Cleaning up while Changing Gears: The Role of Battery Design, Fossil Fuel Power Plants, and Vehicle Policy for Reducing Emissions in the Transition to Electric Vehicles

Plug-in electric vehicles (PEVs) can reduce air emissions when charged with clean power, but prior work estimated that in 2010, PEVs produced 2 to 3 times the consequential air emission externalities of gasoline vehicles in PJM (the largest US regional transmission operator, serving 65 million people) due largely to increased generation from coal-fired power plants to charge the vehicles. We investigate how this situation has changed since 2010, where we are now, and what the largest levers are for reducing PEV consequential life cycle emission externalities in the near future. We estimate that PEV emission externalities have dropped by 17% to 18% in PJM as natural gas replaced coal, but they will remain comparable to gasoline vehicle externalities in base case trajectories through at least 2035. Increased wind and solar power capacity is critical to achieving deep decarbonization in the long run, but through 2035 we estimate that it will primarily shift which fossil generators operate on the margin at times when PEVs charge and can even increase consequential PEV charging emissions in the near term. We find that the largest levers for reducing PEV emissions over the next decade are (1) shifting away from nickel-based batteries to lithium iron phosphate, (2) reducing emissions from fossil generators, and (3) revising vehicle fleet emission standards. While our numerical estimates are regionally specific, key findings apply to most power systems today, in which renewable generators typically produce as much output as possible, regardless of the load, while dispatchable fossil fuel generators respond to the changes in load.

A Piezoelectric-Piezoresistive Coupling Electric Field Sensor for Large Dynamic Range AC Electric Field Measurements

We propose a piezoelectric-piezoresistive coupling electric field sensor capable of performing large dynamic range AC electric field measurements. The electric field sensor utilizes direct coupling between piezoelectric (PE) materials and piezoresistive (PR) strain gauges, in conjunction with an external signal conditioning circuit, to measure AC electric fields effectively. We verified the feasibility of the scheme using a finite element simulation, fabricated a prototype of the electric field sensor, and characterized the properties of the prototype. The testing results indicate that the sensor exhibits an ac resolution of 50 V/m and a linear measurable electric field range of 0 to over 200 kV/m, which keeps the linearity at less than 0.94% from 1 Hz to over 5 kHz. Furthermore, the sensor also has advantages, such as a small size and low power consumption. The sensor can enhance the comprehensive observability and measurability of digital power grids.

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid "close-in", and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.

Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways

The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.

AC/DC Fields Demodulation Methods of Resonant Electric Field Microsensor

Electric field microsensors have the advantages of a small size, a low power consumption, of avoiding wear, and of measuring both direct-current (DC) and alternating-current (AC) fields, which are especially suited to applications in power systems. However, previous reports were chiefly concerned with proposing new structures or improving the resolution, and there are no systematic studies on the signal characteristics of the microsensor output and the demodulation methods under different electric fields. In this paper, the use of an improved resonant microsensor with coplanar electrodes, and the signal characteristics under a DC field, power frequency field, and AC/DC hybrid fields were thoroughly analyzed respectively, and matching demodulation methods derived from synchronous detection were proposed. We theoretically obtained that the frequencies of the detectable electric fields should be less than half of the resonant frequency of the microsensor, and that the sensitivities of the microsensor were identical for AC/DC hybrid fields with different frequencies. Experiments were conducted to verify the proposed demodulation methods. Within electric field ranges of 0–667 kV/m, the uncertainties were 2.4% and 1.5% for the most common DC and 50 Hz power frequency fields, respectively. The frequency characteristic test results of the microsensor were in agreement with those of the theoretical analysis in the range of 0–1 kHz.

Design and Assessment Methodology for System Resilience Metrics

By providing objective measures, resilience metrics (RMs) help planners, designers, and decisionmakers to have a grasp of the resilience status of a system. Conceptual frameworks establish a sound basis for RM development. However, a significant challenge that has yet to be addressed is the assessment of the validity of RMs, whether they reflect all abilities of a resilient system, and whether or not they overrate/underrate these abilities. This article covers this gap by introducing a methodology that can show the validity of an RM against its conceptual framework. This methodology combines experimental design methods and statistical analysis techniques that provide an insight into the RM's quality. We also propose a new metric that can be used for general systems. The analysis of the proposed metric using the presented methodology shows that this metric is a better indicator of the system's abilities compared to the existing metrics.

A General Framework for the Assessment of Power System Vulnerability to Malicious Attacks

The protection and safe operations of power systems heavily rely on the identification of the causes of damage and service disruption. This article presents a general framework for the assessment of power system vulnerability to malicious attacks. The concept of susceptibility to an attack is employed to quantitatively evaluate the degree of exposure of the system and its components to intentional offensive actions. A scenario with two agents having opposing objectives is proposed, i.e., a defender having multiple alternatives of protection strategies for system elements, and an attacker having multiple alternatives of attack strategies against different combinations of system elements. The defender aims to minimize the system susceptibility to the attack, subject to budget constraints; on the other hand, the attacker aims to maximize the susceptibility. The problem is defined as a zero-sum game between the defender and the attacker. The assumption that the interests of the attacker and the defender are opposite makes it irrelevant whether or not the defender shows the strategy he/she will use. Thus, the approaches "leader-follower game" or "simultaneous game" do not provide differences as far as the results are concerned. The results show an example of such a situation, and the von Neumann theorem is applied to find the (mixed) equilibrium strategies of the attacker and of the defender.

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems

In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.

Probabilistic stability analysis: the way forward for stability analysis of sustainable power systems

Future power systems will be significantly different compared with their present states. They will be characterized by an unprecedented mix of a wide range of electricity generation and transmission technologies, as well as responsive and highly flexible demand and storage devices with significant temporal and spatial uncertainty. The importance of probabilistic approaches towards power system stability analysis, as a subsection of power system studies routinely carried out by power system operators, has been highlighted in previous research. However, it may not be feasible (or even possible) to accurately model all of the uncertainties that exist within a power system. This paper describes for the first time an integral approach to probabilistic stability analysis of power systems, including small and large angular stability and frequency stability. It provides guidance for handling uncertainties in power system stability studies and some illustrative examples of the most recent results of probabilistic stability analysis of uncertain power systems.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

Data-driven modeling of solar-powered urban microgrids

Distributed generation takes center stage in today's rapidly changing energy landscape. Particularly, locally matching demand and generation in the form of microgrids is becoming a promising alternative to the central distribution paradigm. Infrastructure networks have long been a major focus of complex networks research with their spatial considerations. We present a systemic study of solar-powered microgrids in the urban context, obeying real hourly consumption patterns and spatial constraints of the city. We propose a microgrid model and study its citywide implementation, identifying the self-sufficiency and temporal properties of microgrids. Using a simple optimization scheme, we find microgrid configurations that result in increased resilience under cost constraints. We characterize load-related failures solving power flows in the networks, and we show the robustness behavior of urban microgrids with respect to optimization using percolation methods. Our findings hint at the existence of an optimal balance between cost and robustness in urban microgrids.

A real-time smart sensor for high-resolution frequency estimation in power systems

Power quality monitoring is a theme in vogue and accurate frequency measurement of the power line is a major issue. This problem is particularly relevant for power generating systems since the generated signal must comply with restrictive standards. The novelty of this work is the development of a smart sensor for real-time high-resolution frequency measurement in accordance with international standards for power quality monitoring. The proposed smart sensor utilizes commercially available current clamp, hall-effect sensor or resistor as primary sensor. The signal processing is carried out through the chirp z-transform. Simulations and experimental results show the efficiency of the proposed smart sensor.