Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods

Abstract This paper investigates the use of renewable energies to supply hotels in island regions. The aim is to evaluate the effect of weather and occupancy fluctuations on the sensitivity of investment criteria. The sensitivity of the chosen energy system is examined using a Monte Carlo simulation considering stochastic weather data, occupancy rates and energy needs. For this purpose, algorithms based on measured data are developed and applied to a case study on the Canary Islands. The results underline that electricity use in hotels is by far the largest contributor to their overall energy cost. For the invested hotel on the Canary Islands, the optimal share of renewable electricity generation is found to be 63%, split into 67% photovoltaic and 33% wind power. Furthermore, a battery is used to balance the differences between day and night. It is found, that the results are sensitive to weather fluctuations as well as economic parameters to about the same degree. The results underline the risk caused by using reference time series for designing energy systems. The Monte Carlo method helps to define the mean of the annuity more precisely and to rate the risk of fluctuating weather and occupancy better.

[1]  Jens Hesselbach,et al.  Assessment of Influencing Factors in Decentralized Energy Supply of Manufacturing Industries Using Probabilistic Methods , 2017, Simul. Notes Eur..

[2]  Philipp Blechinger,et al.  Global analysis of the techno-economic potential of renewable energy hybrid systems on small islands , 2016 .

[3]  Mohammad-Ali Yazdanpanah,et al.  Modeling and sizing optimization of hybrid photovoltaic/wind power generation system , 2014 .

[4]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Z. Şen,et al.  First-order Markov chain approach to wind speed modelling , 2001 .

[6]  Yan Zhang,et al.  The optimal capacity configuration of an independent Wind/PV hybrid power supply system based on improved PSO algorithm , 2009 .

[7]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[8]  Jose M. Yusta,et al.  Stochastic-heuristic methodology for the optimisation of components and control variables of PV-wind-diesel-battery stand-alone systems , 2016 .

[9]  Madeleine Gibescu,et al.  Bottom-up Markov Chain Monte Carlo approach for scenario based residential load modelling with publicly available data , 2016 .

[10]  W. Beckman,et al.  Estimation of degree-days and ambient temperature bin data from monthly-average temperatures , 1983 .

[11]  Florencia Almonacid,et al.  Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks , 2013 .

[12]  Jens Hesselbach,et al.  Classification of global island regarding the opportunity of using RES , 2016 .

[13]  Daniel Scain Farenzena,et al.  SYNTHESIZING SEQUENCES OF HOURLY AMBIENT TEMPERATURE DATA , 2003 .

[14]  S. Simon,et al.  Carbon neutral archipelago – 100% renewable energy supply for the Canary Islands , 2017 .

[15]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[16]  Yonghong Kuang,et al.  A review of renewable energy utilization in islands , 2016 .

[17]  Lin Lu,et al.  First order multivariate Markov chain model for generating annual weather data for Hong Kong , 2011 .

[18]  Santanu Bandyopadhyay,et al.  Optimum sizing of wind-battery systems incorporating resource uncertainty , 2010 .

[19]  Kuo-Tsang Huang,et al.  Energy consumption characteristics of hotel's marketing preference for guests from regions perspective , 2013 .

[20]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .

[21]  Ramon Blasco-Gimenez,et al.  A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties , 2015 .

[22]  Andrés Feijóo,et al.  Assessing wind speed simulation methods , 2016 .

[23]  Mo Chung,et al.  Comparison of building energy demand for hotels, hospitals, and offices in Korea , 2015 .

[24]  Theocharis Tsoutsos,et al.  HOTRES: renewable energies in the hotels. An extensive technical tool for the hotel industry , 2006 .

[25]  Tom E. Baldock,et al.  Feasibility analysis of stand-alone renewable energy supply options for a large hotel , 2008 .

[26]  Antoni Sánchez,et al.  Technical approach for a sustainable tourism development. Case study in the Balearic Islands , 2008 .

[27]  Julieta Schallenberg-Rodriguez,et al.  Photovoltaic techno-economical potential on roofs in regions and islands: The case of the Canary Islands. Methodological review and methodology proposal , 2013 .

[28]  Tarek Y. ElMekkawy,et al.  Stochastic optimization of hybrid renewable energy systems using sampling average method , 2015 .

[29]  Cheng-Dar Yue,et al.  Integration of optimal combinations of renewable energy sources into the energy supply of Wang-An Island , 2016 .

[30]  Carlos Silva,et al.  Design and implementation of hybrid renewable energy systems on micro-communities: A review on case studies , 2014 .

[31]  Ugur Atikol,et al.  A demand-side planning approach for the commercial sector of developing countries , 2004 .

[32]  Francisco Javier Ramos-Real,et al.  Electricity generation cost in isolated system: The complementarities of natural gas and renewables in the Canary Islands , 2010 .

[33]  José L. Bernal-Agustín,et al.  Optimisation of energy supply at off-grid healthcare facilities using Monte Carlo simulation , 2016 .

[34]  Anastasios I. Dounis,et al.  Polygeneration microgrids: A viable solution in remote areas for supplying power, potable water and hydrogen as transportation fuel , 2011 .

[35]  Santanu Bandyopadhyay,et al.  Optimum sizing of photovoltaic battery systems incorporating uncertainty through design space approach , 2009 .

[36]  J. Schallenberg-Rodríguez,et al.  Evaluation of on-shore wind techno-economical potential in regions and islands , 2014 .

[37]  G. Notton.  Importance of islands in renewable energy production and storage: The situation of the French islands , 2015 .