Abstract

Multienergy systems (MES), in which multiple energy vectors are integrated and optimally operated, are key assets in low-carbon energy systems. Multienergy interactions of distributed energy resources via different energy networks generate the so-called distributed MES (DMES). While it is now well recognized that DMES can provide power system flexibility by shifting across different energy vectors, it is essential to have a systematic discussion on the main features of such flexibility. This article presents a comprehensive overview of DMES modeling and characterization of flexibility applications. The concept of “multienergy node” is introduced to extend the power node model, used for electrical flexibility, in the multienergy case. A general definition of DMES flexibility is given, and a general mathematical and graphical modeling framework, based on multidimensional maps, is formulated to describe the operational characteristics of individual MES and aggregate DMES, including the role of multienergy networks in enabling or constraining flexibility. Several tutorial examples are finally presented with illustrative case studies on current and future DMES practical applications.

Highlights

  • In the context of the flexibility provided to the power system by distributed MES (DMES), a further feature not insofar highlighted in any study comes from the fact that electrical power may be broken down into its active and reactive components, so that the DMES model considers active power and reactive power it as if they were two different energy vectors

  • When network constraints are neglected, as shown in [19] for electrical flexibility, the aggregation of the flexibility provided by multiple electrical units mathematically corresponds to add the individual flexibility metrics of each unit through Minkowski summation

  • It should be noted that if wasting heat is not allowed, for example due to environmental regulation, the combined heat and power (CHP) would have no flexibility in varying heat production for a given electricity level as the P-H operational points would be linked by its characteristic line

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Summary

General context

HE historical transition towards low-carbon power and energy systems is primarily based on the integration of renewable energy sources (RES), low-carbon technologies (LCT) and distributed energy resources (DER), and is bringing unprecedented challenges to system operation. Besides the general interest in MES to decarbonise the whole energy sector (beyond electricity) at a lower cost via energy systems integration, there are further benefits arising from multi-energy vector system operation. In this respect, MES exhibit an exceptional potential to unlock value somehow hidden when considering only electricity, and access new forms of flexibility, on the demand side, which, as aforementioned, may be essential in RESbased energy systems [7]. As several major multienergy interactions take place through DER and involve multiple energy networks, there is a great interest in and potential flexibility options arising from distributed MES (DMES). The last section concludes the paper by discussing steps and challenges for practical deployment of DMES flexibility

MES components
Local MES model
ÒMulti-energy nodeÓ formulation
Electrical flexibility from MES
MES flexibility features and applications
Local renewable energy curtailment
Multi-energy service curtailment and comfort level arbitrage
Multi-energy production curtailment
Relaxation of ÒsoftÓ constraints
Visualization of MES operational envelopes: multi-energy flexibility maps
Aggregated multi-energy flexibility in a MES through
AGGREGATING FLEXIBILITY IN DMES
Multi-energy flexibility in DMES: networks as flexibility enablers
Impact of network constraints on multi-energy flexibility
Multi-energy network-related flexibility options and constraints
Energy network arbitrage
Utilization of network-embedded storage
Relaxation of network operational constraints
Illustrative examples
Flexibility for fixed demand
Flexibility with curtailable demand or demand increase
Flexibility for fixed demand with dynamic and griddependent limits
Indicative examples of profitability mapping onto the flexibility region
Findings
CONCLUDING REMARKS
Full Text
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