IEEE 30-bus Power System Research Articles

Multi-period optimal energy flow for electricity-gas integrated systems considering gas inertia and wind power uncertainties

The increasing integration of GFUs promotes the coordinated operation of power and gas systems to enhance the system efficiency and reliability. However, the gas inertia characteristic could have imperative impacts on the coordinated scheduling and this property has not been fully studied. This paper proposes a novel multi-period optimal energy flow (OEF) model considering gas inertia and wind power uncertainties for the scheduling and operation of integrated systems. The nonlinear transient natural gas transmission model is reformulated into a linear programming/quadratic programming (LP/QP) model via the reformulation linearization technique (RLT), yielding computational efficiency improvement. The gas inertia is molded as a time delay to study its impacts on the coordinated scheduling. Then, information gap decision theory (IGDT) based risk averse strategy is advocated to deal with the wind power uncertainties and the impact of the uncertainty level is quantified. Extensive simulation results on the modified 6-bus power system with 6-node gas system and the IEEE 39-bus power system integrated with a realistic 20-node natural gas network demonstrate the effectiveness of the proposed method.

A nonlinear coordinated approach to enhance the transient stability of wind energy-based power systems

This paper proposes a novel framework that enables the simultaneous coordination of the controllers of doubly fed induction generators &#x0028 DFIGs &#x0029 and synchronous generators &#x0028 SGs &#x0029 . The proposed coordination approach is based on the zero dynamics method aims at enhancing the transient stability of multi-machine power systems under a wide range of operating conditions. The proposed approach was implemented to the IEEE 39-bus power systems. Transient stability margin measured in terms of critical clearing time along with eigenvalue analysis and time domain simulations were considered in the performance assessment. The obtained results were also compared to those achieved using a conventional power system stabilizer &#x002F power oscillation &#x0028 PSS &#x002F POD &#x0029 technique and the interconnection and damping assignment passivity-based controller &#x0028 IDA-PBC &#x0029 . The performance analysis confirmed the ability of the proposed approach to enhance damping and improve system &#x02BC s transient stability margin under a wide range of operating conditions.

Optimizing wind turbine participation for frequency regulation in large scale power systems

This paper proposes an optimization approach for the participation of wind turbines in large scale power systems. The wind turbine participation optimization aims to regulate the droop slopes and the synthetic inertia of the aggregated wind turbines to command the power contribution of wind turbines proportional to their power ratings in the primary frequency response schemes. A reduced second order model of power system is firstly presented to simplify the analysis of large scale power system. Meanwhile, the internal dynamics of general wind turbines are analyzed and the concept of stability margin is proposed to describe the wind turbines' capability of enduring frequency dips at the bus node. A convex optimization based approach is then presented to regulate the droop slopes and synthetic inertia of the wind turbines incorporating their stability margin. Five case studies are conducted in New England 39-bus system, IEEE 118-bus power system and the Polish 3375-bus system. The simulation results indicate that the proposed approach is capable of transiting the bus frequency to the new steady state in a smoother and more stable manner.

Measurement‐based wide‐area damping of inter‐area oscillations based on MIMO identification

Interconnected power grid exhibits oscillatory response after a disturbance in the system. One such type of oscillation, the inter-area oscillation has the oscillation frequency in the range of 0.1–1 Hz. The damping of inter-area oscillations is difficult with local controllers, but it can be achieved using a wide area damping controller (WADC). For effective control, the input to the WADC should be the most observable signal, and the WADC output should be sent to the most controllable generator. This study presents a measurement-based novel algorithm for multi-input-multi-output (MIMO) transfer function identification of the power system to estimate such oscillation frequencies. The wide-area control loop is estimated using the MIMO transfer function, and the WADC design is a combination of the discrete linear quadratic regulator and Kalman filtering for the damping of inter-area oscillations. Since the MIMO identification is performed using the actual measurements, the proposed method can accurately monitor changes in the power grid, whereas the conventional methods are derived from small-signal analysis of a linearized model that does not consider changing operating conditions. The overall algorithm is implemented and validated on a RTDS/RSCAD® and MATLAB® real-time co-simulation platform using two-area and IEEE 39-bus power system models.

Protection coordination scheme for directional overcurrent relays considering change in network topology and OLTC tap position

This paper proposes a robust protection coordination scheme of directional overcurrent relays (DOCRs) for multiple network topologies in the presence of on-load tap changers (OLTCs). The robust protection coordination scheme provides single settings of DOCRs which will be valid for all N−1 contingencies created after outage of a line, transformer or generator. An efficient constraint reduction strategy is introduced to reduce the number of coordination constraints caused by consideration of multiple topologies. The impact of tap positions of the OLTCs has been incorporated in the required steady-state and short-circuit current calculations. The robust protection coordination scheme has been posed as an optimization problem and solved using an interior point method based algorithm. The feasibility and the effectiveness of the proposed formulation and solution algorithm has been demonstrated on the IEEE 14-bus power system network.

Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response

The frequency response of a large power system is affected by the penetration of renewable energy sources (RESs), where a utility-scale energy storage system (ESS) can alleviate the problem. This paper presents a strategy for sizing an ESS to improve frequency response of transmission networks. The location of the ESS in the transmission network is determined through a sensitivity analysis targeting minimum line loading around a bus. The ESS sizing strategy considers the minimization of frequency deviation as well as rate of change of frequency (ROCOF) after generator or load tripping events. The tuning of PQ controller parameters of the ESS (active power part) is also performed for frequency response improvement. The proposed approach is tested in a modified IEEE-39 bus power system considering a variety of scenarios where RESs are integrated as four different schemes for peak and off-peak load conditions. DIgSILENT PowerFactory is used for developing, testing, and analyzing the system models. A fitness-scaled chaotic artificial bee colony (FSCABC) optimization algorithm (a hybrid meta-heuristic approach) is used for optimization through a Python script automating simulation events in PowerFactory. The results obtained from the FSCABC approach are verified through the application of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS sizing technique including ESS controller tuning can successfully improve the frequency response of a transmission network.

Hybrid power systems with emission minimization: Multi-objective optimal operation

The demand of electrical power and energy is exponentially increased with the advancement of science and technology in past decades. To fulfil this increasing demand of energy, the burden on conventional fossil fuel based units has continuously been raised, which in turn has severe consequences on the environment. A clean pollution free and healthy environment necessitates the reduction in the use of conventional fossil fuel based units. In this context, integration of non-conventional and renewable power generation facilities with conventional power generators, for instance, hybrid power system is gaining attention. However, the intermittent posed by renewable power generation facilities may make the scheduling of generators and power system operation extremely challenging. These issues and its possible solution have been addressed in this proposed study. An attempt has been made in the evaluation of an optimum generation scheduling and coordination among different hybrid power system configurations in the form of wind-thermal, hydro-thermal-wind and hydro-thermal-wind-solar systems. This stochastic optimal power flow problem of the proposed system has been formulated in a multi-objective optimization framework while incorporating real-time operational constraints. A mathematical model, operational analysis and comparative evaluation between the optimum operational paradigms obtained with a modified bacteria foraging algorithm for wind-thermal, hydro-thermal-wind and hydro-thermal-wind-solar systems have been implemented. The effectiveness and promising solutions of hydro-thermal-wind-solar in response to fulfill the operational objectives like cost effective operation, minimization of transmission loss, emission and voltage variation over other hybrid configurations in multi-objective stochastic optimal power flow environment as implemented in IEEE30 bus power system has been demonstrated.

Fixed Low-Order Wide-Area Damping Controller Considering Time Delays and Power System Operation Uncertainties

This paper presents a procedure for the design of a Wide-Area Damping Controller (WADC) towards improving the low-frequency oscillation modes of the power system considering uncertainties in both nominal power system operation condition and time delay on the WADC communication channels. A set of possible operating conditions is linearized around their equilibrium points and used for the construction of the uncertainty model of the system. The lower and upper limits of the time delay are also included in the set. The WADC operation requires remote signals provided by Phasor Measurement Units and highly susceptible to failures or cyber-attacks that may compromise the controller performance. The proposed procedure evaluates in each stage if the resulting controller presents robustness to communication channel permanent failure. Modal analysis and time-domain nonlinear simulations were conducted in the IEEE 68-bus power system.

A New Approach for Estimating Frequency Variations Due to Smart Grid Functions

This article presents a new approach for estimating the frequency variations (Df) due to applying smart grid functions on a load bus. The proposed approach is based on constructing a ZIP model for the power demands at a load bus that is controlled by smart grid functions.The constructed ZIP model provides a relationship between the active and reactive power demands and the frequency at any load bus. This relationship can be formulated as a set of nonlinear equations, which can be numerically solved for Df. The load-model approach is implemented for performance evaluation using load buses in the IEEE 30-bus power system under different loading levels. Performance results show that the proposed approach has a simple implementation, and can provide an accurate estimation of frequency variations due to slow and small changes load power demands. Furthermore, performance results reveal the insensitivity of the load-model approach to load power demands and/or seasonal changes in load power demands.

False data injection against state estimation in power systems with multiple cooperative attackers

Given the strong cyber–physical interactions in today’s smart grid, false data injection (FDI) attack can readily mislead the state estimation and influence the system operation by manipulating meter measurements. In this paper, a new FDI attack strategy is considered where multiple attackers cooperatively launch an unobservable attack. Firstly, the entire transmission system is partitioned into several subsystems, with each attacker only acquiring and manipulating the measurements in its local area. With limited communications among neighboring attackers, all of them can successfully modify the estimated states without being detected. In addition, by taking practical constraints into account, a least-effort attack problem is formulated and subsequently solved by a distributed alternating direction method of multipliers (ADMM)-based approach. Several case studies implemented on a 4-bus and IEEE 118-bus power systems have finally demonstrated the effectiveness of the proposed approach in the scenario of multiple attackers.

Squirrel Search Algorithm for Solving Optimal Reactive Power Dispatch Problem with FACTS Device

In this paper, a novel algorithm which is being inspired by the natural foraging phenomenon of squirrel, called as Squirrel SSA for solving optimal reactive power dispatch (ORPD) problem of power system, in which FACTS device namely, UPFC is incorporated. Power Injection Modeling (PIM) and Current Injection Modeling (CIM) of UPFC are considered, both are compared for determining the best modeling technique of UPFC which can be incorporated in power system. The performance and possibility of the proposed algorithm are validated on IEEE 30-bus power system. Results obtained are compared with the other recent algorithms to show the superiority of SSA.

Recursive Filtering of Distributed Cyber-Physical Systems With Attack Detection

This article is concerned with the distributed recursive filtering of cyber-physical systems consisting of a set of spatially distributed subsystems. Due to the vulnerability of communication networks, the transmitted data among subsystems could be subject to deception attacks. In this article, attackers do not have enough knowledge of the full network topology and the system parameters and therefore cannot carry out stealth attacks. For this scenario, a defense strategy dependent on the received innovation is proposed to identify the occurring attacks as far as possible. In light of identified attacks, a novel distributed filter is constructed and its gain is designed via a set of recursive formulas on the upper bound of covariance of filtering errors. The utilization of upper bound is to avoid the calculational challenge of cross-covariance matrices and realize the requirement of distributed implementation, simultaneously. Furthermore, the developed scheme only depends on the neighboring information and the information from the subsystem itself, and thereby satisfying the requirement of the scalability. Finally, a standard IEEE 39-bus power system is utilized to verify the effectiveness of the proposed filtering scheme.

Squirrel Search Algorithm for Solving Optimal Reactive Power Dispatch Problem with FACTS Device

In this paper, a novel algorithm which is being inspired by the natural foraging phenomenon of squirrel, called as Squirrel SSA for solving optimal reactive power dispatch (ORPD) problem of power system, in which FACTS device namely, UPFC is incorporated. Power Injection Modeling (PIM) and Current Injection Modeling (CIM) of UPFC are considered, both are compared for determining the best modeling technique of UPFC which can be incorporated in power system. The performance and possibility of the proposed algorithm are validated on IEEE 30-bus power system. Results obtained are compared with the other recent algorithms to show the superiority of SSA.

Eigenvector dimension-reduction method for reliability analysis of power systems under the time-dependent load uncertainty

ABSTRACT Power systems have become central pillars of modern society. Various uncertainties in power systems, however, make reliability analysis difficult. To date, the worst-case scenario was usually employed to tackle uncertainties in the system, which could make the overall system design prohibitively expensive especially when more and more uncertainty sources would be considered in modern power grids. This paper proposes reliability analysis of power systems under the time-dependent load uncertainty using an effective uncertainty quantification (UQ) method, i.e., the eigenvector dimension reduction (EDR) method. As such, reliability can be explicitly calculated in the probability manner for meeting the performance constraints without significantly overdesigning the system. Compared to the Monte Carlo simulation (MCS), the EDR method is much more efficient with satisfactory accuracy. Two case studies, including a 12-bus and a modified IEEE 118-bus power system, are used to demonstrate the feasibility of the proposed work.

Fast Frequency Support From Wind Turbine Systems by Arresting Frequency Nadir Close to Settling Frequency

The recent power cut incident in the UK on 9th August 2019 indicated that frequency control to raise frequency nadir and eliminate frequency second dip is highly desirable for power grids with high penetration of wind energy. This paper proposes a fast frequency support scheme for wind turbine systems (WTSs) that can enable frequency nadir to be significantly raised and close to the settling frequency and eliminate frequency second dip. In the proposed frequency support scheme, in order to achieve similar frequency support performance and ensure stability of WTSs under varying wind speeds, different levels of wind power penetration and system conditions, an adaptive gain, which is a function of real-time rotor speed and wind power penetration level, is proposed. In the proposed scheme, rotor speeds of WTSs are proposed not to be recovered to the optimal operating points during the primary frequency control, but recovered during the secondary frequency control. Simulation results on the IEEE two-area power system with a doubly fed induction generator (DFIG)-based wind farm and the IEEE 39-bus power system with permanent magnetic synchronous generator (PMSG)-based wind farms using real-time digital simulator (RTDS) and Dymola are presented to verify the effectiveness of the proposed scheme.

Stochastic economic dispatch considering the dependence of multiple wind farms using multivariate Gaussian kernel copula

Wind farms usually gather in areas with abundant wind resources. Therefore, spatial dependence of wind speeds among nearby wind farms should be taken into account when modeling a power system with large-scale wind power penetration. This paper proposes a novel non-parametric copula method, multivariate Gaussian kernel copula (MGKC), to describe the dependence structure of wind speeds among multiple wind farms. Wind speed scenarios considering the dependence among different wind farms are sampled from the MGKC by quasi-Monte Carlo (QMC) method, so as to solve the stochastic economic dispatch (SED) problem, for which an improved mean-variance (MV) model is established, which targets at minimizing the expectation and risk of fuel cost simultaneously. In this model, confidence interval is applied in the wind speed to obtain more practical dispatch solutions by excluding extreme scenarios, for which the quantile-copula is proposed to construct the confidence interval constraint. Simulation studies are carried out on a modified IEEE 30-bus power system with wind farms integrated in two areas, and the results prove the superiority of the MGKC in formulating the dependence among different wind farms and the superiority of the improved MV model based on quantile-copula in determining a better dispatch solution.

Consensus-Based Demand Response of PMSG Wind Turbines With Distributed Energy Storage Considering Capability Curves

This article proposes a distributed consensus-based demand response control for permanent magnet synchronous generator (PMSG)-based wind turbines using standalone distributed battery energy storage systems (BESSs). The proposed controller cooperatively regulates the output power of individual wind turbines and BESSs in a wind farm to deliver active and reactive powers to the load in varying wind speed conditions. Capability curves of the wind turbines and BESSs are considered in the design. In addition, a virtual leader is designed to regulate the voltage and frequency of the combined PMSG-BESS units at the point of common coupling. Simulations on modified IEEE 14-bus power system are performed to validate the proposed design.

Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm

In recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

Probabilistic Optimal Power Flow Calculation Method Based on Adaptive Diffusion Kernel Density Estimation

In order to accurately evaluate the influence of the uncertainty and correlation of photovoltaic (PV) output and load on the running state of power system, a probabilistic optimal power flow calculation method based on adaptive diffusion kernel density estimation is proposed in paper. Firstly, based on the distribution characteristics of PV output, the adaptive diffusion kernel density estimation model of PV output is constructed, which can fit the distribution of arbitrary distribution of PV power. This model can improve the local adaptability of PV output model and reflect the uncertainty and volatility of PV output more accurately. Secondly, The Kendall rank correlation coefficient and the least Euclidean distance are used as correlation measure and index of fitting to select the optimal Copula function, and the joint probability distribution model of PV output and load is constructed. After extracting the correlated PV output and load samples, a probabilistic optimal power flow calculation method considering the correlation of PV output and load is proposed. Finally, simulation studies are conducted with the measured data of a PV power plant of China and the IEEE 30-bus power system. The results show that considering the correlation between PV output and load can improve the accuracy of probabilistic optimal power flow calculation and effectively reduce the power generation cost of power system.

Identifying Overlapping Successive Events Using a Shallow Convolutional Neural Network

Real-time identification of successive events in power systems is crucial to avoid cascading failures. Existing identification methods are mainly designed for single events and may not accurately identify a subsequent event that occurs when the system is under going the disturbance of a previous event. Since overlapping successive events do not frequently happen in power systems, insufficient multievent data sets exist for training. We develop a data-driven event identification method that can accurately identify the types of overlapping events. Our approach only requires a small number of recorded phasor measurement unit data of single events to train a two-layer convolutional neural network (CNN) classifier offline. We extract the dominant eigenvalues and singular values as features instead of training on time series directly. That reduces the required number of training data sets and enhances the robustness to measurement inaccuracy. In real time, our method first predicts and subtracts the impact of previous events. It then extracts the dominant features from the residual measurements and applies the classifier. We evaluate the method on simulated events in the IEEE 68-bus power system. Our classifier is demonstrated to be more accurate and stable than a direct application of CNN on time series. The robustness of the proposed method to the delay in event detection and noise is validated.