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.

Many underdeveloped countries are facing acute shortage of electric power and short term measures are important to consider to address the problems of power outage, power plant failures, and disaster areas. Distributed generation (DG) is a promising approach for such cases as it allows quick on-site installation and generation of electric power. Injection of DG can improve the system voltage profile and also reduce the system's total power losses. However, the placement and sizing of the DG unit is an optimization problem in the radial distribution system. As a test case, this study examines voltage profile improvement and system power losses for an 11 KV residential feeder at the Abdul Rehman Baba grid station in Pakistan, which is modelled using the Electrical Transient Analyzer Program (ETAP). For various scenarios, several tests are conducted to assess the effects of DG on the distribution system. The results show that proper design considerations of size and location of a DG, to be inserted in to the system, lead to significant reduction in power losses and improvement in voltage profile and thus improvement in the overall efficiency of the power system. The projections of this work can be used to optimize the expansion of a power system and tackling different issues related to voltage profile in distribution sector worldwide.

In designing a hydropower plant, the design of turbine type depends on the head and water flow. Researchers suggest that Axial/Propeller turbines can perform efficiently in lower heads because of their high specific speeds, Francis turbines can perform efficiently in medium to medium/high heads with high water discharge and medium specific speeds whereas Pelton turbines can perform efficiently in high heads with low water discharge and low specific speeds. In this paper the output performance of Axial/ Propeller, Francis and Pelton turbines are designed, analyzed and compared using TURBNPRO software for the existing Jamshill Turen More HPP. The site head and water discharge are 117m and 305 m³/s. The feasibility recommends 4 vertical shaft Francis turbines. From the results we conclude that if a low head, high specific speed, Axial/Propeller turbine is designed for a medium head site, then its specific speeds exceed its limitations for the same electrical power output per turbine. Similarly, if a high head, low specific speed, Pelton turbine is designed for medium head site, then its runner diameter exceeds its limitations. Four Francis turbines can have the same electrical power output as a minimum of twelve Pelton turbines for the same medium head range

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

Potential and Cost Benefit Analysis of Alternative Sustainable Energy