CIM PROGRAMS AND ACTIVITIES

5:00 p.m.
Thomas Adams, McMaster University
Green power plants of the future (slides)

Although it is possible to capture CO2 emissions from state-of-the-art natural gas and coal power plants, it is extremely expensive and energy intensive to do so. Instead, future green power plants will produce electricity without combustion in air such that CO2 capture is considerably easier by design. One promising possibility is a solid oxide fuel cell power plant integrated with compressed air energy storage. This proposed system has the capability to achieve 100% carbon capture while raising or lowering the power output according to demand. However, in order to make the system work efficiently and economically, a rolling horizon optimization (RHO) control strategy can be applied. The RHO determines the amount of energy to store or release in real time by considering information such as the current state of the system, current and predicted prices of electricity, and current and predicted electricity demands from the grid. The RHO can be modified to achieve either performance or economic objectives, with very different results in behaviour. Overall, the system is quite successful at meeting both environmental and grid performance objectives with only small price premiums over the status quo.

6:00 p.m.
Adam Warren, National Renewable Energy Laboratory
REOpt: Renewable Energy Integration and Optimization

REopt is an energy planning platform offering concurrent,multiple technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision support for project screening and energy asset operation. REopt employs an integrated approach to optimizing the energy costs of a site by considering electricity and thermal consumption, resource availability, and complex tariff structures , incentives, and interconnection limitations.Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency.

5:00 p.m.
Adrian Nachman, University of Toronto
A Variational Problem Arising in Conductivity Imaging from Interior Measurements

Imaging electric conductivity of tissue is both desirable and challenging. The classical Electric Impedance Tomography Problem seeks to determine the conductivity from measurements of voltages and currents at the boundary; it has spurred deep and far-reaching mathematical developments. The ill-posedness of the problem is now well understood, and places severe limitations on the resolution that can be achieved. We will discuss one approach to overcome these limitations: using interior current density data obtainable by a method pioneered by Joy, Scott and Henkelmann at the University of Toronto which makes use of Magnetic Resonance Imagers in a novel way.

We only require knowledge of the magnitude |J| of one current for a given voltage f on the boundary. We show that the corresponding electric potential is the unique solution of a constrained minimization problem with respect to a weighted total variation functional defined in terms of the physical data. Working with the dual variational problem leads naturally to an alternating split Bregman algorithm, for which we prove convergence. The dual problem also turns out to yield a novel method to recover the full vector field J from knowledge of its magnitude, and of the voltage f on the boundary. Time permitting, we will discuss the corresponding problem for anisotropic conductivities.

The results presented are from joint work with Nicholas Hoell, Robert Jerrard, Amir Moradifam, Alexandru Tamasan and Alexander Timonov. Experimental results are joint work with Nahla Elsaid, Michael Joy, Weijing Ma, and Tim DeMonte.

6:00 p.m.
Douglas C. White, Emerson Process Management
Process Plant Optimization in Real Time: Energy and Environmental Interactions

Many industrial plants produce products worth millions of dollars per day and continuous financial optimization of their operations is obviously attractive. Applications of real time optimization technology in the process industries have been attempted for at least the last fifty years; sometimes successfully, sometimes not. In this presentation there will be a short review of the history of these attempts and some of the lessons learned. With the global increase in energy costs and environmental regulations, a current focus is real time optimization of the very complex energy systems in these large industrial sites and the associated environmental impact. Optimization has to fit within the overall control hierarchy and structure at the site which creates special system requirements. The problem structure, issues and current status of these applications is presented as well as open questions that are topics for future developments.

Laura Sanita, University of Waterloo (slides)
Finding small stabilizers for unstable graphs

A vertex v of a graph G is called inessential if there exists a maximum matching in G that exposes v. G is said to be stable if the set of its inessential vertices forms a stable set. Stable graphs play a key role in network bargaining games where we are given a set of players represented as vertices of a graph G, and a set of possible deals between players represented by the edges of G. Kleinberg and Tardos [STOC ’08] defined the notion of a balanced outcome for a network bargaining game, and proved that a balanced outcome exists if and only if the correspondent graph G is stable. This connection motivates the optimization problem of finding a minimum cardinality stabilizer of a given unstable graph G, that is a subset of edges F such that G \ F is stable. In this talk we prove some structural results about this problem and develop efficient approximation algorithms for sparse graphs. Joint work with A. Bock, K. Chandrasekaran, J. Koenemann, and B. Peis.

Ritchie (Yeqi) He, Royal Bank of Canada
An Improved Model for Calculation of Debt Specific Risk VaR with Tail Fitting

Initially introduced in the 1996 Amendment of Basel Accord, the specific (or spread) risk of a debt portfolio (DSR) is the risk due to changes of idiosyncratic credit spreads (bond spreads or CDS spreads) related to individual entities. Financial institutions are allowed to use internal models to calculate the Value-at-Risk (VaR) of DSR. Internal models usually calculate portfolio DSR PnL based on an assumption that idiosyncratic credit spreads follow a tractable closed-form joint distribution such as multi-variate normal or student's t-distribution. This assumption may not give a satisfactory approximation to the joint distribution because the marginal distribution of idiosyncratic credit spreads usually has fat tails. To better model fat tails, we propose an improved Monte Carlo-based model to calculate the DSR VaR. In the proposed model, the marginal distribution is modeled by a normal kernel distribution with Pareto tails, and the dependence structure of idiosyncratic credit spreads are captured by a student's t copula. For Pareto tails, the calibration of shape parameters and scale parameters involves a series of density fitting problems, which are solved by the maximum likelihood estimation. Numerical results show that, the proposed model is capable to generate more accurate distributions, and consequently the quality of estimation of the DSR VaR is improved.

Joint work with Meng Han, Royal Bank of Canada.

Please click here for Dr. He's presentation

September 24, 2013

In the practitioner world, the value of portfolio construction is often viewed with skepticism - a skepticism born of an honest assessment of the dubious forecasting power of the inputs and the opacity of the process. Despite all our wonderful mathematical gymnastics, if we don’t know the nature what is consumed, how can we possibly truthfully convey the nature of what we create? Now suppose we knew the distributions, the dynamics, the Information Ratio (IR) and the interrelationships between a multifactor model and its returns, and further we could disentangle each twist and turn of the raw factors as they are transformed into trades and holdings? Well, then maybe we really could demonstrate the value of portfolio construction.

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4:05 - 4:55 p.m.
Bogie Ozdemir (Sun Life Financial Group) Video of talk
Capital and Business Mix Optimization

Basel III amounts to a climate change in the banking industry. It increased the capital requirements significantly - especially for certain businesses (most notably Capital Markets) and decreased the acceptable forms of capital. Capital has become a scarce resource under Basel III, putting significant downward pressure on ROE. In this new environment, banks will need to change their business mixes, exit or shrink capital heavy businesses and adjust their operating models, while meeting income targets. During this course correction their ROE and Income Targets will be challenged further as some re-balancing of operating models may compromise short term income to improve ROE in future years. Subject to more onerous capital requirements under Basel III banks will need to increase the efficiency of capital utilization and place greater emphasis on optimizing capital allocation and business mix across their operations. In this presentation, we will discuss how to establish an optimization framework incorporating both economic and regulatory capital.