Abstract
Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling helps readers develop their skills in modeling, simulating, and optimizing electric power systems. Carefully balancing theory and practice, it presents novel, cutting-edge developments in forecasting and scheduling. The focus is on understanding and solving pivotal problems in the management of electric power generation systems. Methods for Coping with Uncertainty and Risk in Electric Power Generation Outlining real-world problems, the book begins with an overview of electric power generation systems. Since the ability to cope with uncertainty and risk is crucial for power generating companies, the second part of the book examines the latest methods and models for self-scheduling, load forecasting, short-term electricity price forecasting, and wind power forecasting. Toward Optimal Coordination between Hydro, Thermal, and Wind Power Using case studies, the third part of the book investigates how to achieve the most favorable use of available energy sources. Chapters in this section discuss price-based scheduling for generating companies, optimal scheduling of a hydro producer, hydro-thermal coordination, unit commitment with wind generators, and optimal optimization of multigeneration systems. Written in a pedagogical style that will appeal to graduate students, the book also expands on research results that are useful for engineers and researchers. It presents the latest techniques in increasingly important areas of power system operations and planning. © 2012 by Taylor & Francis Group, LLC.

Abstract
Modelling wind power uncertainty is a critic aspect in the optimal management of power systems with high integration of this renewable resource. It is typically carried out by considering a limited number of representative scenarios that incorporate relevant properties such as hourly auto-correlation and diurnal forecasting profile. Considering a large amount of scenarios improves the wind power modelling, but increases the computational effort. To deal with this problem, a method to incorporate a big set of scenarios in stochastic unit commitment (UC) problem is presented in this paper. The effectiveness of the proposed methodology is evaluated by means of the analysis of a case study and the results are compared to those obtained from a stochastic programming method, concluding that the method presented in this paper offers an approximated solution in a reduced computational time.

Abstract
As a contribution for sustainability, electric vehicles (EVs) are seen as one of the most effective influences in the transport sector. This paper proposes an improved voltage control of the EV operating as uninterruptible power supply (UPS) in smart homes. With the EV plugged-in into the smart home, it can act as an off-line UPS protecting the electrical appliances from power grid outages. The foremost advantages of the proposed voltage control strategy are comprehensively emphasized, establishing a comparison with the classical approach. Aiming to offer a sinusoidal voltage for linear and nonlinear electrical appliances, a pulse width modulation with a multi-loop control scheme is used. A Kalman filter is used for decreasing significantly the time of detecting power outages and, consequently, the transition for the UPS mode. The computer simulations and the acquired experimental results validate the proposed strategy in different conditions of operation. © 2019 Vitor Monteiro et al.

Abstract
This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot-based electric vehicles (EVs). Both thermal and electrical features of the multi-energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price-based and incentive-based demand response (DR) programs are modelled in the proposed multi-energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi-energy MGs.

Abstract
Installation of new power generating units as backup black-start (BBS) sources is a vital issue to improve the acceleration of power network restoration, especially when a serious problem is occurred in main BS units (BSUs) and leads to fail in operation. Accordingly, this work address a new design for the optimal locating of the Gas-based Turbine (GT) as BBS to improve the smart grid performance during both restoration and normal conditions. To this end, there will be incompatible fitness functions to be minimized. Therefore, a multi-objective problem (MOP) including a mixed integer Nonlinear programming (MINLP), is formulated. The Pareto answers of the proposed MOP as the best solutions are modified and extracted by utilizing a meta-heuristic method, called crow search algorithm (CSA). A typical test system is employed for evaluation of the given plan. The extracted outcomes reveal that the network can desirably operate from this design not only to favorably enhance the capability of BSUs, but also to improve the power system performance in normal conditions. It also provides the better start-up program of non-black-start (NBS) power sources with the optimal paths during the restoration process.

Abstract
The many well-established advantages of distributed generation (DG) make their usage in active distribution networks prevalent. However, uncontrolled operation of DG units can negatively interfere with the performance of other equipment, such as tap-changers, in addition to resulting in sub-optimal usage of their potential. Thus, adequate scheduling/control of DG units is critical for operators of the distribution system to avoid those adverse effects. A linearised model of a multi-objective method for coordinating the operation of photovoltaics, battery storage systems, and tap-changers is proposed. Three objective functions are defined for simultaneously enhancing voltage profile, minimising power losses, and reducing peak load power. The formulated multi-objective problem is solved by means of the epsilon-constraint technique. A novel decision-making methodology is offered to find the Pareto optimality and select the preferred solution. To assess to proposed model's performance, it is tested using 33-bus IEEE test system. Consequently, tap-changers suffer lessened stress, the batteries state-of-charge is kept within adequate limits, and the DG units operation is at higher efficiency. The obtained results verify the effectiveness of this approach.