Combining Distributed Flexible Resources into a Virtual Power Plant
A Brief Presentation on Categorization and Application of Distributed Flexible Resources
Published in
3 min readSep 27, 2020
Background
- Flexibility Services Categorization…
- With Different Timescale
- With Different Spatial Features - Distributed Flexible Resources
- Purely Supply
- Purely Demand
- Storage
- Hybrid
Flexibility Services Categorization
With Different Timescale
With Different Spatial Feature
- Some flexibility services are (nearly) spatially homogeneous (effects are same independent of location of supply), e.g. frequency control under normal operation conditions
- Others are spatially heterogeneous (effects depend strongly on location of supply), e.g. voltage control or frequency control under islanded operation conditions
Cost Effectiveness of Different Resources
Flexible Distributed Resources (Demand): Towards VRE-Availability-Based Demand-Side Management
Operation Paradigm — Past
Operation Paradigm — Present
Operation Paradigm — Future
VRE-Availability-Based DSM: Capacity Allocation
- Assumption: N consumers, each has a profile 𝐷_𝑖(𝑡) (a stochastic time series), are to be supplied with a VRE fleet with fixed capacity 𝐶𝐴𝑃_0 and a maximum availability 𝐶𝐹(𝑡) (a stochastic time series)
- Basic case: without adjusting the demand, find the optimal capacity allocation set such that expected total VRE consumption of the consumers is maximized
VRE-Availability-Based DSM: Flexible Demand Scheduling
- Assumption: Each consumer is given fixed capacity allocation 𝐶𝐴𝑃_𝑖. Their demand can also be divided into two parts: a flexible part 𝐹_𝑖(𝑡) and an inflexible part 𝐼𝑛𝑓_𝑖(𝑡)
- The flexible part is of the demand is a deterministic value that can be shifted around its default time occurrence 𝑡_0 between the interval [𝑡_0−∆𝑡, 𝑡_0+∆𝑡]; the inflexible part of the demand is a stochastic time series.
- Objective: find the optimal schedule such that expected total VRE consumption of the consumers is maximized
