Abstract
This paper proposes an estimation strategy in order to address two arising trends in Power System State Estimation (PSSE). Through a hybrid two-stage estimation architecture, high quality measurements gathered by PMUs can be incorporated into PSSE without excluding the widespread employed SCADA measurements. In the first stage of the proposed estimation architecture, SCADA and PMU measurements are individual processed by Maximum Correntropy-based estimators that replace conventional WLS-based methods. The second stage makes use of fusion methods to optimally combine the estimates provided by the individual estimators in order to enhance the quality of final estimates. This architecture allows the inclusion of the new class of measurement while making the whole process bad data-resilient, due to the outlier-rejection properties of Maximum Correntropy-based algorithms.

Abstract

Abstract
This paper addresses the participation of an aggregator of small prosumersin the energy and tertiary reserve markets. A two-stage stochastic optimization model is proposed to exploit the load and generation flexibility of the prosumers. The aim is to define energy and tertiary reserve bids to minimize the net cost of the aggregator buying and selling energy in the day-ahead and real-time markets, as well as to maximize the revenue of selling tertiary reserve during the real-time stage. Scenario-based stochastic programming is used to deal with the uncertainties of photovoltaic power generation, electricity demand, outdoor temperature, end-users' behavior, and preferences. A case study of 1000 small prosumers from MIBEL is used to compare the proposed strategy to two other strategies. The numerical results show that the proposed strategy reduces the bidding net cost of the aggregator by 48% when compared to an inflexible strategy typically used by retailers.

Abstract
This paper proposes a two-stage stochastic optimization model to support an aggregator of prosumers in the definition of bids for the day-ahead energy and secondary reserve markets. The aggregator optimizes the prosumers' flexibility with the objective of minimizing the net cost of buying and selling energy and secondary reserve in both day-ahead and real-time market stages. The uncertainties of the renewable generation, consumption, outdoor temperature, prosumers' preferences, and house occupancy are modeled through a set of scenarios. For a case study of 1000 prosumers, the results show that the proposed bidding strategy reduces the costs of both aggregator and prosumers by 40% compared to a bidding strategy typically used by retailers.

Abstract

Abstract
The total losses volume represents a substantial amount of energy and, consequently, a large cost that is often included in the tariffs structure. Uneven connection of single-phase loads is a major cause for three-phase unbalance and a fundamental cause for active power losses, particularly in Low Voltage (LV) networks. This paper analyzes the impact of load unbalance on LV network losses. In the first phase, several load scenarios per phase are considered to characterize how losses depend on load unbalance. The second phase examines the data collected per phase on a set of real networks, aiming at illustrating real-world cases. The third phase analyzes the effect that public lighting and microgeneration may have in the load unbalance and on the subsequent energy losses. The results of this work clearly demonstrate that it is possible to reduce three-phase unbalance (and losses) through a judicious distribution of loads and microgeneration.