evaluation of foreign investment in power plants using

Extended Summary 本文は pp.2–8

Evaluation of Foreign Investment in Power Plants using Real Options

Moritoshi Kato Member (Tepco Systems Corporation, [email protected])

Yicheng Zhou Senior Member (Tepco Systems Corporation, [email protected])

Keywords: economic evaluation, electricity market, foreign investment, real options, risk, spark spread

This paper proposes new methods for evaluating foreign invest-ment in power plants under market uncertainty using a real optionsapproach. We suppose a thermal power plant project in a deregu-lated electricity market. One of our proposed methods is that wecalculate the cash flow generated by the project in a reference yearusing actual market data to incorporate periodic characteristics ofenergy prices into a yearly cash flow model. We make the stochas-tic yearly cash flow model with the initial value which is the cashflow in the reference year, and certain trend and volatility. Thenwe calculate the real options value (ROV) of the project which hasabandonment options using the yearly cash flow model. Another ourproposed method is that we evaluate foreign currency/domestic cur-rency exchange rate risk by representing ROV in foreign currencyas yearly pay off and exchanging it to ROV in domestic currencyusing a stochastic exchange rate model.

Numerical examples show following results. We suppose that theproject owner purchase an existing gas power plant in USA.

( 1 ) ROV ranges from 345 to 584 USD/kW. It is about threetimes value of the net present value (Fig. 1). The reason is that theproject can choose abandonment options under unfavorable condi-tions.

( 2 ) The lower the heat rate of the power plant is, the larger thedifference between electricity price and fuel price is, and the largerthe capacity factor of the plant is. These two effects make the yearlycash flow large and it leads to the large value of the project. Theeffect of heat rate on ROV is large as the change rate of ROV for thechange rate of thermal efficiency is about 13% (Table 1, Fig. 1).

( 3 ) The change rate of ROV for the change rate of operationand maintenance (O&M) costs is lower than that of NPV. The rea-son is that abandonment options are selected so that the value doesnot decline owing to higher O&M costs (Fig. 2).

Fig. 1. Gas fired power plant value

( 4 ) The distribution of ROV in Japanese yens is calculatedthrough Monte Carlo simulation (Fig. 3). We get 95% value at riskas 70.6 from the distribution. This implies that ROV in JPY maybe 70.6 and under with the probability of 5% and under although itsexpected value is 100.

Our proposed method will be useful for the risk management offoreign investment in power plants.

Table 1. Cash flow model

Fig. 2. Index of gas fired power plant value

Fig. 3. Distribution of ROV in Japanese yens

– 1 –


Transient Stability Study of One-Machine-to-Infinite-Bus Power Systemunder Large Penetration of PV Generation

Naoya Sakamoto Student Member (University of Tokyo)

Haruhito Taniguchi Senior Member (University of Tokyo)

Yutaka Ota Member (University of Tokyo)

Tatsuhito Nakajima Senior Member (University of Tokyo)

Tomoyuki Chinuki Student Member (University of Tokyo)

Keywords: transient stability, one machine to infinite bus model, PV, equal area criterion, critical clearing time, Y method

1. IntroductionLarge penetration of PV may affect transient stability when fault

occurs on a transmission line.To clarify the effects of PV penetration to power system transient

stability, the critical clearing time is evaluated under various con-ditions of a one-machine-to-infinite-bus system with and withoutload. At first the equation of electrical power from the generator asa function of δ is derived. Then by applying the equation to equalarea criterion the critical clearing time (CCT) is determined. To ver-ify the results by equal area criterion, time domain simulation byY-method program with detailed generator model with an AVR, aPSS and a governor was conducted.

2. PV and Power System ModelFigure 1 shows a one-machine-to-infinite-bus system without

load. PV is represented as an inverter, which is a constant poweroutput over 0.83 [pu] voltage and changes to constant current under0.83 [pu]. The generator capacity is assumed to be reduced accord-ing to the output of PV or to be kept constant to prepare for drop ofPV.

Other conditions are assumed as the following standard

Fig. 1. One-machine-to-infinite-bus system without loadmodel

Fig. 2. P-δ curve (without load, reduced capacity andnon-PV-drop)

conditions, in which the power factor of PV is unity, PV drops af-ter instantaneous voltage drop due to power system fault, the initialtransmitting power on the line 3 is kept constant, the load model isconstant impedance, and the load does not drop due to fault.

3. Equal Area Criterion and CCT CalculationTo apply equal area criterion the generator model is assumed as

a classical model, that is, constant voltage behind x′d model. Theoutput of the generator is given by the following equation where thevariables are shown in Fig. 1.

P1 =v1v∞ sin δ1 − x3v1i2 cos (δ1 − δ2 − δ3)

x1 + x3· · · · · · · · · · · · · (1)

δ2 is zero because the power factor of PV is unity and usuallyδ1 − δ3 > 0. Therefore PV output increase means the generatoroutput P1 decrease, which derives that CCT becomes shorter whenPV does not drop than when PV drops after fault clearing. Figure 2shows the P-δ curves under the condition that the system is withoutload, generator capacity is reduced and non-PV-drop. The maxi-mum value of P1c is about 0.8 [pu] compared with 1.2 [pu] with PVdrop (the suffix ‘c’ means ‘after fault clearing’). Hence CCT in theformer condition is 0.269 and the latter 0.363 second.

4. ConclusionTable 1 shows summary of effects to CCT under various condi-

tions. Large PV output has longer CCT than lower PV output underthe condition that the generator output is reduced to keep transmit-ting power constant. Almost all effects to first swing transient sta-bility shown in Table 1 can be explained by the equation of P1 andequal area criterion. And the results are also confirmed by time do-main simulation with the detailed generator model.

Table 1. Summary of effects to CCT under various con-ditions

synchronous generator capacity constant > reductionpower system model without load � with loadsynchronous generator model classic > detailPV output large > lowPV drop after fault* without drop < with dropPV reactive power compensation with > withoutTransmission power constant > increasedLoad model impedance � inverterLoad light > heavyLoad drop after fault without drop > with drop

*) The tendency is reversed in one case (system with load, re-duced capacity and light load)

– 2 –


Approximate Calculation of Voltage in Three-Phase PrimaryDistribution Feeder

Daisuke Iioka Member (Meijo University, [email protected])

Kubou Iwata Non-member (Meijo University)

Hisashi Kondo Non-member (Meijo University)

Takuma Sakaguchi Non-member (Chubu Electric Power Co., Inc.)

Takaya Shigetou Member (Chubu Electric Power Co., Inc.)

Toshiro Matsumura Senior Member (Nagoya University)

Keywords: distribution system, voltage sensitivity, voltage profile

An approximate method to calculate voltage in three-phase pri-mary distribution feeder has been proposed. Figure 1 shows a three-phase three-wire system model with Δ-connected load. The three-phase balanced voltage source supplies the power to the load. Thedependence of load connected between a-phase and b-phase on theload voltage was represented by exponential model:

Pab+ jQab = S Lab cos θLab

(Vab

VLab

)αLab

+ jS Lab sin θLab

(Vab

VLab

)αLab

· · · · · · · · · · · · · · · · · · · · · · · (1)

where Pab, Qab, S Lab are the real, reactive and apparent power, αLab

is voltage sensitivity, VLab is the rated voltage, cos θLab is power fac-tor. Since the complex power is the product of the complex voltageand current, we have the following relationship.

Iab =S Lab cos θLab

VαLabLab

VαLab−1ab − j

S Lab sin θLab

VαLabLab

VαLab−1ab · · · · · · (2)

Applying the Taylor series approximation to Eq. (2), we have foundthe linear relationship between Vab and Iab.

Iab = S Lab cos θLab

((αLab − 1)Vab

V2Lab

+2 − αLab

VLab

)

− jS Lab sin θLab

((αLab − 1)Vab

V2Lab

+2 − αLab

VLab

)· · · · · · (3)

Current Ibc and Ica were obtained in the same way. Neglecting thephase difference between voltage at the sending end and voltage atthe receiving end in the primary distribution system shown in Fig. 1,we obtained the line current Ia, Ib and Ic. Neglecting the imagi-nary part of voltage drop of the distribution line, we obtained theapproximate voltage of load.

Fig. 1. Three-phase three-wire system model

Vab � V0ab − Re{ΔVab} · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (4)

Approximate load voltage Vbc and Vca were obtained in a similarway.

Figure 2 shows the load voltage and error ratio as a function ofline length. The conditions for the calculation were as follows;three-phase constant power load, S Lab = 1.1 MVA, S Lbc = 1.1 MVA,S Lca = 0.8 MVA, cos θLab = cos θLbc = cos θLca = 0.9, L = 2 km. Al-though the error ratio increases with the line length, the approxi-mate voltage agrees with the exact voltage within several hundredvoltages of voltage drop from the voltage source. Figure 3 showsthe relationship between the error of the approximate voltage andthe voltage sensitivity of the load. It was found that the approximatevalue of voltage in the three-phase primary distribution feeder is ingood agreement with the exact value.

Fig. 2. Constant power load voltage and error as a func-tion of line length (S Lab = 1.1 MVA, S Lbc = 1.1 MVA,S Lca = 0.8 MVA, cos θLab = cos θLbc = cos θLca = 0.9, L =2 km)

Fig. 3. Error of approximate voltage (S Lab = 1.1 MVA,S Lbc = 1.1 MVA, S Lca = 0.8 MVA, cos θLab = cos θLbc =cos θLca = 0.9, L = 2 km)

– 3 –


Load Frequency Control by use of a Number of Both Heat Pump WaterHeaters and Electric Vehicles in Power System with a Large Integration

of Renewable Energy Sources

Taisuke Masuta Student Member (The University of Tokyo)

Koichiro Shimizu Student Member (The University of Tokyo)

Akihiko Yokoyama Senior Member (The University of Tokyo)

Keywords: wind power generation, photovoltaic generation, Heat Pump Water Heater (HPWH), Electric Vehicle (EV), Load Fre-quency Control (LFC), smart grid

In recent years, smart grid has gained much attention around theworld, which is a new concept of a better future grid with Infor-mation and Communication Technology (ICT). In Japan, from theviewpoints of global warming countermeasures and energy security,it is expected to establish a smart grid as a power system into whicha large amount of generation from renewable energy sources such aswind power generation and photovoltaic generation can be installed.Measures for the power system stability and reliability are necessarybecause a large integration of these renewable energy sources causessome problems in power systems, e.g. frequency fluctuation and dis-tribution voltage rise, and Battery Energy Storage System (BESS) isone of effective solutions to these problems.

Due to a high cost of the BESS, our research group has studied anapplication of controllable loads such as Heat Pump Water Heater(HPWH) and Electric Vehicle (EV) to the power system control forreduction of the required capacity of BESS. This paper proposesa new coordinated Load Frequency Control (LFC) method for theconventional power plants, the BESS, the HPWHs, and the EVs.Figure 1 shows the block diagram of the proposed LFC method.In this method, the LFC signal is dispatched not only to the con-ventional power plants (LFC generators) but also to the BESS, theHPWHs, and the EVs according to the magnitude and period of theArea Requirement (AR). As a result, the required kW capacity ofthe BESS is reduced by this method compared to that without theHPWHs or the EVs.

Figure 2 shows the control system configuration of HPWH andEV assumed in this paper, which consists of a central load dispatch-ing center and local control centers. The control area of each local

Fig. 1. Block diagram of proposed LFC

control center is the same size as that of each distribution substation.A number of HPWHs and EVs are controlled through a two-waycommunication network. The central load dispatching center col-lects the statistical information on the heating period of the HPWHsand the SOCs of the EVs via the local control centers. Based on theinformation, the central load dispatching center generates and sendsthe LFC signal to the HPWHs and the EVs via the local controlcenters.

The effectiveness of the proposed LFC method is shown by thenumerical simulations conducted on a power system model with alarge integration of wind power generation and photovoltaic gener-ation. Moreover, the impact of the proposed method on reduction ofthe required kW capacity of BESS is quantitatively evaluated.

Fig. 2. Control system configuration of HPWH and EV

– 4 –


Analysis of Energy Saving and Environmental Characteristics of ElectricVehicle in Regionally-Disaggregated World Energy Model

Ryoichi Komiyama Member (The University of Tokyo)

Yasumasa Fujii Member (The University of Tokyo)

Keywords: electric vehicle, plug-in hybrid vehicle, world energy model, optimization, world vehicle penetration model

1. IntroductionThis paper investigates the impact of an extensive introduction of

electric vehicle (EV) and plug-in hybrid vehicle (PHEV) into globalenergy system towards 2050. The significant growth of automobileownership in emerging countries is likely to increase the world oildemand and the associated carbon dioxide emissions. In order to ad-dress these energy security and environmental concerns, the deploy-ment of clean energy vehicles, such as EV and PHEV, are expectedto play a crucial role due to its high fuel efficiency. On these back-grounds, we develop both global energy system model and worldvehicle penetration model, which are able to explicitly analyze theimpact of EV introduction into seasonal daily electric load curveconsidering its specific electricity charging profile to 2050.

2. MethodologyWorld vehicle penetration model yields long-term perspective

on vehicle penetration mix and fuel consumption as shown inFig. 1. Regionally-disaggregated world energy model provides op-timal global energy mix in power generation and primary energysupply. Several scenarios are assumed respectively in vehicle pen-etration (base case, advance case, advance (EV) case), electricitycharge pattern of EV (bottom charge case, bottom/peak charge case)and CO2 emissions constraints (no CO2 regulation case/CO2 regula-tion case). This analysis is original in terms of estimating the impactof EV penetration into global automobile market considering dailyelectric load curve in each country and electricity charging profileof EV as illustrated in Fig. 2.

3. Estimated ResultsSimulation results confirm that EV deployment contributes to

energy conservation, because oil demand reduction outstrips thegrowth in its electricity demand and the associated fuel input intopower generation mix. Concerning carbon dioxide abatement, themagnitude of the impact relies on the carbon-intensity of power gen-eration mix. If the intensity is low enough to make sure the carbonmitigation effect by EV fuel saving, the emissions reduction is wellensured.

It should be noted, however, that, in the regions with high carbon

Fig. 1. Energy demand of passenger vehicle in the world

intensity in power generation mix, carbon emissions per mileageof EV is almost equivalent to that of efficient gasoline vehicle likehybrid vehicle and PHEV is slightly higher than hybrid vehicle aspresented in Fig. 3.

(a) Vehicle: Advance (EV) case, Charge pattern: Bottom

(b) Vehicle: Advance (EV) case, Charge pattern: Bottom/peak

Fig. 2. World optimal dispatch at summer in 2050 (CO2

regulation)

Vehicle: Advance (EV) case, Charge pattern: Bottom

Fig. 3. CO2 emissions per mileage in 2050

– 5 –


Transient Stability Improvement of Multi-Machine Power Systemwith Large-Capacity Battery Systems

Ken-ichi Kawabe Student Member (The University of Tokyo)


Keywords: power system, emergency control, transient stability, FACTS, battery energy storage system, coordinated control

Current trends towards a large penetration of renewable energyresources and deregulation in electrical power sector have heightenthe need to develop a new emergency control scheme since cascad-ing blackouts have been caused in some countries. In developingthe new control scheme applicable to the future grid, it can be moreeffective by utilizing advanced electrical power controllers avail-able in the future grid. Battery energy storage system (BESS) isone of the attractive equipment for emergency control according toits growing installed capacity in the current grid. In Japan, moreand more capacity of BESS will be required for stabilizing systemfrequency and charging surplus power in the daytime, as the pho-tovoltaic (PV) power generation increases to solve energy securityand environmental problems.

This paper explores an application of multiple BESSs to a multi-machine power system to improve transient stability, and a novelcontrol system shown in Fig. 1 is proposed for the BESSs. The con-trol system consists of a stabilizing control system and a correctivecontrol system, and coordinately controls active and reactive powerinjection by the BESSs to improve the stability.

The stabilizing control system adopts two control laws, main andsupplementary ones. The main and the supplementary control lawsdeal with an energy function and a rotor speed of a critical machine,respectively, as stability indices. Each control law calculates a direc-tion into which a control variable should change from its referencevalue to shift the time derivative value of the each stability index intoa desirable direction for the stability enhancement. When the direc-tion calculated by the main control law is coincident with that calcu-lated by the supplementary control law, the control variable shouldbe changed into the direction according to the value determined bythe main control law. Otherwise, the control variable should be keptat its reference value, e.g., an operating point before a fault.

The two control variables of the BESS should be coordinatelycontrolled since they are injected by the same converter. The pro-posed corrective control system can not only keep the apparentpower within its converter capacity, but also find optimal operat-ing points of the two control variables to maximize the variation ofthe time derivative value of the energy function used as a stabilityindex in the main control law.

Fig. 1. Control system for BESS

Digital simulations are conducted on the modified IEEE RTS-24system with total generation capacity of 3405 (MW). The generatorsare represented by the 6-th order model, and automatic voltage reg-ulator and governor of the first-order model are incorporated in eachgenerator. Multiple BESSs are installed at load buses in the systems,since BESSs will be installed for charging surplus power from PVsystems in residential areas in the future, in Japan. The total avail-able power converter capacity of the BESSs is set at 200 (MVA)according to the documents published by Japanese government.

Figure 2 compares swing curves of a critical generator betweencomparative cases. One 200-MVA BESS is installed at bus 10 anda three-phase grounding fault near bus 22 is considered. As shownin Fig. 2, the transient stability is improved by controlling one of thetwo control variables (PB, QB) with the stabilizing control system. Itis also observed that the coordinated control of the two control vari-ables by the stabilizing control system and the corrective controlsystem is most effective for the stability enhancement. In the fullpaper, it is also made clear that distributed installation of BESSs ispreferable in terms of voltage behavior during the transient periodsince absorption of the reactive power by a large-capacity BESS fur-thers the voltage drop during the grounding faults as shown in Fig. 3.

Fig. 2. Rotor angle of 24-th generator

Fig. 3. Voltage at bus 9

– 6 –


SOC Synchronization Control Method of Electric Vehicles ConsideringCustomers’ Convenience for Suppression of System Frequency Fluctuation

Koichiro Shimizu Student Member (The University of Tokyo, [email protected])

Taisuke Masuta Student Member (The University of Tokyo, [email protected])

Yutaka Ota Member (The University of Tokyo, [email protected])

Akihiko Yokoyama Senior Member (The University of Tokyo, [email protected])

Keywords: smart grid, controllable load, Electric Vehicle (EV), Load Frequency Control (LFC), Vehicle-to-Grid (V2G), State OfCharge (SOC)

A large integration of photovoltaic and wind power generationcauses an imbalance between supply and demand in power sys-tems because their output is intermittent. To solve the mentionedproblem, many researches on Load Frequency Control (LFC) us-ing Electric Vehicles (EVs) as controllable loads have been studied.In this paper, we propose a new LFC method using EVs, which isnamed the State Of Charge (SOC) synchronization control. In theproposed control method, a number of EVs can be considered asone large-capacity battery energy storage system. Moreover, theEVs can be plugged-in/out whenever the users like and can storethe sufficient energy for the next trip at plug-out. In addition, SOCof the batteries is different from EV to EV. When some EVs stopcharging/discharging due to their full/empty battery energy or theirplug-out, the performance of the power system control using theEVs becomes worse. The power system needs to reduce the riskof such uncertainty. SOC synchronization control does not need toconsider the uncertainty. In this paper, a lumped EV model is de-signed considering EV users’ convenience and uncertainty. More-over, a frequency control method based on the lumped model is pro-posed. In addition, a dispatching method of the LFC signal to theEVs, which enables the SOCs of all the EVs to be synchronized, isproposed.

The SOC synchronization control system is shown in Fig. 1. Itis assumed that the EVs are under the centralized control with atwo-way communication network between the EVs and the powersystem and the Central Load Dispatching Center (CLDC) sends thecontrol signal to the EVs and receives the information from the EVsvia the LC center. There are 500 local control centers and 50,000EVs in the study area. Each local control center is assumed to con-trol 100 EVs. The SOC synchronization control system consists ofthe system between the CLDC and the LC centers (upper layer) andthat between the LC centers and the EVs (lower layer). The CLDC

Fig. 1. SOC synchronization control system

receives the information on the sum of the power capacities of allthe EVs and the amount of the synchronized SOC from the LC cen-ters. The EVs send the SOC to the LC center every 30 seconds. TheCLDC calculates the LFC signal from the frequency fluctuation. Inthis paper, the CLDC dispatches the LFC signal to the LC centers bythe dispatching method in the upper layer. The LC centers dispatchthe LFC signal to the EVs by the dispatching method in the lowerlayer. The dispatching methods of the LFC signal in the lower layerare presented in Fig. 2, for example. The charging and dischargingpriorities of the EVs are determined according to their SOCs. Thecharging signal is dispatched to the EVs in ascending order of theSOC, whereas the discharging signal is dispatched in descendingorder of the SOC.

Effectiveness of the control proposed methods is evaluated bynumerical simulations conducted on the frequency analysis model.The frequency fluctuation with or without SOC synchronizationcontrol are shown in Fig. 3. When the EVs are controlled withthe SOC synchronization control, the frequency fluctuation is sup-pressed. The SOC synchronization control considering EV cus-tomers’ convenience and uncertainty is effective for suppressing thefrequency fluctuation.

Fig. 2. LFC signal dispatching method

Fig. 3. Frequency fluctuation

– 7 –


Accuracy Verification of Arbitrary Point Insolation Estimation usingthe Automated Meteorological Data Acquisition System Data

Yoshio Yamagishi Member (The University of Tokyo, Hokuriku Electric Power Co.)

Yasumasa Fujii Member (The University of Tokyo)

Keywords: photovoltaic power generation, power fluctuation, insolation, automated meteorological data acquisition system

In this paper, we present the results of the validation of solar ra-diation estimation using sunshine duration information of the Au-tomated Meteorological Data Acquisition System (AMeDAS) Dataprovided by the Japan Meteorological Agency. The validation wascarried out using insolation data of 20 points in the Hokuriku region(Fig. 1). Accuracy was evaluated by correlation coefficients and rootmean square error.

Because of the difference of the position between AMeDASsites and insolation measurement points, the weighting as shownin Eq. (1) was applied.

ci j =1/(r ji)w

Nm∑k=1

1/(r jk)w· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (1)

Where ci j is weighting factor of AMeDAS site i onto insolationmeasuring site j, r jk is distance between AMeDAS site k and insola-tion measuring site j, Nm is number of AMeDAS sites, w is a param-eter of weighting. Weighting inversely proportional to the distance,i.e. w = 1 was the best result (Fig. 2).

Fig. 1. Distribution of measuring sites

The correlation coefficient was 0.84–0.96, if a single point inso-lation was estimated. Time series data of single site are shown onFig. 3. And the accuracy of about 0.98 was obtained when the aver-age of 20 points in the Hokuriku region was estimated. Time seriesdata of area average are shown on Fig. 4. Global solar radiationcalculation method using AMeDAS is considered to have sufficientpractical accuracy.

Fig. 2. Relationship between w and correlation coeffi-cient (measured data)

Fig. 3. Time series data of site 15 (Jul. 1–5, 2010)

Fig. 4. Time series data of area average (Jul. 11–20, 2010)

– 8 –


Parameter Identification Improvement of Dynamic Load Modelin Power System

Kensuke Mizuo Member (Hokuriku Electric Co., [email protected])

Shintaro Komami Member (Hokuriku Electric Co., [email protected])

Keywords: power system, dynamic load model, stability, renewable energy, induction motor, voltage sag

Although it is well known that an adequate dynamic load model inpower system is indispensable for stability analyses especially whenrenewable energies highly penetrate nearby loads, researches on dy-namic load model are quite poor even in the beginning of a low-carbon era. The authors have represented an actual dynamic loadas parallel of an induction motor (IM), a resistor and a capacitor be-hind fixed impedance, and identified parameters of the dynamic loadmodel using measured data during and after voltage sags. Structureof realistic dynamic load model is expressed in Fig. 1. The resultswere already reported. Since some improvements on the identifica-tion were made, the newest results are reported here.

The parameters that should be identified are three of the follow-ing:

( 1 ) IM ratio (power consumption by IM among power con-sumption by load): RM

( 2 ) IM inertia (unit inertia time constant; (sec)): MM

( 3 ) IM loading [IM power consumption (kW) by IM capacity(kVA)]: LM

The parameters are identified and improved using 284 observationaldata by the four following ways:

( 1 ) Bad data exclusion.( 2 ) Consideration of equivalent feeder resistance.( 3 ) Accuracy improvement of given sag voltage.( 4 ) Consideration of local generators.

Figure 2 is IM ratio before sag as affected by demand. The in-fluence of demand before sag is small, and the trend line is roughly50%.

Figure 3 is IM inertia as affected by demand. The influence ofdemand before sag is weak, and the trend line is roughly 0.5 sec.

Figure 4 is IM loading as affected by demand. The influence ofdemand before sag is strong, and the trend line is roughly 80% at alow demand and 50% at a high demand.

Table 1 is a result of presumption; IM ratio is 50%, IM inertia is0.5 sec., and IM loading is 50% at peak demand when power systemstability becomes the worst.

Fig. 1. Dynamic load model

Fig. 2. IM ratio before sag as affected by demand

Fig. 3. IM inertia as affected by demand

Fig. 4. IM loading as affected by demand

Table 1. Comparison with former study

– 9 –


Study on Compensation of Short Term Power Fluctuation by Use ofHeat Pump Air Conditioning System based on Real Machine

Shunsuke Kawachi Student Member (The University of Tokyo)

Hiroto Hagiwara Student Member (The University of Tokyo)

Jumpei Baba Member (The University of Tokyo)

Kei Furukawa Non-member (Shimizu Corporation)

Eisuke Shimoda Member (Shimizu Corporation)

Shigeo Numata Member (Shimizu Corporation)

Keywords: controllable load, heat pump air conditioner, microgrid, power fluctuation compensation

Recently, renewable energy sources such as photovoltaic cells andwind turbines are installed in the power grid on a large scale to re-duce the emission of greenhouse gas. However, the output powerfrom these renewable energy sources are random and intermittent innature. When these fluctuations become substantial, it will be diffi-cult to maintain the balance between power supply and demand inthe power grid. The compensation of power fluctuation using energystorage systems (ESSs) is an effective solution to this problem, butit is preferable to keep their installed energy and power capacity to alow level since the cost to install and maintain ESSs are expensive.

To reduce the capacity of ESSs which is necessary to compensatepower fluctuation in a power grid, the control of power that is usedby electrical equipments on the demand side is considered. In thispaper, the use of heat pump air conditioner for power fluctuationcompensation is focused.

The basic power consumption characteristics and processed heatcharacteristics of heat pump air conditioner is measured by experi-ment using real machine. Figure 1 shows the response characteris-tic of heat pump’s power consumption when sinusoidal power con-sumption reference signal was given. Furthermore, the amplitude ofprocessed heat is shown in Fig. 1.

A simple model of heat pump is proposed based on the measured

Fig. 1. Response characteristic and processed heat char-acteristic of heat pump

characteristics. Using this heat pump model, simulations werecarried out to analyse the amount of ESS’s capacity that can be re-duced by controlling the heat pump to compensate power fluctua-tions.

Figure 2 shows the fluctuation of ESS’s SoC (State of charge)calculated by the simulation. Figure 3 shows the fluctuation of heatpump’s processed heat. The curves in Fig. 2 shows that SoC fluctua-tion is suppressed when the heat pump is used to compensate powerconsumption. The necessary energy capacity of the ESS can be es-timated from the peak-to-peak value of the SoC fluctuation. Fromthe result, the necessary energy capacity of the ESS is reduced toalmost 25% of that of the case without heat pump control. On theother hand, the heat pump’s processed heat is fluctuating in the casewith heat pump control. However, the amplitude of the processedheat’s fluctuation is small enough so that it would not affect the com-fort of the users in the building.

Fig. 2. Fluctuation of ESS’s SoC

Fig. 3. Fluctuation of heat pump’s processed heat

– 10 –


Optimal Operation Scheduling of Pumped Storage Hydro Power Plantin Power System with a Large Penetration of Photovoltaic Generations

Ryota Aihara Student Member (The University of Tokyo, [email protected])

Akihiko Yokoyama Senior Member (The University of Tokyo, [email protected])

Fumitoshi Nomiyama Member (Kyushu Electric Power Co., Inc., Fumitoshi [email protected])

Narifumi Kosugi Member (Kyushu Electric Power Co., Inc., Narifumi [email protected])

Keywords: pumped storage hydro power plant (PSHPP), photovoltaic generation (PV), surplus power, operation scheduling, MonteCarlo simulation, pareto solutions

In recent years, a substantial amount of photovoltaic (PV) gener-ations have been installed in power systems. However, the poweroutput from the PVs is random and intermittent in nature. There-fore, the PV generations pose many challenges to the power systemoperation. To solve these issues, we propose that pumped storagehydro power plant (PSHPP) is used effectively.

This paper presents a new method for planning PSHPP opera-tion pattern considering the surplus power problem, power supplyreliability and fuel cost reduction, which is represented by paretooptimal solutions. The power supply reliability and the fuel costare estimated for each PSHPP operation plan by using Monte Carlosimulation. The proposed evaluation method is summarized as aflowchart in Fig. 1.

In general, improvement of power supply reliability and reduc-tion of operational cost are competing. Optimization of the PSHPPscheduling has no unique solution. Therefore, pareto optimal so-lutions are obtained by a cooperative scheduling method that op-timizes both operation scheduling of PSHPP and thermal powerplants using Genetic Algorithm (GA) and Tabu Search (TS).

Fig. 1. Power supply reliability calculation

(a) Summer

(b) Spring

Fig. 2. Pareto optimal solutions

The pareto optimal solutions obtained by the proposed optimiza-tion method are shown in Fig. 2. In both summer and spring seasons,it can be seen that the pareto optimal solutions are located in a nar-row range of Cost-LOLP space when PV penetration is 0 MW. Onthe contrary, the pareto optimal solutions includes various kinds ofoperation patterns when PV penetration is 1000 MW in both sum-mer and spring seasons.

The simulation results show that the proposed method is very ef-fective in terms of the reliability of power system operation. Thetotal fuel cost of thermal power plants increases in order to securethe power supply reliability using operation of the PSHPP in powersystem with a large integration of PV.

– 11 –


A Study of Reallocation Planning for the Section Switches ConsideringEmployment Cost and Intermittent Renewable Energy

Kazunori Kurihara Student Member (The Chugoku Electric Power Company)

Yutaka Sasaki Member (Hiroshima University)

Yoshifumi Zoka Senior Member (Hiroshima University)

Naoto Yorino Senior Member (Hiroshima University)

Keywords: distribution system, switches reallocation, supply reliability, interruption cost, intermittent renewable energy

1. IntroductionThe power market is to be in highly competitive environment be-

cause of power deregulation. The electric power companies haveraised business efficiency to reduce the number of employee andcentralize the business establishments. Furthermore, an installationof photovoltaic generation (PV) will increase because of energy pol-icy. However, electric power companies must maintain their supplyreliability. Therefore, to reinforce the reliability in distribution sys-tems is one of significant problem.

In this paper, we propose an optimal placement of distribu-tion line switches (automatic and manual switches) to maintain thepower supply reliability under such a situation and exhibit the sim-ulation result in the original distribution system using genetic algo-rithm (GA). The customer service outage cost is then determinedaccordance with the power consumption within the service zone.

2. Switches Reallocation ResultsWe considered a large mount of PV which are installed in distri-

bution test system point no.117 in Fig. 1. The social cost Z to beminimized is expressed as Z = FINV + IC, where FINV is equipmentinvestment cost, IC is interruption cost. Some operation constraintsare considered in this paper, voltage, current, feeder capacity and soon. Table 1 and 3 shows results of reallocation section switches withPV or not installation in urban area, respectively. Number of auto-matic switches mounted each poles are increased after PV installed.Therefore reliability index is improved against original configura-tions in Table 2 and 4.

Fig. 1. Test distribution system in urban area

Table 1. Results of reallocation switches in urban area

Table 2. Results of reliability index and social cost inurban area

Table 3. Results of reallocation switches in urban areawith PV

Table 4. Results of reliability index and social cost inurban area with PV

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Seasonal and Local Characteristics of Lightning Outages of PowerDistribution Lines in Hokuriku Area

Hitoshi Sugimoto Member (Hokuriku Electric Power Company, [email protected])

Katsuhiko Shimasaki Member (Hokuriku Electric Power Company, [email protected])

Keywords: power distribution line, lightning, lightning outage, winter lightning, LLS

The proportion of the lightning outages in all outages on Japanese6.6 kV distribution lines is high with approximately 20 percent, andthen lightning protections are very important for supply reliabil-ity of 6.6 kV lines. It is effective for the lightning performanceto apply countermeasures in order of the area where a large num-ber of the lightning outages occur. Winter lightning occurs in theHokuriku area, therefore it is also important to understand the sea-sonal characteristics of the lightning outages. Figure 1 shows theseasonal proportion of lightning-damaged distribution equipmentsin the Hokuriku area from FY 2003 to FY 2009. In summer 70 per-cent of the lightning-damaged equipments were due to sparkover,such as power wire breakings and failures of pole-mounted trans-formers. However, in winter almost half of lightning-damagedequipments were surge arrester failures. We divided the Hokurikuarea into the meshes every 2 minutes in the north latitude and theeast longitude, counted the number of the lightning strokes detectedby the lightning location system (LLS) in 980 meshes where the dis-tribution poles for 6.6 kV lines existed as in Fig. 3. The number ofthe lightning strokes in winter was 4 times larger than that in sum-mer as in Fig. 2. The number of the lightning outages per lightningstrokes in winter was 4.4 times larger than that in summer.

We have presumed the occurrence number of lightning outages(Y) from lightning stroke density (X1), 50% value of lightning cur-rent (X2) and installation rate of lightning protection equipments(X3) and overhead ground wire (X4). Table 1 shows the partialregression coefficients (k1 − k4) calculated by multiple regressionanalysis of the 50 meshes in order of the mesh with a lot of poles for6.6 kV lines. The Ys of 980 meshes in total were calculated by equa-tion (1). The distribution of presumed number of lightning outagesin winter and summer was similar to the distribution of the actualnumber of lightning outages as in Fig. 3 and Fig. 4. And then the

Table 1. Partial regression coefficients to presume num-ber of lightning outages

Fig. 1. Seasonal proportion of lightning-damaged equip-ments on 6.6 kV lines in Hokuriku area from FY 2003 toFY 2009

presumed results suggest the local difference in the lightning out-ages.

Y = k1 × X1 + k2 × X2 + k3 × X3 + k4 × X4 · · · · · · · · · · · · · (1)

where, Y: lightning outages [outages/1000 poles/year]X1: lightning stroke density [strokes/km2/year]X2: 50% value of lightning current [kA]X3: installation rate of lightning protection equipments [%]X4: installation rate of overhead ground wire [%]k1 − k4: partial regression coefficients

Fig. 2. Comparison between lightning outages on6.6 kV lines and lightning strokes in Hokuriku area fromFY 2003 to FY 2009

Fig. 3. Distribution of presumed number of lightningoutages in winter

Fig. 4. Distribution of presumed number of lightningoutages in summer

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A Novel Method for Enhancement of System Regulating Capacity byusing Seawater Desalination Plant in a Small Island Power System

Toru Yoshihara Student Member (The University of Tokyo)


Masaki Imanaka Student Member (The University of Tokyo)

Yusuke Onda Student Member (The University of Tokyo)

Jumpei Baba Member (The University of Tokyo)

Yusuke Kuniba Non-member (Okinawa Enetech)

Naoto Higa Non-member (Okinawa Enetech)

Sadao Asato Member (Okinawa Enetech)

Keywords: small island power system, wind generation, seawater desalination plant, regulating capacity, load frequency control(LFC), economic load dispatching control (EDC)

Nowadays, much more renewable generations have been installedinto small island power system. However, their output is intermit-tent and the large frequency fluctuation may be caused. In recentresearches, the power consumption control of residential deviceson demand side such as heat pump water heaters, which are calledcontrollable loads, has been proposed. In this research, a seawaterdesalination plant is focused as the controllable load and a novelmethod for ensuring the system regulating capacity by using seawa-ter desalination plant in a small island is proposed.

In this research, the frequency fluctuation is calculated by the useof frequency analysis model shown in Fig. 1. The economic loaddispatching control and the unit commitment, ON-OFF of diesel en-gine generators and a wind turbine generation, are also considered.

In this paper, two kinds of coordinated control are proposed. Theone is a control method for more economic operation, and the otheris a control method for more secure regulation. The control formore economic operation helps avoiding the frequent ON-OFF ofdiesel engine generators or securing more operating time of a windturbine generation. The control for more secure regulation controlcontributes to suppress the system frequency fluctuation. For the co-ordination of these two control methods, both the enhancement ofsystem frequency regulation and the reduction of fuel costs can beexpected.

In order to control the power consumption of the seawater desali-nation plant, the operator’s convenience must be secured. In thispaper, the fresh water tank storage constraint is considered as the

Fig. 1. Frequency analysis model

operator’s convenience. The preventative control of the tank storageconstraint violation is proposed. In this control method, the powerconsumption range of the seawater desalination plant is determinedbased on the tank storage and the water demand forecast. For the co-ordination of the preventative control of the tank storage constraintviolation and the coordinated control for the power system opera-tion, seawater desalination plant can contribute to the system fre-quency suppression and fuel cost reduction with the satisfaction ofthe operator’s convenience.

In order to evaluate these control methods, the system frequencyanalysis simulation is carried out. 3 simulation cases are consid-ered based on the seawater desalination plant operation. Case(1) isthe conventional operation. Case(2) is with the preventative controlof the tank storage constraint violation without the control for thecontribution of power system operation. Case(3) is with both thepreventative control of the tank storage constraint violation and thecontrol for the contribution of power system operation. The sim-ulation results are shown in Tables 1 and 2, from which it can beseen that the proposed control of the seawater desalination plant areeffective for the power system operation in a small island.

Table 1. Staying rate and RMSE

Table 2. Fuel cost, WTG operating time and number ofDEG start-up

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An Optimal Allocation of FACTS Devices for Maximizing Loadabilitywith Hybrid Cording EPSO

Hajime Fujita Student Member (Meiji University)

Hiroyuki Mori Senior Member (Meiji University)

Keywords: FACTS devices, Loadability, hybrid cording EPSO

This paper proposes an optimal allocation of FACTS devices formaximizing Loadability with hybrid EPSO (Evolutionary ParticleSwarm Optimization). Loadability shows the maximum load thatcan be supplied in power network without the violation of thermal,voltage limits, etc. It is important to study it due to the system plan-ning and operating. An allocation of FACTS devices is one of theattractive ways to increase Loadability. The conventional methodson the optimal allocation and the output optimal variables of FACTSmay be classified as follows:

( 1 ) sensitive matrix method( 2 ) meta-heuristics

Meta-heuristics is one of optimization methods that repeatedlymakes used of some rules or heuristics to obtain better solutions.The key point is that meta-heuristics has tools to escape from a lo-cal minimum. As typical meta-heuristics, SA, GA, TS and PSO arewidely spread in the engineering fields. However, it is not easy todetermine the optimal location and the optimal variable output ofFACTS devices. The former is expressed in discrete number whilethe latter is represented in continuous one. As a result, the prob-lem to be solved results in a nonlinear mixed integer problem that isvery hard to solve. In addition, the use of MCS (Monte Carlo Simu-lation) to consider the uncertainty such as variable generations leadsan increase in computational time. In this paper, an efficient meta-heuristic method is proposed to determine the optimal allocation ofFACTS devices to maximize Loadability several nodes in uncertainpower systems. The proposed method makes use of a hybrid cord-ing EPSO that is combination of discrete and continuous EPSO toreduce the computational time and to improve accuracy in uncertainpower network. This paper considers mega-solar as an uncertainty.For convenience, the following methods are defined:

Method A: Two layered PSOMethod B: Two layered EPSOMethod C: Hybrid cording EPSO (Proposed Method)

Three methods were applied to the IEEE 30-node system with twomega-solar. Three hundreds of scenarios were used to examine theperformance of each method. Figure 1 shows the distribution ofLoadability of each method. It can be seen that Method B and C pro-vides a set of solution sets that is far from the origin. That impliesthat the solutions more distant from the origin bring about more ca-pacity of Loadability. Figure 2 shows the frequency of Loadability’such as

Loadability′ =√

Loadability214 + Loadability2

30

where the space in Fig. 1 is divided into four areas; Ar-eas 1, 2, 3 and 4. It can be seen that Method B gives better solution

Fig. 1. Distribution of loadability of each method

Fig. 2. Frequency of loadability of each method

Table 1. Comparison of each method

sets than Method A. Table 1 shows a comparison of three methodswhere the best, the worst and average cost functions are given. Inaddition, the standard deviation of the cost functions and computa-tional time are shown. It can be observed that Method C is betterthan Method A and B in term of cost functions and computationaltimes.

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Probabilistic Reliability Evaluation with Multi-objective Meta-heuristicsin Consideration of Solution Diversity

Hiroki Kakuta Student Member (Meiji University)

Hiroyuki Mori Senior Member (Meiji University)

Keywords: power supply reliability evaluation, probabilistic reliability criteria, multi-objective optimization, multi-objective meta-heuristics (MOMH)

1. IntroductionThis paper proposes a new method for Probabilistic Reliability

Evaluation with Multi-objective Meta-heuristics (MOMH) in con-sideration of the Solution Diversity. Recently, power networks in-creases the degree of uncertainties due to the new environment ofpower network liberations, the emergences of renewable energy, etc.As a result, the importance of improving power supply reliabilityhas been recognized from a standpoint. In this paper, the multi-objective formulation is presented to evaluate a set of the Pareto so-lutions systematically. This paper proposes a new method for Prob-abilistic Reliability Evaluation to obtain better solution sets. Theeffectiveness of the proposed method is successfully demonstratedin the IEEE 24-bus system.

2. Proposed MethodThis paper presents a new efficient Method for Probabilistic Re-

liability Evaluation with MOMH in consideration of the SolutionDiversity. Taboo Area enhances the solution Diversity obtained byGA in the local search. The proposed method combines SolutionDiversity Strategy with SPEA2. SPEA2 has superior the accuracyand the diversity strategies in MOMH. To deal with the uncertaintiesin power systems, this paper evaluates probabilistic reliability indexEENS in consideration of the tradeoff between the system probabil-ity and the energy not supplied.

3. Simulation ResultsFigure 1 shows the simulation results of the proposed method.

The proposed method allows system planners or operators to under-stand the system states more flexibly. The proposed method is fasterthan other methods in the view point of the computational time. Asfor accuracy of the indexes, the proposed method guaranteed thevalues.

Fig. 1. Evaluated EENS of each method

4. ConclusionThis paper proposes a new method for probabilistic reliability

evaluation with Multi-objective meta-heuristics (MOMH) in consid-eration of solution diversity. Recently, the power networks increasethe degree of the uncertainties due to the new environment of powernetwork liberations and the emergence of the renewable energy, etc.The importance of the probabilistic reliability assessment of electricpower systems has been recognized. Probabilistic reliability evalua-tion has been broadly used for power system operation and planningdue to the capability of considering the various system uncertainties.MOMH is useful for evaluating a set of the Pareto solutions system-atically. The effectiveness of the proposed method is successfullydemonstrated to the IEEE Reliability Test System (IEEE-RTS).

Fig. 2. Simulation results of the proposed method

Fig. 3. Computational time in each method

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evaluation of foreign investment in power plants using

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