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Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 25342

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Special Issue Editors

Dr. Licheng Wang

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Guest Editor

College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
Interests: power system operation and control; renewable energy integration into distribution systems; distributed algorithms; deep reinforcement learning and its application in networked systems
Special Issues, Collections and Topics in MDPI journals

Dr. Shuaibing Li

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Guest Editor

School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: data analytics for condition monitoring and fault diagnosis of power equipment; state estimation of electrical power systems; asset operation optimization and assessment
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Dr. Ying Han

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Guest Editor

School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: integrated energy system; transportation electrification
Special Issues, Collections and Topics in MDPI journals

Dr. Fang Yao

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Guest Editor

School of Electric Power and Architecture, Shanxi University, Taiyuan 030006, China
Interests: power system resilience enhancement; micro-grids, machine learning; smart grids; renewable energy integration; forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy sources (RESs), such as wind and photovoltaic (PV) power, are rapidly growing due to a drop in their generation cost and their environmentally friendly nature, and this will become the theme of the next generation of power systems within the goal of carbon neutrality. However, as the penetration of RESs climbs, reverse power flows and distributed power iteration may challenge both the traditional voltage regulation system and protection scheduling in distribution systems. Furthermore, such an ongoing process of RES integration will induce significant power fluctuations on the power supply side, and simultaneously it will also continuously reduce the inertia of the power system. The combined effect of low inertia and significant power fluctuations challenges the frequency stability even in large power systems.
In addition, uncertainties of RESs are significantly introduced into both the generation side and demand side, which consequently challenge the effectiveness of traditional methods in the condition assessment, fault diagnosis, and life cycle management of power components in power systems. This requires innovations in the modeling, control, and state estimation of power systems, as well as life prediction, extension, and condition assessment.
This Special Issue aims to present and disseminate the most recent advances that are able to address challenges induced by large-scale RES integration, as discussed above.

Topics of interest for publication include, but are not limited to, the following:

Operation and control of power systems with new energy sources.
Measurement and control of transportation energy integrated systems.
Modeling and control of renewable power generation.
Life cycle assessment, pricing, policies, and energy planning.
Artificial intelligence for renewable energies.
Advanced monitoring, diagnosis, and big data analytic methods of electrical equipment.
Ultra-low- and near-zero-energy consumption buildings with renewable energy integration.
Power electronic converters and drives.
Modeling of communication–control coupled systems.
Frequency regulation in low-inertia systems with high wind penetration.
Big data for industrial and energy systems.
Smart metering, measurement, instrumentation, and control.
Artificial intelligence for industrial process optimization.
Optimization of industrial applications and energy systems.

Dr. Licheng Wang
Dr. Shuaibing Li
Dr. Ying Han
Dr. Fang Yao
Guest Editors

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Keywords

renewable energy sources (RESs)
photovoltaic (PV)
voltage control
protection
low-inertia system
state estimation
condition assessment
carbon reduction

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Related Special Issue

Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources: 2nd Edition in Energies

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23 pages, 839 KiB

Open AccessArticle

Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning

by Yizheng Wang, Enhao Shi, Yang Xu, Jiahua Hu and Changsen Feng

Energies 2024, 17(21), 5350; https://doi.org/10.3390/en17215350 - 28 Oct 2024

Viewed by 487

Abstract

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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18 pages, 4664 KiB

Open AccessArticle

Optimal Power Model Predictive Control for Electrochemical Energy Storage Power Station

by Chong Shao, Chao Tu, Jiao Yu, Mingdian Wang, Cheng Wang and Haiying Dong

Energies 2024, 17(14), 3456; https://doi.org/10.3390/en17143456 - 13 Jul 2024

Viewed by 973

Abstract

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge (

S O C

) constraint of the battery, partition the

S O C

into different states. Using

S O C

as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery

S O C

to prevent

S O C

from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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20 pages, 3699 KiB

Open AccessArticle

A Two-Layer Control Strategy for the Participation of Energy Storage Battery Systems in Grid Frequency Regulation

by Pan Zhang, Shijin Xin, Yunwen Wang, Qing Xu, Chunsheng Chen, Wei Chen and Haiying Dong

Energies 2024, 17(3), 664; https://doi.org/10.3390/en17030664 - 30 Jan 2024

Cited by 1 | Viewed by 995

Abstract

A two-layer control strategy for the participation of multiple battery energy storage systems in the secondary frequency regulation of the grid is proposed to address the frequency fluctuation problem caused by the power dynamic imbalance between the power system and load when a large number of new energy sources are connected to the grid. A comprehensive allocation model based on area regulation requirement (ARR) signals and area control error (ACE) signals is proposed to obtain the total output of the secondary frequency modulation (FM) demand with a higher degree of adaptation when the FM units respond to the automatic generation control command, and the total output is reasonably allocated to each FM unit by using the two-layer control. Considering the dynamic fluctuation of the grid frequency, the fluctuation is dynamically suppressed in real-time by applying model predictive control to successfully forecast the frequency deviation while realizing the deviation-free correction in the frequency dynamic correction layer. The optimal power distribution of FM units based on the distributed control concept, as well as the power depth of each unit, are coordinated in the equalization control layer while keeping a decent battery charge level. Finally, in Matlab/Simulink, the proposed control approach is simulated and validated. The findings show that the suggested control approach can suppress frequency difference fluctuation, keep the battery charged, and reduce the unit’s FM loss. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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20 pages, 3312 KiB

Open AccessArticle

Distributed Coordinated Operation of Active Distribution Networks with Electric Heating Loads Based on Dynamic Step Correction ADMM

by Shoudong Li, Guangqing Bao and Yanwen Hu

Energies 2024, 17(2), 533; https://doi.org/10.3390/en17020533 - 22 Jan 2024

Viewed by 817

Abstract

In order to change the centralized operation framework of the active distribution network with electric heating loads (EHLs), a distributed optimization method is proposed for the coordinated operation of the active distribution network with EHLs. Firstly, considering the thermal delay effect and heat loss of the thermal system, a centralized optimization operation model for active distribution networks with EHLs is established. Then, based on the centralized optimization operation model, it is rephrased as a standard sharing problem, and a distributed optimization operation model for the EHL active distribution network is established based on the alternating direction multiplier method (ADMM) solution. In the process of solving ADMM, dynamic step correction was further considered. By updating the steps during the iteration process, the number of iterations was reduced, and the convergence and computational efficiency of ADMM were improved. Finally, the effectiveness of the distributed coordinated operation method proposed in this paper was simulated and verified by constructing an IEEE33 distribution system. The results showed that the proposed distributed coordinated operation method has strong robustness to the randomness of the number of distributed units and parameters, and EHLs participating in coordinated operation can expand the consumption space of wind power and photovoltaic power, and improve the economic efficiency of system operation. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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20 pages, 12817 KiB

Open AccessArticle

Numerical and Experimental Investigation of the Decoupling Combustion Characteristics of a Burner with Flame Stabilizer

by Jing Wang, Jingchi Yang, Fengling Yang and Fangqin Cheng

Energies 2023, 16(11), 4474; https://doi.org/10.3390/en16114474 - 1 Jun 2023

Cited by 4 | Viewed by 1448

Abstract

In order to integrate renewable electricity into the power grid, it is crucial for coal-fired power plant boilers to operate stably across a wide load range. Achieving steady combustion with low nitrogen oxide (NO_x) emissions poses a significant challenge for boilers burning low-volatile coal in coal-fired power plants. This study focuses on developing a decoupling combustion technology for low-volatile coal-fired boilers operating at low loads. A three-dimensional numerical simulation is employed to analyze and optimize the geometrical parameters of a burner applied in a real 300 MW pulverized coal fired boiler. Detailed analysis of the burner’s decoupling combustion characteristics, including stable combustion ability and NO_x reduction principles, is conducted. The results indicate that this burner showed three stages of coal/air separation, and the flame holder facilitates the stepwise spontaneous ignition and combustion of low-volatile coal. By extending the time between coal pyrolysis and carbon combustion, the burner enhances decoupling combustion and achieves low nitrogen oxide emissions. Based on optimization, a flat partition plate without inclination demonstrates excellent performance in terms of velocity vector field distribution, coal air flow rich/lean separation, combustion, and nitrogen oxide generation. Compared with the initial structural design, the average nitrogen oxide concentration at the outlet is reduced by 59%. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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28 pages, 7611 KiB

Open AccessArticle

Three-Leg Quasi-Z-Source Inverter with Input Ripple Suppression for Renewable Energy Application

by Chuanyu Zhang, Chuanxu Cao, Ruiqi Chen and Jiahui Jiang

Energies 2023, 16(11), 4393; https://doi.org/10.3390/en16114393 - 29 May 2023

Cited by 17 | Viewed by 1704

Abstract

Single-phase inverters are widely employed in renewable energy applications. However, their inherent 2ω-ripple power can substantially affect system performance, leading to fluctuations in the maximum power points (MPP) of photovoltaic (PV) systems and shortening the lifespans of fuel cell (FC) systems. To alleviate input ripple, a three-leg quasi-Z-source inverter (QZSI) and its associated control strategy are proposed. The QZSI consists of a quasi-Z-source network, an H-Bridge inverter, and an active power filter (APF). The active filtering structure comprises filtering capacitors and the third bridge leg. The proposed control strategy consists of three loops: open-loop simple boost control, output voltage control, and 2ω-ripple suppression control. Open-loop simple boost control is utilized for shoot-through state modulation, output voltage control is applied to the two bridge-legs of the H-Bridge, and the additional third bridge-leg adopts a quasi-PR control (QPR) method that injects specific frequency harmonic voltage and suppresses newly generated low-frequency components of the input current. This method effectively avoids the drawbacks of utilizing passive filtering strategies, such as high-value impedance networks, low power density, and weak system stability. A simulation platform of 300W 144VDC/110VAC50Hz is constructed. The simulation results indicate that the addition of the third bridge leg under full load conditions reduces the input-side inductor current ripple ΔI from 1.89 A with passive filtering to 0.513 A, representing a reduction of 72.86%. The second harmonic ripple of the input current is reduced from 18.2% to 4.5%, and the fourth harmonic ripple is reduced from 16.5% to 2.1%. The DC bus voltage ripple ΔV_PN falls from 70.75 V to 6.54 V, representing a reduction of 90.76%. The Total Harmonic Distortion (THD) of the output voltage and current are both less than 1%. The simulation results validated the feasibility of the proposed approach. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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16 pages, 8712 KiB

Open AccessArticle

Robustly Cooperative Control of Transient Stability for Power System Considering Wind Power and Load Uncertainty by Distribution Preserving Graph Representation Learning (DPG)

by Fang Yao, Xinan Zhang, Tat Kei Chau and Herbert Ho-ching Iu

Energies 2023, 16(5), 2413; https://doi.org/10.3390/en16052413 - 2 Mar 2023

Viewed by 1456

Abstract

Aiming at the influence of wind power and load uncertainty on the transient stability of a power system under low carbon mode, this paper first proposes a collaborative preventive and emergency control model of transient stability by distribution preserving graph representation learning (DPG). Second, the uncertainty set of wind power output and load demand is studied, and the mathematical form of the two-stage robust transient stability collaborative control model is proposed. Then, the latest artificial intelligence technology is embedded into the global optimization algorithm of the model so as to further improve the solving efficiency of the algorithm. Finally, based on the developed improved two-stage robust optimization framework, an effective collaborative control method for transient stability is developed. The transient stability prediction and control system developed in this project is not only conducive to large-scale wind power grid connection but also expected to make academic contributions to development of power system transient stability and practical simulation verification. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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20 pages, 5133 KiB

Open AccessArticle

Economic Dispatch Model of High Proportional New Energy Grid-Connected Consumption Considering Source Load Uncertainty

by Min Xu, Wanwei Li, Zhihui Feng, Wangwang Bai, Lingling Jia and Zhanhong Wei

Energies 2023, 16(4), 1696; https://doi.org/10.3390/en16041696 - 8 Feb 2023

Cited by 10 | Viewed by 1815

Abstract

To solve the problem regarding the large-scale grid-connected consumption of a high proportion of new energy sources, a concentrating solar power (CSP)-photovoltaic (PV)-wind power day-ahead and intraday-coordinated optimal dispatching method considering source load uncertainty is proposed. First, the uncertainty of day-ahead wind power output prediction is described by the multi-scenario stochastic planning method, and the uncertainty of intraday source-load is characterized by the trapezoidal fuzzy number equivalence model. Second, based on the combined scenario set of day-ahead wind power output prediction, the day-ahead optimal dispatch is performed by combining thermal and CSP plants, and the day-ahead thermal and CSP plant dispatch output and intraday source load fuzzy dataset are used as the input quantities for the day-ahead dispatch. Thus, the scheduling output and rotating backup plan for thermal power and CSP plants were determined; finally, the validity and feasibility of the model were verified using arithmetic examples. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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14 pages, 3426 KiB

Open AccessArticle

Sliding Mode Control of Ship DC Microgrid Based on an Improved Reaching Law

by Chuan Xiang, Qi Cheng, Yizheng Zhu and Hongge Zhao

Energies 2023, 16(3), 1051; https://doi.org/10.3390/en16031051 - 18 Jan 2023

Cited by 5 | Viewed by 1887

Abstract

The bus voltage of the ship DC microgrid is sensitive to the change of loads, which has an influence on the power supply quality. This paper introduces a hybrid energy storage system (HESS) that is composed of a battery set and a supercapacitor set, and further studied the control method of HESS. First of all, the topological structures of the ship DC microgrid and HESS are described. Second, combined with the frequency division droop control and voltage PI control, a sliding mode control (SMC) method is proposed to control the charge and discharge of HESS based on an improved reaching law. Finally, the simulation model of the ship DC microgrid is established for the verification of the control method. Simulation results show that: (1) HESS can overcome the shortage of the dynamic response ability of the diesel rectifier generator to the steep change of load power. The supercapacitor set and the battery set successfully respond to the high-frequency and low-frequency components of the differential power in the system, respectively. (2) Compared with the traditional PI control method, SMC can reduce the current chattering of HESS and the voltage fluctuation amplitude of the DC bus. The proposed SMC method can provide a reference for the stable and reliable operation of the ship DC microgrid. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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14 pages, 4762 KiB

Open AccessArticle

Coordinated Control of Distributed Energy Storage Systems for DC Microgrids Coupling Photovoltaics and Batteries

by Quan’e Zhang, Zhigang Song, Qiushi Ru, Jiangwei Fan, Lihui Qiao, Mingche Li, Licheng Wang and Shuaibing Li

Energies 2023, 16(2), 665; https://doi.org/10.3390/en16020665 - 5 Jan 2023

Cited by 8 | Viewed by 2003

Abstract

To adapt to frequent charge and discharge and improve the accuracy in the DC microgrid with independent photovoltaics and distributed energy storage systems, an energy-coordinated control strategy based on increased droop control is proposed in this paper. The overall power supply quality of the DC microgrid is improved by optimizing the output priority of the multi-energy storage system. When photovoltaic and energy storage work simultaneously, the proposed method can dynamically adjust their working state and the energy storage unit’s droop coefficient to meet the system’s requirements. In DC microgrids with energy storage units of different capacities, the proposed strategy can be used to maintain the stability of bus voltage, improve the equalization speed and accuracy of the energy storage state of charge, and avoid the shutdown of energy storage units due to overcharge or discharge. Verification of the proposed strategy is implemented with MATLAB/Simulink. The simulation results show the proposed control strategy’s effectiveness in balancing energy supply and demand and reducing the time of charging and discharging energy storage units. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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12 pages, 3219 KiB

Open AccessArticle

Optimal Dispatch of Agricultural Integrated Energy System with Hybrid Energy Storage

by Wu Yang, Yi Xia, Xijuan Yu, Huifeng Zhang, Xuming Lin, Hongxia Ma, Yuze Du and Haiying Dong

Energies 2022, 15(23), 9131; https://doi.org/10.3390/en15239131 - 2 Dec 2022

Cited by 3 | Viewed by 1445

Abstract

Rural energy is an important part of China’s energy system, and, as China’s agricultural modernization continues, integrated agricultural energy systems (AIES) will play an increasingly important role. However, most of China’s existing rural energy systems are inefficient, costly to run, and pollute the environment. Therefore, meeting various agricultural energy needs while balancing energy efficiency and costs is an important issue in the design and dispatch of integrated agricultural energy systems. In conjunction with hybrid energy storage (HES), which has been developed and matured in recent years, this paper proposes a new type of AIES structure and optimal dispatching strategy that incorporates HES, biogas generation (BG), P2G, and an electric boiler (EB) to provide new ideas for problem solving. Firstly, the structure of AIES is introduced and the mathematical model of the equipment of the system is described; then, an economic optimal dispatching model with the objective of minimizing the comprehensive operating costs of the system is established, and the output of each piece of energy conversion equipment is controlled to achieve the effect of improving the system’s operating performance and reducing the operating costs. The results show that the system with HES and multi-energy coupling equipment has a 20% lower overall cost, 23.2% lower environmental protection cost, and 51% higher energy efficiency than the original system; the stored power of energy storage equipment in the HES mode is primarily determined by the change in demand of the corresponding load, and the number of conversions between different energy sources is limited. The energy conversion loss is minimal. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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16 pages, 5851 KiB

Open AccessArticle

Hierarchical Distributed Coordinated Control for Battery Energy Storage Systems Participating in Frequency Regulation

by Bingqing Yu, Qingquan Lv, Zhenzhen Zhang and Haiying Dong

Energies 2022, 15(19), 7283; https://doi.org/10.3390/en15197283 - 4 Oct 2022

Cited by 4 | Viewed by 1889

Abstract

At present, battery energy storage systems (BESS) have become an important resource for improving the frequency control performance of power grids under the situation of high penetration rates of new energy. Aiming at the problem that the existing control strategy is not sufficient for allocating the frequency regulation power instructions, a hierarchical distributed coordinated control strategy for BESS to participate in the automatic generation control (AGC) of a regional power grid is proposed. At the upper layer, the state of charge (SOC) of BESS and the technical characteristics of different frequency regulation power sources are comprehensively considered to complete the coordinated distribution of frequency regulation commands between BESS and traditional generators; at the lower layer, for the purpose of optimizing the economic operation of the regional power grid, the distributed consistency algorithm is used to control the distributed BESS in order to realize the fine management of power output of BESS. The simulation results indicate that this control strategy can give full play to the technical characteristics of different frequency power sources and improve the frequency regulation of the power grid. The excessive power consumption of BESS is successfully avoided, and the continuous operation of BESS is realized. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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Review

Jump to: Research

28 pages, 5852 KiB

Open AccessReview

A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms

by Ming Zhang, Dongfang Yang, Jiaxuan Du, Hanlei Sun, Liwei Li, Licheng Wang and Kai Wang

Energies 2023, 16(7), 3167; https://doi.org/10.3390/en16073167 - 31 Mar 2023

Cited by 100 | Viewed by 7010

Abstract

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. Full article

(This article belongs to the Special Issue Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources)

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