Abstract
This work addresses a stochastic framework for optimal operation and long-term expansion planning of combined heat and power based microgrid as a part of an active distributing system. The microgrid utilizes renewable energy sources, electricity and heat generation units, energy storage systems, and demand response programs. The proposed model determines the optimal location and capacity of the electrical and thermal facilities, and it considers the impact of renewable energy sources and demand response on the expansion-planning problem. A stochastic mixed-integer linear programming formulation is utilized to minimize the investment and operation costs of system for five years. To evaluate the effectiveness of the proposed model, the algorithm is assessed for the 9-bus system and the 33-bus IEEE test systems. The results demonstrate that the utilization of the proposed algorithm reduces the operational cost and increases system revenues. © 2020 IEEE.

Abstract
Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system's operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

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
Designing appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units. © Springer Nature Switzerland AG 2020.

Abstract
The stability and monitoring of AC microgrids (AC MG) are greatly influenced by gathering sufficient and precise information. Since installing several sensors on AC MGs is costly and increases AC MG ripple, integrating a minimum number of cost-effective sensors is preferred. In this paper, a joint-estimating advanced augmented-Kalman filter (KF) to estimate the current of the AC MG and unknown time-varying loads from the noisy measurement of the AC bus voltage is developed. The proposed approach also provides smooth and noise-less information from the measured voltage. The presented method has less complexity to handle and as a robust approach, it would be capable of dealing with uncertainties due to the load, which can be linear, nonlinear, or unbalanced. The joint-estimating augmented-KF outputs can be then utilized in the monitoring, fault detection, and control design purposes. The developed framework is tested on an AC MG supplying time-varying load and numerical results verify the applicability and accuracy of the developed technique to estimate the load and filter currents.

Abstract
Electric vehicles (EVs) are developing due to concerns over global warming and the major role of the transportation sector in emissions. EVs can also flatten the power curve and increase the reliability of power grids when renewable energy sources are used. Despite of these benefits, EVs impose new loads on distribution networks. Simultaneous charging of EVs, especially at high penetration levels, can create new load peaks in the power curves as well as overloading transformers, shortening their service life. In actual applications, most electric cars are single-phase loads that need to be charged from household or commercial outlets. In this paper, an optimization method is presented to coordinate the dynamic charge operation of single-phase EVs in an unbalanced three-phase distribution network. In the proposed method, the main goal of charging management is to minimize the total cost, which considers both network security constraints and electric vehicle constraints. The proposed method is tested on a sample distribution network and the numerical results prove the effectiveness of the method.