Dynamic incentive framework for demand response in distribution system using moving time horizon control

Panwar, Lokesh Kumar; Reddy, K.S.; Verma, Ashu; Panigrahi, B. K.

doi:10.1049/iet-gtd.2017.0386

Cited by 12 publications

(2 citation statements)

References 32 publications

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“…For example, reference [54] investigated the effect of DR resources on mitigating the uncertainties coming from the penetration of intermittent wind power. In [55], responsive loads were proposed to be rescheduled considering DSO preferences and in order to maximize the profits and load leveling. The proposed load scheduling approach was proved to lead to the highest load reduction (26.8%) which had been requested by the DSO.…”

Section: Flexible Resources At Dso Levelmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

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Section: Flexible Resources At Dso Levelmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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“…However, technical issues such as power losses, voltage deviations are not explored in [25,26]. Under static and dynamic conditions, the responsive load scheduling strategies suggested in [27] provide an optimal trade-off solution with the greatest peak load reduction.…”

Section: Introductionmentioning

confidence: 99%

Heuristic DRP‐DG optimization strategy adopted for maximizing total social welfare in the real time Indian utility network

Sharmila¹,

Mahadevan

Baskaran

et al. 2022

IET Renewable Power Gen

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The energy sector is subjected to an increase in power demand year after year. This increase in energy demand will continue due to the need for energy for modern societies. The service providers should face this situation smartly using renewable energy sources to avoid the intermittent nature of these sources and the fluctuations in the demand. Demand response programs (DRP) can play a vital role in increasing the correlation between the loads and the available generation from renewable energy sources (RES). Modern power systems recommend the use of distributed generation (DG) sources to reduce the transmission losses, contingency, and cost of energy and to increase the system reliability, efficiency, and power system loadability. Optimal DR use in the DG can further improve its performance which is the main focus of this paper. Here, a novel DRP‐DG strategy is introduced to size and allocate the DG units for minimum congestion in IEEE 33‐bus benchmark power system and real Indian power system having 17 buses. The active involvement of the consumers in the DRP facilitates the ultimate objective of maximized total social welfare (TSW) with minimized generation costs. To justify the above multi‐objective optimization, two stages of heuristic optimization are introduced in which the rules are fired in the primary stage for determining the optimal size of DG. Implementation of DRP for achieving comprehensive objective function is vindicated in the secondary stage. A real‐time Indian utility network is considered and a trade‐off output is realized using heuristic fuzzy logic (FL) and genetic algorithm (GA). The results obtained from using the proposed strategy proved its superiority in achieving substantial cost reduction and maximum TSW compared to other soft computing techniques.

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