Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System

Topic Information

Dear Colleagues,

New power energy systems are composed of multiple interconnected energy supplies and consumption networks, such as power grids, oil and gas networks (including natural gas/hydrogen networks), cold and hot networks, transportation networks (including highway/railway/shipping/aviation, etc.), and communication/information networks (including data centers, supercomputing centers, etc.). Their key characteristic is that each energy-related network has a respective source, network, load, and storage in corresponding energy form, and there is a bidirectional coupling of energy flow and information flow between the primary energy network and the secondary communication/information network.

All energy networks are coordinated, scheduled, and controlled/protected by the communication/information network. Each individual energy network and multi-energy system's overall energy network, as well as each communication/information network, have their own planning, operation, control/protection, and market trading, with the goal of achieving balance, security, stability, efficiency, and low-carbon environmental protection during system operations under different time scales. This topic focuses on the innovative methods, key technologies, and applications in the planning, operation, control/protection, and marketing of new power energy systems, involving the integration of multiple disciplines and application areas such as electrical engineering, power engineering, transportation engineering, control engineering, communication engineering, computer science (including artificial intelligence), and mathematics (including network science, optimization theory, etc.). The scope of submissions includes, but is not limited to, the following:

1. Planning methods and key technologies for new power energy systems;
2. Operation methods and key technologies of new power energy systems;
3. Control/protection methods and key technologies for new power energy systems;
4. Progress in market mechanisms and clearing methods for new power energy systems.

Prof. Dr. Tao Lin
Prof. Dr. Qijun Deng
Dr. Xue Cui
Dr. Bowen Zhou
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Mathematics mathematics	2.3	5.4	2013	17.4 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (23 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Electronics Energies Mathematics Sustainability

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

27 pages, 10227 KB  

Open AccessArticle

Low-Carbon Dispatch of Integrated Electricity–Gas Systems Considering Flexible Resources and Uncertainties

by Hong Fan, Jiawen Yu, Feng You and Zhengaoyu Wang

Appl. Sci. 2026, 16(16), 8052; https://doi.org/10.3390/app16168052 - 12 Aug 2026

Viewed by 219

Abstract

High renewable energy penetration and surging electrical demand challenge the operation of integrated electricity–gas systems (IEGS) due to source and load uncertainties. This paper proposes a multi-objective optimal scheduling framework that harnesses flexible resources within the IEGS to balance economic, environmental, and energy [...] Read more.

High renewable energy penetration and surging electrical demand challenge the operation of integrated electricity–gas systems (IEGS) due to source and load uncertainties. This paper proposes a multi-objective optimal scheduling framework that harnesses flexible resources within the IEGS to balance economic, environmental, and energy efficiency goals. First, a liquid storage tank is introduced to reform the traditional carbon capture, utilization, and storage system. Additionally, a hydrogen energy multi-utilization structure—integrating two-stage power-to-gas, hydrogen fuel cells, and hydrogen storage—is developed to improve operational flexibility under renewable fluctuations and carbon constraints. Second, electric vehicles (EVs) schedulability is quantitatively evaluated across different charging scenarios, defining carbon quotas and profit calculation methods to incentivize EV participation. To address source-load uncertainties, a two-stage robust optimization model utilizing a box uncertainty set and budget constraints is constructed to secure the optimal scheduling solution under worst-case scenarios. Finally, by introducing penalty factors for carbon emissions and energy loss, the multi-objective function is transformed into a single-objective problem to minimize operation costs, emissions, and energy wastage. The results show that the coupled CCUS–HEMU configuration reduces the total and environmental costs by 15.50% and 77.13%. Under the worst-case source–load scenario, bidirectional EV charging further reduces the total cost by 22.10%, increases renewable-energy utilization from 87.34% to 94.34%, and decreases load fluctuation and the maximum peak–valley difference by 68.95% and 14.85%, respectively, thereby enhancing the system’s low-carbon flexibility and robustness against operational uncertainties. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

19 pages, 14587 KB  

Open AccessReview

Multi-Robot Systems for Electric Power Inspection: A Review of Cooperative Perception, Collaborative Planning, and Coordinated Execution

by Xianing Jin, Jingsi Huang, Xin Liu and Pei Liu

Electronics 2026, 15(14), 3067; https://doi.org/10.3390/electronics15143067 - 13 Jul 2026

Viewed by 606

Abstract

Electric power systems are expanding toward higher voltage levels, larger renewable-energy bases, denser urban substations, and increasingly complex transmission corridors. These trends make inspection more frequent and more demanding, while conventional manual patrols and single-robot deployments remain constrained by safety risks, limited coverage, [...] Read more.

Electric power systems are expanding toward higher voltage levels, larger renewable-energy bases, denser urban substations, and increasingly complex transmission corridors. These trends make inspection more frequent and more demanding, while conventional manual patrols and single-robot deployments remain constrained by safety risks, limited coverage, endurance, and fragmented situational awareness. Multi-robot systems offer a promising pathway for electric power inspection by combining heterogeneous platforms, distributed sensing, coordinated planning, and human-supervised autonomy. This review synthesizes recent progress in multi-robot inspection for power transmission lines, substations, distribution networks, and related grid assets, with particular attention to transmission corridors and substations where heterogeneous cooperation is operationally valuable. Following a Sense–Think–Act framework, we organize the literature into three interconnected components: cooperative perception for spatial and semantic understanding of grid assets; collaborative planning and task allocation for large-scale, risk-aware inspection; and coordinated execution with human oversight in safety-critical, often energized environments. We highlight how unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), climbing robots, and fixed robotic stations can complement one another in inspection workflows, from wide-area patrol and defect localization to close-range verification and maintenance support. We also discuss persistent challenges, including electromagnetic compatibility, reliable localization near metallic structures, multimodal data fusion, battery endurance, communication robustness, minimum approach distances, cybersecurity, benchmark scarcity, and the need for assurance mechanisms that allow operators to understand, trust, and intervene in multi-robot decisions. Finally, we outline a roadmap for moving from isolated demonstrations toward deployable, human-centered, and grid-integrated multi-robot inspection systems. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

18 pages, 1050 KB  

Open AccessArticle

An Optimization Model Solution Method for Transient Voltage Stability Emergency Control in High-Voltage DC Receiving End

by Weigang Jin, Tao Lin, Jiawei Zhang, Jiayi Wang, Jun Li and Chen Li

Energies 2026, 19(12), 2926; https://doi.org/10.3390/en19122926 - 21 Jun 2026

Viewed by 285

Abstract

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation [...] Read more.

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

25 pages, 5116 KB  

Open AccessArticle

Optimal Sizing of High-Altitude Wind–Solar–Hydrogen Storage Systems Considering Hybrid Electricity–Hydrogen Dispatch

by Longquan Zeng, Ke Li, Yi Yu, Heng Zhang, Yuyin Liang, Chuxian Zhang and Wei He

Sustainability 2026, 18(11), 5515; https://doi.org/10.3390/su18115515 - 1 Jun 2026

Viewed by 391

Abstract

High-altitude regions provide abundant wind and solar resources but impose severe environmental constraints on energy storage systems. To address these challenges, this study proposes a bi-level optimal sizing method for wind–solar–hydrogen storage systems considering altitude-induced impacts. A system model integrating electrochemical storage and [...] Read more.

High-altitude regions provide abundant wind and solar resources but impose severe environmental constraints on energy storage systems. To address these challenges, this study proposes a bi-level optimal sizing method for wind–solar–hydrogen storage systems considering altitude-induced impacts. A system model integrating electrochemical storage and hydrogen storage is established, and a hybrid electricity–hydrogen storage dispatch strategy is designed to exploit their complementary characteristics. The upper-level optimization minimizes lifecycle cost using the Golden Sine Algorithm-Subtraction Average Based Optimizer (GSABO), while the lower level conducts 8760 h simulations to optimize the loss of power supply probability (LPSP) and excess energy rate (EER). A case study in western Sichuan, China, at an altitude of approximately 3500 m, demonstrates the method achieves 0% EER and 0.8% LPSP, reducing total costs by 50.65% compared to single electrochemical storage. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

20 pages, 2439 KB  

Open AccessArticle

A Data-Driven Method for Constructing Planning Evaluation Indicators for Emerging Distribution Networks

by Yuan Zhang, Wei Xiong, Jinsen Liu, Xufeng Yuan, Zhiyang Lu and Fei Zheng

Energies 2026, 19(10), 2310; https://doi.org/10.3390/en19102310 - 11 May 2026

Cited by 1 | Viewed by 486

Abstract

Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning [...] Read more.

Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning evaluation indicators for emerging distribution networks. First, based on an existing comprehensive indicator system, key factors of county-level distribution networks are identified to classify typical planning scenarios, and a preliminary scenario-oriented indicator system is established with expert knowledge. Second, data-driven techniques are employed for indicator selection. The maximum relevance and minimum redundancy (mRMR) method and the Random Forest (RF) algorithm are introduced to evaluate indicator relevance and importance, respectively, and a game-theoretic combination method with coefficient-of-variation (CV) correction is used for comprehensive screening. Finally, a county-level case study is conducted to validate the proposed method. The results show that the proposed method can adjust the planning evaluation indicator system according to changes in distribution network characteristics under different scenarios and performs well in the studied cases. This method provides a practical framework for constructing adaptive indicator systems for distribution network planning evaluation. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

35 pages, 4245 KB  

Open AccessArticle

Fair Cost Allocation Mechanism for Ramping Ancillary Services Based on Responsibility Coefficients

by Yuanhang Zhang and Xianshan Li

Electronics 2026, 15(9), 1891; https://doi.org/10.3390/electronics15091891 - 29 Apr 2026

Viewed by 563

Abstract

In the future market operation of new power systems, establishing a fair and reasonable cost allocation mechanism for ramping ancillary services is crucial. Such a mechanism would incentivize both the generation and load sides to reduce ramping demands. It will also promote the [...] Read more.

In the future market operation of new power systems, establishing a fair and reasonable cost allocation mechanism for ramping ancillary services is crucial. Such a mechanism would incentivize both the generation and load sides to reduce ramping demands. It will also promote the active participation of flexible resources across ramping services. However, the ramping ancillary service market currently piloted in Shandong, China, exhibits significant shortcomings. Net load volatility and uncertainty have increased the ramping service demand. Yet load-side Users, as beneficiaries, do not share the costs. Meanwhile, flexible units that wish to provide service still bear costs even if they fail to win bids. This violates the “who triggers, who pays” principle. To address this, this paper proposes a fair cost allocation mechanism based on ramping responsibility coefficients of market entities. First, a source-load ramping demand assessment model is developed. It quantifies both deterministic demand from net load variations and uncertain demand from net load forecast errors. Second, a two-layer cost allocation model is constructed using source-load ramping responsibility coefficients. In the first layer, system attribution is performed: initial allocation of ramping service costs is based on the responsibility share of each component—net load variations, load forecast deviations, and renewable energy forecast deviations—in total ramping demand. The second layer—responsibility-retrospective allocation—further assigns these costs by source: costs from net load change are allocated to power Users and renewable energy units; costs from load forecast errors and renewable forecast errors are assigned to the power Users and renewable energy units, respectively. For costs from renewable forecast errors, a differentiated allocation method is designed based on the deviation between declared and actual forecast errors. Case study results show that the proposed mechanism improves fairness and traceability in ramping cost allocation. It offers a market-based reference and supports the development of ramping ancillary service markets. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

26 pages, 2136 KB  

Open AccessArticle

Flexible Expansion and Deployment Architecture for Relay Protection Remote Maintenance Master Station Using Low-Code and Containerization Technologies

by Zebing Shi, Honghui Gao, Xiaoliang Chen, Jiang Yu, Yang Diao and Ze Zhao

Energies 2026, 19(9), 2113; https://doi.org/10.3390/en19092113 - 28 Apr 2026

Viewed by 577

Abstract

Traditional relay protection remote maintenance master stations are subject to tight coupling, limited scalability, and cumbersome deployment due to monolithic architectures. This paper proposes a flexible expansion system integrating low-code and containerization technologies. Key innovations include: (1) a domain-specific low-code component library with [...] Read more.

Traditional relay protection remote maintenance master stations are subject to tight coupling, limited scalability, and cumbersome deployment due to monolithic architectures. This paper proposes a flexible expansion system integrating low-code and containerization technologies. Key innovations include: (1) a domain-specific low-code component library with hard-coded core functions for performance, and (2) a collaborative CI/CD pipeline linking low-code development to containerized deployment. The system adopts a four-layer decoupled architecture. Engineering applications in a provincial power grid show that the system supports over 200,000 concurrent devices, improves operation efficiency by 60%, reduces manual configuration workload by 60%, and achieves 99.99% core service availability. This research provides a systematic solution for building scalable and agile intelligent maintenance systems under new power system paradigms. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

33 pages, 2948 KB  

Open AccessArticle

Bi-Level Optimal Scheduling for Bundled Operation of PSH with WP and PV Under Extreme High-Temperature Weather

by Wanji Ma, Hong Zhang, He Qiao and Dacheng Xing

Energies 2026, 19(9), 2048; https://doi.org/10.3390/en19092048 - 23 Apr 2026

Viewed by 370

Abstract

With the increasing occurrence of extreme high-temperature weather events, the traditional bundled operation of wind power (WP), photovoltaic power (PV), and pumped storage hydropower (PSH) is facing dual challenges, namely intensified renewable energy fluctuations and insufficient flexible regulation capability of PSH. Therefore, this [...] Read more.

With the increasing occurrence of extreme high-temperature weather events, the traditional bundled operation of wind power (WP), photovoltaic power (PV), and pumped storage hydropower (PSH) is facing dual challenges, namely intensified renewable energy fluctuations and insufficient flexible regulation capability of PSH. Therefore, this paper proposes an optimal scheduling strategy for bundled operation based on capacity interval matching of PSH with WP and PV under extreme high-temperature weather. First, typical scenarios are generated based on a Time-series Generative Adversarial Network (TimeGAN), and an interval matching transaction model is established based on the forecast intervals of WP and PV capacity and the corrected intervals of PSH capacity. Second, considering PSH as an independent market entity, a bi-level optimization model is constructed, in which the upper-level objective is to maximize the revenue of PSH, while the lower-level objective is to minimize the total cost of the joint clearing of the energy and ancillary service markets. Finally, simulation case studies verify that under extreme high-temperature weather, the proposed optimal scheduling method increases the bundled operation capacity by 17.9% and improves the revenue of PSH in the reserve ancillary service market by 14.8%, thereby effectively enhancing the economic performance of PSH while ensuring the safe and stable operation of the system. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

31 pages, 1340 KB  

Open AccessArticle

Dynamic Robust Generation and Transmission Expansion Planning Incorporating Novel Inter-Area Virtual Transmission Lines and Unit Commitment Ramping Constraints

by Flavio Arthur Leal Ferreira and Clodomiro Unsihuay Vila

Energies 2026, 19(7), 1759; https://doi.org/10.3390/en19071759 - 3 Apr 2026

Viewed by 974

Abstract

Generation and transmission expansion planning (GTEP) faces increasing challenges from variable renewable energy integration, inter-area transmission congestion, and the need for cost-effective flexibility. This study extends a prior data-driven distributionally robust optimization framework by introducing inter-area virtual transmission lines (VTL), enabled through strategic [...] Read more.

Generation and transmission expansion planning (GTEP) faces increasing challenges from variable renewable energy integration, inter-area transmission congestion, and the need for cost-effective flexibility. This study extends a prior data-driven distributionally robust optimization framework by introducing inter-area virtual transmission lines (VTL), enabled through strategic energy storage system (ESS) allocation within network areas, to optimize and potentially defer investments in trunk transmission lines, while adding a unit commitment (UC) level considering ramping constraints to address short-term net demand variability. The model incorporates flexibility from transmission and distribution system operators interconnection (TSO-DSO), quantified via a selected state-of-the-art metric integrated into ramping and flexibility constraints, with required levels derived from associated DSO planning. A linear AC optimal power flow is employed, and uncertainties in demand and variable renewable generation are handled using data-driven distributionally robust optimization within a three-level architecture: column-and-constraint generation with duality-free decomposition at the core, augmented by unit commitment. Case studies on the IEEE RTS-GMLC network demonstrate significant reductions in total system costs (operations, investments, and flexibility provisions), improved transmission efficiency, and enhanced flexibility metrics, confirming the value of localized ESS deployment and high-resolution ramping in modern low-carbon power systems. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

17 pages, 4915 KB  

Open AccessArticle

Optimising Substation Earthing Networks Considering Resistive Coupling with Metal Piping

by Chenglian Ma, Mengqing Song, Zhengduo Zhao, Jinhang Li and Li Sun

Electronics 2026, 15(6), 1257; https://doi.org/10.3390/electronics15061257 - 17 Mar 2026

Viewed by 551

Abstract

With the rapid transition toward modern power systems, ensuring the operational integrity of substation earthing networks has become a critical priority in infrastructure modernisation. This paper investigates the resistive coupling interference between substation earthing grids and adjacent underground metallic pipeline networks within the [...] Read more.

With the rapid transition toward modern power systems, ensuring the operational integrity of substation earthing networks has become a critical priority in infrastructure modernisation. This paper investigates the resistive coupling interference between substation earthing grids and adjacent underground metallic pipeline networks within the context of renovation projects. An integrated field–circuit coupling methodology, synergising CDEGS-based electromagnetic field analysis with ETAP-based circuit modelling, is proposed to quantify critical safety performance metrics. Simulation results demonstrate that resistive coupling induces significant fluctuations in key performance parameters, potentially compromising system safety during faults. Based on these findings, a suite of targeted optimisation strategies and protective measures is developed to ensure the stable operation of both the earthing system and the surrounding metallic infrastructure. This study provides a rigorous theoretical framework and practical technical guidance for the design and optimisation of substation earthing systems in complex electromagnetic environments. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

20 pages, 3102 KB  

Open AccessArticle

Hybrid CNN–GRU-Based Demand–Supply Forecasting to Enhance Sustainability in Renewable-Integrated Smart Grids

by Süleyman Emre Eyimaya and Necmi Altin

Sustainability 2026, 18(5), 2417; https://doi.org/10.3390/su18052417 - 2 Mar 2026

Cited by 1 | Viewed by 856

Abstract

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, [...] Read more.

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative evaluations demonstrate that the hybrid CNN–GRU outperforms single-model approaches, including Long Short-Term Memory (LSTM), GRU, and eXtreme Gradient Boosting (XGBoost), based on Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. On a 14-day test set, the proposed model achieves RMSE values of approximately 34 kW for demand and 28 kW for PV generation, with MAPE of approximately 4% and 6%, respectively. Furthermore, average net-load RMSE is reduced by approximately 15–25% relative to GRU/LSTM baselines, while maintaining controlled errors of approximately 35–40 kW during sharp ≥100 kW/15 min ramp events. By reducing net-load uncertainty and improving forecasting precision, the proposed framework enhances renewable energy utilization, supports more efficient reserve allocation and storage scheduling, and provides a quantitative tool for sustainability-oriented energy management. Consequently, the study contributes to the advancement of sustainable smart grid operation and the broader transition toward low-carbon and resilient energy systems. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

16 pages, 1323 KB  

Open AccessArticle

Coordinated Energy–Reserve Market Clearing and Pricing Mechanism for Regional Power Systems with High Wind Penetration

by Peng Zou, Xiaotao Luo, Xueting Cheng, Yizhao Liu, Jianbin Fan, Jian Le and Zheng Fang

Appl. Sci. 2026, 16(4), 2123; https://doi.org/10.3390/app16042123 - 22 Feb 2026

Cited by 1 | Viewed by 701

Abstract

Addressing the challenges of insufficient reserve capacity allocation and wind power uncertainty-induced security and economic concerns under high wind power penetration, this paper develops an integrated energy–reserve market clearing model for regional electricity markets. Firstly, a comprehensive day-ahead market clearing mechanism is designed, [...] Read more.

Addressing the challenges of insufficient reserve capacity allocation and wind power uncertainty-induced security and economic concerns under high wind power penetration, this paper develops an integrated energy–reserve market clearing model for regional electricity markets. Firstly, a comprehensive day-ahead market clearing mechanism is designed, encompassing market participant bidding, security-constrained unit commitment (SCUC), security-constrained economic dispatch (SCED), nodal marginal price calculation, and market settlement. Secondly, a SCUC model targeting the minimization of total system operating costs and a SCED model targeting the minimization of energy and reserve procurement costs are established, comprehensively incorporating constraints, such as power balance, unit output and ramping limits, reserve requirements, and network power flows, with nodal marginal prices calculated using the Lagrangian multiplier method. Finally, simulation verification is conducted using a modified IEEE 30-bus system as a case study. Results demonstrate that the proposed model effectively coordinates wind power integration with system reserve requirements, achieving economically optimal dispatch while ensuring grid security and stability. Thermal units obtain substantial market revenues by providing reserve ancillary services, while wind units achieve high revenues through zero marginal cost advantages, fully validating the model’s effectiveness and economic efficiency under high wind power penetration conditions. The research findings provide theoretical foundations and practical guidance for constructing electricity spot market mechanisms adapted to large-scale renewable energy integration. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

30 pages, 2950 KB  

Open AccessArticle

Hierarchical Optimization of Integrated RES-NG Provider Participating in Multi-Type Markets with Dynamic HCNG Pricing

by Chunyan Li, Mengdie Li, Renjie Lu, Kun Yang, Bo Hu, Changzheng Shao and Tao Wu

Appl. Sci. 2026, 16(4), 1874; https://doi.org/10.3390/app16041874 - 13 Feb 2026

Viewed by 495

Abstract

With the deployment of Hydrogen-enriched Compressed Natural Gas (HCNG) technology, establishing market mechanisms adapted to its physical characteristics is crucial for renewable energy accommodation. However, existing studies lack HCNG pricing mechanisms that reflect calorific value fluctuations and often overlook the dynamic carbon emission [...] Read more.

With the deployment of Hydrogen-enriched Compressed Natural Gas (HCNG) technology, establishing market mechanisms adapted to its physical characteristics is crucial for renewable energy accommodation. However, existing studies lack HCNG pricing mechanisms that reflect calorific value fluctuations and often overlook the dynamic carbon emission characteristics of Hydrogen Mixed Gas Turbines (HMGTs). To address these gaps, this paper proposes a hierarchical optimization framework for Integrated RES-NG Providers (IRNPs) participating in multi-type markets. In the upper level, a bidding model involving electricity, HCNG, hydrogen, and CEP-GEC joint markets is established. A dynamic HCNG pricing mechanism based on the Wobbe Index is introduced to capture composition variations, and a refined HMGT model based on the modified Arrhenius equation is employed to quantify combustion-emission physicochemical kinetics. The lower level formulates market clearing models for social welfare maximization, which are transformed into a Mathematical Program with Equilibrium Constraints (MPEC) via KKT conditions. Case studies demonstrate that: (1) the refined HMGT model captures the dynamic fluctuation of the carbon emission factor between 0.6074 and 0.6216, correcting the bias of traditional static models; (2) the introduction of the dynamic HCNG pricing mechanism significantly enhances flexibility, increasing the renewable energy accommodation rate from 90.47% to 100%; (3) IRNPs maximize profits through multi-market arbitrage, achieving a daily total revenue of ¥2.231 million, a 30.9% increase compared to participating only in electricity–gas markets; (4) The critical thresholds for cross-market arbitrage are identified, and hydrogen is diverted to the hydrogen market when prices exceed 9.6 ¥/kg and completely prioritized over HCNG blending when prices surpass 15.6 ¥/kg. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

15 pages, 1350 KB  

Open AccessArticle

Investigating Critical Parameters of Maritime Electricity Market

by Efstathios Fostiropoulos, John Prousalidis and Anastasios Manos

Energies 2026, 19(2), 542; https://doi.org/10.3390/en19020542 - 21 Jan 2026

Viewed by 766

Abstract

This paper discusses the economic factors that affect and, hence, must be investigated for the subsequent stage of onshore power supply (OPS) (or cold ironing) applications. In order to be considered a viable alternative to the conventional but pollutant marine gas oil, the [...] Read more.

This paper discusses the economic factors that affect and, hence, must be investigated for the subsequent stage of onshore power supply (OPS) (or cold ironing) applications. In order to be considered a viable alternative to the conventional but pollutant marine gas oil, the cost of electricity must be favorable when determining the optimal choice for vessels at the time of mooring. The financial burden of onshore power supply encompasses the expenses associated with its production, distribution, and the enhancement of port grids. Further research must be conducted on the potential for alternative approaches to recapture the authorized revenue for the DSO, along with the parameters that govern vessel types and the dimensions of ports equipped with operational cold ironing mechanisms. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

29 pages, 5092 KB  

Open AccessArticle

An Optimized Method for Setting Relay Protection in Distributed PV Distribution Networks Based on an Improved Osprey Algorithm

by Zhongduo Chen, Kai Gan, Tianyi Li, Weixing Ruan, Miaofeng Ye, Qingzhuo Xu, Jiaqi Pan, Yourong Li and Cheng Liu

Energies 2026, 19(1), 24; https://doi.org/10.3390/en19010024 - 19 Dec 2025

Cited by 3 | Viewed by 1154

Abstract

The high penetration of distributed photovoltaics (PV) into distribution networks alters the system’s short-circuit current characteristics, posing risks of maloperation and failure-to-operate to conventional inverse-time overcurrent protection. Based on an equivalent model of distributed PV during faults, this paper analyzes its impact on [...] Read more.

The high penetration of distributed photovoltaics (PV) into distribution networks alters the system’s short-circuit current characteristics, posing risks of maloperation and failure-to-operate to conventional inverse-time overcurrent protection. Based on an equivalent model of distributed PV during faults, this paper analyzes its impact on the protection characteristics of traditional distribution networks. With protection selectivity and the physical constraints of protection devices as conditions, an optimization model for inverse-time overcurrent protection is established, aiming to minimize the total operation time. To enhance the solution capability for this complex optimization problem, the standard Osprey Optimization Algorithm (OOA) is improved through the incorporation of three strategies: arccosine chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic spiral search strategy combining mechanisms from the Whale and Marine Predators algorithms. Based on this improved algorithm, an optimized protection scheme for distribution networks with distributed PV is proposed. Simulations conducted in PSCAD/EMTDC (V4.6.2) and MATLAB (R2023b) verify that the proposed method effectively prevents protection maloperation and failure-to-operate under both fault current contribution and extraction scenarios of PV, while also reducing the overall relay operation time. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

26 pages, 1274 KB  

Open AccessArticle

Fair Transmission Expansion Cost Allocation for Renewable Energy Resource Interconnection Based on Stochastic Cooperative Game Theory

by Youngjun Go, Wonseok Choi, Minsung Kim, Jin-Ho Chung, Hyeonjin Kim and Duehee Lee

Mathematics 2025, 13(24), 3898; https://doi.org/10.3390/math13243898 - 5 Dec 2025

Cited by 2 | Viewed by 895

Abstract

We propose a fair transmission expansion cost allocation (CA) algorithm and a fair process to build alternative transmission expansion plans. We define fairness such that each participant’s payment does not exceed its own benefit and the total payment equals the total TEP cost. [...] Read more.

We propose a fair transmission expansion cost allocation (CA) algorithm and a fair process to build alternative transmission expansion plans. We define fairness such that each participant’s payment does not exceed its own benefit and the total payment equals the total TEP cost. In our framework, excessive payments over generator benefits are minimized. Owners of renewable energy resources (RES)s can choose the point of interconnection via the CA algorithm; owners in the same interconnection queue may form an intermediate coalition to persuade owners of expensive bottleneck plans to change at reduced allocation cost. Fairness is implemented using stochastic cooperative game theory (SCGT); the fair CA is obtained by recursively minimizing the largest unfairness, which is the difference between payments and benefits, through coalitions. Benefits consider transmission usage, transmission-induced gains, and the variability of RESs and demand. We design spatially and temporally correlated RESs and demand scenarios using Gibbs sampling specialized for long-term interconnection studies, validate plausibility against a benchmark from the Global Probabilistic Mid-term Load Forecasting Competition 2017, and verify fairness by showing that entities with greater benefits pay larger costs. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

24 pages, 7569 KB  

Open AccessArticle

Multi-Scenario Investment Optimization in Pumped Storage Hydropower Using Enhanced Benders Decomposition and Isolation Forest

by Xu Ling, Ying Wang, Xiao Li, Bincheng Li, Fei Tang, Jinxiu Ding, Yixin Yu, Xiayu Jiang and Tingyu Zhou

Sustainability 2025, 17(23), 10657; https://doi.org/10.3390/su172310657 - 27 Nov 2025

Cited by 1 | Viewed by 875

Abstract

Under the global imperative for climate action and sustainable development, accelerating the transition towards high-penetration renewable energy systems remains a universal priority, central to achieving the United Nations Sustainable Development Goals. However, the inherent uncertainty and volatility of renewables such as wind and [...] Read more.

Under the global imperative for climate action and sustainable development, accelerating the transition towards high-penetration renewable energy systems remains a universal priority, central to achieving the United Nations Sustainable Development Goals. However, the inherent uncertainty and volatility of renewables such as wind and solar PV pose fundamental challenges to power system stability and flexibility worldwide. These challenges, if unaddressed, could significantly hinder the reliable and sustainable integration of clean energy on a global scale. While pumped storage hydropower (PSH) represents a mature, large-scale solution for enhancing system regulation capabilities, existing planning methodologies frequently suffer from critical limitations. These included oversimplified scenario representations—particularly the inadequate consideration of escalating extreme weather events under climate change—and computational inefficiencies in solving large-scale stochastic optimization models. These shortcomings ultimately constrained the practical value of such approaches for advancing sustainable energy planning and building climate-resilient power infrastructures globally. To address these issues, this paper proposed a bi-level stochastic planning method integrating scenario optimization and improved Benders decomposition. Specifically, an integrated framework combining affinity propagation clustering and isolation forest algorithms was developed to generate a comprehensive scenario set that covered both typical and anomalous operating days, thereby capturing a wider range of system uncertainties. A two-layer stochastic optimization model was established, aiming to minimize total investment and operational costs while ensuring system reliability and renewable integration. The upper layer determined PSH capacity, while the lower layer simulated multi-scenario system operations. To efficiently solve the model, the Benders decomposition algorithm was enhanced through the introduction of a heuristic feasible cut generation mechanism, which strengthened subproblem feasibility and accelerated convergence. Simulation results demonstrated that the proposed method achieved a 96.7% annual renewable energy integration rate and completely avoided load shedding events with minimal investment cost, verifying its effectiveness, economic efficiency, and enhanced adaptability to diverse operational scenarios. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

27 pages, 2660 KB  

Open AccessArticle

Game-Based Optimal Scheduling of the Integrated Energy Park, Aggregator, and Utility Considering Energy Supply Risk

by Yunni Zhang, Lu Nan and Ziqi Hu

Energies 2025, 18(23), 6204; https://doi.org/10.3390/en18236204 - 26 Nov 2025

Viewed by 691

Abstract

To address the issues of benefit coordination and energy supply risk management in energy trading between integrated energy parks and the main grid utility, this paper proposes a bi-level game-based optimal scheduling model for the electricity–heat–hydrogen integrated energy system considering energy supply risks. [...] Read more.

To address the issues of benefit coordination and energy supply risk management in energy trading between integrated energy parks and the main grid utility, this paper proposes a bi-level game-based optimal scheduling model for the electricity–heat–hydrogen integrated energy system considering energy supply risks. A bi-level game framework of the integrated energy park (IEP), aggregator, and utility is firstly built, where the aggregator acts as an intermediary coordination entity. The upper-level and lower-level game models, the trading strategies between the aggregator and the utility, as well as the trading strategies between the aggregator and the IEP, are, respectively, optimized after achieving the equilibrium. Furthermore, a conditional value-at-risk (CVaR)-based energy supply risk quantification model is introduced to characterize the operational risks caused by differences in traded energy quantities and then is incorporated into the proposed game-based optimal scheduling model. Finally, a bi-level game-based optimal scheduling model of the IEP, aggregator, and utility considering energy supply risk is proposed. Case studies demonstrate that the proposed model can effectively reduce the operating cost of the utility, reasonably allocate the benefit of the aggregator and the IEP, and can effectively balance energy supply risk and social welfare maximization of the electricity–heat–hydrogen integrated energy system. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

21 pages, 5716 KB  

Open AccessArticle

Optimal Placement and Cost Analysis of Electric Vehicle Charging Stations Using Metaheuristic Optimization

by Hamit Kürşat Demiryürek, Beytullah Bozali and Ali Öztürk

Appl. Sci. 2025, 15(21), 11729; https://doi.org/10.3390/app152111729 - 3 Nov 2025

Cited by 5 | Viewed by 1626

Abstract

The rapid adoption of electric vehicles (EVs) has made the strategic deployment of charging infrastructure a critical task for sustainable mobility. This study formulates the siting of EV charging stations as a p-median problem and applies two metaheuristic approaches—genetic algorithm (GA) and ant [...] Read more.

The rapid adoption of electric vehicles (EVs) has made the strategic deployment of charging infrastructure a critical task for sustainable mobility. This study formulates the siting of EV charging stations as a p-median problem and applies two metaheuristic approaches—genetic algorithm (GA) and ant colony optimization (ACO)—to solve it. The cost function, defined as the combination of transportation and installation costs, was analyzed in various scenarios. The results show that ACO consistently outperforms GA, offering lower total costs and shorter solution times. Crucially, the work uses optimization results published in the literature to expand the comparison beyond GA, using GA as a typical baseline. The suggested framework is adaptable and can be used to solve different spatial planning and facility location issues. This paper offers a data-driven, scientifically based approach for EV charging infrastructure development by combining cost effectiveness and service accessibility. In addition to providing decision-makers with useful tactics for creating dependable and sustainable charging networks, it helps handle the temporal and geographical coordination issues in EV charging. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

19 pages, 1761 KB  

Open AccessArticle

Multi-Objective Optimization Method for Flexible Distribution Networks with F-SOP Based on Fuzzy Chance Constraints

by Zheng Lan, Renyu Tan, Chunzhi Yang, Xi Peng and Ke Zhao

Sustainability 2025, 17(21), 9510; https://doi.org/10.3390/su17219510 - 25 Oct 2025

Cited by 2 | Viewed by 1075

Abstract

With the large-scale integration of single-phase distributed photovoltaic systems into distribution grids, issues such as mismatched generation and load, overvoltage, and three-phase imbalance may arise in the distribution network. A multi-objective optimization method for flexible distribution networks incorporating a four-leg soft open point [...] Read more.

With the large-scale integration of single-phase distributed photovoltaic systems into distribution grids, issues such as mismatched generation and load, overvoltage, and three-phase imbalance may arise in the distribution network. A multi-objective optimization method for flexible distribution networks incorporating a four-leg soft open point (F-SOP) is proposed based on fuzzy chance constraints. First, a mathematical model for the F-SOP’s loss characteristics and power control was established based on the three-phase four-arm topology. Considering the impact of source load uncertainty on voltage regulation, a multi-objective complementary voltage regulation architecture is proposed based on fuzzy chance constraint programming. This architecture integrates F-SOP with conventional reactive power compensation devices. Next, a multi-objective collaborative optimization model for distribution networks is constructed, with network losses, overall voltage deviation, and three-phase imbalance as objective functions. The proposed model is linearized using second-order cone programming. Finally, using an improved IEEE 33-node distribution network as a case study, the effectiveness of the proposed method was analyzed and validated. The results indicate that this method can reduce network losses by 30.17%, decrease voltage deviation by 46.32%, and lower three-phase imbalance by 57.86%. This method holds significant importance for the sustainable development of distribution networks. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

23 pages, 2742 KB  

Open AccessArticle

Optimal Bidding Framework for Integrated Renewable-Storage Plant in High-Dimensional Real-Time Markets

by Yuhao Song, Shaowei Huang, Laijun Chen, Sen Cui and Shengwei Mei

Sustainability 2025, 17(18), 8159; https://doi.org/10.3390/su17188159 - 10 Sep 2025

Cited by 1 | Viewed by 1159

Abstract

With the development of electricity spot markets, the integrated renewable-storage plant (IRSP) has emerged as a crucial entity in real-time energy markets due to its flexible regulation capability. However, traditional methods face computational inefficiency in high-dimensional bidding scenarios caused by expansive decision spaces, [...] Read more.

With the development of electricity spot markets, the integrated renewable-storage plant (IRSP) has emerged as a crucial entity in real-time energy markets due to its flexible regulation capability. However, traditional methods face computational inefficiency in high-dimensional bidding scenarios caused by expansive decision spaces, limiting online generation of multi-segment optimal quotation curves. This paper proposes a policy migration-based optimization framework for high-dimensional IRSP bidding: First, a real-time market clearing model with IRSP participation and an operational constraint-integrated bidding model are established. Second, we rigorously prove the monotonic mapping relationship between the cleared output and the real-time locational marginal price (LMP) under the market clearing condition and establish mathematical foundations for migrating the self-dispatch policy to the quotation curve based on value function concavity theory. Finally, a generalized inverse construction method is proposed to decompose the high-dimensional quotation curve optimization into optimal power response subproblems within price parameter space, substantially reducing decision space dimensionality. The case study validates the framework effectiveness through performance evaluation of policy migration for a wind-dual energy storage plant, demonstrating that the proposed method achieves 90% of the ideal revenue with a 5% prediction error and enables reinforcement learning algorithms to increase their performance from 65.1% to 84.2% of the optimal revenue. The research provides theoretical support for resolving the “dimensionality–efficiency–revenue” dilemma in high-dimensional bidding and expands policy possibilities for IRSP participation in real-time markets. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

18 pages, 5778 KB  

Open AccessArticle

Hierarchical Switching Control Strategy for Smart Power-Exchange Station in Honeycomb Distribution Network

by Xiangkun Meng, Wenyao Sun, Yi Zhao, Xiaoyi Qian and Yan Zhang

Sustainability 2025, 17(17), 7998; https://doi.org/10.3390/su17177998 - 5 Sep 2025

Viewed by 1494

Abstract

The Honeycomb Distribution Network is a new distribution network architecture that utilizes the Smart Power-Exchange Station (SPES) to enable power interconnection and mutual assistance among multiple microgrids/distribution units, thereby supporting high-proportion integration of distributed renewable energy and promoting a sustainable energy transition. To [...] Read more.

The Honeycomb Distribution Network is a new distribution network architecture that utilizes the Smart Power-Exchange Station (SPES) to enable power interconnection and mutual assistance among multiple microgrids/distribution units, thereby supporting high-proportion integration of distributed renewable energy and promoting a sustainable energy transition. To promote the continuous and reliable operation of the Honeycomb Distribution Network, this paper proposes a Hierarchical Switching Control Strategy to address the issues of DC bus voltage (U_dc) fluctuation in the SPES of the Honeycomb Distribution Network, as well as the state of charge (SOC) and charging/discharging power limitation of the energy storage module (ESM). The strategy consists of the system decision-making layer and the converter control layer. The system decision-making layer selects the main converter through the importance degree of each distribution unit and determines the control strategy of each converter through the operation state of the ESM’s SOC. The converter control layer restricts the ESM’s input/output active power—this ensures the ESM’s SOC and input/output active power stay within the power boundary. Additionally, it combines the Flexible Virtual Inertia Adaptive (FVIA) control method to suppress U_dc fluctuations and improve the response speed of the ESM converter’s input/output active power. A simulation model built in MATLAB/Simulink is used to verify the proposed control strategy, and the results demonstrate that the strategy can not only effectively reduce U_dc deviation and make the ESM’s input/output power reach the stable value faster, but also effectively avoid the ESM entering the unstable operation area. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

23 pages, 4283 KB  

Open AccessArticle

Charging Incentive Design with Minimum Price Guarantee for Battery Energy Storage Systems to Mitigate Grid Congestion

by Yujiro Tanno, Akihisa Kaneko, Yu Fujimoto, Yasuhiro Hayashi, Yuji Hanai and Hideo Koseki

Energies 2025, 18(11), 2840; https://doi.org/10.3390/en18112840 - 29 May 2025

Viewed by 1411

Abstract

The large-scale integration of renewable energy sources (RESs) has raised concerns regarding grid congestion in Japan. Battery energy storage systems (BESSs) can mitigate congestion by adjusting charging schedules; however, BESS owners basically prioritize market arbitrage, which may not be aligned with congestion mitigation. [...] Read more.

The large-scale integration of renewable energy sources (RESs) has raised concerns regarding grid congestion in Japan. Battery energy storage systems (BESSs) can mitigate congestion by adjusting charging schedules; however, BESS owners basically prioritize market arbitrage, which may not be aligned with congestion mitigation. This paper proposes a charging incentive design to guide arbitrage-oriented BESS charging toward time periods that are effective for grid congestion mitigation. The system operator predicts congested hours and ensures that BESS owners can purchase electricity at the lowest daily market price. This design intends to shift the BESS charging time towards congestion periods. Because market prices tend to decline during congestion periods, the proposed method reduces the operator’s financial burden while encouraging congestion-mitigating charging behavior. Numerical simulations using a simplified Japanese east-side power system model demonstrate that the proposed method reduced the congestion mitigation costs by 3.86% and curtailed the RES output by 3.89%, compared to using no incentive method (current operation in Japan). Furthermore, additional payments to BESS owners accounted for only around 7% of the resulting cost savings, indicating that the proposed method achieved lower overall system operating costs. Full article

(This article belongs to the Topic Advances in Planning, Operation, Control/Protection, and Market of New Power Energy System)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-23