  The cooperative energy management of aggregated buildings has recently
received a great deal of interest due to substantial potential energy savings.
These gains are mainly obtained in two ways: (i) Exploiting the load shifting
capabilities of the cooperative buildings; (ii) Utilizing the expensive but
energy efficient equipment that is commonly shared by the building community
(e.g., heat pumps, batteries and photovoltaics). Several deterministic and
stochastic control schemes that strive to realize these savings, have been
proposed in the literature. A common difficulty with all these methods is
integrating knowledge about the disturbances affecting the system. In this
context, the underlying disturbance distributions are often poorly
characterized based on historical data. In this paper, we address this issue by
exploiting the historical data to construct families of distributions which
contain these underlying distributions with high confidence. We then employ
tools from data-driven robust optimization to formulate a multistage stochastic
optimization problem which can be approximated by a finite-dimensional linear
program. The proposed method is suitable for tackling large scale systems since
its complexity grows polynomially with respect to the system variables. We
demonstrate its efficacy in a numerical study, in which it is shown to
outperform, in terms of energy cost savings and constraint violations,
established solution techniques from the literature. We conclude this study by
showing the significant energy gains that are obtained by cooperatively
managing a collection of buildings with heterogeneous characteristics.

Optimization and Control;Systems and Control