  Demand response (DR) is a cost-effective and environmentally friendly
approach for mitigating the uncertainties in renewable energy integration by
taking advantage of the flexibility of customers' demands. However, existing DR
programs suffer from either low participation due to strict commitment
requirements or not being reliable in voluntary programs. In addition, the
capacity planning for energy storage/reserves is traditionally done separately
from the demand response program design, which incurs inefficiencies. Moreover,
customers often face high uncertainties in their costs in providing demand
response, which is not well studied in literature.
  This paper first models the problem of joint capacity planning and demand
response program design by a stochastic optimization problem, which
incorporates the uncertainties from renewable energy generation, customer power
demands, as well as the customers' costs in providing DR. We propose online DR
control policies based on the optimal structures of the offline solution. A
distributed algorithm is then developed for implementing the control policies
without efficiency loss. We further offer enhanced policy design by allowing
flexibilities into the commitment level. We perform real world trace based
numerical simulations. Results demonstrate that the proposed algorithms can
achieve near optimal social costs, and significant social cost savings compared
to baseline methods.
