Decentralized optimization approach for power distribution network and microgrid controls

The smart grid vision has led to the development of advanced control and management frameworks using distributed generation (DG) and storage resources, commonly referred to together as distributed energy resources (DERs). Albeit environment-friendly, these DERs in distribution networks including microgrids (MGs) could greatly challenge the operational goal of maintaining adequate power system reliability standards because of their high intermittency, uncertainty, and lack of physical inertia. Meanwhile, these networks are inherently unbalanced and lack high-quality communications to a centralized entity as compared to the bulk transmission grid. Both aspects contribute to the challenge of designing voltage and frequency control frameworks therein. To tackle these problems, we propose decentralized control strategies, which account for cyber-physical network interactions automatically and dynamically while being either cognizant of various communication scenarios or resilient to malicious cyber intrusions. By treating the transmission grid as an infinity bus, voltage stability is the main concern in distribution networks where more DERs are being installed in the near future. Thanks to advances in power electronics, DERs can also be excellent sources of reactive power (VAR), a quantity that is known to have a significant impact on the network voltage level. Accordingly, we first formulate the local VAR-based voltage control design by minimizing a weighted quadratic voltage mismatch error objective using gradient-projection (GP) updates. The step-size design under both static and dynamic settings is further analyzed for practical implementation purposes. Nonetheless, such local design suffers degraded performance due to lack of information exchanges, especially under limited VAR resources. To address this issue, we develop the distributed voltage control (DVC) design based on the alternating direction method of multipliers (ADMM) algorithm. The DVC design has simple node-to-node communication architecture while seamlessly adapting to dynamically varying system operating conditions and being robust against random communication link failures. To further reduce communication complexity and enhance robustness to imperfect communications, especially under the worst-case scenarios of a total communication outage, we integrate both local and distributed control designs to a hybrid voltage control (HVC) scheme that can achieve the dual objectives in terms of flexible adaptivity to variable rate of communications and global optimality of voltage regulation performance. Such an innovative design aims to unify the separated framework of either local or distributed control design. Numerical tests using realistic feeders and real time-series data have been demonstrated for the voltage control designs. The aforementioned decentralized voltage control designs can improve the power system stability while distribution feeders are interconnecting to the bulk transmission grids. With a high penetration of DERs in the networks, it is possible to build a discrete energy system, namely, a microgrid (MG), that is capable of operating in parallel with, or independently from, the transmission grids. Henceforth, MGs are likely to emerge as a means to advance power and cyber physical resiliency in future grid systems. As MGs may operate independently, these mostly power electronics-interfaced DERs exhibiting low-inertia characteristic have raised significant concern over the frequency stability issues. To tackle this problem, we introduce the concept of virtual inertia of DERs and cast the secondary frequency control design for isolated MGs as a consensus optimization problem. We solve it distributively by adopting the partial primal-dual (PPD) algorithm. Interestingly, parts of our specially designed control algorithm turn out to mimic the dynamics of network power flow and virtual synchronous generator-based inverter. Thus, such dynamics is seamlessly governed by the physical system itself. Given a proper control parameter choice, the convergence of the consensus is guaranteed without assuming the time-scale separation of the hierarchical control design methodologies. By extending this work to a practical industrial MG network that follows the IEC 61850 communication protocol, similar frequency regulation objective is introduced and solved by a decentralized ADMM-based algorithm. The countermeasures for malicious attacks on the communication network for both PPD- and ADMM-based control designs are also investigated. Specifically, we analyze two types of malicious attacks on the communication network, namely, the link and node attacks. Meanwhile, anomaly detection and localization strategies are developed based on the metrics of optimization-related variables. We showcase the microgrid frequency regulation operation to demonstrate the effectiveness of the proposed frequency control designs under a real-time simulation environment

Model based forecasting for demand response strategies

The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed energy resources, such as solar and wind, calls for a more active management of the distribution grid. Conventional distribution grids were passive systems, in which the power was flowing unidirectionally from upstream to downstream. Nowadays, and increasingly in the future, the penetration of distributed generation (DG), with its stochastic nature and lack of controllability, represents a major challenge for the stability of the network, especially at the distribution level. In particular, the power flow reversals produced by DG cause voltage excursions, which must be compensated. This poses an obstacle to the energy transition towards a more sustainable energy mix, which can however be mitigated by using a more active approach towards the control of the distribution networks. Demand side management (DSM) offers a possible solution to the problem, allowing to actively control the balance between generation, consumption and storage, close to the point of generation. An active energy management implies not only the capability to react promptly in case of disturbances, but also to ability to anticipate future events and take control actions accordingly. This is usually achieved through model predictive control (MPC), which requires a prediction of the future disturbances acting on the system. This thesis treat challenges of distributed DSM, with a particular focus on the case of a high penetration of PV power plants. The first subject of the thesis is the evaluation of the performance of models for forecasting and control with low computational requirements, of distributed electrical batteries. The proposed methods are compared by means of closed loop deterministic and stochastic MPC performance. The second subject of the thesis is the development of model based forecasting for PV power plants, and methods to estimate these models without the use of dedicated sensors. The third subject of the thesis concerns strategies for increasing forecasting accuracy when dealing with multiple signals linked by hierarchical relations. Hierarchical forecasting methods are introduced and a distributed algorithm for reconciling base forecasters is presented. At the same time, a new methodology for generating aggregate consistent probabilistic forecasts is proposed. This method can be applied to distributed stochastic DSM, in the presence of high penetration of rooftop installed PV systems. In this case, the forecasts' errors become mutually dependent, raising difficulties in the control problem due to the nontrivial summation of dependent random variables. The benefits of considering dependent forecasting errors over considering them as independent and uncorrelated, are investigated. The last part of the thesis concerns models for distributed energy markets, relying on hierarchical aggregators. To be effective, DSM requires a considerable amount of flexible load and storage to be controllable. This generates the need to be able to pool and coordinate several units, in order to reach a critical mass. In a real case scenario, flexible units will have different owners, who will have different and possibly conflicting interests. In order to recruit as much flexibility as possible, it is therefore importan

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Electric Vehicle (EV)-Assisted Demand-Side Management in Smart Grid

While relieving the dependency on diminishing fossil fuels, Electric Vehicles (EVs) provide a promising opportunity to realise an eco-friendly and cost-effective means of transportation. However, the enormous electricity demand imposed by the wide-scale deployment of EVs can put power infrastructure under critical strain, potentially impacting the efficiency, resilience, and safety of the electric power supply. Interestingly, EVs are deferrable loads with flexible charging requirements, making them an ideal prospect for the optimisation of consumer demand for energy, referred to as demand-side management. Furthermore, with the recent introduction of Vehicle-to-Grid (V2G) technology, EVs are now able to act as residential battery systems, enabling EV customers to store energy and use them as backup power for homes or deliver back to the grid when required. Hence, this thesis studies Electric Vehicle (EV)-assisted demand-side management strategies to manage peak electricity demand, with the long-term objective of transforming to a fully EV-based transportation system without requiring major upgrades in existing grid infrastructure. Specifically, we look at ways to optimise residential EV charging and discharging for smart grid, while addressing numerous requirements from EV customer's perspective and power system's perspective. First, we develop an EV charge scheduling algorithm with the objective of tracking an arbitrary power profile. The design of the algorithm is inspired by water-filling theory in communication systems design, and the algorithm is applied to schedule EV charging following a day-ahead renewable power generation profile. Then we extend that algorithm by incorporating V2G operation to shape the load curve in residential communities via valley-filling and peak-shaving. In the proposed EV charge-discharge algorithm, EVs are distributedly coordinated by implementing a non-cooperative game. Our numerical simulation results demonstrate that the proposed algorithm is effective in flattening the load curve while satisfying all heterogeneous charge requirements across EVs. Next, we propose an algorithm for network-aware EV charging and discharging, with an emphasis on both EV customer economics and distribution network aspects. The core of the algorithm is a Quadratic Program (QP) that is formulated to minimise the operational costs accrued to EV customers while maintaining distribution feeder nodal voltage magnitudes within prescribed thresholds. By means of a receding horizon control approach, the algorithm implements the respective QP-based EV charge-discharge control sequences in near-real-time. Our simulation results demonstrate that the proposed algorithm offers significant reductions in operational costs associated with EV charging and discharging, while also mitigating under-voltage and over-voltage conditions arising from peak power flows and reverse power flows in the distribution network. Moreover, the proposed algorithm is shown to be robust to non-deterministic EV arrivals and departures. While the previous algorithm ensures a stable voltage profile across the entire distribution feeder, it is limited to balanced power distribution networks. Therefore, we next extend that algorithm to facilitate EV charging and discharging in unbalanced distribution networks. The proposed algorithm also supports distributed EV charging and discharging coordination, where EVs determine their charge-discharge profiles in parallel, using an Alternating Direction Method of Multipliers (ADMM)-based approach driven by peer-to-peer EV communication. Our simulation results confirm that the proposed distributed algorithm is computationally efficient when compared to its centralised counterpart. Moreover, the proposed algorithm is shown to be successful in terms of correcting any voltage violations stemming from non-EV load, as well as, satisfying all EV charge requirements without causing any voltage violations

Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book