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EV Aggregator Integration Strategy to Improve Distribution System Flexibility under the DERMS Framework

  • 이세미 (아주대학교 일반대학원)

초록/요약

This thesis proposes a new system for improving distribution system flexibility using electric vehicles (EVs) under the distributed energy resource management system (DERMS) framework. To encourage the use of EV flexibility, an opportunity cost-based adaptive mechanism is applied. In addition, a realistic probability distribution-based EV demand model is employed. The proposed DERMS framework comprises four hierarchical levels: device, aggregation, operational, and enterprise, with an optional virtual level. We implement a comprehensive process focused on EV flexibility under the proposed DERMS framework. First, the status of individual EVs is monitored at the EV charging station (EVCS). Based on this information, the quantified EVCS flexibility is transmitted by the EV aggregator (EVA). The EVA aggregates data from all managed EVCSs, simplifies them using curve fitting, and forwards them to the bus aggregator (BA). The BA, in turn, aggregates the data at the bus level and transmits them to the utility DERMS, the top-level system. Subsequently, the utility DERMS and BA sequentially perform optimizations, using optimal power flow and economic load dispatch, to command control signals to each lower system. Finally, the EVA disaggregates the allocated power and distributes it to the EVCSs, and each EVCS updates the charging schedules of individual EVs accordingly. The comprehensive process was validated on the IEEE 33-bus system for both charging only control and combined charging and discharging control scenarios.

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목차

I. Introduction 1
II. System Operational Strategy 4
II.A. Proposed DERMS Framework 4
II.B. Definition of EV Flexibility 6
II.C. System Mechanism 9
III. EV Demand Modeling 12
III.A. Sampling of EV Arrival Time 12
III.B. Sampling of EV Arrival SOC 14
IV. Upstream Process Modeling 16
IV.A. Monitoring of EV Status 16
IV.B. Quantification of EVCS Flexibility 18
IV.C. Aggregation and Simplification of EVA 20
IV.D. Distribution System Model and Aggregation of BA. 24
V. Downstream Process Modeling 26
V.A. Optimization by Utility DERMS 26
V.B. Optimization by BA 29
V.C. Disaggregation by EVA 31
V.D. Update of EV Schedule by EVCS 33
VI. Case Study 35
VI.A. Assumption and Input Data 35
VI.B. Performance Evaluation of the Proposed Approach 39
VI.C. Analysis of Simulation Results for a Year 43
VII. Conclusion 49
References 50

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