Electricity Demand Forecasting Model

The project aimed to improve the decision-making process in the Georgian energy sector using internationally recognized and scientifically proven methodologies. The expected outcomes included developing an electricity demand forecasting tool, a 10-15-year electricity demand forecast for Georgia, and identifying challenges associated with the demand forecasting process. The project was divided into five milestones, with the first three focused on assessing data availability for performing modeling, reviewing existing electricity long-term demand projects applied in Georgia, and consulting with main stakeholders for providing feedback on the selected model and planning the next steps. The assessment of existing data revealed that there was no available data on hourly loads by customer groups in Georgia. Therefore, EEC constructed a model that can capture all relevant customer groups influencing peak demands for hourly, daily, and seasonal perspectives and applied demand proxies of different customer groups based on scientific literature and publicly available sources. According to Georgian electricity sector stakeholders, the existing electricity demand forecasting models are simple and do not incorporate comprehensive demand forecasting analyses. At the same time, forecasting of hourly peak loads is not conducted. Therefore, improving the electricity demand forecasting capacities of stakeholders and implementing comprehensive demand forecasting tools are necessary for proper planning of Georgian electricity sector development. Under this grant project, the Long-range Energy Alternatives Planning (LEAP) Georgia model was constructed in a way to enable forecasting of monthly electricity demand as well as producing monthly peak loads. Additionally, a bottom-up approach was used to create a tool for evaluating energy efficiency scenarios. In total, five scenarios are discussed in the report. The baseline scenario describes how the system might evolve without implementation of any new policies. It takes into account past trends and functional relationships between demand drivers and end-uses. The energy efficiency scenario is based on results and targets provided in the draft National Energy Efficiency Action Plan (NEEAP). The comparison between baseline and energy efficiency scenarios demonstrates the impact of certain energy efficiency policies on the demand. The customer-owned generation scenario was developed to forecast the impact of micro power plants on demand. Finally, as real gross domestic product (GDP) growth is one of the main determinants of electricity usage in the country, two additional scenarios with high and low GDP growth were developed to observe the impact of changes in GDP on electricity demand. The forecast shows that total electricity demand will increase from 10.6 TWh in 2015 to 18.9 TWh in 2030 in the BAU scenario. In other scenarios, a 10% deviation from baseline GDP growth rate results in a 0.8 TWh increase in the case of a high GDP growth rate and a 0.7 TWh decrease in the case of a low GDP growth rate for year 2030 as compared to the business as usual (BAU) scenario. The energy efficiency scenario shows that electricity demand decreases by 2.3 TWh in 2030 as compared to the BAU scenario, while in the customer-owned generation scenario electricity demand decreased by 0.1 TWh in 2030 compared to the BAU scenario. During the last decade, electricity demand in Georgia has increased around 4-5% annually. However, the process of harmonization of Georgian legislation with the Energy Community acquis leads to different policies to be in place and implemented. Initiatives such as development of the NEEAP and obligations to reduce greenhouse gas emissions have been introduced. The Georgian National Energy and Water Supply Regulatory Commission (GNERC) has been working to improve the energy efficiency of the country's energy sector. The electricity demand forecasting model developed under this project will be used to support the decision-making process in the Georgian energy sector. The model will enable stakeholders to forecast electricity demand and identify opportunities for energy efficiency improvements. The results of the model will also be used to inform policy decisions and support the development of the energy sector in Georgia. The model was constructed using the LEAP model, which is a widely used tool for energy planning and policy analysis. The LEAP model was adapted to the Georgian energy sector and was used to develop a comprehensive electricity demand forecasting tool. The model was calibrated using historical data and was validated using sensitivity analysis. The results of the model show that total electricity demand will increase from 10.6 TWh in 2015 to 18.9 TWh in 2030 in the BAU scenario. The energy efficiency scenario shows that electricity demand decreases by 2.3 TWh in 2030 as compared to the BAU scenario. The customer-owned generation scenario shows that electricity demand decreases by 0.1 TWh in 2030 compared to the BAU scenario. The model was also used to develop two additional scenarios with high and low GDP growth rates. The results of these scenarios show that a 10% deviation from baseline GDP growth rate results in a 0.8 TWh increase in the case of a high GDP growth rate and a 0.7 TWh decrease in the case of a low GDP