As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this paper proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Different error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

Wind power generation forecasting is a crucial aspect in renewable energy industry since accurate predictions of wind power generation can support the power grid operators and power plant owners to optimize their energy resources management. This will potentially reduce the purchase of extra electricity from other stakeholders or neighboring countries and will consequence significant cost savings and further reliability. With the increasing importance of wind power utilization in modern power systems, employing accurate forecasting model cannot be understated. In this study, a new probabilistic forecasting model based on the utilization of quantile functions is discussed. The proposed model integrates an adaptive optimal weighted continuous ranked probability score (CRPS) is presented to improve the computational efficiency of the prediction process and enabling more accurate forecast output. Also, an adaptive Adam optimization algorithm is presented for the CRPS loss minimization. A convolutional neural network based long short-term memory (CNN-LSTM) is employed to evaluate the quantile function parameters. To validate the effectiveness of our approach, the specific forecasting models have been compared across a wide range of scenarios. The obtained results unequivocally reveal the superiority and improved accuracy of the proposed forecasting model.

The growing trend of wind generation in power systems and its uncertain nature have recently highlighted the importance of wind power prediction. In this paper a new wind power prediction approach is proposed which includes an improved version of Kriging Interpolation Method (KIM), Empirical Mode Decomposition (EMD), an information-theoretic feature selection method, and a closed-loop forecasting engine. In the proposed approach, EMD decomposes volatile wind power time series into more smooth and well-behaved components. To enhance the performance of EMD, Improved KIM (IKIM) is used instead of Cubic Spline (CS) fitting in it. The proposed IKIM includes the von Karman covariance model whose settings are optimized based on error variance minimization using an evolutionary algorithm. Each component obtained by this EMD decomposition is separately predicted by a closed-loop neural network-based forecasting engine whose inputs are determined by an information-theoretic feature selection method. Wind power prediction results are obtained by combining all individual forecasts of these components. The proposed wind power forecast approach is tested on the real-world wind farms in Spain and Alberta, Canada. The results obtained from the proposed approach are extensively compared with the results of many other wind power prediction methods.

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users’ anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles’ owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs’ batteries during the off‐peak hours and drawing it from the EVs’ batteries during peak‐demand hours of the day. In order to minimise the costs associated with EVs’ charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

The electricity market has become a competitive model due to generation, transmission, distribution and large-scale investments in microgrids where no electricity supplier intends to set the price. However, the competition itself provides opportunities for the buyer to be able to experience more profit. Based on the available optimal bidding model, the supplier can only offer the final bid. Hence, active participation on the demand side creates an efficient and reasonable electricity market, and also promotes additional optimal allocation of economic resources. This study has concentrated over the final marginal bidding price and creating an optimal bidding strategy for consumers. It has specifically focused and proposed a new optimized model for electricity market bidding to improve and obtain a maximum turnover and profit for players. Since the solution of the aforementioned issue is inadequate due to multiple decision variables in mathematical and classical methods; a new enhanced Harris Hawks Optimization (HHO) is proposed. Trapping into local optimum point can be occurred in different optimization algorithms, while the proposed model has simple and efficient structure, without derivative operator which is integrated with mathematical techniques to increase local and global search abilities. The standard HHO algorithm is utilized to increase the probability of entrapment in local optima through increasing the number of optimization variables; however, the challenge of large number of variables is resolved through proposing a novel local/global operators. The proposed model is examined with a standard IEEE system in 2-scenarios, with symmetric and asymmetric information through finding the optimal solution desirably.