Journal of Modern Power Systems and Clean Energy最新文献

{"title":"Parallel Converter-Based Hybrid HVDC System for Integration and Delivery of Large-Scale Renewable Energy","authors":"Hanlin Guo;Zheren Zhang;Zheng Xu","doi":"10.35833/MPCE.2023.001033","DOIUrl":"https://doi.org/10.35833/MPCE.2023.001033","url":null,"abstract":"In this study, a novel parallel converter-based hybrid high-voltage direct current (HVDC) system is proposed for the integration and delivery of large-scale renewable energy. The rectifier uses the line commutated converter (LCC) and low-capacity modular multilevel converter (MMC) in parallel, while the inverter uses MMC. This configuration combines the economic advantages of LCC with the flexibility of MMC. Firstly, the steady-state control strategies are elaborated. The low-capacity MMC operates in the grid-forming mode to offer AC voltage support. It also provides active filtering for the LCC and maintains the reactive power balance of the sending-end system. The LCC efficiently transmits all active power at the rectifier side, fully exploiting its bulk-power transmission capability. Secondly, the fault ride-through strategies of both the AC faults at two terminals and the DC fault are proposed, in which the MMCs at both terminals can remain unblocked under various faults. Thus, the proposed system can mitigate the impact of the faults and ensure continuous voltage support for the sending-end system. Finally, simulations in PSCAD/EMTDC verify the effectiveness and performance of the proposed system.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 2","pages":"688-697"},"PeriodicalIF":5.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698164","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}

{"title":"Path-Aware Market Clearing Model for Inter-Regional Electricity Market via Redundancy Elimination","authors":"Shiyuan Tao;Zhenfei Tan;Chenxing Yang;Zheng Yan;Haihua Cheng","doi":"10.35833/MPCE.2023.000962","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000962","url":null,"abstract":"The inter-regional electricity market is instrumental in enhancing the economic efficiency, reliability, and integration of renewable generation within interconnected power systems. As the market boundary expands, the complexity and solution difficulties of market clearing increase rapidly. The presence of hybrid alternating current (AC)/direct current (DC) interconnector networks further compounds challenges in modeling trading paths and transmission tariffs. To address these issues, this paper proposes a path-aware market-clearing (PAMC) model tailored for the inter-regional electricity market, which accommodates the hybrid AC/DC interconnector network. A variable aggregation strategy is proposed to reduce the problem scale while ensuring equivalent optimality. In addition, a novel redundancy elimination method is developed to expedite the solution of the market-clearing problem. This framework utilizes envelope approximations of residual demand curves to identify bidding blocks that will not affect the marginal price. Corresponding decision variables are then constrained to their bounds to remove redundant information. Comprehensive case studies across different power system scales validate the superiority of the proposed PAMC model in improving social welfare, and verify the effectiveness of the proposed redundancy elimination method in accelerating the solution of the market-clearing problem.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1980-1992"},"PeriodicalIF":5.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543263","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844557","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}

{"title":"Virtual Reality Based Shading Pattern Recognition and Interactive Global Maximum Power Point Tracking in Photovoltaic Systems","authors":"Kangshi Wang;Jieming Ma;Xiao Lu;Jingyi Wang;Ka Lok Man;Kaizhu Huang;Xiaowei Huang","doi":"10.35833/MPCE.2023.000869","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000869","url":null,"abstract":"The performance of photovoltaic (PV) systems is influenced by various factors, including atmospheric conditions, geographical locations, and spatial and temporal characteristics. Consequently, the optimization of PV systems relies heavily on the global maximum power point tracking (GMPPT) methods. In this paper, we adopt virtual reality (VR) technology to visualize PV entities and simulate their performances. The integration of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition (SPR) of PV systems, thereby enhancing their descriptive capabilities. Furthermore, we introduce an interactive GMPPT (IGMPPT) method based on VR technology. This method leverages interactive search techniques to narrow down search regions, thereby enhancing the search efficiency. Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improving the efficiency of GMPPT.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1849-1858"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844440","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}

{"title":"Distributionally Robust Scheduling for Benefit Allocation in Regional Integrated Energy System with Multiple Stakeholders","authors":"Qinglin Meng;Xiaolong Jin;Fengzhang Luo;Zhongguan Wang;Sheharyar Hussain","doi":"10.35833/MPCE.2023.000661","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000661","url":null,"abstract":"A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems (RIESs) with multiple stakeholders. A two-level Stackelberg game model is established, with the RIES operator as the leader and the users as the followers. It considers the interests of the RIES operator and demand response users in energy trading. The leader optimizes time-of-use (TOU) energy prices to minimize costs while users formulate response plans based on prices. A two-stage distributionally robust game model with comprehensive norm constraints, which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage, is constructed to manage wind power uncertainty. Karush-Kuhn-Tucker (KKT) conditions transform the two-level Stackelberg game model into a single-level robust optimization model, which is then solved using column and constraint generation (C&CG). Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders' interests and mitigating wind power risks.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1631-1642"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328383","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}

{"title":"State Transition Modeling Method for Optimal Dispatching for Integrated Energy System Based on Cyber—Physical System","authors":"Yi Yang;Peng Zhang;Can Wang;Zhuoli Zhao;Loi Lei Lai","doi":"10.35833/MPCE.2024.000090","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000090","url":null,"abstract":"The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system (IES). Therefore, this study proposes a state transition modeling method for an IES based on a cyber-physical system (CPS) to optimize the state transition of energy unit in the IES. This method uses the physical, integration, and optimization layers as a three-layer modeling framework. The physical layer is used to describe the physical models of energy units in the IES. In the integration layer, the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model, and the transition conditions between different states of the energy unit are given. The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state. Numerical simulations show that, compared with the traditional modeling method, the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle, which obtains an optimal state of the energy unit and further reduces the system operating costs.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1617-1630"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324294","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}

{"title":"Dynamic Optimal Power Flow Method Based on Reinforcement Learning for Offshore Wind Farms Considering Multiple Points of Common Coupling","authors":"Yang Fu;Zixu Ren;Shurong Wei;Lingling Huang;Fangxing Li;Yang Liu","doi":"10.35833/MPCE.2023.000765","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000765","url":null,"abstract":"The widespread adoption of renewable energy sources presents significant challenges for power system dispatching. This paper proposes a dynamic optimal power flow (DOPF) method based on reinforcement learning (RL) to address the dispatching challenges. The proposed method considers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling (PCCs). First, the operational area model of the offshore power grid at the PCCs is established by combining the prediction results and the transmission capacity limit of the offshore power grid. Built upon this, a dynamic optimization model of the power system and its RL environment are constructed with the consideration of offshore power dispatching constraints. Then, an improved algorithm based on the conditional generative adversarial network (CGAN) and the soft actor-critic (SAC) algorithm is proposed. By analyzing an improved IEEE 118-node example, the proposed method proves to have the advantage of economy over a longer timescale. The resulting strategy satisfies power system operation constraints, effectively addressing the constraint problem of action space of RL, and it has the added benefit of faster solution speeds.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1749-1759"},"PeriodicalIF":5.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10541887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844319","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}

{"title":"Sample Generation for Security Region Boundary Identification Based on Topological Features of Historical Operation Data","authors":"Xiaokang Wu;Wei Xu;Feng Xue","doi":"10.35833/MPCE.2023.000321","DOIUrl":"10.35833/MPCE.2023.000321","url":null,"abstract":"Since the scale and uncertainty of the power system have been rapidly increasing, the computation efficiency of constructing the security region boundary (SRB) has become a prominent problem. Based on the topological features of historical operation data, a sample generation method for SRB identification is proposed to generate evenly distributed samples, which cover dominant security modes. The boundary sample pair (BSP) composed of a secure sample and an unsecure sample is defined to describe the feature of SRB. The resolution, sampling, and span indices are designed to evaluate the coverage degree of existing BSPs on the SRB and generate samples closer to the SRB. Based on the feature of flat distribution of BSPs over the SRB, the principal component analysis (PCA) is adopted to calculate the tangent vectors and normal vectors of SRB. Then, the sample distribution can be expanded along the tangent vector and corrected along the normal vector to cover different security modes. Finally, a sample set is randomly generated based on the IEEE standard example and another new sample set is generated by the proposed method. The results indicate that the new sample set is closer to the SRB and covers different security modes with a small calculation time cost.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 4","pages":"1087-1095"},"PeriodicalIF":5.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10485267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769406","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}

{"title":"Towards Renewable-Dominated Energy Systems: Role of Green Hydrogen","authors":"Sheng Chen;Jingchun Zhang;Zhinong Wei;Hao Cheng;Si Lv","doi":"10.35833/MPCE.2023.000887","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000887","url":null,"abstract":"Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strength-ened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 6","pages":"1697-1709"},"PeriodicalIF":5.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10485266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841993","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}

{"title":"Short-Term Residential Load Forecasting Based on $K-text{shape}$ Clustering and Domain Adversarial Transfer Network","authors":"Jizhong Zhu;Yuwang Miao;Hanjiang Dong;Shenglin Li;Ziyu Chen;Di Zhang","doi":"10.35833/MPCE.2023.000646","DOIUrl":"10.35833/MPCE.2023.000646","url":null,"abstract":"In recent years, the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network. However, due to the scarcity of historical data for these new consumers, it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods. This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering, deep learning, and transfer learning technologies to address this issue. To begin, this paper leverages the domain adversarial transfer network. It employs limited data as target domain data and more abundant data as source domain data, thus enabling the utilization of source domain insights for the forecasting task of the target domain. Moreover, a \u0000<tex>$boldsymbol{K}-mathbf{shape}$</tex>\u0000 clustering method is proposed, which effectively identifies source domain data that align optimally with the target domain, and enhances the forecasting accuracy. Subsequently, a composite architecture is devised, amalgamating attention mechanism, long short-term memory network, and seq2seq network. This composite structure is integrated into the domain adversarial transfer network, bolstering the performance of feature extractor and refining the forecasting capabilities. An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically. In the case study, the relative mean square error of the proposed method is within 30 MW, and the mean absolute percentage error is within 2%. A significant improvement in accuracy, compared with other comparative experimental results, underscores the reliability of the proposed method. The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecasting methods.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 4","pages":"1239-1249"},"PeriodicalIF":5.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769407","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}

{"title":"Synchrophasor Measurement Method Based on Cascaded Infinite Impulse Response and Dual Finite Impulse Response Filters","authors":"Boyu Zhao;Hao Liu;Tianshu Bi;Sudi Xu","doi":"10.35833/MPCE.2023.000824","DOIUrl":"https://doi.org/10.35833/MPCE.2023.000824","url":null,"abstract":"High-precision synchronized measurement data with short measurement latency are required for applications of phasor measurement units (PMUs). This paper proposes a synchrophasor measurement method based on cascaded infinite impulse response (IIR) and dual finite impulse response (FIR) filters, meeting the M-class and P-class requirements in the IEC/ IEEE 60255-118-1 standard. A low-group-delay IIR filter is designed to remove out-of-band interference components. Two FIR filters with different center frequencies are designed to filter out the fundamental negative frequency component and obtain synchrophasor estimates. The ratio of the amplitudes of the synchrophasor is used to calculate the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the FIR filters and the frequency. To shorten the response time introduced by IIR filter, a step identification and processing method based on the rate of change of frequency (RoCoF) is proposed and analyzed. The synchrophasor is accurately compensated based on the frequency and the frequency response of the IIR and FIR filters, achieving high-precision synchrophasor and frequency estimates with short measurement latency. Simulation and experiment tests demonstrate that the proposed method is superior to existing methods and can provide synchronized measurement data for M-class PMU applications with short measurement latency.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"12 5","pages":"1345-1356"},"PeriodicalIF":5.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324326","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}