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Journal articles on the topic 'Active Distribution Networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Kyriakou, Dimitra G., and Fotios D. Kanellos. "Sustainable Operation of Active Distribution Networks." Applied Sciences 13, no. 5 (February 28, 2023): 3115. http://dx.doi.org/10.3390/app13053115.

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The present and future conditions in the energy market impose extremely high standards to the operation of building energy systems. Moreover, distribution networks face new operational and technical challenges as a result of the rapid penetration of renewable energy sources (RES) and other forms of distributed generation. Consequently, active distribution networks (ADNs) will play a crucial role in the exploitation of smart building prosumers, smart grids, and RES. In this paper, an optimization method for the sustainable operation of active distribution networks hosting smart residential building prosumers, plug-in electric vehicle (PEV) aggregators, and RES was developed. The thermal and electrical loads of the residential buildings were modeled in detail and an aggregation method was implemented to the hosted PEVs. Moreover, smart power dispatch techniques were applied at each building prosumer and PEV aggregator hosted by the active distribution network. Simultaneously, all the operational limitations of the active distribution network, building energy systems, and the hosted PEVs were satisfied. The constrained optimal power flow (OPF) algorithm was exploited to keep the voltages of the hosting distribution network between the permissible bounds. A significant operation cost reduction of 17% was achieved. The developed models were verified through detailed simulation results.

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2

Ilea, Valentin, Cristian Bovo, Davide Falabretti, Marco Merlo, Carlo Arrigoni, Roberto Bonera, and Marco Rodolfi. "Voltage Control Methodologies in Active Distribution Networks." Energies 13, no. 12 (June 26, 2020): 3293. http://dx.doi.org/10.3390/en13123293.

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Renewable Energy Sources are becoming widely spread, as they are sustainable and low-carbon emission. They are mostly penetrating the MV Distribution Networks as Distributed Generators, which has determined the evolution of the networks’ control and supervision systems, from almost a complete lack to becoming fully centralized. This paper proposes innovative voltage control architectures for the distribution networks, tailored for different development levels of the control and supervision systems encountered in real life: a Coordinated Control for networks with basic development, and an optimization-based Centralized Control for networks with fully articulated systems. The Centralized Control fits the requirements of the network: the challenging harmonization of the generator’s capability curves with the regulatory framework, and modelling of the discrete control of the On-Load Tap Changer transformer. A realistic network is used for tests and comparisons with the Local Strategy currently specified by regulations. The proposed Coordinated Control gives much better results with respect to the Local Strategy, in terms of loss minimization and voltage violations mitigation, and can be used for networks with poorly developed supervision and control systems, while Centralized Control proves the best solution, but can be applied only in fully supervised and controlled networks.

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3

Ochoa, L. F., C. J. Dent, and G. P. Harrison. "Distribution Network Capacity Assessment: Variable DG and Active Networks." IEEE Transactions on Power Systems 25, no. 1 (February 2010): 87–95. http://dx.doi.org/10.1109/tpwrs.2009.2031223.

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4

Alaskar, Azzan, and Abdulaziz Alkuhayli. "Reliability Evaluation of Active Distribution Systems with Distributed Generations." IOP Conference Series: Earth and Environmental Science 1026, no. 1 (June 1, 2022): 012064. http://dx.doi.org/10.1088/1755-1315/1026/1/012064.

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Abstract Reliability evaluation is essential in designing, planning, operating modern power systems. System operators must operate the network securely and efficiently with minimal interruption events. With the recent advances in power electronics and control, distributed generations (DG) such as photovoltaic (PV), wind turbine, and storage systems are expected to grow in distribution networks. This high level of distributed generations penetration in the grid can increase the complexity of operating the system. This is caused by intermittent nature of solar irradiance and wind speed. This paper proposes a methodology used to assess distribution networks containing stochastic resources such as photovoltaic. This method will use the Monte Carlo simulation with a stochastic model to evaluate the distribution network’s reliability. The system and load point reliability indices such as frequency of loss of load and expected energy not to supplied will be computed in this technique. In addition, the configuration of distribution networks to improve system’s reliability to facilitate system restoration after pre-fault conditions will be assessed.

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Al-Saadi, Hassan, Rastko Zivanovic, and Said F. Al-Sarawi. "Probabilistic Hosting Capacity for Active Distribution Networks." IEEE Transactions on Industrial Informatics 13, no. 5 (October 2017): 2519–32. http://dx.doi.org/10.1109/tii.2017.2698505.

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6

Voropai, N. I., Z. A. Styczynski, I. N. Shushpanov, Pham Trung Son, and K. V. Suslov. "Security model of active distribution electric networks." Thermal Engineering 60, no. 14 (December 2013): 1024–30. http://dx.doi.org/10.1134/s0040601513140097.

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7

Cagnano, Alessia, Enrico De Tuglie, and Marco Bronzini. "Multiarea Voltage Controller for Active Distribution Networks." Energies 11, no. 3 (March 7, 2018): 583. http://dx.doi.org/10.3390/en11030583.

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8

Koutsoukis, Nikolaos C., Pavlos S. Georgilakis, and Nikos D. Hatziargyriou. "Multistage Coordinated Planning of Active Distribution Networks." IEEE Transactions on Power Systems 33, no. 1 (January 2018): 32–44. http://dx.doi.org/10.1109/tpwrs.2017.2699696.

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9

McDonald, Jim. "Adaptive intelligent power systems: Active distribution networks." Energy Policy 36, no. 12 (December 2008): 4346–51. http://dx.doi.org/10.1016/j.enpol.2008.09.038.

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10

U. P., Dimas Fajar, Indri Suryawati, Ontoseno Penangsang, Adi Suprijanto, and Mat Syai’in. "Online State Estimator for Three Phase Active Distribution Networks Displayed on Geographic Information System." Journal of Clean Energy Technologies 2, no. 4 (2014): 357–62. http://dx.doi.org/10.7763/jocet.2014.v2.154.

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11

Li, Xin, Houlei Gao, Tong Yuan, and Bin Xu. "5G Communication Based Distributed Fault Recovery Scheme of Active Distribution Network." E3S Web of Conferences 185 (2020): 01039. http://dx.doi.org/10.1051/e3sconf/202018501039.

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As more and more distributed power sources are connected to low and medium voltage distribution networks, the traditional single-ended passive distribution networks have evolved into multi- terminal, multi-source active distribution networks. When distributed generations with high permeability are connected to a distribution network, the structure and power flow of this network will change significantly, thus the original fault detection method and reclosing scheme may be challenged, which may cause incorrect action of protection or failure of reclosing. On basis of that, this paper proposes an active distribution network fault recovery scheme based on 5G wireless communication, in which the topology recognition technology and smart terminal units with peer-to-peer communication capability are applied. To prove the method’s feasibility, delay of 5G communication is analysed and tested online. In addition, a model of 10 kV active distribution network is built on Real Time Digital Simulation system. Principle investigation and simulation indicate that the proposed scheme can adapt to the change of network structure and implement the fault self-healing quickly.

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12

Sirviö, Katja H., Hannu Laaksonen, Kimmo Kauhaniemi, and Nikos Hatziargyriou. "Evolution of the Electricity Distribution Networks—Active Management Architecture Schemes and Microgrid Control Functionalities." Applied Sciences 11, no. 6 (March 21, 2021): 2793. http://dx.doi.org/10.3390/app11062793.

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The power system transition to smart grids brings challenges to electricity distribution network development since it involves several stakeholders and actors whose needs must be met to be successful for the electricity network upgrade. The technological challenges arise mainly from the various distributed energy resources (DERs) integration and use and network optimization and security. End-customers play a central role in future network operations. Understanding the network’s evolution through possible network operational scenarios could create a dedicated and reliable roadmap for the various stakeholders’ use. This paper presents a method to develop the evolving operational scenarios and related management schemes, including microgrid control functionalities, and analyzes the evolution of electricity distribution networks considering medium and low voltage grids. The analysis consists of the dynamic descriptions of network operations and the static illustrations of the relationships among classified actors. The method and analysis use an object-oriented and standardized software modeling language, the unified modeling language (UML). Operational descriptions for the four evolution phases of electricity distribution networks are defined and analyzed by Enterprise Architect, a UML tool. This analysis is followed by the active management architecture schemes with the microgrid control functionalities. The graphical models and analysis generated can be used for scenario building in roadmap development, real-time simulations, and management system development. The developed method, presented with high-level use cases (HL-UCs), can be further used to develop and analyze several parallel running control algorithms for DERs providing ancillary services (ASs) in the evolving electricity distribution networks.

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13

Allahmoradi, Sarah, Mohsen Parsa Moghaddam, Salah Bahramara, and Pouria Sheikhahmadi. "Flexibility-constrained operation scheduling of active distribution networks." International Journal of Electrical Power & Energy Systems 131 (October 2021): 107061. http://dx.doi.org/10.1016/j.ijepes.2021.107061.

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14

Shaikh, Muhammad Fawad, Sunny Katyara, Zahid Hussain Khand, Madad Ali Shah, Lukasz Staszewski, Veer Bhan, Abdul Majeed, Shoaib Shaikh, and Leonowicz Zbigniew. "Novel Protection Coordination Scheme for Active Distribution Networks." Electronics 10, no. 18 (September 20, 2021): 2312. http://dx.doi.org/10.3390/electronics10182312.

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Distribution networks are inherently radial and passive owing to the ease of operation and unidirectional power flow. Proper installation of Distributed Generators, on the one hand, makes the utility network active and mitigates certain power quality issues e.g., voltage dips, frequency deviations, losses, etc., but on the other hand, it disturbs the optimal coordination among existing protection devices e.g., over-current relays. In order to maintain the desired selectivity level, such that the primary and backup relays are synchronized against different contingencies, it necessitates design of intelligent and promising protection schemes to distinguish between the upstream and downstream power flows. This research proposes exploiting phase angle jump, an overlooked voltage sag parameter, to add directional element to digital over-current relays with inverse time characteristics. The decision on the direction of current is made on the basis of polarity of phase angle jump together with the impedance angle of the system. The proposed scheme at first is evaluated on a test system in a simulated environment under symmetrical and unsymmetrical faults and, secondly, as a proof of the concept, it is verified in real-time on a laboratory setup using a Power Hardware-in-loop (PHIL) system. Moreover, a comparative analysis is made with other state-of-the-art techniques to evaluate the performance and robustness of the proposed approach.

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15

Gill, Simon, Ivana Kockar, and Graham W. Ault. "Dynamic Optimal Power Flow for Active Distribution Networks." IEEE Transactions on Power Systems 29, no. 1 (January 2014): 121–31. http://dx.doi.org/10.1109/tpwrs.2013.2279263.

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16

Al Kaabi, Sultan S., H. H. Zeineldin, and Vinod Khadkikar. "Planning Active Distribution Networks Considering Multi-DG Configurations." IEEE Transactions on Power Systems 29, no. 2 (March 2014): 785–93. http://dx.doi.org/10.1109/tpwrs.2013.2282343.

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17

Zhou, Yuezhi, Yaoxue Zhang, and Jianhua Lu. "CDS: a code distribution scheme for active networks." Computer Communications 27, no. 3 (February 2004): 315–21. http://dx.doi.org/10.1016/s0140-3664(03)00237-8.

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18

Wang, Zhaojian, Feng Liu, Yifan Su, Peng Yang, and Boyu Qin. "Asynchronous distributed voltage control in active distribution networks." Automatica 122 (December 2020): 109269. http://dx.doi.org/10.1016/j.automatica.2020.109269.

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19

Oikonomou, Konstantinos, Masood Parvania, and Roohallah Khatami. "Deliverable Energy Flexibility Scheduling for Active Distribution Networks." IEEE Transactions on Smart Grid 11, no. 1 (January 2020): 655–64. http://dx.doi.org/10.1109/tsg.2019.2927604.

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20

Miri Larimi, Sayyed Majid, Mahmoud Reza Haghifam, and Amin Moradkhani. "Risk-based reconfiguration of active electric distribution networks." IET Generation, Transmission & Distribution 10, no. 4 (March 10, 2016): 1006–15. http://dx.doi.org/10.1049/iet-gtd.2015.0777.

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21

Marujo, Diogo, A. C. Zambroni de Souza, B. I. L. Lopes, and D. Q. Oliveira. "Active Distribution Networks Implications on Transmission System Stability." Journal of Control, Automation and Electrical Systems 30, no. 3 (March 11, 2019): 380–90. http://dx.doi.org/10.1007/s40313-019-00458-x.

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22

Alanazi, Abdulaziz, Hossein Lotfi, and Amin Khodaei. "Market clearing in microgrid-integrated active distribution networks." Electric Power Systems Research 183 (June 2020): 106263. http://dx.doi.org/10.1016/j.epsr.2020.106263.

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23

Prionistis, Giorgos, Theodoros Souxes, and Costas Vournas. "Voltage stability support offered by active distribution networks." Electric Power Systems Research 190 (January 2021): 106728. http://dx.doi.org/10.1016/j.epsr.2020.106728.

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24

Velasco-Gómez, S., S. Pérez-Londoño, and J. Mora-Floréz. "Unbalance compensated distance relay for active distribution networks." Energy Reports 9 (May 2023): 438–46. http://dx.doi.org/10.1016/j.egyr.2022.12.129.

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25

Miri Larimi, Seyyed Majid, Mansoureh Zangiabadi, Mahmoud Reza Haghifam, and Philip Taylor. "Value based pricing of distribution generations active power in distribution networks." IET Generation, Transmission & Distribution 9, no. 15 (November 19, 2015): 2117–25. http://dx.doi.org/10.1049/iet-gtd.2014.1162.

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26

Gholami, Mohammad, Ali Abbaspour Tehrani-Fard, Matti Lehtonen, Moein Moeini-Aghtaie, and Mahmud Fotuhi-Firuzabad. "A Novel Multi-Area Distribution State Estimation Approach for Active Networks." Energies 14, no. 6 (March 23, 2021): 1772. http://dx.doi.org/10.3390/en14061772.

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This paper presents a hierarchically distributed algorithm for the execution of distribution state estimation function in active networks equipped with some phasor measurement units. The proposed algorithm employs voltage-based state estimation in rectangular form and is well-designed for large-scale active distribution networks. For this purpose, as the first step, the distribution network is supposed to be divided into some overlapped zones and local state estimations are executed in parallel for extracting operating states of these zones. Then, using coordinators in the feeders and the substation, the estimated local voltage profiles of all zones are coordinated with the local state estimation results of their neighboring zones. In this regard, each coordinator runs a state estimation process for the border buses (overlapped buses and buses with tie-lines) of its zones and based on the results for voltage phasor of border buses, the local voltage profiles in non-border buses of its zones are modified. The performance of the proposed algorithm is tested with an active distribution network, considering different combinations of operating conditions, network topologies, network decompositions, and measurement scenarios, and the results are presented and discussed.

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27

Radhoush, Sepideh, Maryam Bahramipanah, Hashem Nehrir, and Zagros Shahooei. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges." Sustainability 14, no. 5 (February 22, 2022): 2520. http://dx.doi.org/10.3390/su14052520.

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This paper provides a comprehensive review of distribution system state estimation in terms of basic definition, different methods, and their application. In the last few years, the operation of distribution networks has been influenced by the installation of distributed generations. In order to control and manage an active distribution network’s performance, distribution system state estimation methods are introduced. A transmission system state estimation cannot be used directly in distribution networks since transmission and distribution networks are different due to topology configuration, the number of buses, line parameters, and the number of measurement instruments. So, the proper state estimation algorithms should be proposed according to the main distribution network features. Accuracy, computational efficiency, and practical implications should be considered in the designing of distribution state estimation techniques since technical issues and wrong decisions could emerge in the control center by inaccurate distribution state estimation results. In this study, conventional techniques are reviewed and compared with data-driven methods in order to highlight the pros and cons of different techniques. Furthermore, the integrated distribution state estimation methods are compared with the distributed approaches, and the different criteria, including the level of area overlapping execution time and computing architecture, are elaborated. Moreover, mathematical problem formulation and different measuring methods are discussed.

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28

Liu, Jia, Peter Pingliang Zeng, Hao Xing, Yalou Li, and Qiuwei Wu. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation." Energy 213 (December 2020): 118488. http://dx.doi.org/10.1016/j.energy.2020.118488.

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29

Li, Xingmin, Hongwei Li, Shuaibing Li, Ziwei Jiang, and Xiping Ma. "Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads." Mathematical Problems in Engineering 2021 (November 23, 2021): 1–18. http://dx.doi.org/10.1155/2021/1196369.

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With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

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30

Srećković, Nevena, Miran Rošer, and Gorazd Štumberger. "Utilization of Active Distribution Network Elements for Optimization of a Distribution Network Operation." Energies 14, no. 12 (June 12, 2021): 3494. http://dx.doi.org/10.3390/en14123494.

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Electricity Distributions Networks (DNs) are changing from a once passive to an active electric power system element. This change, driven by several European Commission Directives and Regulations in the energy sector prompts the proliferated integration of new network elements, which can actively participate in network operations if adequately utilized. This paper addresses the possibility of using these active DN elements for optimization of a time-discrete network operation in terms of minimization of power losses while ensuring other operational constraints (i.e., voltage profiles and line currents). The active elements considered within the proposed optimization procedure are distributed generation units, capable of reactive power provision; remotely controlled switches for changing the network configuration; and an on-load tap changer-equipped substation, supplying the network. The proposed procedure was tested on a model of an actual medium voltage DN. The results showed that simultaneous consideration of these active elements could reduce power losses at a considered point of operation while keeping the voltage profiles within the permitted interval. Furthermore, by performing a series of consecutive optimization procedures at a given time interval, an optimization of network operations for extended periods (e.g., days, months, or years) could also be achieved.

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31

Zhou, Xiao Yi, Ling Yun Wang, Wen Yue Liang, and Li Zhou. "Research on the Voltage Influence of Active Distribution Network with Distributed Generation Access." Applied Mechanics and Materials 668-669 (October 2014): 749–52. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.749.

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Distributed generation (DG) has an important influence on the voltage of active distribution networks. A unidirectional power distribution network will be transformed into a bidirectional, multiple power supply distribution network after DGs access to the distribution network and the direction of power flow is also changed. Considering the traditional forward and backward substitution algorithm can only deal with the equilibrium node and PQ nodes, so the other types of DGs should be transformed into PQ nodes, then its impact on active distribution network can be analyzed via the forward and backward substitution algorithm. In this paper, the characteristics of active distribution networks are analyzed firstly and a novel approach is proposed to convert PI nodes into PQ nodes. Finally, a novel forward and backward substitution algorithm is adopted to calculate the power flow of the active distribution network with DGs. Extensive validation of IEEE 18 and 33 nodes distribution system indicates that this method is feasible. Numerical results show that when DG is accessed to the appropriate location with proper capacity, it has a significant capability to support the voltages level of distribution system.

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32

Sun, Dong, Wang, Lv, and War. "Cyber–Physical Active Distribution Networks Robustness Evaluation against Cross-Domain Cascading Failures." Applied Sciences 9, no. 23 (November 21, 2019): 5021. http://dx.doi.org/10.3390/app9235021.

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Active distribution networks (ADNs) are a typical cyber–physical system (CPS), which consist of two kinds of interdependent sub-networks: power networks (PNs) and communication networks (CNs). The combination of typical characteristics of the ADN includes (1) a large number of distributed generators contained in the PN, (2) load redistribution in both the PN and CN, and (3) strong interdependence between the PN and CN, which makes ADNs vulnerable to cross-domain cascading failures (CCFs). In this paper, we focus on the robustness analysis of the ADN against the CCF. Rather than via the rate of the clusters with size greater than a predefined threshold, we evaluate the robustness of the ADN using the rate of the clusters containing generators after the CCF. Firstly, a synchronous probabilistic model is derived to calculate the proportions of remaining normal operational nodes after the CCF. With this model, the propagation of the CCF in the ADN can be described as recursive equations. Secondly, we analyze the relationship between the proportions of remaining normal operational nodes after the CCF and the distribution of distributed generators, unintentional random initial failure rate, the interdependence between the sub-networks, network topology, and tolerance parameters. Some results are revealed which include (1) the more distributed generators the PN contains, the higher ADN robustness is, (2) the robustness of the ADN is negatively correlated with the unintentional random initial failure rate, (3) the robustness of the ADN can be improved by increasing the average control fan in of each node in the PN and the average power fan in of each node in the CN, (4) the robustness of the ADN with Erdos–Renyi (ER) network topological structure is greater than that with Barabasi–Albert (BA) network topological structure under the same average node degree, and (5) the robustness of the ADN is greater, when the tolerance parameters increase. Lastly, some simulation experiments are conducted and experimental results also demonstrate that the conclusions above are effective to improve the robustness of the ADN against the CCF.

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33

Li, Peng, Ming Yang, Yaohua Tang, Yixiao Yu, and Menglin Li. "Robust Decentralized Coordination of Transmission and Active Distribution Networks." IEEE Transactions on Industry Applications 57, no. 3 (May 2021): 1987–94. http://dx.doi.org/10.1109/tia.2021.3057342.

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34

Pola, Saad, and Maher A. Azzouz. "Optimal Protection Coordination of Active Distribution Networks With Synchronverters." IEEE Access 10 (2022): 75105–16. http://dx.doi.org/10.1109/access.2022.3192004.

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35

Pinto, Rafael S., Clodomiro Unsihuay‐Vila, and Fabricio H. Tabarro. "Reliability‐constrained robust expansion planning of active distribution networks." IET Generation, Transmission & Distribution 16, no. 1 (October 29, 2021): 27–40. http://dx.doi.org/10.1049/gtd2.12263.

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36

Valverde, Gustavo, and Thierry Van Cutsem. "Model Predictive Control of Voltages in Active Distribution Networks." IEEE Transactions on Smart Grid 4, no. 4 (December 2013): 2152–61. http://dx.doi.org/10.1109/tsg.2013.2246199.

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37

Mokryani, Geev. "Active distribution networks planning with integration of demand response." Solar Energy 122 (December 2015): 1362–70. http://dx.doi.org/10.1016/j.solener.2015.10.052.

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38

Li, Zhiyi, Mohammad Shahidehpour, Ahmed Alabdulwahab, and Yusuf Al-Turki. "Valuation of distributed energy resources in active distribution networks." Electricity Journal 32, no. 4 (May 2019): 27–36. http://dx.doi.org/10.1016/j.tej.2019.03.001.

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39

Yizhao, Liu, Liu Huanming, Zhang Guangwei, Lei Da, Wang Jinhao, and Chang Xiao. "Improved control strategy of TFAPF in active distribution networks." Journal of Engineering 2019, no. 16 (March 1, 2019): 3402–6. http://dx.doi.org/10.1049/joe.2018.8796.

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40

CHEN, Jiongcong, and Xudong SONG. "Economics of energy storage technology in active distribution networks." Journal of Modern Power Systems and Clean Energy 3, no. 4 (November 5, 2015): 583–88. http://dx.doi.org/10.1007/s40565-015-0148-5.

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41

Rossi, Marco, Giacomo Viganò, Diana Moneta, Maria Teresa Vespucci, and Paolo Pisciella. "Fast estimation of equivalent capability for active distribution networks." CIRED - Open Access Proceedings Journal 2017, no. 1 (October 1, 2017): 1763–67. http://dx.doi.org/10.1049/oap-cired.2017.1273.

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42

Jabr, Rabih A., Izudin Dzafic, and Indira Huseinagic. "Real Time Optimal Reconfiguration of Multiphase Active Distribution Networks." IEEE Transactions on Smart Grid 9, no. 6 (November 2018): 6829–39. http://dx.doi.org/10.1109/tsg.2017.2724766.

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43

Abdelaziz, Morad Mohamed Abdelmageed. "OpenCL-Accelerated Probabilistic Power Flow for Active Distribution Networks." IEEE Transactions on Sustainable Energy 9, no. 3 (July 2018): 1255–64. http://dx.doi.org/10.1109/tste.2017.2781148.

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44

Siano, P., P. Chen, Z. Chen, and A. Piccolo. "Evaluating maximum wind energy exploitation in active distribution networks." IET Generation, Transmission & Distribution 4, no. 5 (2010): 598. http://dx.doi.org/10.1049/iet-gtd.2009.0548.

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45

Li, Gengfeng, Zhaohong Bie, Haipeng Xie, and Yanling Lin. "Customer satisfaction based reliability evaluation of active distribution networks." Applied Energy 162 (January 2016): 1571–78. http://dx.doi.org/10.1016/j.apenergy.2015.02.084.

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46

Li, Gengfeng, Zhaohong Bie, Haipeng Xie, Xiuli Wang, and Xifan Wang. "Reliability Evaluation of Active Distribution Networks Considering Customer Satisfaction." Energy Procedia 61 (2014): 591–94. http://dx.doi.org/10.1016/j.egypro.2014.11.1177.

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47

Ranković, Aleksandar, Branko M. Maksimović, and Andrija T. Sarić. "A three-phase state estimation in active distribution networks." International Journal of Electrical Power & Energy Systems 54 (January 2014): 154–62. http://dx.doi.org/10.1016/j.ijepes.2013.07.001.

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Usman, M., M. Coppo, F. Bignucolo, and R. Turri. "Losses management strategies in active distribution networks: A review." Electric Power Systems Research 163 (October 2018): 116–32. http://dx.doi.org/10.1016/j.epsr.2018.06.005.

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Wang, Lingling, Xu Wang, Chuanwen Jiang, Shuo Yin, and Meng Yang. "Dynamic Coordinated Active–Reactive Power Optimization for Active Distribution Network with Energy Storage Systems." Applied Sciences 9, no. 6 (March 18, 2019): 1129. http://dx.doi.org/10.3390/app9061129.

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Abstract:

This paper proposes a coordinated active–reactive power optimization model for an active distribution network with energy storage systems, where the active and reactive resources are handled simultaneously. The model aims to minimize the power losses, the operation cost, and the voltage deviation of the distribution network. In particular, the reactive power capabilities of distributed generators and energy storage systems are fully utilized to minimize power losses and improve voltage profiles. The uncertainties pertaining to the forecasted values of renewable energy sources are modelled by scenario-based stochastic programming. The second-order cone programming relaxation method is used to deal with the nonlinear power flow constraints and transform the original mixed integer nonlinear programming problem into a tractable mixed integer second-order cone programming model, thus the difficulty of problem solving is significantly reduced. The 33-bus and 69-bus distribution networks are used to demonstrate the effectiveness of the proposed approach. Simulation results show that the proposed coordinated optimization approach helps improve the economic operation for active distribution network while improving the system security significantly.

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50

Kyriakou, Dimitra G., and Fotios D. Kanellos. "Optimal Operation of Microgrids Comprising Large Building Prosumers and Plug-in Electric Vehicles Integrated into Active Distribution Networks." Energies 15, no. 17 (August 25, 2022): 6182. http://dx.doi.org/10.3390/en15176182.

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Abstract:

Active distribution networks and microgrids will be powerful tools for future power systems in their endeavor to integrate more renewable energy sources, increase distributed generation and optimize their operation. In this paper, a method for the coordinated optimal operation scheduling of active distribution networks that are hosting complex microgrids comprising large building prosumers and plug-in electric vehicle aggregators is proposed. The electrical and thermal power systems of the microgrid are modelled in detail while the examined active distribution network is assumed to be able to optimally shift part of its loads in time and comprises renewable energy sources as part of its local generation. Moreover, the microgrid is assumed to be able to shift part of its load in order to assist the active distribution network in order to satisfy all of the network constraints when this is required. The proposed method was developed in such a way that allows both the microgrid and the active distribution network to optimize their operations without exchanging the internal information comprising their technical characteristics and parameters. To this end, the method is organized into five levels wherein only the absolutely necessary information is exchanged, i.e., the power that is exchanged by the microgrid and the active distribution network and the time periods in which the network constraints are violated.

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