M. Hajibeigy; V. Talavat; S. Galvani
Abstract
Due to ever-increasing energy requirements, modern distribution systems are integrated with renewable energy sources (RESs), such as wind turbines and photovoltaics. They also bring economic, environmental, and technical advantages. However, they face the network operator with decision-making challenges ...
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Due to ever-increasing energy requirements, modern distribution systems are integrated with renewable energy sources (RESs), such as wind turbines and photovoltaics. They also bring economic, environmental, and technical advantages. However, they face the network operator with decision-making challenges due to their uncertain nature. Modern distribution systems usually operate at safety margins, and any contingency may lead to power supply losses. In this regard, any attempts to increase the planner/operator's awareness of the network situation will help improve the decision quality. This paper determines the optimal locations of the RESs to enhance the expected power not served as a reliability index. Besides, it reduces power losses and minimizes the 95\% confidence interval of power losses, as much as possible for having more awareness of network states. The K-medoids data clustering method is applied to handle the uncertainties of the RESs and demand loads. The MOPSO, NSGA II, and MOGWO algorithms are used to solve the proposed problem. The efficiency of the proposed approach is tested on the IEEE standard 33-bus and 118-bus distribution networks. The obtained results show that it is possible to reach a better confidence interval while keeping the losses and reliability index at a desired level. Considering solutions with identical losses and reliability index, the confidence interval of power losses using the MOPSO algorithm is 6.86% and 39.82% better rather than the NSGA II and MOGWO algorithms in the 33-bus distribution network and it is 30.23% and 129.63% better in the 118-bus distribution network.
A.M. Alee; S. Golshannavaz; T. Ghanizadehbolandi; V. Talavat
Abstract
This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical ...
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This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical model of PV systems, wind turbines, other non-renewable distributed generations, energy storage systems, and responsive loads, a genetic algorithm (GA)-based approach is used to find the optimal placement and allocation of all units. The modeling also takes into account the uncertainty of PV units and wind turbines to represent real-world operational conditions more accurately. Additionally, although the IEEE 33-bus system is used to formulate the presented method, one can easily extend it to any other network with an arbitrary number of buses. The effectiveness of the proposed method is verified by designing three different scenarios. The simulation results obtained based on MATLAB clearly show the capability of the proposed method to improve the voltage profile and the cost of losses in ADN. This is done by properly utilizing the excess capacity of inverter-interfaced PV units as a static compensator (STATCOM), even in the absence of sunlight. The findings indicate that the inclusion of DERs and PV-STATCOM results to a notable enhancement of approximately 68.46% in power losses reduction and around 65% in the voltage deviation minimization.
