Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state-space approximate dynamic programming

In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programm...

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Veröffentlicht in:IET generation, transmission & distribution Jg. 13; H. 15; S. 3409 - 3420
Hauptverfasser: Lin, Shunjiang, Wang, Yaping, Liu, Mingbo, Fan, Guansheng, Yang, Zhibin, Li, Qifeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 06.08.2019
Schlagworte:
battery storage plants
concave programming
convex programming
diesel generators
diesel‐electric power stations
distributed power generation
hybrid power systems
integer programming
Markov decision process
Markov processes
mixed‐integer nonlinear nonconvex programming
nonlinear programming
photovoltaic inverter
photovoltaic power systems
power generation dispatch
PV inverter
PV/wind/diesel/battery microgrids
Research Article
state‐space approximate dynamic programming algorithm
stochastic MINNP model
stochastic MISOCP model
stochastic mixed‐integer second‐order cone programming model
stochastic optimal dispatch
wind power plants
PV/wind/diesel/battery microgrids
integer programming
nonlinear programming
PV inverter
photovoltaic power systems
convex programming
stochastic MINNP model
distributed power generation
photovoltaic inverter
state-space approximate dynamic programming algorithm
power generation dispatch
stochastic optimal dispatch
stochastic MISOCP model
diesel generators
Markov processes
diesel-electric power stations
mixed-integer nonlinear nonconvex programming
Markov decision process
battery storage plants
wind power plants
stochastic mixed-integer second-order cone programming model
concave programming
hybrid power systems
ISSN:1751-8687, 1751-8695
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Beschreibung
Zusammenfassung:In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programming (MINNP) model is established for the optimal dispatch of MGs. In this model, the expected value of the sum of DGs’ operation as well as start-up cost, the network-loss cost and the PV inverter's auxiliary service cost, is considered as the objective function. The stochastic MINNP model is transformed into a stochastic mixed-integer second-order cone programming (MISOCP) model to reduce the computational complexity. The state-space approximate dynamic programming algorithm is adopted to solve the stochastic MISOCP model. In the algorithm, based on the approximate value functions of typical states that are computed according to the Markov decision process, solving the optimisation model of multiple periods is executed by solving each period's optimisation model one by one to improve the computational efficiency. Meanwhile, parallel computing is executed to greatly improve the efficiency of the proposed algorithm. Test results on two modified IEEE-33 bus and IEEE-123 bus islanded MGs with PV/wind/diesel/battery demonstrate the correctness and efficiency of the proposed model and algorithm.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2018.5840