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Title: Microgrid to enable optimal distributed energy retail and end-user demand response

Abstract

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission whilemore » maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

Authors:
 [1];  [2];  [3];  [4]
  1. Univ. of California, Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Technologies Area
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Technologies Area
  3. McGill Univ., Montreal, QC (Canada). Trottier Inst. for Sustainability in Engineering and Design
  4. niv. of California, Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
SC-23.1 USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23). Climate and Environmental Sciences Division
OSTI Identifier:
1440957
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 210; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Microgrid; Demand response; Energy pricing; Unit commitment; Distributed energy resources; Rate design

Citation Formats

Jin, Ming, Feng, Wei, Marnay, Chris, and Spanos, Costas. Microgrid to enable optimal distributed energy retail and end-user demand response. United States: N. p., 2018. Web. doi:10.1016/j.apenergy.2017.05.103.
Jin, Ming, Feng, Wei, Marnay, Chris, & Spanos, Costas. Microgrid to enable optimal distributed energy retail and end-user demand response. United States. https://doi.org/10.1016/j.apenergy.2017.05.103
Jin, Ming, Feng, Wei, Marnay, Chris, and Spanos, Costas. Thu . "Microgrid to enable optimal distributed energy retail and end-user demand response". United States. https://doi.org/10.1016/j.apenergy.2017.05.103. https://www.osti.gov/servlets/purl/1440957.
@article{osti_1440957,
title = {Microgrid to enable optimal distributed energy retail and end-user demand response},
author = {Jin, Ming and Feng, Wei and Marnay, Chris and Spanos, Costas},
abstractNote = {In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.},
doi = {10.1016/j.apenergy.2017.05.103},
journal = {Applied Energy},
number = C,
volume = 210,
place = {United States},
year = {2018},
month = {6}
}

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Figures / Tables:

Figure 1 Figure 1: System overview. The MG owns a generation facility, and can exchange energy with the grid to meet the building demands. Additionally, the uncertain variables and DR incentives are incorporated during planning.

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Works referencing / citing this record:

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The Impact of Demand Response Programs on Reducing the Emissions and Cost of A Neighborhood Home Microgrid
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Microgrids Real-Time Pricing Based on Clustering Techniques
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