2010 International Conference on Power System Technology
Self Scheduling Program for a VRB Energy Storage in a Competitive Electricity Market I. Gerami Moghaddam and A. Saeidian
The fixed term of operation & maintenance (O&M) cost The varied term of operation & maintenance (O&M) cost Considered time interval A binary value, which indicates whether VRB participates in the market or not
Abstract-- Recent developments and advances in energy storage technologies are making the application of energy storage technologies a viable solution to power system applications. This paper addresses the economical aspects of Vanadium Redox Battery
(VRB)
energy
storage
participation
in
competitive
electricity markets as a power producer. The costs including installation,
&
operation
maintenance
and
the
revenues
containing energy price, reducing transmission access cost and deferring facility investment are among economic parameters considered here. In order to achieve this goal, an appropriate self-scheduling approach must be developed to determine its maximum potential of expected profit among multi-markets such as energy and ancillary service markets. The profit maximization problem faced by the VRB is therefore an optimal scheduling problem
that
is
formulated
as
a
mixed-integer
non-linear
programming (MINLP) problem. Numerical studies have been conducted to evaluate the proposed approach.
Index
Terms-Energy
storage,
MINLP,
self-scheduling,
For index s p e r sp Sp, p Sp, s Spot
Selling mode Purchasing mode Energy Regulation Spinning reserve Spinning reserve in purchasing mode Spinning reserve in selling mode Spot
vanadium redox battery (VRB)
II. I.
NOMENCLATURE
For acronyms A (t) Market-clearing price on hour t ($/MWh) Efficiency of VRB 11 1: Adjusting constant for stored energy Amount of energy stored in the VRB on hour t E(t) (MWh) The lower limit of energy stored in VRB (MWh) The upper limit of energy stored in VRB (MWh) Amount of energy stored in the VRB in the beginning of concerned time interval(MWh) Amount of energy stored in the VRB in the end of concerned time interval(MWh)
pet)
Amount of power bids on hour t (MW) The capacity of VRB (MW) The capacity of converter(MW)
I.Gerami Moghaddam is with the Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, [ran. Asaidian is with Science and Research Center of the Islamic Azad University Shushtar Branch. Emails
[email protected](Gerami Moghaddam),
[email protected](A.Saidian)
978-1-4244-5940-7110/$26.00©2010 IEEE
INTRODUCTION
EREGULAnON of the electric power industry has energy markets in which power producers compete with each other to sell the amount of power that maximizes their profit. In a competitive electricity market, an individual electric energy storage owner can purchase energy form energy market and can sell it in the multi-markets such as energy and ancillary service [I]. Pumped-storage plants are the most conventional electric energy storage technology [2], but the construction of these plants is faced with geographical and environmental impacts [3]. Recently, several emerging electrical energy storage technologies have been introduced such as Vanadium Redox batteries (VRB) [4], Nas batteries, flywheels [5-6], Superconducting Magnetic Energy Storage (SMES) and Compressed Air Energy Storage (CAES) [7]. Due to the flexibility and progress of the energy storage system technology, it will play an important role on the operations of power systems. Several benefits from storage system for utility applications are well known: reduced financial losses due to poor power quality and power outages, energy price arbitrage involving charging with low price "off peak" energy for selling when energy price is high, and to be used as utility ancillary services. One of the merits of incorporation of storage systems is that they provide an opportunity for more penetration renewable resources. This paper will build an economic analysis model and focuses on economic viability for the VRB energy storage system in a competitive electricity market. In order to
D created
implement this goal, a suitable self-scheduling approach must first be developed to determine maximum potential of expected profit among multi-markets that electrical energy storage plant owner decides to participate such as; energy, spinning reserve, and regulation markets. The reminder of the paper is organized as follows. Section 1II describes a background of VRB. Section IV is assigned to explanation of utilization of VRB in different power markets. Section V is devoted to the formulation of self-scheduling problem. Case study is provided is section VI and finally, section VII concludes the paper. III.
BENEFITS OF ENERGY STORAGE SYSTEMS
Several benefits from storage system for utility applications are well known: reduced financial losses due to poor power quality and power outages, energy price arbitrage involving charging with low priced "off-peak" energy for use later when energy cost and price is high, and utility ancillary services.
emissions. Discharging DES during peak demand times also reduces the needed capacity of the DG. 11) System stability: Power and frequency oscillations can be damped by rapidly varying the real and reactive output of storage. The improved stability margin is obtained by electronic controls for the DES. 12) Automatic generation control: Energy stored on a system can be used to minimize area control error. The benefits are easier compliance with North American Electric Reliability Corporation (NERC) standards (CI-C4) and reduced mechanical wear on cycling units. 13) Black start capability: Stored energy can be used to start an isolated generating unit.
1) Support of renewable: Storage can reduce fluctuations in wind and photovoltaic (PV) output, and allows sale of renewable energy at high-value times.
14) Reduced fuel use: Use of less-efficient peaking units is reduced by charging storage with energy from more-efficient base load-generating units. Because peaking units often bum natural gas, this also offers natural gas conservation benefits. Also, by improving the system power factor, losses will be reduced, and there is a concomitant reduction of energy use.
2) Reliability and power quality: Storage will allow loads to operate through outages.
15) Environmental benefits: Reduced fuel use results in reduced emissions and natural gas conservation.
3) Reactive power control, power factor correction, and voltage control: Power electronic interfaces provide the ability to rapidly vary reactive as well as active power.
16) Increased efficiency and reduced maintenance of generating units: Load following by storage units allows prime movers to be operated at more constant and efficient set points, increasing their efficiency, maintenance intervals, and useful life.
4) Load leveling: Storage is charged during light-load periods, using low-cost energy from base-load plants, and discharged during high-load times, when the energy value is higher. The benefits are improved load factor, deferred generation expansion, and reduced purchase at peak times and generation by peaking units. 5) Load following: Storage with power electronic interfaces can follow load changes very rapidly, reducing the need for generating units to follow load. 6) Bulk energy management: Bulk power transfers can be delayed by storing the energy until it is needed, or until its value increases. 7) Spinning reserve: Because of its ability to rapidly change the output, storage with power electronic interfaces can act as spinning reserve, reducing the need for conventional spinning reserve units. 8) Deferral of new transmission capacity: Properly located storage units can be charged during off-peak times, reducing peak loading of transmission lines and effectively increasing transmission capacity. 9) Deferral of new generating capacity: Fewer peaking units are needed when storage reduces peak demand. 10) Support of distributed generation: Storage allows the DG, such as micro turbines and fuel cells, to be operated at constant output at its highest efficiency, reducing fuel use and
17) Increased availability of generating units: During peak periods, charged energy storage added to available generation increases total system capacity [8]. IV. VRB
IN
DIFFERENT POWER MARKETs
The probabilistic nature of calling power producers to generate in the ancillary service markets leads to an uncertain profit of the VRB plant owner. The self-scheduling problem of VRB plant is directly influenced by this uncertainty. When VRB plant owner participates in the ancillary service markets for a specific hour, it receives hourly ancillary service price and also hourly spot price if producer is called to generate. Generally, if VRB plant owner commits in the spinning reserve market, following states may occur: • VRB plant owner is called to generate: In this state, the VRB plant owner receives both of hourly spinning reserve and spot prices. The amount of latter income depends on the amount of extra energy that delivers as spinning reserve power. The probability of being in this state is presented by Pdel• •
VRB plant owner is not called to generate: In this state, the VRB plant owner receives according to hourly spinning reserve price. It is obvious that the probability of being in this state is equal to 1- Pdel.
In addition, if the VRB plant owner participates in a day ahead regulation market, following states may occur: •
Regulation-up: In this state, the amount of generated power must be increased. The VRB plant owner receives all of hourly energy, regulation and spot prices. The latest income depends on the amount of extra energy that is supplicated. Pr up shows the probability of , being in the regulation-up state.
•
Regulation -down: In this state, the amount of generated energy must be decreased. In regulation down state, the VRB plant owner receives according to hourly energy and regulation prices, but must repay for energy not generated according to the hourly spot price. The probability of being in the regulation-down state in specific hour is presented by Pr down. ,
•
No-regulation: In this state, the amount of generated power is not changed and the VRB plant owner receives hourly energy and regulation prices. The probability of being in this state is calculated by 1- Pr up , - Pr down [1]. It is rational that the self-scheduling results , of VRB plant will be changed considering different values for Pdeb Pr up and Pr down. So, these parameters , , must be incorporated into the objective function of the self-scheduling problem.
The VRB plant can operate in three operating modes: selling, purchasing and off-line. The participation status of the VRB in different markets versus its operating modes is illustrated in Table.I. As it can be seen, a VRB can participations in all of energy, spinning reserve and regulation markets when it operates in its selling mode. In its purchasing mode, VRB plant owner purchases electricity, but it can be committed for spinning reserve, because it can reduce its charging power and consequently reduces the overall system load. The VRB plant owner can only be participated in the spinning reserve market when they are in their off-line mode [11]. TABLE!
participation status of VRB versus operation modes Spinning Regulation Energy Operating Reserve Market Market modes Market + + + Selling + + Purchasing + off line +:Participation
-:No participation V.
PROBLEM FORMULATION
The optimization problem is executed to schedule the hourly production bids in the energy and the ancillary service markets to maximize the profit while all the operational constraints are satisfied. The analysis will be performed considering the energy, the spinning reserve and the regulation markets, simultaneously. By assuming the incomes, payments and operation costs of VRB plant, the objective function of optimization problem over a concerned time interval can be represented by (1)-(17). The first three terms of (1) represent the revenues of the VRB plant including the trading in energy,
spinning reserve and regulation markets. In addition, the plant owner expects to receive income when VRB plant is called to generate in one of the spinning reserve or regulation markets or in both of them. This expected income is presented by fourth and fifth terms in (1). For participation of the VRB plant in an hour-based day-ahead market, it is essential that the plant be able to trade at least for one hour. This constraint is applied by (2). In (3)-(5), the lower and upper limits of selling, purchasing and regulation capacities are shown, respectively. Also, the lower and upper limits of spinning reserve power in selling and purchasing modes are represented by (6)-(7), respectively. (8), (9), (12) show the upper limits of
Pp (t)
and
�p,p (t)
with respect to the vacant capacity of
VRB The summation of energy, regulation and spinning reserve in selling and purchasing mode for a specific hour must be less than
Pmax
and
�onv
respectively. This constraint
is applied by (10), (11). Also, (13)-(14) are related to the amount of energy stored in the VRB. The amount of energy stored in each hour is calculated by (17). The lower and upper limits of energy stored amount are presented by (15), where
Emin (t)
is calculated by (13). The lower limit of
energy stored in each hour must be adjusted so that the VRB plant can response to the worst condition from the viewpoint of energy stored level. The worst condition may occur when spinning reserve and regulation-up is required, simultaneously. In addition, in order to reserve enough energy stored for the subsequent concerned time interval, (15) is applied. The parameter r adjusts the amount of energy that should be stored for the subsequent week. If lower prices for the next week are forecasted, the VRB plant owner will choose a low value for r . This parameter can be varied. Upper and lower of capacity is represented by (16). To eliminate conflict between different modes in a specific hour, (17) is considered. VI. CASE STUDY As a case study, a 10MW VRB plant with parameters shown in Table. 2 are considered. In the previous sub-section, the maximum expected profit of VRB plant and total bid in a weekly time horizon was determined through self-scheduling problem development. Here, the expected annual profit of VRB plant is first estimated considering utilization factor, depreciation loss, and tax. Then suitable premium for support and profitable operation of VRB calculated. Otherwise, the VRB plant must be supported financially to persuade the investors on selecting this technology as a candid to invest. Here, we consider two kinds of supports as following: • Decreasing the tax rate • Premium allocation Table.3 shows the results of self scheduling program for VRB plant and economic parameters of this plant.
Maximize T
T
T
T
1=1
1=1
1=1
1=1
� )P, (t) - Pp(t)).Ae (t) +I pr(t).Ae (t) +I (P,p"I' (t) +P'P,p(t)).A"P (t) +I (Pr,up - Pr,down),Pr(t).Ae (t) T
T
1=1
1=1
(1)
+I Pdel (Psp,s (t) +Psp,p(t)).Aspo/t) - I KI+K2'( p". (t)+Pp(t) +(Pr,up - Pr,down),Pr (t) +PdetCPsp,sCt) +Psp,p(t)) Es (t)
P
( 2)
��
17
os
P, (t)
S
Pmax.U, (t)
(3)
Os
Pr(t)
S
Pmax.uJt)
( 4)
Os
Pp(t)
s
Peonv.Up(t)
(5)
Os
Pr (t)
S
Pmax .U, (t)
( 6)
os
Pp (t)
S
Peonv'Up(t)
(7)
Os
P/t) s (Emax - E(t)).U /t)
( 8)
Os
P,p,p(t)
Os
PIP,p(t) +Pp(t)
os
P,p,s (t) +p" (t) +P, (t)
Os
P"p,p(t) +Pp(t) s (Emax - E(t)).Up(t)
S (Emax
- E(t)).Up(t)
(9)
�onv.Up(t)
(10)
S
S
(11)
�nax'Up(t)
(12)
Es ,min (t) =Pr(t +1)+p"p,sCt +1)+p".(t +1) P, (t) - Pdel. EJt) =Es (t -1) 17
Eend =T.Eo E,.,min (t) S EJt)
(13)
P,p,s (t) Pr(t) - (P, up - P, ,deoJ. +17·Pp(t) 17 ' 17
(14) (15)
S
(16)
Emax
UI(t) +Up(t) s 1
(17)
VIII.
C ASE STUDY
As a case study, a lOMW VRB plant with parameters shown in Table. 2 are considered. In the previous sub-section, the maximum expected profit of VRB plant and total bid in a weekly time horizon was determined through self-scheduling problem development. Here, the expected annual profit of VRB plant is first estimated considering utilization factor, depreciation loss, and tax. Then suitable premium for support and profitable operation of VRB calculated. Otherwise, the VRB plant must be supported financially to persuade the investors on selecting this technology as a candid to invest. Here, we consider two kinds of supports as following: • Decreasing the tax rate • Premium allocation Table.3 shows the results of self scheduling program for VRB plant and economic parameters of this plant.
This paper assumes that the annual interest rate for financing the storage system is 7.7% [12). Inflation and escalation rates are not considered in this analysis. The salvage value of VRB plant is assumed to be 15% of its capital cost. Also the annual deprecation loss rate and annual tax rate are assumed to be 1% and 5% of annual profit, respectively. The total capital cost (TCC), is the sum of the total costs for the power electronics and storage units of plant. The annualized capital (AC) cost is, then
AC=TCCxCRF
(18)
The CRF (Capital Recovery Factor) is given as [13]:
CRF= i(i+Ir (1+i r - I
(19)
TABLE.II Adjusted case values for
VRB plant[8]
Rated output(MW)
10
storage energy(MWh)
70
Efficiency
0.75
Unit cost for storage ($/KWh)
500
Variable O&M cost ($/KWh)
o
Fix O&M cost ($/KW/year)
20
life time(year)
20
reserve market. Finally, Fig.7 depicts the expected fluctuation of energy storage in the VRB plant. Based on self -scheduling problem results, the expected weekly profit is equal to $88897.33 and total bidding of VRB plant in all energy and ancillary service market at a week is 2400MWh. For this VRB plant, AC is 5132789.7($) and Net annual profit is 4418407.3($). So the difference of these must be paid to VRB owner to reach the 7.7% annual interest rate. Annual bidding of this VRB plant is 125142.9(MWh). This goal is reachable if a premium about 5.71 cent/KWh be paid to VRB owner. By decreasing the tax rate as a financial support, the amount of needed premium decreases as wei\. Fig.2 shows relation between the tax and the remium to be aid to VRB owner. 120
TABLE.lII The procedure of premium calculation
4635360.5 125142.9 46353.6 231768.0 3500000 4418407.3
Expected annual profit ($/year) Expected annual bid (MWh/year) Annual deprecation loss ($/year) Annual tax ($/year) Salvage value ($) Net annual profit ($/year) Capital cost ($)
35000000
� 100
:::; -.. � � ." ';;
�'"
80 60
:::; 40 6£ :;; 20 � o MO��� � ��fflNMO�� � ���M MMNM���� ��OOM NM��� MMMMMMMM
!l ..c.
3 :.::: �
6
c
5
�
4
::::l
3
E
·E 2
v ... "'-
Hour
7
1
Fig. 3 Hourly forecasted energy market-clearing prices [14] 60
�
=-.
1=
. :-:--
F=
0 0
0.05
Tax(%)
0.1
40
'"
30
:2 '"
20
I "-
===="
� 0.15
Fig2. Relation between tax value and the premium
The time horizon is considered 168 hours. The assumed forecasted prices for the energy, the spinning reserve and the regulation markets are shown in Fig.3 to Fig.7, respectively. Price data are adopted from electric energy market of Mainland, Spain [14] with a few adjustments. The probability of calling plants for generating energy in the spinning reserve
.s �
a-
Iiour Fig . 4 Hourly forecasted spinning reserve market - clearing prices [14]
80
�
regulation-up and regulation-down states are considered 40% and 35%, respectively, which seem to be rational assumptions. In addition, the adjusting constant r is assumed to be 1. The following random-based method shown by (20) is used to forecast the hourly spot price. The hours between 9 and 18 are contemplated as the peak period.
�
AspOl
{ (1 (t)=
) Ae (t) 0::; r ::; 0.25 tE[9,18] (20) (1 + r) Ae (t) - 0.1 ::; Jl ::; 0.1 otherwise +
r
In order to present the spike price in the peak hours, a number of spikes are randomly generated using Freshet distribution. According to the self-scheduling problem results, Fig.6 shows the strategy of VRB to participate in the spinning
10
o
(Pdel ) is assumed to be 3%. Also, the probabilities of
market
50
:::; -.. v".
:::; -..
70 bU
.�
50
�'"
40
� ::E
30
�'"
20
d.)
10
� a::
o
Hour
Fig.S Hourly forecasted regulation market-clearing prices [14]
[6] M.Lazarewicz, "Flywheel based frequency regulation pilot projects," proceeding of 2005 Annual Meeting of Electricity Storage Association , May 23-26 2005,Toronto,Canada. [7] EPRI-DOE
Handbook
of
Energy
Storage
for
transmission
and
Distribution applications. EPRI, Palo Alto, CA, and the US Department Of Energy, Washington DC. [8] R
C Leou , "An Economic Analysis Model for the Energy Storage
Systems in a Deregulated Market, ".IEEE International Conference on Sustainable Energy Technologies 2008, pp.744-749, 24-27 Nov. 2008, Singapore. [9] T.Shigematsu, and
T.Kumamoto, and
H.Deguchi
, and
T.Hara,
"Applications of a Vanadium Redox-flow Battery to Maintain Power Quality," IEEE/PES Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. vol .2,pp.1065-1070. [1O]S.Miyake, and NTokuda, "Vanadium Redox-Flow Battery for a Variety HOllr
of Applications," IEEE Power Engineering Society Summer Meeting, 2001,voLl,pp.450-451.
Fig.6 HOllri
[11]S.1. Kazempour, and M. Parsa Moghadam, "Economic Viability of NaS
bided amount of energ
Battery Plant in a Competitive Electricity Market," proceeding of ICCEP
60
2009,Capri,Italy,June 2009. [12]Energy Information Administration (2006). Annual energy
� � ., '" > .-
>-
'"
50
Available http://wwweia.doe.gov/oiaf/aeo. lO
[13]M.
-:>
"
� 2
R
Lindberg, "Engineering
economic
analysis,"
in
Mechanical
Engineering Review Manual, 8th ed. San Carlos, CA: Professional �o
Publications,1990,ch. 2,pp. 2�3 .
;;
.;:;
outlook
2006with projection to 2030. Report No. DOE/EIA-0383. [Online].
[14]Market Operator of the Electric Energy Market of Mainland Spain. [Online]. Available at: http://www.omel.es.
20
10
V>
XI. Biographies
o
Hour
Fig.7 Expected fluctuation of energy storage in VRB plant
IX. CONCLUSIONS In this paper, an economic viability of VRB plant as emerging energy storage technology in a competitive electricity market was addressed. In order to calculate the maximum potential of expected profit in a specific time interval, a self-scheduling problem for the considered VRB plant was developed. Then, the premium value for support of the considered plant was calculated. This analysis concluded that the VRB has not economic merit and it should be financially supported to encourage investors to select this storage technology. For this purpose, two financial support mechanisms were considered and the amounts of needed financial supports were calculated in a case study. X. REFERENCES [I] R Walwalker, and
I Apt and RMancini, "Economics of electric storage
for energy arbitrage and regulation in New York," Energy Policy, vol. 35, pp. 2558-2568. [2] F.
C Figueiredo, and P C Flynn, "Using diurnal power price to
configure pumped storage," IEEE Trans. Power Conv, vol. 21, pp. 804809,2006.
[3] E. Spahic,and G. Balzer,and B. Hellmich,and W Munch, " Wind energy storages-possibilities," IEEE Power. Teach Conference 2007,pp.615-620, July 2007,Lausanne,Switzerland. [4] A. Price, "Technologies for energy storage-present and future: flow battries ," IEEE Power Engineering Society Summer Meeting 2000, pp. 1541-1545. [5] SJ. Kazempour, and M. P. Moghaddam ,"Static security enhancement of by means of optimal utilization of NaS battery systems ," IEEE Power Teach conference 2007,pp.I-6,July 2007,Lausanne,Switzerland.
Iman Gerami Moghaddam was born in Andimeshk, Iran, on 1979. He received his B.Sc. degree in Electrical Engineering from Isfahan University of Technology in 2002. He is currently a M.sc student in the Department of Electrical Engineering, Tarbiat Moddares University, Tehran, Iran. His research interest includes renewable energy in power system, stochastic programming, operation and CHP systems. Ali Saidian was born in 1946. He received the B.Sc. degree in physics from Shahid Chamran University and He received M.Sc and Phd in Electrical Engineering from the University of Oklahoma Oklahoma and state University(USA), respectively. His research interests are Engineering and power system reliability, power system operation & control, Distributed Generation and renewable energies. Dr.Saidian has been with Shahid Chamran University in Ahvaz, Iran for over 30 years.
