No abstract

SUMMARYParticle swarm optimization (PSO), a meta-heuristic global optimization method, has attracted special interest for its simple algorithm and high searching ability. The updating formula of PSO involves coefficients with random numbers as parameters to enhance diversification ability in searching for the global optimum. However, the randomness makes stability of the searching points difficult to analyze mathematically, and the users need to adjust the parameter values by trial and error. In this paper, stability of the stochastic dynamics of PSO is analyzed mathematically and an exact stability condition taking the randomness into consideration is presented with an index called the "statistical eigenvalue," which is a new concept for evaluating the degree of stability of PSO dynamics. The accuracy and effectiveness of the proposed stability discrimination using the presented index are certified in numerical simulation for simple examples.

The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics; however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.

The authors focus on a microgrid (MG) including hydrogen production processes as a part of demand. The studied MG consists of a photovoltaic (PV), a battery storage system, two different type electrolyzers, and electric/fuel steam boilers and supplies for the electric, thermal and hydrogen demands. This paper proposes detailed operation scheme which consists of two stages, namely, the day ahead scheduling and the real‐time (RT) control. The day ahead scheduling optimizes the operations of system components in the next day based on the forecasts for PV output and demands. The RT control provides the actual control command to individual system component based on the actual PV output and demands monitored online. The effectiveness of the proposed scheme is ascertained through some computational case studies. Performance of the proposed scheme is evaluated in terms of the total energy cost, the curtailed PV output and the hydrogen production volume.

No abstract