An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers

Distribution networks with high electric vehicle (EV) penetration levels can experience transformer overloading and voltage instability issues. A charge scheduling approach is proposed to mitigate against these issues that suits smart home settings in residential areas. It comprises measurement systems located at distribution transformers that communicate directly with fuzzy logic controller (FLC) systems embedded within EV supply equipment (EVSE). This realizes a reduction in data processing requirements compared to more centralized control approaches, which is advantageous for distribution networks with large numbers of transformers and EV scheduling requests. A case study employing the proposed approach is presented. Realistic driver behavior patterns, EV types, and multivariate probabilistic modeling were used to estimate EV charging demands, daily travel mileage, and plug-in times. A Monte Carlo simulation approach was developed to obtain EV charging loads. The effectiveness of mitigation in terms of reducing distribution transformer peak load levels and losses, as well as improving voltage stability is demonstrated for a distribution network in Jakarta, Indonesia.

Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework

Motivated by the benefits of multi-energy integration, this paper establishes a bi-level two-stage framework based on transactive control, to achieve the optimal energy provision among interconnected multi-energy systems (MESs). At the lower level, each MES autonomously determines the optimal setpoints of its controllable assets by solving a cost minimization problem, in which rolling horizon optimization is adopted to deal with the load and renewable energies’ stochastic features. A technique is further implemented for optimization model convexification by relaxing storages’ complementarity constraints, and its mathematical proof verifies the exactness of the relaxation. At the upper level, a coordinator is responsible for minimizing total costs of interconnected MESs while preventing transformer overloading. This collaborative problem is solved iteratively in a proposed two-stage transactive control framework that is compatible with operational time requirement while retaining scalability, information privacy and operation authority of each MES. The effectiveness of the proposed framework is verified by simulation cases that conduct a detailed analysis of the collaborative autonomous optimization mechanism.

STUDI PERENCANAAN PENINGKATAN KINERJA TRAFO DISTRIBUSI DENGAN RELOKASI ANTARA 2 BUAH TRAFO

Dalam penggunaan trafo diketahui bahwa terdapat beberapa trafo yang dibebani tidak optimal sesuai dengan standar yang ada. Sebagai contoh, berdasarkan hasil pengukuran pada bulan Agustus 2015 pada PT. PLN (Persero) Rayon Pare diketahui bahwa pada gardu EF283 di Penyulang Bendo beban puncaknya telah mencapai 103,59 % sedangkan pada gardu EF070 di Penyulang Sambirejo pada beban puncaknya hanya mencapai 32,62 %. Kondisi tersebut akan berakibat negatif pada kinerja dari trafo dimana pada pembebanan yang terlalu tinggi pada trafo akan berakibat trafo tersebut overload dan berimbas mengurangi umur ekonomis dari trafo dan dapat mengakibatkan kerusakan pada trafo akibat panas yang berlebihan. Sebaliknya, jika trafo dibebani jauh dibawah kapasitasnya maka efisiensi trafo akan rendah. Untuk meningkatkan kinerja dari kedua trafo tersebut di atas maka digunakan cara relokasi diantara kedua trafo tersebut dan diharapkan trafo pada gardu EF283 tidak terjadi overload sedangkan prosentase pembebanan gardu EF070 diharapkan meningkat sehingga efisiensi trafo meningkat. Setelah dianalisa dan dibandingkan antara kondisi sebelum dan setelah relokasi pada aspek % pembebanan, effisiensi dan faktor kebutuhan, juga dianalisa tentang pertumbuhan beban dengan menggunakan metode least factor maka dapat disimpulkan bahwa sebelum dilaksanakan relokasi harus dipertimbangkan beberapa persyaratan untuk dilaksanakannya relokasi. Selain itu diketahui bahwa effisiensi trafo akan lebih besar (optimal) jika dibandingkan antara kondisi sebelum (trafo EF283 dan EF070 adalah 97,54 % dan 97,42 %) dan setelah relokasi (98,44 % dan 98,50 %). Sedangkan untuk umur trafo , umur trafo pada gardu EF283 akan relatif lebih panjang dan pada trafo di gardu EF070 akan relatif lebih pendek umur trafonya karena adanya peningkatan pembebanan walaupun effisiensi trafonya ternyata lebih baik dibandingkan sebelum relokasi. In the use of transformers it is known that there are several transformers that are not optimally loaded according to the existing standards. For example, based on the results of measurements in August 2015 at PT. PLN (Persero) Rayon Pare is known that at EF283 substation at Bendo Feeder the peak load has reached 103.59% while at EF070 substation at Sambirejo Feeder at peak load it only reaches 32.62%. These conditions will negatively affect the performance of the transformer where overloading of the transformer will result in the transformer overloading and impacting to reduce the economic life of the transformer and can cause damage to the transformer due to excessive heat. Conversely, if the transformer is burdened far below its capacity, the transformer efficiency will be low. To improve the performance of the two transformers, relocation between the two transformers is used and it is expected that the transformer on the EF283 substation will not overload while the percentage of EF070 substation loading is expected to increase so that the transformer efficiency increases. After analyzing and comparing the conditions before and after relocation in the aspects of% loading, efficiency and needs factors, also analyzed the load growth using the least factor method, it can be concluded that before the relocation is carried out, several conditions must be considered for the relocation. In addition, it is known that the transformer efficiency will be greater (optimal) compared to the conditions before (transformer EF283 and EF070 are 97.54% and 97.42%) and after relocation (98.44% and 98.50%). As for the life of the transformer, the life of the transformer at the EF283 substation will be relatively longer and at the transformer at the EF070 substation will be relatively shorter in the life of the transformer due to an increase in loading even though the transformer efficiency turns out to be better than before relocation.