Most inhabited islands in Indonesia are powered by expensively known diesel generators and isolated from the primary grid due to either geographical or economic reasons. Meanwhile, the diesel generator can be combined with a photovoltaic (PV) system and Battery Energy Storage (BES) system to form a hybrid power generation system to reduce the energy cost and increase renewable energy penetration. For this, proper sizing of each power generation component is required, one of which is influenced by the applied control strategy. This paper proposes an optimal BES dispatch (OBD) control strategy that optimizes the power generation components’ sizing. The method examines the shortcomings of the other popular control strategies, such as load following, cycle charging, or combination. The optimization objectives are to minimize the Levelized Cost of Energy (LCOE) and maximize the renewable energy (RE) penetration, which can be achieved by prioritizing the BES to supply the load over other available generations and charge the BES every time the generator operates. The proposed method is implemented at two different systems with different load profiles. As a result, the proposed control strategy provides lower LCOE while maintaining higher RE penetration than the other control strategies in both locations.

Electricity theft has caused massive losses and damage to electricity utilities. The damage affects the electricity supply’s quality and increases the generation load. The losses happen not only for the electricity utilities but also affect the legitimate users who have to pay excessive electricity bills. That is why the method to detect electricity theft is indispensable. Recently, machine learning algorithms have been used to develop a model for detecting electricity theft. However, most algorithms have problems due to imbalanced data, overfitting issues, and lack of data. Therefore, this paper proposes a solution that implements the oversampling technique to address the problems and increase the developed model’s accuracy. It is used to perform oversampling on the imbalanced dataset. Our proposed method consists of a pre-processing step to remove empty values and extract several parameters. After that, the oversampling technique is performed on the result of the pre-processing step. The logistic regression model combined with the oversampling techniques shows the best performance results on the developed model of electricity theft detection based on the state electricity company customers. The experiment shows that the proposed method, logistic regression combined with the synthetic minority oversampling technique, shows superior performance in terms of the accuracy of the training data and data testing, precision, recall, and F1-scores of 98.97%, 98.7%, 95%, 99%, and 97%, respectively. Moreover, the experiment also shows that the proposed solution outperforms existing methods.

Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable energy resources into a single controllable and deployable entity. This paper examines the opportunity for VPP adoption in Indonesia, which investigates the minimum implementation criteria, provides a gap analysis for VPP implementation, and proposes recommendations for VPP implementation in Indonesia. The implementation criteria are obtained from the literature review, including the lessons learned from other countries, and categorized into four aspects: regulatory, technical, economic, and social. The gap analysis is performed by evaluating the current state of Indonesia’s utility network in correlation with the VPP minimum implementation criteria and then provides a scoring matrix for each criterion. Lastly, the recommendations are arranged to narrow these gaps, organized into ten key focus points, and divided into four phases, initiation, preparation, piloting, and deployment, across a ten-year timeframe.

The excessive government subsidies for electricity and the growing use of Electric Vehicles (EVs) make understanding the electricity market crucial for both suppliers and consumers. This study aims to develop a time-based EV charging tariff method that ensures fair pricing for EV owners in Indonesia, while accounting for the power generation, transmission, and distribution constraints within the Java-Bali power grid. We propose a Real-Time Elasticity Pricing method, considering seven different regions, to address tariff fluctuations. The tariffs are calculated and forecasted one day ahead using Indonesia’s power generation profile, EV charging schemes, and residential EV owners’ charging behaviors during weekdays and weekends. The analysis is conducted using linear regression combined with a market elasticity approach. The results reveal that Time of Use (ToU) and Vehicle to Grid (V2G) schemes cannot follow the load hourly, which is exciting and aligns with the nature of EV charging concerning its stochastic behavior and technical specification, while the Uncontrolled (UnC) scheme is in the contrary. In average, elasticity help reduce government subsidized around 3.15% lesser for ToU scheme and around 4.15% lesser for V2G charging scheme than non elasticity. Moreover elasticity for UnC schemes, help reduce government subsidized around 27.05% higher than non elasticity. Viewing electricity consumption for EV charging in specific schemes of certain groups of hours as distinct commodities could be helpful while considering its regional location, allowing for more precise management and pricing.

Integrating renewable energy through inverter-based generators has decreased the power system’s inertia. Reduced inertia may lead to frequency instability during power imbalance disturbances, particularly in an isolated power system with limited inertia. The Battery Energy Storage System (BESS) and a virtual inertia (VI) emulation control system have become popular to mitigate this issue. Nonetheless, the BESS utilization for VI emulation is highly dependent on the availability of BESS capacity, which may affect the energy cost. Therefore, developing a VI emulation control strategy that requires less energy and can recover the state of charge (SoC) to a desired level to optimize BESS utilization is required. This paper proposes a VI control with an SoC recovery strategy through coordination with the generators’ secondary frequency control. Instead of relying on the frequency, such as in the conventional approach, the controlled signal of the generators’ secondary frequency control also includes the VI power and BESS SoC. Hence, the generators can contribute to lowering the VI required energy and recovering the BESS SoC. The results show that the proposed method outperforms the conventional method by requiring around 36% lower energy and the ability to maintain the BESS SoC.

A custom lithium-Ion battery was built for the payload system on a single-engine two-seaters glider. The stages of software development in forming the Battery Management System as a way to provide security in the charging and discharging processes need some parameters to indicate the conditions of the battery. Therefore, in this study, the process of the State of Charge estimation which used in the Electrical Equivalent Circuit Methods and Adaptive Extended Kalman Filter was carried out in a 42 Ah Li-ion battery. As the results, a Mean Absolute Error, and a Root Mean Square Error in which the value of less than 1%. In the actual process, the real error value has never been discovered. The noise is given to determine the adaptive ability between the extended Kalman Filter and the Adaptive Extended Kalman Filter (AEKF) algorithms. In addition, the primary SoC is set at a certain value to see the estimation accuracy. The investigation shows that this method is possible to be applied in the development of the BMS software for payload systems.