Grid Of Stations Research Articles

Pricing and Routing Mechanisms for Differentiated Services in an Electric Vehicle Public Charging Station Network

We consider a Charging Network Operator (CNO) that owns a network of Electric Vehicle (EV) public charging stations and wishes to offer a menu of differentiated service options for access to its stations. This involves designing optimal pricing and routing schemes for the setting where users cannot directly choose which station they use. Instead, they choose their priority level and energy request amount from the differentiated service menu, and then the CNO directly assigns them to a station on their path. This allows higher priority users to experience lower wait times at stations, and allows the CNO to directly manage demand, exerting a higher level of control that can be used to manage the effect of EV on the grid and control station wait times. We consider the scenarios where the CNO is a social welfare-maximizing or a profit-maximizing entity, and in both cases, design pricing-routing policies that ensure users reveal their true parameters to the CNO.

Smart Remote Nodes Fed by Power Over Fiber in Internet of Things Applications

Smart IoT solutions integrated into power grid stations are important due to their high economic and social value. Power over fiber technology to remotely feeding sensors and control electronics is a good choice in these environments of high electromagnetic interference. A sensing system design for magnetic field monitoring, fire and temperature/presence detection, and remotely fed by optical means is discussed. This design includes two types of nodes, smart and passive. Smart remote nodes have an energy manager to provide power on demand. Asymmetric splitting is proposed to optimize power distribution. Some tests on remote node power consumption, feeding, sensing, and centralized monitoring in one type of those nodes are successfully performed and reported.

A security risk mitigation framework for cyber physical systems

AbstractCyber physical systems (CPSs) are safety‐critical, be it weapon systems, smart medical devices, or grid stations. This makes ensuring security of all the components constituting a CPS unavoidable. The rise in the demand of interconnectedness has made such systems vulnerable to attacks, ie, cyberattacks. Over 170 cases of cyber‐security breaches in CPS were reported over the past two decades. An increase in the number of cyberattack incidents on CPS makes them more exposed and less trustworthy. However, identifying the security requirements of the CPS to pinpoint the relevant risks may help to counteract the potential attacks. Literature reveals that the most targeted security requirements of CPS are authentication, integrity, and availability. However, little attention has been paid on certain crucial security attributes such as data freshness and nonrepudiation. One major reason of security breaches in CPS is the lack of custom or generalized countermeasures. Therefore, we propose a security risk mitigation framework for a CPS focused on constraints, ie, authentication, data integrity, data freshness, nonrepudiation, and confidentiality. Furthermore, we evaluate the proposed work using a case study of a safety critical system. The results show a decrease in the severity of the identified security risks, ie, man‐in‐the‐middle attack, spoofing, and data tempering.

A stochastic programming approach for electric vehicle charging station expansion plans

The projected and current adoption rates of electric vehicles are increasing. Electric vehicles need to be recharged continually over time, and the energy required to ensure that is immense and growing. Given that existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. In this paper, we present a two-stage stochastic programming approach that can be used to determine a power grid expansion plan that supports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. The first stage determines where to expand the power grid, and the second stage determines where to locate charging stations. The key link between the first and second stage decisions is that charging stations can only be located in areas with sufficient power supply enabled by an expanded power grid. To solve the model, we utilize a hybrid approach that combines Sample Average Approximation and an enhanced Progressive Hedging algorithm. We enhance the Progressive hedging algorithm by applying rolling horizon and variable fixing techniques. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.

Multi-objective strategic waste transfer station planning

The production of mixed municipal waste changes due to the increase of separation rate, urbanization and other factors. The future changes in the legislation and technology development influences the way the waste is being treated. Thus the method, location and capacity of processing sites are unknown. The realization of future projects can be supported by the developing of transportation infrastructure. Such a feature may be represented by a robust transfer station grid, which can be designed to handle all possible future realizations and technological solutions (establishment of waste treatment facilities). The paper presents an approach utilising a mathematical model for the design of transfer stations. It is formulated as a multi-objective two-stage mixed-integer stochastic programming problem, where the trade-off between the environmental aspect and the economic viability is considered. The model is tested through a case study for the Czech Republic, where the waste treatment of over 6,000 municipalities is analysed. The solutions for different preferences are assessed throughout the principle called out-of-sample stability with 10,000 scenarios. The optimal decision consists of the robust transfer station grid with selected locations and their respective capacity. The particular solution with respect to the potential trade-off suggests to save 1.148 mil of travelled kilometres with only 0.77 mil EUR. The output in the form of decision support can serve possible stakeholders from the field of waste management to plan more sustainable projects.

Cost-effective telemetry for energy network of an electricity distribution company: part I

We present a novel application of radio frequency wireless mesh network and general packet radio service technologies in a telemetry solution to measure power flow in the energy network of an electricity distribution company. The telemetry solution utilizes some selected circuits of grid stations and calculates total power consumed, total power imported, and total power exported by the distribution company. The selection of circuits for sensors installation is the key for reducing solution cost as compared to the case when sensors are installed on all the power output points. The framework involves installation of specially developed energy sensors (smart energy meters) and data concentrator units at the selected grid stations for measurement of energy data that include active energy, reactive energy, active power, apparent power, current, voltage, and power factor. The measured data reach the data concentrator unit using a 433-MHz wireless mesh network and are transmitted to a remote power control center using general packet radio service. Energy data from different grid stations across the energy network are collected at the power control center and utilized in calculation of total power consumed, total power imported, and total power exported. The approach has been tested on two electricity distribution companies of Pakistan: the Islamabad Electric Supply Company and Peshawar Electric Supply Company. Also in this work, the result of overload detection based on a generalized likelihood ratio test for an industrial feeder of the Islamabad Electric Supply Company is included. Detection probability of 0.96 with a false alarm probability of 0.04 has been achieved for a 30-min data interval.

Study of Smart Grid Communication Network Architectures and Technologies

Smart Grid (SG) is an emerging paradigm of the modern world to upgrade and enhance the existing conventional electrical power infrastructure from generation to distribution to the consumers in a two-way communication fashion to automate the electrical power demand and supply and make this a cyber-physical system. SG infrastructure key elements, such as smart meters, circuit breakers, transformers, feeders, substations, control centers, grid stations, are required well-formed communication network architectures. SG infrastructure is divided into three main communication networks architectures, such as HAH, NAN, and WAN. Each of these communication network architectures requires reliable, stable, secure, high data rate at real-time with the help of different wireline and wireless communication technologies from HAN to WAN networks. To understand the complete concepts about SG, a concise review is presented and it will help the readers to get foundations of communication network architectures and technologies of SG.

Introduction of Smart Grid Station Configuration and Application in Guri Branch Office of KEPCO

Climate change and global warming are becoming important problems around the globe. To prevent these environmental problems, many countries try to reduce their emissions of greenhouse gases (GHGs) and manage the consumption of energy. The Korea Electric Power Corporation (KEPCO) introduced smart grid (SG) technologies to its branch office in 2014. This was the first demonstration of a smart grid on a building, called the Smart Grid Station (SGS). However, the smart grid industry is stagnant despite of the efforts of KEPCO. The authors analyzed the achievements to date, and proved the effects of the SGS by comparing its early targets to its performance. To evaluate the performance, we analyzed the data of 2015 with the data of 2014 in three aspects: peak reduction, power consumption reduction, and electricity fee savings. Furthermore, we studied the economic analysis including photovoltaic (PV) and energy storage system (ESS) electricity fee savings, as well as running cost savings by electric vehicles. Through the evaluation, the authors proved that the performance surpassed the early targets and that the system is economical. With the advantages of the SGS, we suggested directions to expand the system.

Robust Waste Transfer Station Planning by Stochastic Programming

Regarding the infrastructure planning in waste management, the future situation in the Czech Republic or in some other waste developing countries is unknown due to the undecided support from the government or the EU. Furthermore, the production of waste depends on many factors (e.g. separation rate) that are uncertain. The level of recycling will be influenced by technical progress while taking into account the compromise between maximum waste material recovery and the economic impact of the process. Nevertheless, it is important to plan the transportation infrastructure that will be able to support the realization of future projects/development. Such a feature might be the transfer station grid, which can decrease the total transportation costs/impact on the environment. The grid design should be robust with respect to all the possible technological solutions (establishment of waste treatment facilities). In this paper, the mathematical model for grid design of transfer stations is proposed. The model will be presented as a two-stage mixed-integer stochastic programming problem. The model will be tested through a case study on the current situation and possible legislation changes regarding waste management in the Czech Republic. The model scale will be on the micro-regional level proposing a robust transfer station grid design. The realization of these projects takes into consideration possible investments and decides about the capacity of the facility with regards to future government support and donations. The output in the form of recommendation will serve possible investors, municipalities and/or stakeholders from the field of waste management to plan more sustainable projects.

Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment

Medium-term and long-term energy prediction is essential for the planning and operations of the smart grid eco-system. The prediction of next year and next month energy demand of grid station, independent power producers, commercial, domestic and industrial consumers are allowed administrators to optimize and plan their resources. To address the forecasting problems, the basic intention of this study is to propose an accurate and precise medium and long-term district level energy prediction models employing the machine learning based models which are: 1) artificial neural network with nonlinear autoregressive exogenous multivariable inputs model; 2) multivariate linear regression model; and 3) adaptive boosting model. Based on environmental and aggregated energy consumption data as the model's input and output, the load prediction interval is further classified into three main parts, 1-month ahead forecasting, seasonally ahead forecasting and 1-year ahead forecasting. Feature extraction, data transformation and outlier detection are performed through different data tests. The prediction results intimate that the intended models cannot only increase the forecasting accuracy contrasted with previous forecasting models but also produce adequate forecasting intervals in the smart grid environment. Additionally, these techniques describe an essential step-forward, consolidating the spatiotemporal use of energy inconstancies and variations of district level and strong forecasting capabilities of energy usage requirement in future perceptive.

Роль абиотических и биотических факторов в формировании сезонной изменчивости поля биолюминесценции Азовского моря

Впервые на четырёх гидрографических станциях в Азовском море проведены съёмка полей биолюминесценции, значений температуры и электропроводности и анализ видового состава микропланктона. В период дневных измерений получены также вертикальные профили фотосинтетически активной радиации. Практически на всех станциях параллельно измерениям поля биолюминесценции осуществлён отбор проб микропланктона сетями Джеди и батометрами для последующего анализа его видового состава и количественного распределения. Исследования проведены в зимний, весенний и летний периоды на унифицированной сетке станций. Зарегистрирована выраженная сезонная изменчивость интегральных величин поля биолюминесценции и его вертикальной структурированности в Азовском море. Минимальные величины интенсивности поля биолюминесценции в Азовском море (6,05·10-12 Вт·см-2·л-1 на глубине 4 м) зафиксированы в зимний период. Основной причиной этого является низкая (0,5–1,5 °С) температура воды в конце января ― начале февраля, обусловившая малочисленность светящихся планктонтов. С началом весеннего прогрева вод (температура воды 12,5 °С) интенсифицируется развитие одноклеточного планктона и наблюдается повышение интенсивности биолюминесценции в придонном слое до 34,8·10-12 Вт·см-2·л-1. В летний период зафиксированы максимальные величины интенсивности биолюминесценции (до 634,4·10-12 Вт·см-2·л-1) при температуре воды в среднем 27,5 °С и максимальной численности и биомассе светящейся фракции фитопланктона. Полученные результаты указывают на корреляцию биолюминесценции и численности планктонных организмов и позволяют рекомендовать этот оптический метод для мониторинга продукционных районов Азовского моря.

Mitigating the Adverse Impact of Un-Deterministic Distributed Generation on a Distribution System Considering Voltage Profile

Electric power systems are enforced to operate near to their stability limit due to the fast increase in power demand. Therefore, voltage stability has become a primary concern. The main cause of voltage variations is the imbalance between generation and consumption. In order to mitigate variations in voltage profile, most of the modern electric power systems are adopting new emerging technologies such as distributed generation. Validation of standard voltage optimization is a difficult task when distributed generation is integrated to medium and low voltage networks. Integration of distributed generation (DG) will have diverse impacts on voltage levels when connected un-deterministically to the electric distribution system. This paper analyzes both the impacts of un-deterministic large and small size DG on voltage profile. Feasible solutions by incorporating reactors and increasing cross sectional area of cables, variation in voltage profile were mitigated. Detailed simulations were performed in ETAP by modeling and evaluating Kohat road grid station situated in Peshawar, Pakistan. The results anticipated that this approach can be useful to ensure standard voltage profile and better utilization of un-deterministic DG units.

Fuzzy-Estimation Control for Improvement Microwave Connection for Iraq Electrical Grid

The demand for broadband wireless services is increasing day by day (as internet or radio broadcast and TV etc.) for this reason and optimal exploiting for this bandwidth may be other reasons indeed be there is problem in the communication channels. it’s necessary that exploiting the good part form this bandwidth. In this paper, we propose to use estimation technique for estimate channel availability in that moment and next one to know the error in the bandwidth channel for controlling the possibility data transferring through the channel. The proposed estimation based on the combination of the least Minimum square (LMS), Standard Kalman filter, and Modified Kalman filter. The error estimation in channel use as control parameter in fuzzy rules to adjusted the rate and size sending data through the network channel, and rearrangement the priorities of the buffered data (workstation control parameters, Texts, phone call, images, and camera video) for the worst cases of error in channel. The propose system is designed to management data communications through the channels connect among the Iraqi electrical grid stations. The proposed results show that the modified Kalman filter have a best result in time and noise estimation (0.1109 for 5% noise estimation to 0.3211 for 90% noise estimation) and the packets loss rate is reduced with ratio from (35% to 385%).

Assessing the climate change and its impact on rice yields of Haridwar district using PRECIS RCM data

In the past few decades, continuous intervention with the environmental landscape in the form of land use practices (water diversions, deforestation, local agriculture practices, industrialization etc.) in the Haridwar district of Uttarakhand, India, has impacted the region on various accounts. This is likely to be further aggravated in view of the increasing variability in the weather condition. Thus, there is urgent need felt to quantitatively asses the future climatic scenario for initiating and effectively undertaking the adaptation strategies for safe and sustained agricultural growth. For undertaking this study, PRECIS RCM data was downloaded for Roorkee grid station for the period (1961–2090). This PRECIS RCM data was biased corrected by merging two different bias correction methods which involves correcting mean and standard deviation simultaneously. Observed and bias corrected daily data was tested for their significance at 95% probability of occurrence employing various statistical methods such as correlation coefficient, mean bias error, normalized mean squared error, and Z and F statistical tests. These tests revealed that the difference between the observed and bias corrected PRECIS RCM data was insignificant. Trend analysis was done using Mann-Kendall’s test and Theil Sen’s Slope. Analysis of the bias corrected data revealed that the Roorkee station will, in general, record increasing trends in rainfall (at 5.83 mm/year), maximum temperature (at 0.05 °C/year), and minimum temperature (at 0.04 °C/year). This indicates that by 2090 AD present normal rainfall of 1006 mm/year will rise to 1447 mm/year; maximum temperature of 30.1 °C will rise to 33.9 °C and minimum temperature of 17.5 °C will rise to 20.5 °C by 2090. This weather scenario cautions at the future agronomic practices for sustained productivity. Grain yield of rice cv. Sharbati for the period 2014–2090 was simulated by running DSSAT CERES rice model with PRECIS RCM weather data of Haridwar district and RCP CO2 emission scenarios. The simulation result showed that if the crop is grown with the existing soil and crop management conditions, rice productivity of Haridwar in general will decrease 31.7 kgs/ha/year. This could be attributed to the climate change and decreased rice yields from increased maximum temperature. In the case of RCP 2.6 and RCP 4.5, the effect of CO2 on grain yields is more pronounced compared to RCP 6.0 and RCP 8.5. With the higher CO2 concentrations (above 450 ppm), the rice crop cv. Sharbati did not show much CO2 fertilization effect. DSSAT CERES rice simulations under climate change and higher CO2 concentrations showed that the yields will decline with the advancing climate change but CO2 intervention will compensate the loss in yield.

Optimizing the size of a V2G parking deck in a microgrid

Microgrids are usually powered partially and sometimes fully by small scale distributed generators, renewable energy resources and batteries. The batteries of the electric vehicles (EVs) in a parking deck can also be used as a power source of the microgrids. However, unlike fixed batteries, the storage capacity of a parking deck depends on the number of installed V2G (vehicle to grid) stations and the number of available EVs. In this paper, we propose a mathematical model that aims to determine the optimal number of V2G stations in a microgrid parking deck by minimizing the total cost of investment and operation. The EVs in the parking deck are discharged to shift energy purchases from high-price to low-price hours and vice versa. The uncertainty of the demand and the EVs arrivals and departures and the intermittency of the renewable resources are taken into account. We develop a methodology to compute the operation cost of the system using Benders’ decomposition and search for the optimal number of V2G stations using the Nelder-Mead heuristic algorithm. Numerical experiments are conducted on a 14-bus microgrid test system for a planning horizon of five years. We also assess the effects of uncertainties, EVs battery size, chargers’ rated capacity, EVs arrivals and departure rates, dispatch constraints and interest rate on the determined number of V2G station and the discounted payback period. The experimental results show that investing in the V2G technology can reduce the long-term cost of electricity supply for the microgrid.

Research on Real-time Intelligent Load Control Technology among Giant Hydropower Station Group under High-intensity Peak-load and Frequency Regulation Demand

The paper conducts an in-depth study on the real-time dispatching involved in joint operation among giant cascade hydropower stations with high-intensity peak-load and frequency regulation demand, and proposes anintelligent load control technology for cascade hydropower stations in the coordination mode of station and power grid. Aiming to water level safety control of runoff power stations and rapid response to load regulation requirements of the power grid, taking 10 types of constraints such as output, water volume and flow rate into consideration, a model cluster is established through the layered control principle to realize real-time intelligent load allocation and economic operation among Pubugou, Shenxigou and Zhentouba stations. Dadu River has become the first large-scale river basin in China to realize “one-key dispatch” of multiple stations, and has achieved good demonstration effect.

Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management

With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management. In this paper, we introduce generic home energy management control system (HEMCS) to efficiently schedule the household load and integrate RESs. The HEMCS is based on the genetic algorithm, binary particle swarm optimization, wind-driven optimization (WDO), and our proposed genetic WDO algorithm to schedule appliances of single and multiple homes. For energy cost calculation, real-time pricing (RTP) and inclined block rate schemes are combined, because in case of only RTP, there is a possibility of building peaks during off-peak hours that may damage the entire power system. Moreover, to control the demand under the grid station capacity, the feasible region is defined and a problem is formulated using multiple knapsack. Energy efficient integration of RESs in SG is a challenging task due to time varying and their intermittent nature. The simulation results show that the proposed scheme avoids voltage rise problem in areas with high penetration of renewable energy. Moreover, the proposed scheme also reduces the electricity cost up to 48% and peak to average ratio of aggregated load up to 37.69%.

Cluster Analysis of Monthly Precipitation over the Western Maritime Continent under Climate Change

Changes in climate because of global warming during the 20th and 21st centuries have a direct impact on the hydrological cycle as driven by precipitation. However, studying precipitation over the Western Maritime Continent (WMC) is a great challenge, as the WMC has a complex topography and weather system. Understanding changes in precipitation patterns and their groupings is an important aspect of planning mitigation measures to minimize flood and drought risk as well as of understanding the redistribution of precipitation arising from climate change. This paper employs Ward’s hierarchical clustering on regional climate model (RCM)-simulated monthly precipitation gridded data over 42 approximately evenly distributed grid stations from the years 2030 to 2060. The aim was to investigate spatial and temporal groupings over the four major landmasses in the WMC and to compare these with historical precipitation groupings. The results showed that the four large-scale islands of Java, Sumatra, Peninsular Malaysia and Borneo would experience a significant spatial redistribution of precipitation over the years 2030 to 2060, as compared to historical patterns from 1980 to 2005. The spatial groups were also compared for two future forcing scenarios, representative concentration pathways (RCPs) 4.5 and 8.5, and different groupings over the Borneo region were observed.

The velocity field of the Italian area

The rapid development of several permanent GNSS networks in Italy has made available a huge amount of GNSS observations, giving the chance to figure out and significantly improve the spatial and temporal resolutions of the crustal deformation in the Italian area. More than 20 GNSS networks, promoted and managed by different institutions, constitute the grid of monitoring stations that includes over 1000 permanent stations, mainly devoted to real-time positioning services but that has proven to be suitable for monitoring slow deforming processes, such as for instance, intraplate deformation processes. The whole set of raw GPS data is routinely processed at INGV providing daily solutions of station coordinates and estimating linear velocities for each station. The information content of coordinate time series is wide, the station position variations incorporate linear and non-linear effects caused by geophysical phenomena of different nature, of which we show some evidences. The sectors where the permanent network is augmented with non-permanent sites allows to study tectonic processes with a finer resolution.

A Novel Power Flow Algorithm for Hybrid AC/DC Power Grids

This study introduces a novel unified power flow algorithm for hybrid AC/DC power grids. For hybrid AC/DC power grids that incorporate voltage source converters (VSCs), the proposed algorithm uses the voltage amplitude and the phase angle of the AC bus and the voltage of the DC bus as the minimum set of state variables. The power equation of the converter is deduced by these state variables. Then, the bus-power-equilibrium (BPE) equations can be deduced based on the DC node classification and VSC converter station control modes. The Newton–Raphson method can be used to solve these BPE equations with good convergence behavior. In addition, the DC grid and converter station modeling impose no restriction on the hybrid grid topology. As a result, the Jacobian matrix in the modified equations can accurately reflect the coupling relationship between AC and DC grids and simultaneously maintain the sparse feature. The proposed unified algorithm uses the sparse technology and can solve the power flow problem for a large hybrid power grid. A simulation is implemented to illustrate the effectiveness of the proposed algorithm.