- Email: [email protected]
Study on offshore wind farm layout optimization based on decommissioning strategy
Available online at www.sciencedirect.com
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy (2017) 000–000 566–571 EnergyProcedia Procedia143 00 (2017) www.elsevier.com/locate/procedia
World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference, WES-CUE 2017, 19–21 July 2017, Singapore
Study on windSymposium farm layout optimization based on The offshore 15th International on District Heating and Cooling decommissioning strategy Assessing the feasibility of using the heat demand-outdoor a Haiying Sun Yang , Xiaoxia Gaobdemand forecast temperature function fora*,aHongxing long-term district heat Renewable Energy Research Group, Department of Building Services Engineering, The Hong Kong Polytechnic University, a,b,c a a b c c Hong Kong b Department of Power Engineering, North China Electric Power University (Baoding), Baoding, PR China a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France a
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Abstract
In recent years, along with the first generation of wind power reaching retirement age, the high decommissioning cost arises with Abstract more and more attention all around the word. To reduce this huge cost, an innovative offshore wind farm layout optimization method based on decommissioning strategy is presented in this paper. In the optimization method, the decommissioning strategy District networks can are be commonly addressed in the literature as generation one of theofmost decreasing the means thatheating the foundations reused after the retirement of the first windeffective turbines,solutions and thenfor smaller secondgreenhouse gasturbines emissions the building sector. These systems require high investments are on returned through the heat generation wind willfrom be installed on the original foundations. The optimization processwhich is based the Multi-Population sales. Due to the (MPGA). changed climate conditions and building(2D) renovation policies, heat demand in thewind future could decrease, Genetic Algorithm A conceptual two-dimensional wake model is adopted to calculate losses caused by prolonging the investment return period. wake effect. The Cost of Energy (COE) is regarded as the criteria to judge the effectiveness of this new method. The way to The main scope this be paper is to assess the feasibility of using the heat – outdoor temperature heat demand estimate costs willofalso introduced in this study. Finally, a case studydemand in Waglan sea area in Hongfunction Kong isfor analyzed and forecast. The of Alvalade, located in is Lisbon (Portugal), wasdevelop used asthea case study. Theindustry, district is of 665 discussed. Fromdistrict the case results, Hong Kong an ideal region to offshore wind andconsisted the proposed buildings that vary can in both construction period and typology. optimization method reduce the COE down to 1.02 HK$/kWh.Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were results from a dynamic heatLtd. demand model, previously developed and validated by the authors. ©compared 2017 Thewith Authors. Published by Elsevier The resultsunder showed that when only change is considered, the margin of error could be acceptable some applications Peer-review responsibility of theweather scientific committee of the World Engineers Summit – Applied EnergyforSymposium & (the error annualCities demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Forum: LowinCarbon & Urban Energy Joint Conference. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope onDecommissioning average within strategy; the range of 3.8% up genetic to 8% algorithm; per decade, corresponds to the Keywords: Offshore windcoefficient farm layout increased optimization; Multi-population Costthat of energy. decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +852 2766 4815. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 10.1016/j.egypro.2017.12.728
2
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
567
1. Introduction With global population and economy increase annually, the demand of energy, one crucial input for socioeconomic development, is also increasing dramatically [1]. Offshore wind power generation develops rapidly at moment and represents the most potential renewable energy supply to coastal cities in the future. In the year 2016, global new offshore wind capacity additions totaled 2.219 GW, which brought total offshore wind installed capacity to 14.384 GW [2]. On the other hand, as the first generation of offshore wind turbines approaches the end of their 20-year service, a new decommissioning market is emerging [3]. However, unlike onshore wind turbine decommissioning, where service providers have accumulated sufficient experience to enable them to carry out the works rapidly, decommissioning offshore wind turbines requires a much larger spatial and time scale. Thus, a new challenge may arise due to an unexpected increase in decommissioning costs. Offshore wind turbines have a longer life expectancy than onshore turbines, which is around 25–30 years [4]. The offshore wind farm will be decommissioned in order to protect marine ecological environment after its life cycle’s operation. However, the foundation is too often overdesigned [5]. To reduce the huge decommissioning cost, an original decommissioning strategy based offshore wind farm layout optimization method is presented in this paper. In the optimization method, the COE criteria is on account of two generations’ energy output and total cost, the COE comparison of this innovative method and conventional method will be shown in this paper. Nomenclature 2D COE MHK$ MPGA O&M WT
two-dimensional cost of energy million Hong Kong dollar Multi-Population Genetic Algorithm operation and maintenance wind turbine
2. Decommissioning strategy The decommissioning strategy presented means when the first generation offshore wind turbines are decommissioned, foundations can still be utilized after some strengthen strategies in the second generation. In this study, costs include: capital cost, operation and maintenance (O&M) cost and levelised cost. The difference between conventional strategy and decommissioning strategy lies in the capital cost, which consists of WT cost, foundation cost, installation cost and other costs (like grid connection cost and decommissioning cost). The capital costs of conventional strategy and decommissioning strategy are shown in (1) and (2) respectively.
Costcapital CostWT Cost f Costinstallation Costdecommissioning
(1)
Costcapital CostWT 1 Cost f 1 Costinstallation Coststrengthening CostWT 2 Costinstallation Costdecommissioning 2
(2)
In the formulae, CostWT is the WT cost, referenced from CostWT AP BP Prated [6]; Cost f is the foundation cost, assumed to be 6.075 MHK$ per foundation; Costinstalling is the installation cost, assumed to be 80% of the wind turbine cost; Costdecommissioning is the decommissioning cost, assumed to be 60% of the installation cost; Coststrengthening is the strengthening cost, assumed to be 10% of the installation cost.
568
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
3
3. Optimization method 3.1. MPGA A decommissioning strategy based offshore wind farm layout optimization method is developed to optimize the wind turbines’ coordinates in this study. As the decommissioning strategy is adopted, the total service time is two generations’ turbine life cycle (40 years). The optimizing process is based on the MPGA. The initial parameters setting for MPGA is listed in Table 1. In MPGA, the objective COE, contributed by total energy yield and total cost, is the average of two generations’ COEs. Table 1. The initial parameters setting for MPGA. Parameters
Values
Population number
10
Probability of crossover
0.7-0.9
Probability of mutation
0.001-0.05
Number of individual
40
The least keeping generations
500
Binary digits of variable
20
3.2. Wake model Wind turbines not only generate energy but also induce wakes behind their swept areas, which will diminish the power generation performance of downstream wind turbines [7]. The power losses caused by wind turbine wakes are of 10-20% of the total power output in large wind farms [8, 9]. In this study, a conceptual 2D wake model, as shown in Fig. 1, is presented to estimate the wind losses more accurately.
Fig. 1. 2D wake model.
In the formulae, r0 is the radius of wind turbine blade; rw is the radius of wake; s0 is the swept area of a wind turbine; sw is the area of downstream wind turbine under wake effect. Then the single wake formula and multiple wake formula are modified as (3) and (4).
4
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
rw r0 d normal u u0 , 2 Sw 2ar0 u0 1 , rw r0 d normal r0 rw u 2 (r0 d vertical ) S0 2ar02 u u 1 d normal rw r0 , 0 2 r d ( ) 0 vertical ui u0 1
569
(3)
2 u 1 u0 i 1 N
(4)
4. A case study in Hong Kong 4.1. Offshore wind farm site selection According to the previous study of Gao, et al. [10], there are four potential sea areas in Hong Kong that can be selected to build offshore wind farm. Waglan Island sea area is just chosen as a case study (Fig. 2). The size of the offshore wind farm is 3740m×5828m. The hourly wind speed data (from the year 2001 to 2011) comes from Royal Observatory, Hong Kong.
Fig. 2. Offshore wind farm site selection in Hong Kong [10].
4.2. Wind turbines The 2.5MW and 1.65MW wind turbines are considered in the first generation and second generation, respectively. The parameters of wind turbines are listed in Table 2. Table 2. The parameters of wind turbines. Parameters
Turbine 1
Turbine 2
Rated power
2.5 MW
1.65 MW
Cut-in wind speed
3 m/s
3 m/s
Rated wind speed
11.1 m/s
10.5 m/s
Cut-out wind speed
25 m/s
23 m/s
Rotor diameter
108 m
88 m
Hub height
80m
70m
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
570
5
4.3. Results According to the optimization method, both the locations and average power of wind turbines can be obtained as shown in Fig. 3. The main results of the optimized layouts are listed in Table 3. a
b
N=40 5828
1076
1035
1045
1063 1067
5000
1078
1021
1078+803 1023
1066
1049
1034
3000
1078
1047+788 1044+788
1031+780
1049+789 1072+800
1027
998 1075
1034+780 1044
Y(m)
Y(m)
1047
1031 1072
2000
1023+775
1043+786
1066+797 1034
3000
1044+783
1075+801 1071+799
4000
1033+780
1021+771 1025+777 1012+768 1026+777
1063+795 1067+798
1044 1043
1035+783
1076+8021045+785
1025
5000
1075 1071
4000
5828
1033
1012
1026
N=40
1034+778 1027+777
998+762 2000
1075+802 1078+803
1031
1031+780
1046 1063
1000
1073
1072 1078
0 0
1000
1075
1088
X(m)
3000
3740
1033+778
1078+804
1084 1090
2000
1063+795
1000
1033
1078
1046+786
1072+800 1078+804
0 0
1000
1075+803 1073+802 1084+807 1088+809 1090+810
2000
X(m)
3000
3740
Fig. 3. Optimized layout and wind turbine power with (a) convention strategy and (b) with decommissioning strategy. Table 3. Main results of optimized layouts. Number of wind turbine
Convention strategy
Decommissioning strategy
2.5 MW
1.65 MW
Service years
220
40
Energy yield (GWh)
27.55103
13.18103
Total cost (MHK$)
28.96103
13.47103
COE (HK$/kWh)
1.19
1.02
4.4. Discussions From the energy yield aspect, for a conventional strategy, the energy yield within 20 years is 7.55103 GWh. With the new decommissioning strategy, the energy yield is 13.18103 GWh. From the cost aspect, the decommissioning strategy can reduce the cost apparently by 24.83% of the total cost with a conventional strategy. Finally, comparing the COE results, it is obvious that decommissioning strategy can reduce 13.94% of the COE, which is decreased to just 1.02 HK$/kWh. 5. Conclusions The proposed offshore wind farm layout optimization method based on decommissioning strategy is a practical way to decrease the offshore wind energy cost. By reusing the foundations of offshore wind turbines, it is expected to reduce the COE by nearly 14%. Hong Kong is an ideal region to utilize offshore wind energy. Taking the Waglan Island offshore area (3740m5828m) as an example, the potential offshore wind farm can generate 13.18103 GWh electricity in 40 years. The COE will be decreased to 1.02 HK$/kWh if the decommissioning strategy is adopted.
6
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
571
References [1] S. Mathew. (2006). Wind energy : fundamentals, resource analysis and economics. Berlin: Springer. [2] Global Wind Energy Council, , (2017). "GLOBAL WIND STATISTICS 2016." [3] Heba Hashem. (2014). Offshore decommissioning market is emerging, but is wind industry prepared? Available: http://analysis.windenergyupdate.com/operations-maintenance/offshore-decommissioning-market-emerging-wind-industry-prepared [4] J. M. Carrasco, J. T. Bialasiewicz, R. C. P. Guisado, and J. I. León, (2006), "Power-Electronic Systems for the Grid Integration of Renewable Energy Sources A Survey," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. VOL. 53, NO. 4. [5] P. Hou, W. Hu, M. N. Soltani, B. Zhang, and Z. Chen, (2016)."Optimization of Decommission Strategy for Offshore Wind Farms," presented at the Proceedings of the 2016 IEEE Power & Energy Society General Meeting. [6] S. Lundberg, Chalmers University of Technology, (2003). "Performance comparison of wind park configurations," 1401-6176. [7] Z. Song, Z. Zhang, and X. Chen, (2016), "The decision model of 3-dimensional wind farm layout design," Renewable Energy, vol. 85, pp. 248-258, 1//. [8] R. J. Barthelmie, K. Hansen, S. T. Frandsen, O. Rathmann, J. Schepers, W. Schlez, et al., (2009), "Modelling and measuring flow and wind turbine wakes in large wind farms offshore," Wind Energy, vol. 12, pp. 431-444. [9] B. Sanderse, (2009), "Aerodynamics of wind turbine wakes," Energy Research Center of the Netherlands (ECN), ECN-E–09-016, Petten, The Netherlands, Tech. Rep. [10] X. Gao, H. Yang, and L. Lu, (2014), "Study on offshore wind power potential and wind farm optimization in Hong Kong," Applied Energy, vol. 130, pp. 519-531.
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy (2017) 000–000 566–571 EnergyProcedia Procedia143 00 (2017) www.elsevier.com/locate/procedia
World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference, WES-CUE 2017, 19–21 July 2017, Singapore
Study on windSymposium farm layout optimization based on The offshore 15th International on District Heating and Cooling decommissioning strategy Assessing the feasibility of using the heat demand-outdoor a Haiying Sun Yang , Xiaoxia Gaobdemand forecast temperature function fora*,aHongxing long-term district heat Renewable Energy Research Group, Department of Building Services Engineering, The Hong Kong Polytechnic University, a,b,c a a b c c Hong Kong b Department of Power Engineering, North China Electric Power University (Baoding), Baoding, PR China a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France a
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Abstract
In recent years, along with the first generation of wind power reaching retirement age, the high decommissioning cost arises with Abstract more and more attention all around the word. To reduce this huge cost, an innovative offshore wind farm layout optimization method based on decommissioning strategy is presented in this paper. In the optimization method, the decommissioning strategy District networks can are be commonly addressed in the literature as generation one of theofmost decreasing the means thatheating the foundations reused after the retirement of the first windeffective turbines,solutions and thenfor smaller secondgreenhouse gasturbines emissions the building sector. These systems require high investments are on returned through the heat generation wind willfrom be installed on the original foundations. The optimization processwhich is based the Multi-Population sales. Due to the (MPGA). changed climate conditions and building(2D) renovation policies, heat demand in thewind future could decrease, Genetic Algorithm A conceptual two-dimensional wake model is adopted to calculate losses caused by prolonging the investment return period. wake effect. The Cost of Energy (COE) is regarded as the criteria to judge the effectiveness of this new method. The way to The main scope this be paper is to assess the feasibility of using the heat – outdoor temperature heat demand estimate costs willofalso introduced in this study. Finally, a case studydemand in Waglan sea area in Hongfunction Kong isfor analyzed and forecast. The of Alvalade, located in is Lisbon (Portugal), wasdevelop used asthea case study. Theindustry, district is of 665 discussed. Fromdistrict the case results, Hong Kong an ideal region to offshore wind andconsisted the proposed buildings that vary can in both construction period and typology. optimization method reduce the COE down to 1.02 HK$/kWh.Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were results from a dynamic heatLtd. demand model, previously developed and validated by the authors. ©compared 2017 Thewith Authors. Published by Elsevier The resultsunder showed that when only change is considered, the margin of error could be acceptable some applications Peer-review responsibility of theweather scientific committee of the World Engineers Summit – Applied EnergyforSymposium & (the error annualCities demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Forum: LowinCarbon & Urban Energy Joint Conference. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope onDecommissioning average within strategy; the range of 3.8% up genetic to 8% algorithm; per decade, corresponds to the Keywords: Offshore windcoefficient farm layout increased optimization; Multi-population Costthat of energy. decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +852 2766 4815. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the World Engineers Summit – Applied Energy Symposium & Forum: Low Carbon Cities & Urban Energy Joint Conference. 10.1016/j.egypro.2017.12.728
2
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
567
1. Introduction With global population and economy increase annually, the demand of energy, one crucial input for socioeconomic development, is also increasing dramatically [1]. Offshore wind power generation develops rapidly at moment and represents the most potential renewable energy supply to coastal cities in the future. In the year 2016, global new offshore wind capacity additions totaled 2.219 GW, which brought total offshore wind installed capacity to 14.384 GW [2]. On the other hand, as the first generation of offshore wind turbines approaches the end of their 20-year service, a new decommissioning market is emerging [3]. However, unlike onshore wind turbine decommissioning, where service providers have accumulated sufficient experience to enable them to carry out the works rapidly, decommissioning offshore wind turbines requires a much larger spatial and time scale. Thus, a new challenge may arise due to an unexpected increase in decommissioning costs. Offshore wind turbines have a longer life expectancy than onshore turbines, which is around 25–30 years [4]. The offshore wind farm will be decommissioned in order to protect marine ecological environment after its life cycle’s operation. However, the foundation is too often overdesigned [5]. To reduce the huge decommissioning cost, an original decommissioning strategy based offshore wind farm layout optimization method is presented in this paper. In the optimization method, the COE criteria is on account of two generations’ energy output and total cost, the COE comparison of this innovative method and conventional method will be shown in this paper. Nomenclature 2D COE MHK$ MPGA O&M WT
two-dimensional cost of energy million Hong Kong dollar Multi-Population Genetic Algorithm operation and maintenance wind turbine
2. Decommissioning strategy The decommissioning strategy presented means when the first generation offshore wind turbines are decommissioned, foundations can still be utilized after some strengthen strategies in the second generation. In this study, costs include: capital cost, operation and maintenance (O&M) cost and levelised cost. The difference between conventional strategy and decommissioning strategy lies in the capital cost, which consists of WT cost, foundation cost, installation cost and other costs (like grid connection cost and decommissioning cost). The capital costs of conventional strategy and decommissioning strategy are shown in (1) and (2) respectively.
Costcapital CostWT Cost f Costinstallation Costdecommissioning
(1)
Costcapital CostWT 1 Cost f 1 Costinstallation Coststrengthening CostWT 2 Costinstallation Costdecommissioning 2
(2)
In the formulae, CostWT is the WT cost, referenced from CostWT AP BP Prated [6]; Cost f is the foundation cost, assumed to be 6.075 MHK$ per foundation; Costinstalling is the installation cost, assumed to be 80% of the wind turbine cost; Costdecommissioning is the decommissioning cost, assumed to be 60% of the installation cost; Coststrengthening is the strengthening cost, assumed to be 10% of the installation cost.
568
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
3
3. Optimization method 3.1. MPGA A decommissioning strategy based offshore wind farm layout optimization method is developed to optimize the wind turbines’ coordinates in this study. As the decommissioning strategy is adopted, the total service time is two generations’ turbine life cycle (40 years). The optimizing process is based on the MPGA. The initial parameters setting for MPGA is listed in Table 1. In MPGA, the objective COE, contributed by total energy yield and total cost, is the average of two generations’ COEs. Table 1. The initial parameters setting for MPGA. Parameters
Values
Population number
10
Probability of crossover
0.7-0.9
Probability of mutation
0.001-0.05
Number of individual
40
The least keeping generations
500
Binary digits of variable
20
3.2. Wake model Wind turbines not only generate energy but also induce wakes behind their swept areas, which will diminish the power generation performance of downstream wind turbines [7]. The power losses caused by wind turbine wakes are of 10-20% of the total power output in large wind farms [8, 9]. In this study, a conceptual 2D wake model, as shown in Fig. 1, is presented to estimate the wind losses more accurately.
Fig. 1. 2D wake model.
In the formulae, r0 is the radius of wind turbine blade; rw is the radius of wake; s0 is the swept area of a wind turbine; sw is the area of downstream wind turbine under wake effect. Then the single wake formula and multiple wake formula are modified as (3) and (4).
4
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
rw r0 d normal u u0 , 2 Sw 2ar0 u0 1 , rw r0 d normal r0 rw u 2 (r0 d vertical ) S0 2ar02 u u 1 d normal rw r0 , 0 2 r d ( ) 0 vertical ui u0 1
569
(3)
2 u 1 u0 i 1 N
(4)
4. A case study in Hong Kong 4.1. Offshore wind farm site selection According to the previous study of Gao, et al. [10], there are four potential sea areas in Hong Kong that can be selected to build offshore wind farm. Waglan Island sea area is just chosen as a case study (Fig. 2). The size of the offshore wind farm is 3740m×5828m. The hourly wind speed data (from the year 2001 to 2011) comes from Royal Observatory, Hong Kong.
Fig. 2. Offshore wind farm site selection in Hong Kong [10].
4.2. Wind turbines The 2.5MW and 1.65MW wind turbines are considered in the first generation and second generation, respectively. The parameters of wind turbines are listed in Table 2. Table 2. The parameters of wind turbines. Parameters
Turbine 1
Turbine 2
Rated power
2.5 MW
1.65 MW
Cut-in wind speed
3 m/s
3 m/s
Rated wind speed
11.1 m/s
10.5 m/s
Cut-out wind speed
25 m/s
23 m/s
Rotor diameter
108 m
88 m
Hub height
80m
70m
Haiying Sun et al. / Energy Procedia 143 (2017) 566–571 Haiying Sun, Hongxing Yang and Xiaoxia Gao / Energy Procedia 00 (2017) 000–000
570
5
4.3. Results According to the optimization method, both the locations and average power of wind turbines can be obtained as shown in Fig. 3. The main results of the optimized layouts are listed in Table 3. a
b
N=40 5828
1076
1035
1045
1063 1067
5000
1078
1021
1078+803 1023
1066
1049
1034
3000
1078
1047+788 1044+788
1031+780
1049+789 1072+800
1027
998 1075
1034+780 1044
Y(m)
Y(m)
1047
1031 1072
2000
1023+775
1043+786
1066+797 1034
3000
1044+783
1075+801 1071+799
4000
1033+780
1021+771 1025+777 1012+768 1026+777
1063+795 1067+798
1044 1043
1035+783
1076+8021045+785
1025
5000
1075 1071
4000
5828
1033
1012
1026
N=40
1034+778 1027+777
998+762 2000
1075+802 1078+803
1031
1031+780
1046 1063
1000
1073
1072 1078
0 0
1000
1075
1088
X(m)
3000
3740
1033+778
1078+804
1084 1090
2000
1063+795
1000
1033
1078
1046+786
1072+800 1078+804
0 0
1000
1075+803 1073+802 1084+807 1088+809 1090+810
2000
X(m)
3000
3740
Fig. 3. Optimized layout and wind turbine power with (a) convention strategy and (b) with decommissioning strategy. Table 3. Main results of optimized layouts. Number of wind turbine
Convention strategy
Decommissioning strategy
2.5 MW
1.65 MW
Service years
220
40
Energy yield (GWh)
27.55103
13.18103
Total cost (MHK$)
28.96103
13.47103
COE (HK$/kWh)
1.19
1.02
4.4. Discussions From the energy yield aspect, for a conventional strategy, the energy yield within 20 years is 7.55103 GWh. With the new decommissioning strategy, the energy yield is 13.18103 GWh. From the cost aspect, the decommissioning strategy can reduce the cost apparently by 24.83% of the total cost with a conventional strategy. Finally, comparing the COE results, it is obvious that decommissioning strategy can reduce 13.94% of the COE, which is decreased to just 1.02 HK$/kWh. 5. Conclusions The proposed offshore wind farm layout optimization method based on decommissioning strategy is a practical way to decrease the offshore wind energy cost. By reusing the foundations of offshore wind turbines, it is expected to reduce the COE by nearly 14%. Hong Kong is an ideal region to utilize offshore wind energy. Taking the Waglan Island offshore area (3740m5828m) as an example, the potential offshore wind farm can generate 13.18103 GWh electricity in 40 years. The COE will be decreased to 1.02 HK$/kWh if the decommissioning strategy is adopted.
6
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