Topic Editors

Prof. Dr. Junhua Zhao
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518100, China
Prof. Dr. Gaoqi Liang
School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China

Planning, Operation and Control of Low-Carbon Power Systems

Abstract submission deadline
28 February 2027
Manuscript submission deadline
30 April 2027
Viewed by
429

Topic Information

Dear Colleagues,

Decarbonization is rapidly transforming power systems into converter‑dominated, data‑rich, and highly distributed networks. The shift toward high penetrations of wind, solar, storage, electric mobility, and sector‑coupled resources challenges traditional planning and operational paradigms—raising new questions about adequacy, stability, flexibility, and resilience under uncertainty. At the same time, advances in optimization, control, power electronics, and digitalization offer powerful tools to design and operate reliable low‑carbon grids.

This topic welcomes original research articles, reviews, communications, data/resource papers, and real‑world case studies that address the planning, operation, and control of low‑carbon power systems. Submissions reporting field demonstrations, open datasets/code, uncertainty treatment, and reproducible workflows are especially encouraged.

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

  • Long‑term generation and transmission planning under carbon constraints (net‑zero pathways, multi‑criteria planning);
  • Resource adequacy with high VRE and storage (ELCC, firming portfolios, capacity expansion);
  • Distribution system planning with DER hosting capacity, network reconfiguration, and dynamic line rating;
  • Transmission–distribution co‑simulation and TSO–DSO coordination mechanisms;
  • Security‑constrained unit commitment and optimal power flow (deterministic, stochastic, chance‑constrained, distributionally robust);
  • Markets and regulation for low‑carbon systems (carbon pricing, flexibility/ancillary services, capacity and balancing markets);
  • Operation of microgrids, virtual power plants, and aggregators (demand response, transactive energy, V2G/V2B strategies);
  • Energy storage planning and dispatch (BESS sizing, SoC/SoH management, hybrid storage, seasonal storage);
  • Control of converter‑interfaced resources: grid‑forming vs. grid‑following inverters, VSM/synthetic inertia, droop/MPC/robust control;
  • Voltage, frequency, and oscillation damping control with FACTS/STATCOM/HVDC and synchronous condensers;
  • Stability analysis of converter‑dominated grids (small‑signal, transient, voltage, frequency, EMT‑RMS interactions);
  • Inertia estimation, fast frequency response, and restoration/black‑start strategies in low‑inertia systems;
  • Multi‑terminal HVDC grids, offshore hubs, and hybrid AC/DC architectures;
  • Sector coupling and multi‑energy systems planning (power–heat–gas–hydrogen integration);
  • Integration of large EV fleets and charging infrastructure planning and operation;
  • Climate‑ and weather‑driven risk, resilience, and restoration planning (wildfire, flood, heat stress, extreme events);
  • Measurement, monitoring, and state estimation (PMU/WAMS, dynamic state estimation, cyber‑physical observability);
  • Cybersecurity and privacy‑preserving operation for digital substations and IoT‑enabled assets;
  • Emissions accounting, lifecycle assessment, and carbon‑aware scheduling/dispatch;
  • Data‑driven methods, AI/ML, digital twins, and physics‑informed approaches for planning and control;
  • Benchmark systems, standardized test cases, open datasets, and MLOps for power‑system analytics;
  • Practical demonstrations, pilot projects, grid‑code compliance, and lessons learned from real deployments.

Prof. Dr. Junhua Zhao
Prof. Dr. Gaoqi Liang
Topic Editors

Keywords

  • low carbon power systems
  • renewable integration
  • optimal power flow
  • grid forming inverters
  • energy storage and flexibility
  • power system resilience

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Energies
energies
3.2 7.3 2008 16.8 Days CHF 2600 Submit
Applied Sciences
applsci
2.5 5.5 2011 16 Days CHF 2400 Submit
Electronics
electronics
2.6 6.1 2012 16.4 Days CHF 2400 Submit
AI
ai
5.0 6.9 2020 19.2 Days CHF 1800 Submit
Electricity
electricity
1.8 5.1 2020 26.9 Days CHF 1200 Submit
Sci
sci
- 5.2 2019 26.7 Days CHF 1400 Submit
Energy Storage and Applications
esa
- - 2024 15.0 days * CHF 1000 Submit

* Median value for all MDPI journals in the second half of 2025.


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Published Papers (1 paper)

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29 pages, 1215 KB  
Article
Cost-Optimal Coordination of PV Generation and D-STATCOM Control in Active Distribution Networks
by Luis Fernando Grisales-Noreña, Daniel Sanin-Villa, Oscar Danilo Montoya, Rubén Iván Bolaños and Kathya Ximena Bonilla Rojas
Sci 2026, 8(1), 8; https://doi.org/10.3390/sci8010008 - 7 Jan 2026
Viewed by 185
Abstract
This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as [...] Read more.
This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as a nonlinear optimization problem that explicitly represents the P and Q control capabilities of Distributed Energy Resources (DER), encompassing small-scale generation and compensation units connected at the distribution level, such as PV generators and D-STATCOM devices, adjusting their reference power setpoints to minimize daily operating costs, including energy purchasing and DER maintenance, while satisfying device power limits and the voltage and current constraints of the grid. To solve this problem efficiently, a parallel version of the Population Continuous Genetic Algorithm (CGA) is implemented, enabling simultaneous evaluation of candidate solutions and significantly reducing computational time. The strategy is assessed on the 33- and 69-node benchmark systems under deterministic and uncertainty scenarios derived from real demand and solar-generation profiles from a Colombian region. In all cases, the proposed approach achieved the lowest operating cost, outperforming state-of-the-art metaheuristics such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA), while maintaining power limits, voltages and line currents within secure ranges, exhibiting excellent repeatability with standard deviations close to 0.0090%, and reducing execution time by more than 68% compared with its sequential counterpart. The main contributions of this work are: a unified optimization model for joint PQ control in PV and D–STATCOM units, a robust codification mechanism that ensures stable convergence under variability, and a parallel evolutionary framework that delivers optimal, repeatable, and computationally efficient energy management in distribution networks subject to realistic operating uncertainty. Full article
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