Abstract
Premiums for renewable energy are being reduced as a consequence of the world economic crisis. This paper models the trading of the energy generated by a photovoltaic generator. The problem is solved through stochastic mixed integer linear programming where the objective function aims at maximizing the profit of selling the photovoltaic production in the day-ahead market. The model is tested without any premium and market and imbalance market prices are forecasted using AR, MA and ARIMA models while photovoltaic production is simulated using Montecarlo method. The model is tested for a case study where the behaviour of the offer, imbalances, incomes and costs is analyzed.

Abstract
The intermittent nature of wind generation will lead to greater demands for operational flexibility. Traditionally, reserves came from conventional power plants provide the majority of additional required flexibility leading to higher efficiency losses due to technical restrictions of such units. Recently, demand response programs and emerging utility-scale energy storages gained much attention as other flexible options. Under this perspective, this paper proposes a robust optimization scheduling framework to derive an optimal unit commitment decision in systems with high penetration of wind power incorporating demand response programs as well as bulk energy storages in co-optimized energy and reserve markets. In this regard, an improved demand response model is presented using the economic model of responsive loads based on customer's behavior concept that gives choice right opportunity to customers in order to participate in their desired demand response strategy. Moreover, bulk energy storages are considered to be as active market participants. Computational results demonstrate how coordinated operation of different type of demand response programs and bulk energy storages can help accommodate wind power uncertainty from the economic and technical points of view.

Abstract
This paper proposes a load frequency control scheme for an autonomous hybrid generation system consisting of wind turbine generator (WTG), diesel engine generator (DEG), fuel cell (FC), aquaelectrolyzer (AE) and battery energy storage system (BESS). In wind power generation systems, operating conditions are changing continually due to wind speed and load changes, having an effect on system frequency. Therefore, a robust controller is required for load frequency control. The control scheme is based on Linear Matrix Inequality (LMI)-Linear Quadratic Regulator (LQR). The control optimization problem is obtained in terms of a system of LMI constraints and matrix equations that are simultaneously solved. The proposed load frequency control scheme with the advanced LMI-based-LQR (ALQR) design is applied for the autonomous hybrid generation system. The effectiveness and robustness of the proposed controller is demonstrated for different load and wind power perturbations. The results suggest superior performance of the proposed ALQR controller against an optimal output state feedback controller. The integrated control could be realized though the web by applying Internet of Things technologies within the future smart grid.

Abstract
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices. © 2015 IEEE.

Abstract
The variable and uncertain nature of the leading renewable energy resources, such as wind power generation, imposes the development of a sophisticated balance mechanism between supply and demand to maintain the consistency of a power system. In this study, a two stage stochastic programming model is proposed to procure the required load-following reserves from both generation and demand side resources under high wind power penetration. Besides, a novel load model is introduced to procure flexible reserves from industrial clients. Load following reserves from load serving entities (LSE) are also taken into account as well as network constraints, load shedding and wind spillage. The proposed methodology is applied to an illustrative test system, as well as to a 24-node system.

Abstract
In this paper, a new mechanism is proposed to apportion expected reserve costs between electricity market agents in the power system. The uncertainties of generation units, transmission lines, wind power generation and electrical loads are considered in this model. Hence, a Stochastic Unit Commitment (SUC) is used to apply the uncertainty of stochastic variables in the simultaneous energy and reserve market clearing problem. Moreover, electrical customers can participate in the electricity market based on their desired strategies. In this paper, a novel method is proposed to allocate reserve costs between GenCos, TransCos, electrical customers and wind farm owners. Consequently, market agents are responsible for paying a portion of the allocated expected reserve costs based on the economic metrics that are defined for the first,time in this paper. Finally, two cases including a 3-bus test system and IEEE-RTS are utilized to illustrate the performance of the proposed mechanism to share the expected reserve costs.