Abstract
In this paper, the impacts of an incentive-based Demand Response, i.e., Ancillary Service DR (ASDR), and a price-based DR, i.e., Time of Use (ToU), are revealed in a restructured power system which has some wind farms. This network is designed based on the pre-emptive market which is a day-ahead market with a balancing market prognosis. It is a proper mechanism to deal with the stochastic nature of non-dispatchable and outage of all units of the network. With Monte Carlo Simulation (MCS) method, several scenarios are generated in order to tackle the variability and uncertainties of the wind farms generation. The impacts of merging ASDR and ToU are investigated through running a two-stage stochastic security constrained unit commitment (SCUC), separately

Abstract
The theoretical potential for renewable energy resources (RES) to meet the global demands of energy is generally high and the ambitions for introducing RES into energy systems are growing worldwide, which also can contribute to global climate change mitigation if it is produced in a sustainable manner. To address these issues, more and more governments are implementing various programs and energy policies to accelerate the deployment of RES. The aforementioned two reasons lead to an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is to use RES which can inject electric energy to the grid while avoiding greenhouse gas emissions. However, the capacities of RES are not enough to supply all the required power from the side of the load. Facts that are leading to the proposal of original ways to reduce the use of energy in many sectors, namely in commercial, residential, and industrial sectors, in order to reduce the total energy costs of the consumer, to reduce the energy demand specially during on-peak hours and the greenhouse gas emissions while safeguarding end-user preferences. The aim of this paper is to determine the impact of model predictive control (MPC) on energy savings of residential households. Furthermore, the value and impact of generated power by local power sources, such as roof-top-solar, will be determined during off-peak, mid-peak, and on-peak, providing simulations during 24 hours in a house.

Abstract
With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle and demand response have indispensable impacts on future smart grid. Since integration of electric vehicles into the grid is a key element to achieve sustainable energy systems, this paper studies the behavior of plug-in electric vehicle parking lots in the electricity markets. Participation of these new market players, as responsive demands, is modeled in both price-based and incentive-based demand response programs. Moreover, impacts of different demand response programs on optimal operational behavior of the parking lots are investigated. In addition to uncertainties of activated reserve, uncertainties of electric vehicle class, arrival and departure times, and initial battery state-of-charge are also considered. Stochastic programming is used to address the mentioned uncertainties. Numerical studies indicate that the participation of parking lots in different demand response programs can increase their profit.

Abstract
Widespread adoption of Electric Vehicles (EVs) could bring social and economic benefits. The effort of promoting the use of EVs in transportation is indispensable to meet the climate change related targets and to reduce the dependency on the ever unstable prices of diminishing fossil fuels. However, there are still many uncertainties in the market regarding the acceptability of EVs by the final consumers. As a new contribution with respect to earlier studies, this paper assesses the impact of EV charging load on the dielectric oil deterioration of two real Power Distribution Transformers (PDTs), one residential and one industrial, located in the insular grid of Sao Miguel. A PDT thermal model is used to estimate the hot-spot temperature given the load ratio. Real data are used for the main inputs of the model, namely, the daily residential load curve, the daily private industrial client load curve, the PDT parameters, time-of-use rates and EV parameters.

Abstract
The need to generate thermal and electrical energy, global warming caused by increased emissions of greenhouse gases, rising fossil fuel prices and demand for energy independence, have created a new industry focused on energy production through the use of renewable sources. Among the different options, biomass is the third most important source for obtaining electricity, and is the main source for the production of thermal energy. However, problems related to the low density of the different types of biomass, and the difficulty of transportation and storage, have led to the need to find solid fuels with higher density and greater hardness, known as pellets and briquettes. This paper seeks to develop an analysis of the current situation of the production of pellets, mainly with mixed biomass types, and the possible uses they have, with the main emphasis on the review of different combustion processes.

Abstract
The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral- derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Evora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of- Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.