Control Architecture Modeling for Future Power Systems

Uncontrollable power generation, distributed energy resources, controllable demand, etc. are fundamental aspects of energy systems largely based on renewable energy supply. These technologies have in common that they contradict the conventional categories of electric power system operation. As their introduction has proceeded incrementally in the past, operation strategies of the power system could be adapted. For example much more wind power could be integrated than originally anticipated, largely due to the flexibility reserves already present in the power system, and the possibility of interregional electricity exchange. However, at the same time, it seems that the overall system design cannot keep up by simply adapting in response to changes, but that also new strategies have to be designed in anticipation. Changes to the electricity markets have been suggested to adapt to the limited predictability of wind power, and several new control strategies have been proposed, in particular to enable the control of distributed energy resources, including for example, distributed generation or electric vehicles. Market designs addressing the procurement of balancing resources are highly dependent on the operation strategies specifying the resource requirements. How should one decide which control strategy and market configuration is best for a future power system? Most research up to this point has addressed single isolated aspects of this design problem. Those of the ideas that fit with current markets and operation concepts are lucky; they can be evaluated on the present design. But how could they be evaluated on a potential future power system? Approaches are required that support the design and evaluation of power system operation and control in context of future energy scenarios. This work addresses this challenge, not by providing a universal solution, but by providing basic modeling methodology that enables better problem formulation and by suggesting an approach to addressing the general chicken/egg problem of planning and re-design of system operation and control. The dissertation first focuses on the development of models, diagrams, that support the conceptual design of control and operation strategies, where a central theme is the focus on modeling system goals and functions rather than system structure. The perspective is then shifted toward long-term energy scenarios and adaptation of power system operation, considering the integration of energy scenario models with the re-design of operation strategies. The main contributions in the first part are, firstly, by adaptation of an existing functional modeling approach called Multilevel Flow Modeling (MFM) to the power systems domain, identifying the means-ends composition of control levels and development of principles for the consistent modeling of control structures, a formalization of control-as-a-service; secondly, the formal mapping of fluctuating and controllable resources to a multi-scale and multi-stage representation of control and operation structures; and finally the application to some concrete study cases, including a present system balancing, and proposed control structures such as Microgrids and Cells. In the second part, the main contributions are the outline of a formation strategy, integrating the design and model-based evaluation of future power system operation concepts with iterative energy scenario development. Finally, a new modeling framework for development and evaluation of power system operation in context of energy-storage based power system balancing is introduced.<br/

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Coordinating decentralized learning and conflict resolution across agent boundaries

It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems because of scalability, partial information accessibility and complex interaction of agents. It is a challenge for agents to learn good policies, when they need to plan and coordinate in uncertain, dynamic environments, especially when they have large state spaces. It is also critical for agents operating in a multiagent system (MAS) to resolve conflicts among the learned policies of different agents, since such conflicts may have detrimental influence on the overall performance. The focus of this research is to use a reinforcement learning based local optimization algorithm within each agent to learn multiagent policies in a decentralized fashion. These policies will allow each agent to adapt to changes in environmental conditions while reorganizing the underlying multiagent network when needed. The research takes an adaptive approach to resolving conflicts that can arise between locally optimal agent policies. First an algorithm that uses heuristic rules to locally resolve simple conflicts is presented. When the environment is more dynamic and uncertain, a mediator-based mechanism to resolve more complicated conflicts and selectively expand the agents' state space during the learning process is harnessed. For scenarios where mediator-based mechanisms with partially global views are ineffective, a more rigorous approach for global conflict resolution that synthesizes multiagent reinforcement learning (MARL) and distributed constraint optimization (DCOP) is developed. These mechanisms are evaluated in the context of a multiagent tornado tracking application called NetRads. Empirical results show that these mechanisms significantly improve the performance of the tornado tracking network for a variety of weather scenarios. The major contributions of this work are: a state of the art decentralized learning approach that supports agent interactions and reorganizes the underlying network when needed; the use of abstract classes of scenarios/states/actions that efficiently manages the exploration of the search space; novel conflict resolution algorithms of increasing complexity that use heuristic rules, sophisticated automated negotiation mechanisms and distributed constraint optimization methods respectively; and finally, a rigorous study of the interplay between two popular theories used to solve multiagent problems, namely decentralized Markov decision processes and distributed constraint optimization

Human development policy: Theorizing and modeling

Thesis (PhD) - Indiana University, School of Education, 2006For more than half a century after World War II, a tremendous effort has been made for international development, both financially and intellectually. Some development economists believed that national economic growth would contribute to solving the problems of poverty in developing countries; others believed that improving the productivity of human resources was the critical source of economic growth. These concepts of development have justified educational development as a tool of economic growth. Human development theory questions such concepts of development that have focused exclusively on economic growth, and it considers humans themselves as the end of development, not a tool of economic growth. However, this philosophical theory of human development is not readily transformed into practical policy. What does "human development" mean? What role does educational development have in human development? How is it possible to embody this abstract concept as a policy? To answer these questions, this study sets three main aims: (I) to clarify the meaning of human development; (II) to propose a theoretical model of human development to specify a role of education in human development policy; and (III) to test the theoretical model empirically by applying Hierarchical Generalized Linear Modeling to Tanzania's survey data. Theoretical analysis of human development finds that "human development" means the process of expanding individuals' constitutive freedom, and that basic education, especially children's literacy, plays a critical role as a central internal capability in human development. Quantitative models are developed to assess the need for human development policy in Tanzania, paying attention to the domestic gap between rural and urban districts. The models show significant contributions of schooling experience and book possession to children's literacy in both rural and urban areas as well as an imperative need of the rural children for public services such as child health aid staff and primary school teachers. This study analyzes human development theory from an educational perspective and proposed the needs assessment model for human development policy in Tanzania

Servitization strategies & firm boundary decisions

This PhD thesis focuses on a particular manifestation of the servitization of manufacturing phenomenon, namely the offering of advanced asset management services for mature capital equipment in a business to business context. In contrast to past research in the field, the study approaches the issue from the often neglected point of view of the offerings’ intended customers and assumes a strategic perspective to shed light on the considerations that affect the customers’ propensity to accept or reject them. Upon conceptually analysing what the acceptance of such offerings actually requires of customers at an operational level, the study reveals that the latter are in most cases required to outsource a number of activities that have traditionally been handled in‐house. Thus, the issue of accepting servitized offerings of this nature is treated as a make‐or‐buy, or otherwise a firm boundary decision dilemma on behalf of customers. In adopting this treatment, the study then engages with the firm boundary/outsourcing literature and considers the state‐of‐the–art in four contemporary theoretical frameworks of make‐or‐buy decisions that reflect a customer firm’s efficiency, dependence, competence and identity related strategic considerations. In particular, insights are drawn respectively from Transaction Cost Economics, Resource Dependency Theory, a strand of the Resource‐Based View of the firm as well as the tenet of Identity Coherence. Augmented with a number of novel propositions, the collective body of considerations is then empirically explored through a quasi‐experimental cross‐sectional survey of deep‐sea dry and wet cargo shipping firms (considered as customers of servitization) that focuses on six key maintenance activities related to a ship’s main propulsion engine (considered as the object of servitization). In performing a two tier statistical analysis of the empirical data through logistic and multiple regression techniques, the study finds that alternative considerations affect a customer firm’s decision of whether to outsource an activity or not and the decision of how much of an activity to outsource once the first‐tier dilemma is answered positively. Furthermore, the study finds that combined theoretical perspective approaches offer better explanations of the phenomenon in question. With its conclusion, the thesis offers a number of implications directed at the literature streams involved as well as the practice of outsourcing and pursuing a servitization strategy

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications