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Opportunities for quantum computing within net-zero power system optimization

JOULE 2024年第6期8卷 1619-1640页

作者： Morstyn, Thomas Wang, Xiangyue Univ Oxford Dept Engn Sci Oxford England NYU Ctr Data Sci New York NY USA

Optimized power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. The major technological changes associated with net zero, including the rapid adoption of renewables, electrification of transport and heating, and system-wide digitalization, each increase the scope for optimization to create value, but at the cost of greater computational complexity. Although power system optimization problems are now posing challenges for even the largest exa-scale supercomputers, a new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs and is reaching a level of maturity where, for the first time, a practical advantage over classical computing is available for specific applications. In this review, we identify significant and wide-ranging opportunities for quantum computing to offer value for power system optimization. In addition to reviewing the latest work on quantum computing for simulationbased and combinatorial power system optimization applications, we also review state-of-the-art theoretical work on quantum convex optimization and machine learning and map this to power system optimization applications where quantum computing is underexplored. Based on our review, we analyze challenges for industry implementation and scale-up and propose directions for future research.

关键词： dispatch machine learning net zero NISQ optimal power flow planning power system optimization quantum annealing quantum computing unit commitment

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Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power system optimization with FACTS devices

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SCIENTIFIC REPORTS 2025年第1期15卷 1-34页

作者： Agrawal, Sunilkumar P. Jangir, Pradeep Arpita Pandya, Sundaram B. Parmar, Anil Hourani, Ahmad O. Trivedi, Bhargavi Indrajit Khishe, Mohammad Govt Engn Coll Dept Met Engn Gandhinagar 382028 Gujarat India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept Math Dehra Dun India Graph Era Deemed be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Shri KJ Polytech Dept Elect Engn Bharuch 392001 India AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Vishwakarma Govt Engn Coll Ahmadabad Gujarat India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City 320315 Taiwan

In modern power systems, increasing transmission efficiency and responsiveness is necessary to accommodate rising demand and constrained infrastructure development. In this study, the Adaptive Randomized Sine Cosine Algorithm (ARSCA) is introduced to solve the problem of optimal placement and settings of Flexible AC Transmission system (FACTS) devices-Thyristor-Controlled Series Capacitors (TCSC), Thyristor-Controlled Phase Shifters (TCPS), and Static VAR Compensators (SVC)-in the IEEE 30-bus test system. With dynamic load scenarios, ARSCA was shown to perform better by minimizing active power losses to 1.7655 MW, achieving a minimum generation cost of 807.17 $/h, and reducing the gross system cost to 883.53 $/h. The results of these experiments show faster convergence and consistent solution accuracy compared to benchmark algorithms such as Sine Cosine Algorithm (SCA), Improved Grey Wolf optimization (IGWO), Whale optimization Algorithm (WOA), and others. The algorithm also improved voltage stability and reactive power management. ARSCA combines robust exploration and exploitation mechanisms to provide an efficient and scalable solution to power system optimization problem that is cost effective and operationally stable. Its scalability in larger networks and adaptability under expanded uncertainty conditions should be investigated in future studies.

关键词： Sine cosine algorithm (SCA) Opposition-based learning mechanism Optimal power flow (OPF) Wind integration FACTS devices power system optimization

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power system optimization Problems: Game Theory Applications 9th

Power System Optimization Problems: Game Theory Applications

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9th International Conference on Operations Research and Enterprise systems (ICORES)

作者： Balagopalan, Sudha Ravishankar, S. APJ Abdul Kalam Technol Univ Vidya Acad Sci & Technol Trichur India

ISBN: (纸本)9789897583964

We look at the conflict situation in power system problems from an optimization perspective and use Game Theory (GT) concepts for modelling and solving the problem. In order to model the conflicts effectively, we first identify the players, the optimizing quantity and the optimizing platform. This paper details two power system problems and present a case study. We also identify two more areas where the same principles may be applied. Though our work focuses on Cooperative Game Theory (CGT), an extension to the NonCooperative Game Theory (NGT) platform is possible. Since GT is more relevant to a market structure, we use market engineering principles including multilateral trades, differential pricing, inverse elasticity rule, graph theoretical allocation, etc. as tools for organizing the optimization process. A useful addition for inducing stability in the decision making process is the concept of 'power Vectors' borrowed from sports and game parlance for ceding players. Results are encouraging, with a transmission loss reduction of more than 70% in a five bus and 40% in a 24 bus system. We conclude that both versions of GT, the CGT and NGT are powerful tools for optimization in a practical scenario with conflicts and contradictory incentives.

关键词： Game Theory power system optimization Pricing Graph Theory Resource Allocation

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Editorial: power system operation and optimization considering high penetration of renewable energy

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Frontiers in Energy Research 2024年 12卷

作者： Liu, Shengyuan Lin, Zhenzhi Dong, Yuqing Zhao, Jin State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China School of Electrical Engineering Zhejiang University Hangzhou China School of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany

Li et al. address the operational challenges in power systems caused by the increasing penetration of intermittent renewable energy. They propose a multi-regional interconnected transmission network optimization method based on the analytical target cascading (ATC) approach. This method not only tackles the coupling nonlinear problems effectively but also promotes the consumption of renewable energy, offering a robust solution for complex transmission network *** the realm of energy storage systems, Sun et al. propose a switching control strategy based on multi-level logic judgment. Their research aims to improve the utilization rate and economic benefits of energy storage systems, ensuring safe and stable operation of power grids. This strategy is particularly relevant for power systems with high proportions of renewable energy integration, where energy storage plays a critical role in maintaining grid stability. Chen and Chen develop a nonparametric probabilistic forecasting based stochastic scheduling approach for integrated electricity and gas systems (IEGS). Their method integrates the advantages of nonparametric probabilistic forecasting to address the randomness of wind power, establishing a stochastic optimal scheduling model that is efficient and reliable. In summary, this special issue provides a comprehensive examination of the challenges and opportunities in the operation and optimization of power systems with high RES penetration. The diverse research presented here offers innovative strategies and tools that are essential for the successful integration of renewable energy into the power grid, paving the way for a more sustainable and efficient energy *** the global energy landscape undergoes a transformative shift towards sustainability, the integration of renewable energy sources (RES) into power systems has become a critical imperative. The high penetration of RES, characterized by their intermittency and unpredictability , introduc

关键词： power system operation and control power system optimization economic and low-carbon dispatch artificial intelligence power system restoration

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power system optimization with an Inertia Study on the IEEE 30-Bus Test system

Power System Optimization with an Inertia Study on the IEEE ...

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IEEE SoutheastCon Conference

作者： Sadanandan, Sandeep Natarajan, Balasubramaniam Kansas State Univ Elect & Comp Engn Manhattan KS 66506 USA

ISBN: (纸本)9781538615393

Inertia on the power system is an important issue for study and analysis. With the national movement to low inertia green energies, the lack of inertia on the power system could be a significant issue. The purpose of this paper is to include inertia in a power system optimization of the IEEE 30-bus system. The objective function of generation cost is minimized subject to constraints of active power generator limits, active power reserves, and system inertia. As generation is lost on the system, frequency drops. The early response of the system comes from the inertia on the system. With the replacement of large synchronous machines by renewable resources, which are often lower inertia units, the need to maintain a system inertia-constant (Hsys) becomes a necessary goal of power system planners and operators. For our IEEE 30 Bus system with 4 low inertia units, the proposed approach allows the system to maintain 59.7Hz or higher frequency for the loss of 0.0239pu of generation.

关键词： power system optimization Generator Cost Function Reserves Inertia Constant Kinetic Energy

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Stochastic Robust Mathematical Programming Model for power system optimization

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IEEE TRANSACTIONS ON power systemS 2016年第1期31卷 821-822页

作者： Liu, Cong Lee, Changhyeok Chen, Haoyong Mehrotra, Sanjay Argonne Natl Lab Decis & Informat Sci Argonne IL 60439 USA Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA S China Univ Technol Dept Elect Engn Guangzhou 510641 Guangdong Peoples R China

This letter presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with different ... 详细信息

关键词： Distributionally robust power system optimization robust optimization stochastic unit commitment

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Integrating Classification and Regression Tree Algorithm for Operational optimization in AC-DC Hybrid power system Planning: A Novel Approach

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ENERGIES 2025年第4期18卷 783-783页

作者： Yang, Yuyao Zhang, Boyuan Zhang, Jun Gong, Guoxian Pan, Feng Feng, Lei Zheng, Yi Wang, Peng Metrol Ctr Guangdong Power Grid Co Ltd Qingyuan 511545 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100190 Peoples R China

The increasing demand for electricity and the imperatives of climate change have made the optimization of power system planning critical for the energy transition and grid efficiency. This study presents an innovative planning method for inter-regional AC-DC hybrid power systems, leveraging the Classification and Regression Tree (CART) algorithm to optimize the operational characteristics of direct current (DC) channels. By designing a closed-loop iteration, precise operational constraints are considered by the CART algorithm, which immerged into the planning model to achieve safe and economic optimization. Based on the empirical analysis of the HRP-38 system, this study concludes that the CART algorithm offers a constructive approach to managing the operational complexities of modern power grids. By optimizing and refining DC operational characteristics based on actual system requirements, the algorithm contributes to improvements in safety, economic efficiency, and environmental sustainability within the confines of the HRP-38 node system. Consequently, the effectiveness of the CART optimization approach could be corroborated. Meanwhile, this study also acknowledges the limitations in generalizing these results to other power grid configurations and the need for further exploration in developing environmentally conscious planning methods.

关键词： AC-DC hybrid power systems machine learning CART algorithm power system optimization

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Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi-machine systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2024年第0期

作者： Hassan, Mohamed H. Kamel, Salah El-Dabah, Mahmoud A. Abido, Mohammad A. Zeinoddini-Meymand, Hamed Minist Elect & Renewable Energy Cairo Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Al Azhar Univ Fac Engn Dept Elect Engn Cairo Egypt King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran Saudi Arabia Grad Univ Adv Technol Dept Elect & Comp Engn PO 76315-117 Kerman Iran

This paper emphasizes the significance of ensuring adequate damping of electromechanical oscillations in power systems to ensure stable operation. power system Stabilizers (PSSs) are influential in enhancing system damping and refining dynamic characteristics during transient conditions. However, the efficacy of PSSs is notably contingent on parameter values, particularly in the case of lead-lag PSSs. In response to this challenge, the paper introduces a Tilt-Integral-Derivative (TID)-based PSS, optimized through a novel optimization algorithm called Hybrid Manta Ray Foraging and Salp Swarm optimization Algorithms (MRFOSSA). The MRFOSSA algorithm demonstrates robustness and enhanced convergence, validated through benchmark function tests, and outperforms competing algorithms. These superior characteristics of MRFOSSA were employed in optimal tuning of TID-PSSs to uphold the stability of multi-machine power systems. The MRFOSSA algorithm demonstrates robustness and enhanced convergence, outperforms competing algorithms in the optimal tuning of TID-PSS within the Western system Coordinating Council (WSCC)-3-machines 9-bus test system. In summary, the proposed TID-PSS, coupled with the MRFOSSA algorithm, presents a promising avenue for enhancing power system stability. This paper presents a TID-based PSS optimized by MRFOSSA, ensuring robust stability in power systems, validated through WSCC-3 machines 9-bus system. image

关键词： multi-machine power system electromechanical oscillations TID-PSS power system optimization MRFOSSA

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Multi-objective optimization Model for power system Considering CCES 4

Multi-objective Optimization Model for Power System Consider...

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4th power system and Green Energy Conference (PSGEC)

作者： Wang, Chao Ren, Manman Tai, Dequn Gao, Bo Shi, Zhuang Bai, Tianyu He, Xiangning Liu, Jun State Grid Anhui Elect Power Co Ltd Wuhu Power Supply Co Wuhu Peoples R China State Grid Anhui Elect Power Co Ltd Elect Power Res Inst Hefei Peoples R China State Grid Anhui Elect Power Co Ltd Hefei Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9798350365573;9798350365580

Driven by the dual-carbon target, the conversion of power system to low carbon is imminent. Energy storage technologies, especially compressed carbon dioxide energy storage (CCES), have shown great potential in power system due to their advantages of environmental friendliness and flexibility. However, few studies have taken CCES into account in power system optimization models. Therefore, this paper proposes a multi-objective optimization model for power system considering CCES. The model integrates the interactions of factors such as power and air pressure within the CCES, aiming to achieve multi-objective optimization such as reducing operating costs and voltage fluctuations. It also adopts an advanced multi-objective optimization strategy based on NSGA-III and combines the TOPSIS method to dynamically determine the weight allocation of each objective. Through experiments on an IEEE-33 network system, validity and practicality of the proposed method is verified in this paper.

关键词： Compressed carbon dioxide energy storage Multi-objective optimization power system optimization NSGA-III

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Applied MATpower for power system optimization Research 11th

Applied MATPOWER for Power System Optimization Research

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11th Eco-Energy and Materials Science and Engineering Symposium (EMSES)

作者： Buayai, Kittavit Chinnabutr, Kittiwut Intarawong, Prajuap Kerdchuen, Kaan Rajamangala Univ Technol Isan Fac Engn & Architecture Dept Elect Engn Elect Energy Innovat Res Unit Muang 30000 Korat Thailand

This paper provides the applied MATpower techniques for optimization solving in power system research. MATpower has been powerful developed for power flow and optimal power flow solving. These two mains of MATpower are importance for electrical energy management in terms of loss reduction in power systems and system performance improvement. In power flow solving of MATpower process can be applied for conveniently solving in heuristic optimizations. Example of optimal capacitor placement is illustrated for practical guiding. (C) 2014 Elsevier Ltd.

关键词： MATpower power system optimization Heuristic optimization

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