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This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then the fourteen benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs.

Differential evolution (DE) is a simple evolutionaryalgorithm that has shown superior performance in the global continuous optimization. It mainly utilizes the differential information to guide its further search. But the differential information also results in instability of performance. Particle swarm optimization (PSO) has been developing rapidly and has been applied widely since it is introduced, as it can converge quickly. But PSO easily got stuck in local optima because it easily loses the diversity of swarm. This paper proposes a combination of DE and PSO (termed DEPSO) that makes up their disadvantages. DEPSO combines the differential information obtained by DE with the memory information extracted by PSO to create the promising solutions. Finally, DEPSO is tested to solve several benchmark optimization problems. The experimental results show the effectiveness of DEPSO algorithm for the multimodal function, and also verify that DEPSO can perform better than other algorithms (DE, CPSO) in solving the benchmark problems.

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Electric vehicle (EV) charging stations fed by photovoltaic (PV) panels allow integration of various low-carbon technologies, and are gaining increasing attention as a mean to locally manage power generation and demand. This paper presents novel control schemes to improve coordination of an islanded PV-fed DC bus EV charging system during various disturbances, including rapid changes of irradiance, EV connection and disconnection, or energy storage unit (ESU) charging and discharging. A new hybrid control scheme combining the advantages of both master–slave control and droop control is proposed for a charging station supplying 20 EVs for a total power of 890 kW. In addition, a three-level (3L) boost converter with capacitor voltage balance control is designed for PV generation, with the aim to provide high voltage gain while employing a small inductor. The control techniques are implemented in a simulation environment. Various case studies are presented and analysed, confirming the effectiveness and stability of the control strategies proposed for the islanded charging system. For all tested conditions, the operating voltage is maintained within 5% of the rated value.

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