Hybrid Renewable Energy Systems with Battery and Hydrogen Storage

Abstract

As population numbers and people's standards of living increase, so does the global energy demand and carbon dioxide emissions and it is imperative that new sustainable and renewable energy sources are sought, as the world's natural resources are depleting. Electricity generation presents the biggest opportunity to lower CO2 emissions and in an emerging world where the demand for alternative renewable energy systems is growing it is expected that one of the technologies in conjunction with conventional storage which will play a key role in reducing emissions is hydrogen fuel cell technology with hydrogen storage. Many attempts have been made to realise optimisation algorithms of renewable energy system using multiple techniques in literature. These attempts have consisted of using mathematical models combined with rules and object oriented modelling in order to assist in the design of renewable applications. The integration methods described in previous papers up to date seems to offer mainly technical and/or economical optimisation parameters. None of the presented methods seems to be based on a unified model where multi objectives and/constraints are taken into account above technical and economic considerations. There are also few practical examples of analysis and optimisation of hybrid renewable energy systems in a complete optimisation model where the behaviour of renewable energy sources, battery banks, electrolysers, fuel cells and hydrogen storage tanks are reviewed throughout the simulation in detail. For a successful transition to a renewable energy economy, optimisation of renewable energy systems must evolve to take into account metrics additional to technical performance and cost. A Normalised Weighted Constrained Multi-Objective (NWCMO) meta-heuristic optimisation algorithm has been proposed in conjunction with optional constraints for achieving a compromise between mutually conflicting objectives in multiple simultaneous categories; technical, economic, environmental and socio-political objectives, to simulate and optimise a renewable energy system with balanced outcomes. The socio political objective is represented by a proposed socio acceptance matrix which outputs a weighted measured social acceptance indicator towards proposed renewable energy systems. The methodology was implemented using an adjusted Particle Swarm Optimisation algorithm and tested against data and other studies from the literature. In each case the original results could be reproduced, but the newly-implemented algorithm was further able to find a more optimal design solution under the same constraints. In addition, the influence of additional quantified socio-political inputs was explored. This thesis presents a review of issues for integration of hydrogen energy technology into energy systems, emphasising electricity generation using fuel cell hydrogen technology. Integration of energy storage, sizing methodologies, energy flow management and their associated optimization algorithms and software implementation are addressed. The model presented in this thesis offers a streamlined integration of design rules, optimization techniques and constraints merged into one planning system. The outcome is a model offering an end user the possibility to carry out a proper feasibility study prior to embarking on implementing a renewable system. An optimisation methodology based on four classes of objective (technical, economic, environmental, socio-political) is presented, benchmarked and tested against various hybrid renewable energy systems with conventional and hydrogen storage.