Page 83 - Decision Making Applications in Modern Power Systems

Uncertainty management in decision-making Chapter | 2 53

TABLE 2.5 Energy storage system characteristic.
ESS Minimum/maximum charge/ Minimum charge/ Capacity
discharge power (kW) discharge time (h) (kWh)
250/ 1 50 2 100

350
Forecasted power output of PV (kW) 250
300

200
150
100

50
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)
FIGURE 2.2 The forecasted value of PV power generation.

600
Forecaste power output of WT (kW) 450
550
500

400
350
300
250
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (h)
FIGURE 2.3 The forecasted value of WT power generation.

Table 2.6 shows the power market and power exchanged prices for each
hour. The values of the emission factor and the emission cost are fixed at
0.003 kg/kWh and 0.02$/kg, respectively [32].
The optimal scheduling of the microgrid problem under high level of
uncertainties for profit maximization will be solved by the time-varying
acceleration coefficient particle swarm optimization (TVAC-PSO) algorithm.
It has been discovered that parameter adapting is a key factor in PSO to find
an accurate solution [41]. In the TVAC-PSO algorithm, unlike the