Estimating Energy Reduction Amount in the Event of Demand Response Activation: Baseline Model Comparison for the Baltic States

Sadovica, Liga; Lavrinovics, Valentins; Sauhats, Antans Sauļus; Junghans, Oatis; Lehtmets, Karin Maria

doi:10.1109/eem.2018.8469796

Cited by 5 publications

(14 citation statements)

References 4 publications

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“…The aggregated energy/flexibility can be verified and its impact on the balance can be measured and calculated. For example, different methodologies and their suitability for the Baltic market are discussed in [65].…”

Section: Balance Settlement Optionsmentioning

confidence: 99%

Framework to Facilitate Electricity and Flexibility Trading within, to, and from Local Markets

et al. 2021

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Peer-to-peer (P2P) electricity sharing or trading can empower consumers and prosumers, incentivize the balancing of generation and demand locally, increase system resilience and reliability, and help in achieving societal goals, such as increasing renewable energy penetration. Nevertheless, the development of P2P trading in actual environments has been slow due to the unclear position of P2P markets in the power system. Recent developments in the European legislation are promising for the establishment of P2P markets and energy communities. Hence, the interplay between local trading and existing market structures needs to be addressed carefully. Furthermore, P2P trading with distributed resources presumes that electricity end users will become active players in the power system. This paper proposes a bidding and pricing mechanism for local markets, considering the external markets; a new approach to balance settlement and balance responsibility when local trading occurs; and an interface to promote end-user interest in, and interactions with, local energy trading. The proposed local market concept and interface solution promote the coupling between local and existing retail, wholesale and ancillary service markets, and can be seen as a step towards the establishment of local energy markets in real-life settings.

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Section: Balance Settlement Optionsmentioning

confidence: 99%

Framework to Facilitate Electricity and Flexibility Trading within, to, and from Local Markets

et al. 2021

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“…Most baselining research focuses on the American power system. With the ongoing energy transition and increasing use of flexibility, the European perspective is however increasingly studied [7,8,16,17]. In [17], baseline models are compared for the Baltic states.…”

Section: Introductionmentioning

confidence: 99%

“…With the ongoing energy transition and increasing use of flexibility, the European perspective is however increasingly studied [7,8,16,17]. In [17], baseline models are compared for the Baltic states. The statistical model as applied in France (see also [16]) is considered not to be viable, because of the immature flexibility market in the Baltic states.…”

Section: Introductionmentioning

confidence: 99%

“…The statistical model as applied in France (see also [16]) is considered not to be viable, because of the immature flexibility market in the Baltic states. Regression-based models are dropped based on the USA market's rejection in 2009 [17]. The common practice in the EU is considered to be the window-before approach, in which meter readings taken before flexibility activation are used to set a baseline.…”

Section: Introductionmentioning

confidence: 99%

“…The common practice in the EU is considered to be the window-before approach, in which meter readings taken before flexibility activation are used to set a baseline. An alternative is the window before and after approach, however this is considered to be more vulnerable to data manipulation [17].…”

Section: Introductionmentioning

confidence: 99%

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Baselining Flexibility from PV on the DSO-Aggregator Interface

et al. 2021

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Flexibility can be used to mitigate distribution network overloading. Distribution system operators (DSOs) can obtain this flexibility from market parties connected to the distribution network. After flexibility has been delivered to the DSO, it needs to be settled. This is typically done by comparing load measurements with a baseline. This baseline describes an asset’s power profile in case no flexibility would have been delivered. Until recently, baselining research mainly focused on large-scale, predictable and controllable assets. The flexibility used by DSOs however typically comes from small-scale, less predictable and less controllable assets. This paper addresses the baselining problem for photo-voltaic systems. Three existing baselining methods are selected based on their simplicity and transparency and their limitations with respect to application towards photo-voltaic systems are evaluated. Based on this, a proof-of-concept for a new, fourth method is provided. It overcomes some of the limitations of the three existing ones, while still ensuring simplicity and transparency in order to promote market acceptance and practical applicability. All four methods are subjected to two different curtailment strategies: curtailing all peaks above a threshold and curtailing based on a day-ahead flexibility request. Using weather data from three summer weeks in 2019, it is shown that the newly developed method is able to provide a more accurate baseline than the existing methods.

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Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

et al. 2023

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Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.INDEX TERMS Artificial intelligence, blockchain, demand response, demand side management, demand response, Internet of Things (IoT), smart grids, machine learning.

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Estimating Energy Reduction Amount in the Event of Demand Response Activation: Baseline Model Comparison for the Baltic States

Cited by 5 publications

References 4 publications

Framework to Facilitate Electricity and Flexibility Trading within, to, and from Local Markets

Framework to Facilitate Electricity and Flexibility Trading within, to, and from Local Markets

Baselining Flexibility from PV on the DSO-Aggregator Interface

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

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