpreciation reduction. and 20% The BESS results reduction. for the same The are results shown for in the Figure same 20 are. It shown is observed in Figure that due20. It to is theobserved rise in demand,that due rampingto the rise rates in demand, of the grid ra aremping maxed rates out, of andthe hencegrid are the maxed BESS supports out, and thehence grid the during BESS sunset, supports leading the grid to less during backup sunset, power leading for the peakto less time. backup It is observedpower for that the thepeak LCoE time. has It increasedis observed drastically that the toLCoE 0.1577 has USD/kW, increased which drastically is twice to that 0.1577 of the USD/kW, actual, andwhich the is LPSP twice has that increased of the actual, to 1.85%. and Further,the LPSP in has the increased case of reduced to 1.85%. solar Further, power, in the the BESS case cannotof reduced compensate solar power, for the the generation, BESS cannot even compensate when the for ramping the generation, is maxed even out. Itwhen can bethe Energies 2021, 14, 5368 22 of 25 concludedramping is that maxed for reliableout. It can operation, be concluded the size that of for the reliable BESS should operation, be increased the size of when the BESS the LCoEshould conﬁguration be increased is when considered. the LCoE configuration is considered.

FigureFigure 19. 19.Power Power distribution distribution with with ALA-QPSO ALA-QPSO for for the the minimum minimum LPSP LPSP for for 21–26 21–26 June June with with 50% 50% damagedamage to theto the BESS BESS assumed. assumed.

Figure 20. Power distribution with ALA-QPSO for the minimum LCOE for 21–26 June with 20% damage to the BESS and 20% demand appreciation assumed.

6. Conclusions With the increase in energy demands in smart cities, it is more important than ever to integrate renewable energy sources to stop relying on traditional energy sources. In this study, a hybrid renewable energy system with bifacial rooftop PV, floating PV, and wind energy systems, as well as lithium-ion batteries for energy storage, is considered. The renewable energy potential of a city in India is assessed using Google Earth Pro’s Polygon tool, CAD mapper, and SketchUp Pro. The real-time 1-hour interval-based load profile of the selected locations is considered for calculating the upper limits of the BESS. Reliability indices such as loss of load probability (LPSP), levelized cost of energy (LCoE), and life cycle loss (LCL) of the BESS are considered for optimizing the size under certain constraints on the grid. The optimal sizing of the available HRES is achieved using the ALA-QPSO technique and is compared with benchmark algorithms such as variants of the differential algorithm. A mutation strategy is applied to the adopted ALA-QPSO to enhance exploitation. Though the results of this study are superior, they need further investigation. A sensitivity analysis is performed for conditions such as demand appreciation, battery damage, and cost escalation. The results prove that with the financial fitness of the municipal corporation of the region, the proposed system can be implemented in a phased manner with the help of financial institutions such as the Asian De- velopment Bank, with which the municipal corporation has already tied up for some

Energies 2021, 14, 5368 22 of 25

Energies 2021, 14, 5368 21 of 23 Figure 19. Power distribution with ALA-QPSO for the minimum LPSP for 21–26 June with 50% damage to the BESS assumed.

FigureFigure 20. 20.Power Power distribution distribution withwith ALA-QPSOALA-QPSO forfor thethe minimum minimumLCOE LCOEfor for 21–26 21–26 June June with with 20% 20% damage to the BESS and 20% demand appreciation assumed. damage to the BESS and 20% demand appreciation assumed. 6. Conclusions 6. Conclusions With the increase in energy demands in smart cities, it is more important than ever With the increase in energy demands in smart cities, it is more important than ever to integrate renewable energy sources to stop relying on traditional energy sources. In to integrate renewable energy sources to stop relying on traditional energy sources. In this study, a hybrid renewable energy system with bifacial rooftop PV, floating PV, and this study, a hybrid renewable energy system with bifacial rooftop PV, floating PV, and wind energy systems, as well as lithium-ion batteries for energy storage, is considered. wind energy systems, as well as lithium-ion batteries for energy storage, is considered. The renewable energy potential of a city in India is assessed using Google Earth Pro’s The renewable energy potential of a city in India is assessed using Google Earth Pro’s Polygon tool, CAD mapper, and SketchUp Pro. The real-time 1-hour interval-based load Polygon tool, CAD mapper, and SketchUp Pro. The real-time 1-hour interval-based load profile of the selected locations is considered for calculating the upper limits of the BESS. profile of the selected locations is considered for calculating the upper limits of the BESS. Reliability indices such as loss of load probability (LPSP), levelized cost of energy (LCoE), Reliability indices such as loss of load probability (LPSP), levelized cost of energy (LCoE), andand life life cycle cycle loss loss (LCL) (LCL) of of the the BESS BESS are are considered considered for for optimizing optimizing the the size size under under certain certain constraintsconstraints onon thethe grid. grid. TheThe optimaloptimal sizingsizing ofof the the available available HRES HRES is is achieved achieved using using the the ALA-QPSOALA-QPSO techniquetechnique andand isis compared compared with with benchmark benchmark algorithms algorithms such such as as variants variants of of thethe differential differential algorithm. algorithm. AA mutation mutation strategy strategy is is applied applied to to the the adopted adopted ALA-QPSO ALA-QPSO to to enhanceenhance exploitation. exploitation. Though Though the the results results of thisof this study study are superior, are superior they, need they further need further investigation.investigation. A sensitivity A sensitivity analysis analysis is performed is performed for conditions for conditions such as such demand as demand appreciation, appre- batteryciation, damage, battery anddamage, cost escalation.and cost escalation The results. The prove results that prove with thethat financial with the fitness financial of thefitness municipal of the corporationmunicipal corporation of the region, of the proposedregion, the system proposed can besystem implemented can be imple- in a phasedmented manner in a phased with themanner help ofwith financial the help institutions of financial such institutions as the Asian such Development as the Asian Bank, De- withvelopment which theBank, municipal with which corporation the municipal has already corporation tied up has for somealready projects. tied up To for assess some the operational feasibility of the system, stability analysis of the system can be performed in the future, given the transmission data. This work can be extended to other cities by assessing the potential of RES feasible in those areas. This approach will help the world in achieving the RE targets within the time limits.

Author Contributions: Conceptualization, methodology, formal analysis, and writing—original draft preparation, R.S.S.N and R.M.E.; writing—review and editing, D.E., R.S.S.N. and M.R.I.; visualization, M.R.I.; supervision, D.E. and K.S.T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conﬂicts of Interest: The authors declare no conﬂict of interest. Energies 2021, 14, 5368 22 of 23

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