To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans, so the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to recharge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being recharged. Based on these new perspectives, we formulate the EV charging station placement problem (EVCSPP) in this paper. We prove that the problem is nondeterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP, and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.

Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions

To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans. So the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to re-charge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being re-charged. Based on these new perspectives, we formulate the Electric Vehicle Charging Station Placement Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.

Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.

Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

Chemical Reaction Optimization (CRO) is a recently established metaheuristics for optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions.

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Chemical Reaction Optimization: a tutorial

Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.

Real-Coded Chemical Reaction Optimization

Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to supercomputers distributed around the world. One of the major problems is task scheduling, i.e., allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resources into account, and task scheduling is formulated as an optimization problem with three objectives. This is an NP-hard problem, and thus, metaheuristic approaches are employed to find the optimal solutions. In this paper, several versions of the Chemical Reaction Optimization (CRO) algorithm are proposed for the grid scheduling problem. CRO is a population-based metaheuristic inspired by the interactions between molecules in a chemical reaction. We compare these CRO methods with four other acknowledged metaheuristics on a wide range of instances. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications.

Chemical Reaction Optimization for Task Scheduling in Grid Computing

For the low-cost hardware-based intrusion detection systems, this paper proposes a memory-efficient parallel string matching scheme. In order to reduce the number of state transitions, the finite state machine tiles in a string matcher adopt bit-level input symbols. Long target patterns are divided into subpatterns with a fixed length; deterministic finite automata are built with the subpatterns. Using the pattern dividing, the variety of target pattern lengths can be mitigated, so that memory usage in homogeneous string matchers can be efficient. In order to identify each original long pattern being divided, a two-stage sequential matching scheme is proposed for the successive matches with subpatterns. Experimental results show that total memory requirements decrease on average by 47.8 percent and 62.8 percent for Snort and ClamAV rule sets, in comparison with several existing bit-split string matching methods.

A Memory-Efficient Bit-Split Parallel String Matching Using Pattern Dividing for Intrusion Detection Systems

This paper proposes a novel optimization model to maximize the power system observability by placing phasor measurement units (PMUs) in a multistage manner. The problem is constrained by the financial budgets available at each installation stage. The budget may be spent on purchasing and installing new PMUs and relocating PMUs already installed in the power system. This problem is very difficult to solve when the problem size becomes big. Therefore, a newly developed meta-heuristic, called chemical reaction optimization (CRO), is used to solve this optimal multistage PMU placement problem (OMPP), and numerical studies are carried out on the IEEE 57-bus, 118-bus, and 300-bus systems.

Optimal Multistage PMU Placement for Wide-Area Monitoring

A novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO.

On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization

The problem of optimal placement of phasor measurement units (PMUs) was first studied using graph theoretic and mathematical programming methods. However, due to the increase of renewable generations, PMUs may be required not only in transmission networks, but also in sub-transmission, or even distribution networks. Hence, the size of this PMU placement problem will become too big for mathematical programming approaches. As a result, in this paper, we investigated solving this problem using a novel metaheuristic technique, called chemical reaction optimization (CRO). CRO loosely mimics the interactions between molecules in a chemical reaction process. Based on the canonical CRO, we propose a simplified version of CRO (SCRO) for the optimal PMU placement (OPP) problem. Both canonical CRO and SCRO are tested on the full observability of OPP in two scenarios, i.e., considering and not considering zero injections. To investigate the effectiveness of the proposed methods, we evaluate their performances in the IEEE 14-bus, 30-bus, 57-bus, 118-bus, and 300-bus standard systems as well as a large-scale system with 1180 buses. Our simulation results show that, compared with other deterministic and metaheuristc algorithms, SCRO can find the optimal solutions in a shorter time for small-scale systems, and a near-optimal solution within a reasonable time even for a large-scale system.

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Jin Xu

Papers

Chemical Reaction Optimization for Task Scheduling in Grid Computing

A Memory-Efficient Bit-Split Parallel String Matching Using Pattern Dividing for Intrusion Detection Systems

Optimal Multistage PMU Placement for Wide-Area Monitoring

On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization

Optimal PMU placement for wide-area monitoring using chemical reaction optimization