Neural-net based real-time economic dispatch for thermal power plants
- Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems
- Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering
- Electric Power Research Inst., Palo Alto, CA (United States)
This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.
- OSTI ID:
- 438807
- Report Number(s):
- CONF-960725--
- Journal Information:
- IEEE Transactions on Energy Conversion, Journal Name: IEEE Transactions on Energy Conversion Journal Issue: 4 Vol. 11; ISSN 0885-8969; ISSN ITCNE4
- Country of Publication:
- United States
- Language:
- English
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