Abstract
Phasor measurement unit (PMU) provides beneficial information for dynamic power system stability, analysis, and control. One main application of such useful information is data-driven analysis and control. This article presents an approach for optimal signal selection and controller structure determination in PMU-based power system stabilizer (PSS) design. An algorithm is suggested for selecting the optimal input and output signals for PSS, in which a combination of system clustering, modal analysis, and principal component analysis techniques is used. The solution for the optimal PSS input-output selection is determined to increase the observability and damping of the power system. The approach can efficiently reduce the number of input-output signals, while the overall performance is not deteriorated. Then, a linear matrix inequality-based technique is elaborated to design the PMU-based PSS parameters. The stabilizer design approach is formulated as a convex optimization problem and the appropriate stabilizer for pole allocation of the closed-loop model is designed. This method is simulated on two sample power systems. Also, to compare the results with the previous methods, the system is simulated and the results of two previously developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which decreases the number of required PMUs, while the system damping is not affected.

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.

Abstract
The combination of consumer owned Distributed Energy Resources, new Information and Communication Technologies (ICT), as well as changes to the national electricity regulations have created new opportunities for consumer engagement in the electricity sector. In this paper, this combination of technologies and regulations is examined in the Portuguese context. The new regulations dealing with self-consumption from prosumers are combined with smart contracts and distributed ledger technology to formulate an automated energy trading system for residential end-users in local energy markets. Results show that including prosumers in the local energy market brings significant benefits to all market participants. Additionally, results show that the newly created regulatory role of a Market Facilitator is beneficial to these type of local energy exchanges.