Fig 1.
Comparison between actual produced power profile of 250 MW wind farm in Texas, USA in 2018 and calculated power profile assuming 25% efficiency factor for wind turbines.
Fig 2.
Diagram of the proposed BESS.
Fig 3.
Diagram of the conceptual BESS model with the proposed applications.
Better resolutions for the embedded figures are available in Fig 4.
Fig 4.
The simulation model’s data inputs.
a) The calculated curtailed power available for storage at the Chapman Ranch wind farm (Texas, USA) during 2018 after injecting power to the UG, with the real-time generation estimated based on actual weather data in the region. b) The region’s load demand in 2018 along with 5% monthly peak shaving used for ECA. c) An estimation for the hourly energy demand by the EVCSs which serves a future population of 10k EVs in the region for a typical day [42].
Fig 5.
Block diagram representing the simulation model in the User Mode.
The red arrow is explained in the text.
Table 1.
Model constants and their values used in this study.
Table 2.
Control variables and their bounds.
Fig 6.
The flowchart of supplying all demands while prioritizing the DC demands.
Fig 7.
The convergence plot of GA for the equal weights’ scenario (wi = 0.25) and DC priority.
In general, the value for the best fitness can be attained within 100 generations.
Fig 8.
Optimized sets of the six control variables in four different scenarios (1, 2, 3, 4) under the DC priority operation mode.
Fig 9.
Optimized sets of the six control variables in four different scenarios (1, 2, 3, 4) under the AC priority operation mode.
Fig 10.
Four BESS performance indices (PI and Indices 2–4) with the optimized control variables in the four scenarios with the DC priority (case A) and the AC priority (case B).
Table 3.
PIs of different application combinations: 3 applications (EVCS, FR, ECA), 2 applications (FR, ECA), and 1 application (ECA only).
Fig 11.
Optimal solutions of the six control variables with 21 weight factors from 0 (Scenario 1) to 1 (Scenario 21) in step of 0.05 under DC priority mode.
Fig 12.
Optimal solutions of the six control variables with 21 weight factors from 0 (Scenario 1) to 1 (Scenario 21) in step of 0.05 under AC priority mode.
Fig 13.
The profile of feasible solutions under the 21 scenarios and the Pareto set for the two objective functions under the DC priority operation mode.
Fig 14.
The profile of feasible solutions under the 21 scenarios and the Pareto set for the two objective functions under the AC priority operation mode.
Fig 15.
The convergence of the values of the fitness function in five scenarios under the DC priority operation mode.
Fig 16.
The convergence of the values of the fitness function in five scenarios under the AC priority operation mode.