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The cooperative energy management of aggregated buildings has recentlyreceived a great deal of interest due to substantial potential energy savings.These gains are mainly obtained in two ways: (i) Exploiting the load shiftingcapabilities of the cooperative buildings; (ii) Utilizing the expensive butenergy efficient equipment that is commonly shared by the building community(e.g., heat pumps, batteries and photovoltaics). Several deterministic andstochastic control schemes that strive to realize these savings, have beenproposed in the literature. A common difficulty with all these methods isintegrating knowledge about the disturbances affecting the system. In thiscontext, the underlying disturbance distributions are often poorlycharacterized based on historical data. In this paper, we address this issue byexploiting the historical data to construct families of distributions whichcontain these underlying distributions with high confidence. We then employtools from data-driven robust optimization to formulate a multistage stochasticoptimization problem which can be approximated by a finite-dimensional linearprogram. The proposed method is suitable for tackling large scale systems sinceits complexity grows polynomially with respect to the system variables. Wedemonstrate its efficacy in a numerical study, in which it is shown tooutperform, in terms of energy cost savings and constraint violations,established solution techniques from the literature. We conclude this study byshowing the significant energy gains that are obtained by cooperativelymanaging a collection of buildings with heterogeneous characteristics.

The Power of Diversity: Data-Driven Robust Predictive Control for Energy Efficient Buildings and Districts