Distributed Control Methods for Integrating Renewable Generations and ICT Systems

With increased energy demand and decreased fossil fuels usages, the penetration of distributed generators (DGs) attracts more and more attention. Currently centralized control approaches can no longer meet real-time requirements for future power system. A proper decentralized control strategy needs to be proposed in order to enhance system voltage stability, reduce system power loss and increase operational security. This thesis has three key contributions: Firstly, a decentralized coordinated reactive power control strategy is proposed to tackle voltage fluctuation issues due to the uncertainty of output of DG. Case study shows results of coordinated control methods which can regulate the voltage level effectively whilst also enlarging the total reactive power capability to reduce the possibility of active power curtailment. Subsequently, the communication system time-delay is considered when analyzing the impact of voltage regulation. Secondly, a consensus distributed alternating direction multiplier method (ADMM) algorithm is improved to solve the optimal power ow (OPF) problem. Both synchronous and asynchronous algorithms are proposed to study the performance of convergence rate. Four different strategies are proposed to mitigate the impact of time-delay. Simulation results show that the optimization of reactive power allocation can minimize system power loss effectively and the proposed weighted autoregressive (AR) strategies can achieve an effective convergence result. Thirdly, a neighboring monitoring scheme based on the reputation rating is proposed to detect and mitigate the potential false data injection attack. The simulation results show that the predictive value can effectively replace the manipulated data. The convergence results based on the predictive value can be very close to the results of normal case without cyber attack

Tertiary control in microgrids: an optimal power flow approach based on convex optimization and Wirtinger calculus

This work presents a tertiary control for microgrids considering renewable energy sources and energy storage devices. The proposed model considers the operation in 24h including capacity and grid-code limitations. The optimization problem under consideration is non-convex, therefore, the model is approximated to a convex representation, by using a linear formulation of the optimal power flow equations via Wirtinger’s calculus..

Cost-Optimal Operational Security in Transmission Grids with Embedded HVDC Systems and Energy Storage

The future transmission grid for electrical power will face challenges on an unprecedented scale as the transformation of the energy system progresses. The massive integration of renewable energy sources will require new methods and additional equipment to maintain the system secure and cost-efficient. This doctoral thesis presents an approach to securely operate a transmission grid based on optimal power flow. Optimal control of phase shifting transformers, overlaying HVDC grids and large-scale energy storage lead to reduced operating costs. Furthermore, this work discusses efficient approaches to optimally coordinate multiple inter-connected control areas, if one central controller is undesirable for political or technical reasons

Network-Secure Consumer Bidding in Energy and Reserve Markets

Electricity systems are undergoing a fundamental transformation from centralised generation to a distributed paradigm in which electricity is produced at a smaller scale by numerous distributed energy resources (DER). The replacement of centralised facilities by DER brings economic and environmental benefits. However, it also makes it challenging for the market operator to secure the system with sufficient frequency response in the absence of centralised facilities -- dominant providers of such services -- in the electricity markets. Fortunately, the aggregate response of DER can fulfil systems' need for frequency reserve services. However, DER are operated within distribution networks whose technical limits are not accounted for within the wholesale market. This raises the question of how DER can participate in the energy and reserve markets while respecting the distribution network's constraints. To ensure network constraints, consumer and grid constraints / preferences should be modelled simultaneously within a large-scale optimisation problem. Yet, the need for scale, involvement of multiple stakeholders (grid operator and consumers) who possibly have conflicting interests, privacy concerns, and the uncertainty around consumer data and market prices make this extra challenging. This thesis contributes to addressing these challenges by developing network-secure consumer bids that account for the distributed nature of the problem, consumer data and market price uncertainties. Note that when bidding in the market, consumers, and thus, the network operating point is not clear, as it depends on the dispatch in the energy market and whether a contingency occurs. Therefore, we ensure grid feasibility for operating envelopes that include any possible operating points of consumers. We first use the alternating direction method of multipliers (ADMM) to enable network-secure consumer biding. Using ADMM, consumers optimise for their energy and reserve bids and communicate with the grid their required operating envelopes. The network then solves OPFs to see whether any constraint is violated and updates the ADMM parameters. Such communications continue until converging on a consensus solution. We learnt that our ADMM-based solution approach is able to maintain grid's constraints as long as consumers commit to their envelopes -- a requirement that might not hold due to uncertainty. Thus, we further improve our bidding approach by modelling uncertainties around solar PV and demand, using a piecewise affinely adjustable robust constrained optimisation (PWA-ARCO). We observed that not only is PWA-ARCO able to compensate for live uncertainty variabilities, but also it can improve the reliability of consumer bids, especially in reserve markets. We also extend our initial envelopes by enabling consumers to provide reactive power support for the grid. We next enable consumers to bid (possibly) their entire flexibility by developing price-sensitive offers. Such offers include a bid curve chunked into several capacity bands, each being submitted at a different price. We identified that when the prices cannot be forecast accurately, the price-sensitive bidding approach can improve consumer benefit. To ensure network feasibility, instead of an iterative ADMM approach, we propose a more scalable one-shot policy in which the network curtails the part of the consumer bid that violates the network. Compared to ADMM, the one-shot policy significantly reduced the computation complexity at the cost of a slightly less optimum outcome. Overall, this thesis investigates different techniques to provide network-secure energy and reserve market services out of residential DER. It expands the knowledge in the area of consumer bidding solutions, adjustable robust optimisation, and distributed optimisation. It also discovers a range of interesting future research topics, including distribution network modelling and uncertainty characterisation

Achieving High Renewable Energy Integration in Smart Grids with Machine Learning

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest. In recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids. The research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid. The objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation. The research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids. Simulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix

Achieving High Renewable Energy Integration in Smart Grids with Machine Learning

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest. In recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids. The research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid. The objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation. The research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids. Simulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix

Blockchain systems, technologies and applications: a methodology perspective

In the past decade, blockchain has shown a promising vision to build trust without any powerful third party in a secure, decentralized and scalable manner. However, due to the wide application and future development from cryptocurrency to the Internet of things, blockchain is an extremely complex system enabling integration with mathematics, computer science, communication and network engineering, etc. By revealing the intrinsic relationship between blockchain and communication, networking and computing from a methodological perspective, it provided a view to the challenge that engineers, experts and researchers hardly fully understand the blockchain process in a systematic view from top to bottom. In this article we first introduce how blockchain works, the research activities and challenges, and illustrate the roadmap involving the classic methodologies with typical blockchain use cases and topics. Second, in blockchain systems, how to adopt stochastic process, game theory, optimization theory, and machine learning to study the blockchain running processes and design the blockchain protocols/algorithms are discussed in details. Moreover, the advantages and limitations using these methods are also summarized as the guide of future work to be further considered. Finally, some remaining problems from technical, commercial and political views are discussed as the open issues. The main findings of this article will provide a survey from a methodological perspective to study theoretical model for blockchain fundamentals understanding, design network service for blockchain-based mechanisms and algorithms, as well as apply blockchain for the Internet of things, etc