Abstract

Abstract
This paper extensively models the interactions of emerging players in future power systems to analyze their impacts on electricity markets. To this end, renewable energy resources are modeled in such a way that wind power poses uncertainty on the supply side, and rooftop photovoltaics add uncertainty to the demand side. Moreover, both uncontrolled and controlled behaviors of individual electric vehicles (EV) in electricity markets arc addressed through a new EV model. Further, a comprehensive demand response model considering several customer-driven constraints is developed to undertake the practical constraints of customers. A stochastic market clearing formulation is presented to comprehensively account for the unique features of the given resources while evaluating their impacts. The numerical results clearly show the importance of such modeling in electricity markets to investigate the mutual impacts of emerging resources.

Abstract
The decision making framework in power systems has changed due to presence of distributed energy resources (DERs). These resources are installed in distribution networks to meet demand locally. Therefore, distribution companies (Discos) are able to supply energy through these resources to meet their demand at a minimum operation cost. In this framework, the Disco will change its role in the wholesale energy market from price taker to price maker. DERs can provide reserve in their normal operation; this facilitates the provision of reserves by the Disco in the wholesale reserve market. Therefore, in this paper, the strategic behavior of a Disco in wholesale energy and reserve markets is modeled as a bi-level optimization problem. In the proposed model, the operation problem of the Disco and the independent system operator are modeled in the tipper- and lower-level problems, respectively. Karush-Kuhn-Tucker conditions and duality theory are used to transform the proposed nonlinear hi-level problem to a linear single level one. Numerical studies show the effectiveness of the proposed model and its solution methodology.

Abstract

Abstract
This paper presents a new framework for optimizing the operation of Industrial MicroGrids (IMG). The proposed framework consists of three levels. At the first level, a Profit Based Security Constrained Unit Commitment (PB-SCUC) is solved in order to minimize the total expected cost of IMG via maximizing the IMG revenue by transacting in the day-ahead power market and optimizing the scheduling of the units. In this paper, the tendency of IMG for participating in the day-ahead power market is modelled as a quadric function. At the second level, a Security Constrained Unit Commitment is solved at the upper grid for minimizing the upper grid operation and guaranteeing its security. At this level, the accepted IMG bids in the day-ahead power market would be determined. Finally, at the third level, the IMG operator must settle its units on the basis of its accepted bids. Therefore, a rescheduling problem is solved in the third level. Notably, Renewable Energy Sources (RESs), Combined Heat and Power (CHP) units, thermal and electrical storage systems are considered in the IMG. As the RESs and day-ahead market price have stochastic behaviours, their uncertainty is taken into account by implementing stochastic programming. Further, different cases for grid-connected and island modes of IMG are discussed, and the advantages of utilizing RES and storage systems are given. The simulation results are provided based on the IEEE 18-bus test system for IMG and IEEE 30-bus test system for the upper grid.

Abstract
Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.