96 research outputs found
Possibilities and limits of integration of pupils with LMP at the 2nd stage of elementary school
No full textDiplomová práce se zaměří na problematiku inkluzívního vzdělávání žáků s lehkým mentálním postižením na 2. stupni základní školy. Stěžejní část práce bude věnována kazuistické studii chlapce s LMP, kde autorka demonstruje základní teoretická východiska, psychologické aspekty či možnosti a limity determinující vzdělávání této skupiny dětí.The thesis focuses on the issue of inclusive education for students with mild intellectual disabilities in the second stage of primary school. The main part of the thesis will be devoted to a case study of a boy with mild intellectual disabilities, where the author demonstrates basic theoretical foundations, psychological aspects, as well as possibilities and limitations determining the education of this group of children
Analyzing the impact of renewable generation on the locational marginal price (LMP) forecast for California ISO
No full textabstract: Accurate forecasting of electricity prices has been a key factor for bidding strategies in the electricity markets. The increase in renewable generation due to large scale PV and wind deployment in California has led to an increase in day-ahead and real-time price volatility. This has also led to prices going negative due to the supply-demand imbalance caused by excess renewable generation during instances of low demand. This research focuses on applying machine learning models to analyze the impact of renewable generation on the hourly locational marginal prices (LMPs) for California Independent System Operator (CAISO). Historical data involving the load, renewable generation from solar and wind, fuel prices, aggregated generation outages is extracted and collected together in a dataset and used as features to train different machine learning models. Tree- based machine learning models such as Extra Trees, Gradient Boost, Extreme Gradient Boost (XGBoost) as well as models based on neural networks such as Long short term memory networks (LSTMs) are implemented for price forecasting. The focus is to capture the best relation between the features and the target LMP variable and determine the weight of every feature in determining the price.
The impact of renewable generation on LMP forecasting is determined for several different days in 2018. It is seen that the prices are impacted significantly by solar and wind generation and it ranks second in terms of impact after the electric load. The results of this research propose a method to evaluate the impact of several parameters on the day-ahead price forecast and would be useful for the grid operators to evaluate the parameters that could significantly impact the day-ahead price prediction and which parameters with low impact could be ignored to avoid an error in the forecast.Dissertation/ThesisMasters Thesis Electrical Engineering 201
Locational Marginal Pricing: When and Why Not?
No full textThis study establishes that Locational Marginal Pricing (LMP) is conceptually problematic for grid-supported centrally-managed wholesale power markets transitioning to decarbonized grid operations with increasingly diverse participants, hence with increasingly uncertain and volatile net loads. LMP assigns a common per-unit price LMP(b,T) (/MWh) without regard for the true benefits and costs of flexible dynamic power delivery. Another option, illustrated in this study, is to explore alternative conceptually-coherent product definitions, settlement rules, and bid/offer contract formulations that permit electric power grids to function efficiently as flexibility-support
insurance mechanisms enabling just-in-time power deliveries to meet just-in-time customer power demands and grid reliability requirements.JEL Classification: C6, D4, D6, L1, Q4. Length 13 pages. Original Release Date: June 30, 2023. Revision: October 11, 2023
An Efficient Hybrid Intelligent Method for Electricity Price Forecasting
No full textAbstractIn this paper an efficient method is proposed for electricity price forecasting. This paper focuses on Locational Marginal Price (LMP) that efficiently maintains power markets by alleviating transmission network congestion. There are complicated behaviors of the time series due to uncertain factors in the power markets. From a standpoint of market players, a sophisticated method is required to forecast LMP effectively. The proposed method makes use of the hybridization of GP (Gaussian Process) of hierarchical Bayesian estimation, EPSO (Evolutionary Particle Swarm Optimization) of evolutionary computation and fuzzy c-means of allowing data to belong to two or more clusters. EPSO is used to improve the accuracy of parameters in MAP (Maximum a Posteriori) estimation for GP. The use of fuzzy c-mean is useful for increasing the number of learning data for GP to deal with spikes. The effectiveness of the proposed method is demonstrated for real LMP data
Labor-management partnership in US public education: re-examining effects, contingencies, and teacher and student outcomes around COVID-19 and beyond
No full textWhile labor-management partnership (LMP) has been a staple in Industrial Relations (IR), theoretical and empirical questions still remain regarding how, and through which organizational levels, different worker and organizational outcomes may be achieved (i.e., mutual gains), and further, LMP’s ability to address the consequences of a crisis like COVID-19 involving similar outcomes. Mainly drawing from pluralist and unitarist perspectives on worker voice, and from social exchange theory, I develop multiple single level and multilevel models on how LMP may be implemented across levels and operate to reach these outcomes, and further assess the challenges that may be faced. Using four separate but related studies in the setting of US public education around COVID-19, I primarily examine 1) the distinct and complementary impact between dimensions of LMP and managerial (principal) collaborative leadership for school level teacher voice, 2) the direct and mixed-level associations between dimensions of district and school level LMP and student and teacher outcomes at the district and school levels, and 3) the potential for LMP to mitigate negative consequences of COVID-19 involving similar outcomes. Results show support for complementarity and the effects of district level LMP quality on school level implementation and on school level teacher work-related voice and voice about COVID-19. Results also show some support for effects on district and school level student performance and pandemic-impacted performance. Theoretical implications, limitations, and future research directions are discussed.Ph.D.Includes bibliographical reference
Deregulated Real-Time Pricing for the Promotion of Distributed Renewables
No full textabstract: This thesis pursues a method to deregulate the electric distribution system and provide support to distributed renewable generation. A locational marginal price is used to determine prices across a distribution network in real-time. The real-time pricing may provide benefits such as a reduced electricity bill, decreased peak demand, and lower emissions. This distribution locational marginal price (D-LMP) determines the cost of electricity at each node in the electrical network. The D-LMP is comprised of the cost of energy, cost of losses, and a renewable energy premium. The renewable premium is an adjustable function to compensate `green' distributed generation. A D-LMP is derived and formulated from the PJM model, as well as several alternative formulations. The logistics and infrastructure an implementation is briefly discussed. This study also takes advantage of the D-LMP real-time pricing to implement distributed storage technology. A storage schedule optimization is developed using linear programming. Day-ahead LMPs and historical load data are used to determine a predictive optimization. A test bed is created to represent a practical electric distribution system. Historical load, solar, and LMP data are used in the test bed to create a realistic environment. A power flow and tabulation of the D-LMPs was conducted for twelve test cases. The test cases included various penetrations of solar photovoltaics (PV), system networking, and the inclusion of storage technology. Tables of the D-LMPs and network voltages are presented in this work. The final costs are summed and the basic economics are examined. The use of a D-LMP can lower costs across a system when advanced technologies are used. Storage improves system costs, decreases losses, improves system load factor, and bolsters voltage. Solar energy provides many of these same attributes at lower penetrations, but high penetrations have a detrimental effect on the system. System networking also increases these positive effects. The D-LMP has a positive impact on residential customer cost, while greatly increasing the costs for the industrial sector. The D-LMP appears to have many positive impacts on the distribution system but proper cost allocation needs further development.Dissertation/ThesisM.S. Electrical Engineering 201
The Role of Demand-Side Flexibility in Hedging Electricity Price Volatility in Distribution Grids
No full textLocational Marginal Price (LMP) is a dual variable associated with supply-demand matching and represents the cost of delivering power to a particular location if the load at that location increases. In recent times it become more volatile due to increased integration of renewables that are intermittent. The issue of price volatility is further heightened during periods of grid congestion. Motivated by these problems, we propose a market design where, by constraining dual variables, we determine the amount of demand-side flexibility required to limit the rise of LMP. Through our proposed approach a price requesting load can specify its maximum willingness to pay for electricity and through demand-side flexibility hedge against price volatility. For achieving this, an organizational structure for flexibility management is proposed that exhibits the coordination required between the Distribution System Operator (DSO), an aggregator and the price requesting load. To demonstrate the viability of our proposed formulation, we run an illustrative simulation under infinite and finite line capacities.Energy and IndustryIntelligent Electrical Power Grid
Ischemic preconditioning-induced cardioprotection is lost in mice with immunoproteasome subunit low molecular mass polypeptide-2 deficiency
No full textThe ubiquitin-proteasome system plays an important role in many cellular processes through degradation of specific proteins. Low molecular mass polypeptide 2 (LMP-2 or beta(1i)) is one important subunit of the immunoproteasome. Ischemic preconditioning (IPC) activates cell signaling pathways and generates cardioprotection but has not been linked to LMP-2 function previously. LMP-2 knockout mice (C57BL6 background) and wild-type C57BL6 mice were subjected to 30 min of ischemia (I-30) and 120 min of reperfusion (R-120) with or without preceding IPC (10 min of infusion and 5 min of reperfusion). IPC significantly increased left ventricular developed pressure and decreased infarct size in wild-type mice, but this protective effect of IPC was lost in LMP-2 knockout mice. IPC-mediated degradation of phosphatase and tensin homologue deleted on chromosome 10 (PTEN) and activation of the downstream protein kinase Akt were impaired in LMP-2 knockout hearts. The impairment of PTEN degradation was associated with defective immunoproteasomes and decreased proteolytic activities. When LMP-2 knockout mice were pretreated with the PTEN inhibitor bpV(HOpic), cardiac function was significantly improved, and myocardial infarct size was significantly reduced after I-30/R-120. In conclusion, LMP-2 is required for normal proteasomal function and IPC induction in the heart. Its action may be related to PTEN protein degradation.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000261254800024&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biochemistry & Molecular BiologyBiologyCell BiologySCI(E)36ARTICLE124248-42572
A quasi-periodic route to chaos in a parametrically driven nonlinear medium
No full textLMP and DL appreciate the hospitality of the MPI-P (Mainz) during their stay in Germany. LMP, PD and DL acknowledge partial financial support from FONDECYT 1180905. MGC acknowledges partial financial support from Millennium Institute for Research in Optics, ANID-Millennium Science Initiative Program-ICN17_012 and FONDECYT 1180903. JAV and DL acknowledge partial financial support from Centers of excellence with BASAL/CONICYT financing, Grant AFB180001, CEDENNA. The work of BAM is supported, in part, by the Israel Science Foundation through grant No. 1286/17. This author also acknowledges support from Instituto de Alta Investigacion, Universidad de Tarapaca(Arica, Chile)
[[alternative]]Structure Prediction and Dynamical Simulations of Retinal Proteins
No full text[[abstract]]Membrane proteins play a crucial role in many cellular and physiological processes, but the knowledge of their high resolution structures and folding mechanism is very limited due to the difficulties in determining their structures experimentally. Recently, remarkable advances in computer simulations offer a convenient tool to study these problems.
The retinal proteins contain more than 200 amino acids and a retinal molecule and form a seven helix structure. Here we employed a two-step approach to predict the native structures and study the folding dynamics of membrane proteins by using off-lattice coarse-grained Monte-Carlo simulations and all-atom molecular dynamics simulations. In particular, we have applied this approach to predict the structure of retinal proteins found in Halobacterium salinarum membranes. At the first stage, the lowest energy structure with a small root mean square deviation (RMSD) at the lipid midpoint plane (RMSD-LMP) can be obtained. The RMSD-LMP is 1.22 ? for bacteriorhodopsin (BR), 1.64 ? for halorhodopsin (HR), and 1.20 ? for sensory rhodopsin II (SRII). At the second step, the predicted structures are further refined in an all-atom model using Amber force field. The overall RMSD of backbone atoms from the X-ray structures can be reduced from 3.99 ? to 2.64 ? for BR, from 3.12 ? to 1.92 ? for SRII, and from 2.59 ? to 1.89 ? for HR. After successfully predicting the native structures of BR, HR, and SRII by combining Monte-Carlo simulations and molecular dynamics simulations, we further predict the native structure of sensory rhodopsin I.
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