Abstract
The close interaction between the electricity market and the end-users can assist the demand response (DR) aggregator in handling and managing various uncertain parameters simultaneously to reduce their effect on the aggregator's operation. As the DR aggregator's main responsibility is to aggregate the obtained DR from individual consumers and trade it into the wholesale market. Another responsibility of the aggregator is proposing the DR programs (DRPs) to the end-users. This article proposes a model to handle these uncertainties through the development of a novel hybrid stochastic-robust optimization approach that incorporates the uncertainties around wholesale market prices and the participation rate of consumers. The behavior of the consumers engaging in DRPs is addressed through stochastic programming. Additionally, the volatility of the electricity market prices is modeled through a robust optimization method. Two DRPs are considered in this model to include both time-based and incentive-based DRPs, i.e., time-of-use and incentive-based DR program to study three sectors of consumers, namely industrial, commercial, and residential consumers. An energy storage system is also assumed to be operated by the aggregator to maximize its profit. The proposed mixed-integer linear hybrid stochastic-robust model improves the evaluation of DR aggregator's scheduling for the probable worst-case scenario. Finally, to demonstrate the effectiveness of the proposed approach, the model is thoroughly simulated in a real case study.

Abstract
Transmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem.

Abstract
Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The com-bination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is pre-sented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator. Superscript/Subscript Available

Abstract
The trend towards a decentralized, decarbonized, and digital energy system is gaining momentum. A key driver of this change is the rapid penetration increase of Distributed Energy Resources (DER). Commercial consumers can offer significant contributions to future energy systems, especially by engaging in demand response services. Virtual Power Plants (VPP) can aggregate and operate DERs to provide the required energy to the local grid and allowing for the participation in wholesale energy markets. This work considers both the technical constraints of the distribution system as well as the commercial consumer's comfort preferences. A stochastic mixed-integer linear programming (MILP) optimization model is developed to optimize the scheduling of various DERs owned by commercial consumers to maximize the profit of the TVPP. A case study on the IEEE 119-bus test system is carried out. Results from the case study show that the TVPP provides optimal DER scheduling, improved system reliability and increase in demand response engagement, while maintaining commercial consumer comfort levels. In addition, the profit of the TVPP increases by 49.23% relative to the baseline scenario.

Abstract
This paper deals with the bi-level two-stage operation scheduling of hydrogen-based microgrids within a distribution system where the wind and solar generation and load demands are considered as uncertain variables. The distribution system is considered as a leader in the upper level and microgrids as followers in the lower level. Unlike previous approaches, the upper-level is within the day-ahead market and considered a deterministic problem, and the lower-level is considered a stochastic problem and consists of two stages. The first stage determines the purchasing power from the distribution system, while the second stage adjusts the outputs and power dispatch for any realizations of scenarios. This model is transformed from a bi-level to a linear single-level model by applying the Karush- Kuhn-Tucker (KKT) optimally conditions, strong duality, and Fortuny-Amat methods. Several comparisons have been carried out regarding the single clearing price for all microgrids or separate prices for each microgrid. Furthermore, power exchange and dispatch in the distribution system are investigated under the mentioned frameworks.

Abstract
This paper proposes an energy hub management model for residential, commercial, and industrial hubs, considering demand response programs (DRPs). The network configuration and AC optimal power flow (ACOPF) constraints have been applied to the model to prevent any unreal power transaction in the system. The cost due to environmental emissions has also been taken into account and the problem is modeled as a dynamic optimization problem, solved using the CPLEX solver in the GAMS software, interfaced with MATLAB/MATPOWER for the power flow analysis. Besides, the problem is studied in two cases as coordinated and uncoordinated operation modes to investigate their impacts on the operating cost, emission, and power losses. The obtained results show that the coordinated operation would lead to reducing the operating cost, power losses, and emission. Moreover, the impacts of the coordinated and uncoordinated operation modes on the load demand-supply under contingent events and disconnection from the upstream grid are assessed. The results derived from the simulation verify the superior performance of the coordinated operation. It is also noted that the DRP leads to mitigating the operating costs.