Application of Demand-side Technology in Power System Intelligent Regulation
DOI:
https://doi.org/10.13052/dgaej2156-3306.3722Keywords:
Smart grid, demand response, load management.Abstract
To reduce peak demand for electricity, smooth load curve shape, improve
power system safety and efficiency, this paper, by using intelligent home
appliance user operation comfort model is set up to quantify the acceptance,
this paper proposes a maximum minimum load management algorithm based
on optimization strategy to change electric power use time and power con-
sumption mode.The results show that the proposed model and algorithm can
forecast and manage the power load well, and can reduce the peak to average
ratio by 14.3% and the total expenditure by 15.3% while maintaining the
operating comfort of power users to the maximum.The load management
problem of multiple power users can reach Nash equilibrium in a finite
number of iterations, and this Nash equilibrium point is also the global
optimal point.
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References
Guerzoni M, Raiteri E. Demand-side vs. supply-side technology poli-
cies: Hidden treatment and new empirical evidence on the policy mix[J].
Research Policy, 44(3), pp. 726–747, 2015.
Mou Y, Xing H, Lin Z. Decentralized Optimal Demand-Side Manage-
ment for PHEV Charging in a Smart Grid[J]. IEEE Transactions on
Smart Grid, 6(2), pp. 726–736, 2015.
Reddy S S, Abhyankar A R, Bijwe P R. Co-optimization of Energy and
Demand-Side Reserves in Day-Ahead Electricity Markets[J]. Interna-
tional Journal of Emerging Electric Power Systems, 16(2), pp. 195–206,
Badran M F. eHealth in Egypt: The demand-side perspective of imple-
menting electronic health records[J]. Telecommunications Policy, 43(6),
pp. 576–594, 2019.
Hayn M, Zander A, Fichtner W. The impact of electricity tariffs on
residential demand side flexibility: results of bottom-up load profile
modeling[J]. Energy Systems, 9(3), pp. 1–34, 2018.
Klingler A L. Are current regionalisation approaches sufficient to
decompose electricity demand? – A German case study[J]. Environmen-
tal Pollution, 208, pp. 512–522, 2015.
Qi X, Cheng Q, Wu H. Impact of Incentive-Based Demand Response
on Operational Reliability of Distribution Network[J]. Diangong
Jishu Xuebao/Transactions of China Electrotechnical Society, 33(22),
pp. 5319–5326, 2018.
Dhar S, Pathak M, Shukla P R. Transformation of India’s transport
sector under Global Warming of 2oC and 1.5oC Scenario[J]. Journal
of Cleaner Production, 172(PT.1), pp. 417–427, 2018.
Briem L, HS Buck, Mallig N. Integrating public transport into
mobiTopp[J]. Future generation computer systems, 107(Jun.), pp. 1089–
, 2020.
Maciejewski M, Bischoff J, Nagel K. An Assignment-Based Approach
to Efficient Real-Time City-Scale Taxi Dispatching[J]. IEEE Intelligent
Systems, 31(1), pp. 68–77, 2016.