Abstract
The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method.

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Abstract
In this paper, an agent-based model is proposed to improve the electricity market efficiency by using different demand response programs (DRPs). In the proposed model, the strategic self-scheduling of each market player in the electricity market and consequent market interactions are considered by using a game theoretic model powered by a security constrained unit commitment. The tariffs of price-based DRPs and the amount of incentive in the incentive-based DRPs are optimized. Furthermore, a market power index and the operation cost are used to evaluate the market efficiency by using a multi-objective decision-making approach. The results show that different types of DRPs differently affect the oligopolistic behavior of market players, and the potential of market power in power systems can be mitigated by employing the proposed model for DRP optimization. Numerical studies reveal that applying combinational DRPs is more efficient when the regulatory body considers both economic and market power targets.

Abstract
This paper aims to identity the significant impact factors (IFs) of the residential electricity consumption level (RECL) and to better understand the influence mechanism of IFs on RECL. The analysis of influence mechanism is commonly through regression model where feature selection must first be performed to pick out non-redundant IFs that is highly correlated with RECL. In contrast to the existing studies, this study recognizes the problem that majority feature selection methods (e.g., step regression) are limited to the identification of linear relationships and proposes a novel wrapper feature selection (WFS) method to address this issue. The WFS is based on genetic algorithm (GA) and multinomial logistic regression (MLR). GA is a searching algorithm used to generate different feature subsets (FSs) that consist of several IFs. MLR is a modeling algorithm used to score these FSs. Further, maximal information coefficient (MIC) is utilized to verify the validity of WFS for selecting IFs. Finally, MLR based explanatory model is established to excavate the relationship between selected IFs and RECL. The results of Ireland dataset based case study show that WFS can identify the significant and non-redundant IFs that are linearly or nonlinearly related to RECL. The details about how selected IFs affect RECL are also provided via the explanatory model. Such research can provide useful guidance for a wide range of stakeholders including local governments, electric power companies, and individual households.

Abstract
The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO's risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

Abstract