IEEE Electrification Magazine - June 2018 - 48

critical requirement for an operating system. Interdependencies and interactions in a multienergy flow system are
becoming stronger and tighter. on the one hand, disturbances and actions in one system will affect other systems in complicated ways and may cause unexpected
security problems in other systems. on the other hand,
coupling between energy systems can provide additional
resources to enhance security. this module performs
security assessment and control through an integrated
analysis method and has the following functions:
xx
It tells whether a multienergy flow system is in a secure
and normal state and give a warning, if necessary, to alert
operators. the n-1 security guideline is applied to assess
the security of the eI. contingencies include the failure of
each critical component in the electrical, heating, cooling,
and natural gas systems as well as the coupling components. the consequence of each contingency is evaluated, and security violations are calculated using adaptive
simulation models and efficient algorithms. Probability
and risk can be introduced to consider the uncertainty of
intermittent renewable energy.
xx
It identifies the system's weaknesses and bottlenecks.
through contingency analysis, components that may
cause security problems will be recognized so that
operators can pay more attention to them. In addition,
these items can be strengthened or replaced. Security
constraints can be formulated for optimal scheduling
and dispatch.
xx
It offers control suggestions when a system is in a condition of insecurity. this helps to pull the system back
into a secure and normal state as soon as possible and
at an affordable cost. Preventive, corrective, emergency,
or restorative control is activated according to the system operation state. complementarity and mutual
support among different energy systems are utilized,
e.g., by using the flexibility from heating and gas systems' inertia to eliminate electrical power congestion,
control tie line power flow, and increase system reserve
and ramping ability.
With the help of these subsystems, optimal scheduling
and dispatch can be achieved. With the latter, the objective
is to reduce operational costs and curtailment of renewables, so that energy efficiency and facilities' utilization rate
can be improved with a day-ahead/intraday/real-time
scheduling model. In this model, the power demand must
be satisfied and the power supply quality guaranteed,
which means that constraints of multiple-power balance,
multiple-network congestion, voltage level, mass flow rate,
and pipeline temperature and pressure must be considered.
correspondingly, the controllable variables are divided
into four areas:
1) the source area, e.g., electric, heating, and cooling
power generated by combined chP (cchP); gas boilers; heat pumps; lithium bromide refrigeration units;
electric refrigeration units; and importing electricity
from the bulk power system

I EEE E l e c t r i f i c a t i on M a gaz ine / j un e 2018

2) the grid area, encompassing such controllable devices in
the grid as pipeline valves, capacitors, distribution network reconfiguration switches, and transformer taps
3) the demand area, with controllable demand-side resources, e.g., charging and discharging of eVs, shiftable and interruptible loads, and distributed PV and
wind turbine resources
4) the storage area, e.g., the charging and discharging power of battery storage, hot water tanks, and ice storage.
For example, during electricity price valley hours, cchP
will generate less power, while industrial parks will purchase
lots of electricity from the bulk power market, electric refrigeration units will generate cooling power, and gas boilers will
supply heating power. In electricity price peak hours, cchP
will generate more power, so electricity purchased from the
bulk power market can be reduced, while the rest of the heat
can be supplied by gas boilers. that is because the cost of
purchasing power is less than cchP generation during peak
hours and exactly the opposite in valley hours. therefore, for
economic reasons, IeMS optimal scheduling and dispatch
chooses different strategies in different hours.
compared with the traditional eMS for distribution
system operators, the IeMS utilizes all of the possible controllable facilities in the integrated energy system (IeS) to
optimize the total operation cost. Since this cost is closely
related to the energy trading strategy with the bulk power
market, the multienergy virtual power plant becomes one
specific goal of optimal scheduling and dispatch, which
means that it is possible to make the IeS act like a virtual
power plant with the IeMS.
a well-functioning business model could help energy
systems function more economically and efficiently. For
pricing mechanisms, nodal electricity price theory serves
as the benchmark for market design in power systems. as
an extension of nodal electricity price theory, the nodal
energy price is proposed and applied in the IeMS, which
includes not only the electricity price but also the heat
price, cooling price, and gas price.
For example, since a large dhS is inert, many techniques
are different from an ePS, such as the operational regulation mode, transfer delay effects, and pipeline temperature
limitations. the nodal energy price models these effects
and the influence of energy coupling. Specifically, it contains four components: marginal generation cost, marginal
loss cost, marginal congestion cost, and energy coupling
cost. Based on these precise real-cost components, the nodal energy price can be a promising pricing option, reflecting
real production costs, encouraging customers to participate
in energy markets, and guiding in congestion management and loss allocation.

Demonstration of the IEMS in China
Background
an eI demonstration project under construction in guangzhou, china, is introduced here to illustrate the benefits of

Table of Contents for the Digital Edition of IEEE Electrification Magazine - June 2018