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Doctoral Degrees (Physics)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6603

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Browsing Doctoral Degrees (Physics) by Subject "Blackbox optimization."

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    Baseline demand responsiveness framework for the conventional grid through appliance scheduling by evolutionary metaheuristics.
    (2020) Doho, Goncalves Justino.; Matthews, Alan Peter.; Jarvis, Alan Lawrence Leigh.
    A major problem of many energy environments nowadays, is an obsolete and highly inefficient electricity supply system, the Conventional Grid (CG), characterized by a high peak to average ratio, out of an uncontrollable demand, worsened by a native lack of communications infrastructures and resources for performing a proper automated demand side management, which has resulted in blackouts, harsh user discomfort, high electricity cost, huge economic losses and a high carbon footprint. Designed to tackle this problem is the emerging Smart Grid (SG). Most research works are devoted to providing automation and efficiency to the SG (or the intermediate SG-like) environments. There is a scarcity of research devoted to providing automated demand responsiveness to the information layer deprived CG environments, although as evident, an Automated Demand Response (ADR) is badly required, since there is still a long way until we get to the SG, all the more when developing world is concerned. Such context, set our focus towards the CG. So, this research work, developed a framework for providing a "blind" baseline Demand Responsiveness (bbDR) for CG environments, wherein, a pseudo real time electricity pricing function, built from a country load profile, is used as a guiding function for the autonomous scheduling of controllable appliances, which seeks to improve electricity consumption patterns, while also preserving user satisfaction by complying to their preferences. For performance evaluation, the optimized energy consumption patterns (peak load, peak to average ratio, load and cost profiles and mean energy rate) of the controlled use of appliances, are compared to those ones produced by their uncontrolled use. The controlled usage schedules are produced by an evolutionary metaheuristics, whilst the uncontrolled usage is stochastically generated from appliances’ rate-of-use probabilities sourced from the literature. The results proved that, such framework is capable of, without DR communications, delivering meaningful, ADR-like, performances to a communications deprived CG environment. As part of the work for simulating the above bbDR framework, we developed and demonstrated a Real Parameter Blackbox Optimization Approach to Appliance Scheduling (RPBBOAS) model, which describes the household, and provides the logical interface with the optimization algorithms. This real parameter model, vis-a-vis its discrete parameter counterpart, tackles combinatorial explosion by, in a novel way, reducing the problem dimension that is traded with the external blackbox optimization algorithms, in such a way that boosts performance and widens the window of applicable algorithms. While developing the above RPBBOAS model, readily available state-of-the-art metaheuristics showed a lackluster performance, which propelled us to design a novel hybrid evolutionary metaheuristics (Hy- PERGDx) that was eventually used in the bbDR simulations. It showed a better all-around performance and robustness vs the state-of-the-art, when benchmarked on a wide range of non-linear problems. Overall, such deliveries, demonstrated the potential of the proposed bbDR framework for improving demand patterns and quality of service figures, in a communication free way, which with an appropriate follow-up development, makes it suitable for application in severely affected, communications deprived (or communications limited), energy networks such as South Africa or worse energy ecosystems.