Article
Green & Sustainable Science & Technology
Wallisson C. Nogueira, Lina P. Garces Negrete, Jesus M. Lopez-Lezama
Summary: Modern distribution systems and microgrids face uncertainties caused by variations in loads and distributed generation (DG), requiring new tools for more accurate grid state analysis. This paper presents an optimization methodology that addresses uncertainties in the optimal allocation and sizing of DG in distribution networks. The proposal utilizes interval power flow (IPF) to incorporate uncertainties into the combinatorial optimization problem, and implements symbiotic organism search (SOS) and particle swarm optimization (PSO) as metaheuristics for comparison. The results show that the SOS metaheuristic performs better in terms of finding optimal solutions and regulating voltage levels compared to the PSO metaheuristic.
Article
Computer Science, Information Systems
Jinhao Wang, Junliu Zhang, Zhaoguang Pan, Huaichang Ge, Xiao Chang, Bin Wang, Shengwen Li, Haotian Zhao
Summary: This paper proposes a robust interval optimization method to address the challenges associated with wind power integration and enhance the flexibility of a power system. By introducing flexibility resources from a district heating system, the method provides a reliable dispatch plan that can accommodate uncertainties and fluctuations in wind power generation. Case studies demonstrate that the proposed approach effectively enhances the integration of wind power and improves the overall reliability and flexibility of the energy system.
Article
Energy & Fuels
Rufeng Zhang, Yan Chen, Benxin Li, Tao Jiang, Xue Li, Houhe Chen, Ruoxi Ning
Summary: This study investigates the potential of integrated electricity and district heating systems in alleviating the impact of wind power uncertainty, proposing an adjustable robust interval approach. Through mathematical modeling and case studies, the effectiveness of this approach is demonstrated.
Article
Engineering, Multidisciplinary
Shungen Luo, Xiuping Guo
Summary: This paper constructs a 24-hour day-ahead multi-objective complex constrained optimization model to minimize the operational cost and gaseous pollutant emission of a multi-microgrid system. The model uses metaheuristic strategies for solution initialization and repair, and employs fuzzy membership degree and Chebyshev function for decomposition. The results demonstrate that the proposed MOEA/HD algorithm is more efficient and produces higher-quality Pareto optimal solutions compared to other algorithms.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Article
Engineering, Electrical & Electronic
Cong Zhang, Qian Liu, Sheng Huang, Bin Zhou, Long Cheng, Lin Gao, Jiayong Li
Summary: This paper proposes a novel method, the security limits method (SLM), to solve the voltage control strategy issue in power systems by computing security limits and transforming the model into a deterministic one for improved efficiency.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Yue Chen, Wei Wei
Summary: This paper proposes a novel two-stage robust generation dispatch model, where the preparatory curtailment threshold is optimized in the pre-dispatch stage. An adaptive column-and-constraint generation algorithm is developed to solve the problem. Numerical studies validate the advantages and practicability of the proposed model and algorithm.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Xin Fang, Haoyu Yuan, Jin Tan
Summary: This paper proposes a deliverable VG SFR provision model with endogenous VG's power uncertainty decomposition, guaranteeing the deliverable SFR provision through distributionally robust chance constraints. It also validates and evaluates the intra-interval frequency response of PV's SFR, demonstrating that with the deliverable SFR from PV, system cost and frequency reliability can be improved simultaneously.PV's SFR performance can be guaranteed with the proposed model.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Computer Science, Artificial Intelligence
Chaofan Yu, Yuanzheng Li, Yun Liu, Leijiao Ge, Hao Wang, Yunfeng Luo, Linqiang Pan
Summary: This study develops a bi-objective stochastic dispatch model to investigate the relationship between renewable energy utilization and transmission security. The model considers the objectives of renewable energy curtailment and the capacity margin of transmission lines, and proposes a data-driven Bayesian assisted optimization algorithm to improve the searching efficiency.
KNOWLEDGE-BASED SYSTEMS
(2023)
Review
Energy & Fuels
Phani Raghav Lolla, Seshu Kumar Rangu, Koteswara Raju Dhenuvakonda, Arvind R. Singh
Summary: This review paper summarizes the centralized approaches to solve economic dispatch problems and presents a brief analysis on the performance evaluation of centralized algorithms on six standard test systems. It also briefly reviews decentralized and distributed optimization approaches as well as consensus protocols with respect to various network topologies.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Liang Jin, Lu Liu, Juheng Song, Yingang Yan, Xinchen Zhang
Summary: This paper addresses the uncertainties in the manufacturing and service processes of electromagnetic orbital launchers and proposes a method for describing uncertain variables using interval numbers. It converts uncertainty optimization into deterministic optimization problems using interval sequential relations and establishes interval uncertainty optimization methods. By combining a high-precision proxy model with an optimization algorithm, the converted deterministic optimization problem is solved to find the optimal solution set. Finally, reliability estimation is achieved by optimizing the armature of the electromagnetic orbital launcher. The computational example demonstrates that the method can effectively handle optimization problems with uncertain parameters in the parameter interval of an electromagnetic orbital launcher and has good engineering applicability.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Jiayu Wu, Yang Liu, Xianbang Chen, Changhao Wang, Wenfeng Li
Summary: This paper presents a two-stage data-driven adjustable robust optimization (ARO) model for microgrid, considering the uncertainties of wind power generation and electric vehicles. The model utilizes a nonparametric ambiguity set based on an imprecise-Dirichlet model and a polyhedron uncertainty set obtained from historical data. The data-driven ARO problem is converted into a traditional two-stage ARO model, and the optimal day-ahead economic dispatch strategy is achieved using duality theory, Big-M method, and column and constraint generation algorithm. Case studies demonstrate the robustness, economic benefit, flexible adjustment capability, and uncertainty depiction ability of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Mohamed Ebeed, Said Abdel-Fatah, Salah Kamel, Loai Nasrat, Francisco Jurado, Ambe Harrison
Summary: This study proposes an enhanced Artificial Gorilla Troops Optimizer (EGTO) to solve the stochastic optimal reactive power dispatch (SORPD) problem considering uncertainties in load demand and generated power, as well as the reactive power generation capability of photovoltaic (PV) systems. The algorithm is applied on the IEEE 30-bus system and compared with other optimization algorithms, demonstrating improved performance by incorporating the PV unit.
IET RENEWABLE POWER GENERATION
(2023)
Article
Engineering, Multidisciplinary
Jing Ye, Zhenzhen Ma, Pingping Xiong, Xiaojun Guo
Summary: A discrete grey-Markov model based on interval distribution characteristics is proposed in this paper to represent and predict the imbalances in regional socioeconomic indicators. By introducing difference coefficient sequence and median sequences, differential equations for different development level trends are established. The combination of discrete grey models with the Markov model improves the prediction accuracy of nonstationary data sequences.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Electrical & Electronic
Xiaohong Ran, Jiahao Zhang, Kaipei Liu
Summary: Traditionally, uncertainty is described using known probability distribution functions (PDFs). However, in some cases, the PDFs of uncertain variables are inaccurately modeled or the rough PDFs are not clear. To address these issues, this paper proposes a new uncertainty description method using interval and probabilistic models. They introduce a new risk method called interval-probabilistic conditional value-at-risk (IP-CVaR) and establish an optimal model for it. Under interval uncertainty, two interval operators are defined and positive and negative spinning reserve models are established using IP-CVaR, particularly for high penetration wind power integration. The paper also presents a novel risk-aware flexible joint economic dispatch model for assessing the impact of interval uncertainty on power grid's economic operation.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Energy & Fuels
Haonan Zhang, Youwen Tian, Yi Zhao, Qingyu Liu, Nannan Zhang
Summary: This paper proposes the concept of generation load aggregators and develops a robust optimization model to obtain the scheduling scheme with the lowest operating cost. The results show that generation load aggregators can relieve peak and valley pressure on the grid, reduce the cost of electricity for loads, and promote the consumption of renewable energy. The study also provides boundary conditions for the use of energy storage by generation load aggregators under the time-sharing tariff mechanism.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Energy & Fuels
Guangchun Ruan, Haiwang Zhong, Jianxiao Wang, Qing Xia, Chongqing Kang
Article
Engineering, Electrical & Electronic
Hongye Guo, Qixin Chen, Qing Xia, Chongqing Kang
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Green & Sustainable Science & Technology
Hongye Guo, Qixin Chen, Xichen Fang, Kai Liu, Qing Xia, Chongqing Kang
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2020)
Article
Economics
Hongye Guo, Michael R. Davidson, Qixin Chen, Da Zhang, Nan Jiang, Qing Xia, Chongqing Kang, Xiliang Zhang
Article
Engineering, Electrical & Electronic
Ziming Ma, Haiwang Zhong, Qing Xia, Chongqing Kang, Qiang Wang, Xin Cao
IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
Article
Green & Sustainable Science & Technology
Jianxiao Wang, Haiwang Zhong, Zhifang Yang, Xiaowen Lai, Qing Xia, Chongqing Kang
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2020)
Article
Engineering, Electrical & Electronic
Zhenfei Tan, Haiwang Zhong, Qing Xia, Chongqing Kang
Summary: This paper proposes a multi-area coordination framework based on condensed system representation (CSR), which can optimize multi-area systems by fixing non-marginal units and eliminating redundant security constraints without the need for iterative information exchange.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Hongye Guo, Qixin Chen, Qing Xia, Chongqing Kang
Summary: This paper introduces a data-driven framework for identifying bidding objective functions, consisting of three steps: modeling bidding decision processes as a Markov decision process, using a deep inverse reinforcement learning method to identify reward functions, and customizing a deep Q-network method to simulate bidding behaviors. These methods have been tested on real market data from the Australian electricity market.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Hongye Guo, Qixin Chen, Kedi Zheng, Qing Xia, Chongqing Kang
Summary: A novel data-driven ASC forecasting framework based on LSTM model and data processing techniques is proposed in this paper to predict optimal bidding in power markets. The framework integrates data, simplifies high dimensionality, and forecasts ASC with good performance. Real data from the U.S. market are used to demonstrate the effectiveness of the proposed framework.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Zhenfei Tan, Siyu Wang, Haiwang Zhong, Qing Xia, Chongqing Kang
Summary: This paper proposes the use of dynamic line rating (DLR) to expand the flexibility region of a virtual power plant (VPP), in order to better integrate distributed resources. By adjusting the current carrying capacity of the distribution network, transmission limits that hinder the integration of distributed flexibility can be eliminated.
IET RENEWABLE POWER GENERATION
(2022)
Article
Engineering, Electrical & Electronic
Guangchun Ruan, Daniel S. Kirschen, Haiwang Zhong, Qing Xia, Chongqing Kang
Summary: This paper explores demand flexibility in modern power systems through time-varying elasticity, proposing a model-free methodology and a two-stage estimation process using Siamese LSTM networks to accurately estimate price responses and time-varying elasticities. Validated in a case study, the proposed framework achieves higher overall estimation accuracy and better description of abnormal features compared to state-of-the-art methods.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Thermodynamics
Hongye Guo, Qixin Chen, Mohammad Shahidehpour, Qing Xia, Chongqing Kang
Summary: This paper proposes a novel bidding behavior model for generation companies (GENCOs) with bounded rationality and renewable energy. The model incorporates prospect theory and fairness constraints, and analyzes the interaction between GENCOs and the day-ahead power market using a Stackelberg game model. The effectiveness of the proposed model is demonstrated through an illustrative example using a modified IEEE-30 bus system with high renewable energy penetration.
Article
Energy & Fuels
Pengya Wang, Jianxiao Wang, Ruiyang Jin, Gengyin Li, Ming Zhou, Qing Xia
Summary: In recent years, there has been an increasing use of biomass technologies due to its recognition as a natural carbon-neutral fuel. While previous studies have focused on zero-carbon energy systems or buildings, there has been limited exploration of the multi-energy coupling method and techno-economic evaluation of biomass for achieving near-zero carbon systems. The proposed optimal sizing framework integrates waste-to-biogas into regional energy systems to minimize carbon emissions. The case studies demonstrate the significant improvement in supply adequacy, reduction in investment costs, and carbon emission reduction intensity through the integration of biogas.
Article
Engineering, Electrical & Electronic
Guangchun Ruan, Zekuan Yu, Shutong Pu, Songtao Zhou, Haiwang Zhong, Le Xie, Qing Xia, Chongqing Kang
Summary: Intervention policies against COVID-19 have disrupted the power system operation globally, leading to pattern changes. To understand the risks and impacts, an open-access data hub, an open-source toolbox, and evaluation methods were developed for analyzing the U.S. power systems during COVID-19. These resources are valuable for research, public policy, and education. The data hub harmonizes various data, while the toolbox includes reformulated methods and proposes new indices. Empirical studies provide insights and solutions, expanding the understanding of COVID-19's effects.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Proceedings Paper
Engineering, Industrial
Qi An, Jianxiao Wang, Qing Xia, Gengyin Li, Ming Zhou, Zhenyu Chen, Xiaoquan Lu
Summary: This paper investigates the role of integrated demand elasticity in mitigating market power and proposes a bi-level optimization framework for integrated demand response. The research findings show that IDR can significantly improve demand elasticity and effectively mitigate the strategic incentive in the power generation market.
2021 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (IEEE I&CPS ASIA 2021)
(2021)
