Evaluating the Value of Flexibility in Energy Regulation Markets

Neupane, Bijay; Pedersen, Torben Bach; Thiesson, Bo

doi:10.1145/2768510.2768540

Cited by 15 publications

(13 citation statements)

References 11 publications

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“…However, to estimate the impact of the forecast error at an atomic (device) level, we proportionate the aggregated e ect to an individual device. erefore, to evaluate the change in regulation price we use the hypothetical relationship between energy prices and regulation volume as proposed in [22].…”

Section: Change In Regulation Pricementioning

confidence: 99%

Utilizing Device-level Demand Forecasting for Flexibility Markets

Neupane

Pedersen

Thiesson³

2018

Proceedings of the Ninth International Conference on Future Energy Systems

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e uncertainty in the power supply due to uctuating Renewable Energy Sources (RES) has severe ( nancial and other) implications for energy market players. In this paper, we present a device-level Demand Response (DR) scheme that captures the atomic (all available) exibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules that minimize market imbalances. We evaluate the e ectiveness and feasibility of widely used forecasting models for device-level exibility analysis. In a typical device-level exibility forecast, a market player is more concerned with the utility that the demand exibility brings to the market, rather than the intrinsic forecast accuracy. In this regard, we provide comprehensive predictive modeling and scheduling of demand exibility from household appliances to demonstrate the ( nancial and otherwise) viability of introducing exibility-based DR in the Danish/Nordic market. Further, we investigate the correlation between the potential utility and the accuracy of the demand forecast model. Furthermore, we perform a number of experiments to determine the data granularity that provides the best nancial reward to market players for adopting the proposed DR scheme. A cost-bene t analysis of forecast results shows that even with somewhat low forecast accuracy, market players can achieve regulation cost savings of 54% of the theoretically optimal.

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Section: Change In Regulation Pricementioning

confidence: 99%

Utilizing Device-level Demand Forecasting for Flexibility Markets

Neupane

Pedersen

Thiesson³

2018

Proceedings of the Ninth International Conference on Future Energy Systems

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“…It is currently used in the Arrowhead 2 and TotalFlex 3 projects [8]. The benefits of flexible loads have also been quantified in previous research [14]. Note that other models for representing energy flexibility exist, as for example the notion of control space proposed in the Energy Flexibility Platform and Interface (EFPi) from the PowerMatcher 4 suite [15].…”

Section: Flexoffermentioning

confidence: 99%

Energy Consumption Forecast of Photo-Voltaic Comfort Cooling Using UPPAAL Stratego

Agesen

Enevoldsen

Guilly

et al. 2017

Lecture Notes in Computer Science

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To balance the fluctuations of renewable energies, greater flexibility on the consumption side is required. Moreover, solutions are required to handle the uncertainty related to both production and consumption. In this paper, we propose a probabilistic extension to FlexOffers to capture both the interval in which a given energy resource can be operated and the uncertainty that surrounds it. Probabilistic FlexOffers serve as a support for a method to forecast energy production and consumption of stochastic hybrid systems. We then show how to generate a consumption strategy to match a given consumption assignment within a given flexibility interval. The method is illustrated on a building equipped with solar cells, a heat pump and an ice bank used to feed the air conditioning system.

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“…T (f ) = [t es , t ls ] where t es and t ls are the earliest start time and latest start time, respectively. The amount profile is a sequence of (m ∈ N >0 ) consecutive slices, P (f ) = s (1) Figure 2 illustrates FO f = ( [1,5], [3,5], [2,3] An FO captures all possible amount demands and/or supplies of a device for a given time horizon. However, during the scheduling process, an FO is assigned to a specific amount at a specific time resulting in an assignment of the FO defined as follows: Definition 2.…”

Section: Background and Preliminariesmentioning

confidence: 99%

“…N (20, 1), [19,21] N (8, 1), [5,10] U {2, 4} U {3, 4}, U {1, 2} 0 HP (day) N (13, 1), [12,14] Table I.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…The existence of appropriate information and communication technology (ICT) infrastructure [2] and a suitable hierarchical control architecture, offer the capability to market actors to command the DR [3]. Moreover, the establishment of a flexibility market [4] will provide flexibility with the opportunity to be traded [5]. However, the energy captured by individual FOs from small load devices cannot be directly traded in the market [6].…”

Section: Introductionmentioning

confidence: 99%

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Aggregating energy flexibilities under constraints

Valsomatzis

Pedersen

Abelló

et al. 2016

2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-The flexibility of individual energy prosumers (producers and/or consumers) has drawn a lot of attention in recent years. Aggregation of such flexibilities provides prosumers with the opportunity to directly participate in the energy market and at the same time reduces the complexity of scheduling the energy units. However, aggregated flexibility should support normal grid operation. In this paper, we build on the flex-offer (FO) concept to model the inherent flexibility of a prosumer (e.g., a single flexible consumption device such as a clothes washer). An FO captures flexibility in both time and amount dimensions. We define the problem of aggregating FOs taking into account grid power constraints. We also propose two constraintbased aggregation techniques that efficiently aggregate FOs while retaining flexibility. We show through a comprehensive evaluation that our techniques, in contrast to state-of-the-art techniques, respect the constraints imposed by the electrical grid. Moreover, our techniques also reduce the scheduling input size significantly and improve the quality of scheduling results.

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Evaluating the Value of Flexibility in Energy Regulation Markets

Cited by 15 publications

References 11 publications

Utilizing Device-level Demand Forecasting for Flexibility Markets

Utilizing Device-level Demand Forecasting for Flexibility Markets

Energy Consumption Forecast of Photo-Voltaic Comfort Cooling Using UPPAAL Stratego

Aggregating energy flexibilities under constraints

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