A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition

In this paper, we study the problem of consensus-based distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is kept private. That is to say, only one substate is visible to an adversary, protecting the privacy from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach

Distributed Control Strategies for Microgrids: An Overview

There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC/DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications

Power Losses Estimation in Low Voltage Smart Grids

Mención Internacional en el título de doctorOne of the European Union Targets was to replace at least 80% of all traditional energy meters with electronic smart meters by 2020. However, by the end of 2020, the European region (EU 27 including the UK) had installed no more than 150 million smart electricity meters, representing a penetration rate of 50% for smart meters. By 2026, It is expected that there will be more than 227 million smart meters in households due to the updated planning and target numbers, which will affect many European markets, including western and northern Europe. This scenario would contribute to the general purpose of building a more sustainable distribution system for the future. This thesis contributes to the field of power losses estimation and optimization in low-voltage (LV) smart grids in large-scale distribution areas. To contextualize the importance of the research, it has been necessary to explain the unbalanced nature of low voltage distribution networks where there is a huge deployment of smart meter rollout, and there is also uncertainty related to renewable energy generation. Main results of the thesis have been applied in two smart grid research projects: the national project OSIRIS (Optimizaci´on de la Supervisi´on Inteligente de la Red de Distribuci´on) and the European project IDE4L (Ideal Grid For All ). Smart metering infrastructure allows distributor system operators (DSOs) to have detailed information about the customers energy consumption or generation. Smart meters measure the active and reactive energy consumption/generation of customers using different discrete time resolutions which range from 15-60 min. A large-scale smart meter rollout allows service providers to gain information about the energy consumed and produced by each customer in near-real time. This knowledge can be used to compute the aggregated network power losses at any given time. In this case, network power losses are calculated by means of customers’ smart meters measurements, in terms of both active and reactive energy consumption, and by the energy measured by the smart meter supervisor located at the secondary substation (SS). The problem of network losses estimation becomes more challenging as a results of the existence of not-technical losses due to electricity fraud or smart meter measurements anomalous (null or extremely high) or even because there are customers’ smart meters that can be out of service. One of the differential keys of LV smart grids is the presence of single-phase loads and unbalanced operation, which makes it necessary to adopt a complete three-phase model of the LV distribution network to calculate the real value of the power losses. This scenario makes the process of power loss estimation a computationally intensive problem. The challenge is even greater when estimating the power losses of large-scale distribution networks, composed of thousands of SSs. In recent years, environmental concerns have led to the increasing integration of a considerable number of distributed energy resources (DERs) into LV smart grids. This fact prompts DSOs and regulators to provide the maximum energy efficiency in their networks (i.e., the smallest power loss values) and maximum sustainable energy consumption. Detailed understanding of the network’s behavior in terms of power losses and the use of electricity is necessary to achieve this energy efficiency. However, the above scenario presents some drawbacks. The integration of DERs units, such as photovoltaic (PV) panels, into distribution networks can produce an increment of network power losses if the DERs units are not optimally located, coordinated, or controlled. Additionally, the network can experience technical contingencies such as cable’s overloads and nodal over-voltages or can lead to an inefficient system operation due to high energy losses or cables that exceed thermal limits. Moreover, there is a great uncertainty associated with the distributed power generation from PVs because its energy generation depend on weather conditions, including ambient temperature and solar irradiance, which are highly intermittent and fluctuating. Uncertainty is also present in some loads with stochastic behavior, such as plug-in electric vehicles (PEV), which adds an uncertainty layer and makes their optimal integration more complex. Therefore, DSOs require advanced methods to estimate power losses in unbalanced large-scale LV smart grids under uncertain situations. Such estimations would facilitate the deployment of policies and practices that lead to a safe and efficient integration of DERs in the form of flexibility mechanisms. In this context, flexibility mechanisms are essential to achieve optimal operation conditions under extreme uncertainty. Flexibility mechanisms can be deployed to tackle the imbalance between generation and demand that results from the uncertainty that is latent in LV smart grids. These flexibility mechanisms are based on modifying the normal power consumption (for the demand side) or power generation (for the generation side), according to a flexibility scheduling at the request of the network operator. In summary, DSOs face the challenge of managing network losses over large geographical areas where there are hundreds of secondary substations and thousands of feeders, with multiple customers and an ever-increasing presence of renewable DERs. Power losses estimation is thus paramount to improve network energy efficiency in the context of the European Union energy policies. This situation is complicated by the unbalanced operation of those networks and the presence of uncertainty. To address these challenges, this thesis focuses on the following objectives: 1. Power losses estimation in unbalanced LV smart grids under uncertainty. 2. Power losses estimation in unbalanced LV smart grids in large areas with a presence of DERs. 3. Flexibility scheduling for power losses minimization in unbalanced smart grids under uncertainty. The mentioned objectives are achieved by taking advantage of smart metering infrastructures, machine and deep learning models and mathematical programming techniques which allows DSOs to reduce their total power losses within the distribution network. This approach entails using flexibility mechanisms to operate the distribution network optimally and enhance the load management and DG expansion planning. According to the objectives identified earlier, the main contributions of this thesis are the following: 1. Power losses estimation in unbalanced LV smart grids under uncertainty conditions. An optimization-based procedure to estimate load consumption of non-telemetered customers. A Markov chain-based process to estimate intra-hour load demand for data having a low resolution and for non-telemetered customers or customers which smart meters provide incorrect measurements. 2. Power losses estimation in unbalanced LV smart grids in large-scale areas with a presence of DERs. A data mining approach to reduce a high-dimensionality dataset in smart grids to yield a reduced set of relevant features. A clustering process to obtain representative feeders within a large-scale distribution area of smart grids. A deep learning-based power losses estimator for large-scale LV smart grids. The method is formulated as a deep neural network that uses as input features the power load demand and power generation of a set of representative feeders. The model gives, as output, the power losses of the whole area. 3. Flexibility scheduling for power losses minimization in unbalanced smart grids under uncertainty. A robust optimization model for the flexibility scheduling optimization model for unbalanced smart grids with distributed resources, such as PV panels and PEV devices.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Natalia Alguacil Conde.- Secretario: Pablo Ledesma Larrea.- Vocal: Samuele Grill

A Study on the Hierarchical Control Structure of the Islanded Microgrid

The microgrid is essential in promoting the power system’s resilience through its ability to host small-scale DG units. Furthermore, the microgrid can isolate itself during main grid faults and supply its demands. However, islanded operation of the microgrid is challenging due to difficulties in frequency and voltage control. In islanded mode, grid-forming units collaborate to control the frequency and voltage. A hierarchical control structure employing the droop control technique provides these control objectives in three consecutive levels: primary, secondary, and tertiary. However, challenges associated with DG units in the vicinity of distribution networks limit the effectiveness of the islanded mode of operation.In MV and LV distribution networks, the X/R ratio is low; hence, the frequency and voltage are related to the active and reactive power by line parameters. Therefore, frequency and voltage must be tuned for changes in active or reactive powers. Furthermore, the line parameters mismatch causes the voltage to be measured differently at each bus due to the different voltage drops in the lines. Hence, a trade-off between voltage regulation and reactive power-sharing is formed, which causes either circulating currents for voltage mismatch or overloading for reactive power mismatch. Finally, the economic dispatch is usually implemented in tertiary control, which takes minutes to hours. Therefore, an estimation algorithm is required for load and renewable energy quantities forecasting. Hence, prediction errors may occur that affect the stability and optimality of the control. This dissertation aims to improve the power system resilience by enhancing the operation of the islanded microgrid by addressing the above-mentioned issues. Firstly, a linear relationship described by line parameters is used in droop control at the primary control level to accurately control the frequency and voltage based on measured active and reactive power. Secondly, an optimization-based consensus secondary control is presented to manage the trade-off between voltage regulation and reactive power-sharing in the inductive grid with high line parameters mismatch. Thirdly, the economic dispatch-based secondary controller is implemented in secondary control to avoid prediction errors by depending on the measured active and reactive powers rather than the load and renewable energy generation estimation. The developed methods effectively resolve the frequency and voltage control issues in MATLAB/SIMULINK simulations

Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts

Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the communications (e.g. 5G) and the growing proliferation of the Internet of Things devices (such as smart metres and intelligent electronic devices). While the convergence of a significant number of cyber-physical elements has enabled the Smart Grid to be far more efficient and competitive in addressing the growing global energy challenges, it has also introduced a large number of vulnerabilities culminating in violations of data availability, integrity, and confidentiality. Recently, false data injection (FDI) has become one of the most critical cyberattacks, and appears to be a focal point of interest for both research and industry. To this end, this paper presents a comprehensive review in the recent advances of the FDI attacks, with particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts in the Smart Grid infrastructure. This review paper aims to provide a thorough understanding of the incumbent threats affecting the entire spectrum of the Smart Grid. Related literature are analysed and compared in terms of their theoretical and practical implications to the Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack research is identified, and a number of future research directions is recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl

Privacy-Preserving Distributed Optimization and Learning

Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which may expose sensitive information and raise significant privacy concerns. In this survey paper, we overview privacy-preserving distributed optimization and learning methods. We first discuss cryptography, differential privacy, and other techniques that can be used for privacy preservation and indicate their pros and cons for privacy protection in distributed optimization and learning. We believe that among these approaches, differential privacy is most promising due to its low computational and communication complexities, which are extremely appealing for modern learning based applications with high dimensions of optimization variables. We then introduce several differential-privacy algorithms that can simultaneously ensure privacy and optimization accuracy. Moreover, we provide example applications in several machine learning problems to confirm the real-world effectiveness of these algorithms. Finally, we highlight some challenges in this research domain and discuss future directions.Comment: Accepted as a chapter in the Encyclopedia of Systems and Control Engineering published by Elsevie

Fully distributed AC power flow (ACPF) algorithm for distribution systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163883/1/stg2bf00044.pd

Cooperative Strategies for Management of Power Quality Problems in Voltage-Source Converter-based Microgrids

The development of cooperative control strategies for microgrids has become an area of increasing research interest in recent years, often a result of advances in other areas of control theory such as multi-agent systems and enabled by emerging wireless communications technology, machine learning techniques, and power electronics. While some possible applications of the cooperative control theory to microgrids have been described in the research literature, a comprehensive survey of this approach with respect to its limitations and wide-ranging potential applications has not yet been provided. In this regard, an important area of research into microgrids is developing intelligent cooperative operating strategies within and between microgrids which implement and allocate tasks at the local level, and do not rely on centralized command and control structures. Multi-agent techniques are one focus of this research, but have not been applied to the full range of power quality problems in microgrids. The ability for microgrid control systems to manage harmonics, unbalance, flicker, and black start capability are some examples of applications yet to be fully exploited. During islanded operation, the normal buffer against disturbances and power imbalances provided by the main grid coupling is removed, this together with the reduced inertia of the microgrid (MG), makes power quality (PQ) management a critical control function. This research will investigate new cooperative control techniques for solving power quality problems in voltage source converter (VSC)-based AC microgrids. A set of specific power quality problems have been selected for the application focus, based on a survey of relevant published literature, international standards, and electricity utility regulations. The control problems which will be addressed are voltage regulation, unbalance load sharing, and flicker mitigation. The thesis introduces novel approaches based on multi-agent consensus problems and differential games. It was decided to exclude the management of harmonics, which is a more challenging issue, and is the focus of future research. Rather than using model-based engineering design for optimization of controller parameters, the thesis describes a novel technique for controller synthesis using off-policy reinforcement learning. The thesis also addresses the topic of communication and control system co-design. In this regard, stability of secondary voltage control considering communication time-delays will be addressed, while a performance-oriented approach to rate allocation using a novel solution method is described based on convex optimization

Opening of Ancillary Service Markets to Distributed Energy Resources: A Review

Electric power systems are moving toward more decentralized models, where energy generation is performed by small and distributed power plants, often from renewables. With the gradual phase out from fossil fuels, however, Distribution Energy Resources (DERs) are expected to take over in the provision of all regulation services required to operate the grid. To this purpose, the opening of national Ancillary Service Markets (ASMs) to DERs is considered an essential passage. In order to allow this transition to happen, current opportunities and barriers to market participation of DERs must be clearly identified. In this work, a comprehensive review is provided of the state-of-the-art of research on DER integration into ASMs. The topic at hand is analyzed from different perspectives. First, the current situation and main trends regarding the reformation processes of national ASMs are analyzed to get a clear picture of the evolutions expected and adjustment required in the future, according to the scientific community. Then, the focus is moved to the strategies to be adopted by aggregators for the effective control and coordination of DERs, exploring the challenges posed by the uncertainties affecting the problem. Coordination schemes between transmission and distribution system operators, and the implications on the grid infrastructure operation and planning, are also investigated. Finally, the review deepens the control capabilities required for DER technologies to perform the needed control actions