Abstract
As energy demand is increasing, power systems’ complexities are also increasing. With growing energy demand, new ways and techniques are formulated by researchers to increase the efficiency and reliability of power systems. A distribution system, which is one of the most important entities in a power system, contributes up to 90% of reliability problems. For a sustainable supply of power to customers, the distribution system reliability must be enhanced. Distributed generation (DG) is a new way to improve distribution system reliability by bringing generation nearer to the load centers. Artificial intelligence (AI) is an area in which much innovation and research is going on. Different scientific areas are utilizing AI techniques to enhance system performance and reliability. This work aims to apply DG as a distributed source in a distribution system to evaluate its impacts on reliability. The location of the DG is a design criteria problem that has a relevant effect on the reliability of the distribution system. As the distance of load centers from the feeder increases, outage durations also increase. The reliability was enhanced, as the SAIFI value was reduced by almost 40%, the SAIDI value by 25%, and the EENS value by 25% after injecting DG into the distribution network. The artificial neural network (ANN) technique was utilized to find the optimal location of the DG; the results were validated by installing DG at prescribed localities. The results showed that the injection of DG at proper locations enhances the reliability of a distribution system. The proposed approach was applied to thte Roy Billinton Test System (RBTS). The implementation of the ANN technique is a unique approach to the selection of a location for a DG unit, which confirms that applying this computational technique could decrease human errors that are associated with the hit and trial methods and could also decrease the computational complexities and computational time. This research can assist distribution companies in determining the reliability of an actual distribution system for planning and expansion purposes, as well as in injecting a DG at the most optimal location in order to enhance the distribution system reliability.
Highlights
Energy needs are typically managed through energy generated in generating plants.Figure 1 depicts a typical electric power system with generating plants, a transmission system, and distribution networks that are connected with each other
The implementation of the artificial neural network (ANN) technique is a unique approach to the selection of a location for a Distributed generation (DG) unit, which confirms that applying this computational technique could decrease human errors that are associated with the hit and trial methods and could decrease the computational complexities and computational time
Case 2: Reliability Cost Analysis. Many customers, such as residential, industrial, commercial, and governmental customers, that are connected to different feeders in a distribution system are affected by interruptions that are caused due to component failures or deficits in the energy supplied to the load centers
Summary
Energy needs are typically managed through energy generated in generating plants. The power produced depends upon the capacity of generation. This ability ranges from megawatts to gigawatts [1]. These huge levels of power-generating stations are situated far away from load points. The transmission system and distribution system supply electrical power from power-generating plants to different load centers [2,3]
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