A trend of the electric power enterprise construction will be the online monitoring, but the PEA SCADA system cannot monitor from outside by web browser or internet because PEA's SCADA system in some areas is also a centralized computing system that is not designed to interface or monitor with external systems. Normally, the SCADA system in such area should be renovated or upgrade to a network computing system but requires a very high investment. For this reason, we try to use the existing system and develop an online monitoring applied to the problem of the centralized computing system. This paper presents a solution for SCADA substation online monitoring, an online monitoring architecture for the SCADA system to support smart grid, and presents a concept of online monitoring and network virtualization for this system. The aim of this approach is to propose an architecture of SCADA online monitoring that ensures access, available, flexible and consistent for PEA SCADA online monitoring. This paper discussed the feasibility study of handling of monitoring of unmanned substation on internet framework retaining smart grid security in Thailand. The monitoring of the device status and analog value of substation can also be done from the network through cloud-based SCADA with the online monitoring using internet connection. The assessment results allow the network administrator to plan infrastructure expansion with confidence in the security and reliability of the network's operation.Web server hosted or internally hosted SCADA application pushes real-time process and historical data to the web server for data analytics, storage or remote access. A web server offers the highest degree of monitor over performance, reliability, and security.The major advantages of moving SCADA applications to a cloud are: saving on cost, support for system used to monitored and access electric grid from anywhere at any time through internet connection. According to almost one year trial period, this online monitoring is implemented practically, security, and accurately. This implementation will help user or operator to identify power outage, and it can reduce the duration of power outage, increase stability, and enhanced organization standards. Therefore, cloud-based system is told to be a productive automation choice in the future.For the future works, we try to connect the GIS map to the outage management system for implementation about affect customer service using internet connection to access, monitor, identify, and notify the event, such as power outage area, recovery time, and power outage display screen zone.
Abstract. This paper presents solution of optimal multi fuels allocation for electric power generation planning problem via a genetic algorithm ( GA). The objective is to maximize the electric power energy output and minimize the fuels cost. This is a considerably difficult problem because of its data variation.GA can provide an appropriate heuristic search method and return an actual or near optimal solution. This research used some heuristic in addition during crossover and mutation for tuning the system to obtain a better candidate solution.An experimental resultshowed a significantly improve result compared to other techniques.
The ever increasing growth of energy consumption has stimulated an energy crisis, not only in terms of energy demand, but also the impact of climate change from greenhouse gas (GHG) emissions. Renewable energy sources (RES) have high potential toward sustainable development, with a wide variety of socioeconomic benefits, including diversification of energy supply and creation of domestic industry. This paper presents a solution to optimal multi-fuel allocation for the electric power generation planning problem via genetic algorithms (GA). The objective is to maximize the electric power energy output and minimize generation cost. This is a difficult problem because of its data variation and volatility. GA can provide an appropriate heuristic search method and return an actual or near optimal solution. This paper uses some heuristics during crossover and mutation for tuning the system to obtain a better candidate solution. An experimental result showed significantly improved results compared with other techniques. The results in this paper should be useful for connecting power generation with economic growth.
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