  Distributed demand response is a typical distributed optimization problem
that requires coordination among multiple agents to satisfy demand response
requirements. However, existing distributed algorithms for this problem still
face challenges such as unknown system models, nonconvexity, privacy issues,
etc. To address these challenges, we propose and analyze two distributed
algorithms, in which the agents do not share their information and instead
perform local updates using zeroth-order feedback information to estimate the
gradient of the global objective function. One algorithm applies to problems
with general convex and compact feasible sets but has higher oracle complexity
bounded by $O(d/\epsilon^2)$, while the other algorithm achieves lower
complexity bound $O(d/\epsilon)$ but is only applicable to problems with box
constraints. We conduct empirical experiments to validate their performance.
