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Battery swapping station (BSS) is an emerging form of energy storage that can be integrated with microgrid (MG) for economical operation of the system. To manage the scheduling between MG and BSSs, this paper proposes an optimal scheduling model for promoting the participation of BSSs in regulating the MG economic operation. The proposed grid-connected MG consists of wind turbine, photovoltaic (PV) modules, and BSSs. After the BSSs are connected to MG, the batteries in the BSSs can offer auxiliary service to grid through the battery to grid (B2G) operation. Battery swap requests, electricity prices, and MG load influence the decision making in operation scheduling of MG and BSSs. Case studies prove the effectiveness of proposed model by minimizing the MG net costs and maximizing the profits of the BSS with B2G technique and charging strategy that reduces daily charging cost. [ABSTRACT FROM AUTHOR]

According to statistical reports, thermal power plants have long played a critical role in supplying electricity using fossil fuels. However, due to the high investment and operation costs of these power plants and their destructive effects on the environment, renewable energy sources (RESs) in power networks have been considered an effective alternative to traditional power plants. Optimal scheduling of microgrid in smart grids has a significant impact on reducing energy consumption, environmental pollutants and end-user's energy prices. In this paper, the influence of plug-in hybrid electric vehicles (PHEVs) charging on microgrids' optimal operation is evaluated. To assess the behaviour of PHEVs, three various charging patterns consisting of uncontrolled, controlled and smart charging methods are considered. Uncertainties due to forecast error in the PHEVs, loads, prices and renewable source output power are also considered in microgrid modelling energy management. To deal with the optimisation scheduling of microgrid considering uncertainty, a modified harmony search (MHS) algorithm is used. The suggested scheme is validated using simulations and without the PHEV charging effects compared with the conventional methods. [ABSTRACT FROM AUTHOR]

New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered. [ABSTRACT FROM AUTHOR]