Energy Management in Buildings with Intermittent and Limited Renewable Resources

Barata, Filipe; Igreja, José · 2018 · Crossref

DOI: 10.3390/en11102748

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Summary

This paper addresses the challenge of managing energy consumption in buildings within smart grid environments characterized by intermittent and limited renewable energy sources. The research is motivated by the high proportion of primary energy consumed by buildings for heating, ventilation, and air conditioning (HVAC), and the need for demand-side management (DSM) strategies that can balance supply and demand despite the unpredictability of green energy. The authors propose a novel integrative methodology that combines distributed model predictive control (DMPC) with multi-agent systems to optimize energy usage while maintaining indoor thermal comfort. The study employs a distributed control framework where buildings are divided into Thermal Control Areas (TCAs), each acting as an autonomous agent. These agents utilize DMPC algorithms to manage local temperature set-points and energy consumption. The system distinguishes between two energy resources: "green" energy from intermittent renewable sources and "red" energy from the fossil-fuel grid, which is more expensive. To coordinate access to the limited green resource, the authors introduce a day-ahead auction mechanism. Agents submit bids based on their energy needs and the daily price, and a market operator establishes a sequential access order. This order determines the priority for consuming renewable energy, with agents falling back to grid energy if the green resource is exhausted. The control strategy incorporates forecasts for outdoor temperature, renewable power generation, and occupancy, using first-order energy balance models to predict thermal dynamics and disturbances. The proposed solution was evaluated through simulations across different scenarios to demonstrate its flexibility and efficiency. The results indicate that the DMPC-based approach successfully minimizes energy costs while keeping indoor temperatures within comfort bounds. Specifically, the study reports a 37% cost saving compared to baseline methods, alongside reduced overall energy consumption. The system effectively maximizes the utilization of intermittent renewable energy by dynamically shifting loads and prioritizing green energy consumption according to the auction-derived sequence. The simulations also highlight the system's ability to handle constraints related to limited shared resources and thermodynamic coupling between adjacent areas. The significance of this work lies in its contribution to smart grid technologies by providing a robust DSM solution that integrates intelligent control, real-time price negotiation, and energy auction mechanisms. The approach allows consumers to balance thermal comfort with energy savings, thereby reducing CO2 emissions and supporting grid stability in the face of renewable energy variability. The methodology is particularly applicable to scenarios with scarce energy resources, such as remote areas or cruise ships, demonstrating the potential for widespread adoption in sustainable building energy management.

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