Battery Characterization Test Bed
Battery characterization and testing is an important research area at Energy Management department. The test bed is set up to validate the battery life estimation methods and conduct related experiments to estimate degradation based on charge/discharge techniques and environmental factors. The battery test bed consists of a charge/discharge system controllers, power supplies and programmable electronic loads. Custom charge and discharge profiles can be programmed into the system for detailed analysis of battery performance and its life cycle impact.
Cell Tower Energy Management
At NEC Labs, we have developed a novel HVAC demand management system that is based on extensive environmental monitoring. The solution was demonstrated at telecom base stations in Malaysia and has been shown to reduce energy consumption by up to 30%. Multitude of sensors provide an improved environmental snapshot of the room. During the commissioning phase, the temperature information collected from the sensors is used to configure the controller. Based on this information, the controller circumvents existing static control systems to dynamically actuate the air-conditioning devices on and off. This results in maintaining satisfactory temperatures in the base station, while reducing energy consumption caused by heterogeneity in load distribution. The controller is also capable of detecting temperature biases in the sensors and communication issues and reliably recovering from such problems to maintain the desired environmental conditions in the room. For larger base stations and telecom exchanges with multiple devices, the above solution is extended through the definition of zones in a flexible manner by mapping different temperature sensor sets to the actuators in that zone. The solution was implemented and tested on hardware at NECLA and at several locations in Malaysia. It has been implemented in the Energy Management Control (EMC) product available under NEC Smart Energy. ( http://sg.nec.com/en_SG/solutions/by-business/smart-energy/emc.html )
Grid-Scale Energy Storage Management
Development of management algorithms to optimize operation of grid scale energy storage systems is a key area of research. These storage systems are on the MWh scale and our goal is to provide a variety of services to the grid using energy storage. Developed algorithms utilize the grid scale storage to participate in Independent System Operator (ISO) markets for energy, frequency regulation, reserve, and demand response as well as utility applications such as load management and renewable integration ($20B market by 2024). Our research focuses on coordination of grid scale storage dispatch in multiple simultaneous applications while minimizing its operational cost. Various techniques such as nonlinear optimization, optimal control, parameter identification, and artificial intelligence methods are utilized for modeling and management purposes in our research.
Microgrid and Nanogrid Energy Management System
Energy Management Department has a strong background in design and implementation of Energy Management Systems (EMS) for microgrids. Our main objective is to reduce operational cost of a microgrid (including fuel cost, utility bill, battery degradation cost, equipment maintenance etc.) while improving the utilization of renewable energy resources in the microgrid. Our patented EMS can reduce the operational cost of a microgrid by up to 30% compared to scheduled or load-based management approaches. We also work on development of EMS solutions for resilient microgrids which can handle outages and disturbances in a reliable and cost-effective manner. Our research focuses on providing a variety of functionalities and features in a microgrid by intelligent control of distributed generation, load and storage units in real time. Various techniques such as linear, mixed integer, and stochastic optimizations, economic dispatch and unit commitment methods, dynamic cost models, and state estimations are utilized in this research thread.
Power System Optimization Platform
Optimized design, operation, and control of energy systems plays a crucial role in the success of Smart Grid initiatives. Technical, economic, and environmental aspects of these activities for multi-carrier energy systems are under study at the Department of Energy Management. Linear and non-linear optimization solvers (SNOPT) are implemented in different programming environments like Matlab, GAMS to develop sophisticated models and tools to conduct cutting edge research in operation management of energy systems. Economics of microgrids operation, integration of energy storage and electric vehicles, and optimal design of microgrids considering reliability and environmental measures are some examples of the ongoing research projects.
Smart Grid and Energy Systems Laboratory
Smart Grid and Energy Systems Lab at Energy Management Department supports a wide range of research activities related to smart grid and the integration of renewable energy resources. It includes a microgrid consisting of multiple distributed generation sources, demand and storage devices integrating AC and DC distribution systems. Distributed generation and storage systems can be managed in various modes assisted by detailed monitoring of power flows obtained from phasor measurement units and revenue grade meters. Device-level data and inverter data is also collected and analyzed in real-time. In the near future, real time active and reactive power management systems will support various experiments on the network. The energy lab also hosts smart grid compatible communication fabric integrable with IEC 61850.