Modelling and optimisation of decentralised hybrid solar biogas system to power an organic Rankine cycle (ORC-Toluene) and air gap membrane distillation (AGMD) for desalination and electric power generation

The intensive use of fossil fuels to meet the world energy and water demand has caused several environmental issues, such as global warming, air pollution and ozone depletion. Therefore, the integration of stand-alone decentralised hybrid renewable energy systems is a promising solution to satisfy the global energy-water demands and minimize the effects of fossil fuels utilisation. Among these hybrid technologies, concentrated solar power (CSP) combined with waste-based biogas to power organic Rankine cycle for cogeneration provide the means to generate dispatchable, reliable, renewable electricity and water in high direct normal incidence (DNI) regions around the world. Due to the strong inverse correlation between DNI resources and freshwater availability, most of the best potential CSP regions also lack sufficient freshwater resources. The current study proposes and applies a novel multi-dimensional modelling technique based on artificial neural networks (ANN) for hourly solar radiation and wind speed data forecasting over six locations in Oman. The developed model is the first attempt to integrate two ANN models simultaneously by using enormous meteorological data points for both solar radiation and wind speed prediction. The developed model requires only three parameters as inputs, and it can predict solar radiation and wind speed data simultaneously with high accuracy. As a result, the model provides a user-friendly interface that can be utilised in the energy systems design process. Consequently, this model facilitates the implementation of renewable energy technologies in remote areas in which gathering of weather data is challenging. Meanwhile, the accuracy of the model has been tested by calculating the mean absolute percentage error (MAPE) and the correlation coefficient (R). Therefore, the model developed in this study can provide accurate weather data and inform decision makers for future instalments of energy systems. Furthermore, a novel proposed hybrid solar and biogas system for desalination and electric power generation using advanced modelling techniques to integrate the stand-alone off-grid system has been designed. The novelty emerges from some facts, which are centralised around the use of a hybrid electric generation via Concentrated Solar Power (CSP) and anaerobic digestion biogas to achieve higher stability and profitability. Meanwhile, the cogeneration through the waste heat of the ORC drives the AGMD, which benefits as well from the higher stability due to hybridisation. In addition, an innovative and user-friendly modelling approach has been applied, and this efficiently integrates the individual energy components, i.e. PTC, anaerobic biogas boiler, ORC and AGMD, which fosters the optimisation of the proposed system. The models have been developed in the MATLAB/Simulink® software and have been used to investigate the system area, dimensions, and cost and to ensure that the electrical and water demand of the end-user are met. In addition, a new detailed thermo-economic assessment of the proposed hybrid solar biogas for cogeneration in off-grid applications has been investigated. An energy, exergy, and cost analysis has been performed and to fully utilise this, a sensitivity assessment on the developed model has been analysed to examine the effects of various design parameters on the thermo-economic performance. Finally, implementing an in-depth simulation testing of the system in a rural region in Oman is presented. The novel integrated solar and biogas system that has been designed through advanced modelling in the MATLAB/ Simulink® is integrated with a robust multi-objective optimisation technique to determine the best operating configuration. Three objective functions namely, maximising power and water production, and minimising the unit exergy product costs have been formulated. The turbine efficiency, top ORC vapor temperature and ORC condenser temperature has been selected as the decision variables. The non-dominated sorting genetic algorithm (NSGA-II) has been employed to solve the optimisation problem and produce a Pareto frontier of the optimal solutions. Further, the TOPSIS approach has been used to select the optimal solution from the Pareto set. The study constitutes the first attempt to holistically optimise such a hybrid off-grid cogeneration system in a robust manner.