Electric Vehicle Charging Station Location using Fuzzy Optimization

Electric vehicle charging station location is a basic problem in integrating electric vehicles in electric power systems. An electric vehicle plugged into the electric distribution system may absorb or produce active and/or reactive power [1-4] depending on the need of the electric power system, Table 1 below.

Electric vehicle charger operating modes [4].

When finding the location of EV charging stations in order to support the electric distribution systems, the cost of EV charging, the impact on distribution system losses and voltage profile of the system are parameters needed to be considered. These variables are to be looked into when plugged in EV is either acting as a generator or a load given a system demand level.

Recent studies have solved this EV charging station location problem. In [5], a mixed integer programming solution was developed with site accessibility, local jobs and population densities and trip attributes as main constraints. A genetic programming approach is utilized in [6] for simulation of electric vehicles on a real map of a European city where the optimal solution of the charging infrastructure is derived based on mean trip times of electric vehicles. A two step procedure is proposed in [7] where the authors included environmental factors and service radius of EV charging stations in the screening first step and built a modified primal-dual interior point algorithm (MPDIPA) for optimal sizing of EV charging stations with the minimization of total cost associated with EV charging stations to be planned as the objective function with losses and voltage profile included in the problem. Reference [8] introduces an optimization process for sizing and siting of EV charging stations, modeling the charging demand and the structure of road network to where the solution approach was graph theory. Level 1 and level 2 charging stations are discussed in [9] and how to allocate them for residential EV users using simulation-optimization strategy.

Recent studies do not consider uncertainties and imprecision which can be captured using fuzzy optimization. Fuzzy set theory can provide a simpler yet powerful solution for allocating EV charging stations in electric distribution systems. The Civanlar test system [11] will be utilized for the study and assuming that capital investment of the EV charging station is the same for all distribution system candidate nodes while considering time of use (TOU) electricity tariff, distribution system losses and voltage profiles.

References

[1] Chenye Wu, Hamed Mohsenian-Rad, and Jianwei Huang, “PEV-based Reactive Power Compensation for Wind DG Units: A Stackelberg Game Approach”, in Proc. of the IEEE Conference on Smart Grid Communications (SmartGridComm’12), Tainan City, Taiwan, October 2012.

[2]   Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang, Juri Jatskevich, “PEV-Based Combined Frequency and Voltage Regulation for Smart Grid”, the 3rd IEEE Innovative Smart Grid Technologies Conference, Washington DC, Jan 2012.

[3]    M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "V2G Reactive Power Compensation Using a PHEV Bidirectional Charger Interface Rated at Level 1, 2, and 3 Charging Standards," IEEE Energy Conversion Congress and Exposition, Atlanta, Georgia, Sept. 12-16, 2010.

[4]     M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "Examination of a PHEV Bidirectional Charger System for V2G Reactive Power Compensation," IEEE Applied Power Electronics Conference, Palm Springs, California, Feb. 21-25, 2010, pp. 458-465.

[5]    Chen, T. D., et al, “The Electric Vehicle Charging Station Location Problem: A Parking-Based Assignment Method for Seattle”, on-line: http://www.caee.utexas.edu/prof/kockelman/public_html/TRB13EVparking.pdf

[6]    Hess, A. Et al, “Optimal Deployment of Charging Stations for Electric Vehicular Networks”, on-line:http://conferences.sigcomm.org/co-next/2012/eproceedings/urbane/p1.pdf

[7]    Liu, Zhipeng, Wen, F. and  Ledwich, G. F. , “Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems”, IEEE Transactions on Power Delivery, Jan. 2013, Vol. 28 , Issue 1.

[8]    Jia, L., Hu, Z., Song, Y., Luo, Z., “Optimal siting and sizing of electric vehicle charging stations”, 2012 IEEE International Electric Vehicle Conference (IEVC), 4-8 March 2012

[9]    Xi, X., et al, “Simulation-Optimization Model for Location of a Public Electric Vehicle Charging Infrastructure”, on-line:http://www.ise.osu.edu/ISEFaculty/sioshansi/papers/charge_infra.pdf

[10]“Siting Electric Vehicle Charging Stations”, on-line: http://www.sustainabletransportationstrategies.com/wp-content/uploads/2012/05/Siting-EV-Charging-Stations-Version-1.0.pdf

[11]On-line: http://ee.sharif.ir/~e_hajipour/index_files/Test_Cases_Data.pdf

Voltage Stability Impact of Electric Vehicles

Reactive power consumption or injection is a promising feature out of electric vehicle charging. In [1] and [2], the authors present the analysis of the  reactive power control capability of an electric vehicle charging system to support the power system. Reactive power support and economics from the electric vehicles were discussed in references [3] and [4].

The electric vehicle charger follows the modes of operation [1]:

Electric vehicle charger operating modes [1].

Given these modes, analyzing power system voltage stability with electric vehicle charging would be needed. PV and QV curves will be helpful to assess the impact of the different charger operating modes as well as transient voltage stability simulations. This would be a welcome addition to the increasing literature of vehicle to grid (V2G) especially when the grid operator coordinates a large fleet of electric vehicle with the power network.

Does the electric vehicle charging mode provide increased power transfer in terms of static voltage stability? Does the operating mode of an electric vehicle charger gives a better voltage recovery during transient periods?

These research questions can be analyzed by modeling electric vehicle charging operating modes integrated in a power system test case.

References:

1. M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "V2G Reactive Power Compensation Using a PHEV Bidirectional Charger Interface Rated at Level 1, 2, and 3 Charging Standards," IEEE Energy Conversion Congress and Exposition, Atlanta, Georgia, Sept. 12-16, 2010.
2. M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "Examination of a PHEV Bidirectional Charger System for V2G Reactive Power Compensation," IEEE Applied Power Electronics Conference, Palm Springs, California, Feb. 21-25, 2010, pp. 458-465.
3. Chenye Wu, Hamed Mohsenian-Rad, and Jianwei Huang, “PEV-based Reactive Power Compensation for Wind DG Units: A Stackelberg Game Approach”, in Proc. of the IEEE Conference on Smart Grid Communications (SmartGridComm’12), Tainan City, Taiwan, October 2012.
4. Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang, Juri Jatskevich, PEV-Based Combined Frequency and Voltage Regulation for Smart Grid, the 3rd IEEE Innovative Smart Grid Technologies Conference, Washington DC, Jan 2012.

E-Trikes and WESM

Using the 12-12-12 data from WESM, I plotted here the Luzon demand and Luzon LWAP with the inclusion of E-Trike. The peak load for this day was 7,191.7 MW (2 pm) and the lowest LWAP was P1,690.43 per MWh (4 am). For this day, the highest LWAP (P13,145.7/MWh at 6 pm) does not coincide with the peak load.

E-Trikes and WESM.

In the chosen scenario, the 100,000 E-Trikes will be aggregated and charged as one bulk load, but on the other hand, will take advantage of the LWAP at its lowest and be coordinated when the system demand is also at its lowest (off-peak) both from 3 am to 5 am.

Though the 100,000 E-Trikes are envisioned to hit the streets by 2017, it is necessary to study a "what-if" condition on whether the present generation capacity, at least in Luzon, will be able to accommodate the new 493 MW load of E-Trikes. From the NGCP website, the system capacity is 8,091 MW with peak load of 7,318 MW so that's why we have a generation reserve of 773 MW. If the charging of E-Trike is not done during the low load and low LWAP hours, the 493 MW of E-Trike will be added to the current peak load which will bring down the generation reserve to about 280 MW. If the largest contingency is more than 280 MW, the system will be operated at “Alert State”. This shows how important the timing of the E-Trikes’ charging when aggregated as one bulk load. Though when added as a load in the system, the E-Trikes as a load may impact the system price not as depicted in the graph.

The strategy of handling E-Trikes as an additional aggregated load in the power grid would be:

• Charge when the LWAP is at its lowest – from economic standpoint, it makes sense. The E-Trike aggregator will enjoy economic benefits rather than utilizing uncoordinated charging related with LWAP which is to charge at random during a given day.
• Charge when the demand is lowest – from generation capacity and system reliability viewpoints, this will provide support to the power system. Coordination of the E-Trike load with the demand curve may delay the need for additional generation capacity or investments.

In reality, some E-Trikes will be on the streets throughout the day/night. There are transportation needs to be met at night and any time during the day especially in urban locations so it is unlikely there will be 100,000 E-Trikes charging at the same time.

What is discussed here is a worst-case-what-if scenario which may allow us to think if the present generation capacity may be able to accommodate such 493 MW of E-Trike load. So as long as the bulk load does not plug into the grid during peak hours, economic and technical benefits are achievable.

Benefits from Electric Vehicles for the Philippine Power Grid

The last blogs I posted on electric vehicles (EV) may seem to have created the wrong impression in what I’m trying to do. I analyzed the loading impacts of E-Jeepneys and E-Trike to local electric distribution power system, specifically loading up a pole mounted distribution transformer. In practice, these scenarios are very real and can be prevented by planning and coordinating new loads which are the EVs under the government’s program.

The overloading of electrical equipment, at least locally, is just one tiny bit on one side of the fence. EVs when largely manufactured and utilized can be a resource of power grid reliability and security support, just like any other ancillary services.

References [1-4] provide simulations and analysis on the following:

• Frequency regulation – NGCP procures frequency regulation from on-line generators which are called spinning reserves. A big bulk of load can be aggregated and adjust accordingly to maintain system frequency, like a spinning generating reserve. I posted that the vision of DOE to have 100,000 E-Trikes by 2017 will have a MW load greater than the Quezon Power plant which is 480 MW. EV charging is via power electronic converters/inverters which are controllable. The charging of E-Trikes if coordinated accordingly to serve a load serving as a spinning reserve is a promising capability for E-Trikes or any large scale electric vehicle when aggregated.

• Reactive power compensation – Again, NGCP installs and may procure voltage support services from generation or invest on its own reactive power devices. In [3], the authors described an EV charging system which can be a source of reactive power compensation. This system is allowed to inject or consume reactive power whichever is needed by the power system in real time. In [4], the authors provided a pricing methodology for wind farm reactive compensation provided by an EV charging park.

• Contribution to system security – NGCP procures contingency reserves per Philippine Grid Code.  These are generators which are on-line ready to respond (increase or decrease their output) in times of a system disturbance. Authors in reference [2] indicate that EV chargers have response time faster than generators. In this case, going back to the 100,000 E-Trike, you may have a large “generator” providing that contingency reserve to mitigate any undesirable system condition due to a disturbance.

For large scale E-Trike or E-Jeepney charging, which is envisioned in the Philippines, these features would become income generating resources for EV operators or aggregators. Also, the NGCP will have another source of ancillary services which can be counted upon to support grid reliability and security.

References:
[1]    Chenye Wu, Hamed Mohsenian-Rad, Jianwei Huang, Juri Jatskevich, “PEV-Based Combined Frequency and Voltage Regulation for Smart Grid”, in Proc. of the IEEE PES Innovative Smart Grid Technologies Conference (ISGT’2012), Washington, DC, January 2012.
[2]    Sakis Meliopoulos, Jerome Meisel, George Cokkinides and Thomas Overbye, "Power System Level Impacts of Plug-In Hybrid Vehicles." PSERC Document 09-12, PSERC Final Report. October 2009.
[3]    M. Kisacikoglu, B. Ozpineci, L. M. Tolbert, "Examination of a PHEV Bidirectional Charger System for V2G Reactive Power Compensation," IEEE Applied Power Electronics Conference, Palm Springs, California, Feb. 21-25, 2010, pp. 458-465.
[4]    Chenye Wu, Hamed Mohsenian-Rad, and Jianwei Huang, “PEV-based Reactive Power Compensation for Wind DG Units: A Stackelberg Game Approach”, accepted for publication in Proc. of the IEEE Conference on Smart Grid Communications (SmartGridComm’12), Tainan City, Taiwan, October 2012.

E-Trike: Impact on Distribution Transformer Loading

The partnership of DOE and ADB envisions having 100,000 E-Trikes between now and 2017 [1]. Each E-Trike will have about 3 kW to 5 kW electric power usage and will be charging for about 45 minutes to 1 hour.

A study by DOE in collaboration with United Nations [2] indicated that one E-Trike will consume 1.8 MWh in one year. So to check the values here: 1.8 MWh divided by 365 days, an E-Trike will take 4.93 kWh. Below is a table for the kW loading of E-Trike(s). Note that 100,000 E-Trikes is even above the capacity of Quezon Power plant which has 480 MW capacity.

In this post, several scenarios of charging time and number of E-Trikes are presented using 4.93 kW charging power.

Figure 1 presents the connection of 1 E-Trike during three separate hours in the evening versus a 25 kVA distribution transformer. In here, the assumption is the E-Trike driver uses the vehicle from 8am to 5pm, then comes home to his family and charges his vehicle on those random hours. If 1 E-Trike is being connected with the base residential load curve, the 25 kVA distribution transformer will have no overloading.

Figure 1

Figure 2

Figure 2 shows the plots of several number of E-Trike being charged on separate hours of the day. For this case, the assumption is the E-Trike operator/businessman who has several vehicles takes advantage his vehicle charging according to the Time of Use (TOU) rates of the distribution utility. He may utilize a timer-switch to program when the charging begins and ends.  A 25 kVA distribution transformer will overload for the assumed hours for both during evening and during morning except for 3 E-Trikes during morning. A distribution utility coordination with this E-Trike operator will identify that an upgrade from 25 kVA to 37.5 kVA transformer will provide mitigation of the overload unless other households in the service area will shut down all their appliances ( voluntary load shedding).

References:

[1] Consultants sought for $500-M e-Trike project. Available: 

http://business.inquirer.net/74917/consultants-sought-for-500-m-e-trike-project [2] Philippine Electric Vehicle Project. Available: http://cdm.unfccc.int/filestorage/3/K/Y/3KY4J2IW70AZPTX9MS6VGU1QORNE8F/Etrike%20CPA-DD%20ver1.pdf?t=MGF8bWVremlifDCLzXH3EtuSp9elyiR5MR_6

Revision on Loading Impacts of PHUV – E-Jeepney

Ms. Diana Limjoco, manufacturer of E-vehicles in the Philippines responded to my query and corrected my post on charging time of one E-Jeepney which I assumed to be 8 hours. According to her, an E-Jeepney can be charged fully at about 4-5 hours if done right.

Below, I updated the graphs for the loading impact of E-Jeepney(s) charging from 8 hours to 5 hours. No matter what the charging time is, a 25 kVA distribution transformer will suffer overloading for 1 PHUV scenario when charged during the evening and will be heavily loaded in other scenarios. If 2 PHUVs are charged at the same time, the 25 kVA distribution transformer will have severe overloads. And even if the transformer is rated 37.5 kVA, 2 PHUV charging at the same time will overload the transformer when combined with the base residential load.

In this case, the distribution utility has to be proactive in their operations planning in upgrading their distribution transformers or coordinate with consumers who have E-Jeepneys so as to prevent overloading of electrical equipment especially distribution transformers.

Cost Allocation of SPS Service Using Cooperative Game Theory

Power systems planning and operations are usually defined by N-1 criterion. This means that in an event of a single contingency, no remaining connected transmission elements will be thermally overloaded, no bus voltage will be outside of acceptable limits, no system interface limit is violated, and system stability is maintained.

Special Protection Systems or SPS are widely utilized for increasing power transfer in transmission systems at the same time respecting security constraints [1]. SPS applications usually are generation rejection schemes (GRS), line/transformer transfer tripping and load shedding. GRS are designed to mitigate overloading of a transmission line or lines after an N-1 contingency near the vicinity of a generating plant or are employed to arrest increasing dynamic oscillation which may lead to unstable system conditions. Without the GRS, generation output is curtailed to satisfy the N-1 security criterion. With the GRS, the output of the generation is increased thereby increasing power transfer. Further, GRS also mitigates or delays the possibility of transmission expansion or investment due to transmission capacity constraints.

In a locational marginal pricing based electricity market, curtailment of generation (without GRS), specifically of a cheap generation due to the security N-1 criterion can be considered as transmission congestion. Transmission capacity limitations impede the generation output thereby decreasing the profit opportunity of the generation company (GenCo).  If a GRS is installed for this GenCo, the output of the GenCo is increased and thus there is a clear benefit for the GenCo in terms of profit. When transfer capability is limited, without GRS, the profit of a transmission owner (TO) is decreased due to less power wheeling charges. With the GRS, wheeling charges increase as a consequence of the added power transfer. This premise is the same with the electricity system and market administrator, called independent system operator (ISO), since the ISO charges for cost-based services including scheduling, system control and dispatch.  For the demand side, when generation output is curtailed due to congestion, without GRS, the resulting nodal prices at the demand’s location maybe higher than when a GRS is in place to increase generation output from a cheap generation.

GRS installations have embedded cost and actual service cost [3]. Since electricity market participants have various benefits in having a GRS installation, the cost of the SPS/GRS service must be allocated among the participants. Cooperative game theory [4-5] can be utilized in allocating fair cost on the beneficiaries of the SPS service.

The PJM 5 bus test system [6], shown Figure 1, is to be utilized as an example for the application of cooperative game theory in sharing the SPS service cost among power system organizations.

Figure 1. PJM 5 bus test system.

References:

[1]     W. Fu, S. Zhao, J. D. McCalley, V. Vittal, N. Abi-Samra, “Risk Assessment for Special Protection Systems,” IEEE Transactions on Power Systems, vol. 17, no. 1, pp. 63-72. February 2002. Available: home.eng.iastate.edu/~JDM/WebJournalPapers/RiskAssessentSPS.pdf

[2]     Ancillary Services Presentation. Available: http://www.nyiso.com/public/webdocs/services/market_training/workshops_courses/nymoc/Ancillary_Services.pdf

[3]     J. K. Earle, “Functional unbundling of special protection systems as a required interconnected operating service in a deregulated environment,” MSEE Thesis, University of New Brunswick, 1997. Available: dspace.hil.unb.ca:8080/handle/1882/42522

[4]     H. Singh, “Introduction to Game Theory and Its Application in Electric Power Markets,” IEEE Computer Applications in Power, IEEE Computer Application in Power, vol.12, no.2, pp. 18-20, 22, Oct.1999.

[5]     J. Mepokee, D. Enke, B. Chowdhury, “Cost allocation for transmission investment using agent-based game theory,” International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, Iowa, September 12-16, 2004.

[6]     L. Fangxing, B. Rui, "Small Test Systems for Power System Economic Studies," Proceedings of the 2010 IEEE PES General Meeting, Minneapolis, MN, July 25-29, 2010.

Averting The Predicted 2013 Power Crisis

Google this "Rowaldo Del Mundo".

Instantly, you read lines and lines of the upcoming power crisis in the Philippines. At least my title says "predicted". Just like the forecasted electric demand, the power crisis supposedly happening in 2013 is forecasted, too. It is uncertain. Though the numbers tell us the generation capacity is short by that time compared to the demand, not only investments in generation capacity will surely help.

WESM has been employing demand response control in Visayas which prove to be effective in peak shaving. Large customers who own their generation in their facility help out in alleviating the need to put up power plants, which take some time to build, when they voluntarily interrupt their grid connection and depend on their in-house energy sources. In this case, WESM may design a demand response program which can provide peak shaving for normal and emergency grid operations. This may delay the shortage generation from short to medium term. This will also lessen transmission congestion since local generation will supply local loads.

Second, it's time for WESM to design a installed generation capacity market. The spot market in energy brings competition in the short term which is good but it's short term and just pay for the variable cost of power plants. The variable cost covers fuel and operating expenses of putting out MW to the grid. In a capacity market, the market operator will provide signal for investors and reward payment for that fixed costs in building power plants.

Third, DOE must reward large customers who practice energy efficiency programs. Energy efficiency may not be significant in impacting the level of electricity demand, however, better than no action. Measure their consumption during peak hours and see if they are contributing to alleviate the generation shortage.

I hope to come back on this interesting topic by 2013 and see what happened and what did not happen. By that time, I will still use Google.

Philippines' Earth Hour 2011 - A Success!

I woke up reading tweets from VECO on the load being dropped regarding the Earth Hour 2011 on March 26, between 8:30 pm to 9:30 pm. VECO reports:

"EARTH HOUR RESULTS: The highest load drop In the VECO franchise area was 18 MW. Thank you to all those who participated!"

"EARTH HOUR RESULTS: As of 9:30 demand for VECO franchise area dropped to 248.89 MW."

"EARTH HOUR RESULTS: As of 9:15 p.m. demand dropped further to 249.76 MW in the VECO franchise area."

"In the VECO franchise area , As of 8:45 p.m. demand dropped to 257.1 MW. As of 9:00 p.m. it dropped further to 254.68 MW."

"AS of 8:45 p.m. VECO load dropped from 266.646 MW to 257.503 MW. Thank you for your support to Earth Hour!"

So from, VECO reports, about 9MW dropped after 15 minutes of the start of the energy conservation. Less than a MW dropped at 9:00 pm. At 9:15 pm, another chunk of about 7MW was dropped. Fifteen minutes after that, less than a MW was turned off. So at the end of the Earth Hour, VECO tweets that about 18MW was conserved.

Conserving that amount of energy,18MWh, is a big pull. It is a big accomplishment. What more if we look at the overall picture in the Philippines as a whole.

Figure 1

Figure 2

Figures 1-3 illustrates the load trend from the past Saturdays of March 2011. If we want to know the impact of the hour, we look at the load reduction in March 26 compared to March 19, March 12 and March 5. In this way, we are assuming that the load cycle exercised by the residential, industrial and commercial consumers are all alike. The insets in the figures shows that the hour of 8 pm to 10 pm in March 26 is at the lowest level compared with the other Saturdays except for the Luzon between the March 5th and the 26th at around 10:00 pm. 

Figure 3

A simple quantification of the load reduction is conducted by subtracting the average MW difference with the March 26th to other Saturdays in March 2011. The table below shows the numbers. All negative MW difference are good indicators. The positive number is as discussed above.

Table 1

Last year's Earth Hour seems to have failed. This year, the Earth Hour in the Philippines is a resounding success!

Energy Efficiency Impact on Demand

Recent news on the DOE secretary was about his speech at an energy forum attended by electric power industry stakeholders. The secretary was quoted to point at energy efficiency as a solution to the crisis in energy. He said if people would practice the Energy Efficiency Protocol,  the expected demand reduction will be 20% for residential load and 25% for industrial/commercial demand. If residential load is say 30% of the total grid demand then we can follow a formula like,

            TD_Eff = Res(0.8) + Ind(0.75) = (0.3)TD(0.8) + (0.7)TD(0.75) 

TD is the total demand and TD_Eff is the TD with energy efficiency at the demand side.

The DOE secretary also mentioned the energy savings from using energy efficient devices will solve the power shortage for three to four years. Following the DOE forecast on Visayas and applying the formula above, I wanted to verify the declaration.

Looking at the graph above, the DOE demand forecast is above the dependable generation capacity. When energy efficiency is accounted, the demand goes below the dependable capacity for the upcoming four years.

Does this solve the power shortage? No.

Grid operations require generation reserves to maintain system frequency and prepare for unforeseen grid contingencies. In real time, there are generation or transmission outages due to planned maintenance or forced outages.Visayas grid, as per NGCP website requires about 190MW for its generation reserves at the present time. Apparently, load will catch up with the generation capacity in 2013 based on DOE's projections.

Energy efficiency is good not only for the reduction of grid demand but also it makes the grid environment friendly. It will surely help, but given the situation, it does not solve the power shortage for the coming four years.

PS - At present, the dependable capacity in the Visayas alone is at 1,505 MW while peak demand is at 1,430 MW, with a required reserve margin of 335 MW.

Demand-Side Management in Visayas: Why Another Route?

Wholesale Electricity Spot Market (WESM) proposed Visayas Supply Augmentation Auction (VSAA) Program which was approved by the Energy Regulatory Commission (ERC). The VSAA is aimed for demand-side management (DSM) in Visayas where interruptible load of embedded generators will be auctioned just like a competitive market. Please see approved proposed VSAA.

There have been reports that Visayan Electric Co. (Veco) has been practicing (DSM) by having interruptible contracts to its consumers. See this link: Veco, firms to sign power deal.

My question is this: Veco has a working DSM model system. Was there a study providing benefits of a market based DSM compared to the existing practice? Is there a need for a market based DSM that would add another fraction of peso to the rates being charged?

With ERC's approval, market based DSM will go through WESM instead of having Veco and its consumers taking directly. There might be added cost to that. You can say this is only Veco, how about the other distribution utilities? Well, the Veco model works as reported, why not adapt the model that works fine.

Why another route?

Interruptible Load Application in the Philippines

Power drops 30 MW
Posted 01:43pm (Mla time) (Mla time)
By Cris Evert Lato
Cebu Daily News

A 30-megawatt (MW) power shortage last Tuesday forced a mall and hotel in Cebu City to rely on its own generator sets for one hour and reduce its demand on the Visayan Electric Co. (Veco). [ Read more ]

VECO Application to the Energy Regulatory Commission

Philippines' Earth Hour: Success or Failure

One way to evaluate if the Earth Hour in the Philippines is successful or not is to compare the megawatt (MW) demand on March 21, 2009 and March 28, 2009. The Earth Hour was on March 28 at the time of 8:30 pm to 9:30 pm. We choose March 21 since this day is another Saturday which is the same as March 28. Inquirer reports that RP shone in dim during that hour. Philippine Star wrote that RP had record power savings during the event.

The facts say otherwise. The graph below describes the demand in Luzon and Visayas regions for March 21 and 28, 2009. The data were taken from Wholesale Electricity Spot Market (WESM) website. Comparing the demand curve of March 21 and 28 during the Earth Hour, we observed that there was more power utilization on March 28 than March 21. I am not sure where Inquirer and Philippine Star got their data.

Demand-Side Management Practices

The electric power system as driven by economic market forces experiences intensive utilization. To keep electric power systems continue to operate in a reliable and secured manner, resources aside from power generation are needed. Demand-side management has been taking its place in supporting power system operations and planning in the deregulated era. Significant reliability and financial benefits are derived in utilizing various forms of demand-side control.

Demand-side management or control types can be categorized in the order of contribution to power system security and electricity market efficiency.

• Time of Use Pricing (TOU) - this approach shifts power consumption from peak periods where the probability of high market prices and transmission congestion are obviously high and expected.

• Real Time Pricing (RTP) - this demand-side control strategy allows the load to reallocate energy utilization to lower market price hours and when power transmission usage is lower.

• Demand-side Bidding - this category assist the system operator for maintaining generation-load balance and in managing of zonal congestion. With this, it tends to lower the operating cost of the consumer and demand-side assists in alleviating generation resources shortage.

• Demand-side as Ancillary Services - demand-side is utilized as system reserves through emergencies especially when no other possible option provides solution to mitigate existing system-wide operating concerns. In this case, demand-side can be called to support the system operation as Responsive Reserve, Non-spinning Reserve, Regulating Reserve, or as a Replacement Reserve. Most often, the demand is reduced during periods of critical generation reserve margins and when electricity market prices are high due to generation shortage.

• Direct Load Control - this strategy employ the usage of automated control to reduce or curtail demand consumption during the occurrence of price spikes or during summer periods.

• Interruptible Load Program (ILP) - demand is reduced or cut-off from the grid to maintain secured system operation during emergencies where system continuous service can be put at risk. Some system operators utilize interruptible load for economic benefits and eliminating system operating constraints.