A Wide Range and High Swing Charge Pump for Phase Locked Loop in Phasor Measurement Unit

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Phasor Measurement Units are widely utilized in power systems to provide synchrophasor data for a verity of applications, mainly performed by Energy Management Systems (EMS). Synchrophasors are measured at different parts of the network and transmitted to Phasor Data Concentrator (PDC) at a rate of 30-60 samples per second. The synchronization is done by means of a phase locked oscillator inside PMU which uses clock signal of the Global Positioning System (GPS). In this paper a novel charge pump with an appropriate operation capability in phaselocked-loops is presented. By using this phase locked loop in phasor measurement unit, the total performance of this circuit will be improved. The proposed charge pump uses current mirror techniques in order to achieve a wide range of output voltage to control the oscillator and also has a good performance in a wide frequency range from 33MHz to 555MHz. This circuit is designed and simulated in TSMC 0.18μm CMOS technology. The proposed charge pump only consumes 390μW power in supply voltage of 1.8V at 500MHz and has a maximum current of 16.43μA with an acceptable current matching between source and sink currents. It is also capable to be used in a wide frequency range and low power applications

Design of a 41.14-48.11 GHz triple frequency based VCO

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Growing deployment of more efficient communication systems serving electric power grids highlights the importance of designing more advanced intelligent electronic devices and communication-enabled measurement units. In this context, phasor measurement units (PMUs) are being widely deployed in power systems. A common block in almost all PMUs is a phase locked oscillator which uses a voltage controlled oscillator (VCO). In this paper, a triple frequency based voltage controlled oscillator is presented with low phase noise and robust start-up. The VCO consists of a detector, a comparator, and triple frequency. A VCO starts-up in class AB, then steadies oscillation in class C with low current oscillation. The frequency of the VCO, which is from 13.17 GHz to 16.03 GHz, shows that the frequency is tripling to 41.14-48.11 GHz. Therefore, its application is not limited to PMUs. This work has been simulated in a standard 0.18 µm CMOS process. The simulated VCO achieves a phase noise of -99.47 dBc/Hz at 1 MHz offset and -121.8 dBc/Hz at 10 MHz offset from the 48.11 GHz carrier

A Practical Integrated Fault Location Method for Electrical Power Distribution Networks

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Future distribution systems are expected to be self-healing systems which can manage the faults and quickly restore the customers from outage. As the basic function of fault management systems, fault location methods play an important role in reducing the outage time and related costs. This paper aims to present a practical method that provides precise and reliable estimations of the fault location. The method is an integration of a voltage sag-based method and an impedance-based method. Following to any permanent fault, the proposed method first uses the voltage sag magnitudes measured by a limited number of meters to find the closest node to the fault. Then, it investigates the lines connected to the node selected and uses an impedance-based method to find the exact fault location. The method proposed can be applied to large-scale distribution systems having several laterals and load taps. Simulations are on a practical distribution system are performed to test the method performance. The simulation study includes a comparative analysis with two other recent methods reported in the literature. The results show the better accuracy of the proposed method even with measurement inaccuracies and load data errors

Deep learning based forecasting of individual residential loads using recurrence plots

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. High penetration of renewable energy resources in distribution systems brings more uncertainty for system control and management due their intermittent behaviour. In this context, besides generation side, demand side should be also controlled and managed. Since demand side has variant flexibility over time, in order to timely facilitate Demand Response (DR), distribution system operators (DSO) should be aware of DR potential in advance to see whether it is sufficient for different services, and how much and when to send DR signals. This indeed requires accurate short-term or medium-term load forecasting. There are many methods for predicting aggregated loads, but more effective DR schemes should involve individual residential households which would require load forecasting of single residential loads. This is much more challenging due to high volatility in load curves of single customers. In this paper, we present a novel method of forecasting individual household power consumption using recurrence plots and deep learning. We use Convolutional Neural Network (CNN) for such a two-dimensional deep learning approach, and compare it with one-dimensional CNN, as well as Support Vector Machine (SVM) and Artificial Neural Network (ANN). Demonstrating some experimental tests on a real case proved that our approach outperforms the other existing solutions

Markov Chain Modelling-Based Approach to Reserve Electric Vehicles in Parking Lots for Distribution System Energy Management

Integration of renewable energy resources in distribution networks with intermittent behaviour increases the challenge of power balance in transmission systems. To mitigate the undesired impacts, transmission operator involves distribution operators to get local contribution from their flexible resources. In this paper, we address the flexibility offered by some electric car sharing agents which can serve some reserve capacity to distribution system. A Markov Chain modelling based approach is proposed to support system operator to properly estimate the number of electric vehicles required to be booked in advance as reserve. Underestimation would result in imperfect demand correction, and overestimation would imply extra costs. Using a realistic case under a near future scenario of high PV integration and EV accommodation, we demonstrate the contribution of our approach to this problem of planning or scheduling. Obtained results quantifies the performance of the proposed method in terms of average energy difference based on number of EVs. The results can be used as a basis to decide the appropriate number of EV reservations

Single residential load forecasting using deep learning and image encoding techniques

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%

Multi-Objective Optimal Placement of Recloser and Sectionalizer in Electricity Distribution Feeders

© 2019 IEEE. Electricity distribution feeders, due to their geographical dispersion, are subjected to faults caused by adverse weather, vegetation growth, etc., resulting in long outages for customers. Overhead switching devices (i.e. reclosers, sectionalizers, disconnectors and etc.) are known as the most practical solutions to limit the outage area, and consequently increase the distribution system reliability. This paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for Optimal Placement of Recloser and Sectionalizer to minimize customers' outage cost and increase system reliability with an optimal investment. The algorithm determines the number and optimal locations of reclosers and sectionalizers to fulfill the objectives. The obtained results on the standard 85-node distribution feeder validate the effectiveness of the proposed method

Market power appearance through game theoretic maintenance scheduling of distributed generations

The oligopoly structure of the market and the network constraints may produce results far from the perfect competition. Maintenance decisions in an oligopolistic electricity market have a strategic function, because GENCOs usually have impacts on market prices through capacity outages. This paper describes generation maintenance planning in an oligopolistic environment as a strategic decision. In this paper a game theoretic framework is modeled to analyze strategic behaviors of GENCOs. Each GENCO tries to maximize its payoff by strategically making decisions, taking into account its rival GENCOs' decisions. Some GENCOs own DG units, such as wind, diesel, biomass and fuel cell plants. If different GENCOs find out they have the conditions of exerting market power exact in maintenance periods; they will share their data and they will cause some area monopolies. Cournot-Nash equilibrium is used for decision making on maintenance problem in Oligopolistic electricity market. The Cournot-Nash problem is modeled as a mixed integer nonlinear programming optimization problem. The analytic framework presented in this paper enables joint assessment of maintenance and generation strategies. © 2011 Praise Worthy Prize S.r.l. -All rights reserved

Time-of-Use Tariff with Local Wind Generation

Renewable energy, such as wind power, is known to significantly reduce system costs and carbon emissions. However, traditional Time of Use (ToU) tariffs fail to account for local energy generation. To overcome this limitation, we propose a mechanism for calculating new ToU tariffs that incorporates Agile ToU and local energy resources, such as a wind farm. By partially supplying local consumption, wind energy can reduce electricity costs for consumers and encourage load shifting towards peak renewable energy production periods. We demonstrate the effectiveness of the proposed mechanism by testing it on a case study of a residential area in Wales, UK, where electricity would be partially supplied by a nearby wind farm with 5 turbines through a Power Purchase Agreement (PPA). The results show that the new tariff significantly reduces electricity bills

Energy Management Strategy of Microgrids Based on Benders Decomposition Method

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper discusses an optimal energy management system for microgrids, taking into account distribution power flow and dynamic loads, in presence of storage units and all associated constraints, aiming to reduce microgrid costs under two grid-connected and islanded modes. Getting the unit commitment, the microgrid energy management problem is introduced as a mixed integer nonlinear problem (MINLP). Since solving MINLP problems is complex and time consuming, a linearization technique is applied for simplification of the problem as a mixed integer linear programming (MILP) problem. Then, the Benders decomposition method is used to reach an efficient and accurate answer. The model proposed is implemented on a 14-bus microgrid including conventional and renewable distributed resources, storage units, and dynamic loads. The results indicated fair and fast performance of the proposed model