Improvement of demand response using mixed pumped storage hydro plant

Latha, P.; Anand, Shilpaa; Ahamed, T. P. Imthias

doi:10.1109/iset-india.2011.6145380

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Moreover, thermal storage is also discussed in [226,227]. In [228,229] water storage is focused on reserve energy generation. The main purpose of the storage system is linked to flattening of the demand curve during hours of peak consumption.…”

Section: Energy Storagementioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

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In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smartgrid.

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Section: Energy Storagementioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

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“…In the revision, there was a notable trend headed towards the use of batteries and electric accumulators in general [145,163]. However, some articles used thermal storage [155,164] and even water storage [165,166]. The common use of storage systems is tied to the offer of the resource for the hours of higher consumption rates for the flattening of the demand curve.…”

Section: Energy Storagementioning

confidence: 99%

A Review and Analysis of Trends Related to Demand Response

et al. 2018

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This paper provides a review and analysis of trends related to demand response (DR). The authors have considered six different topics for the analysis of DR trends: Users, Network Services, Markets, Complementary Programs and Distributed Energy Resources (DER). A brief summary of the consulted articles is included and the behavior of the different DR trend-related topics is shown up to the year 2017 and their projections for 2020. As a result, the characterization of the main DR topics is obtained as well as its current and future trends. Based on the results of the study, it is concluded that the topic of complementary programs is a trendsetter for current trends and it is expected that there is a future change of focus towards the users and new services.

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“…The German Ordinance on Electricity Network Access stipulates that energy consumption and production must be in balance at all times in an electricity grid (Stromnetzzugangsverordnung -StromNZV, section 4). Any inconsistency in the load generation balance leads to a violation of the grid parameters (Latha et al 2011). A VPP is also subject to these requirements.…”

Section: Introductionmentioning

confidence: 99%

“…This enables to inform customers about potential savings. As storage option hydro pumped storages are a highly used option (Latha et al 2011;Béguin et al 2014). The control of the storages depends on the current state of the power grid.…”

Section: Introductionmentioning

confidence: 99%

Clustering time series applied to energy markets

2019

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In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place. Energy systems are increasingly being made more flexible by decentralised producers and storage facilities, i.e. many consumers are also producers. The aggregation of producers form another type of power plants: a virtual power plant. On the basis of aggregated production and consumption, virtual power plants try to make decisions under the conditions of the electricity market or the grid condition. They are influenced by many different aspects. These include the current feed-in, weather data, or the demands of the consumers. Clearly, a virtual power plant is focusing on developing strategies to influence and optimise these factors. To accomplish this, many data sets can and should be analysed in order to interpret and create forecasts for energy systems. Time series based analytics are therefore of particular interest for virtual power plants. Classifying the different time series according to generators, consumers or customer types simplifies processes. In this way, scalable solutions for forecasts can be found. However, one has to first find the according clusters efficiently. This paper presents a method for determining clusters of time series. Models are adapted and model-based clustered using ARIMA parameters and an individual quality measure. In this way, the analysis of generic time series can be simplified and additional statements can be made with the help of graphical evaluations. To facilitate large scale virtual power plants, the presented clustering workflow is prepared to be applied on big data capable platforms, e.g. time series stored in Apache Cassandra, analysed through an Apache Spark execution framework. The procedure is shown here using the example of the Day-Ahead prices of the electricity market for 2018.

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Improvement of demand response using mixed pumped storage hydro plant

Cited by 3 publications

References 8 publications

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

A Review and Analysis of Trends Related to Demand Response

Clustering time series applied to energy markets

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