Day-ahead Prices Research Articles

Quantum computing for future real-time building HVAC controls

Buildings contribute to more than 70% of overall U.S. electricity usage and greenhouse gas (GHG) emissions. HVAC systems in buildings often consume more than 40% of the total building energy usage. To reduce such high energy use, numerous control strategies including optimal and predictive controls have been developed and demonstrated. To achieve a near real-time solution, most previous research has simplified the non-linearity of building thermodynamics and provided an approximate optimal solution. The future HVAC control optimizes more connected devices in buildings, which requires a rapid and accurate response, not only to the building itself but also to the grid signals. It also poses the challenge of solving non-linear problems with discrete variables. With the recent development of quantum computers, this has become feasible. In this paper, we developed a new optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU). Compared to traditional optimization methods, we obtained similar solutions with less than 2% differences and improved computational speed from hours to seconds. We also demonstrated an 80% reduction in total electricity consumption and a 21% reduction in electricity bills by considering day-ahead price time-of-use demand response signals. Quantum computing has proven capable of solving large-scale non-linear discrete optimization problems for building energy systems.

Foreseeing the worst: Forecasting electricity DART spikes

Statistical learning models are proposed for the prediction of the probability of a spike in the electricity DART (day-ahead minus real-time price) spread. Assessing the likelihood of DART spikes is of paramount importance for virtual bidders, among others. The model’s performance is evaluated on historical data for the Long Island zone of the New York Independent System Operator (NYISO). A tailored feature set encompassing novel engineered features is designed. Such a set of features makes it possible to achieve excellent predictive performance and discriminatory power. Results are shown to be robust to the choice of the predictive algorithm. Lastly, the benefits of forecasting the spikes are illustrated through a trading exercise, confirming that trading strategies employing the model predicted probabilities as a signal generate consistent profits.

Optimal scheduling of a large-scale power-to-ammonia process: Effects of parameter optimization on the indirect demand response potential

In the light of the increasing share of power generation from renewable energy sources, there is a growing interest in flexible operation of power-intensive processes for demand-side management/demand response. In this article, we investigate the potential application of a large-scale power-to-ammonia process in demand response programs. The process system under consideration consists of a polymer exchange membrane electrolysis that is optimally scheduled in accordance with day-ahead prices and a large-scale Haber–Bosch process that serves as a downstream energy storage. We apply a demand-side management framework with a sequential approach to identify the indirect demand response potential of the considered process system. Our results show that the power-to-ammonia process provides significant leverage for demand-side management. Moreover, we find that increasing process flexibility by optimizing operational parameters improves the demand response potential and that price profile dispersion can be used as an indicator to predict the economic performance of demand response scheduling.

Framework for smart grid to implement a price elasticity-based peak time rebate demand response program

The smart grid model is developed with some changes to help in implementing a demand response program which was initially developed for a Pecan Street project. Correspondingly, the real-time solar and load data are collected from the data port for the city of Austin. A single day is selected for our analysis of all four seasons of the year. The flat rate, and real-time and day-ahead pricing information are collected from ComEd. The key challenge for addressing business problems is the flexibility of consumption. However, without considering the properties of loss aversion, the system would not be a practical solution. So, in this article, a dynamic demand response program based on price elasticity that integrates loss aversion characteristics is proposed. The proposed system is compared for all pricing schemes and all seasons. Four scenarios are created for peak time rebate with different combinations of loss aversion factor values and all the possible combinations of rebates. This article directs how these combinations could change the demand curve and how the utility can make a decision about the specific importance of the criteria, such as the total demand carrying capacity, peak demand reduction, and in obtaining optimum profit for utility and the consumer.

Optimal Participation of RES Aggregators in Energy and Ancillary Services Markets

With the advent of bulk volumes of renewable energy, RES aggregators have evolved into key market players of the energy industry. This paper presents a holistic approach to derive the optimal offering strategy of a price-taker RES aggregator in the day-ahead energy and ancillary services markets. The day-ahead, the ancillary services and the real-time balancing market functions are modeled in detail. Fundamental imbalance settlement mechanisms are examined, namely Single Pricing, Two Price settlement and Dual Pricing. Several sources of uncertainty are considered affecting the day-ahead market prices, the ancillary services prices, the imbalance prices and the actual production of the RES aggregator. To cope with these uncertainties, a stochastic risk-constrained optimization model is formulated for the aggregator's optimal bidding. A realistic application in the Greek power system allows for examining the motivations for strategic bidding and accurate forecasting in each imbalance pricing scheme. The interplay between the energy and the ancillary services offers is analyzed, to examine the RES aggregator's motivations to bid for ancillary services.

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

A Hybrid Machine Learning Approach for Price Forecasting in Electricity Market with Smart Bidding Strategies and Wind Energy Influence

The price for renewable power has seen a dip in recent years with increased advanced approaches in the electricity market. For the benefit of both the producers and the consumers of electricity, a forecasting technique to predict the price for the next day is required to handle competitive market participation facilitating the prosumers. Therefore, the study’s main objective is to model a day-ahead electricity price and apply a forecasting tool based on time series analysis, promising accuracy, and minimal average forecast errors. Day-ahead real-time price data of the Nordic pool market forecast study for a period between January 2019 and March 2021 is considered for the study. The impact of wind energy production on day-ahead electricity Nord Pool electricity market for improved participation in the market is forecasted based on time series. Machine learning techniques, namely Support Vector Regression (SVR), Generalized Autoregressive Conditional Heteroskedastic (GARCH), Autoregressive Integrated Moving Average (ARIMA), Multiple Linear Regression (MLR), and hybrid of SVR-GARCH are analyzed in detail to find the best forecasting approach. The proposed forecasting technique on next-day electricity price series influenced by wind power production is validated using performance indices, namely, the Mean Absolute Percentage Error and the Root Mean Squared Error considered for checking the reliability of the proposed new model. A similar study on the Indian market was also conducted to validate the robustness of the model. The electricity price forecasting model is developed and analyzed using a machine learning technique in Python (Jupyter Notebook).

Residential power scheduling based on cost-coupling constraint with distributed generation

Power scheduling is a major task for demand response (DR) to cope with the electricity consumption during the peak period in smart grid. In the residential sector, consumers trend to trigger their electrical appliances when the electric price is low. However, power scheduling including load curtailment, time delay and equipment power adjustment will change consumer’s comfort level to produce discomfort cost. Thus, electricity payment and discomfort cost are coupled to constraint the DR. In this paper, we focus on renewable energy use by smart homes and consider cost-coupling constraints, taking an approach whereby we decouple the primary problem into several independent subproblems. A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers’ expenditure while improving their comfort. To that end, a day-ahead bidding price mechanism including real-time charging management is introduced, and a cost efficiency algorithm is designed based on electricity payments, discomfort and distributed generation. Simulation results demonstrate that the proposed method is effective and feasible when tested in real application scenarios.

A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid

Recently, the integration of renewable energy sources (RESs) in microgrids (MGs) has risen significantly owing to extensive promotion of decarbonization and green energy. However, despite the environmental–economic benefits, RESs are intermittent, and increasing penetration of RESs into MG poses operation challenges in handling uncertainties. In this paper, a novel deep learning-based forecasting model is proposed for MG operation considering uncertainties of RESs, load, and day-ahead price (DAP). To handle the intrinsic uncertainties of MGs, a long short-term memory (LSTM) network is employed and a method for increasing prediction accuracy of the LSTM model based on a genetic algorithm–adaptive weight particle swarm optimization (GA-AWPSO) combination along with a global attention mechanism (GAM) is proposed. Herein, the hyperparameters of the LSTM model are optimized by the GA-AWPSO algorithm, and GAM is added to mine important features from input datasets to improve forecasting performance. To handle uncertainties through the demand side, A DM-CIDR program is developed for providing optimal incentive rate strategies to participants, as different customers exhibit diverse attitudes toward remunerated incentives. In this program, ordering points to identify the clustering structure (OPTICS) and k-nearest neighbor algorithms (k-NN) are used for clustering and classification, respectively, to determine reasonable incentive rates for customers according to their bid/offer data. A simulation was implemented on historical PJM datasets, the results of which revealed the performance and superiority of the proposed approach in handling uncertainties.

Coordinated bidding of multi-product charging station in electricity markets using rolling planning and sample average approximation

Recently, there has been an urgent tendency to steer the transport system towards zero-emission. Along natural gas vehicles, electric and hydrogen vehicles represent a promising way to a low-carbon system. Since the development of these vehicles relies on advances of refueling stations, their economic and operational aspects need to be considered. Therefore, this paper addresses a multi-product charging station for refilling electric, hydrogen and natural gas vehicles. The station participates in day-ahead and intra-day markets where the market price is subject to the uncertainty. For charging station participating in sequential markets with different prices, it is necessary to consider the coordinated bidding. Taking into account the sequential clearing of these markets and the gradual realization of market prices, the bidding problem is formulated as a two-stage stochastic program. In order to solve this large-scale stochastic program with a huge number of scenarios, the rolling planning method in combination with sample average approximation and SCENRED tool are applied. The study aims to attain the optimal operation of devices and bidding curves of the charging station in order to maximize its profit. The obtained average optimality gap based on Monte Carlo sampling approach, which is less than 1%, proves the effectiveness of our proposed algorithm in solving this large scale stochastic optimization. The submitted bidding curves to the intra-day market are found to be dependent on changes in the day-ahead market prices. Tornado diagrams indicate that the selling price and demand of electricity are ranked as the most effective factors on profit.

Deep state space model for predicting cryptocurrency price

There are two fundamental contributions of this work. On the application side, one of the most challenging problems is tackled, predicting day-ahead crypto-currency prices. On the theoretical front, a new dynamical modeling approach is proposed. The proposed approach keeps the probabilistic formulation of the State-Space Model that yields point estimates along with the uncertainty about the estimate and the function approximation ability of the deep neural network. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy.

Energy management for solar-hydrogen microgrids with vehicle-to-grid and power-to-gas transactions

This study addresses the energy management of an integrated solar-hydrogen microgrid. The microgrid includes zero-emission vehicles, renewable energy sources, an electrolyzer, bidirectional charging stations, and a hydrogen refueling station with hydrogen storage. Vehicle-to-grid (V2G) charging stations can alleviate renewable electricity variability by discharging the energy of vehicle batteries back to the grid. The electrolyzer can absorb excess solar generation to balance supply and demand with power-to-gas (P2G) transactions. Given the uncertainties from intermittent renewable generation and energy demand, a two-stage stochastic optimization framework derives the optimal power management strategy for the component subsystems, aiming to minimize operating costs. The first stage of the model determines the day-ahead charging price and power supply for electric vehicle charging and hydrogen production, and the second stage performs real-time energy scheduling under different scenarios. The performance of the proposed optimization strategy is examined through a case study using real-world data, and the results demonstrate that daily operating costs can be reduced by up to 27.5%. Moreover, inclusion of the P2G process with hydrogen storage yields better performance to support peak shaving and reducing costs compared to using V2G or dynamic pricing-based demand response policies only.

Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach

Accurate price forecasts on the EU ETS market are of interest to many production and investment entities. This paper describes the day-ahead carbon price prediction based on a wide range of fuel and energy indicators traded on the Intercontinental Exchange market. The indicators are analyzed in seven groups for individual products (power, natural gas, coal, crude, heating oil, unleaded gasoline, gasoil). In the proposed approach, by combining the Principal Component Analysis (PCA) method and various methods of supervised machine learning, the possibilities of prediction in the period of rapid price increases are shown. The PCA method made it possible to reduce the number of variables from 37 to 4, which were inputs for predictive models. In the paper, these models are compared: regression trees, ensembles of regression trees, Gaussian Process Regression (GPR) models, Support Vector Machines (SVM) models and Neural Network Regression (NNR) models. The research showed that the Gaussian Process Regression model turned out to be the most advantageous and its price prediction can be considered very accurate.

Optimal economic operation of microgrids considering combined heat and power unit, reserve unit, and demand-side management using developed adolescent identity search algorithm

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm (AISA). In the developed model, Powell's local search operator is employed to improve the efficiency of searching for the optimal solution. Due to the existing uncertainties in load consumption and day-ahead market price, the method is presented as a scenario-based stochastic energy management problem. The results reveal the proposed method is highly efficient in solving the problem, and load management can improve economic indicators.

Electricity Markets in the Context of Distributed Energy Resources and Demand Response Programs: Main Developments and Challenges Based on a Systematic Literature Review

Distributed energy resources have been increasingly integrated into electrical grids. Consequently, electricity markets are expected to undergo changes and become more complex. However, while there are many scientific publications on the topic, a broader discussion is still necessary. Therefore, a systematic literature review on electricity markets in the context of distributed energy resources integration was conducted in this paper to present in-depth discussions on the topic, along with shedding light on current perspectives, the most relevant sources, authors, papers, countries, metrics, and indexes. The software R and its open-source tool Bibliometrix were used to perform the systematic literature review based on the widely recognized databases Web of Science and Scopus, which led to a total of 1685 articles after removing duplicates. The results demonstrate that demand response, renewable energy, uncertainty, optimization, and smart grid are the most-used keywords. By assessing highly impactful articles on the theme, emphasis on energy storage systems becomes clear compared to distributed generation and electric vehicles. However, electric vehicles draw attention in terms of citations. Furthermore, multi-level stochastic programming is the most-applied methodology among highly impactful articles. Due to the relevance of the demand response keyword, this paper also conducts a specific review on the topic aligned with electricity markets and distributed energy resources (296 articles). The results demonstrate that virtually all high-impact publications on the topic address day-ahead or real-time pricing. Based on the literature found, this paper presents a discussion on the main challenges and future perspectives related to the field. The complexity of electrical power systems and electricity markets is increasing substantially according to what this study found. Distributed generation development is already advanced, while energy storage systems and electric vehicles are limited in many countries. Peer-to-peer electricity trading and virtual power plant are newer concepts that are currently incipient, and DR programs showcase an intermediate stage of evolution. A particular lack of research on social issues is verified, and also a lack of all-encompassing studies that address multiple interconnected topics, which should be better addressed in the future. The in-depth assessment carried out in this paper is expected to be of high value to researchers and policy-makers and facilitate future research on the topic.

Bi-level optimal scheduling of virtual energy station based on equal exergy replacement mechanism

Integrated Demand Response (IDR) is an effective way to make full use of the resources of the integrated energy system (IES) to achieve a positive interaction between supply and demand. Energy cascade utilization is one of the important technical principles of the IES to improve energy utilization efficiency and realize comprehensive benefit optimization. The design of an incentive mechanism is an important prerequisite for realizing IDR. When formulating the incentive mechanism, the coupling between the conversion efficiency of multiple energy sources and the depreciation of energy quality in the process of energy cascade utilization should be comprehensively considered. The exergy is the index that can evaluate the engineering value of any form of energy. Therefore, this paper unifies the multiple heterogeneous energy sources in the IES in the form of exergy through the energy quality coefficient. To more accurately reflect the difference in energy value of multiple heterogeneous energy sources during the time of invocation, an equal exergy replacement mechanism is designed according to the principle of high energy quality and high price. The regulated energy of the customer participating in the IDR is stored in the form of exergy, and the customer can exchange any form of energy at any time under the same exergy. On this basis, a bi-level optimal scheduling model for virtual energy station (VES) to participate in the day-ahead market is established, which realizes the improvement of multi-subject interests. And use Information Gap Decision Theory (IGDT) to deal with the risks posed by uncertainty in day-ahead market prices. Finally, the case study shows that the designed incentive mechanism can further reduce the energy purchase cost of customers, and VES can obtain higher benefits than only providing internal energy sales services. The IGDT method can reduce operational risk, which verifies the feasibility of the method.

Demand response modeling in a day-ahead wholesale electricity market in Japan, considering the impact of customer risk aversion and dynamic price elasticity of demand

This study aims to develop a mathematical modeling approach to maximize the welfare of the price-responsive customers (CUs) in a wholesale electricity market in Tokyo, Japan. The contributions made by this paper in the quest to determine the role of CUs in a demand response market are twofold. First, the aversion of the CUs to the risk of choosing the Demand Response Programs (DPRs) is taken into account by considering their expected utility from consuming electricity. The proposed model is founded on the customer theory in microeconomics, using the concept of the expected utility function to model the behavior of the risk-averse CUs in response to different DPRs. Second, it introduces an hourly-based model for the short-term price elasticity of demand, considering the day-ahead price mechanism defined in the Japan Electric Power Exchange (JEPX) market. The estimated price elasticities are used in a price elasticity matrix of demand (PEMD) to precisely reflect the different response strategies, including flexible, in-flexible forward-shifting, backward shifting, and optimizing responses. An accurate day-ahead hourly load forecasting is performed, using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, which is trained on four years of data provided by the Tokyo Electric Power Company (TEPCO), with a mean absolute percentage error (MAPE) of 0.94%. The developed model is used to analyze the CUs’ behaviors with different response strategies in the JEPX market in Tokyo. By applying the Time-of-Use (TOU) and Real-Time-Pricing (RTP) programs, the results reveal a peak reduction potential of 10.7% and 7.3%, respectively, for the flexible CUs. Applying the RTP program to the curtailable loads can achieve a 7.7% reduction in daily peak demand and a 1.6% reduction in daily electricity consumption.

Impact of the splitting of the German–Austrian electricity bidding zone on investment in a grid-scale battery energy storage system deployed for price arbitrage with gray and green power in Austrian and German day-ahead power markets

The splitting of the German–Austrian electricity bidding zone in October 2018 established limits for cross-border exchanges of power between Germany and Austria, which led to the decoupling of German and Austrian wholesale power prices. We analyze the impact of the split on the investment in lithium-ion grid-scale battery energy storage systems (BESS) used for intertemporal arbitrage in German and Austrian day-ahead power markets with hourly contracts operated by the Energy Exchange of Austria. The paper was written in response to the increasing share of green power in the national energy mix of the two countries, followed by a similar development in the volume of green power traded. Arbitrage with green power was analyzed and compared to arbitrage with power from conventional sources using a robust mixed-integer linear programming (MILP) model extended with a battery degradation process. The model was solved for different levels of depth of discharge (DOD) to identify the optimum between cycle life and calendar aging with respect to prevailing market conditions. Using predicted costs of battery, a break-even point was reached with 60% DOD identified as the optimal pattern for the dispatch of the BESS. A separate scenario was considered for the analysis of the impact of moving from the perfect knowledge of future prices to a situation where day-ahead prices are forecasted using the seasonal autoregressive integrated moving average (SARIMA) model.

Mitigation of the Inefficiency in Imbalance Settlement Designs Using Day-Ahead Prices

The design of electricity imbalance pricing mechanisms is internationally controversial. Policies on whether to permit virtual trading for market participants and whether, or how, to impose penalty incentives on the imbalance volumes vary widely. Furthermore, market designs vary depending whether the imbalance prices are obtained directly from real-time trading or based upon the offer and demand functions from the day-ahead energy markets. This paper develops an analytical framework for evaluating designs for imbalance settlement mechanisms and we have selected the Japanese electricity market, which has undergone several revisions in its imbalance mechanism, as a good example to assess such variations. We develop a predictive approach for the imbalance volumes and price densities using two-step quantile regressions and derive a new trading optimization for a virtual trader's arbitrage position. We construct supporting models to estimate prediction errors for renewable power and demand as drivers of imbalance volume. The empirical analysis reveals that even in a mechanism with imbalance penalties based upon day-ahead reference prices, virtual trading may still be beneficial to market participants as well as to the system operator. We also find that greater market transparency is crucial for increased benefits. The insights generalize beyond the Japan case study.

An Auction-Based Local Market Clearing for Energy Management in a Virtual Power Plant

Virtual power plant (VPP) concept creates additional benefits for different stakeholders, such as system operators, distributed energy resources’ owners, and aggregators. This article presents an auction-based energy management solution from VPP aggregator point of view. To this end, a network-constrained economic dispatch has been developed taking into account a comprehensive resource portfolio, including the conventional and renewable distributed generations, energy storage units, and flexible loads. The VPP aggregator aims at minimizing its expected costs through optimal dispatch of its own resources as well as purchasing and selling electricity from/to upstream network. To this end, a stochastic programming approach has been employed to consider the generation uncertainty of renewable energy resources as well as day-ahead energy market prices using a set of plausible scenarios. The payments to the prosumers are according to an auction-based pricing method that could be an efficient strategy for the VPP aggregator in order to guarantee fairness among consumers and producers within the VPP. In the auction-based pricing scheme, the electricity price at each local node can be calculated according to the demand and generation in each time slot. The proposed framework is a mixed-integer linear programming problem and solved by the CPLEX solver in the GAMS software. It is noteworthy that the effectiveness of the model has been verified, considering several numerical studies.