Optimization and Energy Management of Battery Energy Storage Systems in Residential and Industrial Applications

• Author / Creator

  Martins, Rodrigo

• Battery Energy Storage System (BESS) provides a fast and high power capability, making them an ideal solution for residential and industrial application.
  However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a BESS depends also on the battery operation, storage technology, and aging of the system. For instance, BESS coupled with residential photovoltaic (PV) generation, designed as PV-BESS, can reduce the energy dependency of individual households while mitigating the impact of the intermittent renewable energy sources on the electric power grid. However, to maximize the benefits, efficient operational strategies must be defined to manage flows of energy in such systems.
  In this thesis, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Then, it shows two cases of how the optimal power flows can be used to develop advanced energy management systems.
  In the first case, the time series of the optimal flows, determined using linear programming, are used to set the parameters of the controller for the next time window.
  In the second case, an energy management system is designed in the form of a fuzzy rule based system, and the time series of the optimal power flows are used to set the parameters of a Takagi-Sugeno fuzzy controller through differential evolution.
  Similarly to the desire to apply BESS in residential areas, the interest in BESSs for industrial peak shaving application has drastically increased. There have been several reports examining the optimal sizing of storage systems. Because most such works make significant assumptions about the key factors that affect battery degradation, this thesis proposes a linear aging model that considers a state of charge dependent calendric aging, and verifies that the depth of discharge cycling aging dependency is not relevant to peak shaving application.
  The linear model reveals that considering a SOC-aware charge control strategy for peak shaving applications the battery storage system lifetime could be significantly prolonged.
  To verify the applicability of the linear aging model, this work proposes a general framework for cost-optimal sizing of the battery and power electronics in peak shaving application.
  A case study conducted with real-world industrial profiles shows the applicability of the approach and reveals the best storage operation patterns when considering the trade-offs between energy purchase, peak-power tariff, and battery aging.
  However, the deployment of BESS for industrial peak shaving applications can substantially reduce the peak power, it is noticed that the storage system is underused, staying idle most of the time. Motivated by that, this thesis proposes a new business model where battery energy storage is offered as a service by a new stakeholder. This new model allows sharing a single battery storage system among multiple clients.
  The results show that sharing batteries in peak shaving applications for multiple clients shortens the payback period.
  The results show that the best economic performances require specific storage technology and component sizing which change depending on the scenario of load demand and PV generation.
  At the same time, it confirms the operational and economic benefits of using the proposed energy management systems.
  The results also show that while batteries used in peak shaving applications are sensitive to calendric aging, the depth of discharge cycling is much less relevant. This is an important observation that will simplify relevant optimization studies and thus contribute to more widespread application of industrial peak shaving systems.
  Each of these topics is justified with experimental case studies, using real-world data sets, demonstrating the feasibility of the proposed models as compared to existing methodologies.

• Subjects / Keywords

  • peak shaving
  • Optimization
  • linear programming
  • Energy Management
  • Battery storage system

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-x86k-m886

• License

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