Integration of renewable energy sources in microgrids can be achieved via demand response programs, which change the electric usage in response to changes in the availability and price of electricity over time. This paper presents a novel control algorithm for joint demand response management and thermal comfort optimization in microgrids equipped with renewable energy sources and energy storage units. The proposed work aims at covering two main gaps in current state-of-the-art demand response programs. The first gap is integrating the objective of matching energy generation and consumption with the occupant behavior and with the objective of guaranteeing thermal comfort of the occupants. The second gap is developing a scalable and robust demand response program. Large-scale nature of the optimization problem and robustness are achieved via a two-level supervisory closed-loop feedback strategy: at the lower level, each building of the microgrid employs a local closed-loop feedback controller that processes only local measurements; at the upper level, a centralized unit supervises and updates the local controllers with the aim of minimizing the aggregate energy cost and thermal discomfort of the microgrid. The effectiveness of the proposed method is validated in a microgrid composed of three buildings, a photovoltaic array, a wind turbine, and an energy storage unit. Comparisons with alternative demand response strategies reveal that the proposed strategy efficiently integrates the renewable sources; energy costs are reduced and at the same time thermal comfort of the occupants is guaranteed. Furthermore, robustness is proved via consistent improvements achieved under heterogeneous conditions (different occupancy schedules and different weather conditions).@en

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.@en

Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the passive design of the building can lead to further improvements in both energy efficiency and comfort. These improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of the building is used to proactively optimize, using both current and future information about the external weather condition and the building state, a combined criterion composed of the energy consumption and the thermal comfort index. Both extensive simulation as well as real-life experiments performed during the unstable German wintertime, demonstrate that the proposed approach can effectively deal with the complex dynamics arising from the high-inertia structure, providing proactive and intelligent decisions that no currently employed rule-based strategy can replicate.@en