  The uncertainties of the renewable generation units and the proliferation of
price-responsive loads make it a challenge for independent system operators
(ISOs) to manage the energy trading market in the future power systems. A
centralized energy market is not practical for the ISOs due to the high
computational burden and violating the privacy of different entities, i.e.,
load aggregators and generators. In this paper, we propose a day-ahead
decentralized energy trading algorithm for a grid with generation uncertainty.
To address the privacy issues, the ISO determines some control signals using
the Lagrange relaxation technique to motivate the entities towards an operating
point that jointly optimize the cost of load aggregators and profit of the
generators, as well as the risk of the generation shortage of the renewable
resources. More, specifically, we deploy the concept of
conditional-value-at-risk (CVaR) to minimize the risk of renewable generation
shortage. The performance of the proposed algorithm is evaluated on an IEEE
30-bus test system. Results show that the proposed decentralized algorithm
converges to the solution of the ISO's centralized problem in 45 iterations. It
also benefits both the load aggregators by reducing their cost by 18% and the
generators by increasing their profit by 17.1%.
