# A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

**Political will**Usually in developing countries, public service providers and legal systems often lack the firmness and reliability over the medium to long term to institute and enforce policies governing private sector and supportive incentives for the integration of renewable energy. Therefore, the need for governments to give their political backing through the promulgation of energy policies with the necessary regulatory framework that would also encourage the private sector to invest in renewable energy systems for power delivery has become imminent [29]. The components of such policies include provision of long term investment security, drive upgrades of existing national grids and accelerate roll-out of mini-grids, address needs of non-electrical energy forms in productive sectors, energy efficiency improvement measures, and as much as possible, apart from operational and deployment activities, enhance domestic renewable energy technology manufacturing and assembly [30].**Resource assessment and suitability**The availability and suitability of any resource ensures that there is sufficient generating capacity to support the demand for targeted level of reliability with minimal cost. Hence carrying out critical assessment to determine the capacity factor of variable, uncertain and spatially diverse renewable energy resource using well-known methods, such as the Weibull distribution for wind assessment [31], is key to establishing the project viability. Data collection and identification of potential constraints that includes assessing of climatic factors, biographical factors, and geographical features such as altitude, temperature, densed urban areas, protected areas, large water bodies, etc. are also essential components in determining the suitability of the project [32].**Social and environmental sustainability assessment**This aspect involves the identification of mitigation measures that will not affect the livelihood of the project area negatively; keeping renewable harvest rates within regeneration rates; keeping emissions to a bare minimum; ensuring that sources of raw materials needed by humanity should not be exhausted for electricity generation especially with regards biomass and hydro power generation; ensuring job creation [33]; and ensuring that livelihood of the inhabitants of the project area is improved.**Technology assessment**Renewable Energy Technologies (RETs) exist in a wide range of options for electricity generation; wind power generation can either be onshore or offshore; solar energy has solar heating, Concentrated Solar Power (CSP) and PV; hydro power has reservoir and run-of-the-river; and biomass power has combustion plants, biogas technology, grate technology, Bubbling Fluidized Bed (BFB) and Circulating Fluidized Bed (CFB), etc. This implies that selection of the preferred technology based on the resources available requires careful considerations ranging from type, fuel flexibility, load ramping capability, investment cost, and plant size [34] and thus the adoption of Clean Energy Technology Assessment Methodology (CETAM) [35].**Economic assessment**Many tools designed to allow policy makers to assess Cost Of Energy (COE) and Levelised Cost Of Energy (LCOE), cost-based incentive rates can be employed across global, regional, local, and project bases; National Renewable Energy Laboratory (NREL) models for economic evaluation of energy systems can be adopted; System Advisor Model (SAM); Cost of Renewable Energy Spreadsheet Tool (CREST), Job and Economic Development Impact (JEDI) Model, etc. [36].

#### 2.1. Study Area

#### 2.2. Resource Assessment

#### 2.2.1. Wind Resource Assessment

**D1**) and (

**D2**) respectively, along the proposed 225 kV as shown in Figure 4. Figure 5a,b shows the wind speed distribution for the two locations at 100 m hub height. Deployment of the renewable energy technology closer to the proposed infrastructure, denoted as (

**B**) in Figure 4, will greatly reduce overall grid connection costs. Point (

**C**) in the figure, denotes the proposed distance (within 30 km) from the existing and proposed grids to install the technologies. Among the many methods known for wind assessment, Rayleigh and Weibull distributions have been widely used to describe the wind speed distribution [44,45]. The literature [44] also highlighted on the versatility of the 2-parameter Weibull distribution method and referenced the work of many researchers that have used it for wind profile characterization of any site. Hence, in this study, we have employed this method to carry out sensitivity analysis to determine the (

**c**) and (

**k**) values of the sites (

**D1**) and (

**D2**).

#### 2.2.2. Solar Resource Assessment

#### 2.2.3. Biomass Resource Assessment

## 3. Configuration and Scheme of Hybrid Systems

## 4. Problem Formulation

#### 4.1. Physical System Criterion

#### 4.1.1. Wind System Model

#### 4.1.2. PV System Model

#### 4.1.3. Biomass System Model

#### 4.1.4. DG System Model

#### 4.1.5. BESS Model

#### 4.2. Economic System Criterion

#### 4.2.1. Wind System Model

#### 4.2.2. PV System Model

#### 4.2.3. Biomass System Model

#### 4.2.4. DG System Model

#### 4.2.5. BESS System Model

#### 4.3. Environmental Criterion

#### 4.4. Hybrid Systems Reliability Criterion

- If the generated power, ${P}_{Gen}$, from the injected renewables (PV and Wind) is greater than the load demand, ${P}_{L}$, for a given time (t), the excess power is used to charge the battery bank as shown in Equation (19), until the $SOC\left(t\right)=SO{C}_{max}$. The new excess power, $EPG$, is unused.
- If ${P}_{L}$ is greater than the energy generated from PV and wind injection, the deficiency of power, $DPS$, is calculated and biomass power, ${P}_{BM}$, is used to meet the deficit until the maximum intended capacity of ${P}_{BM}$ is obtained. If the deficit is less than the intended minimum injection of biomass ${P}_{B{M}_{min}}$, then the capacity of ${P}_{BM}$ is taken to be ${P}_{B{M}_{min}}$. However, if the deficit is greater than the intended maximum biomass power, ${P}_{BM}$, then the capacity of biomass power is taken to be ${P}_{B{M}_{max}}$. The new deficit is calculated and diesel generation, ${P}_{DES}$, is used to meet the deficit. The minimum and maximum diesel generation, ${P}_{DE{S}_{min}}$ and ${P}_{DE{S}_{max}}$, respectively, are used to constrain the diesel generation in the same manner as described for the biomass injection.
- When the calculated ${P}_{DES}\ge {P}_{DE{S}_{max}}$, this implies there is still deficit. In this case the battery bank is discharged according to Equation (20) to satisfy the demand. If the $SOC\left(t\right)<SO{C}_{max}$ the new deficit, $DPS\left(t\right)$, is calculated. Otherwise, $EPG\left(t\right)$ is calculated. The excess and deficient power for any given hour, t, are thus expressed as follows:

#### 4.5. Summary of Objectives

## 5. Case Study and Discussion of Results

#### 5.1. Annual Avoided CO${}_{2}$ Emissions on Varying Reliability Options for Each Combination

#### 5.2. LCC Outcomes on Varying Reliability Options for Each Combination

## 6. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## Abbreviations

k | unitless Weibull shape parameter. |

c | Weibull scale parameter. |

v | observed wind speed. |

$f\left(v\right)$ | probability of occurrence. |

${v}_{max}$ | maximum wind speed. |

${v}_{mod}$ | maximum frequency of wind speed. |

${\rho}_{a}$ | air density. |

A | swept area. |

$\gamma $ | gamma function. |

${P}_{w}$ | output power of wind tubine. |

${P}_{r}$ | rated power of turbine. |

${V}_{ci}$ | cut-in wind speed. |

${V}_{r}$ | rated wind speed. |

${V}_{CO}$ | cut-off wind speed. |

${P}_{w,avg}$ | average output power of wind turbine. |

$C{F}_{w}$ | capacity factor. |

${P}_{pv}$ | generated power from PV panel. |

${P}_{pvr}$ | PV generator capacity. |

${G}_{t}$ | solar radiation data set. |

${G}_{std}$ | standard solar radiation (1000 W/m${}^{2}$). |

${R}_{c}$ | radiation threshold (180 W/m${}^{2}$). |

$C{F}_{pv}$ | capacity factor of PV. |

${T}_{y}$ | total number of hours in a year. |

${\mu}_{{G}_{t}}$ | mean value of the solar radiation data set. |

${\sigma}_{{G}_{t}}$ | standard deviation of the solar radiation data set. |

${Q}_{Bagg}$ | amount of sugarcane bagasse (tons) available for energy production. |

${S}_{ann}$ | annual production of sugarcane. |

${K}_{RB}$ | ratio of sugarcane baggase to primary sugarcane. |

${K}_{AC}$ | accessibility coefficient. |

${K}_{HC}$ | harvest coefficient. |

${K}_{UF}$ | unused fraction. |

${C}_{p}$ | power coefficient. |

${\eta}_{w}$ | efficiency of the wind turbine. |

${A}_{w}$ | total area swept by the wind turbine blades. |

${V}_{H}$ | wind speed at 100 m hub height. |

${V}_{{H}_{R}}$ | wind speed at 10 m height. |

${H}_{R}$ | reference hub height (10 m). |

$\alpha $ | roughness length index. |

${A}_{pv}$ | the total area of PV system. |

${\eta}_{pv}$ | efficiency of the PV module. |

${P}_{BM}$ | plant capacity of biomass. |

$NC{V}_{Bagg}$ | net calorific value of baggase. |

${\eta}_{CFB}$ | efficiency of the biomass plant. |

T | yearly operating time. |

${P}_{DES}$ | diesel generation. |

${Q}_{DG}$ | total number of DG units. |

${N}_{DG}$ | minimum plant unit of diesel generator. |

$SOC\left(t\right)$ | state of charge of battery. |

${\delta}_{h}$ | hourly self discharge |

${P}_{L}\left(t\right)$ | load demand. |

${\eta}_{I}$ | efficiency of inverter. |

${\eta}_{bc}$ | battery charging efficiency. |

${\eta}_{bd}$ | battery discharge efficiency. |

$SO{C}_{min}$ | minimum SOC. |

$SO{C}_{max}$ | maximum SOC. |

$DOD$ | depth of discharge. |

${\alpha}_{w}$ | initial cost of turbine. |

${C}_{W}$ | captial cost of investment. |

$O{M}_{NP{V}_{w}}$ | net present value of annual operation and maintenance cost of wind turbine. |

${\alpha}_{O{M}_{w}}$ | operation and maintenance cost of wind tubine. |

NPV | net present value |

${S}_{NP{V}_{w}}$ | NPV of the resale price of wind turbine. |

${s}_{w}$ | resale price. |

${S}_{{w}_{Tot}}$ | total cost recovered from resale. |

N | total yearly running hours. |

$LC{C}_{w}$ | LCC of wind power system. |

${R}_{NP{V}_{w}}$ | NPV of the replacement cost of wind turbine. |

${C}_{Bag{g}_{Tot}}$ | total cost of supplying the baggase at the plant site. |

${C}_{Baggase}$ | cost of baggase. |

${C}_{storage}$ | cost of storage. |

${C}_{loading}$ | cost of loading. |

${C}_{transport}$ | tansport cost. |

${C}_{BM}$ | capital cost of investment of biomass plant. |

${\alpha}_{BM}$ | initial cost of biamass plant. |

$O{M}_{NP{V}_{BM}}$ | NPV of the total operation and maintenance cost. |

${\mu}_{BM}$ | annual growth rate of the BM cost. |

${\mu}_{BM}$ | annual growth rate of the BM cost. |

$O{M}_{BM}$ | annual operation and maintenance cost of BM. |

${S}_{NP{V}_{BM}}$ | NPV of the resale price of biomass plant. |

${S}_{B{M}_{Tot}}$ | total cost recovered from resale. |

${\delta}_{BM}$ | initial cost of biomass plant. |

$LC{C}_{BM}$ | life cylce cost of biomass power plant. |

$O{M}_{NP{V}_{BM}}$ | NPV of the total operation and maintenance cost of biomass plant. |

${R}_{NP{V}_{BM}}$ | NPV of the replacement cost of biomass plant. |

${C}_{DG}$ | capital cost of the DG power plant. |

${\alpha}_{DG}$ | initial cost of DG. |

$O{M}_{NP{V}_{DG}}$ | NPV of the total operation and maintenance cost of DG. |

$O{M}_{DG}$ | operation and maintenance cost of DG. |

${\mu}_{DG}$ | annual growth rate of the DG cost. |

${S}_{NP{V}_{DG}}$ | NPV of the resale price of DG. |

${S}_{D{G}_{Tot}}$ | total resale price of DG at the end of the project life. |

${\delta}_{DG}$ | initial cost of DG plant. |

$LC{C}_{DG}$ | life cylce cost of DG. |

${C}_{DG}$ | capital cost of investment of DG. |

$O{M}_{NP{V}_{DG}}$ | NPV of the total operation and maintenance cost of DG. |

${R}_{NP{V}_{DG}}$ | replacement cost of DG. |

${C}_{Bat}$ | captial cost of battery system. |

${\alpha}_{Bat}$ | initial cost of battery system. |

${C}_{b}$ | nominal capacity of battery. |

${R}_{NP{V}_{Bat}}$ | replacement cost of battery. |

${\delta}_{Bat}$ | initial cost of battery. |

$LC{C}_{Bat}$ | life cylce cost of battery. |

${C}_{Bat}$ | capital cost of investment of battery. |

$O{M}_{NP{V}_{Bat}}$ | NPV of the total operation and maintenance cost of battery. |

${S}_{NP{V}_{Bat}}$ | NPV of the resale price of battery energy storage. |

${P}_{Renewable}$ | the total renewable generation. |

${Q}_{F,i}$ | quantity of the ith fuel burnt annually. |

$WE{F}_{F,i}$ | weighted average emission factor of the ith fuel. |

$NC{V}_{i}$ | weighted average net calorific value. |

$E{F}_{C{O}_{2,i}}$ | weighted average CO${}_{2}$ emissions factor. |

$An{n}_{C{O}_{2},Avoided}$ | Avoided amount of CO${}_{2}$ emission. |

${R}_{emss}$ | reference emissions. |

${B}_{emss}$ | block emissions. |

$EPG$ | new excess power. |

${P}_{Gen}$ | generated electric power. |

$DPS$ | deficiency of power. |

MOPSO | multi-objective particle swarm optimization. |

DG | diesel generator. |

DEF | diesel energy fraction. |

DPSP | defficiency of power supply probability. |

LCC | life cycle cost. |

PV | photovoltaic. |

RDG | renewable distributed generator. |

GHG | green house gas emissions. |

GA | genetic algorithm. |

PSO | particle swarm optimization. |

RET | renewable energy technology. |

CFB | circulating fluidized bed. |

WAPP | west african power pool. |

AfDB | african development bank. |

GHI | global horizontal irradiance. |

SSA | sub-saharan africa. |

HFO | heavy fuel oil. |

DO | diesel oil. |

## Appendix A

Fuel Consumption for Existing DG units Considered | |||
---|---|---|---|

DG unit | Fuel Operation | Number of Units | Consumption (l/h) |

A | Diesel Oil | 20 | 240 |

B1 | Diesel Oil | 3 | 350 |

B2 | Diesel Oil | 5 | 240 |

K | Heavy Fuel oil | 2 | 700 |

Diesel Oil | 620 | ||

L | Heavy Fuel oil | 3 | 470 |

Diesel Oil | 430 | ||

N1 and N2 | Heavy Fuel oil | 2 | 1024 |

Diesel Oil | 981 | ||

W1 and W2 | Heavy Fuel oil | 2 | 1300 |

Diesel Oil | 1230 | ||

M | Diesel Oil | 2 | 300 |

LO | Diesel Oil | 1 | 300 |

MA | Diesel Oil | 1 | 240 |

Physical and Environmental Parameters | |||
---|---|---|---|

Technology Type | Variable | Notation | Value |

Wind Turbine GAMESA G128-5.0 MW/G132-5.0 MW | Rated Power | ${P}_{r}$ (kW) | 5000 |

Cut-in speed | ${V}_{c}$ (m/s) | 1.5 | |

Rated Speed | ${V}_{r}$ (m/s) | 13 | |

Cut-off speed | ${V}_{co}$ | 27 | |

$HubHeight$ | H (m) | 100 | |

Wind Turbine lifetime | ${L}_{W}$ | 20 | |

PV Panel Sun Power X Series | Maximum Power | ${P}_{PV,max}$ (W) | 360 |

Efficiency of Panel | ${\eta}_{PV}$ | 22.2 | |

Area of PV panel | ${A}_{P{V}_{P}}$ (m${}^{2}$) | 1.63 | |

PV lifetime | ${L}_{PV}$ | 20 | |

Biomass CFB Combustion Plant | Net calorific value of Baggase | $NC{V}_{Bagg}$ (MJ/Kg) | 16 |

Baggase Emissions Factor | $E{F}_{C{O}_{2},Bagg}$ (mmBtu/kg) | 0.0161 | |

Efficiency of Plant | ${\eta}_{CFB}$ | 0.42 | |

Lifetime of Biomass plant | ${L}_{BM}$ | 20 | |

Diesel Generator (DG) Nigatta Dual Fuel Diesel Plant | Unit Plant Capacity | ${N}_{DG}\left(MW\right)$ | 10,000 |

Lifetime of DG plant | ${L}_{DG}$ | 20 | |

Net calorific value of Heavy Fuel Oil (HFO) | $NC{V}_{HFO}$ (mmBtu/gal) | 0.15 | |

Net calorific value of Diesel Oil (DO) | $NC{V}_{DO}$ (mmBtu/gal) | 0.148 | |

HFO Emissions Factor | $E{F}_{HFO,C{O}_{2}}$ (kgCO${}_{2}$/mmBtu) | 75.1 | |

DO Emissions Factor | $E{F}_{DO,C{O}_{2}}$ (kgCO${}_{2}$/mmBtu) | 74.92 | |

Battery Bank Lithium Ion | Hourly Self Discharge | $\delta $ | 0 |

Battery charging efficiency | ${\eta}_{bc}$ | 0.9 | |

Battery Discharging efficiency | ${\eta}_{bd}$ | 0.9 | |

Nominal Capacity of Battery (kWh) | ${C}_{B}$ | 1200 | |

Lifetime of Battery Bank | ${L}_{Bat}$ | 10 | |

Economic Parameters | |||

Project lifetime | N | 20 | |

Interest rate | i (%) | 10 | |

Inflation rate | $\delta $ (%) | 4 | |

Escalation rate | $\mu $ (%) | 5 | |

Inverter efficiency | ${\eta}_{I}$ (%) | 90 | |

Wind Turbine | Capital cost of Wind Turbine | ${C}_{W}$ ($/m${}^{2})$ | 544 |

Yearly Operations and Maintenance Cost | ${\alpha}_{O{M}_{w}}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{W})$ | 1.5 | |

Reselling Price | ${s}_{w}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{W})$ | 30 | |

PV Panel | Capital cost of PV Panel | ${C}_{PV}$ ($/kW) | 519.7 |

Yearly Operations and Maintenance Cost | ${\alpha}_{O{M}_{PV}}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{PV})$ | 1 | |

Reselling Price | ${s}_{pv}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{PV})$ | 25 | |

Biomass Plant | Capital cost of Biomass Plant | ${C}_{BM}$ ($/kW) | 1440 |

Cost of Bagasse | ${C}_{baggase}$ ($/ton) | 25 | |

Cost of Storage | ${C}_{storage}$ ($/ton) | 12 | |

Cost of loading | ${C}_{loading}$ ($/ton) | 5 | |

Cost of Transportation | ${C}_{transport}$ ($/ton/km) | 0.057 | |

Yearly Operations and Maintenance Cost | ${\alpha}_{O{M}_{BM}}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{BM})$ | 0.017 | |

Reselling Price | ${s}_{bm}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{BM})$ | 30 | |

Diesel Generator | Capital cost of DG plant | ${C}_{DG}$ ($/kW) | 1000 |

Cost of HFO | ${C}_{HFO}$ ($/litre) | 0.45 | |

Cost of DO | ${C}_{DO}$ ($/litre) | 0.607 | |

HFO Consumption | ${Q}_{HFO}$ (litre/hour) | 1024 | |

DO Consumption | ${Q}_{DO}$ (litre/hour) | 981 | |

Yearly Operations and Maintenance Cost | $\${\alpha}_{O{M}_{DG}}$ ($/kWh) | 0.032 | |

Reselling Price | ${s}_{dg}\phantom{\rule{3.33333pt}{0ex}}(\%of{C}_{DG})$ | 30 | |

Battery Bank | Capital Cost of Battery | ${C}_{DG}$ ($/kW) | 283 |

Replacement Cost | ${R}_{Bat}$ | - |

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**Figure 5.**Assessment of wind farm locations. (

**a**) D1 wind speed in the Northern region. (

**b**) D2 wind speed in the Southern region.

**Figure 6.**Assessment of wind farm locations continued. (

**a**) Mean daily wind speeds of both locations. (

**b**) Weibull probability density function. (

**c**) Cumulative probability density function. (

**d**) Turbine power curve for both locations.

**Figure 7.**Global horizontal irradiation and PV suitability zone [52].

**Figure 8.**Assessment of solar farm locations. (

**a**) D1 solar irradiation in the Northern region. (

**b**) D2 solar irradiation in the Southern region.

**Figure 10.**Annual sugarcane production [57].

**Figure 17.**Life Cylce Cost (LCC) values evaluated against Defficiency of Power Supply Probability (DPSP).

No. | Source | Capacity (MW) | Location |
---|---|---|---|

Existing Sources | |||

1 | Bumbuna Hydro | 50 | Bumbuna |

2 | Goma Hydro | 6 | Kenema |

3 | Charlotte Hydro | 2 | Western Area |

4 | Bankasoka Hydro | 2 | Port Loko |

5 | Diesel (Goverment) | 27.6 | Western Area |

6 | Diesel (Goverment) | 26.7 | Provincial |

7 | Diesel (IPP1) | 20 | Western Area |

8 | Diesel (IPP3) | 4.8 | Provincial |

9 | Addax Bio-energy | 15 | Makeni(Low availability) |

10 | Total Diesel | 139.1 | |

11 | Total Hydro | 60 | |

12 | Total Biomass | 15 | |

13 | Total Generation | 154.1 | |

Projected National Mining Power Demand [MW] (Approximated) | |||

1 | Sierra Rutile | 23(15) | Moyamba |

2 | Octea mining | 8 | Kono |

3 | London Mining | 50 | Marampa |

4 | Stella Diamonds | 3 | Tongo |

5 | African minerals/Shandong Iron and steel group | 20 (150 PhaseII) | Tonkolili |

6 | Gold Mining and Others | 20 | Various locations |

7 | Total Expected Generation | 296 | |

Research Scope [MW] | |||

2 | Approximated Industrial Demand | 380 | |

3 | Approximated Commercial Demand | 150 | |

4 | Approximated Domestic Demand | 120 |

Weibull Distribution Parameters | ||||
---|---|---|---|---|

Location | c | k | PD (W/m${}^{\mathbf{2}}$) | CF |

D1 | 7.70 | 2.61 | 531.45 | 31.6 |

D2 | 4.75 | 2.40 | 189.40 | 9.95 |

Biomass Feedstock Parameters | ||||
---|---|---|---|---|

Fuel Type | NCV (MJ/kg) | Bulk Density (kg/m${}^{\mathbf{3}}$) | Ash Content (%Dry Bulk) | Moisture Content (%) |

Baggase briquette | 16.7 | 650 | 6 | 8 |

Corncobs | 14 | 185 | 15 | 14 |

Rice straw | 14 | 100 | 20.25 | 10 |

Palm kernel shells | 18.85 | 450 | 5 | N/A |

BLOCK | Parameter | DPSP | ||||||
---|---|---|---|---|---|---|---|---|

0% | 5% | 10% | 20% | 30% | 40% | 50% | ||

DG Only | $Annual\phantom{\rule{4pt}{0ex}}Additional\phantom{\rule{4pt}{0ex}}C{O}_{2}\phantom{\rule{4pt}{0ex}}Emissions$ | $1.75\times {10}^{6}$ | $1.64\times {10}^{6}$ | 1.53$\times {10}^{6}$ | 1.33$\times {10}^{6}$ | $1.11\times {10}^{6}$ | $9.15\times {10}^{5}$ | $7.21\times {10}^{5}$ |

B1-ABCDE | $1.75\times {10}^{6}$ | $1.48\times {10}^{6}$ | $1.34\times {10}^{6}$ | $8.66\times {10}^{5}$ | $7.46\times {10}^{5}$ | $4.90\times {10}^{5}$ | $5.80\times {10}^{5}$ | |

B3-ABD | $1.75\times {10}^{6}$ | $1.64\times {10}^{6}$ | $1.53\times {10}^{6}$ | $1.33\times {10}^{6}$ | $1.11\times {10}^{6}$ | $9.15\times {10}^{5}$ | $7.21\times {10}^{5}$ | |

B7-ADE | $Annual\phantom{\rule{4pt}{0ex}}C{O}_{2}\phantom{\rule{4pt}{0ex}}Avoided\phantom{\rule{4pt}{0ex}}Emissions\phantom{\rule{4pt}{0ex}}\left(tC{O}_{2}\right)$ | $1.75\times {10}^{6}$ | $1.64\times {10}^{6}$ | $5.76\times {10}^{5}$ | $4.67\times {10}^{5}$ | $6.73\times {10}^{5}$ | $7.47\times {10}^{5}$ | $6.70\times {10}^{5}$ |

B8-AD | $1.75\times {10}^{6}$ | $1.64\times {10}^{6}$ | $1.53\times {10}^{6}$ | $1.33\times {10}^{6}$ | $1.11\times {10}^{6}$ | $9.15\times {10}^{5}$ | $7.21\times {10}^{5}$ | |

B9-BD | $1.75\times {10}^{6}$ | $1.64\times {10}^{6}$ | $1.53\times {10}^{6}$ | $1.33\times {10}^{6}$ | $1.11\times {10}^{6}$ | $9.15\times {10}^{5}$ | $7.21\times {10}^{5}$ |

Block | Parameter | DPSP | ||||||
---|---|---|---|---|---|---|---|---|

0% | 5% | 10% | 20% | 30% | 40% | 50% | ||

DG Only | ${Q}_{DG}$ | 63 | 59 | 55 | 48 | 40 | 33 | 26 |

DEF | 95 | 94.4 | 94 | 93.3 | 92.4 | 91 | 89.3 | |

LCC | $7.02\times {10}^{9}$ | $6.58\times {10}^{9}$ | $6.14\times {10}^{9}$ | $5.35\times {10}^{9}$ | $4.47\times {10}^{9}$ | $3.68\times {10}^{9}$ | $2.89\times {10}^{9}$ | |

B1-ABCDE | ${A}_{PV}$ | - | $2.2\times {10}^{6}$ | $1.5\times {10}^{6}$ | $6.1\times {10}^{5}$ | $1.7\times {10}^{5}$ | $1.3\times {10}^{5}$ | $2.9\times {10}^{4}$ |

${A}_{W}$ | - | $1.3\times {10}^{6}$ | $8.1\times {10}^{5}$ | $7.4\times {10}^{5}$ | $6.0\times {10}^{5}$ | $2.6\times {10}^{5}$ | $4.3\times {10}^{5}$ | |

${Q}_{Bagg}$ | - | $2.5\times {10}^{4}$ | $1.7\times {10}^{4}$ | $4.5\times {10}^{3}$ | $4.5\times {10}^{9}$ | $4.5\times {10}^{9}$ | $7.3\times {10}^{9}$ | |

${Q}_{DG}$ | - | 14.0 | 12.5 | 19.6 | 13.0 | 13.0 | 1.0 | |

${Q}_{Bat}$ | - | 35 | 54.7 | 56.5 | 10 | 10 | 97.2 | |

DEF | - | 7.2 | 13.2 | 37.2 | 37.7 | 55.2 | 22.5 | |

REPG | - | 0.44 | 0.25 | 0.12 | 0.06 | 0.00 | 0.02 | |

LCC | - | $2.23\times {10}^{9}$ | $1.52\times {10}^{9}$ | $1.0\times {10}^{9}$ | $6.2\times {10}^{8}$ | $3.9\times {10}^{8}$ | $3.8\times {10}^{8}$ | |

B3-ABD | ${A}_{PV}$ | $1.5\times {10}^{6}$ | $1.5\times {10}^{6}$ | $1.4\times {10}^{6}$ | $1.5\times {10}^{6}$ | $1.5\times {10}^{6}$ | $7.2\times {10}^{5}$ | $3.9\times {10}^{5}$ |

${A}_{W}$ | $9.0\times {10}^{5}$ | $8.8\times {10}^{5}$ | $8.7\times {10}^{5}$ | $8.2\times {10}^{5}$ | $7.7\times {10}^{5}$ | $8.4\times {10}^{5}$ | $8.4\times {10}^{5}$ | |

${Q}_{Bat}$ | 372.13 | 280.1778 | 118.0276 | 126.0121 | 174.812 | 90.516 | 181.9352 | |

DEF | 18.7 | 18.9 | 19.2 | 19.6 | 20.1 | 22.2 | 23.8 | |

REPG | 0.14 | 0.14 | 0.15 | 0.11 | 0.06 | 0.04 | 0.01 | |

LCC | $1.6\times {10}^{9}$ | $1.5\times {10}^{9}$ | $1.4\times {10}^{9}$ | $1.4\times {10}^{9}$ | $1.4\times {10}^{9}$ | $9.7\times {10}^{8}$ | $8.4\times {10}^{8}$ | |

B7-ADE | ${A}_{PV}$ | - | - | $5.8\times {10}^{6}$ | $5.6\times {10}^{6}$ | $5.35\times {10}^{6}$ | $5.0\times {10}^{6}$ | l$4.5\times {10}^{6}$ |

${Q}_{DG}$ | - | - | 35 | 24 | 11 | 4 | 1 | |

${Q}_{Bat}$ | - | - | 130 | 128 | 125 | 120 | 120 | |

DEF | - | - | 19.8124 | 21.2465 | 15.0873 | 20.9012 | 19.5347 | |

REPG | - | - | 0.86 | 0.66 | 0.77 | 0.14 | 0.10 | |

LCC | - | - | $3.57\times {10}^{9}$ | $3.303\times {10}^{9}$ | $3.12\times {10}^{9}$ | $2.94\times {10}^{9}$ | $2.61\times {10}^{9}$ | |

B8-AD | ${A}_{PV}$ | $4.0\times {10}^{6}$ | $3.9\times {10}^{6}$ | $3.8\times {10}^{6}$ | $3.7\times {10}^{6}$ | $3.4\times {10}^{6}$ | $3.2\times {10}^{6}$ | $3.0\times {10}^{6}$ |

${Q}_{Bat}$ | 773.8 | 577 | 470.8 | 523.2 | 430.8 | 319.5 | 156.3 | |

DEF | 19.6 | 19.8 | 20.2 | 20.9 | 21.7 | 23.0 | 23.9 | |

REPG | 0.01 | 0.06 | 0.07 | 0.01 | 0.00 | 0.01 | 0.01 | |

LCC | $2.8\times {10}^{9}$ | $2.6\times {10}^{9}$ | $2.5\times {10}^{9}$ | $2.4\times {10}^{9}$ | $2.3\times {10}^{9}$ | $2.0\times {10}^{9}$ | $1.8\times {10}^{9}$ | |

B9-BD | ${A}_{W}$ | $1.3\times {10}^{6}$ | $1.3\times {10}^{6}$ | $1.2\times {10}^{6}$ | $1.2\times {10}^{6}$ | $1.1\times {10}^{6}$ | $1.1\times {10}^{6}$ | $1.0\times {10}^{6}$ |

${Q}_{Bat}$ | 840 | 799.3 | 765.7 | 737.8 | 541.8 | 410 | 548.5 | |

DEF | 55.4 | 55.2 | 55.7 | 56.1 | 56.5 | 56.8 | 57.3 | |

REPG | 0.32 | 0.36 | 0.28 | 0.22 | 0.18 | 0.16 | 0.11 | |

LCC | $1.43\times {10}^{9}$ | $1.41\times {10}^{9}$ | $1.3\times {10}^{9}$ | $1.3\times {10}^{9}$ | $1.1\times {10}^{9}$ | $9.8\times {10}^{8}$ | $1.1\times {10}^{9}$ |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Konneh, D.A.; Howlader, H.O.R.; Shigenobu, R.; Senjyu, T.; Chakraborty, S.; Krishna, N.
A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization. *Sustainability* **2019**, *11*, 1188.
https://doi.org/10.3390/su11041188

**AMA Style**

Konneh DA, Howlader HOR, Shigenobu R, Senjyu T, Chakraborty S, Krishna N.
A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization. *Sustainability*. 2019; 11(4):1188.
https://doi.org/10.3390/su11041188

**Chicago/Turabian Style**

Konneh, David Abdul, Harun Or Rashid Howlader, Ryuto Shigenobu, Tomonobu Senjyu, Shantanu Chakraborty, and Narayanan Krishna.
2019. "A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization" *Sustainability* 11, no. 4: 1188.
https://doi.org/10.3390/su11041188