Showing papers by "Chen-Ching Liu published in 2019"

Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems

Abstract: When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program (SDP). The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and other operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few SDPs in most cases. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios.

Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters With Voltage Control Devices

Abstract: Conservation voltage reduction (CVR) uses Volt-VAR optimization (VVO) methods to reduce customer power demand by controlling feeder's voltage control devices. The objective of this paper is to present a VVO approach that controls system's legacy voltage control devices and coordinates their operation with smart inverter control. An optimal power flow (OPF) formulation is proposed by developing linear and nonlinear power flow approximations for a three-phase unbalanced electric power distribution system. A bi-level VVO approach is proposed, where Level 1 optimizes the control of legacy devices and smart inverters using a linear approximate three-phase power flow. In Level 2, the control parameters for smart inverters are adjusted to obtain an optimal and feasible solution by solving the approximate nonlinear OPF model. Level 1 is modeled as a mixed integer linear program (MILP) while Level 2 as a nonlinear program with linear objective and quadratic constraints. The proposed approach is validated using 13-bus and 123-bus three-phase IEEE test feeders and a 329-bus three-phase PNNL taxonomy feeder. The results demonstrate the applicability of the framework in achieving the CVR objective. It is demonstrated that the proposed coordinated control approach help us to reduce the feeder's power demand by reducing the bus voltages; the proposed approach maintains an average feeder voltage of 0.96 p.u. A higher energy saving is reported during the minimum load conditions. The results and approximation steps are thoroughly validated using OpenDSS.

DGs for Service Restoration to Critical Loads in a Secondary Network

Abstract: During a major outage in a secondary network distribution system, distributed generators (DGs) connected to the primary feeders as well as the secondary network can be used to serve critical loads. This paper proposed a resilience-oriented method to determine restoration strategies for secondary network distribution systems after a major disaster. Technical issues associated with the restoration process are analyzed, including the operation of network protectors, inrush currents caused by the energization of network transformers, synchronization of DGs to the network, and circulating currents among DGs. A look-ahead load restoration framework is proposed, incorporating technical issues associated with secondary networks, limits on DG capacity and generation resources, dynamic constraints, and operational limits. The entire outage duration is divided into a sequence of periods. Restoration strategies can be adjusted at the beginning of each period using the latest information. Numerical simulation of the modified IEEE 342-node low voltage networked test system is performed to validate the effectiveness of the proposed method.

Intelligent Electronic Devices With Collaborative Intrusion Detection Systems

Abstract: This paper proposes the new concept of intelligent electronic devices (IEDs) with built-in distributed intrusion detection systems. The proposed IEDs have the capabilities to monitor and detect anomalies and abnormal behaviors of the host system of IED and IEC 61850-based messages, e.g., sampled values and generic object oriented substation event. The proposed IEDs collaborate with neighboring IEDs to make an accurate decision and find the origin of cyber attacks. The commodity embedded system has been used to verify the performance of the proposed intrusion detection system with power system protection functions of IEDs. The results show that the proposed mitigation methods work accurately and efficiently with protection functions, e.g., overcurrent and distance protections, on the embedded board.

Risk-Limiting Load Restoration for Resilience Enhancement With Intermittent Energy Resources

Abstract: Microgrids can be used to restore critical load after a natural disaster, enhancing resilience of a distribution network. To deal with the stochastic nature of intermittent energy resources, such as wind turbines (WTs) and photovoltaics (PVs), forecast information is usually required. However, some microgrids may not be equipped with power forecasting tools. To fill this gap, a risk-limiting strategy based on measurements is proposed. A Gaussian mixture model is used to represent a prior joint probability density function of power outputs of WTs and PVs over multiple periods. As time rolls forward, the probability distribution of WT/PV generation is recursively updated using the latest measurement data. The updated distribution is used as an input of the risk-limiting load restoration problem, enabling an equivalent transformation of the original chance constrained problem into a mixed integer linear programming. Simulations on a distribution system with three microgrids demonstrate the effectiveness of the proposed method. Results indicate that networked microgrids can perform better in uncertainty management relative to stand-alone microgrids.

Improving Primary Frequency Response to Support Networked Microgrid Operations

Abstract: Individual microgrids have proven their ability to provide uninterrupted power to critical end-use loads during severe events. Building on the performance of individual microgrids during extreme events, there has been an increasing interest in the operations of network microgrids. By networking microgrids during extreme events, it is possible to share resources, increase the duration for which they can operate islanded, improve efficiency, and increase the resiliency of critical end-use loads. While there are benefits to networking the operations of resiliency-based microgrids, the switching operations that they require introduce transients, which can result in a loss of dynamic stability. The issue of dynamic stability is especially acute in microgrids with high penetrations of inverter-connected generation, and correspondingly low system inertia. While the low inertia of these microgrids can be increased by over-sizing the rotating generators, the increased capital and operating costs can become a barrier to deployment. This paper will present a method of augmenting primary frequency controls to support the switching transients necessary for the operation of networked microgrids, without the need to over-size rotating machines.

Optimal Distribution System Restoration with Microgrids and Distributed Generators

Abstract: Increasing emphasis on reliability and resiliency call for advanced distribution system restoration (DSR). The integration of grid sensors, remote controls, and distributed generators (DG) brings about exciting opportunities in DSR. In this context, this work considers the task of single-step restoration of a single-phase power distribution system. Different from existing works, the devised restoration scheme achieves optimal formation of islands without heuristically pre-identifying reference buses. It further facilitates multiple DGs running within the same island, and establishes a coordination hierarchy in terms of their PV/PQ operation modes. Generators without black-start capability are guaranteed to remain connected to a black-start DG or a substation. The proposed scheme models remotely-controlled voltage regulators exactly, and integrates them in the restoration process. Numerical tests on a modified IEEE 37-bus feeder demonstrate that the proposed mixed-integer linear program (MILP) takes less than four seconds to handle random outages of 1–5 lines. The scalability of this novel MILP formulation can be attributed to the unique use of cycles and paths on the grid infrastructure graph; the McCormick linearization technique; and an approximate power flow model.

Bilateral Electricity Market in a Distribution System Environment

Abstract: Increasing penetration of proactive agents, including distributed energy resources (DERs) and responsive loads (RLs), in distribution systems motivates methods to incentivize market participation from demand-side resources. The complexity resulting from large-scale integration of proactive agents has challenged the viability of centrally managing grid resources in a distribution system environment and motivated decentralized frameworks to coordinate the transactions. Existing decentralized frameworks are primarily focused on designing transaction mechanisms. They neither include a formal market construct nor a computationally feasible approach for market settlement for a large volume of transactions while ensuring the security of grid operation. This paper presents a distributed computational approach for a distribution system market where the coordination framework is based on agreement on bilateral energy transactions reached by market participants. Using the proposed distributed computational method, participants play a role in determining the set of bilateral transactions that maximizes the social welfare. The distribution system operator (DSO) ensures a secure operating condition of the distribution grid by validating each transaction that is agreed upon. Under a potential violation of security constraints by a proposed bilateral transaction, the DSO runs an optimal power flow (OPF) and recommends a new set of transactions (closest to those previously proposed) while maintaining power system security. The proposed framework is validated using IEEE 123-node test system. The results confirm the feasibility of the proposed approach for distribution systems.

Enforcing Radiality Constraints for DER-Aided Power Distribution Grid Reconfiguration

Abstract: Operators can now remotely control switches and update the control settings for voltage regulators and distributed energy resources (DERs), thus unleashing the network reconfiguration opportunities to improve efficiency. Aligned to this direction, this work puts forth a comprehensive toolbox of optimization models leveraging the control capabilities of smart grid assets. We put forth detailed yet practical models to capture the operation of locally and remotely controlled regulators, and customize the watt-var DER control curves complying with the IEEE 1547.8 mandates. Maintaining radiality is a key requirement germane to various feeder optimization tasks. This requirement is accomplished here through an intuitive and provably correct formulation. The developed toolbox is put into action to reconfigure a grid for minimizing losses using real-world data on a benchmark feeder. The results corroborate that optimal topologies vary across the day and coordinating DERs and regulators is critical during periods of steep net load changes.

Resiliency of Distribution Systems Incorporating Asynchronous Information for System Restoration

Abstract: SIEMENS Corporate Technology; Dominion Energy; U.S. Department of Energy (DOE) through the Virginia TechUnited States Department of Energy (DOE) [AT-45607]

Joint Grid Topology Reconfiguration and Design of Watt-VAR Curves for DERs

Abstract: Operators can now remotely control switches and update the control settings for voltage regulators and distributed energy resources (DERs), thus unleashing the network reconfiguration opportunities to improve efficiency. Aligned to this direction, this work puts forth a comprehensive toolbox of optimization models leveraging the control capabilities of smart grid assets. We put forth detailed yet practical models to capture the operation of locally and remotely controlled regulators, and customize the watt-var DER control curves complying with the IEEE 1547.8 mandates. Maintaining radiality is a key requirement germane to various feeder optimization tasks. This requirement is accomplished here through an intuitive and provably correct formulation. The developed toolbox is put into action to reconfigure a grid for minimizing losses using real-world data on a benchmark feeder. The results corroborate that optimal topologies vary across the day and coordinating DERs and regulators is critical during periods of steep net load changes.

A QCQP and SDP Formulation of the Optimal Power Flow Including Renewable Energy Resources

Abstract: The optimal power flow (OPF) problem is a relevant subject for the secure and economic power systems operation. For instance, OPF can be used to reduce the power system technical losses. A reduction in 0.1% in the losses accounts for near 50 billion USD in cost savings. Likewise, OPF can be used along with power production forecast tools, for renewable energy sources, to evaluate their impact in the grid security operation planning. In an OPF problem, an objective function related with demand supply generation cost, power line losses, or violation limits is optimized subject to several system security constraints. These constraints are related with branch power flow limits, voltages limits, power injection limits, among others. The exact model of the objective function and constraints of the OPF problem is neither linear nor convex. In this paper it is presented the OPF problem in a Quadratically Constrained Quadratic Program (QCQP) approach. Objective function is based on a quadratic function of the bus voltages and constraints are formulated as Quadratic forms. A two-bus system is used to demonstrates the non-convexity of the OPF problem. Also, it is presented the Rank- 1 convex relaxation of the OPF problem which relaxes the QCQP model into a positive Semidefinite Programming (SDP) model. Once the OPF problem is relaxed into a SDP convex form, a global optimal solution can be obtained. An application example for the OPF problem is presented for the IEEE 14 system. The QCQP and the SDP results are compared and discussed.

Adaptive Neuro Fuzzy Inference System for Cyber-Intrusion Detection in a Smart Grid

Abstract: The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.

Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters with Voltage Control Devices

Abstract: Conservation voltage reduction(CVR) uses Volt-VAR optimization(VVO) methods to reduce customer power demand by controlling the feeders' voltage control devices. The objective of this paper is to present a VVO approach that controls the systems' legacy voltage control devices and coordinates their operation with smart inverter control. An optimal power flow (OPF) formulation is proposed by developing linear and nonlinear power flow approximations for a three-phase unbalanced electric power distribution system. A bi-level VVOapproach is proposed where Level-1 optimizes the control of legacy devices and smart inverters using a linear approximate three-phase power flow. In Level-2, the control parameters for smart inverters are adjusted to obtain an optimal and feasible solution by solving the approximate nonlinear OPF model. Level-1 is modeled as a Mixed Integer Linear Program(MILP) while level-2 as a Nonlinear Program(NLP) with a linear objective and quadratic constraints. The proposed approach is validated using 13-bus and 123-bus three-phase IEEE test feeders and a 329-bus three-phase PNNL taxonomy feeder. The results demonstrate the applicability of the framework in achieving the CVR objective. It is demonstrated that the proposed coordinated control approach help reduce feeders' power demand by reducing the bus voltages, the proposed approach maintains an average feeder voltage of 0.96 pu. A higher energy saving is reported during the minimum load conditions. The results and approximation steps are thoroughly validated using OpenDSS.