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Multi-Criteria Decision Analysis Framework for the

Assessment of Wind Power Projects: The Case Study of East Durham Wind Centre Project, Ontario, Canada

Orkhan Baghirli

Department of Earth Sciences, Uppsala University Campus Gotland, Cramérgatan 3, Visby 621 67,

Sweden

1. Introduction

In the decision making process for the development of wind power projects, a number of factors, sometimes conflicting, have to be considered. Multi-criteria decision making procedures are widely applied today to ensure a rigorous analysis (Goumas and Lygerou, 1998). The application of multi-criteria analysis can integrate the various aspects into a uniform evaluation procedure and consequently evaluate the potential alternatives (Papadopoulos and Karagiannidis, 2008). As a tool for decision aid, multi-criteria evaluation has demonstrated its usefulness in many environmental and energy policy problems where several stakeholders with conflicting interest are involved (Polatidis and Haralambopoulos, 2007). The PROMETHEE II methods in particular are among the most known and widely applied outranking methods in group energy planning and decision analysis (Pohekar and Ramachandran, 2004; Polatidis and Morales, 2014), and especially suitable for renewable energy developments where environmental and societal criteria are evaluated alongside technological and economic criteria.

In this paper, multi-criteria decision analysis technique has been integrated into decision-making process for the assessment of variant scenarios for a potential wind power project development. For this purpose, a case study, which represents a complex environment with high social sensitivity, is chosen in the province of Ontario, Canada. A number of evaluation criteria are established and assessed via particular related software or comparatively evaluated among each other on a semi-qualitative basis. Relevant stakeholders are identified based on the available project documentation to participate in the decision making process as a part of a role-play workshop. As a result, ranking of the proposed design scenarios for each stakeholder is realized and graphically presented. Key words: wind power, multi-criteria decision analysis, PROMETHEE, project evaluation

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In this paper, multi-criteria decision analysis (MCDA) method is applied on a case study: East Durham Wind Centre Project, in Ontario, Canada to assess the feasibility of proposed design alternatives. For this purpose, several tools such as WindPro, RETScreen, PROMETHEE II and Simos framework (Simos, 1990) are employed. The project documentation published by developers is widely used in different sections of this paper to extract the project related information. Overall, 10 criteria by 4 stakeholders are defined in this study and the final assessment of 4 variant scenarios is realized on the PROMETHEE II platform. As a result, proposed scenarios are ranked based on the preference of identified stakeholders. Results appear to identify a new direction of how a more applicable project could be established. 2. Methodology In this section, a methodological framework is established for the systematical analysis of a case study chosen in the municipality of West Grey County. The chosen case study represents a complex environment for the wind power development in terms of societal issues. Based on the published project documentation, 4 different scenarios which are under considiration by project developers as real potential for the actual development are identified. Afterwards, question and answer session conducted by developers (NextEra Energy Canada, 2013) to addess the project related public concerns is used to identify the participaring stakeholders and evaluation criteria. Each criterion is assigned with an explicit unit of measurement and internally evaluated. Then, criteria are weighted based on their relative importance for each stakeholder using the Simos method. The resulting weights are then transferred into PROMETHEE II platform and finally scenarios are ranked. The results are graphically presented and relevant conclusions are drawn. 3. Case study

3.1 Project Description and background

Development on the East Durham Wind Energy Centre began in 2006. This wind energy project is expected to have a maximum generating capacity of up to 23-megawatts by 14 of 16 proposed wind turbines. Each turbine will be approximately 80 meters tall from the ground to the hub in the center of the blades (Genivar Inc., 2013). The wind farm will be located in the municipality of West Grey County. At the maximum generating capacity, the East Durham Wind Energy Centre will produce enough energy for approximately 7,000 homes in Ontario. This project has been awarded a Feed-in-Tariff contract by the Ontario Power Authority and has received the approval from the Ministry of the Environment on its Renewable Energy Approval application. Life cycle of the project is planned to be minimum 20 years. The project is expected to benefit the regional community by creating employment opportunities, delivering landowner lease payments, and support economy through purchases of regional goods and services. Based on the published project documentation by developers and reflections on local newspapers and blogs created by opposition group (Ontario Wind Resistance, 2014), several conclusions can be drawn about this project. Even though this project has been granted with Renewable Energy Approval (REA) by Ministry of Environment (MOE) for permitting 16 wind turbine locations within the municipality of Grey County, it is not clear enough why developers decided to construct only 14 turbines and if they plan to construct remaining 2 turbines in the near future.

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3.2 Stakeholders` identification

During the stakeholder consultation meeting which took place in January 15, 2013 with the participation of 140 people (Genivar Inc., 2013), several questions have been raised mostly concerning health, noise, property value, decommissioning, landowner contract, wildlife, landscape and technical issues. Stakeholders are identified based on this question and answer session. Based on this information, identified main stakeholders include, but not limited to: Table 1: Stakeholders` identification

Index Stakeholders Description

1 Opposition group Local community, Environmentalist NGO

2 Landowners Farmers

3 NextEra Energy Canada Project developers

4 Regional Authority Province of Ontario

There are about 15 landowners who will have a wind turbine on their property. The number of residences within 1.5km of the project location is 173 (Genivar Inc., 2013). After approval of the project by Ministry of Environment, there are still ongoing oppositions and protests (Ontario Wind Resistance, 2014).

3.3 Creation of variant scenarios

The general situation in the proposed area is that developers have acquired permission for the construction of 16 turbines: 14 GE 1.6-100, 1 GE 1.39-100, 1 GE 1.34-100. It is also mentioned by developers that only 14 of these proposed turbines will be constructed for this project. However, which 14 turbines to build remains as a question. The proposed turbine locations and turbine specifications are as follows.

Figure 1: Wind farm layout Source: Genivar Inc., 2013: Wind Turbine Specification Report Summary

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Turbine locations T2 and T6 are reserved for GE 1.39-100 and GE 1.34-100 turbines correspondingly in case of their construction is necessary. The rest of the locations are determined to host GE 1.6-100 type turbines. Table 2: Turbine specification Source: Genivar Inc., 2013: Wind Turbine Specification Report Summary

Specification Turbine Turbine Turbine

Make General Electric General Electric General Electric

Model 1.34-100 1.39-100 1.6-100

Name Plate Capacity

1.34 MW 1.39 MW 1.62 MW

Hub Height 80 m 80 m 80 m

Rotor Diameter 100 m 100 m 100 m

Minimum Rational Speed

9.75 rpm 9.75 rpm 9.75 rpm

Maximum Rotational Speed

12.8 rpm 13.2 rpm 15.33 rpm

The variant project scenarios under consideration are described in the following table. Table 3: Variant scenarios

Scenarios Scenario 1 Scenario 2 Scenario 3 Scenario 4

Turbine number 14 14 14 16

Turbine model 12 GE 1.6-100 1 GE 1.39-100 1 GE 1.34-100

13 GE 1.6-100, 1 GE 1.39-100

14 GE 1.6-100 14 GE 1.6-100 1 GE 1.39-100 1 GE 1.34-100

Total capacity 22.17 MW 22.45 MW 22.68 MW 25.41 MW

Since developers have already acquired the permission for all of the 16 proposed locations, they may decide to utilize their entire potential. This case is reflected on the scenario 4.

3.4 Establishment of criteria

Different criteria are generated for the purpose of comparison between different scenarios by identified stakeholders. Established scenarios are different in terms of number of turbines and turbine specifications. However, in terms of criteria establishment, some possible criteria are not included such as entrepreneurial risk, employment creation, and potential of reducing blackout since proposed scenarios vary in such a way that changes in these criteria may not have significant effect on comparison. As a result, criteria list is established from the perspective of each stakeholder as it is provided in the following table. Stakeholder consultation meeting is taken as a basis.

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Table 4: Establishment of criteria and way of assessment

Stakeholders Criteria Ways of assessment

Opposition group Social acceptance Visual impact Own assessment

Noise emission Data from project documentation

Avian mortality Data from literature

Inconvenience due to construction of new infrastructure*

Data from project documentation

Landowners Local economic indicator Data from project documentation

NextEra Energy Canada Annual energy production WindPro Net present value RETScreen

Regional Authority CO2 emissions avoided RETScreen

* This criterion is composed of 3 sub-criteria: electricity network, access roads and disturbance area

3.4.1 Social Acceptance Social acceptance criterion is comprised of 4 sub-criteria: visual impact, noise emission, avian mortality and inconvenience due to construction of new infrastructure. Table 5: Social acceptance sub-criteria

Criteria Unit/ Scale

Weight index Direction of preference

Visual impact [1-10] 0.1 MIN

Noise emission [1-10] 0.5 MIN

Avian mortality [1-10] 0.1 MIN

Inconvenience [1-10] 0.3 MIN

Visual impact For the assessment of visual impact, a novel approach is provided. For this purpose, proposed wind farm layout needs to be compressed to have a square shape where distance between turbines is equal and set to 1 blade diameter (least possible in theory). Then, perimeter of this new layout is found and compared with perimeter of the original farm layout. Perimeter of the original farm layout can be found by connecting the vertices of farm layout with a straight line and summing up the length of connecting lines. The following proportionality is proposed for the visual impact assessment. Visual impact ~ Perimeter of proposed layout / Perimeter of compressed layout (1)

Noise impact Noise study report has been provided by developers for 16 turbine locations based on the worst-case wind turbine sound power level of 10 m/s at an 80 m hub height (Genivar Inc., Final Noise Study Report, 2013). It is also shown that, according to this study report, proposed turbines don’t violate MOE`s 40dB noise level requirement. In this report, number of affected residences (given as noise receptors) by each turbine is also provided. Based on this information, noise impact assessment of proposed scenarios are conducted and normalized on a [1-10] scale.

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Avian mortality Based on a study conducted by Sovacool (2009), wind power plants are responsible for 0.3 fatalities per gigawatt-hour (GWh) of electricity produced. Therefore, assessment of this criterion is based on the following function (Polatidis et al., 2014) followed by normalization on a [1-10] scale.

Annual presumed mortality = 0.3 * AEP [GWh] (2) Inconvenience Inconvenience due to construction of new infrastructure is break down into 3 sub-criteria: a) electricity network b) access roads and c) disturbance area. Each sub-criterion is equally weighted and assessed based on a [1-10] scale. Required information pertaining to construction is provided by developers (Genivar Inc., Project Description Report, 2013). The following table illustrates the evaluation of social acceptance criterion based on its sub-criteria. Evaluation of each sub-criterion is done internally according to the proposed methodology in the preceding sections. One should note that direction of preference for the following sub-criteria (MAX) is the opposite of what is provided earlier in this paper (MIN). For example, “visual impact preference” implies less visual intrusion and higher values are preferred for this sub-criterion. Table 6: Evaluation of social acceptance

Criteria Direction Weight Scale Scenario 1 Scenario 2 Scenario 3 Scenario 4

Visual impact preference

MAX 0.1 [1-10] 5.19 5.56 5.56 5.19

Noise emission preference

MAX 0.5 [1-10] 6.4 5.08 3.7 2.5

Avian mortality preference

MAX 0.1 [1-10] 6.61 6.34 6.09 5.23

Inconvenience preference

MAX 0.3 [1-10] 4.79 4.79 4.79 3.62

Social Acceptance

MAX 1.0 [1-10] 5.81 5.16 4.45 3.378

3.4.2 Local economic indicator This criterion reflects on the project`s benefit to local community in terms of landowner payments. It is estimated that this project with 14 proposed turbines would have $4.7 Million landowner payments over 20 years according to project documentation (NextEra Energy Canada: Project Fact Sheet, 2013). To assess the local economic indicator of proposed scenarios, landowner payment per turbine per year has been calculated and used to estimate the local benefit of the proposed scenarios. Direction of preference is maximization. The criterion is described in $/yr. Table 7: Evaluation of local economic indicator

Criterion Unit Scenario 1 Scenario 2 Scenario 3 Scenario 4

Local economic indicator

$/year

222,301

228,741

235,000

268,571

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3.4.3 Annual energy production Annual energy production (AEP) is calculated via WindPro software using the PARK module for different scenarios. The wind climate in the site is obtained by transferring climatological data from the nearest meteorological station available, namely Weathersfield UTM 17 East. It is described in MWh/yr and preference is given to a scenario with a higher AEP. Wake loses and reduction of 10% due to turbine downtime are taken into consideration. Table 8: Evaluation of annual energy production

Criterion Unit Scenario 1 Scenario 2 Scenario 3 Scenario 4

Annual energy

production

MWh/year

104,252.8

107,272.2

110,207.5

119,607.7

3.4.4 Net present value (NPV) The following parameters are needed to calculate the NPV for variant scenarios using RETScreen 4. It is assumed that the salvage value of the existing equipment will equal the cost of decommissioning and removal. Furthermore, it is estimated that operation and maintenance cost will be approximately 15% of annual income due to energy production and investment cost is estimated around $1600/KW (Natural Resources Canada: RETScreen Case studies/ Canada, 2009). Table 9: Evaluation of net present value

NPV parameters

Unit Scenario 4 Scenario 3 Scenario 2 Scenario 1

AEP MWh 119,607 110,208 107,272 104,253

Electricity price $/MWh 60 60 60 60

Investment cost $ 40,656,000 36,288,000 35,920,000 35,472,000

O&M cost $ 1,076,463 991,872 965,448 938,275

Inflation rate % 2.5 2.5 2.5 2.5

Discount rate % 8.5 8.5 8.5 8.5

Debt ratio % 70 70 70 70

Debt interest rate

% 8.5 8.5 8.5 8.5

Income tax rate % 28 28 28 28

Depreciation rate

% 30 30 30 30

NPV $ 14,177,062 14,879,765 14,223,845 13,610,374

* Financial parameters used in this table are extracted from RETScreen Case Studies/ Canada

3.4.5 CO2 emissions avoided

CO2 emissions avoided is an indicator showing the equivalency between electricity production and CO2 emissions. The greenhouse gas emission factor is 0.196 tCO2/MWh according to RETScreen Emission Reduction Analysis tool. Proposed scenarios are assessed by taking this information as a basis. The direction of preference is maximization.

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Table 10: CO2 emissions calculations

Criterion Unit Scenario 1 Scenario 2 Scenario 3 Scenario 4

CO2 emissions avoided

tones/year

20,465

21,058

21,634

23,479

The following table shows the overall impact matrix for the considered variant scenarios for each stakeholder. Table 11: Impact matrix for the variant scenarios

Evaluation of criteria

Preference direction

Units Scenario 1 Scenario 2 Scenario 3 Scenario 4

Social acceptance

MAX

[1-10]

5.81

5.16

4.45

3.378

Local economic indicator

MAX

$/year

222,301

228,741

235,000

268,571

AEP MAX MWh/year 104,252.8 107,272.2 110,207.5 119,607.7

NPV MAX $ 13,610,374 14,223,845 14,879,765 14,177,062

CO2 emissions avoided

MAX

Tons/year

20,465

21,058

21,634

23,479

3.5 Impact matrix and preference elicitation

Each criterion has a corresponding weight assigned by each of the participating stakeholders. Evaluation of weightings is part of the role-play workshop and Simos methodology is employed to assess the weightings. Table 12: Weights of the evaluation criteria for each stakeholder

Criteria weights Opposition group

Landowners NextEra Energy Canada

Regional authority

Social acceptance

80.00 13.33 13.33 26.67

Local economic indicator

5.00 33.33 20.00 6.67

AEP 5.00 26.67 30.00 16.67

NPV 5.00 20.00 30.00 16.66

CO2 emissions avoided

5.00 6.67 6.67 33.33

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4. Results and discussion

PROMETHEE II is utilized to rank the preference of each variant scenario for each stakeholder. For this purpose, following diagrams are created by plotting the net flow for each scenario.

Figure 2: Ranking of the variant scenarios according to stakeholders

The preference will be given to a project which is negotiable for all of the stakeholders. Therefore, scenario 1 and scenario 4 are excluded, since they represent the extremes which are hard to reach at a consensus. On the other hand, scenario 2 and scneario 3 seem more promising. Comparing those scenarios, one can say that both are negotioable, however, scenario 2 is realtively more satisfactory for all parties. Further studies are required two distinquish between scenario 2 and scenario 3.

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5. Conclusion

In this paper, Multi Criteria Decision Analysis (MCDA) technique has been applied to a wind power project case study, namely East Durham Wind Energy Centre Project, in Ontario, Canada to facilitate the decision making process upon several possible design scenarios. The proposed 4 project alternatives are generated based on the published project documentation. The same material is also used for the stakeholder identification and criteria evaluation. Overall, 10 criteria by 4 stakeholders are used for this study. Weighting of criteria is done by Simos method from the perspective of each participating stakeholder in a role-play workshop and transferred to PROMETHEE II framework for the final assessment. The whole process aims to reach at a converged solution in an environment of conflicting interests. Based on the derived results, feasibility of the proposed project alternatives can be assessed and trend for a more applicable project proposal can be identified.

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