IEEE 123-node Test System Research Articles

Resilient Operation of Electric Power Distribution Grids Under Progressive Wildfires

Wildfires have been growingly recognized as a prominent threat in regions with high temperatures during the summer. Power distribution systems, especially those passing through forest regions, are exposed and highly vulnerable to wildfires. This article provides a general formulation to enhance the operational resilience of power distribution networks equipped with renewable energy resources, e.g., wind and solar energy, micro turbines as well as energy storage systems when exposed to progressive wildfires. The wildfire incident is characterized comprehensively and the dynamic heat balance equations of power distribution branches are used to model the impacts of wildfires on overhead line conductors. A mixed-integer quadratic optimization formulation is applied to optimally operate and coordinate all local energy resources to reduce load outages and enhance the system resilience. The applied framework is evaluated on the IEEE 33-node test system. Comprehensive sensitivity analyses are conducted to assess the efficacy of the applied framework, where the numerical results reveal the resilient operation of power distribution networks in the face of wildfire emergencies.

MIRACL Co‐Simulation platform for control and operation of distributed wind in microgrid

This paper presents a co-simulation platform (CSP) developed as a part of the “Microgrids, Infrastructure Resilience and Advanced Controls Launchpad (MIRACL)” project, hereafter called MIRACL-CSP, to allow simulation-based testing, demonstration, and evaluations of distributed wind under various grid operating conditions. MIRACL-CSP provides modular interactions among the power distribution system, distributed wind, and utility decision-making framework. A hierarchical engine for large-scale infrastructure co-simulations (HELICS) is used as the core engine of MIRACL-CSP to establish time and information coordination among the MIRACL-CSP modules. In this study, MIRACL-CSP performance is demonstrated using the IEEE 123-node test distribution grid modelled in GridLAB-D and the utility decision support application modelled in Python. The functionality of MIRACL-CSP is demonstrated through various grid operational scenarios in a microgrid and networked microgrid environment. A co-simulation study is described that demonstrated MIRACL-CSP capabilities for the microgrid operational scenario in the IEEE 123-node test system. MIRACL-CSP is a generic platform that facilitates distributed wind research for different test systems, applications, and valuation of distributed wind.

Implementing multi-settlement decentralized electricity market design for transactive communities with imperfect communication

The ubiquitousness of the low cost Information & Communication Technologies (ICT) provides strategic opportunities for the “prosumers”, customers that both consume and produce energy, to have a more proactive role in system operation. From an operational perspective, though, this creates additional challenges in managing power distribution networks due to such active grid-edge systems with limited to no visibility or control by the utilities. Transactive Energy (TE) has been emerging as a key enabler towards efficiently coordinating prosumers through market based constructs thereby encouraging more consumer-centric economies. However, communication-based controls are susceptible to losses in information or message delays, potentially de-synchronizing solutions. This work presents a transactive implementation of a community-based electricity market design based on a multi-settlement decentralized coordination strategy. The coordination mechanism presents an algorithm that provides robustness to communication delays. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models and delay-prone communication systems. Case studies on the IEEE-123 node test system demonstrate the feasibility of the proposed community-based TE architecture to collaboratively operate prosumer-communities as grid-edge systems and quantifies their potential benefits.

Total supply and accommodation capability curves for active distribution networks: Concept and model

This paper proposes the concept and model of total supply capability (TSC) curve and total accommodation capability (TAC) curve, which is a novel approach to assess the overall capability of an active distribution network (ADN) based on distribution system security region (DSSR). The TSC curve describes the full load supply capability while the TAC curve depicts the full distributed generation (DG) accommodation capability. For the TSC curve and the TAC curve, an ADN has both the upper curves and the lower curves, because both growth and reduction of either load or DG may result in security criticality, and form the upper and lower security boundaries, respectively. A simple test system and IEEE 33-node test system are used to demonstrate the proposed concepts and models. The TAC curve is compared with existing methods to show its advantages and applications. Additionally, it is found that supply capability and accommodation capability are dual; and the operational conditions of the ADN are divided by the four TSC/TAC curves.

Incentive Policy for Battery Energy Storage Systems Based on Economic Evaluation Considering Flexibility and Reliability Benefits

The efficient application of battery energy storage system (BESS) technology can effectively alleviate the uncertainty and volatility caused by distributed generations (DGs) and loads, and reduce their adverse effects on the power grid. More efficient applications could delay equipment capacity upgrades, improve equipment utilization, save costs, and increase the system hosting capacity for renewable energy. However, the application of BESS is restricted by its high cost and limited policy support. It is, therefore, necessary to carry out an economic evaluation of BESS, considering its flexibility and improvement of reliability, alongside incentive policy research to promote its deployment. This study on BESS involves four key aspects: 1) It proposes a reliability-benefit model for BESS, considering the value of electricity in the national economy. 2) It describes a flexibility improvement benefit calculation model for BESS, built with the definition of flexibility indexes of distribution network related to BESS, and considering the capacity, charge, and discharge constraints. 3) A reliability improvement benefit calculation model of BESS was built, and the present study proposes a detailed calculation flow of economic evaluation model for BESS users considering net present value (NPV) index and dynamic payback period (DPP) index. 4) An impact analysis of different prices and incentive policies on BESS business models is also carried out, with the present study finally presenting an incentive policy based on flexibility and reliability improvement. The results of the IEEE 33-node test system show that flexibility and reliability improvement can effectively reflect the benefit and cost of BESS, and that incentive policies can help to promote the development of BESS technology.

Transactive energy systems for distributed blackstart and service recovery

Current transactive controls use marginal benefits and marginal costs to achieve an economic market efficiency during normal grid operations. However, the transactive mechanisms designed for normal economic operations cannot be applied directly for the contingencies because the grid operations during contingencies are often dictated by technical needs rather than purely economic criteria. For instance, one of the key technical requirements for the blackstart is to have at least one blackstart capable resource cleared which cannot be ensured by the transactive mechanism designed for normal economic operations because they work primarily based on the marginal benefit and marginal cost of the participants. This article presents one of the first attempts to develop a transactive mechanism to be used during grid contingencies. A distributed blackstart and service recovery is used as an example contingency to evaluate the performance of the proposed transactive mechanism. The performance of the proposed transactive mechanism is demonstrated for various use cases using a modified IEEE-123 node test system. The simulation results demonstrated the proof of concept of applying a transactive mechanism to enable distributed blackstart and service recovery by engaging the mix of blackstart capable and non-capable distributed energy resources.

Optimal fault location for power distribution systems with distributed generations using synchronized measurements

Abstract Accurate fault location in distribution systems greatly shortens maintenance time and improves reliability. This paper presents novel methods to pinpoint fault location and identify possible bad measurements for enhanced accuracy. It is assumed that network parameters and topology of the distribution network are available. The methods are applicable to a single fault as well as simultaneous faults and are applicable to both balanced and unbalanced networks. The methods utilize synchronized voltage and current phasor measurements to locate the fault. The methods are validated by simulation studies using the modified IEEE 34-Node Test System. Case studies have demonstrated that the methods are suitable for distribution systems with high penetration of distributed generations.

Learning‐based load control to support resilient networked microgrid operations

Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly ApplianceTM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-DTM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.

Seismic-Resilient Electric Power Distribution Systems: Harnessing the Mobility of Power Sources

Mobile power sources (MPSs), including mobile emergency generators, truck-mounted mobile energy storage systems, and electric vehicles, have great potentials to be employed as grid-support resources during power grid emergency operating conditions to supply the critical loads and enhance the resilience of distribution system (DS) via a swift disaster restoration. We here investigate the MPS dispatch (i.e., routing and scheduling) in coordination with DS dynamic network reconfiguration. We propose a two-stage restoration scheme to facilitate the DS restoration following the high-impact low-probability (HILP) seismic disasters. In the first stage, a seismic hazard is simulated through a Monte Carlo simulation engine to estimate the unavailability of power distribution branches under a suite of seismic force scenarios. In the second stage, a mixed-integer nonlinear programming (MINLP) optimization model is formulated for DS restoration that cooptimizes the routing and scheduling of MPSs and DS dynamic network reconfiguration. The MINLP model is then linearized to a mixed-integer linear programming model to reduce the computation complexity, where the seismic-resilience recovery strategies are generated at different timescales. The efficacy of the proposed method is evaluated on the IEEE 33-node test system and the results verify a significant reduction in the load outages and an improved power system resilience to HILP earthquakes.

Distribution system resilience enhancement by microgrid formation considering distributed energy resources

Due to increasing in natural disasters in the recent years, the issue of distribution network resilience has become highly important. Microgrids with different types of distributed energy resources have the capabilities to improve distribution network resilience under extreme events. In this paper, we present a mixed-integer linear program to restore prioritized loads while satisfying topology and operational constraints. In the presented model, we study dynamic microgrid formation and optimal management of various smart grid technologies such as distributed generations, demand response programs, wind turbines and energy storage units. We also investigate the significant impact of renewable energy resources, loads and their uncertainty on distribution system resilience. In addition, we determine the required emergency budgets of operation to restore a distribution system from extreme events. We also intend to examine the effects of demand response programs on improving the distribution system performance in the recovery period. Finally, we verify the effectiveness of the presented model on a modified IEEE 33-node test system and a real distribution system.

Bilateral Electricity Market in a Distribution System Environment

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.

A Novel Machine Learning-Based Short-Circuit Current Prediction Method for Active Distribution Networks

The traditional mechanism models used in short-circuit current calculations have shortcomings in terms of accuracy and speed for distribution systems with inverter-interfaced distributed generators (IIDGs). Faced with this issue, this paper proposes a novel data-driven short-circuit current prediction method for active distribution systems. This method can be used to accurately predict the short-circuit current flowing through a specified measurement point when a fault occurs at any position in the distribution network. By analyzing the features related to the short-circuit current in active distribution networks, feature combination is introduced to reflect the short-circuit current. Specifically, the short-circuit current where IIDGs are not connected into the system is treated as the key feature. The accuracy and efficiency of the proposed method are verified using the IEEE 34-node test system. The requirement of the sample sizes for distribution systems of different scale is further analyzed by using the additional IEEE 13-node and 69-node test systems. The applicability of the proposed method in large-scale distribution network with high penetration of IIDGs is verified as well.

Conic Programming Based Day-ahead Optimization Dispatch in Distribution Network Containing Large-scaled Distributed Generation and Electric Vehicle

Regarding day-ahead optimization dispatch in distribution network containing large-scaled distributed generations (DGs) and electric vehicles (EVs), this paper proposes an optimization dispatch method based on conic programming. A model for dispatch that can optimize the EV aggregator charging power and the output of DGs is established. The proposed model takes the constrains of EV charging energy and charging power into consideration. In addition, DG can curtail the active power in the model to guarantee the operation safety. Then nonlinear optimization dispatch model are transformed and relaxed into second-order cone programming model to be solved. The optimization dispatch method proposed in this paper is verified by the IEEE33 node test system.

Laboratory Test Bed for Analyzing Fault-Detection Reaction Times of Protection Relays in Different Substation Topologies

Visions of energy transition focus on activating end users, meaning that numerous flexible-distribution network-level devices become active participants in power-system operations. This implies a fast, reliable, and secure exchange of data, enabling the distribution-system operators to maintain, or even improve, the quality and delivery of service. With the introduction of the International Electrotechnical Commission (IEC) 61850 standard, the path is set for a single communication topology covering all substation levels. The standard has the potential to change the way substations are designed, built, tested, and maintained. This means that the key segment of the substation, its protection system, will go through a transition period with the end goal of having a digitized substation where all information exchange is performed over an Ethernet communication bus. This paper analyzes the performance impact of the IEC 61850-9-2LE on the protection system. To do this, a laboratory hardware-in-the-loop test setup was developed representing traditional-, hybrid-, and digital-substation topology. The setup serves to simulate faults and create transient waveforms in an extended IEEE 123-node test system, which is then used to detect the reaction times of protection relay devices. To verify the results, a significant number of tests was performed clearly showing the benefits of digitalizing the distribution system.

Research on Droop Control Strategy of Multi-terminal AC/DC Hybrid Distribution System

Multi-terminal AC/DC hybrid distribution system can better meet the requirements of the development of future urban distribution system, but its flexible operation modes also pose new challenges to the control system. In this paper, different droop control strategies are analyzed from the perspective of small signal stability. The test results based on a modified IEEE 33-node test system show that Udc-P and Uac-Q droop curves have the advantages of small voltage fluctuation, short transient time and high level of satisfaction and stability, which is more suitable for multi-terminal AC/DC hybrid distribution systems.

Variance-Based Global Sensitivity Analysis for Power Systems

Knowledge of the impact of uncertain inputs is valuable, especially in power systems with large amounts of stochastic renewable generations. A global sensitivity analysis (GSA) can determine the impact of input uncertainties on the output quantity of interest in a certain physical or mathematical model. The GSA has not been widely employed in power systems due to the prohibitively computational burden. In this paper, it is demonstrated that, via the implementation of a basis-adaptive sparse polynomial chaos expansion, a GSA can be applied to the power system with numerous uncertain inputs. The performance of the proposed method is tested on both the IEEE 13-bus test feeder and the IEEE 123-node test system, in presence of a large amount of independent or correlated uncertain inputs. The possible application of a GSA on the basis of the basis-adaptive sparse polynomial chaos expansion in power systems are discussed in terms of various sensitivities. The findings cannot only be used to rank the most influential input uncertainties with respect to a specific output, such as variances of the nodal power, but also to identify the most sensitive or robust electrical variables such as the bus voltage with respect to input uncertainties.

Three-phase state estimation in the medium-voltage network with aggregated smart meter data

In distribution networks, the lack of measurement data is usually thought to be an inevitable bottleneck of conventional grid operation and planning. Recently, the availability of smart meters in the distribution network has provided an opportunity to improve the network observability. In medium-voltage (MV) distribution networks, there is an increasing demand to use aggregated smart meter data for the state estimation, instead of adopting pseudo-measurements with a low level of accuracy. However, the performance of an estimator requires good knowledge of the available measurements, in terms of both expected values and associated uncertainties. Therefore, this paper intends to firstly pave a new way of utilizing smart meter data gathered from the low-voltage (LV) feeders in a concrete and reliable manner. For the purpose of state estimation in MV distribution networks, smart meter data is to be processed through three steps: phase identification, data aggregation and uncertainty evaluation. The feasibility of the proposed method is verified on the IEEE European LV Test Feeder with a set of real-world smart meter data. Afterwards, the influence of the aggregated smart meter data on the three-phase state estimation are investigated on the modified IEEE 13-node test system and IEEE 34-node test system. Simulation results show that the effect of aggregated smart meter data on the accuracy of state estimators is dependent on both the accuracy level of the aggregated data and the measurement configuration in the network. Furthermore, the use of aggregated smart meter data is shown to be able to provide improved state estimation.

Minimizing the number of remotely controlled switches when the planning the reconfiguration of a primary distribution network

The work shows the possibility to select the permanent configuration of a primary distribution network at which power losses during a week with maximum load and during a week with minimum load the power losses would be close to losses at optimum hourly reconfiguration. Data on the mix of loop branches obtained during the reconfiguration algorithm operation is used for selecting the tie-switches that allow both the regime restoration after emergency opening of a sectionalized switch, and acceleration of this process, especially if the tie-switch is remotely-controlled. The IEEE 33-node test systems are used to demonstrate the proposed algorithms.

Coordinated Dispatch of Active Power Distribution Network Containing Large-scaled Distributed Generation and Electric Vehicle

Regarding the coordinated dispatch of active power distribution network containing large-scaled distributed generations (DGs) and electric vehicles (EVs), this paper proposes a coordinated dispatch method based on voltage iteration. A model for coordinated dispatch that can optimize the node charging power of EVs and the output of DGs is established. Then the network loss in the optimization objective is simplified by the static voltage which obtained in the previous optimization iteration. Meanwhile, the nonlinear restrictions between power and voltage are simplified by the sensitivity equation, also based on the static voltage. Finally, an iteration method is proposed to correct the static voltage used in the simplified model. The coordinated dispatch method proposed in this paper is verified by the IEEE33 node test system.

Research on DG Capacity Selection Based on Power Flow Calculation

It is important to study the access location and capacity of the distributed power supply in the distribution network to reduce the active loss and raise the voltage level. The influence of DG access on power flow in the distribution network is analysed in this paper. It is concluded that the proper location and capacity of DG will change the current distribution in distribution network, and reduce the power loss caused by distribution network of voltage level. The DG test is carried out with IEEE33 node test system, which validates the validity of this paper.