@article{yue_patankar_decarolis_chiodi_rogan_deane_o'gallachoir_2020, title={Least cost energy system pathways towards 100% renewable energy in Ireland by 2050}, volume={207}, ISSN={["1873-6785"]}, DOI={10.1016/j.energy.2020.118264}, abstractNote={Studies focusing on 100% renewable energy systems have emerged in recent years; however, existing studies tend to focus only on the power sector using exploratory approaches. This paper therefore undertakes a whole-system approach and explores optimal pathways towards 100% renewable energy by 2050. The analysis is carried out for Ireland, which currently has the highest share of variable renewable electricity on a synchronous power system. Large numbers of scenarios are developed using the Irish TIMES model to address uncertainties. Results show that compared to decarbonization targets, focusing on renewable penetration without considering carbon capture options is significantly less cost effective in carbon mitigation. Alternative assumptions on bioenergy imports and maximum variability in power generation lead to very different energy mixes in bioenergy and electrification levels. All pathways suggest that indigenous bioenergy needs to be fully exploited and the current annual deployment rate of renewable electricity needs a boost. Pathways relying on international bioenergy imports are slightly cheaper and faces less economic and technical challenges. However, given the large future uncertainties, it is recommended that further policy considerations be given to pathways with high electrification levels as they are more robust towards uncertainties.}, journal={ENERGY}, author={Yue, Xiufeng and Patankar, Neha and Decarolis, Joseph and Chiodi, Alessandro and Rogan, Fionn and Deane, J. P. and O'Gallachoir, Brian}, year={2020}, month={Sep} }
 @article{patankar_queiroz_decarolis_bazilian_chattopadhyay_2019, title={Building conflict uncertainty into electricity planning: A South Sudan case study}, volume={49}, ISSN={["0973-0826"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85060864438&partnerID=MN8TOARS}, DOI={10.1016/j.esd.2019.01.003}, abstractNote={This paper explores electricity planning strategies in South Sudan under future conflict uncertainty. A stochastic energy system optimization model that explicitly considers the possibility of armed conflict leading to electric power generator damage is presented. Strategies that hedge against future conflict have the greatest economic value in moderate conflict-related damage scenarios by avoiding expensive near-term investments in infrastructure that may be subsequently damaged. Model results show that solar photovoltaics can play a critical role in South Sudan's future electric power system. In addition to mitigating greenhouse gas emissions and increasing access to electricity, this analysis suggests that solar can be used to hedge against economic losses incurred by conflict. While this analysis focuses on South Sudan, the analytical framework can be applied to other conflict-prone countries.}, journal={ENERGY FOR SUSTAINABLE DEVELOPMENT}, author={Patankar, Neha and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F. and Bazilian, Morgan D. and Chattopadhyay, Debabrata}, year={2019}, month={Apr}, pages={53–64} }
 @article{patankar_kulkarni_2018, title={Variations of cohort intelligence}, volume={22}, ISSN={["1433-7479"]}, DOI={10.1007/s00500-017-2647-y}, number={6}, journal={SOFT COMPUTING}, author={Patankar, N. S. and Kulkarni, Anand J.}, year={2018}, month={Mar}, pages={1731–1747} }
 @article{kulkarni_patankar_tai_2016, title={Constraint handling in probability collectives using a modified feasibility-based rule}, volume={13}, DOI={10.1504/ijcse.2016.10001035}, abstractNote={Almost all existing heuristic techniques are unconstrained optimisation methods and treat the system in centralised way. A distributed and decentralised optimisation technique in the framework of collective intelligence referred to as probability collectives (PCs) decomposes the entire system into subsystems and treats them as a multi-agent system. Similar to other contemporary heuristic techniques, its performance is significantly affected when constraints are involved. In order to handle constraints, a modified feasibility-based rule is incorporated into the PC algorithm. The approach is validated by solving a variety of constrained test problems. A tension/compression spring design problem, welded beam design problem and pressure vessel design problem are also solved. The approach is shown to be sufficiently robust and other strengths and weaknesses are also discussed. The solution to these problems proves that the constrained PC approach can be applied to a variety of practical/real world problems.}, number={4}, journal={International Journal of Computational Science and Engineering}, author={Kulkarni, A. J. and Patankar, N. S. and Tai, K.}, year={2016}, pages={303–321} }
 @article{patankar_kulkarni_tai_ghate_parvate_2014, title={Multi-criteria probability collectives}, volume={6}, ISSN={["1758-0374"]}, DOI={10.1504/ijbic.2014.066975}, abstractNote={The nature-/bio-/socio-inspired optimisation techniques can efficiently handle unconstrained problems; however, their performance gets significantly affected when applied for solving constrained problems. This paper proposes a variation of the distributed optimisation multi-agent system (MAS) approach of probability collectives (PC) in collective intelligence domain referred to as multi-criteria probability collective (MCPC). In this approach, the constraints are efficiently handled by giving equal importance as the objective function. It is validated by solving a variety of constrained test problems including tension/compression spring design problem and pressure vessel design problem. The solution to these problems proves that the MCPC approach can be applied to a variety of complex practical/real world problems.}, number={6}, journal={INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION}, author={Patankar, Neha S. and Kulkarni, Anand J. and Tai, Kang and Ghate, T. D. and Parvate, A. R.}, year={2014}, pages={369–383} }