  Two types of privacy-preserving decision making paradigms for
utility-community interactions for multi-horizon operation are examined in this
paper. In both designs, communities with renewable energy sources, distributed
generators, and energy storage systems minimize their costs with limited
information exchange with the utility. The utility makes decision based on the
information provided from the communities. Through an iterative process, all
parties achieve agreement. The authors' previous research results on
subgradient and lower-upper-bound switching (LUBS)-based distributed
optimization oriented multi-agent control strategies are examined and the
convergence analysis of both strategies are provided. The corresponding
decision making architectures, including information flow among agents and
learning (or iteration) procedure, are developed for multi-horizon decision
making scenarios. Numerical results illustrate the decision making procedures
and demonstrate their feasibility of practical implementation. The two decision
making architectures are compared for their implementation requirements as well
as performance.
