System-wide Power Research Articles

A self-healing restoration of power grid based on two-stage adaptive decision-making strategy to enhance grid resilience

Large-scale power outages can affect the economy of the country and jeopardize the lives of millions of people, so there is a need for a fast, reliable, and self-healing tool to reduce these losses. Hence, the power system restoration performance can be improved by considering both restoring and operating performance, however, current restoration strategies only consider the restoring performance but ignore the operational performance which can enhance the power system resiliency. Given this background, challenging tasks such as optimal restoration planning (ORP) and optimal real-time operation (ORO) are planned to be tackled by developing an adaptive decision-making strategy (ADMS). In order to reduce the effects of emergency power outages on power systems, this study presents the ADMS, a two-stage self-healing restoration approach. Moreover, a novel metric of capacity accessibility (NMCA) is developed to determine the capacity adequacy status during severe events. The proposed ADMS has two steps, each of which is conducted at a certain time, and is referred to as ORP for the first stage and ORO functions for the second stage. The goal of the ORP optimization model is to maximize system-wide power capacity for electricity demand under extreme events by considering practical constraints, including non-black-start generators (NBSGs) constraints, network topology constraints, and power balance constraints. In order to reduce generation costs, the ORO optimization model adjusts the generated output power to enhance one or more operating performance metrics. The first stage problem is formulated using mixed-integer linear programming (MILP), and it is resolved using the branch-and-bound technique. Moreover, the interior point method solves AC optimal power flow for the second stage. Finally, simulation studies are performed on the modified IEEE 118-bus power systems. The outcomes demonstrate the effectiveness of the proposed strategy for planning (ORP) and real-time operation (ORO); the proposed two-stage strategy is capable of providing an optimal solution for a high-performance online restoration decision support system; the proposed strategy can adapt against the system conditions and contingencies.

Power Loss Minimization in a Radial Distribution Network by Optimal Sizing and Placement of Energy Storage Units

It is possible to reduce distribution losses by strategically placing and sizing DG and BESS sources. Assuring low loss requires strategically placing the aforementioned devices; otherwise, the system may experience either under- or overvoltage. It is preferable to choose bus stations with less risk for loss. The proposed approach tries to pinpoint the optimal BESS size and placement to cut down on investment and operating expenses while still achieving the desired level of energy reduction. The development of optimisation algorithms for finding and scaling BESS units is the fundamental focus of this study. Two such strategies are being explored here: the Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACOA). The goal function, like the original issue, seeks to minimise system-wide power losses while adhering to specified levels of equality and inequality. This article explores the appropriate capacity and placement of the DGs in a 33-bus radial distribution grid to reduce power dissipations. Matlab code is used to perform a simulation, and the results are put to use gauging the method's sturdiness.

ACCURATE: Accuracy Maximization for Real-Time Multicore Systems With Energy-Efficient Way-Sharing Caches

Improving result accuracy in approximate computing (AC)-based real-time applications without violating deadlines has recently become an active research domain. Execution time of AC real-time tasks can individually be separated into: execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve the result accuracy of the initial result within a given deadline. However, obtaining higher result accuracy at the cost of enhanced execution time may lead to deadline violation, along with higher energy usage. We present ACCURATE, a novel hybrid offline–online approximate real-time scheduling approach that first schedules AC-based tasks on multicore with an objective to maximize result accuracy and determines operational processing speeds for each task constrained by system-wide power limit, deadline, and task dependency. At runtime, by employing a way-sharing technique (WH_LLC) at the last level cache (LLC), ACCURATE improves performance, which is further leveraged, to enhance result accuracy by executing more from the optional part and to improve the energy efficiency of the cache by turning off a controlled number of cache ways. ACCURATE also exploits the slacks either to improve the result accuracy of the tasks or to enhance the energy efficiency of the underlying system, or both. ACCURATE achieves 85% QoS with 36% average reduction in cache leakage consumption with a 24% average gain in energy-delay product (EDP) for a 4-core-based chip multiprocessor (CMP) with 6.4% average improvement in performance.

System wide power flow analysis of the Irish distribution network to assess the impact of projected low carbon technology loads and generation including significant increase of electric vehicles connected to the grid

Through the Climate Action and Low Carbon Development Act 2021 the Irish Government have set out a commitment to reach net-zero emissions by 2050 and cut emissions by 51 % by 2030 compared to 2018 levels [1]. Reaching these targets will, in no small part, be attributed to the electrification of heat and transport powered by renewable generation. Today Ireland is a country of 5 million people, and there is installed capacity of about 4.5 GW of renewable generation connected to the grid. By 2030, it is projected that one million electric vehicles (EVs) will be charging on a regular basis, 600,000 homes will be heated via heat pump (HP) technology, roof top photovoltaic (PV) will be installed on hundreds of thousands of homes and there will be 5 GW of additional commercial renewable generation connected at distribution level. It is forecasted that by 2030, if unmanaged, EV load will make up approximately 28 % of the total peak demand in Ireland. ESB Networks, Irelands Distribution System Operator has established the ‘National Network, Local Connections” programme which is tasked with introducing and embedding the new systems and processes in order to facilitate the connection and operation of the new Low Carbon Technologies (LCTs), which are comprised of the EVs, HPs and PVs, and the commercial generation, while maintaining a safe, secure and reliable network. The network levels assessed under this programme include the 10 kV, 20 kV and 38 kV systems for the whole country and the 110 kV network in the Dublin area. The Network System Services (NSS) group within ESB have been tasked with analysing the Irish distribution system to identify network constraints relating to the increase in LCT usage, with the view to establish both traditional and non-traditional network solutions, such as utilising flexibility services, to maintain the integrity of the grid. ESB Networks have defined a number of LCT uptake scenarios, generation scenarios and underlying winter peak and summer night valley demand scenarios. The ESB MV distribution system model is comprised of 20 different network areas, and each of these network areas are assessed from a load flow perspective for an 11 year period from 2020 to 2030. Having carried out analysis to determine the worst-case scenario for each network area, NSS have developed network models using Siemens PSS Sincal software. NSS group are using the models to investigate the system load flows and voltage profiles, for normal and standby feeding operation of the entire distribution network for the period from 2020 to 2030. The development and utilisation of Python and VBA scripting tools to analyse and process large data sets of input data and results aids the efficiency of the large task at hand. This paper will discuss scenario modelling techniques, load flow analysis and constraint identification brought on by the changing energy landscape of the Irish distribution system.

Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare , a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

Optimal power regulation for wind integration in the balancing market environment

Variable renewable generation and load fluctuations induce significant balancing cost in power system operation. To overcome this issue, this paper proposes a control architecture that leverages inherent regulation capabilities of wind turbines to minimize the system-wide balancing costs. Instead of handling wind power fluctuations via power filtering algorithms that are agnostic to system-wide power imbalance, this paper aims to optimize the wind power generation profile from system perspective. In the proposed method, wind turbines are modelled as semi-dispatchable units, where the dispatch command is dynamically generated at every automatic generation control cycle by considering mileage payments as an indicator of system-wide imbalance. As a result, local resources of wind turbines are optimally leveraged in real-time to mitigate system-wide power imbalances. The proposed strategy and state-of-the-art techniques are compared in comprehensive high-fidelity case studies. Our simulation results demonstrate that the proposed system-aware regulation scheme can alleviate system balancing costs without investments into energy storage systems.

Evaluating adaptive and predictive power management strategies for optimizing visualization performance on supercomputers

Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. Our study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.

Dynamic power management for value-oriented schedulers in power-constrained HPC system

High performance computing (HPC) systems are confronting the challenge of improving their productivity under a system-wide power constraint in the exascale era. To measure the productivity of an HPC job, researchers have proposed to assign a monotonically decreasing time-dependent value function, called job-value, to that job. These job-value functions are used by the value-based scheduling algorithms to maximize the system productivity where system productivity is the accumulation of job-value for the completed jobs. In this study, we first show that the relative performance of the competing state-of-the-art static power allocation strategies interchange based on the level of the power constraint when applied to the value-based algorithms. We then investigate the limitations of these static strategies by relating the job completion rate to the resource utilization, and expose that there is non-negligible amount of unused resources for the scheduler to utilize. Even though the system is oversubscribed, these unused resources are insufficient to schedule new high-value jobs. Based on this observation, we propose a novel dynamic power management strategy for the value-based algorithms. Our dynamic allocation policy maximizes the system productivity, resource utilization, and job completion rate by utilizing application power-performance models to reallocate power from running jobs to newly arrived jobs. We simulate a large-scale system that uses job arrival traces from a real HPC system. We demonstrate that the dynamic-variant of each value-based algorithm earns up to 16% higher productivity and completes 13% more jobs compared to its static variants when power becomes a highly constrained resource in the system.

Hybrid fuzzy evaluation algorithm for power system protection security assessment

System Protection Security Assessment is an important task in modern energy grids to ensure system security at all. The assessment system is particularly challenged by multivariate grid structures caused by volatile renewable infeeds.This paper presents an innovative strategy to evaluate the protection relay coordination of system-wide power grids. A way to calculate the quality of all protection relays based on realistic simulation data and independent of the protection method was sought. The hybrid algorithm consists of two major steps. First, a systematic analysis with various fault simulations is performed and the measurement results of all relays of all simulations are used as database. Subsequently, the use of fuzzy sets allows to express the quality of each relay setting regardless of its type.Specifically developed for the use of an optimization algorithm, finally, a new protection coordination is determined for an adapted version of the IEEE 9 bus grid. The results are validated, discussed and the effectiveness of the methodology compared to conventional setting rules.

A Value-Oriented Job Scheduling Approach for Power-Constrained and Oversubscribed HPC Systems

In this article, we investigate limitations in the traditional value-based algorithms for a power-constrained HPC system and evaluate their impact on HPC productivity. We expose the trade-off between allocating system-wide power budget uniformly and greedily under different system-wide power constraints in an oversubscribed system. We experimentally demonstrate that, under the tightest power constraint, the mean productivity of the greedy allocation is 38 percent higher than the uniform allocation whereas, under the intermediate power constraint, the uniform allocation has a mean productivity of 6 percent higher than the greedy allocation. We then propose a new algorithm that adapts its behavior to deliver the combined benefits of the two allocation strategies. We design a methodology with online retraining capability to create application-specific power-execution time models for a class of HPC applications. These models are used in predicting the execution time of an application on the available resources at the time of making scheduling decisions in the power-aware algorithms. We evaluate the proposed algorithm using emulation and simulation environments, and show that our adaptive strategy results in improving HPC resource utilization while delivering a mean productivity that is almost the same as the best performing algorithm across various system-wide power constraints.

Wind Farm Wakes Simulated Using WRF

Optimization of wind turbine (WT) arrays to maximize system-wide power production (i.e. minimize ‘wind-theft’) requires high-fidelity simulations of array-array interactions at the regional scale. This study systematically compares two parameterizations (Fitch and EWP) developed to describe wind farm impacts on atmospheric flow in the Weather Research and Forecasting (WRF) model. We present new year-long simulations for a nested domain centred on Iowa (the state with highest WT density) in the US Midwest that employ real WT characteristics and locations. Simulations with Fitch and EWP indicate similar seasonality in system-wide gross capacity factors (CF) for WT operating in Iowa, but the gross CF are systematically higher in simulations using EWP. The mean gross CF from the Fitch scheme is 44.1%, while that from EWP is 46.4%. These differences in CF are due to marked differences in the intensity and vertical profile of wakes simulated by the two approaches. Output from EWP also indicates much smaller near-surface climate impacts from WT. For example, when summertime hourly near-surface temperature (T2m) from the 299 WT grid cells are compared (i.e. EWP or Fitch minus noWT) the results show warming of nocturnal temperatures (lowest decile of T2m) but the maximum warming is considerably larger in simulations with the Fitch scheme.

Distributed control based power sharing strategy for an islanded AC microgrid

This study presents a novel secondary control strategy for a radial microgrid with end-to-end distributed energy resource (DER) interconnections, operating under islanded conditions. The control strategy utilises sparse communication and precludes the presence of a centralised controller. The distributed algorithm has a twofold objective. The first task is to determine the desired power injection at each DER bus for a load change occurring anywhere in the microgrid. The remaining task includes obtaining the bus angle set points at all the DER units such that the actual power injections converge to the desired power injections at each DER bus. The algorithm enables accurate system wide power injection in proportion to the respective DER base ratings. The control strategy is tested on a three-bus, three-DER system with local loads. The controller is shown to work for two different sets of microgrid ratings. The bounds are evaluated for each design parameter specified according to the system nominal ratings to maintain the stability of the distributed algorithm. Proofs are stated to show the convergence of algorithms to the correct power injection solution for all the nodes. Real-time simulation results are presented to corroborate accurate power sharing through the algorithm.

KAPow

In an FPGA system-on-chip design, it is often insufficient to merely assess the power consumption of the entire circuit by compile-time estimation or runtime power measurement. Instead, to make better decisions, one must understand the power consumed by each module in the system. In this work, we combine measurements of register-level switching activity and system-level power to build an adaptive online model that produces live breakdowns of power consumption within the design. Online model refinement avoids time-consuming characterization while also allowing the model to track long-term operating condition changes. Central to our method is an automated flow that selects signals predicted to be indicative of high power consumption, instrumenting them for monitoring. We named this technique KAPow, for ‘K’ounting Activity for Power estimation, which we show to be accurate and to have low overheads across a range of representative benchmarks. We also propose a strategy allowing for the identification and subsequent elimination of counters found to be of low significance at runtime, reducing algorithmic complexity without sacrificing significant accuracy. Finally, we demonstrate an application example in which a module-level power breakdown can be used to determine an efficient mapping of tasks to modules and reduce system-wide power consumption by up to 7%.

On critical timescale of real-time power balancing in power systems with intermittent power sources

From the perspective of power balancing, we consider in this paper the timescale of real-time dispatch in power systems with high penetration of intermittent power sources. Due to the integration of intermittent power sources, real-time dispatch must be employed in the process of power balancing. The hierarchy of power system operation is typically comprised of the short-term scheduling, the real-time dispatch, and the automatic generation control (AGC). Therein, the AGC is the last-level defense of the systemwide power balancing, which has limited power adjustment capability. Consequently, the real-time dispatch must eliminate as much power imbalance as possible, and its capability of doing so depends on its timescale. Based on the statistical forecast uncertainty functions, a quantified relation between the uncertainty level of the power system and the critical timescale for real-time dispatch is established in this paper. Simulations demonstrate the effectiveness of the proposed formula.

On the Performance of Beam Division Nonorthogonal Multiple Access for FDD-Based Large-Scale Multi-User MIMO Systems

For the frequency division duplexing-based large-scale multiple-input multiple-output (MIMO) systems, we consider a multi-user MIMO (MU-MIMO) non-orthogonal multiple access (NOMA) scheme, referred to as beam division-NOMA (BD-NOMA) , which is featured by a long-term feedback-based user grouping and clustering, along with a short-term feedback-based BD-NOMA scheduling. The performance of BD-NOMA is not warranted in practice because near users are severely interfered by strong signals for the far users of other beams, and furthermore, the long-term feedback-based user grouping and clustering might result in short-term inter-beam interference. We consider the short-term feedback-based joint optimization problem of user selection and power allocation for NOMA in each beam and system-wide power allocation for MU-MIMO, dealing with both intra-beam and inter-beam interference at the same time. We formulate a joint optimization problem for a large-scale MIMO system with the multiple channel quality indicators-based short-term feedback. In our analysis, we reformulate the original problem into an equivalent one that can be solved iteratively with the weighted minimum mean square error-based algorithm. Then, the performance analysis is presented to conclude that the proposed optimization is essential for enabling BD-NOMA to outperform beam division multiple access, especially when inter-beam interference becomes critical.

Compressed On-Chip Framebuffer Cache for Low-Power Display Systems

A framebuffer memory is data storage for the displayed image, which is one of the major power consumers in display systems. This paper proposes a power reduction technique for the on-chip framebuffer cache (FBC) performing a compressed image data management. The proposed architecture stores the compressed image data in the on-chip FBC, and the display controller decompresses the image data on the fly and sends it to the liquid crystal display panel. The compression and decompression processes incur additional power consumption but achieve lower system-wide power consumption. We implement the proposed architecture in a field-programmable gate array platform to confirm power saving by actual measurement. Experiments demonstrate that the proposed on-chip FBC significantly reduces the number of the off-chip framebuffer memory accesses and saves a large portion of the system-wide power consumption accordingly.

TracSim: Simulating and scheduling trapped power capacity to maximize machine room throughput

Abstract The power supplied to machine rooms tends to be over-provisioned because it is specified in practice not by workload demands but rather by high energy LINPACK runs or nameplate power estimates. This results in a considerable amount of trapped power capacity—excess power infrastructure. Instead of being wasted, this trapped power capacity should be reclaimed to accommodate more compute nodes in the machine room and thereby increase system throughput. But to do this we need the ability to enforce a system-wide power cap. In this paper, we present TracSim, a full-system simulator that enables users to measure trapped power capacity and evaluate the performance of different policies for scheduling parallel tasks under a power cap. TracSim simulates the execution environment of a production HPC cluster at Los Alamos National Laboratory (LANL). TracSim enables users to specify the system topology, hardware configuration, power cap, and task workload and to develop resource configuration and task scheduling policies aimed at maximizing machine-room throughput while keeping power consumption under a power cap by exploiting CPU throttling techniques. We use real measurements from the LANL cluster to set TracSim’s configuration parameters. We leverage TracSim to implement and evaluate four resource scheduling policies. Simulation results indicate the performance of those policies and quantify the amount of trapped capacity that can effectively be reclaimed.

Decoupling Capacitor Topologies for TSV-Based 3-D ICs With Power Gating

In traditional decoupling capacitor topologies, power gating can significantly degrade the system-wide power integrity of a 3-D integrated circuit since the decoupling capacitance associated with the power-gated block/plane becomes ineffective for the neighboring, active planes. Two topologies are investigated to alleviate this issue by exploiting: 1) relatively low-resistance through silicon vias (TSVs) and 2) ability of TSVs to bypass plane-level power networks when delivering the power supply voltage. In the proposed topologies, decoupling capacitors placed within a plane can provide charge to neighboring planes even when the plane is power gated, achieving up to 50% and 87% reduction in, respectively, rms power supply and power gating (in-rush current) noise at the expense of a moderate increase in physical area and peak power consumption.

Managing Mismatches in Voltage Stacking with CoreUnfolding

Five percent to 25% of power could be wasted before it is delivered to the computational resources on a die, due to inefficiencies of voltage regulators and resistive loss. The power delivery could benefit if, at the same power, the delivered voltage increases and the current decreases. This article presents CoreUnfolding , a technique that leverages voltage Stacking to improve power delivery efficiency. Our experiments show that about 10% system-wide power can be saved, the voltage regulator area can be reduced by 30%, di / dt improves 49%, and the power pin count is reduced by 40% (≈ 20% reduction in packaging costs), with negligible performance degradation.

Thermal analysis of stochastic DVFS-enabled multicore real-time systems

This paper considers a multicore system, equipped with dynamic voltage/frequency scaling (DVFS), which runs real-time jobs with probabilistic characteristics. The DVFS policy directly affects the performance of this system as well as its power consumption through impacting both dynamic and leakage powers. While power consumption determines the processor thermal behavior, the temperature in turn affects the processor power consumption by impacting the leakage power. Additionally, temperature variation of a core is coupled with the thermal behaviors of the other cores, namely thermal effects of the surrounding components. These inter-effects make sophisticated relations between the nature of the stochastic real-time system and its performance, power consumption, and temperature behavior. In this paper, we first present an exact thermal analysis approach for the specified system considering these inter-effects. We then propose a scalable approximate method for thermal analysis of the system based on the exact analysis. The proposed analytical method outperforms the traditional simulation-based methods in terms of time complexity, by approximately three orders of magnitude, while introducing a relative error of less than 0.8 % for the mentioned setups. Also, we show the efficacy of the proposed analytical method in temperature-aware power minimization, resulting in significant reductions in the system-wide power consumption.