Abstract
Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.
Highlights
Integration of renewable energy, especially solar energy into energy infrastructure is on the rise, driven in part by the economic benefits such as government incentives and money saved on energy bills and in part due to rising awareness of the environmental benefits [1]
The fixed load rate controller in Hyperledger Caliper starts with a configured send rate in transactions per second (TPS) and maintains a defined backlog of transactions in the network by modifying the send rate
The value of average latency remained under 1 second even at a throughput of over 440.3 TPS which was achieved at a send rate of 443 TPS
Summary
Integration of renewable energy, especially solar energy into energy infrastructure is on the rise, driven in part by the economic benefits such as government incentives and money saved on energy bills and in part due to rising awareness of the environmental benefits [1]. Local solar energy production has the advantage of being co-located with the consumption sites, reducing transmission losses inherent in transporting electricity over large distances [9] Another important peak shaving strategy is demand response or demand side management [10]. Demand side response to peak consumption can take the form of increased prices to disincentivize consumers from running shiftable or non-urgent appliances during peak times Another approach is the use of incentivization tokens which is a scheme under which the prosumer can earn tangible benefits for reducing usage at peak times. 2) An aggregation analysis of temporal energy behavior presented in section 3: a) identifies periods of peak usage and high variation b) identifies thresholds for categorizing net producers based on surplus values. 5) Section 6 discusses how our solution builds upon the state of the art, while section 7 presents the salient conclusions of this study
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