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The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed. [ABSTRACT FROM AUTHOR]

Fast depleting fossil fuels and the growing awareness for environmental protection have led us to the energy crisis. Hence, efforts are being made by researchers to investigate new ways to extract energy from renewable sources. ‘Microgrids’ with Distributed Generators (DG) are being implemented with renewable energy systems. Optimization methods justify the cost of investment of a microgrid by enabling economic and reliable utilization of the resources. This paper strives to bring to light the concept of Hybrid Renewable Energy Systems (HRES) and state of art application of optimization tools and techniques to microgrids, integrating renewable energies. With an extensive literature survey on HRES, a framework of diverse objectives has been outlined for which optimization approaches were applied to empower the microgrid. A review of modelling and applications of renewable energy generation and storage sources is also presented. [ABSTRACT FROM AUTHOR]

Islanded microgrids (IMGs) provide a promising solution for reliable and environmentally friendly energy supply to remote areas and off-grid systems. However, the operation management of IMGs is a complex task including the coordination of a variety of distributed energy resources and loads with an intermittent nature in an efficient, stable, reliable, robust, resilient, and self-sufficient manner. In this regard, the energy management system (EMS) of IMGs has been attracting considerable attention during the last years, especially from the economic and emissions point of view. This paper provides an in-depth overview of the EMS optimization problem of IMGs by systematically analyzing the most representative studies. According to the state-of-the-art, the optimization of energy management of IMGs has six main aspects, including framework, time-frame, uncertainty handling approach, optimizer, objective function, and constraints. Each of these aspects is discussed in detail and an up-to-date overview of the existing EMSs for IMGs and future trends is provided. The future trends include the need for improved models, advanced data analytic and forecasting techniques, performance assessment of real-time EMSs in the whole MG's control hierarchy, fully effective decentralized EMSs, improved communication and cyber security systems, and validations under real conditions. Besides, a comprehensive overview of the widely-used heuristic optimization methods and their application in EMSs of IMGs as well as their advantages and disadvantages are given. It is hoped that this study presents a solid starting point for future researches to improve the EMS of IMGs. • The six aspects of the energy management system optimization of islanded microgrids. • Overview of heuristic algorithms for EMS optimization problem. • EMS optimization frameworks and uncertainty schemes: Deterministic, stochastic, robust. • Reviewing constraints, cost functions, and time-frames for EMS optimization problem. • Introducing the future trends in energy management system of islanded microgrids. [ABSTRACT FROM AUTHOR]

Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the ε-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in the form of a HIS finds optimal values for economic and environmental conditions so that the difference between pollution and cost values from its minimum values at the compromise point is roughly 22%. For 17% uncertainty parameters prediction errors, the scheme obtains a robust structure for the IHS. • Planning of islanded hybrid system. • Model of uncertainties by robust optimization. • Using SCA+CSA to solve problem. [ABSTRACT FROM AUTHOR]