Power management and sizing optimisation of renewable energy hydrogen systems

Abstract

Solar and wind renewable energy (RE) sources are widely available and a viable alternative for generating cleaner energy. These RE sources are however intermittent and dependent on location, time of day and season. Adding to this, non-linear components make determining sizes for the components of these systems a complicated and difficult task. Optimisation techniques are currently being used to perform the sizing of these systems, with system sizing and control optimisation performed separately. Objectives other than cost such as efficiency and reliability are not currently considered. The first objective of this study is to develop an integrated sizing and control optimisation strategy for a small-scale stand-alone RE system using multiple objectives while considering sizing and control simultaneously. A sizing and control optimisation algorithm is developed consisting of two optimisation algorithms simultaneously optimising sizing and control variables. A single objective genetic algorithm (GA) is implemented for control optimisation and a strength Pareto evolutionary algorithm (SPEA) is implemented for the sizing optimisation. The developed strategy is referred to as SPEAGA in the rest of the document. Multiple objective functions fo cost, efficiency and reliability are used. An optimal control configuration is determined from the single objective GA which then determines an optimal sizing configuration for all three objective functions using the SPEA. Design, analysis and optimisation require a mathematical model of the system which is developed and validated. The SPEAGA is implemented on the system model. Results obtained from the optimisation process consist of non-dominated solution vectors known as Pareto optimal solutions. Each solution vector consist of six control variables, nine sizing variables and three objective function values. A second objective of this work is a genetic fuzzy system (GFS) developed to analyse the results and provide a reduced set of rules. A GA is implemented to train the fuzzy system which results in a rule-base for each objective of each site. Further a membership function reduction approach is followed to reduce the complexity of each rule-base by eliminating membership functions. A third objective is the insights derived from the fuzzy rule bases. Contributions made by this study include the multi-objective optimisation of sizing and control variables simultaneously through the development of the novel optimisation architecture, SPEAGA. Optimising sizing and control for multiple objectives presents an additional contribution in the sense that it analyses the information in a new way giving new insight. The developed GFS is successfully used to generate rules relating system inputs to outputs and is useful for system design. The SPEAGA successfully optimises a small-scale rural RE hydrogen (H2) system for three sites. Further results include a comparison between the standard SPEA and the developed SPEAGA. The SPEAGA provided improved values for both efficiency and reliability. These results provide new insight into system design in terms of sizing and power management when considering multiple conflicting objectives. Efficiency and reliability are shown to be dependent on control parameters and are therefore improved using the SPEAGA through the additional control optimisation which is highlighted by the results. Insights are obtained from the GFS which are considered useful for future system developments.