A novel pricing scheme for managing virtual energy communities and promoting behavioral change towards energy efficiency

Mamounakis, Ioannis; Efthymiopoulos, Nikolaos; Makris, Prodromos; Vergados, Dimitrios J.; Τσαούσογλου, Γεώργιος; Varvarigos, Emmanouel

doi:10.1016/j.epsr.2018.10.028

Cited by 20 publications

(8 citation statements)

References 19 publications

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“…In the last several years, an increasing interest on the energy themes focused on the reduction of emissions from fossil sources has led the researchers to develop solutions oriented to improve the users' energy awareness in everyday life actions, as well as to assist them to better schedule these actions. Among these solutions, Non-Intrusive Load Monitoring (NILM) is surely one of the most studied [1], in addition to other useful ones, such as energy management and analytic [2,3], load task scheduling [4], or behaviour-based consumption [5]. These algorithms require high computational burden, since their results could be requested in real time to provide an immediate feedback to the user.…”

Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Fagiani

Bonfigli²,

Principi

et al. 2019

Energies

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Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of F 1 -score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time.

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Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Fagiani

Bonfigli²,

Principi

et al. 2019

Energies

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“…In such systems, the idea of local decision making (self-dispatch) is gaining increasing attention, where groups of DERs interact with the system as one aggregated entity. These entities are often called Energy Communities (ECs) [1], where the EC exchanges power with the system as a single entity, and an EC operator entity performs the energy management within the EC, i.e., coordinates the energy profiles of the community's DERs and decides on the power exchange with the main system.…”

Section: Introductionmentioning

confidence: 99%

Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

Τσαούσογλου¹,

Mitropoulou²,

Steriotis³

et al. 2021

Preprint

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<div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty.</div><div>A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.</div>

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“…A complementary approach to supply-side flexibility options for addressing high price volatility and price spikes due to congestion are demand-side flexibility options. Demand-side flexibility can be availed from variable pricing and demand response [23,24], as well as energy storage. In [25], it was demonstrated that through the usage of the two-way (vehicle-to-home/vehicle-to-grid) energy trading capabilities of electric vehicles (EV) under different pricing schemes, EVs can significantly contribute towards the reduction of electricity prices.…”

Section: Introductionmentioning

confidence: 99%

A Coordination Mechanism For Reducing Price Spikes in Distribution Grids

et al. 2020

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Recently, given the increased integration of renewables and growing uncertainty in demand, the wholesale market price has become highly volatile. Energy communities connected to the main electricity grid may be exposed to this increasing price volatility. Additionally, they may also be exposed to local network congestions, resulting in price spikes. Motivated by this problem, in this paper, we present a coordination mechanism between entities at the distribution grid to reduce price volatility. The mechanism relies on the concept of duality theory in mathematical programming through which explicit constraints can be imposed on the local electricity price. Constraining the dual variable related to price enables the quantification of the demand-side flexibility required to guarantee a certain price limit. We illustrate our approach with a case study of a congested distribution grid and an energy storage system as the source of the required demand-side flexibility. Through detailed simulations, we determine the optimal size and operation of the storage system required to constrain prices. An economic evaluation of the case study shows that the business case for providing the contracted flexibility with the storage system depends strongly on the chosen price limit.

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A novel pricing scheme for managing virtual energy communities and promoting behavioral change towards energy efficiency

Cited by 20 publications

References 19 publications

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

A Coordination Mechanism For Reducing Price Spikes in Distribution Grids

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