Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads
A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analy...
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/428 |
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| author | M A Sasi Bhushan M. Sudhakaran Sattianadan Dasarathan Mariappane E |
| author_facet | M A Sasi Bhushan M. Sudhakaran Sattianadan Dasarathan Mariappane E |
| author_sort | M A Sasi Bhushan |
| collection | DOAJ |
| description | A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method. |
| format | Article |
| id | doaj-art-8a9213cf9ad146a5b5ee715489286e50 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8a9213cf9ad146a5b5ee715489286e502025-01-24T13:31:27ZengMDPI AGEnergies1996-10732025-01-0118242810.3390/en18020428Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying LoadsM A Sasi Bhushan0M. Sudhakaran1Sattianadan Dasarathan2Mariappane E3Department of EEE, Puducherry Technological University, Puducherry 605014, Puducherry, IndiaDepartment of EEE, Puducherry Technological University, Puducherry 605014, Puducherry, IndiaDepartment of EEE, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamilnadu, IndiaDepartment of ECE, Christ Institute of Technology, Villianur Commune, Puducherry 605502, Puducherry, IndiaA peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method.https://www.mdpi.com/1996-1073/18/2/428quasi-dynamic load flow analysisdistributed energy resourcesPV-BESSstate of chargepeak shavingmodel predictive control |
| spellingShingle | M A Sasi Bhushan M. Sudhakaran Sattianadan Dasarathan Mariappane E Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads Energies quasi-dynamic load flow analysis distributed energy resources PV-BESS state of charge peak shaving model predictive control |
| title | Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads |
| title_full | Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads |
| title_fullStr | Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads |
| title_full_unstemmed | Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads |
| title_short | Integration of a Heterogeneous Battery Energy Storage System into the Puducherry Smart Grid with Time-Varying Loads |
| title_sort | integration of a heterogeneous battery energy storage system into the puducherry smart grid with time varying loads |
| topic | quasi-dynamic load flow analysis distributed energy resources PV-BESS state of charge peak shaving model predictive control |
| url | https://www.mdpi.com/1996-1073/18/2/428 |
| work_keys_str_mv | AT masasibhushan integrationofaheterogeneousbatteryenergystoragesystemintothepuducherrysmartgridwithtimevaryingloads AT msudhakaran integrationofaheterogeneousbatteryenergystoragesystemintothepuducherrysmartgridwithtimevaryingloads AT sattianadandasarathan integrationofaheterogeneousbatteryenergystoragesystemintothepuducherrysmartgridwithtimevaryingloads AT mariappanee integrationofaheterogeneousbatteryenergystoragesystemintothepuducherrysmartgridwithtimevaryingloads |