The Unmanned Aerial Vehicle (UAV) is controlled remotely by the operator at the Ground Control Station (GCS) during the mission. Reliable electrical energy is needed to supply energy needs in GCS, such as radio communications, workstations, weather stations, HVAC systems, and lamps when the UAV is operating. GCS is located in Ranai City, Natuna Islands. The UAV mission in Ranai can assist the military in monitoring border surveillance, disaster management, and mapping the area. GCS power source in the initial design, using a diesel generator as the main power source with a power of 12 kW for 192.3 kWh per day energy consumption and 10.8 kW load peak. This paper has shown the study on a hybrid system with 3 scenarios, consisting of a PV system, wind turbine, generator, and battery. The system is simulated by using HOMER for accounting the technical and economic aspects. The results showed that the NPC and LCOE of the third scenario, which consisted of PV/wind/battery, were $181,157.38 and $0.15 per kWh, respectively. It has the lowest cost compared to the first scenario with the usage of diesel generator only. The average fuel consumption is 7.97 litre per day, down 87% compared to the existing condition. Thus, this configuration can overcome the costs of delivering fuel supplies for GCS operations and complement the government’s efforts to achieve emission reduction targets.

One solution for diminishing carbon outflows is to provide electric vehicles (EV), which can help the sustainable development of the ecosystem in an environmentally way. Jakarta, as a capital city with high levels of pollution, has forced the government to recognize the need for policymakers to correct environmental failures through effective policy solutions. To support policy-driven adoption of EV, incentives shall be given to stimulate EV users. Current regulations have not yet explained regulations for EV’s, direct and indirect consumer benefits, infrastructure for charging, and complementary policies. This paper will compare the world’s best EV policy which will determine the main policy criteria to be developed for Jakarta’s regulation using the analytical hierarchy process and entropy method in giving scaled preferences of sets of standards and alternatives with acceptable inconsistency. AHP is used to determine initial subjective weights from experts, while then entropy will enhance AHP’s weights into objective weight. This study shows that charging infrastructure is the most influential criterion among other criteria followed by consumer incentive, both direct and indirect, complimentary policies, and regulatory incentives. Therefore, it is highly recommended that Jakarta’s government develop EV’s incentive policy in detail as the order above.