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Risk-Averse Planning, Operation, and Coordination of Energy Systems Considering Uncertainty Modeling and Flexibility Services
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-9013-6494
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Uncertainty sources affect the planning and operation of energy systems. Different system operators need proper alternatives to cope with these uncertainties and improve the operation of their systems from technical and economical viewpoints. This thesis focuses on the risk-averse planning, operation, and coordination of energy systems including the transmission systems, distribution systems, and stand-alone renewable energy-based microgrids. We develop the existing uncertainty modeling methods and propose new mathematical models, pricing strategies, and operational coordination frameworks to enhance the ability of system operators to cope with uncertainties in the real-time operation of the energy systems and the electricity markets.  

From the uncertainty modeling viewpoint, when it comes to planning and operation of power systems with high penetration of renewable energy, since enough flexibility sources may not be available to cope with the uncertainties in the real-time operation, effective uncertainty sources need to be predicted accurately in the planning stage. Consequently, Bayesian statistics and a stochastic-probabilistic method based on Metropolis-coupled Markov chain Monte Carlo simulation are developed to predict the stochastic behavior of uncertainty sources in different energy systems. We utilized our proposed methods to model the stochastic behavior of wind speed, solar radiation, the water flow of a river, electrical load consumption, the behavior of electric vehicle customers, and the harmonic hosting capacity calculation in different case studies. A novel data classification and curve fitting methods are also proposed for deriving appropriate probability distribution functions (PDFs) based on long-term historical data. We consider demand response programs (DRPs), renewable energy sources, and the dynamic line rating as the embedded resources to prepare flexibility services in the ancillary service market. When it comes to utilizing DRPs, the uncertainty in customers' participation and responsiveness profoundly affects the real-time operation of power systems. Therefore, the risk associated with the utilization of uncertain DR is investigated. Moreover, we evaluate the eligibility conditions for risk-averse utilization of DRPs and apply the risk management cost to the pricing policy of DRPs. 

There are several flexibility service buyers in the power system that aim to activate flexibility services based on their objectives. Consequently, there are conflicts between the interest of different buyers that affect the system operation and pay-off mechanism in the electricity market. Accordingly, proper mathematical structures, coordination frameworks, decomposition techniques, and pay-off mechanisms are needed to be introduced to enhance the coordination between different buyers of the flexibility services. Therefore, we propose a look-ahead multi-interval framework for the TSO-DSO operational coordination problem. We develop the logic-based Benders decomposition technique for our large-scale optimization problem, which is a bilevel mixed-integer linear programming (MILP) problem. 

Finally, the results verify that the proposed uncertainty modeling techniques positively affect the planning and operation of different energy systems, especially stand-alone renewable energy-based microgrids. It is shown that the uncertainty of DRPs highly affected the operation of the power system and the ancillary service market. The ramping capability of reserves is introduced as an eligibility condition for risk-averse utilization of DRPs. Dynamic line rating can be used as a reliable flexibility source in the real-time operation of the power system. Furthermore, the results show that the proposed TSO-DSO coordination scheme can properly manage the conflict between the objectives of different flexibility service buyers. Finally, the Logic-based Benders decomposition (LBBD) can properly solve a large-scale bilevel MILP problem. The LBBD method also improves the execution time of MILP problems.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords [en]
Power system planning and operation, energy systems coordination, uncertainty modeling, microgrids, renewable energy
National Category
Energy Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-93401ISBN: 978-91-8048-164-9 (print)ISBN: 978-91-8048-165-6 (electronic)OAI: oai:DiVA.org:ltu-93401DiVA, id: diva2:1700610
Public defence
2022-12-01, Hörsal A, Luleå tekniska universitet, Skellefteå, 09:00 (English)
Opponent
Supervisors
Available from: 2022-10-03 Created: 2022-10-03 Last updated: 2024-04-23Bibliographically approved
List of papers
1. Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation
Open this publication in new window or tab >>Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation
2021 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 290, article id 116719Article in journal (Refereed) Published
Abstract [en]

Due to the lack of available flexibility sources to cope with different uncertainties in the real-time operation of stand-alone renewable energy-based microgrids, the stochastic behavior of uncertainty sources needs to be included in the planning stage. Since there is a high association between some of the uncertainty sources, defining a proper time series to represent the behavior of each source of uncertainty is a challenging issue. Consequently, uncertainty sources should be modeled in such a way that the designed microgrid be able to cope with all scenarios from probability and impact viewpoints. This paper proposes a modified Metropolis–coupled Markov chain Monte Carlo (MC)3 simulation to predict the stochastic behavior of different uncertainty sources in the planning of a stand-alone renewable energy-based microgrid. Solar radiation, wind speed, the water flow of a river, load consumption, and electricity price have been considered as primary sources of uncertainty. A novel data classification method is introduced within the (MC)3 simulation to model the time-dependency and the association between different uncertainty sources. Moreover, a novel curve-fitting approach is proposed to improve the accuracy of representing the multimodal distribution functions, modeling the Markov chain states, and the long-term probability of uncertainty sources. The predicted representative time series with the proposed modified (MC)3 model is benchmarked against the retrospective model, the long-term historical data, and the simple Monte Carlo simulation model to capture the stochastic behavior of uncertainty sources. The results show that the proposed model represents the probability distribution function of each source of uncertainty, the continuity of samples, time dependency, the association between different uncertainty sources, short-term and long-term trends, and the seasonality of uncertainty sources. Finally, results confirm that the proposed modified (MC)3 can appropriately predict all scenarios with high probability and impact.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Uncertainty modeling, Metropolis–coupled Markov chain Monte Carlo simulation, Data classification method, Curve-fitting approach
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-83263 (URN)10.1016/j.apenergy.2021.116719 (DOI)000639137400005 ()2-s2.0-85102060004 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-03-15 (alebob)

Available from: 2021-03-15 Created: 2021-03-15 Last updated: 2024-04-23Bibliographically approved
2. Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids
Open this publication in new window or tab >>Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids
2022 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 199, p. 866-880Article in journal (Refereed) Published
Abstract [en]

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Stochastic planning, Renewable energy-based microgrids, Uncertainty modeling, Metropolis-coupled Markov chain Monte Carlo, Data classification method
National Category
Probability Theory and Statistics Other Mechanical Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93047 (URN)10.1016/j.renene.2022.09.040 (DOI)000888849300002 ()2-s2.0-85138595384 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-11-29 (joosat);

Available from: 2022-09-15 Created: 2022-09-15 Last updated: 2024-04-23Bibliographically approved
3. Risk-Averse Pricing Strategy for Demand Response
Open this publication in new window or tab >>Risk-Averse Pricing Strategy for Demand Response
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Interruptible/curtailable demand response program (ICDRP) is a valuable ancillary service resource in electricity markets. Due to the uncertainty of customer behavior in a market, risk-based pricing for ICDRP is needed. It is also necessary to evaluate the eligibility conditions for utilizing uncertain ICDRP as an ancillary service. In this paper, we first propose a pricing strategy that allocates payoffs to the coalition of ICDRP participants considering risk management costs caused by the uncertain responsiveness of ICDRP participants while maximizing the system operator’s ability to cope with uncertainties and optimizing generation outputs and regulation price in the frequency regulation market. Then, we investigate the flexibility of predetermined reserves in the forward electricity market as an eligibility condition for risk-averse utilization of ICDRP. A risk-averse Shapley value method is developed in the proposed pricing strategy. Finally, we carry out numerical studies to illustrate the feasibility and effectiveness of the proposed pricing strategy to determine the incentives and penalties in a fair way. We also demonstrate the necessity of considering the uncertainties of ICDRP responsiveness in the required reserve selection process to successfully exploit the benefits of ICDRP in the frequency regulation market.

Keywords
Interruptible/curtailable demand response, pric-ing strategy, risk management, Shapley value
National Category
Energy Systems
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93359 (URN)
Available from: 2022-09-30 Created: 2022-09-30 Last updated: 2023-09-05Bibliographically approved
4. TSO-DSO Operational Coordination Using a Look-Ahead Multi-Interval Framework
Open this publication in new window or tab >>TSO-DSO Operational Coordination Using a Look-Ahead Multi-Interval Framework
2023 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 38, no 5, p. 4221-4239Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2023
National Category
Fluid Mechanics and Acoustics Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93357 (URN)10.1109/TPWRS.2022.3219581 (DOI)001054600200018 ()2-s2.0-85141555597 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-11-07 (hanlid);

This article has previously appeared as a manuscript in a thesis

Available from: 2022-09-30 Created: 2022-09-30 Last updated: 2024-03-07Bibliographically approved
5. Risk Assessment Criteria for Utilizing Dynamic Line Rating in Presence of Electric Vehicles Uncertainty
Open this publication in new window or tab >>Risk Assessment Criteria for Utilizing Dynamic Line Rating in Presence of Electric Vehicles Uncertainty
2022 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 212, article id 108643Article in journal (Refereed) Published
Abstract [en]

Dynamic line rating (DLR) is a grid enhancing technology to enable a more effective use of transmission capacity of existing infrastructure.~The growth in load consumption along with a high integration of electric vehicles (EV) highlights the potential of DLR utilization for reducing the congestion costs and overloading risks.~Selecting the proper lines for DLR implementation is necessary to exploit optimally the benefits of DLR. In this paper, we propose risk assessment criteria to select proper lines for DLR implementation to minimize the system operation costs and the risk of overloading caused by high EV integration.A stochastic method is introduced to model the uncertain behavior of EV in charging stations. Furthermore, we analyze the impact of inherent uncertainties in DLR by comparing different DLR percentiles. The benefits of using DLR in different percentiles are then quantified in terms of supply and interruption costs.The results show improvements in system supply cost, system reliability, and operation risks. 

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Dynamic Line Rating, Electric Vehicle, Electric Power Transmission, Optimization, Risk Assessment, Stochastic Approach
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-90181 (URN)10.1016/j.epsr.2022.108643 (DOI)000860499100007 ()2-s2.0-85134786687 (Scopus ID)
Funder
Swedish Energy Agency
Note

Validerad;2022;Nivå 2;2022-07-22 (sofila);

Funder: Skellefteå Kraft Elnät AB; Energiforsk AB

Available from: 2022-04-13 Created: 2022-04-13 Last updated: 2022-11-09Bibliographically approved
6. Including uncertainties in harmonic hosting capacity calculation of a fast EV charging station utilizing Bayesian statistics and harmonic correlation
Open this publication in new window or tab >>Including uncertainties in harmonic hosting capacity calculation of a fast EV charging station utilizing Bayesian statistics and harmonic correlation
2023 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 214, article id 108933Article in journal (Refereed) Published
Abstract [en]

The harmonic emission from an electric vehicle fast charger depends on factors like charger topology, EV type, initial state of charge of EV battery, as well as supply voltage and background distortion. This paper presents the results from harmonic current measurement of a fast charger for a period of one month in Sweden that has charged a variety of EVs from different brands under different state of charge and background distortion. Besides the common harmonic emission pattern, a high level of variation in emission is observed that can affect the aggregation of the emission from multiple chargers. To include such uncertainties, the harmonic hosting capacity is obtained for a fast EV charging station in a stochastic way. A new method, based on Bayesian statistics and the correlation between harmonic magnitude and fundamental magnitude, is proposed for the generation of stochastic samples. It is shown that the proposed method, to a high extent, can model the stochastic behavior of harmonic emission from a fast charger. Furthermore, the results show that neglecting the correlation between harmonic magnitude and fundamental magnitude can underestimate the harmonic hosting capacity.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Electric vehicle charging, harmonic analysis, harmonic distortion, hosting capacity, power system harmonics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-93358 (URN)10.1016/j.epsr.2022.108933 (DOI)000886826000009 ()2-s2.0-85140914689 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-11-08 (hanlid);

Funder: Göteborg Energi Research Foundation; Umeå Energi;

This article has previously appeared as a manuscript in a thesis

Available from: 2022-09-30 Created: 2022-09-30 Last updated: 2023-09-05Bibliographically approved

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