Mine planners utilize production schedules to determine when activities should be executed, e.g., blocks of ore should be extracted;a medium-term schedule maximizes net present value associated with activity execution...
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Mine planners utilize production schedules to determine when activities should be executed, e.g., blocks of ore should be extracted;a medium-term schedule maximizes net present value associated with activity execution while a short-term schedule reacts to unforeseen events. Both types of schedules conform to spatial precedence and resource restrictions. As a result of executing activities, heat accumulates and activities must be curtailed. Airflow flushes heat from the mining areas, but is limited to the capacity of the ventilation system and operational setup. We propose two large-scale production scheduling models: (i) that which prescribes the start dates of activities in a medium-term schedule while considering airspeed, in conjunction with ventilation and refrigeration;and, (ii) that which minimizes deviation between both medium- and short-term schedules, and production goals. We correspondingly present novel techniques to improve model tractability, and demonstrate the efficacy of these techniques on cases that yield short-term schedules congruent with medium-term plans while ensuring the safety of the work environment. We solve otherwise-intractable medium-term instances using an enumeration technique if the gaps are greater than 10%. Our short-term instances solve in 1,800 seconds, on average, to a 0.1% optimality gap, and suggest varying optimal airspeeds based on the maximum heat load on each level.
Concentrating solar power (CSP) plants present a promising path towards utility-scale renewable energy. The power tower, or central receiver, configuration can achieve higher operating temperatures than other forms of...
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Concentrating solar power (CSP) plants present a promising path towards utility-scale renewable energy. The power tower, or central receiver, configuration can achieve higher operating temperatures than other forms of CSP, and, like all forms of CSP, naturally pairs with comparatively inexpensive thermal energy storage, which allows CSP plants to dispatch electricity according to market price incentives and outside the hours of solar resource availability. Currently, CSP plants commonly include a steam Rankine power cycle and several heat exchange components to generate high-pressure steam using stored thermal energy. The efficiency of the steam Rankine cycle depends on the temperature of the plant's operating fluid, and so is a main concern of plant operators. However, the variable nature of the solar resource and the conservatism with which the receiver is operated prevent perfect control over the receiver outlet temperature. Therefore, during periods of solar variability, collection occurs at lower-than-design temperature. To support operator decisions in a real-time setting, we develop a revenue-maximizing non-convex mixed-integer, quadradically-constrained program which determines a dispatch schedule with sub-hourly time fidelity and considers temperature-dependent power cycle efficiency. The exact nonlinear formulation proves intractable for real-time decision support. We present exact and inexact techniques to improve problem tractability that include a hybrid nonlinear and linear formulation. Our approach admits solutions within approximately 3% of optimality, on average, within a five-minute time limit, demonstrating its usability for decision support in a real-time setting.
Ancillary services, such as spinning reserves, can provide grid reliability and contribute to profitability of an energy resource. We exercise an existing dispatch optimization model to estimate the profitability of a...
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Ancillary services, such as spinning reserves, can provide grid reliability and contribute to profitability of an energy resource. We exercise an existing dispatch optimization model to estimate the profitability of a concentrating solar power plant by incorporating the sale of spinning reserves in the ancillary service market using the National Renewable Energy Laboratory's System Advisor Model to simulate operations within a 72-h rolling horizon framework. Assuming a price-taker approach with day-ahead energy and spinning reserve prices from both the California Independent System Operator and the Electricity Reliability Council of Texas, we find that selling spinning reserves in addition to electric energy increases plant profitability by up to 7% with perfect knowledge of day-ahead pricing and solar resource availability. This finding suggests that spinning reserve markets provide significant value streams to concentrating solar power plants that can leverage thermal energy storage to offer reliable production in the short-to-medium term.
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