Energy Management System for Microgrids

Prakash, A.; Tomar, Anuradha; Jayalakshmi, N. S.; Singh, Kulwant; Shrivastava, Ashish

doi:10.1109/rteict52294.2021.9574038

Cited by 1 publication

(1 citation statement)

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“…This backdrop sets the stage for the exploration of advanced approximate methods like stochastic and dynamic optimization, machine learning, and artificial intelligence in improving microgrid energy management [16]. The Stochastic Dual Dynamic Programming (SDDP) algorithm shows promise in optimizing microgrid operations by accounting for the variability and dynamism of renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids

Tabares,

Cortés

2024

Energies

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In recent years, the adoption of renewable energy sources has significantly increased due to their numerous advantages, which include environmental sustainability and economic viability. However, the management of electric microgrids presents complex challenges, particularly in the orchestration of energy production and consumption under the uncertainty of fluctuating meteorological conditions. This study aims to enhance decision-making processes within energy management systems specifically designed for microgrids that are interconnected with primary grids, addressing the stochastic and dynamic nature of energy generation and consumption patterns among microgrid users. The research incorporates stochastic models for energy pricing in transactions with the main grid and probabilistic representations of energy generation and demand. This comprehensive methodology allows for an accurate depiction of the volatile dynamics prevalent in the energy markets, which are critical in influencing microgrid operational performance. The application of the Stochastic Dual Dynamic Programming (SDDP) algorithm within a multi-stage adaptive framework for microgrids is evaluated for its effectiveness compared to deterministic approaches. The SDDP algorithm is utilized to develop robust strategies for managing the energy requirements of 1, 2, and 12 prosumers over a 24 h planning horizon. A comparative analysis against the precise solutions obtained from dynamic programming via Monte Carlo simulations indicates a strong congruence between the strategies proposed by the SDDP algorithm and the optimal solutions. The results provide significant insights into the optimization of energy management systems in microgrid settings, emphasizing improvements in operational performance and cost reduction.

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Section: Introductionmentioning

confidence: 99%