Estimation of Residential Heat Pump Consumption for Flexibility Market Applications

Kouzelis, Konstantinos; Tan, Zheng Hua; Bak‐Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna; Ritchie, Ewen

doi:10.1109/tsg.2015.2414490

Cited by 43 publications

(13 citation statements)

References 27 publications

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“…There are also some forecasting methods that aim to predict the consumption of large appliances or high consumption devices like boilers for district heating systems [28], heat pumps [29], electric vehicles at the station columns [30], etc. Different methods have been introduced in the literature for improving the accuracy of the load forecasting of single devices: data partitioning techniques in [31], or machine learning techniques, probability theory, and statistics in [29] for the prediction of electricity load consumption of heat pumps.…”

Section: Load Forecasting In Distribution Systemsmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

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Section: Load Forecasting In Distribution Systemsmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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“…As a result, the benefit for the DSO has to be evaluated in a probabilistic manner. The benefit of the service for each scenario is calculated according to (3). Figure 12 shows the histogram of the benefit if the flexibility service is applied to the individual load scenarios; note that the y-axis is logarithmic.…”

Section: Baseline Servicesmentioning

confidence: 99%

“…Flexibility can come from demand response (DR), i.e. by using electric vehicles (EVs) [2] and heat pumps [3] as flexible resources, from distributed storage and from distributed generation. These distributed energy resources (DERs) can provide system services [4] and even replace fossil-fuel based flexibility on the generation side [5].…”

Section: Introductionmentioning

confidence: 99%

EcoGrid 2.0: A large-scale field trial of a local flexibility market

et al. 2020

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The number of distributed energy resources installed in distribution networks is steadily increasing. Their flexibility can be leveraged by aggregators and used in the wholesale markets. However, the utilization of this flexibility cannot neglect the operating conditions of distribution networks, and thus some form of coordination between distribution and transmission system operators must exist. The idea of operating local flexibility markets has been proposed to address these issues, but experience from real-life demonstrations is still missing. The Danish project EcoGrid 2.0 has designed, implemented and tested a local flexibility market with 800 participating customers, which runs in parallel with the wholesale markets. First, this paper presents a method to identify and quantify the value of possible flexibility services that are beneficial to the distribution system operator. Next, it provides insight from the operation of a real local flexibility market, with a large number of controllable loads. Results show that aggregators are able to reliably shape the load of residential heating units to deliver flexibility services, despite the presence of large uncertainties. Services can act as an insurance policy against rare but consequential network overloadings and outages. In these rare cases flexibility services can increase social welfare significantly. However, such services should be carefully selected to avoid adverse effects. Finally, we provide with guidelines and some general remarks on the nature and the desirable characteristics of those markets and services.

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“…Abdisalaam et al [15] similarly estimate potential flexibility, and among others assess the potential benefit of interrupting EV charging -however, the EV data is a synthetic model based on typical car usage in The Netherlands from 2007. On the household device side (rather than EVs), studies based on real-world data include [16] (washing machines, dryers, heaters, ACs, refrigerators), [17] (heat pumps) and [18] (wet appliances).…”

Section: Related Workmentioning

confidence: 99%

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

Develder

Sadeghianpourhamami

Strobbe

et al. 2016

2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources).This paper analyzes two data sets, one from a charging-athome field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility:(1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t + ∆.

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Estimation of Residential Heat Pump Consumption for Flexibility Market Applications

Cited by 43 publications

References 27 publications

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

EcoGrid 2.0: A large-scale field trial of a local flexibility market

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

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