Wind Farm Design and Application to Offshore Wind in New Jersey Given Installation and Maintenance Policies

Transcription

1 Wind Farm Design and Application to Offshore Wind in New Jersey Given Installation and Maintenance Policies FERC Workshop/9th Annual Trans-Atlantic Infraday (TAI) Electricity Markets and Planning Energy Infrastructure and Systems, Frank A. Felder, Louis Bowers, Greg Seroka, Rich Dunk, Travis Miles, and David Coit Rutgers University Oct. 30th, 2015

2 Background Introduction Offshore wind power gaining pace World cumulative installed offshore wind power capacity and net annual addition (in megawatts) United States offshore wind resource by region and depth Source: Earth Policy Institute Source: NREL 2 Background Introduction Wind in NJ exists over offshore waters where the wind resources are substantial and land use conflicts can be avoided The Offshore Wind Economic Development Act (2010): the legislation passed in NJ directs the Board of Public Utilities (BPU) to launch an Offshore Wind Renewable Energy Credit (OREC) program Requires a certain percentage of electricity sold in New Jersey to be from offshore wind energy (support 1,100 MW) Credit PJM wholesale market revenue 3

3 Introduction Problem Statement We link the wind, engineering and economic models into an integrative modeling platform to evaluate offshore wind projects. Develop a new model to optimize the installation of wind farm, and apply it to offshore wind in New Jersey. Optimize OREC Delta by making decisions on wind farm capacity, wind turbine spacing, and interconnection points Solve the sub-optimization problem, minimize O&M cost by making decisions on maintenance scheduling Condition based maintenance policy, and period inspection interval is optimized Levelized Cost of Electricity (input), OREC Delta (output), and uncertainty analysis 4 Methodology Two stage decision making model Stage 1 Historical wind speed data Wind farm layout model (Windfarmer by DNV GL or OpenWind by AWS TruePower) Weather Research and Forecasting (WRF) model Installed capacity Spacing Injection point Wind turbine energy production (MWh) Energy Revenue Capacity Revenue Capital Cost O&M Cost LCOE Profit OREC Δ Uncertainty Analysis! Stage 2 Conditionbased maintenance Optimal planned maintenance schedule 5

4 Key Assumptions Assumption Impact of offshore wind on energy and capacity prices are not considered. Decommissioning costs are included in the capital cost. Impact of maintenance cost due to wind farm configuration is not considered. Power curve uncertainty is not considered. 6 Mathematical Model Objective function E offshore wind energy profit years Wind energy profit can be obtained by two parts P% x, x, x, x R size cap in M R% W C% W year 1r 1r years year R R R W E C P R C R W W C C C W C OM Decision variables: Installed wind energy capacities:1100 MW, 2000 MW, and 3000 MW Wind turbine spacing: 8D 10D, 10D 12D, and 10D 15D Optional on-shore interconnection points: Cardiff, Hudson and Larrabee Planned maintenance scheduling (periodic interval) 7

5 Mathematical Model Energy Revenue Energy revenue reflect the units of electricity sold into the wholesale market. R P E R P 1 1 E LM R 0 M H Weather Research and Forecasting (WRF) model (Rutgers Univ. Department of Marine and Coastal Sciences) Hourly data from the daily 3km domain simulations were used Energy losses includes turbine wake loss, ice loss, salt accretion, etc. Hourly OSW energy production data P LM locational marginal price E R energy generated by the wind farm P LM, E R the correlation between these two factors R 0 proportion of time when there are random turbine failures R O P M proportion of time when there is plan maintenance P M 8 Mathematical Model Energy Revenue log(r E ) ~ N(8.59, ) CDF, histogram and fitted probability density function of energy revenue for Cardiff 8D 10D wind farm configuration, 1100 MW P LM, E R =0.015 Boxplot of wind speed data Boxplot of hourly energy price 9

6 Mathematical Model Capacity Revenue E N installed capacity for wind farm, C F1 capacity factor during peak hour P C capacity price R E C P N C N F1 C N number of days in a year Offshore wind capacity factor at PJM peak hour (Cardiff) Capacity Spacing (July 22 nd, 18:00) (July 18 th, 16:00) (July 18 th, 18:00) (July 2 nd, 18:00) (May 12 nd, 18:00) 1100 MW 8D 10D 64.2% 78.9% 78.9% 78.9% 57.9% 10D 12D 63.5% 78.0% 78.0% 78.0% 57.2% 10D 15D 63.4% 77.9% 77.9% 77.9% 57.1% 2000 MW 8D 10D 62.9% 77.3% 77.3% 77.3% 56.7% 10D 12D 62.1% 76.4% 76.4% 76.3% 56.0% 10D 15D 62.4% 76.7% 76.7% 76.6% 56.2% 3000 MW 8D 10D 62.3% 76.6% 76.6% 76.5% 56.2% 10D 12D 61.5% 75.5% 75.5% 75.5% 55.4% 10D 15D 61.6% 75.7% 75.7% 75.7% 55.5% 10 Mathematical Model Capacity Revenue Capacity revenue for different capacity value ($ 000,000) Cap Distribution (Jun-Dec) (Jan-Dec) (Jan-Dec) (Jan-Dec) (Jan-May) 1100 MW ~N(43, (20) 2 ) 2000 MW ~N(77, (35) 2 ) 3000 MW ~N(115, (53) 2 ) 11

7 Total Revenue Mathematical Model Total revenue for different capacity value ($/MWh) Size 2011 (Jun-Dec) 2012 (Jan-Dec) 2013 (Jan-Dec) 2014 (Jan-Dec) 2015 (Jan-May) Energy Cap. Total Energy Cap. Total Energy Cap. Total Energy Cap. Total Energy Cap. Total 1100 MW MW MW Variability in capacity price and ratio of energy and capacity revenue Year Ratio of energy revenue to capacity revenue Ratio of total revenue to LCOE (cost=200 $/MWh) % % % % % 12 Mathematical Model Installed Capital Cost The capital measure includes all the planning, equipment acquisition, construction and installation costs of the wind system. NREL developed an offshore installed capital cost estimate by conducting several parallel assessments Analysis of global market data Review of published literature Interviews with active offshore wind developers in the United States Source: NREL, 2010 Cost of Wind Energy Assume capital cost follows normal distribution C C ~ N( C, 2 C ), with C = 4200 $/kw, and C =

8 Mathematical Model Operation and Maintenance Cost Condition Based Maintenance (CBM) Combines the desirable characteristics of both preventive maintenance and corrective maintenance Avoids repairing/replacing wind turbine too early (waste of wind turbine components life time) Detect system status, and maintain the system before the failure happens and minimize wind turbine system downtime Replace correctively, new life cycle blade Replace preventively, new life cycle Inspection Preventive maintenance Corrective maintenance Blade Gear Box Yaw & Pitch Cost due to labor, etc Repair components Replace components + Loss production 14 Maintenance sub-optimization Operation and Maintenance Cost Start simulation T = 0 P i wind turbine failure due to i th failure mechanism Generate life data according to failure mechanism Replace preventively, and start a new life cycle Replace correctively, and start a new life cycle Fail Inspect wind turbines periodically Below on-condition threshold Not fail, but above oncondition threshold Do nothing Calculate downtime and maintenance cost End 15

9 Maintenance sub-optimization Operation and Maintenance Cost Planned maintenance every 1.2 years 16 Profit, LCOE and OREC Profit, LCOE and OREC Credit Optimal solution: Hudson, 8D 10D 10D 12D, 10D 15D 1100MW 2000MW 3000MW Energy revenue Capacity revenue O&M Capital cost OREC Delta ($/MWh) ($/MWh) ($/MWh) ($/MWh) ($/MWh)

10 Profit, LCOE and OREC LCOE and OREC μ=142.5 σ= Summary Conclusion Developed a model to analyze the revenue and cost for offshore wind energy Develop a general formulation to compare ΔOREC by enumerating optional wind farm installed capacity, turbine spacing, and interconnection points Solve the sub-optimization problem to optimize the wind farm system maintenance policy and frequency for given configuration Study the uncertainty of the factors that have effect on the profit, and quantify the uncertainty of the OREC Future Research plan A more complete model in which new decision variables can be considered: wind turbine orientation, hub height A specific wind farm turbine layout optimization embedded in our model Incorporate effect of climate change on offshore wind modeling 20

11 Reference and Acknowledgement Besnard F, Bertling L. An approach for condition-based maintenance optimization applied to wind turbine blades. Sustainable Energy, IEEE Transactions on 2010;1: Echavarria E, Hahn B, van Bussel G, Tomiyama T. Reliability of wind turbine technology through time. Journal of Solar Energy Engineering 2008;130: Felder FA. Examining electricity price suppression due to renewable resources and other grid investments. The Electricity Journal 2011;24: Hofmann M. A review of decision support models for offshore wind farms with an emphasis on operation and maintenance strategies. Wind Engineering 2011;35:1-16. Miles T, Seroka G, Kohut J, Schofield O, Glenn S. Glider observations and modeling of sediment transport in Hurricane Sandy. Journal of Geophysical Research: Oceans 2015;120: Neil Habig SEaBB. New Jersey Offshore Wind Energy: Feasibility Study p Pryor S, Barthelmie R. Climate change impacts on wind energy: A review. Renewable and sustainable energy reviews 2010;14: Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, et al. A description of the advanced research WRF version 2. DTIC Document; Tegen S, Hand M, Maples B, Lantz E, Schwabe P, Smith A Cost of Wind Energy. Contract 2012;303: Venetsanos K, Angelopoulou P, Tsoutsos T. Renewable energy sources project appraisal under uncertainty: the case of wind energy exploitation within a changing energy market environment. Energy Policy 2002;30: This research is supported by National Science Foundation, OCE and National Science Foundation, OCE , and the New Jersey Board of Public Utilities. 21