Abstract

This paper proposes a strategy for sizing a battery energy storage system (BESS) that supports primary frequency regulation (PFR) service of solar photo-voltaic plants. The strategy is composed of an optimization model and a performance assessment algorithm. The optimization model includes not only investment costs, but also a novel penalty function depending on the state of charge (SoC). This function avoids the existence of a potential inappropriate SoCtrajectory during BESS operation that could impede the supply of PFR service. The performance assessment algorithm, fed by the optimization model sizing results, allows the emulation of BESS operation and determines either the success or failure of a particular BESS design. The quality of a BESS design is measured through number of days in which BESS failed to satisfactorily provide PFR and its associated penalization cost. Battery lifetime, battery replacements, and SoC are also key performance indexes that finally permit making better decisions in the election of the best BESS size. The inclusion of multiple BESS operational restrictions under PFR is another important advantage of this strategy since it adds a realistic characterization of BESS to the analysis. The optimization model was coded using GAMS/CPLEX, and the performance assessment algorithm was implemented in MATLAB. Results were obtained using actual frequency data obtained from the Colombian power system; and the resulting BESS sizes show that the number of BESS penalties, caused by failure to provide PFR service, can be reduced to zero at minimum investment cost.

Highlights

  • One of the most challenging issues for AC power systems is frequency regulation

  • The main contributions of the paper are listed as: (1) a novel penalty function included in the optimization model to ensure that state of charge (SoC) does not pose a risk to Primary frequency regulation (PFR) service; (2) a performance assessment algorithm that emulates battery energy storage system (BESS) operation and permits the calculation of performance indexes such as penalization costs, battery lifetime, battery replacements, and SoC; and (3) a sizing strategy that is composed of the optimization model and the performance assessment algorithm; together, the inclusion of multiple

  • The BESS was designed entirely for providing PFR service to which the solar PV plant was committed. ρ = 3% of the plant capacity had to be dedicated for frequency control, which means the nominal BESS power was PnBESS = 0.3 MW

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Summary

Introduction

One of the most challenging issues for AC power systems is frequency regulation. Instantaneous power generation and consumption must match to avoid frequency deviations from the nominal value. In addition to formulating an optimization problem for sizing BESS, the proposed strategy includes a performance evaluation algorithm that emulates BESS operation. The performance assessment considers a great variety of operational restrictions and is less computationally intensive than the optimization model This algorithm properly complements the BESS sizing strategy since it adds realistic operational aspects to this analysis. The main contributions of the paper are listed as: (1) a novel penalty function included in the optimization model to ensure that SoC does not pose a risk to PFR service; (2) a performance assessment algorithm that emulates BESS operation and permits the calculation of performance indexes such as penalization costs, battery lifetime, battery replacements, and SoC; and (3) a sizing strategy that is composed of the optimization model and the performance assessment algorithm; together, the inclusion of multiple.

BESS Operation under PFR
Proposed Optimization Model for BESS Sizing
BESS Performance Assessment Algorithm
Step 2
Results
Frequency Events’ Characterization
BESS Sizing
Penalization
Investment Costs
Penalization Cost
Conclusions
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