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Investment on generation system and transmission network is an important issue in power systems, and investment reversibility closely depends on performing an optimal planning. In this regard, generation expansion planning (GEP) and transmission expansion planning (TEP) have been presented by researchers to manage an optimal planning on generation and transmission systems. In recent years, a large number of research works have been carried out on GEP and TEP. These problems have been investigated with different views, methods, constraints and objectives. The evaluation of researches in these fields and categorising their different aspects are necessary to manage further works. This study presents a comprehensive review of GEP and TEP problems from different aspects and views such as modelling, solving methods, reliability, distributed generation, electricity market, uncertainties, line congestion, reactive power planning, demand-side management and so on. The review results provide a comprehensive background to find out further ideas in these fields.

The study presents a differential scheme for microgrid protection using time-frequency transform such as S-transform. Initially, the current at the respective buses are retrieved and processed through S-transform to generate time-frequency contours. Spectral energy content of the time-frequency contours of the fault current signals are calculated and differential energy is computed to register the fault patterns in the microgrid at grid-connected and islanded mode. The proposed scheme is tested for different shunt faults (symmetrical and unsymmetrical) and high-impedance faults in the microgrid with radial and loop structure. It is observed that a set threshold on the differential energy can issue the tripping signal for effective protection measure within four cycles from the fault inception. The results based on extensive study indicate that the differential energy-based protection scheme can reliably protect the microgrid against different fault situations and thus, is a potential candidate for wide area protection.

This study proposes a stochastic optimisation model for the day-ahead scheduling in power systems, which incorporates the hourly demand response (DR) for managing the variability of renewable energy sources (RES). DR considers physical and operating constraints of the hourly demand for economic and reliability responses. The proposed stochastic day-ahead scheduling algorithm considers random outages of system components and forecast errors for hourly loads and RES. The Monte Carlo simulation is applied to create stochastic security-constrained unit commitment (SCUC) scenarios for the day-ahead scheduling. A general-purpose mixed-integer linear problem software is employed to solve the stochastic SCUC problem. The numerical results demonstrate the benefits of applying DR to the proposed day-ahead scheduling with variable RES.

The use of renewable energy sources has increased year-on-year. Thus, there is an increasing rate of small generating units connected directly to distribution networks and micro-grids close to consumers. At the same time, these micro-sources must provide stability and reliability of electrical energy to the power network to which they are connected. In the technical literature, several studies have been done to ensure power systems with traditional generating sources to operate in a stable and reliable way, but there is an issue regarding generation uncertainty when a distribution system has many micro-sources. This is because of the uncertainty of primary sources, for example, wind and radiation intensity, and could result in intermittent generation. In this study, stability and reliability of voltage in a power system with distributed generation is analysed using simulation techniques. In the proposed method in this study voltage security analysis is jointly considered with probability laws. Moreover reliability theory is also considered in the proposed voltage collapse analysis methodology. The responsibility of generator in the voltage collapse process, the probabilistic risk of voltage collapse of each operating point and the probability of enlarging the system load as a function of different operating points are the outcome of the methodology, and it is validated by using the IEEE34 test feeder.

Network reconfiguration after complete blackout of a power system is an essential step for power system restoration. A new node importance evaluation method is presented based on the concept of regret, and maximisation of the average importance of a path is employed as the objective of finding the optimal restoration path. Then, a two-stage method is presented to optimise the network reconfiguration strategy. Specifically, the restoration sequence of generating units is first optimised so as to maximise the restored generation capacity, then the optimal restoration path is selected to restore the generating nodes concerned and the issues of selecting a serial or parallel restoration mode and the reconnecting failure of a transmission line are next considered. Both the restoration path selection and skeleton-network determination are implemented together in the proposed method, which overcomes the shortcoming of separate decision-making in the existing methods. Finally, the New England 10-unit 39-bus power system and the Guangzhou power system in South China are employed to demonstrate the basic features of the proposed method.

This study presents a current decomposition technique based on a novel instantaneous power theory for better power quality and reliability of a dc‐grid based wind energy conversion system (WECS) used on a poultry farm. The proposed approach also offers adequate control for the parallel operation of multiple distributed generations independent of the requirement of voltage and frequency synchronisation. In addition to that, a 17‐level hybrid cascaded multilevel inverter is considered and integrated by utilising a three‐level flying capacitor inverter and cascading it with three floating capacitor H‐bridges. The presence of single dc‐link voltage facilitates the back to back operation with a reduced dv /dt ratio, common‐mode voltage variation, and operations under varying load power factors and modulation index. Moreover, for attaining better power management especially in the islanded mode of operation, the battery energy storage device is incorporated. The proposed WECS has been tested through MATLAB/Simulink software simulation under various conditions to facilitate better power quality, increase the flexibility and reliability in the micro‐grid operation.

Frequency security is the premise of realizing the net‐zero transition. A novel assessment scheme is proposed to quantify the frequency security levels of transmission systems under different inverter‐based resources (IBRs) and their control parameter combinations. A novel system frequency dynamic model is proposed as a parametric optimization method, where the saturation of generators, energy storage systems, and renewable energy sources is incorporated as differential algebra equations. This problem is further reformulated as a mixed‐integer linear programming problem to generate a sufficient amount of data under different IBR integration and control parameters. The frequency security assessment problem is formulated as a data‐driven multivariate classification problem, which is solved by the optimal classification tree (OCT) algorithm with better interptretionability. Simulations are conducted on a transmission system. Numerical results indicate that the proposed system frequency dynamic model can capture the frequency dynamic under different IBR reinforcement plans and the OCT can realize accurate classification of the synthetic data regarding frequency security. A novel frequency dynamic problem is formulated as an MIP problem with differential algebra equations, where the inverter‐based resources (IBRs) integration and their non‐linear control characteristics can be captured. The frequency security assessment of transmission systems with different IBR combinations and their control characteristics is formulated as a multivariate classification problem. An optimal classification tree is constructed to explore the main drivers to boost frequency security from a holistic perspective, that is, planning, operation, and control.

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (PSO) algorithm is developed, which integrates the taboo list to broaden the search range and introduces ‘restart’ and ‘disturbance’ operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard PSO algorithm.

Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is the time consuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This study presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on the experimental evidence from the Durham Condition Monitoring Test Rig, a gear condition indicator was proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The performance of the proposed technique was then successfully tested on signals from a full-size wind turbine gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL) programme. The results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into the wind turbine CMSs, this algorithm can automate the data interpretation, thus reducing the quantity of the information that the wind turbine operators must handle.

In the future, energy framework of European Union and other countries, renewable energy plays an important role tackling the problems of the climate change and security of energy supply. The high penetration of renewable energy sources will increase the burden of system operator for maintaining system reliabilities. However, the current strategy of reliability management developed for conventional power systems and existing electricity market design may not cope with the future challenges the power system faces. The development of smart grid will enable power system scheduling and the electricity market to operate in a shorter time horizon for better integrating renewable energy sources into power systems. This study presents an electricity market scheme including a multi-period energy and reserve pre-dispatch model and an energy re-dispatch model for real time operation, respectively. The multi-period energy and reserve pre-dispatch model is formulated using the multi-period optimal power flow technique. During the real time operation, the energy re-dispatch model is used for contingency management and providing balancing services. The proposed market scheme coupled with a contingency analysis methodology has been used to evaluate both real-time electricity price risk and short term reliabilities during the operational hour in the new environment.