(301f) Optimal Scheduling of Industrial Combined Heat and Power Plants Under Time-Sensitive Electricity Prices

Combined heat and power (CHP) plants are widely used in industrial applications, e.g. in pulp and paper mills, aluminum plants, refineries and other chemical processes. In the aftermath of the recession, many of these processes are largely under-utilized, which puts chemical companies under pressure to remain competitive on a global basis.

Fortunately, under-utilization can be an opportunity for tighter interaction with the power grid, which is in transition to the so-called smart grid. Deregulation and an increasing share of renewable energies lead to more variability in time-sensitive electricity prices, offering potential incentives for industrial consumers if they are able to cope with the fluctuations (Samad and Kiliccote, 2012). Therefore, it is essential to characterize the flexibility of a CHP plant and use the obtained insights to determine how to dynamically respond to the time-sensitive electricity prices. An additional challenge is variability in steam demand (Velasco-Garcia et al., 2011).

In this work, we will answer three main questions related to the decision-making for CHP plants under time-sensitive electricity prices:

1. Is it technically and economically feasible to operate an under-utilized industrial CHP plant flexibly in interaction with the smart grid?

2. How large can the potential economic gains be, i.e. what is the optimal way to operate?

3. How can these plants be modeled such that the process flexibility is captured appropriately?

For this purpose, we describe a generalized mode model on a component basis that addresses the operational optimization of under-utilized industrial CHP plants. The mode formulation tracks the state of each plant component in a detailed manner and can account for different operating modes and transitional behavior. Different operating modes include fuel switching for boilers and supplementary firing for gas turbines with a heat recovery steam generator (see Cohen and Ostrowski, 1996; Liu et al., 2009). Transitional behavior such as warm start-ups, cold start-ups, shutdowns and pre-computed trajectories during start-ups is modeled with modes as well. The feasible region of operation for each component is described based on input-output relationships that are thermodynamically sound (Mavromatis and Kokossis, 1998; Varbanov et al., 2004; Aguilar et al., 2007). Furthermore, we emphasize the use of mathematically efficient logic constraints that allow solving the large-scale models fast (Hedman et al., 2009; Rajan and Takriti, 2005). We provide an industrial case study and investigate the impact of different scenarios for under-utilization, where we obtain profit improvements of up to 10%.

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