Energy losses, both technical and non-technical, are incurred during the transfer of electrical energy from the power plant to the customer. Technical energy losses are evident since they result from the properties of materials. Detecting non-technical energy losses is challenging due to their origin in usage. Some of the nontechnical energy losses are caused by meter readings. Electricity meters, which are used to record the electrical energy consumed in electricity distribution networks, are spread over a wide area. Hence, it is unfeasible to gather the data obtained from electricity meters without any errors. Furthermore, the absence of electrical energy monitoring is a significant issue. The automatic meter reading system (AMRS) provides a cost-effective solution to these issues. This study examines the structure, benefits, and implementation challenges of an autonomous meter reading system. This study examines the impact of utilizing power line communication (PLC) meters in the automatic meter reading system application on reducing non-technical energy losses in electricity distribution networks. This study employed energy usage data from Bitlis province. The annual loss and leakage rates presume that the proportion of technical energy losses remains constant compared to the previous year. The examination revealed that the loss and leakage rate was 36.28 % in 2019, 35.10 % in 2020, 29.82 % in 2021, and 25.32 % in 2022. Between 2019 and 2022, the decline in loss and leakage rates is attributed to a reduction in non-technical energy losses. The automatic meter reading system is determined to have a substantial impact on reducing non-technical energy losses in electricity distribution networks. Since the energy needs of countries are increasing day by day, it requires more efficient use of energy. It will lead to more efficient use of energy by reducing non-technical energy losses. In the study, the contribution of automatic meter reading systems to reducing non-technical energy losses is emphasized. The study is valuable as it sets an example for future studies.Multiple power systems, encompassing both fossil fuels and renewable energy sources, play a vital role in the supply side of the smart grid. While research on smart grid electricity pricing has predominantly focused on intelligence and forecasting, there is a notable paucity of studies addressing the fundamental pricing principles and long-term cost management strategies for electricity. The aim of this paper is to propose a foundational framework for estimating energy generation costs, focusing on both fossil fuel and renewable energy sources within the context of smart grid electricity pricing. To assess approximate cost changes over time, the study calculates the Levelized Cost of Electricity (LCOE) utilizing Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) methodologies, which account for economic and environmental impacts. The findings indicate that, assuming a 20-year time horizon, the final levelized costs for each type of power plant are as follows: coal power plant at 96 USD/MWh, gas power plant at 111 USD/MWh, nuclear power plant at 86 USD/MWh, hydroelectric power plant at 87 USD/MWh, solar power plant at 71 USD/MWh, and wind power plant at 69 USD/MWh. Furthermore, the analysis uses Monte Carlo analysis to explore uncertainties associated with carbon prices, the Weighted Average Cost of Capital (WACC), capital costs, and raw material prices, which offers a strategic approach for government institutions to implement regulatory policies of the energy power market.
