<p>This paper presents the importance, issues and challenges related to Smart Grid. It also evaluates various approaches for Smart Grid planning and operation. It discusses tools for asset management and their applicability to the next generation grid. Aging assets, uncertainty in load demand profile and renewable energy resources, and demand management create a challenge for the optimal operation and maintenance of electrical grid. This paper addresses the challenges and opportunities to improve transmission and distribution systems asset maintenance. This paper also presents the asset replacement alternatives. This paper also presents the cost-benefit analysis of asset management using the information/real time data from the utility company. This paper will serve a guide for doing the asset management to the electrification process, investment and recovery to sustain reliable and efficient power delivery.</p>
This paper presents an optimum day-ahead scheduling of thermal and renewable (wind and solar photovoltaic) power generation as a multi-objective optimization (MOO) problem considering the uncertainty. System operating cost (i.e. cost of thermal, wind, solar PV and battery), reliability and emission cost are considered to be optimized simultaneously. The uncertainties due to the generator outages, wind, solar PV power and load forecast errors are incorporated in the proposed optimization problem using the Loss Of Load Probability (LOLP) and Expected Unserved Energy (EUE) reliability indices. In the proposed approach, the amount of spinning reserves (SRs) required are scheduled based on the desired level of system reliability. The proposed multi-objective optimization problem is solved using NSGA-II algorithm. Different case studies are performed considering different objective functions that may be selected by system operator (SO) based on the preference.
This paper formulates the power system Topological Observability (TO) problem as an integer programming problem, and develops a new methodology based on Improved Hopfield Neural Network (IHNN) for the determination of TO in power system networks. These complex power systems require accurate and efficient controls that makes the control centers to work efficiently. These control centers are equipped with Supervisory Control and Data Acquisition (SCADA) systems allowing to acquire information about the power system, and its transmission to control centers in real time. The computations in real time environment are reaching a limit, as far as conventional computer based algorithms are concerned. Hence, it is required to find out newer methods for these applications, which can be implemented on hardware to outperform their software counterpart. Therefore, this paper solves the TO problem using IHNN. This algorithm is based on neural networks and can easily be implemented on dedicated hardware.
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