The penetration rate of grid-tied wind turbine generator systems (WTGSs) into the existing electricity networks is rapidly increasing worldwide. Huge efforts are exerted for improving the characteristics of variable-s...
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The penetration rate of grid-tied wind turbine generator systems (WTGSs) into the existing electricity networks is rapidly increasing worldwide. Huge efforts are exerted for improving the characteristics of variable-speed wind energy conversion systems. This study exhibits a novel symbiotic organisms search algorithm (SOSA)-based optimal control strategy for achieving efficient operation of a grid-connected WTGS. The optimal control strategy relies on proportional-integral (PI) controllers, which are properly fine-tuned using the SOSA. The simulation-based optimisation method is considered in the optimisation process, where the integration of the square error criterion is selected as a fitness function. To obtain realistic performances, practical wind speed data extracted from the Zaafarana power plant in Egypt are used in the analyses. The efficacy of the SOSA-based optimal PI control strategy is compared with that realized using the grey wolf optimiser algorithm (GWOA)-based PI control scheme, considering network disturbances. The feasibility of the proposed control strategy is validated using the simulation studies, which are implemented using MATLAB/Simulink software. Notably, the proposed SOSA-based optimal control strategy is considered to be a precise means of improving the behaviour of grid-tied WTGSs.
Buildings consume approximately 40% of end-use energy worldwide and are responsible for approximately one-third of greenhouse gas (GHG) emissions. Clearly, designing high energy performance buildings and identifying e...
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Buildings consume approximately 40% of end-use energy worldwide and are responsible for approximately one-third of greenhouse gas (GHG) emissions. Clearly, designing high energy performance buildings and identifying effective energy retrofit measures not only decrease CO 2 emissions, but also reduce the need for non-renewable energy sources. While the traditional rules of thumb and building codes improve the building energy efficiency, they are likely to be far from the optimal design as they do not consider the interactions among design variables. Therefore, new methods should be developed to achieve the maximum energy savings. Building energy optimisation (BEO) is a method that considers interactions among design variables and selects the optimal building design from a set of available alternative designs based on the mathematics. A challenge of currently-available optimisationmethods is that they suffer from high computational cost due to high complexities in building optimisation problems including multi-modal and nonlinear behaviour of building thermal performance, discontinuities in the optimisation variables (e. g. window type), uncertainty in building design parameters (e. g. alterations in building operating conditions) and discontinuities in the output of building simulation software (e. g. EnergyPlus). This high computational cost remains a key barrier to the widespread utilisation of optimisation as a design tool. Accordingly, the focus of this research is on developing new efficient solution methods for Building optimisation Problems (BOPs) and deploying them on realistic case studies to evaluate their performance and utility. Generally, BOPs can be categorised into two main groups: simulation-basedoptimisation (software-in-the- loop method) and surrogate-basedoptimisationmethods. In this thesis, new methods were developed to improve the performance of both methods. Furthermore, a new methodology was developed to address uncertainty of building simu
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