Real-time Market Research Articles

Potential and challenges of Peer-To-Peer (P2P) electrical energy trading: investigation and case study

This paper explores the potential and challenges associated with Peer-to-Peer (P2P) electrical energy trading, focusing on both theoretical investigation and practical application through a comprehensive case study. By examining the interactions between prosumers (those who both produce and consume energy) and consumers, we employ utility functions to model these interactions. Optimization is achieved using a non-cooperative game theory approach coupled with real-time pricing mechanisms to reflect market dynamics accurately. Our analysis ensures feasibility and reliability by considering crucial factors such as energy balance and grid capacity constraints, which are essential for maintaining system stability. The simulation results demonstrate the effectiveness of our proposed method. These results highlight the significant potential of P2P trading to enhance overall energy efficiency, promote decentralized energy management, and reduce reliance on traditional energy sources. Furthermore, this study provides detailed insights into maximizing total utility by determining optimal prices and energy trades, ultimately showcasing the transformative impact of P2P trading on real-time energy market operations.

The impact of intermediate option availability on consumers' choice: The moderating role of price roundness

Given the surge in consumer demand and rapid inflation, businesses have turned their attention to premiumization as a means of market segmentation. Building upon previous studies that highlight the significance of the compromise effect in gaining market share for intermediate options, this research examines the feasibility of revenue management strategies using 'psychological pricing' in hospitality and tourism products. By investigating choice behaviors related to intermediate options, we explore how subtle pricing manipulations can enhance the compromise effect. Through five experiments, we validate our hypotheses. Our findings not only extend the literature on pricing behavior and hospitality concerning rounded pricing but also provide insights on how to strategically adjust psychological pricing to align with real-time market conditions.

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency

The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.

Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.

A new bi-level model for the false data injection attack on real-time electricity market considering uncertainties

State Estimation (SE) plays a critical role in various applications of power systems including the electricity market. False Data Injection Attacks (FDIAs) are capable of manipulating the power system SE process aiming to gain financial profits by the players. In this paper, the potential economic influences of FDIA on SE and consequently the Real-Time (RT) electricity market are examined. Therefore, a new bi-level optimization framework is proposed to realistically model the FDIAs from the attacker perspective. At the upper-level, the attacker intends to intelligently choose the most critical congested transmission lines and manipulate their associated meter readings to maximize the expected financial profitability. At the lower-level, the RT market-clearing model is formulated under the influence of FDIA. Considering the incomplete attacker's prior knowledge of the network topology i.e., connection/disconnection of transmission lines, the angles of the phase-shifting transformers, and the real-time load uncertainty, the proposed formulation is extended to a new bi-level scenario-driven stochastic model. The developed attack model, which must remain undetectable by the system operator in all scenarios, is solved using the Karush–Kuhn–Tucker (KKT) approach. The correctness and effectiveness of the proposed optimization scheme have been validated in GAMS software using CONOPT and OQNLP solvers based on the IEEE 14-bus test system and also comparative results are presented to demonstrate the merits of the proposed formulation.

Fair value estimates for illiquid cryptocurrency

To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participants’ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participants’ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.

The use of artificial intelligence and machine learning in e-commerce marketing

The object of this research is the use of artificial intelligence (AI) and machine learning (ML) in e-commerce marketing strategies. Traditional e-commerce marketing approaches often lack a personalized customer experience and find it difficult to adapt to changing consumer behavior. The integration of artificial intelligence and machine learning offers a solution to these problems, enabling real-time marketing initiatives and data analysis. Studies have shown that the use of artificial intelligence and machine learning in e-commerce marketing has led to improved customer relationship management, increased operational efficiency, and more customer-centric advertising strategies. In addition, technologies such as visual search, virtual personal shoppers, and real-time product targeting have changed the e-commerce landscape by providing interactive and personalized shopping experiences. Artificial intelligence and machine learning algorithms analyze vast amounts of customer data to identify patterns, preferences and trends, enabling e-commerce businesses to conduct targeted marketing campaigns and optimize product offerings. Using advanced technologies, companies can streamline operations, increase customer satisfaction and stay ahead of the competition in the digital marketplace. This data suggests that integrating artificial intelligence and machine learning into e-commerce marketing strategies can benefit businesses by improving customer engagement, increasing sales, and gaining a competitive advantage. However, a successful implementation requires access to quality data, a robust AI infrastructure, and ongoing monitoring and optimization to ensure effectiveness and relevance in a dynamic marketplace.

Risk-aware participation in day-ahead and real-time balancing markets for energy storage systems

The increasing volatility of electricity prices due to increased uncertain renewable energy generation gives rise to interesting short-term arbitrage opportunities for Energy Storage Systems (ESS) operators. Whereas prior research has shown the possibility to exploit inter-temporal arbitrage opportunities in the real-time balancing market, this paper formulates a two-stage optimization methodology that allows ESS operators to also engage in inter-market arbitrage by participating in both the day-ahead and real-time balancing markets. However, the effectiveness of such a strategy is heavily influenced by expected inter-market price differentials, which is known to be difficult to predict when it involves the imbalance price. To address this issue, we propose a risk-averse approach by incorporating the conditional value at risk in the ESS objective function. The proposed methodology is applied to a case study of the Belgian electricity market, where we demonstrate the effectiveness of (i) the combined market participation compared to an ESS participating in either market, and (ii) the risk-aware methodology by showcasing improved ex-post out-of-sample profit performance of a risk-averse compared to a risk-neutral ESS.

An Assessment Method of Deviation Electricity Responsibility in China’s Forward Electricity Market Based on Time-Sharing Trading Prices

Electricity forward market contract trading constitutes a principal trading method in the electricity market. To address deviations from or non-compliance with contracts in China’s forward electricity market, an Energy Deviation Settlement (EDS) mechanism has been implemented. However, the current EDS mechanism is marred by unscientific penalties and imprecise deviation cost assessments. Consequently, this paper proposes a deviation responsibility assessment method based on time-sharing trading prices. Initially, the total annual contracted power is decomposed to derive the hourly deviation power for all 8760 h of the year. Subsequently, considering the power supply cost and load level, this paper proposes four pricing strategies for typical supply–demand scenarios and constructs a time-sharing trading pricing model based on cost pricing. Finally, this paper conducts a simulation using actual electricity data from China’s Province H. The results demonstrate that, compared to the traditional EDS mechanism, the proposed method can more accurately and reasonably measure the value of deviation responsibility. Concurrently, it can be better integrated with the future development of real-time spot market operations.

Optimizing Trade Strategies: The Interactive Trade Decision Making Using Weighted Sum Method

The interactive nature of trade decision making enables us to navigate the complexities of international commerce with agility and precision. Through the power of technology and data-driven analysis, we can optimize our strategies, adapt to market conditions, and seize opportunities in real-time. Interactive trade decision making involves a dynamic process that integrates various factors such as market trends, economic indicators, risk assessment, and competitive analysis. It empowers traders to actively engage with the market, gather crucial information, and respond swiftly to changing circumstances. By harnessing advanced tools and algorithms, we can analyse vast amounts of data, uncover patterns, and make informed decisions that maximize profitability and minimize risks. This interactive approach transcends traditional models, allowing us to actively participate in trade negotiations, explore different scenarios, and assess the potential outcomes of our decisions. It encourages collaboration and knowledge sharing, enabling us to leverage collective intelligence and gain valuable insights from experts and peers. With real-time market data and interactive platforms at our fingertips, we can simulate, test, and refine our strategies, ensuring that our trade decisions are based on accurate information and thoughtful analysis.The significance of interactive trade decision making in research lies in its ability to revolutionize the way businesses and individuals approach international trade. We will use the Weighted Sum Method (WSM) in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Alternative parameters taken as Policy, Environment, Economy, Ability. Evaluation parameters parameters taken as Local investment, Technology transfer, Local procurement, Cooperate with R&D, Training, International marketing assistance”. show the final rank of this paper the Economy is in 2nd rank, the Environment is in 4th rank, the Ability is in 3rd rank, the Policy is in 1st rank, The final result is done by using the WSM method. In conclusion, interactive trade decision making is a powerful approach that leverages technology, real-time market information, and data analysis to enable informed and agile decision making in international trade.

Optimizing Trade Strategies: The Interactive Trade Decision Making Using Weighted Sum Method

The interactive nature of trade decision making enables us to navigate the complexities of international commerce with agility and precision. Through the power of technology and data-driven analysis, we can optimize our strategies, adapt to market conditions, and seize opportunities in real-time. Interactive trade decision making involves a dynamic process that integrates various factors such as market trends, economic indicators, risk assessment, and competitive analysis. It empowers traders to actively engage with the market, gather crucial information, and respond swiftly to changing circumstances. By harnessing advanced tools and algorithms, we can analyse vast amounts of data, uncover patterns, and make informed decisions that maximize profitability and minimize risks. This interactive approach transcends traditional models, allowing us to actively participate in trade negotiations, explore different scenarios, and assess the potential outcomes of our decisions. It encourages collaboration and knowledge sharing, enabling us to leverage collective intelligence and gain valuable insights from experts and peers. With real-time market data and interactive platforms at our fingertips, we can simulate, test, and refine our strategies, ensuring that our trade decisions are based on accurate information and thoughtful analysis.The significance of interactive trade decision making in research lies in its ability to revolutionize the way businesses and individuals approach international trade. We will use the Weighted Sum Method (WSM) in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Alternative parameters taken as Policy, Environment, Economy, Ability. Evaluation parameters parameters taken as Local investment, Technology transfer, Local procurement, Cooperate with R&D, Training, International marketing assistance”. show the final rank of this paper the Economy is in 2nd rank, the Environment is in 4th rank, the Ability is in 3rd rank, the Policy is in 1st rank, The final result is done by using the WSM method. In conclusion, interactive trade decision making is a powerful approach that leverages technology, real-time market information, and data analysis to enable informed and agile decision making in international trade.

E-Farming Portal Using Django

Abstract :- Traditional methods and middlemen’s exploitation throughout the crucial produce-selling period pose problems for India’s agriculture sector. E-farming and agromarketing come to light as revolutionary remedies to this problem. One essential component of agro marketing is e-farming, which uses digital platforms to empower farmers. Farmers are less vulnerable to changes in the market because they can now sell their goods across the country thanks to the removal of geographical restrictions. The goal of the article is to improve agricultural practices in India by implementing an E-farming platform using Django. By facilitating national product sales and lowering farmers’ susceptibility to market swings, e-farming makes use of digital channels to empower farmers. The suggested system combines Python Django with front-end tools (HTML, CSS, and Bootstrap) to provide back-end functionality. E-farming increases the revenue and decision-making capabilities of farmers by using SMS communication and real-time market data. Keywords– – Marketplace, online store, portal, Django, e-commerce, e-farming, farming, food, shopping, and web development.

STATISTICAL ANALYSIS OF DILAPIDATED AND DANGEROUS BUILDINGS IN INDIA: OPTIMIZING TDR ALLOCATION FOR URBAN REDEVELOPMENT

This paper presents a comprehensive statistical analysis of dilapidated and dangerous buildings across urban wards in India, focusing on optimising the allocation of Transferable Development Rights (TDR) for effective urban redevelopment. By integrating empirical data on building conditions, urban density, and redevelopment patterns, the study develops a dynamic framework that applies advanced statistical models to evaluate the efficiency of TDR allocations in high-risk zones. The research addresses critical issues of liquidity constraints, TDR hoarding, and inefficient redevelopment processes, proposing a refined mechanism for optimising TDR distribution through data-driven strategies. The proposed model balances redevelopment incentives with environmental and infrastructural sustainability by incorporating real-time market dynamics and environmental cost factors into TDR pricing. This study also includes case studies on slum redevelopment and real estate liquidity, offering actionable insights for policymakers to deploy TDR exchanges to accelerate urban regeneration. The findings contribute to the advancement of sustainable urban growth by offering a novel approach to resolving redevelopment inefficiencies while maintaining ecological balance.

Valuation of forward contract price in energy markets described by a fuzzy-stochastic model and mathematical algorithms: a case study of the PJM Western Hub Real-Time Peak market

Valuation of forward contract price in energy markets described by a fuzzy-stochastic model and mathematical algorithms: a case study of the PJM Western Hub Real-Time Peak market

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

Demand Planning: Riding Disruptive Wave of AI and Accelerated Computing

Traditional demand planning methods often struggle to keep pace with the complexity, volatility, and vast datasets inherent in modern supply chains. Artificial intelligence (AI) offers a transformative solution, revolutionizing demand planning with its ability to analyze vast amount of data, identify complex patterns, and generate highly accurate forecasts. This paper explores the latest advancements in AI for demand planning, encompassing machine learning, deep learning, and natural language processing (NLP). The focus is on how these techniques enhance demand sensing capabilities, incorporating real-time market signals, external data sources, and unstructured text information. Furthermore, the potential of AI to optimize inventory management, enable scenario planning, and increase supply chain resilience in response to unexpected disruptions are examined. The paper also addresses practical challenges in implementing AI-powered demand planning solutions, and outlines areas for future research. Most importantly, the paper provides the robust methologies to integrate the emerging AI developments in demand planning process.

Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark for energy-storage systems in electricity markets, this research focuses on the integration of LFP batteries, considering their unique characteristics and market responsiveness. Regression and regularization techniques, coupled with temporal cross-validation, were employed to ensure model robustness and accuracy in predicting energy trading outcomes. This methodological approach allows for a nuanced analysis of battery degradation, power capacity, energy content, and real-time market prices. The model, validated using Belgium’s system imbalance market data from the 2020–2023 period, incorporates both capital and operational expenditures to assess the economic and operational viability of LFP battery energy-storage systems (BESSs). The findings reveal that considering a broader range of operational parameters in energy arbitrage, beyond just the usual energy prices and round-trip efficiency, significantly influences the cost-effectiveness and performance benchmarking of energy storage solutions. This paper advocates for the strategic use of LFP batteries in energy markets, highlighting their potential to enhance grid stability and energy trading profitability. The proposed benchmark model serves as a critical tool for energy traders, providing a detailed framework for informed decision making in the evolving landscape of energy storage technologies.

Bidding Strategy for Wind and Thermal Power Joint Participation in the Electricity Spot Market Considering Uncertainty

As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will lead to bidding deviation and bring revenue risks. In response to this, a bidding strategy is proposed for wind farms to participate in the spot market jointly with carbon capture power plants (CCPP) that have flexible regulation capabilities. First, a two-stage decision model is constructed in the day-ahead market and real-time balancing market. Under the joint bidding mode, CCPP can help alleviate wind power output deviations, thereby reducing real-time imbalanced power settlement. On this basis, a tiered carbon trading mechanism is introduced to optimize day-ahead bidding, aiming at maximizing revenue in both the electricity spot market and carbon trading market. Secondly, conditional value at risk (CVaR) is introduced to quantitatively assess the risks posed by uncertainties in the two-stage decision model, and the risk aversion coefficient is used to represent the decision-maker’s risk preference, providing corresponding strategies. The model is transformed into a mixed-integer linear programming model using piecewise linearization and McCormick enveloping. Finally, the effectiveness of the proposed model and methods is verified through numerical examples.

Exploring electric vehicle's potential as capacity reservation through V2G operation to compensate load deviation in distribution systems

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy.