Solving Unit Commitment Including Wind Power Generation Using PSS®E

Abstract

Operating the power system in an optimal way and keeping it safe and reliable is very important in power systems planning and operation. The power system currently has a large variety of power plants that operate with different kinds of fossil fuel or use renewable energy to produce power. As the demand on electricity is fluctuated, utilities are obligated to provide consumers with power at any time during a day. Power system operators try to supply electricity in an economic operation by turning on the cheapest generating units and turning off the most expensive ones at off-peak value of the load. Finding the optimal combination of units to supply forecasting load is called unit commitment problem (UC). The second step of the optimization problem is finding the optimal output power from each committed unit, which known as economic dispatch (ED). The main benefits of solving the unit commitment problem and economic dispatch are to minimize generation cost over the objective period horizon while applying all system constraints that come from generating units’ limits and the transmission system’s characteristics, as well as to verify the balance between power generation and power demand. While, the optimal power flow (OPF) tries to find the optimal dispatch for the whole power system, but by taking into account all systems constrain, such as voltage security and transmission line limit. The optimal power flow can control many variables to find the optimal operation of the system, like a transformer tap changer, phase shifter, switched shunt, and load adjustment. In this paper, power system simulator for engineering (PSS®E) is used to solve unit commitment, economic dispatch, and optimal power flow. The implementation is performed on IEEE 30-bus system for a 24-hour period. In the first stage, the UC, ED, and OPF were solved for the systems without including wind power generation. In the second stage, UC, ED, and OPF were solved by including a wind-power farm with 100 MW rated power connected to the system. The solution is based on 24-hour wind data forecasting, and 24-hour power demand's prediction.