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This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

Recent climate changes have created intense natural disasters, such as windstorms, which can cause significant damages to power grids. System resilience is defined as the ability of the system to withstand such high-impact low-probability events. This paper proposes a robust resilient operational schedule for active distribution networks against windstorms. In order to capture dynamic behaviors of these disasters, zonal disaster-specific uncertainty sets associated with the windstorm are proposed. Additionally, the unavailability uncertainties of N-K contingencies as well as the forecast uncertainties of load demand, wind power, and solar power are taken into account. Instead of committing micro-turbines and energy storage systems in the first stage (here-and-now) of the decision-making process, the proposed model considers these commitment decisions in the second stage (wait-and-see) of the decision-making process, which is more consistent with the fast response time of these units. Since the second stage of the proposed model has binary decision variables, recent KKT-based and duality-based methods are not applicable. Therefore, a new solution method based on block coordinate descent (BCD) and line search (LS) techniques is proposed to solve the bi-level problem. Eventually, IEEE 33-bus distribution test system is used to illustrate the effectiveness of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix. [ABSTRACT FROM AUTHOR]

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events. For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints. An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective. The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method. The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events. [ABSTRACT FROM AUTHOR]

The paper analyzed time series of fault in four transmission and distribution systems for evidence of self-organized criticality (SOC). The detrended fluctuation analysis (DFA) of the time series shows prominent long-time correlations. Furthermore, the probability distribution of number of faults per day has a power law tail. Thus, the time series of power system fault seem consistent with SOC. Detailed analysis showed that time series of fault in transmission systems derives its SOC from cascading outage within power system and SOC of atmosphere system, while the time series of fault in distribution systems derive its SOC from that of atmosphere system. The roles that cascade outage play in SOC of the time series of fault in distribution systems remain unknown. Since extreme climate will be more intense and frequent with climate variation, the skewed distribution of power systems fault will be intensified as a consequence. Potential application using SOC feature of time series of fault can be developed to facilitate power systems adaptation to climate change in an economical way. [ABSTRACT FROM PUBLISHER]

This paper presents a multidisciplinary modeling approach to quickly quantify climate change impacts on energy consumption, peak load, and load composition of residential and commercial buildings. This research focuses on addressing the impact of temperature changes on the building cooling load in ten major cities across the Western United States and Canada. Our results have shown that by the mid-century, building yearly energy consumption and peak load will increase in the Southwest. Moreover, the peak load months will spread out to not only the summer months but also spring and autumn months. The Pacific Northwest will experience more hot days in the summer months. The penetration levels of air-conditioning (a/c) systems in this region are likely to increase significantly over the years. As a result, some locations in the Pacific Northwest may be shifted from winter peaking to summer peaking. Overall, the Western U.S. grid may see more simultaneous peaks across the North and South in summer months. Increased cooling load will result in a significant increase in the motor load, which consumes more reactive power and requires stronger voltage support from the grid. This study suggests an increasing need for the industry to implement new technology to increase the efficiency of temperature-sensitive loads and apply proper protection and control to prevent possible adverse impacts of a/c motor loads. [ABSTRACT FROM AUTHOR]

The rise in temperatures induced by climate change may have important implications for Thailand's electricity demand. This paper investigates how changing climate will affect Thailand's daily, seasonal, and long-term electricity demand. Regression models are applied to capture daily load patterns across each month in the year. Temperature projections from the UK Hadley Centre climate model are then used to assess hourly sensitivity to changes in mean temperatures and diurnal temperature range. These are combined with four representative socioeconomic scenarios from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios to project absolute changes in Thailand's electricity demand. The specific climate and socioeconomic scenarios considered here indicate that mean annual temperatures in Thailand will rise by 1.74 to 3.43° C by 2080, implying increases in Thai peak electricity demand of 1.5%-3.1% in the 2020s, 3.7%-8.3% in the 2050s, and 6.6%-15.3% in the 2080s. [ABSTRACT FROM AUTHOR]

National energy models produce aggregate scenarios of generation capacity, energy output, and emissions. However, we need finer scales to study the impact of resource use and air pollution because timing and location determine impacts on sensitive ecosystems and human populations. We present a framework for disaggregating emissions projections to a scale compatible with air quality simulation models. The framework comprises three models that site new power plants consistent with historical patterns while recognizing water, transmission, fuel, and other factors that constrain siting, and then dispatches them consistent with those constraints. The resulting hourly emissions from individual plants are consistent with meteorology, in that peak demands and emissions occur during those hours when temperatures associated with such demands occur. Further, annual emissions vary in a way consistent with year-to-year changes in weather. An application of the framework disaggregates 2030 NOx emissions from a national electricity model in an eight-state region under two climate scenarios: no climate change ("1990s") and accelerated change ("2050s"). Between-year variations in emissions patterns under a particular climate exceed differences between average patterns of the two scenarios. This is in part because NOx emissions are capped; thus, the total cannot change, only its distribution over time and space. [ABSTRACT FROM AUTHOR]