Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Impacts of COVID-19 Pandemic on Italian Electricity Demand and Markets

In this paper, the short-, medium-, and long-term effects of the COVID-19 pandemic on the Italian power system, particularly electricity consumption behavior and electricity market prices, are investigated by defining various metrics. The investigation reveals that COVID-19 lockdown caused a drop in load consumption and, consequently, a decrement in day-ahead market prices and an increase in ancillary service prices. © 2015 CSEE.

Market Conditions and their Effect on Market Clearing Price Forecasting

High volatility in deregulated electricity markets is that characteristic that exposes its participants to higher risks. Volatility is due to many, and most of the times unpredictable, factors, ranging from fuel prices, production from renewable energy sources, electricity demand to Covid-19 and energy crisis. This article describes the initial stages of the work that combines signalling with market conditions in order to analyse the factors affecting the clearing prices of the day ahead market in view of enhancing the forecasting of these prices. The proposed forecasting methodology is based on the extreme learning machine (ELM) and it is tested on the German and Finnish markets. © 2022 IEEE.

ELECTRICITY CONSUMPTION AND MARKET PRICES IN SERBIA Impact of the Pandemic of COVID-19

The COVID-19 pandemic has begun in early 2020 and still continues to strongly affect the entire world delivering a significant global, shock, but varying across countries and commodity sectors. The Government of the Republic of Serbia has been adopting different measures to slow down the dissemination of the corona-virus, specifically nationwide lockdown in March and April 2020. Business activ-ities have been reduced. The pandemic situation has changed the lifestyle as peo-ple are mostly staying home and working from home. This paper provides a re-view of unprecedented impacts of COVID-19 pandemic, with restrictions and lockdown in Serbia, on electricity sector at this stage of the crisis. The outcomes offer a contribution to the body of literature because limited research has been conducted on these relationships in case of Serbia. Sets of statistical indicators are used to analyze changes the electricity sector has been facing. Data visuali-zation is used to compare developments during the pandemic with those of previ-ous years. Our research and data-driven analysis of these impacts should im-prove the understanding of the techno-economic effects of unforeseen events, such as a pandemic, on the power system, scrutinizing if effects could be relative-ly short-lived or longer-lasting. © 2022 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

Reduce the Cold Start of COVID-19 In-hospital Mortality Prediction Models via Transfer Learning

At the beginning of the breakout of a new disease, the healthcare community almost always has little experience in treating patients of this kind. Similarly, due to insufficient patient records at the early stage of a pandemic, it is difficult to train an in-hospital mortality prediction model specific to the new disease. We call this the 'cold start' problem of mortality prediction models. In this paper, we aim to study the cold start problem of 3-days ahead COVID-19 mortality prediction models by the following two steps: (i) Train XGBoost [1] and logistic regression 3-days ahead mortality prediction models on MIMIC3, a publicly available ICU patient dataset [2];(ii) Apply those MIMIC3 models to COVID-19 patients and then use the prediction scores as a new feature to train COVID-19 3-days ahead mortality prediction models. Retrospective experiments are conducted on a real-world COVID-19 patient dataset(n = 1,287) collected in US from June 2020 to February 2021 with a mixed cohort of both ICU and Non-ICU patients. Since the dataset is imbalanced(death rate = 7.8%), we primarily focus on the relative improvement of AUPR. We trained models with and without MIMIC3 scores on the first 200, 400,..., 1000 patients respectively and then tested on the next 200 incoming patients. The results show a diminishing positive transfer effect of AUPR from 5.36% for the first 200 patients(death rate = 5.5%) to 3.58% for all 1,287 patients. Meanwhile the AUROC scores largely remain unchanged, regardless of the number of patients in the training set. What's more, the p-value of t-test suggests that the cold start problem disappears for a dataset larger than 600 COVID-19 patients. To conclude, we demonstrate the possibility of mitigating the cold start problem via the proposed method. © 2022 IEEE.

How Did the COVID-19 Crisis Affect the Efficiency of European Intraday Electricity Markets?

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models is more accurate than others. The focus is on the German intraday market for electricity. We want to find out whether the COVID-19 crisis has an influence on the price development. This paper includes a comprehensive review between Germany, France and Norway (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyse the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions (i) we reviewed the market data linked to COVID-19. We expected a higher volatility in the lockdowns than before and therefore decrease in efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer the question (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis we use data visualization and statistical models as well as sample and out-of-sample data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact of the COVID-19 Pandemic Crisis on the Efficiency of European Intraday Electricity Markets

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway’s (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The Day-Ahead Forecasting of the Passenger Occupancy in Public Transportation by Using Machine Learning

Public transport is one of the main infrastructures of a sustainable city. For this reason, there are many studies on public transportation which mostly answer the question of “when my next bus will arrive?”. However now when the public is under the restrictions of the Covid-19 pandemic and learning to live with new social rules such as “social distance” a new yet crucial question arise on public transportation: “how crowded my next bus will be?” To prevent the crowdedness in public transportation the traffic regulators need to forecast the number of passengers the day ahead. In this study, in cooperation with Synteda, we suggest a machine learning algorithm that forecasts the occupancy in a bus or tram the day ahead for each stop for a route. The input data is past passenger travel data provided by the Västtrafik AB which is the public transportation company in Gothenburg, Sweden. The hourly data for the precipitation and temperature also has been added to the forecasting method;the database of precipitation and temperature is obtained by the SMHI, Swedish Meteorological and Hydrological Institute. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Decomposition-Residuals Neural Networks: Hybrid system identification applied to electricity demand forecasting

Day-ahead energy forecasting systems struggle to provide accurate demand predictions due to pandemic mitigation measures. Decomposition-Residuals Deep Neural Networks (DR-DNN) are hybrid point-forecasting models that can provide more accurate electricity demand predictions than single models within the COVID-19 era. DR-DNN is a novel two-layer hybrid architecture with: a decomposition and a nonlinear layer. Based on statistical tests, decomposition applies robust signal extraction and filtering of input data into: trend, seasonal and residuals signals. Utilizing calendar information, temporal signals are added: sinusoidal day/night cycles, weekend/weekday, etc. The nonlinear layer learns unknown complex patterns from all those signals, with the usage of well-established deep neural networks. DR-DNN outperformed baselines and state-of-the-art deep neural networks on next-day electricity forecasts within the COVID-19 era (from September 2020 to February 2021), both with fixed and Bayesian optimized hyperparameters. Additionally, model interpretability is improved, by indicating which endogenous or exogenous inputs contribute the most to specific hour-ahead forecasts. Residual signals are very important on the first hour ahead, whereas seasonal patterns on the 24th. Some calendar features also ranked high: whether it is day or night, weekend or weekday and the hour of the day. Temperature was the most important exogenous factor. Author