Real-time Energy Price Research Articles

Capacity fade-based energy management for lithium-ion batteries used in PV systems

Battery storage has been widely used in renewable energy systems. Due to the high cost and limited life span of batteries, effective energy management is normally needed to optimize the battery usage. This paper proposes an online energy management (OEM) for a lithium-ion battery bank used in PV-based systems to meet the load demand. An electrical model and a capacity fade model of batteries, which have been experimentally calibrated, are combined and incorporated in the online energy management. By considering the different status of individual batteries in the battery bank, the proposed OEM aims to minimize the total capacity fade of the battery bank, as well as to economize the cost of using energy. The performance of the proposed OEM is tested and compared with a baseline method under three different scenarios using field-collected PV generation data, load demand data and the real-time uniform Singapore energy price. The proposed OEM shows significant advantages over the baseline method in terms of lower cost of using energy as well as smaller battery capacity fade.

Evaluating synergistic effect of optimally controlling commercial building thermal mass portfolios

In order to achieve a sustainable energy future, advanced control paradigms will be critical at both building and grid levels to achieve harmonious integration of energy resources. This research explores the potential for synergistic effects that may exist through communal coordination of commercial building operations. A framework is presented for diurnal planning of multi-building thermal mass and HVAC system operational strategies in consideration of real-time energy prices, peak demand charges, and ancillary service revenues. Optimizing buildings as a portfolio achieved up to seven additional percentage points of cost savings over individually optimized cases, depending on the simulation case study. The magnitude and nature of synergistic effect was ultimately dependent upon the portfolio construction, grid market design, and the conditions faced by buildings when optimized individually. Enhanced energy and cost savings opportunities were observed by taking the novel perspective of optimizing building portfolios in multiple grid markets, motivating the pursuit of future smart grid advancements that take a holistic and communal vantage point.

Real-Time Predictive Analytics, Big Data & Energy Market Efficiency: Key to Efficient Markets and Lower Prices for Consumers

The combination of evolving deregulation of the US and EU energy markets together with recent advances in data analytics and so called 'Big Data' technologies now offers an unprecedented opportunity for optimal real-time energy pricing for buyers and sellers alike. The main challenge to date for optimal pricing has been optimal real-time bidding and variety of traditional data analysis tools have been applied to this challenge. Yet inefficiencies remain due to the volatile nature of the real-time market. Energy data science is the best solution to protect consumers against the electricity market's inefficiencies. This field is the meeting point between computer programming, machine learning, big data, quantitative analysis and economics. Energy data science is used to help the consumer predict what price they should offer to buy at each hour and closes gaps in the electric markets.

Analyzing the impact of electricity price forecasting on energy cost-aware scheduling

Energy cost-aware scheduling, i.e., scheduling that adapts to real-time energy price volatility, can save large energy consumers millions of dollars every year in electricity costs. Energy price forecasting coupled with energy price-aware scheduling, is a step toward this goal. In this work, we study cost-aware schedules and the effect of various price forecasting schemes on the end schedule-cost. We show that simply optimizing price forecasts based on classical regression error metrics (e.g., Mean Squared Error), does not work well for scheduling. Price forecasts that do result in significantly better schedules, optimize a combination of metrics, each having a different impact on the end-schedule-cost. For example, both price estimation and price ranking are important for scheduling, but they carry different weight. We consider day-ahead energy price forecasting using the Irish Single Electricity Market as a case-study, and test our price forecasts for two real-world scheduling applications: animal feed manufacturing and home energy management systems. We show that price forecasts that co-optimize price estimation and price ranking, result in significant energy-cost savings. We believe our results are relevant for many real-life scheduling applications that are currently plagued with very large energy bills.

Plug-In Electric Vehicle and Voltage Support for Distributed Solar: Theory and Application

This paper examines plug-in electric vehicle (PEV) grid integration, which is a specific, yet significant, component of the overall innovation being adopted by the electric power system. We propose a PEV charging policy that considers transmission and distribution integration issues and reacts to market signals across time scales and systems. More specifically, we propose that the PEV should make economic charging decisions every 5 min based on a real-time market energy price signal. On the time scale of seconds, the PEV provides voltage support for the distribution network, which may allow increased penetrations of distributed photovoltaic (PV) solar arrays. Simulation results using Electric Reliability Council of Texas wholesale power market data suggest that this voltage support service may be provided at a low cost to the individual PEV owner ($5-$50 per year). Therefore, this may prove a more attractive option for supporting distributed PV arrays than distribution network upgrades such as tap-changer-equipped transformers. Finally, we demonstrate the feasibility of our control algorithm through a test system located at the National Renewable Energy Laboratory.

Addressable Office Lighting and Control Systems: Technical Considerations and Utility Evaluations

ABSTRACT This article provides a technical overview of the applications and cost-effectiveness of addressable office lighting and control systems. It also examines manufacturer claims of 80 percent plus electricity savings with these systems by reviewing a utility's evaluation installation of one such product in its own office building, in comparison to three other typical office lighting systems. The utility's case study confirms the impressive savings. Combining information technology with fixture-integrated controls, office lighting fixtures can now be controlled individually, in groups, or as an entire building-wide system through the local-area network (LAN). This new approach to office lighting is rapidly gaining acceptance throughout the retrofit and new-construction markets. Given the prospect of more than 80 percent savings on what can represent one-third of a building's electricity usage, addressable lighting controls hold the potential of delivering impressive reductions in electricity usage. With addressable lighting, individual workers can adjust the light levels in their own work areas through software that directs messages to specific light fixtures. Energy managers can enable, reconfigure, and even schedule control settings for selected occupancy and daylight sensors—all from a personal computer. To address the growing concerns about available electricity supplies, load-shedding can be implemented on a system-wide basis for a gradual reduction in power levels during periods of peak demand or expensive real-time energy prices. Given the advances in information technology in recent decades, controls for office lighting have now emerged as easy-to-use, effective tools to assist energy managers in reaching today's mandated goals for energy efficiency.