Generation and Evaluation of Flex-Offers from Flexible Electrical Devices

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

Šikšnys

Neupane

2018

Self Cite

The recent spread of distributed renewable energy sources and smart IoT devices offer exciting new possibilities for the use of energy flexibility, opening a new era of the socalled bottom-up or cellular energy systems. In order to harness the full potential of flexibility, flexibility has to be modeled and represented in a manner that can be efficiently managed, manipulated, and traded on a market. In this paper, we provide a comprehensive overview of the FlexOffer concept, which offers an effective way of modeling and managing energy demand and supply flexibilities from a wide range of flexible resources and their aggregates. First, we define the basic concept and present the different phases of the FlexOffer life-cycle. Then, we discuss more advanced internal FlexOffer constraints as well as algorithms for FlexOffer generation, aggregation, disaggregation, and pricing that can significantly reduce energy management and trading complexities and increase overall efficiency. Finally, we present a general decentralized system architecture for trading flexibility (FlexOffers) in existing and new markets. Our experimental results show that (1) FlexOffers can be extracted with up to 98% accuracy, (2) aggregation and disaggregation can scale to 1000K FlexOffers and more, and (3) flexibility can be traded in the NordPool flexi order market while providing up to 89.9% (of optimal) reduction in the energy cost.

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Flexoffer Generationmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Managing Energy Flexibility Using FlexOffers

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

Šikšnys

Neupane

2018

Self Cite

“…Thus, we need to develop models to analyze user behavior and predict future energy demand [13,26]. The predicted energy demand and the associated flexibility data is used to generate FOs [27]. We explain how FMTS facilitates these phases in Sections 4-5.…”

Section: Flexoffer Life Cyclementioning

confidence: 99%

Flexibility Modeling, Management, and Trading in Bottom-up Cellular Energy Systems

Šikšnys

Proceedings of the Tenth ACM International Conference on Future Energy Systems

Aftab

et al. 2019

Self Cite

Accelerated local deployments of renewable energy sources and energy storage units, as well as increased overall flexibility in local demand and supply through active user involvement and smart energy solutions, open up new opportunities (e.g., self-sufficiency and CO 2 neutrality through local renewables) and yet pose new challenges (e.g., how to maintain the security of supply and get the best yield) to market players in the lower parts of the energy system (including prosumers, energy communities, aggregators, and distribution system operators (DSOs)). One way to cope with the challenges requires "logical" reorganization of the energy system bottom-up as a number of nested (maximally) self-sufficient and interacting cells with their own local (i.e. within a cell) energy management and trading capabilities. This change necessitates effective IT-based solutions. Towards this goal, we propose a unified Flexibility Modeling, Management, and Trading System (FMTS) that generalizes flexibility modeling, management, and intra-cell trading in such cellular energy systems. Our system offers different flexibility provisioning options (Machine Learning based, and Model Predictive Control based), activation mechanisms (indirect and direct device-control), and trading schemes (e.g. flexibility contracts, market-based trading) and suits different cellular system use-cases. In this paper, we introduce the FMTS, overview its core functionality and components, and explain how it practically manages, prices, and trades flexibility from a diverse variety of loads. We then introduce the real-world FMTS instances developed in the GOFLEX project 1 and present experimental results that demonstrate significantly increased flexibility capacities, user gains, and balance between demand and supply when an FMTS instance is used in the simulated cellular energy system setting.

“…When an EV is plugged in, an FO is generated requiring the minimum interaction with the owner of the EV. The FO generation takes into account the historical use of the EV, the SOC ini of the EV, the charging characteristics of the EV, and the technical characteristics of the charging station, e.g., a home charging installation [20]. Identifying the time exibility of an FO is challenging, but appropriate forecast techniques can be designed taking into account daily/weekly driving patterns [6].…”

Section: Market Frameworkmentioning

confidence: 99%

Day-ahead Trading of Aggregated Energy Flexibility

Valsomatzis

Proceedings of the Ninth International Conference on Future Energy Systems

Abelló

2018

Self Cite

Flexibility of small loads, in particular from Electric Vehicles (EVs), has recently attracted a lot of interest due to their possibility of participating in the energy market and the new commercial potentials. Di erent from existing work, the aggregation techniques proposed in this paper produce exible aggregated loads from EVs taking into account technical market requirements. They can be further transformed into the so-called exible orders and be traded in the day-ahead market by a Balance Responsible Party (BRP). As a result, the BRP can achieve at least 20% cost reduction on average in energy purchase compared to traditional charging based on 2017 real electricity prices from the Danish electricity market.