Multiobjective particle swarm optimization: Integration of dynamic population and multiple-swarm concepts and constraint handling

Scope and Method of Study: Over the years, most multiobjective particle swarm optimization (MOPSO) algorithms are developed to effectively and efficiently solve unconstrained multiobjective optimization problems (MOPs). However, in the real world application, many optimization problems involve a set of constraints (functions). In this study, the first research goal is to develop state-of-the-art MOPSOs that incorporated the dynamic population size and multipleswarm concepts to exploit possible improvement in efficiency and performance of existing MOPSOs in solving the unconstrained MOPs. The proposed MOPSOs are designed in two different perspectives: 1) dynamic population size of multiple-swarm MOPSO (DMOPSO) integrates the dynamic swarm population size with a fixed number of swarms and other strategies to support the concepts; and 2) dynamic multiple swarms in multiobjective particle swarm optimization (DSMOPSO), dynamic swarm strategy is incorporated wherein the number of swarms with a fixed swarm size is dynamically adjusted during the search process. The second research goal is to develop a MOPSO with design elements that utilize the PSO's key mechanisms to effectively solve for constrained multiobjective optimization problems (CMOPs).Findings and Conclusions: DMOPSO shows competitive to selected MOPSOs in producing well approximated Pareto front with improved diversity and convergence, as well as able to contribute reduced computational cost while DSMOPSO shows competitive results in producing well extended, uniformly distributed, and near optimum Pareto fronts, with reduced computational cost for some selected benchmark functions. Sensitivity analysis is conducted to study the impact of the tuning parameters on the performance of DSMOPSO and to provide recommendation on parameter settings. For the proposed constrained MOPSO, simulation results indicate that it is highly competitive in solving the constrained benchmark problems

Exploring potential storage-based flexibility gains of electric vehicles in smart distribution grids

Flexibility is one of the most important solutions for facilitating the variability of renewable energy sources (RESs) in a distribution network. It is predicted that electric vehicles (EVs) can play an effective role in improving it in the distribution networks. So, this paper presents multiobjective scheduling of batteries of EVs in parking lots (EVPLs) to improve the storage-based flexibility of smart distribution networks (SDNs). The proposed formulation minimizes the energy cost and the voltage deviation function and maximizes the system flexibility (SF) as multiobjective functions that will be optimized subject to the AC load flow, RES and EV constraints, and the allowable limits of the flexibility and operation indices. The resulting model is in the form of a nonlinear programming (NLP) model. Therefore, an equivalent linear programming (LP) formulation is obtained for the original problem to achieve the global optimum result. The stochastic programming approach is used to model uncertainties of the load, active power generation of RESs, price of energy, and EV parameters. The flexible power management is formulated as one of the objective functions of the proposed multiobjective framework, which is solved by using the ε-constraint method, reaching the best possible compromise solution by a fuzzy decision-maker. The proposed framework is tested by using a 33-bus radial test distribution network in the GAMS software environment to evaluate the EVs capability in improving the flexibility indices. Based on the numerical results, it is observed that the proposed scheme with optimal energy management of EVs is able to obtain a high flexibility for SDN. It can also reduce energy losses in terms of network operation and provide a rather smooth voltage profile

Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging

A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm

How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems

Impact of plug-in hybrid vehicles charging on distribution networks

This thesis is dedicated to study how the charging behaviours of plug-in hybrid vehicles affect the local distribution network. This study focuses on two issues: the power loss and charging cost optimization. The multi-objective particle swarm optimization technique is applied to achieve the optimal charging schedule, resulting in acceptable additional power loss ratio and charging cost. The power loss on electric lines is correlated to the load demand. However,due to the complexity of the distribution network including the transformers and unbalances of loads,it is necessary to understand the power loss-load demand model. The loss-load modelling is based on the distribution network structure and power flow analysis. The two classic distribution networks (IEEE 13-Node and IEEE 34-Node) are employed for power flow analysis.As the consequence of power flow analysis,a new power loss-load demand model is presented. In this thesis, the additional power loss ratio (APLR) is analysed to present the plug-in hybrid electric vehicle (PHEV) impact of power losses on distribution network. To study the charging cost impacts of PHEV,the least square error method is employed to curve fit the data of Australia electricity market and the electricity price-load and further charging cost-load equations are derived. Particle swarm optimization method is used in the optimization and Multi-Objective optimization is conducted to achieve the optimal charging schedule for PHEV to cause less APLR at acceptable charging costs. All the methodologies and algorithms are verified by simulations. The power losses and charging cost impacts and optimizations are simulated by DigSilent Power Factory and MATLAB

Evolutionary Algorithms and Computational Methods for Derivatives Pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems

Optimisation of stand-alone hybrid energy systems for power and thermal loads

Stand-alone hybrid energy systems are an attractive option for remote communities without a connection to a main power grid. However, the intermittent nature of solar and other renewable sources adversely affects the reliability with which these systems respond to load demands. Hybridisation, achieved by combining renewables with combustion-based supplementary prime movers, improves the ability to meet electric load requirements. In addition, the waste heat generated from backup Internal Combustion Engines or Micro Gas Turbines can be used to satisfy local heating and cooling loads. As a result, there is an expectation that the overall efficiency and Greenhouse Gas Emissions of stand-alone systems can be significantly improved through waste heat recovery. The aims of this PhD project are to identify how incremental increases to the hardware complexity of hybridised stand-alone energy systems affect their cost, efficiency, and CO2 footprint. The research analyses a range of systems, from those designed to meet only power requirements to others satisfying power and heating (Combined Heat and Power), or power plus both heating and cooling (Combined Cooling, Heating, and Power). The majority of methods used focus on MATLAB-based Genetic Algorithms (GAs). The modelling deployed finds the optimal selection of hardware configurations which satisfy single- or multi-objective functions (i.e. Cost of Energy, energy efficiency, and exergy efficiency). This is done in the context of highly dynamic meteorological (e.g. solar irradiation) and load data (i.e. electric, heating, and cooling). Results indicate that the type of supplementary prime movers (ICEs or MGT) and their minimum starting thresholds have insignificant effects on COE but have some effects on Renewable Penetration (RP), Life Cycle Emissions (LCE), CO2 emissions, and waste heat generation when the system is sized meeting electric load only. However, the transient start-up time of supplementary prime movers and temporal resolution have no significant effects on sizing optimisation. The type of Power Management Strategies (Following Electric Load-FEL, and Following Electric and Following Thermal Load- FEL/FTL) affect overall Combined Heating and Power (CHP) efficiency and meeting thermal demand through recovered heat for a system meeting electric and heating load with response to a specific load meeting reliability (Loss of Power Supply Probability-LPSP). However, the PMS has marginal effects on COE. The Electric to Thermal Load Ratio (ETLR) has no effects on COE for PV/Batt/ICE but strongly affects PV/Batt/MGT-based hybridised CHP systems. The higher thermal than the electric loads lead to higher efficiency and better environmental footprint. Results from this study also indicate that for a stand-alone hybridised system operating under FEL/FTL type PMS, the power only system has lower cost compared to the CHP and the Combined Cooling, Heating, and Power (CCHP) systems. This occurs at the expense of overall energy and exergy efficiencies. Additionally, the relative magnitude of heating and cooling loads have insignificant effects on COE for PV/Batt/ICE-based system configurations, however this substantially affects PV/Batt/MGT-based hybridised CCHP systems. Although there are no significant changes in the overall energy efficiency of CCHP systems in relation to variations to heating and cooling loads, systems with higher heating demand than cooling demand lead to better environmental benefits and renewable penetration at the cost of Duty Factor. Results also reveal that the choice of objective functions do not affect the system optimisation significantly