Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section... [more]

Peer-to-peer (P2P) electricity sharing or trading can empower consumers and prosumers, incentivize the balancing of generation and demand locally, increase system resilience and reliability, and help in achieving societal goals, such as increasing renewable energy penetration. Nevertheless, the development of P2P trading in actual environments has been slow due to the unclear position of P2P markets in the power system. Recent developments in the European legislation are promising for the establishment of P2P markets and energy communities. Hence, the interplay between local trading and existing market structures needs to be addressed carefully. Furthermore, P2P trading with distributed resources presumes that electricity end users will become active players in the power system. This paper proposes a bidding and pricing mechanism for local markets, considering the external markets; a new approach to balance settlement and balance responsibility when local trading occurs; and an interfa... [more]

Intermittent power interruptions and blackouts with long outage durations are very common, especially on weak distribution grids such as in developing countries. This paper proposes a hybrid photovoltaic (PV)-battery-system sizing optimization through a genetic algorithm to address the reliability in fragile grids measured by the loss of power supply probability (LPSP) index. Recorded historical outage data from a real stochastic grid in Ethiopia and measured customer load is used. The resulting hybrid-system Pareto solutions give the flexibility for customers/power utilities to choose appropriate sizes based on the required reliability level. To evaluate the sizing solutions’ robustness, this work considers and compares grid outage modeling through two different approaches. The first is a Markov model, developed to be minimally implemented with limited outage data available. The second is a Weibull model, commonly used to describe extreme phenomena and failure analysis. It is more fai... [more]

Forecasting time series with multiple seasonal cycles such as short-term load forecasting is a challenging problem due to the complicated relationship between input and output data. In this work, we use a pattern representation of the time series to simplify this relationship. A neural network trained on patterns is an easier task to solve. Thus, its architecture does not have to be either complex and deep or equipped with mechanisms to deal with various time-series components. To improve the learning performance, we propose weighting individual errors of training samples in the loss function. The error weights correspond to the similarity between the training pattern and the test query pattern. This approach makes the learning process more sensitive to the neighborhood of the test pattern. This means that more distant patterns have less impact on the learned function around the test pattern and lead to improved forecasting accuracy. The proposed framework is useful for a wide range of... [more]

The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10... [more]

This paper deals with new challenges regarding power quality in ship technology resulting from the novelisation and implementation of IACS (International Association of Classification Societies) rules and requirements. These rules, known as IACS E24 2016/2018, address harmonic distortion for ship electrical distribution systems, including harmonic filters. The reasons for the legislative changes based on a short overview of power quality-related accidents are discussed, after which a brief presentation of the updated IACS rules illustrated by a related DNV GL (Det Norske Veritas Germanischer Lloyd) case study is shown. A key part of this paper includes proposals concerning harmonics and interharmonics, distortion indices and transient disturbances. The aim of these proposals is to unify power quality indices and measurement procedures to maintain effective and comparable criteria for monitoring distortion and establish requirements for ship owners, designers, shipbuilders, classifiers,... [more]

DC microgrids have become a prevalent topic in research in part due to the expected superior efficiency of DC/DC converters compared to their AC/DC counterparts. Although numerous side-by-side analyses have quantified the efficiency benefits of DC power distribution, these studies all modeled converter loss based on product data that varied in component quality and operating voltage. To establish a fair efficiency comparison, this work derives a formulaic loss model of a DC/DC and an AC/DC PFC boost converter. These converters are modeled with identical components and an equivalent input and output voltage. Simulated designs with real components show AC/DC boost converters between 100 W to 500 W having up to 2.5 times more loss than DC/DC boost converters. Although boost converters represent a fraction of electronics in buildings, these loss models can eventually work toward establishing a comprehensive model-based full-building analysis.

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the... [more]

With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed... [more]

An auto-restoration tool to minimize the impact of faults is one of the critical requirements in a power distribution system. A fault-monitoring system is needed for practical remote supervision to identify faults and reduce their impacts, and thus reduce economic losses. An effective fault-monitoring system is beneficial to improve the reliability of a protection system when faults evolve. Therefore, fault monitoring could play an important role in enhancing the safety standards of systems. Among the various fault occurrences, the transient fault is a prominent cause in Malaysia power systems but gains less attention due to its ability of self-clearance, although sometimes it unnecessarily triggers the operation of protection systems. However, the transient fault is an issue that must be addressed based on its effect that can lead to outages and short-circuits if prolonged. In this study, the authors summarize the guidelines and related standards of fault interaction associated with a... [more]

The paper presents the application of recurrent LSTM neural networks for short-time load forecasting in the Polish Power System (PPS) and a small region of a power system in Central Poland. The objective of the present work was to develop an efficient and accurate method of forecasting the 24-h pattern of power load with a 1-h and 24-h horizon. LSTM showed effectiveness in predicting the irregular trends in time series. The final forecast is estimated using an ensemble consisted of five independent predictions. Numerical experiments proved the superiority of the ensemble above single predictor resulting in a reduction of the MAPE the RMSE error by more than 6% in both forecasting tasks.

Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.

From January 2020, the International Maritime Organization has regulated ship emissions to reduce sulfur content. As an alternative to this, LNG bunkering was proposed, and infrastructure and ships were deployed. Therefore, we used analytic hierarchy process AHP techniques to determine optimal methods of LNG bunkering for shipyard safety. First, we conducted a literature survey on the concept and type of LNG bunkering, global LNG bunkering trends, and features of Japan and South Korea cases and compared them. Thereafter, an expert survey was conducted, and survey data was analyzed using AHP techniques. Finally, we derived optimal methods applicable to shipyard industry. The analytical results revealed that the derived priority of the optimal LNG bunkering method of shipyard was in the order of the STS method, TTS method, and the PTS method. The result of this study can serve as a theoretical basis to make LNG bunkering safer and more economical in shipyards to prepare for the expansion... [more]

The objective of this work is to propose and apply a methodology to obtain the cost of industrial process shutdowns due to voltage sag and short interruption. A field survey, aided by a specific questionnaire, was carried out in several industries connected to medium voltage networks, in the states of Espírito Santo and São Paulo in Brazil. The results obtained were the costs per event and the costs per demand in a total of 33 companies in 12 different types of activities. It is noteworthy that this survey brings a relevant technical contribution to the electricity sector, helping to fill, even partially, an existing gap in both national and international literature.

In this research work, a resilient finite-time consensus-based distributed secondary control protocol is presented for droop-based distributed generating (DG) units of an islanded AC microgrid (MG). Through a multi-agent control structure, the DG units of the microgrid adjust their active power outputs so that they reach an agreed-upon value in a finite time. Concurrently, all the DG units are forced to operate with their frequencies regulated to the reference MG frequency in a finite time, despite time-varying load perturbations. Each DG unit is provided with a hierarchical control architecture, where the primary control is achieved using the droop control method, while the secondary control is established through the proposed distributed control protocol. The communication between DG units takes place over a sparse communication network. The proposed control protocol is robust to both small and sufficiently large communication latencies and it supports the plug-and-play feature of DG... [more]

A lithium-ion capacitor (LiC) is one of the most promising technologies for grid applications, which combines the energy storage mechanism of an electric double-layer capacitor (EDLC) and a lithium-ion battery (LiB). This article presents an optimal thermal management system (TMS) to extend the end of life (EoL) of LiC technology considering different active and passive cooling methods. The impact of different operating conditions and stress factors such as high temperature on the LiC capacity degradation is investigated. Later, optimal passive TMS employing a heat pipe cooling system (HPCS) is developed to control the LiC cell temperature. Finally, the effect of the proposed TMS on the lifetime extension of the LiC is explained. Moreover, this trend is compared to the active cooling system using liquid-cooled TMS (LCTMS). The results demonstrate that the LiC cell temperature can be controlled by employing a proper TMS during the cycle aging test under 150 A current rate. The cell’s to... [more]

The introduction of pervasive telecommunication devices, in the scope of smart grids (SGs), has accentuated interest in the distribution network, which integrates a huge portion of new grid applications. High impact low probability (HILP) events, such as natural hazards, manmade errors, and cyber-attacks, as well as the inherent fragility of the distribution grid have propelled the development of effective resilience tools and methods for the power distribution network (PDN) to avoid catastrophic infrastructural and economical losses. Multiple resilience evaluation frameworks are proposed in the literature in order to assist distribution system operators (DSOs) in managing their networks when faced with exogenous threats. We conduct detailed analysis of existing quantitative resilience studies in both electric and telecommunication domains of a PDN, focusing on event type, metrics, temporal phases, uncertainty, and critical load. Our work adopts the standpoint of a DSO, whose target is... [more]

Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operat... [more]

Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven sche... [more]

The development of renewable clean energy such as hydropower can not only ensure energy security, but also help achieve the United Nation’s Sustainable Development Goals. This paper uses the annual data of 30 provinces in China from 2000 to 2017, and constructs a dynamic spatial Durbin model and a geographically weighted regression model to empirically test the dynamic impact of hydropower on haze pollution and economic growth at the national and provincial levels. The empirical results show that the promoting effect of hydropower on economic growth in Western China is less than that in Eastern China, which further aggravates the economic development gap between the eastern and western regions. In addition, the suppression effect of hydropower on the haze pollution in the western region is greater than that in the eastern region, where the haze pollution is serious. From the national level, hydropower can promote regional economic growth and inhibit haze pollution, and the spatial spil... [more]

Residential demand response empowers the role of electricity consumers by allowing them to change their patterns of consumption, which can help balance the energy grid. Although such type of management is envisaged to play an increasingly important role in the integration of renewables into the grid, the factors that influence household engagement in these initiatives have not been fully explored in Japan. This study examines the influence of interpersonal, intrapersonal, and socio-demographic characteristics of households in Yokohama on their willingness to participate in demand response programs. Time of use, real time pricing, critical peak pricing, and direct load control were considered as potential candidates for adoption. In addition, the authors explored the willingness of households to receive non-electricity related information in their in-home displays and participate in a philanthropy-based peer-to-peer energy platform. Primary data were collected though a questionnaire sur... [more]

The proliferation of renewable energy resources (RES), especially solar photovoltaic (PV) generation resources, causes overvoltage and line overloading in distribution networks. This study proposes a two-level volt−var control method based on multiple timescales. The on-load tap changer (OLTC) operates on an hourly timescale, to regulate the voltage on the secondary winding. In the 15-minutes timescale, PV-connected smart inverters and static var compensators (SVCs) are obliged to compensate the reactive power for the voltage control at the point of common coupling. In the multi-timescale voltage control framework, this study proposes a new multi-sectional volt−var curve (MSVVC) of a PV inverter. The objective of the MSVVC is to minimize the energy loss in the network, improve the voltage profile, and obtain the operational margin of other reactive power compensation devices. In the process of determining the optimal parameters of the MSVVC, stochastic modeling-based load flow analysis... [more]

Three-phase imbalance is a long-term issue existing in low-voltage distribution networks (LVDNs), which consequently has an inverse impact on the safe and optimal operation of LVDNs. Recently, the increasing integration of single-phase distributed generations (DGs) and flexible loads has increased the probability of imbalance occurrence in LVDNs. To overcome the above challenges, this paper proposes a novel methodology based on the concept of “Active Asymmetry Energy-Absorbing (AAEA)” utilizing loads with a back-to-back converter, denoted as “AAEA Unit” in this paper. AAEA Units are deployed and coordinated to actively absorb asymmetry power among three phases for imbalance mitigation in LVDNs based on the high-precision, high-accuracy, and real-time distribution-level phasor measurement unit (D-PMU) data acquisition system and the 5th generation mobile networks (5G) communication channels. Furthermore, the control scheme of the proposed method includes three control units. Specificall... [more]

With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.

The topology of a distribution network can be represented by a bus injection to branch current (BIBC) matrix. It has been introduced and used for load flow analysis of distribution networks. In this paper, a method for BIBC matrix modification is developed to use in applications which require a topology change representation. Proposed method that reflects the changes in configuration in the system BIBC matrix is implemented in distribution network reconfiguration problem. With providing potential solutions for network operational and planning requirements such as necessitate changes in configurations to transfer the loads to a different substation, ease the loading of equipment, conduct planned maintenance and reduce network losses during the normal operation with renewables, storage and other uprising technologies, reconfiguration may also be useful for emergencies, accidents, attacks and weather-related disasters. The BIBC modification process provides the knowledge of possible switc... [more]