Generation expansion planning optimisation with renewable energy integration: A review

Generation expansion planning consists of finding the optimal long-term plan for the construction of new generation capacity subject to various economic and technical constraints. It usually involves solving a large-scale, non-linear discrete and dynamic optimisation problem in a highly constrained and uncertain environment. Traditional approaches to capacity planning have focused on achieving a least-cost plan. During the last two decades however, new paradigms for expansion planning have emerged that are driven by environmental and political factors. This has resulted in the formulation of multi-criteria approaches that enable power system planners to simultaneously consider multiple and conflicting objectives in the decision-making process. More recently, the increasing integration of intermittent renewable energy sources in the grid to sustain power system decarbonisation and energy security has introduced new challenges. Such a transition spawns new dynamics pertaining to the variability and uncertainty of these generation resources in determining the best mix. In addition to ensuring adequacy of generation capacity, it is essential to consider the operational characteristics of the generation sources in the planning process. In this paper, we first review the evolution of generation expansion planning techniques in the face of more stringent environmental policies and growing uncertainty. More importantly, we highlight the emerging challenges presented by the intermittent nature of some renewable energy sources. In particular, we discuss the power supply adequacy and operational flexibility issues introduced by variable renewable sources as well as the attempts made to address them. Finally, we identify important future research directions

A decision support system to follow up and diagnose primary headache patients using semantically enriched data

Abstract Background Headache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting. Methods In this paper, a decision support system is proposed, composed of three components: (i) a cross-platform mobile application to capture the required data from patients to formulate a diagnosis, (ii) an automated diagnosis support module that generates an interpretable decision tree, based on data semantically annotated with expert knowledge, in order to support physicians in formulating the correct diagnosis and (iii) a web application such that the physician can efficiently interpret captured data and learned insights by means of visualizations. Results We show that decision tree induction techniques achieve competitive accuracy rates, compared to other black- and white-box techniques, on a publicly available dataset, referred to as migbase. Migbase contains aggregated information of headache attacks from 849 patients. Each sample is labeled with one of three possible primary headache disorders. We demonstrate that we are able to reduce the classification error, statistically significant (ρ≤0.05), with more than 10% by balancing the dataset using prior expert knowledge. Furthermore, we achieve high accuracy rates by using features extracted using the Weisfeiler-Lehman kernel, which is completely unsupervised. This makes it an ideal approach to solve a potential cold start problem. Conclusion Decision trees are the perfect candidate for the automated diagnosis support module. They achieve predictive performances competitive to other techniques on the migbase dataset and are, foremost, completely interpretable. Moreover, the incorporation of prior knowledge increases both predictive performance as well as transparency of the resulting predictive model on the studied dataset

The Forum: Fall 2005

Fall 2005 journal of the Honors Program at the University of North Dakota. The issue includes stories, poems, essays and art by undergraduate students.https://commons.und.edu/und-books/1058/thumbnail.jp

Smart Tungsten-based Alloys for a First Wall of DEMO

During an accident with loss-of-coolant and air ingress in DEMO, the temperature of tungsten first wall cladding may exceed 1000 °C and remain for months leading to tungsten oxidation. The radioactive tungsten oxide can be mobilized to the environment at rates of 10–150 kg per hour. Smart tungsten-based alloys are under development to address this issue. Alloys are aimed to function as pure tungsten during regular plasma operation of DEMO. During an accident, alloying elements will create a protective layer, suppressing release of W oxide. Bulk smart alloys were developed by using mechanical alloying and field-assisted sintering technology. The mechanical alloying process was optimized leading to an increased powder production by at least 40 %. Smart alloys and tungsten were tested under a variety of DEMO-relevant plasma conditions. Both materials demonstrated similar sputtering resistance to deuterium plasma. Under accident conditions, alloys feature a 40-fold reduction of W release compared to that of pure tungsten.</p

Gas generation and wind power: A review of unlikely allies in the United Kingdom and Ireland

No single solution currently exists to achieve the utopian desire of zero fossil fuel electricity generation. Until such time, it is evident that the energy mix will contain a large variation in stochastic and intermittent sources of renewable energy such as wind power. The increasing prominence of wind power in pursuit of legally binding European energy targets enables policy makers and conventional generating companies to plan for the unique challenges such a natural resource presents. This drive for wind has been highly beneficial in terms of security of energy supply and reducing greenhouse gas emissions. However, it has created an unusual ally in natural gas. This paper outlines the suitability and challenges faced by gas generating units in their utilisation as key assets for renewable energy integration and the transition to a low carbon future. The Single Electricity Market of the Republic of Ireland and Northern Ireland and the British Electricity Transmission Trading Agreement Market are the backdrop to this analysis. Both of these energy markets have a reliance on gas generation matching the proliferation of wind power. The unlikely and mostly ignored relationship between natural gas generation and wind power due to policy decisions and market forces is the necessity of gas to act as a bridging fuel. This review finds gas generation to be crucially important to the continued growth of renewable energy. Additionally, it is suggested that power market design should adequately reward the flexibility required to securely operate a power system with high penetrations of renewable energy, which in most cases is provided by gas generation

Inhibition of Mutation and Combating the Evolution of Antibiotic Resistance

The emergence of drug-resistant bacteria poses a serious threat to human health. In the case of several antibiotics, including those of the quinolone and rifamycin classes, bacteria rapidly acquire resistance through mutation of chromosomal genes during therapy. In this work, we show that preventing induction of the SOS response by interfering with the activity of the protease LexA renders pathogenic Escherichia coli unable to evolve resistance in vivo to ciprofloxacin or rifampicin, important quinolone and rifamycin antibiotics. We show in vitro that LexA cleavage is induced during RecBC-mediated repair of ciprofloxacin-mediated DNA damage and that this results in the derepression of the SOS-regulated polymerases Pol II, Pol IV and Pol V, which collaborate to induce resistance-conferring mutations. Our findings indicate that the inhibition of mutation could serve as a novel therapeutic strategy to combat the evolution of antibiotic resistance

Variability in large-scale wind power generation

The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1?h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well-dispersed wind power