The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar ...irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.
The increasing penetration of Photovoltaic (PV) generation results in challenges regarding network operation, management and planning. Correspondingly, Distribution Network Operators (DNOs) are in ...the need of totally new understanding. The establishment of comprehensive standards for maximum PV integration into the network, without adversely impacting the normal operating conditions, is also needed. This review article provides an extensive review of the Hosting Capacity (HC) definitions based on different references and estimated HC with actual figures in different geographical areas and network conditions. Moreover, a comprehensive review of limiting factors and improvement methods for HC is presented along with voltage rise limits of different countries under PV integration. Peak load is the major reference used for HC definition and the prime limiting constraint for PV HC is the voltage violations. However, the varying definitions in different references lead to the conclusion that, neither the reference values nor the limiting factors are unique values and HC can alter depending on the reference, network conditions, topology, location, and PV deployment scenario.
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Renewable energy sources have recently been integrated into microgrids that are in turn connected to electric vehicle (EV) charging stations. In this regard, the optimal planning of microgrids is ...challenging with such uncertain generation and stochastic charging/discharging EV models. To achieve such ambitious goals, the best sites and sizes of photovoltaic and wind energy units in microgrids with EV are accurately determined in this work using an optimization technique. This proposed technique considers 1) generation profile uncertainty in photovoltaic and wind energy units as well as the total load demand, 2) photovoltaic and wind generation units' DSTATCOM operation capability, and 3) various branch and node constraints in the microgrid. Most importantly, the possible EV requirements are also taken into account, including initial and predetermined state of charge (SOC) arrangements, arrival and departure hours, and diverse regulated and unregulated charging strategies. A bi‐level metaheuristic‐based solution is established to address this complex planning model. The outer level and inner‐level functions optimize renewable energy sources and EV decision variables. Sub‐objectives to be optimized voltage deviations as well as grid power. The results demonstrate the effectiveness of the introduced method for planning renewable energy sources and managing EV to effectively achieve autonomous microgrids.
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Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that ...establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.
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The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, ...such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters' data. The data monitoring is carried based on the industrial digital twins' platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.
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This paper presents a stochastic framework for short-term operation of a distribution company (disco). The proposed framework optimizes disco's operational decisions in two hierarchical stages. The ...first stage, called day-ahead operation stage (DAOS), deals with the operational decisions on purchases from the day-ahead market and commitment of distributed generation (DG) units. The objective of this stage is to minimize the expected operating cost while the financial risk exposed by uncertain real-time prices and loads is restricted to a given level. The model associated with this stage is based on the mixed-integer programming (MIP) format. The second stage, named real-time operation stage (RTOS), deals with disco's activities in real-time. In RTOS, decisions are made on real-time market transactions, dispatch of online DGs, and invocation of load curtailments (LCs) such that the expected operating cost is minimized. This stage is formulated as a nonlinear programming (NLP) problem. To investigate the effectiveness of the developed framework, it is applied to a typical Finnish 20-kV urban distribution network.
Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, ...requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.
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This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the ...traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.
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Real-time electricity prices along with demand-side potentials can provide distribution companies (DisCos) with considerable financial and technical benefits compared to the conventional flat prices. ...This paper incorporates demand response in DisCos' short-term decision model in a real-time pricing (RTP) environment wherein consumers are charged based on hourly varying prices. Besides the hourly RTP sale prices, the established model deals with other DisCo's short-term activities including hourly purchases from the grid, commitment of distributed generation (DG) units, dispatch of shunt compensators, and invocation of load curtailments (LCs). The stochastic nature of wholesale market prices and customers load is also considered in the model. The model is a mixed integer linear programming (MILP) problem which can be easily solved via commercial software packages. The objective is to maximize the DisCo's expected profit while its revenue is limited by regulating bodies. A typical Finnish 20 kV urban distribution network is used to demonstrate the effectiveness of the established model. Simulation results are presented and discussed to investigate the impacts on both financial and technical aspects of using of RTP sale prices.
Recently, various distributed energy resources are significantly integrated into the modern power systems. This introduction of distributed energy resource‐rich systems can cause various power ...quality issues, due to their uncertainties and capacity variations. Therefore, it is crucial to establish energy balance between generation and demand to improve power system's reliability and stability and to minimize energy costs without sacrificing customers’ comfort or utility. In this regard, power system flexibility concept is highlighted as a robust and cost‐effective energy management system, especially on the demand side, to provide consumers’ demands with an acceptable level of power quality. Accordingly, here, a comprehensive review of recent developments in the power system flexibility and demand‐side management strategies and demand response programs are provided to include mainly classifications, estimation methods, distributed energy resource modelling approaches, infrastructure requirements, and applications. In addition, current research topics for applying power system flexibility solutions and demand‐side management strategies based on modern power system operation are deliberated. Also, prominent challenges, research trends, and future perspectives are discussed. Finally, this review article aims to be an appropriate reference for comprehensive research trends in the power system flexibility concept in general and in demand‐side management strategies and demand response programs, specifically.
Investigating the state of the art of power system flexibility.
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