Special Issue "Developing and Implementing Smart Grids: Novel Technologies, Techniques and Models"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: closed (31 August 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Pierluigi Siano

Department of Industrial Engineering, University of Salerno, Fisciano (SA) 84084, Italy
Website | E-Mail
Interests: smart grids; energy management; power systems; demand response
Guest Editor
Dr. Miadreza Shafie-khah

Department of Electromechanical Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
E-Mail
Interests: smart grid; demand response; electric vehicle; power system; electricity market

Special Issue Information

Dear Colleagues,

Due to the rapid growth of technologies and communication systems, electricity demand must be supplied and have the highest quality and reliability. On the other hand, due to increasing concerns about the environment, sustainable energies are highly demanded. On this basis, the conventional energy systems should transition into the smart systems to meet the requirements. Novel technologies, techniques and models in the operation and planning of power systems should enable the current systems to move towards the smart grid. To this end, renewable energy resources, energy storage systems, electric vehicles and demand response are key factors of the transition in different aspects of generation, transmission and distribution levels. This Special Issue aims at encouraging researchers to address the technologies, models and solutions to facilitate and speedup the transition into smart grid.

Prof. Pierluigi Siano
Dr. Miadreza Shafie-khah
Guest Editors

Manuscript Submission Information

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Keywords

  • Power systems
  • Smart grids
  • Transmission and distribution network
  • Renewable energy resources
  • Energy storage systems
  • Reliability
  • Quality
  • Demand response
  • Electric vehicles

Published Papers (18 papers)

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Research

Open AccessArticle Stochastic Wind Curtailment Scheduling for Mitigation of Short-Term Variations in a Power System with High Wind Power and Electric Vehicle
Appl. Sci. 2018, 8(9), 1684; https://doi.org/10.3390/app8091684
Received: 20 August 2018 / Revised: 10 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
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Abstract
Occasionally, wind curtailments may be required to avoid an oversupply when wind power, together with the minimum conventional generation, exceed load. By curtailing wind power, the forecast uncertainty and short-term variations in wind power can be mitigated so that a lower spinning reserve
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Occasionally, wind curtailments may be required to avoid an oversupply when wind power, together with the minimum conventional generation, exceed load. By curtailing wind power, the forecast uncertainty and short-term variations in wind power can be mitigated so that a lower spinning reserve is sufficient to maintain the operational security of a power system. Additionally, the electric vehicle (EV) charging load can relieve the oversupply of wind power generation and avoid uneconomical wind power curtailments. This paper presents a stochastic generation scheduling method to ensure the operation security against wind power variation as well as against forecast uncertainty considering the stochastic EV charging load. In the paper, the short-term variations of wind power that are mitigated by the wind curtailment are investigated, and incorporated into a generation scheduling problem as the mixed-integer program (MIP) forms. Numerical results are also presented in order to demonstrate the effectiveness of the proposed method. Full article
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Open AccessArticle Online Static Security Assessment of Power Systems Based on Lasso Algorithm
Appl. Sci. 2018, 8(9), 1442; https://doi.org/10.3390/app8091442
Received: 27 July 2018 / Revised: 11 August 2018 / Accepted: 21 August 2018 / Published: 23 August 2018
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Abstract
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply
[...] Read more.
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA. Full article
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Open AccessArticle Effectiveness of Distributed vs. Concentrated Volt/Var Local Control Strategies in Low-Voltage Grids
Appl. Sci. 2018, 8(8), 1382; https://doi.org/10.3390/app8081382
Received: 28 June 2018 / Revised: 10 August 2018 / Accepted: 13 August 2018 / Published: 16 August 2018
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Abstract
This paper introduces a novel local Volt/var control strategy in a low-voltage smart grid. Nowadays, various Volt/var local control strategies built on customer photovoltaic inverters, e.g., cosφ(P) and Q(U), are introduced to mitigate the upper voltage limit
[...] Read more.
This paper introduces a novel local Volt/var control strategy in a low-voltage smart grid. Nowadays, various Volt/var local control strategies built on customer photovoltaic inverters, e.g., cosφ(P) and Q(U), are introduced to mitigate the upper voltage limit violations in feeders with high prosumer share. Nevertheless, although these strategies are further refined by including more local variables, their use is still very limited. In this study, the effects of a new concentrated Volt/var local control strategy in low-voltage grids are investigated. Concentrated var-sinks, e.g., coils-L(U), are set at the end of each violated feeder. The concentrated local control strategy L(U) is compared with the distributed cosφ(P) and Q(U) strategies. Initially, both control strategies are theoretically investigated, followed by simulations in a test feeder. Finally, the expected practical significance of the findings is verified through simulations in a real typical urban and rural grid. Additionally, the impact of the different local control strategies used in low-voltage grids on the behavior of the medium-voltage grid is analyzed. The results show that the concentrated Volt/var control strategy eliminates the violation of upper voltage limit even in longer feeders, where both distributed local strategies fail. In addition, the concentrated L(U) local control causes less reactive power exchange on the distribution transformer level than the distributed cosφ(P) and Q(U) strategies. Therefore, the reactive power exchange with the medium-voltage grid and thus the distribution transformer loading are smaller in the case of concentrated local control strategy. Full article
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Open AccessArticle Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
Appl. Sci. 2018, 8(8), 1326; https://doi.org/10.3390/app8081326
Received: 18 July 2018 / Revised: 2 August 2018 / Accepted: 4 August 2018 / Published: 8 August 2018
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Abstract
Battery Energy Storage Systems (BESS) are starting to play an important role in today’s power distribution networks. They provide a manifold of services for fulfilling demands and requests from diverse stakeholders, such as distribution system operators, energy market operators, aggregators but also end-users.
[...] Read more.
Battery Energy Storage Systems (BESS) are starting to play an important role in today’s power distribution networks. They provide a manifold of services for fulfilling demands and requests from diverse stakeholders, such as distribution system operators, energy market operators, aggregators but also end-users. Such services are usually provided by corresponding Energy Management Systems (EMS). This paper analyzes the complexity of the EMS development process resulting from an evolving power utility automation. Full article
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Open AccessArticle Impacts of Integration of Wind Farms on Power System Transient Stability
Appl. Sci. 2018, 8(8), 1289; https://doi.org/10.3390/app8081289
Received: 5 July 2018 / Revised: 25 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
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Abstract
To compensate for the ever-growing energy gap, renewable resources have undergone fast expansions worldwide in recent years, but they also result in some challenges for power system operation such as the static security and transient stability issues. In particular, as wind power generation
[...] Read more.
To compensate for the ever-growing energy gap, renewable resources have undergone fast expansions worldwide in recent years, but they also result in some challenges for power system operation such as the static security and transient stability issues. In particular, as wind power generation accounts for a large share of these renewable energy and reduces the inertia of a power network, the transient stability of power systems with high-level wind generation is decreased and has attracted wide attention recently. Effectively analyzing and evaluating the impact of wind generation on power transient stability is indispensable to improve power system operation security level. In this paper, a Doubly Fed Induction Generator with a two-lumped mass wind turbine model is presented firstly to analyze impacts of wind power generation on power system transient stability. Although the influence of wind power generation on transient stability has been comprehensively studied, many other key factors such as the locations of wind farms and the wind speed driving the wind turbine are also investigated in detail. Furthermore, how to improve the transient stability by installing capacitors is also demonstrated in the paper. The IEEE 14-bus system is used to conduct these investigations by using the Power System Analysis Tool, and the time domain simulation results show that: (1) By increasing the capacity of wind farms, the system instability increases; (2) The wind farm location and wind speed can affect power system transient stability; (3) Installing capacitors will effectively improve system transient stability. Full article
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Open AccessArticle Optimal Operation Analysis of the Distribution Network Comprising a Micro Energy Grid Based on an Improved Grey Wolf Optimization Algorithm
Appl. Sci. 2018, 8(6), 923; https://doi.org/10.3390/app8060923
Received: 3 May 2018 / Revised: 27 May 2018 / Accepted: 29 May 2018 / Published: 4 June 2018
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Abstract
With a focus on the safe, stable, and economical operation of a micro energy grid and a distribution network, this study proposes a bi-level optimal model for the integrated operation of a micro energy grid and a distribution network. The upper model used
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With a focus on the safe, stable, and economical operation of a micro energy grid and a distribution network, this study proposes a bi-level optimal model for the integrated operation of a micro energy grid and a distribution network. The upper model used the minima of three objectives, including the integrated operating cost of the distribution network, the network’s active power loss, and the standard deviation of the voltage deviation in the distribution network. The lower model used the minimum integrated operating cost for the micro energy grid as the objective function. Considering the large number of objectives in the upper model, and that no single optimal solution existed, the judgment-matrix method was used to obtain the weight factors of each objective, and the upper multi-objective optimization problem was transformed into a single-objective problem in this paper. A grey wolf optimization algorithm based on the dynamic adjustment of the proportional weight and convergence factor was proposed to solve the operating model of the distribution network comprising the micro energy grid. This algorithm offers a high solution precision, a high convergence speed, and a strong global searching ability. The nonlinear convergent factor formula proposed in this paper dynamically adjusted the global searching ability of the algorithm, while the proposed proportional weight sped up the convergence of the algorithm. The superiority of the proposed algorithm was verified mathematically by six test functions. The simulation results demonstrated that the model and algorithm proposed in this paper improved the economic benefits, and voltage stability of the distribution network, reduced the active power loss of the distribution network, and enabled the safe, stable, and economical operation of the distribution network comprising a micro energy grid. Full article
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Open AccessArticle Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization
Appl. Sci. 2018, 8(6), 864; https://doi.org/10.3390/app8060864
Received: 15 April 2018 / Revised: 19 May 2018 / Accepted: 20 May 2018 / Published: 25 May 2018
Cited by 1 | PDF Full-text (6386 KB) | HTML Full-text | XML Full-text
Abstract
The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when
[...] Read more.
The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base—which is defined by expert insights and decisions—gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA) techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE) of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014). The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment. Full article
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Open AccessArticle A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
Appl. Sci. 2018, 8(5), 689; https://doi.org/10.3390/app8050689
Received: 20 March 2018 / Revised: 26 April 2018 / Accepted: 26 April 2018 / Published: 28 April 2018
Cited by 2 | PDF Full-text (2344 KB) | HTML Full-text | XML Full-text
Abstract
Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These
[...] Read more.
Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The prediction model based on GBDT uses historical weather data and PV power output data to iteratively train the model, which is used to predict the future PV power output based on weather forecast data. Simulation results show that the proposed model based on GBDT has advantages of strong model interpretation, high accuracy, and stable error performance, and thus is of great significance for supporting the secure, stable and economic operation of power systems. Full article
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Open AccessArticle Energy Management Scheme for an EV Smart Charger V2G/G2V Application with an EV Power Allocation Technique and Voltage Regulation
Appl. Sci. 2018, 8(4), 648; https://doi.org/10.3390/app8040648
Received: 20 March 2018 / Revised: 16 April 2018 / Accepted: 20 April 2018 / Published: 21 April 2018
Cited by 1 | PDF Full-text (2160 KB) | HTML Full-text | XML Full-text
Abstract
The increasing penetration of electric vehicles (EVs) in the distribution grid has established them as a prospective resource for ancillary services. These services require adequate control strategies for prompt and efficient operation. In this study, an energy management scheme (EMS) has been proposed
[...] Read more.
The increasing penetration of electric vehicles (EVs) in the distribution grid has established them as a prospective resource for ancillary services. These services require adequate control strategies for prompt and efficient operation. In this study, an energy management scheme (EMS) has been proposed to employ an off-board EV smart charger to support the grid during short-term variance of renewables and reactive load onset. The scheme operates by calculating power references for the charger instantaneously. The EMS incorporates a proportional power division methodology, proposed to allocate power references to the individual EVs connected to the charger DC-bus. This methodology considers the state-of-charge and battery sizes of the EVs, and it can aggregate energy from various types of EVs. The proposed scheme is compared with another power allocation method, and the entire EMS is tested under the scenarios of power mismatch and voltage sag/swell events. The results show that the proposed scheme achieves the goal of the aggregation of EVs at the charger level to support the grid. The EMS also fulfills the objectives of voltage regulation and four-quadrant operation of the smart charger. Full article
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Open AccessArticle Energy Sharing for Interconnected Microgrids with a Battery Storage System and Renewable Energy Sources Based on the Alternating Direction Method of Multipliers
Appl. Sci. 2018, 8(4), 590; https://doi.org/10.3390/app8040590
Received: 19 March 2018 / Revised: 1 April 2018 / Accepted: 3 April 2018 / Published: 9 April 2018
Cited by 1 | PDF Full-text (1610 KB) | HTML Full-text | XML Full-text
Abstract
In order to facilitate the local sharing of renewable energy, an energy sharing management method of multiple microgrids (MGs) with a battery energy storage system (BESS) and renewable energy sources (RESs) is developed. First, a virtual entity named the energy sharing provider (ESP),
[...] Read more.
In order to facilitate the local sharing of renewable energy, an energy sharing management method of multiple microgrids (MGs) with a battery energy storage system (BESS) and renewable energy sources (RESs) is developed. First, a virtual entity named the energy sharing provider (ESP), which acts as an agent for MGs, is introduced to minimize the power loss cost. Second, a distributed optimal model and a two-level iterative algorithm for the MGs and ESP are proposed, which minimize the total operation cost including purchasing electricity cost, energy storage cost and power loss cost. Based on the energy sharing framework, considering the local objectives of MGs and the objective of ESP, the optimal scheduling can be achieved through the bidirectional interaction between MGs and ESP. During the optimization, the shared information between MGs and ESP is limited to expected exchange power, which protects the privacy of MGs and ESP. Finally, the effectiveness of the proposed model and algorithm in different scenarios is verified through a case study. Full article
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Open AccessArticle A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids
Appl. Sci. 2018, 8(3), 455; https://doi.org/10.3390/app8030455
Received: 14 February 2018 / Revised: 9 March 2018 / Accepted: 9 March 2018 / Published: 16 March 2018
Cited by 1 | PDF Full-text (3204 KB) | HTML Full-text | XML Full-text
Abstract
This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and
[...] Read more.
This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and whose constraints involve technical limitations of both network and distributed resources. Emphasis is given to the power quality issue with particular attention to unbalance reduction and voltage profile improvement. Technological issues, such as those related to the preservation of batteries’ lifetime, were also taken into account. The planning problem is solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. In order to limit the processing time while maintaining reasonable accuracy, the genetic algorithm search space is significantly reduced identifying a subset of candidate buses for the siting of the storage resources. The Inherent Structure Theory of Networks and the Loading Constraints Criterion were used to identify the candidate buses. The proposed method has been applied to a low voltage test network demonstrating the effectiveness of the procedure in terms of computational burden while also preserving the accuracy of the solution. Full article
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Open AccessArticle Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
Appl. Sci. 2018, 8(2), 154; https://doi.org/10.3390/app8020154
Received: 3 January 2018 / Revised: 18 January 2018 / Accepted: 22 January 2018 / Published: 23 January 2018
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Abstract
The effective filtering approach (EFA) is one of the most effective approaches for improving the network traffic performance of high-availability seamless redundancy (HSR) networks. However, because EFA uses port locking (PL) for detecting nondestination doubly-attached nodes with HSR protocol (DANH) rings in HSR
[...] Read more.
The effective filtering approach (EFA) is one of the most effective approaches for improving the network traffic performance of high-availability seamless redundancy (HSR) networks. However, because EFA uses port locking (PL) for detecting nondestination doubly-attached nodes with HSR protocol (DANH) rings in HSR networks, it forwards the first sent frame to all DANH rings in the network. In addition, it uses a control message for discovering passive QuadBox rings in both unidirectional and bidirectional communications. In this study, we propose an enhanced version of EFA called enhanced-EFA (eEFA) that does not forward unicast frames to nondestination DANH rings. eEFA does not use any control message to discover passive QuadBox rings in bidirectional communications. eEFA thus reduces the network traffic in HSR networks compared with EFA. Analytical and simulation results for a sample network show that the traffic reduction of eEFA was 4–26% and 2–20% for unidirectional and bidirectional communications, respectively, compared to EFA. eEFA, thus, clearly saves network bandwidth and improves the network performance. Full article
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Open AccessArticle Phase Coherence Index, HHT and Wavelet Analysis to Extract Features from Active and Passive Distribution Networks
Appl. Sci. 2018, 8(1), 71; https://doi.org/10.3390/app8010071
Received: 27 November 2017 / Revised: 30 December 2017 / Accepted: 4 January 2018 / Published: 7 January 2018
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Abstract
The modern Power Distribution Systems (PDS) operate more and more often with distributed generators and the optimal operation of the utility distribution systems has to take into account the possibility of bi-directional energy flows, although this event may only occur for some of
[...] Read more.
The modern Power Distribution Systems (PDS) operate more and more often with distributed generators and the optimal operation of the utility distribution systems has to take into account the possibility of bi-directional energy flows, although this event may only occur for some of the PDS. For this reason, the analysis methods that are usually employed to investigate the electrical behavior of the PDS can be more or less effective, depending on the typology of electrical loads connected to the line and on the presence or absence of Renewable Energy Sources (RES). This paper proposes either a methodology to select the best performing mathematical tool to investigate the electrical behavior of the PDS—depending on their linearity and stationarity—either an index to discriminate the PDS on the basis of a different amount of PV penetration. The proposed approach is applied to three real cases of PDS with different characteristics: residential and commercial, in the presence or absence of PV plants. In addition, two indices that are able to characterize the PDS in terms of periodicity and disturbance of the electrical signal are considered, specifically the phase coherence between two arbitrary signals and the phase coherence between an arbitrary signal and a reference one. The combined use of these indices can give valuable information about the degree of non-linearity and can be a measure of the PV penetration in a distribution circuit. Full article
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Open AccessArticle Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting
Appl. Sci. 2018, 8(1), 28; https://doi.org/10.3390/app8010028
Received: 30 November 2017 / Revised: 21 December 2017 / Accepted: 21 December 2017 / Published: 25 December 2017
Cited by 7 | PDF Full-text (8571 KB) | HTML Full-text | XML Full-text
Abstract
Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase
[...] Read more.
Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples. Full article
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Open AccessArticle Rule Based Coordinated Control of Domestic Combined Micro-CHP and Energy Storage System for Optimal Daily Cost
Appl. Sci. 2018, 8(1), 8; https://doi.org/10.3390/app8010008
Received: 17 November 2017 / Revised: 16 December 2017 / Accepted: 19 December 2017 / Published: 22 December 2017
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Abstract
This paper presents a novel control algorithm for optimising operational costs of a combined domestic micro-CHP (combined heat and power), battery and heat storage system. Using a minute by minute basic time-step, this work proposes a simple and computationally efficient rule based whole-system
[...] Read more.
This paper presents a novel control algorithm for optimising operational costs of a combined domestic micro-CHP (combined heat and power), battery and heat storage system. Using a minute by minute basic time-step, this work proposes a simple and computationally efficient rule based whole-system management, developed from empirical study of realistic simulated domestic electricity and heat loads. The CHP availability is considered in two binary states which, together with leveraging storage effectively, maximises CHP efficiency, and gives the algorithm increased real world feasibility. In addition, a novel application of a dual battery system is proposed to support the micro-CHP with each battery supplying just one of the distinctive morning and evening electrical load peaks, and thus inherently improving overall battery system lifetime. A case study is presented where the algorithm is shown to yield approximately 23% energy cost savings above the base case, almost 3% higher savings than that of the closest previous work, and 96.8% of the theoretical minimum cost. In general, the algorithm is shown to always yield better than 88% of the theoretical minimum cost, a ratio that will be considerably higher when real-world CHP limitations are factored into the theoretical minimum calculation. Full article
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Open AccessArticle Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
Appl. Sci. 2017, 7(12), 1326; https://doi.org/10.3390/app7121326
Received: 28 October 2017 / Revised: 13 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017
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Abstract
The application of power to gas (P2G) provides a new way to absorb intermittent renewable energy generation, which improves the efficiency of renewable energy utilization and provides the necessary flexibility for operating the integrated energy system. The electro-thermal integrated energy system with P2G
[...] Read more.
The application of power to gas (P2G) provides a new way to absorb intermittent renewable energy generation, which improves the efficiency of renewable energy utilization and provides the necessary flexibility for operating the integrated energy system. The electro-thermal integrated energy system with P2G is a new form of using energy efficiently. In this paper, we first introduce the technology and application of P2G. On the basis of considering the characteristics of P2G facilities, power systems, natural gas systems and heating systems, an optimal dispatching model of electro-thermal multi-energy system with P2G facilities is proposed. Particle swarm optimization is used to solve the optimal scheduling model. The simulation results are discussed for the six-bus and six-node integration system and show that when the volume fraction of hydrogen does not exceed 20% in the gas network, for the same operating mode, an integrated energy grid with P2G function will save about 20 tons of standard coal per day and the abandoned wind rate can be regarded as 0. Full article
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Open AccessArticle A Hardware-in-the-Loop Based Co-Simulation Platform of Cyber-Physical Power Systems for Wide Area Protection Applications
Appl. Sci. 2017, 7(12), 1279; https://doi.org/10.3390/app7121279
Received: 24 October 2017 / Revised: 4 December 2017 / Accepted: 6 December 2017 / Published: 8 December 2017
Cited by 1 | PDF Full-text (17204 KB) | HTML Full-text | XML Full-text
Abstract
With the development of smart grid technology, there has been an increasingly strong tendency towards the integration between the aspects of power and communication. The traditional power system has gradually transformed into the cyber-physical power system (CPPS), where co-simulation technologies can be utilized
[...] Read more.
With the development of smart grid technology, there has been an increasingly strong tendency towards the integration between the aspects of power and communication. The traditional power system has gradually transformed into the cyber-physical power system (CPPS), where co-simulation technologies can be utilized as an effective measure to describe the computation, communication, and integration processes of a power grid. In this paper, the construction methods and application scenarios of co-simulation platforms in the current research are first summarized. Then, a scheme of the real-time hardware-in-the-loop co-simulation platform is put forward. On the basis of power grid simulation developed with the Real-Time Laboratory (RT-LAB), and the communication network simulation developed with OPNET, the control center was developed with hardware devices to realize real-world control behavior instead of digital simulations. Therefore, the mixed-signal platform is capable of precisely simulating the dynamic features of CPPS with high speed. The distributed simulation components can be coordinated in a unified environment with high interoperability and reusability. Moreover, through a case study of a wide area load control system, the performance of the proposed platform under various conditions of control strategies, communication environments, and sampling frequencies was revealed and compared. As a result, the platform provided an intuitive and accurate way to reconstruct the CPPS environment where the influence of the information side of the CPPS control effects was verified. Full article
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Open AccessArticle A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors
Appl. Sci. 2017, 7(11), 1132; https://doi.org/10.3390/app7111132
Received: 3 October 2017 / Revised: 24 October 2017 / Accepted: 27 October 2017 / Published: 3 November 2017
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Abstract
The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper
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The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system’s imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers’ comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts. Full article
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