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The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by ≤33.3% with a 1.5 times increase in the load demand and by ≤50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by ≤80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs. [ABSTRACT FROM AUTHOR]

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs. [ABSTRACT FROM AUTHOR]

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks. [ABSTRACT FROM AUTHOR]

Monitoring and control of electrical power grids are highly reliant on the accuracy of the digital measurements. These digital measurements reflect the precision of the installed sensors, which are vulnerable to the injection of unknown parameters in the form of device malfunction and cyberattacks. This may question the operational security and reliability of many cyberphysical infrastructure such as smart grid. To resolve this issue, a multisensor temporal prediction based wide-area control scheme is proposed in this paper. The feasibility of the designed scheme is verified in an advanced synchrophasor measurements based wide-area monitoring and control system (WAMCS). This WAMCS adopts a flexible ac transmission system device (the primary controller) for controlling the smart grid's voltage profile. The algorithm is validated in a real-time environment with an innovative software-in-the-loop testing setup. The performance of the proposed technique in the presence of false-data-injection attacks shows promising results. [ABSTRACT FROM AUTHOR]

This paper addresses the time-delay effects of the wide-area monitoring and control systems (WAMCS) in smart power grids which may critically impact system stability. The main purpose is to conduct a detailed delay analysis of the WAMCS in case of grid contingences. This analysis is performed via an advanced WAMCS testbed where a flexible ac transmission system (FACTS) device is utilized and controlled via a wide-area controller (WAC). Phasor measurements units (PMUs) are adopted to collect the real-time measurements for the WAC. The testbed results from an interface of four main segments known as the WAC; the actual FACTS device, the local area controller, and the power grid system along with the PMUs are simulated via a real-time digital simulator. To mimic the real case scenario, both hardware-in-the-loop and software-in-the-loop schemes are adopted in the experimental testbed, considering time-delay effects. The results obtained clarify the effect of delay in WAMCS in case of smart grid contingences. [ABSTRACT FROM PUBLISHER]

This study presents a novel interfacing setup for testing and validating wide‐area monitoring and control (WAMC) techniques used in smart grids. The main purpose of this study is to provide a realistic approach for conducting WAMC studies. In here, a wide‐area controller (WAC) for a flexible AC transmission system (FACTS) device is implemented. The measurements for the WAC are collected using phasor measurements units (PMUs). Three main segments are being interfaced in this study. First, the real‐power grid, the local area controller of the actual FACTS device, and the PMUs are simulated using real‐time digital simulator. This simulation represents the central simulation and is interfaced with the WAC, which is realised using a MATLAB‐based program. This interface represents a novel software‐in‐the‐loop (SIL) scheme. On the other hand, an actual FACTS device is designed and interfaced to the central simulation via hardware‐in‐the‐loop (HIL) scheme. This SIL and HIL combination makes the experimental testbed more realistic and closer to the industrial standard. Various tests are conducted to examine the performance of the developed testbed. [ABSTRACT FROM AUTHOR]