  We study the renewable energy generations in Hong Kong based on realistic
meteorological data, and find that different renewable sources exhibit diverse
time-varying and location-dependent profiles. To efficiently explore and
utilize the diverse renewable energy generations, we propose a theoretical
framework for the cooperative planning of renewable generations in a system of
interconnected microgrids. The cooperative framework considers the
self-interested behaviors of microgrids, and incorporates both their long-term
investment costs and short-term operational costs over the planning horizon.
Specifically, interconnected microgrids jointly decide where and how much to
deploy renewable energy generations, and how to split the associated investment
cost. We show that the cooperative framework minimizes the overall system cost.
We also design a fair cost sharing method based on Nash bargaining to
incentivize cooperative planning, such that all microgrids will benefit from
cooperative planning. Using realistic data obtained from the Hong Kong
observatory, we validate the cooperative planning framework, and demonstrate
that all microgrids benefit through the cooperation, and the overall system
cost is reduced by 35.9% compared to the noncooperative planning benchmark.

Computer Science and Game Theory;Systems and Control