Abstract
In this paper, an agent-based model is proposed to improve market efficiency by using different Demand Response Programs (DRPs) in the day-ahead electricity market. To this end, both incentive-based and price-based DRPs are considered. On this basis, time of use, real time pricing, emergency demand response program, interruptible/curtailable services and critical peak pricing are investigated. The tariffs of the considered price-based programs and the amount of incentive in the incentive-based programs are optimized through the proposed model. Furthermore, a market power index, i.e., Share Weighted Average Lerner Index (SWALI) and the operation cost are used to evaluate the market efficiency and the market power. The proposed model optimizes the DRPs to improve the electricity market efficiency by using a multi-attribute decision-making approach. The results show that the market operator can mitigate the potential occurrence of market power in a power system by finding the optimal DRP. © 2016 IEEE.

Abstract

Abstract
In this paper, a new model is developed to optimise the performance of a plug-in Electric Vehicle (EV) aggregator in electricity markets, considering both short- and long-term horizons. EV aggregator as a new player of the power market can aggregate the EVs and manage the charge/discharge of their batteries. The aggregator maximises the profit and optimises EV owners' revenue by applying changes in tariffs to compete with other market players for retaining current customers and acquiring new owners. On this basis, a new approach to calculate the satisfaction/motivation of EV owners and their market participation is proposed in this paper. Moreover, the behaviour of owners to select their supplying company is considered. The aggregator optimises the self-scheduling programme and submits the best bidding/offering strategies to the day-ahead and real-time markets. To achieve this purpose, the day-ahead and real-time energy and reserve markets are modelled as oligopoly markets, in contrast with previous works that utilised perfectly competitive ones. Furthermore, several uncertainties and constraints are taken into account using a two-stage stochastic programing approach, which have not been addressed in previous works. The numerical studies show the effectiveness of the proposed model.

Abstract
A crucial factor for the sustainable development of human society is access to electricity. This fact has motivated the development of renewable energy systems isolated or connected to the electric distribution network. Evaluation of autonomous hybrid energy systems from a technical and economic perspective is a difficult problem that requires using complex mathematical models of renewable sources and generators, such as photovoltaic (PV) panels and wind turbines, and the implementation of optimization techniques in order to obtain an economically successful design. This chapter describes and analyzes traditional isolated energy systems powered by solar PV and wind energies provided with a battery energy storage system. Simulation and optimization are illustrated through the analysis of a rural electrification project in Tangiers (Morocco) in order to provide electricity to rural clinic. Optimization analysis suggests the installation of a PV/BESS system due to the magnitude of the load to be supplied, operating costs, and environmental conditions.

Abstract
The presence of high levels of renewable energy resources (RES) and especially wind power production poses technical and economic challenges to system operators, which under this fact have to procure more ancillary services (AS) through various balancing mechanisms, in order to maintain the generation-consumption balance and to guarantee the security of the grid. Traditionally, these critical services had been procured only from the generation side, yet the current perception has begun to recognize the demand side as an important asset that can improve the reliability of a power system, offering notable advantages. In this study, a two-stage stochastic programming model, representing the day-ahead market clearing procedure on an hourly basis and the actual minute-to-minute operation of the power system, is developed comprising different services that specifically address various disturbance sources of the normal operation of a power system, namely intra-hour load variation, intra-hour wind variation, as well as generating unit and transmission line outages.

Abstract
Distribution automation systems in terms of automatic and remote-controlled sectionalizing switches allows distribution utilities to implement flexible control of distribution networks, which is a successful strategy to enhance efficiency, reliability, and quality of service. The sectionalizing switches play a significant role in an automated distribution network, hence optimizing the allocation of switches can improve the quality of supply and reliability indices. This paper presents a mixed-integer nonlinear programming aiming to model the optimal placement of manual and automatic sectionalizing switches and protective devices in distribution networks. A value-based reliability optimization formulation is derived from the proposed model to take into consideration customer interruption cost and related costs of sectionalizing switches and protective devices. A probability distribution cost model is developed based on a cascade correlation neural network to have a more accurate reliability assessment. To ensure the effectiveness of the proposed formulation both technical and economic constraints are considered. Furthermore, introducing distributed generation into distribution networks is also considered subject to the island operation of DG units. The performance of the proposed approach is assessed and illustrated by studying on the bus 4 of the RBTS standard test system. The simulation results verify the capability and accuracy of the proposed approach.