An Integrated Approach for Demand Response and Wind Curtailment Management in Distribution Systems

Osama Ahmed El-Kashty, Ahmed Ali Daoud, EL-Said El-Sayed EL-Araby

Abstract


Studies have demonstrated that Demand Response (DR) can significantly influence the effectiveness and reliability of future intelligent distribution networks. Despite the increasing deployment of wind energy, the issue of curtailment continues to pose a challenge for utilities that have a high penetration of wind power. This study aims to propose an effective approach for utilizing DR to improve the operating status of distribution networks while simultaneously reducing curtailed wind energy. Thus, considering the curtailed wind energy, this paper proposes a wind curtailment reduction DRP (WCR-DRP) to modify the demand pattern in response to critical times when wind curtailment may occur due to generator ramp limits or minimum output power. In this study, to implement power system operation, an optimal power flow (OPF) problem is utilized while considering the availability of demand response (DR) programs, and the power flow and operational constraints related to the system are considered .

Formulating the optimization problem with two objective functions, including minimizing overall costs and reducing wind power curtailment, is achieved. The ?-constraint method resolves this optimization problem. Finally, the suggested model is employed over the enhanced IEEE 33-bus test system to evaluate its efficacy. Four scenarios are conducted, examining various operational parameters to show the practicality of the suggested approach. Outcomes show that suggested model significantly enhances the operation of the electrical distribution system while establishing optimal employment of wind power.


Keywords


Demand Response (DR), Price elasticity, Optimal power flow (OPF), Curtailed wind energy, Multi-objective, Distribution network.

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References


F. Ayadi, I. Colak, I. Garip, and H. I. Bulbul, “Impacts of renewable energy resources in smart grid,” in 2020 8th International Conference on Smart Grid (icSmartGrid), 2020.

International Energy Agency, Renewables 2022: Analysis and forecast to 2027. OECD, 2022.

“Forecasting of short-term and long-term wind speed of Ras Gharib using time series analysis,” Int. J. Renew. Energy Res., no. V13i1, 2023.

L. Bird et al., “Wind and solar energy curtailment: A review of international experience,” Renew. Sustain. Energy Rev., vol. 65, pp. 577–586, 2016.

O. Agbonaye, P. Keatley, Y. Huang, F. O. Odiase, and N. Hewitt, “Value of demand flexibility for managing wind energy constraint and curtailment,” Renew. Energy, vol. 190, pp. 487–500, 2022.

U. Cetinkaya, R. Bayindir, and S. Ayik, “Ancillary services using battery energy systems and demand response,” in 2021 9th International Conference on Smart Grid (icSmartGrid), 2021.

N. G. Paterakis, O. Erdinç, and J. P. S. Catalão, “An overview of Demand Response: Key-elements and international experience,” Renew. Sustain. Energy Rev., vol. 69, pp. 871–891, 2017.

P. Siano, “Demand response and smart grids—A survey,” Renew. Sustain. Energy Rev., vol. 30, pp. 461–478, 2014.

I. I. R. Gomes, R. Melicio, and V. M. F. Mendes, “Aggregation of wind, photovoltaic and thermal power with demand response,” in 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), 2019.

U. Cetinkaya and R. Bayindir, “Impact of increasing renewable energy sources on power system stability and determine optimum demand response capacity for frequency control,” in 2022 10th International Conference on Smart Grid (icSmartGrid), 2022.

E. E. El-Araby and N. Yorino, “A demand side response scheme for enhancing power system security in the presence of wind power,” Int. J. Electr. Power Energy Syst., vol. 146, no. 108714, p. 108714, 2023.

M. Rahmani-Andebili, “Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets,” Electric Power Syst. Res., vol. 132, pp. 115–124, 2016.

D. S. Kirschen, G. Strbac, P. Cumperayot, and D. de Paiva Mendes, “Factoring the elasticity of demand in electricity prices,” IEEE Trans. Power Syst., vol. 15, no. 2, pp. 612–617, 2000.

A. Asadinejad and K. Tomsovic, “Optimal use of incentive and price-based demand response to reduce costs and price volatility,” Electric Power Syst. Res., vol. 144, pp. 215–223, 2017.

M. Nikzad and B. Mozafari, “Reliability assessment of incentive- and priced-based demand response programs in restructured power systems,” Int. J. Electr. Power Energy Syst., vol. 56, pp. 83–96, 2014.

H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Modeling and prioritizing demand response programs in power markets,” Electric Power Syst. Res., vol. 80, no. 4, pp. 426–435, 2010.

Q. Xu, Y. Ding, and A. Zheng, “An optimal dispatch model of wind-integrated power system considering demand response and reliability,” Sustainability, vol. 9, no. 5, p. 758, 2017.

V. C. Pandey, N. Gupta, K. R. Niazi, A. Swarnkar, and R. A. Thokar, “Modeling and assessment of incentive-based demand response using price elasticity model in distribution systems,” Electric Power Syst. Res., vol. 206, no. 107836, p. 107836, 2022.

J. Jiang, Y. Kou, Z. Bie, and G. Li, “Optimal real-time pricing of electricity based on demand response,” Energy Procedia, vol. 159, pp. 304–308, 2019.

H. Yang, L. Wang, and Y. Ma, “Optimal time of use electricity pricing model and its application to electrical distribution system,” IEEE Access, vol. 7, pp. 123558–123568, 2019.

X. Zhou, J. Shi, and G. Kang, “Optimal demand response aiming at enhancing the economy of high wind power penetration system,” J. Eng. (Stevenage), vol. 2017, no. 13, pp. 1959–1962, 2017.

H. Falsafi, A. Zakariazadeh, and S. Jadid, “The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming,” Energy (Oxf.), vol. 64, pp. 853–867, 2014.

H. Xu, Y. Chang, Y. Zhao, and F. Wang, “A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response,” Int. J. Electr. Power Energy Syst., vol. 147, no. 108832, p. 108832, 2023.

H. Bitaraf and S. Rahman, “Reducing curtailed wind energy through energy storage and demand response,” IEEE Trans. Sustain. Energy, vol. 9, no. 1, pp. 228–236, 2018.

N. Li, X. Wang, Z. Zhu, and Y. Wang, “The reliability evaluation research of distribution system considering demand response,” Energy Rep., vol. 6, pp. 153–158, 2020.

F. Ugranl?, “Analysis of renewable generation’s integration using multi-objective fashion for multistage distribution network expansion planning,” Int. J. Electr. Power Energy Syst., vol. 106, pp. 301–310, 2019.

M. Basu, “An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling,” Electric Power Syst. Res., vol. 69, no. 2–3, pp. 277–285, 2004.

A. Soroudi, M. Ehsan, R. Caire, and N. Hadjsaid, “Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment,” IET Gener Transm Distrib, vol. 5, no. 9, pp. 961–972, 2011.

A. Soroudi, Power system optimization modeling in GAMS, 1st ed. Basel, Switzerland: Springer International Publishing, 2017.

I. J. Ramirez-Rosado and J. A. Dominguez-Navarro, “Possibilistic model based on fuzzy sets for the multi objective optimal planning of electric power distribution networks,” IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1801–1810, 2004.

M.-R. Haghifam, H. Falaghi, and O. P. Malik, “Risk-based distributed generation placement,” IET Gener. Transm. Distrib., vol. 2, no. 2, p. 252, 2008.

S. H. Dolatabadi, M. Ghorbanian, P. Siano, and N. D. Hatziargyriou, “An Enhanced IEEE 33 Bus Benchmark Test System for Distribution System Studies,” IEEE Trans. Power Syst., vol. 36, no. 3, pp. 2565–2572, 2021.

J. J. Chen, B. X. Qi, K. Peng, Y. Li, and Y. L. Zhao, “Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch,” Appl. Energy, vol. 261, no. 114337, p. 114337, 2020.

M. R. Bussieck and A. Meeraus, General Algebraic Modeling System (GAMS),” in Applied Optimization. Boston, MA: Springer US, 2004.

J. Czyzyk, M. P. Mesnier, and J. J. More, “The NEOS Server,” IEEE Comput. Sci. Eng., vol. 5, no. 3, pp. 68–75, 1998.

G. R. Kocis and I. E. Grossmann, “Computational experience with dicopt solving MINLP problems in process systems engineering,” Comput. Chem. Eng., vol. 13, no. 3, pp. 307–315, 1989.




DOI (PDF): https://doi.org/10.20508/ijrer.v13i2.14197.g8734

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