Intraday Market Research Articles

Electrification of transportation: A hybrid Benders/SDDP algorithm for optimal charging station trading

This paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices.

Modeling and Analysis of BESS Operations in Electricity Markets: Prediction and Strategies for Day-Ahead and Continuous Intra-Day Markets

Modeling and Analysis of BESS Operations in Electricity Markets: Prediction and Strategies for Day-Ahead and Continuous Intra-Day Markets

How wind forecast updates are balanced in coupled European intraday markets

AbstractThe market coupling of adjacent market zones is a key element in terms of coping with rising levels of uncertainty in the European energy system due to the adoption of more volatile renewable energy sources. An adequate analysis of this issue yet requires an appropriate consideration of uncertainty along with a detailed modelling of market coupling. Thus, we present a novel approach combining a comprehensive unit commitment and dispatch model with a sophisticated methodology to reflect the uncertainty of wind power generation by considering forecast updates for the German market region in the market clearing process. Subsequently, we investigate the impact of uncertainty on cross‐border electricity flows in a European case study. Our results show that all adjacent market zones and all relevant technologies significantly contribute to balancing forecast updates in Germany, underlining the benefits of intraday market coupling. Further, we observe that a joint and unrestricted intraday market is the most cost‐efficient market solution. Finally, our analyses show that more extensive market coupling is indispensable for the further integration of renewable energy sources in the future.

Blowing against the Wind? a narrative approach to central Bank foreign exchange intervention

Most countries in the world use foreign exchange intervention, but measuring the success of the policy is difficult. By using a narrative approach, I identify interventions when the central bank manages to influence the exchange rate based on pure luck. I separate them from interventions when the central bank actually impacted the exchange rate. Because intervention records are daily aggregates, an intervention might appear to have changed the direction of the exchange rate, when it is more likely to have been caused by market news. This analysis allows to have a better understanding of how successful central bank operations really are. I use new daily data on Bank of England interventions in the 1980 s and 1990 s. Some studies find that interventions work in up to 80 % of cases. Yet, by accounting for intraday market moving news, I find in adverse conditions, the Bank of England managed to influence the exchange rate only in 8 % of cases. I use natural language processing to confirm the validity of the narrative approach. Using lasso and a VAR analysis, I investigate what makes the Bank of England intervene. I find that only movement on the Deutschmark and not US dollar exchange rate made the Bank intervene. Also, I find that interest rate hikes were mostly a tool for currency management and accompanied by large reserve sales.

Agent based modeling for intraday electricity markets

AbstractIn recent years, the strong growth of renewable energy sources has led to considerable instability in the electricity markets. As a consequence, this has increased trading activities in the continuous intraday market, especially close to delivery. This work presents an agent-based model that is able to reproduce the continuous market evolution, distinguishing players in dispatchable and non-dispatchable power plants and analyzing the behavior and interactions between them. All players behave rationally, trying to maximize their revenues and minimize imbalances. The results show that the model is able to reproduce the main characteristics of the continuous intraday electricity market, such as the price path strongly dependent on internal and external information, such as the wind production forecast, possible outages, an increase in order arrival towards the end of the trading session and weak market efficiency. The strategies assigned to each agent have been formulated taking into account statistical analyses of historical orders placed during continuous trading in different European bidding zones. The analyses have been carried out in a scenario composed of thermal plants with different marginal costs and wind agents, but the flexibility of the model gives the possibility to study many different scenarios.

The potential of publicly available weather forecasts for market operations in aggregated photovoltaic plants

Uncertainty in solar generation forecasts affects electricity market operations, forcing producers to bid conservatively and, consequently, reducing the amount of solar energy fed into the grid. This study analyses the potential of aggregated forecasts to reduce the errors of energy generation in medium-sized PV plants. To this objective, publicly available forecasts from the Global Forecasting System were processed with a basic neural network to generate hourly energy forecasts for individual PV plants of 1.1 MW and for the aggregated production of three of them (3.3 MW). The validation was carried out considering typical day-ahead and intraday market horizons, two zones with different atmospheric complexity, and discerning three types of days in terms of the received irradiation. Results show that the aggregation of forecasts would be beneficial as a general strategy in all cases, but especially in the zone of complex atmospheric dynamics and for the day-ahead horizons (36-42 h).

Enhancing Bitcoin Price Volatility Estimator Predictions: A Four-Step Methodological Approach Utilizing Elastic Net Regression

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling.

A mean field game model for optimal trading in the intraday electricity market

AbstractIn this study, we provide a simple one period mean-field-games setting for the joint optimal trading problem for electricity producers in the electricity markets. Based on the Markowitz mean-variance approach from stock trading, we consider a decision problem of an electricity provider when determining the optimal fractions of production that should be traded in the day-ahead and in the intraday markets. Moreover, all such providers are related by a ranking criterion and each one wants to perform as good as possible in this ranking. We first start with a simple model where only the price risk in the intraday market is present and subsequently extend the problem to the cases involving either production and/or demand uncertainty. The key technique is to reduce the optimality conditions to a first order non-linear ordinary differential equation. We will illustrate our findings by various numerical examples. Our findings will in particular be important for electricity producers using renewable resources.

Optimal operation of pumped storage power plants with fixed- and variable-speed generators in multiple electricity markets considering overload operation

This work studies the optimal operation of pumped storage power plants with fixed- and variable-speed generators in different electricity markets. This paper extends the state of the art by systematically considering the detailed plant behavior for heterogeneous pumped storage power plants and the possible short-term electrical overload operation. The Day-Ahead, ancillary service, and Intraday markets are studied in the first part by developing a mixed integer nonlinear problem (MINLP). The results demonstrate that variable-speed units, especially if equipped with doubly-fed induction machines (DFIMs), yield the highest profit, particularly in the presence of automatic Frequency Restoration Reserve (aFRR). The proposed optimization of the Intraday market shows that the total profit of the Day-Ahead, Ancillary services, and Intraday markets are the highest in the case of variable-speed units with aFRR, where it might be a drawback offering Frequency Containment Reserve. Finally, a robust optimization-based bidding curve generation strategy is developed in the last step. The strategy allows the plant to operate with high profits under price prediction uncertainties and enables optimal reservoir management. The developed optimization tasks and the bidding curve generation strategy also exhibit shorter computation times than the state-of-the-art strategies.

FUNCTIONING OF THE MODERN ELECTRICITY MARKET IN UKRAINE AND PRICING FEATURES ON ITS SEGMENTS

Objective. The study aims to investigate the functioning peculiarities of the modern electricity market in Ukraine and pricing on its segments, and to justify proposals for their improvement. Methodology. To address the set tasks, a variety of methods and approaches were utilized in the work, including: abstract-logical – in defining the purpose and objectives of the research; analytical-monographic – in studying literary sources, legislative, and normative legal acts in the sphere of functioning of the modern electricity market in Ukraine; causal-analysis – in identifying a series of barriers hindering the development of the domestic electricity market; statistical-economic – in the process of collecting, processing statistical data and studying the results of energy market operation in 2020-2023. Results. The article examines the main aspects and problems of the functioning of the modern electricity market in Ukraine, based on competitive trading volumes of electricity in integrated market segments such as: day-ahead market, intraday market, bilateral contracts market, balancing electricity market, ancillary services market. Special attention is paid to the mechanism of electricity pricing. Scientific novelty. Recommendations for further liberalization of the electricity market in Ukraine regarding electricity pricing mechanisms on market segments are provided, which will help align price distortions, disproportions in market participants' calculations, and create competitive advantages in energy markets in Ukraine, based on the analysis of its operation results in 2020-2023. Practical significance. The recommendations provided in the article can be applied in the development of individual elements of Ukraine's Energy Strategy, creating conditions for sustainable development of Ukraine.

A Survey on the Spanish Electricity Intraday Market

The Spanish electricity intraday market presents a particular design that makes it unique in Europe. The study is carried out on an hourly basis in order to identify hourly patterns in prices and traded quantities by session. The study evidences an overall growing interest from market participants in intraday trading, particularly in the last-time- negotiated hours. Prices exhibit hourly and daily seasonality as well as mean reversion. Going in depth into the analysis of the current electricity intraday markets is of great relevance nowadays given that there is an open debate concerning the optimal design for intraday markets all over Europe as a consequence of the wholehearted integration of renewables into the power system.

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response

This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity generation and storage system with an integrated demand response scheduling of a continuous power-intensive production process in a multi-stage problem. We optimize both total annualized cost and global warming impact and consider local photovoltaic and wind electricity generation, an electric battery, and electricity trading on day-ahead and intraday market. We find that installing a battery can reduce emissions and enable large trading volumes on the electricity markets, but significantly increases cost. Economically and ecologically-optimal operation of the process and battery are driven primarily by the electricity price and grid emission factor, respectively, rather than locally generated electricity. A parameter study reveals that cost savings from the local system and flexibilizing the process behave almost additively.

Planning of Reserve Storage to Compensate for Forecast Errors

Forecasts and their corresponding optimized operation plans for energy plants never match perfectly, especially if they have a horizon of several days. In this paper, we suggest a concept to cope with uncertain load forecasts by reserving a share of the energy storage system for short-term balancing. Depending on the amount of uncertainty in the load forecasts, we schedule the energy system with a specific reduced storage capacity at the day-ahead market. For the day of delivery, we examine the optimal thresholds when the remaining capacity should be used to balance differences between forecast and reality at the intraday market. With the help of a case study for a simple sector-coupled energy system with a demand for cooling, it is shown that the energy costs could be reduced by up to 10% using the optimal reserve share. The optimal reserve share depends on the forecast quality and the time series of loads and prices. Generally, the trends and qualitative results can be transferred to other systems. However, of course, an individual evaluation before the realization is recommended.

Optimization of an agent-based model for continuous trading energy market

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process, but, on the other hand, the electrical system has to face the problem of unbalances. Renewable energies sources are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real-time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work, we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part, we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part, we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced.

Frequent Auctions for Intraday Electricity Markets

Continuous trading is currently becoming the standard for intraday electricity markets. In this paper, we propose frequent auctions as a viable alternative. We argue that batching orders in auctions potentially leads to lower liquidity cost, more reliable, less noisy price signals, and allows for better alignment of market outcomes with the technical realities of the transmission grid. In an empirical study, we compare the German continuous intraday market with counterfactual outcomes from frequent auctions. We find that traded volumes tend to be higher for continuous trading; however, the auction market benefits from lower liquidity costs and less noisy price signals. Furthermore, we critically discuss the suitability of continuous trading in the presence of network constraints and technical restrictions of conventional units. Taken together these findings suggest that in sparsely traded intraday markets, pooling orders in frequent auctions may be beneficial.

Limits of arbitrage and their impact on market efficiency: Evidence from China

This paper examines the impact of limits of arbitrage (LOA) on market efficiency by considering a large sample of Chinese stocks. Intraday market efficiency is measured using two widely used measures: the intraday return predictability measure and the variance ratio measure. We find that LOA plays a major role in driving market efficiency. We also use an exogenous shock, the introduction of Shenzhen Stock Connect, which increases trade volume and lowers LOA, to examine the impact of LOA on market efficiency. Our results indicate that this event improves market efficiency regarding both our measures. Therefore, we conclude that policy makers in China are acting in the right direction to elevate China's status in world markets by adapting policies that inherently make the country more attractive to global investors by lowering LOA and enhancing market efficiency.

Impact of renewable energy generation on power reserve energy demand

With increasing shares of renewable energy generation, the evolution of reserve needs may represent an important challenge for the power system. The evolution of reserve capacity needs in this context has been extensively studied in the literature (Hirth and Ziegenhagen, 2015). On the other hand, reserve energy needs have been less studied. This work focuses on analysing the relationship between reserve energy demand and load, wind, and solar generation. To this end, autoregressive models with exogenous variables are estimated on 2019 data of the French power system. We find a positive impact of renewable energy generation on downward reserve energy demand and a negative impact on upward reserve energy demand. These results can be interpreted through the relationship between the level of residual load and the probability to find a counterpart on the intraday market to balance forecast errors, thus allowing to reduce the imbalance of the system in real-time and thus the need for reserve energy.

Market integration and TSO-DSO coordination for viable Market-based congestion management in power systems

The article presents the findings on market-based congestion management (CM) in power systems. The main idea is to unlock flexibility from both small and large-scale resources by creating a platform so that flexibility can enter the markets through the platform and be used by system operators (DSOs and TSOs) for CM. The article recognizes two pressing issues in market-based CM: low liquidity and adverse impacts of flexibility activation. The article proposes leveraging market integration and TSO-DSO coordination to address the pressing problems and incorporate them into the platform. Bids from the intraday market at Nord Pool as well as the balancing market bids, were used for CM to show the possibility of addressing the low liquidity issue by receiving bids from well-established markets. In TSO-DSO coordination, an algorithm-agnostic process is proposed and implemented to involve SOs' network limitations before flexibility is traded to mitigate the adverse impacts of flexibility activation. As the market integration and TSO-DSO coordination functionalities rely on flexibility-related data that are often in huge quantities, a metadata register is also implemented to gather, process, and store data to be smoothly accessed by different stakeholders depending on their needs and access rights.

An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets.

DIRECTIONS OF REALIZATION OF THE ORGANIZATIONAL AND ECONOMIC MECHANISM OF FORMATION OF THE ENERGY SECURITY SYSTEM OF UKRAINE: THE ELECTRIC POWER ASPECT

The process of reforming the electricity market has been underway for a long time. The European market liberalization guidelines encourage changes in Ukraine. However, internal contradictions of the domestic market slow down these changes. The basis for these changes is the Law of Ukraine, "On the Electricity Market," adopted in 2017, which lays the foundation for deep reform. This is manifested in the following: all enterprises (producers and suppliers) in the electricity market must operate on separate, self-organized principles; openness and transparency, and free access to the use of networks of all business entities based on public contracts; the law provides for and creates several electricity markets: the intraday market, the balancing market, and the ancillary services market. Each has a separate operating procedure, electricity purchase and sale system, pricing, and the establishment of sales volume standards and quotas. The purpose of the article is to determine the priority areas for implementing the organizational and economic mechanism for the formation of Ukraine's energy security system in the context of the electricity sector. The situation in the electricity market requires significant investments (about USD 25 billion over ten years) to ensure the reliability and continuity of electricity supply. Reform of the electricity market should be based on the following principles: efficiency and transparency of the National Commission for State Regulation of Energy and Public Utilities; separation of electricity generation from distribution; certification of the transmission system operator; updating system, technical, software and information software; optimization of measures and reform of the retail market to meet the needs of consumers; economic justification of pricing to meet the needs and capabilities of consumers, including changes in the cross-subsidization mechanism. Implementing the planned goals and priority areas will help reduce risks in reforming the electricity market, intensify the competitive basis between market participants (entities), create conditions for improving service quality, and limit negative factors' impact on the environment with an incentive pricing policy. The structured structure of the organizational and economic mechanism will allow us to systematize the understanding of the stages of the electricity market functioning. Thus, a unified system of interrelations between the goals of reform, the search for reform methods, sources of financial support, subject and object positions, principles, and methods of their regulation, which directly or indirectly affect the efficiency of the electricity market, will be formed.