Civil Engineering Reference
In-Depth Information
TABLE 13-1
Production Cost of 300 MW Thermal-pv-Battery System
Battery Depletion
MWh/day
Production cost
$/day
Savings
$/day
System Configuration
Thermal only
—
750,000
—
Thermal + pv
—
710,000
40,000
Thermal + pv + battery
344
696,000
54,000
the cost of all thermal and renewable units combined subject to the con-
straints by arriving at the best short-term scheduling. This determines the
hours for which the baseload thermal units of the electrical power company
should be taken either off-line or on-line. The traditional thermal scheduling
algorithms, augmented Lagrangian relaxation, branch and bound, successive
dynamic programming or heuristic method (genetic algorithms and neural
networks), can be used for minimizing the cost of operating the thermal
units with a given renewable-battery system. Marwali et al.
has recently
utilized the successive dynamic programming to find the minimum cost
trajectory for battery and the augmented Langrangian to find thermal unit
commitment. In a case study of a 300 MW thermal-pv-battery power plant,
the authors have arrived at the total production costs shown in
Table 13-1
,
where the battery hybrid system saves $54,000 per day compared to the
thermal only.
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13.5
Utility Resource Planning Tool
The wind and photovoltaic power, in spite of their environmental, financial,
and fuel diversity benefits, are not presently included in the utility resource
planning analysis because of the lack of the familiarity and analytical tools
for nondispatchable sources of power. The wind and pv powers are treated
as nondispatchable for not being available on demand. The Massachusetts
Institute of Technology's Energy Laboratory has developed an analytical tool
to analyze the impact of nondispatchable renewables on the New England's
power systems operation. Cardell and Connors
have applied this tool for
analyzing two hypothetical wind farms totaling 1,500 MW capacity for two
sites, one in Maine and the other in Massachusetts. The average capacity
factor at these two sites is estimated to be 0.25. This is good, although some
sites in California have achieved the capacity factor of 0.33 or higher. The
MIT study shows that the wind energy resource in New England is compa-
rable to that in California. The second stage of their analysis developed the
product cost model, demonstrating the emission and fuel cost risk mitigation
benefits of the utility resource portfolios incorporating the wind power.
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