Abstract
In this study, a direct load control strategy for procuring flexibility from residential heating, ventilation, and air conditioning (HVAC) units and the optimal management of shared energy storage systems connected at different buses of a distribution system is proposed, as a new contribution with respect to earlier studies, aiming to minimize the energy demand during DR event periods. Moreover, an additional objective related to the minimization of the end-users' discomfort induced by the interruption of the HVAC units is considered, leading to the formulation of a bi-level optimization problem based on a second-order conic programming representation of the AC power flow equations. The effectiveness of the proposed methodology is demonstrated by performing simulations on a test system and comparisons with other approaches.

Abstract
This paper deals with a control technique based on inherent characteristics of synchronous generators (SG) for control of interfaced converters with high penetration of renewable energy resources (RERs) into the power grid, as a new contribution to earlier studies. To present an appropriate assessment of the proposed control technique, under dynamic operating condition, a P-Q curve is extracted and analysed based on the different components and characteristics of the interfaced converter as well as the conventional relationship between the active and reactive power. By combining the swing equation of SG and the power-based dynamic model, a P-m-Q curve is achieved and the effects of the variations of embedded virtual inertia on virtual mechanical power are assessed. Moreover, by using small-signal linearization, the grid frequency stability is investigated based on both virtual inertia and mechanical power variations. In order to assess the power sharing ability of the proposed control technique, two transfer functions are obtained and then, the impacts of variations of virtual mechanical power on the active and reactive power of interfaced converter are evaluated through Nyquist and Root Locus diagrams. Simulation results confirm that the proposed control technique can guarantee the operation of interfaced converters, based on inherent characteristics of SG, to deal with the power grid stability with high penetration of RERs.

Abstract
Market cooperation in the electrical sector is crucial for competitiveness improvement. In the particular case of ancillary services, it was only carried out within a national context until the recent past. In the Iberian case, since the middle of 2014, a bilateral mechanism has allowed tertiary reserve sharing between the Portuguese and the Spanish Transmission System Operators (TSOs). This mechanism generates gains for the Portuguese electrical system. However, with a high level of cooperation, these gains could be improved. The Portuguese TSO is one of the most peripheral TSOs in Europe and, as such, it considerably benefits from market integration, in the various dimensions of the electrical sector. Hence, as a new contribution to earlier studies, this paper evaluates, from the Portuguese strategic perspective, what would happen to revenues when sharing replacement reserves without any restriction, considering a full integration of Europe's southwest countries in contrast to the traditional bilateral solution that is currently in place. The new methodology used in this paper is a cross-comparative evaluation between different bilateral combinations of TSOs' offers. A comprehensive study is presented and conclusions are duly drawn.

Abstract
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and future data usually produces larger errors because the different fluctuation patterns in electricity price data show different mapping relations. A daily pattern prediction (DPP) based classification modeling approach for DAEPF is proposed to solve this problem. The basic idea is that first recognize the price pattern of the next day from the "rough" day-ahead forecasting results provided by conventional forecasting methods and then perform classification modeling to further improve the forecasting accuracy through building a specific forecasting model for each pattern. The proposed approach consists of four steps. First, K-means is utilized to group all the historical daily electricity price curves into several clusters in order to assign each daily curve a pattern label for the training of the following daily pattern recognition (DPR) model and classification modeling. Second, a DPP model is proposed to recognize the price pattern of the next day from the forecasting results provided by multiple conventional forecasting methods. A weighted voting mechanism (WVM) method is proposed in this step to combine multiple day-ahead pattern predictions to obtain a more accurate DPP result. Third, the classification forecasting model of each different daily pattern can be established according to the clustering results in step 1. Fourth, the credibility of DPP result is checked to eventually determine whether the proposed classification DAEPF modeling approach can be adopted or not. A case study using the real electricity price data from the PJM market indicates that the proposed approach presents a better performance than unified modeling for a certain daily pattern whose DPP results show high reliability and accuracy.

Abstract
The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

Abstract
In this paper, the integration of a solar power plant to an electric vehicle (EV) Parking Lot is analyzed in terms of reduction of power consumption and losses for various scenarios and operating conditions in a distribution system. The parking lot is designed for EVs and is fed by both grid and roof mounted photovoltaic (PV) panels. The energy management system is designed for charging EVs for various scenarios combined with solar radiation data varying during daytime and the seasons. The energy transactions are simulated in accordance with daytime Solar Power Plant (SPP) generation and EV energy response on Electrical Power System Analysis Software (ETAP) environment. EVs based power consumption is calculated by considering the variation of charging sequences of different car brands. The study represents the results for daily change of power consumption for summer and winter conditions along with the reduction of power consumption, reduction of power losses, decreased main feeder ampacity, restoration of voltage level during SPP operation, and comparison of different scenarios and operation sequences. Finally, the effect of the SPP is presented in terms of reducing the peak and continuous power demand of the parking lot for various EV operations.