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Smart Grid, Integration of Renewable Sources and Improvement of Power Quality

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2793

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Editors

Dr. Haiwen Chen

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Guest Editor

School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China
Interests: low-carbon power system; smart power consumption; big data analytics for power distribution and consumption; behind-the-meter source and load monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Kai Zhou

E-Mail Website
Guest Editor

School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215006, China
Interests: smart grids; stochastic analysis; risk assessment; power system reliability

Dr. Xuan Wang

E-Mail Website
Guest Editor

School of Electrical Engineering, Tiangong University, Tianjin 300387, China
Interests: integrated energy systems; smart distribution networks

Special Issue Information

Dear Colleagues,

Against the backdrop of the pressing global demand for decarbonization and the rapid proliferation of renewable energy sources, the global energy landscape is undergoing a profound transformation. This transition from traditional, centralized power systems based on fossil fuels to distributed networks dominated by renewables presents unprecedented challenges and opportunities. The inherent intermittency and variability of sources such as solar and wind demand that the power grid possess greater intelligence, flexibility, and resilience. The smart grid, integrated with advanced communication, control, and data analytics technologies, serves as the cornerstone for building modern energy infrastructure. However, the grid integration of large-scale renewable energy and emerging loads, such as electric vehicles, can trigger severe power quality issues—including voltage fluctuations, harmonics, and frequency instability—thereby threatening the reliable operation of the entire system.

This Special Issue, "Smart Grid, Integration of Renewable Sources and Improvement of Power Quality," aims to showcase and disseminate the latest advances, innovative research, and comprehensive reviews that address the key intersecting challenges across these three interrelated domains. We seek to foster a multidisciplinary academic discussion on technologies, strategies, and policies to facilitate the seamless integration of renewable energy, enhance the intelligence and resilience of the power grid, and ensure high-standard power quality for all users.

Topics of interest for this Special Issue include, but are not limited to, the following:

Smart Grid Technologies and Architectures:

Data analytics and artificial intelligence applications in grid management.
Demand-side management, demand response, and energy efficiency enhancement.
Design, control, and operation of microgrids, nanogrids, and islanded power systems.
Communication protocols, cybersecurity, and resilience of smart grids.

Renewable Energy Integration:

Grid integration challenges and solutions for large-scale solar PV and wind power.
Energy storage systems for grid stability and renewable energy smoothing.
Advanced power electronic interface technologies.
Grid-forming and grid-following control strategies.
Electric vehicle grid integration and vehicle-to-grid
Virtual power plants and distributed energy resource aggregation technologies.

Power Quality Improvement:

Monitoring, analysis, and diagnosis of power quality disturbances.
Mitigation techniques for harmonics, voltage sags/swells, flicker, and unbalance.
Impact of renewable energy and EV charging on power quality.
Power quality standards, regulations, and economic analysis.

We look forward to receiving your outstanding contributions.

Dr. Haiwen Chen
Dr. Kai Zhou
Dr. Xuan Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart grid
renewable energy integration
power quality
microgrid
energy storage system
electric vehicle
demand-side management

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Published Papers (7 papers)

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Research

25 pages, 2114 KB

Open AccessArticle

Quality-Aware Feasibility-Preserving Unit Aggregation for Smart-Grid Production Simulation

by Jishuo Qin, Bin Yang, Fan Li, Hanqing Liang, Taikun Tao and Yawei Xue

Energies 2026, 19(15), 3487; https://doi.org/10.3390/en19153487 - 24 Jul 2026

Abstract

High renewable penetration, distributed energy resources, and fast-varying electric loads are shifting smart-grid planning from energy-balance simulation toward quality-aware operational assessment. Full-unit benchmark models (FULL) preserve unit commitment, ramping memory, and reserve feasibility but are expensive for repeated annual studies, whereas conventional equivalent aggregation (EQ) can overstate the realizable flexibility of heterogeneous units. This paper proposes quality-aware flexibility-envelope aggregation (QFEA), which separates units by inherited boundary state, ranks them by renewable-following flexibility, constructs conservative cluster envelopes, and couples reduced optimization with feasible disaggregation and state write-back. The model coordinates renewable curtailment, reserve sufficiency, tie-line ramping, and a normalized quality-stress proxy without claiming to replace detailed power-flow, harmonic, or electromagnetic studies. In the nominal single-region case, QFEA reduces the number of commitment objects by 46.2% and computation time by 63.7%, while limiting total-cost deviation to 1.1% and renewable-curtailment deviation to 0.2 percentage points. In 20 matched 24-h stress scenarios, its mean quality-stress index is 2.56%, compared with 2.58% for FULL and 6.57% for EQ. A separate 13–104-unit simplified scaling test keeps inverse-mapping closure error below 5.2 × 10⁻⁹ MWh and disaggregation below 1.3% of measured end-to-end time. The results identify QFEA as a traceable intermediate model for renewable-integration screening when annual computational efficiency and implementable unit trajectories are both required. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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31 pages, 15618 KB

Open AccessArticle

Optimal Operation Strategy Considering Shared Hydrogen Energy Storage and Data Center Load Scheduling

by Guobin Fu, Chengjie Liu, Huanbei Zhao, Zhengkui Zhao, Kaixuan Yang and Xiaoling Su

Energies 2026, 19(14), 3387; https://doi.org/10.3390/en19143387 - 17 Jul 2026

Viewed by 190

Abstract

Data centers are facing rapidly increasing electricity demand and carbon emissions, while the intermittency of renewable energy creates a significant temporal mismatch between renewable generation and data center load demand. To bridge this temporal mismatch, we propose a coordinated optimization strategy that integrates shared hydrogen energy storage facilities with load scheduling mechanisms. A multi-objective MILP model is formulated to minimize annualized cost, renewable energy curtailment, and carbon emissions. Simulation results show that, compared with the no-shared-station case, the proposed electricity–hydrogen coordination strategy with load shifting yields significant benefits: the annualized total cost decreases from 13.65 to 4.74 million yuan; annual carbon emissions are reduced from 6297 to 1432 tons; and peak-period electricity purchases are reduced from 5373 to 906 MWh. Under the representative daily forecast condition, Scenario S4 achieves zero renewable curtailment when grid export is permitted; therefore, the renewable-electricity utilization rate reaches 100.00% within the model boundary. When grid export is prohibited, the utilization rate decreases to 98.59%, with 179,100 kWh of annualized renewable curtailment. The research findings indicate that integrating shared hydrogen energy storage with the load flexibility of data centers can effectively reduce the system’s overall operating costs, promote the integration of renewable energy, and achieve low-carbon operation. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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22 pages, 1525 KB

Open AccessArticle

Risk-Anchored Order-Preserving Scenario-Chain Construction for Renewable Energy Base Planning

by Fan Li, Bin Yang, Jishuo Qin, Jian Meng, Hanqing Liang and Taikun Tao

Energies 2026, 19(14), 3363; https://doi.org/10.3390/en19143363 - 16 Jul 2026

Viewed by 182

Abstract

Large renewable energy bases are increasingly planned under delivery-hour targets, corridor-capacity constraints, and high shares of wind and photovoltaic generation. Chronological planning samples used in expansion and adequacy studies therefore need to retain not only average renewable-load patterns but also low-probability days with high residual balancing demand, large ramps, curtailment pressure, and sustained renewable scarcity. This paper proposes a risk-anchored order-preserving scenario-chain construction method for renewable energy base planning. The proposed method transforms aligned hourly load, wind, photovoltaic, delivery-demand, and loss-adjusted demand trajectories into distinct operational stress indicators, normalizes them into a joint extreme score, anchors the highest-risk natural days together with their adjacent transition days, and applies clustering only to the remaining regular days. Observed medoid days are then inserted back into chronological order to form a compact scenario chain with explicit weights and adjacency information. A representative 8760 h renewable-base case with 4000 MW wind, 5500 MW photovoltaic, 5400 MW coal support, 1200 MWh storage-energy capacity, a 7600 MW delivery corridor, and a 5600 h delivery target is used to run weight-sensitivity tests and same-budget comparisons against monthly typical days, k-means, k-medoids, hierarchical clustering, Carpe Diem, and seasonal time-series aggregation baselines. Under the equal-weight base case, the proposed chain gives an active-metric mean capture ratio of 1.0660, compared with 0.8377 for monthly typical days. Across four non-equal priority-weight vectors, the active-metric mean remains between 1.0041 and 1.0662. Under the same 151-day budget, conventional k-means, k-medoids, hierarchical clustering, Carpe Diem, and seasonal time-series aggregation baselines obtain active-metric means of 0.8643, 0.9025, 0.8732, 0.9239, and 0.9189, respectively. The results indicate that chronological risk anchoring provides a compact yet physically interpretable sampling layer for planning models that must balance renewable utilization, delivery reliability, and storage adequacy. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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31 pages, 2551 KB

Open AccessArticle

Power-Quality-Proxy-Guided Storage State Replay for Renewable-Rich Smart Grids Under Decomposed Production Simulation

by Jishuo Qin, Bin Yang, Fan Li, Yuan Si, Taikun Tao and Dan Wang

Energies 2026, 19(14), 3339; https://doi.org/10.3390/en19143339 - 15 Jul 2026

Viewed by 188

Abstract

Smart grids with high renewable penetration are increasingly evaluated through long-horizon production simulation, but conventional decomposed simulation mainly reports energy balance and unit feasibility, while power-quality stress remains weakly quantified in the storage correction layer. This paper presents a power-quality-proxy-guided state-replay framework for renewable-rich smart grids. Instead of claiming feeder-level electromagnetic simulation, the method defines planning-level proxy indicators that can be exported by production-simulation software: a voltage-deviation proxy obtained from net-power sensitivity, a net-load ramp proxy, an inverter/charger harmonic-risk proxy, and a renewable-curtailment exposure proxy. These normalized indicators are combined into a composite score

S_{P Q}

, which is then used to distinguish two storage values: charge retention during renewable-surplus voltage-rise intervals and discharge support during voltage-dip, ramp-stress, or inverter-stress intervals. A base decomposed production-simulation schedule is first obtained. The proposed layer then constructs storage accounting cycles independent of monthly and rolling-window boundaries, attaches the proxy ledger to each interval, backtracks terminal residual storage energy to low-value charging actions, and reallocates physically feasible discharge to high-

S_{P Q}

intervals. The corrected storage path is projected onto power and energy limits and replayed before storage and conventional-unit states are inherited by the next monthly solve; cycles outside the replay validity envelope are escalated to full redispatch rather than counted as successful corrections. An eight-interval case reports explicit

S_{P Q}

values and shows that a trajectory ending 45 MWh above the 30 MWh reference can be corrected by trimming 35 MWh of low-proxy-value charging and adding 10 MWh of discharge in two high-score intervals. A 96-interval experiment further shows that the full method reduces explicitly discarded residual energy from 214 MWh to 31 MWh, provides 128 MWh of proxy-guided support, and lowers weighted PQ-proxy exposure by 46.3%. The framework links smart-grid data analysis, renewable integration, and power-quality improvement within a traceable production-simulation workflow. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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21 pages, 2106 KB

Open AccessArticle

A Bilevel Programming Framework for Demand Response Incentive Design with Non-Intrusive Load Monitoring-Based Flexibility Estimation

by Ye Ding, Kai Zhou, Xiuming He and Yuan Sun

Energies 2026, 19(12), 2818; https://doi.org/10.3390/en19122818 - 12 Jun 2026

Cited by 1 | Viewed by 233

Abstract

Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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15 pages, 1517 KB

Open AccessArticle

An Optimal Fault Restoration Strategy of Distribution Networks Considering the Dynamic Feature of Distributed Renewable Energy Resources

by Bin Yang, Jilong Tang, Yuhang Guo, Liyuan Zhao, Zhe Li, Yijia Zhu and Xinyu Zhang

Energies 2026, 19(7), 1692; https://doi.org/10.3390/en19071692 - 30 Mar 2026

Viewed by 518

Abstract

Ignoring the dynamic output recovery of distributed renewable energy sources (dRESs) during distribution network restoration may lead to low voltage in the initial stage, which can cause dRESs and loads to trip and even prevent the recovery of the entire distribution system. To address this issue, this paper proposes a dynamic restoration control framework for distribution networks with dRES integration. In this framework, a topology reconfiguration method is established to capture the time-varying characteristics of dRESs during the restoration process, and a double-time-section power flow calculation strategy is incorporated to verify operational constraints throughout the restoration period. The resulting optimization problem is solved by an improved hybrid Aquila Optimizer–Binary Particle Swarm Optimization algorithm, in which pre-scheme initialization and enhanced Gaussian mutation are introduced to improve convergence and solution quality. Case studies demonstrate that the proposed framework can obtain optimal schemes of topology reconfiguration for dRES-penetrated distribution networks within dozens of seconds while avoiding off-normal voltage and unsuccessful dRES reconnection, thereby enhancing the restoration capability of the distribution system. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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18 pages, 2151 KB

Open AccessArticle

A Communication-Free Cooperative Fault Recovery Control Method for DNs Based on Staged Active Power Injection of ES

by Bin Yang, Ning Wei, Yuhang Guo, Jince Ge and Liyuan Zhao

Energies 2026, 19(1), 285; https://doi.org/10.3390/en19010285 - 5 Jan 2026

Cited by 1 | Viewed by 960

Abstract

To address the reclosing failures in the distribution networks (DNs) with high penetration of distributed energy resources (DERs), this paper proposes a communication-free cooperative fault recovery control method based on staged active power injection of an energy storage (ES) system. First, during the initial phase of a fault, a back-electromotive force (b-EMF) suppression arc extinction control strategy was designed for the ES converter, promoting fault arc extinction. Subsequently, the ES switches to grid-forming (GFM) control, providing active power injection to the network following the circuit breaker (CB) tripping. A time-limited variable power control of ES converter is also designed to establish voltage characteristics for fault state detection. And a fault state criterion based on voltage relative entropy is designed, helping reliable reclosing. Simulation results demonstrate that the proposed method achieves coordination solely through local measurements without the need for real-time communication between ES and CB, and can shorten the recovery time of transient faults to hundreds of milliseconds. Full article

(This article belongs to the Special Issue Smart Grid, Integration of Renewable Sources and Improvement of Power Quality)

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