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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (1 March 2023) | Viewed by 22294

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Special Issue Editors

Prof. Dr. Xiaoqing Bai

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Guest Editor

Guangxi Key Laboratory of Power System Optimization and Energy‑Saving Technology, Guangxi University, Nanning, China
Interests: power system analysis; optimization theory and application; data-driven; uncertainty
Special Issues, Collections and Topics in MDPI journals

Dr. Chun Wei

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Guest Editor

College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
Interests: renewable energy; power electronics; wind power generation; intelligent control
Special Issues, Collections and Topics in MDPI journals

Dr. Peijie Li

E-Mail Website
Guest Editor

Guangxi Key Laboratory of Power System Optimization and Energy‑Saving Technology, Guangxi University, Nanning, China
Interests: optimization for power system operation; small-signal stability; power system restoration; optimal power flow

Dr. Dongliang Xiao

E-Mail Website
Guest Editor

School of Electric Power, South China University of Technology, Guangzhou, China
Interests: power and energy system optimization; electricity markets; renewable energy; risk management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To resolve global climate warming issues and promote sustainable development, the installed capacity of renewable energy in power systems has increased rapidly during the last two decades. Large numbers of renewable energy resources with intermittent characteristics, such as wind turbines, solar PVs, and electric vehicles, have been equipped on both the power generation and demand sides, bringing significant challenges to the optimal planning, operation, and control of sustainable power systems. To cope with the uncertainties in sustainable power systems, decision-makers need to utilize various flexible energy resources, such as battery storage, demand-response programs, and gas-fired generators. Additionally, the coordination of power and other energy resources, such as natural gas, heat, and hydrogen, also provide additional flexibility in power transmission and distribution systems.

The significant challenges of sustainable power system optimization are the complexities and uncertainties in both distribution and transmission levels. Under the deregulated power market environment, the power system optimization models need to consider intermittent renewable power productions and account for volatile electricity prices. As a result, it is necessary to investigate efficient optimization techniques for sustainable power systems considering parameter uncertainties and system properties, in order to maximize the economic benefits and minimize reliability concerns.

This Special Issue aims to report the latest advancements in sustainable power systems and optimization to solve its potential difficulties and challenges. Specifically, authors are encouraged to submit their research works in theoretical, methodological, or practical focuses, such as simulation models, algorithms, and applications concerning sustainable power systems and optimization.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Optimization techniques for sustainable power system planning, operation, and control.
Optimization techniques for sustainable power system assisted by energy storage.
Optimization techniques for sustainable power systems with other types of energy, such as gas, heat, and hydrogen.
Demand-side management in sustainable power systems.
Renewable energy trading in wholesale or retail electricity markets.
Multi-objective optimization techniques for sustainable power systems.
Distributed optimization techniques for sustainable power systems
Local energy market mechanisms to accommodate distributed energy resources.
Techniques for handling nonlinearities and non-convexities of sustainable power system problems.
Heuristics algorithms for solving sustainable power system problems.

We look forward to receiving your contributions.

References

Chu S, Majumdar A. Opportunities and challenges for a sustainable energy future. Nature, 2012, 488(7411): 294-303.

Morales J M, Conejo A J, Madsen H, et al. Integrating renewables in electricity markets: operational problems. Springer Science & Business Media, 2013.

Wei, Z. Shen, D. Xiao, L. Wang, X. Bai, and H. Chen, An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining, Applied Energy, 2021.

Prof. Dr. Xiaoqing Bai
Dr. Chun Wei
Dr. Peijie Li
Dr. Dongliang Xiao
Guest Editors

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Keywords

optimization techniques
sustainable power systems
renewable energy
distributed energy resources
demand-side management
electricity markets
energy storages
multi-objective optimization
heuristics algorithms

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Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 164 KiB

Open AccessEditorial

Editorial for the Special Issue on Sustainable Power Systems and Optimization

by Xiaoqing Bai, Chun Wei, Peijie Li and Dongliang Xiao

Sustainability 2023, 15(6), 5164; https://doi.org/10.3390/su15065164 - 14 Mar 2023

Viewed by 1066

Abstract

In recent years, the installed capacity of renewable energy in power systems has increased rapidly to resolve global climate warming issues and promote sustainable development [...] Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

Research

Jump to: Editorial

15 pages, 5287 KiB

Open AccessArticle

Long Life Power Factor Corrected LED Driver with Capacitive Energy Mechanism for Street Light Applications

by Muhammad Faizan, Jinshun Bi, Mengxin Liu, Lixin Wang, Viktor Stempitsky and Muhammad Zain Yousaf

Sustainability 2023, 15(5), 3991; https://doi.org/10.3390/su15053991 - 22 Feb 2023

Cited by 8 | Viewed by 1749

Abstract

Conventional switch-mode LED drivers have problems such as poor performance in harmonic distortion, flickering, power factor correction, stresses on the switches, high switching losses, large size, and high cost. To resolve these problems, we propose a long-life LED driver with the ability of power factor correction. The proposed system is based on the integration of a half-bridge LLC resonant converter and two boundary-conducted boost converters. Both boost converters share a common inductor designed in such a way that both boost converters work in boundary conduction mode to attain the natural power factor correction. Half-bridge LLC resonant converter has soft switching characteristics, which assure the zero-voltage switching (ZVS) of primary-side switches and zero-current switching (ZCS) of diodes on the secondary side. This significantly reduces switching losses and improves the overall efficiency of the system. Voltage divider capacitors are used on the input side, which minimizes the bus voltages. The proposed system has two identical secondary windings with a coupled inductor to eliminate the mismatch between them, which powers two independent LED strings. The simulation of a 100-watt 240 V AC converter yields the approximate sinusoidal shape of the input current. It shows that the switches on the primary side are operated in ZVS and the diodes in ZCS. At 240-volt AC input, the efficiency is 87.4%, the total harmonics distortion (THD) is 10.98%, and the power factor (PF) is 0.98. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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18 pages, 4249 KiB

Open AccessArticle

A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception

by Biyun Chen, Qi Xu, Zhuoli Zhao, Xiaoxuan Guo, Yongjun Zhang, Jingmin Chi and Canbing Li

Sustainability 2023, 15(4), 3376; https://doi.org/10.3390/su15043376 - 12 Feb 2023

Cited by 4 | Viewed by 1682

Abstract

With the massive installation of distributed renewable energy (DRE) generation, many prosumers with the dual attributes of load and power supply have emerged. Different DRE permeability and the corresponding peak-valley timing characteristics have an impact on the power features of prosumers, so new models and methods are needed to reflect the new features brought about by these factors. This paper proposes a method for predicting the power of prosumers. In this method, dynamic segmented curve matching is applied to reduce the complexity of source–load coupling features and improve the effectiveness of the input features, and trend feature perception based on a temporal convolutional network (TCN) was applied to grasp the power trend of prosumers by predicting the multisegment trend indexes. The LST-Atten prediction model based on a temporal attention mechanism (TAM) and a long short-term memory (LSTM) network was applied to predict “day-ahead” power, which combines the trend indexes and similar curve sets as the input. Simulation results show that the proposed model has higher accuracy than individual models. Furthermore, the proposed model can maintain prediction stability under different renewable energy permeability scenarios. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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14 pages, 1992 KiB

Open AccessArticle

Small-Signal Stability Constrained Optimal Power Flow Model Based on BP Neural Network Algorithm

by Yude Yang, Yuying Luo and Lizhen Yang

Sustainability 2022, 14(20), 13386; https://doi.org/10.3390/su142013386 - 17 Oct 2022

Cited by 2 | Viewed by 1504

Abstract

The existing small-signal stability constrained optimal power flow (SC-OPF) generally needs to deduce the sensitivity analytical expression of the small-signal stability index to parameters, which requires a large amount of formula derivation and mathematical computation. In order to overcome the complex problem of sensitivity, this article proposes an approximate sensitivity calculation method based on the back propagation (BP) neural network algorithm in the SC-OPF model. First, the minimum damping ratio of the system is taken as the small-signal stability index, and the algebraic inequality composed of the minimum damping ratio is used as the small-signal stability constraint in this model. Second, the BP neural network is introduced into the SC-OPF to analyze the mapping relationship between the generator power, node power, line power and the minimum damping ratio of the system, and then the numerical differentiation method is used to calculate the approximate first-order sensitivity of the minimum damping ratio in the correction equation. Finally, a series of simulations on the WSCC-9 bus and IEEE-39 bus systems verify the correctness and effectiveness of the proposed model. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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37 pages, 20462 KiB

Open AccessArticle

Frequency Stability Analysis of Multi-Renewable Source System with Cascaded PDN-FOPI Controller

by Aurobindo Behera, Subhranshu Sekhar Pati, Umamani Subudhi, Subhankar Ghatak, Tapas Kumar Panigrahi, Mohammed H. Alsharif and Syed Mohsan

Sustainability 2022, 14(20), 13065; https://doi.org/10.3390/su142013065 - 12 Oct 2022

Cited by 2 | Viewed by 1247

Abstract

The present work describes a multi-area (two and three) renewable-energy-source-integrated thermal-hydro-wind power generation structure along with fleets of plug-in electrical vehicles (PEVs) in each control area. The generation–load balance is the prime objective, so automatic generation control (AGC) is adopted in the system. In the paper, a cascaded combination of proportional derivative with filter PDN and fractional-order proportional integral (FOPI) is proposed and tuned using the hybrid chemical reaction optimization with pattern search (hCRO-PS) algorithm. The hCRO-PS algorithm is designed successfully, and its effectiveness is checked through its application to various benchmark functions. Further, Eigen value analysis is carried out for the test system to verify the system stability. The impacts of diverse step load perturbation (i.e., case I, II, III, and IV) and time-varying load perturbation are also included in the study. Moreover, the impact of renewable sources, PEVs in different areas, and varied state of charge (SOC) levels on the system dynamics are reflected in the work. From the analysis, it can be inferred that the proposed controller provides comparable results with other fractional-order and conventional controllers under varying loading conditions. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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14 pages, 2603 KiB

Open AccessArticle

Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

by Ge Zhang, Songyang Zhu and Xiaoqing Bai

Sustainability 2022, 14(19), 12843; https://doi.org/10.3390/su141912843 - 8 Oct 2022

Cited by 19 | Viewed by 3068

Abstract

Integrated Energy Microgrid (IEM) has emerged as a critical energy utilization mechanism for alleviating environmental and economic pressures. As a part of demand-side energy prediction, multi-energy load forecasting is a vital precondition for the planning and operation scheduling of IEM. In order to increase data diversity and improve model generalization while protecting data privacy, this paper proposes a method that uses the CNN-Attention-LSTM model based on federated learning to forecast the multi-energy load of IEMs. CNN-Attention-LSTM is the global model for extracting features. Federated learning (FL) helps IEMs to train a forecasting model in a distributed manner without sharing local data. This paper examines the individual, central, and federated models with four federated learning strategies (FedAvg, FedAdagrad, FedYogi, and FedAdam). Moreover, considering that FL uses communication technology, the impact of false data injection attacks (FDIA) is also investigated. The results show that federated models can achieve an accuracy comparable to the central model while having a higher precision than individual models, and FedAdagrad has the best prediction performance. Furthermore, FedAdagrad can maintain stability when attacked by false data injection. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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21 pages, 3008 KiB

Open AccessArticle

Error-Tracking Iterative Learning Control for the Constrained Flexible-Joint Manipulator with Initial Errors

by Huihui Shi and Qiang Chen

Sustainability 2022, 14(19), 12453; https://doi.org/10.3390/su141912453 - 30 Sep 2022

Cited by 1 | Viewed by 1269

Abstract

The use of manipulators can improve sustainable energy utilization efficiency and increase sustainable manufacturing practices for solar tracking systems and manufactures, and thus it is significant to guarantee a high tracking accuracy for manipulators. In this paper, an error-tracking adaptive iterative learning control (AILC) method is proposed for a constrained flexible-joint manipulator (FJM) with initial errors. Due to the existence of the repeated positioning drift, the accuracy of the actual manipulator and the sustainable energy utilization efficiency are affected, which motivates the error-tracking approach proposed in this paper to deal with the repeat positioning problem. The desired error trajectory is constructed, such that the tracking error can follow the desired error trajectory without arbitrary initial values and iteration-varying tasks. Then, the system uncertainties are approximated by the capability of fuzzy logic systems (FLSs), and the combined adaptive laws are designed to update the weight and the approximating error of FLSs. Considering the safety operation of the flexible-joint manipulator, both input and output constraints are considered, a quadratic-fractional barrier Lyapunov function (QFBLF) is constructed, such that the system output is always within the constrained region. Therefore, the proposed method can guarantee the output tracking accuracy of manipulators under arbitrary initial values and iteration-varying tasks and keep the system output within the constraints to improve the transient performance, such that the energy utilization and accessory manufacturing efficiency can be improved. Through the Lyapunov synthesis, it is proved that the tracking error can converge to zero as the number of iterations goes to infinity. Finally, comparative simulations are carried out to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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18 pages, 5765 KiB

Open AccessArticle

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

by Yuxin Wen, Peixiao Fan, Jia Hu, Song Ke, Fuzhang Wu and Xu Zhu

Sustainability 2022, 14(16), 10351; https://doi.org/10.3390/su141610351 - 19 Aug 2022

Cited by 10 | Viewed by 1891

Abstract

In recent years, the access of various distributed power sources and electric vehicles (EVs) has brought more and more randomness and uncertainty to the operation and regulation of microgrids. Therefore, an optimal scheduling strategy for microgrids with EVs based on Deep Q-learning is proposed in this paper. Firstly, a vehicle-to-grid (V2G) model considering the mobility of EVs and the randomness of user charging behavior is proposed. The charging time distribution model, charging demand model, state-of-charge (SOC) dynamic model and the model of travel location are comprehensively established, thereby realizing the construction of the mathematical model of the microgrid with EVs: it can obtain the charging/discharging situation in the EV station, so as to obtain the overall output power of the EV station. Secondly, based on Deep Q-learning, the state space and action space are set up according to the actual microgrid system, and the design of the optimal scheduling reward function is completed with the goal of economy. Finally, the calculation example results show that compared with the traditional optimization algorithm, the strategy proposed in this paper has the ability of online learning and can cope with the randomness of renewable resources better. Meanwhile, the agent with experience replay ability can be trained to complete the evolution process, so as to adapt to the nonlinear influence caused by the mobility of EVs and the periodicity of user behavior, which is feasible and superior in the field of optimal scheduling of microgrids with renewable resources and EVs. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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20 pages, 8755 KiB

Open AccessArticle

A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG

by Peixiao Fan, Jia Hu, Song Ke, Yuxin Wen, Shaobo Yang and Jun Yang

Sustainability 2022, 14(14), 8886; https://doi.org/10.3390/su14148886 - 20 Jul 2022

Cited by 4 | Viewed by 1558

Abstract

With the development of micro gas turbines (MT) and power-to-gas (P2G) technology, the electric–gas system plays an important role in maintaining the stable, economical, and flexible operation of the microgrid. When subjected to power load disturbance and natural gas load disturbance, the system controller needs to coordinately control the frequency of the microgrid and the gas pressure at the natural gas pipeline nodes. Additionally, the reliability and stability of a multi-microgrid system are much higher than that of a single microgrid, but its control technology is more complicated. Thus, a frequency–pressure cooperative control strategy of a multi-microgrid oriented to an electric–gas system is proposed in this paper. Firstly, based on the analysis of the operating characteristics of the natural gas network and the coupling equipment, the dynamic model of natural gas transmission is built. Secondly, a multi-microgrid load frequency control model including MT, P2G equipment, electric vehicles (EVs), distributed power sources and loads has been established. In addition, according to the three control objectives of microgrid frequency, node pressure and system coordination and stability, the structure of a Muti-Agent Deep Deterministic Policy Gradient (MADDPG) controller is designed, then the definition of space and reward functions are completed. Finally, different cases are set up in the multi-microgrid, and the simulation results are compared with PI control and fuzzy control. The simulation results show that, the proposed MADDPG controller can greatly suppress the frequency deviation caused by wind power and load disturbances and the air pressure fluctuations caused by natural gas network load fluctuations. Additionally, it can coordinate well the overall stability between the sub-microgrids of multi-microgrid. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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16 pages, 2373 KiB

Open AccessArticle

An Optimal Dispatching Model for Integrated Energy Microgrid Considering the Reliability Principal–Agent Contract

by Biyun Chen, Yanni Chen, Bin Li, Yun Zhu and Chi Zhang

Sustainability 2022, 14(13), 7645; https://doi.org/10.3390/su14137645 - 23 Jun 2022

Cited by 3 | Viewed by 1481

Abstract

As the increasing penetration of sustainable energy brings risks and opportunities for energy system reliability, at the same time, considering the multi-dimensional differentiation of users’ reliability demands can further explore the potential value of reliability resources in Integrated Energy Microgrid (IEM). To activate the reliability resources in a market-oriented perspective and flexibly optimize the operational reservation in dispatch, an optimal dispatching model in IEM considering reliability principal–agent contracts is proposed. We establish the reliability principal–agent mechanism and propose a cooperative gaming model of Integrated Energy Operator (IEO) and Integrated Energy User (IEU) based on the optimal dispatching model. At the upper level, the economic dispatching model of IEO is established to optimize the operation reservation, and the reliability principal–agent contract from users in the lower level would influence reliability improvement. Each IEU in the lower level maximizes its energy utilization and gives the corresponding reliability principal–agent incentives according to the reliability improvement degree and its actual demand. The bi-level model is solved by the KKT condition and strong duality theorem. A case study verifies the effectiveness of the proposed model in reducing the energy dispatch cost, improving the economic benefits of each participant, realizing the optimal allocation of reliability resources and optimizing the IEM energy structure, and the sensitivity analysis of dispatch cost with the user’s energy-using benefits is discussed. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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17 pages, 4998 KiB

Open AccessArticle

An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response

by Dan Zhou, Xiaodie Niu, Yuzhe Xie, Peng Li, Jiandi Fang and Fanghong Guo

Sustainability 2022, 14(7), 3751; https://doi.org/10.3390/su14073751 - 22 Mar 2022

Cited by 4 | Viewed by 2045

Abstract

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when

p

= 0.01,

v

= 100,

t^{0}

= 0.01 and

μ

= 2. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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14 pages, 5125 KiB

Open AccessArticle

A Study on Temperature Distribution within HVDC Bushing Influenced by Accelerator Content during the Curing Process

by Yuanxiang Zhou, Xuewei Wang, Chenyuan Teng, Yunxiao Zhang, Xin Huang and Jianning Chen

Sustainability 2022, 14(6), 3393; https://doi.org/10.3390/su14063393 - 14 Mar 2022

Cited by 1 | Viewed by 1913

Abstract

Power transmission technology plays an important role in energy sustainability. Bushing is an indispensable type of equipment in power transmission. In production, the accelerator changes the temperature distribution during the curing process, influencing the formation of defects and thus the safety output of renewable energy. In this study, uncured epoxy resin samples with different accelerator contents were prepared and measured by differential scanning calorimetry (DSC). The obtained heat flow curves were analyzed for curing kinetics. Then, the curing process of large length–diameter ratio bushings was simulated by using the finite element method combined with a curing kinetics model, transient Fourier heat transfer model, and stress–strain model. The study reveals that the curing system can be established by the Sestak–Berggren autocatalytic model with different accelerator contents. The overall curing degree and the maximum radial temperature difference of the capacitor core tend to increase and then decrease with the accelerator content. This is mainly attributable to the rapid exotherm excluding the participation of some molecular chains in the reaction, resulting in permanent under-curing. As the accelerator content increases, the strain peak decreases and then increases. This paper provides guidance for the comprehensive evaluation and manufacturing of the low-defect capacitor cores of large-size high voltage direct current (HVDC) bushings. Full article

(This article belongs to the Special Issue Sustainable Power Systems and Optimization)

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