Abstract: Future smart grid has been conceived to be able to improve efficiency and stability of the grid operations. Based on the smart meter and advanced mechanism of two-way communications, Energy-Users (EUs) are able to receive real-time signalling (e.g., the electricity price) from the grid and schedule their energy consumption to optimize their objectives of interest correspondingly. Besides the conventional fuel-based energy supply, renewable energy supplies, e.g., solar and wind power, are expected to play important roles in smart grid. Despite their advantages in lowering the electricity-provisioning cost and being environment-friendly, renewable supplies usually suffer from uncontrollable and volatile generations, which result in great fluctuations in their provisioning. Therefore, it is indispensable for EUs equipped with renewable energy suppliers to take a careful tradeoff between exploiting the benefit from the renewable energy and controlling the adversary impact due to its volatility. Based on this motivation, this study aims at jointly optimizing the EU's average energy-acquisition cost as well as its fluctuation. This problem is formulated as a nonconvex optimization problem and this study proposes an efficient Layered Particle Swarm Optimization (L-PSO) algorithm to determine the EU’s optimal scheduling of energy consumptions. Our numerical results show how EU can trade off between benefiting from the renewable supplies and suffering from the associated fluctuation through tuning the weighting-factors.