IEEE Power & Energy Magazine - January/February 2017 - 52

to retain the ability to control their own energy assets and
pursue their business, performance, and reliability objectives, while acknowledging interdependencies among energy
subsystems. Although decomposition methods can conceivably be applied to nonconvex programs, convex surrogates
of the power-water-thermal-gas flow equations and convex
relaxations of binary constraints typically facilitate the
development of distributed schemes with improved convergence properties.
Identifying constraints that couple energy assets and subnetworks managed by different entities is key to achieving these goals. For example, water-power coupling constraints pertain to the power/speed of pumps; coupling among
energy hubs may be via power lines, district heating/cooling pipes, and gas pipes connecting the hubs. After formulating
partial Lagrangian functions based on the coupling constraints,
a variety of techniques (including primal-dual-gradient-type
methods and the alternating direction method of multipliers) can be leveraged to derive a relevant distributed optimization procedure.
In particular, these methods allow one to develop algorithms for cases where a) different systems are managed by
different operators, as illustrated in Figure 8; b) energy hubs
are owned and controlled by different multi-energy actors; c)
commercial and residential customers retain control of their
own assets and participate in the provision of different power
system services (optimization task); and d) a combination of
a)-c). In all these configurations, the entities partaking of the
relevant optimization task retain control of their assets and
pursue their specific operational objectives. However, by
exchanging relevant optimization variables, they will achieve
the solution of the global optimization problem illustrated in
Figure 7, which naturally encapsulates multiflexibility options.
It is worth pointing out that distributed solutions involve
iterative schemes where the set points of the energy assets
are dispatched by each energy actor only upon convergence
of the algorithms. For fast time-varying operational landscapes, it is more desirable to resort to online optimization
schemes, where the set points of the devices are dispatched
as and when available, without necessarily waiting for the
distributed algorithm to converge. A challenge in this direction is to ensure that the set points produced by the online
algorithm do not induce violations of operational and security limits. A cross-fertilization of control and online optimization tools is, therefore, the key to enable the synthesis
of distributed feedback control schemes that ensure satisfaction of physical and security limits while tracking solutions of underlying multi-energy optimization problems.

Conclusions
Coordinated control of multi-energy systems at multiple
spatiotemporal scales promises significant benefits from
socioeconomic, operational efficiency, and environmental perspectives by leveraging the flexibility of various controllable

52

ieee power & energy magazine

assets to ensure a secure and economical supply-demand balance and provide reserve services. To enable such a level of
coordination at appropriate time scales, one possible approach
consists in formulating global optimization problems where
various performance objectives and (economic) indicators that
pertain to single-energy and multi-energy providers as well as
end customers are maximized and, subsequently, in leveraging
relevant optimization and control tools to develop computationally affordable distributed (and online) algorithms.
At a slower time scale, distributed algorithms enable
energy hubs, network operators, and (possibly) customers
to coordinate to achieve solutions of system-level dispatch
problems; at a faster time scale, control algorithms strategically decompose the decision-making process across actors,
while offering fast time-scale flexibility options and steering the operating points of multi-energy systems toward the
solution of global optimization problems. To this end, it is
critical to represent multi-energy system and network models as relevant optimization and control tasks and uncover
intrinsic convexity structures that lead to computationally
efficient distributed solutions.

For Further Reading
P. Mancarella, G. Andersson, J. A. Peças-Lopes, and K. R.
W. Bell, "Modeling of integrated multi-energy systems:
Drivers, requirements, and opportunities," in Proc. 19th
Power Systems Computation Conf., Genova, Italy, 2016, pp.
1-22.
M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klockl, G. Andersson, and K. Frohlich, "Energy hubs for the future," IEEE
Power Energy Mag., vol. 5, no. 1, pp. 24-30, Jan./Feb. 2007.
S. Clegg and P. Mancarella, "Integrated electrical and
gas network flexibility assessment in low-carbon multi-energy systems," IEEE Trans. Sustain. Energy, vol. 7, no. 2, pp.
718-731, Apr. 2016.
P. Mancarella and G. Chicco, "Real-time demand response
from energy shifting in distributed multi-generation," IEEE
Trans. Smart Grid, vol. 4, no. 4, pp. 1928-1938, Dec. 2013.
D. Müller, A. Monti, S. Stinner, T. Schlösser, T. Schütz,
P. Matthes, H. Wolisz, C. Molitor, H. Harb, and R. Streblow,
"Demand side management for city districts," Build. Environ., vol. 91, pp. 283-293, Sept. 2015.
E. Dall'Anese and A. Simonetto, "Optimal power flow
pursuit," IEEE Trans. Smart Grid, May 2016.

Biographies
Emiliano Dall'Anese is with the National Renewable Energy Laboratory, Golden, Colorado, United States.
Pierluigi Mancarella is with the University of Melbourne, Australia, and the University of Manchester, United
Kingdom.
Antonello Monti is with RWTH Aachen University,
Germany.
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