Day-ahead Electricity Research Articles

A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems

Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6 % taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model.

Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

The availability of accurate day-ahead electricity price forecasts is pivotal for electricity market participants. In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes difficult for electricity market participants to obtain because electricity forecasting requires the consideration of features from ever-growing coupling markets. This study provides a method of exploring the influence of market coupling on electricity price prediction. We apply state-of-the-art long short-term memory (LSTM) deep neural networks combined with feature selection algorithms for electricity price prediction under the consideration of market coupling. LSTM models have a good performance in handling nonlinear and complex problems and processing time series data. In our empirical study of the Nordic market, the proposed models obtain considerably accurate results. The results show that feature selection is essential to achieving accurate prediction, and features from integrated markets have an impact on prediction. The feature importance analysis implies that the German market has a salient role in the price generation of Nord Pool.

Impact of Spanish Offshore Wind Generation in the Iberian Electricity Market: Potential Savings and Policy Implications

The European Union considers that offshore wind power will play a key role in making the EU the first climate-neutral continent by 2050. Currently, the potential of offshore wind energy is still untapped in Spain. Furthermore, the characteristics of the coastline in Spain require floating technology, making it challenging to install wind farms due to their current high cost. This work seeks to quantify the impact that Spanish offshore wind energy would have on the Iberian electricity market. Several offshore wind scenarios are evaluated by combining available information in relation to areas suitable for installing wind farms and wind resource data. The impact on the day-ahead electricity market has been obtained by reproducing the market, including these new offshore wind generation scenarios. The introduction of this renewable energy results in a market cost reduction in what is known as the merit-order effect. According to our estimates, for each MWh of offshore wind energy introduced in the market, there would be a market cost reduction of 45 €. These savings can serve as a reference for regulators to adjust their policy framework to boost floating wind offshore generation.

The Merit-Order Effect on the Swedish bidding zone with the highest electricity flow in the Elspot market

Electricity interconnections have become increasingly common as a means of integrating electricity markets, expediting the exchange of electricity, and thereby creating balanced electricity systems. These interconnections also facilitate the incorporation of intermittent renewable energy sources, ensuring greater energy security and reliability in electricity markets. In this study, an assessment is made of the impact of both electricity produced from wind power, and the cross-border flow of electricity, on the mean and volatility of the day-ahead electricity price. To do this, we studied the Swedish bidding zone with the highest flow of electricity imports and exports between 2016 and 2019 – the SE3 bidding zone (BZ). Using a SARMAX/GARCH approach, every hour of the day was assessed individually, by estimating up to 24 different models from 1 January 2016 to 30 April 2020. The results confirm the Merit-Order Effect from wind power, and that its impact is similar in magnitude throughout the day. Furthermore, the flow of electricity from both import and export appear to increase the day-ahead electricity price. This study also suggests that the volatility of electricity prices is primarily increased by unexpected shocks. This may be related to the high dependence of the SE3 BZ electricity system on electricity imports, which makes the system extremely susceptible to larger transmission of shocks.

Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant

Recent studies have indicated a great potential for applying predictive methodologies to the operation of Heating, Ventilation and Air Conditioning (HVAC) systems. Particularly for Centralized Chiller Plants with Thermal Energy Storage (TES) used for District Cooling, there is a substantial opportunity for cost savings when responding to variations in electricity price and ambient temperature. The present work addresses the problem of closed-loop scheduling of a large-scale chiller plant with TES tank under a Day-Ahead (DA) electricity price program. The main contributions include: (i) formulating the problem for a real large-scale complex system; (ii) comparing different dispatching policies with varying degrees of optimality, constraint satisfaction and operational complexity; and (iii) designing an optimization-based scheduling tool and describing its implementation and results. The proposed Mixed-Integer Linear Programming (MILP) formulation extends existing models by explicitly accounting for the effect of chilled water return temperature on the energy balances, considering real plant features, and performing pre-calculation of bounds on equipment and storage capacity. The study is performed with real process data from the central chiller plant located on the campus of the University of California, Davis. The comparison between different dispatching policies highlights the benefits of adopting an optimization-based operational strategy, indicating a cost reduction of up to 32% compared to other suboptimal policies. The study also demonstrates how additional constraints that reduce operational complexity affect the closed-loop performance of the optimization-based method, serving as a guideline for designing the optimization tool.

A Comprehensive Scheduling Framework Using SP-ADMM for Residential Demand Response With Weather and Consumer Uncertainties

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. The results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

Clearing Model and Pricing Analysis of Day-ahead Electricity Market Considering Double-side Auction

Clearing Model and Pricing Analysis of Day-ahead Electricity Market Considering Double-side Auction

Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the models.

A Stochastic Rolling Horizon-Based Approach for Power Generation Expansion Planning

Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and real-time electricity markets within a capacity expansion model. We use Benders’ cuts and a stochastic rolling-horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage, and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs.

Optimum bidding strategy for wind and solar power plants in day-ahead electricity market

Optimum bidding strategy for wind and solar power plants in day-ahead electricity market

Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study

In the next decades, energy storage technologies will play a major role in decarbonizing the European heating and cooling sector. In this regard, the deployment of complementary technologies is of paramount importance for the energy transformation of the Polish heating sector. This work addresses the challenge of sizing large-scale thermal energy storage (TES) systems for combined heat and power (CHP) plants connected to district heating networks and participating in day-ahead electricity markets. In this paper, a method based on a mixed-integer linear programming approach is proposed to find the optimal capacity of TES units connected to coal-fired CHP systems. The model considers the specific investment costs of the storage technology and optimizes the annual operation scheduling of the CHP-TES system. The model is applied to the case study of a coal-fired CHP system. Four scenarios are used to investigate the performance of the CHP system and evaluate the effects of rising carbon prices on the optimal capacity of the TES unit. The results reveal that the integration of the TES leads to a significant drop in the utilization of the heat-only boilers, helping mitigate fuel and environmental costs.

Renewable energy penetration and energy security in electricity markets

The transition from fossil fuels to renewable energies such as wind and solar is being encouraged worldwide. Renewable energy investment costs have been dropping, which, combined with growing incentives, has led to a rapid rise in renewable capacity. These changes in the electricity market entail great challenges and uncertainties, specifically due to resource availability. Wind and solar energy depend on seasonal weather conditions to generate energy, such as wind and radiation, respectively. However, few studies have focused on analyzing and measuring the impact of a progressive increase of renewable energy in a short-term electricity market and its effect on energy security. We developed a System Dynamics model to be able to analyze alternative scenarios and levels of uncertainties. We studied the impact of different shares of renewable sources on economic dispatch and energy security through reliability, reserve margin, resilience, and vulnerability measures. High shares of renewable resources (>60%) impact the energy security—reliability (< 98%), reserve margin (<−2%), resilience (>4%), and vulnerability—and may lower the spot price due to zero variable costs, which reduce the profitability of conventional firms in the short term and give closure to firms in the middle term and long term. Novelty Statement In this study, a system dynamics model is proposed to assess the electricity market configuration and the spot price while renewable energy sources (wind and solar) increase and conventional sources fall (hydropower and fossil fuel). Energy security is measured by including energy measures such as reliability, resilience, vulnerability, and reserve margin in a day-ahead market. The penetration of renewable energy creates a horizontal movement of the supply curve to the right, affecting wholesale electricity price, energy security, and firms' profitability. Highlights The effect of different shares of renewable energy in a day-ahead electricity market was studied. Energy security under renewable energy share on economic dispatch was considered. The analyses were performed using a system dynamics model for a theoretical market. High shares of renewable resources may lower the spot price due to zero variable costs. Low spot price reduces the profitability of both conventional and nonconventional firms.

Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

The energy distance for ensemble and scenario reduction.

Scenario reduction techniques are widely applied for solving sophisticated dynamic and stochastic programs, especially in energy and power systems, but are also used in probabilistic forecasting, clustering and estimating generative adversarial networks. We propose a new method for ensemble and scenario reduction based on the energy distance which is a special case of the maximum mean discrepancy. We discuss the choice of energy distance in detail, especially in comparison to the popular Wasserstein distance which is dominating the scenario reduction literature. The energy distance is a metric between probability measures that allows for powerful tests for equality of arbitrary multivariate distributions or independence. Thanks to the latter, it is a suitable candidate for ensemble and scenario reduction problems. The theoretical properties and considered examples indicate clearly that the reduced scenario sets tend to exhibit better statistical properties for the energy distance than a corresponding reduction with respect to the Wasserstein distance. We show applications to a Bernoulli random walk and two real data-based examples for electricity demand profiles and day-ahead electricity prices. This article is part of the theme issue 'The mathematics of energy systems'.

Strategic offering of producers in the day-ahead coupled gas and electricity market including energy and reserve models

The presence of gas-to-power (G2P) and power-to-gas (P2G) systems in the power system has led to the interdependency of gas and electricity energies. Thus, to increase energy efficiency and achieve desirable financial profit to the mentioned elements, an energy market and ancillary services combined by gas and electricity energies are required so that interdependency of electrical and gas energies and vice versa and the dependency of price to energy can be observable. Hence, this paper presents the strategic offering method for gas and electricity producers (GPs and EPs) in the coupled gas and electricity market as the day-ahead (DA) energy and reserve model. This strategy includes a two-stage problem, where the first stage contains a bi-level model that minimizes the difference between the expected operation cost and expected revenue of all strategic EPs (SEPs) in DA electricity energy and reserve markets subjects to the SEPs model in the upper-level. The lower-level problem minimizes the expected non-SEPs operation cost limited to DC optimal power flow equations, reserve, and non-SEPs formulation. The same problem with the first stage formulation is repeated for GPs in the second stage. Strategic GPs (SGPs) participation model in the DA gas energy and reserve markets presents in the upper-level problem but the market clearing price (MCP) method in the gas network expresses in the lower-level problem. This scheme includes uncertainties of demand and renewable power that are modeled by stochastic programming. The Karush-Kuhn-Tucker (KKT) approach obtains a single-level formulation in each stage, and thus, the master/salve decomposition algorithm solves the proposed strategy to achieve the optimal solution. Finally, this method is simulated on a standard test system to examine its capabilities. The numerical results obtained by the algorithm highlights that it can find the optimal solution in the least possible time considering the least computational errors. Furthermore, units with inexpensive fuel generally participate in the energy market following the proposed strategy; however, all units participate in the reserve market at all operating hours, which aims to achieve high optimal financial profit for the units. Following this, SEPs and SGPs in the mentioned market succeeded to obtain a profit of 37.5% and 13.6%, respectively, in their operating cost.

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

Operational flexibility enhancements using mobile energy storage in day-ahead electricity market by game-theoretic approach

The global share of renewable energy sources (RES) in total generation capacity reached 34.7% in 2019 and has been continuously increasing. Power system flexibility addressing the uncertainty and variability of RES has become a major concern in energy transition. This paper proposes to apply mobile energy storage (MES) from independent MES owners as a flexibility-enhancement ancillary service in the day-ahead electricity market. First, we have proposed a market-based methodology to incent MES owners to provide flexibility service in power systems by simultaneously releasing Locational Marginal price (LMP) and Uncertainty Marginal Price (UMP). The LMP and UMP were obtained by solving robust unit commitment and security-constrained economic dispatch, representing the need for flexibility resources. Second, the optimal operational strategy of MESs was established to respond to LMP and UMP and maximize their profits by providing energy and reserve flexibility subjects to the reserve and traffic constraints. Third, a game-theoretic model was developed to determine market equilibrium by minimizing the operational costs of the power system and maximizing the profits of MES owners. To avoid homogenous behaviors of MESs in solving this model, a sequential dispatching algorithm was developed such that each MES of one MES owner has a unique price signal. An objective function update strategy was accordingly developed to take the interaction among MESs into consideration and ensure a maximum total profit for MES owners. Finally, case studies with IEEE 6-bus system, IEEE 118-bus system, and MATPOWER 300-bus system were provided to validate the proposed methods, demonstrating that MESs increase power system flexibility and their own profits while decreasing the energy and reserve payment of consumers.

Assessing the Value of Demand Response in Microgrids

This paper presents a computer application to assist in decisions about sustainability enhancement due to the effect of shifting demand from less favorable periods to periods that are more convenient for the operation of a microgrid. Specifically, assessing how the decisions affect the economic participation of the aggregating agent of the microgrid bidding in an electricity day-ahead market. The aggregating agent must manage microturbines, wind systems, photovoltaic systems, energy storage systems, and loads, facing load uncertainty and further uncertainties due to the use of renewable sources of energy and participation in the day-ahead market. These uncertainties cannot be removed from the decision making, and, therefore, require proper formulation, and the proposed approach customizes a stochastic programming problem for this operation. Case studies show that under these uncertainties and the shifting of demand to convenient periods, there are opportunities to make decisions that lead to significant enhancements of the expected profit. These enhancements are due to better bidding in the day-ahead market and shifting energy consumption in periods of favorable market prices for exporting energy. Through the case studies it is concluded that the proposed approach is useful for the operation of a microgrid.

Power systems’ performance under high renewables’ penetration rates: a natural experiment due to the COVID-19 demand shock

COVID-19 lockdowns make it possible to investigate the extent to which an unprecedented increase in renewables’ penetration may have brought unexpected limitations and vulnerabilities of current power systems to the surface. We empirically investigate how power systems in five European countries have dealt with this unexpected shock, drastically changing electricity load, the scheduling of dispatchable generation technologies, electricity day-ahead wholesale prices, and balancing costs. We find that low-cost dispatchable generation from hydro and nuclear sources has fulfilled most of the net-load even during peak hours, replacing more costly fossil-based generation. In Germany, the UK, and Spain coal power plants stood idle, while gas-fired generation has responded in heterogeneous ways across power systems. Falling operational costs of generators producing at the margin and lower demand, both induced by COVID-19 lockdowns, have significantly decreased wholesale prices. Balancing and other ancillary services’ markets have provided the flexibility required to respond to the exceptional market conditions faced by the grid. Balancing costs for flexibility services have increased heterogeneously across countries, while ancillary markets’ costs, measured only in the case of Italy, have increased substantially. Results provide valuable evidence on current systems’ dynamics during high renewables’ shares and increased demand volatility. New insights into the market changes countries will be facing in the transition towards a clean, secure, and affordable power system are offered.

Optimal bidding of profit-seeking virtual associations of smart prosumers considering peer to peer energy sharing strategy

This paper presents a risk-averse stochastic framework for virtual associations (VAs), which are dynamic clusters of prosumers. A VA, as a price taker agent, supports the active participation of prosumers in the day-ahead (DA) electricity market. In this regard, a bi-level optimization model is formulated to optimize the decision-making problem of the VA in the DA market with the main goal of maximizing VA profit and minimizing the total energy costs of prosumers. In this framework, the impacts of peer to peer (P2P) trading among the prosumers and VAs on the offering and bidding strategies of VAs are also considered. In a competition among VAs, the prosumers are able to select the most competitive VA to participate in the DA market. Moreover, due to the uncertainties of market prices, the VA should undertake the risks arising from price volatilities that may cause the VA to suffer from financial loss due to occurrence of some scenarios such as price spikes. To compensate the undesired effects of the occurrence of price spikes, the impacts of demand response (DR) actions and peer to peer (P2P) energy trading among prosumers on the decisions of VA are analyzed. Moreover, an index is defined from which the competitive condition in a retailing layer would be analyzed. Using Nordpool data as a practical test system, the undesired effects of occurrence of price spikes are compensated using demand response (DR) actions and peer to peer (P2P) energy trading among prosumers. Moreover, an index is defined from which the competitive condition in a retailing layer would be analyzed.