Modified IEEE 30-bus System Research Articles

Embedded control system for reactive power control in distributed energy resources for voltage regulation in the distributed power system

Since more distributed energy resources (DER) are being linked to the electrical grid, the current distributed power system (DPS) is encountering additional voltage regulation challenges. Traditionally, on-load tap changers, step voltage regulators, and switched capacitor banks have been used for voltage regulation in DPS. However, these sources are insufficient for voltage regulation in current DPS. As a result, reactive power assistance from a DER unit based on power electronics is intended to adjust the voltage in the DPS. In terms of flexibility, security, reliability, and availability, embedded control systems are now being investigated by researchers for use in power converter-fed DER units. This paper presents a method for constructing an embedded controller unit based on XynergyXS for the power converter controller, which includes reactive power regulation in the DER unit. Furthermore, it proposes dynamic reactive power control in the DER unit for enhanced DPS voltage regulation. The suggested voltage control approach is tested in a MATLAB/Simulink model of a practical 85-bus distributed power system located in the northern Tamilnadu region, India as well as a modified IEEE 33-bus system. The new control approach investigates all potential power grid disruptions. The results reveal that the proposed reactive power control method in DER units enhances network voltage regulation while reducing the number of switching operations performed by static voltage regulating units such as on load tap changers and switched capacitor banks.

Underfrequency Load Shedding Strategy With an Adaptive Variation Capability for Multi-Microgrids

Multi-microgrids (MMGs) suffer from power shortages due to the loss of utility grid support when an unintentional transition occurs. This imposes a transient shock on the system voltage and frequency. To maintain the frequency stability and power balance of an islanded MMG, this paper presents an underfrequency load shedding (UFLS) strategy with adaptive variation. A comprehensive load evaluation method based on a composite least squares support vector machine (CLS-SVM) is proposed to ensure uninterrupted power for critical loads. This method considers the comprehensive evaluation influence factors (CEIFs) of loads. Then, a least squares support vector machine (LS-SVM) provides the load shedding determination factors, transforming the problem of determining critical loads into a 0-1 planning problem. A method with adaptive variation is proposed to solve the UFLS model. The effectiveness of the proposed strategy is verified for an MMG model based on a modified IEEE 33-bus system. The test results show that: 1) the average accuracy of the proposed method is 21.05% higher than that based on LS-SVM; 2) compared with UFLS strategies based on the load level alone and on an intelligent algorithm, the frequency fluctuation range of the proposed strategy is 12.50% and 19.23% lower, respectively, and the frequency recovery time is 3.90% and 5.73% shorter, respectively; 3) compared with PSO, GOA and GA, the standard deviation of the iterative mean of the proposed algorithm decreases by 36.22%, 53.42%, and 34.00%, respectively. The proposed strategy can reduce the frequency fluctuation range and frequency recovery time while maintaining strong adaptability.

Analytical Modeling and Control of Grid-Scale Alkaline Electrolyzer Plant for Frequency Support in Wind-Dominated Electricity-Hydrogen Systems

Integration of the hydrogen sector into power systems via electrolyzers could greatly facilitate energy sustainability by producing green hydrogen, moreover, assist in reinforcing frequency stability based on optimal control of electrolyzers. In order to improve the frequency performance of the wind-dominated electricity-hydrogen systems (WEHS), this paper investigates how to analytically evaluate and implement the dynamic frequency regulation (DFR) supported by a grid-scale alkaline electrolyzer (AEL) plant. The DFR's potential of the AEL plant is released by an emulated power-frequency characteristic, and its contributions to improving system frequency response are also analytically evaluated from the aspects of transient, steady-state, and stability indicators. Furthermore, the DFR dependency on the frequency containment reserve provision and pre-contingency operating points of AELs are analytically clarified. A DFR-driven controller to coordinate multiple modules in the AEL plant is also designed in detail. In addition, the economic analysis is presented to evaluate the profitability of AELs providing frequency support. Cases of a simple WEHS and a modified IEEE 9-bus system integrated with grid-scale AEL plants during frequency contingencies are studied to validate the proposed DFR method, which can improve the system frequency response with smoother transients and lower steady-state deviation.

A novel partitioning method for the power grid restoration considering the support of multiple LCC-HVDC systems

The restoration of the modern alternating current-direct current (AC–DC) hybrid power grid after a blackout is difficult and complex. By using the partitioning restoration strategy and the support of high voltage direct current (HVDC) system, the restoration speed of the blackout power grid can be greatly accelerated. Aiming at this problem, this paper proposes a partitioning method for the power grid restoration with multiple line commutated converter-based HVDC (LCC-HVDC) systems. Firstly, by introducing the system strength indices to quantify the interaction between AC and LCC-HVDC systems in the restoration process, a partitioning model considering multiple LCC-HVDCs is proposed. Wherein, the transmission power is optimized to make full use of LCC-HVDCs’ support capability. Then, by combining the system strength characteristic and community structure characteristic of the power grid, a partitioning method based on an improved Girvan Newman (GN) algorithm is proposed. Finally, the results are studied based on a modified IEEE 39-bus system and a realistic partial power grid of southwest China. Compared with two typical partitioning methods for the AC power grid restoration, the results show that the proposed method can effectively use the DC support power and speed up the restoration progress.

A bi-level coordinated dispatch strategy for enhancing resilience of electricity-gas system considering virtual power plants

As extreme weather disasters become more frequent and violent, there is grown emphasis on the resilience of electricity-gas systems, especially in preventing the extreme-weather-induced cascading failures among different subsystems. Meanwhile, with multiple advantages such as high flexibility, virtual power plants (VPPs) are gaining popularity, which shows their potential for providing advanced resilience services. Therefore, this paper proposes a bilevel coordinated dispatch strategy to utilize VPPs, consisting of battery storage devices and dispatchable electric vehicles (EVs), to enhance electricity-gas system resilience. Firstly, the Monte Carlo simulation method is adopted to simulate the probabilistic and sequential process of the extreme-weather-induced cascading failures between gas networks and power grids. Additionally, VPPs dispatch the battery storage through the direct control mode. Due to the price-sensitive nature of EVs, the interaction between VPPs and EV owners can be described as a Stackelberg game to determine the optimal discharging prices and schedule, which ensures the minimization of dispatch cost and the maximization of resilience and EVs owners' revenues. According to the Karush-Kuhn-Tucker optimality conditions, the bilevel game model can transform into single-level mix-integer linear programming. At last, the proposed model is applied to two case systems: a modified IEEE 30-bus system with an 11-node gas network and a modified IEEE 118-bus system with a 20-node gas network. And simulation results verify the effectiveness of the proposed model in enhancing electricity-gas system resilience against the cascading outages among subsystems through dispatching energy from virtual power plants.

A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor—driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system’s overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.

Evaluating grid strength under uncertain renewable generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

Event-driven low-carbon economic dispatch for multi-regional power grid considering power consumption behavior

The trend of low-carbon power puts forward new standards and challenges for the operation of power system, and also makes the optimal dispatching of power system more difficult. In order to better realize the low-carbon economic operation of the power system, this paper proposes a bi-level low-carbon economic dispatching (LCED) model for multi-regional power system. In the upper level, a carbon emission constraint based on event-driven mechanism tailored for coordinated operation of multiple regions is proposed, and the optimal objective is to minimize the total operating cost of the system. In the lower level, the reduction of carbon emission obligation on the load side is considered, and the optimal objective is to maximize the low-carbon utility on the load side. Thus, the synergy of the low-carbon goal on both source and load sides is achieved. Finally, through the simulation of the modified IEEE 39-bus system, it is verified that the proposed bi-level model can effectively reduce operating costs and reduce carbon emissions, and improve the level of renewable energy consumption.

A multidiscipline collaborative optimization approach with acceleration strategies for generator maintenance scheduling in large hydrothermal power systems considering uncertainty of natural inflows

Generator maintenance scheduling (GMS) is an important task in the dispatch and operation of hydrothermal power systems. On a long-term timescale, the uncertainty of natural inflows makes GMS essentially a stochastic optimization problem. The sample average approximation (SAA) method is used to convert the stochastic hydro and thermal generator maintenance model into a large-scale deterministic optimization problem, but it is difficult to solve. This paper proposes a multidisciplinary collaborative optimization approach with acceleration strategies (MCO-AS) for GMS, where the deterministic optimization problem is decomposed into a system-level optimization problem and discipline-level optimization subproblems through decoupling of the nonanticipative constraints and the maintenance variable consistency constraints. Three acceleration strategies, replacement and linearization, information pooling, and parallel calculation, are proposed and incorporated. Finally, computational experiments on a modified IEEE 30-bus system and a real large hydrothermal power system are conducted, and the results illustrate the effectiveness of the approach and acceleration strategies proposed in this paper compared to the traditional SAA method as a benchmark.

Flexible Power Point Tracking Using a Neural Network for Power Reserve Control in a Grid-Connected PV System

Renewable energy penetration in the global energy sector is in a state of steady growth. A major criterion imposed by the regulatory boards in the wake of electronic-driven power systems is frequency regulation capability. As more rooftop PV systems are under installation, the inertia response of the power utility system is descending. The PV systems are not equipped inherently with inertial or governor control for unseen frequency deviation scenarios. In the proposed method, inertial and droop frequency control is implemented by creating the necessary power reserve by the derated operation of the PV system. While, traditionally, PV systems operate in normal MPPT mode, a derated PV system follows a flexible power point tracking (FPPT) algorithm for creating virtual energy storage. The point of operation for the FPPT of the PV is determined by using a neural network block set available in MATLAB. For the verification of the controller, it is applied to a PV array in a modified IEEE-13 bus system modeled in the MATLAB/Simulink platform. The simulation results prove that when the proposed control is applied to the test network with renewable energy penetration, there is an improved system inertia response.

Fast yet balanced trade-offs for multi-timescale multi-objective economic-environmental dispatch under varying conflicts

The conflicts between generation cost and the power generation-induced effects on the environment are always considered to be constant. However, the conflicts can vary with weather conditions and have typical daily fluctuations if the air pollutant dispersion process is considered when minimizing the generation-induced air pollution. Although understanding the conflicting situation by a Pareto front (PF) can contribute to a better economic-environmental balance, the PF calculation is inherently time-consuming and a threat to fast computation, especially when the time granularity is constantly increasing in today’s multi-timescale-based power operation. This paper proposes a novel multi-timescale multi-objective economic-environmental dispatch (MTMO-EED) method to solve the “fast computation or good balance” dilemma from the following two aspects: On the modeling side, we establish a new MTMO-EED framework with intra-day offline–online coordination (IOOC), where the intra-day PF is calculated offline while only a simple single-objective optimization is needed online. We quantify the error bounds of the deviations between the offline estimated PF and the accurate PF induced by offline forecast errors, and then provide explicit operational conditions under which the offline PF can closely approximate the accurate online PF despite the offline forecast errors; On the solution method side, we propose a new multi-objective algorithm, termed basis changing boundary intersection (BCBI), to improve the PF computational efficiency at the day-ahead stage and the intra-day offline stage. Specifically, mathematical properties of the MTMO-EED’s PFs are exploited in the BCBI. Case studies are conducted on a modified IEEE 39-bus system, which validate the proposed method can achieve fast yet balanced economic-environmental trade-offs at both the day-ahead and intra-day stages.

A signed correlation index-based differential protection scheme for inverter-based islanded microgrids

The different fault characteristics of the inverter-based distributed generation (IBDG) units make the protection of the inverter-based islanded microgrids (IBIMGs) a formidable task. According to IEEE Std 1547.4, the limited fault current levels associated with the IBIMGs may result in misleading decisions of the traditional protective relays during fault occurrence. In this paper, a new differential protection scheme for the IBIMGs is introduced based on the signed correlation technique. The proposed scheme is formulated based on the signs of the instantaneous phase currents at both line ends instead of the current magnitudes. In addition, a signed correlation index is defined to measure the similarity between the signs of the instantaneous phase currents at both line ends employing only a time window of half-cycle. Moreover, a fault detection index is introduced to discriminate the internal faults from other events. In order to validate the proposed scheme, the 9-bus IBIMG and the modified IEEE 33-bus system are simulated on the PSCAD/EMTDC platform, and several scenarios are conducted, including load switching, outage of IBDG units, external bus faults, and internal faults. The obtained results emphasize that the proposed scheme operates correctly considering all types of faults, different fault resistances and inception angles, as well as measurement and synchronization errors.

A scalable planning framework of energy storage systems under frequency dynamics constraints

As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. A modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.© 2017 Elsevier Inc. All rights reserved.

Real-time optimal scheduling for active distribution networks: A graph reinforcement learning method

To improve the economy and safety of the active distribution network (ADN) operation, this paper proposes a real-time optimal scheduling strategy based on graph reinforcement learning (GRL), achieving online collaborative optimization of controllable equipment. For realizing the perception of inborn graph-structured features of the ADN and the formulation of the real-time scheduling scheme, the active-reactive power coordination process is firstly characterized as a dynamic graph-structured network interaction. A graph attention network (GAT) block is leveraged to extract and aggregate the topology branch correlation and node power information. Then the obtained topology information embedded with node features, as intermediate latent environment states, is fed into the underlying network architecture of deep reinforcement learning (DRL). Moreover, the power regulation problem of controllable equipment is duly formulated as a finite Markov decision process (FMDP). And the complex decision-making is solved by a deep deterministic policy gradient (DDPG) algorithm. Specially, under the electrical fault scenarios with variations in topology structures and feature information, GRL helps the agent efficiently learn and capture new graph-structured knowledge. Case studies are conducted within a modified IEEE 33-bus system. Extensive experimental results verify the superiority and adaptability of our proposed methodology.

Evaluation of Technical Solutions to Improve Transient Stability in Power Systems with Wind Power Generation

Reliability and safety must be carefully considered in today’s power systems, which are rapidly evolving toward ever higher penetration of renewable, inverter-based generation units. Power systems are constantly stressed by active power disturbances, which can be exacerbated by wind and solar systems that are subject to rapid fluctuations in primary energy. In this framework, a comparative technical analysis of solutions to improve transient stability, both rotor angle stability and frequency stability, is carried out. These solutions can be adopted by the transmission system operator (e.g., an additional parallel transmission line), by the generation companies (e.g., a fast excitation system), or by both, such as SVC (static VAR compensator) and STATCOM (static synchronous compensator). Sensitivity analyses were carried out to assess the impact of the location of the wind turbines in the buses of the grid on their rated power and level of production. On the basis of these analyses, the worst-case fault was considered, and the critical fault recovery time was determined as an engineering parameter to compare the different solutions. For the numerical analysis, a modified IEEE 9-bus system was considered, and the PowerWorld software tool was used. Rotor angle and frequency stability analyses were performed.

Data-driven cooperative trading framework for a risk-constrained wind integrated power system considering market uncertainties

As wind power continues to integrate into modern power systems, the bidding strategies of wind power producers are becoming more important than ever. However, the current trading strategies of wind power producers may be impractical because their market uncertainties, financial risk, and cooperative behaviors are generally not considered. Therefore, this paper proposes a data-driven framework for risk-constrained coordinated bidding strategy for a wind integrated power system that participates in the electricity balancing market. In this framework, a price uncertainty predictor consisting of ridge regression, non-pooling convolutional neural network, and linear quantile regression is first modeled to evaluate the day-ahead electricity price uncertainty. The financial risk for this uncertainty is also formulated as a bidding constraint based on acceptable downside risk. Moreover, a risk-constrained cooperative bidding model considering market uncertainties is presented to maximize the interests of the wind power producer. Then, an improved firefly algorithm is developed to tackle the bidding model, and the adaptive moment estimation method is utilized to improve the convergence speed and exploitation ability of the algorithm. Finally, the Shapley value is introduced for profit distribution for cooperative power producers. The proposed framework and bidding model have been comprehensively evaluated on a modified IEEE 30-bus system. The findings reveal that the daily profit of the proposed uncertainty predictor is increased by U.S.$ 777 compared with the categorical boosting-based uncertainty predictor. Furthermore, the proposed methods also achieve better competitive results in the aspects of cooperative behavior, optimal performance, and forecasting accuracy compared with other algorithms.

Real-Time Topology Detection and State Estimation in Distribution Systems Using Micro-PMU and Smart Meter Data

Distribution network topology detection and state estimation in real-time are critical for modern distribution systems management and control. However, number of sensors in distribution networks are limited and communication links between switch devices and distribution management system are not well-established. In this regard, this paper proposes mixed-integer quadratic programming (MIQP) formulations to determine the topology of distribution network and estimate distribution system states simultaneously using micro-phasor measurement units (micro-PMUs) and smart meter data. Two approaches based on AC optimal power flow are proposed and analyzed: (i) polar power-voltage (PPV) formulation, and (ii) rectangular current-voltage (RIV) formulation. The proposed models include convex objective function while constraints are linearized using first-order approximation of Taylor series and Big M method. The proposed models can identify multiple simultaneous switching actions at each time instant and different topology configurations including radial and meshed networks. Only measurement data at each time interval is needed to identify topology and states of the system correctly. The proposed models are tested on a modified IEEE-33 bus system with realistic load data from Pecan Street Inc. database. The results confirm that both models can identify system topology and states with remarkable accuracy in real-time, while RIV model outperforms PPV model.

Multi-Stage Resilience Management of Smart Power Distribution Systems: A Stochastic Robust Optimization Model

Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening decisions. Second, it properly treats the multitude of uncertainties in planning. The SRO model coordinates hardening and system operational measures for smart power distribution systems equipped with distributed generation units and switches. To capture the uncertainty in the incurred damage by extreme events, an uncertainty set is developed by integrating probabilistic information of hurricanes with the performance of overhead structures. A novel probabilistic model for the repair time of damaged lines is derived to account for the uncertainty in the recovery process. A solution strategy based on the integration of a differential evolution algorithm and a mixed-integer solver is designed to solve the resilience maximization model. The proposed approach is applied to a modified IEEE 33-bus system with 485 utility poles and a 118-bus system with 1841 poles. The systems are mapped on the Harris County, TX, U.S. Results reveal that optimal hardening decisions can be significantly influenced by resilience operational measures.

Adaptive Damping Control Scheme for Wind Grid-Connected Power Systems With Virtual Inertia Control

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines.

Price-based low-carbon demand response considering the conduction of carbon emission costs in smart grids

The rapid development of the Industrial Internet-of-Things extends demand response (DR) research to the aspect of low-carbon emission in smart grids. This study proposed the concept of low-carbon DR (LCDR) in the electricity market as well as the price-based LCDR mechanism and its model. First, carbon cost conduction from the generation side to the demand side was analyzed, and then conduction function was quantifiably deduced. Second, the mechanism and model of price-based LCDR were proposed by considering three DR signals, namely, the electricity price, carbon price, and carbon emission intensity of the demand side, based on the traditional price-based DR (PBDR) mechanism. Third, the proposed LCDR mechanism was applied to the environmental–economic dispatch optimization problem. At last, case studies on the modified IEEE 39-bus system verified that the LCDR mechanism can reduce carbon emissions while maintaining the function of the traditional PBDR. Meanwhile, the applicability of LCDR was illustrated based on carbon emission sensitivity to LCDR model parameters. The proposed mechanism can guide participants in the electricity market in reducing electricity carbon emissions.