Abstract
Economic generation scheduling (EGS) is a non-convex optimization problem for allocating optimal generation among the committed units that can meet given real-world practical limits such as ramp rate limits, prohibited operating zones, valve loading effects, multi-fuel options, spinning reserve and transmission system losses at the minimum fuel cost. Moreover, considering environmental issues results in an environmental/economic generation scheduling (EEGS) problem that is a multiobjective optimization model with two non-commensurable and contradictory objectives. In this paper, a novel method has been presented in order to minimize production cost and emission of the steam power plants in short term periods. The obtained results showed that the proposed method can be used in short-term decision making of steam power plants which will be absolutely effective in long-term emission target oriented strategies. A framework is proposed for solving single objective EGS and multiobjective EEGS problems considering the aforementioned constraints. The problem is solved by a new meta-heuristic optimization called Ray Optimization (RO) to determine the optimal power generation. The performance of the proposed algorithm is investigated by applying it to solve diverse test systems having nonconvex solution spaces. Numerical results have been comprehensively compared with some of the most recently published research works in the area in order to validate the results and confirm the potential of the proposed approach. The obtained results show the application of the proposed framework and effectiveness of the solutions.

Abstract
In the new smart grid paradigm, the residential prosumers can more actively participate in the energy exchange mechanisms by adjusting their consumption through demand response programs and their own available local generation and energy storage system. On these bases, a new model of home energy management system (HEMS) is proposed and analyzed in this paper. Numerical studies show that the proposed HEMS is able to find the optimal operating scenario in different situations and to achieve a reduction of the billing costs by providing some electric flexibility to an aggregator or to a system operator.

Abstract
Due to the rapidly increasing share of electric vehicles (EVs) worldwide, the abundance of EV parking lots (with charging capabilities) is becoming necessary to provide for charging needs in addition to attempting to fully utilize EVs for the benefit of future smart grids. Unmanaged charging of EVs can jeopardize stability and reliability of power systems. Hence, well-operated EV parking lots can be a good solution to enhance system stability. Equipping parking lots with rooftop photovoltaics (PVs) has been gaining interest as a good approach for their design and operation. During the day, when EVs are stationed in the parking lot and particularly in more commercial neighborhoods of cities, the EVs can be charged directly through solar generation, so that minimal stress on the distribution system occurs. This work aims to conduct a comparative study investigating the optimal strategies for the operation of PV-equipped EV parking lots. Multiple parameters are taken into consideration including weather conditions, uncertainty of EV owners' schedules, and EV models. This analysis will result in finding the optimal strategy for the operation of the parking lot from the owner/operator point of view in order to minimize costs and maximize services provided to the grid.

Abstract
This study presents an optimal power management for a microgrid (MG) in distribution market environment. The MG operator is able to have interactions with the distribution market operator (DMO) and adjacent MG (AMG) operator to supply its local loads. The DMO regulates the electricity market, and assigns the electricity price and power profile for the MG. The motivation behind the use of The DMO is that it works as an entity between MG and independent system operator (ISO) in order to guarantee a flexible operation by reducing power fluctuation and reduce the unintentional peak loads in low market price hours. The unused power capacity in AMG is used during the islanded hours through an additional interconnection point connected to the MG. Using this method reduces the need for installing new generation resources, which will be a practical and economical solution for MG developer. This MG which is able to provide power from distribution market and the AMG with two interconnection points is named dependent MG (DMG). A market-based stochastic model containing distribution market constraints and AC power flow formulations employs conditional value at risk (CVaR) methodology to capture the loads and wind uncertainties. The effectiveness of the presented model is evaluated on a 20 kV test system using different case studies. In this test system, the operator is the owner of all generation units. The numerical analysis explicate that presented model reduces the operation cost of DMG with the aim of responsive loads, unused power capacity, energy storage system (ESS) and power generation units. The market-based scheduling also provides operational flexibility for distribution system by adjusting flexibility limit of market constraints. It is also shown that changing risk preferences level changes the power generation pattern in ESS and causes a costly operation of resources in risk-averse strategy. The competence of this model is significant when the preventive maintenance (PM) program is carried out, and the DMG should rely on its flexible resources and AMG's available power capacity, which could reduce the load shedding.

Abstract
This study studies a double-surface sliding-mode observer (DS-SMO) for estimating the flux and speed of induction motors (IMs). The SMO equations are based on an IM model in the stationary reference frame. The DS-SMO is developed based on the equations of a single-surface SMO (SS-SMO) of IM. In DS-SMO method, the observer is designed through combining sliding variables produced by combining estimated fluxes of currents error. The speed is easily determined based on the pass of switching signal through a low-pass filter. Also, an optimal DS-SMO (ODS-SMO) is proposed to improve the transient condition by optimally tuning the observer parameters. To optimise these parameters, the particle swarm optimisation method is adopted. Moreover, an improved DS-SMO (IDS-SMO) is proposed to improve both transient and steady-state conditions, torque ripple and total harmonic distortion. Moreover, the proposed IDS-SMO has a stable performance under sudden load change and the low-speed region. Finally, the accuracy of the proposed ODS-SMO and IDS-SMO methods is substantiated through simulation and experimental results.

Abstract
Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP–TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP–TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration.