An investment decision support tool for horticulture with an adaptive energy management system

In this paper, we provide a simulation-based framework to estimate the electricity cost of cooling and lighting systems in horticulture for a given desired average temperature setting. Since the electricity price follows a Time-of-Use (TOU) pricing program which asks for a higher price during the peak period, the electricity cost could be reduced by having an adaptive Energy Management System (EMS) performing a demand response function, shifting some electricity demand from the high to low pricing periods. To allow demand shifting, we assume that the temperature settings of the cooling system at different times can be varied within a range but their average must meet the desired setting. Following Yoon et al., we build a model of a simple greenhouse and use EnergyPlus, developed by Lawrence Berkeley National Laboratory, to estimate the electricity consumption for the temperature settings determined using genetic algorithm in MATLAB. Our framework is an attempt to provide a simulationbased decision support tool for horticulturists and investors, to determine the best average temperature setting that maximizes the estimated profit, which is the estimated revenue minuses the estimated minimum electricity cost, of producing a given plant with TOU pricing. To illustrate our tool, we consider the Pharachatan-80 strawberry and determine the optimum average temperature setting to grow it indoor with an estimated ambient temperature profile and the TOU pricing in Thailand.