Network reconfiguration has been involved in energy loss reduction by considering the time-varying nature of loads. Nowadays, with the integration of intermittent micro-sources (MSs) to the power grid, optimal configuration should be determined corresponding to the variations in loads and generations. In this study, optimum reconfiguration instants are achieved based on the switching operations and energy losses costs. Moreover, the sequence of selecting loops for reconfiguration is optimised in conjunction with the time intervals by a joint optimisation algorithm. Furthermore, a simple branch exchange method of reconfiguration is modified in the presence of MSs. Discrete genetic algorithm (DGA) is employed to optimise the reconfiguration instants as well as the sequence of selecting loops. By applying the proposed method, the total operating cost of the network is reduced significantly, compared to optimum fixed configuration and hourly reconfiguration policies. The proposed algorithm is evaluated on a real-life 77-bus distribution system with two interconnected MSs.

An analytical approach is presented to evaluate the impacts of price-sensitive loads on congestion in transmission network. To achieve this aim, the Lagrange multipliers (LMs) of transmission lines’ power flow constraints are calculated from independent system operator's social welfare maximisation in which demand-side bidding is considered. It is shown that the LM of each congested line can be decomposed into five components. The first part is a constant value for the specified line, while the next two components are associated to generating units and the last two constitutive parts are related to load service entities (LSEs). The proposed decomposition obtains considerable information regarding the impacts of price-sensitive loads on congestion in transmission network. First, the sensitivity of congestion degree of each congested line to the bidding strategies and maximum price-sensitive demand of LSEs are indicated by weighting coefficients of the last two terms in the decomposed LM. Furthermore, the decomposition of LM to the constitutive components reveals the contribution of each generating unit and LSE to the congestion of corresponding line. The simulation results on a test system confirm the efficiency of the proposed approach.

Integration of renewable energy sources (RESs) may increase the risk of power system blackouts because of the uncertainty nature of their output power. In the meantime, RESs have relatively short starting time when compared with non-black start (BS) generating units. For this reason, RES needs to participate in power system restoration after blackouts. For a complete power system restoration, three stages must be completed. These stages are: generation restoration, transmission system restoration and load pick up. To achieve a faster restoration process, an optimal schedule for the BS units to crank the non-BS (NBS) units is required with optimal transmission path selection. In addition, to maintain the stability of the system and satisfy the system operational constraints, an optimal load pick-up sequence is required. In this study, the firefly algorithm (FA) is used to find the optimal final sequence of NBS units restoration, transmission paths and load pick-up sequence with and without the aid of RES in the system. The objective is to minimise the overall restoration time and the unserved load which maximise the energy capability and improve the sustainability of the system. The proposed algorithm is applied successfully to the IEEE 39-bus system.

In this study, the probabilistic power flow (PPF) problem is modelled as a multiple integral with an implicit integrand. A detailed discussion is given about the dimension reduction (DR) method for solving PPF. To accommodate the dependency of the input random variables, Nataf transformation is introduced to map PPF problems in the independent standard normal space. Emphasis is put on the computational burden of DR method. Based on Taylor expansion, a deep sight is given in the residual error of univariate DR (UDR) method. Using the first four standardised central moments from UDR method, the generalised lambda distribution is employed to represent the probability distribution of the output variable. Finally, an IEEE-118 case is performed to check the proposed method.

The modular multilevel converter (MMC) is one of the best potential candidates for high-voltage direct current (HVDC). Various kinds of faults may occur in an MMC-HVDC system. The system should be restarted by deblocking the converter after a temporary fault is cleared or a fault is isolated, thus recovering the power transmission as soon as possible. This study analyses the recovery process after the converter is deblocked. The analysis shows that the capacitor voltages of the three upper arms or the three lower arms can be rebalanced quickly, and the power transmission and the DC voltage can be recovered with the original main controller. However, the speed of rebalancing the capacitor voltages of the upper arm and the lower arm depends on the arm resistance, the modulation index and so on, and is probably slow. To improve the damping characteristic of the MMC, a dedicated supplementary controller is proposed based on the concept of ‘virtual arm resistance’. Time-domain simulation studies based on PSCAD/EMTDC are performed to verify the analysis and the proposed controller.

This study presents a model of coordinated generation and transmission expansion planning (TEP). The proposed model simultaneously minimises total cost, including planning and total fuel cost, environmental impact in terms of SO₂ and NO _x emission and fuel price risk while maximising the system reliability. Owing to intermittent behaviour of loads and fuel prices, the expansion planning problem should be analysed using probabilistic approaches instead of deterministic ones. Therefore, a new approach is proposed to solve the multi-objective probabilistic coordinated generation and TEP problem. The point estimate method is used to take into account the effect of uncertainty in fuel prices and system demand. The normal boundary intersection (NBI) method is used to obtain the Pareto-optimal solutions. Moreover, fuzzy decision-making process is employed to select one of the Pareto-optimal solutions as the most preferred solution. The proposed model is implemented on the modified Garver6-bus system to evaluate its efficiency. Finally, the results of the NBI method are compared with the classical weighted sum method. Additionally, the model is implemented on the IEEE 24-bus reliability test system, and the results are compared with the virtual database-supported non-dominated sorting genetic algorithm II (VDS-NSGA II) and NSGA II methods.

The future direct current (DC) grids will require additional control functions on voltage source converters (VSC) in order to ensure stability and integrity of DC grids under wide range of disturbances. This study proposes a 3-level cascaded control topology for all the VSC and DC/DC converters in DC grids. The inner control level regulates local current which prevents converter overload. The middle control level uses fast proportional integral feedback control of local DC voltage on each terminal which is essential for the grid stability. The hard limits (suggested ±5%) on voltage reference will ensure that DC voltage at all terminals is kept within narrow band under all contingencies. At the highest level, each station follows power reference which is received from the dispatcher. It is proposed to locate voltage droop power reference adjustment at a central dispatcher, to maintain average DC voltage in the grid and to ensure optimal power flow in the grid. This slow control function has minimal impact on stability. Performance of the proposed control is tested on PSCAD/EMTDC model of the CIGRE B4 DC grid test system. A number of severe outages are simulated and both steady-state variables and transient responses are observed and compared against conventional droop control method. The comparison verifies superior performance of the proposed control topology.

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half-cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ² test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal-operation so it is reliable, accurate and secure.

Simultaneous expansion of the electrical and thermal energies collected with conventional expansion options is scrutinised. A robust, bio-inspired evolutionary optimisation method is proposed, to handle the complex expansion planning of a system consisting of both electrical and thermal forms of energy. Rewiring, network reconfiguration, installation of new lines and also new electrical and thermal generation units are considered as the traditional alternatives in expansion planning. To solve the problem, overall generation requirements of a network are assigned along the planning horizon. The allocation problem is formulated as a mixed-integer non-linear programming problem that minimises the overall system cost owing to generation capacity among the grid nodes and the newly added or upgraded lines. The performance of the original shuffled frog leaping (SFL) optimisation algorithm is advanced to overcome the complexity of the proposed expansion planning problem. Two modification steps were added to the original SFL technique to enable the proposed modified SFL algorithm to extricate from local minima. The two modification phases pledge a fast convergence rate by achieving a rapid adaptive algorithm, besides a better diversification which is the key to extricate from local minima. The efficacy and robustness of the proposed methodology are verified by applying the method to two modified standard test systems.