|M.Sc Student||Omer Eyal|
|Subject||Optimal Design and Operation of "Energy Tower" Combined with|
Pumped Storage and Desalination
|Department||Department of Civil and Environmental Engineering||Supervisors||Professor Ilya Ioslovich|
|Professor Emeritus Per-Olof Gutman|
|Dr. Rami Guetta|
"Energy Tower" is a power plant that uses hot dry air and seawater to produce electricity. The plant is based on a very large cylindrical structure. Water that is sprayed at the top of the tower partially evaporates, thus cooling the surrounding air. The cool air is heavier, which makes it fall down through the tower. Wind turbines-generators that are installed at the bottom of the tower are powered by the artificial wind, and electricity is produced.
The research assumption was that by combining "pumped storage" and a seawater desalination plant with an "Energy Tower", a saving in the initial investment would be obtained, compared to the sum of investments in these systems as stand-alone systems. Other benefits from combining these systems include an increase in the net energy production due to an efficient use of the brine water from the desalination plant, and an increase of the produced net power during peak hours, which is obtained by combining pumped storage with the "Energy Tower".
An optimization algorithm for the design of a system that is combined of an "Energy Tower", pumped storage and seawater desalination plant was formulated and programmed. The objective function of the problem is to maximize the annual net profit from the combined system. The optimization algorithm that was used is block coordinate descent, which includes an iterative process of solving two sub-problems. One sub-problem was formulated as a linear programming problem, using piece-wise linear approximations for highly non-linear relations. The second sub-problem has an analytic solution. If the analytic solution is not feasible, the block coordinate descent is abandoned, and a quasi-Newton algorithm is used instead.
An implementation of the optimization algorithm for a potential project was made. The additional benefit from combining the systems lead to an increase of 14% in the annual net profit, compared to the sum of net profits from the stand-alone systems (when optimally designed).
A sensitivity analysis was performed. The solution was found to be not sensitive to small changes in the climate conditions. The relative change in the annual net profit was found to be twice higher than the relative change in the average electricity price. The break-even point for the price of water that will lead to the decision of not installing a desalination plant with a capacity of 20 [MCM/year] in the combined system was found to be 41 [c/m3].