Day-ahead Prices Research Articles

Energy matching: exploring the value of demand flexibility in educational office buildings

This research presents a case study exploring the potential for demand side flexibility at a cluster of university buildings. The study investigates the potential of a collection of various electrical devices, excluding heating and cooling systems. With increasing penetration of renewable electricity sources and the phasing out of dispatchable fossil sources, matching grid generation with grid demand will become difficult using traditional grid management methods alone. Additionally, grid congestion is a pressing problem. Demand side management in buildings may contribute to a solution to these problems. Currently demand response is, however, not yet exploited at scale. In part, this is because it is unclear how this flexibility can be translated into successful business models, or whether this is possible under the current market regime. This research gives insight into the potential value of energy demand flexibility in reducing energy costs and increasing the match between electricity demand and purchased renewable electricity. An inventory is made of on-site electrical devices that offer load flexibility and the magnitude and duration of load shifting is estimated for each group of devices. A demand response simulation model is then developed that represents the complete collection of flexible devices. This model, addresses demand response as a ‘distribute candy’ problem and finds the optimal time-of-use for shiftable electricity demand whilst respecting the flexibility constraints of the electrical devices. The value of demand flexibility at the building cluster is then assessed using this simulation model, measured electricity consumption, and data regarding the availability of purchased renewables and day-ahead spot prices. This research concludes that coordinated demand response of large variety of devices at the building cluster level can improve energy matching by 0.6-1.5% and reduce spot market energy cost by 0.4-3.2%.

Optimal Power Flow Based DR in Active Distribution Network With Reactive Power Control

Demand response works as a bridge to enable activities between end customer and distribution network operators. Previously, most of the demand response work is concerned about maintaining supply–demand balance and reducing peak load demand without acknowledging distribution network behavior. This article presents a demand response scheme for an active distribution network via the operation of an unbalanced feeder line and consumer loads. The day-ahead dynamic price is coupled with active distribution network constraints to prevent the formation of new peaks. To tackle up the complexities of unbalanced network and distributed generation (DG) integration, the merits of symmetrical domain components are utilized. Power electronically interfaced DG reactive power component is also considered for constant power and voltage mode of operation. For optimizing the distribution network, optimal power flow is modified to achieve social welfare with distribution network constraints. The performance and computational efficiency of the proposed algorithm are verified through extensive case studies.

How are Day-ahead Prices Informative for Predicting the Next Day’s Consumption of Natural Gas? Evidence from France

The purpose of this paper is to investigate, for the first time, whether the next day’s consumption of natural gas can be accurately forecast using a simple model that solely incorporates the information contained in day-ahead market data. Hence, unlike standard models that use a number of meteorological variables, we only consider two predictors: the price of natural gas and the spark ratio measuring the relative price of electricity to gas. We develop a suitable modeling approach that captures the essential features of daily gas consumption and, in particular, the nonlinearities resulting from power dispatching and apply it to the case of France.Our results document the existence of a long-run relation between demand and spot prices and provide estimates of the marginal impacts that these price variables have on observed demand levels. We also provide evidence of the pivotal role of the spark ratio in the short run which is found to have an asymmetric and highly nonlinear impact on demand variations. Lastly, we show that our simple model is sufficient to generate predictions that are considerably more accurate than the forecasts published by infrastructure operators.

Flexible Short-Term Electricity Certificates—An Analysis of Trading Strategies on the Continuous Intraday Market

The most important price for short-term electricity trading in Germany is the day-ahead auction price, which is provided by EPEX SPOT. Basically, short-term fluctuating electricity prices allow cost-optimized production planning by shifting electricity-intensive processes to times of favorable electricity prices. However, the day-ahead price as the outcome of an auction is not directly tradeable afterwards. We propose short-term flexible electricity certificates that pass on the day-ahead auction prices plus a premium for the supplier, enabling users to plan electricity consumption based on realized day-ahead auction prices. We analyze the supplier’s problem of delivering electricity based on such certificates. The supplier can adjust the required electricity volume after the close of the day-ahead auction on the continuous intraday market. We analyze the price fluctuations in this market in relation to the day-ahead price and propose different trading strategies. Using the order book history of EPEX SPOT, we analyze the trading success and risk of these strategies. Furthermore, we investigate to what extent trading success can be explained by changes in market conditions, and, in particular, we identify renewable forecast errors as a driver.

What Incentives Are Required for Renewables to Provide Redispatch Services? A Simulation in a Multi-Market Setting

Renewable energy sources (RES) can provide valuable flexibility potential for multiple markets and grid services in the future. In this paper, the focus lies on the development of algorithms for an optimal dispatch and bidding of a RES-based virtual power plant (VPP) by considering current short-term and balancing market conditions in Austria, as well as a proposed redispatch market. Specifically, different pricing and bidding strategies for a redispatch market are compared and their feasibility is analyzed. The attractiveness of different pricing models and remuneration mechanisms for redispatch, as well as the influence of the redispatch call probability on the bidding behavior of the VPP, is investigated. The simulation of the bidding behavior is carried out using linear optimization techniques. The paper describes the algorithms as well as the assumptions for market rules in redispatch, balancing and short-term electricity markets. The results show that the probability to be activated for redispatch and thus the required incentives depend highly on the location of the plant. Pre-curtailment of the plant to offer positive redispatch is not recommended as the prices would need to be set at up to 200–300% of the day-ahead price.

Optimization of day-ahead pricing electricity markets based on a simplified methodology for stochastic utility function estimation

In the context of smart markets’ popularization, day-ahead pricing of electricity has been deployed more commonly as an attempt to improve the grid’s operation and maximize the socioeconomic welfare. However, estimating the utility function is necessary to maximize the socioeconomic welfare, which is not a trivial task. The utility function models the quality of life added by electricity consumption and depends on several factors, such as the consumer’s class (residential, commercial, industrial, etc.), income, climate conditions, and individual preferences. This paper presents a simplified methodology to estimate the utility function in the context of day-ahead pricing. The main benefit of the proposed method is that its application is straightforward since the modeling is mainly based on simple matrix equations. The demand elasticities (self- and cross-elasticities), which are general concepts in economics, are addressed as input parameters. Due to the inherent uncertainties related to human behavior, a stochastic utility function is proposed to enhance the model’s accuracy/robustness. The proposed model particularly thrives when the effects of cross-elasticities are significant but not massive (although procedures can be applied to mitigate the error if higher cross-elasticities are verified). By applying the proposed model to a hypothetical smart microgrid with 100 consumers and 24 daily periods (hourly prices), results demonstrate that it successfully provides the rates that maximize the socioeconomic welfare created by the market. In the case study, the conventional quadratic utility function implies an error of 73%, whereas the proposed model an error of 10%, highlighting the usefulness of the proposed model.

Machine learning analytics for virtual bidding in the electricity market

In order to solve the problem of the high risks and low efficiency caused by the inconsistency of the day-ahead and real-time prices in two-settlement electricity market, virtual bidding is used to arbitrage on the difference between such two market prices that are unknown to virtual bidders to promote the price convergence. The problem of optimal bidding for virtual bidders from the spatio-temporal dimensions is addressed in this paper. The model takes the budget constraints of virtual bidders into account, as well as considers decrement and increment bids of virtual bidding to maximize the cumulative payoff of virtual bidders, which is formulated as a Markov Decision Process problem. Meanwhile, the conditional value-at-risk is used to quantify and hedge the risks faced by virtual bidders. A deep reinforcement learning algorithm is used to achieve an effective solution to the optimal bidding strategy problem through continuous interaction with a simulated building environment to obtain feedback and update the parameters of the neural network without referring to any prior model knowledge. The PJM data from 2016 to 2018 is used to calculate the cumulative profits and Sharpe ratio of virtual bidders. Compared with greedy algorithm and dynamic programming, the deep reinforcement learning algorithm is verified the effectiveness and superiority in this paper.

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.

Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm

A remodeled sperm swarm optimization (RMSSO) algorithm for a home energy management (HEM) system is proposed, and its real-time efficacy was evaluated using a hardware experimental model. This home environment comprised sixteen residential loads, a smart meter and a Raspberry Pi controller to optimize the energy consumption cost (ECC) in response to the Indian day-ahead pricing (DAP) scheme. A wired/wireless communication network was considered to communicate with the smart meter and controller. To address this optimization problem, the sperm swarm optimization (SSO) algorithm’s constriction coefficient was remodeled to improve its global searching capability and proposed as RMSSO. For the first time, salp swarm optimization (SSA), SSO, and RMSSO algorithms were employed to schedule home appliances in the Indian scenario. To validate the proposed technique’s outcome, the results were compared to those of the conventional SSO and SSA algorithms. This problem was solved using the Python/GUROBI optimizer tool. As a consequence, consumers can use this control strategy in real-time to reduce energy consumption costs.

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

ARIMA Model for Day-Ahead Electricity Market Price Forecasting

Electricity price forecasting becomes a significant challenge on a day-to-day basis and price variations are even more volatile on an hourly basis. Therefore, this paper is used several approaches to analyze the Bulgarian hourly electricity price dynamics in the day-ahead market. Proper analysis crucially depends on the choice of an adequate model. Reviewed are the factors which may influence the electricity spot prices and characteristics of the time series of prices. Methods include and variety of modeling approaches that are applied and evaluated for forecasting electricity prices such as time-series models and regression models. The forecasting technique is to model day-ahead spot prices using well known ARIMA/SARIMA model including stationarity checks, seasonal decompose, differencing, autoregressive modeling, and autocorrelation to analyze and forecast time series hourly data. For each approach, model estimates and forecasts are developed using hourly price data, reshaped, and aggregated data on a daily and monthly basis for the Bulgarian day-ahead market.

The impact of renewables on the incidents of negative prices in the energy spot markets

In this paper we analyze the impact of growing renewable energy generation on the instances of negative day-ahead auction prices. We perform a simulation that is based on the real-world day-ahead wholesale supply and demand curves from the German-Luxembourg coupled market to gain insights about which developments in instances of negative market clearing prices could be expected in the short to middle term period with more intermittent energy in the system. This is achieved through transforming the real-world supply curves to include more renewable generation and recalculating the market clearing prices. The results of the spatial analysis suggest that keeping the strong fiscal support for renewables, auction design, and marginal cost bidding in place unchanged would gradually lead to more instances of negative prices in the coming years. Conclusively, we discuss the implications of the observed phenomena on the energy policy.

A two-layer decentralized charging approach for residential electric vehicles based on fuzzy data fusion

This work presents a two-layer decentralized charging approach (TLDCA) based on fuzzy data fusion concerning the economic and power layers for optimizing the charging cost of residential electric vehicles (EVs). We defined the problem with the fuzzy objective function of minimizing the charging costs and presented a detailed fuzzy integer linear programming formulation for obtaining the optimal solution set. The optimal solution set relies on the decision control variable which is obtained through the fuzzy fusion mechanism that incorporates multiple independent and uncertain day-ahead price patterns and state-of-charge inputs from the utility grid and EV domains. The developed TLDCA reduces the charging cost for EVs while guaranteeing their required energy by determining the optimal charging schedule. We conduct two case studies to investigate the TLDCA behavior, where the first case explores the optimization of charging costs and the changing needs of individual EVs. The second case examines the charging cost, impact on load profile, and peak-to-average ratio against the summer and winter load profiles for the aggregated EVs. The simulation results verify that the developed TLDCA optimizes the charging cost and peak-to-average ratio compared to the uncoordinated charging, standard-rate charging, and time-of-use charging schemes.

Impact of the COVID-19 Pandemic Crisis on the Efficiency of European Intraday Electricity Markets

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway’s (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met.

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.

MILP-Based Imitation Learning for HVAC Control

To optimize the operation of a HVAC system with advanced techniques such as artificial neural network, previous studies usually need forecast information in their method. However, the forecast information inevitably contains errors all the time, which degrade the performance of the HVAC operation. Hence, in this study, we propose MILP-based imitation learning method to control a HVAC system without using the forecast information in order to reduce energy cost and maintain thermal comfort at a given level. Our proposed controller is a deep neural network (DNN) trained by using data labeled by a MILP solver with historical data. After training, our controller is used to control the HVAC system with real-time data. For comparison, we also develop a second method named forecast-based MILP which control the HVAC system using the forecast information. The performance of the two methods is verified by using real outdoor temperatures and real day-ahead prices in Detroit city, Michigan, United States. Numerical results clearly show that the performance of the MILP-based imitation learning is better than that of the forecast-based MILP method in terms of hourly power consumption, daily energy cost, and thermal comfort. Moreover, the difference between results of the MILP-based imitation learning method and optimal results is almost negligible. These optimal results are achieved only by using the MILP solver at the end of a day when we have full information on the weather and prices for the day.

Complexity and Persistence of Price Time Series of the European Electricity Spot Market

The large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyze electricity price time series from the European Power Exchange market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 h. Fluctuations are strongly correlated and persistent below 12 h, which contributes to extreme price events and a strong multifractal behavior. On longer time scales, they get anticorrelated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 h. The long-term behavior is strongly influenced by the evolution of a large-scale weather pattern with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of q-Gaussian distributions as the best fit to the empiric distribution of electricity prices.3 MoreReceived 6 December 2021Revised 21 February 2022Accepted 28 February 2022DOI:https://doi.org/10.1103/PRXEnergy.1.013002Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasComplex systemsEconophysicsEnergy sourcesFractional Brownian motionPatterns in complex systemsSolar energyStochastic processesWind energyPhysical SystemsFractalsMultiple time scale dynamicsTechniquesNon-Markovian processesTime series analysisStatistical PhysicsInterdisciplinary PhysicsGeneral Physics

The relationship between day-ahead and future prices in electricity markets: An empirical analysis on Italy, France, Germany, and Switzerland

We evaluate the relationship between electricity day-ahead and future prices following the hedging pressure theory, which explains the difference between future prices and expected spot prices regarding market players’ risk aversion. We calculate the sign and intensity of the ex-post risk premia in the electricity market of Italy, France, Switzerland, and Germany during the last decade and for all products traded: monthly, quarterly, and yearly futures and distinguish between base-load and peak-price futures. We test the impact of the trading period on the risk premia in panel regressions, controlling for seasonality and the impact of financial markets’ trades and primary energy prices. We show that, in all countries, there is no convergence of future prices to the underlying day-ahead ones; moreover, for most future contracts, the premium rises as contracts approach delivery. In addition, for Italy and Switzerland, there is an inversion of the sign of the premia (mostly for base-load products). Risk premia are negative at the beginning of the trading period and positive as the delivery period approaches. This indicates that, in these countries, premia are, on average, paid by power producers at the beginning of the period and by suppliers (i.e. power buyers) close to delivery. In contrast, in France and Germany, risk premia are both positive at the beginning and at the end of the trading period, signalling that, on average, buyers are willing to pay a premium to cover price volatility.

Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses

By coupling multiple energy networks, an integrated energy system can realize the conversion and complementarity of various energy sources. The development of regional integrated energy systems (RIESs) has brought both opportunities and challenges to the energy market. Without a reasonable integrated energy day-ahead trading structure, it is difficult for energy providers to make profits and for consumers to achieve economic energy utilization. At the same time, various user-side energy loads are controllable, and it is necessary to comprehensively consider multi-energy demand responses in energy trading and management. To address the above issues, the trading behaviors of main market players are clarified, and an integrated energy pricing and management strategy is proposed in this paper. Multi-energy demand response models for data centers, electric vehicles (EVs) and air conditioning loads (ACLs) are also established. Considering multi-energy demand responses, a day-ahead energy pricing and management model for RIESs is proposed. The model is a bilevel Stackelberg game optimization model, in which the upper level considers profit maximization of energy service provider (ESP) while the lower level deals with the cost minimization of energy consumer (EC). Based on simulation analysis, the feasibility of the proposed model is verified. The trading scheme optimized by this model can benefit both ESP and EC. Compared with a traditional integrated energy trading structure, the profit source of ESP is enlarged. The simulation results also show that the application of multi-energy demand responses can improve the economic and collaborative operation effect of RIESs and reduce carbon emissions.

An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when p = 0.01, v = 100, t0 = 0.01 and μ = 2.