Increased share of Renewable Energy Sources (RES) in the generation mix requires higher flexibility in power system resources. The intermittent nature of the RES calls for higher reserves in power systems to smooth out the unpredictable power fluctuations. Grid-tied energy storage systems are practical solutions to facilitate the massive integration of RES. The deployment of Battery Energy Storage Systems (BESS) on the power grids is experiencing a significant growth in recent years. Thanks to intensive research and development in battery chemistry and power conversion systems, BESS costs are reducing. However, much more advancements in battery manufacturing as well as additional incentives from the market side are still needed to make BESS a more cost-effective solution. Planning and operation of the BESS significantly influence its profitability. It is quite important to find optimal sizes of batteries and inverters. Sizing of the BESS for two different applications is addressed in this work. In the first application, the BESS is co-located with Pumped Storage Hydro (PSH) to meet the Day-Ahead (DA) schedule of wind generation. In the second application, a method for BESS sizing in the presence of PV-induced ramp rate limits is proposed. In this thesis, two methods based on Receding Horizon Control (RHC) for the optimal operation of the BESS are introduced. A co-located BESS and wind farm is considered in both methods. In one method, electricity market participation is not considered, and the goal is solely meeting the DA schedule utilizing the BESS. A novel predictive control method is proposed in this part and the efficiency of the method is evaluated through long-run simulations using actual historical wind power. In the second scenario, market participation of the BESS is taken into account. The deviation from the DA schedule can be compensated through the BESS, or by purchasing power from the real-time electricity market. The optimization problem based on physical and operational constraints is developed. The problem is solved through an RHC scheme while using updated wind power and electricity price forecasts. In this thesis, a Ridge-regression forecast model for electricity price and an ARIMA forecast model for wind power are developed. Simulation results using actual historical data for wind power and electricity price demonstrate that the proposed algorithm increases the average daily profit. In order to evaluate the impact of the BESS lifetime and price on average daily profit, different scenarios are defined and simulated. Although they increase the complexity of the problem, much more realistic result might be obtained when all details and constraints are considered.