Pub Date : 2024-08-26DOI: 10.35833/MPCE.2023.001041
Luka V. Strezoski;Nikola G. Simic;Kenneth A. Loparo
In this paper, a robust method for quantifying the impact of short-circuit faults on microgrids is proposed. Microgrids can operate in both islanded (grid-forming) and grid-connected (grid-following) modes, and the ownership and responsibility for the microgrid operation can vary significantly from distribution system operators (DSOs) to third-party microgrid operators. This necessitates the development of a robust short-circuit calculation (SCC) method that can provide accurate results for all the possible microgrid topologies, operational modes, and ownership models. Unlike previously developed SCC methods for microgrids, the SCC method proposed in this paper provides highly accurate results for all possible microgrid topologies: islanded microgrid, grid-connected microgrid, and utility microgrid as a part of a larger distribution grid. In addition, the proposed SCC method solves the short-circuit faults of any complexity, with the same simplicity. The proposed SCC method is tested on a complete model of a real-life microgrid on the Case Western Reserve University campus, operating in both islanded and grid-connected modes. The computational results show the advantages of the proposed SCC method in comparison to the previous ones for microgrids, regarding the robustness (ability to solve complex short-circuit faults with an arbitrary number of faulted buses and phases that affect a microgrid of any topology), as well as the accuracy of the results.
{"title":"A Robust Short-circuit Calculation Method for Islanded, Grid-connected, and Utility Microgrids","authors":"Luka V. Strezoski;Nikola G. Simic;Kenneth A. Loparo","doi":"10.35833/MPCE.2023.001041","DOIUrl":"https://doi.org/10.35833/MPCE.2023.001041","url":null,"abstract":"In this paper, a robust method for quantifying the impact of short-circuit faults on microgrids is proposed. Microgrids can operate in both islanded (grid-forming) and grid-connected (grid-following) modes, and the ownership and responsibility for the microgrid operation can vary significantly from distribution system operators (DSOs) to third-party microgrid operators. This necessitates the development of a robust short-circuit calculation (SCC) method that can provide accurate results for all the possible microgrid topologies, operational modes, and ownership models. Unlike previously developed SCC methods for microgrids, the SCC method proposed in this paper provides highly accurate results for all possible microgrid topologies: islanded microgrid, grid-connected microgrid, and utility microgrid as a part of a larger distribution grid. In addition, the proposed SCC method solves the short-circuit faults of any complexity, with the same simplicity. The proposed SCC method is tested on a complete model of a real-life microgrid on the Case Western Reserve University campus, operating in both islanded and grid-connected modes. The computational results show the advantages of the proposed SCC method in comparison to the previous ones for microgrids, regarding the robustness (ability to solve complex short-circuit faults with an arbitrary number of faulted buses and phases that affect a microgrid of any topology), as well as the accuracy of the results.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 1","pages":"325-337"},"PeriodicalIF":5.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-08-20DOI: 10.35833/MPCE.2024.000348
Fan Zhong;Shaofeng Xie;You Peng;Xinyao Hu
The continuous power supply system, which eliminates the neutral section and realizes safe and reliable operation, shows a development trend in suburban railways. However, the access of a power quality compensator (PQC) may alter the impedance characteristics of the system and introduce additional harmonics with a broader frequency band, potentially increasing the risk of resonance. Accordingly, in this paper, an analytical method is first adopted in conjunction with a field test to construct a simplified harmonic model for an actual continuous suburban line. A modal scanning algorithm is then used to analyze the effects of the controller and filter in the PQC on the harmonic resonance of the suburban railway continuous power supply system. Based on the improved particle swarm optimization algorithm, a multi-objective optimization design for PQC is proposed that can suppress harmonic resonance, filter the harmonics, and reduce the cost while preserving the stability of the control system. Finally, a real case study based on the field test demonstrates the effectiveness of the proposed design.
{"title":"Harmonic Resonance Analysis and Suppression for Suburban Railway Continuous Power Supply Systems","authors":"Fan Zhong;Shaofeng Xie;You Peng;Xinyao Hu","doi":"10.35833/MPCE.2024.000348","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000348","url":null,"abstract":"The continuous power supply system, which eliminates the neutral section and realizes safe and reliable operation, shows a development trend in suburban railways. However, the access of a power quality compensator (PQC) may alter the impedance characteristics of the system and introduce additional harmonics with a broader frequency band, potentially increasing the risk of resonance. Accordingly, in this paper, an analytical method is first adopted in conjunction with a field test to construct a simplified harmonic model for an actual continuous suburban line. A modal scanning algorithm is then used to analyze the effects of the controller and filter in the PQC on the harmonic resonance of the suburban railway continuous power supply system. Based on the improved particle swarm optimization algorithm, a multi-objective optimization design for PQC is proposed that can suppress harmonic resonance, filter the harmonics, and reduce the cost while preserving the stability of the control system. Finally, a real case study based on the field test demonstrates the effectiveness of the proposed design.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 1","pages":"253-264"},"PeriodicalIF":5.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-08-20DOI: 10.35833/MPCE.2024.000105
Chuanshen Wu;Yue Zhou;Jianzhong Wu
This paper establishes a two-layer data-driven robust scheduling method to deal with the significant computational complexity and uncertainties in scheduling industrial heat loads. First, a two-layer deterministic scheduling model is proposed to address the computational burden of utilizing flexibility from a large number of bitumen tanks (BTs). The key feature of this model is the capability to reduce the number of control variables through analyzing and modeling the clustered temperature transfer of BTs. Second, to tackle the uncertainties in the scheduling problem, historical data regarding BTs are collected and analyzed, and a data-driven piecewise linear Kernel-based support vector clustering technique is employed to construct the uncertainty set with convex boundaries and adjustable conservatism, based on which robust optimization can be conducted. The case results indicate that the proposed method enables the utilization of flexibility in BTs, improving the level of onsite photovoltaic consumption and reducing the aggregated load fluctuation.
{"title":"Two-layer Data-driven Robust Scheduling for Industrial Heat Loads","authors":"Chuanshen Wu;Yue Zhou;Jianzhong Wu","doi":"10.35833/MPCE.2024.000105","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000105","url":null,"abstract":"This paper establishes a two-layer data-driven robust scheduling method to deal with the significant computational complexity and uncertainties in scheduling industrial heat loads. First, a two-layer deterministic scheduling model is proposed to address the computational burden of utilizing flexibility from a large number of bitumen tanks (BTs). The key feature of this model is the capability to reduce the number of control variables through analyzing and modeling the clustered temperature transfer of BTs. Second, to tackle the uncertainties in the scheduling problem, historical data regarding BTs are collected and analyzed, and a data-driven piecewise linear Kernel-based support vector clustering technique is employed to construct the uncertainty set with convex boundaries and adjustable conservatism, based on which robust optimization can be conducted. The case results indicate that the proposed method enables the utilization of flexibility in BTs, improving the level of onsite photovoltaic consumption and reducing the aggregated load fluctuation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 1","pages":"265-275"},"PeriodicalIF":5.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-08-07DOI: 10.35833/MPCE.2023.000990
Jin Lu;Xingpeng Li
As renewable energy is becoming the major resource in future power grids, the weather and climate can have a higher impact on grid reliability. Transmission expansion planning (TEP) has the potential to reinforce the power transfer capability of a transmission network for climate-impacted power grids. In this paper, we propose a systematic TEP procedure for renewable-energy-dominated power grids considering climate impact (CI). Particularly, this paper develops an improved model for TEP considering climate impact (TEP-CI) and evaluates the reliability of power grid with the obtained transmission investment plan. Firstly, we create climate-impacted spatio-temporal future power grid data to facilitate the study of TEP-CI, which include the future climate-dependent renewable power generation as well as the dynamic line rating profiles of the Texas 123-bus backbone transmission (TX-123BT) system. Secondly, the TEP-CI model is proposed, which considers the variation in renewable power generation and dynamic line rating, and the investment plan for future TX-123BT system is obtained. Thirdly, a customized security-constrained unit commitment (SCUC) is presented specifically for climate-impacted power grids. The reliability of future power grid in various investment scenarios is analyzed based on the daily operation conditions from SCUC simulations. The whole procedure presented in this paper enables numerical studies on power grid planning considering climate impact. It can also serve as a benchmark for other studies of the TEP-CI model and its performance evaluation.
{"title":"Transmission Expansion Planning for Renewable-Energy-Dominated Power Grids Considering Climate Impact","authors":"Jin Lu;Xingpeng Li","doi":"10.35833/MPCE.2023.000990","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000990","url":null,"abstract":"As renewable energy is becoming the major resource in future power grids, the weather and climate can have a higher impact on grid reliability. Transmission expansion planning (TEP) has the potential to reinforce the power transfer capability of a transmission network for climate-impacted power grids. In this paper, we propose a systematic TEP procedure for renewable-energy-dominated power grids considering climate impact (CI). Particularly, this paper develops an improved model for TEP considering climate impact (TEP-CI) and evaluates the reliability of power grid with the obtained transmission investment plan. Firstly, we create climate-impacted spatio-temporal future power grid data to facilitate the study of TEP-CI, which include the future climate-dependent renewable power generation as well as the dynamic line rating profiles of the Texas 123-bus backbone transmission (TX-123BT) system. Secondly, the TEP-CI model is proposed, which considers the variation in renewable power generation and dynamic line rating, and the investment plan for future TX-123BT system is obtained. Thirdly, a customized security-constrained unit commitment (SCUC) is presented specifically for climate-impacted power grids. The reliability of future power grid in various investment scenarios is analyzed based on the daily operation conditions from SCUC simulations. The whole procedure presented in this paper enables numerical studies on power grid planning considering climate impact. It can also serve as a benchmark for other studies of the TEP-CI model and its performance evaluation.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1737-1748"},"PeriodicalIF":5.7,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10630585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 steady-state security region (SSR) offers robust support for the security assessment and control of new power systems with high uncertainty and fluctuation. However, accurately solving the steady-state security region boundary (SS-RB), which is high-dimensional, non-convex, and non-linear, presents a significant challenge. To address this problem, this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree. First, to better characterize the SSRB, boundary samples are generated using the proposed sampling method. These samples are distributed within a limited distance near the SSRB. Then, to handle the high-dimensionality, non-convexity and non-linearity of the SSRB, boundary samples are converted from the original power injection space to a new feature space using the designed feature non-linear converter. Consequently, in this feature space, boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree. Finally, the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.
{"title":"High-Dimensional Steady-State Security Region Boundary Approximation in Power Systems Using Feature Non-Linear Converter and Improved Oblique Decision Tree","authors":"Yuxin Dai;Jun Zhang;Peidong Xu;Tianlu Gao;David Wenzhong Gao","doi":"10.35833/MPCE.2024.000188","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000188","url":null,"abstract":"The steady-state security region (SSR) offers robust support for the security assessment and control of new power systems with high uncertainty and fluctuation. However, accurately solving the steady-state security region boundary (SS-RB), which is high-dimensional, non-convex, and non-linear, presents a significant challenge. To address this problem, this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree. First, to better characterize the SSRB, boundary samples are generated using the proposed sampling method. These samples are distributed within a limited distance near the SSRB. Then, to handle the high-dimensionality, non-convexity and non-linearity of the SSRB, boundary samples are converted from the original power injection space to a new feature space using the designed feature non-linear converter. Consequently, in this feature space, boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree. Finally, the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1786-1797"},"PeriodicalIF":5.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-07-29DOI: 10.35833/MPCE.2023.001038
Jinghua Li;Hongyu Zeng;Yutian Xie
Joint chance constraints (JCCs) can ensure the consistency and correlation of stochastic variables when participating in decision-making. Sample average approximation (SAA) is the most popular method for solving JCCs in unit commitment (UC) problems. However, the typical SAA requires large Monte Carlo (MC) samples to ensure the solution accuracy, which results in large-scale mixed-integer programming (MIP) problems. To address this problem, this paper presents the partial sample average approximation (PSAA) to deal with JCCs in UC problems in multi-area power systems with wind power. PSAA partitions the stochastic variables and historical dataset, and the historical dataset is then partitioned into non-sampled and sampled sets. When approximating the expectation of stochastic variables, PSAA replaces the big-M formulation with the cumulative distribution function of the non-sampled set, thus preventing binary variables from being introduced. Finally, PSAA can transform the chance constraints to deterministic constraints with only continuous variables, avoiding the large-scale MIP problem caused by SAA. Simulation results demonstrate that PSAA has significant advantages in solution accuracy and efficiency compared with other existing methods including traditional SAA, SAA with improved big-M, SAA with Latin hypercube sampling (LHS), and the multi-stage robust optimization methods.
在参与决策时，联合机会约束（JCC）可以确保随机变量的一致性和相关性。抽样平均近似（SAA）是解决单位承诺（UC）问题中联合机会约束的最常用方法。然而，典型的 SAA 需要大量的蒙特卡罗（MC）样本来确保求解精度，这就导致了大规模的混合整数编程（MIP）问题。为解决这一问题，本文提出了部分样本平均近似法（PSAA），用于处理多区域风力发电系统中 UC 问题中的 JCC。PSAA 对随机变量和历史数据集进行了划分，然后将历史数据集划分为非采样集和采样集。在近似随机变量的期望值时，PSAA 用非采样集的累积分布函数代替 big-M 公式，从而避免了二元变量的引入。最后，PSAA 可以将偶然性约束转化为只有连续变量的确定性约束，避免了 SAA 带来的大规模 MIP 问题。仿真结果表明，与其他现有方法（包括传统 SAA、改进 big-M 的 SAA、拉丁超立方采样 (LHS) 的 SAA 以及多阶段鲁棒优化方法）相比，PSAA 在求解精度和效率方面具有显著优势。
{"title":"Unit Commitment with Joint Chance Constraints in Multi-area Power Systems with Wind Power Based on Partial Sample Average Approximation","authors":"Jinghua Li;Hongyu Zeng;Yutian Xie","doi":"10.35833/MPCE.2023.001038","DOIUrl":"https://doi.org/10.35833/MPCE.2023.001038","url":null,"abstract":"Joint chance constraints (JCCs) can ensure the consistency and correlation of stochastic variables when participating in decision-making. Sample average approximation (SAA) is the most popular method for solving JCCs in unit commitment (UC) problems. However, the typical SAA requires large Monte Carlo (MC) samples to ensure the solution accuracy, which results in large-scale mixed-integer programming (MIP) problems. To address this problem, this paper presents the partial sample average approximation (PSAA) to deal with JCCs in UC problems in multi-area power systems with wind power. PSAA partitions the stochastic variables and historical dataset, and the historical dataset is then partitioned into non-sampled and sampled sets. When approximating the expectation of stochastic variables, PSAA replaces the big-M formulation with the cumulative distribution function of the non-sampled set, thus preventing binary variables from being introduced. Finally, PSAA can transform the chance constraints to deterministic constraints with only continuous variables, avoiding the large-scale MIP problem caused by SAA. Simulation results demonstrate that PSAA has significant advantages in solution accuracy and efficiency compared with other existing methods including traditional SAA, SAA with improved big-M, SAA with Latin hypercube sampling (LHS), and the multi-stage robust optimization methods.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 1","pages":"241-252"},"PeriodicalIF":5.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-07-29DOI: 10.35833/MPCE.2024.000093
Yanqiu Hou;Minglei Bao;Yi Ding
With the implementation of the integrated electricity and gas market (IEGM), the smart energy hubs (SEHs) tend to participate in the market clearing for the optimization of the energy purchase portfolio. Meanwhile, the renewable energy is mushrooming at different scales of energy systems, which can introduce utility-level and distribution-level uncertainties to the operation of the IEGM and SEHs, respectively. Considering the impacts of divergent uncertainties, there exist complicated inter-actions between the IEGM clearing and the robust bidding of SEHs. The lack of consideration of such interactions may lead to inaccurate modeling of the IEGM clearing and cause potential market inefficiency. To handle this, a bi-level robust clearing framework of the IEGM considering the robust bidding of SEHs is proposed, which simultaneously considers the impacts of utility-level and distribution-level uncertainties. The proposed framework is partitioned into two levels. The upper level is the robust clearing mechanism of the IEGM. At this level, the uncertainty locational marginal electricity and gas prices are derived considering the utility-level uncertainties and the uncertainty-based bidding of SEHs. Given the price signals deduced in the upper level, the lower-level robust bidding of the SEH seeks the optimal bidding strategies while hedging against distribution-level uncertainties. To address the proposed framework, an effective algorithm combining column-and-constraint generation (C&CG) algorithm with the best-response decomposition (BRD) algorithm is formulated. The devised algorithm can efficiently solve the individual robust optimization model and coordinate the interaction of two levels. Numerical experiments are carried out to verify the effectiveness of the proposed framework. Moreover, the impacts of uncertainties on the market clearing results along with the optimal biddings of SEHs are further demonstrated within the proposed framework.
{"title":"Bi-Level Robust Clearing Framework of Integrated Electricity and Gas Market Considering Robust Bidding of Smart Energy Hubs","authors":"Yanqiu Hou;Minglei Bao;Yi Ding","doi":"10.35833/MPCE.2024.000093","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000093","url":null,"abstract":"With the implementation of the integrated electricity and gas market (IEGM), the smart energy hubs (SEHs) tend to participate in the market clearing for the optimization of the energy purchase portfolio. Meanwhile, the renewable energy is mushrooming at different scales of energy systems, which can introduce utility-level and distribution-level uncertainties to the operation of the IEGM and SEHs, respectively. Considering the impacts of divergent uncertainties, there exist complicated inter-actions between the IEGM clearing and the robust bidding of SEHs. The lack of consideration of such interactions may lead to inaccurate modeling of the IEGM clearing and cause potential market inefficiency. To handle this, a bi-level robust clearing framework of the IEGM considering the robust bidding of SEHs is proposed, which simultaneously considers the impacts of utility-level and distribution-level uncertainties. The proposed framework is partitioned into two levels. The upper level is the robust clearing mechanism of the IEGM. At this level, the uncertainty locational marginal electricity and gas prices are derived considering the utility-level uncertainties and the uncertainty-based bidding of SEHs. Given the price signals deduced in the upper level, the lower-level robust bidding of the SEH seeks the optimal bidding strategies while hedging against distribution-level uncertainties. To address the proposed framework, an effective algorithm combining column-and-constraint generation (C&CG) algorithm with the best-response decomposition (BRD) algorithm is formulated. The devised algorithm can efficiently solve the individual robust optimization model and coordinate the interaction of two levels. Numerical experiments are carried out to verify the effectiveness of the proposed framework. Moreover, the impacts of uncertainties on the market clearing results along with the optimal biddings of SEHs are further demonstrated within the proposed framework.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 1","pages":"351-364"},"PeriodicalIF":5.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-07-29DOI: 10.35833/MPCE.2023.001027
Wei Kong;Kai Sun;Jinghong Zhao
The hydrogen energy storage system (HESS) integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty. To address the above issues, a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems (CIESs) with power to hydrogen and heat (P2HH) capability. It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads. First, the refined operation model of HESS is established, and its operation model is linearized according to the operation domain of HESS, which simplifies the difficulty of solving the optimization problem under the premise of maintaining high approximate accuracy. Next, considering the flexible start-stop of alkaline electrolyzer (AEL) and the avoidance of multiple energy conversions, the operation sequences of HESS are formulated. Finally, a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established, and the model is simulated and verified using the source-load prediction data of typical days in each season. The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14% while maintaining similar operating cost to the optimal day-ahead economic optimization scheduling. Furthermore, by formulating the operation sequences of HESS, the operating cost of CIES is reduced by up to about 4.4%.
{"title":"Two-stage Optimal Scheduling of Community Integrated Energy System Considering Operation Sequences of Hydrogen Energy Storage Systems","authors":"Wei Kong;Kai Sun;Jinghong Zhao","doi":"10.35833/MPCE.2023.001027","DOIUrl":"https://doi.org/10.35833/MPCE.2023.001027","url":null,"abstract":"The hydrogen energy storage system (HESS) integrated with renewable energy power generation exhibits low reliability and flexibility under source-load uncertainty. To address the above issues, a two-stage optimal scheduling model considering the operation sequences of HESSs is proposed for commercial community integrated energy systems (CIESs) with power to hydrogen and heat (P2HH) capability. It aims to optimize the energy flow of HESS and improve the flexibility of hydrogen production and the reliability of energy supply for loads. First, the refined operation model of HESS is established, and its operation model is linearized according to the operation domain of HESS, which simplifies the difficulty of solving the optimization problem under the premise of maintaining high approximate accuracy. Next, considering the flexible start-stop of alkaline electrolyzer (AEL) and the avoidance of multiple energy conversions, the operation sequences of HESS are formulated. Finally, a two-stage optimal scheduling model combining day-ahead economic optimization and intra-day rolling optimization is established, and the model is simulated and verified using the source-load prediction data of typical days in each season. The simulation results show that the two-stage optimal scheduling reduces the total load offset by about 14% while maintaining similar operating cost to the optimal day-ahead economic optimization scheduling. Furthermore, by formulating the operation sequences of HESS, the operating cost of CIES is reduced by up to about 4.4%.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 1","pages":"276-288"},"PeriodicalIF":5.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces a mixed-integer second-order conic programming (MISOCP) model for the effective management of load and energy in active distribution networks featuring prosumers. A multi-objective function is devised to concurrently minimize various costs, including prosumer electricity costs, network energy loss costs, load shedding costs, and costs associated with renewable energy resource outages. The methodology involves determining optimal active power adjustment points for photovoltaic (PV) resources and integrated energy storage systems (ESSs) within network buildings, in conjunction with a demand-side management program. To achieve the optimal solution for the proposed MISOCP model, a robust hybrid algorithm is presented, integrating the modified particle swarm optimization (MPSO) algorithm and the genetic algorithm (GA). This algorithm demonstrates a heightened capability for efficiently converging on challenging problems. The proposed model is evaluated using a distribution network comprising 33 buses, a practical distribution network, and a distribution network comprising 118 buses. Through comprehensive simulations in diverse cases, the results highlight the innovative contributions of the model. Specifically, it achieves a noteworthy reduction of 26.2% in energy losses and a 17.72% decrease in voltage deviation. Additionally, the model proves effective in augmenting prosumer electricity sales, showcasing its potential to improve the overall efficiency and sustainability of active distribution networks.
{"title":"Integrated Load and Energy Management in Active Distribution Networks Featuring Prosumers Based on PV and Energy Storage Systems","authors":"Alireza Alamolhoda;Reza Ebrahimi;Mahmoud Samiei Moghaddam;Mahmoud Ghanbari","doi":"10.35833/MPCE.2023.000944","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000944","url":null,"abstract":"This study introduces a mixed-integer second-order conic programming (MISOCP) model for the effective management of load and energy in active distribution networks featuring prosumers. A multi-objective function is devised to concurrently minimize various costs, including prosumer electricity costs, network energy loss costs, load shedding costs, and costs associated with renewable energy resource outages. The methodology involves determining optimal active power adjustment points for photovoltaic (PV) resources and integrated energy storage systems (ESSs) within network buildings, in conjunction with a demand-side management program. To achieve the optimal solution for the proposed MISOCP model, a robust hybrid algorithm is presented, integrating the modified particle swarm optimization (MPSO) algorithm and the genetic algorithm (GA). This algorithm demonstrates a heightened capability for efficiently converging on challenging problems. The proposed model is evaluated using a distribution network comprising 33 buses, a practical distribution network, and a distribution network comprising 118 buses. Through comprehensive simulations in diverse cases, the results highlight the innovative contributions of the model. Specifically, it achieves a noteworthy reduction of 26.2% in energy losses and a 17.72% decrease in voltage deviation. Additionally, the model proves effective in augmenting prosumer electricity sales, showcasing its potential to improve the overall efficiency and sustainability of active distribution networks.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1869-1879"},"PeriodicalIF":5.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10608102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-07-15DOI: 10.35833/MPCE.2023.000697
Shice Zhao;Hongshan Zhao
The rapid development of the power system requires high reliability and real-time situational awareness of power equipment. The current agent-based condition-monitoring perception mode is not suitable for widely distributed power equipment due to the potential of single-point failure and high communication and data costs. Therefore, the technical development path of the power equipment perception mode is analyzed based on the development trend of the future power system. The concept of intelligent power equipment (IPE) is introduced, which combines online sensing, data mining, remote communication, and primary and secondary fusion technologies to develop an intelligent object that can realize autonomous situational awareness. IPE can actively interact with the control center and operation and maintenance (O&M) personnel according to its situation. This gives the power company an efficient and comprehensive perception of the equipment. Then, based on the actual situation of the power grid and emerging technology research directions, the challenges faced by each key technology supporting IPE and the corresponding technology enhancement solutions are presented. In addition, the O&M method applicable to IPE is discussed, which achieves proactive maintenance and prognosis management through autonomous equipment perception. Finally, the feasibility and effectiveness of IPE are verified by the performance of current IPE applications in an actual power grid.
{"title":"Intelligent Power Equipment for Autonomous Situational Awareness and Active Operation and Maintenance","authors":"Shice Zhao;Hongshan Zhao","doi":"10.35833/MPCE.2023.000697","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000697","url":null,"abstract":"The rapid development of the power system requires high reliability and real-time situational awareness of power equipment. The current agent-based condition-monitoring perception mode is not suitable for widely distributed power equipment due to the potential of single-point failure and high communication and data costs. Therefore, the technical development path of the power equipment perception mode is analyzed based on the development trend of the future power system. The concept of intelligent power equipment (IPE) is introduced, which combines online sensing, data mining, remote communication, and primary and secondary fusion technologies to develop an intelligent object that can realize autonomous situational awareness. IPE can actively interact with the control center and operation and maintenance (O&M) personnel according to its situation. This gives the power company an efficient and comprehensive perception of the equipment. Then, based on the actual situation of the power grid and emerging technology research directions, the challenges faced by each key technology supporting IPE and the corresponding technology enhancement solutions are presented. In addition, the O&M method applicable to IPE is discussed, which achieves proactive maintenance and prognosis management through autonomous equipment perception. Finally, the feasibility and effectiveness of IPE are verified by the performance of current IPE applications in an actual power grid.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"2081-2090"},"PeriodicalIF":5.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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