Project deployment strategies for community renewable energy: A dynamic multi-period planning approach

Abstract Supplying the energy needs of a community through renewable energy sources is a vital aspect in developing sustainable communities. Many variations and uncertainties affect the development of a renewable-powered net-zero energy system. Community developers face challenges in making the investment decisions when planning community-level renewable energy (RE) projects. This study aims to address the need for reliable methods to assess RE project deployment strategies. To achieve this, the key decision variables were identified and dynamic project performance was assessed for a Canadian RE case study. A framework was developed using system dynamics for rating renewable energy project deployment scenarios. A fuzzy logic-based optimization process was used to identify the optimal system capacities and energy mix. The optimal energy supply mix was identified as follows for the case study: grid electricity- 56%, solar PV – 28%, biomass – 11%, and waste-to-energy– 5%. The results of the system dynamics based rating indicated that stage-by-stage construction that also accounts for community growth in facility capacity sizing provides the best outcomes for the community, with 42.8% of the community’s energy demand supplied with renewables. The developed model can help community developers to identify the best energy choices and investment strategies when planning community energy systems.

[1]  B. Marion,et al.  Performance parameters for grid-connected PV systems , 2005, Conference Record of the Thirty-first IEEE Photovoltaic Specialists Conference, 2005..

[2]  S. Iniyan,et al.  Applications of fuzzy logic in renewable energy systems – A review , 2015 .

[3]  M. Ramachandran,et al.  Application of multi-criteria decision making to sustainable energy planning--A review , 2004 .

[4]  Fabio De Felice,et al.  Life cycle assessment (LCA) and life cycle cost (LCC) analysis model for a stand-alone hybrid renewable energy system , 2016 .

[5]  Varun,et al.  LCA of renewable energy for electricity generation systems—A review , 2009 .

[6]  Ping-Yu Hsu,et al.  A fuzzy-based decision-making procedure for data warehouse system selection , 2007, Expert Syst. Appl..

[7]  Mei Zhao,et al.  Methods and tools for community energy planning: A review , 2015 .

[8]  Louis D. Albright,et al.  Energy Systems Engineering: Evaluation and Implementation , 2008 .

[9]  Rehan Sadiq,et al.  Renewable energy integration into community energy systems: A case study of new urban residential development , 2018 .

[10]  Beyzanur Cayir Ervural,et al.  A multi-objective decision-making approach for sustainable energy investment planning , 2018, Renewable Energy.

[11]  Jamshid Aghaei,et al.  Risk based multiobjective generation expansion planning considering renewable energy sources , 2013 .

[12]  Rehan Sadiq,et al.  Analyzing energy options for small-scale off-grid communities: A Canadian case study , 2020 .

[13]  A. Immanuel Selvakumar,et al.  A comprehensive review on wind turbine power curve modeling techniques , 2014 .

[14]  Kenneth Tze Kin Teo,et al.  Renewable energy project: Project management, challenges and risk , 2014 .

[15]  Gulgun Kayakutlu,et al.  Clean energy investment scenarios using the Bayesian network , 2014 .

[16]  Guohe Huang,et al.  Community-scale renewable energy systems planning under uncertainty—An interval chance-constrained programming approach , 2009 .

[17]  Rehan Sadiq,et al.  Opportunities and challenges in energy demand reduction for Canadian residential sector: A review , 2018 .

[18]  R. Springer.  Framework for Project Development in the Renewable Energy Sector , 2013 .

[19]  Guohe Huang,et al.  Planning of community-scale renewable energy management systems in a mixed stochastic and fuzzy environment , 2009 .

[20]  Poul Alberg Østergaard,et al.  Energy systems scenario modelling and long term forecasting of hourly electricity demand , 2015 .

[21]  Seppo Junnila,et al.  Combining life cycle costing and life cycle assessment for an analysis of a new residential district energy system design , 2013 .

[22]  Hirushie Karunathilake,et al.  A life cycle perspective of municipal solid waste: human health risk-energy nexus , 2017 .

[23]  Chanwoong Jeon,et al.  Optimal subsidy estimation method using system dynamics and the real option model: Photovoltaic technology case , 2015 .

[24]  R. P. Saini,et al.  A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications , 2016 .

[25]  J. Michelez,et al.  Risk quantification and risk management in renewable energy projects. , 2010 .

[26]  Niki Frantzeskaki,et al.  Sustainable energy planning by using multi-criteria analysis application in the island of Crete , 2009 .

[27]  Anastasia Ioannou,et al.  Risk-based methods for sustainable energy system planning: a review , 2017 .

[28]  S. Pelland,et al.  THE DEVELOPMENT OF PHOTOVOLTAIC RESOURCE MAPS FOR CANADA , 2006 .

[29]  Tofigh Allahviranloo,et al.  Ranking fuzzy numbers using fuzzy maximizing-minimizing points , 2011, EUSFLAT Conf..

[30]  Karla Hoffman,et al.  Combinatorial optimization: current successes and directions for the future , 2000 .

[31]  Rehan Sadiq,et al.  Renewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty , 2019, Renewable Energy.

[32]  P. Parker,et al.  Community energy planning in Canada: The role of renewable energy , 2009 .