Rebound effects of demand-response management for frequency restoration

Lutolf, Philipp; Scherer, Marc; Mégel, Olivier; Geidl, Martin; Vrettos, Evangelos

doi:10.1109/energycon.2018.8398849

Cited by 9 publications

(9 citation statements)

References 12 publications

(11 reference statements)

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“…• For a large network or grid-connected systems or slow change of prices and PV power generation, the rebound effect may be trivial. [18] • The study investigates the rebound effects of demand response and its behaviour during frequency restoration. • It shows that in some extreme cases, the rebound effects can lead to oscillations in the power systems [18].…”

Section: Resultsmentioning

confidence: 99%

“…[18] • The study investigates the rebound effects of demand response and its behaviour during frequency restoration. • It shows that in some extreme cases, the rebound effects can lead to oscillations in the power systems [18]. [19] • The study analyses the performance of a demand response (DR) system, installed in the Hartley Bay, British Columbia, to reduce the fuel consumption during peak load periods [19].…”

Section: Resultsmentioning

confidence: 99%

“…A study in [17] investigates the rebound effect of the energy storages under an EMS. Another study [18] analysed the behaviour of rebound effect of demand response during frequency restoration and systems efficiency [19]. However, the rebound effect of the storage devices during PV‐power‐generation fluctuating condition and their overall impact on the microgrid substation needs to be further analysed.…”

Section: Introductionmentioning

confidence: 99%

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Rebound behaviour of uncoordinated EMS and their impact minimisation

2020

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In this paper, the impacts of uncoordinated energy management systems (EMS), with a rebound effect, on a renewable energy-dependent microgrid are discussed and feasible solutions are presented. Two different approaches, i.e. loadbased and price-based EMS are modelled which consider PV units, battery energy storage systems (BESS), and electric vehicles (EVs). Taking account of each component's boundary conditions, the load-based approach intelligently charges the EV and BESS from the grid/PV during off-peak hours, and provides a combined discharge response during peak load hours. In the price-based approach, the charging-discharging of BESSs and EVs from/to grid and PV depends on the time-of-use tariff signal. The primary objective of both models is to minimise the customers' peak electricity consumption and the saturation issues of distribution transformers. It is observed that the simultaneous response of the EMS due to the identical behaviour of load or price curves, and the rebound effect after mode switching transition create large power demand spikes. To mitigate its negative consequence, an improved locking and randomisation technique is designed and implemented. Additionally, the impact of the PV power fluctuations on the load-support systems due to fast-moving clouds and their consequences to the behaviour of the EMS response are investigated.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rebound behaviour of uncoordinated EMS and their impact minimisation

2020

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“…Second, we investigate the use of MARL for voltage regulation, an important emerging issue in highpenetration PV networks, on the IEEE 33-bus network. Third, we validate the use of MARL for building energy management in the extreme case where actions are implemented simultaneously, which can potentially cause oscillatory behavior (e.g., the "rebound effect" [20]).…”

Section: Introductionmentioning

confidence: 83%

GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management

Pigott¹,

Crozier²,

Baker³

et al. 2021

Preprint

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Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network. Intelligent control of smart inverters and other smart building energy management systems can be leveraged to alleviate these issues. GridLearn is a multiagent reinforcement learning platform that incorporates both building energy models and power flow models to achieve grid level goals, by controlling behind-the-meter resources. This study demonstrates how multi-agent reinforcement learning can preserve building owner privacy and comfort while pursuing grid-level objectives. Building upon the CityLearn framework which considers RL for building-level goals, this work expands the framework to a network setting where grid-level goals are additionally considered. As a case study, we consider voltage regulation on the IEEE-33 bus network using controllable building loads, energy storage, and smart inverters. The results show that the RL agents nominally reduce instances of undervoltages and reduce instances of overvoltages by 34%.

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“…The Optimal Operation of Active Distribution Networks with Smart Systems DOI: http://dx.doi.org /10.5772/intechopen.88032 Systems (SHEMS) [15]. While the effect of the rebound load on operating frequency is negligible [16], it can be higher regarding network losses and quality of supply. As described in [17], artificial intelligence algorithms are widely used for managing DR at LV network level.…”

mentioning

confidence: 99%

The Optimal Operation of Active Distribution Networks with Smart Systems

Neagu¹,

Grigoraș²,

Ivanov³

2019

Advanced Communication and Control Methods for Future Smartgrids

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The majority of the existing electricity distribution systems are one-way networks, without self-healing, monitoring and diagnostic capabilities, which are essential to meet demand growth and the new security challenges facing us today. Given the significant growth and penetration of renewable sources and other forms of distributed generation, these networks became "active," with an increased pressure to cope with new system stability (voltage, transient and dynamic), power quality and network-operational challenges. For a better supervising and control of these active distribution networks, the emergence of Smart Metering (SM) systems can be considered a quiet revolution that is already underway in many countries around the world. With the aid of SM systems, distribution network operators can get accurate online information regarding electricity consumption and generation from renewable sources, which allows them to take the required technical measures to operate with higher energy efficiency and to establish a better investments plan. In this chapter, a special attention is given to the management of databases built with the help of information provided by Smart Meters from consumers and producers and used to optimize the operation of active distribution networks.

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Rebound effects of demand-response management for frequency restoration

Cited by 9 publications

References 12 publications

Rebound behaviour of uncoordinated EMS and their impact minimisation

Rebound behaviour of uncoordinated EMS and their impact minimisation

GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management

The Optimal Operation of Active Distribution Networks with Smart Systems

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