Hourly Electricity Prices Research Articles

Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings

A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it allows for easy integration of new agents, enabling seamless functionality expansion, or the disabling of existing agents to tailor the system to specific needs. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.

Integrated e-Methanol and Drop-in Fuels Hydrothermal Liquefaction Platform-Techno-Economic and GHG Emissions Assessment for Grid-Connected Plants under Flexible BECCU(S) Operation.

This study examines the combined production of drop-in fuels and methanol using hydrothermal liquefaction (HTL) as a technological basis in the context of bioenergy and power-to-X (PtX) applications. Given the increasing need for flexibility in a system dominated by fluctuating renewable power, we evaluated flexible methanol operation as a strategy to harness global greenhouse gas (GHG) emissions in a grid-connected HTL setup. In this operation, the biogenic CO2 destination is alternated between methanol synthesis bioenergy with carbon capture and utilization and combined underground storage depending on the hourly electricity price and grid carbon intensity. The results indicate that the strategy has potential to maintain the average fuel carbon intensity within the 65% GHG reduction threshold set by the renewable energy directive III at a minimum methanol price of 870 EUR/t. This approach could facilitate implementation as it does not require dedicated renewable power generation and hydrogen storage, potentially decreasing costs compared to semi-islands and off-grid PtX systems.

Machine and deep learning approaches for forecasting electricity price and energy load assessment on real datasets

This article aims to propose short-term forecasting for a combined model of the hourly electricity price and the hourly load. Where both the independent system operators and the market participants must deal with the uncertainty and volatility of the day-ahead electricity price. Therefore, an accurate prediction model has been needed for a combined short-term forecasting model for both the hourly electricity price and the hourly load in the deregulated market. To get a better prediction, real-time data for the day-ahead electricity price and the hourly load are provided from the ISO New England Control Area (ISO-NE-CA) market. This study presents the combined model based on four featured machine and deep learning algorithms: Feed Forward Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, Long Short-Term Memory, and Gated Recurrent Units. The results are obtained from two scenarios, In the first scenario, the data has been trained and tested without depending on other factors such as temperatures and calendar. In the second scenario, the data has been trained and tested depending on the previously mentioned factors. The obtained results from the four algorithms have been compared to show their performance and their efficiency in the values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE). The four featured algorithms prove their good performance with less error in the forecasting model.

Residential demand response and dynamic electricity contracts with hourly prices: A study of Norwegian households during the 2021/22 energy crisis

Price-responsive demand and dynamic electricity price contracts can play a vital role in balancing renewable energy production and alleviating energy shortages such as those experienced in the European energy crisis. This study focuses on the implicit demand flexibility of residential consumers during extraordinarily high electricity prices in winter 2021/22 in Norway where most households have electric heating and spot price contracts. An econometric model is developed that compares the demand with pre-crisis levels, adjusts for factors influencing electricity consumption, such as outdoor temperature, and utilises a comprehensive dataset including hourly electricity demand data. The results reveal a quick response since the price signal was passed immediately to the customers and substantial energy savings of 11.4 % during winter. While the average household showed no significant short-term price response to daily or hourly price variations, several subgroups did. Particularly, households actively monitoring hourly prices via real-time information channels and those with automatic smart charging of electric cars showed higher load reductions in peak price hours and load shifting to low-price hours. Thus, the study concludes that households are able to respond to variable hourly electricity prices and suggests the promotion of spot price contracts to incentivise residential demand response.

Influencing residential electricity consumption with tailored messages: long-term usage patterns and effects on user experience

BackgroundTo transition our energy system toward sustainable production and consumption, it is important to successfully engage consumers to become active participants in this process. One form this can take is manual demand response, where end users respond to fluctuations in energy production and help balance the grid through adjustment of their consumption. This paper presents a trial of such a system that took place with tenants in subsidized housing in Catalonia, Spain. The aim of the trial was to motivate the load shifting behavior of the participants by forecasting expected consumption curves and tailoring suggestions for optimized behavior. The forecasts and suggestions were based on the users’ past consumption patterns and the hourly day-ahead electricity prices. This information was made available to the users on a web-based platform, and participants were actively informed with text messages sent to their mobile phones in case of attractive saving potentials for the following day. The trial was carried out in 2 phases from November 2019 to May 2020 (Phase 1) and from August to October 2020 (Phase 2). Data were collected on interaction with the platform, the perceived user experience of the platform and text messages, and the perceived energy saving success.ResultsOur results showed that there is a general interest of the participants in the concept, but that there are also important barriers to integrating load shifting behavior into everyday life. The biggest barriers here are limitations in the flexibility potential of households and limited perceived benefits. Feedback from our participants also suggests high acceptance and relevance of more automated demand-side management (DSM) concepts.ConclusionsBased on this, we recommend paying special attention to the accommodation of varying flexibility potential in manual demand response (DR) programs, ensuring that communicated benefits are sufficiently attractive to motivate behavior change, and consideration of a phase of manual DR as an entry point to automated DSM.

Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid

Integrating renewable energy resources (RES) is a challenge for power system operators due to their fluctuations and unpredictability. At the same time, the water shortage problem and the needs to desalinate more freshwater increase the prominence of sufficient energy resources for sustainable operation. Therefore, this paper presents a market-clearing mechanism in a co-optimization model that coordinates the operation of grid-connected reverse osmosis water desalination plants (RO-WDPs) and the operation of renewable-rich power systems. It is assumed that electric demands can participate in the provision of demand response (DR) to the market via multiple DR options. The DR options related to the general loads are demand shifting, load curtailment, distributed generation, and hybrid energy storage systems (HESS). These HESS includes battery storage and hydrogen storage systems. Pertaining to their special characteristics, a new DR option is proposed as a customized option for RO-WDPs. The market clearing mechanism is assumed to be based on the security-constrained unit commitment (SCUC) and formulated as a stochastic mixed-integer linear programming problem (MILP) to optimally schedule the day-ahead operation of the power system. The presented model is applied on 6-bus and IEEE 24-bus reliability test system (RTS) test power systems with significant penetration of RES to demonstrate its merits. The simulation results show that the system efficiency is enhanced by adding the energy flexibility of RO-WDP without endangering the water demand-supply when using the proposed coordinated model. Hence, the total operation cost is minimized, the RES integration is facilitated, and the hourly electricity prices are smoothened.

The tradeoff between electricity cost and CO2 emission in the optimization of photovoltaic-battery systems for buildings

Photovoltaic-battery systems for buildings are increasingly implemented to reduce electricity cost and carbon emission. However, two primary objectives of such systems, namely cost and CO2 minimization, are contradictory goals that cannot be optimized simultaneously. This paper formalizes photovoltaic-battery systems using the multi-objective mixed integer linear programming approach, optimizing the battery installed capacity and operation strategy based on the hourly electricity price and carbon emission factor. The investigation on a commercial building identifies that pursuing cost minimization can partially decrease emission, while minimizing CO2 even causes additional cost. The tradeoff between cost and CO2 diminishes when the grid becomes more volatile, and the incorporation of rooftop photovoltaics can further enhance the battery's effectiveness. As a result, a nearly zero-carbon building is economically realized through 60% renewable penetration in the grid and 50% rooftop photovoltaics, with an electricity cost of 0.05 $/kWh and a CO2 emission of 0.07 kg/kWh, respectively. Moreover, implementing a demand-side carbon tax can be facilitated with intense grid volatility. The novel approach assists buildings in adopting the most cost-effective investment and operation strategy of photovoltaic-battery systems towards zero carbon under various grid conditions.

Large Time‐Varying Volatility Models for Hourly Electricity Prices*

AbstractWe study the importance of time‐varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well‐known time series models in a large dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non‐Gaussian error terms in stochastic volatility. We find that using regressors as fuel prices, forecasted demand and forecasted renewable energy is essential to properly capture the volatility of these prices. Moreover, we show that the time‐varying volatility models outperform the constant volatility models in both the in‐sample model‐fit and the out‐of‐sample forecasting performance.

Optimal design and scheduling of carbon capture power plant based on uncertainty decision-making methods

Post-combustion capture is one of the most mature carbon reduction technologies which plays an important role in pushing forward the process of "carbon neutrality". However, most previous efforts focused merely on the fixed operation of power plants and carbon capture devices, without taking into account the effects of fluctuating electricity prices and the subsequent variations in energy penalty of solvent regeneration process. As a result, the ability of carbon capture power plants (CCPP) to withstand fluctuations has deteriorated, leading to a decrease in robustness. In order to take advantage of the flexible operation characteristics of the power plant and the carbon capture device, the deterministic hourly electricity price and power load demand, as well as uncertainty of power load demand in future are considered in the study to determine the optimal network configuration and the flexible scheduling of carbon capture power plants based on decision-making theories. The uncertain parameters are presented by scenarios, and through MINLP-based optimization, the optimal system configuration under each scenario is obtained. Then these configurations are applied to other scenarios to obtain the scheduling and economic performance of the system. Five uncertainty decision-making theories (optimistic method, pessimistic method, optimistic coefficient method, equal probability method, minimum maximum regret value method) are adopted to select the most appropriate system scheme. Finally, a case study is investigated to demonstrate the proposed method, and the effectiveness and the feasibility are proved by the obtained results. Two network configurations with daily profit from $30,000 to $240,000 by scenarios are obtained and investigated. Results show that the turbine layout in the configuration chosen by optimistic method is radical, while by pessimistic criterion the turbine system shows margin in operation. Configuration selected by the equal possible value method and minimax regret method can achieve good economic performance operating in various scenarios. This research can help decision makers to make scientific decisions and reduce operating risks under uncertainties.

Optimal design and energy management of residential prosumer community with photovoltaic power generation and storage for electric vehicles

The optimum design and management of residential photovoltaic (PV) communities are essential to the reliability to satisfy the demands of communities due to the volatility and intermittence of solar energy. The retailer for energy dispatch management is introduced to improve the economic performances of PV communities through demand-side management measures with the electric grid. This paper proposes a novel two-stage optimization model of design and electricity dispatch strategies for residential PV communities to operate electric vehicles' charges. The first-stage optimization aims to obtain the optimal capacities of PV and batteries. The second-stage optimization optimizes the retailer's hourly electricity prices and electricity dispatch strategies, in which the Stackelberg game is performed between maximizing the retailer's benefit and minimizing the electricity cost of residential basic loads and vehicle charging loads. A case study is implemented to validate the proposed model. The installation capacity of batteries is appropriately increased with the increasing active loads of electric vehicles. The optimal pricing strategies of the retailer based on the trade schemes with the electricity grid are influenced by their ratios of fixed and active loads. Compared to the community without the retailer, the annual electricity cost of users is saved by 21.2 % through the proposed management strategy.

Demand Response Program Integrated With Electrical Energy Storage Systems for Residential Consumers

This article presents a distributed resilient demand response program integrated with electrical energy storage systems for residential consumers to maximize their comfort level. A dynamic real-time pricing method is proposed to determine the hourly electricity prices and schedule the electricity consumption of smart home appliances and energy storage systems commitment. The algorithm is employed in normal and emergency operating conditions, taking into account the comfort level of consumers. In emergency conditions, the power outage of consumers is modeled for different hours and outage patterns. To evaluate the applicability of the proposed model, real samples of Southern California households are considered to model the smart homes and their appliances. Further, a sensitivity analysis is performed to assess the impacts of the number of households and number of persons per household on the output results. The results showed that the proposed model reduced the costs of utility in normal and emergency conditions by about 33.77% and 30.92%, respectively. The values of total payments of consumers in normal and emergency conditions were decreased by about 34.26% and 31.31%, respectively. Further, the consumers comfort level for normal and emergency conditions increased by about 146.78% and 110.2%, respectively. Finally, the social welfare for normal and emergency conditions increased by about 46% and 49.06%, respectively.

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.

Optimal management of a mega pumped hydro storage system under stochastic hourly electricity prices in the Iberian Peninsula

Concern for the environment led in some developed countries to planning future energy use that combines greater electrification with increasing use of Renewable Energy (RE) in the electricity generation mix. These scenarios can affect Security of Supply (SoS) because the intermittence of RE, its stochastic nature as an electricity generation sources and the demand's behaviour. It is necessary to increase storage capacity, among other measures. This paper analyses the effects of an optimal management strategy based on prices for Pumped Hydro Storage plants (PHES) using a daily mean reverting jump diffusion stochastic model of electricity prices in a risk neutral world including daily seasonality. Results show that a) income with this strategy under uncertainty may be insufficient compared to investment costs; b) the strategy does not usually provide proper guarantees as regards SoS at times of high demand for electricity; c) the technical characteristics of PHES such as the maximum upper & lower reservoir volume are highly significant. The effect of a minimum reservoir capacity at times of high demand is also analysed. PHES profitability can be improved under a Generating Company (GENCO) strategy coordinated with a wind farm and if the avoided CO2 emissions are taken into account.

Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs

This paper addresses the question of how much to bid to maximize the profit when trading in two electricity markets: the hourly Day-Ahead Auction and the quarter-hourly Intraday Auction. For optimal coordinated bidding many price scenarios are examined, the own non-linear market impact is estimated by considering empirical supply and demand curves, and a number of trading strategies is used. Additionally, we provide theoretical results for risk neutral agents. The application study is conducted using the German market data, but the presented methods can be easily utilized with other two consecutive auctions. This paper contributes to the existing literature by evaluating the costs of electricity trading, i.e. the price impact and the transaction costs. The empirical results for the German EPEX market show that it is far more profitable to minimize the price impact rather than maximize the arbitrage.

Forecasting electricity prices with expert, linear, and nonlinear models

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.

Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power

In an open electricity market, increased accuracy and real-time availability of electricity price forecasts can help market parties participate effectively in market operations and management. As the penetration of clean energy increases, it brings new challenges to electricity price forecasting. An electricity price forecasting model is constructed in this paper for markets containing a high proportion of wind and solar power, where the scenario with a high coefficient of variation (COV) caused by the high frequency of low electricity prices is particularly concerned. The deep extreme learning machine optimized by the sparrow search algorithm (SSA-DELM) is proposed to make predictions on the model. The results show that wind–load ratio and solar–load ratio are the key input variables for forecasting in power markets with high proportions of wind and solar energy. The SSA-DELM possesses better electricity price forecasting performance in the scenario with a high COV and is more suitable for disordered time series models, which can be confirmed in comparison with LSTM.

Improved Generative Adversarial Behavioral Learning Method for Demand Response and Its Application in Hourly Electricity Price Optimization

In response to the imbalance between power generation and demand, demand response (DR) projects are vigorously promoted. However, customers' DR behaviors are still difficult to be simulated accurately and objectively. To tackle this challenge, we propose a new DR behavioral learning method based on a generative adversary network to learn customers' DR habits. The proposed method is also extended to maximize the economic revenues of generated DR policies on the premise of obeying customers' DR habits, which is hard to be realized simultaneously by existing model-based methods and traditional learning-based methods. To further consider customers' time-varying DR patterns and trace the changes dynamically, we de-fine customers' DR participation positivity as an indicator of their DR pattern and propose a condition regulation approach improving the natural generative adversary framework to generate DR policies conforming to customers' current DR patterns. The proposed method is applied to hourly electricity price optimization to reduce the fluctuation of system aggregate loads. An online parameter updating method is also utilized to train the proposed behavioral learning model in continuous DR simulations during electricity price optimization. Finally, numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain

The installation of wind power technology is growing steadily and the trend can be expected to continue if the objectives proposed by the European Commission are to be achieved. In some countries a considerable percentage of installed wind power capacity is near the end of its useful lifetime. In the case of Spain, the figure is 50% within five years. Over the last 20 years, wind energy technology has evolved considerably and the expected capacity factor has improved, thus increasing annual energy production, and capital expenditure and operational expenditure have decreased substantially. This paper studies the optimal decision under uncertainty between life extension and full repowering for a generic wind farm installed in the Iberian Peninsula when the future hourly electricity prices and the capacity factor evolve stochastically and seasonally. The results show that in economic terms, full repowering is the best option, with a net present value of €702,093 per MW installed, while reblading is the second best option. The methodology can be transferred to other specific wind farms in different electricity markets and can be used to develop national wind energy policy recommendations to achieve projected shares in the electricity mix.

The optimal hourly electricity price considering wind electricity uncertainty based on conditional value at risk

ABSTRACT Wind generation fluctuation results in the electricity supply uncertainty of the company in electricity market. And this uncertainty has an important impact on hourly electricity price. In our paper, we use game models to optimize hourly price for different risk appetites’ companies considering the uncertainty. For the risk-neutral company, we solve the optimal electricity price by maximizing the expected profit. For the risk-averse or risk-preferring company, we consider wind electricity generation as a Weibull distribution and obtain the optimal electricity price based on conditional value at risk. In numerical examples, we find that the risk-preferring company sets the highest electricity price, demonstrating the importance of modeling risk appetites of the company in electricity price. And the results indicate that a risk-averse (risk-preferring) company will set a higher (lower) electricity price for a larger risk confidence level. Besides, we find that the company will lower electricity price with the decrement of users’ price elasticity. Furthermore, our finding shows that the profit of the company increases with the wind penetration due to the decreased cost, which supports the observed and investigated results in electricity market.

A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

ABSTRACT Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule.