Case study of residential energy management systems with solar PV, wind and battery energy storage

As environmental concerns about energy production, distribution, and consumption rise, the energy landscape is evolving. This research examines methods to address these changes by integrating renewable energy and energy storage at the residential level using energy management systems (EMSs). A calibrated simulation residential house model was developed to consistently compare various energy management techniques. The study investigated 1) deterministic EMSs in their simplest forms, 2) adaptive EMSs utilizing machine learning and predictive control algorithms, and 3) a transactional EMS. Deterministic EMSs offered the lowest annual cost savings but were the easiest to implement.

Adaptive EMSs provided the highest estimated cost savings but required more complex controllers. The transactional EMS yielded moderate cost savings and additional benefits such as demand response and community integration capabilities. Experimental work validated key system claims, focusing on battery output control and inter-agent controller communication deployed in practice on a local scale at the Archetype Sustainable House in Vaughan, Ontario, Canada. Future research should focus on implementing predictive control on a larger scale and exploring transactive control at the community level.