Abstract
The paper presents a two-stage approach to cope with the long-term optimal operation of cascaded hydropower systems. This approach combines progressive optimality algorithm (POA) with quadratic programming (QP) to improve the optimization results. POA is used at the first stage to generate a local optimal result, which will be selected as the initial feasible solution of QP method employed at the second stage. Around the initial solution, a rational local search range for QP method is then determined, where the nonlinear water level function and tailrace level function can be linearized nearly with high accuracy. The simplified optimization problem is formulated as a QP model with a quadratic generation function and a linear set of constraints, and solved using the available mathematic optimization software package. Simulation is performed on the long term operation of Hongshui River hydropower system which is located in southwest China and consists of 9 built hydropower plants. Results obtained from the proposed approach show a significant increase in the total energy production compared to the results from POA.
Highlights
Over the past twenty years, China has put much effort on hydropower development [1]
The paper presents a two-stage approach to cope with the long-term optimal operation of cascaded hydropower systems
The developed approach is implemented to the cascaded hydropower plants in the main stream of Hongshuihe River operated by SCPG and Guangxi Power Grid (GXPG)
Summary
Over the past twenty years, China has put much effort on hydropower development [1]. The total installed capacity of hydropower has reached about 280 GW by the end of 2013, ranking first in the world. Shen in future years, such as Hongshuihe River (10 plants), Wujiang River (11 plants), Lancangjiang River (14 plants), Jinshajiang River (13 plants), Daduhe River (16 plants), Yalongjiang River (21 plants) These largescale cascaded hydropower systems are usually installed with the huge generating unit, whose capacity reaches 700 MW, 770 MW, even 800 MW. These methods and models are classified into two types, mathematical programming techniques and heuristic programming methods The former includes linear programming [6] [7], quadratic programming [8], nonlinear programming [7], dynamic programming [9], progressive optimal algorithms (POA) [10]-[12], discrete differential dynamic programming (DDDP) [13], dynamic programming successive approximation (DPSA) [14], Lagrangian relaxation [15], network flow [16], decomposition coordination method [17], etc.
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