IEEE 33-bus Network Research Articles

A New Self-Adaptive Teaching–Learning-Based Optimization with Different Distributions for Optimal Reactive Power Control in Power Networks

Teaching–learning-based optimization has the disadvantages of weak population diversity and the tendency to fall into local optima, especially for multimodal and high-dimensional problems such as the optimal reactive power dispatch problem. To overcome these shortcomings, first, in this study, a new enhanced TLBO is proposed through novel and effective θ-self-adaptive teaching and learning to optimize voltage and active loss management in power networks, which is called the optimal reactive power control problem with continuous and discontinuous control variables. Voltage and active loss management in any energy network can be optimized by finding the optimal control parameters, including generator voltage, shunt power compensators, and the tap positions of tap changers, among others. As a result, an efficient and powerful optimization algorithm is required to handle this challenging situation. The proposed algorithms utilized in this research were improved by introducing new mutation operators for multi-objective optimal reactive power control in popular standard IEEE 30-bus and IEEE 57-bus networks. The numerical simulation data reveal potential high-quality solutions with better performance and accuracy using the proposed optimization algorithms in comparison with the basic teaching–learning-based optimization algorithm and previously reported results.

Identification of Important Locational, Physical and Economic Dimensions in Power System Transient Stability Margin Estimation

Increasing renewable generation can lead to significant spatial and temporal changes to the rotor angle stability boundary, such that critical contingencies may drastically change. Additionally, the inherent variability of renewables increases the number of operational scenarios that require stability assessment. This paper presents a methodology whereby a series of location-specific Decision Tree Regressors are trained, using power system variables to estimate the Critical Clearing Time (CCT) on a locational basis throughout a network. Permutation feature importance is used to reveal the most important power system variables for CCT estimation at each location (capturing aspects related to physical system characteristics, operational parameters as well as economic dispatch). Consequently, estimation of the duration and location of the critical fault can also be made – along with identification of important system variables that explicitly impact the critical fault. Results on the IEEE 39-bus network show accurate estimation of locational CCTs, with a mean absolute percentage error of 1.19% on average. Moreover, the mean absolute percentage error for the minimum CCT is 0.49%. An analysis of important power system variables is provided, demonstrating how the method can assist in the design of targeted locational interventions to improve the stability margin at specific locations.

Multi-objective-based reactive power planning and voltage stability enhancement using FACTS and capacitor banks

Reactive power planning (RPP) and voltage stability improvement (VSI) consider two of the most important problems to meet a major challenge of the power system. In this work, a multi-objective genetic algorithm (MOGA) for RPP with objectives of cost minimization of the power losses, new reactive power (VAR) sources, maximization of the VSI, and enhancement of total transfer capacity (TTC) is introduced. Different optimization variables are considered including generator voltages, transformer tap changers besides load, and different operational constraints. The best compromise solution is determined through a fuzzy min–max approach. Comparison studies among capacitor banks, flexible ac transmission systems (FACTS) or both as new VAR support sources to achieve better performance are explored. Moreover, the optimal allocations of switchable VAR sources are not determined in advance; instead, they are treated as control variables to improve the techno-economic operation of the network. Added to that many voltage stability indicators are presented, and their results are compared. The effectiveness of the proposed algorithm is examined on a modified IEEE 30-bus test system and South Egypt Electricity network where felicitous results have been acquired. The results expound on the effectiveness of the proposed approach compared with other optimization methods.

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer

Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

Monte-Carlo-based data injection attack on electricity markets with network parametric and topology uncertainties

False data injection (FDI) attacks can significantly impact on economic performance of electricity markets in modern power systems. These attacks can be stealthily accomplished by cyber-attackers for the purpose of profitability through financial arbitrage in electricity markets. In this paper, a new strategy of FDI attack based on Monte Carlo is proposed for an attacker participating in an electricity market, who has overmuch imperfect level of the network information. This piece of information, including both the connection /disconnection situation and admittance values of the transmission lines is denominated as topology and parametric uncertainties, respectively. Herein, a probable model is offered for analyzing the uncertainties by the Monte Carlo simulation (MCS). Afterwards, considering the probable errors of uncertainties, the attack strategy is designed in such a manner that the attacker obtains the most profit based on the contribution of each transmission line. The numerical results on two PJM 5-bus and IEEE 30-bus test networks could obviously demonstrate the success of such limited attackers in current electricity markets.

An Adaptive Protection Scheme Based on a Modified Heap-Based Optimizer for Distance and Directional Overcurrent Relays Coordination in Distribution Systems

This paper proposes an adaptive protection scheme (APS) based on the original heap-based optimization (HBO) and a modified HBO (MHBO). APS is used to solve protection relays coordination problems that include directional overcurrent relays (DOCRs) as well as the distance relay’s second zone times. The complexity of the coordination problem increases with the impact of distributed generators (DGs) switching (ON/OFF). Topological changes in grid configuration frequently occur in distributing networks, equipped with DGs, causing changes in the values and direction of short circuit currents. This issue becomes a challenge for protection systems to avoid relays miscoordination and save a network’s reliability. In the proposed MHBO, the Original HBO is modified by three points, population are divided into subgroups, then they are unified into one group gradually, those subgroups are exchanging some search agents between themselves, these search agents are called travelling agents, and the last one is about, upgrading an internal equation in the original algorithm. For validating the proposed relays coordination, the IEEE 8-bus test system, and the IEEE 14-bus distribution network are selected as case studies. The obtained simulated results of the proposed algorithm show better performance compared with those obtained by the previous algorithms.

A new differential current circle diagram based technique for long transmission line protection

Abstract In view of sensitivity, selectivity, and operating speed of the relay, the current differential technique is an alternative against overcurrent and distance-based techniques for long transmission line protection. Moreover, this technique has limited performance due to Line Charging Capacitive Current (LCCC), Current Transformer (CT) saturation further, by using synchronized data, the effect of LCCC can be nullified. However, in case of synchronition is lost, the performance of the current differential is not reliable. Hence, in this paper, the Inverse of Difference of both end Current phasors (IDC) is applied to prevent maloperation due to channel delay and synchronization loss. The simulation experiments are conducted under various operating conditions on a 345 kV, 50 Hz, IEEE 39-bus network using PSCAD 4.5 and MATLAB (R2020a). Results reveal the enhanced security under CT saturation, LCCC and sensitivity under high resistance line to ground faults.

An Incentivized and Optimized Dynamic Mechanism for Demand Response for Managing Voltage in Distribution Networks

The voltage regulation in distribution networks is one of the major obstacles when increasing the penetration of distributed generators (DGs) such as solar photovoltaics (PV), specially during cloud transients, causing potential stress on network voltage regulations. Residential demand response (DR) is one of the cost-effective solutions for voltage management in distribution networks. However, the main barriers of DR implementation are the complexities of controlling a large number and different types of residential loads, satisfying customers’ preferences and providing them fair incentives while identifying the optimum DR implementation locations and sizing as well as cooperating with the existing network equipment for the effective voltage management in the networks. A holistic and practical approach of DR implementation is missing in the literature. This study proposes a dynamic fair incentive mechanism using a multi-scheme load control algorithm for a large number of DR participants coordinating with the existing network equipment for managing voltage at medium voltage (MV) networks. The multi-scheme load control is comprised of short-interval (10-minute) and long-interval (2-hour) DR schemes. The dynamic incentive rates are optimised based on the energy contribution of DR participating consumers, their influence on the network voltage and total power loss improvement. The proposed method minimises the DR implementation cost and size, fairly incentivises the consumers participating in the DR and priorities their consumption preferences while reduces the network power losses and DGs’ reactive power contributions to effectively manage the voltage in the MV networks. An improved hybrid particle swarm optimisation algorithm (IHPSO) is proposed for the load controller to provide fast convergence and robust optimisation results. The proposed approach is comprehensively tested using the IEEE 33-bus and IEEE 69-bus networks with several scenarios considering a large number of DR participants coordinated with the DGs and on-load tap changer (OLTC) in the networks.

Efficient Topology Design Algorithms for Power Grid Stability

The dynamic response of power grids to small disturbances influences their overall stability. This letter examines the effect of network topology on the linearized time-invariant dynamics of electric power systems. The proposed framework utilizes <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm based stability metrics to study the optimal placement of lines on existing networks as well as the topology design of new networks. The design task is first posed as an NP-hard mixed-integer nonlinear program (MINLP) that is exactly reformulated as a mixed-integer linear program (MILP) using McCormick linearization. To improve computation time, graph-theoretic properties are exploited to derive valid inequalities (cuts) and tighten bounds on the continuous optimization variables. Moreover, a cutting plane generation procedure is put forth that is able to interject the MILP solver and augment additional constraints to the problem on-the-fly. The efficacy of our approach in designing optimal grid topologies is demonstrated through numerical tests on the IEEE 39-bus network.

Fuzzy-based real-coded genetic algorithm for optimizing non-convex environmental economic loss dispatch

A non-convex Environmental Economic Loss Dispatch (NCEELD) is a constrained multi-objective optimization problem that has been solved for assigning generation cost to all the generators of the power network with equality and inequality constraints. The objectives considered for simultaneous optimization are emission, economic load and network loss dispatch. The valve-point loading, prohibiting operating zones and ramp rate limit issues have also been taken into consideration in the generator fuel cost. The tri-objective problem is transformed into a single objective function via the price penalty factor. The NCEELD problem is simultaneously optimized using a fuzzy based real-coded genetic algorithm (GA). The proposed technique determines the best solution from a Pareto optimal solution set based on the highest rank. The efficacy of the projected method has been demonstrated on the IEEE 30-bus network with three and six generating units. The attained results are compared to existing results and found superior in terms of finding the best-compromise solution over other existing methods such as GA, particle swarm optimization, flower pollination algorithm, biogeography-based optimization and differential evolution. The statistical analysis has also been carried out for convex multi-objective problem.

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

Backtracking Search Algorithm With Single and Multi-Objective Function for the Solution of Optimal Power Flow Problem

This manuscript investigates the performance of the backtracking search algorithm (BSA) to minimize various objectives for an economical and secure power system. A variety of single and multi - objectives are delineated and solved. This manuscript also includes the valve-point loading effect alongside the objectives considered. The simulation has been computed in the IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test network. The simulation outcomes as obtained by the proposed BSA and various algorithms are compared. Convergence curves are plotted to testify the characteristics of the proposed BSA for proceeding towards the global minima.

Allocation of defensive and restorative resources in electric power system against consecutive multi-target attacks

Because the infrastructure network itself has the characteristics of cascading failures, deliberate attackers will often take advantage of these internal dependencies to perform long-term multi-target strikes, which the paper calls consecutive multi-target attacks. This kind of continuous external destruction on the time scale will increase the chain effect of cascading failures within the system. Among these infrastructure networks, electric power systems are particularly critical, because most of the other infrastructures need electricity for their operation and management, and the cascading failure of the power network is more serious. Thus, this paper comprehensively considers external consecutive multi-target attacks and internal cascading failure propagation, establishes a defense-attack-recovery tri-level game optimization model, and study how to allocate defensive and restorative resources from a macro perspective to improve the strategic resilience of the power supply system. In order to solve the optimization model, the paper proposes a nested heuristic algorithm, and proposes a performance response function that combines the power flow operating characteristics and external failure events, and then uses the IEEE 39-bus network as a case study to verify the effectiveness of the model and algorithm.

Analysing the impact of optimally allocated solar PV-based DG in harmonics polluted distribution network

The optimal installation of a renewable distributed generation (DG) in a radial distribution network (RDN) provides a sustainable and clean solution for the future smart network. A weighted-sum multi-objective approach is applied to determine the optimal location of solar PV-DG followed by a PSO algorithm to find optimal DG size. The impact of optimal DG allocation in distorted and non-distorted distribution network is efficaciously demonstrated and validated on IEEE 33-bus RDN. The harmonic analysis in the presence of the six-pulse converter non-linear load and non-linear DG is carried out for IEEE 34-bus network. The non-linear loads are modelled as harmonic current source. As composite load model provides lowest voltage drop with decreased capital investment cost, hence, its effect and the effect of constant power load model on energy loss saving, network loss and total harmonic distortion at various load levels is being analysed. The harmonic load flow results obtained considering non-linear and linear loads are compared to other analytical methods and nature-inspired techniques to illustrate the robustness of the proposed strategy for enhancing the performance of the network. The statistical significance of the results is verified by the Wilcoxon test.

Seismic resilience assessment of urban interdependent lifeline networks

With increasing interdependence of various lifeline networks, natural disasters, especially seismic disasters damage will further degrade overall network performance. Hence, based on the concept of risk, reliability and vulnerability, some scholars assessed the resilience of multi-layer lifeline networks. However few studies considered bidirectional interdependence of lifelines, which would immensely exacerbates the consequences of seismic disaster. And the existing research of resilience assessment generally assumed that lifeline networks can recover to their initial performance rather than limited recovery which is more practical. Therefore, this study presents a framework and method for assessing the resilience of lifeline networks. And a performance response function for interdependent lifelines is proposed based on its functional characteristics. Then, we use an IEEE 30-bus and Belgium 20-node gas interdependent network as examples to verify the effectiveness of the framework. The results showed that, for each network, the recovery resource and budget had various effects on the resilience. And our framework can provide a quick reference for optimal decision-making in various scenarios under seismic disasters.

Upshot of unified power flow controller on the minimization of the severity of overloading on electric power grid

With the objective of discovering the vital line in an electrical power grid (EPG) where the unified power flow controller (UPFC) could be inserted to minimize the severity of overloading (SOL) during contingencies, an investigation into the stiffness of single line contingencies (SLC) on the EPG is presented in this study. An overloading index (OVI) is developed and used in this contribution to arrange the transmission branches of the EPG based on their cruciality and criticality throughout SLC. A harmony search algorithm (HSA), an optimization method, has been adopted to optimally set the parameters of the UPFC to achieve the best minimum SOL of the system at the optimal location. The IEEE 30-bus network was used as the test bed. Results based on the test bed show that placing a UPFC based on the ranking of a well-known contingency severity index (CSI) minimized the SOL to 2.40×1010 as against the 8.05×105 obtained when the placement is done on the ranking based on the proposed OVI. The minimization of SOL is achieved with a reactance and reactive power of 0.036pu and 0.096pu, respectively, of the UPFC. The result also reveals that the proposed OVI identifies with a higher level of precision the vital line in the test bed for placing the UPFC for the purpose of minimizing the SOL during contingencies.

An Adaptive Protection Scheme for Coordination of Distance and Directional Overcurrent Relays in Distribution Systems Based on a Modified School-Based Optimizer

This paper presents an adaptive protection scheme (APS) for solving the coordination problem that deals with coordination directional overcurrent relays (DOCRs) and distance relays second zone time, in relation to coordination with DOCRs. The coordination problem becomes more complex with the impact of renewable energy sources (RES) when added to the distribution grid. This leads to a change in the grid topology, caused by the on/off states of the distribution generators (DG). The frequency of topological changes in distribution grids poses a challenge to the power system’s protection components. The change in the state of DGs leads to malfunction in reliability and miscoordination between protection relays, since that causes a direct effect to the short circuit currents. This paper used the school-based optimization (SBO) algorithm, which simulates the educational process, in order to deal with coordination problems. That algorithm is modified (MSBO) by modified both learning and teaching processes. The IEEE 8-bus test system and IEEE 14-bus distribution network are used to validate the proposed coordination system’s effectiveness when dealing with the coordination process between distance and DOCRs, at both the near- and far-end in the typical topological grid and with DGs in working order.

Technical and economic analysis of curative actions in distribution networks utilizing battery energy storage systems

Renewable energy generation curtailment increases due to more frequently occurring congestions in power system operation. Post-contingency curative congestion management actions can reduce the necessity of renewable energy curtailment by enabling a more efficient utilization of transmission capacities. In this research, the potential of curative actions to substitute renewable energy curtailment is studied considering technical and economic criteria. Therefore, a novel pricing methodology for the market-based provision of curative actions is introduced. The method is based on the security constraint optimal power flow technique. Simulations are carried out on a modified version of the IEEE 14-bus network and a real-world 110 kV distribution network. Battery energy storage systems are implemented as an exemplary technology to provide curative actions. The developed method achieves a positive power system impact by reducing operational costs and maximizing renewable energy integration. Also, novel business models for merchant-owned battery energy storage systems are unveiled. The provision of curative actions further proves to be competitive to established battery storage applications. Additionally, results of different grid expansion scenarios of the 110 kV network reveal the need to coordinate power system planning and operation more extensively in the future.

Network Reconfiguration Based on an Edge-Cloud-Coordinate Framework and Load Forecasting

With the development of distributed energy resources (DERs), the power flow (PF) in the distribution network (DN) is changed from unidirectional to bidirectional, resulting in complex control and coordinate measures. Network reconfiguration (NR) is a feasible solution for the power grid side to deal with the complex PF. This paper proposed an edge-cloud-coordinated reconfiguration framework with edge servers (ESs) in the prosumer side and cloud servers (CSs) in the utility grid side, where the edge computing (EC) technology is implemented in ES to support load forecasting (LF), while cloud computing (CC) is used in CS to reconstruct the DN. LF is implemented by the long–short-term memory network to acquire the load information in advance, and the social preference of prosumers has been considered. The NR is formulated as a complex combinatorial optimization problem with the goal of minimizing power losses, while satisfying the power flow and voltage requirement. The NR problems are solved by the proposed advanced harmony search algorithm, which can find the optimal global solution, while satisfying the complex constraints of the NR problem. Numerical results are conducted based on an IEEE 33-bus network, which shows the high accuracy of LF and demonstrates the effectiveness of the proposed framework in terms of reducing more than 40% power losses and satisfying the voltage requirement.