Thermal-Safe Test Scheduling for Core-Based System-on-a-Chip Integrated Circuits

Overheating has been acknowledged as a major problem during the testing of complex system-on-chip (SOC) integrated circuits. Several power-constrained test scheduling solutions have been recently proposed to tackle this problem during system integration. However, we show that these approaches cannot guarantee hot-spot-free test schedules because they do not take into account the non-uniform distribution of heat dissipation across the die and the physical adjacency of simultaneously active cores. This paper proposes a new test scheduling approach that is able to produce short test schedules and guarantee thermal-safety at the same time. Two thermal-safe test scheduling algorithms are proposed. The first algorithm computes an exact (shortest) test schedule that is guaranteed to satisfy a given maximum temperature constraint. The second algorithm is a heuristic intended for complex systems with a large number of embedded cores, for which the exact thermal-safe test scheduling algorithm may not be feasible. Based on a low-complexity test session thermal cost model, this algorithm produces near-optimal length test schedules with significantly less computational effort compared to the optimal algorithm

Power systems generation scheduling and optimisation using evolutionary computation techniques

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.Overseas Development Agenc

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Non-cascaded short-term pumped-storage hydro-thermal scheduling using accelerated particle swarm optimization

© 2018 IEEE. This paper presents the implementation of a variant of the famous particle swarm optimization, known as Accelerated Particle Swarm Optimization (APSO), on a non-cascaded or a two-unit hydro-thermal system with consideration of hydal pumping in light loading intervals of hydro-thermal scheduling period. APSO is an easy to program and easy to implement variant of Particle Swarm Optimization (PSO) that has the ability to converge to a good approximate to global optimum within a few iterations. A standard pumped-storage hydrothermal scheduling problem, discussed in existing literature, is considered for the implementation of APSO. A comparison of this implementation is also given with the previously existing implementations of other algorithms

Comparison of Genetic and Reinforcement Learning Algorithms for Energy Cogeneration Optimization

Large process plants generally require energy in different forms: mechanical, electrical, or thermal (in the form of steam or hot water). A commonly used source of energy is cogeneration, also defined as Combined Heat and Power (CHP). Cogeneration can offer substantial economic as well as energy savings; however, its real-time operation scheduling is still a challenge today. Multiple algorithms have been proposed for the CHP control problem in the literature, such as genetic algorithms (GAs), particle swarm optimization algorithms, artificial neural networks, fuzzy decision making systems and, most recently, reinforcement learning (RL) algorithms.This paper presents the comparison of a RL approach and a GA for the control of a cogenerator, using as a case study a thermal power plant serving a factory during the year 2021. The two methods were compared based on an earnings before interest, taxes, depreciation, and amortization (EBITDA) metric. The EBITDA that could be obtained using the RL algorithm, exceeds both the EBITDA that could be generated using a per-week genetic algorithm and the one from the manual scheduling of the CHP. Thus, the RL algorithm proves to be the most cost-effective strategy for the control of a CHP

Green Scheduling of Control Systems

Electricity usage under peak load conditions can cause issues such as reduced power quality and power outages. For this reason, commercial electricity customers are often subject to demand-based pricing, which charges very high prices for peak electricity demand. Consequently, reducing peaks in electricity demand is desirable for both economic and reliability reasons. In this thesis, we investigate the peak demand reduction problem from the perspective of safe scheduling of control systems under resource constraint. To this end, we propose Green Scheduling as an approach to schedule multiple interacting control systems within a constrained peak demand envelope while ensuring that safety and operational conditions are facilitated. The peak demand envelope is formulated as a constraint on the number of binary control inputs that can be activated simultaneously. Using two different approaches, we establish a range of sufficient and necessary schedulability conditions for various classes of affine dynamical systems. The schedulability analysis methods are shown to be scalable for large-scale systems consisting of up to 1000 subsystems. We then develop several scheduling algorithms for the Green Scheduling problem. First, we develop a periodic scheduling synthesis method, which is simple and scalable in computation but does not take into account the influence of disturbances. We then improve the method to be robust to small disturbances while preserving the simplicity and scalability of periodic scheduling. However the improved algorithm usually result in fast switching of the control inputs. Therefore, event-triggered and self-triggered techniques are used to alleviate this issue. Next, using a feedback control approach based on attracting sets and robust control Lyapunov functions, we develop event-triggered and self-triggered scheduling algorithms that can handle large disturbances affecting the system. These algorithms can also exploit prediction of the disturbances to improve their performance. Finally, a scheduling method for discrete-time systems is developed based on backward reachability analysis. The effectiveness of the proposed approach is demonstrated by an application to scheduling of radiant heating and cooling systems in buildings. Green Scheduling is able to significantly reduce the peak electricity demand and the total electricity consumption of the radiant systems, while maintaining thermal comfort for occupants

All Sky Survey Mission Observing Scenario Strategy

This paper develops a general observing strategy for missions performing all-sky surveys, where a single spacecraft maps the celestial sphere subject to realistic constraints. The strategy is flexible such that targeted observations and variable coverage requirements can be achieved. This paper focuses on missions operating in Low Earth Orbit, where the thermal and stray-light constraints due to the Sun, Earth, and Moon result in interacting and dynamic constraints. The approach is applicable to broader mission classes, such as those that operate in different orbits or that survey the Earth. First, the instrument and spacecraft configuration is optimized to enable visibility of the targeted observations throughout the year. Second, a constraint-based high-level strategy is presented for scheduling throughout the year subject to a simplified subset of the constraints. Third, a heuristic-based scheduling algorithm is developed to assign the all-sky observations over short planning horizons. The constraint-based approach guarantees solution feasibility. The approach is applied to the proposed SPHEREx mission, which includes coverage of the North and South Celestial Poles, Galactic plane, and a uniform coverage all-sky survey, and the ability to achieve science requirements demonstrated and visualized. Visualizations demonstrate the how the all-sky survey achieves its objectives