Methods for the management of distributed electricity networks using software agents and market mechanisms: A survey

Lopez-Rodriguez, I.; Hernández-Tejera, Mario; López, Ángel L.

doi:10.1016/j.epsr.2016.03.016

Cited by 17 publications

(11 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a highly electrified economy with high shares of variable solar and wind electricity systems, reducing systemic mismatches between the generation and energy demand assets in an efficient manner requires investments in smart energy management systems. Energy management systems consist of two main categories: (a) supply-side devices from the electric utility-side used to manage the fluctuation of the load demand such as substations, and (b) the demand-side management devices used to manage energy consumption and meet the available power from the generation side [25][26][27][28]. Substations encompass transformers, switchgear, and protection, control and automation systems, and connect parts of the electric grid that operate at different voltage levels and managing these multidirectional power flows while ensuring reliability and security is critical as the share of decentralized and renewable energy increases.…”

Section: Leveraging Digitalization and Business Model Innovations Formentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

View full text Add to dashboard Cite

The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

show abstract

Section: Leveraging Digitalization and Business Model Innovations Formentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

View full text Add to dashboard Cite

show abstract

“…The idea of implementing LEMs as combinatorial auctions is not new. For a survey on some of the methods that have been proposed, the reader is referred to [12]. We briefly discuss two of those herein.…”

Section: Additional Related Workmentioning

confidence: 99%

Combflex: a linear combinatorial auction for local energy markets

Kiedanski¹,

Orda²,

Kofman³

2020

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

View full text Add to dashboard Cite

Local energy markets, platforms in which prosumers in the same Low Voltage network can trade energy among themselves, offer a great opportunity to incentivize the consumption of locally generated energy. Unfortunately, traditionally proposed implementations of local energy markets such as simple double auctions and peer to peer exchanges do not fully exploit the available flexibility in these systems.We design a market mechanism that exploits the characteristics of the players, providing them with expressive bids to represent their flexibility, which we assume is due to energy storage. The proposed market is not obviously manipulable and can be cleared by solving a linear programming problem that grows linearly in the number of participants.Using realistic data, we benchmark the proposed mechanism against sequential auctions and peer to peer exchanges often used in the literature. Our numerical results show that the proposed mechanism outperforms traditional implementations.

show abstract

“…Bayram et al [10] reviewed different concepts of local trading, enabling technologies and required frameworks. Additionally, Lopez-Rodriguez et al [11] elaborated on a survey about different market-based control methodologies for DER. In terms of price settlement, Nanduri et al [12] presented two types of electricity auctions: uniform price and discriminatory auctions at the neighborhood level.…”

Section: Introductionmentioning

confidence: 99%

Local Flexibility Market Design for Aggregators Providing Multiple Flexibility Services at Distribution Network Level

et al. 2018

View full text Add to dashboard Cite

This paper presents a general description of local flexibility markets as a marketbased management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids' hosting capacity.

show abstract

Methods for the management of distributed electricity networks using software agents and market mechanisms: A survey

Cited by 17 publications

References 19 publications

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Combflex: a linear combinatorial auction for local energy markets

Local Flexibility Market Design for Aggregators Providing Multiple Flexibility Services at Distribution Network Level

Contact Info

Product

Resources

About