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21 pages, 1337 KiB

Open AccessArticle

Cost Prediction for Power Transmission and Transformation Projects in High-Altitude Regions Based on a Hybrid Deep-Learning Algorithm

by Shasha Peng, Ya Zuo, Xiangping Li, Mingrui Zhao and Bingkang Li

Processes 2025, 13(7), 2092; https://doi.org/10.3390/pr13072092 - 1 Jul 2025

Abstract

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on [...] Read more.

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on cost predictions for power transmission and transformation projects in high-altitude regions, this paper first constructs a four-dimensional influencing factor system covering climate and environment, engineering scale, material consumption, and technological economy. On this basis, a hybrid deep-learning model combining an improved whale optimization algorithm (IWOA) and a convolutional neural network (CNN) is then proposed. The model improves the training accuracy of CNNs and avoids falling into local optima through the use of an SGDM optimizer, the L2 regularization method, and the Bayesian optimization method. Nonlinear convergence factors and adaptive weights are introduced to enhance the WOA’s ability to optimize the CNN’s learning rate. The case analysis results show that, compared with the comparison model, the proposed IWOA-CNN model exhibits a better convergence performance and fitting effect in the training set and a better prediction effect on the test set. Its mean absolute percentage error is as low as 1.51%, which is 10.1% lower than the optimal comparison model. The root mean square error is reduced to 5.07, and the sum of squared errors is reduced by 72.4%, demonstrating high prediction accuracy. The comparative analysis of scenarios further confirms the crucial role of climate environment; that is, the prediction accuracy of models containing a climate dimension is improved by 51.6% compared to models without such a climate dimension, indicating that the nonlinear impact of low temperatures, frozen soil, and other characteristics of high-altitude regions on costs cannot be ignored. The research results of this paper enrich the method system and application scenarios for the cost prediction for power transmission and transformation projects and provide theoretical reference for engineering predictions in other complex geographical environments. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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25 pages, 2074 KiB

Open AccessArticle

Optimal Operation of a Two-Level Game for Community Integrated Energy Systems Considering Integrated Demand Response and Carbon Trading

by Jing Fu, Li Gong, Yuchen Wei, Qi Zhang and Xin Zou

Processes 2025, 13(7), 2091; https://doi.org/10.3390/pr13072091 - 1 Jul 2025

Abstract

In light of the current challenges posed by complex multi-agent interactions and competing interests in integrated energy systems, an economic optimization operation model is proposed. This model is based on a two-layer game comprising a one-master–many-slave structure consisting of an energy retailer, energy [...] Read more.

In light of the current challenges posed by complex multi-agent interactions and competing interests in integrated energy systems, an economic optimization operation model is proposed. This model is based on a two-layer game comprising a one-master–many-slave structure consisting of an energy retailer, energy suppliers, and a user aggregator. Additionally, it considers energy suppliers to be engaged in a non-cooperative game. The model also incorporates a carbon trading mechanism between the energy retailer and energy suppliers, considers integrated demand response at the user level, and categorizes users in the community according to their energy use characteristics. Finally, the improved differential evolutionary algorithm combined with the CPLEX solver (v12.6) is used to solve the proposed model. The effectiveness of the proposed model in enhancing the benefits of each agent as well as reducing carbon emissions is verified through example analyses. The results demonstrate that the implementation of non-cooperative game strategies among ESs can enhance the profitability of ES1 and ES2 by 27.83% and 18.67%, respectively. Furthermore, the implementation of user classification can enhance user-level benefits by up to 39.51%. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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28 pages, 6211 KiB

Open AccessArticle

Uncertainty-Based System Flexibility Evaluation and Multi-Objective Collaborative Optimization of Integrated Energy System

by Yu Fu, Qie Sun, Ronald Wennersten, Xueyue Pang and Weixiong Liu

Processes 2025, 13(7), 2047; https://doi.org/10.3390/pr13072047 - 27 Jun 2025

Viewed by 116

Abstract

With the advancement of integrated energy systems (IES) and the increasing penetration of variable renewable energy, IES confronts complex uncertainties that necessitate enhanced flexibility. Therefore, this study focuses on improving IES flexibility. To this end, multi-dimensional flexibility evaluation indexes for the “Source–Structure–Demand” dimensions [...] Read more.

With the advancement of integrated energy systems (IES) and the increasing penetration of variable renewable energy, IES confronts complex uncertainties that necessitate enhanced flexibility. Therefore, this study focuses on improving IES flexibility. To this end, multi-dimensional flexibility evaluation indexes for the “Source–Structure–Demand” dimensions were established, and a multi-objective optimization model considering flexibility and source–demand side uncertainties was developed. The flexibility evaluation indexes include the Grid Dependency Level (GDL) for the source side, Insufficient Flexible Resource Probability (IFRP) for the structure side, and Loss of Load Probability (LOLP) for the demand side. Moreover, considering the distinct adjustment response times and inertia of different energy flows during IES operation, thermal and electrical energy are optimized on separate time scales. Thus, the multi-objective optimization constitutes a multi-time scale, high-dimensional, non-convex nonlinear model targeting economy, flexibility, security, and low carbon emissions. This paper employs single-economy objective, single-flexibility objective, and multi-objective optimization to analyze IES configuration, operation, risk, carbon emissions, and flexibility. The results indicate that poor flexibility leads to high operational risk, while excessive pursuit of flexibility incurs high costs and destabilizes operations. By implementing this multi-objective optimization, IES flexibility is enhanced while ensuring system economic performance. It also addresses the flexibility deficiency in traditional single-economy objective optimizations. Additionally, the system increases the renewable energy absorption rate by approximately 10%. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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14 pages, 584 KiB

Open AccessArticle

Carbon Emission Accounting and Identifying Influencing Factors of UHV Project Based on Material List

by Huijuan Huo, Gang Dan, Peidong Li, Shuo Wang, Xin Qie, Yaqi Sun, Cheng Xin and Tianqiong Chen

Processes 2025, 13(7), 2007; https://doi.org/10.3390/pr13072007 - 25 Jun 2025

Viewed by 232

Abstract

China’s UHV power grid, a core “new infrastructure” initiative, is vital for its next-generation power systems. This study quantifies UHV project carbon emissions using a carbon source inventory system, identifies key drivers via Random forest regression (RFR) and SHAP interpretable ML models, and [...] Read more.

China’s UHV power grid, a core “new infrastructure” initiative, is vital for its next-generation power systems. This study quantifies UHV project carbon emissions using a carbon source inventory system, identifies key drivers via Random forest regression (RFR) and SHAP interpretable ML models, and validates findings with a 1000 kV UHV AC project in southwest China. Results highlight material production (97% emissions) and construction phases (3%) as primary carbon sources. The proposed solutions are as follows: ① green materials (low-carbon concrete) and modular construction; ② digital tools for optimized project management. These strategies enable emission reductions while supporting China’s carbon neutrality goals. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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24 pages, 1808 KiB

Open AccessArticle

Research on the Low-Carbon Economic Operation Optimization of Virtual Power Plant Clusters Considering the Interaction Between Electricity and Carbon

by Ting Pan, Qiao Zhao, Jiangyan Zhao and Liying Wang

Processes 2025, 13(6), 1943; https://doi.org/10.3390/pr13061943 - 19 Jun 2025

Viewed by 218

Abstract

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource [...] Read more.

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource characteristics of different virtual power plants (VPPs) within a cooperative alliance, we propose a multi-VPP interaction and sharing architecture accounting for electricity–carbon interaction. An optimization model for VPPC is developed based on the asymmetric Nash bargaining theory. Finally, the proposed model is solved using an alternating-direction method of multipliers (ADMM) algorithm featuring an improved penalty factor. The research results show that P2P trading within the VPPC achieves resource optimization and allocation at a larger scale. The proposed distributed ADMM solution algorithm requires only the exchange of traded electricity volume and price among VPPs, thus preserving user privacy. Compared with independent operation, the total operation cost of the VPPC is reduced by 20.37%, and the overall proportion of new energy consumption is increased by 16.83%. The operation costs of the three VPPs are reduced by 1.12%, 20.51%, and 6.42%, respectively, while their carbon emissions are decreased by 4.47%, 5.80%, and 5.47%, respectively. In addition, the bargaining index incorporated in the proposed (point-to-point) P2P trading mechanism motivates each VPP to enhance its contribution to the alliance to achieve higher bargaining power, thereby improving the resource allocation efficiency of the entire alliance. The ADMM algorithm based on the improved penalty factor demonstrates good computational performance and achieves a solution speed increase of 15.8% compared to the unimproved version. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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28 pages, 3451 KiB

Open AccessArticle

Scheduling Optimization of the Thermoelectric Coupling Virtual Power Plant with Carbon Capture System Under the Energy-Side and Load-Side Dual Response Mechanism

by Ting Pan, Qiao Zhao, Yuqing Wang and Ruining Cai

Processes 2025, 13(6), 1777; https://doi.org/10.3390/pr13061777 - 4 Jun 2025

Viewed by 337

Abstract

To promote low-carbon transformation and achieve carbon peak and neutrality in the energy field, this study proposes an operational optimization model considering the energy- and load-side dual response (ELDR) mechanism for electrothermal coupled virtual power plants (VPPs) containing a carbon capture device. The [...] Read more.

To promote low-carbon transformation and achieve carbon peak and neutrality in the energy field, this study proposes an operational optimization model considering the energy- and load-side dual response (ELDR) mechanism for electrothermal coupled virtual power plants (VPPs) containing a carbon capture device. The organic Rankine cycle (ORC) waste heat boiler (WHB) is introduced on the energy side. The integrated demand response (IDR) of electricity and heat is performed on the load side based on comprehensive user satisfaction (CUS), and the carbon capture system (CCS) is used as a flexible resource. Additionally, a carbon capture device operation mode that makes full use of new energy and the valley power of the power grid is proposed. To minimize the total cost, an optimal scheduling model of virtual power plants under ladder-type carbon trading is constructed, and opportunity-constrained planning based on sequence operation is used to address the uncertainty problems of new energy output and load demand. The results show that the application of the ELDR mechanism can save 27.46% of the total operating cost and reduce CO₂ emissions by 45.28%, which effectively improves the economy and low carbon of VPPs. In particular, the application of a CCS in VPPs contributes to reducing the carbon footprint of the system. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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20 pages, 1490 KiB

Open AccessArticle

Research on Industry–Economy–Energy–Carbon Emission Relationships Based on Panel Vector Autoregressive Modeling

by Qi He, Qiheng Yuan, Xiang Chen, Peng Jiang, Yongli Wang and Yuyang Li

Processes 2025, 13(4), 1107; https://doi.org/10.3390/pr13041107 - 7 Apr 2025

Viewed by 424

Abstract

This study focused on the dynamic relationships among industrial development, energy consumption, economic growth, and carbon emissions in China, with the goal of achieving long-term ecological sustainability. Using the Panel Vector Autoregressive (PVAR) model and Generalized Method of Moments (GMM) estimation, panel data [...] Read more.

This study focused on the dynamic relationships among industrial development, energy consumption, economic growth, and carbon emissions in China, with the goal of achieving long-term ecological sustainability. Using the Panel Vector Autoregressive (PVAR) model and Generalized Method of Moments (GMM) estimation, panel data from 30 Chinese provinces between 2017 and 2021 were analyzed. The impulse response analysis and variance decomposition demonstrated that industrial and economic subsystems significantly influenced carbon emissions, while the energy subsystem had a moderating effect. These results highlight a shift in China’s energy consumption structure, with industrial and economic activities driving carbon emissions, while energy consumption patterns slowed the increase in emissions. These findings have critical implications for understanding the interactions among industry, economy, energy, and carbon emissions. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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22 pages, 4990 KiB

Open AccessArticle

Modeling the Tripartite Coupling Dynamics of Electricity–Carbon–Renewable Certificate Markets: A System Dynamics Approach

by Zhangrong Pan, Yuexin Wang, Junhong Guo, Xiaoxuan Zhang, Song Xue, Wei Li, Zhuo Chen and Zhenlu Liu

Processes 2025, 13(3), 868; https://doi.org/10.3390/pr13030868 - 15 Mar 2025

Viewed by 625

Abstract

To ensure a smooth transition towards peak carbon emissions and carbon neutrality, one key strategy is to promote a low-carbon transition in the energy sector by facilitating the coordinated development of the electricity market, carbon market, and other markets. Currently, China’s national carbon [...] Read more.

To ensure a smooth transition towards peak carbon emissions and carbon neutrality, one key strategy is to promote a low-carbon transition in the energy sector by facilitating the coordinated development of the electricity market, carbon market, and other markets. Currently, China’s national carbon market primarily focuses on the power generation industry. High-energy-consuming industries such as the steel industry not only participate in the electricity market but also play a significant role in China’s future carbon market. Despite existing research on market mechanisms, there remains a significant research gap in understanding how steel enterprises adjust their trading behaviors to optimize costs in multi-market coupling contexts. This study employs a system dynamics approach to model the trading interconnection between electricity trading (ET), carbon emission trading (CET), and tradable green certificates (TGC). Within this multi-market system, thermal power enterprises and renewable generators serve as suppliers of carbon allowances and green certificates, respectively, while steel companies must meet both carbon emission constraints and renewable energy consumption obligations. The results show that companies can reduce future market transaction costs by increasing the proportion of medium to long-term electricity contracts and the purchase ratio of green electricity. Additionally, a lower proportion of free quotas leads to increased costs in the carbon market transactions in later stages. Therefore, it is beneficial for steel companies to conduct cost analyses of their participation in multivariate market transactions in the long run and adapt to market changes in advance and formulate rational market trading strategies. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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22 pages, 1200 KiB

Open AccessArticle

An Interphase Short-Circuit Fault Location Method for Distribution Networks Considering Topological Flexibility

by Hua Xie, Zhe Liu, Kai Li, Qifang Chen, Chao Yang and Tong Li

Processes 2025, 13(3), 782; https://doi.org/10.3390/pr13030782 - 7 Mar 2025

Viewed by 671

Abstract

The location of faults in distribution networks represents a crucial line of defence, ensuring the safe and reliable operation of these networks. This paper puts forth a methodology for the location of short-circuit faults between phases within the context of a distribution network [...] Read more.

The location of faults in distribution networks represents a crucial line of defence, ensuring the safe and reliable operation of these networks. This paper puts forth a methodology for the location of short-circuit faults between phases within the context of a distribution network information physics system. Firstly, a distribution network topology identification model is constructed, and a switching function based on the characteristics of an interphase short-circuit fault current is constructed to form a physical layer interphase short-circuit preconceived fault set. Subsequently, methodologies for processing information perturbations, including distortion, delay, and failure, are proposed. Fault current information is then extracted to form an information layer fault current array. Ultimately, a similarity function is constructed to correlate the fault characteristics of the physical and information layers. This is achieved through the utilization of the variational bee colony algorithm, which is employed to address the aforementioned issue. The efficacy and suitability of the proposed methodology are assessed in the context of single-point and multi-point faults, dynamic topology alterations, and information perturbations in distribution networks. To this end, a real-world project in Hebei and the IEEE system are employed as illustrative examples. The methodology proposed in this paper can facilitate the rapid and precise location of phase-to-phase short-circuits in physical information systems of distribution networks, thereby enhancing the reliability of power supply in new intelligent distribution networks. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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33 pages, 4793 KiB

Open AccessArticle

Designing a Clearing Model for the Regional Electricity Spot Market Based on the Construction of the Provincial Electricity Market: A Case Study of the Yangtze River Delta Regional Electricity Market in China

by Yunjian Li, Lizi Zhang, Ye Cong, Haoxuan Chen and Fuao Zhang

Processes 2025, 13(2), 492; https://doi.org/10.3390/pr13020492 - 10 Feb 2025

Viewed by 784

Abstract

Building the regional electricity spot market (RESM) in a representative area is an important move to promote the electricity market reform and new power system construction in China. In this paper, the RESM operation model and optimization method are established, which take into [...] Read more.

Building the regional electricity spot market (RESM) in a representative area is an important move to promote the electricity market reform and new power system construction in China. In this paper, the RESM operation model and optimization method are established, which take into account the special power grid operation mechanism and market construction achievements in the provincial electricity spot market. Firstly, the influencing factors, core elements, market structure, and operation model of RESM construction in China are analyzed. Secondly, a bi-level optimization model of the RESM is established. The lower layer is the pre-clearing model of the provincial electricity spot market, which is used to optimize the unit combination strategy, considering unit operation constraints and power grid security constraints in the province. The upper layer is the optimization clearing model of the RESM, which is used to optimize the clearing price and adjust the unit operation strategy and inter-provincial electricity trading strategy, considering the security constraints of regional power grid tie lines. Finally, the RESM composed of power grids in the Yangtze River Delta region of China is simulated as an example. The analysis focuses on the operational state of the power grid after the operation of the RESM, considering its safety benefits, economic benefits, and environmental benefits. The optimization of the RESM can effectively solve the serious regional power grid congestion problem, which is achieved through the superposition and printing of pre-clearing results in various provinces, and the average daily cost of electricity purchasing in the region has been reduced by about CNY 11 million, while the annual cost has been reduced by about CNY 4 billion. In addition, the total carbon emissions have been reduced by 11,000 tons per day and 0.18 kg per kilowatt hour on average, and scenes without power abandonment account for more than 95% of the total scenes. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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21 pages, 2727 KiB

Open AccessArticle

Technical and Economic Analysis of a Novel Integrated Energy System with Waste Tire Pyrolysis and Biogas

by Cheng Xin, Jun Liu, Tianqiong Chen, Heng Chen, Huijuan Huo, Shuo Wang and Yudong Wang

Processes 2025, 13(2), 415; https://doi.org/10.3390/pr13020415 - 5 Feb 2025

Cited by 2 | Viewed by 1179

Abstract

To reduce dependence on fossil fuels, cope with the growing energy demand, and reduce greenhouse gas emissions, this paper innovatively designs a novel integrated energy system integrating anaerobic digestion of animal manure, fuel cell technology, gas turbine, and tire pyrolysis. The system maximizes [...] Read more.

To reduce dependence on fossil fuels, cope with the growing energy demand, and reduce greenhouse gas emissions, this paper innovatively designs a novel integrated energy system integrating anaerobic digestion of animal manure, fuel cell technology, gas turbine, and tire pyrolysis. The system maximizes the energy potential of biogas while synergistically treating waste tires, improving waste management’s flexibility, efficiency, and economic viability through multiple outputs such as electricity and by-products, subsystem synergies, equipment sharing, and economies of scale. Thermodynamic performance and economic feasibility are analyzed using Aspen Plus V14 simulation modeling, ensuring the system’s technical and economic viability. In this study, the simulation model of the system is established, and the techno-economic benefits of the system are analyzed. The simulation results show that the net electric power output of the system is 444.79 kW. Combined with the contribution of pyrolysis products, the system’s total efficiency reaches 70.88%. In only 4.79 years, the initial investment can be recovered, and in its 25-year service life, the system has realized a profit of 2,939,130 USD. The system realizes the energy and quality matching between different thermal processes through indirect collaborative treatment of different solid wastes, improves the conversion efficiency of biogas energy, co-treats waste tires, and reduces environmental pollution. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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32 pages, 3435 KiB

Open AccessArticle

Operation Optimization Model of Regional Power Grid Considering Congestion Management and Security Check in Complex Market Operation Environment

by Yunjian Li, Lizi Zhang, Ye Cong, Haoxuan Chen and Fuao Zhang

Processes 2025, 13(2), 336; https://doi.org/10.3390/pr13020336 - 25 Jan 2025

Cited by 3 | Viewed by 764

Abstract

Security checks are essential for ensuring the safe operation of the regional power grid (RPG) and the smooth functioning of the electricity spot market (ESM). Currently, China’s RPG operating environment encompasses a complex mix of centralized ESM, decentralized ESM, and planned power generation. [...] Read more.

Security checks are essential for ensuring the safe operation of the regional power grid (RPG) and the smooth functioning of the electricity spot market (ESM). Currently, China’s RPG operating environment encompasses a complex mix of centralized ESM, decentralized ESM, and planned power generation. This complexity has led to increasingly severe RPG congestion issues. To address this, this paper introduces a security check mechanism design and operational optimization approach tailored for RPGs in complex markets, with a focus on congestion management. Firstly, the paper elaborates on the practical foundations, unique constraints, and requirements for security checks and congestion management during the RPG’s operational mode transitions. Secondly, it outlines the principles underlying the security check mechanism and presents a framework for RPG security checks and congestion management. Through a comparative analysis of three different programs, including their advantages, disadvantages, and applicable scenarios, the paper provides an optimal program recommendation. Building on this, the paper develops an operational optimization method that incorporates congestion management for each of the three security check and congestion management programs. Lastly, an IEEE-39 node test system is simulated to validate the effectiveness of the proposed programs. The mechanism and simulation analysis results show that Program 3, based on market mechanisms, has theoretical and practical advantages over Program 1 (based on multiple adjustments) and Program 2 (based on dispatch plans) for congestion management. Under the same line congestion situation, Program 1 requires two adjustments to relieve the line congestion, while Program 2 and Program 3 can solve the problem with just one optimization adjustment, and the congestion management effect of Program 3 is more obvious and superior. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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25 pages, 3976 KiB

Open AccessArticle

Research on Multi-Scale Electricity–Carbon–Green Certificate Market Coupling Trading Based on System Dynamics

by Tiannan Ma, Lilin Peng, Gang Wu, Yuchen Wei and Xin Zou

Processes 2025, 13(1), 109; https://doi.org/10.3390/pr13010109 - 3 Jan 2025

Cited by 1 | Viewed by 1312

Abstract

While tradable green certificates (TGCs) and carbon emission trading (CET) play key roles in achieving peak carbon and carbon neutrality, the coupling effects between these two policies on the medium- and long-term electricity market and the spot market are still uncertain. In this [...] Read more.

While tradable green certificates (TGCs) and carbon emission trading (CET) play key roles in achieving peak carbon and carbon neutrality, the coupling effects between these two policies on the medium- and long-term electricity market and the spot market are still uncertain. In this study, we firstly construct a multi-scale market trading framework to sort out the information transfer of four markets. Secondly, we establish a multi-scale market system dynamics-coupled trading model with five sub-modules, including the medium- and long-term power markets, the spot market, and the carbon market. Subsequently, we adjust the policy parameters (carbon quota benchmark price, carbon quota auction ratio, and renewable energy quota ratio) and set up five policy scenarios to compare and analyze the impacts of the CET and TGC mechanisms on the power market and carbon emission reduction when they act alone or in synergy, in order to provide a theoretical basis for the adjustment of strategies of market entities and the setting of parameters. The results show that CET can increase spot electricity prices and promote renewable energy to enter the spot market, while TGCs can promote a high proportion of renewable energy consumption but lower spot electricity prices for a long time. The coordinated implementation of the CET and TGC mechanisms can improve the power market’s adaptability to high renewable energy penetration, but it may also result in policy redundancy. Full article

(This article belongs to the Special Issue Evaluation, Decision–Making and Market Simulation of a New Type of Power System)

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