Multi-microgrid is an integrated system of microgrids, distributed generations, and battery energy storage system (BESS). As the significant equipment in microgrid, BESS can perform multitasking, ...such as load management and peak shaving. This study mainly focuses on the energy consumption scheduling of multi-microgrid considering the optimisation of BESS capacity. Energy management with BESS optimisation is studied by considering the cost of distributed generations, cost of BESS, and bidirectional energy trading. The optimisation problem is tackled from two different aspects: an individual-oriented optimisation and a coalition-based optimisation. In the first approach, each microgrid is optimised individually with a non-cooperative game; while in the second approach, the joint optimisation of all microgrids is formulated through cooperation among multi-microgrids. In order to achieve the optimal energy consumption strategy and BESS capacity, distributed algorithms for two formulations are presented, which combine particle swarm optimisation and interior point method. Simulation results show that both approaches can contribute to peak shaving and reducing the daily cost of multi-microgrid.
This book is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research. It provides a structured and integrated ...treatment of the major technologies in optimization and search methodology.
Analog circuit design can be formulated as a nonlinear constrained optimisation problem that can be solved using any suitable optimisation algorithms. Different optimisation techniques have been ...reported to reduce the design time of analog circuits. A hybrid particle swarm optimisation algorithm with linearly decreasing inertia weight for the optimisation of analog circuit design is proposed in this study. The proposed method is used to design a two-stage operational amplifier circuit with Miller compensation. The results show that the proposed optimisation method can substantially reduce the design time needed for analog circuits.
The design parameters of heat pumps are related to each other nonlinearly or in a complicated manner; therefore, it is difficult to determine the optimal combination of design parameters, such as ...superheat, subcooling, and refrigerant type, analytically. To address this limitation, three representative heuristic algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), are applied to optimize a heat pump under the given process conditions. Heuristic algorithms are driven based on randomness; thus, the consistency of the calculation results and computational time represent the decision criteria for the appropriate optimizer. The GA is unsuitable as a heat pump optimizer because it requires an excessive number of iterations. In contrast, PSO and SA have a similar capability of consistency and calculation time with a rational number of iterations. In conclusion, PSO exhibits a slightly better consistency and use of computational resources; therefore, PSO is selected as the heat pump design optimization algorithm in this study. The novelty of this work lies in that the related design parameters of the heat pump are simultaneously globally optimized with minimal physical background, and the heuristic algorithm that is most applicable to heat pump design optimization is determined.
Classification data are usually represented by many features, but not all of them are useful. Without domain knowledge, it is challenging to determine which features are useful. Feature selection is ...an effective preprocessing technique for enhancing the discriminating ability of data, but it is a difficult combinatorial optimization problem because of the challenges of the huge search space and complex interactions between features. Particle swarm optimization (PSO) has been successfully applied to feature selection due to its efficiency and easy implementation. However, most existing PSO-based feature selection methods still face the problem of falling into local optima. To solve this problem, this article proposes a novel PSO-based feature selection approach, which can continuously improve the quality of the population at each iteration. Specifically, a correlation-guided updating strategy based on the characteristic of data is developed, which can effectively use the information of the current population to generate more promising solutions. In addition, a particle selection strategy based on a surrogate technique is presented, which can efficiently select particles with better performance in both convergence and diversity to form a new population. Experimental comparing the proposed approach with a few state-of-the-art feature selection methods on 25 classification problems demonstrate that the proposed approach is able to select a smaller feature subset with higher classification accuracy in most cases.
This book provides recent results on the stochastic approximation of systems by weak convergence techniques. General and particular schemes of proofs for average, diffusion, and Poisson ...approximations of stochastic systems are presented, allowing one to simplify complex systems and obtain numerically tractable models. The systems discussed in the book include stochastic additive functionals, dynamical systems, stochastic integral functionals, increment processes and impulsive processes. All these systems are switched by Markov and semi-Markov processes whose phase space is considered in asymptotic split and merging schemes. Most of the results from semi-Markov processes are new and presented for the first time in this book.
Renewable energy systems (RESs) are affordable, clean and sustainable. However, their output power is intermittent. Therefore, RESs are usually combined with an energy storage system or conventional ...sources to make the overall operation uninterruptable. Optimal sizing of hybrid energy system components is imperative to be financially and technically feasible. In this study, a multi-objective optimisation based on a hybrid optimisation procedure, which combines the exploitation ability of the biogeography-based optimisation (BBO) with the exploration ability of the particle swarm optimisation (PSO), is used to handle the system design. This algorithm is known as greedy particle swarm and BBO algorithm (GPSBBO). Weighted sum method is added to the GPSBBO to handle the multi-objective nature of the design problem. A case study for a hybrid wind-PV energy system design in the standalone and grid-connected configurations is presented to illustrate the proposed method. Coverage of two sets, hypervolume and diversity performance indices are used to compare results of the proposed method to non-dominated sorting genetic algorithm and the multi-objective PSO. These indices show an improved performance of the suggested method in finding the optimal system design.
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