Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid

Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatly reduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decision support system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC power flow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraints generation decomposition algorithms. The developed RDSS tool has been tested on the modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security

The Healing Touch: Tools and Challenges for Smart Grid Restoration

Major electric power disturbances can be triggered by storms, heat waves, solar flares, and many other sources, but all have their roots in the mechanical, cyber, and human vulnerabilities of existing power grids. As shown in ?Figure 1, 2012 was a particularly bad year for extreme weather in the United States. An aging grid infrastructure only exacerbates this problem by creating new concerns over energy reliability and grid resiliency. A single storm can cost billions of U.S. dollars in terms of direct damage to the grid, and it can cause significant power outage-related costs, including lost productivity.published_or_final_versio

System Restoration Navigator: A decision support tool for System Restoration

Power system restoration is well recognized as an important task to reduce the duration of a disturbance that occures in power systems. The complex tasks of emergency recovery require advanced decision support tools to enhance the resilience and, utimately, self-healing capabilities for a smart grid. A piece of software entitled “System Restoration Navigator” (SRN) has been developed based on the Generic Restoration Milestones (GRMs) concept, with the support of EPRI during the last two years. This paper addresses the development and functionality of SRN. Firstly, the basic philosophy of GRMs is introduced. Secondly, the functionality of SRN and integration of SRN with EPRI Operator Training System (OTS) are demonstrated. Thirdly, the Power and Light (PALCO) system is used to illustrate the general restoration plan and concrete restoration actions under a blackout scenario. It is believed that the development of SRN and its integration with OTS is a major step towards the on-line decision-making for system restoration.published_or_final_versio

Benefits of Fast Cut Back Function of Thermal Generating Units in Constructing Self-healing Grids

This paper investigates the benefits of Fast Cut Back (FCB) function of thermal generating units in the self-healing control context. The FCB function enables a generating unit to reduce its output down to the auxiliary power level within seconds. The output can later be restored to normal level promptly without cold start process. This ability can provide dispatchers additional measures in both the emergency control and the restorative control. In this paper, the model of FCB function generating units is established. The benefits of the FCB function in system restoration context are described. Case studies are presented to show: 1) FCB function can be used to maintain generation balance in controlled separation; 2) FCB function reduces the restoration time in the blackstart stage.published_or_final_versio

Spatiotemporal Splitting of Distribution Networks into Self-Healing Resilient Microgrids using an Adjustable Interval Optimization

The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids to substitute microgrids arrangements for effectively coping with any perturbations. To achieve these targets, this paper examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing microgrids. The main intention in the grid-tied state is to maximize the microgrids profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the microgrids less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Multi-agent systems for power engineering applications - part 1 : Concepts, approaches and technical challenges

This is the first part of a 2-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part 1 of the paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part 2 of the paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented