Power consumption is gradually becoming an important issue of designing computing systems. Most of the researches of low power issues have focused on semiconductor techniques or hardware architecture designs, but less utilized the techniques of software optimization. This paper presents a new scheduling methodology in source code level for Intelligent Memory System, which reduces the energy consumption by means of code compilation techniques. The scheduling kernel provides two options for users: performance-oriented low power scheduling and energy-oriented low power scheduling, to achieve the objective of considering high performance and low power issues. The experimental results are also presented and discussed.

L’objectif de cette thèse est d’étudier l’efficacité énergétique des stratégies basse consommation pour des plateformes représentatives. Principalement, nous nous intéresserons aux stratégies énergétiques pour des systèmes embarqués multicœur en étudiant le comportement de politiques logicielles qui permettent la réduction effective de l’énergie tout en répondant aux exigences applicatives. Le travail présenté dans ce mémoire vise à étudier des stratégies de gestion de la consommation pour des plateformes monoprocesseur puis multiprocesseur concrètes. L’approche utilisée pour cette étude fut basée sur des plateformes représentatives afin d’identifier les paramètres significatifs, aussi bien au niveau matériel qu’au niveau applicatif, à l’inverse de nombreux travaux dans lesquels ces paramètres sont assez peu pris en compte voir ignorés. Ce travail analyse et compare diverses expérimentations menées sur des politiques énergétiques basées sur des techniques DVFS (Dynamic Voltage and Frequency Scaling) et DPS (Dynamic Power Switching) et définit les conditions sous lesquelles ces stratégies sont efficaces. Ces expérimentations ont permis d’établir des conclusions remarquables qui peuvent servir de pré-requis lors de la définition de stratégies efficaces de gestion de la consommation. Ces résultats montrent également que pour obtenir des stratégies efficientes il est nécessaire de tenir compte du domaine applicatif. Enfin, il faut noter que les modèles de haut de niveau de consommation ont été définis sur la base des mesures effectuées et afin d’estimer les gains énergétiques dès les premières étapes d’un flot de conception. / The purpose of this study is to investigate how power management strategies can be efficiently exploited in actual platforms. Primarily, the challenges in multicore based embedded systems lies in managing the energy expenditure, determining the scheduling behavior and establishing methods to monitor power and energy, so as to meet the demands of the battery life and load requirements. The work presented in this dissertation is a study of low power-aware strategies in the practical world for single and multiprocessor platforms. The approach used for this study is based on representative multiprocessor platforms (real or virtual) to identify the most influential parameters, at hardware as well as application level, unlike many existing works in which these parameters are often underestimated or sometimes even ignored. The work analyzes and compares in detail various experimentations with different power policies based on Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Switching (DPS) techniques, and investigates the conditions at which these policies are effective in terms of energy savings. The results of these investigations reveal many interesting and notable conclusions that can serve as prerequisites for the efficient use of power management strategies. This work also shows the potential of advanced domain specific power strategies compared to real world available strategies that are general purpose based in their majority. Finally, some high level consumption models are derived from the different energy measurement results to let the estimation of power management benefits at early stages of a system development.

The purpose of this study is to investigate how power management strategies can be efficiently exploited in actual platforms. Primarily, the challenges in multicore based embedded systems lies in managing the energy expenditure, determining the scheduling behavior and establishing methods to monitor power and energy, so as to meet the demands of the battery life and load requirements. The work presented in this dissertation is a study of low power-aware strategies in the practical world for single and multiprocessor platforms. The approach used for this study is based on representative multiprocessor platforms (real or virtual) to identify the most influential parameters, at hardware as well as application level, unlike many existing works in which these parameters are often underestimated or sometimes even ignored. The work analyzes and compares in detail various experimentations with different power policies based on Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Switching (DPS) techniques, and investigates the conditions at which these policies are effective in terms of energy savings. The results of these investigations reveal many interesting and notable conclusions that can serve as prerequisites for the efficient use of power management strategies. This work also shows the potential of advanced domain specific power strategies compared to real world available strategies that are general purpose based in their majority. Finally, some high level consumption models are derived from the different energy measurement results to let the estimation of power management benefits at early stages of a system development.

Traditional forecasting approaches forecast the total system load directly without considering the individual consumer's load. With the introduction of the smart grid, lots of renewable energy resources such as wind and solar are added to the system from consumer side fluctuates the system load and makes forecasting more complex. Thus, it is necessary to forecast individual consumers load. Here, a framework is presented in which individual customer loads is forecasted rather than the system load. At first, a hierarchical cluster analysis is performed to classify daily load patterns into different groups for all the individuals. Then an association analysis is performed to determine critical influential factors that affect the load curve for given day. The next step is the application of a decision tree to establish classification rules between the different groups of the load curve and the critical influential factors. Then, appropriate forecasting models are chosen for different load patterns and the individual load is forecasted. Finally, the forecasted total system load is obtained through an aggregation of an individual load forecasting results. The relative error of forecasting the system load using this framework is compared with the relative errors using SVM regression and this framework had better accuracy. This framework is also used for forecasting the power output of the renewable generation. Also, the results of the day ahead forecast of system load and renewable generation is used for economic power scheduling for the microgrid and peak shaving for the utilities.

A conceptual dust detection mission, KnightSat III, using pico-scale satellites is analyzed. The purpose of the proposed KnightSat III mission is to aid in the determination of the size, mass, distribution, and number of dust particles in low earth orbits through a low cost and flexible satellite or a formation of satellites equipped with a new dust detector. The analysis of a single satellite mission with an on-board dust detector is described; though this analysis can easily be extended to a formation of pico-scale satellites. Many design aspects of the mission are discussed, including orbit analysis, power management, attitude determination and control, and mass and power budgets. Two of them are emphasized. The first is a new attitude guidance and control method, and the second is the online optimal power scheduling. It is expected that the measurements obtained from this possible future mission will provide insight into the dynamical processes of inner solar system dust, as well as aid in designing proper micro-meteoroid impact mitigation strategies for future man-made spacecraft.

This thesis addresses the problem of reactive power scheduling in a power system with several areas controlled by independent transmission system operators (TSOs). To design a fair method for optimizing the control settings in the interconnected multi-TSO system, two types of schemes are developed.<br />First, a centralized multi-TSO optimization scheme is introduced, and it is shown that this scheme has some properties of fairness in the economic sense.<br />Second, the problem is addressed through a decentralized optimization scheme with no information exchange between the TSOs. In this framework, each TSO assumes an external network equivalent in place of its neighboring TSOs and optimizes the objective function corresponding to its own control area regardless of the impact that its choice may have on the other TSOs.<br />The thesis presents simulation results obtained with the IEEE 39 bus system and IEEE 118 bus systems partitioned between three TSOs. It also presents some results for a UCTE-like 4141 bus system with seven TSOs. The decentralized control scheme is applied to both time-invariant and time-varying power systems. Nearly optimal performance is obtained in those contexts.