EPE 2022 - DS3g: Energy Management Systems | ||
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2022 ECCE Europe - Conference > EPE 2022 - Topic 06: Grids, Smart Grids, AC & DC > EPE 2022 - DS3g: Energy Management Systems | ||
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![]() | Comparative Evaluation of Partially-Rated Energy Storage Integration Topologies for High Voltage Modular Multilevel Converters
By Zoe BLATSI | |
Abstract: This paper compares three topologies (Partially Rated Stack - PRS, Stack Parallel Branch - SPB, Induc-tor Parallel Branch - IPB) which integrate energy storage solutions for HVDC-scale modular multilevelconverters to provide ancillary services such as frequency support. The comparison looks (i) at the extra power capability from their energy storage under maximum current and voltage limits of the converter and (ii) the extra penalties in terms of losses and components that these topologies require on top of a conventional MMC. Results indicate that PRS has the most consistent power capability across the power range but comes with higher losses and more extra components, while the SPB and IPB have each trade-offs between power capability, flexibility and efficiency.
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![]() | Energy Management of Smart Homes with Electric Vehicles using Deep Reinforcement Learning
By Xavier WEISS | |
Abstract: The proliferation of electric vehicles (EVs) has resulted in new charging infrastructure at all levels,including domestically. These new domestic EVs can potentially provide vehicle to home (V2H) serviceswhere EVs are used as energy storage systems (ESSs) for the home when they are not in use. Energymanagement systems (EMSs) can control these EVs to minimize the electricity cost to the owner but must satisfy constraints. Uncertainty in EV availability and the microgrid environment is also a challenge and can be addressed through real-time operation. Hence this paper formulates the EV charge/discharge scheduling problem as a Markov Decision Process (MDP). A safe implementation of Proximal Policy Optimization (PPO) is proposed for real-time optimization and compared to a day-ahead Mixed Integer Linear Programming (MILP) benchmark. The resulting PPO agent is able to minimize RA and SD costs for a typical EV user 3\% better than the MILP solution. It obtains a 39\% higher electricity cost than MILP, but unlike MILP does not require accurate forecasting data and operates in real-time.
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![]() | Stability Analysis in an inverter-dominant microgrid facing Inrush current of an induction machine
By Nastaran FAZLI | |
Abstract: In islanding operation with high shares of renewables, transient disturbances may put the systemin danger of instability. In such a situation, the frequency-voltage profile of the remaining powersystem, does not only depend on the depth of the unbalance, which appears following a transient,but also to the percentage of renewables and their control topologies as well as the dominantload on the islanded area. Hence in this paper, the renewables are controlled to act as a grid supporting current and voltage sources. Also the load characteristic is varying from dominantresistive, to dominant induction machine and converter- fed loads. The emergency measuresfor under- and over-frequency and voltage control is also considered the same in all simulationscenarios. The simulations are carried out in Matlab-Simulink via different scenarios to find outif the islanded power system and the generation units in that area are able to handle such atransient event considering different criteria via the emergency measures in the islanded powersystem.
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![]() | System Modeling and Design of a Hybrid Renewable Energy System for a Cable Network Head-End Station in Rural Area
By Tobias SCHILLINGER | |
Abstract: This paper focuses on system modeling of a small scale renewable hybrid energy system for a cable network head-end station in rural areas located in central European lower mountain and lowland regions. Based on one year measured energy demand and local weather data the entire system model allows a location dependent energetic simulation and optimization for individual configurations of the photovoltaic, wind energy and battery storage systems. Using selected examples, different system configurations at varying locations are simulated and compared to each other with regard to the number of photovoltaic modules, size of the battery and power of the small wind turbine.
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