Load forecasting is an important component of power system and resource planning for electrical grids. The adoption of electric vehicles (EVs), behind-the-meter (BTM) solar, and heat pumps will significantly change the amount and variability of loads. Electrification adoption and load forecasting in arctic regions and Alaska is limited. This paper provides the first load and electrification adoption forecast for the Alaska Railbelt transmission system, including yearly adoption rates of EVs, BTM solar, and heat pumps and hourly load data for the forecasted year of 2050. The adoption rates were based on the available historical data and compared to other regional and national trends. Two forecasts were created: (1) a moderate adoption forecast based on projections from current adoption rates and comparisons to other regional and national projections and (2) an aggressive forecast, which provides an illustrative comparison of a high adoption rate of 90% for all technologies. The results of these forecasts demonstrate a significant increase in both energy, 80% and 116%, and peak load demand, 113% and 219%, for the moderate and aggressive electrification adoption scenarios, respectively. These findings highlight a need for resource planning and demand management in this region due to the adoption of EVs, BTM solar, and heat pumps.

Power system models of electric systems are crucial in system planning for operations, economics, and expansion analyses. However, as these models contain critical infrastructure data, they are not publicly available. This poses challenges in future expansion scenarios and evaluating technological advancements in an electric grid. Synthetic models are realistic power system models, both topologically and operationally. However, since the electrical network is typically produced using statistical data and often uses machine learning, it does not contain propriety information. This allows researchers to evaluate system behavior under various operating conditions and as test cases for emerging technologies. These test cases are particularly important in highly evolving electric grids and areas of high renewable energy integration such as Alaska. Currently, no publicly available benchmark power system models of rural Alaskan island microgrids exist. This paper presents a rural Alaskan microgrid synthetic power system model and the methodology adopted to develop the model. The performance of the developed synthetic grid was assessed through steady state and positive-sequence dynamic simulations under various operating conditions.

Load forecasting is an important component of power system and resource planning for electrical grids. The adoption of electric vehicles (EVs), behind-the-meter (BTM) solar, and heat pumps will significant change the amount and variability of loads. Electrification adoption and load forecasting in arctic regions and Alaska is limited. This paper provides the first load and electrification adoption forecast for the Alaska Railbelt transmission system, including yearly adoption rates of EVs, BTM solar, and heat pumps and hourly load data. Adoption rates are based on available historical data and compared to other regional and national trends. Two forecasts are created: 1) a moderate adoption forecast based on projections from current adoption rates and comparisons to other projections, and 2) an aggressive forecast, which provides a bookend comparison at the high adoption rate of 90% for all technologies. The results of these forecasts demonstrate a significant increase in both energy, 80% and 116% for moderate and aggressive, respectively and peak load demand, 113% and 219% for moderate and aggressive, respectively. Additionally, the results indicate a maximum hourly load change of 260% and 381% for the moderate and aggressive forecasts, respectively. These findings highlight a need for resource planning, which accounts for increases in demand and suggests that significant demand management is needed to smooth and control the load fluctuations as a result of the adoption of EVs, BTM solar, and heat pumps.