The operation mode of microgrids influences the dispatching management of distribution system in the power market. The study uses game theory to study dispatching strategies between multi-microgrid in the distribution system. For this purpose, a two-level game model of multi-microgrid dispatching in the electricity market is proposed. Firstly, the upper level of the model researches the answer of a basic question for a multi-microgrid distribution system that what the boundary line between non-cooperation mode and coalition mode is. Secondly, by the lower level of the model, multi-microgrid decides the operation mode ultimately when it is uncertain in the upper level of model. The upper level is a non-cooperative price game between multi-microgrid and distribution system while the lower level is a cooperative trading loss cost game. In order to optimise multi-microgrid dispatching, an algorithm is proposed to allow microgrids merge or split self-adaptively based on NSGA-II. Simulation result shows that the proposed algorithm can find the Nash equilibrium of the upper level of the model and the optimal operation mode for multi-microgrid in the two-level game, which yields a reduction of 52.7% in coalition mode compared to non-cooperation mode.

The integration of solar photovoltaic (PV) systems into the distribution network creates various stability and reliability issues associated with the intermittency of solar PV power generation. Energy storage is a vital component required for overcoming the intermittency of solar PV. This study presents a priority-based demand response management (DRM) for loads with large time constants to create virtual energy storage. The virtual energy storage thus created can be used for partial levelling of intermittent output from solar PVs. The proposed DRM algorithm involves controlling loads with large time constants such as air conditioning systems and refrigerators based on the forecasted solar PV generation. The proposed method is evaluated using data-driven simulations, weather data and mathematical models. The proposed algorithm is highly suitable for megacities that have high number of multi-storey residential buildings. Utilising the virtual storage capacity available from the appliances will reduce the investment as well as the operation cost of renewable energy such as solar PV. Analyses on impact on temperature, percentage of interruptions, cost savings and impact on energy storage sizing are also presented for evaluating the performance of the proposed algorithm.

This paper proposes a new stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids (MGs) under uncertainty from economic, technical, reliability and environmental viewpoints. In the proposed approach, optimal site, size, type, and time of distributed energy resources are determined along with optimal allocation of section switches to partitioning conventional distribution system into a number of interconnected MGs. The uncertainties of the problem are considered using scenario modelling and backward scenario reduction technique is implemented to deal with computational burden. In addition, three different risk averse, risk neutral and risk seeker strategies are defined for distribution network operator. The proposed framework is considered as two unparalleled objective functions which the first objective minimizes the investment cost, operation and maintenance cost, power loss cost and pollutants emission cost and the second objective is defined to minimize energy not supplied in both connected and islanded modes of MGs. Finally, multi objective particle swarm optimization is applied to minimize the proposed bi-objective functions and subsequently fuzzy satisfying method is accomplished to select the best solution proportional to risk based strategies. Efficiency of the proposed framework is validated on 85-bus distribution system and obtained results are presented and discussed.

This study explores optimisation of the hybrid power system in the smart grid framework, in conjunction with the model predictive control (MPC) design. This study also creates a strategy that can maximise the use of renewable energy, e.g. photovoltaic, the wind turbine with battery storage and minimise the utilisation of the utility grid for electricity usage in the industry. This is devised by modelling a discrete state-space model of the hybrid power system for a given industry application. The system design is implemented within a real-time electricity pricing environment that is integrated with renewable energy to optimally meet the demand according to a specific performance of the consumer. The emphasis of this approach is on its capacity to supply optimal power to the demand side by selecting the appropriate source; and its robustness against uncertainties. The results show that MPC design for hybrid power system not only optimises the energy flow but also improves the overall process of energy management. It was also observed that the optimal solution minimises the delay cost of energy demand from the utility grid according to a given reference from the consumer for the specified tuning parameter values of the performance index.

Although grid-connected transformerless photovoltaic (PV) inverters present higher efficiency and power density compared with inverters with a transformer, the leakage current caused by the inverter common-mode voltage introduces several problems. Among the techniques to reduce the leakage current, the modified LCL (MLCL) filter with passive damping is an effective and simple solution. However, the classical design of the filter damping resistance is not adequate for ensuring both proper leakage current attenuation and control system stability. Therefore, this study proposes a methodology to design the resistance in a low-loss passive damping structure applied to the MLCL filter. In addition to the conventional specifications for LCL-type filters, this study includes the leakage current limit in the design procedure. Simulation and experimental results for a 10 kW PV inverter show the damping resistance impact on the leakage current. The results related to the efficiency and grid inductance variation are also presented. Therefore, it is possible to conclude that the proposed design methodology is very useful for obtaining a damping resistance that ensures control system stability and a leakage current in conformity with PV standards.

This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.

This study presents a day-ahead scheduling of a generation portfolio incorporating large shares of intermittent wind generation. The scheduling utilises the cycling of conventional generation as well as the dispatch of energy storage (ES) to mitigate the impact of net load ramps. Inherent system flexibility, expressed as a chance constraint, is quantified in terms of ramping capability and operating reserves of conventional generation and ES. The flexibility chance constraint is then factorised into a set of linear deterministic inequalities, to preserve the mixed-integer linear programming structure of the resulting problem. Numerical simulations are performed and results are analysed for IEEE 24-bus and 118-bus systems. Test results show that implementation of ES improves the flexibility of the system, in terms of alleviation of the cycling of thermal generation as well as wind generation curtailment.

This study investigates different methods of determining the optimum tilt angle (OTA) for photovoltaic (PV) systems to maximise energy yield, and provides an evaluation index system for the static and dynamic performance of PV systems under hazy weather conditions, in view of the nature of solar energy resource and PV energy yield. Due to the fact that the data of total solar radiation on inclined surface in Wuhan is not available and PV power generation is non-linear process, an experimental off-grid PV system with various tilt angles and orientations is established which is located in Hubei Meteorological Administration in Wuhan. The experimental system is then used to analyse the accuracy of five in-plane solar radiation models and the relationship between the model predicted errors and meteorological environmental factors, explore the calculated and measured OTA of PV modules and evaluate the performance of PV system with different tilt angles and orientations based on static and dynamic indexes in real atmosphere condition. Finally, the influence of dust on PV energy production has been reported. After cleaning, the energy yield of PV models can increase by 10–20%.

This study proposes a novel methodology for optimal sizing of a vanadium redox flow battery (VRFB) aiming at providing the load frequency control (LFC) of power system (PSs) with renewable generation, such as wind generation. This methodology utilises a new optimisation problem, where the optimal size of the VRFB is an endogenous result from the model of optimisation problem. To resolve the new optimisation problem, a hybrid optimisation model (HOM) is employed in order to calculate the optimal investment for the VRFB (optimal size) while taking into account both its impact on the PS costs and on the quality of the system frequency. Through stochastic optimisation, the HOM allows computing the variable operating costs and VRFB investment costs, considering the uncertainties associated to the PS. This stochastic optimisation utilises a new quasi-stationary simulations of the PS operation, which demands lower computing effort. To this aim, this study proposes a novel modelling approach of both the VRFB and the PS operation. Moreover, statistical indexes are proposed as new factor used in sizing the LFC operating reserve. The results show that the developed methodology allows evaluating with precision the stochastic characteristics of the PS.

Increasing penetration of intermittent renewable energy sources and the development of advanced information give rise to questions on how responsive loads can be managed to optimise the use of resources and assets. In this context, demand response as a way for modifying the consumption pattern of customers can be effectively applied to balance the demand and supply in electricity networks. This study presents a novel stochastic model from a microgrid (MG) operator perspective for energy and reserve scheduling considering risk management strategy. It is assumed that the MG operator can procure energy from various sources, including local generating units and demand-side resources to serve the customers. The operator sells electricity to customers under real-time pricing scheme and the customers response to electricity prices by adjusting their loads to reduce consumption costs. The objective is to determine the optimal scheduling with considering risk aversion and system frequency security to maximise the expected profit of operator. To deal with various uncertainties, a risk-constrained two-stage stochastic programming model is proposed where the risk aversion of MG operator is modelled using conditional value at risk method. Extensive numerical results are shown to demonstrate the effectiveness of the proposed framework.

This study presents a new multilevel inverter (MLI) with reduced devices, for low/medium- and high-voltage applications. The proposed MLI is evolved from existing cross-connected source-based multilevel inverter (CCS-MLI), results in reduced switches, driver circuits, diodes, and DC voltage sources when compared with the classical CHB, CCS-MLI, and other MLIs. Owing to reduced device numbers, the complexity, size, cost, and maintenance of the proposed topology are greatly reduced. The detailed analysis and working of the proposed topology is presented along with its comparison with classical, CCS-MLI, and other MLIs. Different algorithms are presented for selecting appropriate magnitudes of DC voltage sources to generate different voltage levels in the output. The proposed MLI is suitable for grid integration of renewable energy sources. The concept is presented through modelling and simulation in MATLAB/Simulation environment and validated through real-time simulator OPAL-RT (OP-4500).

Energy storage is becoming increasingly important for isolated power systems having overall low inertia. Among many energy storage devices, superconducting magnetic energy storage (SMES) is most suited for improved frequency control in isolated power systems, due to its outstanding advantages. However, a small rating SMES device has operational constraints, therefore a suitable control strategy is required for its profitable and constrained operation. An adaptive controller which encapsulates on-line identification with model predictive control is proposed in this paper. A recursive least-squares algorithm is used to identify a reduced-order model of wind-diesel power system on-line. Based on the identified model and a simple discrete time model of SMES unit, an adaptive generalized predictive control scheme (AGPC) considering constraints on SMES current level and converter rating is formulated. The scheme yields a control signal which on one hand keeps the system frequency deviations to minimum and on the other hand forces the SMES device to operate within and near its operational constraints, for profitable operation. Simulation studies are performed to illustrate the potency of the proposed strategy in achieving all the control objectives.

Gears are very reliable parts of wind turbines and normally contribute for more than 5 years without failure when operated under fluctuating speed conditions. This case study presents the effectiveness of gear fault diagnosis techniques to highlight cracked tooth, chipped tooth and missing tooth gear under fluctuating speed conditions. Various measuring parameters such as overall vibration acceleration and envelope-detected acceleration were calculated for vibration signal, and sound intensity and sound pressure were calculated for acoustic signal. A statistical indicator kurtosis was also calculated for both vibration signal and acoustic signal. These measuring parameters were then compared with showing the fault detection capability of techniques employed under fluctuating speed and also under different loading conditions. The detection of faults by kurtosis of acoustic signal is found most efficient. For low load conditions, envelope-detected acceleration of vibration signal can also support fault diagnosis under fluctuating speed conditions.