This paper presents an overview of a testbed for intelligent distribution grids, local energy systems, and energy flexible buildings, which is being developed at the campus of Chalmers University of Technology in Gothenburg, Sweden. It describes the test sites, the functionalities, and the planned demonstration activities within the scope of ongoing research projects. The proposed demonstrations include a local energy market platform, energy management solutions for microgrids and smart buildings, as well as voltage control in distribution grids. The paper aims to show how the physical energy supply systems of the university are being adapted to integrate the communication and control setups that provide the technical requirements for smart grid interoperability. As an example, the on-site implementation of remote battery control is presented, where initial results show the feasibility and potential benefits of the external control. Finally, challenges and lessons learned during the development of the testbed are highlighted.
This paper focuses on the optimal energy management of grid-connected microgrids with battery energy storage systems. The microgrid energy management and the optimal power flow of the distribution network are formulated as mixedinteger linear optimization problems to evaluate microgrid energy scheduling strategies including cost minimization, maximum use of own resources, and minimum energy exchange with the upstream network. The real distribution network of Chalmers University of Technology campus is used as a case study. The study results show that economic optimization yields an annual microgrid cost reduction of 4%. Alternatively, if the microgrid minimizes the energy exchange, virtual islanding operation (zero energy exchange) for 3211 hours can be achieved within a year. The results also present the effects on the operation and cost of the distribution system and highlight a trade-off between microgrid cost minimization and battery lifetime.
In this study, an optimisation model is developed for two-stage energy
management of a residential building to minimise energy cost under
monthly power-based tariffs for peak demand and time variable
electricity prices. The expected peak demand is determined in the first
stage, and then the energy management system minimises the energy cost
in the second stage. The optimisation problem of the second stage is
solved in a rolling time window for the real-time operation of the
flexible energy sources in the building. The optimal charging and
discharging of the battery energy system, the charging of the electric
vehicle battery, the operation of the heating system and the optimal
start times of washing machines and dishwashers are determined close to
real-time. The proposed approach allows the user to determine the
expected peak during the month ahead and try to keep the peak demand in
daily operation below that value using a close to real-time energy
management system. The performance of the two-stage approach for
demand-side management of a residential building has been validated by a
realistic case study.
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