Abstract
In this work a new trading framework for demand response (DR) aggregators is proposed using a non-probabilistic model. In this model, DR is acquired from consumers to sell it to the purchasers by aggregators. Two programs, i.e., time-of-use (TOU) and reward-based DR program, are implemented to obtain DR from consumers. Then, the obtained DR is sold to buyers via two considered agreements, i.e., fixed DR contracts and DR options. The information-gap decision theory is also employed to consider the uncertainties for risk-averse aggregators. Consumer's participation behavior is considered as an uncertain parameter. A robustness function is proposed to examine the immunity of the model against adverse variations of uncertain parameters. The feasibility of the proposed model is studied on the real-world data.

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
This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot-based electric vehicles (EVs). Both thermal and electrical features of the multi-energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price-based and incentive-based demand response (DR) programs are modelled in the proposed multi-energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi-energy MGs.

Abstract
Energy arbitrage have monetary benefits for privately owned battery energy storage systems, such as the battery of an electric vehicle or residential batteries. However, the life cycle and degradation cost of the battery storage should be taken into consideration and can decrease obtained income in the long-term. This paper proposes an optimization framework to derive optimal bidding and offering curves for lead-acid battery storage participate in a stepwise energy market. The objective is to maximize the profit comes from participating in energy arbitrage action, while the life cycle of the battery is considered by objective function and constraints. Due to the small capacity of the considered storage unit, it can be assumed that this unit is a pricetaker participant, which its actions cannot influence the market prices. Hence, the energy prices are modeled as uncertain parameters using stochastic programming approach. The second order stochastic dominance constraints are as risk management method.