An Assessment of the CO2 Emission Reduction Potential of Residential Load Management in Developing and Developed Countries
- URL: http://arxiv.org/abs/2504.02811v1
- Date: Thu, 03 Apr 2025 17:55:12 GMT
- Title: An Assessment of the CO2 Emission Reduction Potential of Residential Load Management in Developing and Developed Countries
- Authors: Alona Zharova, Felix Creutzig,
- Abstract summary: Intermittent renewable energies are increasingly dominating electricity grids and are forecasted to be the main force driving out fossil fuels from the grid until 2040.<n>Grids based on intermittent renewables are challenged by diurnal and seasonal mismatch between supply of sun and wind and demand for electricity.<n>We systematically review the literature estimating CO2 savings from residential load management in developing and developed nations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Intermittent renewable energies are increasingly dominating electricity grids and are forecasted to be the main force driving out fossil fuels from the grid in most major economies until 2040. However, grids based on intermittent renewables are challenged by diurnal and seasonal mismatch between supply of sun and wind and demand for electricity, including for heat pumps and electric two and four wheelers. Load management and demand response measures promise to adjust for this mismatch, utilizing information- and price-based approaches to steer demand towards times with high supply of intermittent renewables. Here, we systematically review the literature estimating CO2 savings from residential load management in developing and developed nations. We find that load management holds high potential, locally differentiated with energy mix (including the respective share of renewables and fossils), climate zone, and the regulatory environment and price mechanism. Most identified studies suggest a mitigation potential between 1 and 20%. Load management becomes more relevant with higher shares of intermittent renewables, and when electricity prices are high. Importantly, load management aligns consumers' financial incentives with climate change mitigation, thus rendering accompanying strategies politically feasible. We summarize key regulatory steps to facilitate load management in economies and to realize relevant consumer surplus and mitigation potential.
Related papers
- Do we actually understand the impact of renewables on electricity prices? A causal inference approach [0.0]
Wind power generation has a U-shaped effect on prices.<n>At low penetration levels, a 1 GWh increase in energy generation reduces prices by up to 7 GBP/MWh.<n>Solar power places substantial downward pressure on prices at very low penetration levels.
arXiv Detail & Related papers (2025-01-10T10:45:09Z) - Distributed Energy Management and Demand Response in Smart Grids: A
Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems.
In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
arXiv Detail & Related papers (2022-11-29T01:18:58Z) - Sustainability using Renewable Electricity (SuRE) towards NetZero
Emissions [0.0]
Growth in energy demand poses serious threat to the environment.
Most of the energy sources are non-renewable and based on fossil fuels, which leads to emission of harmful greenhouse gases.
We present a scalable AI based solution that can be used by organizations to increase their overall renewable electricity share in total energy consumption.
arXiv Detail & Related papers (2022-02-26T10:04:26Z) - Modelling the transition to a low-carbon energy supply [91.3755431537592]
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change.
Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely.
Runaway emissions could lead to extremes in weather conditions around the world.
arXiv Detail & Related papers (2021-09-25T12:37:05Z) - Weather-based forecasting of energy generation, consumption and price
for electrical microgrids management [0.0]
The transition towards a carbon-free society goes through an inevitable increase of the share of renewable generation in the energy mix.
This thesis studies the integration of renewables in power systems by investigating forecasting and decision-making tools.
arXiv Detail & Related papers (2021-07-01T09:02:36Z) - The impact of online machine-learning methods on long-term investment
decisions and generator utilization in electricity markets [69.68068088508505]
We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h.
We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm.
We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
arXiv Detail & Related papers (2021-03-07T11:28:54Z) - Exploring market power using deep reinforcement learning for intelligent
bidding strategies [69.3939291118954]
We find that capacity has an impact on the average electricity price in a single year.
The value of $sim$25% and $sim$11% may vary between market structures and countries.
We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market.
arXiv Detail & Related papers (2020-11-08T21:07:42Z) - A Hierarchical Approach to Multi-Energy Demand Response: From
Electricity to Multi-Energy Applications [1.5084441395740482]
This paper looks into an opportunity to control energy consumption of an aggregation of many residential, commercial and industrial consumers.
This ensemble control becomes a modern demand response contributor to the set of modeling tools for multi-energy infrastructure systems.
arXiv Detail & Related papers (2020-05-05T17:17:51Z) - Advancing Renewable Electricity Consumption With Reinforcement Learning [0.0]
We propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation.
We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.
arXiv Detail & Related papers (2020-03-09T20:57:58Z) - Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers.
We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid.
To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
arXiv Detail & Related papers (2020-03-02T20:32:10Z) - NeurOpt: Neural network based optimization for building energy
management and climate control [58.06411999767069]
We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification.
We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
arXiv Detail & Related papers (2020-01-22T00:51:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.