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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 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. 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.