Solar energy is considered as one of the most promising solutions to prevent climate change, and optimal utilization of solar energy contributes to reducing dependence on non-renewable energy sources. Distributed generation can help support the delivery of clean and reliable power to consumers. In this paper, an Energy Management System (EMS) is developed along with load and Photovoltaic (PV) generation forecasting to perform peak shaving of electricity demand. The results from the proposed method are implemented in a real case study, in Port of Gavle in Sweden. Total revenue generated from services of peak-shaving and frequency regulation is maximum in the scenario considering high PV generation and high consumption on a selected experimental day in July. The results show that PV-battery storage systems are effective options to increase the integration of renewable energies in the grid, flatten the load, and achieve stabilized operation.

The port industry is transforming into a smart port thanks to technological advancements and environmental expectations. Developing a sustainable maritime transportation system and its beneficial electrification as a proven approach in emissions reduction are gathering momentum due to technological growth. Global containerization leads to high electricity demand at container terminals, and the electricity demand is highly dynamic and dependent on different operation processes. The approach of this paper is to forecast the hourly peak load demand and short-term electricity demand profile in a container terminal. The correctly forecasted electricity demand profile is crucial for less expensive and reliable power operation and planning. First, Artificial Neural Network (ANN) method is used to predict the container terminal baseload demand. Second, worst-case simultaneous peak load is estimated. Third, the day-ahead load profile is modeled based on the handling operation scheduled for the day. The approach is implemented at the container terminal in Port of Gavle, and the results, including the baseload forecasting, the peak power demand, and the hourly load profile modeling by 2030, have been used in dialogue with the local energy company for the future predicted need of load.

The port industry is transforming into a smart port thanks to technological advancements and environmental expectations. Developing a sustainable maritime transportation system and its beneficial electrification as a proven approach in emissions reduction are gathering momentum due to technological growth. Global containerization leads to high electricity demand at container terminals, and the electricity demand is highly dynamic and dependent on different operation processes. The approach of this paper is to forecast the hourly peak load demand and short-term electricity demand profile in a container terminal. The correctly forecasted electricity demand profile is crucial for less expensive and reliable power operation and planning. First, Artificial Neural Network (ANN)method is used to predict the container terminal baseload demand. Second, the worst-case simultaneous peak load is estimated. Third, the day-ahead load profile is modeled based on the handling operation scheduled for the day. The approach is implemented at the container terminal in Port of Gävle, and the results, including the baseload forecasting, the peak power demand, and the hourly load profile modeling by 2030, have been used in dialogue with the local energy company for the future predicted need of load.

In this paper, a three-step optimization approach is presented in order to obtain the optimal sizing of Standalone Photovoltaic (SPV) system. The main aim is to weigh the cost against the reliability of the system to make a smart design decision. The optimization approach has two objective functions to be minimized, Annual Lost Load (ALL) and Annual Life Cycle Cost (ALCC). The proposed method includes battery lifetime estimation, which is based on the active Depth of Discharge (DOD) of micro-cycles on the battery operation. The method has been applied to a remote area in Zambia. The results indicate that the introduced framework is capable of optimizing the system while satisfying the operational and economical requirements.