Optimal Scheduling of Energy Storage for Energy Shifting and Ancillary Services to the Grid

This thesis is mainly focused on developing optimization-based models for scheduling of energy storage units. At first, a real-time optimal scheduling algorithm is developed seeking to maximize the storage revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices. The electricity price modulation is proposed as an approach to competitively offer incentive by the utility regulator to storage to fill the gap between current and a stable rate of return. Subsequently, the application of large-scale storage for congestion relief in transmission systems as an ancillary service to the grid is investigated. An algorithm is proposed for the following objectives: (i) to generate revenue primarily by exploiting electricity price arbitrage opportunities and (ii) to optimally prepare the storage to maximize its contribution to transmission congestion relief. In addition, an algorithm is proposed to enable independently operated, locally controlled storage to accept dispatch instructions issued by Independent System Operators (ISOs). While the operation of locally controlled storage is optimally scheduled at the owner’s end, using the proposed algorithm, storage is fully dispatchable at the ISO’s end. Finally, a model is proposed and analyzed to aggregate storage benefits for a large-scale load. The complete model for optimal operation of storage-based electrical loads considering both the capital and operating expenditures of storage is developed. The applications of the proposed algorithms and models are examined using real-world market data adopted from Ontario’s electricity market and actual load information from a large-scale institutional electricity consumer in Ontario

Schedulability-driven scratchpad memory swapping for resource-constrained real-time embedded systems

In resource-constrained real-time embedded systems, scratchpad memory (SPM) is utilized in place of cache to increase performance and enforce consistent behavior of both hard and soft real-time tasks via software-controlled SPM management techniques (SPMMTs). Real-time systems depend on time critical (hard) tasks to complete execution before their deadline times. Many real-time systems also depend on the execution of soft tasks that do not have to complete by hard deadlines. This thesis evaluates a new SPMMT that increases both worst-case task slack time (TST) and soft task processing capabilities, by combining two existing SPMMTs. The schedulability-driven ACETRB / WCETRB swapping (SDAWS) SPMMT of this thesis uses task schedulability characteristics to control the selection of either the average-case execution time reduction based (ACETRB) SPMMT or the worst-case execution time reduction based (WCETRB) SPMMT. While the literature contains examples of combined management techniques, until now there have been none that combine both WCETRB and ACETRB SPMMTs. The advantage of combining them is to achieve WCET reduction comparable to what can be achieved with the WCETRB SPMMT, while achieving significantly reduced ACET relative to the WCETRB SPMMT. Using a stripped-down RTOS and an SPMMT simulator implemented for this work, evaluated resource-constrained scenarios show a reduction in task slack time from the SDAWS SPMMT relative to the WCETRB SPMMT between 20% and 45%. However, the evaluated scenarios also conservatively show that SDAWS can reduce ACET relative to the WCETRB SPMMT by up to 60%

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads.Comment: 33 pages, 11 figure

AI-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns in the historical data using data mining techniques. In this research study, two data driven approaches based on predictive modeling techniques are proposed to solve a SC-UC problem in a day ahead electricity market which can be used as alternative backup methods for solving a SC-UC problem. In the first approach, the SC-UC is partially modeled using predictive modeling techniques to enhance the computational speed of the problem, while in the second approach, the optimization problem is completely replaced by data driven predictive models to further enhance the computational efficiency, however, at the cost of some optimality loss. The proposed approaches are validated through numerical simulations on different IEEE case studies to demonstrate and study the effectiveness of the developed approaches. The results obtained from the proposed approaches are compared with those obtained from commercial optimization solvers e.g., IBM CPLEX MIQP and GUROBI MIQP solvers

Fully-deterministic execution of IEC-61499 models for Distributed Avionics Applications

© 2018 by the authors. The development of time-critical Distributed Avionics Applications (DAAs) pushes beyond the limit of existing modeling methodologies to design dependable systems. Aerospace and industrial automation entail high-integrity applications where execution time is essential for dependability. This tempts us to use modeling technologies from one domain in another. The challenge is to demonstrate that they can be effectively used across domains whilst assuring temporally dependable applications. This paper shows that an IEC61499-modeled DAA can satisfy temporal dependability requirements as to end-to-end flow latency when it is properly scheduled and realized in a fully deterministic avionics platform that entails Integrated Modular Avionics (IMA) computation along with Time-Triggered Protocol (TTP) communication. Outcomes from the execution design of an IEC61499-based DAA model for an IMA-TTP platform are used to check runtime correctness through DAA control stability. IEC 61499 is a modeling standard for industrial automation, and it is meant to facilitate distribution and reconfiguration of applications. The DAA case study is a Distributed Fluid Control System (DFCS) for the Airbus-A380 fuel system. Latency analysis results from timing metrics as well as closed-loop control simulation results are presented. Experimental outcomes suggest that an IEC61499-based DFCS model can achieve desired runtime latency for temporal dependability when executed in an IMA-TTP platform. Concluding remarks and future research direction are also discussed

Pushing the Boundaries of Spacecraft Autonomy and Resilience with a Custom Software Framework and Onboard Digital Twin

This research addresses the high CubeSat mission failure rates caused by inadequate software and overreliance on ground control. By applying a reliable design methodology to flight software development and developing an onboard digital twin platform with fault prediction capabilities, this study provides a solution to increase satellite resilience and autonomy, thus reducing the risk of mission failure. These findings have implications for spacecraft of all sizes, paving the way for more resilient space missions

Scheduling Techniques for Operating Systems for Medical and IoT Devices: A Review

Software and Hardware synthesis are the major subtasks in the implementation of hardware/software systems. Increasing trend is to build SoCs/NoC/Embedded System for Implantable Medical Devices (IMD) and Internet of Things (IoT) devices, which includes multiple Microprocessors and Signal Processors, allowing designing complex hardware and software systems, yet flexible with respect to the delivered performance and executed application. An important technique, which affect the macroscopic system implementation characteristics is the scheduling of hardware operations, program instructions and software processes. This paper presents a survey of the various scheduling strategies in process scheduling. Process Scheduling has to take into account the real-time constraints. Processes are characterized by their timing constraints, periodicity, precedence and data dependency, pre-emptivity, priority etc. The affect of these characteristics on scheduling decisions has been described in this paper

Software performance estimation strategies in a system-level design tool

High-level cost and performance estimation, coupled with a fast hardware/software co-simulation framework, is a key enabler to a fast embedded system design cycle. Unfortunately, the problem of deriving such estimates without a detailed implementation available is difficult.In this paper we describe two approaches to solve software cost and performance estimation problem, and how they are used in an embedded system design environment. A source-based approach uses compilation onto a virtual instruction set, and allows one to quickly obtain estimates without the need for a compiler for the target processor. An object-based approach translates the assembler generated by the target compiler to “assembler-level,” functionally equivalent t C. In both cases the code is annotated with timing and other execution related information (e.g., estimated memory accesses) and is used as a precise, yet fast, software simulation model. We contrast the precision and speed of these two techniques comparing them with those obtainable by a state-of-the-art cycle-based processor model