Universidad Pontificia Comillas

Plug-in electric vehicles (PEVs) present environmental and energy security advantages versus conventional gasoline vehicles. In the near future, the number of plug-in electric vehicles will likely grow significantly in the world. Despite the aforementioned advantages, the connection of PEV to the power grid poses a series of new challenges for electric utilities. This paper proposes a comprehensive approach for evaluating the impact of different levels of PEV penetration on distribution network investment and incremental energy losses. The proposed approach is based on the use of a large-scale distribution planning model which is used to analyze two real distribution areas. Obtained results show that depending on the charging strategies, investment costs can increase up to 15% of total actual distribution network investment costs, and energy losses can increase up to 40% in off-peak hours for a scenario with 60% of total vehicles being PEV.

One of the main characteristics of wind power is the inherent variability and unpredictability of the generation source, even in the short-term. To cope with this drawback, hydro pumped-storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time. This paper investigates the combined optimization of a wind farm and a pumped-storage facility from the point of view of a generation company in a market environment. The optimization model is formulated as a two-stage stochastic programming problem with two random parameters: market prices and wind generation. The optimal bids for the day-ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables. A joint configuration is modeled and compared with an uncoordinated operation. A realistic example case is presented where the developed models are tested with satisfactory results.

The outbreak of COVID-19 in Spain started at the end of February. By 9th April 2020 Spain was the second country in confirmed cases and in deaths. On March 14, 2020, the Spanish Government declared the state of alarm to limit viral transmission. During such state, citizens must stay confined at home with few justified exceptions. This whole situation drastically changed the life of the population, which can cause a wide range of psychosocial impacts. This study explored the psychological impact of the COVID-19 pandemic in the general adult population (N = 3055) during the first stages of the outbreak in Spain, as well as their anxiety, stress and depression levels. We also examined the extent to which the following variables were associated to participants’ mental health: 1) demographics; 2) degree of concern about the pandemic; 3) environmental conditions during the home confinement, 5) changes in daily life as a consequence of the pandemic; 6) contact with the COVID-19 disease; 7) actual and perceived severity of the crisis; 8) information about the COVID-19, 9) perceived health status and 10) leisure activities conducted within the last 24 hours. Our results show that Spanish consider the current COVID-19 health crisis as fairly severe, and the majority felt that the COVID-19 crisis had greatly impacted on their daily life, including changes in their daily routines and cancellation of important activities. About 36% of the participants reported moderate to severe psychological impact, 25% showed mild to severe levels of anxiety, 41% reported depressive symptoms, and 41% felt stressed. Women, young, and those who that lost their job during the health crisis showed the strongest negative psychological symptoms. What worried Spaniards the most was the likelihood of suffering an economic crisis derived from the pandemic. We found factors associated with better mental health, such as being satisfied with the information received about the health crisis, conducting leisure activities, and the perception of being in good health. These findings can be used to design psychological interventions to help coping with COVID-19 pandemic, both in Spain and other countries.

High levels of penetration of distributed generation (DG) are a new challenge for traditional electric power systems. Power injections from DGs change network power flows modifying energy losses. Although it is considered that DG reduce losses, this paper shows that this is not always true. This paper presents an approach to compute annual energy losses variations when different penetration and concentration levels of DG are connected to a distribution network. In addition, the impact on losses of different DG technologies, such as combined heat and power, wind power, photovoltaic, and fuel-cells, is analyzed. Results show that energy losses variation, as a function of the DG penetration level, presents a characteristic U-shape trajectory. Moreover, when DG units are more dispersed along network feeders, higher losses reduction can be expected. Regarding DG technologies, it should be noted that wind power is the one that shows the worst behavior in losses reduction. Finally, DG units with reactive power control provide a better network voltage profile and lower losses.

The significant growth in gas-fired units worldwide has increased the grade of interdependency between power and natural gas networks. Since these units are usually required to ramp up during the peak and backup intermittent renewable generation and contingencies, the power system tends to demand more flexibility and reliability from the gas system. This paper contributes with a novel mixed-integer linear programming (MILP) formulation that couples power and gas networks taking into account the gas traveling velocity and compressibility. As a result, the model accounts for the gas adequacy needed to assure the power system reliability in the short term. The robustness of the MILP formulation allows guaranteeing global optimality within predefined tolerances. Case studies integrate the IEEE 24-bus system and Belgian high-calorific gas network for validating the formulation.

The use of conventional manufacturing methods is mainly limited by the size of the production run and the geometrical complexity of the component, and as a result we are occasionally forced to use processes and tools that increase the final cost of the element being produced. Additive manufacturing techniques provide major competitive advantages due to the fact that they adapt to the geometrical complexity and customised design of the part to be manufactured. The following may also be achieved according to field of application: lighter weight products, multimaterial products, ergonomic products, efficient short production runs, fewer assembly errors and, therefore, lower associated costs, lower tool investment costs, a combination of different manufacturing processes, an optimised use of materials, and a more sustainable manufacturing process. Additive manufacturing is seen as being one of the major revolutionary industrial processes of the next few years. Additive manufacturing has several alternatives ranging from simple RepRap machines to complex fused metal deposition systems. This paper will expand upon the structural design of the machines, their history, classification, the alternatives existing today, materials used and their characteristics, the technology limitations, and also the prospects that are opening up for different technologies both in the professional field of innovation and the academic field of research. It is important to say that the choice of technology is directly dependent on the particular application being planned: first the application and then the technology.

In this paper, a probabilistic methodology for estimating the energy costs in the market for wind generators associated with wind prediction errors is proposed. Generators must buy or sell energy production deviations due to prediction errors when they bid in day-ahead or hour-ahead energy markets. The prediction error is modeled through a probability density function that represents the accuracy of the prediction model. Production hourly energy deviations and their associated trading costs are then calculated. Three study cases based on real wind power installations in Spain are analyzed. The three study cases show that the error prediction costs can reach as much as 10% of the total generator energy incomes.

International audience

This paper presents a mixed-integer linear programming (MILP) reformulation of the thermal unit commitment (UC) problem. The proposed formulation is simultaneously tight and compact. The tighter characteristic reduces the search space and the more compact characteristic increases the searching speed with which solvers explore that reduced space. Therefore, as a natural consequence, the proposed formulation significantly reduces the computational burden in comparison with analogous MILP-based UC formulations. We provide computational results comparing the proposed formulation with two others which have been recognized as computationally efficient in the literature. The experiments were carried out on 40 different power system mixes and sizes, running from 28 to 1870 generating units.

In competitive electricity markets, in addition to the uncertainty of exogenous variables such as energy demand, water inflows, and availability of generation units and fuel costs, participants are faced with the uncertainty of their competitors' behavior. The analysis of electricity price time series reflects a switching nature, related to discrete changes in competitors' strategies, which can be represented by a set of dynamic models sequenced together by a Markov chain. An input-output hidden Markov model (IOHMM) is proposed for analyzing and forecasting electricity spot prices. The model provides both good predictions in terms of accuracy as well as dynamic information about the market. In this way, different market states are identified and characterized by their more relevant explanatory variables. Moreover, a conditional probability transition matrix governs the probabilities of remaining in the same state, or changing to another, whenever a new market session is opened. The model has been successfully applied to real clearing prices in the Spanish electricity market.

There is a continuous and fast increase in electric vehicles (EVs) adoption in many countries due to the reduction of EVs prices, governments’ incentives and subsidies on EVs, the need for energy independence, and environmental issues. It is expected that EVs will dominate the private cars market in the coming years. These EVs charge their batteries from the power grid and may cause severe effects if not managed properly. On the other hand, they can provide many benefits to the power grid and get revenues for EV owners if managed properly. The main contribution of the article is to provide a review of potential negative impacts of EVs charging on electric power systems mainly due to uncontrolled charging and how through controlled charging and discharging those impacts can be reduced and become even positive impacts. The impacts of uncontrolled EVs charging on the increase of peak demand, voltage deviation from the acceptable limits, phase unbalance due to the single-phase chargers, harmonics distortion, overloading of the power system equipment, and increase of power losses are presented. Furthermore, a review of the positive impacts of controlled EVs charging and discharging, and the electrical services that it can provide like frequency regulation, voltage regulation and reactive power compensation, congestion management, and improving power quality are presented. Moreover, a few promising research topics that need more investigation in future research are briefly discussed. Furthermore, the concepts and general background of EVs, EVs market, EV charging technology, the charging methods are presented.

Interest in Demand Response (DR) is increasing due to its potential to improve reliability and save costs for electricity systems. DR can provide a sustainable and cost-effective option for supply balancing, especially in a scenario with more volatile inflows from renewable energy sources. End-users can be incentivized to provide DR through time-based pricing in general and dynamic pricing in particular. This paper provides a theoretic framework and practice-oriented review of the status of DR in Europe, outlining the major challenges currently hampering further DR development. Important challenges involve the split-incentive issue for investments in enabling technologies, traditional market rules for flexibility that favor large generation units and the need for electricity market and network operation coordination.

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.

Growing load factors in winter and summer peaks are a serious problem faced by the Spanish electric energy system. This has led to the extensive use of peak load plants and thus to higher costs for the whole system. Wind energy represents a strongly increasing percentage of overall electricity production, but wind normally does not follow the typical demand profile. As generation flexibility is limited due to technical restrictions, and in absence of large energy storages, the other side of the equilibrium generation-demand has to react. Demand side management measures intend to adapt the demand profile to the situation in the system. In this paper, the operation of an electric system with high wind penetration is modeled by means of a unit commitment problem. Demand shifting and peak shaving are considered in this operation problem. Demand shifting is modeled in two different ways. Firstly, the system operator controls the shift of demand; secondly, each consumer decides its reaction to prices depending on its elasticity. The model is applied to the isolated power system of Gran Canaria. The impact of an increased installed wind capacity on operation and the cost savings resulting from the introduction of responsive demand are assessed. Furthermore, results from the different implemented demand response options are compared.

This paper describes the results of a voluntary benchmark initiative concerning the simulation of pantograph–catenary interaction, which was proposed and coordinated by Politecnico di Milano and participated by 10 research institutions established in 9 different countries across Europe and Asia. The aims of the benchmark are to assess the dispersion of results on the same simulation study cases, to demonstrate the accuracy of numerical methodologies and simulation models and to identify the best suited modelling approaches to study pantograph–catenary interaction. One static and three dynamic simulation cases were defined for a non-existing but realistic high-speed pantograph–catenary couple. These cases were run using 10 of the major simulation codes presently in use for the study of pantograph–catenary interaction, and the results are presented and critically discussed here. All input data required to run the study cases are also provided, allowing the use of this benchmark as a term of comparison for other simulation codes.

<p>It is often asserted with confidence that foreign direct investment (FDI) is beneficial for economic growth in the host economy. Empirical evidence has been mixed, and there remain gaps in the literature. The majority of FDI has been directed at developed countries. Single-country studies are needed, due to the heterogeneous relationship between FDI and growth, and because the impact of FDI on growth is said to be largest in open, advanced developed countries with an educated workforce and developed financial markets (although research has focused on developing countries). We fill these gaps with an improved empirical methodology to check whether FDI has enhanced growth in Spain, one of the largest receivers of FDI, whose gross domestic product growth was above average but has escaped scrutiny. During the observation period 1984–2010, FDI rose significantly, and Spain offered ideal conditions for FDI to unfold its hypothesized positive effects on growth. We run a horse race between various potential explanatory variables, including the neglected role of bank credit for the real economy. The results are robust and clear: The favorable Spanish circumstances yield no evidence for FDI to stimulate economic growth. The Spanish EU and euro entry are also found to have had no positive effect on growth. The findings call for a fundamental rethinking of methodology in economics.</p>

The problem of ensuring that there is enough generation capacity to meet future demand has been an issue in market design since the beginning of the deregulation process. Although ideally the market itself should be enough to provide adequate investment incentives, there are several factors that prevent this result from being achieved, and some actual markets have already experienced problems related with a lack of generation capacity. A regulatory framework to address this question is presented. The procedure is based on an organized market where reliability contracts (based on financial call options) are auctioned, so both their price and their allocation among the different plants are determined through competitive mechanisms. This results in a stabilization of the income of the generators and provides a clear incentive for new generation investment, with a minimum of regulatory intervention. Additionally, the method represents a market-compatible mechanism to hedge demand from the occurrence of high market prices.

This paper presents an in-depth analysis of network revenues computed with marginal pricing, and in particular it investigates the reasons why marginal prices fail to recover the total incurred network costs in actual power systems. The basic theoretical results are presented and the major causes of the mismatch between network costs and marginal revenues are identified and illustrated with numerical examples, some tutorial and others of realistic size. The regulatory implications of marginal network pricing in the context of competitive electricity markets are analyzed, and suggestions are provided for the meaningful allocation of the costs of the network among its users.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Distributed control methods based on consensus algorithms have become popular in recent years for microgrid (MG) systems. These kind of algorithms can be applied to share information in order to coordinate multiple distributed generators within a MG. However, stability analysis becomes a challenging issue when these kinds of algorithms are used, since the dynamics of the electrical and the communication systems interact with each other. Moreover, the transmission rate and topology of the communication network also affect the system dynamics. Due to discrete nature of the information exchange in the communication network, continuous-time methods can be inaccurate for this kind of dynamic study. Therefore, this paper aims at modeling a complete dc MG using a discrete-time approach in order to perform a sensitivity analysis taking into account the effects of the consensus algorithm. To this end, a generalized modeling method is proposed and the influence of key control parameters, the communication topology, and the communication speed are studied in detail. The theoretical results obtained with the proposed model are verified by comparing them with the results obtained with a detailed switching simulator developed in Simulink/Plecs.

Continuous liberalization and interconnection of energy markets worldwide has raised concerns about the inherent interdependency between primary energy supply and electric systems. With the growing interaction among energy carriers, limitations on the fuel delivery are becoming increasingly relevant to the operation of power systems. This paper contributes with a novel formulation of a mixed-integer linear programing (MILP) security-constrained optimal power and gas flow. To this end, an iterative methodology, based on development of linear sensitivity factors, determines the stabilized post-contingency condition of the integrated network. The proposed model allows system operators not only to perform security analysis but also to adjust in advance state variables of the integrated system optimally and fast, so that n-1 contingencies do not result in violations. Case studies integrate the IEEE 24-bus system and a modified Belgian high-calorific gas network for analyzing the performance of the formulation and solution methodology.