IEEE Power & Energy Magazine - January/February 2017 - 51
for optimization problems where the operation of power, to problem formulations where multi-energy systems are jointly
water, thermal, and gas systems is jointly optimized. Given optimized is largely unexplored. Ample research opportunities exthe underlying nonconvexity and NP-hardness, suboptimal ist for how to leverage advances in convex relaxation and approxisolutions are largely accepted (e.g., multistart techniques can be mation to develop computationally efficient solution methods for
used to possibly improve the quality of the solution and estimate multi-energy system optimization problems.
Key research issues involve the development of mechaits attraction basin). It is, however, worth pointing out that the
computational complexity becomes rapidly prohibitive with nisms that guarantee that the relaxed or approximated convex
increasing of the system size and the optimization horizon, problem yield feasible operational set points and the derivaespecially in the presence of various forms of energy storage; tions of conditions under which the solution of the convex
consequently, underlying optimization tasks for integrated multi- surrogate retains locally or globally optimized properties.
energy systems may not offer decision-making capabilities at Successfully proven convex relaxation and approximation
appropriate time scales and may not be adequate for operational techniques for nonlinear models can then be leveraged to
landscapes where the rate of set-point updates has to match fast derive mixed-integer convex surrogates for challenging nondynamics of system and ambient conditions. In the context of convex mixed-integer nonlinear programs.
future deregulated multi-energy markets, nonconvexity inherently challenges the development of provably convergent dis- Distributed Control
tributed solvers utilized to strategically decompose the decision- Different energy actors may own and operate different
making process across energy providers, users, and devices.
energy assets. In lieu of centralized problem formulations
Accordingly, recent research has focused on developing that require one single authority to supervise and control the
computationally affordable solution methods by leverag- overall multi-energy infrastructure, powerful decomposition
ing advances in convex relaxations and approximation methodologies can be leveraged to strategically decouple
of nonlinear (nonconvex) flow constraints. In the power- the solution of multi-energy system optimization problems
systems context, convex relaxation techniques enable a solu- across actors. Distributed strategies allow different parties
tion of the ac optimal power-flow
task with reduced computational
Communication Links
burden at both transmission and
distribution scales, while possibly
identifying globally optimal power-flow solutions. Sufficient condiMin f (water)
Min g (Power)
Min h (gas)
tions for tightness of semidefinite
s.t Water Constraints
s.t Power Constraints
s.t Gas Constraints
+ Consensus
+ Consensus
+ Consensus
programming and second-order
Constraints
Constraints
Constraints
code programming relaxations
are available for some classes of
system topologies and problem
Adjacent
setups, while the efficacy of these
Systems Solar
Industrial
methods for general topologies
Area
can be demonstrated by numerical
evidence. Further, powerful linear
Residential
H1
H2
Area
approximation methods enable
one to develop linear and quadratPumped Hydro
ic programming surrogates of the
(Storage)
ac optimal power-low problem,
the computational complexity of
which scales more favorably with
system size. Considering other sectors, relaxations have, for instance,
H3
H4
Commercial
Wind
been recently proposed for waterArea
flow problems based on, e.g., a
Electricity
Heating
Natural Gas
Water
second-order cone relaxation of the
relevant flow equations.
figure 8. Distributed optimization and control methods are leveraged to strategiAlthough the virtues of convex cally decompose the solution of the global optimization problem across energy hubs
optimization tools have been dem- (H ), networks, and operators. Each energy operator retains the ability to control
i
onstrated for specific problems its system, while global coordination is achieved to increase operational flexibility.
within the power-, water-, and gas-en- Online optimization tools can also be leveraged to offer decision-making capabilities
gineering domains, their application at appropriate time scales.
january/february 2017
ieee power & energy magazine
51
Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2017
IEEE Power & Energy Magazine - January/February 2017 - Cover1
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IEEE Power & Energy Magazine - January/February 2017 - Cover3
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