An Aggregator-Based Market Modelling with an Impact of Risk Under Uncertainty
Keywords:
Aggregators, Conditional Value at Risk, Distribution Network Operator, Renewable Energy Sources, Value at Risk.Abstract
In the Electricity market the increased penetration of renewable energy sources (RES) and associated uncertainties impose challenges to determine the day-ahead distribution locational market prices effectively and also these uncertainties can jeopardize grid stability and reliability. RES aggregators compete to increase their profit but their intermittent nature adds financial risks to aggregators (A’s). The main objective of this paper is to model a day-ahead electricity market by considering RES aggregators as participants to trade energy effectively to maintain a dynamic energy balance. Instead of relying on existing probabilistic forecast methods to account for the variable uncertain nature of RES, this paper uses a novel data-driven forecasting method to predict variable RES power generation accurately. The proposed model follows a three stage approach. The first stage involves forecasting PV and wind output power with multiple scenarios. In the second stage, a scenario-based multi-aggregator market modelling is performed where aggregators submit their bids to the distribution network operator, who then clears the market by generating price signals. Uncertainties of RES aggregators lead to financial risk for aggregators. Hence, the third stage involves, risk assessment using value at risk (VaR) and conditional value at risk (CVaR) are applied to different scenarios for evaluating the potential portfolio losses within a specified time horizon and confidence level. To evaluate the effectiveness of the proposed model, it is tested on a modified 33-bus test system which shows effective energy trading at a distribution system with a considerable marginal range of voltage violations. The proposed novel three-stage model aims to improve the distribution level electricity market’s efficiency and reliability, benefiting RES market participants and consumers alike.
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References
Y. Jiang, T. Ortmeyer and M. Fan, "Data-Driven Fast Uncertainty
Assessment of Distribution Systems With Correlated EV
Charging Demand and Renewable Generation," in IEEE Transactions
on Sustainable Energy, vol. 14, no. 3, pp. 1446-1456, July
https://doi.org/10.1109/TSTE.2023.3236446
J. Vargas, C. J. Franco and M. Jiménez, "Electricity Pricing for Renewable
Markets- A Simulation Approach for the Colombian Case," in IEEE Latin
America Transactions, vol. 19, no. 12, pp. 1995-2002, Dec. 2021, doi:
1109/TLA.2021.9480140.
Partha P. Biswas, P.N. Suganthan, R. Mallipeddi, Gehan A.J.
Amaratunga, “Optimal reactive power dispatch with uncertainties
in load demand and renewable energy sources adopting scenariobased
approach”, Applied Soft Computing, Volume 75, 2019
https://doi.org/10.1016/j.asoc.2018.11.042.
T. Niknam, M. Zare and J. Aghaei, "Scenario-Based Multiobjective
Volt/Var Control in Distribution Networks Including Renewable Energy
Sources," in IEEE Transactions on Power Delivery, vol. 27, no. 4, pp.
-2019, Oct. 2012, doi: 10.1109/TPWRD.2012.2209900.
Tavakoli, A., Karimi, A.: Development of Monte-Carlo-based stochastic
scenarios to improve uncertainty modelling for optimal energy management
of a renewable energy hub. IET Renew. Power Gener. 17, 1139–1164
(2023) https://doi.org/10.1049/rpg2.12671.
David L. Woodruff, Julio Deride, Andrea Staid, Jean-Paul Watson, Gerrit
Slevogt, César Silva-Monroy, “Constructing probabilistic scenarios for
wide-area solar power generation, Solar Energy”, Volume 160, 2018,
https://doi.org/10.1016/j.solener.2017.11.067.
Ma, Xi-Yuan & Sun, Yuan-Zhang & Fang, Hua-Liang. (2013). Scenario
Generation of Wind Power Based on Statistical Uncertainty and
Variability. Sustainable Energy, IEEE Transactions on. 4. 894-904.
1109/TSTE.2013.2256807.
Y. Tan, Q. Zhang, C. Li, Q. Chen and N. Yu, "An Improved Modeling
Method for Uncertainty of Renewable Energy Power Generation Considering
Random Variables Correlation," 2022 7th Asia Conference on
Power and Electrical Engineering (ACPEE), Hangzhou, China, 2022, pp.
-89, doi: 10.1109/ACPEE53904.2022.9783818.
B. Zhou, X. Ma, Y. Luo and D. Yang, "Wind Power Prediction Based
on LSTM Networks and Nonparametric Kernel Density Estimation,"
in IEEE Access, vol. 7, pp. 165279-165292, 2019, doi: 10.1109/ACCESS.
2952555.
B. Singh and D. Pozo, "A Guide to Solar Power Forecasting using
ARMA Models," 2019 IEEE PES Innovative Smart Grid Technologies
Europe (ISGT-Europe), Bucharest, Romania, 2019, pp. 1-4,
doi:10.1109/ISGTEurope.2019.8905430.
Ritu Jain, Vasundhara Mahajan,"Load forecasting and risk assessment
for energy market with renewable based distributed generation", Renewable
Energy Focus,Volume 42,2022,Pages 190-205,ISSN 1755-0084,
https://doi.org/10.1016/j.ref.2022.06.007
Q. Tu et al., "Forecasting Scenario Generation for Multiple Wind Farms
Considering Time-series Characteristics and Spatial-temporal Correlation,"
in Journal of Modern Power Systems and Clean Energy, vol. 9,
no. 4, pp. 837-848, July 2021, doi: 10.35833/MPCE.2020.000935.
Abdulqader, Qais. (2016). “Time Series Forecasting Using Arima
Methodology with Application on Census Data in Iraq”. Science Journal
of University of Zakho. 4. 258-268. 10.25271/2016.4.2.116.
Padghan, P. R., Samuel, A. D., & Pitchaimuthu, R. (2023). Aggregatorinduced
demand response and bidding strategy for electricity market
environment based on Ethereum smart contracts. Energy Sources, Part
A: Recovery, Utilization, and Environmental Effects, 45(1), 299–318.
https://doi.org/10.1080/15567036.2023.2165740
Beltran, H., Ayuso, P., Cardo-Miota, J., Segarra-Tamarit, J., Aparicio,
N. and Pérez, E. (2022), Influence of the Intraday Electricity
Market Structure on the Degradation of Li-Ion Batteries Used
to Firm Photovoltaic Production. Energy Technol., 10: 2100943.
https://doi.org/10.1002/ente.202100943
S. Haghifam, H. Laaksonen and M. Shafie-Khah, "Modeling a Local
Electricity Market for Transactive Energy Trading of Multi-Aggregators,"
in IEEE Access, vol. 10, pp. 68792-68806, 2022, doi: 10.1109/ACCESS.
3186309.
Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza. (2022).
Stochastic-Risk Based Approach for Microgrid Participation in Joint
Active, Reactive, and Ancillary Services Markets Considering Demand
Response. IEEE Open Access Journal of Power and Energy. PP. 1-
,doi:10.1109/OAJPE.2022.3206495.
H. Khaloie, F. Vallée, C. S. Lai, J. -F. Toubeau and N. D. Hatziargyriou,
"Day-Ahead and Intraday Dispatch of an Integrated Biomass-
Concentrated Solar System: A Multi-Objective Risk-Controlling Approach,"
in IEEE Transactions on Power Systems, vol. 37, no. 1, pp.
-714, Jan. 2022, doi: 10.1109/TPWRS.2021.3096815.
Y. Zhao, S. Liu, Z. Lin, F. Wen, L. Yang and Q. Wang, "A Mixed CVaRBased
Stochastic Information Gap Approach for Building Optimal Offering
Strategies of a CSP Plant in Electricity Markets," in IEEE Access,
vol. 8, pp. 85772-85783, 2020, doi: 10.1109/ACCESS.2020.2992050.
Y. Zhao, Z. Lin, F. Wen, Y. Ding, J. Hou and L. Yang, "Risk-
Constrained Day-Ahead Scheduling for Concentrating Solar Power Plants
With Demand Response Using Info-Gap Theory," in IEEE Transactions
on Industrial Informatics, vol. 15, no. 10, pp. 5475-5488, Oct. 2019, doi:
1109/TII.2019.2899003.
H. Yang, S. Zhang, J. Qiu, D. Qiu, M. Lai and Z. Dong, "CVaRConstrained
Optimal Bidding of Electric Vehicle Aggregators in Day-
Ahead and Real-Time Markets," in IEEE Transactions on Industrial
Informatics, vol. 13, no. 5, pp. 2555-2565, Oct. 2017, doi:
1109/TII.2017.2662069.
M. K. AlAshery and W. Qiao, "Risk Management via Multiple
Risk Measures," 2018 IEEE Power & Energy Society General
Meeting (PESGM), Portland, OR, USA, 2018, pp. 1-5, doi:
1109/PESGM.2018.8585772.
M. Nazif Faqiry, Lawryn Edmonds, Hongyu Wu, Anil Pahwa, “Distribution
locational marginal price-based transactive day-ahead market
with variable renewable generation”, Applied Energy, Volume 259,
,114103,ISSN0306-2619, //doi.org/10.1016/j.apenergy.2019.114103.
Yu, Dongmin & Ebadi, Abdol & Jermsittiparsert, Kittisak &
Jabarullah, Noor & Vasiljeva, Marina & Nojavan, Sayyad. (2019).
Risk-Constrained Stochastic Optimization of a Concentrating Solar
Power Plant. IEEE Transactions on Sustainable Energy. PP. 1-1.
1109/TSTE.2019.2927735.
Liwei Ju, Qinliang Tan, Yan Lu, Zhongfu Tan, Yuxie Zhang, Qingkun
Tan, A CVaR-robust-based multi-objective optimization model and threestage
solution algorithm for a virtual power plant considering uncertainties
and carbon emission allowances,International Journal of Electrical Power
& Energy Systems,Volume 107,2019,Pages 628-643,ISSN 0142-0615,
https://doi.org/10.1016/j.ijepes.2018.12.012.
Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza
& Catalão, João P.S., 2021. "Day-ahead optimal bidding of
microgrids considering uncertainties of price and renewable
energy resources," Energy, Elsevier, vol. 227(C). https://
ideas.repec.org/a/eee/energy/v227y2021ics0360544221007258.html
Hemmati, M., Mohammadi-ivatloo, B., & Soroudi, A. (2019). Uncertainty
Management in Power System Operation Decision Making. ArXiv,
abs/1911.10358.
Hemmati, Mohammad, Behnam Mohammadi-Ivatloo, and Alireza
Soroudi. "Uncertainty management in power system operation decision
making." arXiv preprint arXiv:1911.10358 (2019).
Andreas V. Ntomaris, Ilias G. Marneris, Pandelis N. Biskas, Anastasios
G. Bakirtzis, Optimal participation of RES aggregators in electricity
markets under main imbalance pricing schemes: Price taker and price
maker approach, Electric Power Systems Research, Volume 206, 2022,
, ISSN 0378-7796, https://doi.org/10.1016/j.epsr.2022.107786.
T. Ghose, H. W. Pandey and K. R. Gadham, "Risk assessment of microgrid
aggregators considering demand response and uncertain renewable
energy sources," in Journal of Modern Power Systems and Clean Energy,
vol. 7, no. 6, pp. 1619-1631, November 2019, doi: 10.1007/s40565-019-
-x.
https://electricgrids.engr.tamu.edu/
Nikpour, Ahmad, Nateghi, Abolfazl, Shafie-khah, Miadreza, & Catalão,
João P.S. (2021). Day-ahead optimal bidding of microgrids considering
uncertainties of price and renewable energy resources. Energy (Oxford),
v. doi:101016/jenergy2021120476.
Das, Saborni & Basu, Mousumi, 2020. "Day-ahead
optimal bidding strategy of microgrid with demand response
program considering uncertainties and outages of
renewable energy resources," Energy, Elsevier, vol. 190(C).
<https://ideas.repec.org/a/eee/energy/v190y2020ics036054421932136x.html>
Farhat, Mohamed & Kamel, Salah & Atallah, Ahmed & Khan, Baseem.
(2021). Optimal Power Flow Solution Based on Jellyfish Search Optimization
Considering Uncertainty of Renewable Energy Sources. IEEE
Access. 9. 100911-100933. 10.1109/ACCESS.2021.3097006.
Paul, Kaushik. (2022). Multi-objective risk-based optimal power system
operation with renewable energy resources and battery energy storage
system: A novel Hybrid Modified Grey Wolf Optimization–Sine Cosine
Algorithm approach. Transactions of the Institute of Measurement and
Control. 014233122210799. 10.1177/01423312221079962.
P. Thallapally and D. Panda, "A Local Electricity Model for DER Aggregator
at a Distribution Network Level," 2023 10th IEEE International
Conference on Power Systems (ICPS), Cox’s Bazar, Bangladesh, 2023,
pp. 1-6, doi: 10.1109/ICPS60393.2023.10428997
