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Title: Dispatch optimization of concentrating solar power with utility-scale photovoltaics

Abstract

Concentrating solar power (CSP) tower technologies capture thermal radiation from the sun utilizing a field of solar-tracking heliostats. When paired with inexpensive thermal energy storage (TES), CSP technologies can dispatch electricity during peak-market-priced hours, day or night. The cost of utility-scale photovoltaic (PV) systems has dropped significantly in the last decade, resulting in inexpensive energy production during daylight hours. The hybridization of PV and CSP with TES systems has the potential to provide continuous and stable energy production at a lower cost than a PV or CSP system alone. Hybrid systems are gaining popularity in international markets as a means to increase renewable energy portfolios across the world. Typically, CSP-PV hybrid systems have been evaluated using either monthly averages of hourly PV production or scheduling algorithms that neglect the time-of-production value of electricity in the market. To more accurately evaluate a CSP-PV-battery hybrid design, we develop a profit-maximizing mixed-integer linear program ($$\mathcal{H}$$) that determines a dispatch schedule for the individual sub-systems with a sub-hourly time fidelity. We introduce the mathematical formulation of such a model and show that it is computationally expensive to solve. To improve model tractability and reduce solution times, we offer techniques that: (1) reduce the problemmore » size, (2) tighten the linear programming relaxation of ($$\mathcal{H}$$) via reformulation and the introduction of cuts, and (3) implement an optimization-based heuristic (that can yield initial feasible solutions for (($$\mathcal{H}$$) and, at any rate, yields near-optimal solutions). Applying these solution techniques results in a 79% improvement in solve time, on average, for our 48-h instances of (($$\mathcal{H}$$); corresponding solution times for an annual model run decrease by as much as 93%, where such a run consists of solving 365 instances of (($$\mathcal{H}$$), retaining only the first 24 h' worth of the solution, and sliding the time window forward 24 h. We present annual system metrics for two locations and two markets that inform design practices for hybrid systems and lay the groundwork for a more exhaustive policy analysis. A comparison of alternative hybrid systems to the CSP-only system reflects that hybrid models can almost double capacity factors while resulting in a 30% improvement related to various economic metrics.« less

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [2]
  1. Colorado School of Mines, Golden, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1573962
Report Number(s):
NREL/JA-5500-75367
Journal ID: ISSN 1389-4420
Grant/Contract Number:  
AC36-08GO28308; EE00025831; EE00030338
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Optimization and Engineering
Additional Journal Information:
Journal Volume: 21; Journal Issue: 1; Journal ID: ISSN 1389-4420
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; dispatch optimization; concentrating solar power; CSP; photovoltaics; lithium-ion battery; mixed-integer linear programming; MILP; CSP-PV hybrid systems; grid integration; system analysis

Citation Formats

Hamilton, William T., Husted, Mark A., Newman, Alexandra M., Braun, Robert J., and Wagner, Michael J. Dispatch optimization of concentrating solar power with utility-scale photovoltaics. United States: N. p., 2019. Web. doi:10.1007/s11081-019-09449-y.
Hamilton, William T., Husted, Mark A., Newman, Alexandra M., Braun, Robert J., & Wagner, Michael J. Dispatch optimization of concentrating solar power with utility-scale photovoltaics. United States. https://doi.org/10.1007/s11081-019-09449-y
Hamilton, William T., Husted, Mark A., Newman, Alexandra M., Braun, Robert J., and Wagner, Michael J. 2019. "Dispatch optimization of concentrating solar power with utility-scale photovoltaics". United States. https://doi.org/10.1007/s11081-019-09449-y. https://www.osti.gov/servlets/purl/1573962.
@article{osti_1573962,
title = {Dispatch optimization of concentrating solar power with utility-scale photovoltaics},
author = {Hamilton, William T. and Husted, Mark A. and Newman, Alexandra M. and Braun, Robert J. and Wagner, Michael J.},
abstractNote = {Concentrating solar power (CSP) tower technologies capture thermal radiation from the sun utilizing a field of solar-tracking heliostats. When paired with inexpensive thermal energy storage (TES), CSP technologies can dispatch electricity during peak-market-priced hours, day or night. The cost of utility-scale photovoltaic (PV) systems has dropped significantly in the last decade, resulting in inexpensive energy production during daylight hours. The hybridization of PV and CSP with TES systems has the potential to provide continuous and stable energy production at a lower cost than a PV or CSP system alone. Hybrid systems are gaining popularity in international markets as a means to increase renewable energy portfolios across the world. Typically, CSP-PV hybrid systems have been evaluated using either monthly averages of hourly PV production or scheduling algorithms that neglect the time-of-production value of electricity in the market. To more accurately evaluate a CSP-PV-battery hybrid design, we develop a profit-maximizing mixed-integer linear program ($\mathcal{H}$) that determines a dispatch schedule for the individual sub-systems with a sub-hourly time fidelity. We introduce the mathematical formulation of such a model and show that it is computationally expensive to solve. To improve model tractability and reduce solution times, we offer techniques that: (1) reduce the problem size, (2) tighten the linear programming relaxation of ($\mathcal{H}$) via reformulation and the introduction of cuts, and (3) implement an optimization-based heuristic (that can yield initial feasible solutions for (($\mathcal{H}$) and, at any rate, yields near-optimal solutions). Applying these solution techniques results in a 79% improvement in solve time, on average, for our 48-h instances of (($\mathcal{H}$); corresponding solution times for an annual model run decrease by as much as 93%, where such a run consists of solving 365 instances of (($\mathcal{H}$), retaining only the first 24 h' worth of the solution, and sliding the time window forward 24 h. We present annual system metrics for two locations and two markets that inform design practices for hybrid systems and lay the groundwork for a more exhaustive policy analysis. A comparison of alternative hybrid systems to the CSP-only system reflects that hybrid models can almost double capacity factors while resulting in a 30% improvement related to various economic metrics.},
doi = {10.1007/s11081-019-09449-y},
url = {https://www.osti.gov/biblio/1573962}, journal = {Optimization and Engineering},
issn = {1389-4420},
number = 1,
volume = 21,
place = {United States},
year = {2019},
month = {9}
}

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