Towards intelligent operation of future power system: bayesian deep learning based uncertainty modelling technique

Abstract

The increasing penetration level of renewable energy resources (RES) in the power system brings fundamental changes of the system operating paradigms. In the future, the intermittent nature of RES and the corresponding smart grid technologies will lead to a much more volatile power system with higher level uncertainties. At the same time, as a result of the larger scale installation of advanced sensor devices in power system, power system engineers for the first time have the opportunity to gain insights from the influx of massive data sets in order to improve the system performance in various aspects. To this end, it is imperative to explore big data methodologies with the aim of exploring the uncertainty space within such complex data sets and thus supporting real-time decision-making in future power system. In this thesis, Bayesian Deep learning is investigated with the aim of exploring data-driven methodologies to deal with uncertainties which is in the following three aspects. (1) The first part of this thesis proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep long short-term memory network. The proposed methodological framework employs clustering in sub-profiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-the-art methods and indicate the importance and effectiveness of sub-profile clustering and high PV visibility. (2) The second part of this thesis studies a novel Conditional Bayesian Deep Auto-Encoder (CBDAC) based security assessment framework to compute a confidence metric of the prediction. This informs not only the operator to judge whether the prediction can be trusted, but it also allows for judging whether the model needs updating. A case study based on IEEE 68-bus system demonstrates that CBDAC outperforms the state-of-the-art machine learning-based DSA methods and the models that need updating under different topologies can be effectively identified. Furthermore, the case study verifies that effective updating of the models is possible even with very limited data. (3) The last part of this thesis proposes a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In particular, the proposed approach replaces deterministic network in traditional Reinforcement Learning with Bayesian probabilistic network in order to obtain an approximation of the value function distribution, which effectively solves Q-value overestimation issue. The proposed model is able to provide both energy management during normal operating conditions and resilient control during extreme events in a multi-energy micro-grid system. Comparing with naive DDPG method and optimisation method, the effectiveness and importance of employing Bayesian Reinforcement Learning approach is investigated and illustrated across different operating scenarios. Case studies have shown that by using the Monte Carlo posterior mean of the Bayesian value function distribution instead of a deterministic estimation, the proposed BDDPG method achieves a near-optimum policy in a more stable process, which verifies the robustness and the practicability of the proposed approach.Open Acces