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
Energy arbitrage have monetary benefits for privately owned battery energy storage systems, such as the battery of an electric vehicle or residential batteries. However, the life cycle and degradation cost of the battery storage should be taken into consideration and can decrease obtained income in the long-term. This paper proposes an optimization framework to derive optimal bidding and offering curves for lead-acid battery storage participate in a stepwise energy market. The objective is to maximize the profit comes from participating in energy arbitrage action, while the life cycle of the battery is considered by objective function and constraints. Due to the small capacity of the considered storage unit, it can be assumed that this unit is a pricetaker participant, which its actions cannot influence the market prices. Hence, the energy prices are modeled as uncertain parameters using stochastic programming approach. The second order stochastic dominance constraints are as risk management method.

Abstract
A key challenge for future energy systems is how to minimize the effects of employing demand response (DR) programs on the consumer. There exists a diverse range of consumers with a variety of types of loads, such as must-run loads, and this can reduce the impact of consumer participation in DR programs. Multi-energy systems (MES) can solve this issue and have the capability to reduce any discomfort faced by all types of consumers who are willing to participate in the DRPs. In this paper, the most recent implementations of DR frameworks in the MESs are comprehensively reviewed. The DR modelling approach in such energy systems is investigated and the main contributions of each of these works are included. Notably, the amount of research in MES has rapidly increased in recent years. The majority of the reviewed works consider power, heat and gas systems within the MES. Over three-quarters of the papers investigated consider some form of energy storage system, which shows how important having efficient, cost-effective and reliable energy storage systems will be in the future. In addition, a vast majority of the works also considered some form of demand response programs in their model. This points to the need to make participating in the energy market easier for consumers, as well as the importance of good communication between generators, system operators, and consumers. Moreover, the emerging topics within the area of MES are investigated using a bibliometric analysis to provide insight to other researchers in this area.

Abstract
Nowadays demand response (DR) is known as one of the main parts of the power system especially in the smart grid infrastructure. Furthermore, to enhance the participation of the consumers in the DR programs, the Independent System Operators (ISOs) have introduced a new entity, i.e. Demand Response Aggregator (DRA). The main contribution of this paper is to investigate a novel framework to increase the profits of the DRA participating in the day-ahead electricity market, i.e. employment of an axillary generation system in the DRA entity. It is supposed that the DRA in this paper has an axillary generation system and it would lead to an increase in the profit of the DRA through avoiding the economic loss in the process of trading DR obtained by the active participation of prosumers in the electricity market. The fuel cell is introduced as the axillary generation unit to the DRA unit. In the framework proposed in this paper, the DR is acquired from end-users during peak periods and will be offered to the day-ahead electricity market. The power flow during the off-peak hours is in another direction, i.e. from the grid to the consumers. In this model, the information-gap decision theory (IGDT) is chosen as the risk measure. The uncertain parameter is the day-ahead electricity market price. The optimization problem's objective is to maximize the profit of the DRA. The behavior of the risk-seeker decision-maker is analyzed and investigated. The feasibility of the program is demonstrated by applying it to realistic data.

Abstract
In this paper, an integrated approach for optimal planning and operation of energy hubs is provided considering the effects of wind energy resources. Inevitable uncertainties of electrical, heating, cooling demands as well as the wind power generation are considered in this study. The proposed model is based on two-stage optimization problems and represented as a stochastic programming problem to address the effects of uncertain parameters. In order to address the uncertain parameters in the model, different scenarios have been generated by Monte-Carlo Simulation approach and then the scenarios are reduced by applying K-means method. In addition, the effects of demand response programs on the operational sub-problem are taken into account. Benders decomposing approach is adopted in this research to solve the complex model of coordinated planning and operation problem. The master problem is supposed to determine the type and capacity of hub equipment, while the operating points of these assets are the decision variables of the operational slave problem. As a result, the proposed mathematical model is expressed as a linear model solved in GAMS. The simulation results confirm that the Benders decomposition method offers extremely high levels of accuracy and power in solving this problem in the presence of uncertainties and numerous decision variables. Moreover, the convergence time is drastically decreased using Benders decomposition method.

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.