(736a) Leveraging the Manufacturing Sector As a Grid Asset through Demand Response – Four Real-World Case Studies

The United States electrical grid is transitioning from fossil fuels to renewable energy sources, primarily wind and solar [1-2]. The intermittency of solar and wind can cause grid instability due to mismatch in electricity generation and demand [3]. One option to rectify the mismatch is to economically incentivize electricity end users to adjust their demand to meet grid requirements [4]. This is succinctly known as demand response. This work presents four real-world case studies of demand response at manufacturing facilities to serve as grid assets.

In the first study, it is observed through power sensors placed on high energy consuming equipment that a food processing facility experienced high intraday electricity demand spikes [5]. To reduce the maximum demand, short-term energy storage in the form of a glycol holding tank is leveraged to control the peak electrical demand. A novel algorithm is developed that determines when to turn on a chiller to charge the glycol tank with cold medium and when to shut off the chiller to preserve the facility demand peak. The result is a 7.9% reduction in the maximum demand. In the second study, a limestone processing facility is examined for its potential to perform ancillary services [6]. Ancillary services are large, short-term changes in the power draw of electricity consumers to help utilities match electricity generation and demand. Large fans at the facility, drawing over 2 MW of power, are the targets of the ancillary service. With model predictive control of a dynamic model of the process, it is demonstrated that the manufacturing facility can maintain product quality and serve as a grid asset. In the third and fourth case studies, two areas of energy consumption flexibility, de-watering and intermediate product storage at two separate mines are illustrated as electrical grid assets [7]. In both cases the objective is to control the peak power consumption at the mine using an algorithm like the food processing facility. Rescheduling de-watering pumping saves $570,000 annually and reduces peak power draw by 5 MW. Rescheduling intermediate product pumping saves $180,000 annually and reduces peak power draw by over 1 MW.

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[3] Yap, Kah Yung, Charles R. Sarimuthu, and Joanne Mun-Yee Lim. "Virtual Inertia-Based Inverters for Mitigating Frequency Instability in Grid-Connected Renewable Energy System: A Review." Applied Sciences 9.24 (2019): 5300.

[4] Zhang, Qi, and Ignacio E. Grossmann. "Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives." Chemical Engineering Research and Design 116 (2016): 114-131.

[5] Machalek, Derek, and Kody Powell. "Automated electrical demand peak leveling in a manufacturing facility with short term energy storage for smart grid participation." Journal of Manufacturing Systems 52 (2019): 100-109.

[6] Machalek, Derek, and Kody M. Powell. "Model predictive control of a rotary kiln for fast electric demand response." Minerals Engineering 144 (2019): 106021.

[7] Machalek, Derek, et al. "Mine operations as a smart grid resource: Leveraging excess process storage capacity to better enable renewable energy sources." Minerals Engineering 145 (2020): 106103.