Trade Price Research Articles

Communication‐resilient and convergence‐fast peer‐to‐peer energy trading scheme in a fully decentralized framework

AbstractThe wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterwards, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.

시계열모형에 의한 서울 아파트시장 가격예측 분석

The purpose of this study is to make a model to forecasting apartment trade price and household rent price in apartment housing market using ARIMA model. On the basis of those models, I tried to forecast the fluctuations of short-term apartment trade price and household rent price in Seoul apartment market. To analyze the ARIMA model, quarterly data during 2011 1/4∼2023 4/4 are used for identification, estimation, diagnosis, and prediction of the ARIMA model. Using ARIMA model, the outcome ARIMA(1,1,0) model is applied to Sepol apartment trade price in the rate of apartment trade price forecasting model, and ARIMA(1,1,1) model is applied to Seoul apartment household rent price in the rate of apartment rent price. According to the forecast results of Seoul apartment trade price and apartment household rent price in the ARIMA model, looking at the quarterly rate of change in Seoul apartment sales prices, it appears that they will continue to go up the 2024 1/4, and will continue to rise but without significant fluctuations. Looking at the quarterly trend of Seoul apartment rental prices on the 2024 1/4 and continue until the 2024 4/4, and the upward trend will be larger than Seoul apt sale price. While the government's housing policy is sluggish, Seoul apartment sales prices and Jeonse prices are expected to be affected by a decrease in apartment supply and interest rates due to a decrease in permits and occupancy this year.

Development pathway and influencing factors of hydrogen energy storage accommodating renewable energy growth

Hydrogen energy is considered an important energy storage mode with medium- and long-term cross-seasonal storage capabilities in scenarios with high penetration of renewable energy (RE). However, there is a lack of research regarding the appropriate scale of hydrogen energy storage (HES) considering different RE power generation scenarios. In response to this gap, the present study aims to establish a power system simulation model to analyze the development pathway of HES while accommodating RE growth with a minimum cost constraint for a power system operation. The impacts of carbon trading prices, advances in HES technology, and RE resource endowment on HES development are also investigated. The findings indicate that the total available solar energy resources in China are 46.94TW, and the total available wind energy resources are 9.52TW. When the share of RE generation reaches 44.33–54.78 % of total power generation, the utilization of HES for balancing power system is advantageous in reducing the overall system costs. Looking ahead to 2060, the year when China is projected to achieve its carbon-neutrality goal, RE is estimated to reach 63.12–91.1 % of total power generation. Consequently, the HES in each province is estimated to contribute to 3.58–18.02 % of total electricity consumption. Provinces with more abundant wind resources relative to solar resources are expected to have a 36.43 % higher demand for HES.

Economic feasibility assessment of coal-biomass co-firing power generation technology

Biomass co-firing technology is a potential solution for low-carbon transition of coal-fired power plants. Existing techno-economic analyses of this technology fail to consider indirect benefits, such as avoided losses from technology retrofitting, and the heterogeneity across regions. To address these limitations, we developed five types of techno-economic indicators to analyze six possible future scenarios for the application of this technology in 29 provinces of China, based on regional heterogeneous data. The results show, first, generally, the higher the local coal price, the local on-grid electricity price, the biomass feed-in tariff subsidy, and the carbon trading price, the greater the incentives and benefits for biomass co-firing. Second, bio-electricity tariff subsidies greatly increase the incentives for technology retrofitting, with 26 of the 29 provinces analyzed having sufficient incentives for retrofits in this scenario. Third, biomass co-firing technology has more revenue potential when emission quota is limited than when it's not. Our results indicate that a timely tightening of the CO2 emission quotas of the coal power sector will stimulate technological retrofits even without additional policy incentives, while more plants retrofitted will achieve positive returns with additional incentives.

Rare earth trade dependence network structure and its impact on trade prices: An industry chain perspective

Rare earth is an important raw material for military manufacturing, artificial intelligence, and the new energy industry, relating to national security and technological development. The article constructs a rare earth trade dependence network to analyze the overall rare earth trade characteristics, trade associations evolution, and the characteristics of world major economies from 2008 to 2022 and explores the impact of the network characteristics on prices using panel regression. The study found that: (1) the rare earth trade dependence network shows an expanding trend, and the trade is more direct; (2) the trade community features, China occupies an important position in the up and middle stream of the industrial chain, and the United States maintains a leading position in the downstream; (3) there are obvious differences in the degree of dependence of the central countries, and their roles are different in each chain segment; (4) the structural features of the network affect the trade price of rare earths, and there are differences in the key influences in each chain segment. Based on the above findings, the article puts forward corresponding policy suggestions.

Low-Carbon supply chain optimisation with carbon emission reduction level and warranty period: nash bargaining fairness concern

This study incorporates fairness concern in a low-carbon supply chain coordination mechanism where a single manufacturer sells its product to consumers through a single retailer. We develop four different scenarios of the Stackelberg master-slave game utility model—both members are neutral (NN), the manufacturer (FN) or retailer (NF) has fairness concern, and both are not neutral (FF), where the Nash bargaining fairness reference is leveraged to capture the impact of fairness preference on low-carbon supply chain optimisation decision-making profits, level of carbon emission reduction, warranty period, and revenue-sharing. Finally, numerical studies are conducted to quantify the impact of the Nash bargaining fairness concern. Research shows that: (1) fairness concern made it worse for the retailer but beneficial for the manufacturer and the system. (2) fairness concern causes a reduction in the level of carbon emission reduction and warranty period. However, the reduction of carbon emission reduction trading price and a certain range of revenue sharing effectively reduces the impact of fairness concern on members. (3) The revenue-sharing contract effectively reduces the negative impacts of fairness concern on supply chain members. The paper is a guide for enterprises development and cooperation but also provides empirical evidence for the government.

Research on Cost Sharing and Coordination of Low Carbon Supply Chain in the Context of Carbon Neutrality Strategy

For carbon emissions under the limit of supply chain cost sharing problem, based on the Shared contract perspective starkerberg game model includes the manufacturers and retailers, analyzes the cost sharing ratio, carbon trading price and unit carbon tax emissions on the supply chain, and the optimal strategy combination and carbon reduction cost allocation ratio is studied. The results show that the one-way cost sharing model is beneficial to the reduction of low carbon supply chain, while the two-way cost sharing model has a positive effect on the reduction of supply chain within the small allocation ratio. In addition, the sharing proportion of manufacturers is within a certain range, and the two-way emission reduction mode is the optimal choice for the supply chain. Meanwhile, the sharing proportion will affect the choice between decentralized and centralized decisions. In addition, the numerical analysis shows that the carbon emission reduction levels increase with the increase of carbon trading prices. The results have good reference significance for the carbon emission reduction decision and carbon reduction cost sharing contract among members of the low-carbon supply chain.

Evolution analysis of low-carbon cooperation of service providers based on Moran process in cloud manufacturing.

Low-carbon cooperation among cloud manufacturing service providers is one way to achieve carbon peak and neutrality. Such cooperation is related to the benefits to service providers adopting low-carbon strategies and stochastic factors such as government low-carbon policies, providers' environmental awareness, and demanders' low-carbon preferences. Focusing on the evolutionary process of service providers' low-carbon strategy selection under uncertain factors, a stochastic evolutionary game model is constructed based on the Moran process, and the equilibrium conditions for low-carbon cooperation among providers are analyzed under benefit-dominated and stochastic factor-dominated situations. Through numerical simulation, the effects of the cloud platform's cost-sharing coefficient for low-carbon investment, matching growth rate, carbon trading price, and group size on providers' low-carbon strategy evolution are analyzed. The research results show that increasing the cloud platform's low-carbon cost-sharing, carbon trading price, and group size can promote low-carbon cooperation among service providers. With greater low-carbon investment costs and greater stochastic factor interference, the providers' enthusiasm for low-carbon cooperation decreases. This study fills the research gap in the low-carbon cooperation evolution of cloud manufacturing providers based on the stochastic evolutionary game and provides decision-making suggestions for governments and cloud platforms to encourage provider participation in low-carbon cooperation and for providers to adopt low-carbon strategies.

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.

The Effect of Energy Security on Economic Growth in ASEAN During 2000–2020

Energy is an important input for economic growth. The increase in non-renewable energy consumption has an impact on energy insecurity. This study aims to calculate energy security and analyze its effect on ASEAN economic growth during the 2000–2020 period. The method used in this study is the Principal Component Analysis to calculate energy security and the Feasible Generalized Least Square panel data regression to analyze its effect on economic growth. The authors use four dimensions to build an energy security index, namely availability, accessibility, acceptability, and efficiency. Several variables are included in the model, are capital, labor, trade, and world oil price. The results of this study indicate that the index and dimensions of energy security have a positive influence on ASEAN economic growth, except for the efficiency dimension. Capital. employment, trade, and oil prices have a positive influence on ASEAN economic growth. Regional Governments need to reduce the gap in the electrification ratio in several countries and build energy infrastructure. The government also needs to increase the application of energy diversification, increase renewable energy production and the need to pay more attention to environmental policies in economic activities.

Local electricity market designs for interconnected nanogrids: Impact on rural electrification in Madagascar

Sustainable, reliable, and affordable energy access is a major challenge in many parts of the world. The paper addresses this challenge by proposing a local electricity market (LEM) design for nanogrids deployed in Madagascar. Each nanogrid shares a solar PV and battery system, and it meets demand locally before trading surplus energy with other nanogrids on a microgrid bus, facilitated through the creation of a LEM. Two distinct market approaches are investigated and compared: central clearing and bilateral trading. Bilateral trading generates trading prices through direct interaction between consumers and producers, whereas central clearing sets a single trading price based on the cost-ordered supply curve. Our study shows that central clearing generates more consumer-friendly prices and facilitates the trading of all technically feasible energy, while bilateral trading may results in unmatched trading capacity. We find an average price for central trading of 0.49€/kWh compared to 1.24€/kWh with a bilateral trading mechanism. To promote the low market prices, a bottom-up retail tariff structure is proposed. The aim of this simplified tariff is to promote initial electrification by minimizing entry prices for end-users’ first electricity access to 0.89€/kWh compared to the current average of 2.11€/kWh. The discussed results were evaluated in consultation with the local company in Madagascar to ensure practical suitability and to achieve maximum significance.

Do military expenditures have an impact on the adoption of renewable energy in OECD nations? Evidence from a panel cointegration test approach

PurposeToday, many industries are implementing creative approaches in response to increasing environmental awareness. It is of great importance to answer the question of whether the military sector, one of the most important sectors, can support renewable energy (RE) adaptation. This study aims to examine how military spending affects the supply of RE in 27 Organization for Economic Cooperation and Development (OECD) nations as well as the regulatory function of factors such as innovation, international trade and oil prices between 1990 and 2021.Design/methodology/approachThe study examines the effects of military spending, income, green innovation, international trade, oil prices and the human development index on the supply of RE using various econometric approaches, which are the cointegration test, moments quantile regression and robustness test.FindingsThe findings demonstrate that all factors, excluding military spending, quite likely affect the expansion of the renewable supply. Military spending negatively influences the RE supply; specifically, a 1% increase in military spending results in a 0.88 reduction in the renewable supply. In addition, whereas income elasticity, trade and human development index in OECD nations are higher in the last quantiles of the regression than in the first quantiles, the influence of military spending and innovation on renewable supply is about the same in all quantiles.Practical implicationsOECD nations must consider the practical implications, which are essential to assess and update the military spending of OECD countries from a green energy perspective to transition to clean energy. Based on the study’s overall findings, the OECD countries should incorporate the advantages of innovation, economic growth and international trade into their clean energy transition strategies to lessen the impact of military spending on renewables.Originality/valueThe study aims to fill a gap in the literature regarding the role of military expenditures in the RE development of an OECD country. In addition, the results of the methodological analysis can be used to guide policymakers on how military spending should be in the field of RE.

The Mathematics behind Cryptocurrencies "A Statistical Analysis of Cryptocurrencies"

This article provides a statistical approach to describe the fit of the most popular cryptocurrencies, building off a previous report, "A Statistical Analysis of Cryptocurrencies." We examined Bitcoin, Ethereum, Tether, Binance, Ripple, Cardano, Solana, and Doge coins. To model our cryptocurrencies, we utilized trading prices between 2017 and 2022 in light of historic events, such as the COVID-19 pandemic. Additionally, we performed a correlation analysis to help understand the relationship between the popular cryptos. Here, we report that the candidate distributions we fit to model the currencies needed to be more independent to describe the return of all popular cryptos. This could be due to the need for Correlation between some of these popular cryptos. We found the generalized hyperbolic and the generalized t showed the best performance of the models tested, though these approaches remained limited in their overall fitness. Their performance also varied by cryptocurrency under investigation, with Tether demonstrating the worst fit across all candidate models. Using our fit models, we also predicted the average daily returns for January 1st, 2023, to February 1st, 2023, and generally found good predictive validity. These results are critical in understanding the movements of cryptos and help better understand the risk associated with trading these currencies.

Low-carbon economic dispatch strategy for integrated power system based on the substitution effect of carbon tax and carbon trading

As the introduction of carbon peak and carbon neutrality, the intensity of carbon regulation is growing. The power system, as the main body of carbon emission, will face the constraints of compound carbon reduction policy. To address this issue, a low-carbon economic dispatch strategy for integrated power system based on the substitution effect of carbon tax and carbon trading is proposed to improve the economic and environmental joint benefits. Firstly, facing the volatility characteristics of carbon trading price, Monte Carlo method is implemented to analyze the uncertainty of carbon trading price. Next, the renewable energy power and electricity load are forecast by long short-term memory method. Subsequently, the gray wolves algorithm is imposed to optimize the dispatch model and obtain the optimal dispatch plan. The comparative analysis reveals that the carbon emission and dynamic payback period of the optimized integrated power system are 4.37 × 106 t and 10.06 years, which are reduced 1.67 × 105 t and 0.3 years compared with the conventional power system. Besides, the economic analysis demonstrates that the substitution effect can decrease the unit cost of carbon emission by 1.03 USD/t. Finally, the comparative analysis manifests that the proposed dispatch model can effectively boost the economic and environmental joint benefits for the integrated power system.

Source tracing and contagion measurement of carbon emission trading price fluctuation in China from the perspective of major emergencies.

Based on monthly economic data spanning from January 2015 to December 2022, we have established an analytical framework to examine the "Russia-Ukraine conflict-financial market pressure and energy market-China carbon emission trading prices." To achieve this objective, we developed indices for financial system pressure, the energy market, and investor sentiment, applying a mediation effects model to validate their transmission mechanisms. Subsequently, the TVP-SV-VAR model was employed to scrutinize the nonlinear impact of the Russia-Ukraine conflict on the valuation of China's carbon emission trading rights. This model integrates time-varying parameters (TVP) and stochastic volatility (SV), utilizing Markov Chain Monte Carlo (MCMC) technology for parameter estimation. Finally, various wavelet analysis techniques, including continuous wavelet transform, cross-wavelet transform, and wavelet coherence spectrum, were applied to decompose time series data into distinct time-frequency scales, facilitating an analysis of the lead-lag relationships within each time series. The research outcomes provide crucial insights for safeguarding the interests of trading organizations, refining the structure of the carbon market, and mitigating systemic risks on a global scale.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

How Does Carbon Trading Impact China’s Forest Carbon Sequestration Potential and Carbon Leakage?

This paper presents an in-depth analysis of the impact of forest carbon sink trading in China, examining its effects from 2018 to 2030 under various carbon pricing scenarios. Using the Global Timber Market Model (GFPM) along with the IPCC Carbon Sink Model, we simulate the potential shifts in China’s forest resources and the global timber market. The study finds that forest carbon trading markedly boosts China’s forest stock and carbon sequestration, aligning with its dual carbon objectives. China’s implementation of forest carbon trading is likely to result in a degree of carbon leakage on a global scale. During the forecast period, our study reveals that the carbon leakage rates under three different forest carbon trading price scenarios, which at estimated at 81.5% (USD 9.8/ton), 64.0% (USD 25/ton), and 57.8% (USD 54/ton), respectively. Notably, the leakage rate diminishes as the forest carbon sink price increases. Furthermore, analysis also suggests that regional variations in the average carbon sequestration capacity of forests, alongside the structure of China’s timber imports, emerge as significant factors influencing the extent of carbon leakage.

The War between Russia and Ukraine and its Impact on Bangladesh: A Comprehensive Study

This comprehensive study investigates the impact of the ongoing war between Russia and Ukraine on the Bangladeshi economy, focusing on various dimensions including trade disruptions, energy and commodity price shocks, remittances, foreign direct investment (FDI), macroeconomic stability, and daily commodities. Drawing on an extensive literature review, the study synthesizes existing research on the economic consequences of geopolitical conflicts, with particular attention to the multifaceted effects of the Russia-Ukraine war on indirectly involved countries like Bangladesh. The analysis reveals that the conflict has led to significant challenges in areas such as trade, energy and commodity prices, remittances, FDI, and macroeconomic stability, with potential consequences for the Bangladeshi economy, including slowed growth, increased inflation, and currency fluctuations. Furthermore, the study highlights the impact of the conflict on the prices and availability of daily commodities, which can affect the cost of living, purchasing power, and overall quality of life. Based on these findings, the study offers recommendations for policymakers and stakeholders to adopt strategies that mitigate the adverse effects of the conflict, promote economic resilience, and ensure the well-being of the population. This study contributes to the understanding of the economic implications of geopolitical conflicts and serves as a valuable resource for policymakers, researchers, and practitioners interested in the broader effects of the Russia-Ukraine war on global economies.

Economic Pricing in Peer-to-Peer Electrical Trading for a Sustainable Electricity Supply Chain Industry in Thailand

The state-owned power Electricity Generating Authority of Thailand (EGAT), a monopoly market in charge of producing, distributing, and wholesaling power, is the focal point of Thailand’s electricity market. Although the government has encouraged people to install on-grid solar panels to sell electricity as producers and retail consumers, the price mechanism, i.e., purchasing price and selling prices, is still unilaterally determined by the government. Therefore, we are interested in studying the case where blockchain can be used as a free trading platform. Without involving buying or selling from the government, this research presents a model of fully traded price mechanisms. Based on the study results of the double auction system, data on buying and selling prices of electrical energy in Thailand were used as the initial data for the electricity peer-to-peer free-trading model. Then, information was obtained to analyze the trading price trends by using the law of demand and supply in addition to the principle of the bipartite graph. The price trend results agree well with those of price equilibrium equations. Therefore, we firmly believe that the model we offer can be traded in a closed system of free-trade platforms. In addition, the players in the system can help to determine the price trend that will occur according to various parameters and will cause true fairness in the sustainable electricity supply chain industry in Thailand.

Optimal decarbonisation pathway for mining truck fleets

The fossil fuel powered mining truck fleets can contribute up to 80% of total emissions in open pit mines. This study investigates the optimal decarbonisation pathway for mining truck fleets. Notably, our proposed pathway incorporates power generation, negative carbon technologies, and carbon trading. Technical, financial, and environmental models of decarbonisation technologies are established, capturing regional variations and time dynamic characteristics such as cost trends and carbon capture efficiency. The dynamic natures of characteristics pose challenges for using the cost-effective analyses approach to find the optimal decarbonisation pathway. To address this, we introduce a mixed-integer programming optimisation framework to find the decarbonisation pathway with minimum life cycle costs during the planning period. A case study for the optimal decarbonisation pathway of truck fleets in a South African coal mine is conducted to illustrate the applicability of the proposed model. Results indicate that the optimal decarbonisation pathway is significantly influenced by factors such as land cost, annual budget, and carbon trading prices. The proposed method provides invaluable guidance for transitioning towards a cleaner and more sustainable mining industry.