One of the most important sources of clean energy in the future is expected to be solar energy which is considered a real time source. Research efforts to optimize solar energy utilization are mainly concentrated on the components of solar energy systems. Photovoltaic (PV) modules are considered the main components of solar energy systems and PVs’ operations typically occur without any supervisory mechanisms, which means many external and/or internal obstacles can occur and hinder a system’s efficiency. To avoid these problems, the paper presents a system to address and detect the faults in a PV system by providing a safer and more time efficient inspection system in real time. In this paper, we proposing a real time inspection and fault detection system for PV modules. The system has two cameras, a thermal and a Charge-Coupled Device CCD. They are mounted on a drone and they used to capture the scene of the PV modules simultaneously while the drone is flying over the solar garden. A mobile PV system has been constructed primarily to validate our real time proposed system and for the proposed method in the Digital Image and Signal Processing Laboratory (DISPLAY) at Western Michigan University (WMU). Defects have been detected accurately in the PV modules using the proposed real time system. As a result, the proposed drone mounted system is capable of analyzing thermal and CCD videos in order to detect different faults in PV systems, and give location information in terms of panel location by longitude and latitude.
In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. ''Budget of uncertainty'' and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.
Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.
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