Generic Semi-Analytical Simulation Tool (GenSAS)
A generic simulation tool for power grid and general dynamic systems based on semi-analytical solutions (SAS) technology.
QUI, FENG↗
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STOCHASTIC OPTIMAL POWER FLOW FOR REAL-TIME MANAGEMENT OF DISTRIBUTED RENEWABLE GENERATION AND DEMAND RESPONSE (Final Report)
To meet the grand challenge of a sustainable energy future, there has been a surge of interest in renewable energy. Today, the uncertainty associated with renewable resources is handled by using operating reserves. The high penetration of renewable resources, however, introduces difficult-to-control dynamics and challenges for power system operation. Decision support tools are necessary at the bulk system operational level to recognize and efficiently utilize renewable resources and distributed demand response products in concert with traditional grid resources. It is envisaged that responsive load can potentially have very significant cost advantages over either spinning or non-spinning ramping reserve. Critical decisions are made during hour(s)-ahead and real-time power system operation regarding the commitment and dispatch of generators to ensure power delivery is both reliable and economic. These decisions are typically made by a security constrained optimal flow, which determines future generator commitments, dispatches, and ensures adequate reserves are available in the event of a contingency (unexpected outage) or if future system conditions deviate from forecasts. However, security has been always based on a pre-specified subset of contingency constraints whose enforcement does not guarantee security under all possible future possibilities while also giving little or no weight to the likelihood of each contingent event or the severity of its consequences. Existing tools, which are based exclusively on deterministic optimization models, do not yield optimal operational decisions to address these new challenges, in terms of both reliability and cost-effectiveness. This project has focused on developing a stochastic optimal power flow (SOPF) framework, which integrates renewable resource uncertainty, load uncertainty, distributed storage (DS), demand response (DR) products, in a holistic manner to address the uncertainty associated with ever-increasing renewable resources, along with the inclusion of distributed demand response products in future power systems. A proof-of-concept problem was created using the Pennsylvania-Jersey-Maryland (PJM) power system network. Synthetic wind generation was added to the system to simulate 50% wind penetration. A 1-hour test of SOPF operation indicated more than 6% operational cost savings. The project continued by adding the Midwestern Independent System Operator (MISO) as a partner, with focus shifting from SOPF to Stochastic Look-Ahead Unit Commitment (SLAC). Unlike PJM, MISO is faced with significant renewable energy resources within its footprint and is challenged with substantial uncertainty in its operations. The SLAC distinguishes itself from existing tools that operators use. At best, today’s tools solve two to three cases independently, where one or two system parameters, such as forecasted load level (e.g., a low, base, and high forecast), are varied and the resulting scenarios are analyzed independently. The stochastic-based optimization of SLAC leverages statistical information from an ensemble of potential operational scenarios and their respective likelihood. The SLAC output can be translated into valuable information to the operator such as suggested commitments, optimal scheduling and dispatch of resources, reserve requirements at both locational and zonal resolutions, ramping availability and requirements, availability of demand response including operational guidance concerning the near-term and real-time coordination between distributed energy resources, and utilization of distributed storage resources. The developed SOPF/SLAC tool, a stand-alone tool compatible with existing EMSs, will provide system operators with unprecedented visibility, flexibility and predictability to these resources and operational guidance concerning the real-time coordination between DERs and DR/DS products. The game changing and practical impact of this disruptive technology will be dramatic and will usher in a new era in the electric power industry, wherein green energy concepts are fully embraced, and electric power costs are lowered throughout the nation.
42 ENGINEERING↗
DRPS Poster for MFC SAC
This LRS entry is for a poster to be displayed at the meeting.
30 DIRECT ENERGY CONVERSION↗
Integrating Learning and Physics based Computation for Fast Online Transient Analysis
In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multi-Timescale Integrated Dynamic and Scheduling Model (MIDAS-Solar)
Solar photovoltaic (PV) installations have experienced unprecedented growth in the United States. PV will become not only an energy producer but also a necessary provider of ancillary services at multiple timescales. Conventional methods to simulate power system operations - such as long-term production simulation (which typically considers schedules from hours to minutes by using an optimization framework) and short-term transient studies (which simulate dynamics from seconds to sub-seconds using state variables and differential equations) are not sufficient for studying the multiple-timescale variation of solar generation and its impact on system reliability. Long-term system economics and short-term system dynamics are highly coupled, particularly when the penetration level of renewable generation is extremely high, because the uncertainty and variability of solar generation will impact both power systems steady-state and dynamic performance. This project helps meet and exceed the Solar Energy Technologies Office goal of systems integration by directly addressing this stability and reliability challenge for electric grid planning and operation. This will be accomplished by developing temporally comprehensive, closed-loop simulation models that seamlessly simulate power systems operations from economic scheduling (day-ahead to hours) to dynamic response analysis (seconds to sub-seconds). Both a multi-timescale grid model and an integrated PV model will be developed in this project to accurately study the impacts of PV variability and uncertainty on system reliability at multiple timescales. Using quasi-dynamic simulation methods and data-driven security assessment (DSA) criteria will allow the dynamic characteristics of PV to be fed forward into longer-timescale scheduling models for a complete understanding of the effect of short-term PV dynamics on bulk systems operations (e.g., reserve scheduling and deployment). Upon completion of the proposed model, this project will help operators accurately assess system reliability by deploying energy and reserve scheduling under critical contingency conditions and studying interactions among all types of essential reliability services provided by modern PV power plants.
14 SOLAR ENERGY↗
Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle
Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.
algorithms↗
Wave Energy Converter Power Take-Off Modeling and Validation From Experimental Bench Tests
This article describes the implementation of a new numerical model of the power take-off system installed in the Monterey Bay Aquarium Research Institute wave energy converter, a device developed to provide power to various oceanic research missions. The simultaneous presence of hydraulic, pneumatic, and electrical subsystems in the power take-off system represents a significant challenge in forging an accurate model able to replicate the main dynamic characteristics of the system. The validation of the new numerical model is addressed by comparing simulations with the measurements obtained during a series of bench tests. Data from the bench tests show good agreement with the numerical model. The validated model provides deeper insights into the complex nonlinear dynamics of the power take-off system and will support further performance improvements in the future.
16 TIDAL AND WAVE POWER↗
Hybrid Modeling of Three-Phase Grid-Supporting Inverters for Dynamic Studies
Grid technologies connected by power electronic converter (PEC) interfaces continually implement grid support functions mandated by grid codes and standards. The transition to converter-based generation demands precise PEC models to assess system dynamics, which have been previously overlooked in conventional power systems. This study proposes a hybrid method for analyzing grid-connected three-phase PEC dynamics with the IEEE standard 1547-2018 Volt-VAr mode that combines physics and data-driven techniques. The physics model reflects the PEC’s internal behavior, whereas the data-driven modeling technique evaluates the grid-supporting capabilities of the smart PEC. The system identification approach is used to generate dynamic PEC models based on changing grid voltage and measured current injected into the grid by the PEC. In the Volt-VAr support mode, a detailed topological model including switches is utilized to compare the goodness-of-fit of the extracted hybrid dynamic model. The results demonstrate that the hybrid PEC model in the Volt-VAr mode accurately matches the dynamics with the topological model.
Subedi, Sunil↗
Model, Design, and Control for Power Conversion in Wave Energy Converter System
Wave energy, if all been harvested along the U.S. coastline, can power around 65% of the energy consumption in U.S.. Comparing to other renewable energy sources like solar or wind, ocean wave can provide up to 90% of steady uptime. With the high energy density (2-3 kW/m 2 ), it can produce more energy with the same amount of installation area comparing to the energy density of wind turbine (0.6 kW/m 2 ) and solar panel(0.2 kW/m 2 ). The predictability of wave provides advantages like planning installation, power dispatching, and maintenance activities. Although with all these advantages, wave energy converter system is still in the research stage due to its high system cost per electricity production. One of the challenges that need to be solved is the irregularity from the wave motion that leads to high instantaneous peak power into the wave energy converter, which usually reaches up to 10 - 20 times of the average power. The high peak power will not only bring high mechanical/electrical stress but also result in an overrating design of the components in the system. Another obstacle that prevents the wave energy system from moving forward is the high testing cost from the validations in wave-energy-test sites or tank-test sites. A high-fidelity multi-disciplinary system model, including hydrodynamics, mechanical dynamics, electromagnetics, and power electronics, is needed to predict the behavior of the system and reduce the cost of design validation.
16 TIDAL AND WAVE POWER↗
Distribution System Segmented Model Simplification with Independent Dynamically Changing End-Use Loads
With more emerging technologies in power distribution systems, to support quasi-static time-series (QSTS) simulation over short time frames, accurately modeling the dynamic behavior of end-use load under various conditions is im- portant. However, time and cost can be a burden for the QSTS simulation of thousands of houses representing dynamic load behaviors on large distribution feeders. There are two main contributions of this paper in order to reduce the QSTS simulation complexity. First, the paper presents a method to stochasti- cally model the dynamic behavior of end-use load for QSTS simulations. Second, using the stochastically modeled load behavior as an input to a distribution sys- tem, and building on our previous QSTS simulation simplification/acceleration method that relies on uniform load behavior, this paper presents a segment substitution approach for the simplification of distribution system models with independent dynamically changing end-use loads. Our numerical results show that compared to GridLAB-D, a software tool that simulates end-use load be- havior through physical models, the proposed stochastic modeling approach achieved an 89.3% simulation time reduction for end-use loads, while accurately capturing the temporally changing total load behavior. Moreover, the QSTS simulation of a large test distribution system through OpenDSS showed that the proposed simplification method decreased the feeder simulation time by a factor of three at less than 0.3% voltage error. Compared to our previous method with independent dynamic loads, the voltage error is reduced by a factor of four.
Power distribution system modeling, stochastic pro↗
Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security
Cyber-Physical Systems (CPSs) like the power grid are critically important but also increasingly vulnerable; ensuring reliable system operation in the face of disruptions is becoming more and more challenging. Multi-Level Optimization (MLO) is a powerful way to model adversarial interactions, which naturally makes it applicable to studying CPS security. However, MLO typically does not address underlying system dynamics, and incorporating nonlinear dynamics is generally infeasible. In this paper, we show how to combine MLO with the Koopman Operator (KO) to remedy this. The KO maps nonlinear dynamics to a lifted space in which those dynamics are linear, thus making it ideal for use with MLO. Moreover, the structure of the KO also provides convenient ways to incorporate domain knowledge into the data-driven process of learning the KO representation of a given system. Here we then demonstrate the use of MLO-KO on a small example problem taken from the power grid domain, discuss the scalability and computational cost of MLO-KO, and identify future research directions for this work.
42 ENGINEERING↗
Dynamic Restoration Strategy for Distribution System Resilience Enhancement
In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/dis-turbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.
distributed energy resources (DERs)↗
Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach
We report that power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Model based co-simulation platform for integrated building system control and design optimization
Both steady-state and dynamic simulations have been widely used by HVAC&R industry to support product/equipment development for decades. Steady-state simulation focuses on the system mass, energy and momentum balance of an equilibrium state. It is based on high-fidelity components models, and thus is suitable for system and component design optimization. Dynamic simulation studies the system transient response and is generally used for controls development and verification. It usually does not require rigorous component models of high accuracy because 1) the commonly used PID control is feedback control whose control performance evaluation doesn’t require high fidelity system/plant model; 2) high-fidelity dynamic model significantly increases the number of equations and variables and creates tremendous challenge for math solver. For supervisory control, transactive control or optimization of an integrated building system, the HVAC&R equipment is often one of the sub-components to be controlled. High-fidelity equipment models are required for accurately evaluating control strategies. In addition, building equipment manufacturers have developed a lot of high-fidelity steady-state equipment/component models per their expertise. Thus, a platform that can integrate OEM high-fidelity steady-state model with dynamic building simulation and/or electric power system & grid simulation to support the development and verification of supervisory control for integrated building systems is necessary. In this study, ORNL’s heat pump design tool (HPDM) is utilized to develop a co-simulation platform for supervisory control and optimization in integrated building systems. It is based on a model that integrates high-fidelity steady-state simulation equipment models with dynamic building simulation. A practical case of using the proposed co-simulation platform to develop and evaluate the supervisory control and optimization is presented and discussed.
Sun, Jian↗
Comparison of Electromagnetic Transient and Phasor Dynamic Simulations: Implications for Inverter Dominated Systems
The simulation of very high shares of inverter-based resources in power systems has begun to draw into question the validity of phasor domain tools in capturing relevant dynamics. Electromagnetic transient simulators can capture the dynamics of power electronics with substantially smaller time steps, but are computationally expensive. This work contrasts the results of phasor domain and electromagnetic transient tools for simulations on a validated model of the Maui power system operating at very high inverter-based resource shares with near zero voltage forming devices. The results show that the phasor domain tool predicts optimistic stability with fewer voltage forming elements on the network, and loses computational stability before the electromagnetic transient tool. As the electromagnet transient model is of the entire system, and system-wide discrepancies are observed, this case study of a physical power system highlights the potential need for system-wide detailed modeling during periods of very high shares of inverter-based resources and few voltage forming devices.
electromagnetic transient-domain↗
Dynamic Model of Back-to-Back Converter for System Level Phasor Simulation
The power system is expected to evolve rapidly with increasing deployment of power electronic interface and conditioning systems, microgrids, and hybrid AC/DC grids. Among power electronic systems, back-to-back (BTB) converters can be a powerful interface to integrate microgrids and networked microgrids. However, in system level dynamic simulations for synchronous machine dominated system with BTB converters, a detailed electromagnetic sinusoidal models is not warranted as these simulations are focused on electromechanical transients. This paper focus on developing a simple phasor model for back- to-back converters that can be easily integrated into powerflow solvers to facilitate large-scale power system simulations. The model is implemented using C++ language and integrated into GridLAB-D software as a potential new capability. The GridLAB-D phasor domain model is validated against the electromagnetic transient (EMT) simulation of the detailed switching model. Simulation results show that the phasor model successfully captures the dominant dynamics of the converters.
Mahmood, Hisham↗
Clean Grid Vision: A U.S. Perspective - Chapter 4. Demand-Side Development
This chapter focuses on recent developments on the demand side of the electricity system in the United States and beyond. After decades of slow-moving change in power system technology, business models, regulatory structures, and even financing schemes, changes on the demand side are accelerating rapidly. These changes are also more tightly linking the supply side with the demand side. This chapter provides high-level summaries of traditional subjects like demand-side management, which includes energy efficiency and demand response, and then transition to a subject that has gathered increasing attention as the most dynamic change likely to impact power systems in the coming decades: beneficial electrification. In each case, we attempt to address the impacts on system flexibility to the degree possible. These demand-side subjects are widely considered essential elements of a clean grid strategy.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Dynamic Human-in-the-Loop Simulated Nuclear Power Plant Thermal Dispatch System Demonstration and Evaluation Study
An Idaho National Laboratory research team performed a human-in-the-loop study with two formerly licensed retired operators to evaluate thermal power dispatch operations supported by the modified GSE Systems GPWR plant simulator. Virtual representations of the analog control panels were presented on touch-screen bays configured to mimic the control room layout in the newly renovated Human Systems Simulation Laboratory. The operators performed 15 scenarios covering normal evolutions to transition the plant from full turbine operation to joint turbine and thermal power dispatch operations in addition to transient response scenarios induced with simulated faults to evaluate the impact of thermal power dispatch system on operator and plant responses. A prototype human-system interface (HSI) was developed and displayed in tandem with the virtual analog panels to support the operators executing the procedurally drive evolutions and transient responses. An interdisciplinary team of operations experts, nuclear engineers, and human factors experts observed the operators performing the scenarios to evaluate the operations. Only a preliminary analysis of the results has been performed. The full analysis will be shared in a milestone report scheduled for release at the end of September; however, some high-level conclusions were readily available. Two high level findings for the system design were captured in the study. The manual control supported by the HSI to transition from standard operations to thermal power dispatch operation imposed a considerable amount of workload on the operators due to tedious manual valve manipulations and system monitoring required to verify their intended effect. An additional operator would be required in the control room to support the daily evolution. Automatic control for the transition was deemed a requirement for plant adoption without imposing additional staffing costs. The second finding was the necessity for an automatic thermal power dispatch system trip isolation function linked to a turbine and reactor trip signal. The operators completed scenarios with automatic isolation functionality and manually required actuation of the thermal power dispatch system. The operator response was sufficiently slower in the manual trip condition, such that operators were unable to manually actuate key post trip safety functions an indicate a degraded control capability that should be avoided. With an automatic trip signal little to no impact of the thermal power dispatch system was identified on the primary plant response and therefore the system could be readily and safely adopted. Together these two findings represent the need to support the adoption of thermal power dispatch capability into existing operations by leveraging automation to augment any additional operator tasking required to control and monitor an additional system beyond existing operations.
99 GENERAL AND MISCELLANEOUS↗