Browsing by Author "Nathwani, Jatin"

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A Beginner's Guide for Policymakers and Governance for a Green Hydrogen Ecosystem
(Balsillie School of International Affairs, 2024-12-04) Herdem, Munur Sacit; Nathwani, Jatin
Green hydrogen has enormous potential for advancing a low-carbon future within the hard-to-abate sectors and also for shaping the issue of energy security. In this paper, we present a beginner’s guide for policymakers and readers interested in green hydrogen.
Conceptualizing Technology Governance: An Ecosystem Perspective
(Balsillie School of International Affairs, 2023-11-20) Nathwani, Jatin; Fitz-Gerald, Ann
Whereas critical challenges to humanity have been tasking international experts for millennia, rapid advancements in emerging transformative technologies (ETT) give rise to a fundamental problem that requires strategies for risk management and governance.
Coronavirus, Climate and a Clean Energy Transition: Is Resiliency Achievable?
(Balsillie School of International Affairs, 2020-04-02) Nathwani, Jatin
In the seeds of this current tragedy lies a historic opportunity for Canada to transition to a low-carbon energy economy — away from dependence on the oil and gas sector.
Data-Driven Simulation and Optimization of Renewable Energy Systems
(University of Waterloo, 2025-01-20) Ye, Wenrui; Wen, John; Nathwani, Jatin
The transition to renewable energy systems is critical in mitigating climate change and reducing fossil fuel dependence. However, integrating these variable and intermittent sources into the existing grid raises challenges such as dynamic energy demand management and resource underutilization, leading to increased operational costs and hindering broader adoption. This thesis develops algorithms to optimize renewable energy systems, enhancing their integration and operational efficiency. The research makes a significant contribution to enhancing the utilization, reliability, and economic viability of renewable energy systems, supporting a smoother transition to sustainable energy practices. This thesis first enhances the energy generation of photovoltaic panels by optimizing their tilt angles to maximize solar energy capture under varying environmental conditions. The machine learning models provided accurate predictions of photovoltaic output, allowing for data-driven insights into optimal system performance. A subsequent optimization process identified the best tilt angles during the operation. The results demonstrated an increase in annual energy output by up to 9.7% compared to fixed-tilt systems. This confirms that dynamic tilt adjustment is an effective strategy for maximizing photovoltaic energy generation. The second part of the research focuses on optimizing the capacity of renewable energy system components, with a particular emphasis on energy storage systems such as batteries. This project addressed the challenge of determining the optimal capacity for each component to efficiently meet energy demands while minimizing costs. A two-stage optimization approach was applied: first, a genetic algorithm generated candidate configurations with specific capacities; second, a simulation was conducted using an energy management algorithm to evaluate the performance of these configurations. The optimized configurations led to an overall system energy independence score of 0.51 and an 18.12% higher internal rate of return, validating the effectiveness of the integrated optimization approach in capacity planning and highlighting the importance of appropriately sized energy storage in enhancing system performance. The final part introduces an advanced energy management algorithm inspired by Model Predictive Control, integrating both batteries and hydrogen storage to enhance renewable energy utilization. By employing time series data and transformer-based models, the system accurately predicts future energy demand. These predictions enable a rolling window optimization technique that utilizes machine learning for dynamic energy management. The inclusion of hydrogen storage allows excess renewable energy to be stored as hydrogen, providing a versatile energy carrier for applications beyond electricity and improving overall renewable energy utilization. This approach improved demand forecasting accuracy by 41.21% and increased the adjusted green hydrogen production rate from 29.54% to 54.3%, demonstrating that advanced predictive energy management strategies, combined with diverse energy storage solutions, significantly enhance system adaptability, efficiency, and renewable energy utilization. These studies demonstrate that optimizing component configurations and energy management strategies—while integrating advanced energy storage systems like batteries and hydrogen—substantially improves the efficiency, reliability, and economic viability of renewable energy systems. The research provides valuable insights for integrating renewable energy into existing grids and supports the transition toward more sustainable and resilient energy infrastructures.
Energy Management System Infrastructure for Solar Photovoltaic Energy System
(University of Waterloo, 2022-10-31) Li, Joey Zheqiang; Wen, John; Nathwani, Jatin
In recent years, more distributed energy resources (DERs) such as solar PV, wind and more are being installed to compensate for the growing energy demand. It is vital to properly monitor and integrate these new energy resources. An Energy Management System (EMS) can be used to achieve this objective. This thesis is focuses on an EMS an infrastructure that can be used for a residential solar PV system. An EMS should be manufacturer independent, affordable, and easy to integrate. This thesis presents an EMS consisting of a Monitoring and Control Platform (MCP), a data management infrastructure, and a client application. The MCP includes a Smart Energy Controller (SEC) and Motes that are placed at the output of the solar PV system, the input to the battery, and the input to the load. The user can send commands to the SEC via the web application. Then, the SEC sends commands to the Motes via Zigbee. The Motes can block or allow the flow of power using a power relay. Thus, the Motes allow precise control over the flow of power. The Motes also contain sensors that can measure the power. The measurements are sent to the SEC via Zigbee. Commands and alarms are logged by SEC. The system uses a neural network to predict the power production of a solar PV energy system. The SEC uploads the power measurements, the predicted power production, and system logs to a database where it is stored. The client application allows users to access the Information stored in the database. The client application displays the measured power from each Mote, the predicted power production, alarms, and system control logs. The EMS was tested on a solar PV system with a single panel, a 12 V battery, and a DC load. It demonstrates the potential for low cost and independent energy management systems. The thesis hasIed an operational Iardware infrastructure for an EMS thIt can be integrated into the existing solar PV systems. The EMS infrastructure is manufacturer independent, affordable, and easy to integrate. These features will help with faster integration of solar PV energy systems into a Smart Energy Network. Furthermore, it provides a platform that can host different energy scheduling and energy prediction algorithms. The functionality has been demonstrated through integration into a solar cart. Furthermore, a software and hardware architecture consisting of a sensor data collection system, control system, and a neural network has been successfully developed. The performance of these components was evaluated through multiple experiments. The sensor data collection system and control system were each tested separately and then tested concurrently. For the sensor data collection, 500 sensor samples were collected and analyzed to demonstrate the reliability of the sensor collection system. The testing results indicate that the system is able to consistently and accurately collect the data. The performance of the remote energy flow control has also been tested. 500 commands were sent from the web application. The response times from the system were evaluated. The results show that the system executes the command and responds within 0.9 to 1.4 seconds. The neural network was trained and tested on actual data collected from a 250-kW solar PV installation in Gaithersburg, Maryland, USA. The mean absolute error of the power production forecasts was 6 kW or 2.4%. Furthermore, the cost of the presented system is estimated to be almost 18 times cheaper compared to existing energy management systems. This thesis provides valuable information for research and development of future energy management systems.
Engineering a Lasting Peace for Gaza and the Middle East
(Balsillie School of International Affairs, 2025-01-24) Jaber, Abdul Malek Al Jaber; Nathwani, Jatin
An economic corridor through Gaza — the India-Middle East-Europe Economic Corridor (IMEC) — could become a gradual pathway for peace, productivity and stability in the region.
Forging links between innovation and sustainability:An empirical examination of the effects on a firm’s financial performance
(University of Waterloo, 2016-04-29) Gabriel, Amir; Nathwani, Jatin
Greenhouse gas (GHG) emissions from the North American energy and energy-intensive materials production sectors account for more than 50 percent of total GHG emissions. Based on the argument that CO2 emissions need to be reduced by more than 50 percent by 2050, energy sector and energy-intensive materials production processes that cause environmental harm are considered a key contributor that cannot be neglected. The challenge is to reduce greenhouse gas emissions from both sectors consistent with corporate sustainability goals and government policy objectives. Energy firms have been auctioning aggressively on carbon-free alternatives to minimize their current footprint. Reducing energy demand and consumption along with related GHG emissions decrease in the production processes of the five key materials: steel, cement, plastic, paper, and aluminum; can have a considerable impact on the environment. Therefore, this research studies the role of innovation and sustainability in the evolution and co-evolution of the energy and energy-intensive materials production firms’ sectors within North America. A quantitative understanding of the causal significance of the association between corporate innovation and corporate sustainability and their combined effects on corporate financial performance would be of great value to decision-makers. Previous academic literature has focused on the importance of innovation, but relying solely on innovation will not guarantee a firm’s success. Sustainability is becoming an increasingly central feature of business operations. Because firms are more likely to apply financial resources to programs that directly affect their profitability, the study offers an analysis of the combined impact of innovation and sustainability on a firm’s financial performance as an aid to support the decision calculus for allocation of scarce resources. This study presents a synthesis of the literature broadly described as the resource-based view, the capability approach, institutional theory and the stakeholder’s theory. A structural equations model is developed with corporate innovation and corporate sustainability as the exogenous (independent) latent constructs and corporate financial performance as the endogenous (dependent) latent construct. The study uses the structural equation modeling (SEM) technique to analyze the hypothesized theory using archived data extracted from different publicly- and privately-available reports. All financial information was obtained from Compustat, an accounting, and financial database for more than 25,000 publicly held companies, as well as research and development expenditures for 2014. All environmental stewardship, social responsibility, and community involvement information was retrieved from public corporate responsibility reports and corporate citizenship documents. All patent information was acquired from the Lens database, an open public resource for innovation cartography, the USPTO, short for, United States patent and trademark office, and the CIPO, short for, Canadian intellectual property office. The structural equation model provides evidence that exogenous (independent) latent constructs have strong, significant positive associations with the endogenous (dependent) latent construct. The model shows that corporate sustainability has a significantly greater association with corporate financial performance than corporate innovation. Based upon key innovative characteristics consisting of R&D expenditures, R&D prior, patent applications, patents granted, and R&D expenditure as a proportion of total revenues, namely R&D intensity, the model displayed a positive association with corporate financial viability. The data analyzed showed a strong and positive association between different sustainability themes’ indicators and the firms’ financial prosperity. Further, it was proven empirically that there is a strong positive association among the innovation manifest variables chosen with the corporations’ financial viability different indicators. Analysis of results indicates a strong reciprocal association between corporate innovation and corporate sustainability which is valid in both directions. Sustainability can drive innovation, and innovation can foster and prompt sustainability. The research illustrates how environmental stewardship, social responsibility, and community involvement manifest indicators can be combined to reflect an organization’s level of sustainable development as well as innovation indicators that describe economic performance. Research results provide insights on how businesses respond to societal demands while maintaining long-term business viability. This study offers a clear understanding of different relationships and capability to evaluate the potential impact of key factors. Results of this study will assist corporate managers to better understand the impact of innovation and sustainability expenditures, therefore, improve the allocation of scarce resources. This dissertation outlines new empirical evidence of North America’s energy and energy-intensive materials production sectors with time dependency of performance. The research outlines the theoretical and practical basis for improved corporate financial performance and offers recommendations for additional studies. The qualitative components of this study provide a greater understanding of the concepts multidisciplinary and linkages, of the relationship between sustainability and innovation. The study has created an instrument that can help shape organizational transitions and evolution. Stakeholders can use this comprehensive document to aid organizations’ response to environmental, social, and economic challenges and issues.
Navigating the Digital Transformation and AI in Education and Peer Review
(Balsillie School of International Affairs, 2024-03-04) Herdem, Munur Sacit; Nathwani, Jatin
This paper presents innovative approaches to rethinking education and peer review in the age of artificial intelligence (AI).
Optimizing EV Routing and Charging/Discharging under Time-Variant Electricity Prices
(University of Waterloo, 2020-08-19) Lin, Bo; Ghaddar, Bissan; Nathwani, Jatin
The integration of electric vehicles (EVs) and the power system has been becoming an increasingly important field of research, due to the rapid EV penetration and the evolvement in vehicle-to-grid (V2G) techniques in the past decade. Under appropriate management of EV charging and discharging, the current grid capacity can satisfy the energy requirements of a considerable number of EVs and EVs could help enhance grid reliability and stability through ancillary service provision. In this thesis, we investigate the operational strategies of commercial EV fleets under the V2G context where energy price signals are utilized to incentivize EV owners to time-shift charging schedule and discharging EVs during peak hours. We propose and formulate a new EV routing problem with time windows under time-variant electricity prices (EVRPTW-TP), considering practical constraints of commercial EV fleets providing logistic services and optimizing over its overall electricity cost. In order to solve the EVRPTW-TP, we then formulate a Lagrangian relaxation as well as a variable neighborhood search and tabu search hybrid (VNS/TS) heuristic to approximate the optimal solution from below and above respectively. Our numerical experiments on small instances suggest that both algorithms are able to provide high quality bounds to the EVRPTW-TP. The VNS/TS heuristic outperforms CPLEX in terms of solution quality and efficiency on instances of $10$ or more customers. In addition, we utilize the VNS/TS heuristic to study a use case of an EV fleet providing grocery delivery service in the Kitchener and Waterloo (KW) region in Ontario, Canada. Insights about the impacts of energy pricing scheme, service time slot design as well as fleet size are presented. Finally, as the first step towards implementing advanced machine learning techniques to solve the EVRPTW-TP, we develop a reinforcement learning (RL) model for the electric vehicle routing problem with time windows (EVRPTW) and provide computational results to assess the performance of the RL model.
The Post-COVID-19 Economy: Financing Canada's Leadership in Sustainable Energy
(Balsillie School of International Affairs, 2020-05-04) Nathwani, Jatin; Ramsara, Raynier
The politicians and policy makers who will be tasked with addressing Canada’s future fiscal situation likely have not yet begun their careers.
A Regional Electricity Hub for Energy Transitions
(University of Waterloo, 2018-07-10) Guler, Burak; Nathwani, Jatin; Celebi, Emre
The transition to a low-carbon energy economy will remain a cornerstone of national energy policies of countries committed to the climate change accord for decades to come. We highlight the need for transmission investment as one key policy instrument among others to achieve an energy economy with lower dependence on fossil fuels. We propose an enhanced role for investing in transmission capacity in support of large-scale inter-regional electricity trade to allow effective fuel switching among countries through a physically connected transmission system and functioning markets. A conceptual framework of Regional Energy Hubs REH is proposed. The cost minimization model for the transmission investment strategy integrates the: 1. key geopolitical parameter for countries that are geographically close in a region but under different political jurisdictions, judged as stable and receptive to firm trading arrangements, 2. economic parameter related to the fuel mix where the differences in a country's supply and demand characteristics are significant enough to allow mutual benefits to be realized through cost reduction, 3. environmental parameter linked to a country's carbon intensity that could benefit from the resources of a neighboring jurisdiction with lower intensities, and 4. financial parameter for each country within a region capable of attracting investment capital for a common interest project. The proposed REH is an innovative framework that is the basis for a cost-effective but environmentally beneficial strategy for integrating the energy supply mix of several countries. The countries are geographically contiguous but operate as different jurisdictions with diverse geopolitical, economic, environmental, and financial constraints. For a regional energy hub, the transmission capacity investments act as one of the key policy instruments allowing recognition of the REH interconnectors as the links. We have applied the REH Framework for two case studies: one in developing markets in South-Eastern Europe and a developed market in the North American context. In the first case study, we have utilized the REH Framework's geopolitical parameter to select a set of countries with developing markets to form a regional electricity hub and applied an economic dispatch model to minimize generation costs and reduce GHG emissions simultaneously in the newly formed REH's total energy fuel mix. The preliminary results for this case study indicated that the total cost minimization approach for the region results in a net benefit in favor of the transmission investment. The REH enables transmission capacity to achieve reduced cost generation and emissions by physically interconnecting markets in a predefined region, essentially enabling fuel switching of carbon-based power generation. In the second case study, we have utilized the REH Framework's financial parameter for a developed market, e.g., the PJM's capacity market, to identify the potential value of interconnectors by employing a financial option theory to value capacity options between a generation and an interconnector. Results of our analysis for the existing and planned projects provide strong evidence of the value of transmission capacity as an option within the REH Framework and points to a pathway to achieve decarbonization at lower costs across a region instead of focusing only on investments in generation capacity. Following ratification of the Paris 2015 Climate Change Accord, all national governments are committed to a reduction of GHG within their jurisdictions. This puts a premium on the identification of practical and cost-effective pathways for achieving the national targets for reducing GHG within a regional context. The case studies demonstrate the critical role of a REH in delivering tangible benefits through interconnectors that would otherwise not be achievable if each country’s energy system was isolated from its contiguous neighbors. The REH allows integration of a diverse mix of generation supply of different countries to yield maximum financial value and GHG reduction potential through transmission interconnectors. To enable the transition to a low carbon energy system of the future, REH's offer an expedient pathway through the development of transmission interconnection capacity consistent with geopolitical, environmental and financial criteria developed here.
Shared Community Energy Storage Allocation and Optimization
(University of Waterloo, 2019-05-13) Chang, Hsiu-Chuan; Ghaddar, Bissan; Nathwani, Jatin
Distributed Energy Resources (DERs) have been playing an increasingly important role for managing households energy costs. DERs consist primarily of energy generation and storage systems utilized by individual households or shared among them as a community. This research proposes a framework to allocate shared energy storage within a community and to then optimize the operational cost of electricity using a mixed integer linear programming (MILP). The allocation options of energy storage include the option of private energy storage (PES) and three options of community energy storage (CES): random, diverse, and homogeneous allocation. With various load options of appliances, photovoltaic (PV) generation and energy storage set-ups, the operational cost of electricity for each household is minimized to provide the optimal operation scheduling. In addition to the electricity operational cost, energy storage utilization, and operation fairness are used to compare different allocation options of storage systems. Computational results are presented on two real use cases: Waterloo, Canada and Ennis, Ireland. For each case, one typical summer day and one common winter day are selected to simulate different scenarios of the two seasons. Given the allocation options and ownership rates of residential energy storage deployment, this research shows the advantage of using CES as opposed to PES and evaluates the cost savings which can facilitate future deployment of CES.
SPECIAL COMMENTARY: How Electricity Can Break the Russian Gas Supply Stranglehold on the EU
(Balsillie School of International Affairs, 2022-06-28) Nathwani, Jatin; Guler, Burak
The Russia-Ukraine war raises the question: what should the EU do with its energy infrastructure and fossil-fuels? We argue against massive investments in additional natural gas infrastructure for increasing supply
Technical and Economic Assessment of Ground Source Heat Pump Systems (GSHPs) in Ontario
(University of Waterloo, 2017-08-28) Al-Haq, Armughan; Nathwani, Jatin; Basu, Dipanjan
Ground Source Heat Pump Systems (GSHPs) are one of the most promising clean and low-carbon source of geothermal renewable energy technologies for heating, ventilation and cooling of homes. Geothermal heat pump (GHP) technologies, referred to as GeoExchange, comprise ground-source and/or water-source heat pumps that use the constant temperature of the earth as the exchange medium instead of the outside air temperature. This study is a technical and economic assessment of use of GSHPs to support the policy options for increasing the share of geothermal energy sources within the residential sector of Ontario. The study identifies the technical and economic barriers to the wide-spread adoption of ground source heat pumps in Ontario and is an assessment of the impacts of large-scale deployment of GSHPs on greenhouse gas (GHG) emissions. In this study, I have established the basis for evaluating the cost and environmental benefits of GSHPs in Ontario. The results provide a sound economic and technical foundation for supporting investment decisions in favour of implementing GSHPs as a viable alternative to traditional heating, ventilation, air-conditioning systems (HVACs), specifically, natural gas use for space heating and hot water usage in buildings. The study reveals that geothermal ground source heat pumps have a great potential to reduce GHG emissions for Ontario’s residential sector by a magnitude of 21.7 megatonnes (Mt) that will in turn reduce the overall emissions of Ontario by 13%. GSHPs are a cost-effective solution for implementation on a wide-scale. The economic analysis clearly indicates the horizontal ground source heat pump system (H.GSHPs) is a strong winner in multiple sensitivity analysis when considering different lifespans, discount factors, and base case scenario against comparative scenarios. The rankings of the twenty-seven (27) cities selected for this study identify that the GSHPs are more attractive compared to traditional HVACs from an investment point of view in cities of the southern and distinct region as compared to the northern regions because of low present value (PV) of costs. The PV compares the cash outflows based on the initial investment, operating costs, maintenance costs, and disposal costs in a project lifespan of 60 years that span life cycles of 20 – 30 years for GSHPs and 12 years for traditional HVAC applications. This study has conducted a comprehensive technical and economic assessment for twenty-seven (27) cities in Ontario to address the geographic variation of benefits. While there is a variation across regions of Ontario – and this is based on weather, soil condition and level of energy use – the overall conclusion is a compelling case for GSHPs as a viable alternative to the use of natural gas.
Typology of Business Models for Emerging Grid-scale Energy Storage Technologies
(University of Waterloo, 2019-03-26) Malek, Kourosh; Nathwani, Jatin
The main goals of this thesis are to develop, validate, and analyze emerging business models to ensure near-term market success of the grid-scale Energy Storage (ES) technologies. The main research contributions are a typology (i.e. classification according to general type) of emerging business models for investment and operational viability of grid–scale storage, validation of business models for valuation analysis of diverse grid-scale storage, and a unique technology management framework for value analysis of emerging technologies. It is widely accepted that the intermittency of primary renewable energy sources is a limiting factor for inclusion of these technologies in autonomous power applications. ES technologies can be seen as valuable flexibility assets with their capabilities to control grid power intermittency or power quality services in generation, transmission, and distribution, as well as in end-user consumption side. When combined with sophisticated and reliable business models, grid-scale storage technologies can contribute significantly to enhance asset utilization rate and reliability of the power systems. The latter is particularly critical for deployment of regional and national energy policies of implementing renewable sources. Despite the fact that energy storage systems increase operational cost of the distributed electricity system, energy storage technologies can play a vital role in reducing overall upgrade cost of the electricity grids when renewable sources need to be integrated locally. The main challenge of adopting ES technologies among utilities is how to match the right energy storage technology to appropriate business-operation models for a site-specific grid configuration. Current know-how and assessment tools provide substantial information around technical specifications and requirements for adopting ES technologies for various grid configurations. However, only few of the existing approaches use market driven information. The majority of the tools also suffer from a lack of detailed information relevant for business managers for decision making purposes. Currently, none of the existing tools and investment methodologies evaluate the benefits of electricity storage from the perspective of a detailed techno-economic and business-operation models. The choice of appropriate business model, complexity of regulatory and policy environment, ownership and governance structure of storage asset, financing strategies, managing revenue streams, and associated operational risks are critical for providing an accurate assessment of the viability of the emerging ES technologies. In order to fully assess the value proposition of ES technologies, formulate their risks and opportunities profile, and develop implementation plans, a comprehensive analysis framework is needed to support integration of technical, economic and business operation perspectives. This research aims to develop a typology of different business models in the context of grid-scale ES technologies. A bottom-up approach is proposed, demonstrated, and validated to identify a generalized business model framework. The business model framework is tailored to provide a customized analysis platform for adopting emerging energy storage technologies. Several case studies are carried out based on the proposed business model framework and energy storage valuation analysis therein. Each business model, combined with thorough valuation analysis, provides insights on when deployment of individual storage technologies can be economically and technically viable. For industry looking to adapt new energy storage technologies, such analysis can provide multi-dimension considerations (cost, efficiency, reliability, best practice business operation model, and policy instruments), which can potentially lead to complete insights for strategic decision-making purposes.
Zero Fossil Fuels Dependence Requires a National Commission
(Balsillie School of International Affairs, 2021-05-17) Nathwani, Jatin; Fitz-Gerald, Ann
Canada’s dependence on fossil fuels is both a blessing and a curse: the oil and gas sector currently delivers massive economic benefits, but with an environmental sting that precludes its expansion as global targets for greenhouse gas emissions (GHGs) tighten.