Have you ever gotten a song stuck in your head?

1. Ep Evaluator Alternative Definition
2. Ep Evaluator Lab
3. Ep Evaluator 12
4. Ep Evaluator Tool
5. Ep Evaluator Training

No matter how hard you try to shake it, it’s there—over and over and over again.

DATAINNOVATIONSNORTHAMERICA 120KimballAve.,Suite100 SouthBurlington,VTUSA05403Fax:(802)658-2782 Support:Phone:(802)658-1955 Email:northamerica-support@datainnovations.com. The Assessment Documentation will be an adequate, accurate and objective evaluation and presentation of the environmental and social risks and impacts, whether prepared by the client, consultants or external experts. For Category A and, as appropriate, Category B. Alternatives analysis completed which evaluates lower Greenhouse Gas (GHG). When testing regulated products such as Pharmaceuticals (Prescription and Over-the-counter), Medical Devices, Dietary Supplements or even Cosmetics, the test methods employed must meet certain performance requirements or the results obtained from using the methods will not be accepted by regulatory agencies such as FDA.

An electrophysiology (EP) study is a test that records the electrical activity and the electrical pathways of your heart in order to determine the best treatment for an abnormal heart rhythm.

That’s actually a sign of pretty clever song writing.

Wouldn’t it be amazing if the same could happen with ESL teaching methods?

How helpful would it be to find teaching methods that are so clever, they just stick with you all the time?

Imagine having handy teaching hints constantly on recall, similar to the manner in which the melody to “Sweet Caroline” is never more than two seconds away from your mind’s reach.

The difference being, these methods are actually helpful. Caught in a communication conundrum? Grab a visual! Frustrated that your students don’t seem to be getting it? Recall those ever-important stages of language acquisition!

Prepare to have all these, and more, permanently imprinted on your brain. Just for the time being, clear your mind of the lyrics to “Yellow Submarine” and make some space for five ESL teaching methods that’ll rock your world!

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1. Total Physical Response

Total Physical Response, or TPR, is a language acquisition method developed by psychology professor James Asher. TPR uses a combo of language and physical actions to engage students in the language learning process. Much has been learned about TPR from observing infants throughout the acquisition of their home language.

Think about a time you may have interacted with your 6-month-old niece. Maybe her mom said “Give your auntie a high five!” Your niece responded with the physical action of slapping your hand. Can she fluently communicate yet? No, but she does pick up on a few verbal clues like “high five,” and the hand slap is nearly an automatic response.

While the brain is generating this automatic response, it’s also taking note of things like syntax and speech patterns. After enough exposure, your niece will eventually generate language spontaneously. If it’s easy enough for an infant, why wouldn’t it be just as effective with your students?

Total Physical Response is a fairly low-stress strategy, which is one of the keys to its success. There’s no pressure for a student to speak when using TPR. Instead they simply listen and respond in a physical manner. TPR can be a great pre-cursor to verbal communication.

There are many ways to use TPR within the classroom, but as a warm-up or transition between activities are two great ways to get your students up and moving.

• It’s best to begin with simple, explicit instructions or commands, such as “sit” or “stand.” It’s imperative that clear visuals are provided when introducing the concept. A physical demonstration paired with the corresponding action is a most effective strategy when beginning.

• Once the group has been familiarized with the terms and the reciprocal actions, it must be revisited consistently. Meaningful repetitions will deepen understanding, as well as provide familiarity with the language.

• Once your students have mastered these simple commands, you can move on to more involved, multi-step verbal tasks like “Stand up and touch your toes” or “Sit on the floor.” As the complexity of the tasks grow, so will your students’ confidence.

Before you know it, your students will be able to “Shake, Rattle and Roll” like rock stars!

2. Introducing Items of Cultural and Personal Relevance

Where do you feel most comfortable? Most likely at home, among familiar things. When we are in our natural environment we are typically secure and relaxed. Creating feelings of security and comfort within your students can help mentally prepare them for successful language acquisition. Though we usually can’t reach our students in their literal home environment, there are some things we can do to create a welcoming and familiar classroom.

Just as your home probably reflects your culture and personality, your classroom should reflect the background and interests of each culture represented among your students. Items of cultural relevance might include books in a student’s home language and maps or flags from their homeland. Of course, it’s always important to know your students on personal level, too. Appropriate pop culture references and music can also be incorporated into your classroom.

Providing these items not only creates a feeling of familiarity, but it also sends the message that your students are valued. It shows that you care about every part of them, culturally and personally.

When integrating items of cultural and personal relevance into your classroom, get creative! Again, think about your own home:

• It has your favorite books. Create a classroom library comprised of your student’s favorites, including books in their home language.

• It has photos of family and friends. Make certain the visuals in your classroom represent people from varying backgrounds.

• If you like to paint, it has all the stuff you need to paint. Find out what your students like to do and have some of that stuff for them, too.

By adding just a few items to their classroom environment, your students can feel “Homeward Bound” regardless of how far away their home may be.

3. Using Authentic Materials

I have clear memories from my own time as a student in which the teacher requested that we perform seemingly mindless and mundane tasks. I’m sure they fit neatly into the lesson plan, but they didn’t seem to serve any real purpose. I remember a particularly bland piece of prose that revolved around an older gentleman’s trip to the library and the books he selected.

The point of reading the selection was to check comprehension. Why would I care to read about this fictional geezer, let alone prove my comprehension of his literary adventures by regurgitating the tale? Your students feel the same way.

Learning can be much more meaningful and motivating if it actually serves a purpose. Would you be in interested in reading this post if you didn’t think you’d take something away from it? Probably not. Your students don’t want to waste their time on something without a “take-away,” either. This is the true benefit of using authentic materials.

Authentic materials can be described as materials that have been created for native speakers and are used as teaching tools in the ESL classroom. These aren’t necessarily manufactured pieces from a classroom curriculum. It’s important to note that ESL considerations were not made as the materials were being created. This could include books, directions and maps, newspaper articles or recipes. It could also include videos or music.

Really, any source of language designed for the native speaker could be considered an authentic material. As a student, I would much rather have the end product of a lesson be a pizza I made following a recipe than a bunch of useless information about a useless subject (i.e. Grandpa’s trip to the library!).
There are tons of ways to incorporate authentic materials into your classroom:

• Set up a makeshift restaurant in your classroom. Grab menus, recipes, signs and even little notepads for writing down orders.

• Plan a trip to a local museum. You’ll need brochures, bus schedules (if you’re taking school transportation—pretend!), maps and directions. It might even be fun to provide a little history on the anticipated exhibits through written text or videos.

• Plant a class garden. You’ll need to start with gardening and plant research. This can be done with books, videos and Internet resources. There are seed types to read about and predictions to make. Once again, you can incorporate recipes using the fruits of your labor.

This is just the beginning—all you need is a little creativity. In no time you and your students will realize there “Ain’t Nothing Like the Real Thing.”

4. Displaying Visuals and Realia

I must admit that one of my biggest pet peeves is when people try to describe an episode of a television show to me that I don’t watch. I have no background information on the plot or characters, so it doesn’t really make sense. They’re talking and talking about this thing I don’t really understand, with no reference, other than their wordy explanation.

I honestly feel like screaming, “I don’t know what you’re talking about and I don’t care!” But, I don’t want to be perceived as rude or unintelligent, so most times I just smile and act interested, all the while, mentally zoning out. Sound familiar?

This very scenario plays out daily in classrooms across the country with our ELL students. Unfortunately, the stakes are much higher than choosing your new favorite TV show.

How can we prevent our students from tuning us out? Well, If I had had any familiarity or background on that television show, I might have been more willing to participate in the conversation. This is exactly what we need to provide for our students.

Visuals and realia are one of the most effective ways to provide a relatable reference for our students. Visuals are just what you might think they are: a universal picture that accompanies your lesson. For instance, if you’re teaching about elephants, have a pictures of elephants available to share with students. Easy, right?

Using realia is just as simple. It just means having a tangible object that students can fully “experience” to help deepen understanding. If you’re teaching a science lesson about trees, have bark, leaves and twigs available. If you’re teaching an English lesson about plurals, have one bean available to show the meaning of “singular,” and two or more beans available to show the meaning of “plural.”

Remember that elephant lesson? A picture could be easy and helpful, but you can take it a step further by adding small elephant figurines, or by providing a scrap of leathery material to better describe their skin.

We’ve already talked about how realia can be used to help with an English lesson, and math is a no brainer, too. Provide something tangible when teaching addition, subtract or any other operation.

Visuals are invaluable when teaching routines and social emotional skills. Think about those feeling charts or visual schedules. What a great way to reach not only your ELL students, but all of your kids.

By providing just a few inexpensive and interesting items for your students, you’ll soon have your students “Seeing Clearly Now.”

5. Remembering the Stages of Second Language Acquisition

I recall as a young teacher I had a Hmong student named Cindy. I was quite inexperienced and I easily got frustrated with her silence, day after day. All the other kids greeted me with hugs, laughed at my jokes and sang silly songs with me. Cindy just sat for months, stone faced and silent. I honestly took it personally.

She didn’t appear to like anything about me or our classroom. I was constantly in her face, prompting her to repeat after me and “use her words.” I thought if I could just make her speak it would mean she liked me and I was doing my job as her teacher. Obviously, this was useless and I eventually just gave up, and so did she—before she even started. Cindy will forever be one of my biggest regrets as a teacher.

I didn’t realize it at the time, but Cindy was simply going through the stages of second language acquisition. She was stuck in the first stage, the silent or receptive stage, and I was doing nothing to help move her forward.

There are actually five stages that your students might go through on their language acquisition journey:

1. Silent or Receptive Stage: During this stage students may be silent or use non-verbal communication, like pointing or nodding their head. The focus is on building the confidence it takes to actually speak and on learning basic vocabulary. There’s no language fluency at this stage in the game.

2. Early Production: Students might begin speaking in one- to two-word responses or short phrases and could acquire upwards of 1,000 new vocabulary words during this stage. Confidence grows even though a student might not be comfortable with the language yet.

3. Speech Emergence: This is where the real communication begins. Sentences and phrases become longer and more complex, though the rules of grammar might still be foggy. Greater comprehension is gained in this stage and students might begin reading or writing in the acquired language.

4. Intermediate Fluency: Learners begin thinking in the second language during this stage. Take, for instance, a French student to whom you’re teaching English. Previously when they had encountered a small, fury rodent gathering nuts they would think ecureuil. At this point they might see that same fury rodent and think “squirrel.” Comprehension and fluency greatly increase at this level.

5. Advanced Fluency: This is full mastery of the language. It can take between two and 10 years to get to this stage. The work doesn’t stop once the language has been mastered, either. There must be ongoing opportunities to engage in the language to keep sharp.

It’s fairly easy to determine which stage your students are in. Language acquisition charts and checklists are widely available. You might display these around your classroom or work area as a reference for reasonable expectations during each stage. It’s also a good self-reminder to relax. Don’t take it personally if your students aren’t getting it just yet. You truly have to let nature take its course on this one.

It’s of great importance that we remember to never push our students through these stages, or expect more than what they’re ready for. We must observe students, catch them where they are and work with them from there.

Though my intentions were good, all of my urging with Cindy certainly caused anxiety, which resulted in a complete shut down. This could happen during any stage if you neglect to take time and pick up on the needs of your students. As we guide our students through the stages of language acquisition, always remember: “Time Is on Our Side.”

These five methods can be used with your students at all levels and stages of acquisition. Each method can practically be adapted to fit all classrooms, across the curriculum, within every lesson.

They’re clever and memorable—just like that catchy eighties tune. Time to put ’em on instant recall and rock it out in your classroom!

Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Click here to get a copy. (Download)

And One More Thing…

Looking for authentic ESL materials for your classroom? Then you’re going to love FluentU!

It’s got a huge collection of authentic English videos that people in the English-speaking world actually watch on the regular. There are tons of great choices there when you’re looking for songs for in-class activities. You’ll find music videos, musical numbers from cinema and theater, kids’ singalongs, commercial jingles and much, much more.

On FluentU, all the videos are sorted by skill level and are carefully annotated for students. Words come with example sentences and definitions. Students will be able to add them to their own vocabulary lists, and even see how the words are used in other videos.

For example, if a student taps on the word “brought,” they’ll see this:

Plus, these great videos are all accompanied by interactive features and active learning tools for students, like multimedia flashcards and fun games like “fill in the blank.”

It’s perfect for in-class activities, group projects and solo homework assignments. Not to mention, it’s guaranteed to get your students excited about English!

Jackie Hostetler has worked in the field of education for 15 years, earning her ESOL Masters in 2010. Her passions include early childhood education and language acquisition in our youngest learners. She is the director of an early learning center and the mother of two of her own little learners.

If you liked this post, something tells me that you'll love FluentU, the best way to teach English with real-world videos.

Clement Malanda ^{1, 2}, Augustine B. Makokha ^{1, 2}, Charles Nzila ^{2, 3}, Collen Zalengera ⁴

¹Department of Energy Engineering, Moi University, Eldoret, Kenya

²Africa Center of Excellence in Phytochemicals, Textiles and Renewable Energy, Moi University, Eldoret, Kenya

³Department of Manufacturing, Industrial and Textile Engineering, Moi University, Eldoret, Kenya

⁴Department of Energy Studies, Mzuzu University, Luwinga Mzuzu 2, Malawi

Correspondence to: Clement Malanda , Department of Energy Engineering, Moi University, Eldoret, Kenya.

Email:

Copyright © 2020 The Author(s). Published by Scientific & Academic Publishing.

This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/

Abstract

Off-grid villages in Malawi continue to suffer from limited access to electricity due to under performance of the installed generation systems. This is largely attributable to inappropriate methodologies applied for sizing the systems that ignore sustainability indicators (technical, economic and environmental) as well as communities’ existing energy demand and future projections. This paper presents the sustainability evaluation of five types of hybrid renewable energy systems considered for deployment in three villages in Malawi. The study employed a Multi-Criteria Decision Analysis (MCDA) based on TOPSIS (Technique for Order of Preference by Similarity to the Ideal Solution) algorithm. The PV-Battery (PB), PV-Wind-Battery (PWB), PV-Diesel-Battery (PDB), Wind-Diesel-Battery (WDB) and PV-Wind-Diesel-Battery (PWDB) systems were evaluated. The study envisaged to identify suitable systems for deployment in each of the villages based on the pre-set technical, economic and environmental criteria. Under these criteria, the sub-criteria were identified which included; renewable fraction, excess electricity, total system capacity, battery autonomy, total electrical production, return on investment, simple payback, Net Present Cost (NPC), initial capital cost, operating cost, Cost of Energy (COE) and carbon dioxide (CO₂) emissions. The indicative values for these sub-criteria were derived from the optimization results from HOMER simulation software. The TOPSIS analysis entailed definition of energy alternatives and criteria, formulation of the decision matrices, normalization of the decision matrices, generation of weighted normalized matrices, determination of ideal and negative ideal solutions, calculation of relative separations from the ideal and negative ideal solutions and determination of relative closeness of each energy alternative to the ideal solution. For Chigunda, the PWB system was the most suitable with the highest closeness to ideal solution (C_i) value of 0.749 while for Mdyaka and Kadzuwa; the best alternative was the PB configuration with the highest C_i values of 0.708 and 0.717 respectively.

Keywords: Multi-Criteria Decision Analysis, TOPSIS, HOMER, Sustainability, Renewable Energy

Cite this paper: Clement Malanda , Augustine B. Makokha , Charles Nzila , Collen Zalengera , Sustainability Evaluation of Hybrid Renewable Electrification Alternatives in Malawi’s Villages Using a Multi-Criteria Approach, Energy and Power, Vol. 10 No. 1, 2020, pp. 11-19. doi: 10.5923/j.ep.20201001.02.

1. Introduction

Global energy generation, distribution and consumption patterns are rapidly evolving with growth in human population. The focus is quickly shifting towards renewables as a means of unlocking economic development. The interest in renewable energy (RE) sources is derived from the fact that they are sustainable and environmentally benign when compared to conventional energy sources. Between the years 2000 and 2017, renewables were the fastest-growing energy sources, contributing up to 40% to all primary energy increases [1]. Solar PV and wind energy systems recorded the highest deployment rate during this period. The primary impact of solar PV and wind energy systems lies in making production and consumption of energy accessible and inclusive. In locations where grid expansion is prohibitively expensive, off-grid RE systems could be an economically viable substitute. From IRENA report [2], the total global installed capacity of RE systems has leapt from less than 2 GW in 2008 to about 6.5 GW in 2017. In 2016 alone, it was estimated that worldwide, over 122 million people benefited from electricity from off-grid schemes for lighting and other electrical energy related services [2,3]. Asia has proven to be the hub of off-grid renewable capacity expansion as by 2016, 76 million people were electrified using solar lights and solar home systems [2]. There has also been a rapid recognition of off-grid RE systems in Africa. From 2011 to 2016, the number of people accessing electricity from off-grid sources rose from 2 to 58 million and solar lights, solar mini-grids and solar home systems have been the major drivers of this transition [3]. From 2008 to 2017, electricity generation using off-grid means jumped from 231 MW to 1.2 GW and 820 MW was derived from solar lights, solar mini-grids and solar home systems [3]. Although there has been a noticeable increase in generation from hydropower mini-grids from 124 MW to 126 MW in this period, the contribution from off-grid capacity has fallen sharply from 53% to 15% [2]. While the outlook for Kenya, Tanzania, Ethiopia, Nigeria and North African countries heralds huge electrification successes, most of the Sub-Saharan Africa countries to the contrary continue to face acute electricity shortages. As of 2017, 61% of people living in this region’s rural communities did not have access to electricity [3]. A unique case is for Malawi, where the national electricity access rate stands at 11% (42% urban, 4% rural) [4]. At 365 MW, hydro fired electricity meets most of the country’s demand although standby diesel generators complement this capacity [4]. Off-grid RE exploitation remains very low. As of 2016, only 10.4 MW of solar were reported to have been installed although none of the installed systems are currently functional [5]. Electricity generation from off-grid wind systems can also not be traced except for the cases where wind was hybridized with solar to electrify six villages [5]. A closer look at the world’s most deployed off-grid RE systems reveals serious sustainability challenges, which are rendering the systems defunct. Among several factors, lack of technology and reverse engineering skills’ transfer, rigid bureaucracies, lack of community engagement prior to installations, lack of financing and comprehensive tariff collection strategy to make the projects self-financing, scarcity of spare parts and exposure of equipment to harsh environmental conditions are some of the challenges which are concomitant to the failure of the systems [6,7]. These challenges fall into the broader categories of technical, economic, social and environmental aspects. Likewise, hybrid RE systems are challenged with multiple but conflicting sustainability factors which require thorough consideration before the systems are introduced to the real world conditions [8]. Objective decision making is therefore of paramount importance in the planning and deployment of the systems as it enhances the sustainability of the systems [8]. Suffice to say, one of the tools, which aids in rational decision-making is the Multi-Criteria Decision Analysis (MCDA). MCDA is a technique which helps in the selection of an optimal system based on its ability to satisfy several criteria [9]. The evaluation criteria encompasses the technical, economic, environmental and social aspects of the systems which form the basis for judgement [3]. Several methods, which are used in the performance of MCDA, have been discussed in literature. Elimination Choice and Translation Reality (ELECTRE), Preference Ranking Organization Method for Evaluations Enrichment (PROMETHEE), Analytical Hierarchy Process (AHP), Weighted Aggregate Sum Product Assessment (WASPAS), Grey Relation Method, Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), Complex Proportional Assessment (CORPAS), Z-numbers, VIKOR, Strength, Weaknesses, Opportunities and Threats (SWOT) and MCDA Combined Fuzzy Method are some of the methods which have been presented [8–28]. However, in scenarios where comprehensive evaluations of the systems are desired, TOPSIS has been earmarked as the ideal method. TOPSIS draws strength from its ability to perform analyses using varied energy system alternatives and different criteria without overlaps at any point in the evaluation. This technique performs its evaluation by determining the relative closeness of each energy alternative to the ideal energy solution [9]. Ideally, the best energy system is supposed to have the shortest Euclidean distance from the ideal solution and the longest Euclidean distance from the negative ideal solution [9,21]. This general subject of sustainability analysis of RE systems using TOPSIS has never been dearth of research as a number of studies have been reported. To begin with, Diemuodeke et al [19] used TOPSIS to identify a best hybrid energy system among Diesel-PV-Wind, Diesel-Wind, Diesel-PV and Diesel-Battery energy systems for Nigeria’s coastal regions. Ranking of hydropower, geothermal, biofuel, hydrogen, wind and solar power generation systems using TOPSIS was also done in Turkey [22]. Another study by [23] capitalized on this technique to size hybrid solar PV-wind RE systems. TOPSIS was also used alongside SWOT method to identify an ideal sustainable energy alternative among large hydro, small hydro, wind, solar PV, concentrating solar, geothermal and biomass [20]. In order support policy formulation for energy planning, one study also used TOPSIS to evaluate the sustainability of 33 electricity generation systems [24]. In related work, TOPSIS and AHP were used to evaluate and select the best system among solar, wind, geothermal and biomass energies [25]. A multi-site approach to energy supply systems’ selection was also presented for Nigerian cities of Benin, Warri, Yenagoa, Port Harcourt, Uyo and Calabar [26]. The study used HOMER optimization results and TOPSIS algorithm to perform an optimal system assessment among eight energy alternatives namely; diesel, PV-battery, diesel-battery, wind-battery, PV-diesel-battery, wind-diesel-battery, PV-wind-diesel and PV-wind-diesel-battery [26]. This study therefore sought to apply the TOPSIS method to evaluate RE system alternatives, which were identified for rural electrification in Malawi. The systems were established through HOMER simulations. The study was based on three villages of Chigunda (CH), Mdyaka (MD) and Kadzuwa (KA).

2. Description of Study Locations

The three villages considered in this study are Chigunda, Mdyaka and Kadzuwa located in three different geographical regions of Malawi. Chigunda is located in Nkhotakota district in the central region of Malawi and lies within the geographical coordinates 12° 25’ 34.7” S and 034° 01’ 06.7” E. Mdyaka is in Nkhata Bay district in the northernregion and lies along 11° 47’ 09.5” S and 034° 13’ 39.0” E. Kadzuwa is a village in Thyolo district in the southern region of Malawi and lies within 15°59’ 48.4” S and 035° 15’ 01.5” E. The annual average wind speeds and solar irradiation (GHI) are presented in color maps as shown Figure 1.

Figure 1.Solar and wind resource maps for the study locations in Malawi

3. Methodology

Five hybrid energy system alternatives were considered in the MCDA process. These are namely; PV-Battery (PB), PV-Wind-Battery (PWB), PV-Diesel-Battery (PDB), Wind-Diesel-Battery (WDB) and PV-Wind-Diesel-Battery (PWDB). The energy systems and their representative performance scores, which were put under microscope, were derived from HOMER’s optimization results. The evaluation of the energy alternatives in this study proceeded in the subsequent stages.

3.1. Defining the Energy Alternatives and Criteria

In the first step of the evaluation, the energy systems for each village were defined. The evaluation of the systems was based on the technical, economic and environmental criteria or attributes. Under these attributes, twelve sub-attributes or performance indicators were identified to assist in the analysis. The technical criterion was represented by renewable fraction, excess electricity, total system capacity, battery autonomy and total electrical production. Return on investment, simple payback, net present cost (NPC), initial capital cost, operating cost and cost of energy (COE) stood for the economic criterion. For the environmental attribute, the representative sub-attribute was the amount of carbon dioxide (CO₂) emissions. Inherently, some of these sub-criteria have positive and some have negative impact on an energy system. In principle, all costs and emissions have to be kept as low as possible in any energy enterprise and therefore, these were considered as negative [19]. In this regard, NPC, initial capital, COE, operating costs, excess electricity and CO₂ emissions were taken as negative attributes while renewable fraction, total system capacity, battery autonomy, total electricity production, return on investment and simple payback were considered to be positive attributes. The technical, economic and environmental sub-criteria considered in the study are described as follows;Renewable Fraction (%): Quantifiesthe proportionate contribution of renewable power sources in satisfying the load.Excess Electricity (kWh/yr.):This is thesurplus electricity, which must be disposed of because it cannot serve the load or charge the battery storage.Total System Capacity (kW): It relates to the cumulative size of the electricity generation components. Battery Autonomy (hr.): A quantity obtained by calculating the ratio of the total battery size to the total electrical load.Total Electrical Production (kWh/yr.): Represents the total amount of generated electrical energy in a year obtained through aggregation of individual component’s contribution.Return on Investment (%):Compares the yearly savings in costs to the initial investment which was made.Simple Payback (yrs.): The time taken to recoup the initially invested amount of money.Net Present Cost (US$): This is the sum of the present value of installation and operation costs of an energy system over the course of its lifetime less the generated revenue over the same period. Initial Capital Cost (US$): Total cost of installing an energy system when the project is being rolled out. Operating Costs (US$/yr.): The difference between the total costs and revenues incurred in a year and the initial capital costs.Cost of Energy (US$/kWh): The cost of producing 1 kWh of electricity.CO₂ Emissions (kg): Yearly amount of carbon dioxide emissions resulting from operating an energy system. The optimization results from HOMER, which guided this work, are outlined in Table 1, Table 2 and Table 3.

Table 1. HOMER Optimization Results for Chigunda

Table 2. HOMER Optimization Results for Mdyaka

Table 3. HOMER Optimization Results for Kadzuwa

3.2. Formulation of the Decision Matrix

Central to the sustainability evaluation of RE alternatives using TOPSIS was the formulation of a deterministic decision matrix with m energy alternatives and n criteria. The matrix members, x_ij, were perceived as the energy systems’ performance scores linking the energy alternatives to their criteria [9]. Specifying the scores for each sub-criterion resulted into matrices, which took the form of equation (1).

(1)

In equation (1), C₁ to C_n represent the criteria or sub-criteria and A₁ to A_m stand for the energy alternatives under study and x₁₁ to x_ijindicate the scores.

3.3. Normalization of the Decision Matrix

The decision matrices were then subjected to normalization. This procedure helped in getting rid of the measurement units associated with the sub-criteria so that the analyses proceeded with dimensionless quantities [21]. This was done by using equation (2).

(2)

3.4. Generating Weighted Normalized Matrix

Weighted normalized matrices were generated using equation (3) through multiplication of the normalized decision matrix by the sub-criteria weights, which were determined using the Analytical Hierarchy Process (AHP). AHP generated sub-criteria weights (w_i) by comparing two sub-criteria at a time on a judgemental scale of 1-9. This was done in order to determine ranks which depicted how each sub-criterion was affecting an energy system [27].

(3)

The sub-criteria weights, which were used in all the villages, are presented in Table 4. As a general rule, the sum of the weights should be equal to 1 [28].

Table 4. Sub-criteria Weights from AHP Analysis

From the data presented in Table 4, it is evident that the determination of suitable energy systems was being influenced by the total system capacity designated to meet the communities’ electricity demand as this quantity was carrying more weight. However, net present, initial capital and operating costs were also having an impact.

3.5. Determination of Ideal and Negative Ideal Solutions

In order to determine the ideal (A^*) and negative ideal (A^-) solutions, equation 4 and 5 were employed respectively.

(4)

(5)

In equation (4), A^*

Ep Evaluator Alternative Definition

represents the ideal solution which is basically a set generated by choosing a largest member in each weighted normalized matrix’s row for the positive criteria and the smallest member in each weighted normalized matrix’s row for the negative criteria and combining them to form a single set [19]. To the contrary, A^-in equation (5) stands for the negative ideal solution obtained by choosing a smallest member in each weighted normalized matrix’s row for the positive criteria and the largest member in each weighted normalized matrix’s row for the negative criteria and combining them to form a single set [19].

3.6. Calculation of the Relative Separations

The Euclidean distances of each energy alternative from the ideal and negative ideal solutions were calculated by applying equation 6 and 7 respectively;

(6)

(7)

Whereby S_i^* refers to the separation of each energy alternative to the ideal solution and S_i^-

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is the separation of each energy alternative to the negative ideal solution [28].

3.7. Determination of Relative Closeness of each Energy Alternative to the Ideal Solution

Ep Evaluator 12

The relative closeness of each energy alternative to the ideal solution was computed through application of equation (8).

(8)

From equation (8), C_iis the relative closeness of an energy alternative to the ideal solution (A^*

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).

4. Results

The results on the realization of the optimal energy system configurations suitable for deployment in Malawi’s rural communities of Chigunda, Mdyaka and Kadzuwa are presented. The results are based on TOPSIS’s step-wise matrix calculations, which were done in Microsoft Excel program.

4.1. Resultant Decision Matrices

Consolidation of the energy system alternatives and the sub-criteria values led to the formulation of decision matrices depicted in Tables (1,2,3).

4.2. Weighted Normalized Decision Matrices

The normalized values for the weighted decision matrices emanating from the systems’ characteristics for the three villages are presented graphically in Figures (2,3,4).

Figure 2. Normalized System Characteristics for Chigunda

Figure 3.Normalized System Characteristics for Mdyaka

Figure 4. Normalized System Characteristics for Kadzuwa

4.3. Ideal and Negative Ideal Solutions

The ideal and negative ideal solutions for each village are presented in Table 5. The sub-criteria have been identified with their respective positive or negative impacts on the energy systems.

Table 5. Ideal and Negative Ideal Solutions for Chigunda, Mdyaka and Kadzuwa

4.4. Relative Separations

The relative separations of each energy alternative from the ideal and negative ideal solutions are illustrated in Table 6 for Chigunda, Mdyaka and Kadzuwa.

Table 6. Ideal and Negative Ideal Solutions

4.5. Closeness to Ideal Solutions

The relative closeness to ideal solution values (C_i) for each energy alternative for each village have been presented diagrammatically in the radar plot in Figure 5.

Figure 5. Ci values’ Radar Plot

5. Analysis and Discussion

The goal of this study was to identify an energy system for each village with the shortest Euclidean distance to the ideal solution (a system having a C_i value close to 1). In this regard, with reference to Figure 1, it is apparent that the system befitting deployment in Chigunda is the PV-Wind-Battery. This is so because this configuration has the closest distance to the ideal solution as evident by its C_i value of 0.749. This is seconded by the PV-Battery combination with a C_ivalue of 0.746. For Mdyaka, Figure 5 shows that the PV-Battery system is the best as it has the highest C_ivalue of 0.708. Following it is the PV-Wind-Battery configuration with a C_ivalue of 0.696. Finally, Figure 5 also indicates that for Kadzuwa, the PV-Battery configuration fits the assessment criteria by having a C_ivalue of 0.717. It is seconded by the PV-Wind-Battery configuration with C_ivalue of 0.715. One importation observation worth noting however is the absence of the diesel generator component in the ideal system configurations in all the villages. This is largely due to the high environmental footprint manifested by higher CO₂ emissions associated with the combustion of diesel and the higher operation costs, which come along with diesel generator usage. Viewing these results from a broader perspective, it can be hypothesized that on the overall, the PV-Wind-Battery system, which suit deployment in Chigunda, is the overall ideal solution for all the villages as it has the highest C_i value among all the systems under investigation. Extending the scope of comparison also reveals notable variabilities with the results from literature. To begin with, in a study which set out to identify a suitable system among Diesel-PV-Wind, Diesel-Wind, Diesel-PV and Diesel-Battery under technical, economic and environmental criteria, it was established that the ideal system was the Diesel-PV-Wind which had a C_i value of 0.489 Diemuodeke et al [19]. This indicates some disparity with the results of the current study as not all the suitable energy alternatives contain a diesel generator component and the C_i value is lower when compared to those for all the suitable energy systems established by this study. In comparison with the results of the study by Diemuodeke et al [26], it is observed that for Benin, Yenagoa and Port Harcourt cities, the PV-wind-diesel-battery configuration was the suitable system with respective C_i value of 0.7226, 0.727759 and 0.728202. For Warri, Uyo and Calabar, the ideal system for deployment consisted of PV-wind-battery combination and had respective C_i values of 0.70036, 0.706276 and 0.685015. From these findings, it can also be observed that the findings for Benin, Yenagoa and Port Harcourt portray contrasting opinions with the results in this study based on both the ideal systems for deployment and the magnitudes of the C_i values. Much as the results on the optimal system configuration for Warri, Uyo and Calabar cities resonate well with the findings for Chigunda, the magnitudes of the C_i values are different. These differences in the findings are however inevitable due to the fact that different numbers of energy alternatives, criteria and sub- criteria were used and the magnitudes of the weights were also different.

6. Conclusions

The study aimed at establishing the optimal systems for deployment in rural areas of Malawi namely; Chigunda, Mdyaka and Kadzuwa. This was achieved with the aid of the TOPSIS algorithm, which is under the Multi-Criteria Decision Analysis. Five hybrid renewable energy systems were evaluated based on their ability to meet the technical, economic and environmental criteria. Based on the village-by-village analyses, the following key findings were established:i. For Chigunda, the best system was the one having PV-Wind-Battery components. For Mdyaka, the optimal system comprised PV-Battery components and the same result held for Kadzuwa. ii. System configurations with a diesel generator component were not preferred in the analyses.With regard to the findings of this study, the following conclusions can be made:i. Among several other existing methods for optimal system selection, TOPSIS can also act as powerful tool for evaluation and decision making on system selection. The tool can also be used to validate findings obtained when using different approaches. ii. The multiplicity and multi-dimensional nature of TOPSIS qualifies it to be an effective energy planning tool for RE systems as the research has managed to establish the suitable energy systems for multiple locations through elimination of unfeasible systems.

ACKNOWLEDGEMENTS

Ep Evaluator Training

The authors wish to acknowledge the Africa Center of Excellence in Phytochemical, Textiles and Renewable Energy (ACEII-PTRE) at Moi University for the financial support rendered towards this research.

References

[1]	International Energy Agency, “World Energy Outlook: Chapter 1 - Introduction and Scope,” 2017.
[2]	IRENA, “Off-grid renewable energy solutions,” 2018.
[3]	REN21, “Renewables 2019 Global Status Report,” Paris: REN21 Secretaria, 2019.
[4]	World Bank, “The World Bank Malawi-Electricity Access Project (P164331),” pp. 1–18, 2019.
[5]	E. Unyolo, “Renewable Energy Mini-grids in Malawi: Status; Barriers and Opportunities,” 2018.
[6]	T. Urmee, D. Harries, and A. Schlapfer, “Issues related to rural electrification using renewable energy in developing countries of Asia and Pacific,” Renew. Energy, vol. 34, no. 2, pp. 354–357, 2009.
[7]	P. Bertheau, J. Dionisio, C. Jütte, and C. Aquino, “Challenges for implementing renewable energy in a cooperative-driven off-grid system in the Philippines,” Environ. Innov. Soc. Transitions, no. December 2018, pp. 0–1, 2019.
[8]	J. Wang, Y. Jing, C. Zhang, and J. Zhao, “Review on multi-criteria decision analysis aid in sustainable energy,” Renew. Sustain. Energy Rev., vol. 13, pp. 2263–2278, 2009.
[9]	E. Lozano-minguez, A. J. Kolios, and F. P. Brennan, “Multi-criteria assessment of offshore wind turbine support structures,” Renew. Energy, vol. 36, no. 11, pp. 2831–2837, 2011.
[10]	A. Kumar et al., “A review of multi criteria decision making (MCDM) towards sustainable renewable energy development,” Renew. Sustain. Energy Rev., vol. 69, no. October 2016, pp. 596–609, 2017.
[11]	M. O. Babatunde and D. E. Ighravwe, “A CRITIC-TOPSIS framework for hybrid renewable energy systems evaluation under techno-economic requirements,” J. Proj. Manag., vol. 4, pp. 109–126, 2019.
[12]	A. Malik, M. Al Badi, A. Al Kahali, Y. Al Nabhani, A. Al Bahri, and H. Al Barhi, “Evaluation of renewable energy projects using multi-criteria approach,” Proc. 4th IEEE Glob. Humanit. Technol. Conf. GHTC 2014, pp. 350–355, 2014.
[13]	A. Mardani, A. Jusoh, E. K. Zavadskas, F. Cavallaro, and Z. Khalifah, “Sustainable and renewable Energy: An overview of the application of multiple criteria decision making techniques and approaches,” Sustain., vol. 7, no. 10, pp. 13947–13984, 2015.
[14]	R. A. Taha and T. Daim, “Multi-Criteria Applications in Renewable Energy Analysis , a Literature Review,” Res. Technol. Manag. Electr. Ind. Green Energy Technol., pp. 17–31, 2013.
[15]	M. Troldborg, S. Heslop, and R. L. Hough, “Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties,” Renew. Sustain. Energy Rev., vol. 39, pp. 1173–1184, 2014.
[16]	M. Cinelli, S. R. Coles, and K. Kirwan, “Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment,” vol. 46, pp. 138–148, 2014.
[17]	K. Chatterjee and S. Kar, “A Multi Criteria Decision Making for Renewable Energy Selection Using Z Number,” Technol. Econ. Dev. Econ., vol. 24, no. 2, pp. 739–764, 2018.
[18]	R. Attalage and P. Daassanayake, “A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems A hybrid tool to combine multi-objective optimization and multi-criterion decision,” no. May, 2014.
[19]	E. O. Diemuodeke, S. Hamilton, and A. Addo, “Multi-criteria assessment of hybrid renewable energy systems for Nigeria’s coastline communities,” Energy. Sustain. Soc., vol. 6, no. 1, 2016.
[20]	C. Bhowmik, A. Baruah, S. Bhowmik, and A. Ray, “Green energy sources selection for sustainable energy planning using multi-criteria decision-making approach,” IOP Conf. Ser. Mater. Sci. Eng., vol. 377, no. 1, pp. 0–8, 2018.
[21]	V. Lakshmi, T., Venkatesan, “A Comparison of various normalization in techniques for order performance by similarity to ideal solution (TOPSIS),” Int. J. Comput. Algorithm, vol. 3, no. 3, pp. 255–259, 2014.
[22]	Ü. Şengül, M. Eren, S. Eslamian Shiraz, V. Gezder, and A. B. Sengül, “Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey,” Renew. Energy, vol. 75, pp. 617–625, 2015.
[23]	M. Alsayed, M. Cacciato, G. Scelba, and A. Consoli, “Optimal sizing of hybrid power generation systems based on multi criteria decision analysis,” SPEEDAM 2012 - 21st Int. Symp. Power Electron. Electr. Drives, Autom. Motion, vol. 1000, no. 2, pp. 1442–1447, 2012.
[24]	D. Streimikiene, T. Balezentis, I. Krisciukaitien, and A. Balezentis, “Prioritizing sustainable electricity production technologies: MCDM approach,” Renew. Sustain. Energy Rev., vol. 16, no. 5, pp. 3302–3311, 2012.
[25]	H. Al Garni, A. Kassem, A. Awasthi, D. Komljenovic, and K. Al-Haddad, “A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia,” Sustain. Energy Technol. Assessments, vol. 16, pp. 137–150, 2016.
[26]	E. O. Diemuodeke, A. Addo, C. O. C. Oko, Y. Mulugetta, and M. M. Ojapah, “Optimal mapping of hybrid renewable energy systems for locations using multi-criteria decision-making algorithm,” Renew. Energy, vol. 134, pp. 461–477, 2019.
[27]	M. Amer and T. U. Daim, “Selection of renewable energy technologies for a developing county: A case of Pakistan,” Energy Sustain. Dev., vol. 15, no. 4, pp. 420–435, 2011.
[28]	J. R. S. C. Mateo, Multi-Criteria Analysis in the Renewable Industry, First Edit. Springer-Verlag London, 2012.