Process-variation resilient and voltage-scalable dct architecture for robust low-power computing

In this paper, we present a novel discrete cosine transform (DCT) architecture that allows aggressive voltage scaling for low-power dissipation, even under process parameter variations with minimal overhead as opposed to existing techniques. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors appear only from the long paths that are designed to be less contributive to output quality. The proposed architecture allows a graceful degradation in the peak SNR (PSNR) under aggressive voltage scaling as well as extreme process variations. Results show that even under large process variations (±3σ around mean threshold voltage) and aggressive supply voltage scaling (at 0.88 V, while the nominal voltage is 1.2 V for a 90-nm technology), there is a gradual degradation of image quality with considerable power savings (71% at PSNR of 23.4 dB) for the proposed architecture, when compared to existing implementations in a 90-nm process technology. © 2006 IEEE.

Process variation tolerant low power DCT architecture

2-D Discrete Cosine Transform (DCT) is widely used as the core of digital image and video compression. In this paper, we present a novel DCT architecture that allows aggressive voltage scaling by exploiting the fact that not all intermediate computations are equally important in a DCT system to obtain "good" image quality with Peak Signal to Noise Ratio(PSNR) > 30 dB. This observation has led us to propose a DCT architecture where the signal paths that are less contributive to PSNR improvement are designed to be longer than the paths that are more contributive to PSNR improvement. It should also be noted that robustness with respect to parameter variations and low power operation typically impose contradictory requirements in terms of architecture design. However, the proposed architecture lends itself to aggressive voltage scaling for low-power dissipation even under process parameter variations. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors would only appear from the long paths that are less contributive towards PSNR improvement, providing large improvement in power dissipation with small PSNR degradation. Results show that even under large process variation and supply voltage scaling (0.8V), there is a gradual degradation of image quality with considerable power savings (62.8%) for the proposed architecture when compared to existing implementations in 70 nm process technology.

Low-Power Process-Variation Tolerant Arithmetic Units Using Input-Based Elastic Clocking

In this paper we propose a design methodology for low-power high-performance, process-variation tolerant architecture for arithmetic units. The novelty of our approach lies in the fact that possible delay failures due to process variations and/or voltage scaling are predicted in advance and addressed by employing an elastic clocking technique. The prediction mechanism exploits the dependence of delay of arithmetic units upon input data patterns and identifies specific inputs that activate the critical path. Under iso-yield conditions, the proposed design operates at a lower scaled down Vdd without any performance degradation, while it ensures a superlative yield under a design style employing nominal supply and transistor threshold voltage. Simulation results show power savings of upto 29%, energy per computation savings of upto 25.5% and yield enhancement of upto 11.1% compared to the conventional adders and multipliers implemented in the 70nm BPTM technology. We incorporated the proposed modules in the execution unit of a five stage DLX pipeline to measure performance using SPEC2000 benchmarks [9]. Maximum area and throughput penalty obtained were 10% and 3% respectively.

The importance of gas infrastructure in power systems with high wind power penetrations

Gas fired generation currently plays an integral support role ensuring security of supply in power systems with high wind power penetrations due to its technical and economic attributes. However, the increase in variable wind power has affected the gas generation output profile and is pushing the boundaries of the design and operating envelope of gas infrastructure. This paper investigates the mutual dependence and interaction between electricity generation and gas systems through the first comprehensive joined-up, multi-vector energy system analysis for Ireland. Key findings reveal the high vulnerability of the Irish power system to outages on the Irish gas system. It has been shown that the economic operation of the power system can be severely impacted by gas infrastructure outages, resulting in an average system marginal price of up to €167/MWh from €67/MWh in the base case. It has also been shown that gas infrastructure outages pose problems for the location of power system reserve provision, with a 150% increase in provision across a power system transmission bottleneck. Wind forecast error was shown to be a significant cause for concern, resulting in large swings in gas demand requiring key gas infrastructure to operate at close to 100% capacity. These findings are thought to increase in prominence as the installation of wind capacity increases towards 2020, placing further stress on both power and gas systems to maintain security of supply.

Managing Future offshore wind power variability in a European Supergrid

The future European power system will have a hierarchical structure created by layers of system control from a Supergrid via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the context of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called 'back-up generation' needed to support an 80% renewable energy portfolio in Europe by 2050. © 2013 IEEE.

A review of offshore wind power development in the United Kingdom

Currently wind power is dominated by onshore wind farms. However, as the demand for power grows driven by security of energy supply issues, dwindling fossil fuel supplies and greenhouse gas emissions reduction targets, offshore wind power will develop rapidly because of the decline of viable onshore sites. The United Kingdom has a target of 21% renewable electricity by 2020 and this is expected to come mostly from wind power. Britain is the most active internationally in terms of offshore wind farm development with almost 48GW in some stage of development. In addition the Scottish Government, the Northern Ireland Executive and the Government of Ireland undertook the 'Irish-Scottish Links on Energy Study' (ISLES), which examined the feasibility of creating an offshore interconnected transmission network and subsea electricity grid based on renewable energy sources off the coast of western Scotland and the Irish Sea. The aim of this paper is to provide an appraisal of offshore wind power development with a focus on the United Kingdom. © 2013 IEEE.

Dynamic Modeling and Interaction of Hybrid Natural Gas and Electricity Supply System in Microgrid

Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.

Inexact-aware architecture design for ultra-low power bio-signal analysis

This study introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimise energy consumption. In this scenario, the reliability of static RAM (SRAM) memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, the authors propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e. their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. The authors illustrate their scheme on an industrial benchmark application performing the power spectrum analysis of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.

Heterogeneous error-resilient scheme for spectral analysis in ultra-low power wearable electrocardiogram devices

Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.

A Microgrid Testbed for Interdisciplinary Research on Cyber-Secure Industrial Control in Power Systems

This paper describes a smart grid test bed comprising embedded generation, phasor measurement units (PMUs), and supporting ICT components and infrastructure. The test bed enables the development of a use case focused on a synchronous islanding scenario, where the embedded generation becomes islanded from the mains supply. Due to the provisioned control components, control strategy, and best-practice ICT support infrastructure, the islanded portion of the grid is able to continue to operate in a secure and dependable manner.

Logistics management and costs of biomass fuel supply

Discusses part of a project conducted by the authors into the logistics planning and management and costs of supplying biomass fuels to biomass-fired power stations in the UK. Defines biomass fuels and the reasons for the growth in interest in their use for electricity generation. The activities and parties involved in the biomass fuel supply chain are discussed together with the management of the chain in order to achieve smooth and consistent flow of biomass fuel to power stations. Explains the approach used to modelling the delivered costs of biomass fuels for four types of biomass fuel included in the project: forest fuel, short rotation coppice, straw and miscanthus. Comments are given on the environmental impacts of the fuel supply chains. The results indicate that straw supply systems are capable of producing the lowest delivered costs of the four fuels studied. Short rotation coppice and miscanthus, two new energy crops, are likely to have the highest delivered costs at present. This is due to the cost of growing these fuels and the financial incentives required by farmers to persuade them to grow these crops. Logistics costs (i.e. transport, storage and handling) are shown to represent a significant proportion of total delivered cost in biomass supply. Careful supply chain planning and logistics management will be of central importance to the success of the biomass industry.

Demand response in electrical energy supply: an optimal real time pricing approach

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. This paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. The use of DemSi by a retailer in a situation of energy shortage, is presented. Load reduction is obtained using a consumer based price elasticity approach supported by real time pricing. Non-linear programming is used to maximize the retailer’s profit, determining the optimal solution for each envisaged load reduction. The solution determines the price variations considering two different approaches, price variations determined for each individual consumer or for each consumer type, allowing to prove that the approach used does not significantly influence the retailer’s profit. The paper presents a case study in a 33 bus distribution network with 5 distinct consumer types. The obtained results and conclusions show the adequacy of the used methodology and its importance for supporting retailers’ decision making.

Is the electric vehicle a solution for the wind power integration in the portuguese power system?

The integration of Plug-in electric vehicles in the transportation sector has a great potential to reduce oil dependency, the GHG emissions and to contribute for the integration of renewable sources into the electricity generation mix. Portugal has a high share of wind energy, and curtailment may occur, especially during the off-peak hours with high levels of hydro generation. In this context, the electric vehicles, seen as a distributed storage system, can help to reduce the potential wind curtailments and, therefore, increase the integration of wind power into the power system. In order to assess the energy and environmental benefits of this integration, a methodology based on a unit commitment and economic dispatch is adapted and implemented. From this methodology, the thermal generation costs, the CO2 emissions and the potential wind generation curtailment are computed. Simulation results show that a 10% penetration of electric vehicles in the Portuguese fleet would increase electrical load by 3% and reduce wind curtailment by only 26%. This results from the fact that the additional generation required to supply the electric vehicles is mostly thermal. The computed CO2 emissions of the EV are 92 g CO2/kWh which become closer to those of some new ICE engines.

Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power

The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system.