Energy Management in Buildings with Intermittent and Limited Renewable Resources

Abstract

This work reports a contribution, in a model predictive control multi-agent systems context, introducing a novel integrative methodology to manage energy networks from the demand-side point of view, in the strong presence of intermittent energy sources, including energy storage in households or car batteries. In particular, the article presents a control-based solution for indoor comfort, which, in addition, optimizes the usage of a limited shared energy resource. The control management is applied, in a distributed way, to a set of so-called thermal control areas (TCAs) and demand units, with the objective of minimizing the cost of energy while maintaining the indoor temperature within the comfort zone bounds, and simultaneously not exceeding a limited amount of shared renewable energy. The thermal control areas are, in general, thermodynamically connected, and are also coupled by energy interrelation constraints established in the particular optimization solution. Energy management is performed with a fixed sequential order established from a previously carried out auction, wherein the bids are made by each unit’s demands, acting as demand-side management agents, based on the daily energy price. The developed solution is explained by a basic algorithm that has been applied to different scenarios, and the results have been compared so as to illustrate the benefits and flexibility of the proposed approach, showing less energy consumption and a 37% cost saving.

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

“…In this case, the home arrival time of EVs owners in the evening is also described by a normal distribution with mean value 7:00 p.m. and standard deviation 1 h; and the home departure time in the morning follows a normal distribution with mean value 8:00 a.m. and standard deviation 1 h. The stochastic characteristics of EVs are represented by the reduced scenarios of the available number of the aggregated EVs at each time [43,44]. The capacity of each EV is assumed to be 20 kWh, and the total number of aggregated EVs is 100 in this case.…”

Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems

“…Studies in literature have extensively proven Model Predictive Control strategies to be suitable control methods for EMS, allowing taking control actions by considering the evolution of the state variables of the systems over a time horizon rather than instantaneously [10][11][12][13][14][15][16][17]. The decision variables are in fact optimized by considering not only the current state, but also future predictable events, which can affect the system behavior.…”