Day-ahead Electricity Market Research Articles

Bid-Aggregation Based Clearing of Day-Ahead Electricity Markets

In this work we propose a heuristic clearing method for a simplified model of European day-ahead electricity auction markets, considering divisible standard bids and block orders. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints, reducing the feasibility region of the clearing problem. By removing bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block orders the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to run multiple threads of the algorithm, using different aggregation patterns. We evaluate the performance of the algorithm via numerical tests.

Has Real Time Spot Electricity Market in India Impacted Day-Ahead Spot Electricity Market?

Introduction of Real-Time Market (RTM) on June 1st, 2020, marked pivotal advancement in electricity trading in India featuring bi-hourly auctions and power transmission based on temporal blocks. Adherence to operational protocols outlined in Procedure for Scheduling Collective Transactions in RTM along with statutory directives like CERC Power Market Regulations 2010 ensures efficient market operation. Notable features include 15-minute contract trading, clandestine bidding and authorization from SLDC. Day-Ahead Market (DAM) facilitates electricity exchange through double-sided closed auctions ensuring compliance with regulatory protocols. In this study we explore whether Real Time Spot Electricity Market in India has impacted Day-Ahead Spot Electricity Market. Findings of the study provide crucial insights for market participants, stakeholders, open access consumers and electric utilities is India.

The effect of hydropower bidding strategy on the iberian day-ahead electricity market

In 2021, the Iberian Electricity Market, which includes Portugal and Spain, experienced a significant increase in electricity prices. On average, prices tripled compared to pre-pandemic levels in 2019, despite stable demand. This trend, though less pronounced in other European markets, has raised concerns about the efficiency and competitiveness of wholesale electricity markets. This study investigates the bidding behavior of non-pumped hydropower plants in the Iberian market, employing a merit order model based on real data from 2019 to 2021. The analysis reveals a clear mismatch between hydropower bid prices and actual production costs. Although this practice is legal, it conflicts with the European Union's goal of promoting fair competition within the internal electricity market.Our findings suggest that aligning hydropower bid prices with 2019 levels could have reduced annual market costs by approximately 2324 million euros under a moderate scenario. This indicates substantial potential for cost savings and improved integration of hydropower. However, this strategy also benefits companies that own these hydropower plants, as they often possess large portfolios and can capitalize on increased market prices through other plants, particularly renewable energy units, which offer electricity at very low prices and have a high likelihood of being dispatched.Furthermore, the study highlights regulatory gaps allowing hydropower plants to price low-cost energy similarly to thermal plants, raising consumer costs and distorting market dynamics. These findings emphasize the need for stronger regulatory oversight and reforms to prevent excessive price inflation by hydropower, which plays a critical role in renewable energy integration and grid stability. Policymakers must ensure fair pricing while compensating hydropower plants for their flexibility and essential contributions to the energy system.

A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series

Day-ahead electricity market is crucial for ensuring the balance of electricity supply and demand. Its electricity price serves as a key guide for market participants and power dispatching agencies, making accurate short-term price forecasting essential. In this paper, a hybrid short-term electricity price forecasting method considering the long-term dependence of wavelet transform (WT) series is proposed. Different from the existing studies that do not distinguish the characteristics of WT series, this paper uses Hurst exponent and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots to analyze the long-term dependence of subseries, and chooses seasonal autoregressive integrated moving average (SARIMA) or long short-term memory (LSTM) method to construct a hybrid forecasting model according to the characteristics of each subseries. Validation using real electricity prices from the NYISO and PJM markets shows that, compared to methods that do not consider long-term dependencies of subseries, the proposed method can improve prediction accuracy by up to 50.98 % and stability by up to 68.09 %.

Distributed economic model predictive control for the joint energy dispatch of wind farms and run-of-the-river hydropower plants

This study addresses the energy dispatch problem of a virtual power plant (VPP) acting as a price-tacker in the day-ahead electricity market. The VPP comprises wind farms and a cascade of run-of-the-river hydropower plants. Even if the storage capacity of the cascade is limited, it can still be exploited to compensate the variability of wind. This implies dispatching the water reservoirs near to real-time, while accounting for complex constraints and various sources of uncertainty. To this aim, we present a control strategy based on economic model predictive control (MPC), which is then decomposed using the auxiliary problem principle. As a distinctive feature, the proposed algorithm is fully-distributed, i.e. no central coordinator is required. Compared to centralized MPC, the distributed algorithm brings a ∼10% reduction in the average execution time of the controller. Moreover, the joint operation of hydropower and wind is shown to enhance the economic value of both assets.

Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in the optimisation framework. Considering this cost in economic analysis and operational strategies is essential to optimise long-term performance and economic viability. Neglecting degradation costs can lead to suboptimal operation and dispatch strategies. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximising net revenues, maximising profits considering capacity degradation, and maximising profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. This paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

Risk-aware microgrid operation and participation in the day-ahead electricity market

This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the ɛ-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.

Bidding zero? An analysis of solar power plants’ price bids in the electricity day-ahead market

Typically, solar power is offered for price bids at the level of their near zero marginal costs to electricity markets. While aggregate effects of this behaviour on prices (merit-order effect) and profits (cannibalisation effect) have been studied extensively, potential deviations from this strategy still lack an understanding. We observe a group of firms to offer solar power for prices larger than zero to the Iberian electricity day-ahead market. Based on a literature review and analysing incentives set for solar power by the Spanish electricity market design, we suggest these price bids to result from revenue opportunities in sequential markets. Results of our regression analyses confirm that the observed group of firms is more likely to conduct arbitrage. This motive also allows for explaining the level of a case-study firm’s price bids.

Enhancing Day-Ahead Electricity Market Planning with a Novel Probabilistic Strategy for Wind Power and Uncertain Customers

Enhancing Day-Ahead Electricity Market Planning with a Novel Probabilistic Strategy for Wind Power and Uncertain Customers

Time-frequency connectedness between electricity prices in Romania and its determinants in the competitive markets

PurposeIn this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.Design/methodology/approachOur study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.FindingsWe discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.Originality/valueWe combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).

Merit-order of dispatchable and variable renewable energy sources in Turkey's day-ahead electricity market

This study employs linear and non-linear quantile regression models to assess the merit-order effect (MOE) of integrating renewable energy sources (RESs) into the Turkish day-ahead electricity market. The analysis spans nine different quantiles from January 2019 to December 2022. Our results show that both dispatchable and variable renewable energy sources (VREs) are essential for reducing market clearing prices (MCPs) in the Turkish day-ahead market. However, they have different impacts on quantiles of MCPs and price variability. VREs enhances the price variability across all hours in linear and non-linear models, while dispatchable renewable energy reduces price variability except at night.

Market zone configuration under collusive bidding among the conventional generators and renewable energy sources in the day-ahead electricity market

The European cross-zonal day-ahead (DA) electricity market is transitioning to the flow-based market coupling model for market clearing. With the increasing integration of renewable energy sources (RESs), market participants have opportunities for collusive bidding, resulting in decreased social welfare (SW). Thus, this paper is the first to propose an approach to configure the market zone (MZ) considering collusive bidding among conventional generators (CGs) and RESs in the DA market. Specifically, a bi-level model is developed to analyze collusive bidding among the CGs and RESs. Then, multi-dimensional market performance indices are used to determine the critical branches (CBs), on which the configuration of MZs is based. Finally, test 6-bus and simplified European systems are used to demonstrate the validity and merit of the proposed approach. Our simulation on the 6-bus system shows that when compared with the initial zonal market (ZM), the SW of the optimized ZM increased by 18 %, while the re-dispatch surrogate cost decreased to 0. Also, the penetration of RESs improved by 12 %, which guarantees the development of RESs.

Literature review. Pricing rule alternatives for the European day-ahead market

This literature review delves into the intricate realm of pricing rules in the day-ahead electricity market, spurred by a notable surge in 2022 electricity prices. Employing a comparative analysis of uniform pricing (UP), discriminatory pricing (DP), and the Vickrey–Clarke–Groves (VCG) pricing rule, the study navigates the complexities of these mechanisms. The evaluation unveils contrasting perspectives: the political push for consumer-friendly low prices versus the efficiency aspiration for a self-regulating market. DP, minimizing average prices but increasing volatility, and VCG, excelling in productive efficiency, are pitted against UP, the prevailing yet imperfect norm. The findings underscore the nuances of each rule; DP leads to reduced average prices and heightened volatility, while VCG ensures superior productive efficiency. Despite UP’s simplicity, it prompts inefficiencies and political costs. These insights illuminate the pivotal choice faced by policymakers, balancing efficiency, fairness, and market sustainability.

Forecasting with an N-dimensional Langevin equation and a neural-ordinary differential equation.

Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stationary methods is comparatively scarce, despite the common prevalence of non-stationary features in electricity markets. Specifically, existing non-stationary techniques will often aim to address individual non-stationary features in isolation, leaving aside the exploration of concurrent multiple non-stationary effects. Our overarching objective here is the formulation of a framework to systematically model and forecast non-stationary electricity-price time series, encompassing the broader scope of non-stationary behavior. For this purpose, we develop a data-driven model that combines an N-dimensional Langevin equation (LE) with a neural-ordinary differential equation (NODE). The LE captures fine-grained details of the electricity-price behavior in stationary regimes but is inadequate for non-stationary conditions. To overcome this inherent limitation, we adopt a NODE approach to learn, and at the same time predict, the difference between the actual electricity-price time series and the simulated price trajectories generated by the LE. By learning this difference, the NODE reconstructs the non-stationary components of the time series that the LE is not able to capture. We exemplify the effectiveness of our framework using the Spanish electricity day-ahead market as a prototypical case study. Our findings reveal that the NODE nicely complements the LE, providing a comprehensive strategy to tackle both stationary and non-stationary electricity-price behavior. The framework's dependability and robustness is demonstrated through different non-stationary scenarios by comparing it against a range of basic naïve methods.

Load Flow Assignments’ Definition from Day-Ahead Electricity Market Interconnection Power Flows: A Study for Transmission Networks

The mass introduction of renewable energy sources (RESs) presents numerous challenges for transmission system operators (TSOs). The Italian TSO, Terna S.p.A., aims to assess the impact of inverter-based generation on system inertia, primary regulating energy and short-circuit power for the year 2030, characterized by a large penetration of these sources. The initial working point of the Italian transmission network has to be defined through load flow (LF) calculations before starting dynamical analyses and simulations of the power system. Terna 2030 development plan projections enable the estimation of active power generation and load for each hour of that year in each Italian market zone, as well as cross-zonal active power flows; this dataset differs from conventional LF assignments. Therefore, in order to set up a LF analysis for the characterization of the working point of the Italian transmission network, LF assignments have to be derived from the input dataset provided by Terna. For this purpose, this paper presents two methods for determining canonical LF assignments for each network bus, aligning with the available data. The methodologies are applied to a simplified model of the Italian network, but they are also valid for other transmission networks with similar topology and meet the future needs of TSOs. The methods are tested at selected hours, revealing that both approaches yield satisfactory results in terms of compliance with the hourly data provided.

Pricing in the Day-Ahead Electricity Market Subject to Electric Network Constraints

Pricing in the Day-Ahead Electricity Market Subject to Electric Network Constraints

A sample robust optimal bidding model for a virtual power plant

In many energy markets, the trade amount of electricity must be committed to before the actual supply. This study explores one consecutive operational challenge for a virtual power plant—the optimal bidding for highly uncertain distributed energy resources in a day-ahead electricity market. The optimal bidding problem is formulated as a scenario-based multi-stage stochastic optimization model. However, the scenario-tree approach raises two consequent issues—scenario overfitting and massive computation cost. This study addresses the issues by deploying a sample robust optimization approach with linear decision rules. A tractable robust counterpart is derived from the model where the uncertainty appears in a nonlinear objective and constraints. By applying the decision rules to the balancing policy, the original model can be reduced to a two-stage stochastic mixed-integer programming model and then efficiently solved by adopting a dual decomposition method combined with heuristics. Based on real-world business data, a numerical experiment is conducted with several benchmark models. The results verify the superior performance of our proposed approach based on increased out-of-sample profits and decreased overestimation of in-sample profits.

Predicting Day-Ahead Electricity Market Prices through the Integration of Macroeconomic Factors and Machine Learning Techniques

Several events in the last years changed to some extent the common understanding of the electricity day-ahead market (DAM). The shape of the electricity price curve has been altered as some factors that underpinned the electricity price forecast (EPF) lost their importance and new influential factors emerged. In this paper, we aim to showcase the changes in EPF, understand the effects of uncertainties and propose a forecasting method using machine learning (ML) algorithms to cope with random events such as COVID-19 pandemic and the conflict in Black Sea region. By adjusting the training period according to the standard deviation that reflects the price volatility, feature engineering and by using two regressors for weighing the results, significant improvements in the performance of the EPF are achieved. One of the contributions of the proposed method consists in adjusting the training period considering the price variation. Thus, we introduce a rule-based approach given an empirical observation that for days with a higher growth in prices the training interval should be shortened, capturing the sharp variations of prices. The results of several cutting-edge ML algorithms represent the input for a predictive meta-model to obtain the best forecasting solution. The input dataset spans from Jan. 2019 to Aug. 2022, testing the proposed EPF method for both stable and more tumultuous intervals and proving its robustness. This analysis provides decision makers with an understanding of the price trends and suggests measures to combat spikes. Numerical findings indicate that on average mean absolute error (MAE) improved by 48% and root mean squared error (RMSE) improved by 44% compared to the baseline model (without feature engineering/adjusting training). When the output of the ML algorithms is weighted using the proposed meta-model, MAE further improved by 2.3% in 2020 and 5.14% in 2022. Less errors are recorded in stable years like 2019 and 2020 (MAE = 6.71, RMSE = 14.67) compared to 2021 and 2022 (MAE = 9.45, RMSE = 20.64).

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

Optimal Short-Term Charge/Discharge Operation for Electric Vehicles With Volt-Var Control in Day-Ahead Electricity Market

Optimal Short-Term Charge/Discharge Operation for Electric Vehicles With Volt-Var Control in Day-Ahead Electricity Market