Electricity Trading Research Articles

Assessing Financial Distress in Emerging Markets: An Industry-Level Study from Albania

This research investigates financial health of 18 financial industries in Albania between 2011 and 2014. The authors have built a dataset of 12 financial ratios, averaging the profitability, liquidity, efficiency and leverage ratio groups of 200 non-financial firms with highest revenues reported in 2014, according to the Albanian National Business Register. We compute the ratios for 4 consecutive years, grouping the companies into industries and tracking industry performance financial health. We rank the top and worst industry performers per ratio group and per year. We compare the financial health of industries at the beginning of the observation period (2011) and at the end of the observation period). This study classifies the companies into financially distressed and non-financially distressed. The authors average the financial ratios of these two groups and observe the most impactful differences that discriminate between financially distressed and non-distressed firms. The empirical results of this research confirm that drilling is the industry with highest financial health, while health care is the worst performing industry over the period 2011-2014, based on the metric and data used. Furthermore, the authors observe that financial health industries such as e-commerce, electricity trade and hydropower have improved over time, based on the observation period. According to this study, liquidity, leverage and efficiency ratios discriminate well between distressed and non-distressed firms, while profitability indicators show limited differences between the two groups.

High Voltage Direct Current (HVDC) Transmission: Advancing Renewable Energy Integration and Long-distance Power Transfer

Focus on the development of renewable energy in UK and some few advanced countries is driving a major shift in the characteristics and control requirements of the electricity grid. Currently, attention is shifting away from traditional load generators near consumers and towards more decentralized suppliers, particularly wind farms and hydropower plants located far from areas of consumption. An additional paradigm shift in Europe is the pursuit to establish a single European energy market, which will make it simple and effective to trade electricity across international borders. Long-distance transmission is now required because of the distance between energy resources and load centres. For short-distance transmission, High Voltage Direct Current (HVDC) technology is more expensive due to power electronic converters. However, for longer distances, HVAC becomes more expensive due to line cost as compared to the cost of DC line. Hence, the transition to a single energy market, which necessitates effectively transporting electricity across numerous borders and over vast distances, will be nearly impossible without a major upgrade to the current AC grid infrastructure to make it more durable and adaptable. Modernizing existing AC grids with HVDC grid technology can provide the adaptability and resilience necessary to absorb increasingly intermittent renewable energy sources by the grid and over long-distance transmission using HVDC converters. The foundation for power flow regulation and system stability is the converter control angles. The study further examines the relationship between converter firing angles and system stability. This paper will demonstrate the modelling of steady-state operation of HVDC transmission system incorporating generating source, converters technology, and transmission system using MATLAB/Simulink for probing under a variety of operating conditions which is the basis for this work.

Mechanisms and motivations: Green electricity trading in China's high-energy-consuming industries

Mechanisms and motivations: Green electricity trading in China's high-energy-consuming industries

An inter-provincial renewable electricity trading model under renewable portfolio standards policy

An inter-provincial renewable electricity trading model under renewable portfolio standards policy

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

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.

A Two-Stage Robust Optimization Strategy for Long-Term Energy Storage and Cascaded Utilization of Cold and Heat Energy in Peer-to-Peer Electricity Energy Trading

This study addresses the optimization of urban integrated energy systems (UIESs) under uncertainty in peer-to-peer (P2P) electricity trading by introducing a two-stage robust optimization strategy. The strategy includes a UIES model with a photovoltaic (PV)–green roof, hydrogen storage, and cascading cold/heat energy subsystems. The first stage optimizes energy trading volume to maximize social welfare, while the second stage maximizes operational profit, considering uncertainties in PV generation and power prices. The Nested Column and Constraint Generation (NC&CG) algorithm enhances privacy and solution precision. Case studies with three UIESs show that the model improves economic performance, energy efficiency, and sustainability, increasing profits by 1.5% over non-P2P scenarios. Adjusting the robustness and deviation factors significantly impacts P2P transaction volumes and profits, allowing system operators to optimize profits and make risk-aligned decisions.

Optimal Peer-to-Peer Coupled Electricity and Carbon Trading in Distribution Networks

Optimal Peer-to-Peer Coupled Electricity and Carbon Trading in Distribution Networks

How to extend the Photovoltaic value chain? A blockchain-based strategy for photovoltaic-storage-hydrogen integration with green electricity trading

How to extend the Photovoltaic value chain? A blockchain-based strategy for photovoltaic-storage-hydrogen integration with green electricity trading

Two‐Layer Optimal Electricity Trading Method for Distribution Networks with Multi‐MG

Two‐Layer Optimal Electricity Trading Method for Distribution Networks with Multi‐MG

Unified carbon emissions and market prices forecasts of the power grid

Unified carbon emissions and market prices forecasts of the power grid

The Nonlinear Dynamics of CO2 Emissions in Pakistan: A Comprehensive Analysis of Transportation, Electricity Consumption, and Foreign Direct Investment

CO2 emissions are major drivers of climate change, causing global warming, extreme weather, and biodiversity loss. They disrupt ecosystems, deplete resources, and threaten public health and economic stability. Reducing CO2 emissions is essential for climate stability and sustainability. This study explores the complex relationships between CO2 emissions and factors such as the transportation sector, electricity consumption, foreign direct investment (FDI), international trade, and gross domestic product (GDP). The focus is on small- and medium-sized enterprises (SME) in Pakistan. Using time series data from 2000 to 2022. This study applies advanced econometric techniques, including nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least squares (DOLS), and fully modified ordinary least squares (FMOLS). The findings highlight that increased electricity consumption, international trade, transportation activities, and FDI contribute to higher CO2 emissions. However, FDI can also help reduce emissions, particularly through investments in green technologies. This study emphasizes the importance of transitioning to renewable energy and adopting sustainable practices across sectors such as electricity, trade, and transportation. Specifically, transportation and electricity consumption were found to significantly impact CO2 emissions, with a 10% increase in transportation activities resulting in a 5% rise in emissions. Conversely, FDI can reduce emissions by approximately 3% per unit of investment, largely due to green technology adoption. Additionally, integrating renewable energy and energy-efficient technologies in transportation can lead to a 20% reduction in emissions. Policymakers and experts must prioritize strategies that promote renewable energy adoption and integrate sustainable practices to reduce CO2 emissions and ensure long-term environmental sustainability. This research is innovative in its analysis of the interconnected effects of electricity consumption, trade, transportation, and FDI on CO2 emissions. By applying sophisticated econometric methods, it highlights the potential of FDI, particularly green investments, to mitigate environmental damage. This study, focusing on Pakistan, offers insights into how economic growth can be balanced with environmental sustainability.

A Reputation-Based Pricing Strategy for Distributed Diverse Entity Systems: Enhancing Market Efficiency Through Real-Time Reputation Updates

Although existing studies address the reduction of default rates by adjusting electricity trading rankings based on reputation values, the mechanisms for penalizing electricity trading defaults remain incomplete. Therefore, this paper proposes a real-time reputation-based pricing method for distributed diverse entity systems to mitigate electricity trading defaults. First, a reputation reward and penalty mechanism evaluates the trading behavior of diverse entities. Next, a ‘price-dominant, reputation-auxiliary’ pricing concept guides the process. Following this, a reputation-driven pricing strategy model for distributed adjustable resources allows for bid adjustments based on real-time market dynamics. Upon electricity trading completion, the reputation values of all entities are recalculated and disclosed, enabling entities to adjust future pricing and electricity trading quantities to optimize their profits. This method effectively reduces default rates while alleviating the impact of market electricity tradings on peak-to-valley fluctuations. Finally, simulations conducted on the MATLAB 2018b platform confirm the rationality and feasibility of the proposed real-time reputation-based pricing strategy within distributed diverse entity systems.

Respiratory Ill-Health and Welding Exposures: A Canadian Cohort Study.

Respiratory ill-health in welders is well documented but without a clear indication of exposures responsible. In a Canadian cohort study of welders and electrical workers, we collected self-reports of asthma/wheeze and rhinitis at each 6-monthly contact for up to 5 years. Physician diagnoses of asthma and chronic obstructive pulmonary disease (COPD/bronchitis)were extracted from the Alberta administrative health database (AHDB). Welders provided task-specific information at each contact. Estimates were derived for cumulative exposure to particulates, chromium, and nickel. Factors associated with time to first and recurrent events were identified by proportional hazards regression, adjusting for sex, age, and smoking. Of 1001 welders and 884 workers in electrical trades recruited, 1338 in Alberta were matched to the AHDB. Welders were more at risk of physician-diagnosed COPD/bronchitis than those in the electrical trades (HR for first report = 1.87; 95% CI = 1.27-2.77) but not of asthma. Times to first self-report of asthma/wheezing (HR = 1.58; 95% CI = 1.23-2.04) and rhinitis (HR = 1.29; 95%CI = 1.11-1.49) were shorter in welders. Among welders, time to physician-diagnosed asthma was weakly related to cumulative nickel exposure (mg/m3_h/100) (HR = 1.08; 95% CI = 1.00-1.17). COPD/bronchitis was related to cumulative exposure to total dust (g/m3_h) (HR = 1.01; 95% CI = 1.00-1.03) and to chromium (mg/m3_h/100) (HR = 1.14; 95% CI = 1.04-1.26). The risk of both asthma and COPD/bronchitis reduced with time using local exhaust ventilation. Self-reported rhinitis increased with cumulative nickel exposure (HR = 1.00; 95% CI = 1.00-1.01). Welders were at increased risk of COPD/bronchitis, with risk related to cumulative dust and chromium exposure. Nickel exposure increased the risk of asthma and rhinitis.

Shocking shortage: the electrical trade crisis in the Eastern Cape, South Africa

This study examines the shortage of professional electricians in the Eastern Cape, South Africa, exploring the root causes, consequences, and potential solutions. Using an interpretivist paradigm and inductive approach, qualitative data from semi-structured interviews with local electricians were analysed through thematic analysis. Findings indicate that inadequate training opportunities and low industry awareness are major contributors to this shortage, leading to an increased presence of unqualified electricians. The analysis identified three primary themes: causes, including insufficient training and awareness; consequences, such as diminished industry standards and rising unqualified practitioners; and potential solutions centred on educational reform and expanded vocational programs. While this study is limited to the Eastern Cape, it provides valuable insights that may inform similar regions, offering actionable recommendations for industry stakeholders. By addressing these training and awareness gaps, stakeholders can help reduce the shortage, enhance workforce skills, and improve overall industry standards. The research underscores the critical need for educational initiatives and awareness campaigns to counteract skilled labour shortages, emphasising that targeted strategies could stimulate sustainable workforce development. These findings contribute to a broader understanding of labour market dynamics in the construction sector, with particular relevance for the sustainable growth of skilled trades.

Mutual Influences Among the Electricity Market, Carbon Emission Market, and Renewable Energy Certificate Market

With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term.

Research on cross-provincial power trading strategy considering the medium and long-term trading plan

To accommodate China’s electricity market reforms integrating medium and long-term (MLT) transactions and spot transactions, and to boost renewable energy consumption through the spot market, this paper proposes an optimized cross-provincial electricity trading strategy model based on a two-layer game framework. The proposed model incorporates an MLT green certificate contract decomposition method, enabling nested optimization of green certificate contracts and scheduling plans for cross-provincial power transactions. To encourage broader participation, a bilateral green certificate trading framework is established, which globally optimizes green certificate allocation to increase benefits for market participants. A Nash-Stackelberg game model is introduced to address complex game interactions among multiple participants under the green certificate mechanism and the limitation of assuming complete rationality. The game model combines supply and demand sides with an embedded demand-side evolutionary game. Additionally, an improved Aquila optimization algorithm (IAOA) is developed to accurately calculate electricity supply and demand. The algorithm integrates a Circle chaotic map, Sobol sequence, random walk strategy, and filtering technology to enhance optimization capabilities and manage complex constraints. The algorithm is then embedded with a distributed iterative approach to achieve equilibrium strategies. A real-world case study was conducted to validate the feasibility and effectiveness of the proposed model. The results demonstrate that the proposed approach effectively achieves equilibrium, optimizes trading strategies, and fosters win-win, coordinated development among participants in the cross-provincial electricity market.

Optimal Scheduling of Virtual Power Plants Under a Multiple Energy Sharing Framework Considering Joint Electricity and Carbon Trading

The virtual power plant (VPP) is an excellent approach for mitigating the intermittency and fluctuation of renewable energy sources. The present work proposes an optimal scheduling model for VPPs to leverage the benefits of joint electricity and carbon trading from the perspective of multiple energy-sharing mechanisms. First, the optimal sharing scheduling model of the electric, thermal, and hydrogen energy was established. The model integrates various components, including wind turbines, photovoltaic units, electrolytic cells, combined heat and power units, hydrogen-doped gas boilers, electric energy storage, thermal storage tanks, and hydrogen storage tanks. Then, the model incorporates a tiered carbon trading mechanism to minimize operating and trading costs. Finally, numerical results indicate that, compared with the independent operation of virtual power plants and the lack of joint electricity and carbon trading, the optimal scheduling scheme proposed in this paper reduces the total cost and carbon emissions of the three VPPs by 3.3% and 49.7%, respectively. This demonstrates that the proposed model can effectively reduce the total operating expenses of VPPs by facilitating the allocation of electric, thermal, and hydrogen energy and achieving low-carbon emission operations.

A framework for high resolution coupled global electricity & hydrogen models based on integrated assessment model scenarios

A framework for high resolution coupled global electricity & hydrogen models based on integrated assessment model scenarios

Modelling the socio-economic impacts of coal production reduction: A macro provincial analysis of China

Modelling the socio-economic impacts of coal production reduction: A macro provincial analysis of China

Blockchain-based smart grid power trading technology

To improve the security and efficiency of smart grid power trading, this study designs a smart grid trading system based on blockchain technology. The transaction module of the system composed of smart contracts and order-matching algorithms is proposed. The credit consensus mechanism of the system adopts a designed credit-based hybrid consensus mechanism. The experimental results showed that under this system, the successful transaction prices of consumers bidding were between the grid price and the recycling price, making bidding more reasonable. The highest power utilization rate was 0.84, the lowest was 0.67, and the transaction throughput exceeded 350TPS. The highest CPU usage was 0.74, the lowest was 0.5, and the highest memory usage was only 0.44. Compared with other systems, the order matching of this system was more reasonable and its performance was better. Furthermore, the system has been subjected to rigorous security testing and is reliable, thereby providing a secure and efficient trading platform for smart grid electricity trading.