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Large-scale solar promises a low-carbon energy alternative. However, solar production in North America given anticipated climate change has been studied only seasonally in terms of solar irradiance. This work integrates more of the predictive potential of climate-change models by exploring other environmental variables, such as humidity and temperature. Here, a Continental US (CONUS) model is produced by deep learning using 2593 NREL simulated solar power stations. Daily forecasts using 17 Global Climate Models (GCM’s) through 2099 are summarized monthly. Results suggest power production factors change between +4 % and 􀀀 19 % over 93 years. These results suggest more, but still modest, potential declines than previous solar irradiance-based studies. The modest impact is encouraging. For some areas, climate model variability unfortunately yielded statistically insignificant trends and practical application is less clear. For future evaluations, this work suggests the potential importance of additional variables, monthly interval summary, and accounting for model variability. 

In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing cleanhybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation. 

Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants. 

Most large-scale ammonia production typically relies on natural gas or coal, which causes harmful carbon pollution to enter the atmosphere. The viability of a small-scale “green” ammonia plant is investigated where renewable electricity is used to provide hydrogen and nitrogen via electrolysis and air liquefaction, respectively, to a Haber-Bosch system to synthesize ammonia. A green ammonia plant can serve as a demandresponsive load to the electricity distribution system and provide long-term energy storage through chemical energy storage in ammonia. A coordinated operational model of an electricity distribution system and an electricity-run ammonia plant is proposed in this paper. Case studies are performed on a modified PG&E 69-node electricity distribution system coupled with a small-scale ammonia plant. Results indicate the ammonia plant can adequately serve as a demand response resource and positively impact the distribution locational marginal price (DLMP). 

This paper proposes a home energy management system (HEMS) while considering the residential occupant’s clothing integrated thermal comfort and electrical vehicles (EV) state-of-charge (SOC) concern. An adaptive dynamic program- ming (ADP) based HEMS model is proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), the donning/doffing decisions for the clothing conditions and charging/discharging of EV while taking into account the uncertainties in outside temperature and EV arrival SOC. We use model predictive control (MPC) to simulate a multi-day energy management of a residential house equipped with the proposed HEMS. The proposed HEMS is compared with a baseline case without the HEMS. The simulation results show that a 47.5% of energy cost saving can be achieved by the proposed HEMS while maintaining satisfactory occupant thermal comfort and negligible EV SOC concerns. 

Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.