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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, ACCEPTED SEPTEMBER 2015 1

Long-Term Renewable Energy Planning Model forRemote Communities

Mariano Arriaga, Member, IEEE, Claudio A. Cañizares, Fellow, IEEE,Mehrdad Kazerani, Senior Member, IEEE

Abstract—The paper presents a novel long-term RenewableEnergy (RE) planning model for Remote Communities (RCs),considering the characteristics of diesel-based RCs in Canadaand other parts of the world such as Alaska and northern Chile.Over the past few years, there has been a significant increasein assessing and deploying RE projects in northern remotelocations. The model proposed in this paper adds to such effortsby creating a multiple-year community planning tool that can beused to determine economic and technically-feasible RE solutions,considering the current operating structures, electricity pricingsystems, subsidy frameworks, and project funding alternativesunder which RE can be deployed in RCs. The proposed model isimplemented in a case study for the Kasabonika Lake First Na-tion community in northern Ontario. The case study shows thatRE projects can be feasible under current operating conditions,for a set of funding alternatives that share the economic risks.

Index Terms—Microgrids, remote community, renewable gen-eration, power generation planning, microgrid economics, dieselgenerators.

NOMENCLATURE

System parameters

BLTk Loan term [years].CDi Selling or savings electricity applicable rate for

customer i [$/kWh].CPI Customer Price Index.CTi Binary value used for distinguishing between RE

curtailment classes.CFUEL Actual oil-based fuel price [$/litre].ELTk Operation lifetime for equipment k [years].INCi External incentive rate for customer i [$/kWh].IRRmax

i Maximum Internal Rate of Return (IRR) requiredfor a feasible deployment plan [%].

IRRmini Minimum IRR required for a feasible deployment

plan for customer i [%].PDi,t,h

Customer i load on year t at hour h [kW].PDt,h

Community demand on year t at hour h [kW].PFGBASEj,t,h

Required active power for baseline scenario pro-duced by Fuel-based Generator (FG) j on year tat hour h [kW].

PREmk,g,t,hActive power output obtained for each equip-ment unit k combined with complementary unit

This work was supported in part by the National Science and EngineeringResearch Council (NSERC) of Canada and Hatch Ltd., with supporting fund-ing coming from Natural Resources Canada (NRCan) under the ecoENERGYInnovation Initiative program.

The authors are with the Department of Electrical and Computer Engineer-ing, University of Waterloo, Waterloo, ON, N2L 3G1, Canada (e-mail: [email protected]; [email protected]; [email protected]).

g from the respective RE model on year t athour h [kW].

PREmk,t,hActive power output obtained for each equipmentunit k from the respective RE model on year tat hour h [kW].

RCCAPkPresent capital cost for Renewable Energy (RE)equipment k per installed capacity unit [$/kW].

RCOMkPresent Operation and Maintenance (O&M)cost RE equipment k per installed capacityunit [$/kW].

RDi Discount rate for customer i [%].RPmin

t Rated power for the smallest nominal power FGoperating on year t [kW].

RPk Nominal rated power for equipment k [kW].bmaxCB Maximum Capacity Building (CB) reduction per-

centage [%].bmaxICt

Maximum allowable RE installed capacity ratiowith respect to the average annual load on year t.

bCBb Constant coefficient for CB linear equation.bCBm Linear coefficient for CB linear equation.bCBu Number of RE units to be installed to achieve

maximum CB cost reduction.bCEP In-hand capital contribution ratio available at the

start of the project.bCFPt

Percentage of the total fuel consumption suppliedby the community to the utility on year t [%].

bDiCustomer i load ratio of the total communitydemand.

bEFP Project external funding ratio of the total projectcapital cost.

bOMt Annual O&M variation due to technology changeon year t.

dminGS Minimum operation load ratio for FGs.

dGSLIMi,tFG operation limit at which RE is curtailed whenreached [kW].

dGSajLinear fuel consumption coefficient for dieselgenerator j [litre/kW].

dGSbj Constant fuel consumption coefficient for dieselgenerator j [litre].

fBASEt Fuel consumption baseline obtained fromcurrently installed generation equipment onyear t [litre/year].

rB Loan interest rate [%].ri Inflation rate for the applicable electricity tariff

for customer i [%].rFR Percentage revenue obtained by the community

from fuel sales for electricity generation [%].

2 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, ACCEPTED SEPTEMBER 2015

Solar parameters

CPV INVgCapital cost for solar inverter type g [$/unit].

CPV OMkO&M cost for Photovoltaic (PV) equipmenttype k [$/unit].

CPV UkCapital cost per PV solar panel for equipmenttype k [$/unit].

Wind parameters

CWTOMkO&M cost for Wind Turbine (WT)type k [$/unit].

CWTTWRk,gCapital cost for tower type g for WTtype k [$/unit].

CWTUkCapital cost per WT for equipmenttype k [$/unit].

Variables

CBk,t Cost reduction ratio due to capacity building forequipment type k on year t.

CBLi,k,tLoan obtained for purchasing equipment type kfor customer i on year t [$].

CBPMTi,k,tAmortization loan payments for equipmenttype k for customer i on year t [$].

CCAPi,k,tCapital cost of equipment k for customer i in-stalled on year t [$/kW].

CCEi,k,tInitial capital expense contribution for equipmenttype k for customer i on year t [$].

CEFi,k,tExternal funding available for equipment type kfor customer i on year t [$].

COMi,k,tO&M cost of on-site equipment k for customer ion year t [$/kW].

CTEQk,gTotal Net Present Value (NPV) for equipmenttype k using complementary unit type g [$].

Ci,t Total capital and O&M of RE equipment forcustomer i on year t [$/year].

EOSi,k,t Number of RE equipment units on-site for equip-ment k for customer i on year t.

EQCk,t Cumulative number of on-site equipment type kby year t.

IRRi IRR for RE operation for customer i [%].ISi,t Income or saving obtained from RE for cus-

tomer i on year t [$/year].LCk,g Levelized Cost of Energy (LCOE) for equipment

type k using complementary unit type g [$/kWh].LRi,t Community loss opportunity cost from diesel

sell due to RE generation for customer i onyear t [$/year].

PDBi,t,hActive power difference between the availableRE for customer i and the respective load onyear t and hour h [kW].

PFGprojj,t,hRequired active power for RE projects scenariosby FG j on year t at hour h [kW].

PFGt,hRequired active power output from FG plant onyear t at hour h [kW].

PREai,k,t,hRenewable active power available for customer ifor equipment k on year t at hour h [kW].

PREci,k,t,hRenewable active power curtailed for customer ifor equipment k on year t at hour h [kW].

PREui,k,t,hRenewable active power used to supply the loadfor customer i and equipment k on year t athour h [kW].

Wi,t Social welfare for customer i on year t [$/year].fPROJt Fuel consumption obtained when considering RE

projects on year t [litre/year].xPV Pk,g

Integer variable for the number of solar PVpanels connected in parallel for equipment type kwhen connected to inverter type g.

xPV Sk,gInteger variable for the number of solar PVpanels connected in series for equipment type kwhen connected to inverter type g.

xi,k,t Integer variable for the number of RE units to bedeployed for equipment type k for customer i onyear t.

yi,k,u,s Auxiliary variable to represent on-site equipmentk during its operation lifetime.

zi,t,h Renewable active power used ratio for customer ion year t at hour h.

Indices

g Complementary equipment to be used with unitk; g = 1, ..., G.

h Hours in a year; h = 1, ...,H .i Subsidized unsubsidized or avoided fuel cost

customer type; i = 1, ..., I .j FG unit to be considered; j = 1, ..., J .k Solar or wind technology equipment to be con-

sidered; k = 1, ...,K.t Year; t = 1, ..., T .

I. INTRODUCTION

H IGH energy costs, together with technical, environmen-tal, and social aspects, have gradually triggered Re-

newable Energy (RE) assessments, projects, and policies inRemote Communities (RCs). In particular, previous and on-going projects have been paving the way for further REdevelopment in Canada’s northern locations from the Yukon toNewfoundland, (e.g., [1]–[3]), with recent efforts to increasethe access to energy-related information from RCs in order topromote further developments [4], [5]; also, there has beenescalating efforts to assess appropriate equipment, naturalresources, and economic feasibility of such projects (e.g., [6],[7]). While pointing out the potential of RE projects, theseefforts highlight the shortcomings as well as the technicaland economic barriers that are yet to be overcome [5],[8], which include electric energy storage systems that, formedium or high RE penetration levels, are yet to significantlydecrease the overall cost of energy under current deploymentconditions [9], [10]. Furthermore, to properly evaluate thetechnical and economic RE aspects of such projects, from aplanning perspective, there are still limited tools that considerthe specific challenges of RCs; this is the focus of this paper.

There has been significant work regarding RE microgridplanning and sizing considering different model detail levels,

AUTHORS: ARRIAGA, M.; CAÑIZARES, C.A.; KAZERANI,M. 3

objective function(s), and equipment characteristics [11]. De-tailed dynamic models have been proposed to assess feasibilityof RE configurations based on energy availability and equip-ment downtime [12], thus resulting in technically-feasiblesolutions, but not necessarily economically viable. Furtherdeterministic models have been used for economic evaluationsconsidering trade-offs between reliability and cost [13], as wellas proposing multi-objective Interval Linear Programming(ILP) to assess risk levels for different configurations [14].These previous approaches consider significant levels of detailwhile accounting in some cases for some of the uncertaintiesthat result from microgrid sizing; however, the long-term roleof the associated community and funding details have notbeen considered. On the latter, investment opportunities havebeen analyzed for grid-connected microgrid models dealingwith investment periods and uncertainties [15]; some of theseconcepts can be translated to RCs, as shown in this paper.

Microgrid sizing software considering different approachesare available. Thus, HOMER is a widely-used software formicrogrid sizing that determines the minimum Net PresentValue (NPV) configuration [16]; however, the software con-siders individual projects with the same operating structure,which can be a limiting factor when creating a RE plan fora community. Also, DER-CAM is a comprehensive economicand environmental model that can be used for determining theminimum cost equipment configuration for a microgrid [17];however, multiple-year investment and project funding alter-natives are not considered. These shortcomings are addressedin the present paper.

Previous projects, assessments, planning models, and soft-ware tools have helped understand the complexity and re-quirements of deploying RE in RC microgrids. Nevertheless,currently, there is a gap from the planning perspective tohelp communities understand and quantify the potential of REand the benefits that RE projects can bring, given the currentoperating and framework constraints. Hence, the objective ofthe present paper is to use existing equipment and economicmodels to propose a comprehensive RE long-term planningmodel that accounts for all relevant and current technical,economical, and operating conditions of RCs. The proposedmodel considers the characteristics of some of the previouslydeployed RE northern projects and previously described plan-ning models which apply directly to RCs. In addition, themodel acknowledges some of the significant roles that thecommunity plays regarding potential RE project deployments,as well as quantifying the project benefits.

The rest of the paper is organized as follows: Section II dis-cusses the different electricity rates and subsidy framework inNorthern and Remote Communities (N&RCs) in Canada, andtheir relation with RE project operation schemes, which arerelevant to the proposed planning model. Section III presentsthe proposed mathematical model for long-term RE planning.Section IV presents the case study developed in collaborationwith Kasabonika Lake First Nation (KLFN), and discussesthe results of applying the proposed model to develop amultiple-year RE plan considering multiple scenarios. Finally,Section V highlights the main conclusions and contributions.

II. ELECTRICITY RATES AND SUBSIDIES

Community Applicable Electricity Rates (AERs) need tobe understood in order to assess the potential benefit of REprojects in RCs. AERs in Canada vary significantly dependingon the subsidy level which generally aims to set electricityprices for off-grid residents at par with the on-grid counterpartrates [5]. The details and subsidy levels differ by location;however, a generalized rate classification can be as follows:

• Unsubsidized Customer (UbC): These customers payapproximately the actual cost of electricity since they donot receive a direct subsidy. These rates apply mainly tofederal government clients and some community-ownedbuildings. This type of customers can and have installedRE equipment for Self-Consumption (SC) purposes.

• Subsidized Customer (SbC): These customers pay pricesthat match the electricity rates of southern locations forprovinces and capitals for territories. In general, theserates apply to residential customers and are approximately10% to 20% of the actual electricity cost. Due to thehighly subsidized tariff, RE is not likely to be economi-cally feasible for these customers.

• Avoided Fuel Cost (AFC): This rate does not refer toan RC customer type, but to the fuel displacementcost resulting from electricity generation, including ad-ministration and transportation costs. Hence, the AFCultimately represents the energy cost that RE projectscompete against. The rate is approximately 40% to 60%of the energy cost depending on the RC location. A PowerPurchase Agreement (PPA) can be established with theutility to export RE power to the microgrid, fixing therate to the AFC.

Similar rate structures exist in other parts of the world(e.g., [18]).

III. PLANNING MATHEMATICAL MODEL

The main objective of this paper is to develop a multiple-year RE planning model that can help RCs determine thefeasibility of energy projects considering the characteristicsof remote microgrids. The model maximizes the potentialbenefit or social welfare W perceived by the community whileidentifying:

• RE equipment type and capacity to be deployed;• operation schemes under which RE units can operate;• installation time-frame for RE equipment; and• RE equipment location for customers whose current load

demand is known.

A. Model Architecture

Figure 1 shows the structure of the proposed model, whichis composed of four stages. The input data stage includeshistorical data for natural resources, community location andenergy-related information, and Fuel-based Generator (FG)and RE equipment specifications. The forecast stage createsthe time-series estimates for the electric load, and the on-site RE resources for the planning horizon. The pre-processingstage calculates the dispatch strategy details for FGs, estimates


Fig. 1. Mathematical model architecture.

the power profile, pre-selects configuration details for eachRE equipment type, and overall generation costs. Finally,the optimization stage solves a proposed Mixed Integer Non-linear Programming (MINLP) problem that maximizes the REplanning social welfare for the community.

It is important to note that, based on [9], [10], battery energystorage is not considered as a viable alternative in the proposedmodel. Under the current conditions, battery energy storage forRCs present several challenges such as thermal managementand investment and Operation and Maintenance (O&M) costs,that do not make it a feasible option in the medium term.

B. Historical Data and Equipment Specifications

1) Load and Installed Equipment: Detailed historical in-formation for the majority of RCs is available from off-gridutilities, once a community grants access to such information.The minimum data requirements for this model are the hourlyload time-series and the annual electricity demand growth rate.In some cases, seasonal growth rates are preferred due tothe wide load level range throughout the year. In addition,electricity consumption for large individual customers mightbe available for the RE planning model.

2) Solar-related Resources: Solar irradiation data is widelyavailable in the literature with different levels of resolution.However, there are significant drawbacks when consideringremote northern locations. Hourly solar irradiation is easilyavailable only for sites south of latitude 58◦N. In Canada, suchnorthern sites account for approximately 100 communitiesand 100,000 people, covering one third of the total N&RCs.In addition, there is limited correlation between satellite andground data for northern latitudes, which decreases the accu-racy of the available data by approximately 10% to 15% [19].Temperature data is usually available from the same sources.

3) Wind Resource Data: Low resolution wind speed datacan be easily obtained from different sources; however, thisdata is usually limited to seasonal or annual averages. On-site wind speed data is seldom available for RCs; thus,synthetic meso-scale data is the next alternative data source.

As with the solar resource, wind speed has the aforementionedcorrelation issue between ground and modelled data. Thelimited available studies give a significant wind speed rangefor northern locations, in most cases, greater than a ±0.5m/sannual average, which for some locations represents a ±10%difference. An additional alternative in remote locations is toobtain wind speed data from the local airport to validate meso-scale estimations; however, some remote airports do not storewind speed data and hence historical logs may not be available,as in the case of most of the communities in Northern Ontario,Canada.

C. Forecasts

1) Electric Load Forecast: A historical multiple-yearhourly data can be used to create a load forecast that followsthe current load profile in the community. In this paper, anormal distribution function is used to perturb the existingdata, based on historical annual growth rates; in some loca-tions, seasonal growth rates can be used to more accuratelyrepresent the growth variation within a year. In addition tothe historical growth, the current electric generation installedcapacity in the community needs to be considered to createthe forecast, since in some situations it can restrict electricitydemand and eventually infrastructure growth; growth decrease,and eventual halt, may take place when the peak load is closeto the FG plant rated capacity [7].

2) Solar Irradiation and Temperature Forecasts: Likewiseto the electric load forecast, the solar and temperature fore-casts are obtained by perturbing the historical hourly data byassuming a normal distribution of the annual average valuefor the respective parameters. In most cases, available dataexpands for 10 years or more; hence, a representative datasample can be used to create these forecasts.

3) Wind Speed Forecast: This forecast can evidently beobtained following the previous described simple forecastmethod; however, in some instances, historical data might belimited. In such cases, synthetic wind speed time-series canbe used to create the respective forecast, as described in [20].Such methodology is followed here to create the forecastassuming a normal distribution for the annual wind speedaverages.

D. Generation Equipment Considerations

The proposed long-term planning model requires electricitygeneration equipment calculations that precede the optimiza-tion step. The calculations include the dispatch strategy detailsfor FGs and on-site available power, and Levelized Cost ofEnergy (LCOE) for the RE equipment under consideration, asdescribed in the following sections.

The model equations presented next use the following sub-indexes: j represents different FGs, i.e., both on-site units andthose planned for future deployment; k refers to different REequipment types to be considered, i.e., solar Photovoltaic (PV)and Wind Turbine (WT); g refers to complementary equipmentused along with RE units, i.e., inverters for solar PV andtower options for WT equipment; i represents the RE operationschemes under which projects can be installed; t refers to the


year in the planning horizon; and h defines the hour within aplanning year.

1) Fuel-based Generators: The unit commitment and eco-nomic dispatch problem for FG facilities in RCs is trivialwhen compared to an on-grid system, simply because ofthe limited installed capacity and consequently less operatingalternatives [21]. The dispatch strategy is determined by theoperating limits in the plant Programmable Logic Controller(PLC), which simultaneously deals with the spinning reserveand the economic unit commitment problem. The PLC limitsand setpoints keep approximately a 15% spinning reserve mar-gin, as well as committing units with the minimum marginalcost, under normal operating conditions.

2) RE Reduced Search Space: The processing time of theproposed MINLP optimization model benefits from reducingthe RE equipment search space by pre-selecting equipmentunits that better fit the community location. The correspondingselections for each equipment type is done by minimizing thegenerally used LCOE given by:

min LCk,g =CTEQk,g

ELTk∑t=1

H∑h=1

PREmk,g,t,h

(1+rd)t

(1)

where CTEQk,grepresents the NPV for the capital and O&M

cost; ELTk is the equipment operational lifetime; rd is thediscount rate; and PREmk,g,t,h

is the RE power calculated fromthe wind and solar equipment models, described next.

3) Solar PV: The solar PV pre-selection process createsfeasible cost-effective solar arrays for each type of PV moduletype, considering the available inverters and their operatingconstraints such as currents and voltages, and yields thecorresponding power output profiles. The main objectives areto identify the best PV array configuration for each moduletype and, at the same time, to reduce the search space for theoptimization process. Such pre-selection is done by estimatingthe solar PV array total cost, given by:

CTEQk,g=

(CPV Uk

+

ELTk∑t=1

CPV OMk

(1 + rd)t

)xPV Pk,g

xPV Sk,g

+ CPV INVg

(2)

where CPV Ukand CPV INVg

are the PV module and invertercost, respectively; CPV OMk

is the annual O&M cost per mod-ule; and xPV Pk,g

and xPV Sk,gare the variables representing

the number of PV modules in parallel and series for eacharray. The RE power output for the solar PV modules in (1)is determined based on [22].

4) Wind Turbine: The WT pre-selection process determinesthe most cost-effective WT/tower height set selection for eachunit type, as well as the corresponding power output profiles.The process calculates the wind speeds at the different heightsand determines the total deployment costs; thus reducing,as with solar PV pre-selection, the search space during the

optimization process. The WT total cost equation is given by:

CTEQk,g= CWTUk

+ CWTTWRk,g+

ELTk∑t=1

CWTOMk

(1 + rd)t(3)

where CWTUkand CWTTWRk,g

are the equipment cost forthe WT and tower, respectively; and CWTOMk

is the O&Mannual cost for each turbine. The WT output power calculationused in this paper follows the small WT guidelines fromthe International Electrotechnical Commission (IEC) 61400standards [23].

E. RE Long-Term Planning

1) Objective Function: The long-term planning model is anMINLP problem that maximizes the benefit to the RC, giventhe deployment and operational constraints of such locations.Hence, the optimization problem is defined as follows:

max

I∑i=1

T∑t=1

Wi,t

(1 +RDi)t(4)

where

Wi,t = ISi,t − Ci,t − LRi,t (5)

with ISi,t referring to the direct income and/or saving obtainedfrom deploying RE equipment; Ci,t comprises the associatedproject costs incurred through the planning horizon; and LRi,t

refers to direct community economic losses encountered as aconsequence of RE deployment. These variables consider theRE economic impact under different operation schemes.

As described in Section II, AERs change significantlyamong customer types and, evidently, these have a significanteffect on potential RE deployments. Thus, the income/savingsare defined as:

ISi,t =(CDi(1 + ri)

t + INCi

) K∑k=1

H∑h=1

PREui,k,t,h(6)

where CDi is the present electricity rate; ri is the respectiveannual price change; INCi is an external energy incentive,if available; and PREui,k,t,h

is the renewable power used tosupply the load, as defined later in this section.

The project costs Ci,t comprise initial capital contributions,financial/loan, and O&M costs throughout the projects’ life-time, and is given by:

Ci,t =

K∑k=1

(CCEi,k,t+ CBPMTi,k,t

+ COMi,k,t) (7)

where

CCEi,k,t= CCAPi,k,t

bCEP (1− bEFP ) (8)

and

CCAPi,k,t= RCCAPk

RPk CBk,t xi,k,t (9)

The parameters bCEP and bEFP are the percentage of avail-able capital contribution at the start of the project, andthe percentage of available external funding with respect to


the total project cost, respectively; RCCAPkis the present

equipment cost per kW; RPk is the equipment rated capacity;xi,k,t is the number of RE units to be deployed; and CBk,t

is the Capacity Building (CB) factor. The latter representsthe installation learning curve likely to be experienced at thecommunity as more RE units are deployed, which is modelledby linearly reducing the deployment costs as additional similarunits are installed; such cost reduction continues until a pre-defined minimum limit is reached. This factor is thus definedas:

CBk,t =

{bCBmEQCk,t + bCBb if EQCk,t ≤ bCBu

bmaxCB otherwise

(10)where

EQCk,t =

I∑i=1

t∑v=1

xi,k,v (11)

with bCBm and bCBb are the linear and constant cost reductioncoefficients, respectively; bmax

CB is the maximum cost reductionallowed; and bCBu is the number of units at which bmax

CB isreached.

The available project capital ratio bCEP is likely to bemodest, since current Aboriginal Band budgets are typicallydedicated to higher priority issues, such as education, health,and infrastructure. Thus, if an RC engages in RE projects, itwould require to seek external federal or provincial govern-ment funding, as well as financing instruments through, forexample, bank or other institution loans, with the latter havingtwo main positive effects from the RE project perspective:The loan is likely to increase the community’s real andperceived project ownership, and creates a higher level ofO&M responsibility due to the required periodic loan paymentschedule. This external funding is given by:

CEFi,k,t= CCAPi,k,t

bEFP (12)

and the loan mechanism is defined as:

CBLi,k,t= CCAPi,k,t

− CCEi,k,t− CEFi,k,t (13)

where

CBPMTi,k,t= CBLi,k,t

rB(1 + rB)BLTk

(1 + rB)BLTk − 1(14)

This equation represents the generally used amortization pay-ment equation, where rB is the interest rate and BLTk isthe total number of loan payments. The O&M costs extendthrough the RE equipment lifetime and are given by:

COMi,k,t= RCOMk

RPk bOMtEOSi,k,t (15)

where

EOSi,k,t = eosi,k,s =

T∑u=1

yi,k,u,s (16)

and

yi,k,u,s = xi,k,t ∀ s = t, t+ 1, · · · , t+ ELTk ≤ T,

u = 1, 2, · · · , T(17)

The parameter RCOMkis the present O&M cost per kW, and

bOMt is the cost variation through the equipment’s operationallifetime, which is likely to increase in later years. In addition,EOSi,k,t represents the number of units on-site, and yi,k,u,sis an auxiliary variable relating EOSi,k,t to xi,k,t.

Some RCs could experience a potential revenue loss LRi,t

as a direct consequence of RE deployment. This economic lossis incurred if the community is the sole or partial fuel supplierfor the utility company. This loss of community revenue dueto fuel displacement is given by:

LRi,t =rFR CFUEL bCFPt(1 + CPI)t)(

fBASEt − fPROJt

)zi,t,h

(18)

where rFR is the percentage of fuel revenue obtained by thecommunity; CFUEL is the actual on-site fuel price; bCFPt

is the percentage of the total fuel supply purchased fromthe community; CPI is the customer price index fuel pricegrowth; fBASEt

is the fuel consumption of the baselinescenario, i.e., with no RE equipment installed; fPROJt

is theexpected fuel consumption after RE equipment deployment;and zi,t,h is the ratio of RE used for each operation scheme,which is defined as:

zi,t,h =

K∑k=1

PREui,k,t,h

I∑i=1

K∑k=1

PREui,k,t,h

(19)

The fuel consumption for the baseline and project scenariosare represented by:

fBASEt =

J∑j=1

H∑h=1

(dGSajPFGBASEj,t,h+ dGSbj ) (20)

and

fPROJt=

J∑j=1

H∑h=1

(dGSajPFGprojj,t,h

+ dGSbj ) (21)

where dGSajand dGSbj represent the linear and constant

coefficients for fuel consumption vs. power relationship; sincethe rated power of such generators is relatively low (< 2MW),a linear approximation is adequate for planning purposes.

2) Model constraints: The power per generator for thebaseline and project scenarios PFGBASEj,t,h

and PFGprojj,t,h

are calculated by:

PFGt,h= PDt,h

−I∑

i=1

K∑k=1

PREui,k,t,h(22)

where PDt,hrepresents the community load demand.

There are three categories of RE power used in the pro-posed planning model: The available RE power PREa is thecalculated power output from the respective wind and solarmodel. The RE used power PREu is the expected power tobe consumed by the community. Finally, the curtailed RE


power PREc is the excess power resulting from the dispatchconstraints for RE equipment. These RE dependant variablesare given by:

PREui,k,t,h= PREai,k,t,h

− PREci,k,t,h (23)

PREai,k,t,h= PREmk,t,h

EOSi,k,t (24)

PREci,k,t,h=

{PDBi,t,h

if PDBi,t,h> 0

0 otherwise(25)

where EOSi,k,t is determined in (16) and

PDBi,t,h=

K∑k=1

PREai,k,t,h− PDi,t,h

+ dGSLIMi,t(26)

dGSLIMi,t= (1− CTi)d

minGSRPmin

t (27)

PDi,t,h= PDt,h

bDi (28)

with PDBi,t,hrepresenting the difference between the available

renewable power and the respective load; dGSLIMi,t is the FGlower limit at which, when reached, RE is curtailed; CTi isa pre-defined binary constant used to distinguish between REcurtailment classes, with CTi = 1 representing the case whenRE is only for SC (no grid feeding), and CTi = 0 representingthe case when RE can inject power to the grid; dmin

GS is theminimum FG load ratio; RPmin

t is the rated power of thesmallest FG; and bDi

is the load ratio of the total demand

for each customer i whereI∑

i=1

bDi = 1. The distinction

between curtailments in the model is based on previous REnorthern projects. Thus, CTi = 1 involves disconnecting theRE source from the microgrid to avoid sending any powerto the distribution system; this has been the case of certainSC projects, since no contract with the utility was in place toallow electricity export (3 SC projects in northern Ontario havebeen supported in the last few years [24]). On the other hand,CTi = 0 allows for microgrid export, as long as the loweroperating limit for the smallest FG unit is not reached [3]. Itshould be mentioned that in northern RCs there is the potentialfor using the curtailed RE for thermal storage and/or spaceheating [25]; however, this particular option is not consideredin this paper.

The intended RE installed capacity is likely to encounterlimits set by the utility to avoid any disruptions to theexisting microgrid system. In the Northwest Territories, forexample, this RE installed capacity restriction has been setas a percentage of the RC annual average load. Hence, thisconstraint is modelled as:

I∑i=1

K∑k=1

RPkEOSi,k,t ≤ bmaxICt

H∑h=1

PDt,h

H(29)

where bmaxICt

is the maximum RE installed capacity ratio.The economic feasibility of the planning scenario is given

by the combined result of the NPV and the Internal Rate ofReturn (IRR). In the model, NPV is directly accounted for

as part of the social welfare objective function (4). However,maximizing Wi,t does not necessarily mean that the projectis financially attractive; thus, the project’s resulting IRR mustbe moderately above the pre-defined discount rate to appealto the involved stakeholders. This constraint is given by:

T∑t=1

Wi,t

(1 + IRRi)t= 0 ∀

K∑k=1

T∑t=1

xi,k,t ≥ 1 (30)

where IRRmini ≤ IRRi ≤ IRRmax

i . The parameter IRRmini

defines the minimum required to consider financial projectfeasibility. The upper limit IRRmax

i is only to be implementedif an incentive program is available (e.g., a Feed-In-Tariff(FIT) program), the incentive rate aimed at making RE projectsfinancially feasible within a desirable IRR range.

RE projects for the same equipment type and operationscheme are likely to be funded and deployed only onceover the planning horizon. This consideration avoids repeatingproject activities in different planning years, such as equipmenttransportation for the same project over two different years ordecommissioning. This constraint is given by:

xi,k,t = EOSi,k,t ∀ xi,k,t > 0 (31)

F. Financial Indicators

The proposed model uses various financial indices at differ-ent stages to quantify the economic feasibility of the proposedprojects. The main index is the NPV of the social welfare Win (5), which the model aims to maximize, so that the commu-nity best benefits from RE deployment projects. However, onits own, the social welfare does not assure that the proposedprojects are financially attractive, since the resulting W maynot cover the operating and financial expenses over the projectlifetime. Hence, the financial return represented by the IRRindex for each type of project is bounded between appropriatelimits in (30) to guarantee appropriate returns on investments.Furthermore, the proposed model considers RE equipmentthat produces the highest cost/benefit return through the min-imization of the LCOE for each type of technology underconsideration, as per (1). Overall, the intention of the financialindices is to obtain not only technically but financially feasiblescenarios that can be currently implemented in RCs.

IV. CASE STUDY: KASABONIKA LAKE FIRST NATION

The case study aims to apply the model to create a multiple-year RE plan for KLFN, an Oji-Cree First Nation communitylocated in northern Ontario, 53◦ 31’ 59"N and 88◦ 36’ 21"W.The KLFN diesel plant consists of three generators, rated at400 kW, 600 kW, and 1 MW, using a single-unit dispatchstrategy under normal operating conditions. Currently, thecommunity and utility have plans to increase capacity byinstalling a 1.6 MW generator likely over the next year.The local utility and community are rather familiar with REprojects, since WT and solar PV units have been deployed.The detailed information obtained for this case study is basedon the collaboration efforts among Hydro One Remote Com-munities Inc. (HORCI), the community, and the authors.


TABLE ICASE STUDY PARAMETER VALUES

Param. Value Param. ValuebmaxCB (%) 20 CFUEL ($/litre) 1.85bmaxICt

(%) 50 CPI (%) 4.5bCBu 20 dmin

GS (%) 40bCBm -0.01 dGSLIMi,t(%) 40bCBb 1.01 ELTk (years) 15bCEP (%) 10 INCi ($/kWh) 0bCFPt (%) 0 IRRmin

i (%) 8bEFP (%) 50 RDi (%) 6bOMt (%) 0 rFR (%) 10BLTk (years) 15 RCCAPk $/W 9c12d

CDi ($/kWh) 0.926a

0.394bRPmin

t (kW) 400e

600f

aUnsubsidized electricity rate. cSolar PV cost, dWT cost.bAvoided fuel cost rate. e,fBefore and after the 400 kW FG is decomissioned.

The solar PV panels considered are: 230 kWp Kyocera,220 kWp Sanyo, and 230 kWp and 240 kWp Canadian solarmodules. The solar inverters are: 6.3 kW SMA, 10 kWFronius, 10 kW Mastervolt, and 10 kW Aurora/PowerOne.And, the WTs used are: 50 kW Endurance; 60 kW, 95 kW,and (2x) 100 kW Northern Power; 30 kW Wenvor; and 10 kWBergey WTs. The equipment was selected since these unitsare commercially available in Canada and can potentially betransported to the community (e.g., winter roads).

The objective of the case study is to determine the mostfeasible RE alternatives over a 20-year planning horizon,considering a project investment period of 5 years. The op-eration schemes are: SC for the community-owned buildingswhere load data is available (i.e., school and water treatmentplant), and AFC for the rest of the load. Table I presentsthe parameters used for all scenarios in the case study, whichwere obtained from KLFN, HORCI, and historical estimates.In addition, the model assumes that project investments takeplace at the start of each year, while their operation doesnot start until middle of the year. The model is implementedin MATLAB using a genetic algorithm from the GlobalOptimization Toolbox to solve the MINLP problem.

Table I shows the capital costs RCCAPkassociated with the

deployment of solar PV and WT equipment used in this study.These capital costs are based on information from previousRE equipment deployed in the last two years at KLFN, whichdue to the location of the community, are higher than REdeployment costs in more accessible locations. Thus, for solarPV in Canada, the average price for on-grid turnkey projectsranged between $2,800/kW and $5,000/kW, but for off-gridsystems it was $8,100/kW in 2012 [26]. In the case of WTsin Canada, the average cost for turnkey large WTs projects was$2,259/kW in 2013, while for small on-grid WT the averageprice ranged between $6,000/kW and $8,000/kW in 2008 [27],[28]. Note that the WT selection criteria must consider thewind regime; hence, in the case of KLFN, due to its lowwind potential, a commercial WT was selected to make WTseconomically feasible (e.g., Class 3 WT). For RCs with higherwind speed regimes, different WT classes would have to beconsidered in the planning process.

A. Scenarios

The following studied scenarios are based on some of thealternatives and parameters of concern while planning REproject(s) in RCs:

• Funding alternatives (bCEP and bEFP ): Scenario 1 con-siders the baseline where one stakeholder funds the totalprojects’ cost. Scenario 2 incorporates a loan alternativeto finance the projects, since initial economic resourcesare likely to be limited. Scenarios 3-12 considers the loanalternative plus external government funding aimed topromote northern development available for community-driven projects.

• Discount rate (rd): Scenario 3 considers the social dis-count rate of 4% used by the Ontario Power Authority(OPA) for project assessment. Scenarios 4 and 5 considerhigher discount rates, 6% and 8%, respectively, to assessthe higher risk or uncertainty of future cash-flows. Forthe rest of the scenarios, a discount rate of 6% was used.

• Fuel cost growth (CPI): Scenario 6 considers a 5%annual growth rate for the diesel-fuel cost which isequal to the historical average of the 10-year compoundgrowth rate for fuel prices in northern Ontario since 2000.Scenario 7 considers a higher rate, 7% cost growth, andall other scenarios use a 4.5% annual growth, the 5-yearhistorical compound growth rate.

• RE capacity limit (bmaxICt

): Scenario 8 eliminates the in-stalled capacity constraint described in (29). For all otherscenarios, the RE installed capacity limit is set to 50%of the annual average community load.

• Solar irradiation: Scenarios 9 and 10 represent the re-spective lower and upper expected variation limits for theannual average solar global irradiation. Based on the cor-relation data available for a similar latitude location, thevariation considered is approximately ±6%. For the restof the scenarios, an annual average of 2.9 kWh/m2/dayis assumed.

• Wind speed: Scenarios 11 and 12 analyze the effect oflower and higher wind speeds, ±10%; however, based onthe meso-scale correlation information and performanceof the currently installed WTs, the actual value is closerto the lower bound. The rest of the scenarios are basedon an annual average of 5.61 m/s.

B. Results

Each scenario gives a multiple-year RE plan for the commu-nity. A detailed explanation of Scenario 3 that encompasses allthe available options of the model is first presented. Followedby a general discussion of the twelve selected scenarios.

1) Scenario 3: This is the first scenario that considers thediverse funding mechanisms which are likely to be availablefor community-driven RE projects. Hence, this scenario showsthe capabilities of the proposed model, highlighting the ben-efits of assessing multiple projects over a planning horizon.Figure 2 (a) shows the solar PV cost reduction over theyears resulting from the CB process considered by the model,which intrinsically promotes further solar PV deployment.Figure 2 (b) presents the proposed installed capacity for each


Fig. 2. Scenario 3. RE long-term plan: (a) deployment costs and (b) capacity.

Fig. 3. Power output per generator type for Scenario 3 (June 23th-25th).

type of project and operation scheme; in this case, both SCand AFC schemes are economically feasible, and since theloads for the water treatment plant and the school are known,the capacities at such locations can also be identified. Thetotal planned RE capacity is 260 kW, which corresponds toapproximately 47% of the annual average load (the maximumRE installed capacity limit was set to 50%).

Figure 3 presents the power supplied per type of generationunit for 3 sample days; the FG units switch accordinglybetween high and low load requirements, as expected, whereRE can be considered a negative load due to the low pene-tration level, having a maximum hourly and highest annualRE contributions of 35% and 7% over the planning horizon,respectively.

Figure 4 shows the components of the annual social wel-fare W over the planning horizon. First, the combinationof the external funding and the loan alternatives assure thatthe initial cash contribution from the community remainslow, so that RE projects do not compete with other priorityprojects within the community. Second, the RE projects bringa direct benefit to the community, since they will be theequipment owners; such benefit comes with the responsibilityof covering the loan repayment schedule. The intention ofthe loan is not only to obtain financial feasibility, but also tobe a commitment to maintain the equipment operational andinvest in the relatively high O&M costs, which in this casecorresponds to approximately $0.09/kWh and $0.15/kWh forthe solar PV and WT equipment, respectively.

Fig. 4. Cash-flow for Scenario 3.

2) Summary of All Scenarios: Figure 5 shows the proposedRE deployed capacity by technology and operating schemefor each scenario. Scenarios 1 and 2 are the most limitedwith 100 kW of installed capacity, since the investments arenot distributed among different funding alternatives, and asa result only PV-SC projects are marginally feasible. Sce-narios 3 to 12 consider external funding, thus reducing thecommunity project expenses, and resulting in higher feasibleRE deployment capacities. For these scenarios, the selectionof the discount rate value has the highest effect in the REcapacity output. Hence, the social discount rate of 4% allowsfor 274 kW of RE deployment, while the more conservative8% discount rate only allows for 236 kW; this reduction ismainly seen in the AFC operating scheme. Scenarios 6 and 7show that changing the compound annual fuel growth ratefrom 5% to 7% has an installed capacity difference of only12 kW; the reason for the relatively minor change is that thecurrent subsidy framework decreases the direct effect of fuelprice in the electricity rate. Scenario 8 proposes RE projects of300 kW capacity when no pre-defined installed capacity limitis set, which corresponds to bmax

ICt= 54%; hence, there is no

further economic benefit of increasing RE capacity beyond thislevel under current operating conditions. Scenarios 9 and 10show that even with the potential solar irradiation variation,the expected RE installed capacity is maintained at 274 kW.Finally, Scenarios 11 and 12 show that WT technology is notfeasible when considering the expected variation in annualwind speed; if the actual wind speed decreases by 10%, WTtechnology is not included in the deployment plan. Note thatfor all scenarios presented, the technological risks are similar,since the type of equipment to be deployed is the same andonly the RE installed capacity changes.

Figure 6 presents the IRR values from the communityperspective for each type of project and scenario. Scenarios 1and 2 have relatively low IRR values without even consideringthe parameter variation of the remaining scenarios, and asa result are not attractive alternatives. On the other hand,Scenarios 3 to 12 have significantly higher IRR, due to thepartial contribution of 50% of the capital expenses comingfrom the government agency, and thus the community capitalcosts are halved, while still obtaining the total economicbenefit from the proposed projects. Furthermore, due to thesame funding contribution, the potential loan is also reducedfor Scenarios 3 to 12, which also reduces the annual loanpayments and thus further contributes to an increase in theIRR values. This figure also shows the IRR for the receivedgovernment funding when considering that government fuel


Fig. 5. Proposed RE deployment capacity per scenario: (a) funding alterna-tives, (b) discount rates, (c) fuel costs, (d) no RE deployment limit, (e) solarirradiation levels, and (f) wind speed levels.

Fig. 6. Expected IRR per stakeholder and scenario: (a) funding alternatives,(b) discount rates, (c) fuel costs, (d) no RE deployment limit, (e) solarirradiation levels, and (f) wind speed levels.

subsidies are also reduced. In Ontario, approximately 66% ofthe total fuel cost in HORCI operated communities comesfrom a provincial government subsidy; hence, if RE generationreduces fuel consumption, the total subsidy contribution fromthe government will also be modestly reduced. Therefore,from a policy perspective, supporting such remote RE projectswould also benefit the government on top of other socialbenefits. It should be noted that as RE is further integratedinto RCs, there will be a need to account for the potentialbenefits in the rate structure itself; this would require furtherstudy, and is not addressed in this paper.

V. CONCLUSIONS

A novel RE long-term planning model for RCs has beenpresented in this paper. The proposed model can be used toevaluate several RE projects through multiple-years to obtaina long-term plan regarding RE development in RCs. Theproposed model considers different operation schemes underwhich RE can be deployed, considering current economicand technical constraints. Furthermore, it considers communityfunding alternatives which results in higher economic feasibil-ity for the community by sharing the risk among stakeholders.Finally, the model also allows quantifying the governmentbenefit from supporting such projects under a given energysubsidy framework.

The results demonstrate that realistic RE community planscan be obtained with the proposed model, considering windand solar equipment that have or can be deployed and operatedin such remote locations, while producing a direct economicbenefit to the community. The model should be applicable to

RCs in jurisdictions with similar characteristics as CanadianRCs, such as in Alaska and Chile.

ACKNOWLEDGMENT

The authors would like to thank Hydro One Inc.;Mitchell K. Diabo, General Manager at KLFN DevelopmentCorporation; and Konstantinos Karanasios and Profs. PaulParker and Kankar Bhattacharya at the University of Waterloofor their valuable input.

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j h k i t g

Mariano Arriaga (S’11–M’15) received theB.A.Sc. and M.A.Sc. degrees in industrial systemsengineering from the University of Regina, Canada,in 2003 and 2004, respectively. He received theM.Sc. degree in renewable energy from the Uni-versity of Zaragoza, Spain, in 2009., and the Ph.D.degree in Electrical and Computer Engineering fromthe University of Waterloo in 2015.He is currently a Postdoctoral Fellow at the Facultyof Electrical and Computer Engineering, Universityof Waterloo, Canada. He worked as a Field Engineer

in the oil/gas industry in exploration fields in northern Canada, and has alsoworked as Senior Project Engineer in the automotive industry. He has workedin Canada, Mexico, Tanzania, and Lao PDR in developing new alternatives forelectrification of rural communities. His research interests include renewableenergy integration in remote communities, microgrids, socioeconomic issuesof RE deployment in isolated places.Dr. Arriaga is a Registered Professional Engineer in the province of Ontario.

Claudio Cañizares (S’86–M’91–SM’00–F’07) is aFull Professor at the Electrical and Computer Engi-neering Department of the University of Waterloo,where he has held various academic and administra-tive positions since 1993, and currently serves asthe Hydro One Endowed Chair and an AssociateDirector of the Waterloo Institute for SustainableEnergy (WISE). He received the Electrical Engineerdegree from the Escuela Politénica Nacional (EPN)in Quito-Ecuador in 1984, where he held differentteaching and administrative positions between 1983

and 1993, and his MSc (1988) and PhD (1991) degrees in Electrical Engi-neering are from the University of Wisconsin-Madison. His research activitiesfocus on the study of stability, modeling, simulation, control, optimization, andcomputational issues in large and small girds and energy systems in the contextof competitive energy markets and smart grids. He is a Fellow of the IEEE,the Royal Society of Canada and the Canadian Academy of Engineering, andhas been the recipient of various IEEE PES Technical Council and Committeeawards and recognitions, holding several leadership positions in various IEEE-PES Technical Committees.

Mehrdad Kazerani (S’88–M’96–SM’02) receivedthe B.Sc. degree from Shiraz University, Iran, the M.Eng. degree from Concordia University, Canada, andthe Ph.D. degree from McGill University, Canada, in1980, 1990, and 1995, respectively.From 1982 to 1987, he was with the Energy Ministryof Iran. He is currently a Professor at the Departmentof Electrical and Computer Engineering, Universityof Waterloo, Waterloo, ON, Canada. His research in-terests include power electronic circuits and systemsdesign, power quality/active power filters, matrix

converters, distributed power generation, utility interface of alternative energysources, microgrids, battery electric, hybrid electric and fuel cell vehicles, andFACTS.Dr. Kazerani is a Registered Professional Engineer in the province of Ontario.

https://www.aadnc-aandc.gc.ca/eng/1334855478224/1334856305920

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