Transmission Line Contingencies Research Articles

A study on the effect of the stability of the electric power system on the internal and external contingency of the combined energy hub

In this study, the combined energy hub on the gas pressure converter station in South Korea was configured in a detailed model form rather than a conventional model form. In addition to power sources such as fuel cells and TEG, energy storage systems (ESS), photovoltaics (PV), combined heat and power systems (CHP), and transformers and buses are configured, and loads are configured to observe various problems that can occur in each change. And the contingencies that can occur inside and outside the combined energy hub are simulated for the Turbo Expander Generator (TEG) to operate effectively in connection with the power system.Four fault locations of connected load and power generation facilities inside the combined energy hub are selected, and we observe the voltage change of each bus as the circuit breaker operates. In addition, as an external change, the increase in renewable energy and the voltage change due to transmission line contingencies were also observed. The analysis intends to establish a system analysis environment to check whether a problem occurs in terms of voltage and reactive power during the operation of future combined energy hubs.

Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.

Investigation of Disruptive Effect on Voltage Stability Caused by Line Contingency in Power Systems and Improvement of System Stability

There are many situations that cause voltage instability in power systems. One of these situations is line contingency that may occur in power systems. In this study, the effects of line contingency on the steady state are investigated in the IEEE 9 buses power system. Firstly, the static and dynamic analyses of the power system are made in normal operating condition. Continuous power flow analysis was used as static analysis and time domain simulation method were used as dynamic analysis. Then each transmission line is deactivated and the analyses are repeated. The changes in the maximum loading capacity and voltage stability of the system are investigated in case of each transmission line contingency. Finally, Static Var Compensator (SVC) is connected to the power system to improve the stability level. When the SVC is connected to the power system, the maximum loading capacity of the system is examined and the effects of this situation on the voltage stability are analyzed. All analyses are performed in the MATLAB® PSAT (Power System Analysis Toolbox). The results showed that the line contingency reduces the maximum loading capacity and deteriorates the voltage stability. It has been observed that both the problems can be eliminated with the SVC integration into the power system.

A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty

Currently, optimization models for the safe and reliable operation of power systems deal with two major challenges: the first one is the reduction of the computational load when considering N−1 contingencies; the second one is the adequate modeling of the uncertainty of intermittent generation and demand. This paper proposes a new affinely adjustable robust model to solve the security constrained unit commitment problem considering these sources of uncertainty. Linear decision rules, which take into account the forecasts and forecast errors of the different sources of uncertainty, are used for the affine formulation of the dispatch variables, thus allowing the tractability of the model. Another major novelty is that the evaluation of the N−1 security constraints is performed by incorporating a novel method, proposed in the literature, based on the user-cuts concept. This method efficiently and dynamically adds only the binding N−1 security constraints, increasing the computational efficiency of the model when transmission line contingencies are considered. Finally, Monte Carlo simulations on the post-optimization results were run to demonstrate the effectiveness, feasibility and robustness of the solutions provided by the proposed model.

Optimal offshore wind farms connection considering the wind capacity maximisation – a Benders decomposition approach

This study presents a new methodology to determine the optimal Offshore Wind Farms connection point in a power system, with the purpose of maximising the wind power penetration, considering a Benders decomposition approach to represent the security constraints related to the ‘’ contingency criterion. The proposed algorithm uses a mixed-integer non-linear programming approach associated with a non-linear programming, and it is evaluated in a tutorial system (IEEE 14-Bus) and two networks from the literature: a small case (IEEE RTS-79), and a medium and real case (Brazilian Birds System). In each case, three points geographically close to each other are selected as candidates in the onshore grid for the wind power injection. The experiments conducted clearly show that the connection decision from the proposed approach led to the maximum safe wind power penetration among the candidate options, which proves its applicability and effectiveness, even for more complex networks, and considering all single transmission line contingencies for safe operation.

Resilient Transmission Hardening Planning in a High Renewable Penetration Era

Hardening components in transmission systems is a practice to improve system resilience against possible disturbances caused by natural disasters. In a power system with a very high penetration of renewable energy, the system hardening will be further complicated by the uncertainty and variability of renewable energy. In this paper, we study the transmission line hardening planing problem in the context of probabilistic power flows injected by the high penetration of renewable energy. We assume that the probabilistic information of renewable energy is incomplete and ambiguous and propose a data-driven approach to approximate the renewable uncertainty sets. We then extend the $N-1$ security criteria to multiple simultaneous contingencies and seek to prepare a hardening plan for the worst-case scenarios. A two-stage data-driven stochastic model is formulated by considering the joint worst-case wind output distribution and transmission line contingencies. Then, we apply the Column-and-Constraints generation method to solve the proposed model. To test the effectiveness of the proposed approach, we conduct experiments on 24-bus and 118-bus test systems. We numerically show that the data-driven approach can effectively address the uncertainty ambiguity and the proposed approach can produce effective hardening plans that improve the system resilience.

Investigation of Combined SVC and TCSC versus IPFC in Enhancing Power System Static Security

Electrical power systems are often required to operate at full loading capacity due to ever increasing demand and transmission line contingencies with limited grid expansion. This results in line overload and operating near system limit, thereby threatening system security. Utilization of existing system can be achieved using Flexible Alternating Current Transmission System (FACTS) devices without violating system limits. This research investigation involves static security assessment of a modelled IEEE 30-bus test system in MATLAB/SIMULINK/PSAT environment. The security status with the incorporation of combined Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Interline Power Flow Controller (IPFC) were determined. Prior to this, Contingency Severity Index (CSI) based on Performance Index (PI) of Voltage and Active Power was employed to determine the optimal location of the FACTS devices. Sequential Quadratic Programming (SQP) was applied to determine the optimal sizing/percentage compensation of FACTS. Subsequently, power system with and without the incorporation of FACTS devices were modelled. The ability of the compensated system to withstand credible transmission line contingencies without violating the normal operating limits (bus voltage and line thermal) was examined and presented. The paper presents how combined SVC/TCSC and an IPFC aided the power system to boost its steady state security in the face of possible line contingencies.

A probabilistic model for estimating the effects of photovoltaic sources on the power systems reliability

As the power grid is continuously expanding, the increased amount of loading and the corresponding amount of generation is causing the grid to become more and more complex. The addition of intelligent devices for control and communications is also making the grid more vulnerable to failures. The addition of distributed renewable energy sources can potentially alleviate some of the stress on the grid, and help improve its reliability; however, their effects on the grid are not yet fully understood.In this paper, we study the effects of adding photovoltaic (PV) sources to the grid from a reliability perspective. We use monte carlo simulation to assess the reliability of the system in the presence of transmission line contingencies and potential cascading failures, and we use the particle swarm optimization to find the optimal placement of PV sources in the network that would maximize the system reliability.Our results show that while adding PV sources to the network can, under certain conditions, improve the system reliability, increasing the amount of PV indefinitely does not necessarily continue to improve it. There is a certain optimal amount of PV power that would improve the system reliability, and any further increase can have negative effects.

Market pricing with single‐generator‐failure security constraints

Regional transmission organisations and independent system operators include different types of security requirements to approximate system security issues. Transmission line contingencies are well handled in state-of-art market models with line outage distribution factors and, at the same time, the impacts of transmission line contingencies are reflected in energy prices. However, there is a lack of efficient mechanisms to handle generator contingencies and reflect the impacts of generator contingencies on energy prices. In this study, a set of security constraints to withstand single-generator-failure contingencies are presented and the market implications are studied. A new component of locational marginal prices, a marginal security component, which is a weighted shadow price of the security constraints, is proposed to better represent energy prices. A 3-bus system example is given to illustrate the market implications. The results are confirmed on a 73-bus system test case.

Optimal location of SVC for improvement in voltage stability of a power system under normal and contingency condition

Voltage instability is a major problem in power system it seldom results in voltage collapse and blackout. FACTS devices can be used to maintain voltage stability by controlling active and reactive power and increase voltage stability margin. Optimal location of these devices is also an important issue. In this paper static var compensator (SVC) is used to improve voltage stability. Optimal location of SVC is found by continuation power flow and by finding fast voltage stability index. SVC is placed at optimal location and its effect on improving voltage stability, loadability limit and reducing power losses have been studied under normal as well as contingency conditions. Two types of contingencies are considered, transmission line (N − 1) contingency and generator outage contingency. The proposed work is tested in IEEE 14 bus test system.

An N-k Analytic Method of Composite Generation and Transmission with Interval Load

N-k contingency estimation plays a very important role in the operation and expansion planning of power systems, the method of which is traditionally based on heuristic screening. This paper stringently analyzes the best and worst states of power systems given the uncertainties of N-k contingency and interval load. For the sake of simplification and tractable computation, an approximate direct current (DC) power flow model was used. Rigorous optimization models were established for identifying the worst and best scenarios considering the contingencies of generators and transmission lines together with their uncertain loads. It is very useful to identify the worst N-k contingencies with interval loads. If the worst existing scenario meets security standards, all scenarios must satisfy it. The mathematical model established for finding the worst N-k contingency with interval load is a bi-level optimization model. In this paper, strong duality theory and mathematical linearization were applied to the solution of bi-level optimization. The computational results of standard cases validate the effectiveness of the proposed method and illustrate that generator contingency has more impact on minimum load shedding than transmission line contingency.

Firefly Algorithm in Determining Maximum Load Utilization Point and Its Enhancement through Optimal Placement of FACTS Device

In a Power System, load is the most uncertain and extremely time varying unit. Hence it is important to determine the system’s supreme acceptable loadability limit called maximum loadability point to accommodate the sudden variation of load demand. Nowadays the enhancement of the maximum loadability point is essential to meet the rapid growth of load demand by improvising the system’s load utilization capacity. Flexible AC Transmission system devices (FACTS) with their speed and flexibility will play a key role in enhancing the controllability and power transfer capability of the system. Considering the theme of FACTS devices in the loadability limit enhancement, in this paper maximum loadability limit determination and its enhancement are prepared with the help of swarm intelligence based meta-heuristic Firefly Algorithm(FFA) by finding the optimal loading factor for each load and optimally placing the SVC (Shunt Compensation) and TCSC (Series Compensation) FACTS devices in the system. To illuminate the effectiveness of FACTS devices in the loadability enhancement, the line contingency scenario is also concerned in the study. The study of FACTS based maximum system load utilization acceptability point determination is demonstrated with the help of modified IEEE 30 bus, IEEE 57 Bus and IEEE 118 Bus test systems. The results of FACTS devices involvement in determining the maximum loading point enhance the load utilization point in normal state and also help to overcome the system violation in transmissionline contingency state. Also the firefly algorithm in determining the maximum loadability point provides better search capability with faster convergence rate compared to that of Particle swarm optimization (PSO) and Differential evolution algorithm.

Enhancing Corrected Transient Energy Margin in Electricity Energy Market Operation Using Stochastic Multiobjective Mathematical Programming

In this paper, a new stochastic multiobjective model for the market clearing of the joint energy and reserve auctions is proposed wherein both supply and demand sides can participate as reserves. The proposed framework considers the dynamic security aspect of the power system in the scenario-based market operation. To solve the problem, first, on the basis of lattice Monte Carlo simulation, different scenarios are generated to model the uncertainties of power system (including generating units and transmission line contingencies). Then, the competing objective functions of the proposed framework, including the expected social welfare function and expected corrected transient energy margin (CTEM), are simultaneously optimized for each scenario in the multiobjective framework. To calculate the CTEM, a sensitivity analysis based on the ac power flow formulation is incorporated before solving the optimization problem of the market clearing. The optimization problem is solved considering dc power flow constraints and system reserve requirements. Eventually, the scenarios are aggregated based on the expected value of decision variables to produce the final results of the market clearing framework. The model is applied to the New England test system, and simulation studies are implemented to demonstrate the effectiveness of the proposed method.

Two-stage robust optimization for N-k contingency-constrained unit commitment

This paper proposes a two-stage robust optimization approach to solve the N- k contingency-constrained unit commitment (CCUC) problem. In our approach, both generator and transmission line contingencies are considered. Compared to the traditional approach using a given set of components as candidates for possible failures, our approach considers all possible component failure scenarios. We consider the objectives of minimizing the total generation cost under the worst-case contingency scenario and/or the total pre-contingency cost. We formulate CCUC as a two-stage robust optimization problem and develop a decomposition framework to enable tractable computation. In our framework, the master problem makes unit commitment decisions and the subproblem discovers the worst-case contingency scenarios. By using linearization techniques and duality theory, we transform the subproblem into a mixed-integer linear program (MILP). The most violated inequalities generated from the subproblem are fed back into the master problem during each iteration. Our approach guarantees a globally optimal solution in a finite number of iterations. In reported computational experiments, we test both primal and dual decomposition approaches. Our computational results verify the effectiveness of our proposed approach.

Determination of Reactive Power Compensation Considering Large Disturbances for Power Flow Solvability in the Korean Power System

This paper proposes a methodology using a tool based on the branch-parameter continuation power flow (BCPF) in order to restore the power flow solvability in unsolvable contingencies. A specified contingency from a set of transmission line contingencies is modeled, considering the transient analysis and practice in the Korean power system. This tool traces a solution path that satisfies the power flow equations with respect to the variation of the branch parameter. At a critical point, in which the branch parameter can move on to a maximum value, a sensitivity analysis with a normal vector is performed to identify the most effective compensation. With the sensitivity information, the location of the reactive power compensation is determined and the effectiveness of the sensitivity information is verified to restore the solvability. In the simulation, the proposed framework is then applied to the Korean power system.

Analysis of overload conditions in distance relay under severe system contingencies

Distance relay protection is widely used worldwide for protection scheme on high voltage transmission lines. This protection tends to be prone to load encroachment condition causing possible undesired tripping condition. Investigations of two system disturbances that occurred in TNB’s (Tenaga Nasional Berhad) Grid in the year 2003 and 2005 have clearly revealed the occurrence of load encroachment condition following line overloads after large number of line tripping. Examination of the impedance locus trajectory during the overload events have enabled a distinction be made between power swing and load encroachment phenomena. This paper presents the analysis and findings of the investigation of the load encroachment phenomena of the two events, including other related transmission line contingencies.

An Approach to Optimal Dispatch Scheduling Incorporating Transmission Security Constraints

The introduction of competition in electricity markets emphasizes the importance of sufficient transmission capacities to guarantee effective power transactions. Therefore, for the economic and stable electric power system operation, transmission security constrains should be incorporated into the dispatch scheduling problem. With the intent to solve this problem, we decompose a dispatch scheduling problem into a master problem(MP) and several subproblems(SPs) using Benders decomposition. The MP solves a general optimal power flow(OPF) problem while the SPs inspect the feasibility of OPF solution under respective transmission line contingencies. If a dispatch scheduling solution given by the MP violates transmission security constraints, then additional constraints corresponding to the violations are imposed to the MP. Through this iterative process between the MP and SPs, we derive an optimal dispatch schedule incorporating the post-contingency corrective rescheduling. In addition, we consider interruptible loads as active control variables since the interruptible loads can participate as generators in competitive electricity markets. Numerical examples demonstrate the efficiency of the proposed algorithm.