# Special Issue on Developing and Implementing Smart Grids: Novel Technologies, Techniques and Models
###### Abstract  
The proposed algorithm yields approximately 23% energy cost savings. [PERSON] et al. (2020) used a 20-busy-based 20-busy-based 20-busy-based 20-busy-based 20  
proposed to be solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. [PERSON] et al. analyzed the complexity of the energy management system development process resulting from an evolving power utility automation in [7]. As a result, flexibility, complexity, interoperability, and overlapping issues were identified as main concerns to be faced during the design and implementation stages of battery energy storage system (BESS) control applications. Considering the variability of solar power, [PERSON] et al. aimed to reveal how the applied classifiers and the training data impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale [8]. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) were applied to classify the daily local weather types for the day-ahead short-term solar photovoltaic (PV) power forecasting. [PERSON] et al. introduced either a methodology to select the best performing mathematical tool to investigate the electrical behavior of the power distribution systems--depending on their linearity and stationarity--or an index to discriminate the power distribution systems on the basis of a different amount of PV penetration [9]. A grey wolf optimization algorithm based on the dynamic adjustment of the proportional weight and convergence factor was presented by [PERSON] et al. to solve the operating model of the distribution network comprising the micro energy grid [10]. The simulation results demonstrated that the model and algorithm 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. To develop the bi-directional communication in smart grids, an enhanced version of effective filtering approach (EFA) called enhanced-EFA (eEFA) was proposed by [PERSON] et al. that does not forward unicast frames to nondestination doubly-attached nodes with high-availability seamless redundancy (HSR) protocol rings [11]. One of the features of the approach is that it does not use any control message to discover passive QuadBox rings in bidirectional communications. [PERSON] et al. [12] have summarized the construction method as an effective measure to describe the computation, communication, and integration processes of a power grid in the current research. Then, they proposed a platform providing an intuitive and accurate way to reconstruct the cyber-physical power system (CPPS) environment where the influence of the information side of the CPPS control effects was verified. A novel, wide-area measurement system (WAMS)-based optimal under-frequency load-shedding (UFLS) technique was proposed by [PERSON] et al. aiming to operating complex systems in a stable mode [13]. In order to absorb renewable power variations, one method can be to alleviate the error of forecast in smart grids. Thus, [PERSON] et al. 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 [14]. Simulation results stated that their proposed model has advantages of strong model interpretation, high accuracy, and stable error performance. For the purpose of load forecasting improvement, [PERSON] et al. tried to handle the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods [15]. The proposed models has proved to sufficiently improve the performance of hourly short-term load forecasting. The paper authored by [PERSON] et al.