Article
Energy & Fuels
Chao (Chris) Qin, Eric Loth
Summary: The study proposes a concept that leverages underground reservoirs of abandoned oil or gas wells to enhance the dispatchability of wind farms and reduce overall costs. This approach can not only lower the cost of generator sizing, but also increase energy production and decrease storage system costs, while attracting more infrastructure investments.
Article
Energy & Fuels
Pranda Prasanta Gupta, Vaiju Kalkhambkar, Prerna Jain, Kailash Chand Sharma, Rohit Bhakar
Summary: This paper proposes a method of using BES Train to solve transmission congestion and curtailment issues in solar power. By establishing a stochastic security-constrained unit commitment model with BES Train, the uncertainties of solar power and critical line contingencies are taken into account. Simulation results show that BES Train can effectively reduce operation costs, relieve congestion, and curtail solar power.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Xinjing Zhang, Chao(Chris) Qin, Yujie Xu, Wen Li, Xuezhi Zhou, Ruimin Li, Ye Huang, Haisheng Chen
Summary: A small-scale CAES system utilizing scroll machines was developed and integrated into a wind generation system. Simulation analysis revealed that the integration of distributed renewables with an energy storage system can enhance flexibility and meet fluctuating power demand. The energy utilization efficiency of the system reached as high as 88.75% in a typical day, with a 15% reduction in maximal installation capacity.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Green & Sustainable Science & Technology
Tong Wu, Ying-Jun Angela Zhang, Shuoyao Wang
Summary: This paper proposes a new model for scenario-based security-constrained unit commitment (SCUC) with BESSs and solves it using a mixed-integer programming and convolutional neural network algorithm. The algorithm eliminates the need for explicitly considering the scenario-based security constraints, greatly reducing computational complexity and achieving promising results.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Thermodynamics
Dogan Erdemir, Ibrahim Dincer
Summary: The present study describes the development and performance assessment of a novel hydrogen storage system that integrates compressed air storage to maintain the desired storage pressure. The system utilizes a two-chamber storage system, where air and hydrogen are stored in separate chambers, and operates in a synchronized manner to compress air while expanding hydrogen. The study also incorporates an ammonia-fueled Brayton cycle to maximize the energy efficiency of the system. The results show promising energy and exergy efficiencies for the charging and discharging periods, with the overall system efficiencies calculated for a specific charging and discharging duration.
APPLIED THERMAL ENGINEERING
(2023)
Article
Mathematics
Georgios E. E. Arnaoutakis, Gudrun Kocher-Oberlehner, Dimitris Al Katsaprakakis
Summary: The utilization of solar and wind energy is increasing worldwide, and micro-compressed air energy storage (micro-CAES) is considered as a low-cost storage option that can provide demand shifting when coupled with photovoltaics and wind turbines. A model based on preset criteria is presented to determine the distribution of generated energy. Comparisons between micro-CAES and storage batteries show that micro-CAES has higher annual stored energy by 114 kWh.
Article
Energy & Fuels
Houshang Abiari, Tahere Daemi, Seyyed Amin Saeid
Summary: By increasing the penetration of low-inertia renewable energy systems in the power systems, the grid frequency stability becomes even more challenging. This paper proposes a frequency constrained unit commitment model considering the uncertain primary frequency response of power electronic converters-based energy storage systems. The proposed method prevents frequency instability by spending 6.38% more cost than the model with perfect operation of converters.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Julian David Hunt, Behnam Zakeri, Andreas Nascimento, Jonas Rafael Gazoli, Fabio Tales Bindemann, Yoshihide Wada, Bas van Ruijven, Keywan Riahi
Summary: This paper presents a novel isothermal compressed air energy storage (CAES) system that uses two floating storage vessels in the deep ocean to balance the pressure with the oceanic pressure. The system has a maximum compression ratio of four, significantly increasing efficiency and reducing costs. The estimated cost for electric energy storage is between 10 and 50 USD/kWh, and for the installed power capacity is between 800 and 1500 USD/kW. It provides an alternative option for long-term energy storage in islands and coastal regions.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Electrical & Electronic
Jianqiang Luo, Fei Teng, Siqi Bu, Zhongda Chu, Ning Tong, Anbo Meng, Ling Yang, Xiaolin Wang
Summary: This paper proposes a solution to address the traditional operational constraint issue in power systems considering converter-driven stability and elaborates on a power sensitivity-based power dispatch method to enhance stability margin and update operational constraint solutions.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Yonghong Chen, Feng Pan, Feng Qiu, Alinson S. Xavier, Tongxin Zheng, Muhammad Marwali, Bernard Knueven, Yongpei Guan, Peter B. Luh, Lei Wu, Bing Yan, Mikhail A. Bragin, Haiwang Zhong, Anthony Giacomoni, Ross Baldick, Boris Gisin, Qun Gu, Russ Philbrick, Fangxing Li
Summary: This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established to review and analyze the current state of the security-constrained unit commitment (SCUC) business model and its solution techniques in electricity market clearing problems. It also investigates future challenges in market clearing problems and presents efforts in developing benchmark models.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Thermodynamics
Geraldo Lucio Tiago Filho, German Andres Lozano Vela, Luciano Jose da Silva, Maisa Tonon Bitti Perazzini, Estefania Fernandes dos Santos, Davi Febba
Summary: The study examined the technical and economic feasibility of connecting a Compressed Air Energy Storage (CAES) system to a wind farm in the state of Minas Gerais, Brazil, but found it difficult to locate suitable abandoned mines for storing compressed air and windy locations for energy generation. Analysis of geological characteristics, wind flow areas, thermodynamic calculations, and economic factors revealed that the project was ultimately not economically viable and would require policy support to scale up.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Energy & Fuels
Farayi Musharavati, Shoaib Khanmohammadi, Mohammad Rahmani, Saber Khanmohammadi
Summary: The study involved thermodynamic modeling and exergy analysis of a compressed air energy storage system boosted with thermoelectric generator (CAES/TEG). By adding thermoelectric modules to the CAES system, the net output power of the system was increased, indicating the potential of improving the performance of the system.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Zisheng Lu
Summary: Since the industrial revolution, carbon dioxide has been emitted into the atmosphere through the burning of coal, oil, and natural gas. The use of renewable energy should be greatly increased, from the current 26% to 86% by 2050. However, solar energy and wind energy face challenges such as intermittency and instability. This study proposes the use of compressed air for energy storage and demonstrates that the power capacity increases with turbine inlet gas pressure, while the overall efficiency decreases. The highest total efficiency achieved is approximately 0.519, with a peak power generation capacity of 663.6 W in the compressed air system.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Stanislaw Salyga, Lukasz Szablowski, Krzysztof Badyda
Summary: The article introduces three constant volume CAES systems and builds dynamic mathematical models using Aspen HYSYS software. Among them, the adiabatic compressed air energy storage system with liquid thermal energy storage achieved a round trip efficiency of 64.8%.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Marcus King, Anjali Jain, Rohit Bhakar, Jyotirmay Mathur, Jihong Wang
Summary: Compressed air energy storage (CAES) is a technology that can aid in achieving decarbonisation goals for electrical power systems, requiring locations with suitable geological features for development. In India, approximately 1.05% of land is deemed suitable for CAES plant development, but due to the presence of other competing energy storage technologies, the actual development potential is relatively low.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Electrical & Electronic
Subir Majumder, Niloy Patari, Anurag K. Srivastava, Priyank Srivastava, Anuradha M. Annaswamy
Summary: Despite the recent development of control approaches for distributed energy resources (DERs), oversights in cognitive aspects simplify the cyber-physical power system in controller development. By identifying the limitations of classical controller definitions, alternative definitions of voltage control approaches classifiers are justified to enable real-world comparative performance analysis and deployability. The combination of classical and domain-based definitions can better classify control algorithms.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Chuan Qin, Surendra Bajagain, Sanjeev Pannala, Anurag K. Srivastava, Anamika Dubey
Summary: This paper proposes an integrated real-time model update (RTMU) module for enhancing situational awareness in distribution systems. The module includes estimators for BTM PV power, network topology, and cold load pickup, leveraging multiple data resources, deep learning approaches, and domain knowledge. The proposed RTMU enhances situational awareness under normal conditions and during power outages.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Article
Engineering, Electrical & Electronic
Xin Lin, Ramon Zamora, Craig A. Baguley, Anurag K. Srivastava
Summary: This article proposes a hybrid method (HM) consisting of an ensemble model (EM), deep ensemble model (DEM), and thermal dynamic model expressed by resistance-capacitance (RC). The EM includes three predictors of support vector machine (SVM), back propagation neural network (BPNN), and generalized regression neural network (GRNN), optimized by genetic algorithm (GA). The DEM includes multiple bi-directional long-short term memory (Bi-LSTM) networks, optimized by Bayesian algorithm (BA). The presented model provides accurate short-term individual load forecasting, as demonstrated by comparison with existing models.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Article
Computer Science, Information Systems
Syed Muhammad Hur Rizvi, Anurag K. K. Srivastava
Summary: Electric distribution systems are undergoing a significant upgrade due to the increased penetration of distributed energy resources (DERs), which affects the analysis of transmission system operation. Traditionally, the voltage stability margin of the transmission system is determined through a continuation power flow (CPF) analysis. However, this traditional CPF method neglects the active management of distribution networks and the presence of distributed energy resources. This paper proposes two integrated T & D CPF methods to address these issues and demonstrates their effectiveness through co-simulation of large-scale T & D systems.
Article
Engineering, Electrical & Electronic
N. Patari, A. K. Srivastava, N. Li
Summary: This paper presents a new online distributed voltage control algorithm for three-phase unbalanced distribution systems. The algorithm regulates the active and reactive power of voltage control agents considering the operational limits of power converters. Simulation results and convergence analysis demonstrate the robustness and superior performance of the proposed algorithm.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Amirkhosro Vosughi, Sanjeev Pannala, Anurag K. K. Srivastava
Summary: This study develops algorithms for event detection, classification, and localization in an active distribution network. The proposed techniques consider system dynamics and achieve enhanced accuracy compared to existing data-driven techniques. Real-time testbed and field system data are used for evaluation and validation.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Computer Science, Information Systems
Partha S. Sarker, Sajan K. Sadanandan, Anurag K. Srivastava
Summary: This work focuses on developing metrics for monitoring the resiliency of the cyber-power distribution system while preserving consumers' privacy. The developed metrics integrate factors affecting resiliency using neural networks and fuzzy multiple-criteria decision making. The resiliency of the distributed system is computed using game-theoretic data envelopment analysis-based optimization. These metrics are valuable for monitoring and selecting the best possible mitigation strategies in the distribution system.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Chuan Qin, Anurag K. Srivastava, Ahmed Y. Saber, Dax Matthews, Kevin Davies
Summary: As building-level PV panels and other inverter-based resources (IBR) are increasingly being used, market retailers and distribution operators face challenges in managing the unobserved energy flow and predicting the net load due to the intermittent and stochastic nature of IBR-based solar power. This article proposes a deep-learning-based algorithm that uses a limited number of sensors to forecast behind-the-meter (BTM) PV power generation. The algorithm utilizes geometric deep learning and spatiotemporal graph neural networks to analyze the data in a non-Euclidean graph structure, resulting in improved load forecasting accuracy.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Matteo Menazzi, Chuan Qin, Anurag K. Srivastava
Summary: This article presents a pioneering approach that utilizes DERs and loads to enhance the restoration capability of distribution grids. The proposed network equivalent model allows real-time management and restoration of the system. By developing a data-driven load model and an optimal restoration strategy, the reliability and efficiency of the system can be improved.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Srayashi Konar, Anurag K. Srivastava, Anamika Dubey
Summary: Autonomous service restoration (ASR) of active distribution network (ADN) reduces service restoration time by utilizing measurement devices, smart switches, and distributed energy resources (DERs). This can be achieved through centralized or distributed deployment, but with increasing data points, centralized optimization becomes challenging and distributed optimization provides robustness and scalability. This work presents a penalty-driven distributed alternating direction method of multipliers algorithm (PD-ADMM) to solve the distributed ASR problem, along with a switch level decomposition and coordination technique that considers load restoration, DER utilization, and radiality of ADN using a commodity flow model.
IEEE TRANSACTIONS ON POWER DELIVERY
(2023)
Article
Engineering, Electrical & Electronic
Arman Ahmed, Sajan K. Sadanandan, Shikhar Pandey, Sagnik Basumallik, Anurag K. Srivastava, Yinghui Wu
Summary: This research develops a method for event analysis using Phasor Measurement Units (PMUs) measurement data. The method utilizes the spatial and temporal features of PMU measurement data for event detection, localization, and classification, employing an unsupervised deep learning model and a classification scoring algorithm based on physics informed rules. Experimental results demonstrate that the proposed method outperforms other existing techniques for event analysis.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
P. Banerjee, S. C. Srivastava, Anurag K. Srivastava
Summary: This work presents a method for accurately estimating phasors for M class phasor measurement units (PMUs). The method involves filtering the aliased signal by performing model order estimation for different data windows and sampling frequencies. The estimated model order is then used to estimate the fundamental phasor and the derivatives of amplitude and phase using the TLS-ESPRIT method. The performance of the estimation is evaluated in the presence of noise and compared with previous work in the literature.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Srayashi Konar, Anurag K. Srivastava
Summary: This work proposes a novel model predictive control (MPC) based efficient BS & RS strategy for reconfigurable distribution grid with DERs. The proposed algorithm coordinates smart switches, black start DERs (BS-DERs), and energy storage systems (ESSs) to generate optimal switching sequence and solves the non-convexity problem of sequential service restoration with a mixed integer linear programming approach. The proposed scheme also incorporates errors associated with forecasting, DER capacity, and load demand.
Article
Computer Science, Information Systems
Subir Majumder, Amirkhosro Vosughi, Hussain M. Mustafa, Tori E. Warner, Anurag K. Srivastava
Summary: With the increase in distributed energy resources and usage of ICT in decision-making, there is a need for new mechanisms and metrics to control/optimize grid voltage. This paper discusses the classification and performance evaluation of control algorithms for inverter-based DERs, as well as the communication requirements and deployment challenges in the presence of cyber vulnerabilities. It also identifies the modeling requirements for real-world deployment and proposes the development of a unified test-bed.
Article
Automation & Control Systems
Satabdy Jena, Narayana Prasad Padhy, Anurag K. Srivastava
Summary: This article investigates the effects of cyber attacks and proposes a comprehensive detection and mitigation strategy for protecting the feasible operation region of global economic dispatch in interconnected DC MG clusters. Separate defense actions are designed for different nodes in a MG, and a variable averaging gain method is proposed for processing neighbor's information. The results show the efficacy of the proposed algorithm for achieving secure global economic dispatch, prioritizing resiliency over economics by supplying critical loads under adverse events.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
