Pierre Henneaux

A level-1 probabilistic risk assessment to blackout hazard in transmission power systems

The blackout risk in power systems is difficult to estimate by actual probabilistic methods because they usually neglect, or do not properly consider, the dependencies between failures and the dynamic evolution of the grid in the course of a transient. Our purpose is therefore to develop an integrated probabilistic approach to blackout analysis, capable of handling the coupling between events in cascading failure, and the dynamic response of the grid to stochastic initiating perturbations. This approach is adapted from dynamic reliability methodologies. This paper focuses on the modeling adopted for the first phase of a blackout, ruled by thermal transients. The goal is to identify dangerous cascading scenarios and better calculate their frequency. A Monte Carlo code specifically developed for this purpose is validated on a test grid. Some dangerous scenarios are presented and their frequency calculated by this method is compared with a more classical estimation neglecting thermal effects, showing significant differences. In particular, our method can reveal dangerous scenarios neglected or underestimated by the more classical method because they do not take into account the increase of failure rates in stress conditions.

Blackout Probabilistic Risk Assessment and Thermal Effects: Impacts of Changes in Generation

Renewable energy integration and deregulation imply that the electric grid will be operated near its limits in the future, and that the variability of cross-border flows will increase. Therefore, it is becoming more and more crucial to study the impact of these changes on the risk of cascading failures leading to blackout. We propose in this paper to emphasize important factors leading to blackouts, to review methodologies which were developed to simulate cascading failure mechanisms and to study specifically the impact of thermal effects on the risk of blackout for several changes in generation (variations in cross-border flows, wind farms penetration, shut-down of power plants). This is studied by applying to a test system the first level of a dynamic probabilistic blackout risk assessment developed previously. We show that taking into account thermal effects in cascading failures is important not only to have a good estimation of the risk of blackout in different grid configurations, but also to determine if a specific change in generation has a positive or a negative impact on the blackout risk.

Probabilistic Security Analysis of Optimal Transmission Switching

Optimal transmission switching (OTS) optimizes simultaneously the generation dispatch and the topology of a power system. It has been shown that taking some transmission lines out of service can significantly reduce the operating cost of the system while respecting the traditional deterministic N-1 security criterion of operational reliability. However, topology modifications could adversely affect probabilistic security metrics. The operational reliability of a power system can be translated into a cost by multiplying the expected energy not served by the value of lost load. This paper therefore explores whether it is possible to maintain a positive economic balance with OTS when considering not only the cost of generation but also the expected socio-economic cost of disruptions in the supply. This is done in two steps: the computation of an N-1 secure OTS and then the calculation of a probabilistic estimate of the operational reliability of the OTS solution. Based on the results obtained with two test systems, it is shown that OTS tends to significantly degrade probabilistic measures of security. It is thus not obvious that OTS can lead to a positive economic balance, even when N-1 security is enforced. Consequently, a probabilistic security analysis should be performed before implementing an OTS solution.

Benchmarking and Validation of Cascading Failure Analysis Tools

Cascading failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on cascading failure reviews and synthesizes how benchmarking and validation can be done for cascading failure analysis, summarizes and reviews the cascading test cases that are available to the international community, and makes recommendations for improving the state of the art.

A Two-Level Probabilistic Risk Assessment of Cascading Outages

Cascading outages in power systems can lead to major power disruptions and blackouts and involve a large number of different mechanisms. The typical development of a cascading outage can be split in two phases with different dominant cascading mechanisms. As a power system is usually operated in N-1 security, an initiating contingency cannot entail a fast collapse of the grid. However, it can trigger a thermal transient, increasing significantly the likelihood of additional contingencies, in a “slow cascade.” The loss of additional elements can then trigger an electrical instability. This is the origin of the subsequent “fast cascade,” where a rapid succession of events can lead to a major power disruption. Several models of probabilistic simulations exist, but they tend to focus either on the slow cascade or on the fast cascade, according to mechanisms considered, and rarely on both. We propose in this paper a decomposition of the analysis in two levels, able to combine probabilistic simulations for the slow and the fast cascades. These two levels correspond to these two typical phases of a cascading outage. Models are developed for each of these phases. A simplification of the overall methodology is applied to two test systems to illustrate the concept.

Optimizing Primary Response in Preventive Security-Constrained Optimal Power Flow

Preventive security-constrained optimal power flow (PSCOPF) dispatches controllable generators at minimum cost while ensuring that the system adheres to all operating constraints. All the transmission and generation limits are respected during both the pre- and post-contingency states without relying on post-contingency redispatch. Therefore, all credible generation contingencies should be modeled in PSCOPF and the system-wide automatic primary response should be allocated accordingly among synchronized generators by adjusting their droop coefficients. This paper proposes a new PSCOPF model that optimizes the droop coefficients of the synchronized generators. The cost savings attained with the proposed approach and its computational performance are evaluated. Different wind penetration levels and reserve policies are tested using annual simulations on the one- and three-area IEEE Reliability Test System.

Towards a 3-level blackout probabilistic risk assessment: Achievements and challenges

Although blackouts are infrequent, they result in major societal and economical negative consequences. These massive disruptions to electricity service are due to cascading outages, in which a lot of different phenomena occur. Even if a variety of methods are emerging to study cascading outages, it remains difficult to estimate the risk of blackout for a real grid and dangerous scenarios. Based on the analysis of past blackouts, a 3-level blackout probabilistic risk assessment can be developed in order to consider the main phenomena occurring in a cascading failure in a realistic way. But such an approach encounters several challenges and difficulties. The aim of this paper is to present such an approach, to assess present achievements and to discuss ways of solving future challenges.

Role of thermal effects in blackout probabilistic risk assessment

The blackout risk in power systems is difficult to estimate by actual probabilistic methods because they usually neglect, or do not properly consider, the dependencies between failures and the dynamic evolution of the grid in the course of a transient. Our purpose is therefore to develop an integrated probabilistic approach to blackout analysis, capable of handling the coupling between events in cascading failure, and the dynamic response of the grid to stochastic initiating perturbations. This approach is adapted from dynamic reliability methodologies. This paper focuses on the modeling adopted for the first phase of a blackout, ruled by thermal transients. The goal is to identify underestimated dangerous cascading scenarios and better calculate their frequency. A Monte Carlo code specifically developed for this purpose is validated on a test grid. Some dangerous scenarios are presented and their frequency calculated by this method is compared with a classical estimation, showing important differences.

Improving Monte Carlo simulation efficiency of level-I blackout probabilistic risk assessment

Blackouts in power systems are due to cascading failures whose typical development can be split in two phases: a slow cascade and a fast cascade. Once a blackout occurred, the restoration is as an additional (and last) phase. The blackout Probabilistic Risk Assessment (PRA) can be decomposed in three levels, according to three phases. An analog Monte Carlo simulation has been developed for the level-I, in order to simulate independent and thermal failures during the slow cascade. The main limitation of such an analog simulation is the small fraction of runs leading to interesting consequences. The aim of this paper is then to propose biasing techniques in order to improve the blackout PRA level-I Monte Carlo simulation efficiency. Two methods are explored: favoring failures during the cascade by forcing them to occur before a time limit and favoring thermal failures by biasing weather conditions sampling. Results obtained on a test case show that a significant gain can be reached.

Confidence intervals for adequacy assessment using Monte Carlo sequential simulation

The most common way for probabilistic adequacy assessments of composite generation/transmission systems is based on Monte Carlo (MC) simulation. This simulation can be either non-sequential or sequential. Non-sequential MC simulation has a better efficiency, but sequential MC simulation is required to consider time-related phenomena, such as storage systems, or to compute interruption frequency and duration indices. A large number of independent 1-year sequential MC simulations are usually run to estimate confidence intervals based on classical formula for independent and identically distributed random variables. For real-size systems, this technique is hardly applicable due to the computing time needed to have a good estimation of the sample variance. The aim of this paper is to propose alternative techniques to estimate confidence intervals of reliability indices given by a single 1-year sequential MC simulation. These techniques are applied to two systems, the Reliability Test System and a model of the United Kingdom transmission network, and are compared to the usual approach.