Quasi-static Time-series Simulation Research Articles

Revenue-Based Allocation of Electricity Network Charges for Future Distribution Networks

This paper investigates the economic implications that high penetrations of distributed energy resources (DER) have in future distribution networks, and proposes a novel scalable scheme for the assignment of use of network charges based on individual participant nodes’ revenue. For validation purposes, a techno-economic simulation is proposed to understand how power and revenue flows will change. A year-long high-resolution quasi-static time series (QSTS) simulation, two price schemes, four trading environments, and four DER allocation methods from the literature are used to study economic benefits for individual participants and the supplier. Testing is performed using the IEEE 33-bus and 123-bus networks, and an Irish urban medium voltage feeder. Revenue flow is presented as an indicator of which participant nodes are profiting more from grid usage, and therefore should be responsible for greater network charges, this is validated against traditional and alternative schemes. Important reductions in use of network charges are seen especially by participant nodes with a higher PV generation-to-load and self-consumption rates. The proposed method is only relevant when dynamic tariffs are in place and/or local trading is enabled. Ultimately, results suggest that the income from network charges received by the supplier is increased when dynamic tariffs are used.

A Framework to Predict Variability Characteristics in Building Load Profiles

The expansion of Advanced Metering Infrastructure (AMI) has provided building operators and researchers detailed information on building energy consumption. The majority of AMI systems, however, record data at relatively low resolutions of 15, 30, or 60 minutes, due to cost, storage and bandwidth limitations. Emerging applications in power flow analysis, Quasi-Static Time-Series Simulation (QSTS), smart grid integration and load matching, however, require data at higher resolutions. Short-term energy demand can deviate significantly from long-term averages, with an unknown magnitude and frequency when only low-resolution load profile data is available. This paper presents a novel data-driven approach to predict characteristics of the missing high-resolution information in a low-resolution signal, applicable to both measured and modeled building load profile data, utilizing machine learning regression algorithms. In the proposed framework, the relationship between characteristics of high-resolution and low-resolution signals is learned from the decomposition and characterization of a subset of high-resolution building data. This paper validates the underlying hypotheses and methodology of this approach through a single-building case study, training a variety of machine learning models on one year of data, and using the resulting models to predict high-resolution characteristics in a different year. An Ensemble Tree regression model demonstrates a high predictive accuracy (R2 of 0.79-0.92) for several statistical metrics of the high-resolution load profile. These results support the broader potential for leveraging low-resolution information to accurately constrain predictions of missing high-resolution information in building load profiles, which may greatly increase the utility of both measured and modeled data in many practical and research applications. Generalizing such models will require analysis of high-resolution data from a diverse set of building types.

Temporally-adaptive Robust Data-driven Sparse Voltage Sensitivity Estimation for Large-scale Realistic Distribution Systems with PVs

This letter proposes a new robust data-driven sparse voltage sensitivity estimation approach for large-scale distribution systems with PVs. It has a high statistical efficiency to mitigate the impacts of PV stochasticity and unknown measurement noise under various system operating conditions. A new adaptively-weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> sparsity-promoting regularization is developed, exploiting the temporal characteristic of time-varying sensitivities for better accuracy. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization is used to mitigate collinearity impacts. The Huber loss function and a concomitant scale estimate are adopted to mitigate the impacts of unknown and non-Gaussian noise. These techniques are implemented in a fast recursive parallel computing framework. The proposed estimator is tested by quasi-static time series simulations of a large three-phase unbalanced system with PVs and various discrete time-delayed control devices. Results validate the superior robustness and efficiency of the proposed estimator over existing alternatives.

Fast Quasi-Static Time-Series Simulation for Accurate PV Inverter Semiconductor Fatigue Analysis with a Long-Term Solar Profile

Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the proposed simulation is 0.16% compared to the fatigue simulation results using the complete thermal stress profile. The error of the proposed method is significantly less than the conventional averaged thermal profile. A PV inverter that responds to a transactive energy system is simulated to demonstrate the use of the proposed fatigue simulation. The proposed simulation has the potential to cosimulate with system-level simulation tools that also adopt the quasi-static time-series concept.

Non-Bias Allocation of Export Capacity for Distribution Network Planning With High Distributed Energy Resource Integration

A novel distributed energy resources (DER) allocation method focused on grid constraints that avoids topological bias is proposed for distribution networks. A technology-agnostic approach is used, where a non-bias allocation of export capacity (NAEC) not specific to generation type is calculated. Moreover, the proposed NAEC is extended from an export capacity into a hosting capacity (HC) using a statistical approach. The methods are tested using the IEEE 33-bus distribution system, and two typical Irish distribution feeders -one urban, one rural- as case studies. Using a high-resolution year-long quasi-static time series simulation (QSTS) and three different generation profiles, the proposed NAEC method is validated against current practices and state of the art allocation methods in terms of active balancing, security of supply, interactions between users, operational concerns, and fairness. Results show that an equivalent or higher level of DER penetration is achieved using the proposed methodology. There are no additional constraint violations using the NAEC methodology, moreover, time slots with violations are reduced, improving security of supply. Furthermore, results suggest that avoiding topological bias makes the network accessible for more users, and prioritises self-consumption.

Simplified A-Diakoptics for Accelerating QSTS Simulations

The spread of distributed energy resources (DERs) across the distribution power system demands complex planning studies based on quasi-static-time-series (QSTS) simulations, requiring a significant amount of computing time to complete, leading planners to look for alternatives to QSTS. Diakoptics based on actors (A-Diakoptics) is a technique for accelerating simulations combining two computing techniques from different engineering fields: diakoptics and the actor model. Diakoptics is a mathematical method for tearing networks, reducing their complexity by using smaller subcircuits that can be solved independently. The actor model is used to coordinate the interaction between these subcircuits and their control actions, given the pervasive inconsistency that can be found when dealing with large-scale models. A-Diakoptics is a technique that simplifies the power flow problem for improving the simulation time performance, leading to faster QSTS simulations. This paper presents a simplified algorithm version of A-Diakoptics for modernizing sequential power simulation tools to use parallel processing. This simplification eliminates critical points found in previous versions of A-Diakoptics, improving the performance of the algorithm and facilitating its implementation to perform QSTS simulations. The performance of the new version of A-Diakoptics is evaluated by its integration into EPRI’s open-source simulator OpenDSS, which uses standard computing architectures and is publicly available.

Design and field implementation of smart grid-integrated control of PV inverters for autonomous voltage regulation and VAR ancillary services

Ancillary services from Photovoltaic (PV) inverters can increase distribution system flexibility and alleviate the voltage regulation challenges associated with high PV penetration levels. However, the required communication infrastructure and smart grid integration challenges limit the broad deployment of PV ancillary services. This paper presents a cost-effective volt/var control (VVC) of multi-string PV inverters for active voltage regulation and reactive power dispatch using the existing smart distribution infrastructure to avoid the upfront costs of providing PV ancillary services. The proposed VVC model is developed in MATLAB/Simulink to adapt PV reactive power compensation according to the X/R characteristics of the distribution feeder. An IEC 61131-3 compliant prototype is designed based on Simulink PLC code and deployed to a commercially available remote terminal unit (RTU) using CODESYS standardization tool to address smart grid integration challenges. The proposed VVC scheme is tested in a real-world grid-connected PV system with multi-string inverters for experimental validation. Quasi-static time-series simulation and experimental results demonstrate the validated effectiveness of the proposed control scheme in controlling fast PV fluctuations, resolving voltage violations, voltage flickers, and enabling higher PV penetration.

Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations

This article introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. The architecture successfully captured PV local volt-volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

Aggregation of Radial Distribution System Bus with Volt-Var Control

The high penetration of the distributed energy resources (DERs) encourages themselves to implement grid-supporting functions, such as volt-var control. The quasi-static time-series (QSTS) simulation is an essential technique to evaluate the impact of active DERs on the grid. Meanwhile, the increase of complexity on the circuit model causes a heavy computational burden of QSTS simulation. Although circuit reduction methods have been proposed, there have been few methods that can appropriately handle the distribution system (DS) with multiple voltage control devices, such as DERs implementing volt-var control. To address the remaining issues, this paper proposes an offline bus aggregation method for DS with volt-var control. The method determines the volt-var curve for the aggregated bus on the basis of historical data to reduce error in the aggregated model, and its offline process solves the computational convergence issue concerned in the online one. The effectiveness of the proposed method is validated in the simulation using a Japanese low-voltage DS model. The simulation results show that the proposed method can reduce the voltage error and computational time. Furthermore, the versatility of the proposed method is verified to show the performance does not heavily depend on how to select historical data for model-building.

Electric Vehicles Charging Management for Real-Time Pricing Considering the Preferences of Individual Vehicles

The paper proposes a real-time model for electric vehicles (EVs) controlled load charging. The proposed demand-side management (DSM) of EVs is implemented based on queuing analysis with a nonhomogeneous arrival rate and charging service periods dataset. An electric vehicle model is used which is based on a statistical survey to represent the uncontrolled demand of the EVs. A probability distribution for the time at which EVs are plugged and the corresponding value of the state of charges (SOCs) are considered. The preferences of individual EVs have been fully exploited through a set of instructions to fulfill the needs of the vehicles’ owners. The designated preferences include the owner setting for both, charging price preferences (OPR), and the maximum estimated parking time duration (EPTD). The quasi-static time-series (QSTS) simulation is used to simulate real-time scenarios of the 24-h simulation period. The IEEE 123 nodes radial test feeder is analyzed with different daily load curves, EV charging scenarios, and wind power penetrations. The results show the effectiveness of the proposed DSM in avoiding excessive levels of charging with/without penetration of non-dispatchable wind power generation. The proposed DSM enables the EVs to charge with low tariff rates either at excessive renewable power generation or late evening hours with available committed bulk power plants and light loading conditions.

Enhancing PV Hosting Capacity Using Smart Inverters and Time of Use Tariffs

Optimizing the Photovoltaic (PV) hosting capacity (HC) considering the irradiance's variability properly and the load during the day represents a critical matter. Typically, the high PV HC results in overvoltage and high voltage fluctuations at the point of common coupling (PCC) with the utility. In this paper, a new algorithm is proposed for enhancing PV HC by considering the smart inverter functions to overcome key PCC issues. The Volt-Var and dynamic reactive current functions of the smart inverter are suggested to increase the PV HC. Furthermore, the time of use tariff is utilized for mitigating the duck curve issue at the utility by reducing the peak to valley difference of the substation net load curve. Quasi-static time-series simulations are performed using the OpenDSS program to prove the effectiveness of the proposed algorithm. The proposed algorithm is validated by extensive numerical analysis on the standard IEEE 123 node test feeder. Deduced outcomes are very encouraging in mitigating the overvoltage, reducing the energy losses, limiting the considerable number of on-load tap changes, and alleviating the high voltage fluctuations.

Evaluating the Curtailment Risk of Non-Firm Utility-Scale Solar Photovoltaic Plants under a Novel Last-In First-Out Principle of Access Interconnection Agreement

With the increasing share of distributed energy resources on the electric grid, utility companies are facing significant decisions about infrastructure upgrades. An alternative to extensive and capital-intensive upgrades is to offer non-firm interconnection opportunities to distributed generators, via a coordinated operation of utility scale resources. This paper introduces a novel flexible interconnection option based on the last-in, first-out principles of access aimed at minimizing the unnecessary non-firm generation energy curtailment by balancing access rights and contribution to thermal overloads. Although we focus on solar photovoltaic (PV) plants in this work, the introduced flexible interconnection option applies to any distributed generation technology. The curtailment risk of individual non-firm PV units is evaluated across a range of PV penetration levels in a yearlong quasi-static time-series simulation on a real-world feeder. The results show the importance of the size of the curtailment zone in the curtailment risk distribution among flexible generation units as well as that of the “access right” defined by the order in which PV units connect to the grid. Case study results reveal that, with a proper selection of curtailment radius, utilities can reduce the total curtailment of flexible PV resources by up to more than 45%. Findings show that non-firm PV generators can effectively avoid all thermal limit-related upgrade costs.

Evaluating Distributed PV Curtailment Using Quasi-Static Time-Series Simulations

By strategically curtailing active power and providing reactive power support, photovoltaic (PV) systems with advanced inverters can mitigate voltage and thermal violations in distribution networks. Quasi-static time-series (QSTS) simulations are increasingly being utilized to study the implementation of these inverter functions as alternatives to traditional circuit upgrades. However, QSTS analyses can yield significantly different results based on the availability and resolution of input data and other modeling considerations. In this paper, we quantified the uncertainty of QSTS-based curtailment evaluations for two different grid-support functions (autonomous Volt-Var and centralized PV curtailment for preventing reverse power conditions) through extensive sensitivity analyses and hardware testing. We found that Volt-Var curtailment evaluations were most sensitive to poor inverter convergence (−56.4%), PV time-series data (−18.4% to +16.5%), QSTS resolution (−15.7%), and inverter modeling uncertainty (+14.7%), while the centralized control case was most sensitive to load modeling (−26.5% to +21.4%) and PV time-series data (−6.0% to +12.4%). These findings provide valuable insights for improving the reliability and accuracy of QSTS analyses for evaluating curtailment and other PV impact studies.

분산전원 연계 허용용량 개선을 위한 스마트인버터의 전압-무효전력 기능 적용에 따른 영향 분석

Herein, This paper analyzed the effect of applying the volt-var curve on the smart inverter to solve the over-voltage problem caused by increasing the distributed generation. To analyze the voltage characteristics due to the increase in distributed power connection, the voltage characteristics at the end of the test model were analyzed while increasing the distributed power capacity. The potential hosting capacity was analyzed by calculating the voltage margin up to the upper limit according to volt-var curve types with different reactive power contributions and dead-band. In addition, mounting the volt-var curve to many distributed generation connected to the distribution system faced a practical limitation. Considering this limitation, the voltage characteristics and hosting capacity margins for the penetration level of the volt-var curve were analyzed. In this paper, the effect of the volt-var curve was analyzed using OpenDSS, a quasi-static time-series simulation, considering the characteristics of the distribution system test model in the worst case.

A techno-economic comparison of traditional upgrades, volt-var controls, and coordinated distributed energy resource management systems for integration of distributed photovoltaic resources

In some cases, increased deployment of distributed photovoltaic (PV) systems can impact the distribution grid, including causing steady-state voltages and thermal loading of lines and equipment to deviate from operational limits. We present techno-economic analysis of three possible solutions for mitigating these effects on two real feeders: traditional infrastructure upgrades, autonomous volt-var controls, and a distributed energy management system (DERMS). We focus on issues arising in aggregate from residential and small commercial PV systems at different penetration levels and use a bottom-up approach that couples cost modeling with sequential hosting capacity analysis and quasi-static time-series simulation. We compare trade-offs for each solution in terms of effectiveness, upfront capital costs, operating costs, PV output curtailment, and distribution system losses. We find that volt-var controls offer the lowest cost option for hosting capacity expansion but cannot mitigate all violations at high penetration levels. For the feeders studied, the range of upfront cost for the DERMS and traditional upgrades is similar, but the DERMS results in a greater expansion of the hosting capacity. However, unlike traditional solutions, DERMS also involves higher level of PV curtailment than is observed with autonomous volt-var controls.

Distribution system segmented model simplification with independent dynamically changing end-use loads

With more emerging technologies in power distribution systems, accurately modeling the dynamic behavior of end-use load under various conditions has become important. One approach has been quasi-static time-series (QSTS) power flow simulation over short time frames. However, time and cost can be a burden for the QSTS simulation of thousands of houses with dynamic loads on large distribution feeders. This may limit sensitivity analysis, design optimization or exploration of alternatives on the distribution system. There are two main contributions of this paper in order to reduce the QSTS simulation complexity. First, the paper presents a method to stochastically model the dynamic behavior of end-use load for QSTS simulations. Second, using the stochastically modeled load behavior as part of a distribution system, and building on our previous QSTS simulation simplification/acceleration method for uniform load behavior, this paper presents a segment substitution approach for the simplification of distribution system models with multiple independent dynamically changing end-use loads. Our numerical results show that compared to GridLAB-D, a software tool that simulates end-use load behavior 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, the voltage error with independent dynamic loads is reduced by a factor of four. This makes it usable for transactive system studies, including battery integration, compared to earlier methods that were tested on photovoltaic data.

Parallel Computing and Multicore Platform to Assess Electric Vehicle Hosting Capacity

The impact generated by the massive connection of electric vehicles (EVs) in the distribution networks needs to be evaluated through methodologies that consider the stochastic behavior of the EV connections. These methodologies must allow obtaining representative results of annual simulations, considering different charging scenarios, with high resolution of time steps. This kind of analysis implies a combinatorial explosion of case studies with a high computational cost. This article presents a methodology aimed at reducing the simulation time required to determine the EV hosting capacity (HC), understood as the maximum EV penetration level that a distribution network could host before affecting its operating limits, through the co-simulation of hardware and software. Monte Carlo methods and quasi-static time series simulation concepts are used to model the connection features of EVs. The simulation and implementation are developed using parallel computing, and MATLAB and OpenDSS software. The methodology is tested on the IEEE 123 nodes system which shows the dependence of the HC indicator with the location and type EV chargers. Finally, the benefits of parallel computing in the proposed methodology are exposed to get a significant reduction in the execution time to evaluate the HC in the IEEE 123 nodes system.

Optimal Parameters of Volt–Var Function in Smart Inverters for Improving System Performance

This paper proposes a method to improve the performance of a distribution system by optimizing volt–var function of a smart inverter to alleviate the voltage deviation problem due to distributed generation connection. In order to minimize voltage deviation and line losses which represent the performance of a distribution system, this paper proposes an algorithm that optimally sets the parameters of the volt–var function. In the process of optimizing the parameters of the volt–var function, the algorithm proposed in this paper considers minimizing the contribution of the reactive power in order not to affect the output of the distributed generation. In order to apply to the field, the distribution system in South Korea considering the configuration and operation regulation was selected as a test model for algorithm verification. As a result, the system performance was successfully improved by optimally setting the volt–var function of the smart inverter which is an effective way to solve the over-voltage problem caused by distributed generation connection. This paper verified the proposed method through OpenDSS, a quasi-static time-series simulation, for the test model considering the characteristics of the distribution system in South Korea.

Optimal Volt–Var Curve Setting of a Smart Inverter for Improving Its Performance in a Distribution System

We propose an algorithm to set the parameters of volt-var curves to improve the performance of distributed generation. Specifically, we optimally set the volt-var curves of the smart inverter for proper control of the distributed generation output. To improve the overall distribution system performance, we consider the minimization of voltage deviation, system loss, and peak of reactive power in an objective function, thereby providing optimal volt-var curve parameters. The proposed algorithm overcomes various limitations of existing studies. First, we use distribution system factors in a multiobjective optimization function to adjust volt-var curve parameters that deviate from the default settings. Second, we consider the service transformers that affect the system voltage according to the photovoltaic generation output in the test model during optimization. Third, we analyze the system improvement according to the number of parameters to be optimized. Fourth, we analyze the implementation of the optimized volt-var curve according to the practical periods when the smart inverter settings can be updated. Fifth, we evaluate different suitable volt-var curves for multiple photovoltaic generators grouped using clustering. Using the proposed algorithm, the distribution system operator can set the optimal volt-var curve according to the system conditions and requirements. We conducted simulations of the proposed method using OpenDSS, a quasistatic time-series simulation, on a test model reflecting the characteristics of a South Korean distribution system. The simulation results confirm that the overall system performance increases when the optimal volt-var curve settings obtained from the proposed algorithm are used.

Improved load modelling for emerging distribution system assessments

Distribution system modelling and analysis with growing penetration of distributed energy resources (DERs) requires more detailed and accurate distribution load modelling. Smart meters, DER monitors, and other distribution system sensors provide a new level of visibility to distribution system loads and DERs. However, there is a limited understanding of how to efficiently leverage the new information in distribution system load modelling. This study presents the assessment of 11 methods to leverage the emerging information for improved distribution system active and reactive power load modelling. The accuracy of these load modelling methods is assessed both at the primary and the secondary distribution levels by analysing over 2.7 billion data points of results of feeder node voltages and element phase currents obtained by performing annual quasi-static time series simulations on EPRI&apos;s Ckt5 feeder model.