With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9 % recall, 100% F1, 100% precision, 99.21 % training accuracy, and 99.70% testing accuracy.)

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.

Despite the universal importance of price based demand response (DR) for managing electric vehicle (EV) charging load, the academic literature has explored various mechanisms to its implementation. The prequel to this work has demonstrated that implementation of load management schemes on the basis of price based DR programs leads to costlier scheduling for low or constant energy consumers. In this regard, the proposed work has considered and expanded the same idea from analytical as well as implementation point of view to multiple EV charging regions and respective loads. We present a novel mechanism to calculate EV charging prices using individualized energy consumption patterns of EVs in each region. In this regard, all EV regions/stations receive a dynamic price signal which is non-discriminatory in nature. The dynamic price signals are specifically designed to mitigate the impact of discriminatory prices on end user’s cost. Furthermore, the other objectives of these non-discriminatory prices are to lower energy cost and rebound peaks without affecting utility objective (i.e., net revenue). Initially, a new mathematical model is presented to calculate charging prices based on real time load demand and market dynamics. Then relatively a well behaved functional form of the optimization problem is formulated and the cost minimization objective function is solved by using genetic algorithm (GA). The optimization program successfully converges to give global optimum solution validating the effectiveness of proposed mechanism. Finally, the analytical and simulation results are conducted to show the achievements of our proposed work in terms of fair cost distribution with high user satisfaction. It is also proved that in both mechanisms, the utility’s revenue remains unaffected.

Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.

In this paper, an efficient optimization technique, called improved moth-flame optimization (IMFO) is proposed to improve the performance of conventional Moth-flame optimization (MFO). Then, both of MFO and IMFO are applied to solve the coordination problem of standard and non-standard directional overcurrent relays (DOCRs). In the proposed IMFO, the leadership hierarchy of grey wolf optimizer is used to improve the performance of conventional MFO with the aim of finding the best optimum solution. The major goal for optimal coordination of DOCRs is to minimize the total operation time for all primary relays as well as satisfy the selectivity criteria between relay pairs without any violation in the operating constraints. The performance and feasibility of proposed IMFO are investigated using three different networks (8-bus network, 9-bus network, and 15-bus). The proposed IMFO is compared with conventional MFO and other well-known optimization techniques. The results show the effectiveness of the proposed IMFO in solving both standard and non-standard DOCRs coordination problems without any violation between primary and backup relays. In addition, the results show the power of proposed IMFO in finding the best optimal relay settings and minimizing the total operating time of relays which its reduction ratio reaches more than 28% with respect to the conventional MFO. Furthermore, the reduction in the total operating time of primary relays reaches more than 50 % with the usage of the non-standard relay curve.