Abstract
Two increasingly popular distributed energy resources (DERs), especially within the European context, are photovoltaic (PV) installations and electric vehicles (EVs). Numerous models have been proposed for optimal management thereof, such as Home Energy Management Systems (HEMSs) and EV parking lot management systems (EVPLMS). However, these approaches are often designed to benefit only one party without taking into account the effect of any other management systems. I.e., HEMSs are designed to only maximize the economic benefit of home owners, while EVPLMSs are designed to only maximize the profit of parking lot owners. In this study, the coordinated use of these systems is modeled and simulated to investigate whether a synergistic relationship exists in which consumers (EV owners) have an added economic benefit by the simultaneous operation of HEMSs and EVPLMSs. As such, a cost-benefit analysis is conducted from the point of view of the EV owners, utilizing a HEMS at home and an EVPLMS at work. The analysis was performed on case studies that are based on real facilities, locations, meteorological data, and electricity market prices in Porto, Portugal.

Abstract
The advantages of wind power integration over other renewable resources are well-known information and the natural results are the massive worldwide integration. Such massive integration, without the correct management together with conventional generation leads to an augmented complexity and the inflexibility of conventional power systems. For several reasons, forecasting tools are one of the most valuable tools in the power systems field, because they helps to decide in advance the way to manage correctly and with profits the electrical mix production. In this work, an extended hybrid wind power forecasting approach, with probabilistic features, is proposed to forecast twenty-four hours-ahead, considering only real historical wind power data. To validate the proposed forecasting approach, a comparison with other validated models is performed to offer a fair and proportional analysis. The outcomes show that the suggested forecasting approach performs adequately even considering the reduced data available as input.

Abstract
Decentralization of power systems is creating a need for tools which can provide fast and accurate optimal power flow (OPF) solutions, without being dependent on the availability of all system information and/or uncertain variables. In this study, a hybrid probabilistic algorithm is proposed to accurately and efficiently predict ideal generation levels of individual generators to minimize the total system cost (as per AC-OPF), while having no information on the grid structure and with limited information on system variables. The proposed hybrid algorithm combines the use of correlation analysis, k-means clusters, and kernel density estimation (KDE), to predict ideal generation levels of each generator based only on historical datasets of local information (i.e. adjacent load centers). By simulating the AC-OPF problem on the IEEE 9-bus test system, a historical dataset of 1000 samples is synthetically generated and randomized local information is given as input for each agent. Quasi-deterministic Monte-Carlo simulations with 100000 samples were used for validation. In the most uncertain operating conditions, the proposed algorithm was capable of predicting the ideal generation level of the most expensive generator with a 1.65% error, while being three times faster than a Neural Network (NN), taking only 0.39 seconds to run on a standard laptop computer.

Abstract
Self-scheduling of Home Energy Management Systems (HEMS) is one of the most interesting problems for active end-users to reduce their electricity bills. The electricity bill reduction by adopting Demand Response Programs (DRP) considering the flexibility of the end-users is addressed in this paper. The problem is addressed as a multi-objective optimization problem. The first objective function is the minimization of the daily bill, while the second objective aims to minimize the Discomfort Index (DI) regarding shifting the home appliances plugging-in time. The Time-of-Use (ToU) tariff is adopted in this paper and therefore, the end-users can benefit from shifting their flexible loads from peak hours to the off-peak hours and this reduces their bills, accordingly. In this case, the end-users have to change their energy consumption which imposes a level of discomfort on the end-users. Therefore, a two-stage model is proposed in this paper to deal with the mentioned objective functions. The proposed model is represented as standard mixed-integer linear programming (MILP) and for solving this problem the epsilon-constraint method is adopted in this study. The obtained Pareto front from the epsilon-constraint multi-objective framework is fed to the fuzzy satisfying method for final plan selection. These results show that by providing the Pareto set of optimal solutions to the user, they are more informed and can make decisions that better suit their preferences.

Abstract
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 article 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. Several 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.

Abstract
The deployment of large scale photovoltaic (PV) power generation has been witnessed in several countries worldwide with different installed capacities. Accordingly, codes and regulations to ensure secure and economical operation have been revised to address the challenges related with PV integration into electrical networks. This paper presents an H8 mixed sensitivity robust control design for enhancing the overall damping of low frequency oscillations. The presented architecture will implement the output signal of the power oscillator damper (POD) at the control loop of the PV-based solar power plant. The effectiveness of the proposed approach is tested using the New-England, 10machines test system. © 2020 IEEE.