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1. Pricing Multi-Interval Dispatch under Uncertainty Part I: Dispatch-Following Incentives

   https://doi.org/10.1109/TPWRS.2021.3055730  

   Guo, Ye; Chen, Cong; Tong, Lang (January 2021, IEEE Transactions on Power Systems)

   null (Ed.)

   Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two- part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors. 

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2. Storage Degradation Aware Economic Dispatch

   https://doi.org/10.23919/ACC50511.2021.9482838  

   Bansal, Rajni Kant; You, Pengcheng; Gayme, Dennice F.; Mallada, Enrique (May 2021, American Control Conference)
3. Contextual dispatch for function specialization

   https://doi.org/10.1145/3428288  

   Flückiger, Olivier; Chari, Guido; Yee, Ming-Ho; Ječmen, Jan; Hain, Jakob; Vitek, Jan (November 2020, Proceedings of the ACM on Programming Languages)

   In order to generate efficient code, dynamic language compilers often need information, such as dynamic types, not readily available in the program source. Leveraging a mixture of static and dynamic information, these compilers speculate on the missing information. Within one compilation unit, they specialize the generated code to the previously observed behaviors, betting that past is prologue. When speculation fails, the execution must jump back to unoptimized code. In this paper, we propose an approach to further the specialization, by disentangling classes of behaviors into separate optimization units. With contextual dispatch, functions are versioned and each version is compiled under different assumptions. When a function is invoked, the implementation dispatches to a version optimized under assumptions matching the dynamic context of the call. As a proof-of-concept, we describe a compiler for the R language which uses this approach. Our implementation is, on average, 1.7× faster than the GNU R reference implementation. We evaluate contextual dispatch on a set of benchmarks and measure additional speedup, on top of traditional speculation with deoptimization techniques. In this setting contextual dispatch improves the performance of 18 out of 46 programs in our benchmark suite. 

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4. Preventive Dispatch for Transmission De-icing

   https://doi.org/10.1109/TPWRS.2020.3004079  

   Jafarishiadeh, Fatemehalsadat; Mohammadi, Farshad; Sahraei-Ardakani, Mostafa (September 2020, IEEE Transactions on Power Systems)
5. High-performance symbolic-numerics via multiple dispatch

   https://doi.org/10.1145/3511528.3511535  

   Gowda, Shashi; Ma, Yingbo; Cheli, Alessandro; Gwóźzdź, Maja; Shah, Viral B.; Edelman, Alan; Rackauckas, Christopher (September 2021, ACM Communications in Computer Algebra)

   As mathematical computing becomes more democratized in high-level languages, high-performance symbolic-numeric systems are necessary for domain scientists and engineers to get the best performance out of their machine without deep knowledge of code optimization. Naturally, users need different term types either to have different algebraic properties for them, or to use efficient data structures. To this end, we developed Symbolics.jl, an extendable symbolic system which uses dynamic multiple dispatch to change behavior depending on the domain needs. In this work we detail an underlying abstract term interface which allows for speed without sacrificing generality. We show that by formalizing a generic API on actions independent of implementation, we can retroactively add optimized data structures to our system without changing the pre-existing term rewriters. We showcase how this can be used to optimize term construction and give a 113x acceleration on general symbolic transformations. Further, we show that such a generic API allows for complementary term-rewriting implementations. Exploiting this feature, we demonstrate the ability to swap between classical term-rewriting simplifiers and e-graph-based term-rewriting simplifiers. We illustrate how this symbolic system improves numerical computing tasks by showcasing an e-graph ruleset which minimizes the number of CPU cycles during expression evaluation, and demonstrate how it simplifies a real-world reaction-network simulation to halve the runtime. Additionally, we show a reaction-diffusion partial differential equation solver which is able to be automatically converted into symbolic expressions via multiple dispatch tracing, which is subsequently accelerated and parallelized to give a 157x simulation speedup. Together, this presents Symbolics.jl as a next-generation symbolic-numeric computing environment geared towards modeling and simulation. 

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