Plant efficiency and profitability are becoming increasingly important and operating at the most optimal point is a necessity. The definition of proper operational bounds on output variables such as product quality, production rates etc., is critical for plant optimisation. The use of operational bounds that do not lie within the region of the output operational space of the plant can result in the control system attempting to operate the plant in a non attainable region. The use of operational bounds that lie within the bounds of the output operational space of the plant and if the output operational space is non convex can also result in the control system attempting to operate the plant in a non attainable region. This results in non feasible optimisation. A numerical intersection algorithm has been developed that identifies the feasible region of operation known as the desired operational space. This is accomplished by finding the intersection of the required operational space and the achievable output operational space. The algorithm was simulated and evaluated on a case study under various scenarios. These scenarios included specifying operational bounds that lie partially within the bounds of the achievable operational space and also specifying operational bounds that lie within the bounds of the operational space which was non convex. The results yielded a desired operational space with bounds that were guaranteed to lie within an attainable region on the output operational space. The desired operational space bounds were also simplified into a rectangle with high and low limits that can be readily used in control systems. / Dissertation (MEng)--University of Pretoria, 2012. / Chemical Engineering / unrestricted

The research is motivated by the need to address two major challenges in wind power integration: how to mitigate wind power fluctuation and how to ensure stability of the farm and host grid. It is envisaged that wind farm power output fluctuation can be reduced by using a specific type of buffer, such as an energy storage system (ESS), to absorb its negative impact. The proposed solution, therefore, employs ESS to solve the problems. The key research findings include a new technique for calculating the desired power output profile, an ESS charge-discharge scheme, a novel direct-calculation (optimization-based) method for determining ESS optimal rating, and an ESS operation scheme for improving wind farm transient stability. Analysis with 14 wind farms and a compressed-air energy storage system (CAES) shows that the charge-discharge scheme and the desired output calculation technique are appropriate for ESS operation. The optimal ESSs for the 14 wind farms perform four or less switching operations daily (73.2%-85.5% of the 365 days) while regulating the farms output variation. On average, the ESSs carry out 2.5 to 3.1 switching operations per day. By using the direct-calculation method, an optimal ESS rating can be found for any wind farm with a high degree of accuracy. The method has a considerable advantage over traditional differential-based methods because it does not require knowledge of the analytical form of the objective function. For ESSs optimal rating, the improvement in wind energy integration is between 1.7% and 8%. In addition, a net increase in grid steady-state voltage stability of 8.3%-18.3% is achieved by 13 of the 14 evaluated ESSs. For improving wind farm transient stability, the proposed ESS operation scheme is effective. It exploits the use of a synchronous-machine-based ESS as a synchronous condenser to dynamically supply a wind farm with reactive power during faults. Analysis with an ESS and a 60-MW wind farm consisting of stall-regulated wind turbines shows that the ESS increases the farm critical clearing time (CCT) by 1 cycle for worst-case bolted three-phase-to-ground faults. For bolted single-phase-to-ground faults, the CCT is improved by 23.1%-52.2%. / text