IEEE Power & Energy Magazine - January/February 2017 - 45
The generic schematics provided in Figure 2 include a
number of input energy vectors (electricity, water, natural gas,
hydrogen, and district heating); typical conversion elements
(such as CHP units, electric and absorption chillers, and water
pumps); and relevant outputs representing local generation and/
or electricity, heat and cooling, gas, and water demands. The
energy hub depicted in Figure 2 also contains a heat exchanger
for district-heating-network connection, energy-storage systems, and TES in the form of, e.g., hot water storage and icethermal storage. Additional elements not explicitly represented
in Figure 2 could include reversible EHPs, absorption chillers, power-electronics-interfaced renewable energy resources,
electrolyzers (with a hydrogen output), and different types of
engines or turbines.
The conversion stages can be captured through coupling
factors and/or conversion efficiencies; this black-box approach
reduces the level of complexity and the number of parameters
necessary to describe the energy hub's operation and, hence,
facilitates the development of computationally affordable control/optimization schemes, while maintaining adequate accuracy in the representation of the underlying system physics. A
generic energy hub model of Figure 2 can be tailored to specific configurations (e.g., the unit, facility, plant, or geographical area to be modeled) by retaining relevant components and
conversion stages and incorporating different levels of simplifications/reductions. Overall, the resulting model involves
an input-output efficiency matrix that can serve as a building
block for the formulation (and solution) of multisystem-level
optimization problems. Relevant optimization problems can be
used to compute the optimal energy mix for the hub to minimize operational costs or to optimize the operation of an interconnected system of energy hubs.
This article provides an overview of possible joint control and optimization approaches for multi-energy systems;
it also elaborates on core challenges related to the development of distributed control and optimization algorithms that
allow different parties to retain the ability to control their
own energy assets and pursue their individual performance
and reliability objectives, while acknowledging interdependencies among energy subsystems.
Example of Transmission-Level
Modeling and Applications
Recent efforts have looked at the transmission-level infrastructure from a multi-energy system viewpoint, with the
objective of assessing the potentials for flexible and reliable operation and developing innovative control approaches
that tap into the identified opportunities for flexibility. This
refers primarily to interaction between electricity and gas
networks, as well as their interaction with other energy sectors and vectors, such as heating and potentially hydrogen.
Conceivably, the gas network could represent a very large
storage facility that can assist in managing any excess
renewable-based power generation. Using renewable electricity to produce hydrogen (via electrolizers, through the
january/february 2017
so-called "power-to-gas" process) that could be blended
with natural gas or processed into methane, this otherwise
curtailed clean energy could then be transported and stored
in the gas network for successive use (even across seasons).
This process could also contribute to the decarbonization of
the hard-to-decarbonize gas sector.
A multi-energy setting could also highlight hidden operational cross-system flexibility constraints that may arise due
to, for example, limits in the local amount of gas that can be
provided to gas turbines to follow the net load, e.g., due to a
sudden, unexpected decrease in wind power. In fact, recent
events in various countries have pointed out the importance
of considering limitations of the gas infrastructure for simultaneous energy supply to the electricity and heating sectors,
especially under very cold conditions. From the standpoint
of future low-carbon settings, with more and more variable
renewable energy sources as well as greater options for delivery of heat (e.g., through EHPs and/or CHP), the evolving
electricity and heat sectors could bring about further new flexibility requirements in the gas network.
For instance, it might happen that there is not enough flexibility in the gas infrastructure (in the form of line pack, i.e.,
gas stored in the pipelines, especially in the presence of local
bottlenecks) to deliver fuel to gas-fired power plants to provide balancing and reserve power in the case of uncertain and
fast-changing renewable production. In cases like this, there
may be a need to constrain gas plants' ability to follow variations in the net electrical load they see and, instead, provide
reserves by introducing intertemporal (across time scales that
consider the line-pack storage and its limitations) and intersector (across gas and electricity) ramp-like constraints in the
(electrical) optimal-power-flow problem. Delivery of heat can
exacerbate this issue, because a more or less electrified heating sector may change the magnitude of the ramps in covering
the net load variations and the volume of gas (and, thus, the
line pack) in the gas network.
A challenge in this direction pertains to how to capture
this feature in mathematical terms and accommodate it into
+/-
Electricity
Water
Electricity
Water
Natural Gas
Cooling
Hydrogen
Distributed
Heat
Heating
figure 2. A generic energy hub model capturing a variety
of inputs/outputs and conversion stages, along with storage
of different energy types.
ieee power & energy magazine
45
Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2017
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