Stochastic optimization of interconnected multireservoir power systems

Journal Article · Thu Nov 01 04:00:00 EST 1990 · IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (USA)

DOI:https://doi.org/10.1109/59.99403· OSTI ID:6030128

Li, C ^[1]; Yan, R; Zhou, J Y ^[2]

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1. ESCA Corp., Bellevue, WA (USA)
2. EPRI, Beijing (CN)

A new decomposition-coordination (DC) methodology for solving the long-term optimal scheduling of interconnected multi-reservoir power systems with stochastic river inflows is presented. The problem (with M reservoirs and N regional power systems) is composed of M hydro and N thermal subproblems. The optimization of the individual hydro subproblem can be solved by a 2-state stochastic dynamic programming (SDP) taking into account the stochasticity and time series correlation of the river inflows. The thermal and hydro coordination is accomplished by successively updating dual variables so that the total average hydro and thermal generations meet the predicted system load demands. A computer program has been developed and tested for the Central China and East China interconnected power system with up to 9 reservoirs. Numerical result shows the effectiveness of the proposed method as compared with existing methods.

• OSTI ID: 6030128
• Journal Information: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (USA), Journal Name: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (USA) Vol. 5:4; ISSN ITPSE; ISSN 0885-8950
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
240100* -- Power Systems-- (1990-)
240700 -- Power Transmission & Distribution-- Economic
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29 ENERGY PLANNING, POLICY, AND ECONOMY
296000 -- Energy Planning & Policy-- Electric Power
ASIA
CHINA
DESIGN
DYNAMIC PROGRAMMING
ENERGY SYSTEMS
FORECASTING
INTERCONNECTED POWER SYSTEMS
LOAD MANAGEMENT
MANAGEMENT
OPTIMIZATION
PLANNING
POWER GENERATION
POWER SYSTEMS
PROGRAMMING
STOCHASTIC PROCESSES