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24 pages, 2782 KB

Open AccessArticle

Optimization of Electricity–Carbon Coordinated Scheduling Process for Virtual Power Plants Based on an Improved Snow Ablation Optimizer Algorithm

by Haiji Wang, Ming Zeng, Xueying Lu, Zhijian Chen and Jiankun Hu

Processes 2025, 13(9), 3027; https://doi.org/10.3390/pr13093027 - 22 Sep 2025

Viewed by 275

Abstract

Given the strong coupling between electricity flow and carbon flow, promoting the low-carbon transformation of the energy sector is a crucial measure to actively responding to climate challenges. As a pivotal hub linking the electricity market with the carbon market, promoting electricity–carbon coordinated [...] Read more.

Given the strong coupling between electricity flow and carbon flow, promoting the low-carbon transformation of the energy sector is a crucial measure to actively responding to climate challenges. As a pivotal hub linking the electricity market with the carbon market, promoting electricity–carbon coordinated scheduling of Virtual Power Plants (VPPs) is of great significance in expediting the energy transition process. Based on the introduction of carbon potential, this manuscript constructs a VPP electricity–carbon coordinated scheduling model that incorporates various typical elements, including renewable energy units and demand response. Furthermore, this paper utilizes Brain Storm Optimization (BSO) to improve the Snow Ablation Optimizer (SAO) algorithm and applies the improved algorithm to solve the model developed in this manuscript. Finally, an analysis was conducted using a small-scale VPP project in eastern China, and the results are the following: Firstly, the SAO improved by BSO demonstrates a significant enhancement in solution efficiency. In particular, for the cases presented in this manuscript, the algorithm’s convergence speed increased by 42.85%. Secondly, under the multi-market conditions and with real-time carbon potential, VPPs will possess greater flexibility in scheduling optimization and stronger incentives to fully explore their emission reduction potential through collaborative electricity–carbon scheduling, thereby improving both economic and environmental performance. However, constrained by factors such as the currently low carbon price level, the extent of improvement in VPPs’ performance under real-time carbon potential, compared to fixed carbon potential, remains relatively limited, with a 1.07% increase in economic benefits and a 2.63% reduction in carbon emissions. Thirdly, an increase in carbon prices can incentivize VPPs to continuously tap into their emission reduction potential, but beyond a certain threshold (120 CNY/t in this case study), the marginal contribution of further carbon price increases to emission reductions will progressively decline. Specifically, for every 20-yuan increase in the carbon price, the carbon emission reduction rate of VPPs drops below 1%. 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, 4073 KB

Open AccessArticle

Modeling the Carbon–Electricity Coupled System with Multi-Entity Participation Under Carbon Emission Trading Market Coverage Expansion: A System Dynamics Approach

by Guiyuan Xue, Wenjuan Niu, Zheng Xu, Xiaojun Zhu, Yin Wu and Chen Chen

Processes 2025, 13(9), 2969; https://doi.org/10.3390/pr13092969 - 18 Sep 2025

Viewed by 385

Abstract

China’s carbon emission trading market (CETM), initially covering only power generators, is expanding to include key carbon emitters, like steel and cement enterprises. These high-energy-consuming industries also participate in the electricity market as major consumers. Current research lacks a systemic analysis of multi-market, [...] Read more.

China’s carbon emission trading market (CETM), initially covering only power generators, is expanding to include key carbon emitters, like steel and cement enterprises. These high-energy-consuming industries also participate in the electricity market as major consumers. Current research lacks a systemic analysis of multi-market, multi-entity coupling under CETM coverage expansion. This study employs system dynamics to model coupling among steel, cement, thermal power, and renewable energy enterprises within both electricity and carbon markets. Multi-scenario analysis examines key indicator changes as the policy deepens. The results indicate that the impact of CETM coverage expansion unfolds in two phases: initial and deepening stages. Policy deepening will significantly influence key indicators, such as carbon prices and grid feed-in tariffs. Allowance tightening will lead to a pronounced rise in carbon prices, and the carbon trading costs for steel enterprises are significantly higher than those for cement enterprises. The increase in Renewable Portfolio Standards obligations will affect the supply–demand dynamics in the electricity market and contribute to reducing carbon trading costs for high-emission enterprises. All entities should tailor their strategies according to their specific characteristics to proactively adapt to the market changes induced by the CETM coverage expansion. 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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17 pages, 5466 KB

Open AccessArticle

Research on Photovoltaic Power Stations and Energy Storage Capacity Planning for a Multi-Energy Complementary System Considering a Combined Cycle of Gas Turbine Unit for Seasonal Load Demand

by Yongneng Ding, Yuxuan Lu, Weitao Yi, Yan Huang and Xi Zhu

Processes 2025, 13(9), 2897; https://doi.org/10.3390/pr13092897 - 10 Sep 2025

Viewed by 369

Abstract

Multi-energy systems could utilize the complementary characteristics of heterogeneous energy to improve operational flexibility and energy efficiency. However, seasonal fluctuations and uncertainty of load would have a great influence on the effectiveness of the system planning scheme. Regarding this issue, this paper proposes [...] Read more.

Multi-energy systems could utilize the complementary characteristics of heterogeneous energy to improve operational flexibility and energy efficiency. However, seasonal fluctuations and uncertainty of load would have a great influence on the effectiveness of the system planning scheme. Regarding this issue, this paper proposes a photovoltaic power (PV) station and thermal energy storage (TES) capacity planning model with considering the electrical load uncertainty based on a stochastic optimization method. And four-season load demand scenarios are built by Generative Adversarial Networks (GANs). At last, the proposed capacity configuration model is tested in a case study, and the results show the influence of seasonal fluctuations in load, scenario number, and TES capacity. 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, 2920 KB

Open AccessArticle

Thermoelectric Optimisation of Park-Level Integrated Energy System Considering Two-Stage Power-to-Gas and Source-Load Uncertainty

by Zhuo Song, Xin Mei, Cheng Huang, Xiang Jin, Min Zhang, Junjun Wang and Xin Zou

Processes 2025, 13(9), 2835; https://doi.org/10.3390/pr13092835 - 4 Sep 2025

Viewed by 416

Abstract

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A [...] Read more.

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. 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, 2777 KB

Open AccessArticle

Economic Optimal Scheduling of Virtual Power Plants with Vehicle-to-Grid Integration Considering Uncertainty

by Lei Gao and Wenfei Yi

Processes 2025, 13(9), 2755; https://doi.org/10.3390/pr13092755 - 28 Aug 2025

Viewed by 379

Abstract

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory [...] Read more.

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability. 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, 1050 KB

Open AccessArticle

Power Spot Market Clearing Optimization Based on an Improved Low-Load Generation Cost Model of Coal-Fired Generator

by Xujia Yin, Hongxun Tian, Ce Zhou, Peng Zou, Caihuan Wu, Meng Qin and Jun Shu

Processes 2025, 13(9), 2745; https://doi.org/10.3390/pr13092745 - 28 Aug 2025

Viewed by 464

Abstract

With the rapid expansion of variable renewable energy, coal-fired units are increasingly operated at low load, where non-convex cost characteristics pose challenges for spot market clearing. This study reviews and improves existing low-load generation cost models, introducing three key enhancements: (1) integrating piecewise [...] Read more.

With the rapid expansion of variable renewable energy, coal-fired units are increasingly operated at low load, where non-convex cost characteristics pose challenges for spot market clearing. This study reviews and improves existing low-load generation cost models, introducing three key enhancements: (1) integrating piecewise linearization with the marginal cost approach to reduce computational burden; (2) removing redundant binary variables and incorporating previously omitted cost components to improve clearing efficiency; and (3) developing a fuel cost model that combines quasi-fixed and marginal costs for low-load generation with firing and combustion support (FCS), enabling the joint optimization of low-load and normal operations. Applied to 6-bus and provincial systems, the proposed approach achieves speed-ups of 11.3× and 6.3× over the benchmark model (Model I) while maintaining accuracy, demonstrating both its efficiency and practical applicability. 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, 2281 KB

Open AccessArticle

Life Cycle Cost Modeling and Multi-Dimensional Decision-Making of Multi-Energy Storage System in Different Source-Grid-Load Scenarios

by Huijuan Huo, Peidong Li, Cheng Xin, Yudong Wang, Yuan Zhou, Weiwei Li, Yanchao Lu, Tianqiong Chen and Jiangjiang Wang

Processes 2025, 13(8), 2400; https://doi.org/10.3390/pr13082400 - 28 Jul 2025

Cited by 1 | Viewed by 796

Abstract

The large-scale integration of volatile and intermittent renewables necessitates greater flexibility in the power system. Improving this flexibility is key to achieving a high proportion of renewable energy consumption. In this context, the scientific selection of energy storage technology is of great significance [...] Read more.

The large-scale integration of volatile and intermittent renewables necessitates greater flexibility in the power system. Improving this flexibility is key to achieving a high proportion of renewable energy consumption. In this context, the scientific selection of energy storage technology is of great significance for the construction of new power systems. From the perspective of life cycle cost analysis, this paper conducts an economic evaluation of four mainstream energy storage technologies: lithium iron phosphate battery, pumped storage, compressed air energy storage, and hydrogen energy storage, and quantifies and compares the life cycle cost of multiple energy storage technologies. On this basis, a three-dimensional multi-energy storage comprehensive evaluation indicator system covering economy, technology, and environment is constructed. The improved grade one method and entropy weight method are used to determine the comprehensive performance, and the fuzzy comprehensive evaluation method is used to carry out multi-attribute decision-making on the multi-energy storage technology in the source, network, and load scenarios. The results show that pumped storage and compressed air energy storage have significant economic advantages in long-term and large-scale application scenarios. With its fast response ability and excellent economic and technical characteristics, the lithium iron phosphate battery has the smallest score change rate (15.2%) in various scenarios, showing high adaptability. However, hydrogen energy storage technology still lacks economic and technological maturity, and breakthrough progress is still needed for its wide application in various application scenarios in the future. 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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23 pages, 2992 KB

Open AccessArticle

Research on Two-Stage Investment Decision-Making in Park-Level Integrated Energy Projects Considering Multi-Objectives

by Jiaxuan Yu, Wei Sun, Rongwei Ma and Bingkang Li

Processes 2025, 13(8), 2362; https://doi.org/10.3390/pr13082362 - 24 Jul 2025

Viewed by 566

Abstract

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment [...] Read more.

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. 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, 632 KB

Open AccessArticle

An Electricity Market Pricing Method with the Optimality Limitation of Power System Dispatch Instructions

by Zhiheng Li, Anbang Xie, Junhui Liu, Yihan Zhang, Yao Lu, Wenjing Zu, Yi Wang and Xiaobing Zhang

Processes 2025, 13(7), 2235; https://doi.org/10.3390/pr13072235 - 13 Jul 2025

Viewed by 442

Abstract

The electricity market can optimize the resource allocation in power systems by calculating the market clearing problem. However, in the market clearing process, various market operation requirements must be considered. These requirements might cause the obtained power system dispatch instructions to deviate from [...] Read more.

The electricity market can optimize the resource allocation in power systems by calculating the market clearing problem. However, in the market clearing process, various market operation requirements must be considered. These requirements might cause the obtained power system dispatch instructions to deviate from the optimal solutions of original market clearing problems, thereby compromising the economic properties of locational marginal price (LMP). To mitigate the adverse effects of such optimality limitations, this paper proposes a pricing method for improving economic properties under the optimality limitation of power system dispatch instructions. Firstly, the underlying mechanism through which optimality limitations lead to economic property distortions in the electricity market is analyzed. Secondly, an analytical framework is developed to characterize economic properties under optimality limitations. Subsequently, an optimization-based electricity market pricing model is formulated, where price serves as the decision variable and economic properties, such as competitive equilibrium, are incorporated as optimization objectives. Case studies show that the proposed electricity market pricing method effectively mitigates the economic property distortions induced by optimality limitations and can be adapted to satisfy different economic properties based on market preferences. 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, 2288 KB

Open AccessArticle

Virtual Power Plant Optimization Process Under the Electricity–Carbon–Certificate Multi-Market: A Case Study in Southern China

by Yanbin Xu, Yi Liao, Shifang Kuang, Jiaxin Ma and Ting Wen

Processes 2025, 13(7), 2148; https://doi.org/10.3390/pr13072148 - 6 Jul 2025

Cited by 1 | Viewed by 753

Abstract

Over the past decade, China has vigorously supported the development of renewable energy and has initially established the electricity–carbon–certificate multi-market. As a typical market-oriented demand-side management model, studying the optimization process and cases of virtual power plants (VPPs) under the multi-market has significant [...] Read more.

Over the past decade, China has vigorously supported the development of renewable energy and has initially established the electricity–carbon–certificate multi-market. As a typical market-oriented demand-side management model, studying the optimization process and cases of virtual power plants (VPPs) under the multi-market has significant importance for enhancing the operation level of VPPs, as well as promoting corresponding experiences. Based on the mechanisms and impacts of the electricity–carbon–certificate multi-market, this manuscript takes a VPP project in southern China as a case, constructs a sequential decision-making optimization model for the VPP under a diversified market, and solves it using reinforcement learning and Markov decision theory. The case analysis shows that, compared to energy supply income, although the proportion of income from certificate trading and carbon trading in the multi-market is relatively limited, participating in the electricity–carbon–certificate multi-market can significantly enhance VPPs’ willingness to accommodate the uncertainties of renewable energy and can significantly improve the economic and environmental performances of VPPs, which is of great significance for improving the energy structure and accelerating the process of low-carbon energy transformation. 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, 1337 KB

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

Viewed by 574

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 KB

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

Viewed by 367

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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33 pages, 6211 KB

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

Cited by 1 | Viewed by 515

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 KB

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 594

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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23 pages, 1808 KB

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 618

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 KB

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 582

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 KB

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

Cited by 1 | Viewed by 621

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 KB

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 860

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 KB

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

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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 KB

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

Cited by 1 | Viewed by 1149

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 KB

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 3 | Viewed by 1767

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 KB

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 986

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 KB

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 2 | Viewed by 1724

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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