Gas-Electricity Coordination in Competitive Markets under Renewable Energy Uncertainty

As climate concerns, low natural gas prices, and renewable technologies increase the electric power sector’s dependence on natural gas-fired power plants, operational and investment models for gas and electric power systems will need to incorporate the interdependencies between these two systems to accurately capture the impacts of one on the other. Currently, few hybrid gas-electricity models exist. This paper reviews the state of the art for hybrid gas-electricity models and presents a new model and case study to illustrate a few potential coupling effects between gas and electric power systems. Specifically, the proposed model analyzes the optimal operation of gas-fired power plants in a competitive electricity market taking into consideration gas purchases, gas capacity contracting, and residual demand uncertainty for the generation company due to renewable energy sources

Wind power long-term scenario generation considering spatial-temporal dependencies in coupled electricity markets

This article belongs to the Section A3: Wind, Wave and Tidal EnergyWind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France

Air temperature forecasting using machine learning techniques: a review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined

A ventromedial prefrontal dysrhythmia in obsessive-compulsive disorder is attenuated by nucleus accumbens deep brain stimulation

Background: Obsessive-compulsive disorder (OCD) has consistently been linked to abnormal frontostriatal activity. The electrophysiological disruption in this circuit, however, remains to be characterized. Objective/hypothesis: The primary goal of this study was to investigate the neuronal synchronization in OCD patients. We predicted aberrant oscillatory activity in frontal regions compared to healthy control subjects, which would be alleviated by deep brain stimulation (DBS) of the nucleus accumbens (NAc). Methods: We compared scalp EEG recordings from nine patients with OCD treated with NAc-DBS with recordings from healthy controls, matched for age and gender. Within the patient group, EEG activity was compared with DBS turned off vs. stimulation at typical clinical settings (3.5 V, frequency of stimulation 130 Hz, pulse width 60 ms). In addition, intracranial EEG was recorded directly from depth macro electrodes in the NAc in four OCD patients. Results: Cross-frequency coupling between the phase of alpha/low beta oscillations and amplitude of high gamma was significantly increased over midline frontal and parietal electrodes in patients when stimulation was turned off, compared to controls. Critically, in patients, beta (16-25 Hz)-gamma (110-166 Hz) phase amplitude coupling source localized to the ventromedial prefrontal cortex, and was reduced when NAc-DBS was active. In contrast, intracranial EEG recordings showed no beta-gamma phase amplitude coupling. The contribution of non-sinusoidal beta waveforms to this coupling are reported. Conclusion: We reveal an increased beta-gamma phase amplitude coupling in fronto-central scalp sensors in patients suffering from OCD, compared to healthy controls, which may derive from ventromedial prefrontal regions implicated in OCD and is normalized by DBS of the nucleus accumbens. This aberrant cross-frequency coupling could represent a biomarker of OCD, as well as a target for novel therapeutic approaches. (C) 2021 The Authors. Published by Elsevier Inc.This work was supported by Project grants SAF2015-65982-R from the Spanish Ministry of Economy and Competitiveness to BS and PSI2014-58654-JIN to JGR, an FPI Predoctoral Fellowship (BES-2016-079470) to ST, and BIAL Foundation Grant 119/12 to BS. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC-2018-COG 819814)

Analysis of the effect of voltage level requirements on an electricity market equilibrium mode

This paper presents a conjectural-variation-based equilibrium model of a single-price electricity market. The main characteristic of the model is that the market equilibrium equations incorporate the effect of the voltage constraints on the companies’ strategic behavior. A two-stage optimization model is used to solve the market equilibrium. In the first stage, an equivalent optimization problem is used to compute the day-ahead market clearing process. In the second stage, some generation units have to modify their active and reactive power in order to meet the technical constraints of the transmission network. These generation changes are determined by computing an AC optimal power flow

Impact of the Kyoto Protocol on the Iberian Electricity Market: A scenario analysis

This paper presents an assessment of the impact of the Kyoto Protocol on the Iberian Electricity Market during two periods: the first phase (2005-2007) and the second phase (2008-2012). A market-equilibrium model is used in order to analyze different conditions faced by generation companies. Scenarios involving CO2-emission prices, hydro conditions, demand, fuel prices and renewable generation are considered. This valuation will show the significance of CO2-emission prices as regards Spanish and Portuguese electricity prices, generation mix, utilities profits and the total CO2 emissions. Furthermore, the results will illustrate how energy policies implemented by regulators are critical for Spain and Portugal in order to mitigate the negative impact of the Kyoto Protocol. In conclusion, the Iberian electricity system will not be able to reach the Kyoto targets, except in very favorable conditions (CO2-emission prices over [euro]15/ton and the implementation of very efficient energy policies).

A New Model to Simulate Local Market Power in a Multi-Area Electricity Market: Application to the European Case

The work presented in this article proposes an original method that models the medium-term market equilibrium under imperfect competition circumstances in multi-area electricity systems. It provides a system analysis considering multiple market splitting possibilities, where local market power may appear according to the status of the interconnections. As a result of new policies and regulations, power systems are increasingly integrating the existing electricity markets in unified frameworks. The integration of electricity markets poses highly challenging tasks due to the uncertainty that comes from the agents’ strategic behaviors which depend on multiple factors, for instance, the state of the interconnections. When it comes to modeling these effects, the purpose is to identify each strategy by using conjectured-price responses that depend on the different states of the system. Consequently, the problem becomes highly combinatorial, which heightens its size as well as its complexity. Therefore, the purpose of this work’s methodology is the reduction of the possible network configurations so as to ensure a computational tractability in the problem. In order to validate this methodology, it has been put to the test in a realistic and full-scale two-year operation planning model of the European electricity market that consists of a group of nine countries

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions

A New Methodology to Obtain a Feasible Thermal Operation in Power Systems in a Medium-Term Horizon

Nowadays, electricity market paradigms are constantly changing. On the one hand, the deployment of non-dispatchable renewable energy sources is bringing out the necessity of representing hourly dynamics in medium-term fundamental models. On the other, the promotion of new interconnection capacity and the integration of markets (as is the case of the European market) makes necessary the simultaneous modeling of multiple electricity systems. Thus, the large size of power markets, together with the consideration of uncertainty in some inputs, make it computationally intractable to work rigorously on an hourly detailed time span. Temporal aggregation, integer programming relaxation or less accurate generation modeling are usually employed to obtain reasonable computation times. However, the application of these techniques often leads to infeasible or suboptimal operational outputs. This paper proposes a new soft-linking methodology to meet reliable results from medium-term models, such as hourly prices or aggregated productions, with a feasible and detailed representation of the thermal generation, considering technical constraints and risk aversion. The results of a fundamental model that represents the competitive behavior between market players in a multi-area power system are used as the starting point for the methodology. Then, a post-processing method is applied to optimize and make feasible the thermal portfolio of a market agent. The final output is a feasible hourly scheduling and an ample space for optimization, where the introduction of a strategic term represents the rational behavior of a player who tries to maximize its profit