Pricing strategy for device-level demand response in a microgrid

Li, Pan; Guan, Xiaohong; Wu, Jiang; Wang, Dai

doi:10.1109/chicc.2014.6896262

Cited by 5 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [33], instead, the objective to maximize while remunerating the customers is the profit for the Aggregator. Finally, in [34] a positive/negative (limited) quantity is added to the retail price in each period of a day in order to encourage load shifting from a price period to another. (R2.2),(R2.3),(R3.5) In this paper, a quadratic remuneration function is used.…”

Section: (R37)mentioning

confidence: 99%

Aggregation and remuneration in Demand-Response with a blockchain-based framework

Silvestre

Gallo

Sanseverino

et al. 2020

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

This paper describes the possibility to use the blockchain technology for load and generation aggregation in a new distributed Demand Response (DR) service and customers remuneration system. The blockchain technology and the use of smart contracts for DR allow the creation of a distributed system in which customers can communicate directly, in a transparent, secure and traceable way, with the grid operator to provide their flexibility. In this paper, the DR problem formulation takes into account several aspects, which are periodically executed. First, the blockchain records customers' energy consumption or production, then, the smart contract starts calculating the baseline and the potential support provided by each customer to fulfill the requested load adaptation. Customers' availability for generation and load profile modulation is also taken into account, as well as their privacy and an updated definition of the roles of grid and market operators in a new Demand-Response scenario supported by the blockchain technology. The blockchain used is Hyperledger Fabric, since it turned to be flexible for smart contracts implementation while supporting multi-tenancy. Results show the possibility to successfully apply the blockchain technology to this particular topic, even considering privacypreserving issues.

show abstract

Section: (R37)mentioning

confidence: 99%

Aggregation and remuneration in Demand-Response with a blockchain-based framework

Silvestre

Gallo

Sanseverino

et al. 2020

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

show abstract

Dissecting demand response mechanisms: The role of consumption forecasts and personalized offers

Benegiamo

Loiseau

Neglia

2018

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

Demand-Response (DR) programs, whereby users of an electricity network are encouraged by economic incentives to re-arrange their consumption in order to reduce production costs, are envisioned to be a key feature of the smart grid paradigm. Several recent works proposed DR mechanisms and used analytical models to derive optimal incentives. Most of these works, however, rely on a macroscopic description of the population that does not model individual choices of users.In this paper, we conduct a detailed analysis of those models and we argue that the macroscopic descriptions hide important assumptions that can jeopardize the mechanisms' implementation (such as the ability to make personalized offers and to perfectly estimate the demand that is moved from a timeslot to another). Then, we start from a microscopic description that explicitly models each user's decision. We introduce four DR mechanisms with various assumptions on the provider's capabilities. Contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. We present numerical simulations that compare the different mechanisms and their sensitivity to forecast errors. At a high level, our results show that the performance of DR mechanisms under reasonable assumptions on the provider's capabilities are significantly lower than those suggested by previous studies, but that the gap reduces when the population's flexibility increases.

show abstract

Dissecting demand response mechanisms: the role of consumption forecasts and personalized offers

Benegiamo

Loiseau

Neglia

2016

2016 American Control Conference (ACC)

View full text Add to dashboard Cite

Pricing strategy for device-level demand response in a microgrid

Cited by 5 publications

References 13 publications

Aggregation and remuneration in Demand-Response with a blockchain-based framework

Aggregation and remuneration in Demand-Response with a blockchain-based framework

Dissecting demand response mechanisms: The role of consumption forecasts and personalized offers

Dissecting demand response mechanisms: the role of consumption forecasts and personalized offers

Contact Info

Product

Resources

About