Battery Energy Storage Solution for PV Integrated Campus Distribution Networks

Written by Tripti Gangwar, Narayana Prasad Padhy, and Premalata Jena

The key objective of the smart grids is to develop affordable and reliable future grids powered by decentralized renewable electricity systems, thereby reducing fossil fuel consumption in the near future. The two key renewable sources in India are wind and solar, contributing 54% and 26% to the overall renewable generation. India, the second most populated country in the world and with a high solar insolation, is an ideal combination for rooftop solar installations. The solar installations are predicted to increase exponentially in the upcoming years to achieve the renewable target of 175GW set by the Government of India by the end of 2022, which includes 100GW of solar power. Currently, 114 GW installation is complete, including 57 GW of solar energy. However, the increased solar installations of low or medium power capacity are associated with prominent issues in the distribution network, such as voltage fluctuations, an unintended trip of solar inverters, and limited utilization of renewable energy during power outages. Additional problems might occur during large-scale renewable integration, such as peak energy deficit, reverse power flow, system instability, etc. To overcome the mentioned objectives, the article describes the integration of battery energy storage system (BESS) in a distributed manner at different locations of the distribution system.

The integration of BESSs in the distribution system needs to be standardized and should be deployed on a functioning solar integrated distribution system. The deployment will consider the modeling of the distribution network followed by optimization and control methods and later power hardware in-loop (PHIL) simulation for successful validation before field deployment. The distribution network modeling consists of system-level as well as component-level modeling. BESS's unplanned and uncontrolled operation is a loss to the operator and the consumer from both economic and technical perspectives. Therefore, optimal planning and operation are needed to exploit the benefit of BESSs in a distribution grid. Optimization techniques were adopted to determine the size and location of BESS. With energy storage assisting in improving operating conditions, the main challenge for integration in the distribution network is the optimal allocation while taking into consideration diverse factors such as the characteristics of multiple technologies, depth of discharge, the impact of the charging / discharging cycle on the cycle life, distributed or aggregated placement, and so on. Therefore, an optimization problem is formulated to minimize the investment and operation cost of the distributed energy storage systems to be installed at various 11 kV/415 V substations on campus. The total distribution system was modeled and simulated offline with varying load and PV data. The objective of investment, maintenance and operation cost was followed along with the network and battery constraints to determine a global optimal solution. However, other objectives like minimizing system losses, degradation of batteries, input from grid and other multi-objective problems can be formulated for BESS allocation. Instead of classical optimization techniques, other methods like artificial intelligence, analytical and meta-heuristics are also preferred. The analytical and mathematical optimization methods require significant computing for complex and large-scale problems but provide a robust solution. In addition, mathematical optimization is limited to addressing linear/quadratic/mixed-integer programming problems. Meta-heuristics are model-free and flexible, while artificial intelligence search ability of optimal solution depends on the effectiveness of the algorithm. Although the type of developed problem influences the selection of solution methods, researchers used a combination of these methods to achieve improved performance.
Market digitization, electrification, and decarbonization accelerate the deployment of BESS. However, the driving push behind the project was the transformation of a low-voltage passive distribution network into a smart and active distribution network, performance improvements in terms of voltage and frequency regulation, declining cost of lithium-ion batteries, and government policy of increasing renewable energy generation. The main risks identified through the deployment process and the solutions adopted for the same are highlighted below:

1. Upscaling the passive distribution network to a smart distribution network without altering the existing network

Solution:

• The component and system-level modeling of each device on the Indian Institute of Technology Roorkee (IITR) distribution network was carried out. The modeled system was validated on a real-time simulation platform RTDS. Theoretical and offline analysis was also carried out on various simulation platforms like OpenDSS and MATLAB.
• Smart meters and SCADA system are installed on the campus for data monitoring and controlling.  Also, the system is modernized by installing remote terminal units RTUs at all the substations.
• A central energy management system is installed to control and monitor the BESS stations of the distribution network.

2. Research and analysis of renewable integrated distribution networks are confined to theoretical simulations or small-scale power hardware in-loop prototypes, which do not manifest the real-time scenario

Solution:

• The proposed solution to deploy BESS will be a benchmark for further similar types of deployments.  This will provide a platform to perform real-time analysis on a highly PV penetrated distribution network.  
• A lab prototype is built in the lab considering seamless transition and voltage control capabilities of the BESS inverter, the control algorithm developed in-house was tested through a PHIL set up. The tested algorithms and modes of operation of BESS are then scaled to a deployable model of BESS.

 

3. Random storage allocation evokes poor performance, increased expenditure, and reduced resiliency and reliability. In addition, the unplanned and uncontrolled storage operation is a loss to the operator

Solution:

• Optimization techniques were adopted to determine the size and location of BESS.
• With energy storage assisting in improving operating conditions, the main challenge for integration in the distribution network is the optimal allocation while taking into consideration diverse factors such as the characteristics of multiple technologies, depth of discharge, the impact of the charging / discharging cycle on the cycle life, distributed or aggregated placement, and so on. Therefore, an optimization problem is formulated aiming at minimization of investment and operation cost of the distributed energy storage systems to be installed at various substations.
• The total distribution system was modeled and simulated offline with varying load and PV data. The objective of investment, maintenance and operation cost was followed along with the network and battery constraints to determine a global optimal solution.

 

4. Integrating the battery, battery management system (BMS), energy management system (EMS), and power conversion system (PCS) into a complete operative system. Then synchronizing the complete deployed BESS to institute the SCADA system

Solution:

• Rigorous lab testing was done before the deployment of the total system to validate the functioning concerning the IITR distribution network.
• Support from various industries like Tata Power and ABB was utilized for testing the prototype.
• IITR Dean Infrastructure was involved with the R&D team to synchronize BESS and SCADA successfully.

 

5. Battery energy storage deployment, particularly lithium-ion batteries, is growing rapidly. However, the lack of standardization, regulations and policies is a roadblock to further deployment

Solution:

• Federal Energy Regulatory Commission (FERC) includes storage as the 'generation facility'. However, the storage is unique as it shares features of both load and generation. The solution adopted for the regulatory risks is deploying batteries alongside rooftop PV with stable revenue and market participation rules.  It will mitigate the risks of changing market strategies and may reduce the revenue from storage devices.
• The BESS inverter's modes of operation and control approaches are designed following IEEE standard 1547.
  A total of 800 kWh/500 kW of BESS is proposed to be installed at five locations with a central energy management system. One location of 200 kWh/200 kW is installed, and the other one of 150 kWh/75 kW is ongoing. The rest of the locations will be planned and installed after successfully testing the already deployed systems.

 

The main lessons learned during and after the deployment of BESS are briefly described below:

1. Controls and monitoring are crucial for the profitable operation of the system. Lithium-ion batteries are most advantageous due to their high energy density and longer life cycle. However, failure to monitor the cells will lead to faster degradation, earlier end of life and poor performance.
2. A complete integrated system is essential for commercial deployment, and successful and cost-effective operation.  BESSs with power conversion system, battery management system, and energy management system is deployed at optimal sites.  A vendor with all the civil, electrical, and transportation works for integrating the total system is preferred. However, multiple vendors will introduce complexity but at the same time may give more flexibility to achieve a customized design.
3. Long-duration lithium-ion batteries are a feasible solution to mitigate the challenges due to the proliferation of renewable energy resources. The declining price of lithium-ion batteries, high efficiency and high energy density can firmly demonstrate the profit to customers and distribution networks.
4. The analysis of the lifecycle of BESS is essential for planning the energy and power rating in a distribution network. It is concluded in the offline study that with partial cycles of discharging, the batteries will last longer than complete cycles of discharge. The lifecycle of the battery is determined by its calendar life and cycle life. The calendar life depends on the state of charge and temperature of the cell. The cycle life depends on the state of charge, temperature, depth of discharge and the number of charge/discharge cycles. Therefore, the temperature is controlled by installing a containerized system, and the number of charge/discharge cycles are maintained by discharging the battery at a partial depth of discharge, which will automatically lead to longer life of the battery.

 

References

1. T. Gangwar, N. P. Padhy and P. Jena, "Storage Allocation in Active Distribution Networks considering Life-cycle and Uncertainty," IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2022.3167382.
2. R. Sioshansi et al., "Energy-Storage Modeling: State-of-the-Art and Future Research Directions," in IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 860-875, March 2022.
3. R. Khezri, A. Mahmoudi, N. Ertugrul, M. F. Shaaban, and A. Bidram, "Battery lifetime modelling in planning studies of microgrids: A review," in 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021, pp. 1–6.
4. M. Rouholamini, C. Wang, H. Nehrir, X. Hu, Z. Hu, H. Aki, B. Zhao, Z. Miao, and K. Strunz, "A review of modeling, management, and applications of grid connected li ion battery storage systems," IEEE Transactions on Smart Grid, pp. 1–1, 2022.
5. T. Gangwar, N. P. Padhy and P. Jena, "Management of Energy Storage Dispatch in Unbalanced Distribution Networks using OpenDSS," 2022 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE), 2022, pp. 1-6.

 

This article was edited by Geev Mokryani

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