Modified IEEE 30-bus Test System Research Articles

A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework

The ever-increasing number of grid-connected electric vehicles (EVs) has led to emerging new opportunities and threats in electrical distribution systems (DS). Developing a realistic model of EV interaction with the DS, as well as developing a strategy to optimally manage these interactions in line with distribution system operators (DSOs) intentions, are the most important prerequisites for gaining from this phenomenon especially in modern smart distribution systems (SDS). In this paper, a comprehensive model describing the electric vehicle integration to an SDS is presented by considering the real-world data from EV manufacturers and DSOs. Moreover, a novel energy management strategy (EMS) based on the multi-objective optimization problem (MOOP) is developed to fulfill the operational objectives of DSO and EV owner, including peak load shaving, loss minimization, and EV owner profit maximization. In this regard, an innovative dimension reduction approach is presented, to make it feasible to apply the heuristic optimization methods to a MOOP with a large number of decision variables. Thanks to this method, the improved electromagnetism like algorithm (IEMA) is employed to perform the multi-objective energy scheduling for a large-scale EV fleet. In addition, a novel method is devised for estimating the optimal hosting capacity of an SDS in adopting EVs without the need for sophisticated computations. The presented method is applied to the modified IEEE-33 bus test system. Obtained results reveal that employment of a realistic model concludes to more accurate results than a simplified model. In addition, the efficiency of the proposed EMS in satisfying EV owner and DSO objectives are approved by analyzing obtained computation results.

Coordinated planning of transmission expansion and coal‐fired power plants flexibility retrofits to accommodate the high penetration of wind power

As one of the promising renewable energy technologies, wind power generation has developed rapidly over the last decade. However, the rapid growth of wind power resulted in significant wind curtailment due to transmission congestion and lack of flexible resources. Although energy storage is the most efficient way to increase the flexibility of the power system, large capacities of cost-effective energy storage are not yet available today. Considering that coal-fired power plants are still the dominant suppliers of electricity in many countries, it has a great potential to increase large amounts of flexibility by retrofitting the existing coal-fired power plants. In this study, the coordinated planning model for transmission expansion and coal-fired power plants flexibility retrofits is proposed to accommodate high penetration of wind power. Robust optimisation is employed to handle the uncertainties of peak load demand and wind power capacity, and the robust planning model is solved by nested column-and-constraint generation method. The validity of the proposed planning model is demonstrated using the modified IEEE 24-bus test system and modified IEEE 118-bus test system.

Improved plug–play SV with virtual inertia for enhancing the stability of high RES‐penetrated grids

The synchronverter (SV) is currently one of the most promising grid-friendly inverters that mimic synchronous generators (SGs). Almost all the available SV use virtual current signal for synchronization, which require a complicated controller that lacks plug–play capability. This paper proposes a new generation of self-synchronised SV with a simpler controller, which provides plug–play capability with no need for switching between actual controller current and a virtual current for each ON–OFF process. The proposed SV (PSV) can also share the load with other SGs and SVs and also it is able to take part in regulating system frequency and voltage. A linearised small-signal model is derived for the controller to investigate its stability. The PSV is also validated through multiple simulation studies carried out in MATLAB/Simulink environment. The results show the capability of the PSV to achieve self-synchronisation and plug–play operation without a dedicated PLL or a virtual current. Compared to the current self-synchronised SV, the PSV guarantees a smoother grid connection and gives more accurate reactive power sharing. Moreover, the PSV is applied to a modified IEEE 14-bus test system considering three different contingencies to prove its impact on enhancing system dynamics and stability.

Optimal Coordinated Expansion Planning of Transmission and Electrical Energy Storage Systems Under Physical Intentional Attacks

In this paper, a new model for “coordinated expansion planning of transmission and electrical energy storage systems (CEP)” to decrease the vulnerability against physical intentional attacks (PIAs) is proposed. In this model, generators and the amount of load at the planning horizon year are assumed to be known and load variations during four sample days are considered as the representatives of daily load variations in different seasons. To consider PIAs and their impacts on the power system, a scenario-based method is used in which credible PIA scenarios during the horizon year are obtained. Using the obtained scenarios, the problem of CEP is modeled as a mixed integer linear programming problem. The model is implemented on a modified IEEE 30-bus test system and numerical results are presented for numerous case studies. Using numerical results, the proposed model is verified by comparison with the models suggested in literature. Also, the sensitivity analysis of the results is presented.

Two‐stage stochastic programming for the joint dispatch of energy and reserve considering demand response

This paper formulates a two-stage stochastic programming model for energy and reserve joint dispatch considering wind power uncertainty and the participation of incentive-based demand response (IBDR). The model consists two stages, the first stage is to determine the day-ahead energy dispatch and the reserve capacity need, the second stage is to determine the deployment of reserves to accommodate the realisation of wind power uncertainty in real time. The IBDR program and the thermal power units provide reserve together to cope with the uncertainty of wind power. The IBDR model considers the maximum response times of a DR agent during a day to assure the reliability of using electricity for end users. To reduce the computation burden caused by wind power scenarios, the inactive constraints identification method is utilised to eliminate redundant transmission line constraints before solving the original model. Finally, the effectiveness of the proposed model and the solving method are tested on the modified IEEE 118-bus test system with 50 DR agents.

Multi-period Robust Dispatch of Active Distribution Network Based on Scene Analysis Method

With the large-scale access of intermittent renewable energy, the active distribution networks (ADN) is facing great challenges on achieving full consumption and efficient use of intermittent renewable energy on the premise of ensuring safe operation. To resolve these issues, a novel multi-period robust dispatch strategy for ADN based on scene analysis method is proposed in this paper. Then, the second-order cone relaxation (SOCR) is used to transform the original model into a mixed integer second-order cone programming (SOCP), which gains as much improvement as possible in computational performance at the expense of as few losses as possible in accuracy. Finally, numerical test on the modified IEEE 33-bus test system demonstrates that on the premise of realizing the maximum utilization of DG, the proposed strategy can also achieve the purposes of energy-saving, loss reduction and improvement in voltage level.

Sensitivity Analysis of Renewable Energy Integration on Stochastic Energy Management of Automated Reconfigurable Hybrid AC–DC Microgrid Considering DLR Security Constraint

This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac-dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac-dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac-dc MG operation cost while all of the constraints of the network are satisfied.

Optimization of damping controller for PSS and SSSC to improve stability of interconnected system with DFIG based wind farm

This paper studies the stability issues of the Double Fed Induction Generator (DFIG) based wind farm in the power system with variable wind speed in strong and weak grid. With the intermittent generation of the wind power, it experiences instability and malfunction. The nature of Electromechanical Modes of Oscillations (EMOs) for DFIG incorporated into the multi-machine system under various conditions is explored considering system uncertainties. In this paper, the impact of Power System Stabilizer (PSS) and Static Series Synchronous Compensator (SSSC) on the stability of wind power system is analyzed by utilizing modified IEEE 14-bus test system. The rotor speed deviation is the objective function for particle swarm optimization and is formulated based on the transient and small signal stability. The transient and small signal stability investigations are performed by using time domain simulation and eigenvalue analysis respectively. The sensitivity analysis is carried out with degree of compensation of SSSC and coefficient of damping gain of PSS as sensitivity parameter. The performance of the modified test system is examined for transient and small signal stability for DFIG based wind farm for variable wind speed and grid strength considering different combinations of PSS and SSSC. From the results, it is noticed that oscillations in the system are minimized, damping, transient and small signal stability are enhanced and also the capability of synchronous generator and DFIG under extreme disturbance is intensified by optimization the coordinated controllers’ parameters. The analyses also show that damping ratio is improved for most of the EMOs. Sensitivity analysis identifies EMOs that have detrimental and beneficial impacts on the system.

Dynamic Security Assessment for Power System Under Cyber-Attack

In recent years, power systems have become more dependent on new technological advancement in the communication network to send and receive data and commands through the wide area power network. This dependence has created a new threat to the network, known as a cyber-attack. Such attacks could lead to blackouts and the consequences on the security of the power system would be severe. This study presents a new approach, which aims at assessing the dynamic behavior of the test system’s model under a cyber-attack contingency. The methodology used in this study was based on the scenarios of cyber-attacks on the protection relay devices to generate the database for dynamic security assessment. Then, a data mining framework was used for the database preparation and classification via feature selection algorithm and decision tree classifier. The results of the modified IEEE 30-bus test system model in this study showed a high accuracy of 99.537%, and a short time frame that makes this application suitable for real-time application to protect the power network from an insecure state and ensure that the power system remains reliable.

Hybrid Harris Hawk Optimization Based on Differential Evolution (HHODE) Algorithm for Optimal Power Flow Problem

Harri’s Hawk Optimization (HHO) algorithm manifests as a new meta-heuristic algorithm in literature. When we look at studies that have used with this algorithm, we can see that its results in test functions and in the solutions of some test functions in IEEE Congress on Evolutionary Computation (CEC) are much better compared to other heuristic and meta heuristic algorithm results. In this study, an algorithm has been developed which has been hybridized with the mutation operators of Differential Evolution (DE) to further improve the HHO algorithm. This algorithm is named as Hybrid Harris Hawk Optimization based on Differential Evolution (HHODE). Performance of the proposed HHODE algorithm has been first compared with HHO and then compared with the results of other algorithms which have been most commonly used in the literature. In this comparison process, the most commonly used test functions in the literature and some of the other test functions in CEC2005 and CEC2017 as a new application field, have been solved. When the results of the comparison of HHODE with other algorithms are analyzed, it is observed that the balance between the exploratory tendency and exploitative tendency of the algorithm is well consistent. Formula 1 ranking method is used in the order of HHODE according to HHO and other algorithms. When a general evaluation of HHODE was performed, it was found to be an even more powerful algorithm as a result of the combination of strong features of both HHO and DE. The optimal power flow (OPF) problem is one of the most important problems of the modern power system. The HHODE algorithm is proposed to solve the OPF problem, which is considered without valve-point effect and prohibited zones (1) and with prohibited zones (2) in this paper. The effectiveness of the HHODE hybrid algorithm is tested on modified IEEE 30-bus test system. The result of HHODE algorithms are compared with other optimization algorithms in the literature.

Optimal placement of unified power flow controller using differential search algorithm

Optimal power flow (OPF) problem plays a crucial role to run an economically efficient and well-planned power system. It is a challenging task for the power system researchers to cope with the ever-increasing load-demand while getting the minimum system loss. The development of flexible ac transmission system (FACTS) has added a new dimension both to the system operation and research. Unified power flow controller (UPFC) is the most reliable FACTS controller, which can provide both series and shunt compensation. As a matter of fact, UPFC can reliably control the different power system parameters. In this article, UPFC is incorporated into the modified IEEE 5-bus and modified IEEE 30-bus test system. Differential search algorithm (DSA) is proposed and implemented to run the OPF with and without UPFC and the results are listed, analysed and compared with the same that is obtained by genetic algorithm (GA) and BAT search algorithm.

Parallel Restoration Method for AC-DC Hybrid Power Systems Based on Graph Theory

High voltage direct current (HVDC) transmission systems have been used for interconnection among multiple power grids. As a result, the conventional AC power systems are transforming into AC-DC hybrid power systems. In this paper, a parallel restoration method for AC-DC hybrid power systems is proposed. First, the effects of line-commutated-converter-based (LCC) and voltage-source-converter-based (VSC) HVDC systems on service restoration are explored. The startup characteristics of HVDC systems are analyzed. Second, an optimization model for service restoration of AC-DC hybrid power systems is developed, aiming at maximizing the generating power and minimizing the restoration time. The startup constraints of HVDC systems are incorporated into the proposed model. Third, a graph-theoretic algorithm is proposed to obtain a near-optimal parallel restoration scheme, verified by power flow calculations. An 11-bus test system is used to illustrate the effects of using HVDC systems for system restoration and a modified IEEE 39-bus test system is used to verify the effectiveness of the proposed parallel restoration method. The simulation results indicate that the proposed method can make full use of the benefits of HVDC systems, allocate cranking power flexibly during the parallel restoration, and reduce the restoration time.

Market-based participation of energy storage scheme to support renewable energy sources for the procurement of energy and spinning reserve

Energy Storage Scheme (ESS) is of great importance to realize energy management and to optimally utilize Renewable Energy (RE) integration in the electricity system. An increasing exploitation of RE in electricity system raises the concern about the need for Ancillary Services (AS) in a power system. These services are required for maintaining the reliability and security of the supply. This paper proposes a market-based participation of ESS to support large-scale RE penetration for the procurement of energy and AS using Virtual Power Plant (VPP) in a deregulated environment. The proposed VPP consists of a pumped-storage system as one of the recognized ESS and Renewable Power Producers (RPPs). This optimization problem is formulated and solved using an optimal power flow technique which considers network constraints and power flow limits. Spinning Reserve (SR) as one of the main AS is considered in this paper, which is procured under Spinning Reserve Market (SRM). The ability of the proposed approach to provide both energy and SR is tested on 3 case studies and demonstrated by considering a modified IEEE-30 bus test system. Results show that the VPPs can play a significant role in increasing the penetration of RE for the procurement of energy and AS.

Probabilistic Power Flow Methodology for Large-Scale Power Systems Incorporating Renewable Energy Sources

In this paper, we propose a new scheme for probabilistic power flow in networks with renewable power generation by making use of a data clustering technique. The proposed clustering technique is based on the combination of Principal Component Analysis and Differential Evolution clustering algorithm to deal with input random variables in probabilistic power flow. Extensive testing on the modified IEEE-118 bus test system shows good performance of the proposed approach in terms of significant reduction of computation time compared to the traditional Monte Carlo simulation, while maintaining an appropriate level of accuracy.

A systematic approach for the joint dispatch of energy and reserve incorporating demand response

The intermittent nature of wind power increases the need for flexibility of the power system. This paper proposes the systematic approach for the joint dispatch of energy and reserve incorporating demand response, including the formulation of the two-stage optimization, dynamic scenario generation, and inactive constraint identification. The incentive-based demand response model is adopted to improve flexibility by its cooperation with conventional units. The dynamic scenario generation method is developed to provide reasonable input for the two-stage optimization, considering the temporal correlations of the wind power. Three indicators are proposed to evaluate the quality of scenarios. To speed up the solution, the inactive constraint reduction has been applied to reduce the computational burden raised by the number of the scenarios and the system scale. Finally, the modified IEEE 118-bus test system with fifty incentive-based demand response aggregators is utilized to evaluate the effectiveness of the proposed method to improve operational economics and to promote wind power utilization. Simulation results show that 89.53% of the transmission line constraints can be removed, leading to a maximal reduction of 69.81% of the computational time. Compared to the conventional sampling method, the dynamic scenario set performs better in terms of three proposed indicators, and can reduce the total cost by 1.99%. Neglecting the constraint of response times, the economic efficiency would be overestimated by 0.98%.

Numerical method for probabilistic load flow computation with multiple correlated random variables

Grid complexity is increasing progressively as the deepening penetration of renewable power generation and unpredictable demand, which necessitates an exhaustive assessment of system parameters in a probabilistic manner. In this study, the authors employ a dimensional reduction integral method to tackle the above problems challenged by dimensionality. Their approach transforms the multivariate raw moments into a linear sum of several one-dimensional integrals, which could be solved by Gauss quadrature. To handle the correlation between non-Gaussian input variables, Nataf transformation is used to map the inputs into the independent normal domain. Instead of commonly used series expansion such as A-type Gram-Charlier, Edgeworth or Cornish-Fisher, the probability distributions of output variables can be better approximated by C-type Gram-Charlier series with the calculated moments. The salient feature of the proposed method is demonstrated in a modified IEEE 118-bus test system with respect to both accuracy and run times.

Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

This paper proposes a novel algorithm based on an advanced feature selection technique for the decision tree (DT) classifier to assess the dynamic security in a power system. The proposed methodology utilizes symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier-based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of the DT classifier. The effectiveness of the proposed technique is demonstrated on the modified IEEE 30-bus test system model. The results show that the DT classifier with SU outperforms the DT classifier without SU. The performance of the proposed algorithm indicates that the DT classifier with SU is able to assess the dynamic security of the system in near real time. Therefore, it is able to provide vital information for protection and control applications in power system operation.

A scenario-based planning framework for energy storage systems with the main goal of mitigating wind curtailment issue

This paper provides a new multi-objective (MO) framework for expansion studies of energy storage systems (ESSs) in high wind penetrated power system. The proposed approach well considers the issues originated from the wind power curtailment via introducing expected of wind curtailment cost as an objective function of the studies. All other imposed costs of installing storage units are modeled as expected of total social cost. Also, the effect of uncertainties is modeled through an internal scenario analysis. In this regard, two criteria including maximum regrets of wind curtailment cost and total social cost are considered as the other objectives of the proposed MO optimization procedure. These all the crucial and maybe contradictory aspects of the ESSs expansion problem are treated via a Posteriori MO optimization algorithm, i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA II). The proposed methodology is implemented on the modified IEEE 24-bus test system and its anticipated applicability is well verified.

Developing a multi-objective framework for expansion planning studies of distributed energy storage systems (DESSs)

This paper presents a framework for expansion planning studies of distributed energy storage systems (DESSs) in high wind penetrated power systems. The main objective is to find optimal location and capacity of DESSs in the viewpoint of independent system operator (ISO) while ensuring the maximum usage of wind farms output generation. Three different criteria are introduced for expansion planning studies. Minimizing wind curtailment cost together with transmission congestion cost are considered to properly deal with the issues associated with the curtailment of wind energy and constraints of transmission network. Furthermore, the minimum normalized profit for all DESSs' owners needs to be maximized to model the requirements of DESSs' owners in the studies. These all the crucial aspects of the DESSs expansion problem are treated via a well-organized posteriori multi-objective (MO) optimization algorithm, i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed method is applied to the modified IEEE 24-bus test system, and the results are presented to verify the applicability and efficiency of the proposed DESSs planning in a renewable-based power system.

Power system security assessment with high wind penetration using the farms models based on their correlation

Increasing the wind energy penetration in a power system presents some technical challenges to the transmission expansion planning (TEP). In the first stage of TEP, the transmission bottlenecks should be determined and ranked via security assessment. The high penetration of wind energy means a large number of wind farms exist in various geographic areas. It is found that there is a correlation between the wind speed series of different farms. Therefore, integration of the correlated wind farms into security assessment is addressed in this study. Hence, a new model of wind speed prediction is presented based on the correlated autoregressive moving average time series. The copula functions are used to make the predefined correlation among wind speed series. Here a linear correlation using a Gaussian copula is implemented. Then, the risk-based security assessment is performed using the proposed sequential time simulation. The proposed process is applied to the modified IEEE 39-bus test system which comprises 15 wind farms. Then, the transmission bottlenecks are identified and ranked regarding their overload risks. Finally, the impacts of the wind farms correlation on the risk index are investigated and those contingencies which impose the highest influence on this risk are identified.