TY - JOUR
T1 - Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids
AU - Baldi, S
AU - Karagevrekis, A
AU - Michailidis, IT
AU - Kosmatopoulos, EB
N1 - Accepted Author Manuscript
PY - 2015
Y1 - 2015
N2 - Electrical smart microgrids equipped with small-scale renewable-energy generation systems are emerging progressively as an alternative or an enhancement to the central electrical grid: due to the intermittent nature of the renewable energy sources, appropriate algorithms are required to integrate these two typologies of grids and, in particular, to perform efficiently dynamic energy demand and distributed generation management, while guaranteeing satisfactory thermal comfort for the occupants. This paper presents a novel control algorithm for joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy demand shaping is achieved via an intelligent control mechanism for heating, ventilating, and air conditioning units. The intelligent control mechanism takes into account the available solar energy, the building dynamics and the thermal comfort of the buildings’ occupants. The control design is accomplished in a simulation-based fashion using an energy simulation model, developed in EnergyPlus, of an interconnected microgrid. Rather than focusing only on how each building behaves individually, the optimization algorithm employs a central controller that allows interaction among the buildings of the microgrid. The control objective is to optimize the aggregate microgrid performance. Simulation results demonstrate that the optimization algorithm efficiently integrates the microgrid with the photovoltaic system that provides free electric energy: in particular, for each building composing the microgrid, the energy absorbed from the main grid is minimized, the energy demand is balanced with the solar energy delivered to each building, while taking into account the thermal comfort of the occupants.
AB - Electrical smart microgrids equipped with small-scale renewable-energy generation systems are emerging progressively as an alternative or an enhancement to the central electrical grid: due to the intermittent nature of the renewable energy sources, appropriate algorithms are required to integrate these two typologies of grids and, in particular, to perform efficiently dynamic energy demand and distributed generation management, while guaranteeing satisfactory thermal comfort for the occupants. This paper presents a novel control algorithm for joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy demand shaping is achieved via an intelligent control mechanism for heating, ventilating, and air conditioning units. The intelligent control mechanism takes into account the available solar energy, the building dynamics and the thermal comfort of the buildings’ occupants. The control design is accomplished in a simulation-based fashion using an energy simulation model, developed in EnergyPlus, of an interconnected microgrid. Rather than focusing only on how each building behaves individually, the optimization algorithm employs a central controller that allows interaction among the buildings of the microgrid. The control objective is to optimize the aggregate microgrid performance. Simulation results demonstrate that the optimization algorithm efficiently integrates the microgrid with the photovoltaic system that provides free electric energy: in particular, for each building composing the microgrid, the energy absorbed from the main grid is minimized, the energy demand is balanced with the solar energy delivered to each building, while taking into account the thermal comfort of the occupants.
KW - Interconnected microgrids
KW - Demand response
KW - Thermal comfort
UR - http://resolver.tudelft.nl/uuid:9df49a54-cf8d-43f6-95b9-9ccd9f2e5349
U2 - 10.1016/j.enconman.2015.05.049
DO - 10.1016/j.enconman.2015.05.049
M3 - Article
SN - 0196-8904
VL - 101
SP - 352
EP - 363
JO - Energy Conversion and Management
JF - Energy Conversion and Management
ER -