ANALISIS TIPE ARUS DAN LOKASI GANGGUAN PADA PENYULANG DISTRIBUSI GI.BANDUNG UTARA

Perubahan kebutuhan energi masyarakat yang meningkat menyebabkan arus gangguan pada penyulang gardu induk. Penelitian ini memaparkan hasil fault diagnosis pada distribution substation feeder menggunakan pendekatan soft computing yaitu, logika fuzzy. Studi fault diagnosis ini dilakukan dengan cara mengolah data arus gangguan dan lokasi titik gangguan pada gardu induk. Data – data tersebut kemudian dijadikan input pembelajaran pada logika fuzzy. Hasil pengujian menunjukan bahwa Fuzzy Rule-Based memiliki tingkat keakuratan yang baik dalam mengindikasi tipe arus gangguan, tetapi tingkat keakuratannya menurun ketika melakukan indikasi lokasi arus gangguan. Changes in the increased energy demand of the community cause a fault current in the substation feeder this research describes fault diagnosis result on distribution substation feeder using soft computing approach, that is fuzzy logic Fault diagnosis study is done by processing the data fault current and the location of the disturbance point on the substation, The data are then used as learning input on fuzzy logic. Test results show that Fuzzy Rule-Based has a good accuracy level in indicating the type of fault current but the accuracy level decreases when indicating the location of the fault current

Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage

This paper presents a two-level optimization model for the optimal scheduling of an active distribution system in day-ahead and real-time market horizons. The distribution system operator transacts energy and ancillary services with the electricity market, plug-in hybrid electric vehicle parking lot aggregators, and demand response aggregators. Further, the active distribution system can utilize a switching procedure for its zonal tie-line switches to mitigate the effects of contingencies. The main contribution of this paper is that the proposed framework simultaneously models the arbitrage strategy of the active distribution system, electric vehicle parking lot aggregators, and demand response aggregators in the day-ahead and real-time markets. This paper's solution methodology is another contribution that utilizes robust and lexicographic ordering optimization methods. At the first stage of the first level, the optimal bidding strategies of plug-in hybrid electric vehicle parking lot aggregators and demand response aggregators are explored. Then, at the second stage of the first level, the day-ahead optimization process finds the optimal scheduling of distributed energy resources and switching of electrical switches. Finally, at the second level, the real-time optimization problem optimizes the scheduling of system resources. Different case studies were carried out to assess the effectiveness of the algorithm. The proposed algorithm increases the system's day-ahead and real-time revenues by about 52.09% and 47.04% concerning the case without the proposed method, respectively.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Capacity Share Optimization for Multiservice Energy Storage Management Under Portfolio Theory

Energy storage (ES) is playing a vital role in providing multiple services in several electricity markets. However, the benefits and risks vary across markets and time, which justifies the importance to optimize ES capacity share in different markets. In this paper, a novel portfolio theory-based approach is proposed for optimally managing ES in various markets to maximize benefits and reduce the risk for ES owners. Three markets are considered, which are energy arbitrage, ancillary services, and distributed network operator's market. They are modeled based on energy cost, frequency response cost, and system congestion cost. Portfolio theory is utilized to quantify ES capacity allocated to each market over time for various levels of risk aversions. The relation between risks and expected return of different markets is efficiently reflected by the portfolio theory, providing implications to storage operation. The extensive demonstration illustrates that the markets that storage can participate in are fundamentally different regarding to its risk aversion. In addition, the optimum portfolio of the markets for storage is on the efficient frontier, providing the maximum return at a certain risk aversion level. This study is particularly useful for guiding market participation and operation of ES to gain maximum economic return at minimum risk.</p