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Demand Response

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José Horta, Eitan Altman, Mathieu Caujolle, Daniel Kofman, David Menga

Proc. of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), October 2018.
DOI: 10.1109/SmartGridComm.2018.8587495

Abstract: Future electricity distribution grids will host a considerable share of the renewable energy sources needed for enforcing the energy transition. Demand side management mechanisms play a key role in the integration of such renewable energy resources by exploiting the flexibility of elastic loads, generation or electricity storage technologies. In particular, local energy markets enable households to exchange energy with each other while increasing the amount of renewable energy that is consumed locally. Nevertheless, as most ex-ante mechanisms, local market schedules rely on hour-ahead forecasts whose accuracy may be low. In this paper we cope with forecast errors by proposing a game theory approach to model the interactions among prosumers and distribution system operators for the control of electricity flows in real-time. The presented game has an aggregative equilibrium which can be attained in a semi-distributed manner, driving prosumers towards a final exchange of energy with the grid that benefits both households and operators, favoring the enforcement of prosumers’ local market commitments while respecting the constraints defined by the operator. The proposed mechanism requires only one-to-all broadcast of price signals, which do not depend either on the amount of players or their local objective function and constraints, making the approach highly scalable. Its impact on distribution grid quality of supply was evaluated through load flow analysis and realistic load profiles, demonstrating the capacity of the mechanism ensure that voltage deviation and thermal limit constraints are respected.

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Sawsan Al Zahr

[Invited paper] Proc. of the IEEE MENACOMM Confrence, Jounieh, Lebanon, Apr. 2018
DOI: 10.1109/MENACOMM.2018.8371044

Abstract: Along with the growing penetration of renewable energy sources, demand side management (DSM) is becoming a key component of future energy systems such as smart grids. DSM aims at balancing the demand for power with intermittent renewable energy sources such as wind and solar units. DSM deploys various mechanisms to influence customer’s capability and willingness to modify their power consumption according to the utility’s energy production and the distribution capacity. DSM aims at either saving energy in sustainable manner (i.e. energy response) or/and shifting the time of energy use to off-peak hours (i.e. demand response). Indeed, DSM does not necessarily reduce the total customer’s power consumption but reshapes consumption patterns. Hence, DSM is expected to reduce the need for investments in networks and power plants in order to meet peak demands. In this paper, we propose an advanced demand response (DR) solution for individual households. Considering a household equipped with various domestic loads, we aim at optimally scheduling the day-ahead power consumption under timevariable rates while taking advantage of modular and deferrable loads, e.g. electric vehicle. Our proposal is numerically illustrated through real-life scenarios, elaborated using an existing simulator of human behavior regarding power consumption.

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Sawsan Al Zahr, Elias A. Doumith, Philippe Forestier

Proc. of the IEEE Globecom Confrence, Singapore, Singapore, Dec. 2017
DOI: 10.1109/GLOCOM.2017.8255068

Abstract: As the global energy policy is changing from a demand-driven to a supply-driven approach, demand side management (DSM) is becoming a key component of future energy systems. Indeed, it helps power grids’ operators to balance the demand for power with intermittent renewable energy sources such as wind and solar units. DSM consists in optimizing/adapting the power consumption to meet the production through various methods such as improving the energy efficiency by using better equipment and materials, implementing demand response (DR) solutions, etc. DSM mechanisms do not necessarily reduce the total power consumption, but reshape the consumption pattern. Hence, DSM is expected to reduce the need for investments in networks and power plants in order to meet peak demands. In this paper, we propose an advanced DR solution for individual households. Considering a household equipped with various domestic loads, we aim at optimally scheduling the day-ahead power consumption under time-variable rates while taking advantage of modular and deferrable loads, e.g., electric vehicle. For this purpose, we propose an exact approach to solve the problem of energy management within a household under both system’s and user’s constraints. Our proposal is numerically validated through real- life scenarios, elaborated using an existing simulator of human behavior regarding power consumption.

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