Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing

By Matteo Muratori, Giorgio Rizzoni. IEEE Transactions on Power Systems, 2016

374 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/2401a27f9af2b43fd907cc1c4d19f0c65ef4ece4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Mengmeng Yu
2. Renzhi Lu
3. Seung Ho Hong

In Renewable Energy, Sustainability and the Environment

1. Mengmeng Yu
2. Seung Ho Hong

Citations in the corpus

• The rise of electric vehicles—2020 status and future expectations (2021, neutral): “At the local level, however, multiple consumers responding to the same signal might cause ‘rebound peaks’ (Li et al 2012, Muratori and Rizzoni 2016) that can overstress distribution systems, calling for coordination among consumers connected to the same distribution network (e.g. direct EV-charging…”
• Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019, background): “C ONTINUAL changes in variables relevant to the electricity market, such as the electricity price, energy consumption and dynamic operation, require the energy management systems (EMSs) to take optimal real-time actions instantly and adaptively [1].”