A modified power management algorithm with energy efficiency and GHG emissions limitation for hybrid power ship system

With the integration of energy storage system (ESS), photovoltaic cell (PV) and generator, hybrid power ship system (HPSS), as one of promising technology, is regarded as an advanced method to improve energy efficiency and marine environment quality. However, the computational complexity and non-convexity of energy scheduling in hybrid power ship system make it challenging to obtain the feasible solution. To address this crucial issue, a heuristic optimization algorithm named multi-populations particle swarm optimization (MPPSO) is proposed for economic and feasible energy scheduling. Firstly, a hybrid power ship system, comprising generator, ESS, PV, service loads and propulsion system, is formulated. On this basis, a load shedding coefficient is given for the secure and stable operation of hybrid power ship system under fault model. Then, to achieve energy scheduling, several improvements are proposed to enhance PSO. Considering the problem of premature, a nonlinear adaptive inertial weight strategy is proposed to improve the searching ability. With the fitness value of population, learning coefficients are adjusted in nonlinear so that particle can accurately learn from individual or population position. Further, a modified velocity update formula with the information of historical experience and center particle is proposed to employ the particle information fully. Finally, the effectiveness of MPPSO is illustrated on simulation experiment by three cases.