Real-time Market Research Articles

Research on the optimal allocation method of source and storage capacity of integrated energy system considering integrated demand response

The optimal allocation of energy storage capacity is an important issue for integrated energy systems (IES). To reduce the impact of volatility and intermittency of renewable energy sources, the impact of volatility needs to be smoothed out by rational allocation of energy storage. To address the above issues, this paper proposes a method for the optimal allocation of source storage capacity considering integrated demand response(IDR). Firstly, the basic mechanism of IES based on energy hub(EH) is constructed, and the model and coupling relationship of equipment components in the system are introduced. The IDR model is built to optimize the load curve according to the real-time market tariff scheme, and then the confidence interval method is used to analyze the scenery uncertainty in IES and determine the scenery output curve under different confidence levels. On this basis, a bi-level optimization model is built that takes into account both source and storage capacity allocation and operational optimization. The upper-level planning model takes into account the uncertainty of wind power and photovoltaic output, and solves the allocation scheme of energy storage intending to minimize the total planning cost; the lower-level operation optimization model takes into account the output constraints of each unit and optimizes the output of the equipment to minimize the operation cost. Finally, it is verified that considering IDR can reasonably optimize the allocation of the system’s source storage capacity and reduce the system’s investment and operating costs.

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

Graph-theoretic approach to detecting real-time intents within purchase conversion funnel using clickstream data

PurposePurchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.Design/methodology/approachThe authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.FindingsThe graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.Practical implicationsThe findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.Originality/valueTo the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

Efficient energy management of wireless charging roads with energy storage for coupled transportation–power systems

Wireless charging roads equipped with energy storage systems are promising electric vehicle charging solutions by virtue of their strong advantages in time saving and reduced pressure on the existing power infrastructure. Integration of wireless charging roads into the existing electricity market and efficient management of the corresponding energy storage system are crucial for successful implementation of the wireless charging road systems. In this work, we develop a coupled transportation–power system framework for incorporation of a wireless charging road system into the real-time electricity market. In addition, we propose a Lyapunov optimization-based control strategy to manage the energy storage system in a cost-efficient manner. Our simulation study demonstrates that efficient control of the energy storage system not only reduces the energy costs of the entire wireless charging road system but also alleviates the pressure produced by the wireless charging load on the existing power grid. In our two numerical examples, the energy costs are reduced by 2.61% and 15.34%, respectively. Two indicators of power grid pressure: time average of maximum and time average of standard deviation of locational marginal prices, are reduced by 10.65% and 69.33% for the first numerical example and 5.11% and 34.73% for the second numerical example.

Load forecasting and risk assessment for energy market with renewable based distributed generation

This paper projects the risk associated with social benefit gained by Independent System Operator (ISO) due to unpredictability of load demand and intermittent nature of Renewable Energy Sources (RES). The hybrid method i.e. Wavelet Transform–Auto Regression Integrated Moving Average (WT-ARIMA) is used for forecasting the load in day-ahead market. The load demand for 24 hours is forecasted using the historical hourly data of 2 months. The mean absolute percentage error (MAPE) is calculated for both classical ARIMA and WT-ARIMA model. When compared to the ARIMA model, the WT-ARIMA model has a better fit to the time series data. This paper relates the load forecasting with energy market and shows the impact of forecasting error on market parameters. The system parameters like generation cost, social benefit and power loss are analyzed in day-ahead and real-time market. This work also elaborates the effect of intermittent nature of RES on the system parameters. The optimal location of RES based distributed generation is identified by higher Locational Marginal Price (LMP) based ranking method. The optimal energy management problem is formulated with the aim to minimize the total generation cost taking into account the variability in wind power and solar power. The uncertainty modeling of wind and solar is projected by the Rayleigh and Beta distribution respectively. The risk associated with social benefit of ISO due to uncertainty of wind and solar is calculated using indices, Value at Risk and Conditional Value at Risk. The proposed approach is implemented on IEEE 30 bus system.

Flexibility Aggregation of Temporally Coupled Resources in Real-Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio’s actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator’s offer. Once trained, the ML algorithms can make fast decisions about the portfolio’s balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator’s portfolio and minimizing the aggregator’s imbalances.

Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

The uncertainty of wind power and electricityprice restrict the profitability of wind-storage integrated system (WSS) participating in real-time market (RTM). This paper presents a self-dispatch model for WSS based on deep reinforcement learning (DRL). The designed model is able to learn the integrated bidding and charging policy of WSS from the historical data. Besides, the maximum entropy and distributed prioritized experience replay frame, known as Ape-X, is used in this model. The Ape-X decouples the acting and learning in training by a central shared replay memory to enhance the efficiency and performance of the DRL procedures. Besides, the maximum entropy framework enables the designed agent to explore various optimal possibilities, thus the learned policy is more stable considering the uncertainty of wind power and electricity price. Compared with traditional methods, this model brings more benefits to wind farms while ensuring robustness.

Automated Real Estate Valuation With Machine Learning: A Case Study on Apartment Sales in Yerevan

Real estate is one of the major sectors of the Armenian economy and has been developing dynamically since Armenia transitions from planned to market economies in early 1990s. More recently, large online platforms have been developed in Armenia to advertise real estate offerings, thus reducing information asymmetry, and increasing liquidity in both sales and rental markets. Simultaneously, granular geospatial data became increasingly affordable via platforms such as OpenStreetMap, Google Maps and Yandex Maps. With granular data concerning a representative portion of the real estate offering available online, it is increasingly tenable to monitor the real estate market in real time and develop analytical tools that can automatically and accurately estimate the value of real estate assets based on their internal and external features. This paper sets out to analyze Armenia real estate market and assess the performance of a special class of machine learning models while predicting the price of a square meter of apartments in Yerevan. Furthermore, it is presented the way to determine the most decisive factors which have an influence on the price of apartments on sale.

미국 내 다중 간격 실시간 시장 운영 및 가격 결정 방식 분석

With the increase of renewable energy in the power system, the volatility of net load and uncertainty in the power system continue to increase. This can increase the number of ramp events and supply and demand imbalances in the power system, which can lead to abnormal and inefficient operations such as price instability and price spikes in the power market. Accordingly, securing ramping capability to strengthen flexibility is becoming important. In this paper, we will look at the markets introduced by ISO, especially CAISO and NYISO in the United States to secure ramping up/down resources. MIRTM(multi-interval real time market) is a market based on real time unit commitment and look-ahead dispatch considering continuous time. MIRTM operation can reflect the volatility of the net load in economic dispatch, but there is a limit to reflecting shortterm uncertainty of net load, and there is an issue related to the uplift in determining electricity prices. It is analyzed that these limitations of MIRTM can be partially supplemented through the FRP(flexible ramping product) introduced by CAISO. The FRP is a product that ensures a 95% confidence interval for net load forecasting error in economic dispatch. Through FRP, pure energy prices and ramping related costs can be distinguished in MIRTM, and and it is analyzed that FRP can be a reasonable decision method to calculate the uplift in MIRTM.

Stock Price Prediction and Analysis using Machine Learning Techniques

Abstract: Stock market is one of the most sought-after investment options today. Investing in the stock market has the potential to multiply people's money in a short period of time, but it also carries many risks. To be successful in the stock market, it is necessary to conduct extensive research about the market and the forces influencing it. This examination involves a review of historical stock prices as well as real-time market developments. When deciding the position in the market, investors need to make an informed choice. Market-related information may be obtained from a variety of sources, including news articles, stock research websites, apps, and television channels. However, stock movement prediction by taking into consideration the elements influencing it would undoubtedly assist investors in making sound decisions. The research area of stock movement prediction has seen numerous advancements throughout the years. In the domain of machine learning, various methods of stock price prediction have been introduced. In this paper, we have discussed different approaches to stock movement prediction based on diverse factors and technical indicators. We also propose a method for detecting market trends, which is a critical aspect in maintaining long-term market positions. Keywords: Machine Learning, Stock Market, Prediction, Classification, Technical Indicators, Trend prediction

Publisher’s Note

High-voltage direct current (HVDC) transmission systems are widely employed to transmit energy among systems with renewable energy integrated. However, the reduced system inertia and the risk of HVDC failure pose significant challenges to operators. To ensure system reliability especially frequency stability under extreme circumstances, a robust frequency-constrained unit commitment (FCUC) model is proposed in this paper. Unlike previous studies, an extra power-rescheduling procedure is embedded in our model, and consequently the FCUC model is formulated as a three-layer structure, where the day-ahead schedule model, emergency corrective model (primary frequency response), and power rescheduling (secondary frequency response) are co-optimized. Accordingly, an improved tri-level Benders Decomposition (BD) algorithm was developed to solve the problem. By leveraging the optimality cuts of the algorithm, the characteristics of fast power support and flexible regulation capabilities of HVDC system in real-time (RT) market are preemptively considered in DA market. Consequently, the model feasible region is more adaptive to the plausible contingencies The outperformance of our model was validated by the numerical experiments in an asynchronous HVDC interconnected system. The results demonstrate that by formulating the coordination of AC–DC dispatch framework, the global optimal operational cost is further captured when ensuring the system reliability.

A Study on Farmers’ Perception about Uzhavan App in Select Districts of Tamil Nadu

ICT provides more facilities to farmers by introducing agricultural mobile apps and encourage the farmers for effective farming. These mobile apps provide agriculture related information to farmers in timely and cost effective manner. The present study is carried out to find the farmers perception on uzhavan app which is developed by Government of Tamil Nadu. More than 5 lakh farmers have registered in this app and get various benefits. The uzhavan app has been used by many farmers in gaining more information about agricultural activities. Respondents revealed that dissemination of information through the uzhavan app is valuable and user friendly. The major challenges faced by the farmers were network problem mainly during the ATMA training registration, lack of price details of few commodity and in minor problem time consumes to use all services, app is quite difficult to use due to poor content visibility. From the study, it has been found that majority of farmers had good perception about the uzhavan app and they are satisfied with the features that support them in farming and they also expect some other features need to be integrated in uzhavan mobile app. The uzhavan app provides real time market price of some agricultural commodities and it has greater impact in selling farmers produce at fair price in mandis. Meanwhile, the features of e market online platform helps farmers to connects directly with traders and sell their produce at good price. The availability of farm guide in app provides information regarding fertilizer recommendations practices where most of sample respondents adopted recommended usage level and has positive impact in farming by increasing the yield of farmers.

An Asynchronous Online Negotiation Mechanism for Real-Time Peer-to-Peer Electricity Markets

Participants in electricity markets are becoming more proactive owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management (DSM), which also boosts the emergence of Peer-to-Peer (P2P) market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. It is highly desirable to propose and analyse novel approaches suitable for real-time P2P market mechanisms. These are challenging since most often involving a heavy computational burden, while the time available for negotiation in real-time is very short. Our core contribution is to design and analyse a novel asynchronous online optimization framework and related real-time P2P market negotiation mechanism, which can greatly reduce the computation and communication burden from two aspects. First, a novel online consensus alternating direction method of multipliers (ADMM) algorithm is proposed. It significantly reduces computation since only one iteration is performed for each agent at every time period. Second, the market operates in an asynchronous mode so that all agents can freely trade without waiting for idle or inactive neighboring agents. The sublinear regret upper bound is proved for our asynchronous online algorithm, which indicates that social welfare in the market can be maximized in the long run on average (over time). Simulations show that our algorithm enjoys good convergence performance, robustness, and fairness.

Modeling the risk-based decisions of the microgrid in day-ahead energy and reserve markets considering stochastic dispatching of electrical and thermal energy storages

• The MGO’s bids in the markets are optimized with dispatching of local resources. • Uncertainties of electrical demand and power generation of PV are modeled. • A robust two-stage stochastic optimization model is developed. • The most capacity of electrical energy storage is used to provide reserve capacity. • With increasing the robust parameter, the MGO’s cost increases. In this paper, the electrical and thermal energy management problem of a micro-grid operator (MGO) is addressed under uncertainties aiming at participating in the day-ahead energy and reserve markets. For this purpose, a robust two-stage stochastic model is developed to protect the first stage MGO’s decisions, i.e., its bids in the energy and reserve markets, against the uncertainty of the real-time energy market price. This is done through stochastic dispatching of the MG resources which includes the electrical and thermal energy storages and the combined heat and power unit as the second-stage decisions. The results showed that the MGO’s expected total cost decreases when it participates in both the energy market and the reserve market in comparison with the case it only participates in the energy market. Also, the risk-based behavior of the MGO showed that increasing the robust parameter decreases the reserve provided for the market and the net power trading with the market. However, the proposed robust two-stage stochastic model leads to a smaller reduction of the MGO’s first-stage decisions in the worst case in comparison with the conventional methods, i.e. deterministic and probabilistic ones. This issue proves the effectiveness of the proposed approach to protect the MGO’s decisions against the uncertainties.

Optimal Scheduling of a Self-Healing Building Using Hybrid Stochastic-Robust Optimization Approach

This article provides a two-stage robust energy management method for a self-healing smart building that can handle contingencies that occur during real-time operation. Aside from an electrical link with the distribution network, the smart building is equipped with a diesel generator and photovoltaic solar power generating systems. The energy management system should be smart enough to plan different resources based on the situation. At first, bilevel programming identifies critical faults for affected components based on mean time to repair. After identifying major failures, the faults are described in operational scenarios, and a two-stage hybrid robust-stochastic programming technique is used to determine the bid/offer in day-ahead and real-time energy markets, in which stochastic programming is responsible for considering the uncertainty of faults, and the robust optimization approach is used to cope with the uncertainty of real-time market prices. After linearization, the final optimization is modeled as mixed-integer linear programming in the GAMS optimization package. For the studied smart building, the daily operational cost is expected to increase from $25.794 (for the deterministic case) to $ 28.097 (for the most conservative case) due to the uncertainty of real-time market prices. Due to power shortages caused by the failure of components, the total expected not-supplied load is 6.72 kW (2.53%). A comparison between naive and self-healing scheduling indicated that naive energy management will charge an additional $ 2.75 without considering the probability of components failures under the deterministic case.

A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants’ Performance: A Case of California ISO

Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world. We address this open problem with focus on the electricity market that is operated by the California ISO. In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the existing real-world strategies to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy selection algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used.

Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study

Demand response program is being implemented in the National Electricity Market of Singapore, which boosts the flexibility of demand side to actively participate in the real-time electricity market. Meanwhile, it is also significant to implement such a program in the day-ahead market, since generation companies could arrange their generating plans and load providers are able to adjust their hourly purchasing schedules. However, uncertain factors should be considered in the demand response program of the day-ahead market, such as the uncertain electricity load. Regarding the issue, this paper proposes a day-ahead bidding and clearing framework considering demand response with uncertain and correlated nature of electricity loads. To this end, a data-driven Dirichlet process mixture model is introduced to represent the load uncertainty, which might bring about the economic risk. To further reduce such a risk, a worst-case conditional value at risk is integrated into our proposed framework, and a WCVaR based two-step risk averse market clearing model is proposed. Finally, we conduct numerical studies based on the Singapore electricity market. Numerical studies demonstrate the outperformance of Dirichlet process mixture model for the load uncertain representation, and also verify that the worst-case conditional value at risk based market clearing model could effectively reduce the economic risk while maximizing the social welfare.

HVAC system dynamic management in communities via an aggregation–disaggregation framework

The aggregation and disaggregation problem of Heating Ventilation and Air Conditioning (HVAC) systems in the building at the community-level have been considered in this work. The aim is to provide capacity reserves as a Virtual Power Plant (VPP) to support power system operation, improving grid reliability and efficiency in the face of increased use of renewable energy. Motivated by the layered operating structure of the power grid, an aggregation–disaggregation framework which operates at both slow and fast time-scales is proposed. At the slow time-scale, each building forecasts its HVAC system future electric power consumption information using predictions of disturbance and information of thermal zone via solving certain optimization problems. These values will be collected by a control center such as a community aggregator. Acting as a VPP, the control center determines each HVAC system future nominal electric power consumption by participating in the real-time electricity market or an independent power allocation plan. At the fast time-scale, a modified primal–dual gradient method based distributed control scheme is designed such that under real disturbances, the system can track the nominal consumption as closely as possible using only neighboring communications and local measurements. Finally, a numerical study demonstrates the effectiveness of the proposed framework.

Investigating the informativeness of technical indicators and news sentiment in financial market price prediction

Real-time market prediction tool tracking public opinion in specialized newsgroups and informative market data persuades investors of financial markets. Previous works mainly used lexicon-based sentiment analysis for financial markets prediction, while recently proposed transformer-based sentiment analysis promise good results for cross-domain sentiment analysis. This work considers temporal relationships between consecutive snapshots of informative market data and mood time series for market price prediction. We calculate the sentiment mood time series via the probability distribution of news embedding generated through a BERT-based transformer language model fine-tuned for financial domain sentiment analysis. We then use a deep recurrent neural network for feature extraction followed by a dense layer for price regression. We implemented our approach as an open-source API for real-time price regression. We build a corpus of financial news related to currency pairs in foreign exchange and Cryptocurrency markets. We further augment our model with informative technical indicators and news sentiment scores aligned based on news release timestamp. Results of our experiments show significant error reduction compared to the baselines. Our Financial News and Financial Sentiment Analysis RESTFul APIs are available for public use.

Robust bidding strategy for aggregation of distributed prosumers in flexiramp market

Distributed prosumers (DPs) are the grid customers that own energy production/storage assets. Due to the flexibility and fast response of their assets, they can procure ancillary service products (ASP) in the wholesale market. An appealing ASP offered by California ISO in the real-time market (RTM) is flexiramp for which market participants do not submit direct offers, and the compensation is based on their energy opportunity costs. In this paper, we propose a bidding strategy model for DP aggregator participation in the RTM considering energy and flexiramp. First, we develop a risk-averse optimization to determine the optimal energy and reserve product to trade in day-ahead market while considering proper amounts of flexiramp to trade in the RTM. In the RTM, to obtain optimal amounts of energy and flexiramp, the aggregator must submit hourly multi-level price-quantity energy bids for multiple RTM intervals with 15 min time-steps. On this basis, we propose a robust hourly economic bidding strategy model that determines the optimal energy bids in the RTM. We develop an adjustable robust counterpart of the model to address the RTM energy and flexiramp price uncertainties. The simulation results justify the efficacy of our proposed framework in gaining profits from the wholesale market.