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Measuring and understanding the evolution of the electricity generation efficiency sector is a relevant task for policymakers. This study focuses on assessing the determinants of electricity generation efficiency across 24 countries in Latin America and the Caribbean between 2000 and 2020. A hurdle model was employed to account for the censored nature of the dependent variable. The findings show a positive impact of opening up the electricity sector in fully efficient countries. Market liberalization and the involvement of privately owned power plants contribute to efficiency gains in inefficient countries. Additionally, the tariff protectionism level exhibits positive effects for completely efficient and inefficient countries. These results provide valuable insights, as less-efficient countries can glean strategies from their more efficient counterparts to enhance their electricity generation efficiency. [ABSTRACT FROM AUTHOR]

Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn. [ABSTRACT FROM AUTHOR]

Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power. [ABSTRACT FROM AUTHOR]

In prosumers' communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company. [ABSTRACT FROM AUTHOR]

Trolleybus transport refers to contemporary challenges related to a reduction in emissions of greenhouse gases and CO2 into the atmosphere formulated by international institutions, such as the United Nations, the Organisation for Security and Co-operation in Europe, or the European Union. Departure from fossil fuels in urban transport is one of the key challenges for the coming years. Trolleybuses are an important tool in this task, even though their importance was declining in the past. Nowadays, due to, among others, technological development, in particular the availability of high-capacity batteries, their long life and low weight, trolleybus transport is becoming popular again. The use of the existing overhead contact infrastructure of the trolleybus network and small on-board batteries allow expanding the spatial accessibility of zero-emission public transport. Thus, this reduces the social differentiation in access to environmentally friendly transport that does not emit pollutants at the place of operation. The article presents possibilities of using on-board batteries in shaping trolleybus connections with the optimal use of the existing overhead contact lines (OHL). It presents a procedure that allows for the evaluation of the extent to which the OHL should cover the routes of bus lines in order to qualify for trolleybus service in the In-Motion-Charging (IMC) technology. Analysis of the literature shows inadequate scientific studies on combining the advantages of overhead wiring and the development of on-board battery technology in popularising zero-emission transport. This article addresses the key issues related to the use of partially autonomous trolleybuses. [ABSTRACT FROM AUTHOR]

The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {12VL}. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {23VL}—serviceability condition and {13VL}—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object's elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement. [ABSTRACT FROM AUTHOR]

A key drawback of multirotor unmanned aerial vehicles (UAVs) with energy sources based solely on electrochemical batteries is related to the available on-board energy. Flight autonomy is typically limited to 15–30 min, with a flight duration upper limit of 90 min currently being achieved by high-performance battery-powered multirotor UAVs. Therefore, propulsion systems that utilize two or more different energy sources (hybrid power systems) may be considered as an alternative in order to increase the flight duration while retaining key performance benefits of battery energy storage use. The research presented in this work considers a multirotor UAV power unit, based on the internal combustion engine (ICE) powering an electricity generator (EG) connected to the common direct current (DC) bus in parallel with the lithium-polymer (LiPo) battery, and the respective modeling and identification of individual power unit subsystem, along with the dedicated control system design. Experimental verification of the proposed hybrid power unit control system has been carried out on the custom-build power unit prototype. The results show that the proposed control system combines the two power sources in a straightforward and effective way, with subsequent analysis showing that a two-fold energy density increase can be achieved with a hybrid energy source, consequently making it possible to achieve higher flight autonomy of the prospective multirotor (hover load around 1000–1400 W) equipped with such a hybrid system. [ABSTRACT FROM AUTHOR]

The need for deploying fast-charging stations for electric vehicles (EVs) is becoming essential in recent years. This need is justified by the increasing charging demand and supported by new charging technologies making EV chargers more efficient. In this paper, we provide a survey on EV fast-charging models and introduce a data-driven approach with an optimization model for deploying EV fast-chargers for both electric vehicles and heavy trucks traveling through a network of suburban highways. This deployment aims at satisfying EV charging demands while respecting the limits imposed by the electric grid. We also consider the availability of local photovoltaic (PV) farm and integrate its produced power to the proposed charging network. Finally, through a case study on Paris-Saclay area, we provide locations for EV charging stations and analyze the benefits of integrating PV power at different prices, production costs and charging capacities. The obtained results also suggest potential enhancements to the charging network in order to accommodate the increasing charging demand for EVs in the future. [ABSTRACT FROM AUTHOR]

We propose a machine learning-based methodology which makes use of ensemble methods with the aims (i) of treating missing data in time series with irregular observation times and detecting anomalies in the observed time behavior; (ii) of defining suitable models of the system dynamics. We applied this methodology to US wholesale electricity price time series that are characterized by missing data, high and stochastic volatility, jumps and pronounced spikes. For missing data, we provide a repair approach based on the missForest algorithm, an imputation algorithm which is completely agnostic about the data distribution. To identify anomalies, i.e., turbulent movements of power prices in which jumps and spikes are observed, we took into account the no-gap reconstructed electricity price time series, and then we detected anomalous regions using the isolation forest algorithm, an anomaly detection method that isolates anomalies instead of profiling normal data points as in the most common techniques. After removing anomalies, the additional gaps will be newly filled by the missForest imputation algorithm. In this way, a complete and clean time series describing the stable dynamics of power prices can be obtained. The decoupling between the stable motion and the turbulent motion allows us to define suitable jump-diffusion models of power prices and to provide an estimation procedure that uses the full information contained in both the stable and the turbulent dynamics. [ABSTRACT FROM AUTHOR]

Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large scale nonlinear programming problem is solved using genetic algorithm, simulated annealing and mixed integer second-order cone programming method. The feasibility and effectiveness of the proposed algorithm have been verified. [ABSTRACT FROM AUTHOR]

Certified electric vehicle power converters can inject DC current into the AC grid if they fail. Verification of DC injection by electric vehicle supply equipment can be a cost-effective extra measure to ensure power quality from a variety of plugged-in electric vehicles. As electric vehicle supply equipment typically performs high-accuracy revenue energy metering, we propose that measurement of AC current and DC injection with a single sensor is the most economically efficient design. This article presents an integrated shunt current sensing system with separation of AC and DC signals for concurrent revenue metering and DC injection detection. It also shows how the combined sensor is integrated into 19.2 kW single-phase electric vehicle supply equipment, and outlines how the design would be extended to 100 kW three-phase electric vehicle supply equipment. The prototype can detect DC injection of ≥400 mA in an AC current up to 80 A in accordance with the IEEE 1547-2018 standard. The prototype can also conduct revenue metering within the 1.0 accuracy class. The prototype does not have high power dissipation at high currents typical for shunt systems. Finally, the prototype is less costly than common electric vehicle supply equipment revenue metering CT systems with the addition of the popular Hall-effect sensor. [ABSTRACT FROM AUTHOR]

Wind power generators face risks derived from fluctuations in market prices and variability in power production, generated by their high dependence on wind speed. These risks could be hedged using weather financial instruments. In this research, we design and price an up-and-in European wind put barrier option using Monte Carlo simulation. Under the existence of a structured weather market, wind producers may purchase an up-and-in European wind barrier put option to hedge wind fluctuations, allowing them to recover their investments and maximise their profits. We use a wind speed index as the underlying index of the barrier option, which captures risk from wind power generation and the Autoregressive Fractionally Integrated Moving Average (ARFIMA) to model the wind speed. This methodology is applied in the Colombian context, an electricity market affected by the El Niño phenomenon. We find that when the El Niño phenomenon occurs, there are incentives for wind generators to sell their energy to the system because their costs, including the put option price, are lower than the power prices. This research aims at encouraging policymakers and governments to promote renewable energy sources and a financial market to trade options to reduce uncertainty in the electrical system due to climate phenomena. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical analysis of mobility detection in connectivity-based localization, which exploits connectivity information as range measurements to anchors at a known location, to investigate how well and how precise mobility can be detected with connectivity in short-range networks. We derive mobility detection, miss detection, and false alarm probabilities in terms of a mobility detection threshold, defined as the minimum distance to detect the mobility, under the shadow fading channel and arbitrary mobility models to take into account practical and general scenarios. Based on the derivations, we address the threshold determination with the criteria in the sense of the minimum average error from miss detection and false alarm. Numerical and simulation evaluations are performed to verify our theoretical derivations, to show that increasing anchor numbers can improve the mobility detection probability with a smaller detection threshold, and that the probabilities are bounded by the weights of miss detection and false alarm. This work is the first attempt at addressing the performance of mobility detection using connectivity, and it can be utilized as a baseline for connectivity-based mobility tracking. [ABSTRACT FROM AUTHOR]

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments. [ABSTRACT FROM AUTHOR]

This research tackles effective and functional management practices in energy consumption using advanced technological solutions to mitigate unexpected events. This study specifically focuses on the recent COVID-19 pandemic that led to a reduction in energy consumption and therefore meddled with the project management's status quo for the energy sources. Moreover, this research analyzes the role of information and communication technology (ICT) solutions in energy management before and after the recent coronavirus pandemic turmoil. This study also examined the role and place of sustainable energy resources in the effective management of energy consumption during the COVID-19 pandemic. The main purpose of this research is to demonstrate how the recent coronavirus pandemic affected energy consumption and energy prices in many countries and how it paved the way for the new ICT in managing energy consumption. This study elaborated a model to show how these changes evolved in selected European Union (EU) Member States. It turns out that adjusted electricity consumption, as well as the average daily spot electricity prices for the baseload power in the EU countries, declined, and this trend is likely to continue in the next years to come. Due to the COVID-19 pandemic, substantial changes in the economy led to reduced office space usage, cutting commuting and business trips, and bringing commercial travel almost to a halt, further shifting the energy balance towards renewable energy sources (RES). Besides, it becomes apparent that ICT solutions used in maintaining and superintending the energy balance are gaining popularity and wide recognition worldwide and will help pave the way towards modern and smart high-renewables grids. [ABSTRACT FROM AUTHOR]

Medium and heavy-duty battery electric trucks (BETs) may play a key role in mitigating greenhouse gas (GHG) emissions from road freight transport. However, technological challenges such as limited range and cargo carrying capacity as well as the required charging time need to be efficiently addressed before the large-scale adoption of BETs. In this study, we apply a geospatial data analysis approach by using a battery electric vehicle potential (BEVPO) model with the datasets of road freight transport surveys for analyzing the potential of large-scale BET adoption in Finland and Switzerland for trucks with gross vehicle weight (GVW) of over 3.5 t. Our results show that trucks with payload capacities up to 30 t have the most potential for electrification by relying on the currently available battery and plug-in charging technology, with 93% (55% tkm) and 89% (84% tkm) trip coverage in Finland and Switzerland, respectively. Electric road systems (ERSs) would be essential for covering 51% trips (41% tkm) of heavy-duty trucks heavier than 30 t in Finland. Furthermore, range-extender technology could improve the trip electrification potential by 3–10 percentage points (4–12 percentage points of tkm). [ABSTRACT FROM AUTHOR]

As a large number of distributed generations are connected to the distribution system, research on the hosting capacity is actively being conducted. In particular, various methods, such as smart inverter functionality, co-located energy storage systems (ESS), and the use of on-load tap changers (OLTC), have been proposed to improve the hosting capacity. In this paper, a method to improve the hosting capacity by utilizing a solid-state transformer (SST) and its unique control capability is proposed. Lastly, the proposed method is verified in the distribution system of the Republic of Korea using the OpenDSS program. [ABSTRACT FROM AUTHOR]

Studying the evolution of the efficiency of the electricity generation sector is a relevant task for policy makers, and requires the undesirable outputs derived from the activity to be considered in the evaluation. In this work, we propose a dynamic slack-based Data Envelopment Analysis (DEA) model that incorporates the assumption of weak disposability between the generation of electricity from fossil sources and the CO2 emissions caused by the sector to measure the technical efficiency of 24 Latin American and Caribbean countries in the period 2000–2016. The results show that, of the total number of countries studied, four are efficient overall, and four groupings of countries in relation to the levels of efficiency achieved are also identified. These results are important given that less-efficient countries can, through learning, increase their efficiency in electricity generation or emulate the future strategies proposed by the most efficient countries. [ABSTRACT FROM AUTHOR]

Recently, with the penetration of numerous Distributed Energy Resources (DER) in Smart Distribution Networks (SDN), Local Transactive Markets have emerged. Exchanging energy between all participants of local markets results in the satisfaction of producers and consumers. Based on these issues, this study provides a novel framework for the participation of SDN-independent entities in wholesale and local electricity markets simultaneously. In this regard, the considered system's players, namely Distribution System Operator (DSO) and DER Aggregator (AG), take part within local as well as wholesale markets in two-day ahead and real-time stages. Moreover, to deal with the inherent conflict between the existing players' interests, a Stackelberg game-based technique is proposed. In the raised competition, the leader, DSO, attempts to minimize its operating costs, while the follower, DER AG, tends to maximize its profit. Therefore, actors' actions choices within both markets are made non-cooperatively. On the other hand, to handle the uncertain nature of stochastic parameters in the depicted problem, Monte Carlo Simulation (MCS), together with a fast backward/forward scenario reduction approach, is exploited. Ultimately, to evaluate the efficiency of the proposed scheme, two different case studies, with and without considering the competitive environment, are implemented on a modified IEEE-33 bus SDN. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective approach to carry out the planning of medium-voltage (MV) and low-voltage (LV) distribution systems, considering renewable energy sources (RES) and robustness, is proposed. Due to the uncertainties associated with RES and demand, the proposed planning methodology takes into account a robust planning index (RPI). This RPI allows us to evaluate the robustness estimation associated with each planning solution. The objective function in the mathematical model considers the costs of investment and operation and the robustness of the planning proposals. Due to the computational complexity of this problem, which is difficult to solve by means of classical optimization techniques, MV/LV planning is solved by a decomposition search and a general variable neighborhood search (GVNS) algorithm. To demonstrate the efficiency and robustness of this methodology, tests are performed in an integrated distribution system with 50 MV nodes and 410 LV nodes. Our numerical results show that the proposed methodology makes it possible to minimize costs and improve robustness levels in distribution system planning. [ABSTRACT FROM AUTHOR]

In electricity markets, bilateral contracts (BC) are used to hedge against price volatility in the spot market. Pricing these contracts requires scheduling from either the buyer or the seller aiming to achieve the highest profit possible. Since this problem includes different players, a Generation Company (GC) and an Electricity Supplier Company (ESC) are considered. The approaches to solve this problem include the Nash Bargaining Solution (NBS) equilibrium and the Raiffa–Kalai–Smorodinsky (RKS) bargaining solution. The innovation of this work is the implementation of an algorithm based on the RKS equilibrium to find a compromise strategy when determining the concessions to be made by the parties. The results are promising and show that the RKS approach can obtain better results compared to the Nash equilibrium method applied to a case study. [ABSTRACT FROM AUTHOR]

A model suitable to obtain where and when renewable energy sources (RES) should be allocated as part of generation planning in distribution systems is formulated. The proposed model starts from an existing two-stage stochastic mixed-integer linear programming (MILP) problem including investment and scenario-dependent operation decisions. The aim is to minimize photovoltaic and wind investment costs, operation costs, as well as total substation costs including the cost of the energy bought from substations and energy losses. A new Benders' decomposition framework is used to decouple the problem between investment and operation decisions, where the latter can be further decomposed into a set of smaller problems per scenario and planning period. The model is applied to a 34-bus system and a comparison with a MILP model is presented to show the advantages of the model proposed. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including several forecasting techniques applied to the forecasting of electricity prices like statistical time series models, artificial neural networks, and wavelet transform models.

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension. [ABSTRACT FROM AUTHOR]