Abstract
This paper presents a pool trading model within a local energy community considering home energy management systems (HEMSs) and other consumers. A transparent mechanism for market clearing is proposed to incentivise active prosumers to trade their surplus energy within a rule-based pool market in the local energy community. A price-based demand response program (PBDRP) is considered to increase the consumers' willingness to modify their consumption. The mathematical optimization problem is a standard mixed-integer linear programming (MILP) problem to allow for rapid assessment of the trading market for real energy communities which have a considerable number of consumers. This allows for novel energy trading strategies amongst different clients in the model and for the integration of a pool energy trading model at the level of the local energy community. The objective function of the energy community is to minimize the overall bills of all participants while fulfilling their demands. Two different scenarios have been evaluated, independent and integrated operation modes, to show the impacts of coordination amongst different end-users. Results show that through cooperation, end-users in the local energy community market can reduce the total electricity bill. This is shown in a 16.63% cost reduction in the independent operation and a 21.38% reduction in the integrated case. Revenues for active consumers under coordination increased compared to independent operation of the HEMS.

Abstract
Protection and control systems represent an essential part of distribution networks by ensuring the physical integrity of components and by improving system reliability. Protection devices isolate a portion of the network affected by a fault, while control devices reduce the number of de-energized loads by transferring loads to neighboring feeders. The integration of distributed generation has the potential to enhance the continuity of energy services through islanding operation during outage conditions. In this context, this study presents a multi-objective optimization approach for sizing and allocating protection and control devices in distribution networks with microgrids supplied by renewable energy sources. Reclosers, fuses, remote-controlled switches, and directional relays are considered in the formulation. Demand and generation uncertainties define the islanding operation and the load transfer possibilities. A non-dominated sorting genetic algorithm is applied in the solution of the allocation problem considering two conflicting objectives: cost of energy not supplied and equipment cost. The compromise programming is then performed to achieve the best solution from the Pareto front. The results show interesting setups for the protection system and viability of islanding operation.

Abstract
The increasing penetration of renewable energy sources in areas with wholesale energy markets may have significant impacts on the prices of electricity within these markets. These renewable energy sources typically have low or zero marginal prices and thus can bid into energy markets at prices which might be below plants using other generating technologies. This work seeks to understand the impact of these zero marginal cost plants in the Iberian Energy Market. This work makes use of an Artificial Neural Network (ANN) to evaluate the impact of growing renewable energy generation on the market-clearing price. Real data from the Iberian Energy Market is chosen and used to train the ANN. The scenarios used for renewable energy generation are taken from the newly published national energy and climate plans for both Spain and Portugal. Results show that increasing penetration of renewable energy leads to significant reductions in the forecasted energy price, showing a price decrease of about 23 (sic)/MWh in 2030 compared to the baseline. Increasing solar PV generation has the largest effect on market prices.

Abstract
Hydrogen vehicle stations (HVSs) that convert electricity into hydrogen have appeared as a new arrival asset to the power system with the raising interest in hydrogen vehicles (HVs). In order to safely power these new assets, microgrids, including different flexible resources, are an ideal option. This paper presents an efficient MG scheduling in the presence of HVSs, renewable energy resources, energy storage systems (ESS) and demand response. This model also takes the uncertainties associated with electrical loads, renewables, and HVs into consideration. In order to create an MILP problem, linearized AC optimal power flow equations are considered. A 21-bus MG is examined by applying the proposed model to various case studies, thereby proving that the MG schedule meets the demand of HVs and electrical load. Employing DR programs can reduce operation costs and reduce the load during peak usage hours. Furthermore, the physical constraints of the network satisfy the security in operation. Finally, numerical analysis illustrates the effectiveness of the proposed method.

Abstract
There are significant changes occurring both in the electricity system and the natural gas system. These two energy carries can be combined to form what is known as an energy hub. These energy hubs can play a significant role in the energy system and thus understanding of their optimization, especially their costs, is important. This paper proposes a risk management framework for an energy-hub through the utilization of the information-gap decision theory (IGDT). The uncertainties introduced from the various load profiles, such as the electric and heating loads, are considered in this risk management framework. The modeled energy-hub consists of several distributed generation systems such as a microcombined heat and power (mu CHP), electric heat pump (EHP), electric heater (EH), absorption chiller (AC) and an energy storage system (ESS). A demand response (DR) program is also considered to shift a percentage of electric load away from the peak period to minimize the operational cost of the hub. A feasible test system is also applied to demonstrate the proposed model's effectiveness.

Abstract
Energy hub systems improve energy efficiency and reduce emissions due to the coordinated operation of different infrastructures. Given that these systems meet the needs of customers for different energies, their optimal design and operation is one of the main challenges in the field of energy supply. Hence, this paper presents a two-stage stochastic model for the integrated design and operation of an energy hub in the presence of electrical and thermal energy storage systems. As the electrical, heating, and cooling loads, besides the wind turbine's (WT's) output power, are associated with severe uncertainties, their impacts are addressed in the proposed model. Besides, demand response (DR) and integrated demand response (IDR) programs have been incorporated in the model. Furthermore, the real-coded genetic algorithm (RCGA), and binary-coded genetic algorithm (BCGA) are deployed to tackle the problem through continuous and discrete methods, respectively. The simulation results show that considering the uncertainties leads to the installation of larger capacities for assets and thus a 8.07% increase in investment cost. The results also indicate that the implementation of shiftable IDR program modifies the demand curve of electrical, cooling and heating loads, thereby reducing operating cost by 15.1%. Finally, the results substantiate that storage systems with discharge during peak hours not only increase system flexibility but also reduce operating cost.