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Sizing of battery energy storage for end-user applications under time of use pricing

Sizing of battery energy storage for end-user applications under time of use pricing

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This chapter focuses on the optimal sizing of BESSs in end-user applications in the frame of time-varying energy pricing structures by adopting a probabilistic approach. More in detail, the proposed procedure focuses on one of the most used time-varying tariff structures (i.e. the ToU tariff) but it can be easily extended to other structures. In this chapter, starting from the procedure proposed, the probabilistic optimal sizing is performed applying the point estimate method (PEM), an algorithm that guarantees accuracy of the results with computational effort significantly lower than that implied by the Monte Carlo procedure.

Chapter Contents:

  • 6.1 Introduction
  • 6.2 Energy tariff structures
  • 6.3 The cost of the storage system
  • 6.4 Probabilistic approach for sizing battery systems
  • 6.4.1 Brief background on PEM algorithm
  • 6.4.2 Applications of PEM for the BESS sizing procedure
  • 6.5 Numerical applications
  • 6.5.1 Industrial load
  • 6.5.2 Commercial load
  • 6.5.3 Residential load
  • 6.6 Conclusions
  • References

Inspec keywords: Monte Carlo methods; power generation economics; battery storage plants; pricing; tariffs; probability

Other keywords: probabilistic approach; point estimate method; battery energy storage sizing; end-user applications; time of use pricing; time-varying energy pricing structures; time-varying tariff structures; Monte Carlo procedure; probabilistic optimal sizing; PEM; BESS optimal sizing

Subjects: Monte Carlo methods; Other power stations and plants; Power system management, operation and economics

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