Flexibility Management for Residential Users Under Participation Uncertainty

Krasopoulos, Christos T.; Papaioannou, Thanasis G.; Stamoulis, George D.

doi:10.1109/smartgridcomm52983.2022.9961041

Cited by 1 publication

(5 citation statements)

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“…Assuming that the users are symmetric is by no means a limitation of the proposed optimization framework. In fact, a similar mathematical formulation for user targeting for energy flexibility provision has been considered in [9] with asymmetric users, which shows that the problem is mathematically tractable. The main objective of the assumption in the current paper regarding symmetric users was to numerically investigate how different user characteristics and different demand-production energy imbalances affect the RES and DR aggregator profits.…”

Section: Discussionmentioning

confidence: 99%

“…The provision of incentives in this work is carried out according to the model proposed in [9] (with certain adaptations dictated by the dual pricing scheme) and more thoroughly explained in [12]. Our focus is on a provider/aggregator that aims to engage users in demand response (DR) and provides them with incentives.…”

Section: Dr Incentives Modellingmentioning

confidence: 99%

“…The optimization problem of the DR aggregator is specified in this section. While similar to the approach presented in the third investigation of Section IV of [9], one of the main differences here is that the available budget for the DR aggregator is now based on the RES aggregator's energy deviation D RES . The impact of D RES on the model's results needs to be considered, and for this reason, in our optimization algorithm, a range of D RES values shall be examined.…”

Section: Optimization Frameworkmentioning

confidence: 99%

“…However, while the DR profit is clean, the RES profit corresponds to the decrease in its revenue loss. The r > r min constraint has been employed to achieve more robust targeting even though users might accept somewhat smaller incentives than r min [9]. Additionally, from another point of view, a fair optimization framework would dictate that targeted users are offered higher incentives than the minimum acceptable ones so that all three types of stakeholders are engaged in a win-win situation.…”

Section: Optimization Frameworkmentioning

confidence: 99%

“…In this paper, we investigate bilateral cooperation between RES and DR aggregators for mitigating imbalances in energy generation within an augmented common portfolio of RES units and end-user DSF resources so that both of them achieve higher profits in relation to their direct and independent participation in the balancing market. We consider DSF uncertainty by appropriately adapting the user model of [9], which includes the probability of user participation according to the provided incentives, a user selection process that considers their positive attitude towards the provision of flexibility, and a healthy incentivization policy promoting fair remuneration for their services, under a suitable optimization framework combining those features. We also consider dual-tariff penalties for positive or negative imbalances and develop an optimization framework to achieve the required flexibility to offset imbalances while addressing the trade-off between maximizing the profit of RES and DR aggregators and appropriately incentivizing the users.…”

Section: Introductionmentioning

confidence: 99%

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Win–Win Coordination between RES and DR Aggregators for Mitigating Energy Imbalances under Flexibility Uncertainty

Krasopoulos,

Papaioannou,

Stamoulis

et al. 2023

Energies

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The integration of renewable rnergy sources (RESs) into the power grid involves operational challenges due to the inherent RES energy-production variability. Imbalances between actual power generation and scheduled production can lead to grid instability and revenue loss for RES operators and aggregators. To address this risk, in this paper, we introduce a mutually beneficial bilateral trading scheme between a RES and a DR aggregator to internally offset real-time energy imbalances before resorting to the flexibility market. We consider that the DR aggregator manages the energy demand of users, characterized by uncertainty in their participation in DR events and thus the actual provision of flexibility, subject to their offered monetary incentives. Given that the RES aggregator faces penalties according to dual pricing for positive or negative imbalances, we develop an optimization framework to achieve the required flexibility while addressing the trade-off between maximizing the profit of the RES and DR aggregators and appropriately incentivizing the users. By using appropriate parameterization of the solution, the achievable revenue for the imbalance offsetting can be shared between the RES and the DR aggregators while keeping users satisfied. Our analysis highlights the interdependencies of the demand–production energy imbalance on user characteristics and the RES and DR aggregator profits. Based on our results, we show that a win–win outcome (for the RES and DR aggregators and the users) is possible for a wide range of cases, and we provide guidelines so that such bilateral agreements between RES and DR aggregators could emerge in practical settings.

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Section: Discussionmentioning

confidence: 99%

Section: Dr Incentives Modellingmentioning

confidence: 99%

Section: Optimization Frameworkmentioning

confidence: 99%

Section: Optimization Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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