Analysis of North Sea offshore wind power variability

This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing the hourly power production for offshore wind farms, and this total production is analyzed to identify the largest aggregated hourly power variations. Based on publicly available information, a simplified representation of the coastal power grid is built for the countries bordering the North Sea. Wind farms less than 60 km from shore are connected radially to the mainland, while the rest are connected to a hypothetical offshore HVDC (High-Voltage Direct Current) power grid, designed such that wind curtailment does not exceed 1% of production. Loads and conventional power plants by technology and associated cost curves are computed for the various national power systems, based on 2030 projections. Using the MATLAB-based MATPOWER toolbox, the hourly optimal power flow for this regional hybrid AC/DC grid is computed for high, low and medium years from the meso-scale database. The largest net load variations are evaluated per market area and related to the extra load-following reserves that may be needed from conventional generators.Parts of this work were funded by Agentschap.NL, the Netherlands, now RVO.nl (Rijksdienst voor Ondernemend Nederland [25], under the project North Sea Transnational Grid (NSTG). The NSTG project is a cooperation between Delft University of Technology and the Energy Research Center of the Netherlands

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

Synthesis of a polymyxin derivative for photolabeling studies in the gram-negative bacterium Escherichia coli

The antimicrobial activity of polymyxins against Gram-negative bacteria has been known for several decades, but the mechanism of action leading to cell death has not been fully explored. A key step after binding of the antibiotic to lipopolysaccharide (LPS) exposed at the cell surface is 'self-promoted uptake' across the outer membrane (OM), in which the antibiotic traverses the asymmetric LPS-phospholipid bilayer before reaching the periplasm and finally targeting and disrupting the bacterial phospholipid inner membrane. The work described here was prompted by the hypothesis that polymyxins might interact with proteins in the OM, as part of their self-promoted uptake and permeabilizing effects. One way to test this is through photolabeling experiments. We describe the design and synthesis of a photoprobe based upon polymyxin B, containing photoleucine and an N-acyl group with a terminal alkyne suitable for coupling to a biotin tag using click chemistry. The resulting photoprobe retains potent antimicrobial activity, and in initial photolabeling experiments with Escherichia coli ATCC25922 is shown to photolabel several OM proteins. This photoprobe might be a valuable tool in more detailed studies on the mechanism of action of this family of antibiotics

New Cycle-based Formulation, Cost Function, and Heuristics for DC OPF Based Controlled Islanding

This paper presents a new formulation for intentional controlled islanding (ICI) of power transmission grids based on mixed-integer linear programming (MILP) DC optimal power flow (OPF) model. We highlight several deficiencies of the most well-known formulation for this problem and propose new enhancements for their improvement. In particular, we propose a new alternative optimization objective that may be more suitable for ICI than the minimization of load shedding, a new set of island connectivity constraints, and a new set of constraints for DC OPF with switching, and a new MILP heuristic to find initial feasible solutions for ICI. It is shown that the proposed improvements help to reduce the final optimality gaps as compared to the benchmark model on several test instances.Comment: https://doi.org/10.1016/j.epsr.2022.10858

A Grounded Theory of Interdisciplinary Communication and Collaboration in the Outpatient Setting of the Hospital for Patients with Multiple Long-Term Conditions

Interdisciplinary communication and collaboration are crucial in the care of people with multiple long-term conditions (MLTCs) yet are often experienced as insufficient. Through the lens of complexity science, this study aims to explain how healthcare professionals (HCPs) adapt to emerging situations in the care of patients with MLTC by examining interdisciplinary communication and collaboration in the outpatient hospital setting. We used the constant comparative method to analyze transcribed data from seven focus groups with twenty-one HCPs to generate a constructivist grounded theory of ‘interdisciplinary communication and collaboration in the outpatient setting of the hospital for patients with multiple long-term conditions’. Our theory elucidates the various pathways of communication and collaboration. Why, when, and how team members choose to collaborate influences if and to what degree tailored care is achieved. There is great variability and unpredictability to this process due to internalized rules, such as beliefs on the appropriateness to deviate from guidelines, and the presence of an interprofessional identity. We identified organizational structures that influence the dynamics of the care team such as the availability of time and financial compensation for collaboration. As we strive for tailored care for patients with MLTC, our theory provides promising avenues for future endeavors.</p

Parametric Evaluation of Different ANN Architectures: Forecasting Wind Power Across Different Time Horizons

The participation of volatile wind energy resources in the generation mix of power systems is increasing. It is therefore becoming more and more crucial for system operators to accurately predict the wind power generation across different short term horizons (5 to 60 minutes ahead) in order to adequately balance the system and maintain system security. This paper presents a comprehensive assessment of the influence of different parameters in artificial neural networks, such as the amount of historic data, batch size, number of hidden layers, number of neurons per hidden layer, and the amount of training data on the short term forecast accuracy. In order to identify the parameters which are most influential with respect to forecast accuracy, a sensitivity study isolating the various factors on a one-At-A-Time basis has been performed. To minimize the forecast error across the investigated forecast horizons, the developed neural networks use the feed forward back propagation algorithm. From the investigated cases it is concluded that a neural network with two hidden layers is most suitable for wind forecasting on the timeframes considered. Furthermore, with increasing forecast horizons (from 5 to 60 minutes ahead), better performance is achieved when neural networks contain increased neurons in the hidden layers and have enlarged training data sets.Intelligent Electrical Power Grid