Real-time Market Research Articles

A computationally efficient pumped storage hydro optimization in the look-ahead unit commitment and real-time market dispatch under uncertainty

A computationally efficient pumped storage hydro optimization in the look-ahead unit commitment and real-time market dispatch under uncertainty

Impact of Real-Time Data, Market Sentiment, and Economic Factors on Investment Profitability in Indonesia

The study investigates the complex interplay of real-time data, market sentiment, and economic factors in shaping investment profitability in the context of Indonesia. Real-time data, including stock prices and trading volumes, market sentiment derived from sentiment analysis and expert interviews, and economic factors such as GDP growth, inflation rates, and interest rates are examined for their impact on investment outcomes. Quantitative methods, including correlation analysis, regression analysis, and time series analysis, are employed to analyze a comprehensive dataset spanning an extended period. The findings reveal that real-time data has a significant positive influence on investment profitability, underlining the importance of timely access to financial information. Market sentiment also exerts a notable impact, as more positive sentiment correlates with higher profitability. However, the relationship between economic factors and profitability is nuanced, with GDP growth exhibiting a positive influence and inflation rates and interest rates demonstrating weak negative effects. These results offer practical insights for investors and financial analysts, emphasizing the need to consider a holistic approach that incorporates real-time data, sentiment analysis, and macroeconomic indicators in investment decision-making. The study contributes to a deeper understanding of investment dynamics in the emerging market of Indonesia.

Digital marketing program design based on abnormal consumer behavior data classification and improved homomorphic encryption algorithm

This article endeavors to delve into the conceptualization of a digital marketing framework grounded in consumer data and homomorphic encryption. The methodology entails employing GridSearch to harmonize and store the leaf nodes acquired post-training of the CatBoost model. These leaf node data subsequently serve as inputs for the radial basis function (RBF) layer, facilitating the mapping of leaf nodes into the hidden layer space. This sequential process culminates in the classification of user online consumption data within the output layer. Furthermore, an enhancement is introduced to the conventional homomorphic encryption algorithm, bolstering privacy preservation throughout the processing of consumption data. This augmentation broadens the applicability of homomorphic encryption to encompass rational numbers. The integration of the Chinese Remainder Theorem is instrumental in the decryption of consumption-related information. Empirical findings unveil the exceptional generalization performance of the amalgamated model, exemplifying an AUC (area under the curve) value of 0.66, a classification accuracy of 98.56% for online consumption data, and an F1-score of 98.41. The enhanced homomorphic encryption algorithm boasts attributes of stability, security, and efficiency, thus fortifying our proposed solution in facilitating companies’ access to precise, real-time market insights. Consequently, this aids in the optimization of digital marketing strategies and enables pinpoint positioning within the target market.

Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets, i.e., the daily and real-time markets, characterized by different time-scales. Thus, a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market, respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs, even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally, the strategy is tested on a lab-scale MG set up at Khalifa University, Abu Dhabi, UAE.

Modeling market trading strategies of the intermediary entity for microgrids: A reinforcement learning-based approach

Participation of a large number of microgrids (MGs) in the energy markets faces several challenges. To address these challenges, MGs can be aggregated by an Intermediary entity (IE). In this paper, a new decision-making framework is proposed to solve the energy management problem of multiple MGs and their participation in the energy market through the IE. This framework has three main functions. First, energy price forecasting is conducted using the long short-term memory (LSTM) recurrent neural networks method. Then, the power exchange of the MGs with the main grid is optimized by solving their energy management problem. In this stage, the integrated power exchanges of the MGs with the grid are determined based on the predicted prices. In the final function, the Monte Carlo reinforcement learning technique is employed to optimize real-time pricing decisions and identify potential obstacles that may affect proposed bid prices. This approach addresses energy management challenges and enables profitable energy trading in multiple MGs within energy markets, with confirmation supported by simulation results. The results show a maximum increment of 4.55% in the profit of the IE when purchasing energy and a 3.79% maximum increment when selling energy in the real-time market compared to day-ahead decisions, respectively.

Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data

Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46%. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly.

Feasibility Constrained Online Calculation for Real-Time Optimal Power Flow: A Convex Constrained Deep Reinforcement Learning Approach

Due to the increasing uncertainties of renewable energy and stochastic demands, quick-optimal control actions are necessary to retain the system stability and economic operation. Existing optimal power flow (OPF) solution methods need to be enhanced to guarantee the solution optimality and feasibility in real-time operation under such uncertainties. This paper proposes a convex constrained soft actor-critic (CC-SAC) deep reinforcement learning (DRL) algorithm for the AC-OPF problem. First, this problem is standardized as a Markov decision process model to be solved by DRL algorithms. Second, the operational constraints are satisfied by a novel convex safety layer based on the penalty convex-concave procedure (P-CCP). Then, the control policy is updated by the state-of-the-art off-policy entropy maximization-based SAC algorithm. Therefore, the CC-SAC is a combination of data-driven and physics-driven approaches. The former speedups the solution time by predicting near-optimum control actions through a deep neural network. The latter effectively guarantees the solution feasibility. Simulation results demonstrate the computational performance of the proposed CC-SAC to effectively find AC-OPF decisions with no constraint violation, zero optimality gap and high speed up to 34 times compared to a state-of-the-art solver. The proposed approach indicates its practicability for power system real-time operation and marketing.

Deep-Reinforcement-Learning-Based Dynamic Ensemble Model for Stock Prediction

In stock prediction problems, deep ensemble models are better adapted to dynamically changing stock market environments compared to single time-series networks. However, the existing ensemble models often underutilize real-time market feedback for effective supervision, and base models are pre-trained and fixed in their optimization, which makes them lack adaptability for evolving market environments. To address this issue, we propose a deep-reinforcement-learning-based dynamic ensemble model for stock prediction (DRL-DEM). Firstly, we employ deep reinforcement learning to optimize the weights of deep-learning-based time-series models. Secondly, existing deep-reinforcement-learning methods only consider environmental rewards. Thus we improve the reward function by introducing real-time investment returns as additional feedback signals for the deep-reinforcement-learning algorithm. Finally, an alternating iterative algorithm is used to simultaneously train the base predictors and the deep-reinforcement-learning model, allowing DRL-DEM to fully utilize the supervised information for global coordinated optimization. The experimental results show that in SSE 50 and NASDAQ 100 datasets, the mean square error (MSE) of the proposed method reached 0.011 and 0.005, the Sharpe ratio (SR) reached 2.20 and 1.53, and the cumulative return (CR) reached 1.38 and 1.21. Compared with the best results in the recent model, MSE decreased by 21.4% and 28.6%, SR increased by 81.8% and 82.1%, and CR increased by 89.0% and 89.1%, with higher forecasting accuracy and stronger investment return capability.

Enhancing Data Engineering Frameworks for Scalable Real-Time Marketing Solutions

This report discusses improvements to the data engineering frameworks for cost-efficient real-time marketing environment, with the applications in mobile advertising and smart city analytics. The proposed framework combines the processing of time-series data with map-reduce (M-R) systems in order to compensate for inefficiencies in the execution of temporal queries and the processing of real-time data streams. It enables effective behavioral targeting in mobile marketing and helps integrate various domains’ real-time data for smart city utilization. Specific examples that illustrate further refinements in the algorithms are showcased through case study and experimental evidence where gains in throughput, data reliability, and system effectiveness are quantified in real-time Marketing and Smart City applications.

Risk-based optimization for facilitating the leasing services of shared energy storage among renewable energy stations

Due to the inherent power output correlation and uncertainty, renewable energy stations normally incur the deviation penalty in the day-ahead and real-time electricity market. Meanwhile, shared energy storage operators have been appearing to provide energy storage leasing services for neighboring renewable energy stations. In this context, this paper presents a novel optimization strategy to provide leasing services for renewable energy station clusters while improving the utilization rate and revenue of shared energy storage simultaneously. Especially, the proposed strategy utilizes a two-stage optimization model to incorporate the overselling risk. In the first stage, a matching index is defined to select a cluster of wind and solar power stations in the geographically-close region, when a set of highly complementary stations are selected by matching the typical output curve of the shared energy storage. In the second stage, an optimization strategy is determined to explore the benefit and risk of overselling for shared energy storage with the goal of maximizing the total revenue, when the correlation of wind and solar power output is realized in the scenario generation and sampling process. The results of numerical experiments have demonstrated that employing a moderate overselling method can provide an economical and efficient operational solution to improving the utilization of shared energy storage.

Predicting online customer purchase: The integration of customer characteristics and browsing patterns

Despite the relentless growth of online retail, e-commerce platforms still suffer from a low purchase conversion rate. Researchers and practitioners have attempted to understand customer purchase behavior, but it remains elusive due to customers' heterogeneous and complex decision-making processes. This study addresses a gap in existing research by combining two types of factors affecting purchase behavior: past customer characteristics and current website browsing behavior. This study employs recency, frequency, and monetary value (RFM) to extract variables of customer characteristics and utilizes graph metrics to comprehensively measure browsing patterns. Based on these variables, this study conducts predictive analysis for purchase behavior and clustering analysis for session-level customer segmentation. Our findings reveal that integrating customer characteristics and browsing patterns significantly enhances purchase prediction and proposes a novel customer segmentation. This study not only provides theoretical and methodological contributions to decision support and e-commerce literature but also offers practical insights for real-time personalized marketing strategies.

ДІДЖИТАЛІЗАЦІЯ В МАРКЕТИНГУ: АНАЛІЗ ВИКЛИКІВ І МОЖЛИВОСТЕЙ

The study found that digital marketing is actively developing and gaining wide popularity in Ukraine and the world. Digital marketing (or digital marketing) is a marketing strategy that uses Internet technologies and digital communication channels to promote goods or services. This paper identifies the main components of digital marketing (websites, search engine marketing, social media, email, content marketing, online advertising, analytics and measurement). In order to understand the extent to which digitalisation affects marketing, it is important to analyse the transformation of the main information, strategic and operational marketing activities in the digital revolution. The article establishes that despite the incredible benefits, certain challenges arise on the way to the development of digital marketing (marketing budget management, the possibility of getting lost among competitors, traffic and lead generation, ROI optimisation, multichannel marketing strategies, use of tools and technologies, building and maintaining brand visibility, understanding of your audience) and suggests ways to solve them. The author presents the main statistical indicators of digital marketing and digital advertising. Comparing the three main digital advertising markets - the USA, China and Europe, the USA can be considered the largest, as it accounts for more than a third of global digital advertising spending. The presented correlation and regression model allowed to establish the relationship between the indicators of digital advertising and the indicator of the overall advertising media market. It is determined that the key ways of digital marketing transformation include personalised, interactive and real-time marketing. Eight key opportunities of digital marketing have been allocated, in particular: global reach, target audience and personalisation, customer loyalty, cost-effectiveness, multiplier effect, control, measurable results in real time, brand development.

Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach

The high-renewable-penetrated power system frequently requires frequency regulation services. By aggregating heterogeneous demand-side flexible resources, the virtual power plants (VPP) are able to quickly respond to the frequency regulation signal, enabling them promising frequency regulation service providers. However, due to the difference in ownership, it is unlikely that the self-interested demand-side resources are willing to follow the VPP-oriented dispatch results. To bridge this gap, this paper proposes a decentralized game-based mechanism for the frequency regulation service provision inside the VPP. Firstly, the supply function-based bidding models of aggregated demand-side resources, namely, demand-side flexible resources and renewable distributed generations, are established. Then, a non-cooperative generalized Nash game for frequency regulation service provision is formulated. The existence, uniqueness, and competitive efficiency loss of the game are analyzed by designing an equivalent best response potential game of the original game. Additionally, a decentralized look-ahead real-time market clearing mechanism for frequency regulation service provision inside the VPP is proposed based on dual decomposition algorithm. Finally, the effectiveness and computation efficiency of the proposed method are validated in the case studies. Meanwhile, it is shown that the competition efficiency loss can be effectively reduced from around 80% to 6% when increasing the demand-side resource population from 20 to 2000 as well as balancing the demand-side resource composition.

Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms

The wholesale electricity market is composed of real-time market and procurement. Since the fully liberalization of the energy market in Singapore in 2018, competition among the market participants has become intensive. Therefore, the forecast of the electricity price, including both Uniform Singapore Energy Price (USEP) and regulation price (REG), is essential for market participants to trade at the best possible price. This study proposes an event-driven forecast model to provide a real-time automatical price forecast of the wholesale electricity price using the cutting-edge regression tree ensembled algorithm, XGBoost. The model is triggered every half of an hour by the dispatch of the latest electricity price, which will be also used as the input together with other necessary training data from the historical price database. The training is finished within 1–2 min and the model makes the predictions on prices of the next 6 h. A better prediction has been demonstrated benchmarked to the forecast provided commercially by Energy Market Company. The forecast model only takes commercially available data as the input and is embedded with messaging broker, enabling online collaboration of different modules. The model is readily implemented, and thus can serve as an effective supplementary tool in virtual power plant operation with a higher renewable energy penetration in the future.

Impulse Purchase Behaviour on The Shopee Platform and The Role of Real-Time Commerce Marketing

The objective of this research is to investigate the potential correlation, encompassing good and negative aspects, between individuals' purchasing decisions and Live Commerce events, which are presently prevalent among the Indonesian population. The present study used survey methodology to collect data from participants through the use of structured questionnaires, employing a quantitative research strategy. The research study is comprised of a sample size of 400 persons. The researcher employs a cross-sectional methodology. The data analysis technique employed in this study is descriptive analysis, utilising the SmartPLS 4 software. The research findings suggest that there is a notable and favourable impact of various factors, namely Anchor characteristic, Online Comment, Logistic Service Quality, and Promotion Incentive Information, on the perception of trust. Additionally, the findings also indicate that Anchor characteristic, Logistic Service Quality, and Promotion Incentive Information have a positive influence on the perceived value. Furthermore, the mediator Perceived Trust and Value has a beneficial impact on Impulse Buying Behaviour.

A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets

Publicly available electric vehicle charging hubs are expected to grow, to meet the increasing charging demand of EVs. A dominant class of these will be fast-charging hubs where the EVs will arrive for charging at all hours of the day, get the requested charge, and leave promptly. The profitability of these fast-charging hubs will be highly dependent on the variation of the day-ahead prices of electricity, volatility of the real-time power market, and the randomness of EV charging demand. The hubs can hedge against these uncertainties by committing power purchases in the day-ahead electricity market and by adopting dynamic real-time power management strategies. We develop a novel two-step methodology. The first step entails a mixed integer linear program (MILP) that assists the hubs in their day-ahead power commitment. The second step employs a Markov decision process (MDP) model that derives the real-time power management control actions. The MILP is solved using a commercial solver and the MDP is solved using a deep reinforcement learning algorithm. We demonstrate the effectiveness of our methodology for a fast-charging hub, housing 150 charging stations and a battery storage system, that operates in the Pennsylvania-New Jersey- Maryland interconnection (PJM) power grid.

Real Time Marketing in Accommodation Businesses: A Review on Social Media Shares

Dijitalleşme ve teknolojik gelişmeler, tüketim alışkanlıklarının yanı sıra işletmelerin iş süreçlerini çevrimiçi ortamlara aktarmıştır. Diğer bir ifadeyle bir turistik ürün hakkında bilgi edinmek isteyen turistler ile ürünlerini daha geniş kitleye ulaştırmak isteyen turizm işletmeleri, elektronik ortamlarda yürütülen modern uygulamaları tercih etmeye başlamışlardır. Konaklama işletmeleri de modern pazarlama olarak ifade edilen bu süreç içerisinde turizm pazarlama karması elemanlarının faaliyete geçirilmesi için teknolojiden yararlanma amacıyla elektronik pazarlama (e-pazarlama) uygulamalarına yönelmişlerdir. Son yıllarda sosyal medya kullanımının oldukça artması ise e-pazarlamaya yönelik eğilimleri sosyal medya tabanlı yürütülen gerçek zamanlı pazarlama uygulamalarına dönüştürmüştür. Bu araştırmada, grup konaklama işletmelerinin gerçek zamanlı pazarlamaya yönelik sosyal medya sayfalarında yapmış oldukları paylaşımların içerik uyumluluğu ve sağladığı etkileşimin incelenmesi amaçlanmıştır. Nitel araştırma yöntemleri dâhilinde amaçlı örneklem yönteminin benimsendiği bu araştırmada, araştırmanın örneklemini oluşturan dünyanın en büyük grup konaklama işletmeleri olarak saptanan 15 konaklama işletmesinden sosyal medya sayfasına sahip olan 11’i incelemeye alınmıştır. Araştırma ikincil verilere dayalı olarak gerçekleştirilmiş olup etik kurulu izni gerektirmemektedir. Araştırma sonuçlarına göre, gerçek zamanlı pazarlama uygulamalarının daha fazla etkileşim alarak daha geniş kitleye ulaşılması bakımından önemli bir araç olduğu ve konaklama işletmelerinin bu amaçla yapmış oldukları paylaşımlarının içerik bakımından uyumluluk gösterdiği saptanmıştır.

Brands talking on events? Brand personification in real-time marketing tweets to drive consumer engagement

PurposeThis study aims to explore how brands use brand personification techniques in real-time marketing on social media, particularly Twitter, and examine how these techniques impact consumer engagement, moderated by brand-event congruence levels.Design/methodology/approachData included 464 tweets posted by 95 brands around three large events in 2019. The types of brand personification techniques and the level of brand-event congruence applied by the tweets were content-analyzed, and regression analyses were conducted to examine their linkages to consumer engagement metrics.FindingsResults confirmed the use of diverse personification techniques in brands’ real-time marketing tweets as in the previous literature. The study also revealed a new personification technique, tacit expression, not reported in previous literature. The study also showed that the overall effectiveness of multimedia-based (vs caption-based) personification techniques in increasing consumer engagement on social media was greater, but their relative effectiveness varied depending on whether or not the event was functionally congruent with the brand.Practical implicationsThe findings offer valuable suggestions to brand managers regarding prioritizing brand personification techniques and aligning brands’ social media marketing with real-time events to maximize the effectiveness of real-time marketing in boosting consumer engagement.Originality/valueThis research offers insights into the dynamic effects of different brand personification techniques in the new context of real-time marketing, extending the scope of literature on brand personification and anthropomorphism. The revelation of a new type of brand personification not captured in the extant literature is also a significant contribution.

Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices’ operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy.

Cyber-Impact Analysis for ISO Revenue Adequacy Considering FDIA in Real-Time Market Operations

The consensus on the potential of market-targeting cyberattacks to cause catastrophic damage has driven recent research on electricity market cybersecurity analysis. This paper identifies two missing components in current literature. First, ISO revenue adequacy has not been analyzed under the context of cyberattacks. The false data injection attacks (FDIAs) could disturb the market settlement impacting revenue adequacy for ISOs. The lack of such analysis prevents ISOs from comprehensively assessing the financial consequences of market cyberattacks. Second, market attackers need to anticipate the market-clearing results to maximize their attack objectives. Thus, current literature focuses on formulating the attacker model and the market-clearing model as a bilevel problem. However, the coupling between the attack decision, the dispatch at ex-ante, and the price calculation at ex-post have not been explored. To fill those two research gaps, this paper first analytically explores the impact of FDIAs on real-time market operations on ISO revenue adequacy. Then, cyber-impact analysis is proposed to numerically analyze the revenue adequacy. The attacker model, ex-ante dispatch model, and ex-post incremental model are formulated as a trilevel problem to provide a reliable cyber-impact analysis on revenue adequacy. The proposed analysis and platform are demonstrated with the New-England 39-bus system.