According to recent developments, the application of distributed generations (DGs) has become popular especially in distribution systems. The high utilization of distributed generating resources in modern power systems can cause new challenges from protection coordination perspectives. Changing the distribution system structure from single-supply radial system to multi-source ring network, leads to the bidirectional power flow and also has a vital impact on protection coordination issues. In addition, micro-grids can be operated under gridconnected as well as islanded mode, and fault current is extensively different for these two operation modes. Therefore, traditional protection algorithms cannot be used in the advancement of power systems. In recent years, several research studies have been conducted to investigate the improvement of protection schemes in micro-grids. This paper presents a comprehensive review on protection problems resulting from micro-grids embedded with DGs, and discusses some alternate protection strategies.

The introduction of smart grid technologies and the impending removal of incentive schemes is likely to complicate the cost-effective selection and integration of residential PV systems in the future. With the widespread integration of smart meters, consumers can leverage the high temporal resolution of energy consumption data to optimize a PV system based on their individual circumstances. In this article, such an optimization strategy is developed to enable the optimal selection of size, tilt, azimuth and retail electricity plan for a residential PV system based on hourly consumption data. Hourly solar insolation and PV array generation models are presented as the principal components of the underlying objective function. A net present value analysis of the potential monetary savings is considered and set as the optimization objective. A particle swarm optimization algorithm is utilized, modified to include a penalty function in order to handle associated constraints. The optimization problem is applied to real-world Australian consumption data to establish the economic performance and characteristics of the optimized systems. For all customers assessed, an optimized PV system producing a positive economic benefit could be found. However not all investment options were found to be desirable with at most 77.5% of customers yielding an acceptable rate of return. For the customers assessed, the mean PV system size was found to be 2 kW less than the mean size of actual systems installed in the assessed locations during 2015 and 2016. Over-sizing of systems was found to significantly reduce the potential net benefit of residential PV from an investor's perspective. The results presented in this article highlight the necessity for economic performance optimization to be routinely implemented for small-scale residential PV under current regulatory and future smart grid operating environments.

Abstract-Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.

Continued advances in PV and battery energy storage technologies have made hybrid PV-battery systems an attractive prospect for residential energy consumers. However the process to select an appropriate system is complicated by the relatively high cost of batteries, a multitude of available retail electricity plans and the removal of PV installation incentive schemes. In this paper, an optimization strategy based on an individual customer's temporal load profile is established to maximize electricity cost savings through optimal selection of PV-battery system size, orientation and retail electricity plan. Quantum-behaved particle swarm optimization is applied as the underlying algorithm given its well-suited application to problems involving hybrid energy system specification. The optimization strategy is tested using real-world residential consumption data, current system pricing and available retail electricity plans to establish the efficacy of a hybrid PV-battery solution.

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This paper presents an economic optimization case study for a medium-scale hybrid PV-battery system. PV energy yield and battery operation models based on hourly satellite insolation and daily temperature data form the basis of an underlying objective function aiming to maximize the net present value of potential energy cost savings. Forecasted system prices and energy tariffs over a nine-year period are considered enabling the opportune year to invest and the characteristics of the corresponding optimal system to be determined.

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