Abstract
The development of small-scale power generation units based on biomass gasification is an effective mean to meet the growth interest of deployment of local power generation exploiting endogenous renewable energy sources. However, significant research and development activities are required towards the deployment of cost-effective solutions suitable to be used in several applications and with different biomass feedstock. For this purpose, a flexible experimental setup is required to be developed. This paper proposes a critical review of the current state of the art of the available technologies suitable for small-scale power generation using biomass gasification. The main guidelines to develop cost-effective solutions are identified and the conceptual framework of the experimental setup is proposed. Also, the operational specifications are presented.

Abstract
This paper presents a hybrid optimization model based on the metaheuristic Evolutionary Particle Swarm Optimization (EPSO) and Linear Programming for solving the problems of sizing, location and network interface technology selection of battery energy storage system (BESS). The batteries are integrated in a distribution network that has dispersed photovoltaic (PV) generation. Thus, a stochastic scenario generation model is also proposed for creating a database for the PV generation, load and energy prices curves considering historical data. The proposed approach is applied with success to the CIGRE MV benchmark network in the European configuration. Several tests were carried out in order to evaluate the EPSO approach for planning and operate BESS into the modern distribution networks. Experimental results indicate that dispersed solutions to locate the batteries throughout of the network were privileged over concentrated solutions.

Abstract
This paper contrasts the Genetic Algorithm (GA) strategies to solve the problem of Economic Dispatch (ED) in a Microgrid (MG). An environmental ED strategy for MG is proposed through maintaining the system reliability requirements at acceptable level. Two additional cost terms such as the CO2 emission penalties and load curtailment charges are added to the traditional objective function of the ED problem. Also, cost and reserve supply of the network are considered. Since the load curtailment cost is highly dependent to the network reliability indices, specifically those determining network inability to supply demand, the Expected Energy Not Served (EENS) reliability index is used to calculate the curtailment costs. By illustrating the advantages of Inherit Based Genetic Algorithm (IBGA) over the other two strategies, namely Simple Genetic Algorithm (SGA) and 3-matrices Genetic Algorithm (3MGA), IBGA is used to solve the proposed Reliable, Environmental Economic Dispatch (REED) problem in a MG.

Abstract
In this study, a non-probabilistic program is proposed to a trading framework for demand response (DR) aggregators. Both sides of the aggregator, including upper side and down side of this entity, have been taken into account. In the down-side of the aggregator, two popular programs are considered such as reward-based program and time-of-use (TOU) program, where DR is obtained from these resources. The acquired DR is being sold to the purchasers in the other side of the aggregator through DR options and fixed DR contracts. To the aim of increasing the desired target profit of risk-seeker aggregator, an opportunity function of information-gap decision theory (IGDT) is used to handle the uncertainty, which is solved in General Algebraic Modeling System (GAMS) software. This model is implemented in a realistic case study.

Abstract
The proposed model analyzes the profit of a demand response (DR) aggregator from trading DR in the day-ahead electricity market in a way that it tends to gain profit from the favorable deviations of the uncertain parameters. Two types of DR programs are implemented in this model, i.e., time-of-use and reward based DR program. The information-gap decision theory is being employed as a risk measure to address the uncertainties. Two uncertain parameters from both sides of the aggregator have been taken into account in this model, such as the participation rate of the consumers in reward-based DR program in the consumer-side of the aggregator and the day-ahead market prices in the wholesale-side of it. The program is simulated in GAMS software using the available commercial solver. Real data is considered to check the feasibility of the proposed program.

Abstract
During recent decades with the power system restructuring process, centralized energy sources are being replaced with decentralized ones. This phenomenon has resulted in a novel concept in electric power systems, particularly in distribution systems, known as Distributed Generation (DG). On one hand, utilizing DG is important for secure power generation and reducing power losses. On the other hand, widespread use of such technologies introduces new challenges to power systems such as their optimal location, protection devices' settings, voltage regulation, and Power Quality (PQ) issues. Another key point which needs to be considered relates to specific DG technologies based on Renewable Energy Sources (RESs), such as wind and solar, due to their uncertain power generation. In this regard, this paper provides a comprehensive review of different types of DG and investigates the newly emerging challenges arising in the presence of DG in electrical grids.