Abstract
Operation of distributed energy resources is taking importance nowadays. This paper proposes an optimal planning and operation model of distributed energy resources in a district taking into account the mobility of consumers using conventional fuel vehicles (FV) or electric vehicles (EV). The stochastic model considers the uncertainty of the type of vehicle, availability and distance traveled, and then it manages the available resources to obtain the maximum benefit from the grid. Results show that the EVs assist to achieve greater benefits of the distributed resources. Moreover, the costs per driven km are mainly independent of the type of vehicle considered. © 2015 IEEE.

Abstract
Simulation is often used to forecast human populations. In this paper we use a novel approach by combining Micro-Macro (MicMac) models into an Agent-Based perspective to simulate and forecast the behavior of the Portuguese population. The models include migrations and three scenarios corresponding to three different expected economic growth rates. We conclude that the increase in the number of emigrants leads to a reduction of the Portuguese women that are in the fertile age. This justifies the decrease of births and therefore the general decrease of the total Portuguese Population.

Abstract
This paper proposes a two-phase approach for optimal short-term operational scheduling with intermittent renewable energy resources (RES) in an active distribution system. The first phase determines the amounts of purchased power from the market and the unit status of distributed generation (DG) and feeds the data into the second phase, a real-time scheduling coordination with hourly network reconfiguration. The two-phase proposed approach is applied to a case study of a sixteen-bus test system that uses synthetic data from renewable power generators and forecasts local user demands with a sampling time of five minutes.

Abstract
Smart grid can be expressed as a combination of power network substructures with an extensive telecommunication network which is able to provide a two-way communication and use of advanced sensors in order improve efficiency, system reliability, transport security, and power consumption. Loads in this network are divided into two groups, linear and non-linear. The majority of these loads on the network, such as rectifiers, electric vehicles are non-linear. The non-linear loads can cause odd harmonics in the network and can damage transformers. In this article, management and planning of hybrid vehicles for total harmonic index reduction and also annual cost reduction has been considered.

Abstract
Allocation of flexible AC transmission systems (FACTS) devices is a challenging power system problem. This paper proposes a new particle swarm optimisation (PSO) variant, called enhanced leader PSO (ELPSO), for solving this problem. This algorithm is capable of solving FACTS allocation problem in a way leading to lower amounts of power flow violations, voltage deviations and power losses with respect to other optimisation algorithms. Distributed thyristor controlled series compensators (D-TCSC's) are used. D-TCSC's are installed at all branches except those with regulating transformers. The reactances of D-TCSC's are found in optimisation process. ELPSO features a five-staged successive mutation strategy which mitigates premature convergence problem of conventional PSO. ELPSO and other optimisation algorithms are applied to IEEE 14 bus and 118 bus power systems for N-1 contingencies and also for simultaneous outage of four branches. The results show that it leads to lower amounts of power flow violations, voltage deviations and power losses with respect to conventional PSO (CPSO) and eight other optimisation algorithms including genetic algorithm (GA), gravitational search algorithm (GSA), galaxy based search algorithm (GBSA), invasive weed optimisation (IWO), asexual reproduction optimisation (ARO), threshold acceptance (TA), pattern search and nonlinear programming (NLP).

Abstract
The objective of the retailer in medium-term planning is managing the portfolio of contracts from different sources as well as determining the optimal selling price offered to its customers. When supplying the electricity sold to the costumers, two main challenges are faced by retailers. The first problem occurs during the electricity procurement procedure. In this stage, the retailer must deal with the uncertainty due to the pool price that propels the retailers to move towards agreeing to forward contracts signed at higher average prices. Besides, when the retailer decides on selling the electricity, another problem is to face the uncertainty caused by the demand while taking into consideration the possibility of reducing its clients in the case of high selling price. In this regard, this paper proposes a stochastic multi-objective framework for the retailer with profit maximization and risk minimization as two objective functions. The risk, due to the market price uncertainty, is modeled, employing the expected downside risk. The problem is formulated as mixed-integer programming while the stochastic optimization problem is characterized using the roulette wheel mechanism and lattice Monte Carlo simulation. Furthermore, lexicographic optimization and augmented epsilon-constraint method are used to solve the proposed multi-objective problem, and the best compromise solution is determined employing a fuzzy satisfying method. The presented model has been implemented using a realistic case study to verify the effectiveness of the method used in this paper. © 2015 John Wiley & Sons, Ltd.