Efficiency and Market Power in Electricity Markets with Inelastic Demand, Energy Storage, and Hybrid Energy Resources

This thesis studies electricity markets and analyzes market mechanisms - operation rules for participants in achieving supply-demand balance, to understand how competition between resources, e.g., conventional generators, inelastic loads, and new market participants, such as energy storage and hybrid resources, affects market efficiency. We first consider the participation of conventional resources in a two-stage market, i.e., day-ahead and real-time settlement. Although designed to allocate resources efficiently and prevent speculation, price manipulation by strategic participants can undermine these goals. To address price manipulation, some markets have proposed system-level market power mitigation (MPM) policies, which substitute noncompetitive bids with default bids based on estimated generation costs. Using equilibrium analysis, we illustrate that such a policy in the day-ahead stage is more robust to price manipulations than in real-time, which may lead to non-equilibrium solutions. Despite being inelastic, loads can shift their allocation between the two stages to manipulate prices and reduce their payments. Further, heterogeneity in cost coefficients, estimation of dispatch cost in excess, and demand uncertainty tend to benefit generators. Together, system-level MPM policies can have unintended consequences when implemented without accounting for the conflicting interests of participants. We then study how integrating energy storage affects market efficiency. Our analysis indicates that the existing participation mechanism, where energy storage bids power in a market, may diminish market benefits due to its unique operational characteristics, e.g., the operating cost depends on charge-discharge cycles, unlike conventional generators. We propose a novel market mechanism based on an energy-cycling function that maps cycle depth to per-cycle prices. An equilibrium analysis illustrates the efficient competitive equilibrium that aligns with the social planner problem, i.e., net surplus maximization. However, at the Nash equilibrium, storage incurs reductions in profit relative to the competitive equilibrium due to price manipulation by strategic generators. Finally, we study the participation of hybrid resources combining energy storage and renewable energy sources. We use the New York zonal model as an example of a large-scale electricity market to benchmark the performance of the following two types of market models. We consider (i) a granular model, where the market operator manages the operation of constituent energy storage, and (ii) an integrated model, where the owner manages the storage operation. Our analysis shows that granular models lead to lower operating costs but add computational complexity, which may not be desirable from the operator's perspective. Though less computationally intensive, integrated models result in more intervals violating the physical limits of constituent energy storage

Market Power Mitigation in Two-stage Electricity Market with Supply Function and Quantity Bidding

The main goal of a sequential two-stage electricity market -- e.g., day-ahead and real-time markets -- is to operate efficiently. However, the price difference across stages due to inadequate competition and unforeseen circumstances leads to undesirable price manipulation. To mitigate this, some Independent System Operators (ISOs) proposed system-level market power mitigation (MPM) policies in addition to existing local policies. These policies aim to substitute noncompetitive bids with a default bid based on estimated generator costs. However, these policies may lead to unintended consequences when implemented without accounting for the conflicting interest of participants. In this paper, we model the competition between generators (bidding supply functions) and loads (bidding quantity) in a two-stage market with a stage-wise MPM policy. An equilibrium analysis shows that a real-time MPM policy leads to equilibrium loss, meaning no stable market outcome (Nash equilibrium) exists. A day-ahead MPM policy, besides, leads to a Stackelberg-Nash game with loads acting as leaders and generators as followers. In this setting, loads become winners, i.e., their aggregate payment is always less than competitive payments. Moreover, comparison with standard market equilibrium highlights that markets are better off without such policies. Finally, numerical studies highlight the impact of heterogeneity and load size on market equilibrium

A modified proximity approach in the fusion of heavy-ions

By using a suitable set of the surface energy coefficient, nuclear radius, and universal function, the original proximity potential 1977 is modified. The overestimate of the data by 4 % reported in the literature is significantly reduced. Our modified proximity potential reproduces the experimental data nicely compared to its older versions.Comment: 9 pages, 5 figures, Chin. Phys. lett.(2010) in pres

Evaluation of commercial probiotic lactic cultures against biofilm formation by

Background/Aims Cronobacter sakazakii, an emergent pathogen is considered as a major concern to infants and neonates fed on reconstituted powdered infant milk formula. In conjunction with many other factors, biofilm forming capacity adds to its pathogenic potential. In view of the facts that infants are at highest risk to C. sakazakii infections, and emerging antibiotic resistance among pathogens, it is imperative to evaluate probiotic cultures for their efficacy against C. sakazakii. Therefore, pure probiotic strains were isolated from commercial probiotic products and tested for their antimicrobial and anti-biofilm activities against C. sakazakii. Methods A total of 6 probiotic strains were tested for their antibiotic susceptibility followed by antimicrobial activity using cell-free supernatant (CFS) against C. sakazakii. The inhibitory activity of CFS against biofilm formation by C. sakazakii was determined using standard crystal violet assay and microscopic observations. Results All the probiotic strains were sensitive to ampicillin, tetracycline, vancomycin and carbenicillin whereas most of the strains were resistant to erythromycin and novobiocin. Four of the 6 probiotic derived CFS possessed antimicrobial activity against C. sakazakii at a level of 40 μL. A higher biofilm inhibitory activity (>80%) was observed at initial stages of biofilm formation with weaker activity during longer incubation upto 48 hours (50%–60%). Conclusions The study indicated the efficacy of isolated commercial probiotics strains as potential inhibitor of biofilm formation by C. sakazakii and could be further explored for novel bioactive molecules to limit the emerging infections of C. sakazakii

Mapping of variations in child stunting, wasting and underweight within the states of India: the Global Burden of Disease Study 2000–2017

Background To inform actions at the district level under the National Nutrition Mission (NNM), we assessed the prevalence trends of child growth failure (CGF) indicators for all districts in India and inequality between districts within the states. Methods We assessed the trends of CGF indicators (stunting, wasting and underweight) from 2000 to 2017 across the districts of India, aggregated from 5 × 5 km grid estimates, using all accessible data from various surveys with subnational geographical information. The states were categorised into three groups using their Socio-demographic Index (SDI) levels calculated as part of the Global Burden of Disease Study based on per capita income, mean education and fertility rate in women younger than 25 years. Inequality between districts within the states was assessed using coefficient of variation (CV). We projected the prevalence of CGF indicators for the districts up to 2030 based on the trends from 2000 to 2017 to compare with the NNM 2022 targets for stunting and underweight, and the WHO/UNICEF 2030 targets for stunting and wasting. We assessed Pearson correlation coefficient between two major national surveys for district-level estimates of CGF indicators in the states. Findings The prevalence of stunting ranged 3.8-fold from 16.4% (95% UI 15.2–17.8) to 62.8% (95% UI 61.5–64.0) among the 723 districts of India in 2017, wasting ranged 5.4-fold from 5.5% (95% UI 5.1–6.1) to 30.0% (95% UI 28.2–31.8), and underweight ranged 4.6-fold from 11.0% (95% UI 10.5–11.9) to 51.0% (95% UI 49.9–52.1). 36.1% of the districts in India had stunting prevalence 40% or more, with 67.0% districts in the low SDI states group and only 1.1% districts in the high SDI states with this level of stunting. The prevalence of stunting declined significantly from 2010 to 2017 in 98.5% of the districts with a maximum decline of 41.2% (95% UI 40.3–42.5), wasting in 61.3% with a maximum decline of 44.0% (95% UI 42.3–46.7), and underweight in 95.0% with a maximum decline of 53.9% (95% UI 52.8–55.4). The CV varied 7.4-fold for stunting, 12.2-fold for wasting, and 8.6-fold for underweight between the states in 2017; the CV increased for stunting in 28 out of 31 states, for wasting in 16 states, and for underweight in 20 states from 2000 to 2017. In order to reach the NNM 2022 targets for stunting and underweight individually, 82.6% and 98.5% of the districts in India would need a rate of improvement higher than they had up to 2017, respectively. To achieve the WHO/UNICEF 2030 target for wasting, all districts in India would need a rate of improvement higher than they had up to 2017. The correlation between the two national surveys for district-level estimates was poor, with Pearson correlation coefficient of 0.7 only in Odisha and four small north-eastern states out of the 27 states covered by these surveys. Interpretation CGF indicators have improved in India, but there are substantial variations between the districts in their magnitude and rate of decline, and the inequality between districts has increased in a large proportion of the states. The poor correlation between the national surveys for CGF estimates highlights the need to standardise collection of anthropometric data in India. The district-level trends in this report provide a useful reference for targeting the efforts under NNM to reduce CGF across India and meet the Indian and global targets. Keywords Child growth failureDistrict-levelGeospatial mappingInequalityNational Nutrition MissionPrevalenceStuntingTime trendsUnder-fiveUndernutritionUnderweightWastingWHO/UNICEF target

Mining Non-Functional Requirements using Machine Learning Techniques

Background: Non-Functional Requirements (NFR) have a direct impact on the architecture of the system, thus it is essential to identify NFRs in the initial phases of software development. Aim: The work is based on extraction of relevant keywords from NFR descriptions by employing text mining steps and thereafter classifying these descriptions into one of the nine types of NFRs. Method: For each NFR type, keywords are extracted from a set of pre-categorized specifications using Information-Gain measure. Then models using 8 Machine Learning (ML) techniques are developed for classification of NFR descriptions. A set of 15 projects (containing 326 NFR descriptions) developed by MS students at DePaul University are used to evaluate the models. Results: The study analyzes the performance of ML models in terms of classification and misclassification rate to determine the best model for predicting each type NFR descriptions. The Naïve Bayes model has performed best in predicting “maintainability” and “availability” type of NFRs. Conclusion: The NFR descriptions should be analyzed and mapped into their corresponding NFR types during the initial phases. The authors conducted cost benefit analysis to appreciate the advantage of using the proposed models