Hybrid microgrids which consist of AC and DC subgrids interconnected by power electronic interfaces have attracted much attention in recent years. They not only can integrate the main benefits of both AC and DC configurations, but also can reduce the number of converters in connection of distributed generation sources, energy storage systems and loads to AC or DC buses. In this study, the structure of hybrid microgrids is discussed, and then a broad overview of the available protection devices and approaches for AC and DC subgrids is presented. After description, analysis and classification of the existing schemes, some research directions including communication infrastructures, combined control and protection schemes, and promising devices for the realisation of future hybrid AC/DC microgrids are pointed out.

In this study, a novel small-scale electromagnetic energy harvester is designed and optimised, which aims at addressing the limitations of existing approaches in output power scavenged from low-velocity flowing water. Instead of conventional cantilever beams, an asymmetric arc-shaped elastic beam is implemented to disrupt flow, leading to its oscillation in the induced unsteady flow field. An electromagnetic transducer is utilised to convert kinetic energy into electrical energy. Furthermore, the effects of the structure asymmetry and curvature on the electrical outputs are investigated to optimise the scavenging energy capability. A prototype with the volume of 152.4 cm³ is fabricated and tested. A maximum open-circuit voltage of 1440 mV is obtained at 0.409 m/s, and the harvester generates an output power of 0.503 mW when it is connected to an external load of 110 Ω. The small-scale harvester shows great potential for applying in wireless sensor networks.

This study proposes a zone partitioning protection strategy for DC systems incorporating offshore wind farm. Regarding a DC system which consists of multiple local zones with radial topology, a multi-port DC hub is introduced to serve as firewall between different zones. By blocking the converter with DC fault blocking capability in the corresponding DC hub port, the fault can be prevented from spreading among zones. On the other hand, an inter-zone protection centre is designed and built in the DC hub to implement the continuous operation under the situation of any port outage. Furthermore, in every local zone, the hybrid DC circuit breaker is installed in each branch at the side near the star point in order to isolate the faulty branch quickly and selectively via installing the smoothing reactor at the other side to restrict the fault current. Especially, a zone protection centre is designed and built in the star point of each zone to implement the fault detection and fault isolation. A detailed model of the DC system with the proposed protection strategy is built under PSCAD/EMTDC^TM environment. Simulation results under different operating conditions demonstrate the feasibility and validity of the proposed strategy.

This study exhibits the optimum design procedure to tune controller parameters for grid-connected distributed generation system based on cuckoo search algorithm (CSA). To investigate the effectiveness of proposed algorithm, a grid-tied photovoltaic (PV) system consisting of two power electronic converters controlled by five proportional integral (PI) controllers is chosen. Setting proper values for all the PI controllers is a complicated task, notably when the system is non-linear. In this study, response surface methodology (RSM) is used to develop the mathematical design of the PV system which is required to apply the optimisation algorithm. To minimise the design efforts of RSM, an alternate approach based on artificial neural network is introduced to develop the mathematical model of the PV system which is another salient feature of this research. Moreover, two modifications in the CSA are proposed to extract optimum parameters for the controllers which are found suitable in power system applications. Both the transient and dynamic performances of the system with the optimum values obtained through CSA are studied for different types of grid fault conditions using PSCAD/EMTDC. The design values are compared with values obtained through genetic algorithm and bacterial foraging optimisation. Experimental validation is also given for the proposed method.

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters present the end-users of the power system with new opportunities to decrease their electricity costs or become active electricity market participants. However, the intermittent nature of RES and dynamic electricity prices require tools against uncertainty to protect the end-users from underutilizing their assets. In this work, we examine the effectiveness of Robust Optimization (RO) in maximizing the economic benefit of the owner of a home battery storage system in the presence of uncertainty in dynamic electricity prices. The advantage of the robust model is that it keeps its linear class, thus it is not too computationally intensive to be included in the control algorithm of a residential energy storage controller. In the use-case, the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal, while the storage controller is doing dynamic electricity price arbitrage. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations, while, in summer, when the price fluctuations are higher, uncertainty can be ignored.

To mitigate the degraded power system inertia and undesirable primary frequency response caused by large-scale wind power integration, the frequency support capabilities of variable-speed wind turbines is studied in this work. This is made possible by controlled inertial response, which is demonstrated on a research turbine – controls advanced research turbine, 3-bladed (CART3). Two distinct inertial control (IC) methods are analysed in terms of their impacts on the grids and the response of the turbine itself. The released kinetic energy in the IC methods are determined by the frequency measurement or shaped active power reference in the turbine speed–power plane. The wind turbine model is based on the high-fidelity turbine simulator fatigue, aerodynamic, structures and turbulence, which constitutes the aggregated wind power plant model with the simplified power converter model. The IC methods are implemented over the baseline CART3 controller, evaluated in the modified 9-bus and 14-bus testing power grids considering different wind speeds and different wind power penetration levels. The simulation results provide various insights on designing such kinds of ICs. The authors calculate the short-term dynamic equivalent loads and give a discussion about the turbine structural loadings related to the inertial response.

The performance of a grid-connected photovoltaic (PV) system, under the Surinamese weather conditions, is monitored and reported. A measurement and data-logging system provides inputs for the calculation of selected standard key performance indicators (KPI). Calculated KPI's are compared to expected values obtained from modelling the system using the PVSyst software. In addition, results from selected comparable studies are also used to compare the computed KPI's. Using one year data, the annual energy yield (E_ac ) totalled 37 MWh. This value is 6% higher than the one obtained from the modelling software. The calculated performance ratio (PR) and capacity factor (CF) of 74.5 and 15.5%, respectively, are also higher than the ones obtained from PVSyst. The difference can be attributed to the irradiance data (satellite data, monthly averages) used for input in the PVSyst software. The above mentioned calculated values for the PR and CF compared favourably with internationally reported values for systems located in regions with similar weather patterns. Using a total investment cost of 109,000 USD the levelised cost of energy (LCOE) calculated with the RETScreen software equals USD 0.36 /kWh. This LCOE is three times the current energy price in Suriname.

The increases in renewable current sources, prosumers and decentralised control generation in centralised grids have increased the fluctuations in electricity costs, increased the bi-direction power flow problems and changed the operation and investment of the centralised grid. These new constraints have to be observed to manage the design of the market, the new management near the load and the new operators for the power system. This study proposes a stochastic framework for the centralised grid with a market-based, decentralised management and bi-directional power flow of mixed generators of electrical energy. A decentralised and bi-directional market-based management system (DBMBMS) model is developed which considers the operation costs, security and reliability of the centralised grid, the spot market price, weather changes and the fluctuations in the load. A differential evolution technique with a Monte Carlo program is used in aggregation with bi-directional power flows to find the optimal solutions, depending on the uncertainties of the centralised grid. Using a DBMBMS model, optimal load and price management are then realised, based on the decision-maker's choices. The impacts of this new management system on the reduction of the total electricity prices of the different power sources are analysed and illustrated with practical case studies.

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.

This work deals with real-time implementation of a three-phase single-stage solar photovoltaic (SPV) grid-tied system using a three-level voltage source converter (TL-VSC) working at lower switching frequency of 900 Hz under varying solar intensity levels for large power applications. The proposed system consists of SPV array, TL-VSC, interfacing inductors, and three-phase AC grid. The decoupled current controller with the capacitor voltage balancing control and an incremental conductance maximum power point tracking (IC-MPPT)-based algorithms are used to control the TL-VSC to track and transfer maximum active power to AC grid at unity power factor from SPV array. Owing to low switching frequency and single-stage topology, the efficacy of the system is increased. The performance of SPV grid integrated system using TL-VSC is validated under varying solar intensity levels in the laboratory at different switching frequencies and interfacing inductors in accordance to IEEE 519 standard.

The penetration rate of renewable energy into the power systems has been increasing in many countries. The governments and international energy organisations have announced the long-term visions such as power generation and penetration rate of renewable energy. However, the specific installation plans and scenarios have not been discussed and determined. Although plenty of researches related to optimal allocation problem of renewable energy-based distributed generations (DGs) have been proposed in past decade, most of the studies have considered only 1 year's allocation and daily annual system operation. Therefore, this study proposes a novel scenario-based two-stage stochastic programming problem for long-term allocation of DGs. Also, a scenario generation procedure is presented for solving the problem. Furthermore, few studies have focused on the evaluations and analyses of the optimal solutions in various aspects. Thus, this study shows the optimal allocation results on 34-bus distribution network, considering the different scenario generation methods and number of scenarios. The authors finally discuss the important points and indicate that decision makers have to consider several important issues. In addition, they developed and released a general-purpose framework for optimal allocation of DGs as an open source.

The wind turbine gearbox, an important component of wind turbines, has a high failure rate and maintenance cost; therefore, several vibration sensors are installed to execute effective condition monitoring, fault diagnosis, and prediction, for reducing the downtime caused by faults. The sensor positions should directly and accurately reflect the gearbox operating status and provide several signals to the condition monitoring system. This article studies a gearbox model and the gear tooth faults, and builds a gearbox gear vibration model involving these faults. Using structure analysis, sensor configuration solutions rendering all the tooth faults detectable and all the faults isolable are, respectively, obtained, based on the gear vibration model. The sensor configuration solutions of the wind turbine gearbox can guide wind turbine gearbox sensor installation in practice and support common wind turbine gearbox sensor configuration solutions in theory.

Increasing penetration of photovoltaic (PV) generation may significantly impact existing voltage regulation schemes. In particular, the lack of coordination between control devices and fast change in PV generation can cause voltage magnitude to go beyond limits. This study addresses the issue by providing a novel real-time centralised voltage control algorithm to tackle voltage fluctuations caused by variability of PV power generation. The new method considers autonomous response of upstream voltage regulation devices and provides optimised set-points for the chosen downstream controllers. Furthermore, the investigated method was tested in the real-time digital simulator on a model of 11 kV grid located in a semi-rural area of Queensland, Australia. The control algorithm was designed in MATLAB with a multidimensional, non-linear, mixed-integer optimisation. The parallel computing implementation of the KNITRO optimiser helped to provide quicker and more accurate response than the existing uncoordinated control schemes. As a result, optimised control coordination leads to a substantial reduction in voltage fluctuations and decrease in regulation time. Moreover, the proposed method detects and eliminates unnecessary control operations. Thus, it can also prevent voltage hunting in distribution networks.