Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights

Yanmin Wang; Yangqing Li; Rajbondit Pin; Andrey A. Achitaev

doi:10.1109/CCDC69976.2026.11560673

Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights

Yanmin Wang
, Yangqing Li
, Rajbondit Pin
, Andrey A. Achitaev

School of Electrical Engineering and Automation, Harbin Institute of Technology
Siberian Federal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, a novel model predictive control approach is proposed for photovoltaic (PV) energy storage systems to address the two inherent issues of stochastic fluctuations of PV power source and the overcompensation of energy storage part imposed by the fixed weights at the same time. First a discrete state-space model is established by combining the PV power source and energy storage system. Based on MPC, the traditional approach with fixed weight is analyzed and three optimal objectives are chosen to improve it, i.e., the minimum deviation of grid connected power, the minimum output of energy storage and the expected median value of state of energy (SOE). Then an optimal approach based on Harris Hawks optimization algorithm is adopted to achieve the dynamic weights for the improved MPC approaches characterized by multiple optimal objectives and dynamic weights. Finally, a port PV system is chosen as an example to validate the effectiveness of this paper.

Original language	English
Title of host publication	38th Chinese Control and Decision Conference, CCDC 2026
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	299-304
Number of pages	6
ISBN (Electronic)	9798331550707
DOIs	https://doi.org/10.1109/CCDC69976.2026.11560673
State	Published - 2026
Externally published	Yes
Event	38th Chinese Control and Decision Conference, CCDC 2026 - Nanjing, China Duration: 15 May 2026 → 18 May 2026

Publication series

Name	38th Chinese Control and Decision Conference, CCDC 2026

Conference

Conference	38th Chinese Control and Decision Conference, CCDC 2026
Country/Territory	China
City	Nanjing
Period	15/05/26 → 18/05/26

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

PV power source
energy storage system
model predictive control
optimal control

Access to Document

10.1109/CCDC69976.2026.11560673

Cite this

Wang, Y., Li, Y., Pin, R., & Achitaev, A. A. (2026). Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights. In 38th Chinese Control and Decision Conference, CCDC 2026 (pp. 299-304). (38th Chinese Control and Decision Conference, CCDC 2026). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC69976.2026.11560673

@inproceedings{f16df28593f3495d9b8732d7413a1dfe,

title = "Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights",

abstract = "In this paper, a novel model predictive control approach is proposed for photovoltaic (PV) energy storage systems to address the two inherent issues of stochastic fluctuations of PV power source and the overcompensation of energy storage part imposed by the fixed weights at the same time. First a discrete state-space model is established by combining the PV power source and energy storage system. Based on MPC, the traditional approach with fixed weight is analyzed and three optimal objectives are chosen to improve it, i.e., the minimum deviation of grid connected power, the minimum output of energy storage and the expected median value of state of energy (SOE). Then an optimal approach based on Harris Hawks optimization algorithm is adopted to achieve the dynamic weights for the improved MPC approaches characterized by multiple optimal objectives and dynamic weights. Finally, a port PV system is chosen as an example to validate the effectiveness of this paper.",

keywords = "PV power source, energy storage system, model predictive control, optimal control",

author = "Yanmin Wang and Yangqing Li and Rajbondit Pin and Achitaev, \{Andrey A.\}",

note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 38th Chinese Control and Decision Conference, CCDC 2026 ; Conference date: 15-05-2026 Through 18-05-2026",

year = "2026",

doi = "10.1109/CCDC69976.2026.11560673",

language = "英语",

series = "38th Chinese Control and Decision Conference, CCDC 2026",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "299--304",

booktitle = "38th Chinese Control and Decision Conference, CCDC 2026",

address = "美国",

}

Wang, Y, Li, Y, Pin, R & Achitaev, AA 2026, Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights. in 38th Chinese Control and Decision Conference, CCDC 2026. 38th Chinese Control and Decision Conference, CCDC 2026, Institute of Electrical and Electronics Engineers Inc., pp. 299-304, 38th Chinese Control and Decision Conference, CCDC 2026, Nanjing, China, 15/05/26. https://doi.org/10.1109/CCDC69976.2026.11560673

Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights. / Wang, Yanmin; Li, Yangqing; Pin, Rajbondit et al.
38th Chinese Control and Decision Conference, CCDC 2026. Institute of Electrical and Electronics Engineers Inc., 2026. p. 299-304 (38th Chinese Control and Decision Conference, CCDC 2026).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights

AU - Wang, Yanmin

AU - Li, Yangqing

AU - Pin, Rajbondit

AU - Achitaev, Andrey A.

PY - 2026

Y1 - 2026

N2 - In this paper, a novel model predictive control approach is proposed for photovoltaic (PV) energy storage systems to address the two inherent issues of stochastic fluctuations of PV power source and the overcompensation of energy storage part imposed by the fixed weights at the same time. First a discrete state-space model is established by combining the PV power source and energy storage system. Based on MPC, the traditional approach with fixed weight is analyzed and three optimal objectives are chosen to improve it, i.e., the minimum deviation of grid connected power, the minimum output of energy storage and the expected median value of state of energy (SOE). Then an optimal approach based on Harris Hawks optimization algorithm is adopted to achieve the dynamic weights for the improved MPC approaches characterized by multiple optimal objectives and dynamic weights. Finally, a port PV system is chosen as an example to validate the effectiveness of this paper.

AB - In this paper, a novel model predictive control approach is proposed for photovoltaic (PV) energy storage systems to address the two inherent issues of stochastic fluctuations of PV power source and the overcompensation of energy storage part imposed by the fixed weights at the same time. First a discrete state-space model is established by combining the PV power source and energy storage system. Based on MPC, the traditional approach with fixed weight is analyzed and three optimal objectives are chosen to improve it, i.e., the minimum deviation of grid connected power, the minimum output of energy storage and the expected median value of state of energy (SOE). Then an optimal approach based on Harris Hawks optimization algorithm is adopted to achieve the dynamic weights for the improved MPC approaches characterized by multiple optimal objectives and dynamic weights. Finally, a port PV system is chosen as an example to validate the effectiveness of this paper.

KW - PV power source

KW - energy storage system

KW - model predictive control

KW - optimal control

UR - https://www.scopus.com/pages/publications/105043892777

U2 - 10.1109/CCDC69976.2026.11560673

DO - 10.1109/CCDC69976.2026.11560673

M3 - 会议稿件

AN - SCOPUS:105043892777

T3 - 38th Chinese Control and Decision Conference, CCDC 2026

SP - 299

EP - 304

BT - 38th Chinese Control and Decision Conference, CCDC 2026

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Chinese Control and Decision Conference, CCDC 2026

Y2 - 15 May 2026 through 18 May 2026

ER -

Multi-Objective Model Predictive Control of PV Energy Storage Systems with Dynamic Weights

Abstract

Publication series

Conference

UN SDGs

Keywords

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