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AbstractÐIn the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing clean hybrid 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. 

Hidden moving target defense (HMTD) is a proactive defense strategy that is kept hidden from attackers by changing the reactance of transmission lines to thwart false data injection (FDI) attacks. However, alert attackers with strong capabilities pose additional risks to the HMTD and thus, it is much-needed to evaluate the hiddenness of the HMTD. This paper first summarizes two existing alert attacker models, i.e., bad-data-detection-based alert attackers and data-driven alert attackers. Furthermore, this paper proposes a novel model-based alert attacker model that uses the MTD operation models to estimate the dispatched line reactance. The proposed attacker model can use the estimated line reactance to construct stealthy FDI attacks against HMTD methods that lack randomness. We propose a novel random-enabled HMTD (RHMTD) operation method, which utilizes random weights to introduce randomness and uses the derived hiddenness operation conditions as constraints. RHMTD is theoretically proven to be kept hidden from three alert attacker models. In addition, we analyze the detection effectiveness of the RHMTD against three alert attacker models. Simulation results on the IEEE 14-bus systems show that traditional HMTD methods fail to detect attacks by the model-based alert attacker, and RHMTD is kept hidden from three alert attackers and is effective in detecting attacks by three alert attackers. 

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.