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Existing literature on microgrids (MGs) has either investigated the dynamics or economics of MG systems. Accordingly, the important impacts of battery energy storage systems (BESSs) on the economics and dynamics of MGs have been studied only separately due to the different time constants of studies. However, with the advent of modern complicated microgrids, BESSs are bridging these two domains. Thus, there is a need to study how these two are related in conjunction with each other. Looking at both the economics and dynamics of MGs and exploring their links will help researchers develop joint Econo-dynamics models, notably in the context of digital twin technology, that could be the future trend toward net-zero emission systems. This paper reviews, analyses, and classifies BESSs applications based on their time constants. The classified BESS applications are: 1) synthetic inertia response; 2) primary frequency support to compensate for the slow response micro-sources; 3) real-time energy management for covering intermittent renewables; 4) economic dispatch for improving steady-state performance, and 5) slack bus realization. Research gaps and future trends have been discussed throughout the paper and are summarized in the future trend section. This work was supported in part by the Australian Research Council (ACR) under Grant DP190102501. This work is partially supported by AlphaESS through ARC Research Hub for Integrated Energy Storage Solutions (IH180100020).

Large-scale compressed air energy storage (CAES) is conventionally used in power systems. However, application of CAESs at the distribution level is limited because of differences in design and efficiency. On the other hand, application of electrical batteries suited for distribution networks (DNs) faces also challenges from high investment cost and significant degradation. In this regard, this paper presents the participation of an active distribution system equipped with a small-scale CAES (SCAES) in the day-ahead wholesale market. To make CAES applicable to DNs, thermal-electrical setting design of the SCAES coupled with a packed-bed heat exchanger is adopted in the operation of the grid, where SCAES performs as an energy storage for DNs to surpass existing deficiencies of battery banks. The electrical/thermal conversion rate has been modeled for the SCAES operation. Moreover, the operation strategy of the SCAES is optimally coordinated with an electric vehicle charging station (EVCS) as an alternative energy storage technology in deregulated DNs. To make EVCS simulation more realistic, Gaussian Copula probability distribution function is used to model the behavior of the EVCS. The results obtained from different case studies confirm the value of SCAES as a reliable energy storage technology for DNs.

The literature on microgrid (MG) studies can be categorized as those that investigated the dynamics or economics of MG systems. Due to the important roles of battery energy storage systems (BESSs) in MGs, the BESSs have been involved in both economics and dynamics studies but mostly separately due to the different time constants of studies. Whereas, in the complicated modern MGs, the BESS is the joint point that bridges these two studies. Thus, studying merely one aspect of economy-dynamic without considering the other aspect would not be accurate. To bridge this gap, this paper reviews, analysis and classifies the BESSs applications, based on their time constants, by merging of which helps researchers to develop joint economy-dynamics models that would be the future trend in the field. The classified BESS applications are 1) inertia synthesis, 2) primary frequency response to compensate slow response time of micro-sources (MSs) for load tracking, 3) real-time energy management for the integration of intermittent renewables, 4) economic dispatch and optimal power flow for improving steady-state performance, and 5) slack bus realization. The research gaps and future trends have been discussed throughout the paper and are summarized in the future trend section.

Participation of wind generation in electricity markets is mainly restricted by the intermittent nature of wind and their possible outages. The great potential of flexible loads from demand response (DR) can be seen as a cost-effective option to handle such issues. In this regard, this study investigates the operation of a virtual power plant (VPP) that is constructed by a DR aggregator and wind power aggregator to handle the inherent volatility of wind generators as well as the possible wind power outage. A stochastic programming formulation in three stages is offered for the VPP that participates in the balancing, intraday and day-ahead markets. The model for DR is developed based on the elasticity concept, and the scenarios related to severe outages of the wind generators are considered. In order to manage the risk of the problem, conditional value-at-risk has also been employed in offering strategy. Case studies demonstrate that the VPP offering strategy can efficiently solve the balancing problem as well as outage risk of the wind generation while increasing the net profit in case of joint operation.

This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi‐objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57‐bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.

© 2019 Elsevier Ltd Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.

© 2019 Elsevier Ltd Restructuring of power systems along with the integration of renewable energy resources in electricity networks have transformed traditional distribution networks (DNs) into new active distribution systems (ADSs). In addition, rapid advancement of technology has enabled the bulk utilization of power generation units and energy storage (ES) systems in distribution networks. The next step in this trend is to decentralize ADSs to microgrids (MGs). This paper aims to present a review on the recent advancements in the development of ADSs and MGs. In this respect, the regulatory requirements and economic concepts, by which the traditional passive DNs are evolved into ADSs, are categorized and illustrated first. Then, the state-of-the-art of ADS formation is detailed based on the novel standpoint of grid operation factors which are involved in deregulated electricity markets at the distribution level. After presenting highlighted projects of MGs across the world, a similar review approach has been adopted to explain the formation of MGs which play a vital role in the decentralization of ADSs. This survey can provide both policy makers and distribution system planners with new perspectives to establish or participate in day-ahead wholesale markets.

© 2019 Elsevier Ltd As the number of electric vehicles (EVs) is steadily increasing, their aggregation can offer significant storage to improve the electric system operation in many aspects. To this end, a comprehensive stochastic optimization framework is proposed in this paper for the joint operation of a fleet of EVs with a wind power producer (WPP) in a three-settlement pool-based market. An aggregator procures enough energy for the EVs based on their daily driving patterns, and schedules the stored energy to counterbalance WPP fluctuations. Different sources of uncertainty including the market prices and WPP generation are modeled through proper scenarios, and the risk is hedged by adding a risk measure to the formulation. To obtain more accurate results, the battery degradation costs are also included in the problem formulation. A detailed case study is presented based on the Iberian electricity market data as well as the technical information of three different types of EVs. The proposed approach is benchmarked against the disjoint operation of EVs and WPP. Numerical simulations demonstrate that the proposed strategy can effectively benefit EV owners and WPP by reducing the energy costs and increasing the profits.

© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods.

© 2019 Elsevier B.V. The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data mining of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settlements. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers’ smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems operators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various approaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use.

Interest in language teacher reflection has grown massively after the emergence of post-method era. This study aims to explore the potential associations between teacher reflectiveness and experience among EFL teachers. To this end, a standard English Language Teaching Reflection Inventory (ELTRI) was employed to measure the reflectiveness of 179 English language teachers. The results revealed that there is a negative correlation between teaching experience of teachers and their reflectiveness that is aligned with the tacit claims of Cognitive Consistency Theories, specifically Congruity Theory. It is suggested that as teachers grow more experienced, they tend to become more stabilized in their instructional praxis. The findings of this study bear important implications for those who are in the realm of teaching in general and language teaching in particular.

© 2017 IEEE. Offering strategy of a price-maker demand response aggregator (DRA) in a two-settlement market is presented in this paper. The aggregator minimizes its cost by offering energy and price bids in the day-ahead market and energy bids in the balancing market. On the other hand, DRA optimally manages the aggregated demands of a large number of electric vehicles and properly distributes them through the time. The problem is formulated as a stochastic mixed-integer nonlinear optimization problem. The risk of the problem is managed by conditional value-at-risk measure and finally, the proposed approach is numerically evaluated through a detailed case study.

© 2017 IEEE. This paper presents a new framework for risk consideration of participants in electricity markets. Current approaches add risk measures to stochastic problem formulation in order to control the variation of the profits (costs). However, in the proposed method, cumulative distribution function of profit is utilized and it is shaped based on the preferred risk levels of the producer (buyer). The features of this new method are compared with the current approaches based on a detailed case study performed on the problem of a producer participating in a three settlement market.

© 2017 IEEE. Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation and load profiling. The recent changes in power system structure and introduction of new technologies necessitate the new investigation of applications and benefits of clustering methods for power systems. In this regard, this paper aims at reviewing the new research for clustering techniques for residential customers.

© 2017 IEEE. The concepts of demand response (DR) and demand side management have been in practice by power utilities for many decades. Due to fundamental changes in the electricity structure in recent years, the provision of DR from small loads like residential buildings or electric vehicles has become possible. However, there are still many challenges to the real implementation of DR programs for such customers. Based on a detailed survey of the literature, this paper tries to illustrate the role of aggregators in the implementation and expansion of DR in future smart grids. Then different technical and managerial challenges of DR aggregation for residential customers will be discussed thoroughly.

A microgrid in form of a distributed system must work in both modes of grid connected and islanding. With considering to the islanding mode of a microgrid, the synchronism conditions should be established to connect it back to the main grid. To synchronism this part of the network to the main power system, at first, phase estimation have to be performed by a static switch. The synchronizers those are used for three-phase systems are based on the use of phase locked loop (PLL) in synchronous reference frame. In this paper a static switch is designed which has a new PLL with neural network controller and can correctly diagnosis the phase angle for an unbalance input voltage vector. This type of PLL relative to the initiative and modified ones has more simple structure. Here, in order to verify the effectiveness of the proposed method, simulation is carried out on Davarzan area microgrid in Iran using MATLAB software.

This paper present a new method for design of power system stabilizer (PSS) based on sliding mode control (SMC) technique. The control objective is to enhance stability and improve the dynamic response of the single machine infinite bus (SMIB) system. In order to test effectiveness of the proposed scheme, simulation will be carried out to analyze the small signal stability characteristics of the system about the steady state operating condition following the change in reference mechanical torque and also parameters uncertainties. For comparison, simulation of a conventional control PSS (lead-lag compensation type) will be carried out. The main approach is focusing on the control performance which later proven to have the degree of shorter reaching time and lower spike.

Among ac drives, the permanent magnet synchronous motor has gained popularity owing to its high torque to current ratio, large power to weight ratio, high efficiency, high power factor and robustness .Because of its technical and economic advantages, Permanent Magnet Synchronous Motor drive technology is a serious contender for replacing the existing technologies. In this paper we report the utilization of a modern controller (BELBIC) based on emotion processing mechanism in brain. Our results show superior control characteristics especially very fast response, simple implementation and robustness with respect to disturbances and manufacturing imperfections. Our proposed method enables the designer to shape the response in accordance with the multiple objectives of his/her choice. And it may be asserted, the system with more suitable regulation of parameters achieves higher performance too.

This paper is concerned with the effect of power system harmonics and voltage unbalances on the thermal aging of the insulation of electric motors. A thermal model is proposed where the motor is divided geometrically into a number of lumped components, with interconnections to neighboring components through a linear mesh of thermal impedances. The lumped parameters are derived from entirely dimensional information, the thermal properties of the materials used, and constant heat transfer coefficients. Arrhenius chart is then used to estimate motor insulation remaining life. The proposed approach is used to determine the effect of supply harmonics and voltage unbalances on the useful life of a three phase induction motor and also validated by experimental results.

Nowadays, the valuation of reactive power is an important research issue in restructured power systems. Although different methods established for the valuation or pricing reactive power but they have a little attention to power system security and reliability. Here a new concept of equivalent reactive power compensation method based on contingency selection and probability characteristic of power system is introduced. Also modification of this method by using voltage control areas is discussed. Results procured from these improvements show the effectiveness of the proposed modifications.