A Proposal for an Environmental Decision Support System at the Regional Level: Concepts, Support Methodology, Tools and their Terminology

One of the goals of IIASA's research activities in the area of environmental quality modeling is the integration of data and models in a unified framework to assist decision makers with the management of complex environmental systems. Building on IIASA's work undertaken within the WELMM (Water, Energy, Land, Materials and Manpower) project of the former Resources and Environment Area and the work on Decision Support Systems of the former Management and Technology Area, a conceptual framework for an environmental decision support system (EDSS) has been developed and is presented in this paper. The proposed EDSS has been developed with the interest and the financial support of the CSI, the Center for Information Systems of the Regional Government of Piemonte, Italy. The main issue addressed by this paper is to devise a system assisting decision makers in tackling environmental problems at the regional level. These decisions are typically characterized by a combination of both structured (formalizable, described in a quantitative model) and unstructured elements (incomplete information, undefined cause-effect relationships, influence of political objectives, public perception, consideration of aesthetics, etc.). The proposed EDSS enables the user to use models and data, of relevance to a particular task, which are embedded in the EDSS in the form of a process information system. The specific feature of this process information system is that it contains processes of anthropogenic nature (the socio-economic activities being the cause of environmental impacts like power plants, industrial production units, etc.) as well as natural processes determining the spatial/temporal distribution and the extent of environmental quality changes (like the dispersion and deposition of air pollutants and their effect on human population, vegetation and wildlife). The system ensures that the data and models, which have been developed in the context of specific EDSS applications are documented right from the outset and become thus equally available for further use. This becomes especially important in view of the long-term effort to be put into the development of data and models dealing with the large number of environmental problems that governments, industry and academic institutions are confronted with at the regional level

Power Management for Energy Systems

The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences

Technology Diffusion in Climate Mitigation Modeling and Implications for Mitigation Targets

Global climate mitigation analyses have been used to evaluate the challenges of reducing greenhouse gases and to inform climate change policymaking for over 30 years. Studies traditionally focus on projections of greenhouse gases over the 21st century based on key drivers such as population growth, economic growth, and the rate of technological change especially in climate mitigation or energy technologies. Any one of these factors can have an appreciable impact on emissions levels and the cost of mitigation particularly in the face of stringent mitigation targets. One area that has not been sufficiently studied is the impact of different rates of technology diffusion of advanced energy technologies between high-income and low- and middle-income countries. This is the topic of this dissertation. The standard approach in climate economic modeling is to assume that all technologies are available at the same time and rate across countries with different incomes and technological capabilities. This study applies the literature related to economic and technological convergence to first develop new estimates of technology diffusion for energy-related sectors across 112 countries of varying income levels. Then new greenhouse gas scenarios are developed with the Global Change Assessment Model (GCAM) to test the importance of different assumptions on technology diffusion versus other key modeling assumptions. The modeling results from this research show that the cost of meeting the same climate target could be as high as 60% to 80% in marginal cost terms and about 30% greater in total policy costs when different assumptions on diffusion rates of climate mitigation technologies between countries are used. These results clearly point to the need for greater evaluation on the importance of technology diffusion in climate mitigation modeling and also in the consideration of these results for climate change policy decision making

Nonlinear robust H∞ control.

A new theory is proposed for the full-information finite and infinite horizontime robust H∞ control that is equivalently effective for the regulation and/or tracking problems of the general class of time-varying nonlinear systems under the presence of exogenous disturbance inputs. The theory employs the sequence of linear-quadratic and time-varying approximations, that were recently introduced in the optimal control framework, to transform the nonlinear H∞ control problem into a sequence of linearquadratic robust H∞ control problems by using well-known results from the existing Riccati-based theory of the maturing classical linear robust control. The proposed method, as in the optimal control case, requires solving an approximating sequence of Riccati equations (ASRE), to find linear time-varying feedback controllers for such disturbed nonlinear systems while employing classical methods. Under very mild conditions of local Lipschitz continuity, these iterative sequences of solutions are known to converge to the unique viscosity solution of the Hamilton-lacobi-Bellman partial differential equation of the original nonlinear optimal control problem in the weak form (Cimen, 2003); and should hold for the robust control problems herein. The theory is analytically illustrated by directly applying it to some sophisticated nonlinear dynamical models of practical real-world applications. Under a r -iteration sense, such a theory gives the control engineer and designer more transparent control requirements to be incorporated a priori to fine-tune between robustness and optimality needs. It is believed, however, that the automatic state-regulation robust ASRE feedback control systems and techniques provided in this thesis yield very effective control actions in theory, in view of its computational simplicity and its validation by means of classical numerical techniques, and can straightforwardly be implemented in practice as the feedback controller is constrained to be linear with respect to its inputs