Stochastic Unit Commitment in Microgrids based on Model Predictive Control

被引：0

作者：

Alvarado Barrios, Lazaro ^{[1
]}

Boza Valerino, Juan ^{[2
]}

Rodriguez del Nozal, Alvaro ^{[1
]}

Manuel Escano, Juan ^{[1
]}

Martinez-Ramos, Jose L. ^{[2
]}

Gonzalez-Longatt, Francisco ^{[3
]}

机构：

[1] Univ Loyola Andalucia, Dept Engn, Seville, Spain

[2] Univ Seville, Dept Elect Engn, Seville, Spain

[3] Loughborough Univ, Sch Elect Elect & Syst Engn, Loughborough, Leics, England

来源：

2018 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST) | 2018年

关键词：

Stochastic Unit Commitment; Model Predictive Control; Microgrid; SYSTEM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This article deals with the problem of Stochastic Unit Commitment (SUC), considering the stochastic nature of demand and meteorological phenomena. This paper shows the optimal operation of a hybrid microgrid composed of the following generation units: wind turbine (WT), photovoltaic solar panel (PV), diesel engine generator (DE), micro-turbine (MT), as well as storage devices such as Battery Energy Storage (BES), considering its constraints and the requirements of the reserve generation. For this purpose, a Model-based Predictive Control (MPC), which uses dynamic models of prediction of renewable power and demand in real time, is developed, allowing feedback at each step of time, which corrects the uncertainty of the models. A comparison with a classic UC formulation has been made. The results reach a lower cost solution.

引用

页数：5

共 50 条

[21] A stochastic programming model for the tertiary control of microgrids
Citores, L.
Corchero, C.
Heredia, F. -J.
2015 12TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), 2015,
[22] A Parametrized Model Predictive Control Approach for Microgrids
Pippia, Tomas
Sijs, Joris
De Schutter, Bart
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, : 3171 - 3176
[23] A Model Predictive Control Framework for Residential Microgrids
Mehleri, E. D.
Papageorgiou, L. G.
Markatos, N. C.
Sarimveis, H.
22 EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, 2012, 30 : 327 - 331
[24] Model predictive control and optimization of networked microgrids
Kamal, Faria
Chowdhury, Badrul
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 138
[25] Stochastic Iterative Learning Model Predictive Control based on Stochastic Approximation
Park, ByungJun
Oh, Se-Kyu
Lee, Jong Min
IFAC PAPERSONLINE, 2019, 52 (01): : 604 - 609
[26] Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study
Parisio, Alessandra
Rikos, Evangelos
Glielmo, Luigi
JOURNAL OF PROCESS CONTROL, 2016, 43 : 24 - 37
[27] Stochastic Unit Commitment: Model Reduction via Learning
Liu X.
Conejo A.J.
Constante Flores G.E.
Current Sustainable/Renewable Energy Reports, 2023, 10 (02): : 36 - 44
[28] A Robust and Model Predictive Control based Energy Management Scheme for Grid-Connected Microgrids
Zhu, Junjie
Liu, Yajie
Lei, Hongtao
Zhang, Tao
2018 2ND IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2), 2018,
[29] Model-Predictive-Control-Based Distributed Control Scheme for Bus Voltage Unbalance and Harmonics Compensation in Microgrids
Liu, Jia
Miura, Yushi
Ise, Toshifumi
2017 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2017, : 4439 - 4446
[30] Cooperative control for lane departure prevention based on model predictive control and gated recurrent unit model
Qin, Zengke
Guo, Lie
Wu, Jian
Ge, Pingshu
Liu, Xin
Zhao, Liyuan
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2024,

← 1 2 3 4 5 →