Information Gap Decision Theory-Based Congestion and Voltage Management in the Presence of Uncertain Wind Power

Murphy, Conor; Soroudi, Alireza; Keane, Andrew

doi:10.1109/tste.2015.2497544

Cited by 74 publications

(44 citation statements)

References 24 publications

(10 reference statements)

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“…An agent‐based energy management is proposed to facilitate power trading in MG with the help of DR . An information gap decision theory based for congestion management using reactive power of the wind turbines as well as DR is proposed . The uncertainty associated with variable generation technologies can create risk in their bidding strategies.…”

Section: Role Of Dr In Microgrids and Integration Of Renewable Resourcesmentioning

confidence: 99%

Opportunities and challenges of demand response in active distribution networks

Ponnaganti

Pillai

Bak‐Jensen

2017

WIREs Energy & Environment

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In power systems, the installed generation capacity must exceed the annual peak demand, even though some capacity is kept idle most of the time. However, if it is uneconomical or not feasible to augment a sufficient capacity, the demand might exceed the available capacity. This mandates the system operator to shed the load in order to maintain security of the system. With the advent of advanced smart metering infrastructure, communication between system operator and end-use customers makes it possible to adjust/curtail/shift the demand with respect to the state of the system. The response of the demand commonly termed as demand response (DR) can be attained either by incentive-based or pricebased. With the help of DR, the renewable energy generation capacity can be increased by tuning the demand to match the variable and unpredictable power from renewable generation. It can also bring other benefits such as peak shaving, hosting capacity enhancement, and generation cost reduction. Furthermore, electric vehicles, heat pumps, and electric water heater can also be used as distributed storage resources to contribute to ancillary services, such as frequency/ voltage regulation, peak-shaving power or help to integrate fluctuating renewable resources. All these DR modes of operation need conventional regulatory frameworks and market design for capitalizing the available resources. Therefore, the objective of the study is to discuss the DR classification and their control strategies, DR role in microgrids and integration of renewable energy resources. Also, highlighted the opportunities and challenges along with the insights for the research scope associated with DR.

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Section: Role Of Dr In Microgrids and Integration Of Renewable Resourcesmentioning

confidence: 99%

Opportunities and challenges of demand response in active distribution networks

Ponnaganti

Pillai

Bak‐Jensen

2017

WIREs Energy & Environment

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“…As an optimization method without requiring a probability distribution of uncertainty variables, IGDT theory can model the deviation between the forecasting and actual value, and determine the range of variable fluctuation according to decision maker's acceptance of the threat caused by uncertainty [19,20],which has been widely used in power system. Based on the IGDT method, the impact of the uncertainty of load recovery on the safe and stable recovery of power grid [21,22], the uncertainty of forced stop of generator set on thermal Gencos [23], the uncertainty of load demand and distributed power output on active distribution network operation [24], the uncertainty of wind power on power flow calculation of HVDC transmission and voltage management [25][26][27], and the uncertainty of the spot market price for EV aggregators to develop scheduling strategy and Gencos to develop a bidding strategy and allocate trading power rationally [28][29][30][31] have been studied. Therefore, using IGDT robust optimization method to mitigate the uncertainty of wind power is feasible for the joint scheduling of wind-storage-EVs hybrid system.…”

Section: Introductionmentioning

confidence: 99%

IGDT-Based Wind–Storage–EVs Hybrid System Robust Optimization Scheduling Model

et al. 2019

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Wind power has features of uncertainty. When wind power producers (WPPs) bid in the day-ahead electricity market, how to deal with the deviation between forecasting output and actual output is one of the important topics in the design of electricity market with WPPs. This paper makes use of a non-probabilistic approach—Information gap decision theory (IGDT)—to model the uncertainty of wind power, and builds a robust optimization scheduling model for wind–storage–electric vehicles(EVs) hybrid system with EV participations, which can make the scheduling plan meet the requirements within the range of wind power fluctuations. The proposed IGDT robust optimization model first transforms the deterministic hybrid system optimization scheduling model into a robust optimization model that can achieve the minimum recovery requirement within the range of wind power output fluctuation, and comprehensively considers each constraint. The results show that the wind–storage–EVs hybrid system has greater operational profits and less impact on the safe and stable operation of power grids when considering the uncertainty of wind power. In addition, the proposed method can provide corresponding robust wind power fluctuation under different expected profits of the decision-maker to the wind–storage–EVs hybrid system.

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“…This will consume a lot of manpower and material resources and lack of intelligence [3]. Therefore, it is urgent to develop a real-time video surveillance, feedback and disposal system [4].…”

Section: Introductionmentioning

confidence: 99%