Pub Date : 2024-12-13DOI: 10.1016/j.segan.2024.101595
Jiwen Qi, Li Li, Jahangir Hossain, Gang Lei
An electric vehicle (EV) parking lot model with distributed energy resources, addressing challenges such as market volatility, renewable energy variability, and unpredictable EV behavior, is presented in this paper. The model integrates renewable energy sources with vehicle-to-grid (V2G) functionalities and uses a long short-term memory network to forecast uncertainties like Frequency Control Ancillary Service (FCAS) and spot market prices, solar irradiance, and wind speed. To optimize limited bi-directional chargers, an EV allocation method is introduced, and the Monte Carlo method is employed to simulate diverse EV user behaviors. Additionally, a refined information gap decision theory-based method is developed to determine optimal V2G incentives, enhancing profitability for the parking lot and reducing costs for EV owners. Validation through comparisons with alternative incentive scenarios shows that participation in both FCAS and spot markets yields the highest profitability, with profit increases ranging from 91.67% to 125.45% compared to fixed incentives of $0 or $0.1.
{"title":"Optimizing electric vehicle parking lot profitability through vehicle-to-grid incentive decision-making in multiple energy markets","authors":"Jiwen Qi, Li Li, Jahangir Hossain, Gang Lei","doi":"10.1016/j.segan.2024.101595","DOIUrl":"10.1016/j.segan.2024.101595","url":null,"abstract":"<div><div>An electric vehicle (EV) parking lot model with distributed energy resources, addressing challenges such as market volatility, renewable energy variability, and unpredictable EV behavior, is presented in this paper. The model integrates renewable energy sources with vehicle-to-grid (V2G) functionalities and uses a long short-term memory network to forecast uncertainties like Frequency Control Ancillary Service (FCAS) and spot market prices, solar irradiance, and wind speed. To optimize limited bi-directional chargers, an EV allocation method is introduced, and the Monte Carlo method is employed to simulate diverse EV user behaviors. Additionally, a refined information gap decision theory-based method is developed to determine optimal V2G incentives, enhancing profitability for the parking lot and reducing costs for EV owners. Validation through comparisons with alternative incentive scenarios shows that participation in both FCAS and spot markets yields the highest profitability, with profit increases ranging from 91.67% to 125.45% compared to fixed incentives of $0 or $0.1.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101595"},"PeriodicalIF":4.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The transition to battery electric vehicles (BEVs) necessitates the establishment of an effective charging infrastructure, especially in countries like Greece where BEV adoption has been slow. This paper presents a comprehensive multi-criteria decision analysis (MCDA) framework, utilizing the PROMETHEE II method, to facilitate the optimal siting of electric vehicle charging stations (EVCSs) in Greek municipalities. The framework considers a broad range of economic, environmental, social, and technological factors and is supported by a user-friendly, web-based application that enables stakeholders to assess potential EVCS locations by dynamically adjusting criteria weights in areas of their interest. The application is tested via a case study involving municipalities in Crete, identifying major cities, such as Heraklion, Chania, and Rethymno, as the optimal alternatives to the problem, with average rankings of 1.07, 2.89, and 3.68 respectively. Sensitivity analysis results confirm the stability and robustness of the proposed locations with ranking deviations generally limited to a range of one to three positions. Overall, the framework effectively distinguishes key factors influencing EVCS deployment and supports strategic infrastructure planning and investment decisions.
{"title":"Optimizing electric vehicle charging station placement in Greek municipalities through multi-criteria decision analysis","authors":"Panagiotis Skaloumpakas , Alexandros Kafouros , Evangelos Spiliotis , Elissaios Sarmas , Vangelis Marinakis","doi":"10.1016/j.segan.2024.101589","DOIUrl":"10.1016/j.segan.2024.101589","url":null,"abstract":"<div><div>The transition to battery electric vehicles (BEVs) necessitates the establishment of an effective charging infrastructure, especially in countries like Greece where BEV adoption has been slow. This paper presents a comprehensive multi-criteria decision analysis (MCDA) framework, utilizing the PROMETHEE II method, to facilitate the optimal siting of electric vehicle charging stations (EVCSs) in Greek municipalities. The framework considers a broad range of economic, environmental, social, and technological factors and is supported by a user-friendly, web-based application that enables stakeholders to assess potential EVCS locations by dynamically adjusting criteria weights in areas of their interest. The application is tested via a case study involving municipalities in Crete, identifying major cities, such as Heraklion, Chania, and Rethymno, as the optimal alternatives to the problem, with average rankings of 1.07, 2.89, and 3.68 respectively. Sensitivity analysis results confirm the stability and robustness of the proposed locations with ranking deviations generally limited to a range of one to three positions. Overall, the framework effectively distinguishes key factors influencing EVCS deployment and supports strategic infrastructure planning and investment decisions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101589"},"PeriodicalIF":4.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1016/j.segan.2024.101590
Xiaocheng Wang , ZeLong Li , Qiaoni Han , Pengjiao Sun
With the rapid development of the electric vehicle industry, there are games about charging between electric vehicles (EVs) and charging stations (CSs) that have been extensively studied. Due to the mileage problem that EVs still have, this paper addresses the charging interactions between EVs and CSs in a midway charging scenario. Firstly, in the information exchange process with the involvement of navigation system, each EV chooses under the influence of the pricing strategy of CSs to minimize the expenditure after considering factors including distance and road conditions. After getting EVs’ strategy, CSs will adjust the charging strategy to maximize the revenue while obtaining the minimum load factor. Then, we use a Stackelberg game with multi-leader and multi-follower to model the interaction between CSs and EVs. Moreover, considering the particularity of midway charging, we add fair charging to limit the charging capacity of EVs. Lastly, to address the Stackelberg equilibrium problem, the backward induction method is adopted, that is, we derive the charging capacity strategies of EVs (i.e., followers) given the charging price of CSs (i.e., leaders), and then design the optimal pricing strategy of CSs based on the EVs’ optimal strategy. Besides, a distributed algorithm is also proposed to obtain the game equilibrium iteratively. Furthermore, the simulation results show that the average charging cost of EVs is reduced by 25% using the proposed strategy, and the load balance of CSs is relatively high, which shows the effectiveness of this strategy in reducing costs and balancing loads.
{"title":"A midway charging strategy for electric vehicles based on Stackelberg game considering fair charging","authors":"Xiaocheng Wang , ZeLong Li , Qiaoni Han , Pengjiao Sun","doi":"10.1016/j.segan.2024.101590","DOIUrl":"10.1016/j.segan.2024.101590","url":null,"abstract":"<div><div>With the rapid development of the electric vehicle industry, there are games about charging between electric vehicles (EVs) and charging stations (CSs) that have been extensively studied. Due to the mileage problem that EVs still have, this paper addresses the charging interactions between EVs and CSs in a midway charging scenario. Firstly, in the information exchange process with the involvement of navigation system, each EV chooses under the influence of the pricing strategy of CSs to minimize the expenditure after considering factors including distance and road conditions. After getting EVs’ strategy, CSs will adjust the charging strategy to maximize the revenue while obtaining the minimum load factor. Then, we use a Stackelberg game with multi-leader and multi-follower to model the interaction between CSs and EVs. Moreover, considering the particularity of midway charging, we add fair charging to limit the charging capacity of EVs. Lastly, to address the Stackelberg equilibrium problem, the backward induction method is adopted, that is, we derive the charging capacity strategies of EVs (i.e., followers) given the charging price of CSs (i.e., leaders), and then design the optimal pricing strategy of CSs based on the EVs’ optimal strategy. Besides, a distributed algorithm is also proposed to obtain the game equilibrium iteratively. Furthermore, the simulation results show that the average charging cost of EVs is reduced by 25% using the proposed strategy, and the load balance of CSs is relatively high, which shows the effectiveness of this strategy in reducing costs and balancing loads.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101590"},"PeriodicalIF":4.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-10DOI: 10.1016/j.segan.2024.101588
Mohammad Amin Amrovani , Hossein Askarian-Abyaneh , Mohamad Amin Gharibi , Mohammad Mozaffari
In recent decades, the world has experienced both natural disasters and deliberate attacks on power systems, resulting in significant impacts on the structural integrity of electricity infrastructures. This has led to profound economic consequences and substantial blackouts. One of the primary challenges faced by distribution networks during High-Impact Low-Probability (HILP) events is the timeframe within which repair crews can take appropriate action. This article introduces a comprehensive framework for analyzing and bolstering urban distribution system resilience, particularly during extreme weather events, with a focus on the role of electric vehicles (EVs). Demonstrating efficacy through simulations on a test system, the study considers power generation resources, EV involvement, and evaluates resilience indices. Emphasizing the restoration phase's significance to prevent blackouts and prioritize critical loads, the research also explores the impact of the proposed framework on repair crew timeframes to enhance network resilience. The framework and algorithms aim to address gaps in current research and improve distribution network resilience. Ultimately, the efficiency of the suggested framework has been confirmed using an IEEE 33-bus test case. The results indicate that increasing the participation of EVs from 25 % to 75 % leads to an average increase in response time for critical loads by 143 %, 262 %, 161 %, and 344 % in the respective studied islands.
{"title":"Urban grid resilience assessment framework: Leveraging electric vehicles, time-based analysis, and mobile distributed generators for repair crew strategic deployment","authors":"Mohammad Amin Amrovani , Hossein Askarian-Abyaneh , Mohamad Amin Gharibi , Mohammad Mozaffari","doi":"10.1016/j.segan.2024.101588","DOIUrl":"10.1016/j.segan.2024.101588","url":null,"abstract":"<div><div>In recent decades, the world has experienced both natural disasters and deliberate attacks on power systems, resulting in significant impacts on the structural integrity of electricity infrastructures. This has led to profound economic consequences and substantial blackouts. One of the primary challenges faced by distribution networks during High-Impact Low-Probability (HILP) events is the timeframe within which repair crews can take appropriate action. This article introduces a comprehensive framework for analyzing and bolstering urban distribution system resilience, particularly during extreme weather events, with a focus on the role of electric vehicles (EVs). Demonstrating efficacy through simulations on a test system, the study considers power generation resources, EV involvement, and evaluates resilience indices. Emphasizing the restoration phase's significance to prevent blackouts and prioritize critical loads, the research also explores the impact of the proposed framework on repair crew timeframes to enhance network resilience. The framework and algorithms aim to address gaps in current research and improve distribution network resilience. Ultimately, the efficiency of the suggested framework has been confirmed using an IEEE 33-bus test case. The results indicate that increasing the participation of EVs from 25 % to 75 % leads to an average increase in response time for critical loads by 143 %, 262 %, 161 %, and 344 % in the respective studied islands.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101588"},"PeriodicalIF":4.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing penetration level of distributed wind power results in significant fluctuations and poses a great challenge to the voltage security of distribution systems. This paper proposes a coordinated day-ahead reactive power dispatch (RPD) method to improve voltage quality. First, a scenario generation method based on the copula autoregressive moving average (copula-ARMA) model is proposed to describe the spatialtemporal correlation of wind power and capture the fluctuations in wind power more accurately. Based on the constructed scenario set, the day-ahead RPD optimization is formulated as a two-stage stochastic programming model. A novel objective function, which minimizes the maximum voltage deviation over all buses and total power losses during the RPD process, is proposed to improve the voltage stability margin of distribution systems. The proposed RPD method coordinates multiple power sources and voltage regulators, i.e., on-load tap changer, and capacitor banks. Moreover, the location and hourly charging shcedule of mobile energy storage units are also optimized to provide flexible voltage support. The proposed day-ahead RPD method was simulated in the modified IEEE 33-bus distribution system. The results show that, in contrast with a traditional day-ahead RPD model, the proposed method reduces the rate of voltage violation by 14 % when unexpected scenarios occur.
{"title":"Coordinated day-ahead reactive power dispatch in distribution system considering spatial-temporal correlation of wind power","authors":"Yunyun Xie , Sheng Cai , Xiaohui Qin , Hao Wu , Qian Zhou , Dandan Zhu , Qiuwei Wu","doi":"10.1016/j.segan.2024.101591","DOIUrl":"10.1016/j.segan.2024.101591","url":null,"abstract":"<div><div>The increasing penetration level of distributed wind power results in significant fluctuations and poses a great challenge to the voltage security of distribution systems. This paper proposes a coordinated day-ahead reactive power dispatch (RPD) method to improve voltage quality. First, a scenario generation method based on the copula autoregressive moving average (copula-ARMA) model is proposed to describe the spatial<img>temporal correlation of wind power and capture the fluctuations in wind power more accurately. Based on the constructed scenario set, the day-ahead RPD optimization is formulated as a two-stage stochastic programming model. A novel objective function, which minimizes the maximum voltage deviation over all buses and total power losses during the RPD process, is proposed to improve the voltage stability margin of distribution systems. The proposed RPD method coordinates multiple power sources and voltage regulators, i.e., on-load tap changer, and capacitor banks. Moreover, the location and hourly charging shcedule of mobile energy storage units are also optimized to provide flexible voltage support. The proposed day-ahead RPD method was simulated in the modified IEEE 33-bus distribution system. The results show that, in contrast with a traditional day-ahead RPD model, the proposed method reduces the rate of voltage violation by 14 % when unexpected scenarios occur.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101591"},"PeriodicalIF":4.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-06DOI: 10.1016/j.segan.2024.101582
Damianos Cheilas, Henrik W. Bindner, Tilman Weckesser
The ongoing electrification in distribution networks induces congestion issues resulting in the need for network reinforcement. However, demand flexibility and intelligent utilization of grid components have the potential to defer network investments. This paper introduces a procedure for the evaluation of flexibility services in distribution grids, considering the thermal model of the substation transformer, impact of outage and cost of flexibility. A case study involving electric vehicles as the flexible load is presented, where the cost and flexibility benefit values are computed. Uncertainty in the load forecast is included in the process and an assessment of the risk level and aptitude is performed. The results show the dominating impact of disconnection in relation to ageing, meaning that allowing some levels of overloading could be more preferable than procuring flexibility in certain situations. Taking into account the uncertainty of the load, the associated cost and flexibility benefit can be assessed depending on the choice of service parameters and risk consideration, supporting decision-making for flexibility in the presence of uncertainty.
{"title":"Investigating the benefit of flexibility services in distribution grids under uncertainty","authors":"Damianos Cheilas, Henrik W. Bindner, Tilman Weckesser","doi":"10.1016/j.segan.2024.101582","DOIUrl":"10.1016/j.segan.2024.101582","url":null,"abstract":"<div><div>The ongoing electrification in distribution networks induces congestion issues resulting in the need for network reinforcement. However, demand flexibility and intelligent utilization of grid components have the potential to defer network investments. This paper introduces a procedure for the evaluation of flexibility services in distribution grids, considering the thermal model of the substation transformer, impact of outage and cost of flexibility. A case study involving electric vehicles as the flexible load is presented, where the cost and flexibility benefit values are computed. Uncertainty in the load forecast is included in the process and an assessment of the risk level and aptitude is performed. The results show the dominating impact of disconnection in relation to ageing, meaning that allowing some levels of overloading could be more preferable than procuring flexibility in certain situations. Taking into account the uncertainty of the load, the associated cost and flexibility benefit can be assessed depending on the choice of service parameters and risk consideration, supporting decision-making for flexibility in the presence of uncertainty.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101582"},"PeriodicalIF":4.8,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-04DOI: 10.1016/j.segan.2024.101568
Kamil Erdayandi , Lucas C. Cordeiro , Mustafa A. Mustafa
This paper proposes a novel Privacy-preserving and Accountable Billing (PA-Bill) protocol for peer-to-peer energy trading markets. It addresses the challenges of discrepancies between committed and delivered energy volumes, ensuring accurate billing, privacy, and accountability. PA-Bill employs a universal cost-splitting mechanism to enhance fairness and prevent indirect privacy leakage. The protocol leverages homomorphic encryption to protect user data and uses blockchain technology to maintain accountability through an immutable and transparent distributed ledger. Additionally, it includes a dispute resolution mechanism to rectify erroneous bill calculations and identify responsible parties, thus ensuring non-repudiation. Our experimental and theoretical evaluations demonstrates that PA-Bill effectively supports large communities of up to 2000 households, offering a computationally efficient, privacy-preserving, and accountable billing solution in a semi-decentralised manner.
{"title":"Privacy-preserving and accountable billing in peer-to-peer energy trading markets with homomorphic encryption and blockchain","authors":"Kamil Erdayandi , Lucas C. Cordeiro , Mustafa A. Mustafa","doi":"10.1016/j.segan.2024.101568","DOIUrl":"10.1016/j.segan.2024.101568","url":null,"abstract":"<div><div>This paper proposes a novel Privacy-preserving and Accountable Billing (PA-Bill) protocol for peer-to-peer energy trading markets. It addresses the challenges of discrepancies between committed and delivered energy volumes, ensuring accurate billing, privacy, and accountability. PA-Bill employs a universal cost-splitting mechanism to enhance fairness and prevent indirect privacy leakage. The protocol leverages homomorphic encryption to protect user data and uses blockchain technology to maintain accountability through an immutable and transparent distributed ledger. Additionally, it includes a dispute resolution mechanism to rectify erroneous bill calculations and identify responsible parties, thus ensuring non-repudiation. Our experimental and theoretical evaluations demonstrates that PA-Bill effectively supports large communities of up to 2000 households, offering a computationally efficient, privacy-preserving, and accountable billing solution in a semi-decentralised manner.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101568"},"PeriodicalIF":4.8,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.segan.2024.101576
Mario Useche-Arteaga , Oriol Gomis-Bellmunt , Marc Cheah-Mane , Vinicius Lacerda , Pieter Gebraad
The increasing need for integrating offshore wind generation into power systems has highlighted energy islands as a promising solution. Such islands also could incorporate responsive infrastructure for improving grid flexibility such as energy storage and hydrogen production. AC energy islands could be particularly cost–benefit effective for short and medium distances between wind power parks and onshore grids. However, the implementation of AC energy islands presents challenges from the viewpoints of voltage stability, power dispatch and reliable operation. This paper develops optimal operation strategies for AC energy islands including: i) a nonlinear optimal power flow approach for controlling the reactive power compensation systems; ii) a finite horizon mathematical programming approach for the management of BESS and hydrogen production systems; and iii) a multi-objective programming approach for maximizing the active power delivered to the onshore power grid while minimizing the nodal voltage deviations simultaneously. Results show that the single-objective approach for real and reactive power dispatch reduces voltage deviations by around 20 % and line currents by over 70 % in the evaluated test system, compared to the non-optimal scenario. Moreover, the incorporation of energy storage in AC energy island test system increased the capacity for power dispatch over 10 %. Additionally, the multi-objective approach is effective in aligning the nodal voltages with nominal values and maximizing the power delivered to the onshore grid. Results show that with a small 1% reduction in power delivery, the proposed multi-objective approach achieved an approximate 50 % decrease in nodal voltage compared to the single-objective approach.
{"title":"AC energy islands for the optimal integration of offshore wind energy resources: Operation strategies using multi-objective nonlinear programming","authors":"Mario Useche-Arteaga , Oriol Gomis-Bellmunt , Marc Cheah-Mane , Vinicius Lacerda , Pieter Gebraad","doi":"10.1016/j.segan.2024.101576","DOIUrl":"10.1016/j.segan.2024.101576","url":null,"abstract":"<div><div>The increasing need for integrating offshore wind generation into power systems has highlighted energy islands as a promising solution. Such islands also could incorporate responsive infrastructure for improving grid flexibility such as energy storage and hydrogen production. AC energy islands could be particularly cost–benefit effective for short and medium distances between wind power parks and onshore grids. However, the implementation of AC energy islands presents challenges from the viewpoints of voltage stability, power dispatch and reliable operation. This paper develops optimal operation strategies for AC energy islands including: i) a nonlinear optimal power flow approach for controlling the reactive power compensation systems; ii) a finite horizon mathematical programming approach for the management of BESS and hydrogen production systems; and iii) a multi-objective programming approach for maximizing the active power delivered to the onshore power grid while minimizing the nodal voltage deviations simultaneously. Results show that the single-objective approach for real and reactive power dispatch reduces voltage deviations by around 20 % and line currents by over 70 % in the evaluated test system, compared to the non-optimal scenario. Moreover, the incorporation of energy storage in AC energy island test system increased the capacity for power dispatch over 10 %. Additionally, the multi-objective approach is effective in aligning the nodal voltages with nominal values and maximizing the power delivered to the onshore grid. Results show that with a small 1% reduction in power delivery, the proposed multi-objective approach achieved an approximate 50 % decrease in nodal voltage compared to the single-objective approach.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101576"},"PeriodicalIF":4.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.segan.2024.101586
Jian Wang , Mingyi Li , Dong Yin , Jinxin Ouyang
To address the problem of fault recovery in distribution networks during extreme weather, this paper proposes a strategy that actively mitigates loop closing impulse currents and supports the fault recovery process. This method leverages the flexibility of photovoltaic-storage-charging integrated stations (PSCIS) while considering the spatial-temporal evolution of weather disasters. First, the power variation characteristics of photovoltaic (PV), energy storage systems (ESS), and electric vehicles (EVs) are examined, leading to the development of a charging and discharging model for the PSCIS. Second, the loop closing impulse current during fault recovery is derived, and the impact of PSCIS on impulse current suppression through active power regulation is analyzed. Then, to enhance the resilience of distribution network recovery, a strategy incorporating transient safety checks for generating fault recovery scheme is proposed. Finally, a modified IEEE 123-node distribution network is used to validate the effectiveness of the proposed method. The results demonstrate that the active involvement of PSCIS in power regulation can effectively manage dynamic fault recovery in distribution networks affected by extreme weather, suppress loop closing impulse currents, improve the feasibility of network reconfiguration schemes, and enhance the overall resilience of distribution networks.
{"title":"Fault recovery strategy of distribution network considering active participation of photovoltaic-storage-charging integrated station under extreme weather disasters","authors":"Jian Wang , Mingyi Li , Dong Yin , Jinxin Ouyang","doi":"10.1016/j.segan.2024.101586","DOIUrl":"10.1016/j.segan.2024.101586","url":null,"abstract":"<div><div>To address the problem of fault recovery in distribution networks during extreme weather, this paper proposes a strategy that actively mitigates loop closing impulse currents and supports the fault recovery process. This method leverages the flexibility of photovoltaic-storage-charging integrated stations (PSCIS) while considering the spatial-temporal evolution of weather disasters. First, the power variation characteristics of photovoltaic (PV), energy storage systems (ESS), and electric vehicles (EVs) are examined, leading to the development of a charging and discharging model for the PSCIS. Second, the loop closing impulse current during fault recovery is derived, and the impact of PSCIS on impulse current suppression through active power regulation is analyzed. Then, to enhance the resilience of distribution network recovery, a strategy incorporating transient safety checks for generating fault recovery scheme is proposed. Finally, a modified IEEE 123-node distribution network is used to validate the effectiveness of the proposed method. The results demonstrate that the active involvement of PSCIS in power regulation can effectively manage dynamic fault recovery in distribution networks affected by extreme weather, suppress loop closing impulse currents, improve the feasibility of network reconfiguration schemes, and enhance the overall resilience of distribution networks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101586"},"PeriodicalIF":4.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-01DOI: 10.1016/j.segan.2024.101579
Jie Yang , Qi Teng , Kai Ma , Mengshi Yang
The focus of this paper is the fairness problem in carbon trading markets caused by allocation systems, and the problem that the punishment for the excessive carbon emissions in existing carbon trading markets are not strict enough and the control of carbon emissions is not obvious. The proposed solution is a novel carbon quota trading model based on a two-stage sealed-bid auction. This model adopts a remunerated distribution method, grouping multiple integrated energy systems under a single agent, and introduces an auction strategy among these systems. Through a series of simulation results, it is found that compared with the gratuitous distribution model, the trading model proposed in this paper can reduce system carbon emissions by more than 8% within the carbon trading price range of 20–40 CNY/t. In addition, the simulation results show that the model proposed in this paper and the traditional auction model can generally improve the operating revenue of the integrated energy system by more than 20%. The experimental results verify that the model and strategy proposed in this paper play a crucial role in promoting the internal carbon trading system and reducing the carbon emission potential of the integrated energy system.
{"title":"Remunerated distribution strategy of carbon quotas in integrated energy systems based on sealed-bid auctions","authors":"Jie Yang , Qi Teng , Kai Ma , Mengshi Yang","doi":"10.1016/j.segan.2024.101579","DOIUrl":"10.1016/j.segan.2024.101579","url":null,"abstract":"<div><div>The focus of this paper is the fairness problem in carbon trading markets caused by allocation systems, and the problem that the punishment for the excessive carbon emissions in existing carbon trading markets are not strict enough and the control of carbon emissions is not obvious. The proposed solution is a novel carbon quota trading model based on a two-stage sealed-bid auction. This model adopts a remunerated distribution method, grouping multiple integrated energy systems under a single agent, and introduces an auction strategy among these systems. Through a series of simulation results, it is found that compared with the gratuitous distribution model, the trading model proposed in this paper can reduce system carbon emissions by more than 8% within the carbon trading price range of 20–40 CNY/t. In addition, the simulation results show that the model proposed in this paper and the traditional auction model can generally improve the operating revenue of the integrated energy system by more than 20%. The experimental results verify that the model and strategy proposed in this paper play a crucial role in promoting the internal carbon trading system and reducing the carbon emission potential of the integrated energy system.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"40 ","pages":"Article 101579"},"PeriodicalIF":4.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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