Importance of social welfare point for the analysis of demand response

Gadham, Kumar Raja; Ghose, T. K.

doi:10.1109/cmi.2016.7413735

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…A DR program introduces load flexibility, where different entities will benefit, starting with RCs and the electricity supplier [23]. The main goal of these DR programs is to regulate the operational and economical parameters of the power system [24]. PBDR programs are capable of offering a scheme based on the price variation.…”

Section: Benefits and Risks Of A Smart House And Demand Response Conceptmentioning

confidence: 99%

Analysis Application of Controllable Load Appliances Management in a Smart Home

et al. 2019

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The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

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Section: Benefits and Risks Of A Smart House And Demand Response Conceptmentioning

confidence: 99%

Analysis Application of Controllable Load Appliances Management in a Smart Home

et al. 2019

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“…Market operators, for example, implement DR programs to satisfy both the demand of the electricity market and power system security issues. The necessity of expanding the generation and transmission facili-ties could be postponed [1] by implementing proper demand-side programs. DR programs are also bene cial for transmission network providers to operate their network in a more reliable and secure condition.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

2019

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This paper proposes a new cooperative scheduling framework for Demand Response Aggregators (DRAs) and Electric Vehicle Aggregators (EVAs) in a day-ahead market. The proposed model implements the Information-Gap Decision Theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees to obtain the predetermined pro t by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenariobased and bi-level IGDT-based methods, respectively. The DRA provides DR from two demand-side management programs including Time-Of-Use (TOU) and reward-based DR. Then, the obtained DR is o ered in day-ahead markets. Furthermore, the EVAs not only meet the EV owners' demand economically, but also participate in the day-ahead market while are willing to set DR contracts with the DRA. The objective function is to maximize the total pro t of DR and EV aggregators by pursuing two di erent strategies to deal with price uncertainty, i.e., risk-seeking strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated with respect to a case study with realistic data of electricity markets.

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Demand response program using incentive and dis-incentive based scheme

Gadham

Ghose

2019

Energy Syst

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Importance of social welfare point for the analysis of demand response

Cited by 8 publications

References 9 publications

Analysis Application of Controllable Load Appliances Management in a Smart Home

Analysis Application of Controllable Load Appliances Management in a Smart Home

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

Demand response program using incentive and dis-incentive based scheme

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