Benchmark (computing)

Summary

In computing, a benchmark is the act of running a computer program, a set of programs, or other operations, in order to assess the relative performance of an object, normally by running a number of standard tests and trials against it. The term benchmark is also commonly utilized for the purposes of elaborately designed benchmarking programs themselves. Benchmarking is usually associated with assessing performance characteristics of computer hardware, for example, the floating point operation performance of a CPU, but there are circumstances when the technique is also applicable to software. Software benchmarks are, for example, run against compilers or database management systems (DBMS). Benchmarks provide a method of comparing the performance of various subsystems across different chip/system architectures. Purpose As computer architecture advanced, it became more difficult to compare the performance of various computer systems simply by looking at their specifications

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https://en.wikipedia.org/wiki/Benchmark_(computing)

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On Polynomial Algorithms for Normalizing Formulas

Mario Bucev, Simon Guilloud, Viktor Kuncak

We propose a new approach for normalization and simplification of logical formulas. Our approach is based on algorithms for lattice-like structures. Specifically, we present two efficient algorithms for computing a normal form and deciding the word problem for two subtheories of Boolean algebra, giving a sound procedure for propositional logical equivalence that is incomplete in general but complete with respect to a subset of Boolean algebra axioms. We first show a new algorithm to produce a normal form for expressions in the theory of ortholattices (OL) in time O(n^2). We also consider an algorithm, recently presented but never evaluated in practice, producing a normal form for a slightly weaker theory, orthocomplemented bisemilattices (OCBSL), in time O(n log(n)^2). For both algorithms, we present an implementation and show efficiency in two domains. First, we evaluate the algorithms on large propositional expressions, specifically combinatorial circuits from a benchmark suite, as well as on large random formulas. Second, we implement and evaluate the algorithms in the Stainless verifier, a tool for verifying the correctness of Scala programs. We used these algorithms as a basis for a new formula simplifier, which is applied before valid verification conditions are saved into a persistent cache. The results show that normalization substantially increases cache hit ratio in large benchmarks.

2022

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This paper presents a method to determine the optimal location, energy capacity, and power rating of distributed battery energy storage systems at multiple voltage levels to accomplish grid control and reserve provision. We model operational scenarios at a one-hour resolution, where deviations of stochastic loads and renewable generation (modeled through scenarios) from a day-ahead unit commitment and violations of grid constraints are compensated by either dispatchable power plants (conventional reserves) or injections from battery energy storage systems. By plugging-in costs of conventional reserves and capital costs of converter power ratings and energy storage capacity, the model is able to derive requirements for storage deployment that achieve the technical-economical optimum of the problem. The method leverages an efficient linearized formulation of the grid constraints of both the HV (High Voltage) and MV (Medium Voltage) grids while still retaining fundamental modeling aspects of the power system (such as transmission losses, effect of reactive power, OLTC at the MV/HV interface, unideal efficiency of battery energy storage systems) and models of conventional generator. A proof-of-concept by simulations is provided with the IEEE 9-bus system coupled with the CIGRE’ benchmark system for MV grids, realistic costs of power reserves, active power rating and energy capacity of batteries, and load and renewable generation profile from real measurements.

2020

A VSC-Based BESS Model for Multi-Objective OPF Using Mixed Integer SOCP

Xiaoying Tang, Tong Wu

This paper presents a new model of voltage source converter (VSC)-based battery energy storage systems (BESSs) that interface with power grids. A VSC-based BESS is made up of a series connection of a VSC, its connecting transformer and a BESS. The VSC allows a BESS to generate both active and reactive powers in all four quadrants. The proposed model captures the coupling between active power, reactive power and the voltage of a BESS. In addition, the proposed model explicitly describes the relationship between the control configuration of Pulse Width Modulation (PWM)-VSC and the power output of the BESS. Therefore, the proposed model possesses unparalleled control capabilities in the operational parameters of both the AC and DC sides of the converter. By incorporating such a model into the active- reactive optimal power flow ( A-R-OPF) formulation, we can not only optimize the active and reactive power output of the BESS in power systems, but also understand how the optimal powers are generated by setting the operational parameters of both the BESS and PWM-VSC. To solve the A-R-OPF problem with the proposed model, we propose a sequence of strong relaxations to transform the problem into a mixed-integer second order cone programming (SOCP) problem. Such a formulation is amendable for efficient solutions using off-the-shelf solvers. Case studies on the IEEE benchmark systems show that more than 17.73% of power losses in transmission lines and more than 0.961% of interface losses can be reduced by using the proposed model in comparison to the traditional BESS model.

IEEE2020