  In this paper, we propose a theoretical framework for the joint optimization
of investment and operation of a microgrid, taking the impact of energy
storage, renewable energy integration, and demand response into consideration.
We first study the renewable energy generations in Hong kong, and identify the
potential benefit of mixed deployment of solar and wind energy generations.
Then we model the joint investment and operation as a two-period stochastic
programming program. In period-1, the microgrid operator makes the optimal
investment decisions on the capacities of solar power generation, wind power
generation, and energy storage. In period-2, the operator coordinates the power
supply and demand in the microgrid to minimize the operating cost. We design a
decentralized algorithm for computing the optimal pricing and power consumption
in period-2, based on which we solve the optimal investment problem in
period-1. We also study the impact of prediction error of renewable energy
generation on the portfolio investment using robust optimization framework.
Using realistic meteorological data obtained from the Hong Kong observatory, we
numerically characterize the optimal portfolio investment decisions, optimal
day-ahead pricing and power scheduling, and demonstrate the advantage of using
mixed renewable energy and demand response in terms of reducing investment
cost.
