In each year, the Computational Optimization and Applications (COAP) editorial board

selects a paper from the preceding year’s COAP publications for the “Best Paper Award. ” The recipients of the award for papers published in 2006 are Walter Murray of Stanford University and Uday V. Shanbhag of the University of Illinois at Urbana-Champaign for their paper “A Local Relaxation Approach for the Siting of Electrical Substations,” published in Volume 33, pages 7–49. This paper presents work done when Uday was a doctoral student at the Systems Optimization Laboratory at Stanford University. The effort began after a former student Mukund Thapa mentioned that Stephen Chapel at the Electric Power Research Institute (EPRI) had been approached for help by Robert (Bob) Fletcher of the Snohomish Public Utility (SPU) to solve a problem of placing substations in an electrical network. Bob had apparently approached a number of companies with little success since the general response had been that the problem looked intractable. Murray, with his doctoral student Kien-Ming Ng, had been working on another discrete problem from Ericsson to assign frequencies in a cellular network and had a lot of success [5]. After an initial survey funded by SPU through Stephen Chapel at EPRI, a full-scale research effort began in 2002 with SPU and with Bob Fletcher serving as our principal utility contact. The project’s core problem lay in developing efficient optimization methods for the location of electrical substations on rectangular grids, with the goal of minimizing installation and operating costs (from transmission losses). It may be thought that rectangular grids are a poor approximation to the actual grid. However, the nodes on the grid represent the load that may be drawn in a particular 1/2 mile by 1/2 mile block. The utility has a requirement to meet such a load. It was not immediately apparent how best to pose the problem mathematically. From previous work on optimal power flow (OPF) under contingencies, it was apparent that any change to the circuit such as moving a substation from one node to another required a different set of powerflow equations. Indeed even the set of unknowns changes. In the OPF, the various configurations of the distribution in the set of constraints were simply duplicated. Typically about 10 contingencies would be accounted for. To do that here would have required an astronomically large number of constraints. Fortunately, the authors came up with a compact representation of the problem, which resulted in the need to solve a mixedinteger quadratic program (MIQP). The downside was that the number of integer and real variables could be 5000 of each. There did not appear to be any off-the-shelf software at that time that would come close to solving such a problem. At the time CPLEX could not solve discrete quadratic programs, but even later when such software was available, the largest problem which could be solved with CPLEX was about 100 variables. In designing a new algorithm, the authors concluded that given a configuration of substations it may be necessary to move all substations simultaneously to get a better configuration. It certainly looked like such an approach was necessary to obtain the efficiency needed to solve large problems, the reasoning being, that the number of such moves would, by and large, be independent of the size of the problem if the “density” of substations was fairly constant.