期刊
IET RENEWABLE POWER GENERATION
卷 14, 期 13, 页码 2507-2516出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2019.1282
关键词
approximation theory; stochastic processes; dynamic programming; power generation economics; wind power plants; power generation dispatch; pumped-storage power stations; integer programming; stochastic programming; power system security; optimisation; linear programming; multiple wind farms; pumped-storage hydro stations; approximate dynamic programming; stochastic economic dispatch problem; specific stochastic dynamic programming model; stochastic storage model; approximate DP method; mixed-integer linear programming models; approximate value functions; AVFs; convex piecewise linear functions; deterministic model; forecast scenario; wind farm output; stochastic sampling scenarios; successive projective approximation routine algorithm; near-optimal day-ahead dispatch scheme; MILP models; actual provincial power system; modified IEEE 39-bus system
资金
- National Basic Research Program of China (973 Program) [2013CB228205]
- National Natural Science Foundation of China [51977080, 51207056]
The stochastic economic dispatch problem of power system with multiple wind farms and pumped-storage hydro stations is formulated as a specific stochastic dynamic programming (DP) model, i.e. stochastic storage model, it is impossible to obtain an accurate solution due to the curse of dimensionality. Based on the approximate DP (ADP) method, the stochastic storage model can be transformed into a series of mixed-integer linear programming (MILP) models by describing the approximate value functions (AVFs) as convex piecewise linear functions in post-decision states. The AVFs are first initialised using the results of the deterministic model under a forecast scenario of wind farm output and then trained by scanning stochastic sampling scenarios consecutively with the successive projective approximation routine algorithm. To obtain a near-optimal day-ahead dispatch scheme, the forecast scenario is substituted into the MILP models expressed by the trained AVFs and is solved forward through each time interval. The network constraints are incorporated by the while-loop detection of critical lines. Test results on an actual provincial power system and the modified IEEE 39-bus system, including the comparison among the ADP, DP, scenario-based and chance-constrained programming methods, demonstrate the feasibility and efficiency of the proposed model and algorithm.
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