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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. [ABSTRACT FROM AUTHOR]

Emerging smart-grid-enabling technologies will allow an unprecedented degree of observability and control at all levels in a power system. Combined with flexible demand and storage, they could allow procuring energy at minimum cost and environmental impact. That however presupposes real-time coordination of demand of individual households and industries down at the distribution level, with generation at the transmission level. This is closely related with the balancing market economic dispatch (ED) problem, which currently does not take into account distribution network constraints and flexible demand characteristics. Still, assuming a suitably modified form of that problem was available, due to both computational and communications requirements, its centralized solution in its full detail would not be tractable. While there is currently a wealth of literature dealing with distributed optimization applications in power systems, it typically focuses on smaller parts of the overall energy management problem (e.g., transmission area synchronization or electric vehicles management) often without considering its full scale or establishing any association with energy market mechanisms. The target of this paper is twofold: 1) identify a flexible demand and distribution network inclusive formulation for ED and 2) propose a solution method. [ABSTRACT FROM AUTHOR]

The need for improved utilization of existing system assets and energy sources, as well as the smooth incorporation of new technologies (such as electric vehicles) into the grid, has prompted the participation of small power consumers and generators in the energy markets. A problem of such scale however cannot be managed in a centralized manner in its full detail. This paper examines the idea of a decentralized approach in clearing the energy market. A general framework for the problem decomposition and its distributed solution is presented and analyzed. A key point of interest in this work is the fundamental question of how far decomposition may be pursued for a given system, while still achieving reasonable convergence properties. The corresponding optimization problem is formulated and solved through a parallel implementation of the alternating direction method of multipliers (ADMM). A thorough investigation of its convergence properties is conducted, and through its coordination with an additional proximal based decomposition method, we improve its scalability characteristics. [ABSTRACT FROM AUTHOR]

The target of this work is the determination of the optimal solution in transmission expansion planning in the particular case of isolated systems involving dc interconnections. Therefore special attention is given to dc‐links modelling aspects for steady state analysis, as well as security constraints relevant to small sized systems operation. Therein lies the first contribution of this work, namely in the incorporation of operating reserve related considerations and simplified dc‐links models in isolated systems planning. The proposed method combines a Monte‐Carlo simulation, which enables the probabilistic assessment of power system operation, with an adaptive enumeration technique which recurrently determines and evaluates investment scenarios of interest, based on suitable probabilistic indices. This constitutes the second contribution of this work, as a map of all meaningful solutions to the planning problem may be generated, which in turn allows not only the determination of the optimal solution, but also the evaluation of the effects of over‐ or under‐investment in certain parts of the grid. [ABSTRACT FROM AUTHOR]