MOD-DR: Microgrid optimal dispatch with demand response

TitleMOD-DR: Microgrid optimal dispatch with demand response
Publication TypeJournal Article
LBNL Report NumberLBNL - 1006770
Year of Publication2016
AuthorsJin, Ming, Wei Feng, Ping Liu, Chris Marnay, and Costas Spanos
JournalApplied Energy
Start Page758
Date Published12/2016
KeywordsChina Energy, China Energy Group, Energy Analysis and Environmental Impacts Division, Energy System Planning & Grid Integration, International Energy Department

In the face of unprecedented challenges of upcoming fossil fuel shortage and reliability and security of the grid, there is an increasing interest in adopting distributed, renewable, energy resources, such as microgrids (MGs), and engaging flexible electric loads in power system operations to potentially drive a paradigm shift in energy production and consumption patterns. Prior work on MG dispatch has leveraged decentralized technologies like combined heat and power (CHP) and heat pumps to promote efficiency and economic gains; however, the flexibility of demand has yet to be fully exploited in cooperation with the grid to offer added benefits and ancillary services. The object of the study is to develop microgrid optimal dispatch with demand response (MOD-DR), which fills in the gap by coordinating both the demand and supply sides in a renewable-integrated, storage-augmented, DR-enabled MG to achieve economically viable and system-wide resilient solutions. The key contribution of this paper is the formulation of a multi-objective optimization with prevailing constraints and utility trade-off based on the model of a large-scale MG with flexible loads, which leads to the derivation of strategies that incorporate uncertainty in scheduling. Evaluation using real datasets is conducted to analyze the uncertainty effects and demand response potentials, demonstrating in a campus prototype a 17.5% peak load reduction and 8.8% cost savings for MOD-DR compared to the non-trivial baseline, which is on par with the Oracle for perfect predictions.