Energies

2021

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20 pages, 9119 KiB

Open AccessArticle

Analysis of Prosumer Behavior in the Electrical Network

by Dušan Medveď, Michal Kolcun, Marek Pavlík, Ľubomír Beňa and Marián Mešter

Energies 2021, 14(24), 8212; https://doi.org/10.3390/en14248212 - 7 Dec 2021

Cited by 15 | Viewed by 2642

Abstract

This article deals with the prosumer behavior, specifically on an on-grid electrical network that is connected to a larger synchronous electrical network, as well as an off-grid system. In the Simulink (Matlab) application, two models were constructed for this purpose. The modeling of [...] Read more.

This article deals with the prosumer behavior, specifically on an on-grid electrical network that is connected to a larger synchronous electrical network, as well as an off-grid system. In the Simulink (Matlab) application, two models were constructed for this purpose. The modeling of the operation of the electrical network’s on-grid system takes place in one of the models. The simulation of the operation of the electrical network’s off-grid system takes place in the other. We examined the model’s behavior in the provided simulated period from the standpoint of transient states and qualitative indicators of electrical energy under various connection configurations in both systems. The simulations resulted in the possibility of incorporating new sources of energy accumulation, such as pumped storage hydropower plants based on energy storage systems (ESSs), and modifying the model to the user’s needs. Full article

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14 pages, 22383 KiB

Open AccessArticle

A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning

by Xiaoming Xie, Meiping Li and Du Zhang

Energies 2021, 14(21), 7333; https://doi.org/10.3390/en14217333 - 4 Nov 2021

Cited by 7 | Viewed by 2039

Abstract

The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important [...] Read more.

The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important part of the orderly and efficient operation of the electricity market. Especially during the COVID-19 pandemic, the prices of raw materials for electricity production, such as coal, have risen sharply. Forecasting electricity prices has become particularly important. Currently, existing EPF prediction models face two main challenges: First, how to integrate multiscale electricity price data to obtain a higher prediction accuracy. Second, how to solve the problem of data noise caused by the fusion of EPF samples and multiscale data. To solve the above problems, we innovatively propose a tensor decomposition method to integrate multiscale electricity price data and use

L_{1}

regularization and wavelet transform to remove data noise. In general, this paper proposes a deep learning EPF prediction model, named the WT_TDLSTM model, based on tensor decomposition, a wavelet transform, and long short-term memory (LSTM). Among them, the LSTM method is used to predict electricity prices. We conducted experiments on three datasets. The experimental results of three data prove that the WT_TDLSTM model is better than the compared EPF model. The indicators of MSE and RMSE are 33.65–99.97% better than the comparison model. We believe that the WT_TDLSTM model is a good supplement to the EPF model. Full article

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26 pages, 3498 KiB

Open AccessReview

A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications

by Sadaqat Ali, Zhixue Zheng, Michel Aillerie, Jean-Paul Sawicki, Marie-Cécile Péra and Daniel Hissel

Energies 2021, 14(14), 4308; https://doi.org/10.3390/en14144308 - 17 Jul 2021

Cited by 134 | Viewed by 8971

Abstract

The fast depletion of fossil fuels and the growing awareness of the need for environmental protection have led us to the energy crisis. Positive development has been achieved since the last decade by the collective effort of scientists. In this regard, renewable energy [...] Read more.

The fast depletion of fossil fuels and the growing awareness of the need for environmental protection have led us to the energy crisis. Positive development has been achieved since the last decade by the collective effort of scientists. In this regard, renewable energy sources (RES) are being deployed in the power system to meet the energy demand. The microgrid concept (AC, DC) is introduced, in which distributed energy resources (DERs), the energy storage system (ESS) and loads are interconnected. DC microgrids are appreciated due to their high efficiency and reliability performance. Despite its significant growth, the DC microgrid is still relatively novel in terms of grid architecture and control systems. In this context, an energy management system (EMS) is essential for the optimal use of DERs in secure, reliable, and intelligent ways. Therefore, this paper strives to shed light on DC microgrid architecture, control structure, and EMS. With an extensive literature survey on EMSs’ role, different methods and strategies related to microgrid energy management are covered in this article. More attention is centered on the EMS for DC microgrids in terms of size and cost optimization. A very concise analysis of multiple optimization methods and techniques has been presented exclusively for residential applications. Full article

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19 pages, 1779 KiB

Open AccessArticle

Acceptance of Demand Response and Aggregators as a Solution to Optimize the Relation between Energy Producers and Consumers in order to Increase the Amount of Renewable Energy in the Grid

by Adrian Tantau, András Puskás-Tompos, Laurentiu Fratila and Costel Stanciu

Energies 2021, 14(12), 3441; https://doi.org/10.3390/en14123441 - 10 Jun 2021

Cited by 10 | Viewed by 3103

Abstract

Demand response plays a very important role in balancing the intermittent production of an increasing share of renewable energy sources on the energy market. This article analyses the importance of demand response and the role of aggregators for the new development of the [...] Read more.

Demand response plays a very important role in balancing the intermittent production of an increasing share of renewable energy sources on the energy market. This article analyses the importance of demand response and the role of aggregators for the new development of the electricity market, where the renewables will play a more important role. The main objective of this research is to determine the acceptance level of demand response and its implementation on the energy consumer side. This acceptance should include a professional actor, the aggregator which is assuming the role of optimizing the relation between energy producers and consumers, and to monitor the implementation and use of demand response. The research is based on semi-structured interviews with experts in energy from Hungary, Romania and Serbia, on workshops with experts and a wider online survey with end customers for electricity. The results indicate that there is a willingness potential to implement demand response programs with aggregators as intermediaries between energy providers and end consumers of electrical energy. Full article

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21 pages, 6534 KiB

Open AccessArticle

A Practical GERI-Based Method for Identifying Multiple Erroneous Parameters and Measurements Simultaneously

by Ruipeng Guo, Lilan Dong, Hao Wu, Fangdi Hou and Chen Fang

Energies 2021, 14(12), 3390; https://doi.org/10.3390/en14123390 - 8 Jun 2021

Viewed by 1842

Abstract

Even with modern smart metering systems, erroneous measurements of the real and reactive power in the power system are unavoidable. Multiple erroneous parameters and measurements may occur simultaneously in the state estimation of a bulk power system. This paper proposes a gross error [...] Read more.

Even with modern smart metering systems, erroneous measurements of the real and reactive power in the power system are unavoidable. Multiple erroneous parameters and measurements may occur simultaneously in the state estimation of a bulk power system. This paper proposes a gross error reduction index (GERI)-based method as an additional module for existing state estimators in order to identify multiple erroneous parameters and measurements simultaneously. The measurements are acquired from a supervisory control and data acquisition system and mainly include voltage amplitudes, branch current amplitudes, active power flow, and reactive power flow. This method uses a structure consisting of nested two loops. First, gross errors and the GERI indexes are calculated in the inner loop. Second, the GERI indexes are compared and the maximum GERI in each inner loop is associated with the most suspicious parameter or measurement. Third, when the maximum GERI is less than a given threshold in the outer loop, its corresponding erroneous parameter or measurement is identified. Multiple measurement scans are also adopted in order to increase the redundancy of measurements and the observability of parameters. It should be noted that the proposed algorithm can be directly integrated into the Weighted Least Square estimator. Furthermore, using a faster simplified calculation technique with Givens rotations reduces the required computer memory and increases the computation speed. This method has been demonstrated in the IEEE 14-bus test system and several matpower cases. Due to its outstanding practical performance, it is now used at six provincial power control centers in the Eastern Grid of China. Full article

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2020

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16 pages, 17135 KiB

Open AccessArticle

Monitoring System of Transmission Line in Mountainous Area Based on LPWAN

by Han Zeng, Pengqi Zuo, Fangming Deng and Pei Zhang

Energies 2020, 13(18), 4898; https://doi.org/10.3390/en13184898 - 18 Sep 2020

Cited by 6 | Viewed by 2792

Abstract

In light of the difficulty of the inspection and maintenance of a transmission line condition monitoring system in remote mountainous areas, this paper proposes a long-term online monitoring scheme based on a low power wide area network (LPWAN). Considering different failure rates, three [...] Read more.

In light of the difficulty of the inspection and maintenance of a transmission line condition monitoring system in remote mountainous areas, this paper proposes a long-term online monitoring scheme based on a low power wide area network (LPWAN). Considering different failure rates, three monitoring periods of transmission lines in mountainous areas are proposed. An online monitoring framework of transmission lines in mountainous areas was designed based on long range radio (LoRa) and a cellular mobile network, and a dynamic group network model of LoRa was established. The multi-objective particle swarm optimization algorithm can be used to optimize the energy and delay of the system, and then the suitable working mode for the three monitoring periods can be obtained. The simulation results showed that the minimum packet loss rate of the system could be less than 1%, the energy consumption of the system was 80% lower than the existing monitoring system, and the service life of the system can reach 15.13 years under the normal failure rate. Compared with the existing schemes, the proposed work shows the advantages of high reliability transmission, low cost and long-term monitoring, which is especially for transmission line monitoring in mountainous areas. Full article

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18 pages, 3316 KiB

Open AccessArticle

Designing Solar Power Purchase Agreement of Rooftop PVs with Battery Energy Storage Systems under the Behind-the-Meter Scheme

by Chawin Prapanukool and Surachai Chaitusaney

Energies 2020, 13(17), 4438; https://doi.org/10.3390/en13174438 - 27 Aug 2020

Cited by 6 | Viewed by 2922

Abstract

With a significant growth of rooftop photovoltaic systems (PVs) with battery energy storage systems (BESS) under the behind-the-meter scheme (BTMS), the solar power purchase agreement (SPPA) has been developed into one of the most attractive models. The SPPA is a scheme where the [...] Read more.

With a significant growth of rooftop photovoltaic systems (PVs) with battery energy storage systems (BESS) under the behind-the-meter scheme (BTMS), the solar power purchase agreement (SPPA) has been developed into one of the most attractive models. The SPPA is a scheme where the investors propose to directly sell electricity from rooftop PVs to the customers. The proposed rates are typically performed in terms of the discount rates on the time-of-use (TOU) tariff with demand charges. The operation modes of the BESS should also be designed in accordance with the proposed rates. Therefore, this paper proposes a methodology to design the discount rates and operation modes of the BESS which will minimize the electricity charges of the customers while maintaining the revenue of the investors under the SPPA and BTMS. The reverse power flow is considered as additional revenue to the investors. This paper also implements the proposed methodology with tariff structure in Thailand. The result showed that the installed capacity of rooftop PVs and battery capacity directly affect the discount rates and operation modes of the BESS. The rate of excess energy also has a significant impact on the discount rates but not affect the operation modes. Full article

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16 pages, 1285 KiB

Open AccessArticle

Virtualization Management Concept for Flexible and Fault-Tolerant Smart Grid Service Provision

by Shadi Attarha, Anand Narayan, Batoul Hage Hassan, Carsten Krüger, Felipe Castro, Davood Babazadeh and Sebastian Lehnhoff

Energies 2020, 13(9), 2196; https://doi.org/10.3390/en13092196 - 2 May 2020

Cited by 20 | Viewed by 4271

Abstract

In modern power systems, reliable provision of grid services (e.g., primary and ancillary services) are highly dependent on automation systems in order to have monitoring, processing, decision making and communication capabilities. The operational flexibility of automation systems is essential for the reliable operation [...] Read more.

In modern power systems, reliable provision of grid services (e.g., primary and ancillary services) are highly dependent on automation systems in order to have monitoring, processing, decision making and communication capabilities. The operational flexibility of automation systems is essential for the reliable operation of power systems during and after disruptive events. However, this is restricted by integrated hardware-software platforms. Therefore, it will be difficult to reconfigure control strategies during run time. This paper presents the concept of Grid Function Virtualization (GFV) as a potential approach to improve the operational flexibility of grid automation systems. GFV has been proposed to offer a new way to deploy and manage grid services by leveraging virtualization technology. The main idea of GFV is to run grid services (i.e., software implementation of services) independently from underlying hardware. To realize the important design considerations, the GFV architecture and its building blocks is elaborated in details. To this end, an exhaustive review of applications of virtualization in several domains is provided to show the importance of virtualization in improving flexibility and resource utilization. Finally, the advantages of the proposed concept to deal with disruptions in power systems is demonstrated in a proof of concept based on a CIGRE MV benchmark grid. Full article

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21 pages, 2630 KiB

Open AccessArticle

Intelligent Fault Detection System for Microgrids

by Cristian Cepeda, Cesar Orozco-Henao, Winston Percybrooks, Juan Diego Pulgarín-Rivera, Oscar Danilo Montoya, Walter Gil-González and Juan Carlos Vélez

Energies 2020, 13(5), 1223; https://doi.org/10.3390/en13051223 - 6 Mar 2020

Cited by 43 | Viewed by 4220

Abstract

The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust [...] Read more.

The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications. Full article

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26 pages, 3921 KiB

Open AccessArticle

Influence and Impact of Data Averaging and Temporal Resolution on the Assessment of Energetic, Economic and Technical Issues of Hybrid Photovoltaic-Battery Systems

by Alessandro Burgio, Daniele Menniti, Nicola Sorrentino, Anna Pinnarelli and Zbigniew Leonowicz

Energies 2020, 13(2), 354; https://doi.org/10.3390/en13020354 - 10 Jan 2020

Cited by 24 | Viewed by 3098

Abstract

The temporal resolution of the demand and generation profiles may have a significant impact on the estimation of self-sufficiency and self-consumption for consumers and prosumers. As an example, measuring the load profile, with a low temporal resolution, may lead to the under-estimation of [...] Read more.

The temporal resolution of the demand and generation profiles may have a significant impact on the estimation of self-sufficiency and self-consumption for consumers and prosumers. As an example, measuring the load profile, with a low temporal resolution, may lead to the under-estimation of energy consumption, while measuring solar irradiation with a low temporal resolution may lead to the over-estimation of on-site energy generation. Storage systems may reduce errors due to the lower temporal resolution by 8–10 times or even more, depending on the capacity of the batteries. Besides self-generation and self-consumption, there are other indicators that can be influenced by temporal resolution that deserve to be investigated. This is a detailed study of the influence of temporal resolution and the time averaging on a hybrid photovoltaic-battery system; this study encompasses both economic and technical aspects, from the calculation of savings on the electricity bill to the estimation of the equivalent cycles of battery storage system. To this end, the three-minute load profile of a real case study is used to obtain other three load profiles with temporal resolution equal to 15, 30, and 60 min via data averaging. Therefore, the authors analyze the influence and the impact of temporal resolution and data averaging in terms of: The size of the photovoltaic generator and the capacity of the storage system; the savings in the electricity bill and the balance between costs and savings; the peak values and the average values of power flows during high generation and low generation; the profile of the storage system over the year; the utilization rate of the storage system and the rated power of the electronic converter that regulates the charge and the discharge; the profile of the state of charge of the storage system and the life-time estimation of batteries through the calculation of the equivalent number of cycles. Full article

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2019

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16 pages, 4403 KiB

Open AccessArticle

Bumpless Optimal Control over Multi-Objective Microgrids with Mode-Dependent Controllers

by Ying Wu, Josep M. Guerrero, Juan C. Vasquez and Yanpeng Wu

Energies 2019, 12(19), 3619; https://doi.org/10.3390/en12193619 - 23 Sep 2019

Cited by 5 | Viewed by 2781

Abstract

To avoid transient jumps at the switching time between two operating modes in microgrids, this paper proposes a linear quadratic-based optimal bumpless controller with two degrees of freedom (DOF) to suppress the transient disturbance and realize seamless switching between mode-dependent controllers. By minimizing [...] Read more.

To avoid transient jumps at the switching time between two operating modes in microgrids, this paper proposes a linear quadratic-based optimal bumpless controller with two degrees of freedom (DOF) to suppress the transient disturbance and realize seamless switching between mode-dependent controllers. By minimizing the transient performance criteria, which contains both the reference tracking error and the controller tracking error, this bumpless algorithm not only effectively forces the latent controller to track the active controller, but also guarantees the plant output track the reference as close as possible. For the different control objectives of the two modes, a current-based networked PI controller is proposed in islanded mode to achieve power sharing, as well as suppressing circulating current, and a power-based PI controller is designed in grid connected mode to supply required P and Q, as well as effectively synchronize f and v safely with the main grid. A microgrid test system with two operation modes was built in Matlab/Simulink. Several operating cases were executed to verity the feasibility and effectiveness of this optimal bumpless control strategy. Full article

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17 pages, 2948 KiB

Open AccessArticle

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

by Gangqiang Li, Huaizhi Wang, Shengli Zhang, Jiantao Xin and Huichuan Liu

Energies 2019, 12(13), 2538; https://doi.org/10.3390/en12132538 - 1 Jul 2019

Cited by 132 | Viewed by 6756

Abstract

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting [...] Read more.

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 min. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model. Full article

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22 pages, 5616 KiB

Open AccessFeature PaperArticle

Aggregating Large-Scale Generalized Energy Storages to Participate in the Energy and Regulation Market

by Yao Yao, Peichao Zhang and Sijie Chen

Energies 2019, 12(6), 1024; https://doi.org/10.3390/en12061024 - 15 Mar 2019

Cited by 7 | Viewed by 3144

Abstract

This paper proposes a concept of generalized energy storage (GES) to facilitate the integration of large-scale heterogeneous flexible resources with electric/thermal energy storage capacity, in order to participate in multiple markets. First, a general state variable, referred to as the degree of satisfaction [...] Read more.

This paper proposes a concept of generalized energy storage (GES) to facilitate the integration of large-scale heterogeneous flexible resources with electric/thermal energy storage capacity, in order to participate in multiple markets. First, a general state variable, referred to as the degree of satisfaction (DoS), is defined, and dynamic models with a unified form are derived for different types of GESs. Then, a real-time market-based coordination framework is proposed to facilitate control, as well as to ensure user privacy and device security. Demand curves of different GESs are then developed, based on DoS, to express their demand urgencies as well as flexibilities. Furthermore, a low-dimensional aggregate dynamic model of a GES cluster is derived, thanks to the DoS-equality control feature provided by the design of the demand curve. Finally, an optimization model for large-scale GESs to participate in both the energy market and regulation market is established, based on the aggregate model. Simulation results demonstrate that the optimization algorithm could effectively reduce the total cost of an aggregator. Additionally, the proposed coordination method has a high tracking accuracy and could well satisfy a diversified power demand. Full article

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2018

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18 pages, 2856 KiB

Open AccessArticle

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

by Tobias Heidrich, Jonathan Grobe, Henning Meschede and Jens Hesselbach

Energies 2018, 11(12), 3461; https://doi.org/10.3390/en11123461 - 11 Dec 2018

Cited by 10 | Viewed by 4551

Abstract

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is [...] Read more.

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible Renewable Energies levy (EEG levy) was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering the electricity price and weather forecasts as well as an internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system. Full article

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20 pages, 668 KiB

Open AccessArticle

Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures

by Eng Tseng Lau, Kok Keong Chai, Yue Chen and Jonathan Loo

Energies 2018, 11(12), 3418; https://doi.org/10.3390/en11123418 - 6 Dec 2018

Cited by 12 | Viewed by 3762

Abstract

The traditional grid operation is unfortunately lacking the resilience and responsiveness in reacting to contingency events due to the poor utilization of available resources in mitigating the shortfalls. Such an unaddressed issue may affect the grid stability and the ultimate grid blackout. Therefore, [...] Read more.

The traditional grid operation is unfortunately lacking the resilience and responsiveness in reacting to contingency events due to the poor utilization of available resources in mitigating the shortfalls. Such an unaddressed issue may affect the grid stability and the ultimate grid blackout. Therefore, this paper models a grid optimization module consisting of a mid and low (microgrid) voltage level grid component of an urban grid network for a disaster recovery. The model minimizes the cost of generation required to meet the demand through the economic dispatch in combination with the unit commitment. Two optimization problems are formulated that resemble the grid operation: normal (grid-connected) and islanded. A constrained-based linear programming optimization problem is used to solve the formulated problems, where the dual-simplex algorithm is used as the linear solver. The model ensures sufficient demand to be met during the outages through the N-1 contingency criterion for critical infrastructures. The simulation length is limited to 24 h and is solved using the MATLAB^® R2017b software. Three different cases are established to evaluated the modelled grid resilience during the grid-connected or the islanding of operations subject to adversed events. The simulated results provide the economical outage recovery that will maintain the grid resilience across the grid. Full article

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20 pages, 2910 KiB

Open AccessArticle

The Role of Charging Infrastructure in Electric Vehicle Implementation within Smart Grids

by Qing Kong, Michael Fowler, Evgueniy Entchev, Hajo Ribberink and Robert McCallum

Energies 2018, 11(12), 3362; https://doi.org/10.3390/en11123362 - 1 Dec 2018

Cited by 18 | Viewed by 5053

Abstract

In the integration of electric vehicle (EV) fleets into the smart grid context, charging infrastructure serves as the interlinkage between EV fleets and the power grid and, as such, affects the impacts of EV operation on the smart grid. In this study, the [...] Read more.

In the integration of electric vehicle (EV) fleets into the smart grid context, charging infrastructure serves as the interlinkage between EV fleets and the power grid and, as such, affects the impacts of EV operation on the smart grid. In this study, the impacts of charging infrastructure on the effectiveness of different EV operational modes were simulated using a multi-component modelling approach, which accounts for both stochastic EV fleet charging behaviors as well as optimal energy vector dispatch operation. Moreover, a campus microgrid case study was presented to demonstrate the various design factors and impacts of charging infrastructure implementation affecting EV fleet adoption and operation. Based on results from the study, it was shown that charging infrastructure should be adopted in excess of the minimum required to satisfy EV charging for driving needs. In addressing uncontrolled charging behaviors, additional charging infrastructure improves EV owner convenience and reduces queuing duration. Meanwhile, controlled charging strategies benefit from increased resilience against uncertain charging behavior and operate more optimally in systems subject to time-of-use (TOU) electricity pricing. Lastly, it was demonstrated that successful vehicle-to-grid (V2G) implementation requires charging infrastructure to emulate the availability and fast response characteristics of stationary energy storage systems, which translates to excess charging port availability, long EV plug-in durations, and bi-directional power flow capabilities well beyond the level 2 charging standard. Full article

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20 pages, 3148 KiB

Open AccessArticle

A PLC Channel Model for Home Area Networks

by Xinyu Fang, Ning Wang and Thomas Aaron Gulliver

Energies 2018, 11(12), 3344; https://doi.org/10.3390/en11123344 - 30 Nov 2018

Cited by 3 | Viewed by 3791

Abstract

Smart meters (SMs) are key components of the smart grid (SG) which gather electricity usage data from residences and businesses. Home area networks (HANs) are used to support two-way communications between SMs and devices within a building such as appliances. This can be [...] Read more.

Smart meters (SMs) are key components of the smart grid (SG) which gather electricity usage data from residences and businesses. Home area networks (HANs) are used to support two-way communications between SMs and devices within a building such as appliances. This can be implemented using power line communications (PLCs) via home wiring topologies. In this paper, a bottom-up approach is designed and a HAN-PLC channel model is obtained for a split-phase power system which includes branch circuits, an electric panel with circuit breakers and bars, a secondary transformer and the wiring of neighboring residences. A cell division (CD) method is proposed to construct the channel model. Furthermore, arc fault circuit interrupter (AFCI) and ground fault circuit interrupter (GFCI) circuit breaker models are developed. Several HAN-PLC channels are presented and compared with those obtained using existing models. Full article

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17 pages, 1068 KiB

Open AccessArticle

A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid

by Yuwen Chen, José-Fernán Martínez, Pedro Castillejo and Lourdes López

Energies 2018, 11(11), 2972; https://doi.org/10.3390/en11112972 - 1 Nov 2018

Cited by 14 | Viewed by 3310

Abstract

Smart meters are applied to the smart grid to report instant electricity consumption to servers periodically; these data enable a fine-grained energy supply. However, these regularly reported data may cause some privacy problems. For example, they can reveal whether the house owner is [...] Read more.

Smart meters are applied to the smart grid to report instant electricity consumption to servers periodically; these data enable a fine-grained energy supply. However, these regularly reported data may cause some privacy problems. For example, they can reveal whether the house owner is at home, if the television is working, etc. As privacy is becoming a big issue, people are reluctant to disclose this kind of personal information. In this study, we analyzed past studies and found that the traditional method suffers from a meter failure problem and a meter replacement problem, thus we propose a smart meter aggregation scheme based on a noise addition method and the homomorphic encryption algorithm, which can avoid the aforementioned problems. After simulation, the experimental results show that the computation cost on both the aggregator and smart meter side is reduced. A formal security analysis shows that the proposed scheme has semantic security. Full article

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14 pages, 4598 KiB

Open AccessArticle

Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System

by Jimyung Kang and Soonwoo Lee

Energies 2018, 11(11), 2905; https://doi.org/10.3390/en11112905 - 25 Oct 2018

Cited by 15 | Viewed by 3003

Abstract

Demand response, in which energy customers reduce their energy consumption at the request of service providers, is spreading as a new technology. However, the amount of load curtailment from each customer is uncertain. This is because an energy customer can freely decide to [...] Read more.

Demand response, in which energy customers reduce their energy consumption at the request of service providers, is spreading as a new technology. However, the amount of load curtailment from each customer is uncertain. This is because an energy customer can freely decide to reduce his energy consumption or not in the current liberalized energy market. Because this uncertainty can cause serious problems in a demand response system, it is clear that the amount of energy reduction should be predicted and managed. In this paper, a data-driven prediction method of load curtailment is proposed, considering two difficulties in the prediction. The first problem is that the data is very sparse. Each customer receives a request for load curtailment only a few times a year. Therefore, the k-nearest neighbor method, which requires a relatively small amount of data, is mainly used in our proposed method. The second difficulty is that the characteristic of each customer is so different that a single prediction method cannot cover all the customers. A prediction method that provides remarkable prediction performance for one customer may provide a poor performance for other customers. As a result, the proposed prediction method adopts a weighted ensemble model to apply different models for different customers. The confidence of each sub-model is defined and used as a weight in the ensemble. The prediction is fully based on the electricity consumption data and the history of demand response events without demanding any other additional internal information from each customer. In the experiment, real data obtained from demand response service providers verifies that the proposed framework is suitable for the prediction of each customer’s load curtailment. Full article

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18 pages, 3156 KiB

Open AccessArticle

Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid

by Sujie Shao, Qingtao Zeng, Shaoyong Guo and Xuesong Qiu

Energies 2018, 11(11), 2871; https://doi.org/10.3390/en11112871 - 23 Oct 2018

Cited by 3 | Viewed by 2664

Abstract

Smart gird, integrated power network with communication network, has brought an innovation of traditional power for future green energy. Optical fiber technology and synchronous digital hierarchy (SDH) technology is widely used in smart grid communication transmission network. It is a challenge to reduce [...] Read more.

Smart gird, integrated power network with communication network, has brought an innovation of traditional power for future green energy. Optical fiber technology and synchronous digital hierarchy (SDH) technology is widely used in smart grid communication transmission network. It is a challenge to reduce impact of the availability of smart grid communication services caused by random failures and random time to repair. Firstly, we create a service channel violation risk degree (SCVRD) model to precisely track the violation risk change of communication service channel. It is denoted by the probability of service channel cumulative failure duration exceeding the prescribed duration. Secondly, a service channel violation risk degree routing mechanism is proposed to improve the availability of communication service. At last, the simulation is implemented with MATLAB and network data in one province are used as data instance. The simulation results show that the average service channel failure rate of availability-aware routing based on statistics (AAR-OS) algorithm and risk-aware provisioning algorithm are reduced by 15% and 6%, respectively. Full article

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18 pages, 889 KiB

Open AccessArticle

Efficient and Provably Secure Key Agreement for Modern Smart Metering Communications

by An Braeken, Pardeep Kumar and Andrew Martin

Energies 2018, 11(10), 2662; https://doi.org/10.3390/en11102662 - 6 Oct 2018

Cited by 57 | Viewed by 4200

Abstract

Security in modern smart metering communications and in smart grid networks has been an area of interest recently. In this field, identity-based mutual authentication including credential privacy without active involvement of a trusted third party is an important building block for smart grid [...] Read more.

Security in modern smart metering communications and in smart grid networks has been an area of interest recently. In this field, identity-based mutual authentication including credential privacy without active involvement of a trusted third party is an important building block for smart grid technology. Recently, several schemes have been proposed for the smart grid with various security features (e.g., mutual authentication and key agreement). Moreover, these schemes are said to offer session key security under the widely accepted Canetti-Krawczyk (CK) security model. Instead, we argue that all of them are still vulnerable under the CK model. To remedy the problem, we present a new provably secure key agreement model for smart metering communications. The proposed model preserves the security features and provides more resistance against a denial of service attack. Moreover, our scheme is pairing-free, resulting in highly efficient computational and communication efforts. Full article

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17 pages, 5765 KiB

Open AccessArticle

An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

by Rongheng Lin, Budan Wu and Yun Su

Energies 2018, 11(9), 2466; https://doi.org/10.3390/en11092466 - 17 Sep 2018

Cited by 16 | Viewed by 4026

Abstract

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the [...] Read more.

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering. Full article

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16 pages, 2325 KiB

Open AccessArticle

Matching of Local Load with On-Site PV Production in a Grid-Connected Residential Building

by Arslan Ahmad Bashir, Mahdi Pourakbari Kasmaei, Amir Safdarian and Matti Lehtonen

Energies 2018, 11(9), 2409; https://doi.org/10.3390/en11092409 - 12 Sep 2018

Cited by 23 | Viewed by 4249

Abstract

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation [...] Read more.

Efficient utilization of renewable generation inside microgrids remains challenging. In most existing studies, the goal is to optimize the energy cost of microgrids by working in synergy with the main grid. This work aimed at maximizing the self-consumption of on-site photovoltaic (PV) generation using an electrical storage, as well as demand response solutions, in a building that was also capable of interacting with the main grid. Ten-minute resolution data were used to capture the temporal behavior of the weather. Extensive mathematical models were employed to estimate the demand for hot-water consumption, space cooling, and heating loads. The proposed framework is cast as mixed-integer linear programming model while minimizing the interaction with the grid. To evaluate the effectiveness of the proposed framework, it was applied to a typical Finnish household. Matching indices were used to evaluate the degree of overlap between generation and demand under different PV penetrations and storage capacities. Despite negative correlation of PV generation with Finnish seasonal consumption, a significant portion of demand can be satisfied solely with on-site PV generation during the spring and summer seasons. Full article

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19 pages, 1972 KiB

Open AccessArticle

A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data

by Zigui Jiang, Rongheng Lin and Fangchun Yang

Energies 2018, 11(9), 2235; https://doi.org/10.3390/en11092235 - 26 Aug 2018

Cited by 33 | Viewed by 5302

Abstract

Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management [...] Read more.

Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy. Full article

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17 pages, 4020 KiB

Open AccessArticle

Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality

by Sara Deilami

Energies 2018, 11(9), 2187; https://doi.org/10.3390/en11092187 - 21 Aug 2018

Cited by 21 | Viewed by 4372

Abstract

This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs [...] Read more.

This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs that minimizes the costs associated with generation and losses considering network and bus total harmonic distortion (THD). The aim is to first attend the high priority customers and charge their vehicles as quickly as possible while postponing the service to medium and low priority consumers to the off-peak hours, considering network, battery and power quality constraints and harmonics. The vehicles were randomly plugged at different locations during a period of 24 h. The proposed PEV coordination is based on the maximum sensitivity selection (MSS), which is the sensitivity of losses (including fundamental and harmonic losses) with respect to the PEV location (PEV bus). The proposed algorithm uses the decoupled harmonic power flow (DHPF) to model the nonlinear loads (including the PEV chargers) as current harmonic sources and computes the harmonic power losses, harmonic voltages and THD of the smart grid. The MSS vectors are easily determined using the entries of the Jacobian matrix of the DHPF program, which includes the spectrums of all injected harmonics by nonlinear electric vehicle (EV) chargers and nonlinear industrial loads. The sensitivity of the objective function (fundamental and harmonic power losses) to the PEVs were then used to schedule PEVs accordingly. The algorithm successfully controls the network THDv level within the standard limit of 5% for low and moderate PEV penetrations by delaying PEV charging activities. For high PEV penetrations, the installation of passive power filters (PPFs) is suggested to reduce the THDv and manage to fully charge the PEVs. Detailed simulations considering random and coordinated charging were performed on the modified IEEE 23 kV distribution system with 22 low voltage residential networks populated with PEVs that have nonlinear battery chargers. Simulation results are provided without/with filters for different penetration levels of PEVs. Full article

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18 pages, 2448 KiB

Open AccessArticle

Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings

by Evgeny Nefedov, Seppo Sierla and Valeriy Vyatkin

Energies 2018, 11(8), 2165; https://doi.org/10.3390/en11082165 - 19 Aug 2018

Cited by 29 | Viewed by 5556

Abstract

Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also [...] Read more.

Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also beneficial for making buildings sustainably greener on account of using V2B technology in conjunction with local photovoltaic (PV) generation. It is assumed that there is no local battery storage other than EVs and that the EV batteries are fully available for driving, so that the EVs batteries must be at the intended state of charge at the departure time announced by the EV driver. Our goal is to exploit the potential of the EV batteries capacity as much as possible in order to permit a large area of solar panels, so that even on sunny days all PV power can be used to supply the building needs or the EV charging at the parking lot. A system architecture and collaboration protocols that account for uncertainties in EV behaviour are proposed. The proposed approach is proven in simulation covering one year period for three locations in different climatic regions of the US, resulting in the electricity bill reductions of 15.8%, 9.1% and 4.9% for California, New Jersey and Alaska, respectively. These results are compared to state-of-the-art research in combining V2B with PV or wind power generation. It is concluded that the achieved electricity bill reductions are superior to the state-of-the-art, because previous work is based on problem formulations that exploit only a part of the potential EV battery capacity. Full article

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20 pages, 1256 KiB

Open AccessArticle

Efficient and Privacy-Preserving Data Aggregation and Dynamic Billing in Smart Grid Metering Networks

by An Braeken, Pardeep Kumar and Andrew Martin

Energies 2018, 11(8), 2085; https://doi.org/10.3390/en11082085 - 10 Aug 2018

Cited by 13 | Viewed by 4513

Abstract

The smart grid enables convenient data collection between smart meters and operation centers via data concentrators. However, it presents security and privacy issues for the customer. For instance, a malicious data concentrator cannot only use consumption data for malicious purposes but also can [...] Read more.

The smart grid enables convenient data collection between smart meters and operation centers via data concentrators. However, it presents security and privacy issues for the customer. For instance, a malicious data concentrator cannot only use consumption data for malicious purposes but also can reveal life patterns of the customers. Recently, several methods in different groups (e.g., secure data aggregation, etc.) have been proposed to collect the consumption usage in a privacy-preserving manner. Nevertheless, most of the schemes either introduce computational complexities in data aggregation or fail to support privacy-preserving billing against the internal adversaries (e.g., malicious data concentrators). In this paper, we propose an efficient and privacy-preserving data aggregation scheme that supports dynamic billing and provides security against internal adversaries in the smart grid. The proposed scheme actively includes the customer in the registration process, leading to end-to-end secure data aggregation, together with accurate and dynamic billing offering privacy protection. Compared with the related work, the scheme provides a balanced trade-off between security and efficacy (i.e., low communication and computation overhead while providing robust security). Full article

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22 pages, 1763 KiB

Open AccessArticle

Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices

by Christian Giovanelli, Seppo Sierla, Ryutaro Ichise and Valeriy Vyatkin

Energies 2018, 11(7), 1906; https://doi.org/10.3390/en11071906 - 21 Jul 2018

Cited by 21 | Viewed by 5893

Abstract

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price [...] Read more.

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively. Full article

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22 pages, 1241 KiB

Open AccessEditor’s ChoiceReview

Peer to Peer Distributed Energy Trading in Smart Grids: A Survey

by Juhar Abdella and Khaled Shuaib

Energies 2018, 11(6), 1560; https://doi.org/10.3390/en11061560 - 14 Jun 2018

Cited by 187 | Viewed by 14296

Abstract

Due to the expansion of distributed renewable energy resources, peer to peer energy trading (P2P DET) is expected to be one of the key elements of next generation power systems. P2P DET can provide various benefits such as creating a competitive energy market, [...] Read more.

Due to the expansion of distributed renewable energy resources, peer to peer energy trading (P2P DET) is expected to be one of the key elements of next generation power systems. P2P DET can provide various benefits such as creating a competitive energy market, reducing power outages, increasing overall efficiency of power systems and supplementing alternative sources of energy according to user preferences. Because of these promising advantages, P2P DET has attracted the attention of several researchers. Current research related to P2P DET include demand response optimization, power routing, network communication, security and privacy. This paper presents a review of the main research topics revolving around P2P DET. Particularly, we present a comprehensive survey of existing demand response optimization models, power routing devices and power routing algorithms. We also identify some key challenges faced in realizing P2P DET. Furthermore, we discuss state of the art enabling technologies such as Energy Internet, Blockchain and Software Defined Networking (SDN) and we provide insights into future research directions. Full article

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18 pages, 3705 KiB

Open AccessArticle

Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information

by Yaxin Huang, Yunlian Sun and Shimin Yi

Energies 2018, 11(6), 1532; https://doi.org/10.3390/en11061532 - 12 Jun 2018

Cited by 4 | Viewed by 3295

Abstract

The normal communication between smart meter and concentrator is a key factor influencing the normal function of users’ power consumption systems. To solve the communication failure of the smart meter caused by the signal conflict as well as the collected consecutive information abnormality [...] Read more.

The normal communication between smart meter and concentrator is a key factor influencing the normal function of users’ power consumption systems. To solve the communication failure of the smart meter caused by the signal conflict as well as the collected consecutive information abnormality from the same smart meter, according to the chain optimization index, the networking method of static and dynamic combination proposed in this paper is first used to picked out the optimal relay for a smart meter belonging to multiple relay communication ranges. Meanwhile, the communication with other secondary relays is closed to avoid signal conflict. Then the paper forms different combinations of collected data and these combinations are trained in the extreme learning machine (ELM) to find the characteristics value of power consumption information. Finally, in MATLAB simulation, if ELM detects the abnormal information, new communication path could be promptly found through dynamic adjustment of chain optimization weighted coefficient and the weighted coefficient of the number of the relayed smart meters. It solves the problem of consecutive information abnormality from the same smart meter and raises the reliability of smart meter’s communication, having a significantly meaning to guarantee the normal function of users’ power consumption system. Full article

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16 pages, 3977 KiB

Open AccessArticle

Applications of Complex Network Analysis in Electric Power Systems

by Mahmoud Saleh, Yusef Esa and Ahmed Mohamed

Energies 2018, 11(6), 1381; https://doi.org/10.3390/en11061381 - 29 May 2018

Cited by 69 | Viewed by 14484

Abstract

This paper provides a review of the research conducted on complex network analysis (CAN) in electric power systems. Moreover, a new approach is presented to find optimal locations for microgrids (MGs) in electric distribution systems (EDS) utilizing complex network analysis. The optimal placement [...] Read more.

This paper provides a review of the research conducted on complex network analysis (CAN) in electric power systems. Moreover, a new approach is presented to find optimal locations for microgrids (MGs) in electric distribution systems (EDS) utilizing complex network analysis. The optimal placement in this paper points to the location that will result in enhanced grid resilience, reduced power losses and line loading, better voltage stability, and a supply to critical loads during a blackout. The criteria used to point out the optimal placement of the MGs were predicated on the centrality analysis selected from the complex network theory, the center of mass (COM) concept from physics, and the recently developed controlled delivery grid (CDG) model. An IEEE 30 bus network was utilized as a case study. Results using MATLAB (MathWorks, Inc., Nattick, MA, USA) and PowerWorld (PowerWorld Corporation, Champaign, IL, USA) demonstrate the usefulness of the proposed approach for MGs placement. Full article

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17 pages, 429 KiB

Open AccessArticle

Achieving Cost Minimization and Fairness in Multi-Supplier Smart Grid Environment

by Amna Malik, Zain Ali, Ahmed Bilal Awan, Ahmed G. Abo-Khalil and Guftaar Ahmad Sardar Sidhu

Energies 2018, 11(6), 1367; https://doi.org/10.3390/en11061367 - 28 May 2018

Cited by 2 | Viewed by 3552

Abstract

In this paper, we study the energy management techniques in the smart grid with multiple energy providers. We seek to minimize the electricity cost. In this paper, the desired objectives are achieved through scheduling of different consumers to different utilities at different time [...] Read more.

In this paper, we study the energy management techniques in the smart grid with multiple energy providers. We seek to minimize the electricity cost. In this paper, the desired objectives are achieved through scheduling of different consumers to different utilities at different time slots. We consider a practical system where multiple users can be allocated to a single utility, but, a user cannot be assigned to more than one utility. As a first goal, we formulate a sum cost minimization problem subject to independent generation capacity of each utility. A dual decomposition approach is exploited to find an efficient solution where the sub-gradient approach is adopted to update the dual variables. Later, a min-max based optimization framework is adopted to achieve the fairness among different customers. Moreover, suboptimal schemes are also designed to reduce the computational complexity. Simulation results are presented to validate the performance of the proposed solutions. Full article

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25 pages, 6811 KiB

Open AccessArticle

Value of Residential Investment in Photovoltaics and Batteries in Networks: A Techno-Economic Analysis

by Damian Shaw-Williams, Connie Susilawati and Geoffrey Walker

Energies 2018, 11(4), 1022; https://doi.org/10.3390/en11041022 - 24 Apr 2018

Cited by 31 | Viewed by 6143

Abstract

Australia has one of the highest rates of residential photovoltaics penetration in the world. The willingness of households to privately invest in energy infrastructure, and the maturing of battery technology, provides significant scope for more efficient energy networks. The purpose of this paper [...] Read more.

Australia has one of the highest rates of residential photovoltaics penetration in the world. The willingness of households to privately invest in energy infrastructure, and the maturing of battery technology, provides significant scope for more efficient energy networks. The purpose of this paper is to evaluate the scope for promoting distributed generation and storage from within existing network spending. In this paper, a techno-economic analysis is conducted to evaluate the economic impacts on networks of private investment in energy infrastructure. A highly granular probabilistic model of households within a test area was developed and an economic evaluation of both household and network sectors performed. Results of this paper show that PV only installations carry the greatest private return and, at current battery prices, the economics of combined PV and battery systems is marginal. However, when network benefits arising from reducing residential evening peaks, improved reliability, and losses avoided are considered, this can more than compensate for private economic losses. The main conclusion of this paper is that there is significant scope for network benefits in retrofitting existing housing stock through the incentivization of a policy of a more rapid adoption of distributed generation and residential battery storage. Full article

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18 pages, 16272 KiB

Open AccessArticle

A Distributed Secondary Control Algorithm for Automatic Generation Control Considering EDP and Automatic Voltage Control in an AC Microgrid

by Mi Dong, Li Li, Lina Wang, Dongran Song, Zhangjie Liu, Xiaoyu Tian, Zhengguo Li and Yinghua Wang

Energies 2018, 11(4), 932; https://doi.org/10.3390/en11040932 - 13 Apr 2018

Cited by 5 | Viewed by 4274

Abstract

This paper introduces a distributed secondary control algorithm for automatic generation control (AGC) and automatic voltage control (AVC), which aims at matching area generation to area load and minimizing the total generation cost in an alternating current (AC) microgrids. Firstly, the control algorithm [...] Read more.

This paper introduces a distributed secondary control algorithm for automatic generation control (AGC) and automatic voltage control (AVC), which aims at matching area generation to area load and minimizing the total generation cost in an alternating current (AC) microgrids. Firstly, the control algorithm utilizes a continuous-time distributed algorithm to generate additional control variables to achieve frequency-voltage recovery for all distributed generators (DGs). Secondary, it solves the economic dispatch problem (EDP) by a distributed economic incremental algorithm in the secondary control level, which avoids the problem caused by communication speed inconsistency between secondary and tertiary control levels. This study also utilizes a fully distributed strategy based on secondary communication network to estimate the total load demand. In addition, the proposed algorithm can be used to realize a seamless handover from the islanded mode to the grid-connected mode, run under the condition of short time communication system out of action, and help to realize the plug and play function. Lastly, the stability of the proposed control algorithm is analyzed and proved, and the effectiveness of the method is verified in some case studies. Full article

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21 pages, 32902 KiB

Open AccessArticle

Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems

by Lun Yang, Xia Zhao, Xinyi Li and Wei Yan

Energies 2018, 11(4), 917; https://doi.org/10.3390/en11040917 - 12 Apr 2018

Cited by 16 | Viewed by 4625

Abstract

The existing studies on probabilistic steady-state analysis of integrated energy systems (IES) are limited to integrated electricity and gas networks or integrated electricity and heating networks. This paper proposes a probabilistic steady-state analysis of integrated electricity, gas and heating networks (EGH-IES). Four typical [...] Read more.

The existing studies on probabilistic steady-state analysis of integrated energy systems (IES) are limited to integrated electricity and gas networks or integrated electricity and heating networks. This paper proposes a probabilistic steady-state analysis of integrated electricity, gas and heating networks (EGH-IES). Four typical operation modes of an EGH-IES are presented at first. The probabilistic energy flow problem of the EGS-IES considering its operation modes and correlated uncertainties in wind/solar power and electricity/gas/heat loads is then formulated and solved by the Monte Carlo method based on Latin hypercube sampling and Nataf transformation. Numerical simulations are conducted on a sample EGH-IES working in the “electricity/gas following heat” mode to verify the probabilistic analysis proposed in this paper and to study the effects of uncertainties and correlations on the operation of the EGH-IES, especially uncertainty transmissions among the subnetworks. Full article

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15 pages, 1583 KiB

Open AccessArticle

Power Transformer Operating State Prediction Method Based on an LSTM Network

by Hui Song, Jiejie Dai, Lingen Luo, Gehao Sheng and Xiuchen Jiang

Energies 2018, 11(4), 914; https://doi.org/10.3390/en11040914 - 12 Apr 2018

Cited by 31 | Viewed by 6038

Abstract

The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the [...] Read more.

The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status. Full article

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31 pages, 1734 KiB

Open AccessReview

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

by Seyedeh Narjes Fallah, Ravinesh Chand Deo, Mohammad Shojafar, Mauro Conti and Shahaboddin Shamshirband

Energies 2018, 11(3), 596; https://doi.org/10.3390/en11030596 - 8 Mar 2018

Cited by 206 | Viewed by 13050

Abstract

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and [...] Read more.

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management. Full article

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13 pages, 2827 KiB

Open AccessArticle

Secure Protocol and IP Core for Configuration of Networking Hardware IPs in the Smart Grid

by Marcelo Urbina, Naiara Moreira, Mikel Rodriguez, Tatiana Acosta, Jesús Lázaro and Armando Astarloa

Energies 2018, 11(3), 510; https://doi.org/10.3390/en11030510 - 27 Feb 2018

Cited by 5 | Viewed by 5321

Abstract

Nowadays, the incorporation and constant evolution of communication networks in the electricity sector have given rise to the so-called Smart Grid, which is why it is necessary to have devices that are capable of managing new communication protocols, guaranteeing the strict requirements of [...] Read more.

Nowadays, the incorporation and constant evolution of communication networks in the electricity sector have given rise to the so-called Smart Grid, which is why it is necessary to have devices that are capable of managing new communication protocols, guaranteeing the strict requirements of processing required by the electricity sector. In this context, intelligent electronic devices (IEDs) with network architectures are currently available to meet the communication, real-time processing and interoperability requirements of the Smart Grid. The new generation IEDs include an Field Programmable Gate Array (FPGA), to support specialized networking switching architectures for the electric sector, as the IEEE 1588-aware High-availability Seamless Redundancy/Parallel Redundancy Protocol (HSR/PRP). Another advantage to using an FPGA is the ability to update or reconfigure the design to support new requirements that are being raised to the standards (IEC 61850). The update of the architecture implemented in the FPGA can be done remotely, but it is necessary to establish a cyber security mechanism since the communication link generates vulnerability in the case the attacker gains physical access to the network. The research presented in this paper proposes a secure protocol and Intellectual Property (IP) core for configuring and monitoring the networking IPs implemented in a Field Programmable Gate Array (FPGA). The FPGA based implementation proposed overcomes this issue using a light Layer-2 protocol fully implemented on hardware and protected by strong cryptographic algorithms (AES-GCM), defined in the IEC 61850-90-5 standard. The proposed secure protocol and IP core are applicable in any field where remote configuration over Ethernet is required for IP cores in FPGAs. In this paper, the proposal is validated in communications hardware for Smart Grids. Full article

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15 pages, 450 KiB

Open AccessArticle

Improving Control Efficiency of Dynamic Street Lighting by Utilizing the Dual Graph Grammar Concept

by Igor Wojnicki and Leszek Kotulski

Energies 2018, 11(2), 402; https://doi.org/10.3390/en11020402 - 9 Feb 2018

Cited by 24 | Viewed by 4714

Abstract

The paper introduces a definition of dual graph grammar. It enables two graphs to share information in a synchronized way. A smart city example application, which is an outdoor lighting control system utilizing the dual graph grammar, is also demonstrated. The [...] Read more.

The paper introduces a definition of dual graph grammar. It enables two graphs to share information in a synchronized way. A smart city example application, which is an outdoor lighting control system utilizing the dual graph grammar, is also demonstrated. The system controls dimming of street lights which is based on traffic intensity. Each luminaire’s light level is adjusted individually to comply with the lighting norms to ensure safety. Benefits of applying the dual graph grammar are twofold. First, it increases expressive power of the mathematical model that the system uses. It becomes possible to take into account complex geographical distribution of sensors and logical dependencies among them. Second, it increases the system’s efficiency by reducing the problem size during run-time. Experimental results show a reduction of the computation time by a factor of 2.8. The approach has been verified in practice. Full article

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2017

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1168 KiB

Open AccessArticle

Smart Grid Applications for a Practical Implementation of IP over Narrowband Power Line Communications

by Noelia Uribe-Pérez, Itziar Angulo, David De la Vega, Txetxu Arzuaga, Igor Fernández and Amaia Arrinda

Energies 2017, 10(11), 1782; https://doi.org/10.3390/en10111782 - 6 Nov 2017

Cited by 28 | Viewed by 6079

Abstract

Currently, Advanced Metering Infrastructure (AMI) systems have equipped the low voltage section with a communication system that is being used mainly for metering purposes, but it can be further employed for additional applications related to the Smart Grid (SG) concept. This paper explores [...] Read more.

Currently, Advanced Metering Infrastructure (AMI) systems have equipped the low voltage section with a communication system that is being used mainly for metering purposes, but it can be further employed for additional applications related to the Smart Grid (SG) concept. This paper explores the potential applications beyond metering of the available channel in a Power Line Communication-based AMI system. To that end, IP has been implemented over Narrow Band-Power Line Communication (NB-PLC) in a real microgrid, which includes an AMI system. A thorough review of potential applications for the SG that might be implemented for this representative case is included in order to provide a realistic analysis of the potentiality of NB-PLC beyond smart metering. The results demonstrate that existing AMI systems based on NB-PLC have the capacity to implement additional applications such as remote commands or status signals, which entails an added value for deployed AMI systems. Full article

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Graphical abstract

3083 KiB

Open AccessArticle

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

by Jimyung Kang and Jee-Hyong Lee

Energies 2017, 10(10), 1537; https://doi.org/10.3390/en10101537 - 4 Oct 2017

Cited by 9 | Viewed by 4636

Abstract

Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of [...] Read more.

Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider’s perspective. Full article

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1001 KiB

Open AccessArticle

A Data Analysis Technique to Estimate the Thermal Characteristics of a House

by Seyed Amin Tabatabaei, Wim Van der Ham, Michel C. A. Klein and Jan Treur

Energies 2017, 10(9), 1358; https://doi.org/10.3390/en10091358 - 8 Sep 2017

Cited by 9 | Viewed by 5234

Abstract

Almost one third of the energy is used in the residential sector, and space heating is the largest part of energy consumption in our houses. Knowledge about the thermal characteristics of a house can increase the awareness of homeowners about the options to [...] Read more.

Almost one third of the energy is used in the residential sector, and space heating is the largest part of energy consumption in our houses. Knowledge about the thermal characteristics of a house can increase the awareness of homeowners about the options to save energy, for example by showing that there is room for improvement of the insulation level. However, calculating the exact value of these characteristics is not possible without precise thermal experiments. In this paper, we propose a method to automatically estimate two of the most important thermal characteristics of a house, i.e., the loss rate and the heat capacity, based on collected data about the temperature and gas usage. The method is evaluated with a data set that has been collected in a real-life case study. Although a ground truth is lacking, the analyses show that there is evidence that this method could provide a feasible way to estimate those values from the thermostat data. More detailed data about the houses in which the data was collected is required to draw stronger conclusions. We conclude that the proposed method is a promising way to add energy saving advice to smart thermostats. Full article

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3046 KiB

Open AccessArticle

A PMU-Based Method for Smart Transmission Grid Voltage Security Visualization and Monitoring

by Heng-Yi Su and Tzu-Yi Liu

Energies 2017, 10(8), 1103; https://doi.org/10.3390/en10081103 - 27 Jul 2017

Cited by 13 | Viewed by 7758

Abstract

With the rapid growth of usage of phasor measurement units (PMUs) for modern power grids, the application of synchronized phasors (synchrophasors) to real-time voltage security monitoring has become an active research area. This paper presents a novel approach for fast determination of loading [...] Read more.

With the rapid growth of usage of phasor measurement units (PMUs) for modern power grids, the application of synchronized phasors (synchrophasors) to real-time voltage security monitoring has become an active research area. This paper presents a novel approach for fast determination of loading margin using PMU data from a wide-area monitoring system (WAMS) to construct the voltage stability boundary (VSB) of a transmission grid. Specifically, a new approach for online loading margin estimation that considers system load trends is proposed based on the Thevenin equivalent (TE) technique and the Mobius transformation (MT) technique. A VSB is then computed by means of real-time PMU measurements and is presented in a complex load power space. VSB can be utilized as a visualization tool that is able to provide real-time visualization of the current voltage stability situation. The proposed method is fast and adequate for online voltage security assessment. Furthermore, it enables us to significantly increase a system operator’s situational awareness for operational decision making. Simulation studies were carried out using different sized power grid models under various operating conditions. The simulation results are shown to validate the capability of the proposed method. Full article

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2094 KiB

Open AccessArticle

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

by César Hernández-Hernández, Francisco Rodríguez, José Carlos Moreno, Paulo Renato Da Costa Mendes, Julio Elias Normey-Rico and José Luis Guzmán

Energies 2017, 10(7), 884; https://doi.org/10.3390/en10070884 - 30 Jun 2017

Cited by 15 | Viewed by 5634

Abstract

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing [...] Read more.

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation. Full article

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4057 KiB

Open AccessArticle

A Multiconductor Model of Power Line Communication in Medium-Voltage Lines

by Lesek Franek and Petr Fiedler

Energies 2017, 10(6), 816; https://doi.org/10.3390/en10060816 - 15 Jun 2017

Cited by 14 | Viewed by 5426

Abstract

Most power line communication (PLC) models are designed to date simulate power lines as two-wire lines. However, in alternating current (AC) electrical distribution, the two-wire option is seldom applied, and medium-voltage lines are most often based on the three-phase configuration. In this context, [...] Read more.

Most power line communication (PLC) models are designed to date simulate power lines as two-wire lines. However, in alternating current (AC) electrical distribution, the two-wire option is seldom applied, and medium-voltage lines are most often based on the three-phase configuration. In this context, the influence of the ground, which constitutes another conductor with specific parameters, cannot be neglected. Two-wire models are characterized by limited accuracy, not allowing us to simulate certain major phenomena affecting PLC. This, for example, could embody the answer to the question of whether it is more advantageous to transmit a signal independently through each phase, reference the signal with respect to another phase or to use the ground as a reference. This paper discusses a multi-conductor model that eliminates the disadvantages outlined above; the proposed model exploits the multi-conductor telegrapher’s equations. In order to be able to include medium-voltage (MV)/ low-voltage (LV) transformers in medium-voltage network models, we constituted a transformer model. The designed models were validated on a real medium-voltage network. To be able to evaluate the suitability of the PLC, the noise in the medium voltage network was measured in order to determine the signal-to-noise ratio (SNR). Full article

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3494 KiB

Open AccessArticle

A Dual Half-Bridge Converter with Adaptive Energy Storage to Achieve ZVS over Full Range of Operation Conditions

by Lei Zhao, Chuangyu Xu, Xuemei Zheng and Haoyu Li

Energies 2017, 10(4), 444; https://doi.org/10.3390/en10040444 - 28 Mar 2017

Cited by 6 | Viewed by 6132

Abstract

The phase-shifted full-bridge (PSFB) converter is widely employed in high-power applications. However, circulating current, duty-cycle loss, secondary voltage oscillation, and narrow zero-voltage-switching (ZVS) range are the main drawbacks of the conventional PSFB converter. This paper proposes a novel full-bridge converter to improve the [...] Read more.

The phase-shifted full-bridge (PSFB) converter is widely employed in high-power applications. However, circulating current, duty-cycle loss, secondary voltage oscillation, and narrow zero-voltage-switching (ZVS) range are the main drawbacks of the conventional PSFB converter. This paper proposes a novel full-bridge converter to improve the performance of the conventional PSFB converter. The proposed converter contains two paralleled half-bridge inverters and an auxiliary inductor on the primary side. The rectifier stage is composed of six diodes connected with the form of full-bridge rectification. This structure allows the stored energy for ZVS operation to change adaptively with duty-cycle. The power can be transferred from the primary side to the secondary side during the whole period. Therefore, the requirement of output filter inductance is reduced and the circulating current is removed. The proposed converter is a good candidate for high power, high voltage and variable input voltage applications. The operation principle and performance are verified on a laboratory prototype. Full article

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4120 KiB

Open AccessArticle

Power Controlling, Monitoring and Routing Center Enabled by a DC-Transformer †

by Syed Ashad Mustufa Younus, Matteo Nardello, Pietro Tosato and Davide Brunelli

Energies 2017, 10(3), 403; https://doi.org/10.3390/en10030403 - 21 Mar 2017

Cited by 1 | Viewed by 5588

Abstract

The penetration of various types of renewable sources and on-site storage devices have recently focused attention towards DC power distribution in consumer grids to achieve the target of zero/positive energy buildings and communities. To achieve this target, the most important component is the [...] Read more.

The penetration of various types of renewable sources and on-site storage devices have recently focused attention towards DC power distribution in consumer grids to achieve the target of zero/positive energy buildings and communities. To achieve this target, the most important component is the DC consumer grid architecture which can integrate not only renewable sources and storage, but also enable the implementation in any conventional AC distribution network without any significant upgrade. To this end, a unique DC Transformer enabled DC microgrid architecture is presented in this paper. The architecture, called PCmRC (power controlling monitoring routing center) is proposed to manage distributed energy sources and storage at any stage and also directly interconnects the DC consumer grid with the conventional AC power grid. This paper also investigates detailed control algorithms of each component and the DC Transformer topology in addition to proposing four unique stages of grid operational modes to enhance the overall grid stability in any operational condition. The main objectives are to maximize the exploitation of renewable sources, to decrease reliance on fossil fuels, to boost the overall efficiency of the grid by reducing the power conversion losses and demand side management in all possible forms. The simulation platform is designed in MATLAB/Simulink. Simulation results of several types of case studies show the effectiveness of the proposed power distribution and management model. Full article

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3251 KiB

Open AccessArticle

International Electronical Committee (IEC) 61850 Mapping with Constrained Application Protocol (CoAP) in Smart Grids Based European Telecommunications Standard Institute Machine-to-Machine (M2M) Environment

by In-Jae Shin, Byung-Kwen Song and Doo-Seop Eom

Energies 2017, 10(3), 393; https://doi.org/10.3390/en10030393 - 20 Mar 2017

Cited by 21 | Viewed by 11868

Abstract

As power systems develop rapidly into smarter and more flexible configurations, so too must the communication technologies that support them. Machine-to-machine (M2M) communication in power systems enables information collection by combining sensors and communication protocols. In doing so, M2M technology supports communication between [...] Read more.

As power systems develop rapidly into smarter and more flexible configurations, so too must the communication technologies that support them. Machine-to-machine (M2M) communication in power systems enables information collection by combining sensors and communication protocols. In doing so, M2M technology supports communication between machines to improve power quality and protection coordination. When functioning in a “smart grid” environment, M2M has been labelled by the European Telecommunications Standard Institute (ETSI). International Electronical Committee (IEC) 61850 as the most important standard in power network systems. As evidence, this communication platform has been used for device data collection/control in substation automation systems and distribution automation systems. If the IEC 61850 information model were to be combined with a set of contemporary web protocols, the potential benefits would be enormous. Therefore, a constrained application protocol (CoAP) has been adopted to create an ETSI M2M communication architecture. CoAP is compared with other protocols (MQTT, SOAP) to demonstrate the validity of using it. This M2M communication technology is applied in an IEC61850, and use the OPNET Modeler 17.1 to demonstrate intercompatibility of CoAP Gateway. The proposed IEC 61850 and CoAP mapping scheme reduces the mapping time and improves throughput. CoAP is useful in the ETSI M2M environment where device capability is able to be limited. Full article

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6362 KiB

Open AccessArticle

Electric Field Simulations and Analysis for High Voltage High Power Medium Frequency Transformer

by Pei Huang, Chengxiong Mao and Dan Wang

Energies 2017, 10(3), 371; https://doi.org/10.3390/en10030371 - 16 Mar 2017

Cited by 15 | Viewed by 7759

Abstract

The electronic power transformer (EPT) raises concerns for its notable size and volume reduction compared with traditional line frequency transformers. Medium frequency transformers (MFTs) are important components in high voltage and high power energy conversion systems such as EPTs. High voltage and high [...] Read more.

The electronic power transformer (EPT) raises concerns for its notable size and volume reduction compared with traditional line frequency transformers. Medium frequency transformers (MFTs) are important components in high voltage and high power energy conversion systems such as EPTs. High voltage and high power make the reliable insulation design of MFT more difficult. In this paper, the influence of wire type and interleaved winding structure on the electric field distribution of MFT is discussed in detail. The electric field distributions for six kinds of typical non-interleaved windings with different wire types are researched using a 2-D finite element method (FEM). The electric field distributions for one non-interleaved winding and two interleaved windings are also studied using 2-D FEM. Furthermore, the maximum electric field intensities are obtained and compared. The results show that, in this case study, compared with foil conductor, smaller maximum electric field intensity can be achieved using litz wire in secondary winding. Besides, interleaving can increase the maximum electric field intensity when insulation distance is constant. The proposed method of studying the electric field distribution and analysis results are expected to make a contribution to the improvement of electric field distribution in transformers. Full article

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4000 KiB

Open AccessArticle

A Novel High-Frequency Voltage Standing-Wave Ratio-Based Grounding Electrode Line Fault Supervision in Ultra-High Voltage DC Transmission Systems

by Yufei Teng, Xiaopeng Li, Qi Huang, Yifei Wang, Shi Jing, Zhenchao Jiang and Wei Zhen

Energies 2017, 10(3), 309; https://doi.org/10.3390/en10030309 - 5 Mar 2017

Cited by 15 | Viewed by 5224

Abstract

In order to improve the fault monitoring performance of grounding electrode lines in ultra-high voltage DC (UHVDC) transmission systems, a novel fault monitoring approach based on the high-frequency voltage standing-wave ratio (VSWR) is proposed in this paper. The VSWR is defined considering a [...] Read more.

In order to improve the fault monitoring performance of grounding electrode lines in ultra-high voltage DC (UHVDC) transmission systems, a novel fault monitoring approach based on the high-frequency voltage standing-wave ratio (VSWR) is proposed in this paper. The VSWR is defined considering a lossless transmission line, and the characteristics of the VSWR under different conditions are analyzed. It is shown that the VSWR equals 1 when the terminal resistance completely matches the characteristic impedance of the line, and when a short circuit fault occurs on the grounding electrode line, the VSWR will be greater than 1. The VSWR will approach positive infinity under metallic earth fault conditions, whereas the VSWR in non-metallic earth faults will be smaller. Based on these analytical results, a fault supervision criterion is formulated. The effectiveness of the proposed VSWR-based fault supervision technique is verified with a typical UHVDC project established in Power Systems Computer Aided Design/Electromagnetic Transients including DC(PSCAD/EMTDC). Simulation results indicate that the proposed strategy can reliably identify the grounding electrode line fault and has strong anti-fault resistance capability. Full article

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1597 KiB

Open AccessArticle

A Dynamic Economic Dispatch Model for Uncertain Power Demands in an Interconnected Microgrid

by Young-Sik Jang and Mun-Kyeom Kim

Energies 2017, 10(3), 300; https://doi.org/10.3390/en10030300 - 3 Mar 2017

Cited by 13 | Viewed by 5917

Abstract

In this paper, we propose a dynamic economic dispatch (DED) model with sharing of responsibility for supply–demand balance under uncertain demands in a microgrid (MG). For developing the proposed model, an energy band operation scheme, including a tie-line flow (TLF) contraction between the [...] Read more.

In this paper, we propose a dynamic economic dispatch (DED) model with sharing of responsibility for supply–demand balance under uncertain demands in a microgrid (MG). For developing the proposed model, an energy band operation scheme, including a tie-line flow (TLF) contraction between the main grid and the microgrid (MG), is constructed for preventing considerable changes in the TLFs caused by DED optimization. The proposed scheme generalizes the relationship between TLF contractions and MG operational costs. Moreover, a chance-constrained approach is applied to prevent short- and over-supply risks caused by unpredictable demands in the MG. Based on this approach, it is possible to determine the reasonable ramping capability versus operational cost under uncertain power demands in the MG. Full article

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5868 KiB

Open AccessArticle

Optimal Cooperative Management of Energy Storage Systems to Deal with Over- and Under-Voltages

by Ghassem Mokhtari, Ghavameddin Nourbakhsh, Amjad Anvari-Moghadam, Negareh Ghasemi and Aminmohammad Saberian

Energies 2017, 10(3), 293; https://doi.org/10.3390/en10030293 - 2 Mar 2017

Cited by 8 | Viewed by 4187

Abstract

This paper presents an optimal cooperative voltage control approach, which coordinates storage units in a distribution network. This technique is developed for storage systems’ active power management with a local strategy to provide robust voltage control and a distributed strategy to deliver optimal [...] Read more.

This paper presents an optimal cooperative voltage control approach, which coordinates storage units in a distribution network. This technique is developed for storage systems’ active power management with a local strategy to provide robust voltage control and a distributed strategy to deliver optimal storage utilization. Accordingly, three control criteria based on predefined node voltage limits are used for network operation including normal, over-voltage, and under-voltage control modes. The contribution of storage units for voltage support is determined using the control modes and the coordination strategies proposed in this paper. This technique is evaluated in two case studies to assess its capability. Full article

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