Optimizing Greenhouse Lighting for Advanced Agriculture Based on Real Time Electricity Market Price

Abstract

The world’s growing demand for food can be met by agricultural technology. Use of artificial light to supplement natural sunlight in greenhouse cultivation is one of the most common techniques to increase greenhouse production of food crops. However, artificial light requires significant electrical energy, which increases the cost of greenhouse production and can reduce profit. This paper models the increments to greenhouse productivity as well as the increases in cost from supplemental electric lighting, in a situation where the greenhouse is one of the elements of a smart grid, a system where the electric energy market is dynamic and prices vary over time. We used our models to calculate the optimum values for supplemental light and the required electrical energy for HPS lamps in the greenhouse environment, using cherry tomato cultivation as a case study crop. We considered two optimization techniques: iterative search (IS) and genetic algorithm (GA). The two approaches produced similar results, although the GA method was much faster. Both approaches verify the advantages of using optimal supplemental light in terms of increasing production and hence profit.

Distributed Real‐Time Pricing Method Incorporating Load Uncertainty Based on Nonsmooth Equations for Smart Grid

“…We define the utility function of an individual as ( ), where is the user's electricity consumption. In previous studies, some researchers adopted a quadratic function as the utility function [9,10]. In this work, we also use a quadratic function as the utility function, which is expressed as…”

“…Similar to [8][9][10], we perform the numerical simulations according to the users' electricity usage. Each method was tested more than 50 times, where the related parameters were selected randomly.…”

“…Currently, the main pricing methods based on DR programs contain fixed, multistep, time-of-use, and real-time pricing. The latter has advantages over the multistep and time-of-use pricing methods, which is the most effective for demand side management and can avoid new peaks caused by user electricity consumption during low-price periods [7][8][9][10]. When the real-time pricing method is implemented, users can shift their usage and save on electricity loads to reduce electricity costs while satisfying building inhabitant comfort level and satisfaction requirements.…”

Real-Time Pricing for Demand Response in Smart Grid Based on Alternating Direction Method of Multipliers

“…For instance, a joint optimization model of generation side and user side based on TOU pricing in China has been discussed by Tan et al [3]. But RTP has been accepted as the most effective pricing scheme in DSM [4].…”

“…When the penalty term added to the objective function is zero for any feasible ( ), problem (10) is clearly equivalent to the original problem (4). In this specific problem, the penalty item depicts the uncertainty of the use of electricity.…”