Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783415
Chunyan Ma, Qing Duan, Haoqing Wang, Yi Mu
A large number of Distributed Generation (DG) access to the distribution network, which changes the power flow distribution of the distribution network, and the randomness of distributed generation and electric vehicle load also brings new problems to the network reconfiguration of the power grid. Aiming at the influence of distributed generation and electric vehicle access on distribution network, this paper aims at reducing node voltage deviation and active power loss, and introduces the optimal concept, using Multi-Objective Genetic Algorithm ( MOGA ) to reconstruct the distribution network. The IEEE33 node example is calculated and analyzed. The results show that this method can obtain a network structure with better steady-state economic operation capability and safe power supply capability.
{"title":"Multi-objective Dynamic Network Reconstruction Method for Active Distribution Network Including Distributed Generation and Electric Vehicles","authors":"Chunyan Ma, Qing Duan, Haoqing Wang, Yi Mu","doi":"10.1109/CEEPE55110.2022.9783415","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783415","url":null,"abstract":"A large number of Distributed Generation (DG) access to the distribution network, which changes the power flow distribution of the distribution network, and the randomness of distributed generation and electric vehicle load also brings new problems to the network reconfiguration of the power grid. Aiming at the influence of distributed generation and electric vehicle access on distribution network, this paper aims at reducing node voltage deviation and active power loss, and introduces the optimal concept, using Multi-Objective Genetic Algorithm ( MOGA ) to reconstruct the distribution network. The IEEE33 node example is calculated and analyzed. The results show that this method can obtain a network structure with better steady-state economic operation capability and safe power supply capability.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125831637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783289
Libin Wen, Hong Hu, Jun Li, Zhiyuan Sun, Yihui Zhang, Jianxu Wu
Due to the randomness and volatility of wind power, the regulation capacity of power system is insufficient after large-scale grid connection. After the heating transformation of pure condensing thermal power unit, its regulating capacity has changed significantly. Therefore, the technical research of test and theoretical modeling is carried out. The minimum power value of the generator under steam extraction condition is tested through the test, and then the maximum output power value of the generator is obtained by considering the power loss caused by steam extraction. In this way, the steam extraction flow electric power model is obtained to calculate the regulating capacity in real time. According to the online visible regulation capacity, this can guide the stable operation of the power grid. This can be used for reference to promote the development of wind power.
{"title":"Extraction Steam Flow-generator Power Modeling and Its Application in Wind Power Consumption","authors":"Libin Wen, Hong Hu, Jun Li, Zhiyuan Sun, Yihui Zhang, Jianxu Wu","doi":"10.1109/CEEPE55110.2022.9783289","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783289","url":null,"abstract":"Due to the randomness and volatility of wind power, the regulation capacity of power system is insufficient after large-scale grid connection. After the heating transformation of pure condensing thermal power unit, its regulating capacity has changed significantly. Therefore, the technical research of test and theoretical modeling is carried out. The minimum power value of the generator under steam extraction condition is tested through the test, and then the maximum output power value of the generator is obtained by considering the power loss caused by steam extraction. In this way, the steam extraction flow electric power model is obtained to calculate the regulating capacity in real time. According to the online visible regulation capacity, this can guide the stable operation of the power grid. This can be used for reference to promote the development of wind power.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126587415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783429
Shuxian Yi, Rui Chen, Xiaofei Wang, Tianzhi Cao, Li Zheng
In order to promote the penetration of distributed renewable energy and the hybrid connection of AC and DC distribution network, regional energy router is proposed. By integrating regional energy router into smart distribution network, safe and stable economic network operation can be better realized. However, traditional manual design and parameter optimization method have faced many difficulties such as low efficiency and large quantity of manpower. This paper proposed an automatic parameter optimization method based on high-efficiency simulation model of regional energy router. By using genetic algorithm (GA) and integral of time multiplied by squared error criterion (ITSE), efficient and fast parameter optimization can be achieved. Proposed parameter optimization method has been verified based on simulation of a four-port 10kV/1MW regional energy router.
{"title":"Automatic Parameter Optimization Based on High-Efficiency Simulation Model of Regional Energy Router","authors":"Shuxian Yi, Rui Chen, Xiaofei Wang, Tianzhi Cao, Li Zheng","doi":"10.1109/CEEPE55110.2022.9783429","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783429","url":null,"abstract":"In order to promote the penetration of distributed renewable energy and the hybrid connection of AC and DC distribution network, regional energy router is proposed. By integrating regional energy router into smart distribution network, safe and stable economic network operation can be better realized. However, traditional manual design and parameter optimization method have faced many difficulties such as low efficiency and large quantity of manpower. This paper proposed an automatic parameter optimization method based on high-efficiency simulation model of regional energy router. By using genetic algorithm (GA) and integral of time multiplied by squared error criterion (ITSE), efficient and fast parameter optimization can be achieved. Proposed parameter optimization method has been verified based on simulation of a four-port 10kV/1MW regional energy router.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121593580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Energy storage technology already has the potential advantages of being mobile, modular, and "plug and play". The results of existing energy storage planning of the Distribution Network (DN) are energy storage configurations with fixed access points and capacity. There is still a lot of room for the flexibility and value of energy. With many renewable energies distributed generation (DG) connected to the distribution network, it can be divided into different operating scenarios according to the seasonality of distributed power and loads in the distribution network. This paper proposes a Mobile Energy Storage (MES) configuration planning method of the DN. Through this method, the MES devices are dynamically allocated between different operating scenarios of the DN regarding the varying load demands as well as DG outputs. The access points, capacities and operation modes of MES devices in various scenarios are taken as the decision variables, the maximum comprehensive economic benefit brought by MES is taken as the objective, and thus the mixed-integer second-order cone programming(MISOCP) model for MES configuration is constructed. Case studies are performed in the modified IEEE 33-node distribution systems. The feasibility and effectiveness of the proposed method are verified The proposed method can be treated as a useful expansion to the flexible operation of energy storage device, through which the MES can well service for various operating scenarios of the DN and thus its value can be fully developed.
{"title":"A Configuration Planning Method of Mobile Energy Storage in Various Scenarios","authors":"Hongzhou Chen, Jizhong Zhu, Xiaofu Xiong, Jian Wang, Wei Wang, Junjie Cheng","doi":"10.1109/CEEPE55110.2022.9783288","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783288","url":null,"abstract":"Energy storage technology already has the potential advantages of being mobile, modular, and \"plug and play\". The results of existing energy storage planning of the Distribution Network (DN) are energy storage configurations with fixed access points and capacity. There is still a lot of room for the flexibility and value of energy. With many renewable energies distributed generation (DG) connected to the distribution network, it can be divided into different operating scenarios according to the seasonality of distributed power and loads in the distribution network. This paper proposes a Mobile Energy Storage (MES) configuration planning method of the DN. Through this method, the MES devices are dynamically allocated between different operating scenarios of the DN regarding the varying load demands as well as DG outputs. The access points, capacities and operation modes of MES devices in various scenarios are taken as the decision variables, the maximum comprehensive economic benefit brought by MES is taken as the objective, and thus the mixed-integer second-order cone programming(MISOCP) model for MES configuration is constructed. Case studies are performed in the modified IEEE 33-node distribution systems. The feasibility and effectiveness of the proposed method are verified The proposed method can be treated as a useful expansion to the flexible operation of energy storage device, through which the MES can well service for various operating scenarios of the DN and thus its value can be fully developed.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122598185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the stability consideration of power systems, the meshed topology of the network has become common. This paper proposes a second-order cone relaxation based method for the optimal power flow problem of meshed networks. The method imposes four sets of second-order cone relaxations to convexify the non-convex power flow constraints. Besides, the convex concave procedure with penalty has been implemented to prompt exact relaxations. Within few times of iterations, a feasible solution which is near the global optimum can be obtained. The superiority of the proposed approach has been tested over the case study.
{"title":"A Second-Order Cone Relaxation Based Method for Optimal Power Flow of Meshed Networks","authors":"Yuwei Chen, Bingqing Xia, Chenggen Xu, Qing Chen, Zhaohui Shi, Songge Huang","doi":"10.1109/CEEPE55110.2022.9783365","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783365","url":null,"abstract":"Due to the stability consideration of power systems, the meshed topology of the network has become common. This paper proposes a second-order cone relaxation based method for the optimal power flow problem of meshed networks. The method imposes four sets of second-order cone relaxations to convexify the non-convex power flow constraints. Besides, the convex concave procedure with penalty has been implemented to prompt exact relaxations. Within few times of iterations, a feasible solution which is near the global optimum can be obtained. The superiority of the proposed approach has been tested over the case study.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122831882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783438
Xin Cun, Rong Yan, Guangchao Geng, Q. Jiang
Small-signal stability boundary (SSSB) plays a significant role in operation and control in power system. The small-signal stability is able to assessed quickly and improved by the useful information of SSSB. While SSSB generation faces plenty of challenges in existing methods, such as high computation complexity, massive operating points (OPs) and contingencies should be considered. The proposed paper proposes a data-driven method to generate the stability boundary, by quickly sampling as many as possible the OPs which the damping ratio of its are close to the stability boundary with critical N-1 contingencies. To achieve this goal, a sample method with the self-adapting step size based on the damping ration sensitivity and critical contingencies selection based on the network theory are proposed in this paper. The effectiveness of the proposed method is demonstrated by IEEE 9-bus system and NESTA 162-bus system.
{"title":"Data-Driven Small-Signal Stability Boundary Based on Damping Ratio Sensitivity","authors":"Xin Cun, Rong Yan, Guangchao Geng, Q. Jiang","doi":"10.1109/CEEPE55110.2022.9783438","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783438","url":null,"abstract":"Small-signal stability boundary (SSSB) plays a significant role in operation and control in power system. The small-signal stability is able to assessed quickly and improved by the useful information of SSSB. While SSSB generation faces plenty of challenges in existing methods, such as high computation complexity, massive operating points (OPs) and contingencies should be considered. The proposed paper proposes a data-driven method to generate the stability boundary, by quickly sampling as many as possible the OPs which the damping ratio of its are close to the stability boundary with critical N-1 contingencies. To achieve this goal, a sample method with the self-adapting step size based on the damping ration sensitivity and critical contingencies selection based on the network theory are proposed in this paper. The effectiveness of the proposed method is demonstrated by IEEE 9-bus system and NESTA 162-bus system.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121582509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783408
Zhichao Shi, Tao Zhang, Yajie Liu, Rui Wang, Shengjun Huang
With the increasing penetration of renewable generation into power system, the energy management of multi-microgrid (MMG) system has become an important problem. In this paper, a bi-level distributionally robust energy management model for MMG system with uncertain renewable generation is proposed. The distribution system operator (DSO) and each microgrid (MG) are considered as different entities with various objectives. In each MG, the Wasserstein distance based ambiguity set is utilized to describe the uncertain distribution of renewable generation. Due to the coupling power exchange, the alternating direction method of multipliers (ADMM) is applied to solve the problem in a decentralized manner. For individual MG energy management, the column and constraint generation (CCG) algorithm is used to solve it. Simulation experiments are carried out to validate the effectiveness of the proposed model based on a modified IEEE 33-bus system with three MGs.
{"title":"Distributionally Robust Energy Management of Multi-microgrid System with Uncertainty","authors":"Zhichao Shi, Tao Zhang, Yajie Liu, Rui Wang, Shengjun Huang","doi":"10.1109/CEEPE55110.2022.9783408","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783408","url":null,"abstract":"With the increasing penetration of renewable generation into power system, the energy management of multi-microgrid (MMG) system has become an important problem. In this paper, a bi-level distributionally robust energy management model for MMG system with uncertain renewable generation is proposed. The distribution system operator (DSO) and each microgrid (MG) are considered as different entities with various objectives. In each MG, the Wasserstein distance based ambiguity set is utilized to describe the uncertain distribution of renewable generation. Due to the coupling power exchange, the alternating direction method of multipliers (ADMM) is applied to solve the problem in a decentralized manner. For individual MG energy management, the column and constraint generation (CCG) algorithm is used to solve it. Simulation experiments are carried out to validate the effectiveness of the proposed model based on a modified IEEE 33-bus system with three MGs.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122128879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-22DOI: 10.1109/CEEPE55110.2022.9783344
Yan Cheng, P. Yu, Shumin Sun, Nan Wang, Yuanhang Zhang, Peng Kou
The development of modern power system is accompanied by many problems. The growing proportion of wind generation in power grid gives rise to frequency instability problem. The increasing load demand in power grid worsens the load peak-to-valley difference problem. Battery Energy Storage System (BESS) has the capability of frequency regulation and peak load shaving, but its high economic costs need to be taken into consideration. To address this issue, this paper proposes a sizing strategy for BESS with wind integration under the condition of frequency regulation and peak load shaving. In the case of eliminating frequency deviations, the sizing of BESS is decided by the approximated Laplace distribution based on wind power prediction errors. As for peak load shaving, the load interval control method is used for the sizing of BESS. Simulation results demonstrate the effectiveness of the proposed scheme.
{"title":"Sizing of Battery Energy Storage for Wind Integration: Considering Frequency Regulation and Peak Load Shaving","authors":"Yan Cheng, P. Yu, Shumin Sun, Nan Wang, Yuanhang Zhang, Peng Kou","doi":"10.1109/CEEPE55110.2022.9783344","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783344","url":null,"abstract":"The development of modern power system is accompanied by many problems. The growing proportion of wind generation in power grid gives rise to frequency instability problem. The increasing load demand in power grid worsens the load peak-to-valley difference problem. Battery Energy Storage System (BESS) has the capability of frequency regulation and peak load shaving, but its high economic costs need to be taken into consideration. To address this issue, this paper proposes a sizing strategy for BESS with wind integration under the condition of frequency regulation and peak load shaving. In the case of eliminating frequency deviations, the sizing of BESS is decided by the approximated Laplace distribution based on wind power prediction errors. As for peak load shaving, the load interval control method is used for the sizing of BESS. Simulation results demonstrate the effectiveness of the proposed scheme.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131477469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, An artificial intelligence algorithm-genetic algorithm is adopted. Through the improvement of the genetic algorithm, the grid reconfiguration of the distribution network is carried out, and the optimal grid structure under the constraint conditions is sought, and finally the line loss is minimized under the normal operation of the grid. Achieve the effect of reducing line loss and increase corporate efficiency.
{"title":"Research on Optimization of Line Loss of Distribution Network Based on Genetic Algorithm","authors":"Lingyi Li, Chunwei Wang, Yueming Li, Shujian Zhao, Xinya Wang, Siyu Han","doi":"10.1109/CEEPE55110.2022.9783383","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783383","url":null,"abstract":"In this paper, An artificial intelligence algorithm-genetic algorithm is adopted. Through the improvement of the genetic algorithm, the grid reconfiguration of the distribution network is carried out, and the optimal grid structure under the constraint conditions is sought, and finally the line loss is minimized under the normal operation of the grid. Achieve the effect of reducing line loss and increase corporate efficiency.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"1998 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of the Electric Vehicle (EV), the charging demand increases rapidly. To release the electricity supply pressure, time of use price is implemented in some cities to transfer the charging demand of household EVs from peak period to valley period. However, plenty of EVs choose to charge intensively at the beginning of valley period, which causes a new load peak and wastes the potential of peak shaving and valley filling. To address the problem, this paper proposed the optimal charging scheduling strategy for household EVs under time of use price. Firstly, the charging model and process of the EV are developed to describe the various charging demands accurately. Subsequently, EVs with optimization potential are screened to improve the efficiency of time scheduling. In order to shorten the peak-valley difference of the residential load, the charging periods of EVs are optimized utilizing the genetic algorithm. Finally, based on the actual data of residential load and EVs, the time scheduling simulation is studied to show the optimization performance. By making full use of the peak-shaving and valley-filling capacity of EVs, the simulation results proved the effectiveness of the proposed method on charging time scheduling, the peak load caused by centralized charging demands decreased. Besides, the residential power during the peak period is effectively reduced and the power during the valley period is improved.
{"title":"Optimal Charging Scheduling for Household Electric Vehicles under TOU Prices","authors":"Ruoyun Hu, Qi Ding, Qingjuan Wang, Ran Shen, Yifan Wang, Taoyi Qi","doi":"10.1109/CEEPE55110.2022.9783267","DOIUrl":"https://doi.org/10.1109/CEEPE55110.2022.9783267","url":null,"abstract":"With the development of the Electric Vehicle (EV), the charging demand increases rapidly. To release the electricity supply pressure, time of use price is implemented in some cities to transfer the charging demand of household EVs from peak period to valley period. However, plenty of EVs choose to charge intensively at the beginning of valley period, which causes a new load peak and wastes the potential of peak shaving and valley filling. To address the problem, this paper proposed the optimal charging scheduling strategy for household EVs under time of use price. Firstly, the charging model and process of the EV are developed to describe the various charging demands accurately. Subsequently, EVs with optimization potential are screened to improve the efficiency of time scheduling. In order to shorten the peak-valley difference of the residential load, the charging periods of EVs are optimized utilizing the genetic algorithm. Finally, based on the actual data of residential load and EVs, the time scheduling simulation is studied to show the optimization performance. By making full use of the peak-shaving and valley-filling capacity of EVs, the simulation results proved the effectiveness of the proposed method on charging time scheduling, the peak load caused by centralized charging demands decreased. Besides, the residential power during the peak period is effectively reduced and the power during the valley period is improved.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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