Pub Date : 2024-04-25DOI: 10.1109/TSTE.2024.3393764
Ziyang Yin;Shouxiang Wang;Qianyu Zhao;Mingjian Cui
Against the backdrop of the low-carbon energy transition, distribution system operators face the urgent challenge of balancing the contradictory demands of high photovoltaic (PV) accommodation capacity and low operation cost. Meanwhile, most iteration-based PV accommodation capacity improvement methods are limited by imprecise line resistance and the conflicting relationship between decision efficiency and modeling accuracy. To this end, a two-timescale distribution network dispatching approach based on muti-objective DRL is proposed. This approach is an online decision-making method based on real-time data and robust to temperature-dependent resistance via constructing a two-stage decision-making model based on multi-objective Markov decision process considering the weather factors. Also, the proposed model has a vectorized reward function to assess the trade-off between the economy and accommodation capacity for better operation. A novel multi-objective DRL (MODRL) algorithm based on the tchebycheff norm is proposed, which decomposes the proposed decision-making model into multiple sub-models for learning Pareto optimal policies. Comparative tests on the IEEE 33-bus system validate that the proposed method effectively acquires optimization strategies under varying user preferences to improve economic and PV accommodation capacity. The proposed algorithm obtains more diverse Pareto fronts and high-quality solutions than other state-of-the-art MODRLs.
{"title":"Temperature-Dependent Resistance Constrained PV Accommodation Capacity Improvement Based on Multi-Objective DRL","authors":"Ziyang Yin;Shouxiang Wang;Qianyu Zhao;Mingjian Cui","doi":"10.1109/TSTE.2024.3393764","DOIUrl":"10.1109/TSTE.2024.3393764","url":null,"abstract":"Against the backdrop of the low-carbon energy transition, distribution system operators face the urgent challenge of balancing the contradictory demands of high photovoltaic (PV) accommodation capacity and low operation cost. Meanwhile, most iteration-based PV accommodation capacity improvement methods are limited by imprecise line resistance and the conflicting relationship between decision efficiency and modeling accuracy. To this end, a two-timescale distribution network dispatching approach based on muti-objective DRL is proposed. This approach is an online decision-making method based on real-time data and robust to temperature-dependent resistance via constructing a two-stage decision-making model based on multi-objective Markov decision process considering the weather factors. Also, the proposed model has a vectorized reward function to assess the trade-off between the economy and accommodation capacity for better operation. A novel multi-objective DRL (MODRL) algorithm based on the tchebycheff norm is proposed, which decomposes the proposed decision-making model into multiple sub-models for learning Pareto optimal policies. Comparative tests on the IEEE 33-bus system validate that the proposed method effectively acquires optimization strategies under varying user preferences to improve economic and PV accommodation capacity. The proposed algorithm obtains more diverse Pareto fronts and high-quality solutions than other state-of-the-art MODRLs.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"2006-2020"},"PeriodicalIF":8.6,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-24DOI: 10.1109/TSTE.2024.3392882
Jiaqi Li;Hua Geng
A novel control strategy of platform pitch motion suppression is presented for floating offshore wind turbines (FOWTs). System analysis shows that the platform pitch motion acts as unstable zero dynamics, resulting in non-minimum phase characteristics. The proposed strategy consists of a nonlinear generator torque compensator and a multiplicative feedback-based gain-scheduled proportional-integral (GSPI) blade pitch controller. The proposed torque compensator directly compensates the non-minimum phase system to a minimum phase one. It breaks the limitation of the platform pitch motion on the bandwidth of the blade pitch controller. Moreover, the simultaneous multiplicative feedback of rotor speed and platform pitch angular velocity is proposed as a new framework for platform pitch suppression. It gets rid of the requirements by cascade control for decoupled dynamics of dual loops. Meanwhile, a GSPI controller is used to determine the blade pitch angle. Stability proof is given for the proposed method. Compared with the traditional methods, such as PI gain-detuning and cascade control, simulation results demonstrate that the proposed control strategy performs better in platform pitch suppression.
本文针对浮式海上风力涡轮机（FOWT）提出了一种抑制平台俯仰运动的新型控制策略。系统分析显示，平台变桨运动是不稳定的零动态，导致非最小相位特性。所提出的策略包括一个非线性发电机扭矩补偿器和一个基于乘法反馈的增益调度比例积分（GSPI）叶片变桨控制器。拟议的扭矩补偿器可将非最小相位系统直接补偿为最小相位系统。它打破了平台俯仰运动对叶片俯仰控制器带宽的限制。此外，还提出了转子速度和平台螺距角速度的同步乘法反馈作为平台螺距抑制的新框架。它摆脱了级联控制对双环解耦动力学的要求。同时，使用 GSPI 控制器来确定叶片俯仰角。提出的方法给出了稳定性证明。与 PI 增益调整和级联控制等传统方法相比，仿真结果表明所提出的控制策略在平台螺距抑制方面表现更佳。
{"title":"Platform Pitch Motion Suppression for Floating Offshore Wind Turbine in Above-Rated Wind Speed Region","authors":"Jiaqi Li;Hua Geng","doi":"10.1109/TSTE.2024.3392882","DOIUrl":"10.1109/TSTE.2024.3392882","url":null,"abstract":"A novel control strategy of platform pitch motion suppression is presented for floating offshore wind turbines (FOWTs). System analysis shows that the platform pitch motion acts as unstable zero dynamics, resulting in non-minimum phase characteristics. The proposed strategy consists of a nonlinear generator torque compensator and a multiplicative feedback-based gain-scheduled proportional-integral (GSPI) blade pitch controller. The proposed torque compensator directly compensates the non-minimum phase system to a minimum phase one. It breaks the limitation of the platform pitch motion on the bandwidth of the blade pitch controller. Moreover, the simultaneous multiplicative feedback of rotor speed and platform pitch angular velocity is proposed as a new framework for platform pitch suppression. It gets rid of the requirements by cascade control for decoupled dynamics of dual loops. Meanwhile, a GSPI controller is used to determine the blade pitch angle. Stability proof is given for the proposed method. Compared with the traditional methods, such as PI gain-detuning and cascade control, simulation results demonstrate that the proposed control strategy performs better in platform pitch suppression.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1994-2005"},"PeriodicalIF":8.6,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1109/TSTE.2024.3390808
Yang Xia;Yan Xu;Xue Feng
Multiple individual microgrids can be integrated as a networked microgrid system for enhanced technical and economic performance. In this paper, a two-stage data-driven method is proposed to hierarchically coordinate individual microgrids towards decentralized operation in a networked microgrid (NMG) system. The first stage schedules active power outputs of micro-turbines and energy storage systems (ESSs) on an hourly basis for energy balancing and cost minimization, where ESSs are controlled by a local P/SoC droop scheme. In the second stage, the reactive power outputs of PV inverters are dispatched every three minutes based on a Q/V droop controller, aiming to reduce network power losses and regulate the voltage under real-time uncertainties. At offline training stage, a multi-agent deep reinforcement learning model is trained to learn an optimal coordination policy, enhanced by a safety model framework. For online application, the trained agent can work locally in a decentralized manner without information exchanges, and the safety model can also be applied to monitor and guide online actions for safety compliance. Numerical test results validate the effectiveness and advantages of the proposed method.
{"title":"Hierarchical Coordination of Networked-Microgrids Toward Decentralized Operation: A Safe Deep Reinforcement Learning Method","authors":"Yang Xia;Yan Xu;Xue Feng","doi":"10.1109/TSTE.2024.3390808","DOIUrl":"10.1109/TSTE.2024.3390808","url":null,"abstract":"Multiple individual microgrids can be integrated as a networked microgrid system for enhanced technical and economic performance. In this paper, a two-stage data-driven method is proposed to hierarchically coordinate individual microgrids towards decentralized operation in a networked microgrid (NMG) system. The first stage schedules active power outputs of micro-turbines and energy storage systems (ESSs) on an hourly basis for energy balancing and cost minimization, where ESSs are controlled by a local P/SoC droop scheme. In the second stage, the reactive power outputs of PV inverters are dispatched every three minutes based on a Q/V droop controller, aiming to reduce network power losses and regulate the voltage under real-time uncertainties. At offline training stage, a multi-agent deep reinforcement learning model is trained to learn an optimal coordination policy, enhanced by a safety model framework. For online application, the trained agent can work locally in a decentralized manner without information exchanges, and the safety model can also be applied to monitor and guide online actions for safety compliance. Numerical test results validate the effectiveness and advantages of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1981-1993"},"PeriodicalIF":8.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140628593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1109/TSTE.2024.3390782
Guan Bai;Sheng Huang;Yaojing Feng;Qiuwei Wu;Pengda Wang;Jiani Mao
This study proposes a distributed optimal power control (OPC) scheme to reduce the structural loads in WFs for extending the service life of component via a consensus approach. First, a nonlinear cost function of the thrust force and the shaft torque is formulated to minimize structural loads by coordinating the active power and pitch angle of wind turbines (WTs). Then, the nonlinear cost function is linearized via the state variables of WTs and transformed into a linear equation respected to the control variables. Moreover, a fully distributed alternating direction method of multipliers is developed for the optimal structural loads problem to calculate the optimal values of cost function, which could distribute computational burden enhance information privacy protection. Based on the proposed distributed framework, only the intermediate information is exchanged among adjacent WTs controller. More importantly, when several WTs controller occur the communication failure, the communication disconnected WTs can work in decentralized control mode to regulate the pitch angle, and the other WTs with communication still track the power command, which could improve the robustness of the control system. A WF simulation is established as a testing system to verify the effectiveness of the proposed OPC scheme.
{"title":"Distributed Optimal Power Control Scheme for Structural Loads Minimization in Wind Farms via a Consensus Approach","authors":"Guan Bai;Sheng Huang;Yaojing Feng;Qiuwei Wu;Pengda Wang;Jiani Mao","doi":"10.1109/TSTE.2024.3390782","DOIUrl":"10.1109/TSTE.2024.3390782","url":null,"abstract":"This study proposes a distributed optimal power control (OPC) scheme to reduce the structural loads in WFs for extending the service life of component via a consensus approach. First, a nonlinear cost function of the thrust force and the shaft torque is formulated to minimize structural loads by coordinating the active power and pitch angle of wind turbines (WTs). Then, the nonlinear cost function is linearized via the state variables of WTs and transformed into a linear equation respected to the control variables. Moreover, a fully distributed alternating direction method of multipliers is developed for the optimal structural loads problem to calculate the optimal values of cost function, which could distribute computational burden enhance information privacy protection. Based on the proposed distributed framework, only the intermediate information is exchanged among adjacent WTs controller. More importantly, when several WTs controller occur the communication failure, the communication disconnected WTs can work in decentralized control mode to regulate the pitch angle, and the other WTs with communication still track the power command, which could improve the robustness of the control system. A WF simulation is established as a testing system to verify the effectiveness of the proposed OPC scheme.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2143-2154"},"PeriodicalIF":8.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140628674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1109/TSTE.2024.3390578
Han Yue;Musaab Mohammed Ali;Yuzhang Lin;Hongfu Liu
Ultra-short-term power forecasting for distributed solar photovoltaic (PV) generation is a largely unaddressed, highly challenging problem due to the prohibitive real-time data collection and processing requirements for a sheer number of distributed PV units. In this paper, we propose an innovative idea of forecasting the power output of a large fleet of distributed PV units using limited real-time data of a sparsely selected set of PV units, referred to as pilot units. We develop a two-stage method to address this problem. In the planning stage, we use the K-medoids clustering algorithm to select pilot units for the installation of real-time remote monitoring infrastructure. In the operation stage, we devise a deep learning framework integrating Long Short-Term Memory, Graph Convolutional Network, Multilayer Perceptron to capture the spatio-temporal power generation patterns between pilot units and other units, and forecast the power outputs of all units in a large PV fleet using the real-time data from the few selected pilot units only. Case study results show that our proposed method outperforms all baseline methods in forecasting for power outputs of individual PV units as well as the whole PV fleet, and the forecasting time resolution is not dependent on that of weather data.
{"title":"Ultra-Short-Term Forecasting of Large Distributed Solar PV Fleets Using Sparse Smart Inverter Data","authors":"Han Yue;Musaab Mohammed Ali;Yuzhang Lin;Hongfu Liu","doi":"10.1109/TSTE.2024.3390578","DOIUrl":"10.1109/TSTE.2024.3390578","url":null,"abstract":"Ultra-short-term power forecasting for distributed solar photovoltaic (PV) generation is a largely unaddressed, highly challenging problem due to the prohibitive real-time data collection and processing requirements for a sheer number of distributed PV units. In this paper, we propose an innovative idea of forecasting the power output of a large fleet of distributed PV units using limited real-time data of a sparsely selected set of PV units, referred to as pilot units. We develop a two-stage method to address this problem. In the planning stage, we use the K-medoids clustering algorithm to select pilot units for the installation of real-time remote monitoring infrastructure. In the operation stage, we devise a deep learning framework integrating Long Short-Term Memory, Graph Convolutional Network, Multilayer Perceptron to capture the spatio-temporal power generation patterns between pilot units and other units, and forecast the power outputs of all units in a large PV fleet using the real-time data from the few selected pilot units only. Case study results show that our proposed method outperforms all baseline methods in forecasting for power outputs of individual PV units as well as the whole PV fleet, and the forecasting time resolution is not dependent on that of weather data.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1968-1980"},"PeriodicalIF":8.6,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1109/TSTE.2024.3390394
Yujia Zhang;Guang Li;Mustafa Al-Ani
This paper proposes a robust learning-based model predictive control (MPC) strategy tailored for sea wave energy converters (WECs). The control algorithm aims to maximize power extraction efficiency and maintain the WECs' operational safety over a wide range of sea conditions, subject to system constraints and plant-model mismatches. The theoretical basis is the robust tube-based MPC (RTMPC), enabling WEC system state trajectories to evolve around the noise-free nominal WEC model state trajectories. The disturbances can be bounded by pre-computed uncertainty sets for tightening the WEC's physical constraints to guarantee the constraint satisfaction of an uncertain WEC system. Typically, RTMPC constructs a tube with constant sets of uncertainties, which is likely to be overly conservative and hence potentially degrades energy conversion performance. In this work, a machine learning-based uncertainty set is introduced to dynamically predict and quantify the model uncertainties at each sampling instant, which can effectively enlarge the feasible region of the WEC TMPC control problem. The proposed RTMPC not only ensures improved energy conversion efficiency but also guarantees the operational safety of WECs under uncertain conditions. Numerical simulations demonstrate the efficacy of the proposed controller.
{"title":"Robust Learning-Based Model Predictive Control for Wave Energy Converters","authors":"Yujia Zhang;Guang Li;Mustafa Al-Ani","doi":"10.1109/TSTE.2024.3390394","DOIUrl":"10.1109/TSTE.2024.3390394","url":null,"abstract":"This paper proposes a robust learning-based model predictive control (MPC) strategy tailored for sea wave energy converters (WECs). The control algorithm aims to maximize power extraction efficiency and maintain the WECs' operational safety over a wide range of sea conditions, subject to system constraints and plant-model mismatches. The theoretical basis is the robust tube-based MPC (RTMPC), enabling WEC system state trajectories to evolve around the noise-free nominal WEC model state trajectories. The disturbances can be bounded by pre-computed uncertainty sets for tightening the WEC's physical constraints to guarantee the constraint satisfaction of an uncertain WEC system. Typically, RTMPC constructs a tube with constant sets of uncertainties, which is likely to be overly conservative and hence potentially degrades energy conversion performance. In this work, a machine learning-based uncertainty set is introduced to dynamically predict and quantify the model uncertainties at each sampling instant, which can effectively enlarge the feasible region of the WEC TMPC control problem. The proposed RTMPC not only ensures improved energy conversion efficiency but also guarantees the operational safety of WECs under uncertain conditions. Numerical simulations demonstrate the efficacy of the proposed controller.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1957-1967"},"PeriodicalIF":8.6,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.1109/TSTE.2024.3389023
Jingwei Tang;Zhi Liu;Jianming Hu
Spatial-temporal wind power prediction is of enormous importance to the grid-connected operation of multiple wind farms in the wind power system. However, most of the conventional methods are usually limited to predicting an individual wind farm's power, and thus lack enough effectiveness of wind power forecasting of multiple adjacent wind farms. This paper proposes a novel spatial-temporal wind power probabilistic prediction approach, named ZF-GCN-MHTQF, based on time zigzags and flexible convolution at graph convolutional network, point-wise loss function and the heavy-tailed quantile function. The proposed framework combines the advantages of time zigzags and flexible convolution at graph convolutional networks that can extract temporally conditioned topological information from multiple wind farms efficiently and incorporate the extracted topological information to predict wind power. At the same time, the proposed method incorporates the strengths of point-wise loss functions and heavy-tailed quantile functions which can effectively tackle the problem of the traditional multi-quantile regression and accurately capture the full conditional distribution information of wind power. In our experiments, two real-world wind power datasets from Australia are utilized to validate the proposed model. Numerical experiments demonstrate the effectiveness and robustness of the proposed method compared to the state-of-the-art spatial-temporal models.
{"title":"Spatial-Temporal Wind Power Probabilistic Forecasting Based on Time-Aware Graph Convolutional Network","authors":"Jingwei Tang;Zhi Liu;Jianming Hu","doi":"10.1109/TSTE.2024.3389023","DOIUrl":"10.1109/TSTE.2024.3389023","url":null,"abstract":"Spatial-temporal wind power prediction is of enormous importance to the grid-connected operation of multiple wind farms in the wind power system. However, most of the conventional methods are usually limited to predicting an individual wind farm's power, and thus lack enough effectiveness of wind power forecasting of multiple adjacent wind farms. This paper proposes a novel spatial-temporal wind power probabilistic prediction approach, named ZF-GCN-MHTQF, based on time zigzags and flexible convolution at graph convolutional network, point-wise loss function and the heavy-tailed quantile function. The proposed framework combines the advantages of time zigzags and flexible convolution at graph convolutional networks that can extract temporally conditioned topological information from multiple wind farms efficiently and incorporate the extracted topological information to predict wind power. At the same time, the proposed method incorporates the strengths of point-wise loss functions and heavy-tailed quantile functions which can effectively tackle the problem of the traditional multi-quantile regression and accurately capture the full conditional distribution information of wind power. In our experiments, two real-world wind power datasets from Australia are utilized to validate the proposed model. Numerical experiments demonstrate the effectiveness and robustness of the proposed method compared to the state-of-the-art spatial-temporal models.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1946-1956"},"PeriodicalIF":8.6,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1109/TSTE.2024.3388388
Anping Zhou;Mohammad E. Khodayar;Jianhui Wang
Distributionally robust optimization (DRO) has emerged as a favored methodology for addressing the uncertainties stemming from renewable energy sources. However, existing DRO frameworks primarily focus on single types of uncertainty characteristics, such as moments. Exploring novel ambiguity sets that encompass heterogeneous uncertainty information to mitigate decision conservatism is thus an essential and strategic move. This paper introduces a day-ahead optimal scheduling model tailored for electricity-hydrogen systems under renewable uncertainty, with embedded technologies of hydrogen production, storage, and utilization. Three novel ambiguity sets enriched with the moment, Wasserstein distance, and unimodality information are adeptly devised. Building upon these elaborated ambiguity sets, we develop efficient and scalable reformulations of the expected objective function and uncertain constraints, leading to either a tractable mixed-integer second-order cone programming problem or a linear programming problem. We validate the effectiveness and operating flexibility of the proposed electricity-hydrogen model using both a 6-bus test system and the IEEE 118-bus test system. Furthermore, we demonstrate the superior cost performance and computational efficiency of our developed DRO approaches.
{"title":"Distributionally Robust Optimal Scheduling With Heterogeneous Uncertainty Information: A Framework for Hydrogen Systems","authors":"Anping Zhou;Mohammad E. Khodayar;Jianhui Wang","doi":"10.1109/TSTE.2024.3388388","DOIUrl":"10.1109/TSTE.2024.3388388","url":null,"abstract":"Distributionally robust optimization (DRO) has emerged as a favored methodology for addressing the uncertainties stemming from renewable energy sources. However, existing DRO frameworks primarily focus on single types of uncertainty characteristics, such as moments. Exploring novel ambiguity sets that encompass heterogeneous uncertainty information to mitigate decision conservatism is thus an essential and strategic move. This paper introduces a day-ahead optimal scheduling model tailored for electricity-hydrogen systems under renewable uncertainty, with embedded technologies of hydrogen production, storage, and utilization. Three novel ambiguity sets enriched with the moment, Wasserstein distance, and unimodality information are adeptly devised. Building upon these elaborated ambiguity sets, we develop efficient and scalable reformulations of the expected objective function and uncertain constraints, leading to either a tractable mixed-integer second-order cone programming problem or a linear programming problem. We validate the effectiveness and operating flexibility of the proposed electricity-hydrogen model using both a 6-bus test system and the IEEE 118-bus test system. Furthermore, we demonstrate the superior cost performance and computational efficiency of our developed DRO approaches.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1933-1945"},"PeriodicalIF":8.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), reasonable hydrogen supply infrastructure (HSI) planning is a premise for promoting the popularization of HFCVs. However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is prompted by the chicken-egg conundrum. Meanwhile, there is a cost contradiction between energy planning and hydrogen refueling convenience of HFCVs caused by HRSs siting planning. To this end, this work establishes a multi-network HSI planning model coordinating hydrogen, power, and transportation networks. Then, to reflect the causal relation between HFCVs and HRSs effectively in the early stage of hydrogen infrastructure investment planning without sufficient historical data, hydrogen demand decision-dependent uncertainty (DDU) and a distributionally robust optimization framework are developed. The uncertainty of hydrogen demand is modeled as a Wasserstein ambiguity set with a decision-dependent empirical probability distribution. Subsequently, to reduce the computational complexity caused by the introduction of a large number of scenarios and high-dimensional nonlinear constraints, we developed an improved distribution shaping method and techniques of scenario and variable reduction to derive the solvable form with less computing burden. Finally, the simulation results demonstrate that this method can reduce costs by at least 7.7% compared with traditional methods and will be more effective in large-scale HSI planning issues. Further, we put forward effective suggestions for the policymakers and investors.
{"title":"A Decision-Dependent Hydrogen Supply Infrastructure Planning Approach Considering Causality Between Vehicles and Stations","authors":"Haoran Deng;Bo Yang;Mo-Yuen Chow;Dafeng Zhu;Gang Yao;Cailian Chen;Xinping Guan;Dipti Srinivasan","doi":"10.1109/TSTE.2024.3388274","DOIUrl":"10.1109/TSTE.2024.3388274","url":null,"abstract":"In the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), reasonable hydrogen supply infrastructure (HSI) planning is a premise for promoting the popularization of HFCVs. However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is prompted by the chicken-egg conundrum. Meanwhile, there is a cost contradiction between energy planning and hydrogen refueling convenience of HFCVs caused by HRSs siting planning. To this end, this work establishes a multi-network HSI planning model coordinating hydrogen, power, and transportation networks. Then, to reflect the causal relation between HFCVs and HRSs effectively in the early stage of hydrogen infrastructure investment planning without sufficient historical data, hydrogen demand decision-dependent uncertainty (DDU) and a distributionally robust optimization framework are developed. The uncertainty of hydrogen demand is modeled as a Wasserstein ambiguity set with a decision-dependent empirical probability distribution. Subsequently, to reduce the computational complexity caused by the introduction of a large number of scenarios and high-dimensional nonlinear constraints, we developed an improved distribution shaping method and techniques of scenario and variable reduction to derive the solvable form with less computing burden. Finally, the simulation results demonstrate that this method can reduce costs by at least 7.7% compared with traditional methods and will be more effective in large-scale HSI planning issues. Further, we put forward effective suggestions for the policymakers and investors.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1914-1932"},"PeriodicalIF":8.6,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1109/TSTE.2024.3387296
Jun Wang;Feilong Fan;Yue Song;Yunhe Hou;David J. Hill
To mitigate the stability issues in the droop-controlled isolated microgrids brought by aleatory renewable energy sources (RESs), which can be added at any given time, this paper proposes a two-stage robust coordination strategy to optimize the operation of multiple flexible resources. In the first stage, a day-ahead unit commitment (UC) schedule of microturbines(MTs) is formulated considering the uncertainty of RESs and loads. In the second stage, an hourly power dispatch and droop gains adjustment scheme for the energy storage devices are developed to minimize the operation cost and ensure the small signal stability. An adaptive column and constraint generation (C&CG) algorithm is developed to solve the stability-constrained two-stage robust optimization problem. Simulation results on a 33-bus microgrid system reveal that compared to benchmarking approaches, the proposed coordination strategy is able to guarantee the small-signal stability with lower cost. And a sensitivity analysis validates the robustness of the methodology against the uncertainties of RESs.
{"title":"Stability Constrained Optimal Operation of Inverter-Dominant Microgrids: A Two Stage Robust Optimization Framework","authors":"Jun Wang;Feilong Fan;Yue Song;Yunhe Hou;David J. Hill","doi":"10.1109/TSTE.2024.3387296","DOIUrl":"10.1109/TSTE.2024.3387296","url":null,"abstract":"To mitigate the stability issues in the droop-controlled isolated microgrids brought by aleatory renewable energy sources (RESs), which can be added at any given time, this paper proposes a two-stage robust coordination strategy to optimize the operation of multiple flexible resources. In the first stage, a day-ahead unit commitment (UC) schedule of microturbines(MTs) is formulated considering the uncertainty of RESs and loads. In the second stage, an hourly power dispatch and droop gains adjustment scheme for the energy storage devices are developed to minimize the operation cost and ensure the small signal stability. An adaptive column and constraint generation (C&CG) algorithm is developed to solve the stability-constrained two-stage robust optimization problem. Simulation results on a 33-bus microgrid system reveal that compared to benchmarking approaches, the proposed coordination strategy is able to guarantee the small-signal stability with lower cost. And a sensitivity analysis validates the robustness of the methodology against the uncertainties of RESs.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 3","pages":"1900-1913"},"PeriodicalIF":8.6,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：