Abstract
Multienergy systems (MES) can optimally deploy their internal operational flexibility to use combinations of different energy vectors to meet the needs of end-users and potentially support the wider system. Key relevant applications of MES are multienergy districts (MEDs) with, for example, integrated electricity and gas distribution and district heating networks. Simulation and optimization of MEDs is a grand challenge requiring sophisticated techno–economic tools that are capable of modeling buildings and distributed energy resources (DERs) across multienergy networks. This article provides a tutorial-like overview of the state-of-the-art concepts for techno–economic modeling and optimization of integrated electricity–heat–gas systems in flexible MEDs, also considering operational uncertainty and multiple grid support services. Relevant mixed integer linear programming (MILP) formulations for two-stage stochastic scheduling of buildings and DER, iteratively soft-coupled to nonlinear network models, are then presented as the basis of a practical network-constrained MED energy management tool developed in several projects. The concepts presented are demonstrated through real-world applications based on The University of Manchester MED case study, the details of which are also provided as a testbed for future research.
Highlights
The energy sector has been planned, developed, and operated as a group of decoupled systemsMartínez Ceseña et al.: Integrated Electricity–Heat–Gas Systems: Techno–Economic Modeling, Optimization, and Application to multienergy districts (MEDs) that supply electricity, gas, and other energy vectors to end-users
The multienergy system (MES) concept challenges that idea by recognizing that what end-users require are services, and these may be provided through multiple systems and various combinations of energy vectors [1]–[3]
MEDs benefit from resource diversity and flexibility opportunities across space, energy network/vector, and time: for example, by allowing multivector exchanges between buildings that take surplus electricity generation [e.g., from photovoltaics (PV)] from one building and store it as a different vector in another building, for example, using electric heat pumps (EHPs) and thermal energy storage (TES) [14], [15]
Summary
The energy sector has been planned, developed, and operated as a group of decoupled systems. The multienergy system (MES) concept challenges that idea by recognizing that what end-users require are services (e.g., lighting and heating), and these may be provided through multiple systems and various combinations of energy vectors (e.g., integrated electricity–heat–gas systems) [1]–[3]. M E S , FLEXIBILITY , A N D DISTRICT’S DER AND BUILDING O P E R AT IONALOPTIMIZ AT I O N This section overviews the concept of MES (compared to decoupled energy systems) and operational flexibility for MEDs. Multienergy flexibility, in particular, arises as an extension of demand side flexibility enabled by combinations of different energy vectors. As a too general MES representation of buildings may be unsuitable to capture their flexibility, specific building models are introduced
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