Development of PV hosting-capacity prediction method based on Markov Chain for high PV penetration with utility-scale battery storage on low-voltage grid

• Published in: International journal of sustainable energy Vol. 42; no. 1; pp. 1297 - 1316
• Main Authors: Atmaja, Wijaya Yudha, Sarjiya, Putranto, Lesnanto Multa
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 14-12-2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Batteries; BESS; Computer applications; Computing costs; Energy storage; Load distribution; Markov chain; Markov chains; Monte Carlo; Monte Carlo simulation; Photovoltaic cells; Photovoltaics; Predictions; PV hosting capacity; stochastic; Stochasticity
• ISSN: 1478-6451; 1478-646X
• DOI: 10.1080/14786451.2023.2261759

The previous stochastic hosting capacity prediction method using the Monte Carlo method for high photovoltaic (PV) penetration with a battery energy storage system (BESS) required a large number of computations to achieve the expected accuracy. The problem of high computational load must be addressed so that the electrical distribution planner can practically use the PV hosting capacity prediction in actual situations. Therefore, this study developed a Markov-chain-based PV hosting capacity prediction method for high PV penetration using BESS. The proposed method is described in detail, followed by case and validation studies. The obtained hosting capacity was 123.58 kW, which increased to 3676.4 kW after the utility-scale BESS implementation. The results demonstrate that the proposed Markov-chain-based PV hosting capacity prediction method outperforms the Monte Carlo method, which is the most popular stochastic hosting capacity method, in terms of accuracy and computational cost.