Abstract
With a significant deployment of smart meters across end-user platforms, the dynamic visibility of energy flow among the end-users has been increased significantly. The granular information of smart meters can be used to improve the load forecast accuracy and to influence energy consumption patterns with demand side management (DSM) schemes. This paper addresses the challenges of smart meter data size, complexity, variability and volatility for efficient use in load forecast and DSM. A novel clustering-based approach for analysis of smart meter data, aimed at more accurate and detailed load profiling, reduced profile complexity, improved load forecast accuracy and providing optimal DSM solutions is proposed. The proposed approach utilizes an advanced clustering algorithm to reduce the data size. The approach addresses data complexity, variability and volatility by linearizing the load profiles and minimizing the errors. The validity of the approach is demonstrated on an Irish smart meter dataset and on a simulated solar photovoltaic (PV) data and showed an improved load forecast accuracy, improved DSM solutions, and reduced computational burden. The improvements in the DSM solution are evidenced by a higher cost saving with a higher peak load reduction at the lower level of demand flexibility.
Highlights
The electrical power system is undergoing significant changes to provide a secure, reliable, affordable and low carbon electricity
A novel approach is proposed for the end user energy demand management through the effective load forecasting using smart meter data in a power distribution network
The case studies have evidenced the ability of the proposed approach to select appropriate cluster for demand side management (DSM) application and the effective energy management
Summary
The electrical power system is undergoing significant changes to provide a secure, reliable, affordable and low carbon electricity. Increasing integration of loads such as electric vehicles and heat pumps into distribution system exacerbates the uncertainties in a power system with varied loading patterns To manage these uncertainties, new sources of flexibility are required to manage secure, reliable and affordable supply of electricity. The alternative load and generation profiles transpose the N-dimensional non-linear data functions into a concatenation of continuous differentiable linear functions that reduces the data complexity while preserving the data accuracy in the application These profiles are used for load forecasting and in DSM with the presence of RES. Build-up of extended k-mean clustering algorithm, alternative profiling for the use in load forecasting, new cluster selection index, controlling feature of the pricing signal until the desired outcome is achieved, and micro level DSM application.
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