The proliferation of distributed and renewable energy resources introduces additional operational challenges to power distribution systems. Transactive energy management, which allows networked neighborhood communities and houses to trade energy, is expected to be developed as an effective method for accommodating additional uncertainties and security mandates pertaining to distributed energy resources. This paper proposes and analyzes a two-layer transactive energy market in which houses in networked neighborhood community microgrids will trade energy in respective market layers. This paper studies the blockchain applications to satisfy socioeconomic and technological concerns of secure transactive energy management in a two-level power distribution system. The numerical results for practical networked microgrids located at IliinoisTech-Bronzevilie in Chicago illustrate the validity of the proposed blockchain-based transactive energy management for devising a distributed, scalable, efficient, and cybersecured power grid operation. The conclusion of the paper summarizes the prospects for blockchain applications to transactive energy management in power distribution systems.
{"title":"Blockchain for transactive energy management in networked neighborhood microgrids","authors":"Zhikun Hu;Mingyu Yan;Chongyu Wang;Ahmed Alabdulwahab;Mohammad Shahidehpour","doi":"10.23919/IEN.2025.0026","DOIUrl":"https://doi.org/10.23919/IEN.2025.0026","url":null,"abstract":"The proliferation of distributed and renewable energy resources introduces additional operational challenges to power distribution systems. Transactive energy management, which allows networked neighborhood communities and houses to trade energy, is expected to be developed as an effective method for accommodating additional uncertainties and security mandates pertaining to distributed energy resources. This paper proposes and analyzes a two-layer transactive energy market in which houses in networked neighborhood community microgrids will trade energy in respective market layers. This paper studies the blockchain applications to satisfy socioeconomic and technological concerns of secure transactive energy management in a two-level power distribution system. The numerical results for practical networked microgrids located at IliinoisTech-Bronzevilie in Chicago illustrate the validity of the proposed blockchain-based transactive energy management for devising a distributed, scalable, efficient, and cybersecured power grid operation. The conclusion of the paper summarizes the prospects for blockchain applications to transactive energy management in power distribution systems.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 4","pages":"235-246"},"PeriodicalIF":5.1,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11247892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Radiator cooling configurations need to account for both efficient heat dissipation and energy conservation requirements. Rapid and rational determination of cooling system configurations constitutes a critical aspect of transformer design, enhancing electrical power energy utilization efficiency. Computational fluid dynamics (CFD) is widely recognized as a well-established technique for simulating and optimizing heat dissipation systems. However, this approach is time-consuming because of preprocessing procedures, such as meshing. This paper proposes a fast iterative optimization model for calculating the outlet oil temperature and airflow distribution. Based on the analytical model results, this paper identifies the optimal energy-saving range for radiator cooling configurations, incorporating the cooperative effects of cooling efficiency, air pressure drop during heat transfer, and inlet-outlet temperature difference. The analytical model demonstrated errors in energy dissipation and temperature difference calculations within an acceptable range. The calculation time was reduced by more than 99%. Radiator configurations within the optimal range effectively minimize energy waste while meeting the target temperature difference and enhancing cooling efficiency. Finally, the PC2600-22/520 radiator was utilized to validate the accuracy of the analytical model and the rationality of the co-optimal intervals.
{"title":"A fast configuration method for external cooling system of power transformer considering energy loss","authors":"Lujia Wang;Mengzhi Sun;Zhenlu Cai;Haitao Yang;Xibo Wu","doi":"10.23919/IEN.2025.0028","DOIUrl":"https://doi.org/10.23919/IEN.2025.0028","url":null,"abstract":"Radiator cooling configurations need to account for both efficient heat dissipation and energy conservation requirements. Rapid and rational determination of cooling system configurations constitutes a critical aspect of transformer design, enhancing electrical power energy utilization efficiency. Computational fluid dynamics (CFD) is widely recognized as a well-established technique for simulating and optimizing heat dissipation systems. However, this approach is time-consuming because of preprocessing procedures, such as meshing. This paper proposes a fast iterative optimization model for calculating the outlet oil temperature and airflow distribution. Based on the analytical model results, this paper identifies the optimal energy-saving range for radiator cooling configurations, incorporating the cooperative effects of cooling efficiency, air pressure drop during heat transfer, and inlet-outlet temperature difference. The analytical model demonstrated errors in energy dissipation and temperature difference calculations within an acceptable range. The calculation time was reduced by more than 99%. Radiator configurations within the optimal range effectively minimize energy waste while meeting the target temperature difference and enhancing cooling efficiency. Finally, the PC2600-22/520 radiator was utilized to validate the accuracy of the analytical model and the rationality of the co-optimal intervals.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 4","pages":"269-277"},"PeriodicalIF":5.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets. However, most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships, particularly in the presence of nonlinearities and confounding factors. This limits their value for informing policy and market design in the context of the energy transition. To address this gap, we propose a novel causal inference framework based on local partially linear double machine learning (DML). Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for nonlinear, context-dependent effects. This represents a substantial methodological advancement over standard econometric techniques. Applying this framework to the UK electricity market over the period 2018–2024, we produce the first robust causal estimates of how renewables affect day-ahead wholesale electricity prices. We find that wind power exerts a U-shaped causal effect: at low penetration levels, a 1 GWh increase reduces prices by up to £7/MWh, the effect weakens at mid-levels, and intensifies again at higher penetration. Solar power consistently reduces prices at low penetration levels, up to £9/MWh per additional GWh, but its marginal effect diminishes quickly. Importantly, the magnitude of these effects has increased over time, reflecting the growing influence of renewables on price formation as their share in the energy mix rises. These findings offer a sound empirical basis for improving the design of support schemes, refining capacity planning, and enhancing electricity market efficiency. By providing a robust causal understanding of renewable impacts, our study contributes both methodological innovation and actionable insights to guide future energy policy.
{"title":"Do we actually understand the impact of renewables on electricity prices? A causal inference approach","authors":"Davide Cacciarelli;Pierre Pinson;Filip Panagiotopoulos;David Dixon;Lizzie Blaxland","doi":"10.23919/IEN.2025.0027","DOIUrl":"https://doi.org/10.23919/IEN.2025.0027","url":null,"abstract":"Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets. However, most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships, particularly in the presence of nonlinearities and confounding factors. This limits their value for informing policy and market design in the context of the energy transition. To address this gap, we propose a novel causal inference framework based on local partially linear double machine learning (DML). Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for nonlinear, context-dependent effects. This represents a substantial methodological advancement over standard econometric techniques. Applying this framework to the UK electricity market over the period 2018–2024, we produce the first robust causal estimates of how renewables affect day-ahead wholesale electricity prices. We find that wind power exerts a U-shaped causal effect: at low penetration levels, a 1 GWh increase reduces prices by up to £7/MWh, the effect weakens at mid-levels, and intensifies again at higher penetration. Solar power consistently reduces prices at low penetration levels, up to £9/MWh per additional GWh, but its marginal effect diminishes quickly. Importantly, the magnitude of these effects has increased over time, reflecting the growing influence of renewables on price formation as their share in the energy mix rises. These findings offer a sound empirical basis for improving the design of support schemes, refining capacity planning, and enhancing electricity market efficiency. By providing a robust causal understanding of renewable impacts, our study contributes both methodological innovation and actionable insights to guide future energy policy.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 4","pages":"247-258"},"PeriodicalIF":5.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11231232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To enhance the low-voltage ride-through (LVRT) capability of emerging power systems with increasing penetration of renewable energy while addressing issues such as the slow response speed of traditional proportional-integral (PI) control, high model accuracy requirements, and complex system parameter tuning, this paper proposes a droop-controlled converter reactive power support strategy based on first-order linear active disturbance rejection control (LADRC). First, a mathematical model of a droop-controlled grid-forming (GFM) converter is established. A model equivalence method is then proposed to transform the dynamic characteristics of the control loop into equivalent impedance parameters. Based on the equivalent impedance parameter model, the influencing factors of the converter terminal voltage and point of common coupling (PCC) voltage are derived. Next, a first-order linear active disturbance rejection control strategy is introduced into the traditional droop control framework, and the controller parameters are optimized via the bandwidth tuning method. Finally, a simulation model of the droop-controlled GFM converter based on the linear active disturbance rejection controller is constructed on the PSCAD/EMTDC platform, and through comparative experiments under typical grid fault conditions, the effectiveness of the proposed control strategy in improving the system fault ride-through capability and voltage support is verified.
{"title":"Reactive voltage support strategy for droop-controlled grid-forming converters based on LADRC","authors":"Dejian Yang;Zhijie Cao;Chaoquan Li","doi":"10.23919/IEN.2025.0021","DOIUrl":"https://doi.org/10.23919/IEN.2025.0021","url":null,"abstract":"To enhance the low-voltage ride-through (LVRT) capability of emerging power systems with increasing penetration of renewable energy while addressing issues such as the slow response speed of traditional proportional-integral (PI) control, high model accuracy requirements, and complex system parameter tuning, this paper proposes a droop-controlled converter reactive power support strategy based on first-order linear active disturbance rejection control (LADRC). First, a mathematical model of a droop-controlled grid-forming (GFM) converter is established. A model equivalence method is then proposed to transform the dynamic characteristics of the control loop into equivalent impedance parameters. Based on the equivalent impedance parameter model, the influencing factors of the converter terminal voltage and point of common coupling (PCC) voltage are derived. Next, a first-order linear active disturbance rejection control strategy is introduced into the traditional droop control framework, and the controller parameters are optimized via the bandwidth tuning method. Finally, a simulation model of the droop-controlled GFM converter based on the linear active disturbance rejection controller is constructed on the PSCAD/EMTDC platform, and through comparative experiments under typical grid fault conditions, the effectiveness of the proposed control strategy in improving the system fault ride-through capability and voltage support is verified.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 4","pages":"259-268"},"PeriodicalIF":5.1,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11218761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Grid-forming (GFM) control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation (CIG), including wind power, photovoltaic, and battery energy storage. In this paper, we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy. The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation, denoted as the frequency-fixed zone. With the proposed strategy, the system frequency is almost always fixed at its rated value, achieving system active power balance independent of frequency, and intentional power flow adjustments are implemented through direct phase angle control. This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations. Furthermore, synchronization dynamics are significantly diminished, and synchronization stability is enhanced. The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.
{"title":"Frequency-fixed grid-forming control for less-dynamic and safer renewable power systems","authors":"Yong Min;Zhenyu Lei;Lei Chen;Fei Xu;Boyuan Zhao;Zongxiang Lu;Ling Hao","doi":"10.23919/IEN.2025.0024","DOIUrl":"https://doi.org/10.23919/IEN.2025.0024","url":null,"abstract":"Grid-forming (GFM) control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation (CIG), including wind power, photovoltaic, and battery energy storage. In this paper, we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy. The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation, denoted as the frequency-fixed zone. With the proposed strategy, the system frequency is almost always fixed at its rated value, achieving system active power balance independent of frequency, and intentional power flow adjustments are implemented through direct phase angle control. This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations. Furthermore, synchronization dynamics are significantly diminished, and synchronization stability is enhanced. The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 4","pages":"219-234"},"PeriodicalIF":5.1,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11218760","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As power systems expand, solving the unit commitment problem (UCP) becomes increasingly challenging due to the curse of dimensionality, and traditional methods often struggle to balance computational efficiency and solution optimality. To tackle this issue, we propose a problem-structure-informed quantum approximate optimization algorithm (QAOA) framework that fully exploits the quantum advantage under extremely limited quantum resources. Specifically, we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems, which are solvable in parallel by limited number of qubits. This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse. Consequently, our approach can be extended to future power systems that are larger and more complex.
{"title":"Problem-structure-informed quantum approximate optimization for large-scale unit commitment with limited qubits","authors":"Jingxian Zhou;Ziqing Zhu;Linghua Zhu;Siqi Bu","doi":"10.23919/IEN.2025.0025","DOIUrl":"https://doi.org/10.23919/IEN.2025.0025","url":null,"abstract":"As power systems expand, solving the unit commitment problem (UCP) becomes increasingly challenging due to the curse of dimensionality, and traditional methods often struggle to balance computational efficiency and solution optimality. To tackle this issue, we propose a problem-structure-informed quantum approximate optimization algorithm (QAOA) framework that fully exploits the quantum advantage under extremely limited quantum resources. Specifically, we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems, which are solvable in parallel by limited number of qubits. This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse. Consequently, our approach can be extended to future power systems that are larger and more complex.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 4","pages":"215-218"},"PeriodicalIF":5.1,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11218762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yaqi Sun;Wenchuan Wu;Yue Zhou;Haotian Zhao;Shuwei Xu;Qi Wang
The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply, increasing the demand for energy storage in distribution networks. Shared energy storage in distribution networks can participate in energy storage allocation as a provider of reliability ancillary services. This paper proposes a novel Nash bargaining based energy storage coordinated allocation method to fully incentivize shared energy storage to participate in reliability services within the distribution network. First, an analytical reliability assessment model is constructed and embedded into the energy storage allocation model, where the impact of renewable energy uncertainty is described using chance constraints. Considering the interests of both the distribution network and shared energy storage operators, a Nash bargaining based energy storage coordinated allocation and benefit sharing mechanism is established, which is then transformed into a mixed-integer linear programming (MILP) model for efficient solution. Case studies show that the proposed method, through cooperation between the distribution system operator and shared energy storage operators, significantly reduces investment cost of energy storage and ensures a rational distribution of the benefits obtained.
{"title":"Energy storage configuration model for reliability services of active distribution networks","authors":"Yaqi Sun;Wenchuan Wu;Yue Zhou;Haotian Zhao;Shuwei Xu;Qi Wang","doi":"10.23919/IEN.2025.0015","DOIUrl":"https://doi.org/10.23919/IEN.2025.0015","url":null,"abstract":"The volatility introduced by the integration of renewable energy poses challenges to the reliability of power supply, increasing the demand for energy storage in distribution networks. Shared energy storage in distribution networks can participate in energy storage allocation as a provider of reliability ancillary services. This paper proposes a novel Nash bargaining based energy storage coordinated allocation method to fully incentivize shared energy storage to participate in reliability services within the distribution network. First, an analytical reliability assessment model is constructed and embedded into the energy storage allocation model, where the impact of renewable energy uncertainty is described using chance constraints. Considering the interests of both the distribution network and shared energy storage operators, a Nash bargaining based energy storage coordinated allocation and benefit sharing mechanism is established, which is then transformed into a mixed-integer linear programming (MILP) model for efficient solution. Case studies show that the proposed method, through cooperation between the distribution system operator and shared energy storage operators, significantly reduces investment cost of energy storage and ensures a rational distribution of the benefits obtained.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 3","pages":"149-156"},"PeriodicalIF":5.1,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose STPLF, which stands for the short-term forecasting of locational marginal price components, including the forecasting of non-conforming hourly net loads. The volatility of transmission-level hourly locational marginal prices (LMPs) is caused by several factors, including weather data, hourly gas prices, historical hourly loads, and market prices. In addition, variations of non-conforming net loads, which are affected by behind-the-meter distributed energy resources (DERs) and retail customer loads, could have a major impact on the volatility of hourly LMPs, as bulk grid operators have limited visibility of such retail-level resources. We propose a fusion forecasting model for the STPLF, which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices. Additionally, data preprocessing and feature extraction are used to increase the accuracy of the STPLF. The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes. We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
{"title":"Fusion of deep learning and machine learning methods for hourly locational marginal price forecast in power systems","authors":"Matin Farhoumandi;Sheida Bahramirad;Ahmed Alabdulwahab;Mohammad Shahidehpour;Farrokh Rahimi;Ali Ipakchi;Farrokh Albuyeh;Sasan Mokhtari","doi":"10.23919/IEN.2025.0019","DOIUrl":"https://doi.org/10.23919/IEN.2025.0019","url":null,"abstract":"In this paper, we propose STPLF, which stands for the short-term forecasting of locational marginal price components, including the forecasting of non-conforming hourly net loads. The volatility of transmission-level hourly locational marginal prices (LMPs) is caused by several factors, including weather data, hourly gas prices, historical hourly loads, and market prices. In addition, variations of non-conforming net loads, which are affected by behind-the-meter distributed energy resources (DERs) and retail customer loads, could have a major impact on the volatility of hourly LMPs, as bulk grid operators have limited visibility of such retail-level resources. We propose a fusion forecasting model for the STPLF, which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices. Additionally, data preprocessing and feature extraction are used to increase the accuracy of the STPLF. The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes. We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 3","pages":"193-204"},"PeriodicalIF":5.1,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151718","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As future ship system, hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis. However, current optimization scheduling works lack the consideration of sea conditions and navigational circumstances. Therefore, this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system. The proposed framework considers multiple optimizations of route, speed planning, and energy management under the constraints of sea conditions during navigation. First, a complex hybrid ship power model consisting of diesel generation system, propulsion system, energy storage system, photovoltaic power generation system, and electric boiler system is established, where sea state information and ship resistance model are considered. With objective optimization functions of cost and greenhouse gas (GHG) emissions, a two-stage optimization framework consisting of route planning, speed scheduling, and energy management is constructed. Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route, speed, and energy optimization scheduling. Finally, simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption, operating costs, and carbon emissions by 17.8%, 17.39%, and 13.04%, respectively, compared with the non-optimal control group.
{"title":"Two-stage optimization of route, speed, and energy management for hybrid energy ship under sea conditions","authors":"Xiaoyuan Luo;Jiaxuan Wang;Xinyu Wang;Xinping Guan","doi":"10.23919/IEN.2025.0017","DOIUrl":"https://doi.org/10.23919/IEN.2025.0017","url":null,"abstract":"As future ship system, hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis. However, current optimization scheduling works lack the consideration of sea conditions and navigational circumstances. Therefore, this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system. The proposed framework considers multiple optimizations of route, speed planning, and energy management under the constraints of sea conditions during navigation. First, a complex hybrid ship power model consisting of diesel generation system, propulsion system, energy storage system, photovoltaic power generation system, and electric boiler system is established, where sea state information and ship resistance model are considered. With objective optimization functions of cost and greenhouse gas (GHG) emissions, a two-stage optimization framework consisting of route planning, speed scheduling, and energy management is constructed. Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route, speed, and energy optimization scheduling. Finally, simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption, operating costs, and carbon emissions by 17.8%, 17.39%, and 13.04%, respectively, compared with the non-optimal control group.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 3","pages":"174-192"},"PeriodicalIF":5.1,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of renewable energy sources (RESs) with inverter interfaces has fundamentally reshaped power system dynamics, challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids. Conventional classifications, which decouple voltage, frequency, and rotor angle stability, fail to address the emerging strong voltage-angle coupling effects caused by RES dynamics. This coupling introduces complex oscillation modes and undermines system robustness, necessitating novel stability assessment tools. Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability. This work proposes a transient energy-based framework to resolve these gaps. By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange, the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers. The coupling strength is also quantified by defining the relative coupling strength index, which is directly related to the transient energy interpretation of the coupled stability. Angle-voltage coupling may induce instability by injecting transient energy into the system, even if the individual phase angle and voltage dynamics themselves are stable. The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability, addressing the urgent need for tools for managing modern power system evolving stability challenges.
{"title":"Stability assessment of inverter-dominated power systems considering coupling between phase angle and voltage dynamics","authors":"Cong Fu;Shuiping Zhang;Shun Li;Feng Liu","doi":"10.23919/IEN.2025.0016","DOIUrl":"https://doi.org/10.23919/IEN.2025.0016","url":null,"abstract":"The integration of renewable energy sources (RESs) with inverter interfaces has fundamentally reshaped power system dynamics, challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids. Conventional classifications, which decouple voltage, frequency, and rotor angle stability, fail to address the emerging strong voltage-angle coupling effects caused by RES dynamics. This coupling introduces complex oscillation modes and undermines system robustness, necessitating novel stability assessment tools. Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability. This work proposes a transient energy-based framework to resolve these gaps. By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange, the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers. The coupling strength is also quantified by defining the relative coupling strength index, which is directly related to the transient energy interpretation of the coupled stability. Angle-voltage coupling may induce instability by injecting transient energy into the system, even if the individual phase angle and voltage dynamics themselves are stable. The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability, addressing the urgent need for tools for managing modern power system evolving stability challenges.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"4 3","pages":"157-164"},"PeriodicalIF":5.1,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11125854","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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