2019
DOI: 10.1049/iet-rpg.2019.0536
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Real‐time stochastic operation strategy of a microgrid using approximate dynamic programming‐based spatiotemporal decomposition approach

Abstract: This study focuses on the real-time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real-time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single-period and single-unit sub-problems, and the value functions are used to describe the interaction among the sub-problem… Show more

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Cited by 14 publications
(9 citation statements)
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“…MDP is a sequential optimization problem whose goal is to find a policy to maximise expected profits or minimise expected costs (Zhu et al, 2019). Let S t and x t be the state variables and decision variables of the battery at stage t, respectively, W t is the exogenous information that arrives during the stage interval from t to t + 1; V(S t ) is the value function of state S t .…”
Section: Markov Decision Processmentioning
confidence: 99%
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“…MDP is a sequential optimization problem whose goal is to find a policy to maximise expected profits or minimise expected costs (Zhu et al, 2019). Let S t and x t be the state variables and decision variables of the battery at stage t, respectively, W t is the exogenous information that arrives during the stage interval from t to t + 1; V(S t ) is the value function of state S t .…”
Section: Markov Decision Processmentioning
confidence: 99%
“…An MDP model for the real-time operation of the MG is established under uncertain conditions. Then, the best output of the battery is determined in the MG (Shuai et al, 2019;Zhu et al, 2019). The above studies all combine the operation of batteries with the variability and uncertainty of renewable energy sources and load in the MG or distribution network.…”
Section: Introductionmentioning
confidence: 99%
“…To overcome the ‘curse of dimensionality’ for practical applications such as HEMS, approximate DP (ADP) emerges as a promising class of algorithms that use various approaches to speed up DP and enable its use for larger optimal decision making under uncertainty [24, 25 ]. Past HEMS efforts have used ADP for grid‐battery management [26–31 ] and real‐time MG operations [32 ]. In [26 ], Keerthisinghe et al employed ADP with temporal difference (TD) learning to implement a computationally efficient HEMS that optimised photovoltaic (PV)–battery system scheduling for minimal cost and user discomfort.…”
Section: Introductionmentioning
confidence: 99%
“…In [31 ], Wei et al developed an iterative ADP algorithm to solve the optimal battery control problem for the smart home energy systems while considering the battery efficiency and the charging/discharging constraints. Zhu et al [32 ] developed an ADP‐based spatiotemporal decomposition approach that supported the effective management of distributed energy storage systems in real‐time MG operation under renewable generation uncertainty. A few other studies have used ADP to control the energy use of building cooling systems to minimise energy consumption while preserving occupant comfort [33, 34 ].…”
Section: Introductionmentioning
confidence: 99%
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