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There is no doubt that there is an increase in the penetration of electrical energy into the operation of high-speed railway systems (HSR). This is even more pronounced with the increasing trends in smart electric multiple units (EMU). The operational speed serves as a metric for punctuality and safety, as well as a critical element to maintain the balance between energy supply and consumption. The speed-based regenerative energy from EMU’s braking mode could be utilized in the restoration of system operation in the aftermath of a failure. This paper optimizes the system resiliency with respect to the operational speed for the purpose of restoration by minimizing the total cost of implementing recovery measures. By simultaneously valuating the dual-impact of any given fault on the speed deterioration level from the railway operation systems (ROS) side and the power supply and demand unbalance level from the railway power systems (RPS) side, this process develops an adaptive two-dimension risk assessment scheme for prioritizing the handling of different operational zones that are cascaded in the system. With the aid of an integrated speed-based resilience cost model, we determine the optimal resilience time, speed modification plan, and energy allocation strategy. The outcome from implementing thismore »routine in a real-world HSR offers a pioneering decision-making strategy and perspective on optimizing the resilience of an integrated system.« less

Failure identification and prediction in a power system are essential components that are prerequisites for optimizing the maintenance of the system. The incidences of power system failures have increased dramatically in recent times due to the uncertainties inherent in the advent of both man-made and natural disasters. This problem is further exacerbated due to the increasing demand for higher operational efficiency in power systems. Currently, there is a paucity of studies that predict and identify failure in a distribution power system. In this paper, we propose an integrated methodology for selecting the optimal maintenance plan based on predicting and identifying failure modes with the aid of Hidden Markov Models (HMM) and a probabilistic decision-making tool. While the model parameters of previous studies were determined utilizing observable prior knowledge, the use of HMM offers a different approach especially in the absence of such observable prior distributions. Thus, we determine the status of health of a power system by using an HMM to capture the relationship between unobservable degradation state and observed parameters. The preliminary outcome is instructive for the management of power systems especially in response to fortifying the system against aging and degradation.

The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of themore »suggested assessment techniques.« less

This paper presents a survey of the literature on the strategies to enhance the resilience of power systems while shedding lights on the research gaps. Using a deductive methodology on the literature covering the resilience of power systems, we reviewed more than two hundred peer-reviewed articles spanning the 2010–2019 decade. We find that there is vacuum on the level of integration that considers the interdependence of local or decentralized decision making in an adaptive power system. This gap is widened by the absence of policies to enhance resilience in power networks. While there is significant coverage and convergence of research on algorithms for solving the multi-objective problem in optimization routines, there are still uncharted territories on how to incorporate system degradation while designing these self-restoration systems. We posit that a shift to a smarter, cleaner and more resilient power network requires sustained investments rather than disaster-induced responses.

Purpose of Review This paper focuses on the advances in the resilience of electricity systems and energy markets. The objective is to identify how the progress on system resilience may influence market rules while uncovering the gaps in the literature. Recent Findings This review distills three findings. First, significant advances have been achieved both in the design and configuration of power systems for resilience. Second, topological and architectural advances appear isolated from market operations. Third, there is room to integrate self-healing resilience into power systems and bridge the bifurcation between increasing network resilience and having the market adequately value resilience. Summary Evidently, the incidences of disruptions to electricity networks are on the rise, making a change from having a merely reliable electricity network to one that is resilient and adaptive a necessity. This review showcases the qualitative value inherent in processes to enhance adaptive resilience while promoting the requisite signals for power market integration.

This article investigates the relationship between firms’ carbon intensity, carbon management practices, and their financial performance. The extant literature on firms’ financial performance and their environmental performance has mostly considered a single dimension of firms’ environmental performance leading to restricted, and often, mixed outcomes. With panel data collected on financial statements and climate change related activities from 136 corporate firms in the U.S. between 2011 and 2018, this article integrates a process dimension based on an environmental management score with an outcome dimension represented by firms’ carbon emissions intensity. A regression model is employed to investigate the relationships between corporate environmental performance and corporate financial performance. We find evidence of a nonlinear relationship between corporate firms’ environmental performance and financial performance across both high and low-carbon intensive sectors. Specifically, we find that for firms in the high-carbon intensive sector, a U-shaped relationship exists between firms’ corporate environmental performance outcome dimension and their financial performance while for the low-carbon intensive sector, the converse is the case. The results show that the interaction between the outcome dimension of environmental performance and financial performance is moderated by the process dimension of environmental performance for firms in the low- and high-carbon intensive sectors.

The growth of renewable energy technologies creates significant challenges for the stability of the system because of their intermittency. Nonetheless, we can value these technologies with storage systems. We model the supply by a renewable technology, wind, into a storage facility using the leaky bucket mechanism. The bucket is synonymous with storage while the leakage is equivalent to meeting load. Modelica is used to capture: (i) the time-dependence of the state of the bucket based on a physical model of storage; (ii) the stochastic representation of wind energy using wind speed data that is fed into a physical model of a wind technology; and (iii) the load, modeled as a resistor-inductor circuit. The strength of Modelica in using non-causal equations for basic sub-systems that are linked together is harnessed through its libraries. We find that there is a diminishing return to storage. Beyond a certain level of storage, the integration of a reliable baseload power supply is required to diminish the risk due to reduced reliability. The need for storage systems as a hedge against intermittency is dependent on the interplay between the supply volatilities and the stochastic load to guarantee an acceptable level of quality of service and reliability.