Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation

Li, Kangping; Wang, Fei; Mi, Zengqiang; Fotuhi-Firuzabad, M.; Duić, Neven; Wang, Tieqiang

doi:10.1016/j.apenergy.2019.113595

Cited by 173 publications

(51 citation statements)

References 37 publications

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“…It is noted that distributed renewable generations are playing important roles in the distribution network. Particularly, more and more households are installing distributed photovoltaic systems [37]. Therefore, distributed renewable generations will be considered in the optimal scheduling model in future works.…”

Section: Discussionmentioning

confidence: 99%

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Zhang

et al. 2019

IEEE Access

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Home energy management systems (HEMSs) enable residential customers to efficiently participate in demand response programs in order to obtain optimal benefits. Traditional HEMSs only manage household electric appliances to reduce the electricity consumption cost while the optimal scheduling of natural gas appliances has been overlooked. Due to the increasing popularity of natural gas appliances in modern smart homes, the electricity consumption of residential customers connected to the natural gas network is significantly affected by the use of natural gas appliances. To consider the interaction between electric and natural gas appliances in households, a day-ahead optimal joint scheduling model of electric and natural gas appliances for HEMS is proposed. Firstly, all household appliances are classified into several categories and the mathematical model of each appliance is presented. Then, a day-ahead optimal joint scheduling model of both electric and natural gas appliances for HEMS is formulated, in which the objective function is to minimize the household's energy cost and the dissatisfaction level caused by the shifting, reduction and replacement of loads in response to the time varying prices. Case studies using realistic data indicate that the proposed model can save the total energy costs up to 30% for customers whilst ensuring their satisfaction levels. INDEX TERMS Home energy management system, natural gas, demand response, joint scheduling, dissatisfaction. A. SUPERSCRIPTS bch Battery charging bdch Battery discharging dw Dishwasher eac Electric air conditioner el Electric light es Electric stove ev Electric vehicle ewh Electric water heater gahs Gas air heating system The associate editor coordinating the review of this manuscript and approving it for publication was Alexander Micallef. gs Gas stove gwh Gas water heater ua Uncontrollable appliance s Stove wh Water heater wm Washing machine B. INDEXES AND SETS

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Section: Discussionmentioning

confidence: 99%

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Zhang

et al. 2019

IEEE Access

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“…In order to incent customers to participate in the DR program, the DR aggregators need to make decisions based on the customer baseline (CBL). Therefore, it is vital to predict the CBL accurately [23]. However, in a residential distribution network, the distributed PV owned by customers generally is located behind-the-meter (BTM) [24], which measures the net load and denotes actual electricity load minus PV power generation.…”

Section: Background and Motivationmentioning

confidence: 99%

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

et al. 2020

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Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.

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“…The contextually supervised source separation model is used to disaggregate the net load signals of feeder-level measurements in [11]. A supervised machine learning algorithm is utilized in [14] to solve the solar PV generation capacity estimation problem as a part of the net load disaggregation method under the assumption that actual measured solar PV generation and capacity data are available for a small number of representative solar PV sites.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the data-driven methods [10], [15], [16] often use a highly simplified linear model, which is incapable of capturing the nonlinear relationship among the solar irradiance, solar PV system geometry, and solar PV generation. In many cases, the pure data-driven methods [8]- [10], [12], [14] require historical solar PV generation data of a subset of customers, which can be difficult for electric utilities to obtain. Some data-driven methods [9], [10] could suffer from transposition errors if solar PV systems of different geometry are not available to serve as solar proxies.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Kabir

Yao

et al. 2021

IEEE Trans. Sustain. Energy

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Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation

Cited by 173 publications

References 37 publications

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Day-Ahead Optimal Joint Scheduling Model of Electric and Natural Gas Appliances for Home Integrated Energy Management

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

Joint Estimation of Behind-the-Meter Solar Generation in a Community

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