Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources

The application of renewable energy resources in a power system has received significant attention owing to the environmental impacts and fluctuations of fossil fuel prices. Consequently, renewable energy resources have become important sources to generate power at the commercial level due to their various benefits, coupled with the government incentives and public supports. This research work is focused on the evaluation of the reliability, economic and environmental benefits of renewable energy resources in a microgrid system. The lifecycle analysis of a microgrid system that consists of the photovoltaic, wind turbine generator, electric storage system and diesel generator is implemented in this study to test their commercial prospects in rural communities that have no access to electricity due to economic and technical constraints. The objective of this research work is to minimize the cost of energy, lifecycle cost, the annual cost of load loss and lifecycle greenhouse gas emission cost as well as to improve the overall benefit of green technologies in the proposed microgrid system. This objective is achieved by utilizing the basic probability concept to obtain the reliability performance indicators such as expected energy not served, loss of load expectation and loss of load probability, in addition to utilizing an fmincon optimization tool in the MATLAB environment to investigate the environmental and economic effects of renewable energy resources in a power system. The suitability of the model is tested on six case studies by using the same load profile, wind speed and irradiation of the site and diesel generator power capacity. The market factors such as interest rate and price of diesel fuel as well as forced outage rate, annual peak load variation and distributed generation penetration level are utilized to study their impacts on the operation of a microgrid system. The results obtained in this study demonstrate the optimal feasibility of renewable energy resources in a microgrid system. This indicates that it offers a significant reduction in the values of lifecycle cost, cost of energy, greenhouse gas emission cost and the annual cost of load loss when compared with case study 1. This research work shows that the utilization of green technologies in a microgrid system optimizes the reliability, cost savings, lifecycle cost and environmental impact. The technique adopted in the study can serve as a reference for rural electrification projects and solve socioeconomic problems that are associated with power outages.

[1]  Ramesh C. Bansal,et al.  Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources , 2017 .

[2]  Temitope Raphael Ayodele,et al.  Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building , 2016 .

[3]  Ramesh C. Bansal,et al.  The Impacts of PV-Wind-Diesel-Electric Storage Hybrid System on the Reliability of a Power System , 2017 .

[4]  Marko Čepin,et al.  Assessment of Power System Reliability: Methods and Applications , 2011 .

[5]  Srinivasa Rao Gampa,et al.  Optimum placement and sizing of DGs considering average hourly variations of load , 2015 .

[6]  V. Rajini,et al.  Cost benefit and technical analysis of rural electrification alternatives in southern India using HOMER , 2016 .

[7]  Akbar Maleki,et al.  Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms , 2015 .

[8]  Dange Huang Basic considerations in electrical generating capacity adequacy evaluation , 2005 .

[9]  Dhaker Abbes,et al.  Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems , 2014, Math. Comput. Simul..

[10]  Mukesh Singh,et al.  Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system , 2016 .

[11]  Ana Estanqueiro,et al.  Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems , 2015 .

[12]  F M Rabiul Islam,et al.  Smart energy grid design for island countries: challenges and opportunities , 2017 .

[13]  Ramesh C. Bansal,et al.  Integration of renewable distributed generators into the distribution system: a review , 2016 .

[14]  Joao P. S. Catalao,et al.  Impacts of optimal energy storage deployment and network reconfiguration on renewable integration level in distribution systems , 2017 .

[15]  Alibakhsh Kasaeian,et al.  Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability , 2017, Energy.

[16]  R. P. Saini,et al.  A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control , 2014 .

[17]  Francesco Tajani,et al.  GIS application and econometric analysis for the verification of the financial feasibility of roof-top wind turbines in the city of Bari (Italy) , 2017 .

[18]  Lu Zhang,et al.  Optimal sizing study of hybrid wind/PV/diesel power generation unit , 2011 .

[19]  Charles Mbohwa,et al.  Replicability and scalability of mini-grid solution to rural electrification programs in sub-Saharan Africa , 2017 .

[20]  W. V. Sark,et al.  Techno-economic analysis of household and community energy storage for residential prosumers with smart appliances , 2018 .

[21]  Louis J. Durlofsky,et al.  Optimal design and operation of integrated solar combined cycles under emissions intensity constraints , 2018, Applied Energy.

[22]  Ahmad Salemnia,et al.  Long-term chronological load modeling in power system studies with energy storage systems , 2015 .

[23]  C. K. Das,et al.  Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm , 2018, Applied Energy.

[24]  M. A. Kabir,et al.  Relative life cycle economic analysis of stand-alone solar PV and fossil fuel powered systems in Bangladesh with regard to load demand and market controlling factors , 2012 .

[25]  J. Catalão Smart and Sustainable Power Systems : Operations, Planning, and Economics of Insular Electricity Grids , 2015 .

[26]  Mark Gillott,et al.  Optimum community energy storage for renewable energy and demand load management , 2017 .

[27]  José L. Bernal-Agustín,et al.  Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage , 2011 .

[28]  Eugene Fernandez,et al.  Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy System , 2016 .

[29]  Bo Zhao,et al.  Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island , 2014 .

[30]  Tao Zhang,et al.  Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach , 2015 .

[31]  Alireza Maheri,et al.  Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming , 2016 .

[32]  Jose M. Yusta,et al.  Optimisation of PV-wind-diesel-battery stand-alone systems to minimise cost and maximise human development index and job creation , 2016 .

[33]  M. Kashif Shahzad,et al.  Techno-economic feasibility analysis of a solar-biomass off grid system for the electrification of remote rural areas in Pakistan using HOMER software , 2017 .

[34]  Ramesh C. Bansal,et al.  Techno-economic analysis of a PV–wind–battery–diesel standalone power system in a remote area , 2009 .

[35]  A. Hamidat,et al.  Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms , 2017 .

[36]  Yang Li,et al.  Optimal distributed generation planning in active distribution networks considering integration of energy storage , 2018, 1808.05712.

[37]  Hongxing Yang,et al.  A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island , 2014 .

[38]  Rasoul Azizipanah-Abarghooee,et al.  Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm , 2014 .

[39]  Mengshi Li,et al.  Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula , 2018, Applied Energy.

[40]  S.M.T. Bathaee,et al.  Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response , 2017 .

[41]  Ramesh C. Bansal,et al.  Reliability and economic evaluation of a microgrid power system , 2017 .

[42]  Mehrdad Abedi,et al.  Design of an optimum hybrid renewable energy system considering reliability indices , 2010, 2010 18th Iranian Conference on Electrical Engineering.

[43]  Rita Puig,et al.  Optimal sizing of a hybrid grid-connected photovoltaic and wind power system , 2015 .

[44]  Ramesh C. Bansal,et al.  Reliability assessment of distribution system with the integration of renewable distributed generation , 2017 .

[45]  Omid Abrishambaf,et al.  Demand response implementation in smart households , 2017 .

[46]  Yasir M. Al-Abdeli,et al.  Optimisation of stand-alone hybrid energy systems supplemented by combustion-based prime movers , 2017 .

[47]  Stein W. Wallace,et al.  Synergy of smart grids and hybrid distributed generation on the value of energy storage , 2016 .

[48]  Marcus Gallagher,et al.  Multiple community energy storage planning in distribution networks using a cost-benefit analysis , 2017 .

[49]  David Dvorak,et al.  Feasibility study of wind-to-hydrogen system for Arctic remote locations – Grimsey island case study , 2015 .

[50]  Henerica Tazvinga,et al.  Distributed Renewable Energy Technologies , 2017 .

[51]  Zhou Wu,et al.  Optimal switching renewable energy system for demand side management , 2015 .

[52]  H. Shayeghi,et al.  Demand side management in a smart micro-grid in the presence of renewable generation and demand response , 2017 .

[53]  Gengyin Li,et al.  Optimal residential community demand response scheduling in smart grid , 2018 .

[54]  Mauro Gamberi,et al.  Economic and environmental bi-objective design of an off-grid photovoltaic–battery–diesel generator hybrid energy system , 2015 .

[55]  Henerica Tazvinga,et al.  Non-renewable Distributed Generation Technologies: A Review , 2017 .

[56]  Mark Gillott,et al.  Optimum community energy storage system for PV energy time-shift , 2015 .

[57]  Seyed Hossein Hosseinian,et al.  GA-based optimal sizing of microgrid and DG units under pool and hybrid electricity markets , 2012 .