New Sustainable Operation Method for a Power Grid without an Energy Storage System: A Case Study of a Hospital in Japan

Yuji Mizuno, Masaharu Tanaka, Yoshito Tanaka, Fujio Kurokawa, Nobumasa Matsui

Abstract


This paper presents a new sustainable operation method for running the power system of a disaster base hospital without the use of an energy storage device. There is a diesel generator for islanded operation in the hospitals in the event of a disaster, but it keeps emptying due to the issue that the fuel stored in the tank deteriorates. In consequence, diesel generators fail to start up and medical services cannot be kept. To prevent fuel deterioration, it is deemed necessary to refuel the tank with occasional use of fuel. Even in hospitals, installing backup power systems like photovoltaics is a common way to reduce energy use. In such hospitals, there is a demand to combine diesel generators and photovoltaics to respond a demand side response. Since it is challenging to operate a complicated system of diesel generators and photovoltaics, it is necessary to install more energy storage system.But several hospitals do not want to install it because energy storage system is so pricey. This paper proposes a method that can correspond demand side response as a virtual power plant in a power grid without an energy storage system to improve the operational issues of a complex combination of diesel generators and photovoltaics. It requires a load prediction first. The prediction method is the load one step ahead prediction and providing the optimized output distribution and rate setting to the diesel generators, stable operation is possible without energy storage system. The proposed method is evaluated by employing a simulation model using the measured photovoltaics output and the actual load at a hospital. As a result, it shows that it can correspond a demand side response of ± 10 % in the season when the load is low at the hospital with a contract demand 980 kW with 20 % of a photovoltaics. Furthermore, it is clarified that it can correspond a demand side response of ± 25 % in the season during peak load seasons.  


Keywords


Hospital; virtual power plant (VPP); diesel generator (DG); photovoltaic (PV); demand side response (DR); predicted load; machine learning (ML)

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References


“Damage from Typhoon Jebi,” White Paper Disaster Management in Japan, Director General for Disaster Management Cabinet Office, Government of Japan, pp. 17-21, 2019.

“About the damage situation to affect an earthquake to assume Kumamoto,” Cabinet Office of Japan, 6th Jul. 2016.

“Damage to the power supply infrastructure due to the Hokkaido Eastern Iburi Earthquake,” White Paper Disaster Management in Japan, Director General for Disaster Management Cabinet, Government of Japan, pp. 22-33, 2019.

“Disaster Management in Japan,” Director General for Disaster Management Cabinet, Government of Japan, pp. 1-49, 2014.

(IEIEJ/JSA) Japanese Industrial Standards (JIS) T1022, 2018.

Allianz Risk Consulting (ARC), “Diesel Fuel Degradation,” Tech Talk, Vol. 22, 2021.

Y. Mizuno, Y. Tanaka, F. Kurokawa, and N. Matsui, “A New Approach of Optimum Energy Scheduling of Emergency Generators Using Linear Programing in a Large Hospital,” in Proc. 2016 International Conference on Renewable Energy Research and Application (ICRERA), pp. 832-836, Nov. 2016.

T. Baba, Y. Mizuno, Y. Tanaka, M. Tanaka, F. Kurokawa, I. Colak, and N. Matsui, “Comparison of Optimum Energy Schedule of Emergency Generators of Large Hospital with Renewable Energy System Using Mathematical Programing Method,” in Proc. 2017 International Conference on Renewable Energy Research and Application (ICRERA), pp. 519-523, Nov. 2017.

M. Tanaka, H. Eto, Y. Mizuno, N. Matsui, and F. Kurokawa, “GA based Optimization for Configuration and Operation of Emergency Generators in a Medical Facility Using Renewable Energy,” International Journal of Renewable Energy Research (IJRER), Vol. 8, No. 1, pp. 200-207, 2018.

Y. Mizuno, T. Baba, Y. Tanaka, M. Tanaka, F. Kurokawa, I. Colak, and N. Matsui, “A New Load Prediction Method and Management of Distributed Power System in Island Mode of a Large Hospital,” in Proc. 2018 International Conference on Renewable Energy Research and Application (ICRERA), pp. 1215-1220, Nov. 2018.

T. Baba, Y. Mizuno, Y. Tanaka, F. Kurokawa, and N. Matsui, “Evaluation of An Island Operation Method of Smart Hospital Grid Using A Power Emulation System,” in Proc. 2018 international conference on Smart Grid (icSmartGrid), pp. 98-101, Dec. 2018.

Y. Mizuno, Y. Tanaka, F. Kurokawa, and N. Matsui, “A Hospital Grid with Renewable Energy System Applied to Virtual Power Plant,” in Proc. 2020 international conference on Smart Grid (icSmartGrid), pp. 203-207, June 2020.

S. V. Cotto and W. Lee, “Microgrid Modular Design for Tribal Healthcare Facilities: Kayenta Health Center PV System Case Study,” IEEE Transactions on Industry Applications, Vol. 53, No. 6, pp. 5121-5129, 2017.

B. N. Silva, M. Khan, and K. Han, “Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Side Response Strategies, Challenges, and Opportunities,” Sustainability, pp. 1-23, Dec. 2020.

H. Yang, M. Li, Z. Jiang, and P. Zhang “Multi-Time Scale Optimal Scheduling of Regional Integrated Energy Systems Considering Integrated Demand Side Response,” IEEE Access, pp. 5080-5090, Jan. 2020.

Z. Jiang, Q. Ai, and R. Hao “Integrated Demand side response Mechanism for Industrial Energy System Based on Multi-Energy Interaction,” IEEE Access, vol. 7, pp. 66336-66346, Jan. 2019.

M. G. M. Abdolrasol, M. A. Hannan, A. Mohamed, U. A. U. Amiruldin, I. B. Z. Abidin, and M. N. Uddin, “An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm,” IEEE Transactions on Industry Applications, Vol. 54, No. 3, pp. 2834-2844, May/June. 2018.

D. J. C. MacKay, “Bayesian Interpolation,” Neural Computation, Vol. 4, No. 3, pp. 415-447, 1992.

Q. Long, H. Yu, F. Xie, N. Lu, and D. Lubkeman, “Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm,” IEEE Transactions on Smart Grid, Vol. 12, No. 2, pp. 943-952, Sep. 2020.

F. Y. Xu, X. Cun, M. Yan, H. Yuan, Y. Wang, and L. L. Lai, “Power Market Load Forecasting on Neural Network With Beneficial Correlated Regularization,” IEEE Transactions on Industrial Informatics, Vol. 14, No. 11, pp. 5050-5059, Nov. 2018.

K. Methaprayoon, W-J. Lee, S. Rasmiddatta, J. R. Liao, and R. J. Ross, “Multistage Artificial Neural Network Short-Term Load Forecasting Engine With Front-End Weather Forecast,” IEEE Transactions on Industry Applications, Vol. 43, No. 6, Nov./Dec. 2007.

T. W. S. Chow and C.T. Leung, “Neural Network Based Short-Term Load Forecasting Using Weather Compensation,” IEEE Transactions on Power Systems, Vol. 11, No. 4, Nov., 1736-1742, 1996.

R. Darshi, M. A. Bahreini, and S. A. Ebrahim, “Prediction of Short-Term Electricity Consumption by Artificial Neural Networks Levenberg-Marquardt Algorithm in Hormozgan Province, Iran,” in Proc. 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), pp. 1-4, Dec. 2019.

Dantzig, G.B., A. Orden, and P. Wolfe. “Generalized Simplex Method for Minimizing a Linear Form Under Linear Inequality Restraints.” Pacific Journal Math., Vol. 5, pp. 183-195, 1955.

K. Hiromasa, Y. Takabayashi, K. Shiota, K. Tanomura, S. Osaki, Y. Onoue, and K. Shimomura, “A Study of Generation Control System in Consideration of Renewable Energy Source,” The Transactions of the Institute of Electrical Engineers of Japan B, A Publication of Power and Energy Society, Vol. 137, No. 2, pp. 93-101, 2007.

“Cabinet Decision Made on the FY 2019 Annual Report on Energy,” Agency for Natural Resources and Energy, Jun. 2020. Available: https://www.enecho.meti.go.jp/en/

D. Santhusitha and K. Karunasingha, “Root Mean Square Error or Mean Absolute Error? Use their ratio as well,” Information Sciences, Vol. 585, pp. 609-629, Mar. 2022.

N. Nguyen and J. Mitra, “An Investigation into the Role of Gas Turbines in Supporting Renewable Energy Integration,” in Proc. 2018 North American Power Symposium (NAPS), pp. 1-6, Sep. 2018.




DOI (PDF): https://doi.org/10.20508/ijrer.v12i3.13240.g8514

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