Abstract
Transactive energy (TE) is a viable framework to tackle the load-generation mismatch in energy systems with high penetration of renewable energy resources (RERs). In this paper, we propose a TE framework for prosumers with heating, ventilation, and air conditioning (HVAC) systems to address real-time power shortage in a residential microgrid. Our framework consists of two phases. First, to mitigate load-generation mismatch, we develop an online appliance scheduling method to determine the optimal operation schedule of each prosumer's appliances. In particular, we apply receding horizon optimization (RHO) to tackle the load and renewable generation uncertainties and to better match the real-time power consumption of the appliances with the priorly-purchased power from the day-ahead market. Second, in case that there still exists power shortage at the microgrid level, a TE market based on pay-as-market clearing price (MCP) is proposed among prosumers to reduce the power consumption of their HVAC systems. We capture the competition among the participating prosumers as a non-cooperative game and develop an algorithm to achieve the Nash equilibrium, while considering prosumers' willingness to participate in the TE market. Extensive simulations are performed to demonstrate the efficiency of our proposed TE framework.

Abstract
In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives.

Abstract
Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing the grid. This article proposes an ultra-short-term PV power forecasting model based on the optimal frequency-domain decomposition and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency-domain analysis. Then, the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then, a convolutional neural network (CNN) is used to forecast the low-frequency and high-frequency components, and the final forecasting result is obtained by addition reconstruction. Based on the actual PV data in heavy rain days, the mean absolute percentage error (MAPE) of the proposed forecasting model is decreased by 52.97%, 64.07%, and 31.21%, compared with discrete wavelet transform, variational mode decomposition, and direct prediction models. In addition, compared with recurrent neural network and long-short-term memory model, the MAPE of the CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of the CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this article can improve both forecasting accuracy and time efficiency significantly.

Abstract
In recent years, simultaneous participation in energy and ancillary services (AS) markets has been very profitable for microgrids (MG). High penetration of renewable energy sources (RES) in energy supply, due to the uncertainties of these products, increases the need for AS. Also, active and reactive powers are completely related, so in this paper the microgrid simultaneous participation in the active and reactive power and ancillary services (regulation up and regulation down, spinning reserve and non-spinning reserve) markets is modeled considering uncertainty of wind and solar generations. The relation between active and reactive power generation of each generator is calculated based on capability diagrams and mathematical equations. Conditional value at risk (CVaR) is used for risk management, and probability of calling ancillary services is calculated. Uncertainties of wind and solar generations are modeled using their probability distribution functions (PDFs). The ERCOT market simulation is discussed to calculate the participation of each unit in all the mentioned markets based on realworld data.

Abstract
Energy hubs (EHs) are units wherein multiple energy carriers can be converted, stored and conditioned to simultaneously supply different energy demands. In this paper, a new model is proposed for unit commitment in renewable EHs with electric, thermal and cooling demands, different storage systems, combined heat and power (CHP) unit, boiler, electric chiller, absorption chiller, PV module, wind turbine and battery charging station (BCS). Using information gap decision theory (IGDT), day-ahead EH scheduling is done from risk-neutral, risk-averse and risk-seeking perspectives, considering the un-certainties of electric demands, BCS demands, heat demands, cooling demands, PV and wind power and electricity prices. Comprehensive models are used for storage systems considering their degradation, charging loss, discharging loss and storage loss; the ramp-up and ramp-down rate limits, start-up and shut-down costs of CHP, boiler and cooling components are considered. The effect of risk as well as effect of critical cost deviation factor and target cost deviation factor on EH operation cost and schedule of EH components is investigated. The findings indicate that the sensitivity of EH operation cost may be very different with respect to different sets of uncertain input data. The findings also show the significant effect of risk-awareness on schedule of EH components and its operation cost.

Abstract
The ever-increasing penetration of wind energy generation (WEG) and electric vehicle (EV) batteries in power systems could bring two significant challenges to day-ahead energy balancing markets. First challenge, the uncertainty of WEG and random behavior of the EV batteries can raise energy imbalance in energy markets. Second challenge, the intermittent nature of WEG and uncontrolled EV batteries charging, bring high congestion costs and new congestion patterns in transmission system. In this condition, there is no guarantee that the WEG is deliverable throughout the power system. The hourly coordination of WEG units with EV batteries can play important roles in addressing the first challenge. Similarly, to addressing the second challenge, a transmission impedance adjustment (TIA) device is taken as a promising way to reduce transmission congestion and promote the integration of large-scale WEG and EV batteries through controlling the power flows. However, utilization of TIA device is limited nowadays due to the complexity of this device in the optimization problem of day-ahead energy market clearing with the DC approximation of the power flow network. Consequently, a computationally effi-cient methodology, which is compatible with existing customary solvers, is proposed to adjust the TIA device set point with minimal modification efforts. An adaptive robust optimization formulation is adapted in the proposed problem to handle the WEG uncertainty. Finally, the simulation results on six and IEEE 118 bus test systems suggest that: 1) substantial economic savings can be achieved through utilization of WEG and storage capability of EV batteries, beyond the independent capabilities of TIA technology; 2) the TIA device plays a critical role in removing transmission congestion; and 3) the storage capability of EV batteries could relieve the uncertainty of WEG and increase its dispatchability.