Search Results (125)

Wind power has become a strategic cornerstone of China’s renewable-energy transition and industrial upgrading, making it essential to understand how policy interventions shape the behaviour of its industry chain. This study examines how major wind power policies issued between 2015 and 2024 transmit shocks across nine upstream, midstream, and downstream sectors. Using four contagion tests based on higher-order co-moments, combined with a policy sensitivity index, the analysis identifies distinct transmission patterns across policy types. The results show that market-mechanism reforms induce the strongest and most systemic contagion effects, reflecting their ability to align financial incentives with renewable-integration objectives. Upstream sectors—particularly equipment and key material industries—exhibit the highest responsiveness, while midstream construction and downstream operation and maintenance display more moderate and delayed adjustments. Development and construction policies generate broader but less intensive contagion, whereas industry-support measures trigger selective, sector-specific responses. These findings offer practical guidance for improving policy coordination, investment planning, and industrial upgrading within China’s wind power value chain. Future research could extend the analysis by incorporating firm-level data, longer policy cycles, and interactions with other structural shocks such as electricity-market reforms and climate-related risks. Full article

With the deepening of power market reform and the large-scale integration of bidirectional systems such as energy storage and electric vehicles, achieving sustainable carbon management has become increasingly urgent. Traditional carbon emission accounting methods face challenges, including insufficient dynamics and unclear responsibility boundaries. To address these issues, this paper proposes a sustainability-oriented accounting method for indirect carbon emissions from electricity in the context of bidirectional system integration in the power market environment. First, the dynamic carbon emission characteristics of bidirectional systems such as energy storage and vehicle-to-grid (V2G) systems are analyzed, and a carbon emission accounting model is constructed to address the fairness issue of emission responsibility allocation during charging and discharging. Second, on the basis of the theory of carbon emission flows and incorporating electricity trading contract data, an accounting method for indirect carbon emissions from electricity in green electricity trading, coal-fired electricity trading, and hybrid scenarios under bidirectional system integration is developed. Finally, the case study demonstrates that the proposed method accurately captures the temporal variation of carbon emission factors, ensures conservation of total emissions, and fairly redistributes carbon responsibility among users under different market scenarios, while revealing how bidirectional systems and green electricity trading reshape nodal carbon intensities and spatial emission distributions without causing double counting. Full article

Under the context of rapid distributed energy development and ongoing electricity market reforms, this paper investigates bidding strategies for virtual power plants (VPPs) formed by aggregated distributed renewable energy (DRE) in China’s evolving day-ahead electricity market. To address privacy concerns of DRE participants and VPP aggregators during dynamic aggregation, an enhanced Benders decomposition framework is proposed. The methodology first characterizes market uncertainties (e.g., electricity prices and renewable generation output) by clustering them into representative scenarios using K-medoids clustering. A privacy-preserving decentralized optimization model is then formulated: the VPP aggregator solves a master problem to determine bidding decisions, while DRE units independently address subproblems via privacy-protected mathematical constraints that avoid revealing explicit operational details. The framework ensures secure information exchange and computational efficiency. Case studies demonstrate that the proposed model effectively balances privacy protection and bidding performance, outperforming traditional centralized optimization approaches in terms of solution quality and scalability. Full article

This study explores how carbon pricing and electricity market liberalization jointly contribute to China’s sustainable energy transition. Using a dynamic computable general equilibrium (CGE) model (CEEEA2.0), we simulate three policy scenarios—business as usual, emissions trading scheme (ETS) with regulated electricity prices, and ETS with market-based pricing—under a unified emissions cap. The results demonstrate that electricity market liberalization enhances carbon pricing efficiency by eliminating price distortions, leading to a 0.06% increase in GDP and a 12% reduction in emission abatement costs. However, liberalization also raises electricity and consumer prices, disproportionately affecting rural and low-income households. These findings underscore the need to balance economic efficiency and social equity in sustainability-oriented energy reforms. Our analysis emphasizes the importance of designing inclusive and just transition policies to ensure that carbon mitigation efforts support long-term environmental, economic, and social sustainability goals. Full article

This study is one of the first empirical attempts to quantify the social benefit of virtual power plants (VPPs) in South Korea using the contingent valuation method (CVM). As Korea pursues its ambitious carbon neutrality goal by 2050, VPPs have emerged as a critical technology for managing the intermittency of renewable energy sources and ensuring grid stability. Despite their recognized technical potential, the social and economic value of VPPs remains largely unexplored. Through a nationwide survey of 1105 households, we employed a double-bounded dichotomous choice spike model to estimate willingness to pay (WTP) for government-led VPP implementation. The analysis revealed two distinct dimensions influencing VPP valuation: electricity bill perceptions and electricity generation mix preferences. Results indicated that Korean households exhibited significant but heterogeneous WTP for VPP implementation, with unconditional mean annual WTP ranging from KRW 23,474 to KRW 26,545 per household. Notably, support for renewable energy transition showed stronger positive effects on WTP compared to nuclear expansion preferences, suggesting VPPs are primarily valued as renewable energy enablers. The substantial spike probability (32–34%) indicated that approximately one-third of the population has zero WTP, highlighting challenges in introducing novel energy technologies. Key determinants of positive WTP included perceived fairness of electricity pricing, support for market-based mechanisms, and preferences for transitioning from coal and nuclear to renewables. These findings provide critical policy insights for VPP deployment strategies, suggesting the need for phased implementation, targeted communication emphasizing renewable integration benefits, and coordination with broader electricity market reforms. The study contributes to energy transition economics literature by demonstrating how public preferences for emerging grid technologies are shaped by both economic considerations and environmental values. Full article

Electricity market reforms and decreasing technology costs have propelled residential solar PV growth leading distribution network operators to face operational challenges including reverse power flows and voltage regulation during peak solar generation. Traditional mono-facial south-facing PV systems concentrate production at midday when demand may be low, leading to high curtailment, especially for downstream households. This study proposes vertically installed east–west-facing bifacial PV systems (BiE and BiW), characterized by two energy peaks (morning and evening), which are better aligned with residential demand and alleviate grid constraints. Using load flow simulations, the performance of vertical bifacial configurations was compared against mono-facial systems across PV capacities from 1 to 20 kW. Fairness in curtailment was evaluated at 10 kW using Jain’s fairness index, the Gini index, and the Curtailment index. Simulation results show that BiE and BiW installations, especially at higher capacities, not only generate more energy but also are better at managing curtailment. At 10 kW, BiE and BiW increased bid energies by 815 kWh and 787 kWh, and reduced curtailed energy by 1566 kWh and 1499 kWh, respectively. These findings highlight the potential of bifacial PV installations in mitigating curtailment and improving fairness in energy distribution, supporting the demand for residential PV systems. Full article

The huge penetration of renewable energy sources poses several challenges for the function of electricity markets, such as increased price volatility and massive curtailments. This paper investigates the current structure of the wholesale electricity market in Greece under the Target Model guidelines. Our analysis put under scrutiny the formation and function of both spot and balancing markets by highlighting key challenges and reforms. Empirical evidence reveals that the domestic market is currently in accordance with the European Target Model; however, the anticipated benefits in terms of more competitive prices are not evident yet. The oversupply of electricity accompanied by low demand that is apparent in the Greek market points to the rapid participation of storage units in the system. The paper provides a detailed description of the recent support mechanism to facilitate the integration of BESS into the system. Eventually, this is anticipated to reduce price volatility and smoothen the price curves. Full article

The global campaign to reach net zero will necessitate the use of hydrogen as an efficient way to store renewable electricity at large scale. Methane pyrolysis is rapidly gaining traction as an enabling technology to produce low-cost hydrogen without directly emitting carbon dioxide. It offers a scalable and sustainable alternative to steam reforming whilst being compatible with existing infrastructure. The process most commonly uses thermal energy to decompose methane (CH₄) into hydrogen gas (H₂) and solid carbon (C). The electrification of this reaction is of great significance, allowing it to be driven by excess renewable electricity rather than fossil fuels, and eliminating indirect emissions. This review discusses the most recent technological advances in electrified methane pyrolysis and the relative merits of the mainstream reactor technologies in this space (plasma, microwave, fluidised bed, and direct resistive heating). This study also examines the economic viability of the process, considering energy costs, and the market potential of both turquoise hydrogen and solid carbon products. Whilst these technologies offer emission-free hydrogen production, challenges such as carbon deposition, reactor stability, and high energy consumption must be addressed for large-scale adoption. Future research should focus on process optimisation, advanced reactor designs, and policy frameworks to support commercialisation. With continued technological innovation and sufficient investment, electrified methane pyrolysis has the potential to become the primary route for sustainable production of hydrogen at industrial scale. Full article

As China’s power market reforms deepen, the scale of market operations and the number of participants have reached new highs, introducing increasingly complex threats and heightened risk scenarios. Traditional risk early warning systems for electricity sales companies are heavily influenced by subjective factors, incomplete data, and poor real-time performance, which cannot meet the requirements of sustainable development. To achieve efficient, full-chain, and sustainable risk control, this paper proposes a data-driven risk warning method for electricity sales companies, encompassing the entire sales process. Firstly, based on data correlations across the electricity sales process, appropriate data sources for risk warnings are identified. Key elements are then extracted using Principal Component Analysis (PCA), while historical business data is adaptively clustered, with risk warning levels classified using the Adaptive Sparrow Optimization Density Peak Clustering Algorithm (DPC-SSA). Lastly, dynamic risk warnings are generated through the stacking identification model. The effectiveness and practicality of the proposed method are validated through an analysis using real data from a provincial power trading management platform. Full article

China’s power generation system has undergone reforms, leading to a competitive electricity market where independent producers participate through competitive bidding. With the rise of low-carbon policies, producers must optimize bidding strategies while reducing carbon emissions, creating complex interactions with local governments. Evolutionary game theory (EGT) is well-suited to analyze these dynamics. This study begins by summarizing the fundamental concepts of electricity trading markets, including transaction models, bidding mechanisms, and carbon reduction strategies. Existing research on the application of evolutionary game theory in power markets is reviewed, with a focus on theoretical constructs such as evolutionary stable strategies and replicator dynamics. Based on this foundation, the study conducts a detailed mathematical analysis of symmetric and asymmetric two-group evolutionary game models in general market scenarios. Building upon these models, a three-group evolutionary game framework is developed to analyze interactions within power producer groups and between producers and regulators under low-carbon mechanisms. A core innovation of this study is the incorporation of a case study based on China’s electricity market, which examines the evolutionary dynamics between local governments and power producers regarding carbon reduction strategies. This includes analyzing how regulatory incentives, market-clearing prices, and demand-side factors influence producers’ bidding and emission reduction behaviors. The study also provides a detailed analysis of the bidding strategies for small, medium, and large power producers, revealing the significant impact of carbon pricing and market-clearing prices on strategic decision-making. Specifically, the study finds that small producers tend to adopt more conservative bidding strategies, aligning closely with market-clearing prices, while large producers take advantage of economies of scale, adjusting their strategies at higher capacities. The study explores the conditions under which carbon emission reduction strategies achieve stable equilibrium, as well as the implications of these equilibria for both market efficiency and environmental sustainability. The study reveals that integrating carbon reduction strategies into power market dynamics significantly impacts bidding behaviors and long-term market stability, especially under the influence of governmental penalties and incentives. The findings provide actionable insights for both power producers and policymakers, contributing to the advancement of low-carbon market theories and supporting the global transition to sustainable energy systems. Full article

The energy transition of Cyprus presents a distinctive case study influenced by its geographic isolation, regulatory evolution, and the imperative to integrate renewable energy sources (RESs). This paper critically examines the chronological progression of Cyprus’ energy transition, beginning with the formulation of a liberalized electricity market aligned with the European Union’s Target Model. The analysis explores key drivers underpinning increased RES investments, while addressing the transformative impacts of global disruptions on energy security and policy priorities. Furthermore, it assesses pivotal regulatory reforms and the advancement of enabling infrastructure, such as advanced metering systems and cross–border interconnections, which underpin the island’s energy modernization efforts. Finally, this paper identifies opportunities for Cyprus to position itself as a regional smart energy hub, offering valuable insights into the challenges and prospects faced by isolated energy systems within the context of the European energy transition. Full article

Jiangxi Province relies heavily on thermal power and energy imports but is rich in natural resources, particularly lithium. This study explores strategies for advancing wind–solar–storage systems to help Jiangxi transition to a low-carbon energy structure. Using LEAP and NEMO models, four scenarios are examined: the reference (REF) scenario, new energy storage policy scenario (NPS), high wind–solar power capacity scenario (HWSS), and comprehensive optimization scenario (COS). Key findings show that the COS and HWSS offer significant advantages over the REF scenario and NPS in terms of energy storage efficiency, carbon emission reduction, and cost savings. By 2035, under the COS, wind and solar power share rises to 48%, reducing coal use by 5.9 million tons and electricity imports by 40.0 TWh compared to the REF scenario. Battery storage utilization increases by 1499.8 GWh, nearly four times that of the REF scenario. This scenario also cuts CO₂ emissions by 16.8% and lowers cumulative social costs by 5.19 billion USD, delivering optimal economic efficiency. The study also identifies challenges such as high investment costs, underdeveloped business models, and low resource utilization, and recommends setting higher targets, implementing flexible solutions, promoting market reforms, and increasing R&D efforts, among other measures. Full article

As the share of electric vehicles (EVs) continues to rise, the distribution grid faces the risk of overload. In Germany, the reform of § 14a Energiewirtschaftsgesetz (German Energy Industry Act, EnWG) was introduced to address this problem. The reform has changed the revenue potential of smart chargeable uni and bidirectional EVs (smart EVs). The reform introduces variable grid fee tariffs for smart EVs, but also possible emergency power reduction measures to relieve grid overload situations. While variable grid fee tariffs can increase revenues, power reduction measures can lead to revenue losses. This work investigates the dual impact of these factors by implementing variable grid fee tariffs and the option of power reduction signals in a flexibility marketing model. Our findings indicate that smart EVs can generate significant additional revenues through variable grid fee tariffs in 2035. Conversely, the impact of emergency power signals on these revenues and the EV load profile is marginal. As a result, the effect of power reduction measures on the German load profile is also negligible. Full article

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

With the deepening of power market reform and the increasingly fierce competition in the power market, the accurate prediction of electricity price has become an important demand for power market participants to make scientific decisions, optimize resource allocation, and reduce risks. Electricity price forecast can provide a key reference for the power market, help market participants make wise decisions, promote competition and efficient operation and cope with complex market fluctuations, provide a scientific basis for various entities to optimize resource allocation, reduce risks and improve benefits, and promote the sustainable development of the power industry. This study presents a dynamic retail price prediction method for smart grid based on the Stackelberg game model. Firstly, the correlation test is used to verify the strong correlation between electric load and electricity price. Secondly, the parameters of the Stackelberg model are determined, and the load and electricity price are tested using the white noise test. Finally, by comparing the BP neural network model and quantifying the model parameters, the superiority of the model is verified. The results show that the Stackelberg game model has higher prediction accuracy than the BP neural network model in electricity price prediction. Full article