A Baseline Load Estimation Approach for Residential Customer based on Load Pattern Clustering

Li, Kangping; Wang, Bo; Wang, Zheng; Wang, Fei; Mi, Zengqiang; Zhen, Zhao

doi:10.1016/j.egypro.2017.12.408

Cited by 37 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work in [59] also classifies CBL evaluation methods into three general categories: 1) Averaging methods: based on the hypothesis that the load profiles of an individual customer in adjacent several days are similar, thus, the CBL can be simply estimated based on the average load of days prior to the event day; 2) Regression methods: try to fit a linear function to describe the relationship between the load and explanatory variables such as historical load and weather data (e.g. temperature, humidity and wind speed) and then use this function to estimate the CBL of the event days; 3) Machine Learning methods: try to find the potential relation between the load and its impact factors.…”

Section: B Baseline Calculation Methodologymentioning

confidence: 99%

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

et al. 2020

View full text Add to dashboard Cite

The use of Distributed Ledger Technologies such as Blockchain for certifying Demand Response services allows for the creation of a distributed system in which customers can communicate with the system operator to provide their flexibility, in a secure, transparent and traceable way. Blockchain technology also supports incentive mechanisms for users taking part in the service through the generation of utility tokens to recognize the user's contribution. This paper presents the experimental test of a novel methodology for Demand Response programs implementation by using the Blockchain technology. The latter is employed for defining a distributed Demand Response service and a new system for its tracing and certification. For this work, a Smart Contract has been conceived and written to execute Demand Response events, calculate users' baseline, compute the support provided by each user towards the fulfilment of the requested load curve modification and remunerate each user with utility tokens proportionally to their contribution. To test the methodology, a Hyperledger Fabric network and a Smart Contract were deployed on four nodes of the Microgrid Laboratory of the Department of Energy Technology at Aalborg University (DK). Subsequently, a realistic scenario comprising two consumer nodes was developed using power electronic converters for generating the household profiles and Smart Meters for the measurement of the consumption profiles. Theoretical and experimental results show the feasibility of Distributed Ledger Technologies in smart grids management with a minimum investment in new hardware while enabling the active participation of customers in Demand Response more transparently and fairly.

show abstract

Section: B Baseline Calculation Methodologymentioning

confidence: 99%

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

et al. 2020

View full text Add to dashboard Cite

show abstract

“…As outlined in Reference 15 another consideration that must be made is the accuracy and bias of a forecasted baseline load profile. The ensuring the highest accuracy from a forecast involves minimizing the error that is made to measure demand response 49 . Minimizing forecast error involves minimizing the bias and variance produced by the forecasted load profile.…”

Section: Development Of Mathematical Modelsmentioning

confidence: 99%

“…Minimizing forecast error involves minimizing the bias and variance produced by the forecasted load profile. This was be represented by Expression (15) 49 …”

Section: Development Of Mathematical Modelsmentioning

confidence: 99%

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

et al. 2021

View full text Add to dashboard Cite

Summary To forecast the demand response of a residential customer, forecasted load profiles must first be calculated and the demand response produced by residential energy management devices determined. Finding an efficient method to select an optimal forecasted load profile to assess the demand response of these devices is a fundamental issue currently overlooked. Poorly forecasted load profiles can affect customer compensation for demand reduction, estimation of retail profits and allocation and reconciliation processes for electricity markets. This research resolved to develop an economic model for selecting an optimal forecasted load profile from which the demand response of residential energy management devices can be determined. Using 2 years of half‐hourly electricity consumption data for 5379 households obtained from the “Low Carbon” London project (UK), a novel economic model was created to assess the economic advantage of nine different forecast methods. Results suggest that when creating forecasted load profiles to measure demand response of these devices the sociodemographic of customers, tariff type and the operational objectives of the device must be taken into account to maximize the economic benefit of demand response initiatives for the customer and retailer.

show abstract

“…INDEPENDENT DR AGGREGATION IN BALANCING MARKET DR service is a temporal change in consumer's energy consumption due to a reaction to price signals or other measures [7]. DR is associated with multiple benefits, such as increased system flexibility, improved network congestion management, cost-effective deferral of grid investments and improved energy efficiency [8], [9]. DR can be broadly divided in two groups: implicit and explicit DR.…”

Section: Imentioning

confidence: 99%

Case study - Assessing Economic Potential for Demand Response in Baltic Balancing Market

Sadovica

Junghāns

Sauhats

et al. 2018

2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

View full text Add to dashboard Cite

Demand response integration in balancing energy markets can provide significant financial savings for grid operators and market participants and promote optimal resource allocation. To facilitate demand response participation in power system balancing, the service must not only provide economic gains for the existing market participants, but it also has to present a viable business case for demand response service providers. Currently, in the Baltic states, there is no demand side participation in balancing markets. To support balancing market development, we analysed the economic potential of demand response for service providers. To forecast market conditions, we employed stochastic simulations for energy market prices and balancing product activation. Furthermore, to calculate the economic gains of a service provider, we used technical parameters of fridges obtained in a demand response pilot study and the demand response aggregation settlement model proposed by the Baltic TSOs and Finnish TSO. The preliminary results suggest sufficient financial incentives for future investments.

show abstract

A Baseline Load Estimation Approach for Residential Customer based on Load Pattern Clustering

Cited by 37 publications

References 13 publications

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

Demand Response Service Certification and Customer Baseline Evaluation Using Blockchain Technology

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

Case study - Assessing Economic Potential for Demand Response in Baltic Balancing Market

Contact Info

Product

Resources

About