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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.

It is expected that electric vehicles (EVs) will soon represent a large share of the demand for electricity. Several research works have extolled the advantages of these devices as flexible demands, not only to charge their batteries when it is cheaper to do so, but also to provide services in the form of vehicle-to-grid (V2G) power injections to the system. These services, however, could reduce the useful life of the battery and thus introduce a cost that needs to be taken into account when scheduling the charging of these vehicles. This study presents a scheduling algorithm for EVs under a real time pricing scheme with uncertainty. The objective function explicitly takes into account the cost of battery degradation not only when used to provide services to the system but also in terms of the EV utilisation for motion. The results show that the scheduling of the V2G services is sensitive to the electricity prices uncertainty and to the degradation costs derived from the energy arbitrage. Also, the optimal energy state of charge of the batteries is highly dependent on whether the cost of battery degradation is taken into account or not.

Artificial Neural Network (ANN) has been recognized as a powerful method for short-term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by gradient-based learning algorithms which usually suffer from excessive training and tuning burden as well as unsatisfactory generalization performance. Based on the ensemble learning strategy, this paper develops an ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high-quality STLF of Australian National Electricity Market (NEM). The model consists of a series of single ELMs. During the training, the ensemble model generalizes the randomness of single ELMs by selecting not only random input parameters but also random hidden nodes within a pre-defined range. The forecast result is taken as the median value the single ELM outputs. Owing to the very fast training/tuning speed of ELM, the model can be efficiently updated to on-line track the variation trend of the electricity load and maintain the accuracy. The developed model is tested with the NEM historical load data and its performance is compared with some state-of-the-art learning algorithms. The results show that the training efficiency and the forecasting accuracy of the developed model are superior over the competitive algorithms.

New smart load management (SLM) approach for the coordination of multiple plug-in electric vehicle (PEV) chargers in distribution feeders is proposed. PEVs are growing in popularity as a low emission and efficient mode of transport against petroleum-based vehicles. PEV chargers represent sizeable and unpredictable loads, which can detrimentally impact the performance of distribution grids. Utilities are concerned about the potential overloads, stresses, voltage deviations and power losses that may occur in distribution systems from domestic PEV charging activity as well as from newly emerging charging stations. Therefore this study proposes a new SLM control strategy for coordinating PEV charging based on peak demand shaving, improving voltage profile and minimising power losses. Furthermore, the developed SLM approach takes into consideration the PEV owner preferred charging time zones based on a priority selection scheme. The impact of PEV charging stations and typical daily residential loading patterns are also considered. Simulation results are presented to demonstrate the significant performance improvement offered by SLM for a 1200 node test system topology consisting of several low-voltage residential networks populated with PEVs.

The scope of this study is the optimal siting and sizing of distributed generation within a power distribution network considering uncertainties. A probabilistic power flow (PPF)-embedded genetic algorithm (GA)-based approach is proposed in order to solve the optimisation problem that is modelled mathematically under a chance constrained programming framework. Point estimate method (PEM) is proposed for the solution of the involved PPF problem. The uncertainties considered include: (i) the future load growth in the power distribution system, (ii) the wind generation, (iii) the output power of photovoltaics, (iv) the fuel costs and (v) the electricity prices. Based on some candidate schemes of different distributed generation types and sizes, placed on specific candidate buses of the network, GA is applied in order to find the optimal plan. The proposed GA with embedded PEM (GA–PEM) is applied on the IEEE 33-bus network by considering several scenarios and is compared with the method of GA with embedded Monte Carlo simulation (GA–MCS). The main conclusions of this comparison are: (i) the proposed GA–PEM is seven times faster than GA–MCS, and (ii) both methods provide almost identical results.

The study presents an improved particle swarm optimisation (IPSO) method for the multi-objective optimal power flow (OPF) problem. The proposed multi-objective OPF considers the cost, loss, voltage stability and emission impacts as the objective functions. A fuzzy decision-based mechanism is used to select the best compromise solution of Pareto set, obtained by the proposed algorithm. Furthermore, to improve the quality of the solution, particularly to avoid being trapped in local optima, this study presents an IPSO that profits from chaos queues and self-adaptive concepts to adjust the particle swarm optimisation (PSO) parameters. Also, a new mutation is applied to increase the search ability of the proposed algorithm. The 30-bus IEEE test system is presented to illustrate the application of the proposed problem. The obtained results are compared with those in the literatures and the superiority of the proposed approach over other methods is demonstrated.

A new estimation method for a power system dynamic state estimation, the unscented Kalman filter (UKF), is presented. It is based on the application of the unscented transformation (UT) combined with the Kalman filter theory. One of the challenges in the process of power system estimation is coping with a highly non-linear mathematical model of network equations, which is usually approximated through a linearisation. The new derivative free estimation method overcomes this limitation using the UT and achieves better accuracy with simpler implementation. The UKF is derived and demonstrated using three different test power systems under typical network and measurement conditions. Its performance is compared with the classical extended Kalman filter. The simplicity of the new estimator and its low computational demand make it a better option to be applied in the next generation of dynamic system estimators.

A probabilistic power flow model that takes into account, spatially correlated power sources and loads is proposed. It is particularly appropriate to assess the impact of intermittent generators, such as wind power ones on a power network. The proposed model is solved using an extended point estimate method, that accounts for dependencies among the input random variables (i.e., loads and power sources). The proposed probabilistic power flow model is illustrated through 24-bus and 118-bus case studies. Finally, conclusions are duly drawn.

This article presents an application of an efficient and reliable heuristic technique inspired by swarm behaviours in nature namely, gravitational search algorithm (GSA) for solution of multi-objective optimal power flow (OPF) problems. GSA is based on the Newton's law of gravity and mass interactions. In the proposed algorithm, the searcher agents are a collection of masses that interact with each other using laws of gravity and motion of Newton. In order to investigate the performance of the proposed scheme, multi-objective OPF problems are solved. A standard 26-bus and IEEE 118-bus systems with three different individual objectives, namely fuel cost minimisation, active power loss minimisation and voltage deviation minimisation, are considered. In multi-objective problem formulation fuel cost and loss; fuel cost and voltage deviation; fuel cost, loss and voltage deviation are minimised simultaneously. Results obtained by GSA are compared with mixed integer particle swarm optimisation, evolutionary programming, genetic algorithm and biogeography-based optimisation. The results show that the new GSA algorithm outperforms the other techniques in terms of convergence speed and global search ability.

A probabilistic load flow (PLF) method using Copula and improved Latin hypercube sampling is proposed. The stochastic dependence between input random variables is considered. Copula theory is adopted to establish the probability distribution of correlated input random variables. Based on discrete data, an improved Latin hypercube sampling is proposed. The accuracy of probability distribution of correlated input random variables established by Copula theory is evaluated by adopting the power output of wind farms located at New Jersey. The performance of the proposed PLF method is investigated using IEEE 14-bus and IEEE 118-bus test systems.