As a large number of small-scale PV plants have been deployed in distribution systems, generation forecasting of such plants has recently been gaining interest. Because the PV power mainly depends on weather conditions, it is important to accurately collect weather data for relevant PV sites to enhance PV forecasting accuracy. However, small-scale PV plants do not often have their own measuring apparatus to get historical weather data, so they have used weather datasets from relatively nearby weather data centers (WDCs). Therefore, these small-scale PV plants have difficulty delivering robust and reliable forecasting accuracy because of inappropriate predicted weather data from a distance. In this paper, two weather data mixing models are proposed: (a) inverse distance weighting (IDW), and (b) inverse correlation weighting (ICW). These models acquire adequate mixed weather data for the day-ahead generation forecasting for small-scale PV plants. Furthermore, the mixed weather data are c... [more]

The current electrification status in West African countries presents rural electrification rates below 40%, national grid losses above 39% with frequent disruptions, and electricity prices averaging $0.35/kWh, up to national values of $0.66/kWh. With this, off-grid systems have gained great attention during the last decade as energy solutions; especially solar home systems (SHS) and mini grids. Nowadays, 385 mini grids with a power of near 30 MW are operating in West Africa, with 95% based on PV. Since 2019, result-based tenders with international aid funding—more effective than previous competitive tenders—seek to install at least 317 new mini grids in Togo, 250 in Nigeria, 100 in Burkina Faso, and two in Mali. Besides, the market for mini-grid energy access start-ups grew from $19 million in 2013 to $339 million in 2018. Despite this recent development in West Africa, research and data for mini grids in this region is scarce, and it is mostly approached from the technological side,... [more]

The lack of a systematic definition of intermittency in the power sector blurs the use of this term in the public debate: the same power source can be described as stable or intermittent, depending on the standpoint of the authors. This work tackles a quantitative definition of intermittency adapted to the power sector, linked to the nature of the source, and not to the current state of the energy mix or the production predictive capacity. A quantitative indicator is devised, discussed and graphically depicted. A case study is illustrated by the analysis of the 2018 production data in France and then developed further to evaluate the impact of two methods often considered to reduce intermittency: aggregation and complementarity between wind and solar productions.

Within the frame of upgrading and modernisation of the Water Supply System (WSS), our work is focussing on the safety systems/devices implemented or that should be implemented in the WSS. The implementation of safety systems is supposed to reduce hazard occurrence and hazardous consequences in case of a WSS unsafe disruption. To assess this reduction, we preconise the use of the safety integrity levels standards. The implementation of the safety systems/devices is undertaken on the ground of the multi-barriers safeguard approach. The “Water Contamination Hazard” is considered in the paper. A case study is presented, assessed and conclusions are drawn. The methodology presented in the paper and the results of the case study assessment will contribute to the decision-making regarding the upgrading of the safety and the performance of the WSS.

This article identifies and characterizes the network of dominant businesses which owned wind farms in Galicia (Spain) from 1995 to 2017. This research contributes to reduce the research gap about identifying the investment groups involved and to appreciate the real size of the wind sector. The novelty of the research lies in identifying the network of real owners of wind farms through the application of Domhoff’s power structure analysis, normally used in the analysis of the power structure of corporations and political institutions. With this method, it is possible to observe how the individual wind farm companies are assessed as well as the matrix company, and/or the principal shareholders to which the farm belongs are identified. The article shows that the installed wind power in Galicia is owned by large energy firms with the participation of international investment funds as well, even though the smaller number of local companies in the sector were given favored status under the... [more]

This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% ass... [more]

This paper proposes the concept “active energy agent (AEA)” to characterize the autonomous and interactive entities of power system. The future distribution network is a peer-to-peer (P2P) community based on numbers of AEAs. A two-stage “P2P Plus” mechanism is developed to address the electricity transaction within AEA community. In the first “P2P” stage, electricity is directly traded among AEAs via P2P price bidding. The model of P2P transaction is established, and the method of multi-dimensional willingness is adopted in price bidding. In the second “Plus” stage, the centralized coordination by distribution company (DisCo) is formulated as a constrained optimization problem, in which the objective is to maximize profit and the constraints are the basic rights of AEAs and line ratings of distribution network. A 30-bus test system including 29 AEAs and main grid is investigated. Numeric simulation results verify the effectiveness of the proposed models and methods regarding flow const... [more]

Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies. The low computational cost and powerful performance of ESN make it widely used in a range of applications including forecasting tasks and nonlinear modeling. This paper presents a Bayesian optimization algorithm (BOA) of ESN hyperparameters in load forecasting with its main contributions including helping the selection of optimization algorithms for tuning ESN to solve real-world forecasting problems, as well as the evaluation of the performance of Bayesian optimization with different acquisition function settings. For this purpose, the ESN hyperparameters were set as variables to be optimized. Then, the adopted BOA employs a probabilist model using Gaussian process to find the best set of ESN hyperparameters using three different options of ac... [more]

In this study, we propose a voltage estimation method for the radial distribution network with distributed generators (DGs) using high-precision measurements (HPMs). The proposed method uses the section loads center for voltage estimation because individual loads are not measured in the distribution system. The bus voltage was estimated through correction of the section load center by using an HPM at the end of the main feeder. The correction parameter of the section load center was calculated by comparing the initial voltage estimates and the measurements of the HPMs. After that, the voltage of the main feeder was re-estimated. Finally, the bus voltage in the lateral feeder was estimated based on the voltage estimates in the main feeder and the current measurements in the lateral feeder. The accuracy of the proposed algorithm was verified through case studies by using test systems implemented in MATLAB, Simulink, and Python environments. In order to verify the utilization of the propo... [more]

In this paper, the variations in hydropower generation are addressed considering the seasonality and ENSO (El Niño-Southern Oscillation) episodes. The dynamic hypothesis and the stock-flow structure of the Colombian electricity market were analyzed. Moreover, its dynamic behavior was analyzed by using Dynamic Systems tools aimed at providing deep insight into the system. The MATLAB/Simulink model was used to evaluate the Colombian electricity market. Since we combine System Dynamics and Dynamic Systems, this methodology provides a novel insight and a deeper analysis compared with System Dynamics models and can be easily implemented by policymakers to suggest improvements in regulation or market structures. We also provide a detailed description of the Colombian electricity market dynamics under a broad range of demand growth rate scenarios inspired by the bifurcation and control theory of Dynamic Systems.

This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box−Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt−Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).

This paper presents a novel deep learning architecture for short-term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.

We analyze the solar influence of space weather events on Polish energy infrastructure via geomagnetic disturbances. We consider two time intervals of very different levels of solar activity (SA) in the course of the solar activity cycle 24: 2010—an early rising phase of SA, near the solar minimum and 2014—solar maximum. From the total number of electrical grid failures, we extract almost five thousand failures in 2010 and more than ten thousand in 2014, which could be related to space weather effects. We analyze data of failures having unidentified causes, failures coupled with the aging of transmission network elements and electronic devices, which appeared during the time intervals of enhanced geomagnetic activity. We show that around the times of increasing geomagnetic activity level, the amount of transmission lines failures in the groups mentioned above grows, but mostly with some delay. It might be connected to some cumulative effect due to the result of transient states and the... [more]

The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape... [more]

For wide-area measurement systems and smart grids, phasor measurement units (PMUs) have become key elements since they provide synchronized information related to the fundamental frequency components of voltages and currents. In recent years, some works have extended the concept of PMU to harmonic analysis due to the proliferation of nonlinear loads. In this work, as a first contribution, the reference model for P-class and M-class PMUs provided by the IEEE Standard C37.118.1 is expanded with the aim of obtaining the harmonic information and electric power quantities. Additionally, as a second contribution, the approach of global harmonic parameters (GHPs) for PMUs is proposed. Specifically, GHPs are introduced in this work as unified quantities regarding the overall harmonic content of voltages and currents signals. With the help of these parameters, the estimation of power quality indices (PQIs) according to the IEEE Standard 1459 can be carried out but with an important advantage, i... [more]

This paper employs the cluster analysis to classify the energy sector into three types, namely, technology-, capital-, and labor-intensive energy company. It then studies the interactive endogenous relationship between R&D investment and financially sustainable performance and the moderate effect of the executive incentive through three-stage least squares (3SLS) of the simultaneous equations model (SEM). The results show that for the technology-intensive energy company, an increase in the previous period in R&D investment improves the current period of financially sustainable performance, and the improvement in the current period in financially sustainable performance results in a decline in financially sustainable performance in the next period, which demands an increase in R&D investment subsequently. In contrast, for the capital-intensive energy company, R&D investment can significantly improve the financially sustainable performance in the current period, and the improvement in fi... [more]

Power distribution networks are vulnerable to different faults, which compromise the grid performance and need to be managed effectively. Automatic and accurate fault detection and location are key components of effective fault management. This paper proposes a new framework for fault detection and location for smart distribution networks that are equipped with data loggers. The framework supports networks with mixed overhead lines and underground cables. The proposed framework consists of area detection, faulty section identification, and high impedance fault location. Firstly, the faulty zone and section are detected based on the operation of over-current relays and digital fault recorders. Then, by comparing the recorded traveling times at both ends of lines, which are related to the protection zone, the faulty line is identified. In the last step, the location of the fault is estimated based on discrete wavelet transform. The proposed method is tested on a 20 kV 13 node network, wh... [more]

(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using histor... [more]

Together with renewable energy sources, nuclear power represents an important contribution to a sustainable energy mix in many countries and has an important impact on sustainable development. Nuclear energy production is also a source of high-level radioactive waste (HLW) and spent nuclear fuel (SNF), which require special concern. Disposal in deep geological formations is one of the solutions for the long-term management of HLW and SNF. It requires the development of a concept ensuring long-term safe isolation of waste and its validation applying the safety case methodology, which is a formal compilation of evidence, analyses and arguments that quantify and justify a claim that the repository will be safe. The results of laboratory testing of a potential repository host rock are an important component of the evidence that helps in the safety assessment of the deep geological disposal concept. This paper presents results of research focused on the physical, geomechanical and sorption... [more]

The Special Issue “Distributed Energy Resources Management 2018” includes 13 papers [...]

This paper presents a unique integrated approach to meter placement and state estimation to ensure the network observability of active distribution systems. It includes observability checking, minimum measurement utilization, network state estimation, and trade-off evaluation between the number of real measurements used and the accuracy of the estimated state. In network parameter estimation, observability assessment is a preliminary task. It is handled by data analysis and filtering followed by calculation of the triangular factors of the singular, symmetric gain matrix using an algebraic method. Usually, to cover the deficiency of essential real measurements in distribution systems, huge numbers of virtual measurements are used. These pseudo measurements are calculated values, which are based on the network parameters, real measurements, and forecasted load/generation. Due to the application of a huge number of pseudo-measurements, large margins of error exists in the calculation pha... [more]

A coupled structural decomposition analysis (SDA) and sensitivity analysis approach is developed to explore the drivers of China’s CO2 emission intensity at both general and sectoral levels and from both ex-post and ex-ante perspectives. Two steps are involved—structural decomposition and sensitivity analysis. First, the popular factor decomposition method, SDA, is implemented to identify which drivers “have” made the largest contribution to emission intensity changes. Second, an emerging ex-ante approach, sensitivity analysis, is introduced to answer how and to what extent such drivers “will” influence future emission intensity at a sectoral level. Based on China’s input-output tables for 1997−2012, the empirical study provides a hotspot map of China’s energy system. (1) Direct-emission coefficient and technology coefficient are observed as the top two overall drivers. (2) For the former, reducing direct-emission coefficient in an emission-intensity sector (e.g., electricity and heat... [more]

A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches.

Flow-induced vibration is a canonical issue in various engineering fields, leading to fatigue or immediate damage to structures. This paper numerically investigates flow-induced vibrations of a cylinder interacting with the wake of another cylinder at a Reynolds number Re = 150. It sheds light on the effects of mass ratio m*, damping ratio, and mass-damping ratio m*ζ on vibration amplitude ratio A/D at different reduced velocities Ur and cylinder spacing ratios L/D = 1.5 and 3.0. A couple of interesting observations are made. The m* has a greater influence on A/D than ζ although both m* and ζ cause reductions in A/D. The m* effect on A/D is strong for m* = 2−16 but weak for m* > 16. As opposed to a single isolated cylinder case, the mass-damping m*ζ is not found to be a unique parameter for a cylinder oscillating in a wake. The vortices in the wake decay rapidly at small ζ. Alternate reattachment of the gap shear layers on the wake cylinder fuels the vibration of the wake cylinder for... [more]

Integration of renewable energy resources and conventional grids leads to an increase in power quality issues. These power quality issues require different standards to be followed for accurate measurement and monitoring of various parameters of the power system. Conventional power quality analyzers (PQAs) are programmed to a particular standard and cannot be reconfigured by the end user. Therefore, conventional PQAs cannot meet the challenges of a rapidly changing grid. In this regard, a Compact RIO-based (CRIO-based) PQA was proposed, that can be easily reprogrammed and cope with the challenges faced by conventional PQAs. The salient features of the proposed PQA are a high processing speed, interactive interface, and high-quality data-storage capacity. Moreover, unlike conventional PQAs, the proposed PQA can be monitored remotely via the internet. In this research, a hardware-in-loop (HIL) simulation is used for performing the power-quality assessment in a systematic manner. Power qu... [more]