In recent years, the emerging of renewable energy resources (RERs) in distribution networks has been rapidly raised by network planners. Although the integration of RERs brings a lot of advantages to distribution networks, they impose some disadvantages such as high initial capital investment on distribution companies (DISCO). This paper proposes a new two‐stage multi‐period distribution network expansion planning (DNEP), in which the potential of private investor's (PIs) participation is considered in the problem. In the proposed model, the planner presents an incentive price to buy energy from PIs so that it attracts them to participate in the planning projects. This incorporation is based on a long‐term contract that guarantees the benefits of both contract correspondents. The proposed model is presented as a hierarchical two‐stage optimization problem where the upper stage determines the general structure of the system and the lower level considers the operational conditions. Moreover, the photovoltaic plants (PVs) and energy storage systems (ESSs) are investigated as clean and new technologies in the planning, and DISCO determines the optimal sizing and sitting of these resources regarding technical and economical assessments. The fuzzy clustering method (FCM) is used to capture the intermittency of the system loads and solar irradiance by creating a day‐ahead scheme. The effectiveness of the proposed DNEP problem is evaluated through a 54‐bus distribution test system and a real 104‐bus test system as well. Simulation and economic results show that how PIs can play an important role in long‐term planning by investing RERs.
In recent years, supporting schemes have been legislated by several governments to encourage private investors in installing renewable energy resources (RER). In such cases, the supportive policies are mainly enacted based on the either investor or distribution companies’ standpoint. In this paper, a distribution network expansion planning (DNEP) framework with the cooperation of residential private investors (RPI) is proposed. Due to the presence of a couple of main participants, the proposed framework is arranged in a bi‐level framework, where the RPI participation is optimized at the upper level, and the system structure is determined at the lower level. In order to assess the profitability of the project from the investors’ attitude, payback period years (PBY) is utilized. Meanwhile, due to the presence of uncertainty resources, fuzzy clustering method (FCM) is developed to catch the intermittency of the problem. The proposed framework is implemented on a real 81 bus distribution test system in Iran. Moreover, the existing scheme in Iran and also a modified plan are investigated to make cost‐effective decisions. Finally, sensitivity analysis is performed to reach a more beneficial result. Obtained results demonstrated how distribution companies can utilize the potential of residential customers in long‐term planning.
High‐impact, low‐probability events that cause significant annual damages seriously threaten the health of distribution networks. The effects of these events have made the expansion planning for distribution systems something beyond the traditional reliability criteria, so there is an ever‐increasing need for modifications in current planning approaches and focusing on the resilience in the expansion planning of distribution networks. The new attitude dealing with resilience and distributed generation sources in distribution networks necessitates a fundamental reconsidering of traditional distribution network planning methods. Here, by modelling common natural disasters such as floods and storms, an appropriate index is introduced to evaluate the distribution network resilience in the presence of distributed generation (DG) sources, including conventional gas‐fired and photovoltaic sources. Then, by presenting an appropriate model for load and photovoltaic production, the problem of comprehensive distribution network planning, including substations, feeders, and DG sources, is mathematically formulated as a multi‐objective optimization problem to improve resilience and optimize costs. Furthermore, a non‐dominated sorting genetic algorithm is used to solve the problem of comprehensively planning a resilient distribution network. Implementation of the proposed model on the IEEE 54‐bus sample network shows that network resilience can be improved with minimum cost by optimal network planning.
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