(544a) Optimal Scheduling of Demand Responsive Industrial Production with Hybrid Renewable Energy Systems | AIChE

(544a) Optimal Scheduling of Demand Responsive Industrial Production with Hybrid Renewable Energy Systems


Palazoglu, A. - Presenter, University of California, Davis
El-Farra, N. H. - Presenter, University of California, Davis

The operational scheduling problem, including the management of energy demand and utilization associated with production processes, has received significant attention over the last decade, especially in light of the increased penetration levels of renewable and smart grid technologies, along with the adoption of time-varying electricity pricing policies. While the benefits of integrating renewable generation into the existing energy infrastructure have been widely recognized, the resulting time variations in energy flows between generation and consumption may significantly influence the cost of electricity. Taking the varying energy costs into account over a future time horizon in the production scheduling phase has already been shown to be technically feasible and economically plausible in a number of previous studies (e.g., see [1-3]).

 One of the merging trends aimed at achieving optimal economic performance is the smart scheduling between the power grid and the consumers. A two-way communication scheme is envisioned in the future smart grid. A major focus from the power system research side is on the multi-agent communication and scheduling of electricity generation [4]. However, the demand side should also take an active role in optimizing the grid operations and end-user economic performance, especially when renewable energy generation systems are incorporated and selling excess power back to the grid is allowed to gain benefits.

 This paper presents a methodology to apply real-time optimization and game theory techniques to the problem of optimally scheduling and managing the interaction between electricity providers and consumers so that the grid and loads can come to an agreement to achieve optimal economic performance. The energy flows in a typical energy-intensive industrial process, such as the chlor-alkali production, are simulated to conduct day-ahead scheduling and real-time demand response behaviors, while the smart grid allows for buying or selling electricity. The chlor-alkali production plant also combines photovoltaic/thermal hybrid solar systems, wind energy conversion systems and fuel cells, focusing on the supply and recovery of power, heat and materials. A communication and incentive scheme is first proposed for the complete energy scheduling process. The trade-off between following the contracted electricity profiles and paying penalties for any deviations from the contract to ensure maximum real-time profits is analyzed. Energy management strategies are then developed to realize the objectives of meeting production requirements while minimizing the overall operating costs and environmental impact. Production demand response to maximize profits is also integrated into the proposed methodology. Finally, it is demonstrated that load contracting can bring benefits such as reduced uncertainties to the grid as well.


[1] K. Nolde and M. Morari, “Electrical load tracking scheduling of a steel plant,” Computers & Chemical Engineering, vol. 34, no. 11, pp. 1899–1903, 2010.

[2] X. Wang, H. Teichgraeber, A. Palazoglu, and N. H. El-Farra, “An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation,” Journal of Process Control, vol. 24, no. 8, pp. 1318–1327, 2014.

[3] S. Mitra, I. E. Grossmann, J. M. Pinto, and N. Arora, “Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes,” Computers & Chemical Engineering, vol. 38, pp. 171–184, 2012.

[4] W. Saad, Z. Han, H. V. Poor, and T. Basar, “Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications,” Signal Processing Magazine, IEEE, vol. 29, no. 5, pp. 86–105, 2012.


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