# Engineering & Technology Management and Operations Research Projects

**ENGINEERING & TECHNOLOGY MANAGEMENT**

**Innovation and Firm Performance: The Value of Dense Versus Porous Thickets (with Jeffrey G. Covin, Indiana University, Kelley School of Business)
- Dr. Michael Heeley**

The importance of patented inventions in generating firm value has long been recognized. In this research we investigate the assumption that dense patent thickets are generally associated with increased value and argue that in certain contexts, porous thickets will actually be more positively associated with firm value. We hypothesize and find that the recency (or age) of the knowledge underlying the patent thicket and the competitiveness of the technological environment moderate the thicket density–firm value relationship. In doing so, we make an important theoretical contribution by showing when firm value is best promoted through creating stronger versus weaker barriers around innovations.

**CVC Funding From the Start-Up Perspective: A Source of Uncertainty or Clarity in the Market for IPOS? (with Sharon F. Matusik, CU-Boulder, Markus Fitza, Bentley University)**

- Dr. Michael Heeley

- Dr. Michael Heeley

Corporate venture capital (CVC) investments are an important component of technology entrepreneurship for established firms and can serve as a vehicle for identifying new technology opportunities. If the CVC benefits from using these investments strategically, though, what does this mean to the start-up receiving them? We investigate how the potential for behavioral uncertainty related to possible value appropriation by CVCs can create information asymmetries at technology start-ups’ IPOs. We find CVC investments are associated with increased underpricing. In contexts that further obscure whether the CVC might appropriate value (i.e., low IP protection, high industry concentration), underpricing is more pronounced.

**OPERATIONS RESEARCH**

**Stochastic Hydro-Thermal Scheduling
- Steffen Rebennack
- collaborators: Bruno Flach (IBM research), Timo Lohmann (CSM), Mario Pereira (PSR), and Thomas Vosson (University of Colorado)**

In hydro-thermal scheduling problems, one is interested in determining the optimal operating policy for the use of hydro and thermal resources in order to minimize total expected costs of fulfilling the demand for electricity over a given time horizon. The mix of hydro-power assets with thermal power generation units is particularly challenging as they operate on different time scales. Power from hydro has basically marginal generation cost in the short term (as long as water is available) while thermal generation cost are governed by fuel prices. In order to adequately price the water in the reservoirs, optimization models, spanning several years, have to be solved. As the rainfall cannot be forecasted accurately over such long time horizons, point estimates of rainfall are replaced by distributions. The mathematical models then seek to find optimal solutions providing a trade-off between various inflow scenarios.

In this line of research, we make the following contributions:

- incorporation of CO2 emission constraints into the

stage-decomposition framework of dynamic programming methods

- expansion planning with CO2 emissions constraints

- incorporation of fuel price and electricity demand uncertainty

- forecasting of CO2 emission allowances spot market prices

- unification of sampling and scenario tree approaches

- incorporation of risk constraints / measure

- convergence improvements of current solution techniques

**Optimal Power Flow (OPF)
- Steffen Rebennack
- collaborators: Stephen Frank (CSM), Josef Kallrath (BASF), P.K. Sen (CSM), and Ingrida Steponavice (University of Jyvaskyla)**

OPF seeks to optimize the power flow within an electrical network. Aspects of optimization are operational cost, planning cost, reliability or network losses. The physical laws governing the flow of power in the grid make this problem particularly challenging. In the optimization language, the general OPF problem is a nonlinear, highly non-convex, large-scale mixed-integer optimization problem. In practice, OPF has been the predominant method for power systems analysis since its introduction in the 1960's.

In this line of research, we make the following contributions:

- comprehensive literature review on existing solution techniques

- primer in OPF problems, targeted for Operations Researchers

- apply novel relaxation techniques to obtain globally optimal solutions

- optimization of building efficiency using DC power distribution