Pub Date : 2024-09-04DOI: 10.1109/TSTE.2024.3454298
Woon-Gyu Lee;Hak-Man Kim
The optimal operation of an islanded AC microgrid system is achieved by proper power sharing among generators. The conventional distributed cost optimization strategies use a communication system to converge incremental costs. However, these methods are dependent on the distributed communication network and do not consider frequency deviations for real-time load variability. Thus, this paper proposes a DRL-based dynamic droop control strategy. The proposed twin delayed DDPG-based DRL interacts with the environment to learn the optimal droop gain for reducing generation cost and frequency deviation. The trained agent uses local information to transmit dynamic droop gains to the primary controller as demand load changes. It can simplify the control structure by omitting the secondary layer for optimal operation and power quality. The proposed control strategy is designed with a centralized DRL training process and distributed execution, enabling real-time distributed optimal operation. The comparison results with conventional distributed strategy confirms better control performance of the proposed strategy. Finally, the feasibility of the proposed strategy was verified by experiment on AC microgrid testbed.
{"title":"Deep Reinforcement Learning-Based Dynamic Droop Control Strategy for Real-Time Optimal Operation and Frequency Regulation","authors":"Woon-Gyu Lee;Hak-Man Kim","doi":"10.1109/TSTE.2024.3454298","DOIUrl":"10.1109/TSTE.2024.3454298","url":null,"abstract":"The optimal operation of an islanded AC microgrid system is achieved by proper power sharing among generators. The conventional distributed cost optimization strategies use a communication system to converge incremental costs. However, these methods are dependent on the distributed communication network and do not consider frequency deviations for real-time load variability. Thus, this paper proposes a DRL-based dynamic droop control strategy. The proposed twin delayed DDPG-based DRL interacts with the environment to learn the optimal droop gain for reducing generation cost and frequency deviation. The trained agent uses local information to transmit dynamic droop gains to the primary controller as demand load changes. It can simplify the control structure by omitting the secondary layer for optimal operation and power quality. The proposed control strategy is designed with a centralized DRL training process and distributed execution, enabling real-time distributed optimal operation. The comparison results with conventional distributed strategy confirms better control performance of the proposed strategy. Finally, the feasibility of the proposed strategy was verified by experiment on AC microgrid testbed.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"284-294"},"PeriodicalIF":8.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219451","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 : 2024-09-04DOI: 10.1109/TSTE.2024.3454606
Jiabing Hu;Wei Wang;Yingbiao Li;Jianbo Guo
Power electronics (PE) equipment contains multiple timescale energy storage components and control loops. As a result, the dynamic process presents multiple timescale characteristics in PE-dominated power systems. For simplicity, single timescale dynamics are often the focus of corresponding analysis, and the influence of different timescales (i.e., cross-timescale analysis) is rarely considered. However, there is an interaction effect between multiple timescale controls and energy storage components, which complicates system dynamics. In this study, the cross-timescale impact of current control on the dynamics of rotor speed control timescale are evaluated. First, based on a two-machine two-area system comprising a phase-locked loop (PLL)-synchronized doubly fed induction generator (DFIG)-based wind turbine (WT), the influence of the PLL on cross-timescale interactions is revealed via modal analysis. Then, a current control equivalent circuit is derived for analyzing its cross-timescale influence on rotor motion, and the LC resonance mechanism related to the current control is revealed. Moreover, the impact of the PLL on cross-timescale interactions is elucidated. Finally, the cross-timescale influence phenomena and mechanisms are verified via real-time digital simulations.
{"title":"Cross-Timescale Interaction Analysis Between Current Control and Rotor Speed Control Timescale Dynamics in a High-Proportion DFIG-WT System","authors":"Jiabing Hu;Wei Wang;Yingbiao Li;Jianbo Guo","doi":"10.1109/TSTE.2024.3454606","DOIUrl":"10.1109/TSTE.2024.3454606","url":null,"abstract":"Power electronics (PE) equipment contains multiple timescale energy storage components and control loops. As a result, the dynamic process presents multiple timescale characteristics in PE-dominated power systems. For simplicity, single timescale dynamics are often the focus of corresponding analysis, and the influence of different timescales (i.e., cross-timescale analysis) is rarely considered. However, there is an interaction effect between multiple timescale controls and energy storage components, which complicates system dynamics. In this study, the cross-timescale impact of current control on the dynamics of rotor speed control timescale are evaluated. First, based on a two-machine two-area system comprising a phase-locked loop (PLL)-synchronized doubly fed induction generator (DFIG)-based wind turbine (WT), the influence of the PLL on cross-timescale interactions is revealed via modal analysis. Then, a current control equivalent circuit is derived for analyzing its cross-timescale influence on rotor motion, and the \u0000<italic>LC</i>\u0000 resonance mechanism related to the current control is revealed. Moreover, the impact of the PLL on cross-timescale interactions is elucidated. Finally, the cross-timescale influence phenomena and mechanisms are verified via real-time digital simulations.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"295-307"},"PeriodicalIF":8.6,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219449","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 : 2024-09-03DOI: 10.1109/TSTE.2024.3453269
Yang Mi;Changkun Lu;Chunxu Li;Jinpeng Qiao;Jie Shen;Peng Wang
In order to improve the voltage distribution and operation cost for ADN, A scheduling strategy is designed to integrate flexible resources, particularly mobile energy storage systems, within the coupling of ADN and TN, under an uncertain environment. A day-ahead Volt-VAR coordinated scheduling framework for ADN and TN can be proposed through incorporating a data-driven day-ahead scenario generation method based on denoising diffusion probabilistic model. First, the historical data may be employed to learn the error relationship between real power curves and predicted power curves for generating RES scenarios. The probability distribution for prediction error is constructed which can describe the day-ahead output power curve of RES. Subsequently, a SCCO approach is employed to measure voltage operating risk in uncertain environment, which can effectively utilize the controllability of different resource at timescale and spatial scale in ADN to fulfill the anticipated operational requirement. Finally, The ADN coupled with TN model can be linearized and converted into the mixed-integer linear programming method. Numerical simulations based on the IEEE 33-bus distribution system coupled with 15-node transportation network may verify the effectiveness of the proposed method.
{"title":"Data-Driven Volt-VAR Coordinated Scheduling With Mobile Energy Storage System for Active Distribution Network","authors":"Yang Mi;Changkun Lu;Chunxu Li;Jinpeng Qiao;Jie Shen;Peng Wang","doi":"10.1109/TSTE.2024.3453269","DOIUrl":"10.1109/TSTE.2024.3453269","url":null,"abstract":"In order to improve the voltage distribution and operation cost for ADN, A scheduling strategy is designed to integrate flexible resources, particularly mobile energy storage systems, within the coupling of ADN and TN, under an uncertain environment. A day-ahead Volt-VAR coordinated scheduling framework for ADN and TN can be proposed through incorporating a data-driven day-ahead scenario generation method based on denoising diffusion probabilistic model. First, the historical data may be employed to learn the error relationship between real power curves and predicted power curves for generating RES scenarios. The probability distribution for prediction error is constructed which can describe the day-ahead output power curve of RES. Subsequently, a SCCO approach is employed to measure voltage operating risk in uncertain environment, which can effectively utilize the controllability of different resource at timescale and spatial scale in ADN to fulfill the anticipated operational requirement. Finally, The ADN coupled with TN model can be linearized and converted into the mixed-integer linear programming method. Numerical simulations based on the IEEE 33-bus distribution system coupled with 15-node transportation network may verify the effectiveness of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"242-256"},"PeriodicalIF":8.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219452","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 : 2024-09-03DOI: 10.1109/TSTE.2024.3454080
Qiong Liu;Ye Guo;Lirong Deng;Haotian Liu;Dongyu Li;Hongbin Sun
A residual deep reinforcement learning (RDRL) based on an approximate-model-driven optimization approach is proposed for inverter-based volt-var control (IB-VVC) in active distribution networks. A modified Markov decision process is introduced to formulate the model-based and RDRL-based IB-VVC simultaneously, and then RDRL learns a residual action based on the action of the model-based approach with an approximate model. It inherits the control capability of the approximate-model-based optimization and enhances the policy optimization capability by residual policy learning. Since the approximate model acquired by operators is generally relatively reliable, the action solved by model-based optimization approaches is not far away from the optimal one. This allows RDRL to search for the residual action in a smaller residual action space, which further improves the approximation accuracy of the critic and reduces the search difficulties of the actor. Simulations demonstrate that RDRL improves the optimization performance considerably throughout the learning stage and verifies their three rationales for superior performance point-by-point on 69 and 141 bus balanced distribution networks.
{"title":"Residual Deep Reinforcement Learning With Model-Based Optimization for Inverter-Based Volt-Var Control","authors":"Qiong Liu;Ye Guo;Lirong Deng;Haotian Liu;Dongyu Li;Hongbin Sun","doi":"10.1109/TSTE.2024.3454080","DOIUrl":"10.1109/TSTE.2024.3454080","url":null,"abstract":"A residual deep reinforcement learning (RDRL) based on an approximate-model-driven optimization approach is proposed for inverter-based volt-var control (IB-VVC) in active distribution networks. A modified Markov decision process is introduced to formulate the model-based and RDRL-based IB-VVC simultaneously, and then RDRL learns a residual action based on the action of the model-based approach with an approximate model. It inherits the control capability of the approximate-model-based optimization and enhances the policy optimization capability by residual policy learning. Since the approximate model acquired by operators is generally relatively reliable, the action solved by model-based optimization approaches is not far away from the optimal one. This allows RDRL to search for the residual action in a smaller residual action space, which further improves the approximation accuracy of the critic and reduces the search difficulties of the actor. Simulations demonstrate that RDRL improves the optimization performance considerably throughout the learning stage and verifies their three rationales for superior performance point-by-point on 69 and 141 bus balanced distribution networks.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"269-283"},"PeriodicalIF":8.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219453","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 : 2024-09-03DOI: 10.1109/TSTE.2024.3454060
Ande Bala Naga Lingaiah;Narsa Reddy Tummuru
This paper proposes a photovoltaic (PV)-Utility integrated system with inductive power transfer (IPT) for electric vehicle (EV) charging applications in residential management applications. To realize this objective, a buck boost-interleaved H-bridge (BBIHB) configuration is proposed to integrate the PV source with the DC link of the front end converter (FEC) while achieving maximum power from the PV and power delivery to the IPT simultaneously. The DC bus of the IPT in the proposed system is obtained through the additive connection of the PV and FEC DC-link, which results in inherent boost of the DC bus voltage for the IPT system thus enhances the transmission power for the EV charging. Furthermore, the FEC allows bidirectional power flow between the utility and BBIHB converter by sharing the deficient or excess power in the system based on PV power availability. The complete modeling of the system and a control algorithm to achieve the above mentioned objectives is presented in this paper. Other features of the proposed system include inherent voltage boosting, a simple control strategy, and the absence of a separate converter for the PV to extract maximum power. Finally, an experimental hardware setup is developed in the laboratory and tested up to 4 kW of output power to validate the proposed system's performance, achieving a maximum DC-to-DC efficiency of 94% at this condition.
{"title":"A Photovoltaic-Grid Integrated System for the Residential Power Management","authors":"Ande Bala Naga Lingaiah;Narsa Reddy Tummuru","doi":"10.1109/TSTE.2024.3454060","DOIUrl":"10.1109/TSTE.2024.3454060","url":null,"abstract":"This paper proposes a photovoltaic (PV)-Utility integrated system with inductive power transfer (IPT) for electric vehicle (EV) charging applications in residential management applications. To realize this objective, a buck boost-interleaved H-bridge (BBIHB) configuration is proposed to integrate the PV source with the DC link of the front end converter (FEC) while achieving maximum power from the PV and power delivery to the IPT simultaneously. The DC bus of the IPT in the proposed system is obtained through the additive connection of the PV and FEC DC-link, which results in inherent boost of the DC bus voltage for the IPT system thus enhances the transmission power for the EV charging. Furthermore, the FEC allows bidirectional power flow between the utility and BBIHB converter by sharing the deficient or excess power in the system based on PV power availability. The complete modeling of the system and a control algorithm to achieve the above mentioned objectives is presented in this paper. Other features of the proposed system include inherent voltage boosting, a simple control strategy, and the absence of a separate converter for the PV to extract maximum power. Finally, an experimental hardware setup is developed in the laboratory and tested up to 4 kW of output power to validate the proposed system's performance, achieving a maximum DC-to-DC efficiency of 94% at this condition.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"257-268"},"PeriodicalIF":8.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219450","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}
The serious uncertainties from the extensive integration of renewable energy generations put forward a higher real-time requirement for power system dispatching. To provide economic and feasible generation operations in real-time, a physics-informed reinforcement learning (PIRL) method based on constrained reinforcement learning (CRL) for optimal power flow (OPF) is presented in this paper. In the proposed method, a physics-informed actor based on the power flow equations is designed to generate generation operations that satisfy the equality constraints of OPF. To specify inequality constraints in actor optimization, the policy gradient is augmented with the constraints to correct unfeasible generation operations. In particular, the cost functions related to inequality constraints can be directly calculated based on the output of the actor, which is more accurate than using networks to approximate in general CRL methods. The proposed method is tested on the IEEE 118-bus system, and the simulation result shows that the proposed method achieves a significant improvement in computation speed compared with the traditional interior point method while obtaining a similar generation cost.
{"title":"Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow With Renewable Energy Resources","authors":"Zhuorui Wu;Meng Zhang;Song Gao;Zheng-Guang Wu;Xiaohong Guan","doi":"10.1109/TSTE.2024.3452489","DOIUrl":"10.1109/TSTE.2024.3452489","url":null,"abstract":"The serious uncertainties from the extensive integration of renewable energy generations put forward a higher real-time requirement for power system dispatching. To provide economic and feasible generation operations in real-time, a physics-informed reinforcement learning (PIRL) method based on constrained reinforcement learning (CRL) for optimal power flow (OPF) is presented in this paper. In the proposed method, a physics-informed actor based on the power flow equations is designed to generate generation operations that satisfy the equality constraints of OPF. To specify inequality constraints in actor optimization, the policy gradient is augmented with the constraints to correct unfeasible generation operations. In particular, the cost functions related to inequality constraints can be directly calculated based on the output of the actor, which is more accurate than using networks to approximate in general CRL methods. The proposed method is tested on the IEEE 118-bus system, and the simulation result shows that the proposed method achieves a significant improvement in computation speed compared with the traditional interior point method while obtaining a similar generation cost.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"216-226"},"PeriodicalIF":8.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219454","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}
The proliferation of distributed renewable resources increases the uncertainty in distribution systems. Coupling the distribution system and district heating system helps leverage the flexibility of thermal storage and thus supports the operation of the electrical grid. This paper proposes a method to characterize flexibility from district heating system via polyhedral sets. First, a recursive robust feasibility condition that ensures heat supply adequacy under uncertain demand is established. Then, stagewise robust feasible sets of thermal storage levels are calculated using a customized projection algorithm. Finally, dynamic bounds of electric heaters are computed by a further projection step. With those dynamic bounds, the electric heaters behave like reducible loads, and the demands in each period are decoupled over time, although the dispatch of thermal storage units must comply with inter-temporal constraints. The proposed method allows the two coupled systems to be operated in a distributed way without forecasts and extensive communications. Numerical simulations on small and practically sized testing systems validate the advantage of the proposed method. On average, the set calculation takes about 8 minutes for the day-ahead problem and 11 seconds for real-time dispatch on a portable laptop, and the prediction-free operation policy has an average optimality gap of 3.6% compared to the hindsight optimum.
{"title":"Exploiting the Flexibility of District Heating System for Distribution System Operation: Set-Based Characterization and Temporal Decomposition","authors":"Weitao Chen;Xiaojun Wang;Wei Wei;Yin Xu;Jianzhong Wu","doi":"10.1109/TSTE.2024.3452560","DOIUrl":"10.1109/TSTE.2024.3452560","url":null,"abstract":"The proliferation of distributed renewable resources increases the uncertainty in distribution systems. Coupling the distribution system and district heating system helps leverage the flexibility of thermal storage and thus supports the operation of the electrical grid. This paper proposes a method to characterize flexibility from district heating system via polyhedral sets. First, a recursive robust feasibility condition that ensures heat supply adequacy under uncertain demand is established. Then, stagewise robust feasible sets of thermal storage levels are calculated using a customized projection algorithm. Finally, dynamic bounds of electric heaters are computed by a further projection step. With those dynamic bounds, the electric heaters behave like reducible loads, and the demands in each period are decoupled over time, although the dispatch of thermal storage units must comply with inter-temporal constraints. The proposed method allows the two coupled systems to be operated in a distributed way without forecasts and extensive communications. Numerical simulations on small and practically sized testing systems validate the advantage of the proposed method. On average, the set calculation takes about 8 minutes for the day-ahead problem and 11 seconds for real-time dispatch on a portable laptop, and the prediction-free operation policy has an average optimality gap of 3.6% compared to the hindsight optimum.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"227-241"},"PeriodicalIF":8.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219455","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 : 2024-08-28DOI: 10.1109/TSTE.2024.3450503
Yangjun Zeng;Yiwei Qiu;Jie Zhu;Shi Chen;Buxiang Zhou;Jiarong Li;Bosen Yang;Jin Lin
Utility-scale renewable power-to-hydrogen (ReP2H) systems typically consist of multiple electrolyzers (ELZs), many of which are powered by thyristor rectifiers (TRs). A TR-powered ELZ has a nonlinear and nondecouplable relation between its active and reactive loads. The on-off switching and load allocation across multiple ELZs impact the efficiency of P2H energy conversion and the active and reactive power flows in the electrical network. Improper scheduling may result in an excessive reactive load from the hydrogen plant, causing voltage violations and increased network losses, which compromise both safety and profitability. To address these issues, this paper first explores the tradeoffs between the energy efficiency and reactive loads of ELZs. Then, we propose a joint active-reactive power management method to coordinate the loads and thermal properties of the ELZs, renewables, energy storage, and var compensation to improve the overall productivity and profitability. Mixed-integer second-order cone programming (MISOCP) is established to achieve these goals, and a decomposition algorithm enables its applicability in large-scale systems. Case studies show that the proposed method, at best, increases the hydrogen yield by 2.49% while reducing network losses by 3.12% compared to the state-of-the-art strategies based on wind and solar power data from Inner Mongolia, China. The optimal var resource configuration for ReP2H systems is also briefly discussed.
{"title":"Scheduling Multiple Industrial Electrolyzers in Renewable P2H Systems: A Coordinated Active-Reactive Power Management Method","authors":"Yangjun Zeng;Yiwei Qiu;Jie Zhu;Shi Chen;Buxiang Zhou;Jiarong Li;Bosen Yang;Jin Lin","doi":"10.1109/TSTE.2024.3450503","DOIUrl":"10.1109/TSTE.2024.3450503","url":null,"abstract":"Utility-scale renewable power-to-hydrogen (ReP2H) systems typically consist of multiple electrolyzers (ELZs), many of which are powered by thyristor rectifiers (TRs). A TR-powered ELZ has a nonlinear and nondecouplable relation between its active and reactive loads. The on-off switching and load allocation across multiple ELZs impact the efficiency of P2H energy conversion and the active and reactive power flows in the electrical network. Improper scheduling may result in an excessive reactive load from the hydrogen plant, causing voltage violations and increased network losses, which compromise both safety and profitability. To address these issues, this paper first explores the tradeoffs between the energy efficiency and reactive loads of ELZs. Then, we propose a joint active-reactive power management method to coordinate the loads and thermal properties of the ELZs, renewables, energy storage, and var compensation to improve the overall productivity and profitability. Mixed-integer second-order cone programming (MISOCP) is established to achieve these goals, and a decomposition algorithm enables its applicability in large-scale systems. Case studies show that the proposed method, at best, increases the hydrogen yield by 2.49% while reducing network losses by 3.12% compared to the state-of-the-art strategies based on wind and solar power data from Inner Mongolia, China. The optimal var resource configuration for ReP2H systems is also briefly discussed.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"201-215"},"PeriodicalIF":8.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219457","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 : 2024-08-28DOI: 10.1109/TSTE.2024.3448366
Haohui Ding;Qinran Hu;Tao Qian;Zaijun Wu
The power-to-hydrogen-and-heat (P2HH) method is a potential means of improving energy efficiency while reducing carbon emissions. However, existing P2HH methods are ineffective at low temperatures, and do not consider physical constraints on heat exchange between the electrolyzer and heating network. Hence, this paper proposes an improved P2HH (IP2HH) method to overcome these shortcomings. First, this paper proposes a novel collaborative mechanism between the electrolyzer and the heating network. In this system, the electrolyzer can not only transfer excess heat to the heating network when at high temperatures, but can also be heated by the heating network when at low temperatures. Second, this paper takes the physical limitations of heat exchange between electrolyzer and heating network into consideration, and models the regulating valve of heat exchanger. These considerations allow produce simulation results that better approximate real-world scenarios. Finally, a convex model integrated with the IP2HH approach is established. Compared with the existing P2HH method, the IP2HH method may allow electrolyzer to work intermittently based on fluctuations in renewables supplies while maintaining high hydrogen production efficiency. Such improvements could reduce carbon emissions by 35.5%, costs by 10.7%, and renewables curtailment by 26.4% at ambient temperature of 1 $^{circ }$ C.
{"title":"Modeling and Optimization Operation of Improved Power-to-Hydrogen-and-Heat Method at Low Temperature for Reducing Carbon Emissions","authors":"Haohui Ding;Qinran Hu;Tao Qian;Zaijun Wu","doi":"10.1109/TSTE.2024.3448366","DOIUrl":"10.1109/TSTE.2024.3448366","url":null,"abstract":"The power-to-hydrogen-and-heat (P2HH) method is a potential means of improving energy efficiency while reducing carbon emissions. However, existing P2HH methods are ineffective at low temperatures, and do not consider physical constraints on heat exchange between the electrolyzer and heating network. Hence, this paper proposes an improved P2HH (IP2HH) method to overcome these shortcomings. First, this paper proposes a novel collaborative mechanism between the electrolyzer and the heating network. In this system, the electrolyzer can not only transfer excess heat to the heating network when at high temperatures, but can also be heated by the heating network when at low temperatures. Second, this paper takes the physical limitations of heat exchange between electrolyzer and heating network into consideration, and models the regulating valve of heat exchanger. These considerations allow produce simulation results that better approximate real-world scenarios. Finally, a convex model integrated with the IP2HH approach is established. Compared with the existing P2HH method, the IP2HH method may allow electrolyzer to work intermittently based on fluctuations in renewables supplies while maintaining high hydrogen production efficiency. Such improvements could reduce carbon emissions by 35.5%, costs by 10.7%, and renewables curtailment by 26.4% at ambient temperature of 1 \u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000C.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"189-200"},"PeriodicalIF":8.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219456","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}
To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism. Initially, the strategy assesses electric energy interactions between microgrids and distribution networks to establish a foundation for collaborative scheduling. The two-stage pricing mechanism initiates with a leader-follower game, wherein the microgrid operator acts as the leader and users as followers. Subsequently, it adjusts EV tariffs based on the game's equilibrium, taking into account factors such as battery degradation and travel needs to optimize EVs' electricity consumption. Furthermore, a bi-level optimization model refines power interactions and pricing strategies across the network, significantly enhancing demand response capabilities and economic outcomes. Simulation results demonstrate that this strategy not only increases renewable energy consumption but also reduces energy costs, thereby improving the overall efficiency and sustainability of the system.
{"title":"Optimal Dispatch Strategy for a Multi-Microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism","authors":"Yonghui Nie;Zhi Li;Jie Zhang;Lei Gao;Yang Li;Hengyu Zhou","doi":"10.1109/TSTE.2024.3449909","DOIUrl":"10.1109/TSTE.2024.3449909","url":null,"abstract":"To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism. Initially, the strategy assesses electric energy interactions between microgrids and distribution networks to establish a foundation for collaborative scheduling. The two-stage pricing mechanism initiates with a leader-follower game, wherein the microgrid operator acts as the leader and users as followers. Subsequently, it adjusts EV tariffs based on the game's equilibrium, taking into account factors such as battery degradation and travel needs to optimize EVs' electricity consumption. Furthermore, a bi-level optimization model refines power interactions and pricing strategies across the network, significantly enhancing demand response capabilities and economic outcomes. Simulation results demonstrate that this strategy not only increases renewable energy consumption but also reduces energy costs, thereby improving the overall efficiency and sustainability of the system.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"174-188"},"PeriodicalIF":8.6,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219458","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}
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