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Some Optimization Problems in Electrical Power Systems
Electrical power grids are a rich source of problems in optimization and data analysis. This talk describes work on two such problems. In the first, we formulate a bilevel optimization problem to identify possible vulnerabilities by finding the attack that causes maximal disruption. In the second, we describe multivariate logistic regression (MLR) and deep learning approaches for identifying outages in a grid from real-time sensor network data. We show that when these classifiers are trained to recognize the “signature” of outages under a variety of network conditions, they can identify outages correctly in the vast majority of cases. An extension of our approach allows identification of optimal sensor locations.
Optimisation in deregulated electricity markets – Australian experience
There are six kinds of optimization processes that are conducted in deregulated electricity markets in Australia which are all conducted with generally weak relationships across the supply chain. These are described as generation planning, transmission planning, distribution planning and demand side (customer) planning, fuel supply planning and emission abatement planning. This is further complicated by certificate schemes for large and small scale renewable energy technologies. The talk will seek to describe the optimization methods applied within each process, the current mechanisms applied for the integration of these planning processes which could potentially optimise the whole electricity supply chain, share anecdotes of how and where this integration process has broken down in the past, and speculate on how integrated planning across multiple entities could be made more effective having regard to future uncertainties.
Optimisation applications at the Australian Bureau of Statistics
The Australian Bureau of Statistics uses optimisation methods in a range of applications. As well as “textbook” OR problems such as job allocation, we apply optimisation to some less conventional problems. I will concentrate on two examples:
Secondary cell suppression occurs when we need to censor publication of some cells within a statistical table for privacy reasons. As well as suppressing the confidential cells (“primary suppression”) we often need to suppress additional cells in the table, so that readers cannot deduce the confidential data by “differencing” non-confidential cells.
However, suppressing cells reduces the value of the published data. Hence, we need a method that minimises the “cost” of suppression (e.g. by assigning each cell a value) while guaranteeing adequate protection for confidential cells. This can be formulated as a mixed integer problem, but computation requirements quickly become high, especially for tables in more than two dimensions.
Table balancing occurs in economics and demography, when we collect data from multiple sources that give different perspectives on the same phenomena: for instance, the sale of a car can be measured from both buyer’s and seller’s side. In such cases optimisation methods can help reconcile conflicting sources to produce a self-consistent and accurate estimate of what’s really going on.
Optimization and Games in Transportation
In this talk I will present a survey of some applications of optimization and game theory in equilibrium models for transportation systems. Starting from my early work on equilibrium flows for congested transit systems and some of the applications that these models have had along the years, I will move to more recent work on stochastic traffic equilibrium, risk-averse route choice, dynamic equilibrium, and the limiting behavior of the Price-of-Anarchy for highly congestion networks.
Operations Research: for and with industry
In this talk, the speaker traces his applied operations research work for, and with industry. Through specific examples of work in industry sectors such as airports, tourism & travel, wine, coal, the police, and the postal services, this talk traces real applications of operations research. While these applied OR projects have resulted in benefits and outcomes to the companies involved, they have also resulted in a stream of publications. The speaker concludes that business impact and science impact are not necessarily orthogonal. What is needed is a research practitioner’s mindset.
Stochastic optimization and game theory on energy markets
In this talk we develop a stochastic optimization-game theory model representing an energy market, which includes the transmission network and a few number of agents with oligopolistic behavior. We consider a general network with nonlinear externalities and nonlinear pricing rules. As a tool for the modeling and analysis we also use mechanism design theory.
Optimization in Data Analysis: Survey and Recent Developments
Optimization methodology has proved to be essential to formulating and solving problems in data analysis, machine learning, and computational statistics. Such problems are characterized by fairly elementary objective functions but a very large amount of data. Algorithms need to take account of the statistical / learning context, the expense of computing function and derivative information, nonsmoothness, and (increasingly) nonconvexity. The talk will sketch canonical problem formulations, fundamental algorithmic techniques, and issues of current research focus.