Browsing by Author "Das, Indrajit"

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Optimal Incentive Design for Targeted Penetration of Renewable Energy Sources
(Institute of Electrical and Electronics Engineers (IEEE), 2014-08-28) Das, Indrajit; Bhattacharya, Kankar; Canizares, Claudio
Environmental concerns arising from fossil-fuel-based generation have propelled the integration of less-polluting energy sources in the generation portfolio and simultaneously have motivated increased energy conservation programs. In today's deregulated electricity market, most participants [e.g., GENCOs and local distribution companies (LDCs)] focus on maximizing their profits, and thus they need to be incentivized to invest in renewable generation and energy conservation, which are otherwise not profitable ventures. Therefore, this paper proposes a novel holistic generation expansion plan (GEP) model that enables the central planning authority (CPA) to design optimal incentive rates for renewable integration and energy conservation targets, considering the investor interests and constraints. The model also determines the siting, sizing, timing, and technology required to adequately supply the projected demand over the planning horizon. The model is applied to the generation planning of Ontario, Canada, based on realistic data, to determine appropriate incentives for investors in renewable generation and energy conservation by LDCs. The obtained optimal incentives are shown to be similar to the ones currently in place in Ontario, with a slightly shorter payback period for investors. The effect of uncertainties associated with solar and wind energy availability on the GEP model is also examined using Monte Carlo simulations.
Renewable Energy Integration in Diesel-Based Microgrids at the Canadian Arctic
(Institute of Electrical and Electronics Engineers (IEEE), 2019-08-14) Das, Indrajit; Canizares, Claudio A.
The effect of climate change is significant in the arctic regions of the world, with the carbon footprint from diesel-only based electricity generation in remote arctic communities adding to the environmental degradation through greenhouse gas (GHG) emission, oil spills, and black carbon. Moreover, the dependence on diesel and its associated costs are an economic problem for these communities, particularly in the Canadian Arctic, where governments subsidize this fuel. Thus, this article presents specific studies including new variable-speed generator (VSG) technologies that demonstrate the feasibility, impact, and benefits of introducing renewable energy (RE) together with VSGs in remote microgrids in the Canadian Arctic. More specifically, this article describes a two-step procedure to select remote communities for detailed feasibility studies of deployment of RE sources, including a generation expansion planning (GEP) framework and optimization model for RE and new VSG integration applied to the selected communities, to minimize diesel dependence of isolated microgrids and maximize the incorporation of environmentally friendly generation technologies. The proposed approach is applied to communities in Nunavut and the North West Territories in the Canadian Arctic, based on actual data, to study the technoeconomic feasibility of RE integration and develop business cases for diesel generation replacement with RE and VSG generation in these communities. The obtained optimal plans contain diesel-RE hybrid combinations that would yield substantial economic savings and reductions on GHG emissions, which are being used as the base for actual deployments in some of the studied communities.
Sensitivity-Indices-Based Risk Assessment of Large-Scale Solar PV Investment Projects
(Institute of Electrical and Electronics Engineers (IEEE), 2013-12-13) Das, Indrajit; Bhattacharya, Kankar; Canizares, Claudio; Muneer, Wajid
Large-scale solar photovoltaic (PV) generation is now a viable, economically feasible and clean energy supply option. Incentive schemes, such as the Feed-in-Tariff (FIT) in Ontario, have attracted large-scale investments in solar PV generation. In a previous work, the authors presented an investor-oriented planning model for optimum selection of solar PV investment decisions. In this paper, a method for determining the sensitivity indices, based on the application of duality theory on the Karush–Kuhn–Tucker (KKT) optimality conditions, pertaining to the solar PV investment model is presented. The sensitivity of the investors' profit to various parameters, for a case study in Ontario, Canada are presented and discussed and these are found to be very close to those obtained using the Monte Carlo simulation and finite-difference (individual parameter perturbation) based approaches. Furthermore, a novel relationship is proposed between the sensitivity indices and the investor's profit for a given confidence level to evaluate the risk for an investor in solar PV projects.