Network-based optimization techniques for wind farm location decisions
Jorge Ignacio CISNEROS-SALDANA, Seyedmohammadhossein HOSSEINIAN, Sergiy BUTENKO
Network-based optimization techniques for wind farm location decisions
This study aims to find appropriate locations for wind farms that can maximize the overall energy output while controlling the effects of wind speed variability. High wind speeds are required to obtain the maximum possible power output of a wind farm. However, balancing the wind energy supplies over time by selecting diverse locations is necessary. These issues are addressed using network-based models. Hence, actual wind speed data are utilized to demonstrate the advantages of the proposed approach.
wind energy / wind farm location / network analysis / optimization / clique / s-plex
[1] |
Abello J, Pardalos P M, Resende M G C (1999). On maximum clique problems in very large graphs. In: Abello J, Vitter J, eds. External Memory Algorithms and Visualization. Providence: American Mathematical Society
|
[2] |
Archer C, Jacobson M (2005). Evaluation of global wind power. Journal of Geophysical Research, 110: 148–227
|
[3] |
Archer C L, Jacobson M Z (2007). Supplying baseload power and reducing transmission requirements by interconnecting wind farms. Journal of Applied Meteorology and Climatology, 46: 1701–1717
|
[4] |
Balasundaram B, Butenko S, Hicks I V (2011). Clique relaxations in social network analysis: The maximum k-plex problem. Operations Research, 59(1): 133–142
|
[5] |
Bañuelos-Ruedas F, Rios-Marcuello S, Camacho C Á (2011). Methodologies used in the extrapolation of wind speed data at different heights and its impact in the wind energy resource assessment in a region. In: Suvire G O, eds. Wind Farm – Technical Regulations, Potential Estimation and Siting Assessment. Rijeka: InTech
|
[6] |
Boginski V, Butenko S, Pardalos P M (2003). Modeling and optimization in massive graphs. In: Pardalos P M, Wolkowicz H, eds. Novel Approaches to Hard Discrete Optimization. Providence: American Mathematical Society
|
[7] |
Boginski V, Butenko S, Pardalos P M (2006). Mining market data: A network approach. Computers & Operations Research, 33: 3171–3184
|
[8] |
Degeilh Y, Singh C (2011). A quantitative approach to wind farm diversification and reliability. International Journal of Electrical Power & Energy Systems, 33: 303–314
|
[9] |
Drake B, Hubacek K (2007). What to expect from a greater geographic dispersion of wind farms? A risk portfolio approach. Energy Policy, 35: 3999–4008
|
[10] |
Garey M, Johnson D S (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness. San Francisco: W. H. Freeman and Co
|
[11] |
GeoBolivia (2008). Mapa eolico republica de Bolivia. https://geo.gob.bo/geonetwork/srv/spa/catalog.search#/metadata/f1ea9466-7b18-4f69-92ea-b625a3e4e6ce, 2018-11-11
|
[12] |
Global Times (2017). China’s wind power capacity continues to grow. http://www.globaltimes.cn/content/1030911.shtml, 2018–2-27
|
[13] |
Grothe O, Schnieders J (2011). Spatial dependence in wind and optimal wind power allocation: A copula based analysis. Energy Policy, 39: 4742–4754
|
[14] |
Jacobson M (2009). Review of solutions to global warming, air pollution, and energy security. Energy & Environmental Science, 2: 148–173
|
[15] |
Jaramillo O, Borja M, Huacuz J (2004). Using hydropower to complement wind energy: A hybrid system to provide firm power. Renewable Energy, 29(11): 1887–1909
|
[16] |
Kahn E (1979). The reliability of distributed wind generators. Electric Power Systems Research, 2: 1–14
|
[17] |
Liu S, Jian J, Wang Y, Liang J (2013). A robust optimization approach to wind farm diversification. International Journal of Electrical Power & Energy Systems, 53: 409–415
|
[18] |
Mantegna R N, Stanley H E (2000). An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge: Cambridge University Press
|
[19] |
McElroy M B, Lu X, Nielsen C P, Wang Y (2009). Potential for wind-generated electricity in China. Science, 325(5946): 1378–1380
Pubmed
|
[20] |
Milligan M (2000). Optimizing the geographic distribution of wind plants in Iowa for maximum economic benefit and reliability. Wind Engineering, 24: 271–290
|
[21] |
Milligan M, Artig R (1999). Choosing wind power plant locations and sizes based on electric reliability measures using multiple-year wind speed measurements. US Association for Energy Economics Annual Conference
|
[22] |
Novoa C, Jin T (2011). Reliability centered planning for distributed generation considering wind power volatility. Electric Power Systems Research, 81(8): 1654–1661
|
[23] |
Pardalos P M, Rebennack S, Pereira M V F, Iliadis N A, Pappu V (2013). Handbook of Wind Power Systems. New York: Springer
|
[24] |
Pattillo J, Youssef N, Butenko S (2012). Clique relaxations in social network analysis. In: Thai M T, Pardalos P M, eds. Handbook of Optimization in Complex Networks: Communication and Social Networks. New York: Spinger
|
[25] |
Pattillo J, Youssef N, Butenko S (2013). On clique relaxation models in network analysis. European Journal of Operational Research, 226: 9–18
|
[26] |
Pei J, Liu X B, Fan W J, Pardalos P M, Lu S J (2017). A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers. Omega,
CrossRef
Google scholar
|
[27] |
Piacquadio M, De la Barra A (2014). Multifractal analysis of wind velocity data. Energy for Sustainable Development, 22: 48–56
|
[28] |
Roques F, Hiroux C, Saguan M (2010). Optimal wind power deployment in Europe – a portfolio approach. Energy Policy, 38: 3245–3256
|
[29] |
Sethuraman S, Butenko S (2015). The maximun ratio clique problem. Computational Management Science, 12: 197–218
|
[30] |
Soder L (2004). Simulation of wind speed forecast errors for operation planning of multiarea power systems. In: Proceedings of the 8th International Conference on Probabilistic Methods Applied to Power System, Ames, Iowa. IEEE: 723–728
|
[31] |
Takle G, Lundquist J (2010). Wind turbines on farmland may benefit crops. https://www.ameslab.gov/node/8364
|
[32] |
Touma J (1977). Dependence of the wind profile power law on stability for various locations. Journal of the Air Pollution Control Association, 27: 863–866
|
[33] |
Trukhanov S, Balasubramaniam C, Balasundaram B, Butenko S (2013). Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations. Computational Optimization and Applications, 56: 113–130
|
[34] |
Vaughan E, Kelley P (2016). U.S. number one in the world in wind energy production. Washington, DC: American Wind Energy Association
|
[35] |
Verma A, Buchanan A, Butenko S (2015). Solving the maximum clique and vertex coloring problems on very large sparse networks. INFORMS Journal on Computing, 27: 164–177
|
[36] |
Yang T, Fu C, Liu X, Pei J, Liu L, Pardalos P M (2018). Closed-loop supply chain inventory management with recovery information of reusable containers. Journal of Combinatorial Optimization, 35(1): 266–292
|
/
〈 | 〉 |