The development of digital programs for predicting the performance of multi-cylinder, turbo-charged diesel engines, and the temperature distribution in engine components is described. The performance program incorporates the relatively simple 'filling and emptying' method, with the turbo-charger compressor and turbine as boundary conditions, to calculate transient gas conditions.In the second part of the paper a description is given of the metal temperature program assumptions and use, including the deduction of the surrounding fluid conditions.The accuracy and limitations of the performance program is demonstrated by comparing predictions and measurements on an experimental 2-stroke single cylinder diesel engine operating under simulated turbo-charged conditions.The practical applications of the programs to medium speed 2-and 4-stroke engines are illustrated, and the economics of their use as a design aid are discussed.
With the proliferation of advanced metering infrastructure (AMI), more real-time data is available to electric utilities and consumers. Such high volumes of data facilitate innovative electricity rate structures beyond flat-rate and time-ofuse (TOU) tariffs. One such innovation is real-time pricing (RTP), in which the wholesale market-clearing price is passed directly to the consumer on an hour-by-hour basis. While rare, RTP exists in parts of the United States and has been observed to reduce electric bills. Although these reductions are largely incidental, RTP may represent an opportunity for large-scale peak shaving, demand response, and economic efficiency when paired with intelligent control systems. Algorithms controlling flexible loads and energy storage have been deployed for demand response elsewhere in the literature, but few studies have investigated these algorithms in an RTP environment. If properly optimized, the dynamic between RTP and intelligent control has the potential to counteract the unwelcome spikes and dips of demand driven by growing penetration of distributed renewable generation and electric vehicles (EV). This paper presents a simple reinforcement learning (RL) application for optimal battery control subject to an RTP signal.
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