Optimal Load Management Application for Industrial Customers by Jefferson Fabricio

The urgent need for greater planning and operation efficiency of current and future electrical networks, as well as the reduction of environmental impact due to current levels of greenhouse gas emissions have led to the development of the Smart Grid concept. In this context, the integration of renewable sources to the transmission and distribution systems, and the management of customer’s consumption through direct or indirect control methods are two important components of smart grids. The latter, in particular, has led to the emergence of Demand Side Management (DSM) programs with the main purpose of controlling demand levels considering end-user preferences or the final service quality. In the case of developed countries, the industrial sector requires significant and growing amounts of energy year after year. For this reason, considering the special characteristics imposed by industrial processes, DSM programs that focus on rational and efficient electric consumption have been designed to improve the current operation practice of this sector. In particular, load shifting, which is part of Demand Response (DR) programs in the context of DSM, together with dynamic pricing schemes, such as Time of Use (TOU) and Real Time Pricing (RTP), are attractive approaches for demand management. With this goal in mind, the present research focuses on the development and evaluation of an optimization model to optimally schedule water-cooled chillers in industrial applications. The proposed optimization model is capable of minimizing energy and/or peak demand costs associated with normal operation of chillers, depending on the priority of the industrial consumer, while meeting demand-supply balance, process, peak demand constraints, and operating limits at the same time. To represent the chiller active power demand at every time interval, a polynomial regression model is proposed, and estimated by means of a robust regression technique using actual load demand and process measurements at an actual industrial facility, showing that the resulting regression model determines the chiller electric consumption accurately for normal operating conditions; a Chilled Water Storage (CWS), i.e. a thermal storage device for water cooling systems, is also considered in this model. The final optimization model is tested to find the optimal scheduling of chillers in a water cooling system of an automotive frame manufacturing plant in Ontario. Two different cost minimization scenarios are simulated to determine the better operation strategy and contrasted with the actual operation to evaluate the possible monthly bill savings that can be achieved. Finally, the optimal size of the CWS is determined, to maximize savings, for the current number of chillers as well as with the possible decommissioning of one of them, as requested by the facility technical staff. The final simulation results suggest that load shifting of chillers could be a successful strategy for industrial customers, since important electricity bill savings without affecting iii the normal plant operation were attained. This was possible due to an optimal chiller scheduling and indirect incentives provided by current industry energy price schemes in Ontario. Furthermore, the optimization model presented permitted to optimally size the CWS in the water cooling system studied, so that electricity costs were minimized depending on the total chiller capacity considered. Therefore, optimization approaches to schedule industrial processes could be a powerful tool to increase the operational efficiency of industrial plants to reduce their significant energy costs.