The recent legalization of cannabis is facilitating very rapid growth in the cannabis cultivation industry, with the energy intensive indoor cultivation facilities becoming more prevalent. This presents a challenge to utilities as the high energy demand from this industry can overburden the existing utility infrastructure. Hence, from both planning and operational perspectives, it is crucial to understand the energy consumption of the rapidly growing load. This paper proposes a deterministic energy consumption model for indoor cannabis cultivation operations for the two major loads in these facilities, i.e., lighting and HVAC, over a 24-hour period based on equipment specifications and operational requirements of the facility. This model can further be used to estimate or forecast short-term and long-term energy demands and costs of indoor cannabis operation(s). The proposed model successfully simulated the environmental conditions in a real-world cannabis facility, and the model's energy consumption output is validated using actual measurements taken from this facility as well as model output using GridLab-D.

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With the expectation of increasing market share of Plug-in Electric Vehicles (PEVs), utilities expect to see a significant increase in energy demand and system peak as a result of PEVs recharging their batteries from the grid, if the charging is not controlled at the distribution system level. By making the grid "smarter", utilities would be able to maximize utilization of existing assets and defer capital investments, while maintaining system security and reliability. The current research proposes a modeling framework for day-ahead dispatch and dynamic control of PEV loads as well as scheduling of taps and capacitors. The first step of the proposed work, which is presented in this paper, involves the development of a static Genetic Algorithm (GA)-based optimization model that determines the day-ahead schedule for PEV loads, taps and capacitors, with the base load and relevant PEV information provided as inputs to the model. In this case, the objective is to minimize the system peak, while satisfying the physical and operational limits of the distribution system, as a higher system peak translates into higher operational costs for the utility. The proposed approach is tested in an actual distribution feeder, demonstrating its feasibility for realistic applications.

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