Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
1

Quantitative Value Evaluation of Power Generation
Facilities Using Simulation Technologyfor Power Supply Operation

HISAKI YAMAUCHI*1

KENTAROU SHIKATA*2

TAKEHIRO NAKA*3

MASAYA DEGUCHI*4

With the expansion of renewable energy and the liberalization of the electricity market , thecomplexity and uncertainty of the future power system and market are increasing and it is required
to provide high-value products and services to mitigate them and to contribute the realization of adecarbonized society. We use simulation technology for power supply operation to forecast theoperating schedule of each generator and the electric power price to minimize total social cost
under future scenarios of power demand, capacity mix, etc. while considering the constraints of thetransmission system. This report explains an overview of this technology and the originalevaluation framework of Mitsubishi Heavy Industries, Ltd. (MHI) for the improvement of ourproducts and services, while also giving application examples.

|1. Introduction
At present, decarbonization in the global power industry against the backdrop of global
warming issues and power system reforms toward the reduction of total social cost are being
concurrently promoted and we are facing drastic changes in policies and systems, such as themassive introduction of renewable energy and the establishment of trading markets for electricity
and environmental value. In Japan, studies on the use of renewable energy as the main power
resource toward the achievement of CO2 emissions reduction targets by 2050 are being carried out,
however, the further introduction of renewable energy will have a major influence on the powersystem and market. For example, it will have an effect on wholesale market prices, require moreinstalled capacity for system reliability, and lead to competitive markets for capacity and flexibility
values. In such a trend, it will become more complex than ever to evaluate the value of our power
generation products and services.
As shown in Table 1, we performed simulations for power supply operations for about 20countries and regions, and accumulated expertise especially through those for Europe and the U.S.
where renewable energy and liberalized electricity markets have been introduced ahead of othercountries. In this report, an outline of the simulation is described first, and then MHI’s original
evaluation framework for our product value—developed through application to various overseas
markets—and application examples are described.

*1

Research Manager, Service Engineering Department, Research & Innovation Center, Mitsubishi Heavy Industries, Ltd.

*2

Heat Transfer Research Department, Research & Innovation Center, Mitsubishi Heavy Industries, Ltd.

*3

Electricity & Applied physics Research Department, Research & Innovation Center, Mitsubishi Heavy Industries, Ltd.

*4

Service Engineering Department, Research & Innovation Center, Mitsubishi Heavy Industries, Ltd.

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
2Table 1Countries and regions for which we performed power supply operation
simulations

GroupEurope
Asia
Middle East
North America

Countries/regions
U.K., Ireland, Poland
Japan, China, Taiwan, Korea, Singapore, Thailand, Philippines, Indonesia, Vietnam,
Bangladesh, Myanmar, Australia
Saudi Arabia, Bahrain
U.S. (PJM, MISO, SPP, ERCOT, CAISO), Mexico|2. Simulation technology for power supply operation
The simulation for power supply operation is a method using a series of calculations for
obtaining the capacity mix and operation pattern of each generator that minimize total social cost to
meet power demand. The values of a generator are broadly classified under kW value for capacity,
kWh value for energy and ΔkW value for flexibility, and each value can be quantitatively evaluatedby this method. In this report, we mainly focus on the evaluation of kWh value and ΔkW value andprovide an outline of the simulation technology, major input items and evaluation framework for
product value.

2.1

Outline of the simulation technology for power supply operationThe kWh value of a generator is derived by solving the Unit Commitment/Economic
Dispatch problem. In this approach, generators are dispatched sequentially in ascending order ofgeneration cost (merit order) to meet power demand, and the operation schedule of each generator
is decided accordingly. Mixed-integer linear programming is used to consider the schedules ofON/OFF of each generator and charge/discharge of pumped-storage or storage batteries. The
objective function of this approach is the minimization of the total generation cost required to meetthe demand and is calculated using the generation costs of each generator consisting of fuel cost,variable operating and maintenance costs, CO2 countermeasures cost, etc., under the constraints
shown in Table 2. This demand-and-supply balance is evaluated at a certain time interval (every 30minutes or one hour in many cases) and the generation cost of a marginal generator which ispositioned at a point where supply and demand curves intersect is regarded as the kWh market
price for the time, based on which the future price is forecasted.
ΔkW value can be also derived at the same time with kWh value (Co-optimization). With
the increase of renewable energy, larger balancing capabilities are required and the importance ofthe value evaluation for flexibility provided by generators even increases. In modeling of theevaluation, generators, sequentially in ascending order of the amount of opportunity loss in thekWh market, supply flexibility according to the respective ramp rates until a given ΔkW demand issatisfied, under constraints such as the LFC (Load Frequency Control) band set for each generator.
Thus, the schedule of output distribution of each generator to the ΔkW market is forecasted as wellas the ΔkW market price.
In cases where transmission line constraints affect the operation schedule of each generator,
the power flow of each transmission line is evaluated, based on a grid model in which the points(nodes) are connected, by the DC-OPF method for mainly evaluating the active power. The
Locational Marginal Price (LMP) at each node is obtained as a shadow price against thetransmission capacity constraint of each transmission line. If the transmission capacity limit isexceeded and grid congestion occurs, the price at the demand-side node becomes higher than that at
the supply-side node. Therefore, the LMP is used as a price signal for promoting power generation,
construction of generators, etc., in regions with significant demand. It is predicted that with theincrease of renewable energy connected to the power system, grid congestion will occur in morecountries and regions. This method provides more accurate evaluation of the value of generators.

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
3Table 2 Major constraint conditions inpower supply operation simulation
Category
Power market

Thermal generator

Energy storage system(pumped-storage, etc.)
Transmission line

2.2Constraint conditions
Demand and supply in balance
Upper and lower limits of power output
LFC lower limit, LFC capacity
Upper limit of ramp rate (up/down)Start-up time
Minimum up time, minimum down time
Upper and lower limits of power output
Storage capacity
SOC (State of Charge) range
Upper limit of transmission capacity

Major input values

The representative items in input data used in this analysis model are shown in Table 3. For
future power demand and fuel prices, we often use information published by governmentalorganizations of each country, while for generators, we use information/knowledge that MHI
possesses as a manufacturer. Generation efficiency, in particular, is an important input value forforecasting the kWh market. The efficiency not only at rated load, but also at partial load, and theconsideration of deterioration due to long-term use affect the calculation accuracy. In addition, therefined input settings for operational flexibility such as minimum load and ramp rate provide moreaccurate evaluation of the ΔkW market.
Table 3Category
DemandFuel
Generator
Transmission line
Emissions (CO2)
Ancillary service
marketItem
Energy demand
Balancing demand
Fuel priceSee table on the right
Connection point
Transmission capacity
Resistance/reactance
Emission unit price
Required reaction time

Major input data
Category
Facility
information

Capacity/output

Performance

Cost
Maintenance
Others

Item
Location information
Operation start year/Retirement year
Fuel used
Installed capacity(generating end/transmission end)
Minimum load
LFC lower limit/LFC capacity
Rated generation/Partial load efficiency
Ramp rate (up/down)
Minimum operating time, minimum
stopping time
Emissions (CO2, etc.) per unit ofgeneration
Startup and shutdown cost
Variable operation and maintenance cost(consumables, etc.)
Planned outage period, Unplanned outage
probability
Assumed generation pattern (renewableenergy)
Charge and discharge efficiency

As an analysis model example, the Japanese grid model developed by MHI is shown in
Table 4. About 500 thermal and nuclear generators and top-two classes of transmission
lines/substations in each power supply area were modeled. For the demand and the generation ofrenewable energy in each power supply area, those published by each transmission and distribution
business operator were used. The power demand of each substation was distributed by thepopulation rate of the municipality where the substation is located.

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Table 4
Area
Hokkaido
TohokuTokyo
Chubu
Hokuriku
Kansai
Chugoku
Shikoku
KyushuOkinawa
Total

2.3

Configuration of Japanese grid model (2017)
Number oftransmission lines666114494
1794
494285108760

Number of nodes
604911078
1277
37327778
610

Number ofgenerators
244316573
1270
47
245215525

Value evaluation framework for generators

The schematic flow diagram in the simulation for the power market/system is shown in
Figure 1. In Step 1, the total cost minimization calculation for required installed capacity including
reserve capability to meet future demand is performed with the price trend of existing and futuretechnologies, feasibility of each market participant, value of lost load, etc., taken into account, andthis kW market model for planning future resources is also used to derive the kW market price.
In Step 2, based on the future capacity mix derived in Step 1, the aforementioned Unit
Commitment/Economic Dispatch problem is solved to derive the kWh market price and ΔkW
market price. In this step, schedules of all generators can be evaluated, but the calculation load islarge. Therefore, to examine any number of combination of product/service specifications, anefficient method with a smaller calculation load is required.
In Step 3, in order to evaluate the value of products/services in more detail, the calculation
method (Price-based Unit Commitment) which can output a schedule that maximizes the revenueof the target generator is adopted for the power prices obtained in Step 2. This calculation load isenough small that, by changing various parameters of the generator, we can evaluate the effects on
the schedule more easily. However, if the specification of the generator is largely changed from thespecification set in Step 2, whether it has an effect on the power price must be checked in Step 2again.
By comparing the expected future revenue obtained from the schedule and the power priceadding the kW revenue derived in Step 1 with the initial cost, effects of the specifications of
products/services on the return on investment are evaluated. By applying a statistical method, suchas the design of experiments, to this step, the sensibility of specifications can be evaluated more
efficiently.
With the aforementioned framework, products/service solutions under various futurescenarios can be studied in a relatively short time.

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
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Figure 1

Schematic flow diagram in the simulation for the power market/system|3. Evaluation example of future value of generator
In this chapter, the differences between the result of the analysis and the actual data arepresented, and then the value evaluation example for generators is described.

3.1

Comparison between the analysis result and the actual data

The generation amount by fuel and the kWh price derived in Step 2 were compared with theactual generation by fuel(1) and the area prices by Japan Electric Power Exchange (JEPX)(2) inFY2017, respectively (Figures 2 and 3). The comparison showed that the generation by fuel could
be predicted within ±10% and the weekly average area price could be predicted 8% to 14% incomparison with the actual values. It was also confirmed that the result reproduced qualitative
trends such as a rise in prices in high-demand periods in summer and winter.

Figure 2 Comparison ofactual power
generation by fuel inJapan (in FY2017)

3.2

Figure 3 Comparison of kWh price between analysis data andJapan Electric Power Exchange (JEPX) prices(2017, Kyushu area)

Evaluation example for the whole power system

By solving the Unit Commitment/Economic Dispatch problem in Step 2, the behaviors of allgenerators belonging to a target power system, as well as the power prices, can be forecasted.
Figure 4 shows an example of the calculation results, in which generators are indicated in meritorder on the horizontal axis, while annual average capacity factor, operating time ratio by load andannual number of starts of each generator are indicated on the vertical axis. This example shows thebasic relationship of merit order in which a generator with a lower generation cost has a higher
capacity factor and operates for a longer time at a higher load with a smaller number of starts.
However, this relationship is affected by the occurrence of grid congestion or the degree of
operational flexibility of each generator.

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
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Figure 4 Evaluation example for power supply operation
simulation

The value of a product/service can be derived from a change of schedule between before andafter the introduction of the product/service. Figure 5 shows the change of the annual schedule
over time in cases where the ramp rate of one thermal generator was increased. It is confirmed thatthe increase of the ramp rate allowed the generator to take on the balancing capability that had been
supplied by other generators, resulting in an increase of the operating time. The value can bequantitatively evaluated by the difference of the concurrently calculated kWh and ΔkW market
price between before and after this service was applied.
This result can be used not only for the evaluation of the target generator, but also for theevaluation of the whole system. In this example, the target facility took on a larger balancing
capability, a higher-efficiency generator increased the supply of kWh and reduced the operatingtime of a lower-efficiency generator, resulting in a decrease of the total generation cost of thewhole system. In addition, the generation by renewable energy, which had been curtailed due toinsufficient balancing capability, could be used in some time zones, resulting in the reduction ofenvironmental load and total generation cost. This method allows quantitative evaluation of thesevalues (Figure 6).

Figure 5

Evaluation example of schedule of generator

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
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Figure 6 Evaluation example of value ofwhole power system

3.3

Evaluation example of specification of generator

As described in section 2.3, in order to evaluate the effect of each product/service
specification, we adopt the method for determining the operation schedule that maximizes therevenue of the target generator based on a given power price in Step 3, because a generator hasvarious parameters.
As an example, the process for studying the operational flexibility modification service forthe target generator is shown in Figure 7.

Figure 7

Evaluation example of generator/service specification

There are many items in terms of flexibility, such as start-up cost, start-up time, minimum
load and ramp rate. Therefore, the design of experiments is introduced to extract only theparameters that contribute to the increase of product/service values. Specifically, for the kWh/ΔkW
market price in one future scenario which was forecasted in Step 2, the parameter values are
combined and the return on investment of the generator is evaluated from the schedule thatmaximizes the revenue. For combinations of other parameters, the same calculation can be applied
to determine the sensibility of each parameter to the value of the target product/service. As a result,only the parameters that contribute to an increase in the value of product/service can be easily
extracted. As an application of this method, after power prices in several future scenarios are
forecasted in Step 2, product/service specifications can be studied in consideration of a change of
future scenarios by using quality engineering, etc.
This series of methods can be used not only for the value evaluation of generators, but also
for suggestion on their operation. For example, it is possible to suggest the operation method thatmaximizes revenue based on the predicted power price and the periodic inspection timing inconsideration of the mechanical condition of the generator. In addition, as described in section 3.2,
it is possible to examine the solution which realize reduction of environmental load and system
reliability economically for the whole power system. On the basis of the evaluation result by thismethod, it becomes possible to share the value with various stakeholders and to offer

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
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products/services which realize this.

|4. Conclusion
We established an analysis method for quantitatively evaluating the value of
products/services and increasing it by forecasting the schedules of all generators and power pricethat minimize total social cost required to meet power demand.
This method allows the study of specifications of products/services under various futurescenarios in various countries and regions, as well as suggestions toward the reduction of total
generation cost and CO2 emissions in the whole system.
Toward the realization of carbon-neutral society, we will use this technology as a tool fordeveloping and providing products/services that match the needs of target countries/regions, and asa tool for sharing value with customers and co-creating the products/services that they are seeking.

References
(1)

(2)

Electric Power Investigation Statistics, Agency for Natural Resources and Energy, Ministry of Economy,
Trade and Industry
https://www.enecho.meti.go.jp/statistics/electric_power/ep002/results.html
Spot Market/Intraday Market, JEPX,
http://www.jepx.org/market/index.html