International Journal of Electrical and Computer Engineering (IJECE) Vol.
No.
April 2013, pp.
ISSN: 2088-8708
Evaluation of Power System Reliability Considering Direct Load Control Effects Ali Mansouri*.
Ali Aazami**.
Amin Omidian*.
Ehsan Mohamadian*.
Rahmat Aazami* * Department of Electrical Engineering.
Science and Research Branch.
Islamic Azad University.
Ilam.
Iran ** Department of Electrical Engineering.
Quchan Institute of Engineering and Technology.
Quchan.
Iran
Article Info
ABSTRACT
Article history:
With the development of deregulated power systems and increase of prices in some hours of day, demand side management programs were noticed more by customers.
In restructured power systems.
DSM programs are introduced as DEMAND RESPONSE.
In this paper we try to evaluate the effect of DR programs on power system reliability and nodal reliability.
In order to reach to this target.
Direct Load Control program, as the most common demand response program, is considered.
Effects of demand response programs on system and nodal reliability of a deregulated power system are investigated using direct load control and economic load model.
DC power-flow-based optimal load curtailment objective and reliability evaluation techniques.
The proposed method is evaluated by numerical studies based on a small reliability test system (RBTS), and simulation results show that demand response program can improve the system and nodal reliability.
Received Feb 3, 2013 Revised Mar 19, 2013 Accepted Mar 24, 2013 Keyword:
Demand response (DR) Direct load control Power system deregulation Reliability Copyright A2013 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ali Mansouri.
Department of Electrical Engineering.
Science and Research Branch.
Islamic Azad University.
Ilam.
Iran Email: dra_1364@yahoo.
INTRODUCTION
Demand Side Management (DSM) introduced by Electric Power Research Institute (EPRI) in the DSM consists of a series of activities that governments or utilities design to change the amount or time of electric energy consumption, to achieve better social welfare or some times for maximizing the benefits of utilities or consumers.
In fact.
DSM is a global term that covers activities such as: Load Management.
Energy Efficiency.
Energy Saving and so on .
Electric power industry bas been faced with restructuring and deregulation.
Meanwhile a few new terms created in this new environment, such as "Demand Response" (DR).
DR can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time.
Further.
DR can be also defined as the incentive paymentsdesigned to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized .
Demand response consists of a series of activities that governments or utilities design to change the amount or time of electric energy consumption, to achieve better social welfare or some times for maximizing the benefits of utilities or consumers.
The benefits of DR include increased static and dynamic efficiency, better capacity utilization, pricing patterns that better reflect actual costs, reduction of price spikes, decentralized mitigation of market power, and improved risk management.
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A recent study estimated the prospective benefits of active demand response at $7.
5 billion by 2010 (ICF 2.
Other studies, described in GAO .
, give further details of the benefits that have already been generated because of demand response and active retail choice.
Federal Energy Regulatory Commission (FERC) reported the results of DR investigations and implementations in US utilities and Power Markers .
, .
In the mentioned report.
DR is divided into two basic categories and several subgroups:
A- Incentive-based programs:
A-1- Direct Load Control (DLC) A-2- Interruptible/curtail able service (I/C) A-3- Demand Bidding/Buy Back A-4- Emergency Demand Response Program (EDRP) A-5- Capacity Market Program (CAP) A-6- Ancillary Service Markets (A/S) B- Time-based programs:
B-1- Time-of-Use (TOU) program B-2- Real Time Pricing (RTP) program B-3- Critical Peak Pricing (CCP) Program The most usual demand response program is Direct Load Control (DLC), in which utilities have the ability to remotely shut down participantAos load on a short notice.
DR is able to change the amount and time of electric energy usage so that the best efficiency of consumption takes place in the peak interval .
There is a growing concern about the reliability of power systems under a market environment, especially after the blackouts in North America and Europe in 2003.
Bulk power system operators primarily rely on adjustments in generation output (MW movements up or dow.
to keep the system reliability.
In principle, changes in electricity demand could serve as well as generator movements in meeting the reliability requirements .
So, customer loads could be able to participate in these markets.
The participation of these resources will either enhance reliability or lower costs of maintaining reliability for all customers and will save money for participating customers.
This paper investigates the impacts of DR programs on system and nodal reliability in state enumeration approach.
A small reliability test system.
RBTS, is studied, and simulation results show that demand response can improve the system and nodal reliability.
This paper is organized in five sections.
Section 2 defines the load economic model which is used to evaluate the participation in DLC program and explains the economic analysis formulation.
Reliability Index Calculation is discussed in section 3.
Section 4 presents the numerical results which are tested on RBTS and finally section 5 is dedicated to conclusions.
MODELING OF DEMAND RESPONSEM
In the beginning of the deregulation, usually consumers had not effective participation in the power markets and therefore they were not able to response to the prices effectively.
However, the development of the restructured power systems has been accompanied by many problems, for example reduced system Figure 2 shows how the demand elasticity could effect on electricity prices .
Figure1.
Effect of demand variation on the electric energy price .
Evaluation of Power System Reliability Considering Direct Load Control Effects (Ali Mansour.
A ISSN:2088-8708 Elasticity is defined as the ratio of the relative change indemand to the relative change in price .
C q A 0 dq C A q 0 dp Where:
AEd .
i ) : Demand changes in time interval t i AEA .
i ) AEA .
j ) : Price changes in interval t i : Price changes in time interval According to Equation .
, self elasticity ( AE d .
i ) A ii A AE A .
i ) AE d .
i ) A ij A AE A .
j ) A ii ) and cross elasticity ( A ij ) can be written as .
Where:
AEd .
i ) : Demand changes in time interval t i AEA .
i ) : Price changes in interval t i AEA .
j ) : Price changes in time interval j Self elasticity and cross elasticity are negative and positive values, respectively.
If the relative change in demand is larger than the relative change in price, the demand is said to be elastic, on the other hand, if the relative change in demand is smaller than the relative change in price, the demand is said to be So the elasticity coefficients can be arranged in a 24 by 24 matrix E.
The detailed process of modeling and formulating how the DR program affects on the electricity demand and how the maximum benefit of customers is achieved, are discussed in .
Accordingly the final responsive economic model is presented by .
E .
[ A .
A A 0 .
A A.
] E d .
A Ed 0 .
A Eu E0 .
, j ).
0 C A( j ) A E i A 1,2,.
,24.
A 0 .
j A1 The above equation shows how much should be the customer's demand in order to achieve maximum benefit in a 24-hours interval.
Time period is assumed to be one hour.
Variable load curve for 24 hours within one day are considered in the simulations.
So the elasticity coefficients can be arranged in a 24 by 24 matrix E .
E A 1,1 A 1, 2
E 2,1 A 2, 2
As EAE As E
23, 2
E 23,1
EA 24,1 A 24, 2
A1, 23
As AU A 2, 23
AU A 23, 23
AU A 24, 23
A 1, 24 E
A 2, 24 EE
A 23, 24 E
A 24, 24 EE
As .
REALIBILITY INDEX CALCULATION Reliability evaluation methodologies of power systems are systematically described in reference .
According to the method of selecting system state, there are two basic methods: state enumeration and Monte IJECE Vol.
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April 2013: 254Ae259
IJECE
ISSN: 2088-8708
Carlo sampling.
The composite system reliability assessment is a complex calculation project, which generally includes the following procedures .
Determination of component failures and load curve models.
Selection of system states.
Identification and analysis of system problems.
Calculation of reliability indices.
Both the state enumeration and Monte Carlo simulation method can be applied to composite system reliability evaluation.
These two methods use different approaches to select system states and have different forms of formulas to calculate reliability indices.
The techniques of identifying and analyzing problems in a system state are the same.
These include power flow and contingency analysis for problem recognition and optimal power flow for remedial actions.
In our following simulation, the enumeration simulation method is adopted to select system states.
The formulation of load curtailment determination under contingency s using DC load flow and customer interruption load can be depicted by the optimization of Equation .
E min Eu LC is iEa ND E Eu A LC i A Eu B ij (A i A A j ) E iEa NG PG i PD i E C B ij (A i A A j ) C Pij E A Pij E 0 C LC i C PD i E min C PG i C PG i E PG i E LC i is the load curtailment bus i under contingency s.
PGi and PDi are generation output and load power bus i.
NG.
ND are the sets of generation buses, load buses in the system.
PGimax PGimin is the minimum Pijmax is the maximum output of real power of generator i.
output of real power of generator i.
the maximum real power flow allowed through line ij.
Bij is the susceptance between nodes i and j.
This optimal model of load curtailment is a linear programming problem that is easily solved by conventional linear programming methods.
Indices of system reliability are:
LOLE: Loss of load expected is Index load Average Interruption Duration, in .
, is the mean duration per interruption.
ENS: Energy Not Supplied, in [MWh/da.
, is the total amount of energy which is expected not to be delivered to the loads.
AENS: Average Energy Not Supplied, in [MWh/C/da.
, is the average amount of energy not supplied, for all customers.
Additional calculated indices for the load points are:
AID: Average Interruption Duration .
LPENS: Load Point Energy Not Supplied [MWh/da.
NUMERICAL RESULT
A case study based on the Ie 6-bus system is presented in this section.
In order to show the effect of demand response program on system reliability of a deregulated power system .
The RBTS has 11 generating units of various sizes, with the total installed capacity of 240 MW and a total system peak demand of 185MW spreading out among 5 of the 6 system buses.
The single line diagram of RBTS is shown in Figure 2.
The amount of incentive and the price of electrical energy in DLC program formulation are assumed to be same and equal to 50 $/MWh.
The elasticity of the load is shown in Table.
A typical load curve of a real world network is selected to test and analyze the effect of DLC program.
Figure 3 .
The load curve is divided into three intervals: Low load period .
00 p.
to 9:00 ), off-peak period .
:00 a.
to 19:00 p.
) and peak period .
:00 p.
to 12:00 p.
Evaluation of Power System Reliability Considering Direct Load Control Effects (Ali Mansour.
A ISSN:2 Figure 2.
Sinngle line diagrram of the RB
BTS
Table 1.
Self and croselasticities Peak Off-Peak Low Peak Off-Peak Low Load (MW) 11 13 15 Time .
Figure 3.
Load of tesst system Figure Effecct of different scenarios of DLC Three scennarios are assumed for the different partticipantAos poteential in DLC program.
It means the pootential of thee customers that take partt in DLC program assumeed to be 10%, 20%, and 30% of all The loaad curve befoore and after implementattion of DLC program for different scen narios are repressented in Figuure 4.
As it can be b seen, by im mplementationn of DLC program, based on o the differennce between elasticities in diferent periods, loads are transferred froom peak periods to valleyy periods.
Witthout demand d response prograams, the systeem peak loadd is 185 MW.
considering demand respoonse program ms, however, th he system peak load 877MW.
Load duuration curve with and with hout DR progrram shows in Figure 5.
Load (MW) load duratio
on curve without DLC
load duratio
on curve with DLC
10 12 14 16 18
Time .
Fig.
Load durationn curve with an nd without DR R programs E Vol.
No.
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IJECE
IJECE
ISSN: 2088-8708
In order to show the effect of demand response on the load curve and system and nodal reliability of a deregulated power system, a test system.
RBTS .
, has been simulated using the reliability evaluation Above program maximize the profit of customers moreover influencing the system and nodal Two scenarios will be observed in this paper: 1-Test of system without considering DR programs, 2Test of system with considering DR programs.
By Simulation and test system, these results will be driven for the reliability of total system (Table .
Table 2.
System reliability Idices of The RBTS
LOLE
Without Considering ENS
AENS
Considering DR For system nodes these results are driven (Table 3, .
Table 3.
Average Interruption Duration [Hou.
Without Considering DR Considering DR Load 6 Load 3 Load 4 Load 5 Table 4 Load Point Energy Not Supplied (MWh/da.
Load 6 Load 3 Load 4 Load 5 Without Considering DR Considering DR The results show that direct load control programs improves the reliability of the system and from the comparison of nodal reliability indices with andwithout considering demand response programs especially DLC program, it is shown that thenodal reliability is also improved considering demand response CONCLUSION This paper evaluate the effects of demand response programs especially direct load control on system and nodal reliability of a deregulated power system using direct load control and economic load model.
DC power-flow-based optimal load curtailment objective and reliability evaluation techniques.
From the simulation results it can be seen that demand response programs improves the system reliability and nodal reliability of a deregulated power system.
REFERENCES