Robust model predictive control for optimal energy management of island microgrids with uncertainties

Zhang, Yan; Fu, Lijun; Zhu, Wanlu; Bao, Xianqiang; Liu, Cang

doi:10.1016/j.energy.2018.08.200

Cited by 106 publications

(23 citation statements)

References 22 publications

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“…The controller shows the ability to guarantee peak shaving of the integrated system and lower operation costs. In [37], a robust MPC is described as being aimed at optimizing microgrids in the presence of uncertainty, which has great relevance towards system performance, as described in the previous section. Eventually, learning-based control systems are those controls that do not have any need of modelling the systems, because they learn their behavior on the basis of their dynamics [35].…”

Section: Optimal Controlmentioning

confidence: 99%

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Arteconi

2018

Environments

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The urgent need for the reduction of greenhouse gas (GHG) emissions requires efficient and integrated energy communities in order to decrease final energy demand and sustain energy transitions towards renewable energy sources (RES). This introduces a big research challenge for future energy systems design and optimization, given the multi-level and inter-sectorial dimensions of the problem. Buildings cover a central role in this context, because they represent the nexus between different energy carrier networks. The study of the management of multi-sector and multi-energy systems presents several challenges, such as the need to take into account uncertainties, to represent the heterogeneity of the energy demand and the scalability of the problem. This paper aims at outlining such a framework, and at showing the state-of-the-art nature of the above-mentioned challenges.

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Section: Optimal Controlmentioning

confidence: 99%

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Arteconi

2018

Environments

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“…Robust optimization for the scheduling of microgrids has been used for different configurations, such as wind power optimization [21], provisional microgrids [22], and distributed EMSs [23], among others. Robust MPC is a family of MPC controllers which includes robust optimization for handling uncertainties in the predictions, and has also been used for the microgrid EMSs [24][25][26][27]. An EMS where the bounds of the uncertainty are given by fuzzy interval models is proposed in [25].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

et al. 2019

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This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.

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“…However, renewable energy sources were not a part of this hybrid configuration, and their fluctuations were not present on the power generation side. The uncertainty of renewable sources were indeed observed in the robust MPC presented in Reference . Nevertheless, a single ESS was considered on the microgrid evaluated.…”

Section: Introductionmentioning

confidence: 99%

Predictive energy management for a wind turbine with hybrid energy storage system

et al. 2019

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Summary Hybrid energy storage systems (HESSs) help mitigating the fluctuations and variable availability of certain renewable sources, such as wind power, as they can provide support in different time scales. Therefore, regulating their state‐of‐charge (SOC) becomes crucial to ensure that the hybrid system complies with generation commitments agreed in time‐ahead markets despite subsequent unexpected wind speed variations. So far, research has been mainly targeted at avoiding extreme SOC situations in the storage devices, whereas the regulation of this parameter to specific values has often been disregarded. A novel approach is proposed in this work, where model predictive control (MPC) is used to regulate the SOC of a HESS under variable wind and grid demand scenarios. The MPC‐based supervisory controller developed for the hybrid system has been implemented and simulated under different situations. This controller monitors the future variation of the SOC with the aim of having the HESS available to develop its assigned functions successfully. The results show that a proper regulation of the SOC in the HESS increases the capacity to manage the active power supplied to the grid by the hybrid system based on wind power, as well as the level of compliance with generation commitments established time ahead.

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Robust model predictive control for optimal energy management of island microgrids with uncertainties

Cited by 106 publications

References 22 publications

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

An Overview about Criticalities in the Modelling of Multi-Sector and Multi-Energy Systems

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

Predictive energy management for a wind turbine with hybrid energy storage system

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