|Title||Optimal utilization of a boiler, combined heat and power installation, and heat buffers in horticultural greenhouses|
|Author(s)||Beveren, P.J.M. van; Bontsema, J.; Straten, G. van; Henten, E.J. van|
|Source||Computers and Electronics in Agriculture 162 (2019). - ISSN 0168-1699 - p. 1035 - 1048.|
Biobased Chemistry and Technology
|Publication type||Refereed Article in a scientific journal|
|Availibility||Full text available from 2021-07-01|
|Keyword(s)||Dynamic optimization - Energy cost saving - Equipment deployment - Greenhouse - Greenhouse operational management - Zero-or-range constraint|
In the daily operation of a greenhouse, decisions must be made about the best deployment of equipment for generating heat and electricity. The purpose of this paper is twofold: (1)To demonstrate the feasibility and flexibility of an optimal control framework for allocating heat and electricity demand to available equipment, by application to two different configurations used in practice. (2)To show that for a given energy and electricity demand benefit can be obtained by minimizing costs during resource allocation. The allocation problem is formulated as an optimal control problem, with a pre-defined heat and electricity demand pattern as constraints. Two simplified, yet realistic, configurations are presented, one with a boiler and heat buffer, and a second one with an additional combined heat and power generator (CHP)and a second heat buffer. A direct comparison with the grower is possible on those days where the other equipment that was at the grower's disposal was not used (63 days in the available 2012 data set). On those days overall costs savings of 20% were obtained. This shows that a given heat demand does not come with a fixed price to pay. Rather, benefits can be obtained by determining the utilization of the equipment by dynamic optimization. It also appears that prior knowledge of gas and electricity prices in combination with dynamic optimization has a high potential for cost savings in horticultural practice. To determine the factors influencing the outcome, different sensitivities to the optimization result were analyzed.