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Special Issue "Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (28 February 2017)

Special Issue Editor

Guest Editor
Assoc. Prof. Dr. Paras Mandal

Power & Renewable Energy Systems (PRES) Lab., Department of Electrical & Computer Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA
Website | E-Mail
Interests: cyber-physical systems; smart grid; power systems operations and control; machine learning; intelligent systems; data analytics

Special Issue Information

Dear Colleagues,

In recent years, Microgrids have become one of the power industry’s most attractive topics due to a set of changes and technology advancements that have taken place in various sectors of the power system leading to a more decentralized approach. This has been possible due to the value and positive impacts that Microgrids’ deployment offers which may be through new energy investments and business opportunities as well as making the grid more resilient and sustainable. This Special Issue of Energies will explore the latest developments in technology to enable the application of Microgrids at a large scale in the power grid. The Special Issue will encompass:

  • Microgrids and advanced distribution systems
  • Smart Grid technology applications in microgrids
  • Decentralized electricity markets in microgrids
  • Multi-agent system (MAS) architectures for microgrids
  • Solar, wind, and other renewable forecasting and integration to microgrids
  • Energy storage and electric vehicle applications for microgrids
  • Smart microgrid energy management system

This Special Issue will bring together researchers and practitioners from industry, research laboratories, and academia to present and discuss challenges and opportunities related to Microgrids and future electric power distribution grid.

Dr. Paras Mandal
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • microgrid and smart grid
  • microgrid electricity markets
  • electric vehicles
  • energy management
  • energy storage
  • distribution networks
  • renewable energy

Published Papers (18 papers)

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Research

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Open AccessArticle
Harmonic Distortion Minimization in Power Grids with Wind and Electric Vehicles
Energies 2017, 10(7), 932; https://doi.org/10.3390/en10070932
Received: 1 March 2017 / Revised: 12 May 2017 / Accepted: 25 June 2017 / Published: 5 July 2017
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Abstract
Power-electronic interfacing based devices such as wind generators (WGs) and electrical vehicles (EVs) cause harmonic distortions on the power grid. Higher penetration and uncoordinated operation of WGs and EVs can lead to voltage and current harmonic distortions, which may exceed IEEE limits. It [...] Read more.
Power-electronic interfacing based devices such as wind generators (WGs) and electrical vehicles (EVs) cause harmonic distortions on the power grid. Higher penetration and uncoordinated operation of WGs and EVs can lead to voltage and current harmonic distortions, which may exceed IEEE limits. It is interesting to note that WGs and EVs have some common harmonic profiles. Therefore, when EVs are connected to the grid, the harmonic pollution EVs impart onto the grid can be reduced to some extent by the amount of wind power injecting into the grid and vice versa. In this context, this work studies the impact of EVs on harmonic distortions and careful utilization of wind power to minimize the distortions in distribution feeders. For this, a harmonic unbalanced distribution feeder model is developed in OpenDSS and interfaced with Genetic Algorithm (GA) based optimization algorithm in MATLAB to solve optimal harmonic power flow (OHPF) problems. The developed OHPF model is first used to study impact of EV penetration on current/voltage total harmonic distortions (THDs) in distribution grids. Next, dispatch of WGs are found at different locations on the distribution grid to demonstrate reduction in the current/voltage THDs when EVs are charging. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Stability Analysis of the Cyber Physical Microgrid System under the Intermittent DoS Attacks
Energies 2017, 10(5), 680; https://doi.org/10.3390/en10050680
Received: 14 January 2017 / Revised: 5 May 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
Cited by 4 | PDF Full-text (2609 KB) | HTML Full-text | XML Full-text
Abstract
Recent research has demonstrated the vulnerabilities of cyber physical microgrid to different rates of denial-of-service (DoS) attacks, which send internal requests to degrade the victim’s performance. However, the interaction between the attacks and the security of microgrid remains largely unknown. In this paper, [...] Read more.
Recent research has demonstrated the vulnerabilities of cyber physical microgrid to different rates of denial-of-service (DoS) attacks, which send internal requests to degrade the victim’s performance. However, the interaction between the attacks and the security of microgrid remains largely unknown. In this paper, we address two fundamental questions: (1) What is the impact of intermittent DoS (IDoS) attacks on the security of cyber physical microgrid and (2) how can we analyze the stability of the cyber physical microgrid under IDoS attacks? To tackle these problems, we firstly model the cyber physical microgrid system considering the IDoS attacks on the network server. Based on the model, the interaction between the cyber system and the physical system is analyzed. Then, the impacts of IDoS attacks on the security of the cyber physical microgrid system are studied. It shows that the attack may lead to the system level oscillation with the information variation during the attack period. Therefore, a risk assessment method is proposed to investigate the stability of the cyber physcial microgrid system under IDoS attacks. Lastly, the proposed methodology is verified by simulation results. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids
Energies 2017, 10(4), 566; https://doi.org/10.3390/en10040566
Received: 22 February 2017 / Revised: 10 April 2017 / Accepted: 18 April 2017 / Published: 21 April 2017
Cited by 2 | PDF Full-text (532 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we propose a framework for optimal energy scheduling combined with a transaction mechanism to enable multiple microgrids to exchange their energy surplus/deficit with others while the distributed networks of microgrids remain secure. Our framework is based on two layers: a [...] Read more.
In this paper, we propose a framework for optimal energy scheduling combined with a transaction mechanism to enable multiple microgrids to exchange their energy surplus/deficit with others while the distributed networks of microgrids remain secure. Our framework is based on two layers: a distributed network layer and a market layer. In the distributed network layer, we first solve optimal power flow (OPF) using a predictor corrector proximal multiplier algorithm to optimally dispatch diesel generation considering renewable energy and power loss within a microgrid. Then, in the market layer, the agent of microgrid behaves either as a load agent or generator agent so that the auctioneer sets a reasonable transaction price for both agents by using the naive auction-inspired algorithm. Finally, energy surplus/deficit is traded among microgrids at a determined transaction price while the main grid balances the transaction. We implement the proposed mechanism in MATLAB (Matlab Release 15, The MathWorks Inc., Natick, MA, USA) using an optimization solver, CVX. In the case studies, we compare four scenarios depending on whether OPF and/or energy transaction is performed or not. Our results show that the joint consideration of OPF and energy transaction achieves as minimal a cost as the ideal case where all microgrids are combined into a single microgrid (or called grand-microgrid) and OPF is performed. We confirm that, even though microgrids are operated by private owners who are not collaborated, a transaction-based mechanism can mimic the optimal operation of a grand-microgrid in a scalable way. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization
Energies 2017, 10(3), 339; https://doi.org/10.3390/en10030339
Received: 20 December 2016 / Accepted: 7 March 2017 / Published: 9 March 2017
Cited by 5 | PDF Full-text (1735 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based [...] Read more.
In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based optimization model for maximum profits is developed. To reduce the computational complexity in solving the model, the possibility degree comparison between an interval and a real number is used to convert the interval constraints into the general ones; meanwhile, some slack variables and complementary conditions are introduced to eliminate the absolute-value operation. Unlike the stochastic optimization, the interval optimization only needs the upper-lower bounds of the uncertain variables instead of their probability distribution functions, which is beneficial to the practical application. Furthermore, the possible profit interval and the expected optimal profit can be determined by solving the optimization model. Numerical simulations are performed on a microgrid system modified from the benchmark low voltage network in the European Union project “Microgrid”, and the results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Development of Middleware Applied to Microgrids by Means of an Open Source Enterprise Service Bus
Energies 2017, 10(2), 172; https://doi.org/10.3390/en10020172
Received: 30 September 2016 / Revised: 25 January 2017 / Accepted: 29 January 2017 / Published: 10 February 2017
Cited by 1 | PDF Full-text (4395 KB) | HTML Full-text | XML Full-text
Abstract
The success of the smart grid relies heavily on the integration of Distributed Energy Resources (DERs) and interoperability among the hardware elements that are present as part of either the smart grid itself or in a smaller size deployment, such as a microgrid. [...] Read more.
The success of the smart grid relies heavily on the integration of Distributed Energy Resources (DERs) and interoperability among the hardware elements that are present as part of either the smart grid itself or in a smaller size deployment, such as a microgrid. Therefore, establishing an accurate design for software architectures that guarantee interoperability and are able to abstract hardware heterogeneity in this application domain, along with a clearly defined procedure on how to implement and test a solution like this, becomes a desirable objective. This paper describes the requirements needed to design a secure, decentralized and semantic middleware architecture for microgrids and the procedures used to develop it, so that the mandatory software components that have to be encased by the solution, as well as the steps that should be followed to make it happen, become clear for any designer, software architect or programmer that has to tackle similar challenges. In order to demonstrate the usability of the ideas put forward here, two successful pilots where middleware solutions were created according to these principles have been described. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Decision Support System for a Low Voltage Renewable Energy System
Energies 2017, 10(1), 118; https://doi.org/10.3390/en10010118
Received: 6 November 2016 / Revised: 4 January 2017 / Accepted: 9 January 2017 / Published: 18 January 2017
Cited by 9 | PDF Full-text (5691 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the development of a decision support system (DSS) for a low-voltage grid with renewable energy sources (photovoltaic panels and wind turbine) which aims at achieving energy balance in a pilot microgrid with less energy consumed from the network. The DSS [...] Read more.
This paper presents the development of a decision support system (DSS) for a low-voltage grid with renewable energy sources (photovoltaic panels and wind turbine) which aims at achieving energy balance in a pilot microgrid with less energy consumed from the network. The DSS is based on a procedural decision algorithm that is applied on a pilot microgrid, with energy produced from renewable energy sources, but it can be easily generalized for any microgrid. To underline the benefits of the developed DSS two case scenarios (a household and an office building with different energy consumptions) were analyzed. The results and throw added value of the paper is the description of an implemented microgrid, the development and testing of the decision support system on real measured data. Experimental results have demonstrated the validity of the approach in rule-based decision switching. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid
Energies 2017, 10(1), 45; https://doi.org/10.3390/en10010045
Received: 3 August 2016 / Revised: 29 December 2016 / Accepted: 29 December 2016 / Published: 3 January 2017
Cited by 3 | PDF Full-text (3627 KB) | HTML Full-text | XML Full-text
Abstract
A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs [...] Read more.
A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs in a grid, an MG operates in an islanded manner from the grid and protects its power generations and loads from disturbance by means of intelligent load shedding. A load shedding is a control procedure that results in autonomous decrease of the power demands of loads in an MG. In this study, we propose a load shedding algorithm for the optimization problem to maximize the satisfaction of system components. The proposed algorithm preferentially assigns the power to the subdemand with a high preference to maximize the satisfaction of power consumers. In addition, the algorithm assigns the power to maximize the power sale and minimize the power surplus for satisfaction of power suppliers. To verify the performance of our algorithm, we implement a multi-agent system (MAS) on top of a conventional development framework and assess the algorithm’s adaptability, satisfaction metric, and running time. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks
Energies 2017, 10(1), 37; https://doi.org/10.3390/en10010037
Received: 13 October 2016 / Revised: 16 December 2016 / Accepted: 19 December 2016 / Published: 1 January 2017
Cited by 10 | PDF Full-text (3286 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. [...] Read more.
This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Multi-Agent System Fault Protection with Topology Identification in Microgrids
Energies 2017, 10(1), 28; https://doi.org/10.3390/en10010028
Received: 11 October 2016 / Revised: 15 December 2016 / Accepted: 20 December 2016 / Published: 27 December 2016
Cited by 5 | PDF Full-text (43598 KB) | HTML Full-text | XML Full-text
Abstract
Data acquisition and supervisory control are usually performed using client-server architecture and centralized control in conventional power systems. However, the message transmission and fault clearing are too slow for large-scale complex power systems. Microgrid systems have various types of distributed energy resources (DERs) [...] Read more.
Data acquisition and supervisory control are usually performed using client-server architecture and centralized control in conventional power systems. However, the message transmission and fault clearing are too slow for large-scale complex power systems. Microgrid systems have various types of distributed energy resources (DERs) which are quite different in characteristics and capacities, thus, the client-server architecture and centralized control are inadequate to control and operate in microgrids. Based on MATLAB/Simulink (ver.R2012a) simulation software and Java Agent Development Framework (JADE) (JADE 4.1.1-revision 6532), this paper proposes a novel fault protection technology that used multi-agent system (MAS) to perform fault detection, fault isolation and service restoration in microgrids. A new topology identification method using the YBus Matrix Algorithm is presented to successfully recognize the network configurations. The identification technology can respond to microgrid variations. Furthermore, the interactive communications among intelligent electronic devices (IEDs), circuit breakers (CBs), and agents are clarified during fault occurrence. The simulation results show that the proposed MAS-based microgrids can promptly isolate faults and protect the system against faults in real time. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids
Energies 2016, 9(12), 1031; https://doi.org/10.3390/en9121031
Received: 20 October 2016 / Revised: 21 November 2016 / Accepted: 29 November 2016 / Published: 7 December 2016
Cited by 3 | PDF Full-text (572 KB) | HTML Full-text | XML Full-text
Abstract
With the development of microgrids (MGs), interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG) with a battery energy storage system (BESS) [...] Read more.
With the development of microgrids (MGs), interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG) with a battery energy storage system (BESS) and renewable energy resources (RESs). The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM), a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP) of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Mitigation of the Impact of High Plug-in Electric Vehicle Penetration on Residential Distribution Grid Using Smart Charging Strategies
Energies 2016, 9(12), 1024; https://doi.org/10.3390/en9121024
Received: 25 July 2016 / Revised: 16 November 2016 / Accepted: 25 November 2016 / Published: 3 December 2016
Cited by 12 | PDF Full-text (4347 KB) | HTML Full-text | XML Full-text
Abstract
Vehicle electrification presents a great opportunity to reduce transportation greenhouse gas emissions. The greater use of plug-in electric vehicles (PEVs), however, puts stress on local distribution networks. This paper presents an optimal PEV charging control method integrated with utility demand response (DR) signals [...] Read more.
Vehicle electrification presents a great opportunity to reduce transportation greenhouse gas emissions. The greater use of plug-in electric vehicles (PEVs), however, puts stress on local distribution networks. This paper presents an optimal PEV charging control method integrated with utility demand response (DR) signals to mitigate the impact of PEV charging to several aspects of a grid, including load surge, distribution accumulative voltage deviation, and transformer aging. To build a realistic PEV charging load model, the results of National Household Travel Survey (NHTS) have been analyzed and a stochastic PEV charging model has been defined based on survey results. The residential distribution grid contains 120 houses and is modeled in GridLAB-D. Co-simulation is performed using Matlab and GridLAB-D to enable the optimal control algorithm in Matlab to control PEV charging loads in the residential grid modeled in GridLAB-D. Simulation results demonstrate the effectiveness of the proposed optimal charging control method in mitigating the negative impacts of PEV charging on the residential grid. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Local Alternative for Energy Supply: Performance Assessment of Integrated Community Energy Systems
Energies 2016, 9(12), 981; https://doi.org/10.3390/en9120981
Received: 17 August 2016 / Revised: 31 October 2016 / Accepted: 14 November 2016 / Published: 25 November 2016
Cited by 16 | PDF Full-text (5036 KB) | HTML Full-text | XML Full-text
Abstract
Integrated community energy systems (ICESs) are emerging as a modern development to re-organize local energy systems allowing simultaneous integration of distributed energy resources (DERs) and engagement of local communities. Although local energy initiatives, such as ICESs are rapidly emerging due to community objectives, [...] Read more.
Integrated community energy systems (ICESs) are emerging as a modern development to re-organize local energy systems allowing simultaneous integration of distributed energy resources (DERs) and engagement of local communities. Although local energy initiatives, such as ICESs are rapidly emerging due to community objectives, such as cost and emission reductions as well as resiliency, assessment and evaluation are still lacking on the value that these systems can provide both to the local communities as well as to the whole energy system. In this paper, we present a model-based framework to assess the value of ICESs for the local communities. The distributed energy resources-consumer adoption model (DER-CAM) based ICES model is used to assess the value of an ICES in the Netherlands. For the considered community size and local conditions, grid-connected ICESs are already beneficial to the alternative of solely being supplied from the grid both in terms of total energy costs and CO2 emissions, whereas grid-defected systems, although performing very well in terms of CO2 emission reduction, are still rather expensive. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach
Energies 2016, 9(11), 973; https://doi.org/10.3390/en9110973
Received: 25 October 2016 / Revised: 15 November 2016 / Accepted: 17 November 2016 / Published: 22 November 2016
Cited by 7 | PDF Full-text (5167 KB) | HTML Full-text | XML Full-text
Abstract
A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases [...] Read more.
A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Smart Grid Cost-Emission Unit Commitment via Co-Evolutionary Agents
Energies 2016, 9(10), 834; https://doi.org/10.3390/en9100834
Received: 29 June 2016 / Revised: 10 October 2016 / Accepted: 11 October 2016 / Published: 17 October 2016
Cited by 1 | PDF Full-text (2922 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the uncertainty of wind, solar and load; smart charging and discharging of plug-in hybrid electric vehicles (PHEVs) to and from various energy sources; and the coordination of wind, solar power, PHEVs and cost-emission are considered in the smart grid unit [...] Read more.
In this paper, the uncertainty of wind, solar and load; smart charging and discharging of plug-in hybrid electric vehicles (PHEVs) to and from various energy sources; and the coordination of wind, solar power, PHEVs and cost-emission are considered in the smart grid unit commitment (UC). First, a multi-scenario simulation is used in which a set of valid scenarios is considered for the uncertainties of wind and solar energy sources and load. Then the UC problem for the set of scenarios is decomposed into the optimization of interactive agents by multi-agent technology. Agents’ action is represented by a genetic algorithm with adaptive crossover and mutation operators. The adaptive co-evolution of agents is reached by adaptive cooperative multipliers. Finally, simulation is implemented on an example of a power system containing thermal units, a wind farm, solar power plants and PHEVs. The results show the effectiveness of the proposed method. Thermal units, wind, solar power and PHEVs are mutually complementarily by the adaptive cooperative mechanism. The adaptive multipliers’ updating strategy can save more computational time and further improve the efficiency. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
Optimal Bidding of a Microgrid Based on Probabilistic Analysis of Island Operation
Energies 2016, 9(10), 814; https://doi.org/10.3390/en9100814
Received: 2 September 2016 / Revised: 27 September 2016 / Accepted: 2 October 2016 / Published: 12 October 2016
Cited by 2 | PDF Full-text (470 KB) | HTML Full-text | XML Full-text
Abstract
Island operation of a microgrid increases operation survivability and reliability when there is a large accident in a main grid. However, because a microgrid typically has limited generation capability, a microgrid operator (MGO) has to take the risk of island operation into account [...] Read more.
Island operation of a microgrid increases operation survivability and reliability when there is a large accident in a main grid. However, because a microgrid typically has limited generation capability, a microgrid operator (MGO) has to take the risk of island operation into account in its market participation and generation scheduling to ensure efficient operation. In this paper, a microgrid islanding event is interpreted as a trade suspension of a contract, and a set of islanding rules is presented in the form of a market rule. The risk of island operation is evaluated by modeling the microgrid islanding stochastically using an islanding probability function, which is defined in the form of a conditional probability to reflect the influence of outside conditions. An optimal bidding strategy is obtained for the MGO by formulating and solving an optimization problem to minimize the expected operating cost. The effectiveness of the proposed method was investigated by numerical simulations in which the proposed method and two other methods were applied to the same microgrid. Numerical sensitivity analyses of the coefficients of the islanding probability function were conducted to determine how an MGO copes with changes in outside conditions. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
A Price-Based Demand Response Scheme for Discrete Manufacturing in Smart Grids
Energies 2016, 9(8), 650; https://doi.org/10.3390/en9080650
Received: 30 May 2016 / Revised: 26 July 2016 / Accepted: 9 August 2016 / Published: 17 August 2016
Cited by 7 | PDF Full-text (5142 KB) | HTML Full-text | XML Full-text
Abstract
Demand response (DR) is a key technique in smart grid (SG) technologies for reducing energy costs and maintaining the stability of electrical grids. Since manufacturing is one of the major consumers of electrical energy, implementing DR in factory energy management systems (FEMSs) provides [...] Read more.
Demand response (DR) is a key technique in smart grid (SG) technologies for reducing energy costs and maintaining the stability of electrical grids. Since manufacturing is one of the major consumers of electrical energy, implementing DR in factory energy management systems (FEMSs) provides an effective way to manage energy in manufacturing processes. Although previous studies have investigated DR applications in process manufacturing, they were not conducted for discrete manufacturing. In this study, the state-task network (STN) model is implemented to represent a discrete manufacturing system. On this basis, a DR scheme with a specific DR algorithm is applied to a typical discrete manufacturing—automobile manufacturing—and operational scenarios are established for the stamping process of the automobile production line. The DR scheme determines the optimal operating points for the stamping process using mixed integer linear programming (MILP). The results show that parts of the electricity demand can be shifted from peak to off-peak periods, reducing a significant overall energy costs without degrading production processes. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Open AccessArticle
A Novel Protection Method for Single Line-to-Ground Faults in Ungrounded Low-Inertia Microgrids
Energies 2016, 9(6), 459; https://doi.org/10.3390/en9060459
Received: 16 March 2016 / Revised: 8 June 2016 / Accepted: 8 June 2016 / Published: 16 June 2016
Cited by 5 | PDF Full-text (6965 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a novel protection method for single line-to-ground (SLG) faults in ungrounded low-inertia microgrids. The proposed method includes microgrid interface protection and unit protection. The microgrid interface protection is based on the difference between the zero-sequence voltage angle and the zero-sequence [...] Read more.
This paper proposes a novel protection method for single line-to-ground (SLG) faults in ungrounded low-inertia microgrids. The proposed method includes microgrid interface protection and unit protection. The microgrid interface protection is based on the difference between the zero-sequence voltage angle and the zero-sequence current angle at the microgrid interconnection transformer for fast selection of the faulty feeder. The microgrid unit protection is based on a comparison of the three zero-sequence current phase directions at each junction point of load or distributed energy resources. Methods are also included to locate the minimum fault section. The fault section location technology operates according to the coordination of microgrid unit protection. The proposed method responds to SLG faults that may occur in both the grid and the microgrid. Simulations of an ungrounded low-inertia microgrid with a relay model were carried out using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including DC (EMTDC). Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids
Energies 2017, 10(5), 620; https://doi.org/10.3390/en10050620
Received: 26 January 2017 / Revised: 24 April 2017 / Accepted: 26 April 2017 / Published: 3 May 2017
Cited by 6 | PDF Full-text (7168 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power [...] Read more.
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system. Full article
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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