Effective solar prosumer identification using net smart meter data

Donaldson, Daniel L.; Jayaweera, Dilan

doi:10.1016/j.ijepes.2020.105823

Cited by 53 publications

(34 citation statements)

References 33 publications

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“…From the above discussion, it can be deduced that disaggregation can be applied to various problems. A number of these studies attempt to estimate the installed PV capacity at a customer level and are reported in ( [17], [24]- [26]). None of the studies is focused on estimating PV at a distribution level.…”

Section: ) Disaggregation Methodsmentioning

confidence: 99%

A Probabilistic Estimation of PV Capacity in Distribution Networks From Aggregated Net-Load Data

Waswa¹,

Chihota

Bekker

2021

IEEE Access

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Globally, solar photovoltaic (PV) installations on distribution LV feeders have increased significantly. The increased penetration leads to several technical problems on existing networks and impacts utilities' business models as energy sales drop. For proactive management of these challenges, utilities need to continually monitor the capacity of installed PV. To this end, some utilities typically require PV installations to be registered and sometimes use GIS mapping to approximate the installed PV capacity. However, these GIS PV capacity estimations methods are unreliable. Therefore, to obtain reliable PV capacity estimates at a distribution level, comprehensive modeling is required to accurately represent the generation output and the distribution load. This paper proposes a novel probabilistic PV estimation method that uses time-series historical sets of load and irradiance data to estimate the embedded PV capacity while considering the uncertainty in solar irradiance and the measured net load. Uncertainty characterization is implemented using empirical probability density functions, and simulation is performed stochastically using Monte-Carlo methods. A novel quantile analysis approach is developed and used in the computation of the final PV estimates. The proposed methodology is tested using measured load data from Ausgrid customers, Australia, and achieves reasonable accuracy (between 86% and 90% for the tested cases) and a 10% mean absolute percentage error. This approach is robust to the effects of input uncertainty and can be used by distribution utilities to estimate and monitor PV capacity installed on the distribution networks without incurring extra advanced metering investment costs.

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Section: ) Disaggregation Methodsmentioning

confidence: 99%

A Probabilistic Estimation of PV Capacity in Distribution Networks From Aggregated Net-Load Data

Waswa¹,

Chihota

Bekker

2021

IEEE Access

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“…In other words, by categorizing the electrical power consumption, it is possible to approximate the load profile of a household sufficiently. It is therefore not unexpected that most of the existing load profiling techniques rely on clustering algorithms, such as k-means [143][144][145], fuzzy k-means [143], hierarchical clustering [143,146], Self-Organizing Maps (SOM) [143], neural networksbased clustering [147][148][149], Gaussian Mixture Models (GMM) [150,151], Density-Based Spatial Clustering (DBSCAN) [152], and agglomerative clustering [153]. Due to the high stochasticity and irregularity of household-level consumption, clustering techniques that analyze the variability and uncertainty of smart meter data have also been considered in the literature [150,151].…”

Section: Load Profilingmentioning

confidence: 99%

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

et al. 2021

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The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

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“…The efficient frontier as shown in Fig. 2 is then drawn by varying the weights, satisfying equation (5). For the same energy production risk level D in Fig.…”

Section: Processing Bids and Optimising Energy Portfoliomentioning

confidence: 99%

“…Consequently, local energy trading among peers subscribed to a common market framework is encouraged, leading to a P2P transactive environment [1] - [3]. To facilitate this framework, local energy networks incorporate bidirectional power flow and smart metering, information & communication technology, cyber-physical interaction, decentralized control [4] and trading platforms [5]. These technologies cater to attain different social, environmental and technical objectives based on 1) extent of liberalization of the market in the country of deployment and 2) type of energy sources contained in the system.…”

Section: Introductionmentioning

confidence: 99%

Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

Mohan¹,

Bu²,

Jisma³

et al. 2020

Preprint

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As the local energy sources are mostly uncertain and fluctuating in nature, the ‘energy risk’ due to discrepancies between committed energy transactions and metered measurements is prominent in peer to peer (P2P) markets. We propose a P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers. The risk in energy production is modeled using Markowitz portfolio theory and the best point where energy return per unit risk is maximum is obtained from the efficient frontier by using the modified Sharpe ratio. The energy portfolio thus obtained is used as a constraint while optimizing the conflicting prosumer benefits using multi-objective stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC-NSPSO). In effect, only a reliable proportion of total energy demand submitted in the bid is cleared in the market, foreseeing the real-time fluctuations. The proposed market settlement mechanism also gives room to the existing distribution system operators by assigning them the duty of 1) optimally allocating energy among buyers and sellers in accordance with their competitive bids 2) providing the infrastructure, managing the market and charging for the service and 3) checking the technical feasibility by performing load flow and monitoring power transfer sensitivities to encourage short distance transactions. The energy allocation is done in CIGRE LV benchmark microgrid with ten peers having solar and wind generation. The allocated energy is found to be closer to the metered measurements and hence the reserve cost is observed to be low.

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Effective solar prosumer identification using net smart meter data

Cited by 53 publications

References 33 publications

A Probabilistic Estimation of PV Capacity in Distribution Networks From Aggregated Net-Load Data

A Probabilistic Estimation of PV Capacity in Distribution Networks From Aggregated Net-Load Data

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

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