High-Performance Nonlinear Optimization Software for Power Applications

Award Information
Agency:
Department of Energy
Branch
n/a
Amount:
$148,080.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
DE-FG02-12ER90229
Award Id:
n/a
Agency Tracking Number:
98656
Solicitation Year:
2012
Solicitation Topic Code:
02 d
Solicitation Number:
DE-FOA-0000577
Small Business Information
Ziena Optimization LLC (Currently Ziena Optimization Inc.)
IL, Evanson, IL, 60208-3150
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
038873308
Principal Investigator:
Richard Waltz
Dr.
(847) 894-8384
waltz@ziena.com
Business Contact:
Richard Waltz
Dr.
(847) 894-8384
waltz@ziens.com
Research Institution:
Stub




Abstract
Problems of large-scale nonlinear optimization are central to the solution of difficult and novel problems that arise from the need to distribute electric power in the most efficient and reliable way. In recognition of the importance of optimization in this area, DOE & apos;s Office of Advanced Scientific Computing (ASCR) has made substantial investments in research that have led to powerful, robust software for very general classes of nonlinear optimization problems. There remains however a substantial need to extend and adapt these efforts to better take advantage of high-performance computing (HPC) and to unlock their value for new users. Power industry optimization is a particularly promising area for such an undertaking, in light of the complex decisions involved and the challenges of changing costs and technologies. We propose to focus on two complementary areas of investigation to meet this need. First, we will re-engineer nonlinear optimization software, previously developed with ASCR support, so as to take advantage of high-performance computing (HPC) concepts that address energy efficiency problems too large for current codes. Second, we will adapt and focus software technology from previous work for ASCR to address the specific needs of real-time power grid applications, in which HPC techniques are needed in order to re-optimize very quickly a large number of problems of similar structure. Both of these initiatives will strengthen the internals of algorithms to give the software wider applicability in HPC settings and to better deal with greatly increased problem size due to uncertainty. This work will be of direct public benefit by enabling scarce and costly resources to be used much more effectively for purposes of power distribution. The first area of investigation will be applied in particular to optimal power flow with contingencies and to short-term stochastic dispatch for systems with a high level of renewable penetration. The second area will be applied in improving grid short-term management, from both technical and economic stand- points, particularly in taking full advantage of new demand side management and generation technologies to reduce costs and environmental impact subject to security constraints.

* information listed above is at the time of submission.

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