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Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

--This paper presents the system integration and hierarchical control implementation in an inverter-based microgrid research laboratory (MGRL) in Aalborg University, Denmark. MGRL aims to provide a flexible experimental platform for comprehensive studies of microgrids. The structure of the laboratory, including the facilities, configurations and communication network, is first introduced. The complete control system is based on a generic hierarchical control scheme including primary, secondary and tertiary control. Primary control loops are developed and implemented in digital control platform, while system supervision, advanced secondary and tertiary management are realized in a microgrid central controller. The software and hardware schemes are described. Several example case studies are introduced and performed in order to achieve power quality regulation, energy management and flywheel energy storage system control. Experimental results are presented to show the performance of the whole system.

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Due to the decreasing revenues from the surplus renewable energy injected into the grid, mechanisms promoting self-consumption of this energy are becoming increasingly important. Demand Response (DR) and local storage are among the widely used mechanisms for reaching higher self-consumption levels. Deploying a shared storage unit in a residential microgrid is an alternative scenario that allows households to store their surplus renewable energy for a later use. However, this creates some challenges in managing the battery and the available energy resource in a fair way. In this paper, a reputation-based centralized Energy Management System (EMS) is proposed to deal with these issues by considering households' reputations in the reallocation of available energy in the shared storage unit. This framework is used in an optimization problem, in which the EMS jointly schedules households' appliances power consumption and the energy that each household can receive from the storage unit. The scheduling problem is formulated as a Mixed Integer Linear Programming (MILP) with the objective of minimizing the amount and price of energy absorbed from the main grid. The MILP problem is coded in GAMS and solved using CPLEX. Numerical analysis is conducted using real data of renewable energy production and appliances' demand profiles for different classes of households and different annual periods in Spain. Simulation results of the different scenarios show that by using the proposed framework higher cost savings can be achieved, in comparison with the classical scheduling scenario. The saving can reach up to 68% when different classes of households exist in the microgrid. The results also show that the fairness in energy allocation is guaranteed by the reputation-based policy, and that the total power absorbed from the main grid by the whole microgrid is significantly decreased.

This paper proposes a comprehensive planning framework including a main problem and two subproblems to enhance the resilience of power distribution network (PDN) and water distribution network (WDN) with multiple microgrids against hurricanes. The main problem which is formulated in stochastic programming aims to minimize the investment cost of resilience improvement strategies and the expected inaccessibility values of loads to power and water under hurricanes. Line hardening in PDN, upgrading the energy storage size in microgrids and water tanks in WDN are considered as three clean candidate strategies. In analyzing each scenario of the main problem, the microgrids which are connected to the PDN are modeled as emergency sources through the first stochastic sub-problem that can restore disconnected loads and water pumps. Water pumps as critical loads are equipped with emergency generators with limited fuel capacity. If there are some water pumps which cannot be restored in each scenario of the main problem, their emergency generators will be scheduled with the second sub-problem of the model. The proposed model is tested on the modified IEEE 33bus PDN with multiple microgrids and a designed WDN, and the effectiveness of the proposed method is validated accordingly.

In this paper, a novel wireless load sharing controller for parallel connected online UPS inverters is proposed. As opposed to the conventional droop method, the proposed method achieves stable steady-state frequency and phase and an good dynamic response is obtained. A virtual output impedance is proposed in order to reduce its line impedance impact and to properly share nonlinear loads. Experimental results are presented from two 6-kVA UPS inverters controlled by DSP boards, showing the feasibility of the proposed approach.

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