Proactive energy management of solar greenhouses with risk assessment to enhance smart specialisation in China

For better time-allocation of stored energy, the solar greenhouse (SGH) is equipped with some storage devices designed economically for local weather: wall storage actively managed with energy-store/retrieve fans and Safety Energy (SE which is a solar collector and fully thermally isolated heat tank) designed for non-regular extreme weather. A proactive energy management process, addressing the optimal energy utilisation through dynamic cooperation of the wall and the SE, is presented in this paper. Based on probabilistic weather forecast and a SGH thermal model, found by system identification, the operation set-points are optimised proactively by minimising the plant probable thermal “cost” and weather-related risk in a scheduling period to take pre-emptory action against potential emergencies. The optimisation is formulated in a two-level control scheme. A master problem optimises the primary (wall-soil) storage operation against the expected weather, and a sub-problem operates the SE as a supplement to the limited wall storage in order to create a better indoor environment. The main task of the slave problem manager is to find the optimal SE operation under probable extreme weather to keep reserves to minimise any risk of severe crop loss. The overall optimisation is solved by a hybrid evolutionary algorithm based on a genetic algorithm. The tests show good potential for energy saving and crop cold stress minimisation, as well as great tolerance to forecast errors for most of the cases in Monte-Carlo simulation. The capacity of the proposed real-world system to implement the tested risk management scheme over web “recommendations” satisfies the need to close the loop of an effective Internet of Things (IoT) system, based on the MACQU (Management And Control for QUality) technological platform.