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Center for Nonlinear Studies |
This presentation describes an effective algorithm for optimizing distribution system operation in a smart grid, from cost and system stability points of view. The proposed algorithm mainly aims at controlling the power available from different sources such that they satisfy the load demand with the least possible cost while giving the highest priority to renewable energy sources. Moreover, a smart battery charger was designed to control the batteries in such a way that they are allowed to discharge only when there is no very big load predicted within an immediate future period. This will make such a storage available to act as a buffer for the predicted large load to increase the stability of the system and reduce voltage dips. In addition, batteries are used to serve another purpose from an economic point of view, which is peak- shifting during the day in order to avoid the relatively high prices of grid power during peak periods. Since this algorithm is mainly dependent on forecasted data of the power available from different renewable energy sources as well as the load demand, a full attention has been paid to the forecasting process. Hence, a non-linear regression technique was applied to build accurate forecasting models for different sources and for the load. These models help in monitoring and predicting the total power generation and demand online. Furthermore, a fuzzy controller was utilized to make use of the forecasted data of the coming peak period then decide dynamically the amount of power that should be taken out of energy storage. Different case studies were investigated to verify the validity of the proposed algorithm and define the system behavior under several conditions. The presentation will also describe efforts currently underway on the development of a wide area measurement (WAMS) system for smart grid applications. This system is based on synchronized phasor measurement technology with the access of a broadband communication capability. The purpose is to increase the overall system efficiency and reliability for all power stages via significant dependence on WAMS as distributed intelligence agents with improved monitoring, protection, and control capabilities of the power network. An example of consisting of a 50 kW generation station, 20 kW wind turbine, three transformers, four circuit breakers, four buses, two short transmission lines, and two 30 kW loads is presented. The communication layer consists of three PMUs, located at generation and load buses, and one Phasor data concentrator (PDC) collecting the data received from remote PMUs and send it to the control center for analysis and control actions. The power system status can be easily monitored and controlled in real time by using the measured bus values online which improves the overall system reliability and avoids cascaded blackout during fault occurrence. The simulation results confirm the validity of the proposed WAMS technology for smart grid applications.
Host: Misha Chertkov, chertkov@lanl.gov, 665-8119