  In this paper, we address an optimal management problem of community energy
storage in the real-time electricity market under a stochastic renewable
environment. In a real-time electricity market, complete market information may
not be assessable for a strategic participant, hence we propose a paradigm that
uses partial information including the forecast of real-time prices and slopes
of the aggregate supply curve to model the price impact of storage use in the
price-maker storage management problem. As a price maker, the community energy
storage can not only earn profits through energy arbitrage but also smooth
price trajectories and further influence social welfare. We formulate the
problem as a finite-horizon Markov decision process that aims to maximize the
energy arbitrage and social welfare of the prosumer-based community. The
advance of the management scheme is that the optimal policy has a threshold
structure. The structure has an analytic form that can guide the energy storage
to charge/discharge by comparing its current marginal value and the expected
future marginal value. Case studies indicate that welfare-maximizing storage
earns more benefits than profit-maximizing storage. The proposed
threshold-based algorithm can guarantee optimality and largely decrease the
computational complexity of standard stochastic dynamic programming.
