Hourly Energy Prices Research Articles

Representing Hourly Energy Prices in a Large-Scale Monthly Water System Model

Water system management models represent different purposes, such as water supply, flood control, recreation, and hydropower. When building large-scale system models to represent these diverse objectives, their most appropriate time steps for each purpose often do not coincide. A monthly time step is usually sufficient for water supply modeling, but it can be too coarse for flood control, hydropower, and energy operations, where hourly time steps are preferred. Large-scale water management and planning models mostly employ monthly time steps, but using monthly average energy prices underestimates hydropower revenue and overestimates pumping energy cost because these plants tend to operate during times with above- or below-average energy prices within any month. The approach developed here uses hourly varying prices depending on the percent of monthly operating hours. This paper examines an approach that approximately incorporates hourly energy price variations for hydropower and pumping into large-scale monthly time-step water system model operations without affecting water delivery results. Results from including hourly varying energy prices in a large-scale monthly water supply model of California (CALVIN) are presented. CALVIN is a hydroeconomic linear programming optimization model that allocates water to agricultural and urban users with an objective to minimize total scarcity costs, operating costs, and hydropower revenue loss. Thirteen hydropower plants are modeled with hourly varying prices, and their revenue increased by 25 to 58% compared to revenue calculated with monthly average constant energy prices. Hydropower revenue improvements are greater in critically dry years. For pumping plants modeled with hourly varying prices, the energy use cost decreased by 10 to 59%. This study improves system representation and results for large-scale modeling.

Hourly Energy Prices in Spain: Evidence of Persistence Across Different Months

We examine the hourly structure of energy prices in Spain using 12 one-month-long series (from November 2020 to October 2021) of samples of hourly pricing (from 00:00 to 23:00) for each day of the month and applying a long memory approach with fractional integration analysis. Our results indicate that the series are highly persistent, with shocks having permanent effects with non–mean reversion properties, with no month-specific effects on the data series.

CO2 price effects on the electricity market and greenhouse gas emissions levels: an application to the Spanish market

This research proposes an empirical method to estimate the impact on the wholesale electricity market of an increase in the price of CO2 emission allowances. The current literature in this field is mainly focused on long-term simulation analyses, while this study carries out a short-term analysis with microdata from the electricity market. A higher price of CO2 implies an increase in the electricity generation costs of polluting units and therefore an increase in the price of the electricity market. When CO2 becomes more expensive, polluting electricity generators are shifted in the hourly electricity supply curve towards less competitive positions (in favour of less polluting/cheaper units). Displaced polluting units could even be taken out of the market, which would imply a reduction in CO2 emissions. These short-term movements can be reproduced with our microdata of the day-ahead electricity market –data Provided by the Spanish Market Operator (OMIE). According to our results, increases in the carbon price of 10, 20, or 30 € per ton, respectively, cause increases of 1.8%, 4.2% and 5.3% in the electricity price (year 2018), while the negative effect on emissions is relatively small. Our analysis concludes with the estimation of an ARIMA-SARIMA model that looks for the main determinants of the variations in the hourly energy prices and the carbon emissions. The estimations show that the marginal supply technology in the electricity market is important in explaining these variations.Graphical abstract

Optimal cost management of the CCHP based data center with district heating and district cooling integration in the presence of different energy tariffs

Data centers are expected to be one of the major prosumers in energy networks because of their high electricity demands as well as being a possible source of waste heat energy utilization. A combined cooling, heat, and power (CCHP) system can be a perfect match with a data center for supplying the data center's less fluctuated, uninterrupted power and cooling demand. Moreover, CCHP based data center integration with regional district heating and cooling system can gain data center's excess heat, improve the efficiency of the primary energy sources, and give flexibility to district energy operators. This paper aims to offer a comprehensive energy cost management for a CCHP based data center that considers power, heating, cooling, and data center's excess heat utilization in the presence of hourly energy prices and varying outdoor temperatures. Mixed Integer Linear Programming (MILP) is used to determine a cost-optimized operational strategy for the overall system. The result shows that CCHP and regional energy integration to the data center decrease the overall energy cost of the operation while providing more flexibility to the data center and district energy network operators. Optimized operation of the CCHP system with the data center's excess heat recovery, district heating, and cooling integration can cut its annual electricity costs by 40.3% compared to the local operation of the CCHP based data center. Return of investment (ROI) is estimated to be in 6.6 years for additional required systems for the conventional data center.

Hourly Energy Prices in Spain. Evidence of Persistence Across Different Months

Hourly Energy Prices in Spain. Evidence of Persistence Across Different Months

Decision support methodologies and day-ahead optimization for smart building energy management in a dynamic pricing scenario

Nowadays identifying techniques aimed at a rational use of electric power has become even more important than the production of energy itself. This is caused by different factors, as the progressive saturation of the electricity grid, which is increasingly subject to connection requests, mainly due to the development of plants which exploit renewable energy sources. This work suggests a new approach based on the combination of the optimizer and the simulator developed in the MATLAB/Simulink environment, in order to reduce the energy costs in buildings during the summer while taking into consideration the user comfort. The electrical consumption of the entire building is taken into consideration is here examined with the aim of applying an air-conditioning system. The goal is to find, the day before, which is the optimal hourly scheduling of the control variables that must be applied the next day, taking into consideration all external conditions; weather conditions and the hourly energy price. In order to achieve this objective, the control variables, that have been changed, are the room temperature set points and the flow water temperature set point. As required by the UNI EN ISO 7730:2006 standard, comfort measurement is calculated by PPD (Predicted Percentage of Dissatisfied) index. Different scenarios are investigated and two optimization algorithms are compared. The results show that there is an average of 10−28% potential cost saving, while maintaining a high level of comfort (PPD ≤ 12). The study is carried out by simulating a real office building in Italy, and the comparisons are shown regarding the actual settings applied to it.

A power market-based operation support model for sub-daily hydropower regulation practices

With increasing power production from renewable energy sources, sub-daily variations in energy demand need to be balanced. Today, hydropower is commonly used as balancing power. In this study, we quantified the impact of capacity constraints, in terms of reservoir volume and hydropower capacity, on the potential to comply with instant energy demand. To evaluate the impact, we developed two new metrics, power market impact and system efficiency ratio, which are based on two threshold flow regimes derived from natural flow as lower threshold release and regulated flow (based on hourly energy prices) as upper threshold release. The operation support model comprises 96 different regulation scenarios based on varying combinations of hydropower and reservoir capacities. For each scenario, an hourly water balance was simulated, to obtain the highest complying with upper threshold release based on actual energy demand. We tested the framework on the Kemijoki river with defined thresholds based on the natural flow regime (tributary river Ounasjoki) and the hourly energy price in Finland in 2017, and estimated the impact of regulation on hourly flow regime at the Taivalkoski hydropower station. The annual flow regime impact in 2013, 2014 and 2015 was estimated to be 74%, 84% and 61%, respectively, while the monthly impact varied from 27% to 100%. Our framework for evaluating interactions between the power market and sub-daily regulation practices is a useful novel tool for sustainable river management and can be easily applied to different rivers and regions and evaluated for different timescales.

Critical review and economic feasibility analysis of electric energy storage technologies suited for grid scale applications

Radical changes in production paradigms are affecting electric systems. This is due to Renewable Energy Sources (RES) introduction, which changed production from being centralized and dispatchable, to be more decentralized and unpredictable. Further transformations are expected as the RES share into electric grids will increase. RES further development will likely rely mostly on additional solar PV capacity, then relevant energy quantities will be needed to be shifted from day hours to evening and night hours. Such “Load Shifting” could be performed with Electric Energy Storage (EES) technologies. Few technologies suited for this are already commercially available, whereas others have been proposed. In this paper, the EES technologies suited for load shifting are reviewed with a focus on economic costs. After that, current and future EES economic feasibility are assessed by using Italian hourly energy prices from 2018. Since EES resulted to be currently uneconomic, the minimum price modification required to make EES feasible is calculated.

Algorithm development for night charging electric vehicles optimization in big data applications

Abstract: In this paper a night charging method that optimizes the recharging process of electric vehicles (EVs) depending on hourly energy price in a peer to peer (P2P) energy trading system is presented. This algorithm determines how much energy should be recharged in the battery of each EV and the corresponding time slot to do it, avoiding the discontinuities in the charging process and considering the users’ personal mobility constraints.

Robust energy procurement strategy of large consumers

Summary This paper presents a risk-constrained model for large consumers to determine the optimal energy acquisition framework and the participation level in load-reduction programs based on the real-time pricing scheme. Additionally, the proposed pricing framework for the load-reduction enables the system operator to control the consumers' load reduction levels, according to energy prices. In competitive electricity markets, the uncertainty of hourly energy prices is the main problem faced by large consumers. Therefore, the information gap decision theory approach is addressed to manage the financial risk arising from uncertain prices. In this work, instead of profit maximization, the optimal robust framework is specified in a way that guarantees the desired profit. The robustness of the optimal procurement strategy is demonstrated by determining the variation interval of uncertain prices such that the profit associated with this strategy will be more than an acceptable threshold, which is defined by consumers. The performance of the presented model is analyzed via a case study. Copyright © 2015 John Wiley & Sons, Ltd.

Combined cooling, heating, and power system optimal pricing for electricity and natural gas using particle swarm optimization based on bi-level programming approach: Case study of Canadian energy sector

Combined cooling, heating, and power (CCHP) systems are considered energy efficient and they can be economically rewarding only when the inter-dependencies of energy prices for electricity and natural gas (NG) are properly accounted for. The goal of this study is to determine the optimal hourly energy prices for electricity and NG for a non-autonomous CCHP system using particle swarm optimization algorithm based on bi-level programming approach. Based on optimal sizing and configuration of the CCHP system, the rewards for both the distribution utility and the industrial energy consumer are analyzed. For simulation purposes in this study, a CCHP system with known loads operated in Ontario, Canada, based on actual energy prices is examined. For a CCHP system with gas turbine (GT) as prime mover, the optimal energy prices for on-peak and off-peak periods for spring, summer, fall, and winter for electricity and NG are determined. It is found that for the on-peak period, if the electricity prices in winter are 1.16, 1.56, and 1.75 times greater than those in summer, fall, and spring, respectively, and if the NG prices in winter are 1.19, 1.64, and 1.86 times greater than those in summer, fall, and spring, respectively, the rewards for distribution utility and industrial energy consumer are guaranteed. Based on the simulation results, it is determined that, under the optimal hourly energy prices for electricity and NG, the industrial energy consumer meets in excess of 92% of its needed electricity by the CCHP system that is sized and configured optimally with capital recovery of 2–3 years.

Estimated impacts of climate warming on California’s high-elevation hydropower

California’s hydropower system is composed of high and low elevation power plants. There are more than 150 high-elevation power plants, at elevations above 1,000 feet (300 m). Most have modest reservoir storage capacities, but supply roughly 74% of California’s in-state hydropower. The expected shift of runoff peak from spring to winter due to climate warming, resulting in snowpack reduction and increased snowmelt, might have important effects on power generation and revenues in California. The large storage capacities at low-elevation power plants provide flexibility to operations of these units under climate warming. However, with climate warming, the adaptability of the high-elevation hydropower system is in question as this system was designed to take advantage of snowpack, a natural reservoir. With so many high-elevation hydropower plants in California, estimation of climate warming effects by conventional simulation or optimization methods would be tedious and expensive. An Energy-Based Hydropower Optimization Model (EBHOM) was developed to facilitate practical climate change and other low-resolution system-wide hydropower studies, based on the historical generation data of 137 high-elevation hydropower plants for which the data were complete for 14 years. Employing recent historical hourly energy prices, the model was used to explore energy generation in California for three climate warming scenarios (dry warming, wet warming, and warming-only) over 14 years, representing a range of hydrologic conditions. The system is sensitive to the quantity and timing of inflows. While dry warming and warming-only climate changes reduce average hydropower revenues, wet warming could increase revenue. Re-operation of available storage and generation capacities help compensate for snowpack losses to some extent. Storage capacity expansion and to a lesser extent generation capacity expansion both increase revenues, although such expansions might not be cost-effective.

A soft computing system for day-ahead electricity price forecasting

Hourly energy prices in a competitive electricity market are volatile. Forecast of energy price is key information to help producers and purchasers involved in electricity market to prepare their corresponding bidding strategies so as to maximize their profits. It is difficult to forecast all the hourly prices with only one model for different behaviors of different hourly prices. Neither will it get excellent results with 24 different models to forecast the 24 hourly prices respectively, for there are always not sufficient data to train the models, especially the peak price in summer. This paper proposes a novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. SOM is used to cluster the data automatically according to their similarity to resolve the problem of insufficient training data. SVM models for regression are built on the categories clustered by SOM separately. Parameters of the SVM models are chosen by Particle Swarm Optimization (PSO) algorithm automatically to avoid the arbitrary parameters decision of the tester, improving the forecasting accuracy. The comparison suggests that SOM–SVM–PSO has considerable value in forecasting day-ahead price in Pennsylvania–New Jersey–Maryland (PJM) market, especially for summer peak prices.