Electric vehicles in Smart Grids: Performance considerations

Distributed power system is the basic architecture of current power systems and demands close cooperation among the generation, transmission and distribution systems. Excessive greenhouse gas emissions over the last decade have driven a move to a more sustainable energy system. This has involved integrating renewable energy sources like wind and solar power into the distributed generation system. Renewable sources offer more opportunities for end users to participate in the power delivery system and to make this distribution system even more efficient, the novel Smart Grid concept has emerged. A Smart Grid: offers a two-way communication between the source and the load; integrates renewable sources into the generation system; and provides reliability and sustainability in the entire power system from generation through to ultimate power consumption. Unreliability in continuous production poses challenges for deploying renewable sources in a real-time power delivery system. Different storage options could address this unreliability issue, but they consume electrical energy and create signifcant costs and carbon emissions. An alternative is using electric vehicles and plug-in electric vehicles, with two-way power transfer capability (Grid-to-Vehicle and Vehicle-to-Grid), as temporary distributed energy storage devices. A perfect fit can be charging the vehicle batteries from the renewable sources and discharging the batteries when the grid needs them the most. This will substantially reduce carbon emissions from both the energy and the transportation sector while enhancing the reliability of using renewables. However, participation of these vehicles into the grid discharge program is understandably limited by the concerns of vehicle owners over the battery lifetime and revenue outcomes. A major challenge is to find ways to make vehicle integration more effective and economic for both the vehicle owners and the utility grid. This research addresses problems such as how to increase the average lifetime of vehicles while discharging to the grid; how to make this two-way power transfer economically viable; how to increase the vehicle participation rate; and how to make the whole system more reliable and sustainable. Different methods and techniques are investigated to successfully integrate the electric vehicles into the power system. This research also investigates the economic benefits of using the vehicle batteries in their second life as energy storage units thus reducing storage energy costs for the grid operators, and creating revenue for the vehicle owners

A critical review of forest biomass estimation equations in India

Plant biomass is an integral part of the global carbon cycle and a renewable energy source that can deaccelerate the rising global temperature. India has 71 million ha (M ha) of land under forests represented by tropical to alpine ecosystems. Numerous direct and indirect species-specific and mixed-species equations have been used for biomass estimation in India. Biomass estimation equations that facilitate the prediction of aboveground biomass (AGB) stocks non-destructively across India are still lacking. Therefore, the objective of this review is to (i) assess the existing species-specific biomass estimation equations for trees, bamboos, palms, and bananas in India, (ii) assess and identify the most appropriate multi-species biomass estimation equations for AGB estimation across India, and (iii) define the critical research gaps in biomass estimation in India. The literature search found 85 species-specific and six multi-species AGB estimation equations reported from India. It was also found that a 50% of these equations were based on the power-law function using diameter at breast height (D) as the predictor variable. We carried out a multi-fold validation to compare the multi-species equation's compatibility by comparing the root mean square error (RMSE). The estimated RMSE values of the six reported multi-species equations showed that the following two equations could be effectively used for estimation of AGB: (i) lnAGB= 0.349+1.316 lnGBH and (ii) AGB= (0.18D2.16) × 1.32. These are adequate for predicting biomass of any woody species across a range of conditions in India

Allometric Models for Estimation of Forest Biomass in North East India

In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest biomass C is needed to provide information on C fluxes and balances in such systems. The objective of this study was to develop generalized biomass models using harvest data covering tropical semi-evergreen, tropical wet evergreen, sub-tropical broad leaved, and sub-tropical pine forest in North East India (NEI). Among the four biomass estimation models (BEMs) tested AGBest = 0.32(D2Hδ)0.75 × 1.34 and AGBest = 0.18D2.16 × 1.32 were found to be the first and second best models for the different forest types in NEI. The study also revealed that four commonly used generic models developed by Chambers (2001), Brown (1989), Chave (2005) and Chave (2014) overestimated biomass stocks by 300–591 kg tree−1, while our highest rated model overestimated biomass by 197 kg tree−1. We believe the BEMs we developed will be useful for practitioners involved in remote sensing, biomass estimation and in projects on climate change mitigation, and payment for ecosystem services. We recommend future studies to address country scale estimation of forest biomass covering different forest types