Multi-objective Optimization of Digital Management for Renewable Energies in Smart Cities

Abstract

Smart cities mark the shift from the traditional model of urban construction to the planned construction of a composite system between smartness and energy. Considering the defects of renewable energies, e.g., intermittency, randomness, and low dispatchability, it is imperative to explore the digital and unified management of urban renewable energies. Therefore, this paper presents a multi-objective optimization algorithm for digital management, which can quantify the multiple energy models of smart cities. Firstly, the dimensions of renewable energy construction in smart cities were detailed, and the functions, hierarchy, and data flows of the digital management system for renewable energies were plotted in turn. After that, the output probability models of typical renewable energy power generation systems were established, plus the objective functions of digital management for renewable energies. Finally, particle swarm optimization (PSO) was combined with genetic algorithm (GA) for multi-objective optimization of the digital management for renewable energies in smart cities. The proposed models and algorithm were proved effective through experiments.

An Investigation of Hybrid Renewable Energy Potential by Harnessing Traffic Flow

“…Indonesia which is on the equator should be able to take advantage of the potential of existing renewable energies such as solar energy, wind energy, water energy, wave energy, and geothermal energy as alternative energy [8]. Renewable energy sources are expected to have an active role in future energy diversification scenarios because they are friendly to the environment and have reserves that never run out [9]. For example, the potential for solar energy in Indonesia reaches around 4.8 KWh/m 2 or the equivalent of 112,000 GWp, while wind energy has a potential of 978 MW [8,10].…”