Automated Dynamic Demand Response Implementation On A Micro Grid
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Automated Dynamic Demand Response Implementation on a Micro-grid
In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D2R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D2R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability to dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D2R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.
Microgrid Dynamics and Control
This book discusses relevant microgrid technologies in the context of integrating renewable energy and also addresses challenging issues. The authors summarize long term academic and research outcomes and contributions. In addition, this book is influenced by the authors’ practical experiences on microgrids (MGs), electric network monitoring, and control and power electronic systems. A thorough discussion of the basic principles of the MG modeling and operating issues is provided. The MG structure, types, operating modes, modelling, dynamics, and control levels are covered. Recent advances in DC microgrids, virtual synchronousgenerators, MG planning and energy management are examined. The physical constraints and engineering aspects of the MGs are covered, and developed robust and intelligent control strategies are discussed using real time simulations and experimental studies.
Applications of Synthetic Biology in Health, Energy, and Environment
The application of genetic engineering techniques by redesigning and repurposing biological systems for novel biotechnical applications has paved the way for the field of synthetic biology. This field boosted the evolution and discovery of various novel technologies essential to the conquest of biological problems related to health, disease, the environment, and energy. The field of synthetic biology is growing rapidly, and further research is required. Applications of Synthetic Biology in Health, Energy, and Environment deliberates on principles and the advancement of synthetic biology and their translation in the fields of health, disease, energy, and the environment. Covering topics such as climate change, bioremediation, and smart drugs, this premier reference source is an excellent resource for students and educators of higher education, industrialists, medical professionals, hospital administrators, policymakers, environmental scientists, pharmacists, librarians, researchers, and academicians.