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Future Urban Energy Systems: Harnessing Demand Side Flexibility and Managing Data Uncertainty.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Future Urban Energy Systems: Harnessing Demand Side Flexibility and Managing Data Uncertainty./
Author:	Azari, Delaram .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	156 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
Subject:	Mathematical programming. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28232986
ISBN:	9798728201311

Future Urban Energy Systems: Harnessing Demand Side Flexibility and Managing Data Uncertainty.
Azari, Delaram .

Future Urban Energy Systems: Harnessing Demand Side Flexibility and Managing Data Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 156 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--Wageningen University and Research, 2020.

This item must not be sold to any third party vendors.

There is a significant interest in harnessing the available flexibility that exists on the demand side of the distribution energy systems by implementing energy storage or demand response programs. Appropriate choices regarding technical design parameters, involvement of flexibility providers in the demand response program, and economic preferences of different actors are necessary for proper planning and operation of future energy systems. However, such choices require data and information which are either not available to the decision makers, or if available, are prone to uncertainty. Proper accounting of such uncertainties is key to understanding of the performance of energy system of the future. The quest to understand the performance of future energy system with flexible resources, demands an adequate framework that accounts for uncertainties in the data.This thesis looks into the impact of data uncertainty on the techno-economic decisions of the actors in a future energy system, with flexibility resources. It considers three actors, namely the distribution system operator, aggregators (as potential operators of a demand response program), and end-users, like building energy managers and residential consumers. A comprehensive framework is developed to quantify the sensitivity of a hypothetical energy system with flexible sources, which might be put in place in the future, to unknown and inaccurate data. The proposed framework consists of three modules: a simulation module, a local sensitivity analysis (LSA), and a global sensitivity analysis (GSA). Hypothetical energy systems that harness the available flexibility from the demand side are simulated as optimization problems. Such models reflect the requirement of existing, or emerging actors in the system.After an introduction in Chapter 1, in Chapter 2 a data-driven framework is proposed to assess the performance of a hypothetical demand response (DR) program in the absence of historical data on flexibility potential of flexible consumers (i.e., DR providers) and grid parameters. A continuous optimization problem is formulated to simulate load shifting of residential consumers for an aggregator, as future DR operator. The optimization problem minimizes the daily load variation, while limiting the total energy cost not to increase. One important aspect of the proposed framework is that it reflects the preferences of the flexible consumers. Such characteristics allows for investigating the sensitivity of the results to different flexibility levels. In addition, the proposed optimization problem ensures that the objective of the system operator can be reached without modeling the distribution grid. In Chapter 3 the impact of uncertainties in load and price data (i.e., input uncertainty) on the performance of a potential DR program was investigated. The main contribution of this chapter was in developing a sensitivity analysis framework to perform local and global sensitivity analysis on the DR optimization model, introduced in Chapter 2. Two sources of data uncertainty were considered: load and price forecast errors, and flexibility preferences of the consumers. The local sensitivity was used to investigate the sensitivity of the performance of the proposed DR program in reducing load variability to load and price forecast error. An analytical expression was derived for the sensitivities, and from there, the local sensitivities were computed. In addition, by performing global sensitivity analysis, first, various scenarios on the preferences of the flexibility providers were defined. Then, the optimal value of the variance of daily load under different flexibility preferences of the consumers were compared, and the dependency of the DR model to such preferences were identified.The optimal flexibility dispatch (OFD) is a recently-introduced, power flow based method that a distribution system operator (DSO) can use to effectively determine the amount of flexibility it needs to procure from the flexible resources available on the demand side. However, the drawback of this method is that the optimal flexibility dispatch is inexact due to its relaxation error. In Chapter 4 a novel error-free OFD framework is introduced, as a bi-level optimization problem where the upper level problem seeks to minimize the relaxation error and the lower level solves the second-order cone convex optimal flexibility dispatch problem. Furthermore, the sensitivity of the optimal flexibility schedules and the locational flexibility prices to uncertainty in load forecast and flexibility ranges of the flexible resources, which are input to the problem, are investigated using local sensitivity analysis. The sensitivity analysis is performed based on the perturbed KKT conditions of the lower level optimization problem. The proposed problem considered a local flexibility market framework and accounted for distribution network constraints.In Chapter 5 a simulation framework is used to capture the impact of modifications in energy consumption of a residential building to improve the energy efficiency. The proposed simulation tool enables investigating the combined effects of, and the interactions between, the technological measure, as well as consumers' involvement in a DR program. To investigate the impact of uncertain parameters on the total energy demand of the building, and identify the most impactful modification, a global sensitivity analysis was performed. Feasible combinations of technological modifications were investigated in various scenarios (referred to as configurations).This thesis ends with Chapter 6, in which a synthesis of the results on future energy system with flexibility resources at the demand side is presented. This chapter places the results obtained in this thesis in a broader perspective of urban energy systems. The synthesis reflects the importance of investigating the impact of data uncertainty, on the operational decisions of the system operator, the aggregator, and pro-active energy consumers and thereof. The results provide projections on the potential factors different actors need to consider when engaging in contractual agreements with the others. This research shows that the demand side flexibility can contribute to an efficient network management, as well as, efficient utilization of the local energy sources in a building energy system. However, there is an uncertainty associated with the compliance of the flexible consumers in the execution of the accepted flexibility program, which requires further research for practical implications.

ISBN: 9798728201311Subjects--Topical Terms:

3683785
Mathematical programming.

Future Urban Energy Systems: Harnessing Demand Side Flexibility and Managing Data Uncertainty.
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502 $a Thesis (Ph.D.)--Wageningen University and Research, 2020.
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520 $a There is a significant interest in harnessing the available flexibility that exists on the demand side of the distribution energy systems by implementing energy storage or demand response programs. Appropriate choices regarding technical design parameters, involvement of flexibility providers in the demand response program, and economic preferences of different actors are necessary for proper planning and operation of future energy systems. However, such choices require data and information which are either not available to the decision makers, or if available, are prone to uncertainty. Proper accounting of such uncertainties is key to understanding of the performance of energy system of the future. The quest to understand the performance of future energy system with flexible resources, demands an adequate framework that accounts for uncertainties in the data.This thesis looks into the impact of data uncertainty on the techno-economic decisions of the actors in a future energy system, with flexibility resources. It considers three actors, namely the distribution system operator, aggregators (as potential operators of a demand response program), and end-users, like building energy managers and residential consumers. A comprehensive framework is developed to quantify the sensitivity of a hypothetical energy system with flexible sources, which might be put in place in the future, to unknown and inaccurate data. The proposed framework consists of three modules: a simulation module, a local sensitivity analysis (LSA), and a global sensitivity analysis (GSA). Hypothetical energy systems that harness the available flexibility from the demand side are simulated as optimization problems. Such models reflect the requirement of existing, or emerging actors in the system.After an introduction in Chapter 1, in Chapter 2 a data-driven framework is proposed to assess the performance of a hypothetical demand response (DR) program in the absence of historical data on flexibility potential of flexible consumers (i.e., DR providers) and grid parameters. A continuous optimization problem is formulated to simulate load shifting of residential consumers for an aggregator, as future DR operator. The optimization problem minimizes the daily load variation, while limiting the total energy cost not to increase. One important aspect of the proposed framework is that it reflects the preferences of the flexible consumers. Such characteristics allows for investigating the sensitivity of the results to different flexibility levels. In addition, the proposed optimization problem ensures that the objective of the system operator can be reached without modeling the distribution grid. In Chapter 3 the impact of uncertainties in load and price data (i.e., input uncertainty) on the performance of a potential DR program was investigated. The main contribution of this chapter was in developing a sensitivity analysis framework to perform local and global sensitivity analysis on the DR optimization model, introduced in Chapter 2. Two sources of data uncertainty were considered: load and price forecast errors, and flexibility preferences of the consumers. The local sensitivity was used to investigate the sensitivity of the performance of the proposed DR program in reducing load variability to load and price forecast error. An analytical expression was derived for the sensitivities, and from there, the local sensitivities were computed. In addition, by performing global sensitivity analysis, first, various scenarios on the preferences of the flexibility providers were defined. Then, the optimal value of the variance of daily load under different flexibility preferences of the consumers were compared, and the dependency of the DR model to such preferences were identified.The optimal flexibility dispatch (OFD) is a recently-introduced, power flow based method that a distribution system operator (DSO) can use to effectively determine the amount of flexibility it needs to procure from the flexible resources available on the demand side. However, the drawback of this method is that the optimal flexibility dispatch is inexact due to its relaxation error. In Chapter 4 a novel error-free OFD framework is introduced, as a bi-level optimization problem where the upper level problem seeks to minimize the relaxation error and the lower level solves the second-order cone convex optimal flexibility dispatch problem. Furthermore, the sensitivity of the optimal flexibility schedules and the locational flexibility prices to uncertainty in load forecast and flexibility ranges of the flexible resources, which are input to the problem, are investigated using local sensitivity analysis. The sensitivity analysis is performed based on the perturbed KKT conditions of the lower level optimization problem. The proposed problem considered a local flexibility market framework and accounted for distribution network constraints.In Chapter 5 a simulation framework is used to capture the impact of modifications in energy consumption of a residential building to improve the energy efficiency. The proposed simulation tool enables investigating the combined effects of, and the interactions between, the technological measure, as well as consumers' involvement in a DR program. To investigate the impact of uncertain parameters on the total energy demand of the building, and identify the most impactful modification, a global sensitivity analysis was performed. Feasible combinations of technological modifications were investigated in various scenarios (referred to as configurations).This thesis ends with Chapter 6, in which a synthesis of the results on future energy system with flexibility resources at the demand side is presented. This chapter places the results obtained in this thesis in a broader perspective of urban energy systems. The synthesis reflects the importance of investigating the impact of data uncertainty, on the operational decisions of the system operator, the aggregator, and pro-active energy consumers and thereof. The results provide projections on the potential factors different actors need to consider when engaging in contractual agreements with the others. This research shows that the demand side flexibility can contribute to an efficient network management, as well as, efficient utilization of the local energy sources in a building energy system. However, there is an uncertainty associated with the compliance of the flexible consumers in the execution of the accepted flexibility program, which requires further research for practical implications.
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