Abstract
The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.
Highlights
Comparing the three sectors of households, commercial businesses, and industries, as discussed earlier, for the first two sectors, load curve reshaping is possible with HVAC and lighting control systems or appliance time-shifting, commercial businesses are less flexible than households
The industrial demand-side management (DSM) requires the integration of production and energy management programmes and a detailed consumption data analysis for developing DSM programmes tailored for each category
Considering that load curves on weekdays increased at 8 a.m. and decreased at 9 p.m., these were set as the time limits for processes, with the exception of forklifts
Summary
This can be done by playing the role of different operating reserve forms, which can reduce the need for overinvestment in generation capacity and improve the utilisation factor of the existing generators, altogether leading to a reduced cost of delivered electricity for customers This is essential considering that the average generation capacity utilisation is less than 65% in some cases, and the load of transmission lines is below. To provide appropriate DSM programmes for the domestic sector, smart sensor data and diaries of appliances are needed These are not always available, especially in developing countries. Commercial load characteristics make load shifting an inappropriate option since it interferes with regular commercial business For these customers, DSM solutions such as HVAC control or energy storage systems installation can be more suitable [1,5]. The key concern is that, generally, utility’s DSM-related measurement equipment involves massive data collection and handling, which reduces industrial business owners’ interests in DSM programmes [9]
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