Decarbonizing Canada’s agricultural sector is critical to meeting national net-zero emissions targets by 2050. This study presents a techno-economic framework for evaluating the feasibility of Small Modular Reactor (SMR)-based Hybrid Energy Systems (HESs) for large-scale greenhouse operations in Southern Ontario’s Leamington-Kingsville corridor, Canada's largest controlled-environment agriculture hub. Eight energy-system configurations incorporating SMRs, solar PV arrays, wind turbines, battery storage, boilers, combined heat and power (CHP) units, and grid interconnections are modelled using the Hybrid Energy System Optimization (HESO) platform. Hourly operational data from five industrial greenhouses are used to characterize thermal and electrical load profiles. Results indicate that a fully nuclear configuration (SMR2/Battery) achieves the greatest CO₂ reduction of approximately 437 kt relative to the natural-gas baseline, albeit at a substantially higher cost, with a levelized cost of electricity (LCOE) of 13,282 $/MWh and a levelized cost of heat (LCOH) of 59.4 $/MWhₜₕ (all costs reported in USD) and a net present cost (NPC) of 383 M$. By contrast, hybrid configurations such as SMR2/Boiler/Grid and SMR2/Boiler/PV/Battery yield more moderate CO₂ reductions ranging from 4 to 35 kt, with LCOE values of 160-343 $/MWh, LCOH of 62.7-65.7 $/MWhₜₕ, and NPC ranging from 94 M$ to 282 M$, while maintaining annual operating costs close to the fossil-fuel baseline (6.3-6.9 M$/yr). Sensitivity analyses reveal that extended project lifetimes and lower inflation rates improve cost-effectiveness, whereas elevated nuclear-capital or grid-electricity prices substantially increase total system expenses. Hybrid configurations integrating SMRs with renewables and battery storage can reduce annual CO₂ emissions by up to 35 kt, maintain LCOH near 63 $/MWhₜₕ, and keep lifetime system costs below approximately 280 M$, offering a technically viable and economically competitive pathway for greenhouse decarbonization within Canada's clean-energy transition.

Maritime shipping accounts for over 80 % of global trade and about 3 % of global CO₂ emissions due to its reliance on fossil fuels for propulsion and auxiliary power generation. This study assesses in detail the energy demand, CO₂ emissions, and renewable energy infrastructure required to electrify container ships operating from Los Angeles Harbor (LAH) across three representative capacity ranges: 5000–7999 TEU, 8000–11,999 TEU, and 12,000–14,499 TEU. The estimated annual propulsion energy demand for these vessel classes is 227,842 MWh, 253,884 MWh, and 253,055 MWh, respectively, reflecting the growing energy requirements with increased displacement and engine capacity. Larger vessels show higher total power consumption but greater transport efficiency, as energy demand per TEU decreases from 45.6 MWh/TEU for the smallest class to 21.1 MWh/TEU for the largest, representing a 54 % improvement in energy intensity and overall fuel economy. Average cruising speeds are 24.6, 23.9, and 23.8 knots for the three classes, respectively, indicating that scale optimization does not significantly compromise operational performance. Under heavy fuel oil (HFO) operation, annual CO₂ emissions reach 72,245 t, 92,797 t, and 96,845 t per vessel, while full battery-electric operation powered entirely by renewable sources nearly eliminates these emissions. Reducing sailing speed by 25 % lowers annual fuel consumption by roughly 30 %, decreasing CO₂ emissions to about 67,700 t for the smallest class, demonstrating the strong non-linear relationship between speed and fuel use. Renewable methanol reduces emissions to around 29.8 gCO₂/TEU-n.mile, LNG achieves approximately 59.6 gCO₂/TEU-n.mile, and low-sulfur HFO produces nearly 99 gCO₂/TEU-n.mile. Battery weight increases propulsion power by roughly 3 % for the largest ship, but remains a secondary factor compared with voyage energy requirements.

This study presents a comprehensive energy modeling and optimization analysis for a proposed hybrid renewable energy system to power a middle school on the remote, off-grid Pelee Island, Canada. A detailed eQUEST energy model incorporates the school’s architectural design, HVAC configuration, occupancy schedules, and local climatic data to generate an hourly load profile. The simulation reveals an annual electricity demand of 152.8 MWh, with space heating constituting the largest end-use (40%) and exhibiting pronounced winter peaks (January: 16.82 MWh). To meet this demand, techno-economic optimization identifies an optimal hybrid configuration under a cost-based dispatch optimization strategy comprising a 50-kW solar PV array, two 20-kW wind turbines, a 160-kW biogenerator, and a 221-kWh battery storage system (with a 146-kW converter). The optimal system produces 247,658 kWh/year and achieves a levelized cost of energy (COE) of $0.269/kWh and a net present cost (NPC) of $604,702, ensuring zero unmet load and an 8.7-year payback period. Component-level results indicate complementary operation: PV supplies 58,375 kWh/year (~27.5%), wind supplies 68,148 kWh/year (~23.6%), and the biogenerator supplies 121,135 kWh/year (~48.9%) while consuming 21.5 tonnes/year of biomass fuel and operating 1,482 h/year. Operational performance and savings are rigorously validated using the International Performance Measurement and Verification Protocol (IPMVP) with weather-normalized weekday/weekend baselines (R²=0.7566 and 0.7104, respectively, at Tb=23°C). The enthalpy wheel (ECM I) achieves quantified heating savings of 35,216 kWh annually, and the electric pre-heater (ECM II) delivers 6,035 kWh annual savings with end-use reductions of 8% (heating), 15.4% (ventilation), and 1% (hot water).

As wind energy and increasingly integrated grid topologies evolve, wind farm operators will have more options for the sale of their desirable low carbon, low water footprint power. Potential markets could include transmission, but also local sales to distribution grids and/or directly connected customers. The increase of coupled energy storage also increases the optionality of wind energy sales. Conventional static Power Purchase Agreements (PPAs) and/or market auctions may not be sufficiently flexible or efficient at enabling the optimization of wind energy sale options in an increasingly opportunistic market environment. The utilization of semi-autonomous agents to represent energy trading entities could increase the dynamism and widen market opportunities for all stakeholders. Subsequently, this Paper demonstrates how semi-autonomous agents could be developed to negotiate the sale of power between a central grid and two wind farms. We develop a framework for negotiation-based energy trading between a grid and two battery equipped wind farms, Airbreeze and Jetflow, using Deep Q-Learning (DQL) to optimize the grid’s energy procurement. Wind generation and battery storage levels for each wind farm are modeled as dynamic constraints in the negotiation process. The proposed system negotiates energy every 10 minutes over a 7-day period, simulating offers and counteroffers to balance grid demand with wind energy availability. The negotiation system integrates a DQL-based agent with a strategy-switching mechanism that adapts to wind farm behavior. Experimental results show the system's effectiveness in negotiating energy, maintaining battery levels, and securing sufficient power for grid needs.

This study develops an integrated modeling and optimization framework for a renewable microgrid for a proposed middle school on Pelee Island, Canada, a grid-constrained community served by an aging submarine power cable. A detailed building energy model was constructed in eQUEST using site-specific climate data, occupancy schedules, and HVAC configurations, yielding an hourly electricity demand profile that totals 152.8 MWh/year. Space heating dominates end-use consumption at 40%, and heating demand peaks at 16.82 MWh in January. Techno-economic optimization identifies an optimal hybrid configuration comprising a 50 kW photovoltaic array, two 20 kW wind turbines, a 160 kW biogenerator, and a 221 kWh battery bank coupled to a 146 kW converter. Under a cost-based dispatch strategy, the system delivers 247,658 kWh/year with zero unmet load, achieving a cost of energy (COE) of $0.269/kWh, a net present cost (NPC) of $604,702 over the project lifetime, and a payback period of 8.7 years. Wind, photovoltaic, and biogenerator sources contribute 23.6%, 27.5%, and 48.9% of total generation, respectively. System performance is verified using an IPMVP-informed, weather-normalized baseline that couples separate weekday and weekend regression models at a balance-point temperature of 23 °C, producing R2 values of 0.76 and 0.71, respectively. Two energy conservation measures (ECMs) are evaluated: an enthalpy wheel that recovers 35,216 kWh/year of heating energy and an electric preheater that delivers 6,035 kWh/year of auxiliary savings. The biogenerator operates for 1,482 hours/year and consumes 21.5 tonnes of biomass fuel, while the wind turbines reach a 38.9% capacity factor, reflecting strong year-round resource availability.

The transition towards low-carbon thermal processes requires an objective, systematic evaluation of alternative fuels in industrial drying applications. This study investigates the thermodynamic performance of hydrogen compared with current fuels, natural gas and propane, in a continuous-flow grain dryer using a detailed exergy analysis. A BROCK BCT-2500 dryer was modelled to dry corn grains from 25% to 15% moisture content under steady state and adiabatic conditions, evaluated at stoichiometric and lean combustion ratios. Exhaust gas properties for each fuel were determined from equilibrium calculations using NASA CEA software, which is then diluted in the mixing chamber with excess air to attain the required temperature and flow rate to dry these grains. The exergy balances were established across each component of the system while maintaining identical grain inlet and outlet conditions, corresponding to a fixed drying load basis. In addition, a complementary analysis was performed on a fixed fuel mass basis to evaluate the intrinsic performance of each fuel per unit mass. Results indicate that combustion is the primary source of exergy destruction among all fuels. Under stoichiometric conditions, the exergy destruction was found to be significantly higher for hydrogen compared to natural gas and propane under identical drying requirements, primarily due to higher exhaust gas temperatures; however, lean combustion substantially improves the exergy performance of hydrogen, making it a more viable option for low-carbon drying processes. Hydrogen exhibits the highest drying capacity of 3.3–5.6 kg of corn per gram of hydrogen, while propane maintains the highest exergy efficiency of up to 61% under current operating scenarios. Evaluating drying performance on a per-unit-mass-of-fuel basis provides a direct measure of the intrinsic fuel capability, independent of system-scale operating conditions. These findings reveal a critical trade-off between thermodynamic efficiency and drying capacity of fuels, offering a quantitative insight into the feasibility of hydrogen in grain drying systems.

Onshore wind is a mature technology, its capacity is expected to grow from 7.8% of the global energy mix in 2023 to 12.1% in 2028. The ability to understand the current health of wind assets through the remaining useful life (RUL) of specific components has advanced through improved reliability engineering. This has led to advances in operational cost minimization through strategic maintenance scheduling. However, turbine uptime and maintenance schedules are not the full economic landscape of today's wind industry, as farms are now being sold as transactional commodities. Yet, the riskiness of wind assets is not fully appreciated through these transactions. Wind assets arc inherently different than traditional civil infrastructure as they can provide a dynamic revenue stream. This mcans investment can potentially create opportunities for additional and/or larger profits later in asset life depending on the market. While decision support systems exist in the wind industry, they have not been adapted for end-of-life support. A working framework has been developed as a comprehensive investment decision support platform for the wind industry. Thc integrated framework combines traditional project finance principles like objective functions and discounted cash flow (DCF) analysis, with more complex methods like scenario testing, real-options valuation, and probabilistic techniques like Monte Carlo simulations to provide the asset owner with transparent, realistic, and open-ended decision support. The framework is applied in a case study to recommend actions for four different future market scenarios relating to a real commercial wind farm.

This study investigates the potential of integrating Small Modular Reactors (SMRs) into greenhouse operations and urea production to tackle rising energy demands and environmental concerns stemming from emissions in the food production sector. The research offers a comprehensive techno-economic assessment of a system utilizing SMRs to supply both heat and electricity to a greenhouse while generating sustainable urea fertilizer. The evaluation includes key metrics such as Levelized Cost of Urea (LCOU), Payback Period (PBT), Discounted Payback Period (DPB), and Internal Rate of Return (IRR). The analysis indicates a total capital expenditure of approximately 400 million USD, with the SMR representing 88 % of the cost. The LCOU is estimated at USD 1394 per metric ton, which is significantly higher than conventional market prices, leading to a prolonged PBT of 15.4 years and a lower IRR of 4.1 %. Sensitivity analyses demonstrate that fluctuations in urea prices and SMR capital costs significantly affect the system's financial viability. Despite the high initial costs, the SMR-powered system has the potential to reduce natural gas consumption and greenhouse gas emissions, thereby promoting long-term sustainability in agriculture. These findings emphasize how SMRs can deliver a cleaner, more sustainable energy solution for greenhouse heating and nitrogen fertilizer production, which are vital for supporting agricultural growth while minimizing environmental impact.

Energy quality plays a critical role in the commercialization of thermoelectric generators (TEGs). In this study, a hyperbolic shape is introduced into the design of the thermoelectric (TE) couple, which is a core component of a TEG module. To this end, a one-dimensional thermodynamic model, solved using the particle swarm optimization (PSO) method, is developed to evaluate its exergy performance compared to that of the traditional cubic design. The results reveal that the TEG module with hyperbolic-shaped TE couples exhibits lower irreversibility, along with higher exergy efficiency and reduced levelized cost of energy (LCOE). Specifically, the hyperbolic design achieves improvements of up to 10.4

Power Purchase Agreements (PPAs) typically involve a power producer and a power purchaser. Historically, these two primary parties agree to pricing terms that will be honored over multiple year contracts. Successful wind farm enterprises also rely on third parties for critical services like operations and maintenance (O&M). These O&M service contracts are often provided for the wind farm (Principal) by the original equipment manufacturer (OEM) (Agent). In a case where the Agent billed per work completed, and had superior knowledge as to required maintenance, the Agent could be incentivized to overbill the Principal. On the other hand, a Pixed, annual cost contract might lead the Agent to under-maintain the asset. The information asymmetry between the Principal and Agent could be eliminated if the wind farm and the OEM are both parties to the PPA. With aligned interests, there will be incentive to minimize downtime and increase farm performance. This study employs a Monte Carlo simulation to model 10,000 performance comparisons of a three-party PPA that includes the O&M provider, against a standard two-party PPA version that includes only the wind farm and power purchaser. The investigation is based on two years of operations data from a 200 MW Canadian wind farm. More than 50% of cases tested show power producer proPit increases greater than $16.9M (4.1%) and greater than $11.7M (3.5%) increases for the OEM over a traditional two-party PPA. A global sensitivity analysis was also completed which showed that the power sale price and the shared revenue percentage were most inPluential in terms of total earnings for each party.

Maritime shipping is a cornerstone of global commerce, enabling the transport of goods across continents and fueling economic development. However, its substantial dependence on fossil fuels positions it as a major source of CO2 emissions and environmental impact. This study assesses energy demands, CO2 emissions, and infrastructure for electrifying container ships across three size categories operating from Los Angeles Harbor (LAH). Findings indicate annual energy consumption of 227,842 MWh, 253,884 MWh, and 253,055 MWh, respectively, for each class. Notably, larger vessels achieve enhanced energy efficiency per TEU, with energy demand per TEU decreasing from 45.57 MW-hr/TEU for 5000–7999 TEU ships to 21.09 MW-hr/TEU for 12,000–14,499 TEU vessels. While smaller vessels maintain slightly higher average speeds (24.6 knots for 5000–7999 TEU ships compared to 23.8 knots for 12,000–14,499 TEU), larger ships offer superior sustainability in fuel efficiency and emissions per kilometer traveled. The analysis also shows that increased battery weight elevates power requirements, raising fuel use by 3

Large-scale compressed gas storage is a critical part of green hydrogen production with offshore renewable energies. Compared with traditional floating onboard storage, subsea storage is a safer, all-weather and long-term alternative. This study presents a subsea hydrogen storage accumulator concept with a low aspect ratio (AR = 0.5). The hydrodynamics of accumulators with different free ends, namely flat tip (FT), radiused tip (RT), and approximate hemispherical tip (AHT) are investigated. The Reynolds number is about 3.3 × 106. The large-eddy (LES) turbulence model is used in the numerical simulation. The time-averaged and transient flow structures, force characteristics, and surface pressure coefficient of three accumulators are compared and analyzed. In addition, the strength and influence range of the horseshoe vortex around the accumulators are quantified by the time-average bed pressure coefficient and bed shear stress amplification for the study of local scour. The results show that complex and abundant flow structures have formed in the wake of the three accumulators, including obvious arch-type vortex and hairpin vortex. The dominating frequency of vortex shedding is not obvious, and the lift coefficient also indicates a non-zero lateral force exerted on the accumulators. The main reason is that the flow is in the regime of supercritical transition. The mean lift coefficient and drag coefficient of the three accumulators are 0.63/0.30 (FT), 0.66/0.21 (RT), and 0.70/0.19 (AHT), respectively. On comparing the AHT and RT accumulators, the influence range of the horseshoe vortex in front of the FT accumulator is larger, and the fluid recirculation and downwash behind the tank are stronger. To some extent, the AHT design reduces the fluid recirculation and downwash in front and behind the accumulator, and has an inhibitory effect on local scour.

Underwater compressed hydrogen storage is poised to become a promising enabler for harnessing intermittent offshore renewable energy. The substantial buoyancy generated by the density difference between hydrogen and seawater presents significant anchoring challenges for seabed foundations. This study introduces a novel composite suction caisson (CSC) foundation integrated with compressed hydrogen accumulators and investigates its anti-uplift mechanisms under operational buoyancy variations. Scaled physical modeling tests are conducted with controlled buoyancy loading rates ranging from 1 to 100 N/s, representing realistic gas injection scenarios. Comparative analyses of pullout resistance among regular suction caissons (RSCs), modified suction caissons (MSCs), and CSCs reveal that the proposed CSC configuration enhances bearing capacity by 8.4

This study provides one of the first systematic exergy-based comparisons of hydrogen, natural gas, and propane in a commercial continuous-flow grain dryer, combining operational and fuel-based performance perspectives. A BROCK BCT-2500 dryer was modelled to dry corn grains from 25% to 15% moisture content under steady state and adiabatic conditions, evaluated at stoichiometric and lean combustion ratios. Exhaust gas properties for each fuel were determined from equilibrium calculations using NASA CEA software at both stoichiometric and lean combustion, which is then diluted in the mixing chamber with excess air to attain the required temperature and flow rate to dry these grains. The exergy balances were established across each component of the system while maintaining identical grain inlet and outlet conditions, corresponding to a fixed drying load basis. In addition, a complementary analysis was performed on a fixed fuel mass basis, varying exhaust gas and diluted exhaust gas temperatures to evaluate the intrinsic performance of each fuel per unit mass. Results indicate that combustion is the primary source of exergy destruction among all fuels. Hydrogen shows approximately 1.3% and 7% higher exergy destruction from natural gas and propane, respectively, under stoichiometric combustion and identical drying conditions, primarily due to higher exhaust gas temperatures; however, lean combustion substantially improves the exergy performance of hydrogen, making it a more viable option for low-carbon drying processes. Hydrogen exhibits the highest drying capacity of 3.3-5.6 kg of corn per gram of hydrogen, while propane maintains the highest exergy efficiency of up to 61% under current operating scenarios. Evaluating drying performance on a per-unit-mass-of-fuel basis provides a direct measure of the intrinsic fuel capability, independent of system-scale operating conditions. These findings reveal a critical trade-off between the thermodynamic efficiency and the drying capacity of fuels, offering quantitative insight into the feasibility of hydrogen in grain-drying systems. Furthermore, the evaluation of a 5% hydrogen blend with NG demonstrated that low-level hydrogen integration can be implemented within existing drying systems with limited infrastructure modifications.

As Canada advances toward its target of zero-emission heavy commercial vehicle sales by 2040, the success of long-haul electric vehicle (LHEV) adoption will depend on the availability of well-placed, high-capacity public charging infrastructure. This study evaluates how two critical real-world constraints, namely daily utilization time and site space capacity, affect the optimal design of Ontario's future on-route charging network by simulating a total of 63 scenarios. The results show that modest utilization thresholds (e.g., 8 h per day) can reduce the number of required stations by up to 20 % with minimal impact on service coverage. In contrast, restricted space capacity leads to steep declines in the number of supported trips unless more locations are added. When both constraints are applied together, their impacts are largely additive, increasing the need for infrastructure expansion while shifting grid demand across space and time. The study highlights the need to align transportation and electricity infrastructure planning, prioritize high-demand freight corridors, and support regulatory frameworks that promote efficient, high-utilization, and grid-resilient charging solutions.

Maritime shipping underpins global logistics but remains a source of greenhouse gas emissions, motivating the evaluation of low-carbon fuels and port-based supply infrastructure. This study assesses the techno-economic and environmental feasibility of hydrogen-powered small feeder container ships at the Port of Los Angeles (POLA) using a framework combining optimal sizing, techno-economic modeling, and sensitivity analysis. A hybrid hydrogen supply system is optimized, consisting of 500 kW PV, a 438 kW converter, a 7 MW electrolyzer, and a 300 kg H2 tank, producing 716,538 kg H2/yr to meet ship fueling demand. The optimal configuration achieves a levelized cost of hydrogen (LCOH) of $3.54/kg H2, a 14-year payback period, and a net present cost (NPC) of $104.9M over a 30-year lifetime. Sensitivity analysis indicates that ship speed is the dominant operational driver: increasing speed from 8 to 24 knots raises hydrogen demand from 89,600 kg/y to 2.42 million kg/yr, while reducing LCOH from $11.99/kg H2 to $1.33/kg H2 due to higher utilization. Vessel weight further affects system performance, with favorable cost-power trade-offs observed near 8000-8500 t displacement. Solar resource availability also influences economics, with lower NPC and operating costs occurring near 5 kWh/m2/day. Environmental performance is evaluated using the normalized metric grams CO2/TEU-nautical mile, highlighting scale-related efficiency effects across vessel classes: small feeder ships exhibit higher emissions intensity (153 g CO2/TEU-n.mi) than large container ships (35 g CO2/TEU-n.mi), despite substantially lower absolute hydrogen demand. A real-world POLA-Shanghai case study further demonstrates scalability requirements for long-distance routes and port-level hydrogen supply planning.

This study presents a comparative life cycle and economic assessment of using clean hydrogen as a sustainable alternative to natural gas and propane for corn grain drying. The study compares the environmental performance limited to GWP100 and cost-effectiveness of hydrogen from various renewable sources (hydro, wind, solar) and plasma pyrolysis of natural gas against conventional fossil fuels under two delivery scenarios: pipeline and trucking. A life cycle assessment is conducted using Open LCA to quantify the carbon intensity of each fuel from cradle to combustion at multiple energy requirements, based on four burner efficiencies across each scenario. In parallel, economic analysis is conducted by calculating the fuel cost required per ton of dried corn grains at each efficiency across both scenarios. The results indicate that green hydrogen consistently outperforms current fuels in terms of emissions, but it is generally more expensive at lower burner efficiencies and in trucking scenarios. However, the cost competitiveness of green hydrogen improves significantly at higher efficiency, and with pipeline infrastructure development, it can become more economical when compared to propane. Hydrogen produced via plasma pyrolysis offers high environmental and economic costs due to its electricity and natural gas requirements. Sensitivity analysis further explores the impact of a 50% reduction in hydrogen production and transportation costs, revealing that hydrogen could become a viable option for grain drying in both pipeline and trucking scenarios. This study highlights the long-term potential of hydrogen in reducing carbon emissions and offers insights into the economic feasibility of hydrogen adoption in agricultural drying processes. The findings suggest that strategic investments in hydrogen infrastructure could significantly enhance the sustainability of agricultural practices, paving the way for a greener future in food production.

Transient energy simulation software TRNSYS is used to model and simulate the temperature of stormwater ponds and determine the relative importance of each energy-transfer mechanisms that affect the energy gains or losses of a pond. This model distinguishes itself from prior research by analyzing how runoff entering and connecting the pond to a heat exchanger (serving as a coolant) affects its temperature. The model was validated by comparing the predicted pond temperature with field data collected from a retention pond in Leamington, Ontario, Canada. The results indicate that the model’s average error in predicting the temperature of the pond relative to the observed values is 0.13 °C. A sensitivity study has been conducted on the system to verify the impact of each parameter on the pond temperature.The air temperature, solar radiation, wind speed, relative humidity, pond surface area, rain temperature, and rain flow rate were considered in the sensitivity analysis. The sensitivity analysis showed that, among the investigated parameters, air temperature had the most significant effect on pond temperature. A 3 °C increase in air temperature causes a 1.24 °C increase in pond temperature. The magnitude of the energy transfer mechanisms was investigated. The results showed that the contribution of runoff and return flow from the heat exchanger had the least effect on changing the average temperature of the pond. In addition, solar gain and evaporation had the largest share of the energy received and lost from the pond with 50