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Sri Lanka: Electricity Supply Chain Analysis and Proposals for Revamping

APPENDIX

Operational Planning and Dispatch

This report has been prepared by a team of consultant representing the Pathfinder

Foundation-Sri Lanka, Economic Consulting Associates-UK, KPMG Sri Lanka, on

behalf of the Public Utilities Commission of Sri Lanka. However the interpretations and

opinions expressed in this report should not be construed to be the official viewpoint of

the Public Utilities Commission of Sri Lanka

Electricity Supply Chain Analysis and Proposals for Revamping


Contents

i

Contents

Contents i

Abbreviations and Acronyms iii

1 Introduction 1

ASSESSMENT 2

2 Operational planning, dispatch and audit 3

2.1 Timetable 3

2.2 Year-ahead and month-ahead operational planning 3

2.3 Day-ahead operational schedule 15

2.4 Real-time dispatch 21

2.5 Auditing 22

3 Infrastructure and software 24

3.1 Data collection and management 24

3.2 Software 24

3.3 Comments 28

4 Conclusions 29

RECOMMENDATIONS 30

5 Methodologies 31

5.1 Load forecasting 31

5.2 Hydro management 38

6 Projected Assessment of System Adequacy (PASA) 42

6.1 Inputs and Outputs of the PASA process 42

6.2 Sample Medium Term PASA (MTP) 43

6.3 Sample Short Term PASA (STP) 47

7 Operational planning, dispatch and audit 52

7.1 Year-ahead 52

7.2 Month-ahead operational planning 54

7.3 Week-ahead operational planning 57



Contents

ii

7.4 Day-ahead operational schedule 59

7.5 Real-time dispatch 61

7.6 Ex-post analysis 62

8 Infrastructure and software 71

8.1 Data management infrastructure 71

8.2 Software 72

9 Conclusions 75



Abbreviations and Acronyms

iii


AESK AES Kelanitissa

AM & CS Asset Management and Central Services

BST Bulk Supply Tariff

BSTA Bulk Supply Transactions Account

CCGT Combined Cycle Gas Turbine

CCRR Capital Cost Recovery Rate

CEB Ceylon Electricity Board

CPC Ceylon Petroleum Corporation

ECA Economic Consulting Associates

EMS Energy Management System

FOB Free-On-Board

GL CEB Generation Licensee

GT Gas Turbine (open-cycle)

HQ CEB Corporate Headquarters

HSFO High Sulphur Fuel Oil

IPP Independent Power Producer

LCC Lanka Coal Company

LECO Lanka Electricity Company

LSFO Low Suphur Fuel Oil

LTGEP Long-Term Generation Expansion Plan

MFP Ministry of Finance and Planning

MGEA Minimum Guaranteed Energy Amount

MOPS Mean Of Platts Singapore

NCRE Non-Conventional and Renewable Energy

O&M Operations and Maintenance

PF Pathfinder Foundation

PPA Power Purchase Agreement

PUCSL Public Utilities Commission of Sri Lanka

Rs Sri Lanka Rupee

SCADA System Capture And Data Acquisition

SCC CEB System Control Centre

SOE State-Owned Enterprise

WCP West Coast Power




iv

Currency

The abbreviations Rs and SLR are used interchangeably to refer to Sri Lanka Rupees.

The exchange rate used in this report is 1 US$ : 130 Rs.

Note on plant names

A number of power plants in Sri Lanka are given different names in different publications. For the purposes of this report, where alternative names exist, the following have been used.

Report name Alternative names

Puttalam Coal Power Plant Lakvijaya Power Plant

New Chunnakam Power Plant Jaffna Power Plant

Colombo Power Barge Power Plant

West Coast Power Kerawalapitiya Power Plant



Introduction

1

1 Introduction

This is the appendix to the Draft Final Report submitted to the Public Utilities Commission of Sri Lanka (PUCSL) by the Pathfinder Foundation (PF) of Sri Lanka in association with Economic Consulting Associates Ltd (ECA) of the United Kingdom and KPMG Sri Lanka for the project:

The electricity supply chain analysis and proposals for revamping

This appendix contains more detail on the issues we have identified with respect to operational planning and dispatching as currently conducted by the System Control Centre (SCC) of the Ceylon Electricity Board (CEB) than can be provided in the main report. It is organised into two parts, following the arrangement of the relevant section of the main report. The first part assesses the efficiency of current operational planning and dispatching and represents an update of the assessment contained in our Interim Report. The second part contains our recommendations on improvements.



ASSESSMENT

2

ASSESSMENT



Operational planning, dispatch and audit

3

2 Operational planning, dispatch and audit

2.1 Timetable

At present, SCC’s planning process horizons are as follows: year-ahead, six month-ahead, month-ahead and day-ahead. The year-ahead plan is repeated every month, and has a planning horizon of 12 months into the future, the first month result is month-ahead operational planning. The daily plan is taken rolling for the next three days, the water values of monthly plan are inputs into the daily plan.

There are some disadvantages with this process as follows:

The water value (or FCF) is calculated for the next 12 months with time interval is one month, it have many uncertain factors such as load forecast, changed in maintenance schedule of power plant, the hydro situation condition is not as expected... hence the result usually is inaccurate.

The water value (or FCF) then becomes the input for daily plan; but with inaccurate water value, the power system operation will not be optimized and increase the generation cost.

Currently, there is no week-ahead plan. SCC is building SDDP file for week-ahead operational planning and it will be completed in near future. As the Consultant commented, the week-ahead operational planning is very important, it will reduce the error of month-ahead planning and help the planning to be more realistic, therefore the system will be operated more economically.

2.2 Year-ahead and month-ahead operational

planning

2.2.1 Optimisation function

It is unclear to use what optimisation function is being applied in the planning process. The choice of function can have significant impacts on outcomes and costs.

In essence, there are two alternative functions that could be used. The first would be to minimise the total financial costs to CEB of generation and power purchases. The second would be to minimise the total economic costs to Sri Lanka.

These are not necessarily the same. To give an example, there is some evidence that the actual heat rates of some IPPs are lower (less efficient) than that used to calculate energy charges in their PPA and that some are higher. If minimising financial costs




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to CEB, the energy charge in the PPA should be used as the prices applied in the planning and dispatching process. However, if minimising total economic costs, the actual heat rates of the generators and their resulting fuel costs should be used.

For an IPP with a lower (less efficient) heat rate than that used in its PPA, the use of the contract energy charge will give a lower cost for planning purposes than using the actual heat rate leading to greater levels of dispatch of the generator. This may benefit CEB. However, for Sri Lanka as a whole, it increases the costs of fuel supply relative to using a generator with a higher energy charge but more efficient heat rate.

The difference between the contract and actual heat rate in this example will be borne by the IPP rather than CEB but this may still represent a loss to Sri Lanka as a whole. The profitability of the IPP with an actual heat rate that is less efficient than the contract rate is lowered—it makes a loss on its fuel charges for each unit dispatched. Meanwhile, the alternative IPP with an actual heat rate that is more efficient than the contract rate also sees a reduction in its profitability relative to that under economic dispatch. Although it would make a profit on fuel costs if dispatched, it is not being selected for dispatch as this is based on contract not actual heat rates. Both IPPs, therefore, make a loss relative to the outcomes under economic dispatch.

2.2.2 Load forecast

There is no software used by SCC and other Licensees to forecast the load. Load forecast in long term is taken based on extrapolation from historical data, after that they can adjust by experience.

Total energy: Distribution power companies (Discos) forecast its total energy of the year and submit to SCC, SCC summarizes these to whole system and apportions to 12 months based on historical data. SCC does not forecast whole system load and compare with the forecast of Discos.

Peak load and low load forecast: Generation Planning Department of CEB forecasts peak load and low load then provides to SCC.

Based on monthly historical data, each month, SCC allocates the energy and peak load, low load in corresponding with each block of load and define daily load block for each month.

SCC does not compare load forecast and actuality to find out the accuracy.




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Figure 1 Load forecast

Input data

Load Blocks

0

500

1,000

1,500

2,000

2,500

MW

Hour




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Daily Load Blocks modelled in SDDP software.

Comments

Some factors (depending on the forecasting horizon) which could affect to load growth should also be taking into account, e.g. GDP growth, social economic development... The load forecast is also should be developed into many scenarios instead of only 1 scenario as in present. The consultant proposes to forecast the load in 3 scenarios: high scenario, basic scenario and low scenario to prevent from uncertain factors. SCC should establish a process to assess the accuracy of their load forecasts by comparing the forecasts to actual load and assess the reasons for inaccuracies and subsequently refine their approach.

2.2.3 Hydro management

Inflow forecast

To date SCC do not have separate software tools that enable them to forecast inflows for hydro power plants, SCC does not forecast inflow and are not provided with information from an established weather forecasting organization. SCC instead execute ARP model hidden under SDDP to forecast inflows based on historical data then execute with number of scenarios following SDDP methodology based on forecasted inflow data. In year-ahead plan and month-ahead plan, the historical data is taken from 1979.




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Figure 2 Historical inflow data modelled in SDDP

How to choose the historical data into calculation is very important as it will be used to forecast for the future; therefore, it should review and calculate carefully for each power plant.

In general, each reservoir will has its own hydrological period, therefore it could forecast quite accurately inflow in each year. The Consultant proposes to supplement at least 1 more scenario (in deterministic mode) in this case, combine with the result in stochastic running will bring optimized operational planning.

Reservoir and coefficient

Because of the limitation of the software, the curve of reservoir volume and upstream elevation and production coefficient (MW/m3/s) is just approximately simulated with a few points (less than 6 points), hence the calculation of hydropower plant output is not accurate.




8

Figure 3 Reservoir and coefficient data modelled in SDDP

Pricing

The pricing of hydro power plants is set zero, this makes sense because water is the national resources and it must be used at a maximum efficiency to avoid the case hydropower plant must discharge and thermal power plant was dispatch instead of hydropower plant because of high price.

2.2.4 Thermal power plants

Most of PPA between CEB and power plants has the following structure:

Capacity Charge: CCi = CCnei+ CCei

CCnei :Capacity Charge (Non-Escalable Component), it reflects, in part, the debt servicing obligations of the Company

CCei :Capacity Charge (Escalable Component), it covers all administration costs, fixed O&M Fees, and related expenses.

Energy Charge : ECi = ECnfi + ECfi

ECnfi : Energy Charge (Non-fuel Component), it is the component of the Energy Charge which includes the variable operation and maintenance costs

ECfi : Energy Charge (Fuel Component), it comprises of two sub components, namely, Fuel Oil Rate and Fuel Transport Rate




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The software is not able to simulate in detail the terms and conditions of PPAs, instead it simulates only the heat rate and fuel cost, therefore it comprehensively determine the cost that CEB pays to power plants for supply of power. The heat rate is simulated as a point or a curve upon PPA and start-up costs are not modelled.

Figure 4 Heat rate modelled in SDDP

Generation energy constraints (max and min limit) were also simulated (if any), fuel cost and limitation of fuel is updated every month, this data is provided by CPC.

Figure 5 Fuel cost modelled in SDDP




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Figure 6 Thermal generator modelled in SDDP

2.2.5 Renewable Energy

Renewable energy takes the proportion of 8-10% of the whole system. Based on historical data, SCC forecasts renewable energy, which is not modelled in SDDP, and it will be deducted in the whole system load.

2.2.6 Transmission system

There are some congestion points in transmission grid. SCC imports transmission system data from PSS/E file to SDDP and run co-optimized (network power flows are being co-optimised with generation)

2.2.7 Outage management

Power plants will make the maintenance schedule and submit to SCC for approval. With year-ahead and month-ahead operational planning, SCC will approve based on comparison with total available generation capacity and whole system load, then decide to approve or revise the maintenance plan of power plants.




11

Figure 7 Outage management

Figure 8 Maintenance modelled in SDDP

Comment: SCC should take into account the economic of whole system to arrange adequate maintenance schedule for power plants.




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2.2.8 Forced outage

Based on historical data, SCC calculates the forced outage of power plants. Normally, it’s 3% for thermal power plants and 1% for hydro power plant. Basically, that percentage is adequate.

Figure 9 Forced outage modelled in SDDP

2.2.9 Spinning reserves

At present, for all calculation and analysis, the spinning reserve is set to 5% of whole system load, SCC allocates this for hydro power plants. In case of hydro power plants run out of water, spinning reserves shall be set to zero.




13

Figure 10 Spinning reserves modelled in SDDP

Grid Code does not regulate the Spinning reserve, but it has regulation on Reserve Margin1 as follows:

Minimum: 2.5%

Maximum: 20%

Typical: 15%

Spinning reserves is 5%, it’s too much and impacts on economic operation. Spinning reserve should not be a constant, it should be more flexible. For example, 5% for off-peak, 2% for peak load. However, how to set up spinning reserve will need careful consideration of other elements to ensure the reliability of the power system operation.

2.2.10 Calculation

SCC calculates with stochastic method algorithm with 100 scenarios of inflow, inflow is forecasted by ARP model. The month-ahead and year-ahead planning is taken rolling for the next 12 months.

12.26.2 POWER SUPPLY SECURITY STANDARDS, GRID CODE, April 2014




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Figure 11 Parameters set in SDDP

As spinning reserves are set to 5% of demand (which is a high proportion), the expected generation cost may significantly increases in comparison with actual cost. So far SCC does not calculate and compare generation cost in two scenarios: spinning reserve and non-spinning reserve.

2.2.11 Output data

The analysis of calculation is very important. SCC ranks 100 scenarios base on energy and summarizes as below:

10 scenarios with maximum of hydro energy:

Average calculation to determine low limit

10 scenarios with average of hydro energy:

Average calculation to determine base case

10 scenarios with minimum of hydro energy:

Average calculation to determine high limit

The result of yearly and half-yearly generation plan is used to adjust the retail price.

The yearly and monthly operational planning is used to estimate the generation cost of CEB, this is one of main issues for CEB to balance its finance.

The output of long term operational planning is used for short team plan and for direction of short term operation.




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2.3 Day-ahead operational schedule

2.3.1 Load forecast

Similar with annual and monthly load forecast, SCC has not got tool for short term load forecast. SCC uses excel tool to extrapolate the load based on historical data, after that they can adjust it by their experience. The historical data is bases on 3 preceding day historical data.

Figure 12Load forecast

Historical data

Forecasted load




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Because domestic load takes about 66%2, so in short term forecast, it should take into account some factors such as weather, working day or non – working day etc. The current process may not adequately do so.

2.3.2 Hydro management

As long term operational planning, SCC has not been equipped with software to forecast inflow of hydro power plant, SCC is also not provided information from professional weather forecast organization. SCC operates hydro power plant based on historical water flow data. In the daily plan, the historical data is for the preceding 3 days. The reservoir elevation is updated daily then provided as input to the NCP software.

Figure 13 Inflow forecast

2.3.3 Thermal power plants

Thermal power plant mostly is modelled as above but heat rate is a function, unlike with SDDP. Limitations and start-up costs are modelled as in the PPA.

2Table 1: Detailed Sales Forecast 2013, Decision on Electricity Tariff 3013, June 2013




17

Figure 14 Heat rate modelled in NCP

Figure 15 Start-up cost modelled in NCP

2.3.4 Renewable energy

Renewable energy is modelled as in the year-ahead plan.




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2.3.5 Transmission system

Transmission system is modelled as in the year-ahead plan. SCC imports transmission system data from PSS/E file to NCP and run co-optimized (network power flows are being co-optimised with generation)

Figure 16 Transmission network modelled in NCP

2.3.6 Outage management

Power plants submit to SCC their maintenance schedule based on long-term maintenance planning and actual situation of power system. SCC will consider both on system security and economic of power system before making approval of power plants’ maintenance schedule. Nevertheless, the short-term maintenance schedule may be revised in month-ahead planning.

2.3.7 Spinning reserves

Hydro power plants are in charge of spinning reserves and frequency control. The spinning reserves are set 5% of whole system load, in there, 2% is used for frequency control. SCC allocates this for all hydro power plants and which one is for spinning reserves, which one is for frequency control.

For example:

System load is 870 MW, 5% is 43 MW, 2% is 17 MW. There are 5 hydro power plants A, B, C, D and E, A+B used for frequency control.




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So 17 MW of A+B is not scheduled and 26 MW of C+D+E is not scheduled.

Figure 17 Spinning reserves modelled in NCP

Figure 18 Frequency control modelled in NCP

The consultant’s view is that spinning reserve should not be a constant, it should be more flexible.




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2.3.8 Calculation

The daily plan is taken rolling for the next three days, SCC just run one scenarios with spinning reserve, they does not run and compare total generation cost with non-spinning reserve scenarios. To run NCP, SCC uses water value data from file *.fcf, it’s an output of SDDP, and objective function is minimise cost.

Figure 19 Using water value in NCP

Figure 20 Parameters set in NCP




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2.4 Real-time dispatch

To real time operation, SCC can supervise power system operation as total system load (MW & MVar), generation of power plants and MW, MVar, A of feeders at substations… via SCADA.

Figure 21 Supervision of power system operation via SCADA

Load curve Real-time operational data at a substation

Generation summary Single line diagram of a substation

System Operator (SO) supervises frequency to control system and uses telephone for dispatch instruction based on merit order.




22

Figure 22 Merit order used for dispatch

SO completely follow day-ahead plan except that there is a very big change in the system, for example there is a very big generator shut down or forecasted load is incorrect, in this case, SO runs NCP software for revising the operational planning.

In emergency case, if there is a generator shutdown at peak load (2200 MW), it will be as below:

If greatest generator with capacity 300 MW shutdown, frequency will decrease lower than 48.75 Hz and F81 will shed load.

If a generator with capacity 100 MW shutdown, frequency will not decrease lower than 48.75 Hz.

With the existing infrastructure, the operation as on-going is adequate.

2.5 Auditing

There are three main departments at SCC which are planning department, operation department and auditing department. Auditing department updates actual conditions of previous day and compares with day-ahead schedule, evaluates deviations between actual conditions and conditions expected in the day-ahead dispatch, and analyse how such deviations affected generation scheduling, operational economics, reserve and the quality of service. SCC also compares daily forecasted load and actual load to analyse accuracy.




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Figure 23 Unit commitment check

Figure 24 Comparing schedule and actual dispatch and remark



Infrastructure and software

24

3 Infrastructure and software

3.1 Data collection and management

Operational data is very important for system operations and also for system planning over the short term to long term. However, SCC is not equipped fully with a Supervisory Control And Data Acquisition/Energy Management System (SCADA/EMS) (SCC does not have EMS system, the SCADA system was installed in 1990 and upgraded in 2008).Instead most of data is collected manually. In particular:

For substations:

Data is collected 3 times per day at peak load, average load and min load, including: MW, MVar, kV

For hydro power plants:

Data is collected every 30 minutes, including: MW, MVar, kV

Elevation: once per day at 6AM

Inflow: power plants do not have measurement system, SCC calculate inflow base on elevation, energy, efficiency curve.

For thermal power plants:

Data is collected every 30 minutes, including: MW, MVar, kV

Fuel: Total fuel use

To date, SCC also has not established a central database to store management data, technical data and operation data of each elements in power system such as power plants, network, load and so on. Instead, data is stored in separate excel files or in hard copies.

SCC does not have an automated system capable of issuing dispatch instructions to generators from a centralised power system control centre; instead telephone communications are used.

3.2 Software

SCC has the following software packages in place:




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Long-term and medium-term generation operational planning: SDDP (PSR)

Day-ahead and three day-ahead generation operational planning: NCP (PSR)

Power system study: PSS/E (Siemens)

3.2.1 SDDP software

SDDP is a hydrothermal dispatch model with representation of the transmission network and used for short, medium and long term operation studies. The model calculates the least-cost stochastic operating policy of a hydrothermal system, taking into account the following aspects: 3

Operational details of hydro plants (water balance, limits on storage and turbine outflow, spillage, filtration etc.)

Detailed thermal plant modelling (unit commitment, generation constraints due to "take or pay" fuel contracts, concave and convex efficiency curves, fuel consumption constraints, bi-fuel plants etc.)

Representation of spot markets and supply contracts

Hydrological uncertainty: it is possible to use stochastic inflow models that represent the system hydrological characteristics (seasonality, time and space dependence, severe droughts etc.)

Detailed transmission network: Kirchhoff laws, power flow limit in each circuit, losses, security constraints, export and import limits for each electrical area

Load variation per load level and per bus, with monthly or weekly stages (medium or long term studies) or hourly stages (short term studies).

SCC uses SDDP for year-ahead planning, six month-ahead planning and month-ahead planning. SDDP has the following advantages and disadvantages:

Advantages:

With the database on historical inflow, SDDP could make automatic synthesis and analysis with different inflow scenarios based on Stochastic method algorithm;

Simulating complicated cascade systems;

Simulating thermal power plant with multi fuel;

3 Reference source: http://www.psr-inc.com.br

http://www.psr-inc.com.br/




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Simulating network, import data from PSS/E file;

Assessing the accuracy of historical inflow data.

Disadvantages:

Time interval: The simulation mode of SDDP is on a week or a month; hence SDDP is not able to simulate in detail the maintenance schedule of power plants, COD progress of new construction or the hydrological situation of hydro power plants. Therefore, the analysis will not be accurate and will not be able to reflect comprehensively the operation mode of power system.

5 blocks of load: Every month, system load will be simulated by 5 blocks; therefore it will not be able to reflect in detail the daily load curve, weekend, weekday, Pmax and Pmin. In many cases, the month-ahead planning or year-ahead planning will not be able to calculate energy output of oil thermal power plant to cover the peak load.

Hydropower plant simulation: The curve of reservoir volume and upstream elevation, production coefficient (MW/m3/s) was simulated too sketchily with just a few points: The simplicity in simulation will bring error on power output of a hydropower plant and affect other power plant. Hence, the planning for power plants will not be accurate as expected.

Thermal power plant simulation: Only simulate the heat rate and fuel cost, start up cost, cannot simulate all detail in PPA, electricity price in PPA, capacity charge, fix cost.... hence cannot simulate the generation cost of power plants.

Customized ability: The Customized ability is very limitary.

3.2.2 NCP software

NCP determines the operation of a transmission-constrained hydrothermal system in order to minimize costs or to maximize revenues of energy sales to the market. Costs include fuel usage (variable production and start-up costs), deficit costs, and penalties for the violation of operational constraints, among others. The following features are modelled:4

Load balance for each bus of the transmission network, including quadratic losses in the transmission grid

Linear power flow model, including circuit flow capacity constraints for the base case and contingencies

4Reference source: http://www.psr-inc.com.br

http://www.psr-inc.com.br/




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Water balance equation for hydro plants in river cascades considering the water travel time and wave propagation

Minimum and maximum power levels for each power plant, including unit commitment decisions

Reservoir minimum, alertness and flood control storage volumes

Minimum and maximum downstream water outflows and constraints for the rate of change of these outflows

Coupling alternatives with mid-long term studies include: generation target, end-of-period storage target or reading a future cost function

Thermal plant constraints: minimum up-time and down-time, power ramping rates, fuel availability and number of startups

Hydropower function at unit level considering the turbine-generator efficiency curve, the hydraulic losses, the tail water elevation and the head x storage relationship

System security constraints (primary and secondary reserves, sum of circuit flow constraints, general generation constraints, etc)

At present, SCC uses NCP software for daily planning and real time dispatch. NCP software has the following advantages and disadvantages:

Advantages:

Time interval: The simulation mode of NCP is on 15/30/60 minutes

Simulating complicated cascade system;

Simulating multi fuel thermal power plant;

Simulating network, import data from PSS/E file;

Interoperates with SDDP

Disadvantages:

Future cost function (FCF) – the result of SDDP relating the expected value of future production cost to the water volume stored in the reservoirs is used as terminal (boundary) condition input data in NCP. The results of SDDP are not accurate as expected so FCF will adversely affect the result of NCP.




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3.2.3 PSS/E software

PSS/E is quite standard power system study software that is used in many countries throughout the world. The complete PSS/E package consists of a comprehensive suite of programs that allow the user to conduct a number of different types of power system studies, including: transmission network and generation performance for both steady-state and dynamic conditions. There are two primary simulation engines, one is suitable for steady-state (alternating current) analysis and the other for dynamic power system simulations. Collectively these enable the following types of analyses to be performed:5

Power flow and related network functions

Optimal power flow

Balanced and unbalanced faults

Network equivalent construction

Dynamic power system simulations

3.3 Comments

The infrastructure of SCC does not at present satisfy the requirements for an efficient planning and dispatch process. SCC lacks the capabilities to collect and manage data. It also has no automatic control over generation.

SDDP and NCP have many advantages as software tools for operational planning. However, there are also a number of disadvantages. SDDP and NCP are designed as longer-term operational planning tools. Each run of the model takes considerable time, making them unsuitable for real-time dispatching. The two models are also unable to represent individual power plants in detail. If both long term and short term plans are made only by SDDP and NCP, the result will have some errors and bring the unreal and not optimized operational planning.

SCC does not have the capability to simulate relay protection systems in order to ensure that they can set the system correctly and that faults can be quickly isolated which in turn improves power system reliability.

SCC also needs to be equipped with additional tools and software applications for power system analysis, load forecasting and water inflow forecasting.

5Reference source: PSS/E User’s Guide



Conclusions

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4 Conclusions

At present, the overall working situation of SCC is good; nevertheless, it still contains some issues which are required to change or improve to make power system operating more economic.

SCC has not fully equipped with IT infrastructure, i.e: SCADA/EMS, data collection and management system, central database.

SCC has not fully equipped with forecast software, SDDP and NCP still contain disadvantages could affect the quality of operational planning.

The optimisation function used and whether this uses economic costs or financial prices needs to be clarified.

Completion of a week-ahead model and database which can be used for operational planning is required.



RECOMMENDATIONS

30

RECOMMENDATIONS



Methodologies

31

5 Methodologies

5.1 Load forecasting

The demand of electricity is increasing drastically day by day. This increase in demand of electrical energy has drawn the attention of power system engineers towards the reliable operation of power system. For reliable operation of integrated power supply systems, a close tracking of electrical load is required. Load forecast predicts the load which is going to be required at a particular time of day or on any particular day. Load forecast plays an important role in the smooth operation of any power system. It is absolutely essential for load switching, area planning and also load flow analysis during contingencies. It is also a determining factor during infrastructure development or capital expenditure decision making. Therefore, load forecast is important for proper management of any power system.

Depends on the time range, the load forecast can be divided into three categories which include long term, medium term and short term. Long term load forecast (LTLF) is applicable for system and long term network planning. Mid Term Load forecast (MTLF) refers to quarterly, half yearly and yearly load forecast needs. Short Term Load forecast (STLF) means day-ahead and week-ahead load forecast needs. There are many techniques that could be employed for loads forecasting like linear regression, statistical method, exponential smoothening, neural network based artificial intelligence technique, fuzzy logic, genetic algorithm, autoregressive model, similar day approach, time series, expert system, support vector machine, and data mining model.

For short-term load forecast several factors should be considered, such as time factors, weather data, and possible customers’ classes. The medium- and long-term forecasts take into account the historical load and weather data, the number of customers in different categories, the appliances in the area and their characteristics including age, the economic and demographic data and their forecasts, the appliance sales data, and other factors.

The time factors include the time of the year, the day of the week, and the hour of the day. There are important differences in load between weekdays and weekends. The load on different weekdays also can behave differently. For example, Mondays and Fridays being adjacent to weekends, may have structurally different loads than Tuesday through Thursday. This is particularly true during the summer time. Holidays are more difficult to forecast than non-holidays because of their relative infrequent occurrence.

Weather conditions influence the load. In fact, forecasted weather parameters are the most important factors in short-term load forecasts. Various weather variables could be considered for load forecast. Temperature and humidity are the most commonly used load predictors.

Basically, there are some popular methods using for load forecast as below:



Methodologies

32

5.1.1 Long term and medium load forecast

Trend Analysis

Trend analysis extends past rates of electricity demand in to the future, using techniques that range from hand-drawn straight lines to complex computer- produced curves. These extensions constitute the forecast. Trend analysis focuses on past changes or movements in electricity demand and uses them to predict future changes in electricity demand. Usually, there is not much explanation of why demand acts as it does, in the past or in the future. Trending is frequently modified by informed judgment, wherein utility forecasters modify their forecasts based on their knowledge of future developments which might make future electricity demand behave differently than it has in the past.

The advantage of trend analysis is that, it is simple, quick and inexpensive to perform.

The disadvantage of a trend forecast is that it produces only one result, future electricity demand. It does not help analyze why electricity demand behaves the way it does, and it provides no means to accurately measure how changes in energy prices or government polities influence electricity demand.

End-use models

The end-use approach directly estimates energy consumption by using extensive information on end use and end users, such as appliances, the customer use, their age, sizes of houses, and so on. Statistical information about customers along with dynamics of change is the basis for the forecast.

End-use models focus on the various uses of electricity in the residential, commercial, and industrial sector. These models are based on the principle that electricity demand is derived from customer’s demand for light, cooling, heating, refrigeration, etc. Thus end-use models explain energy demand as a function of the number of appliances in the market.

Ideally this approach is very accurate. However, it is sensitive to the amount and quality of end-use data. For example, in this method the distribution of equipment age is important for particular types of appliances. End-use forecast requires less historical data but more information about customers and their equipment.

This method predicts the energy consumptions. If we want to calculate the load, we have to have the load factor in each sections and different types of energy consumptions and then by load factor we can calculate the load in each section.



Methodologies

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Econometric models

The econometric approach combines economic theory and statistical techniques for forecasting electricity demand. The approach estimates the relationship between energy consumption (dependent variables) and factors influencing consumption. The relationships are estimated by the least-square method or time series methods.

One of the options in this framework is to aggregate the econometric approach, when consumption in different sectors (residential, commercial, industrial, etc.) is calculated as a function of weather, economic and other variables, and then estimates are assembled using recent historical data. Integration of the econometric approach in to the end- use approach introduces behavioural components in to the end-use equations.

The advantage of econometrics are that it provides detailed information on future levels of electricity demand, why future electricity demand increases, and how electricity demand is affected by all the various factors.

A disadvantage of econometric forecasting is that in order for an econometric forecast to be accurate, the changes in electricity remain the same in the forecast period as in the past.

5.1.2 Short-term load forecast

Similar-day approach

This approach is based on searching historical data for days within one, two, or three months with similar characteristics to the forecast day. Similar characteristics include weather, day of the week, and the date. The load of a similar day is considered as a forecast. Instead of a single similar day load, the forecast can be a linear combination or regression procedure that can include several similar days.

Time series

Time series methods are based on the assumption that the data have an internal structure, such as autocorrelation, trend, or seasonal variation. Time series forecasting methods detect and explore such a structure. Time series have been used for decades in such fields as economics, digital signal processing, as well as electric load forecast. In particular, ARMA (autoregressive moving average), ARIMA (autoregressive integrated moving average), ARMAX (autoregressive moving average with exogenous variables), and ARIMAX (autoregressive integrated moving average with exogenous variables) are the most often used classical time series methods. ARMA models are usually used for stationary processes while ARIMA is an extension of ARMA to non- stationary processes. ARMA and ARIMA use the time and load as the only input parameters. Since load generally depends on the weather and time of the day, ARIMAX is the most natural tool for load forecast among the classical time series models.



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Neural networks

Artificial neural networks (ANNs) have succeeded in several power system problems, such as planning, control, analysis, protection, design, load forecast, security analysis, and fault diagnosis. The last three are the most popular. The ANNs ability in mapping complex non-linear relationships is responsible for the growing number of its application to load forecast.

The outputs of an artificial neural network are some linear or nonlinear mathematical function of its inputs. The inputs may be the outputs of other network elements as well as actual network inputs. In practice network elements are arranged in a relatively small number of connected layers of elements between network inputs and outputs. Feedback paths are sometimes used.

The most popular artificial neural network architecture for electric load forecast is back propagation. Back propagation neural networks use continuously valued functions and supervised learning. That is, under supervised learning, the actual numerical weights assigned to element inputs are determined by matching historical data (such as time and weather) to desired outputs (such as historical electric loads) in a pre-operational “training session”. Artificial neural networks with unsupervised learning do not require pre-operational training.

In developing a short-term load forecast, the following are some of the degrees of freedom which must be iterated upon with the objective to increase the potential for an accurate load forecast: (1) fraction of the database that will be used for training and testing purpose, (2) transformations to be performed on the historical database, (3) ANNs architecture specifications, (4) optimal stopping point during ANNs training, and, (5) relative weights for use in forecast combination

Figure 25 ANN structure

Note:

- Pi input layer (temperature, humidity, working day or non-working day…);



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- b1and b2: unknown layer;

- b3: Output layer;

- d: Result.

The selection of the number of input signal depends on the specific problem and can only be determined by assessing the impact of the inputs and load. The accuracy of output depends on the structure of neural network and historical data.

Fuzzy logic

Fuzzy logic is a generalization of the usual Boolean logic used for digital circuit design. An input under Boolean logic takes on a truth value of “0” or “1”. Under fuzzy logic an input has associated with it a certain qualitative ranges. For instance a transformer load may be “low”, “medium” and “high”. Fuzzy logic allows one to (logically) deduce outputs from fuzzy inputs. In this sense fuzzy logic is one of a number of techniques for mapping inputs to outputs (i.e. curve fitting).

Among the advantages of fuzzy logic are the absence of a need for a mathematical model mapping inputs to outputs and the absence of a need for precise (or even noise free) inputs. With such generic conditioning rules, properly designed fuzzy logic systems can be very robust when used for forecasting. Of course in many situations an exact output (e.g. the precise 12PM load) is needed. After the logical processing of fuzzy inputs, a “defuzzification” process can be used to produce such precise outputs.

Support vector machines

Support Vector Machines (SVMs) are a more recent powerful technique for solving classification and regression problems. Unlike neural networks, which try to define complex functions of the input feature space, support vector machines perform a nonlinear mapping (by using so-called kernel functions) of the data into a high dimensional (feature) space. Then support vector machines use simple linear functions to create linear decision boundaries in the new space.

Expert systems

Rule based forecasting makes use of rules, which are often heuristic in nature, to do accurate forecasting. Expert systems, incorporates rules and procedures used by human experts in the field of interest into software that is then able to automatically make forecasts without human assistance.

Expert systems work best when a human expert is available to work with software developers for a considerable amount of time in imparting the expert’s knowledge to the expert system software. Also, an expert’s knowledge must be appropriate for codification into software rules (i.e. the expert must be able to explain his/her



Methodologies

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decision process to programmers). An expert system may codify up to hundreds or thousands of production rules.

Using correlation – trend method in Excel for load forecast

This method is extremely suitable with hour-ahead load forecast. It determines correlations between electricity load demand (capacity power, energy) and important affecting factors (economic increasing, electricity price, weather, foreign exchange, etc). This is deployed in Excel with following steps.

Forecasting hour-ahead load curve

Historical daily load curve is compared with the current day (day D) to find the day that is most similar to load curve day D. The comparison is automatically undertaken by function:

Correl(array1, array2)

With the following algorithm:

22 )()(

))((),(

yyxx

yyxxYXCorrel

Therein:

- X is load curve in 24 hours of day ( iD );

-

x is the average load in 24 hours of day ( iD );

-

y is the average load in 24 hours forecast for day D ;

- Y is load curve in 24 hours forecast for day D .

The Correl function will bring the correlation between two variation array X, Y. The more similar between X are Y are, the closer to 1 of result of Correl function is. Therefore, after comparison of [21]6 historical days with current day D, it could find the day that have the load curve which is most similar with day D, assumed day (D-i).

Trend function to forecast the capacity for the next 4 hours (from hour H to hour (H+3) with function FORECAST is following:

)'_,'_,( sxknownsyknownxFORECAST

Therein:

6It depends on historical data and power system



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- x is load of hour ( 1H );

- syknown '_ is load of [5]7 historical hours from hour ( 1H ) of day ( iD );

- sxknown '_ is load of [5]8 historical hours from hour ( 1H ) of day D .

Comparing forecasted load curve with historical load curve

Once getting the forecast load curve for the next 4 hours, compare these load curves with the historical load based on Correl result from the highest to the lowest (compare the most similar one then the least). This comparison is to handle the wrong historical data because of load shedding, error in data collection system (not able to collect historical data).

Revising load curve

After comparing forecast load curve with historical load curve, if Correl result is < 0.9 then it is allowed to multiply the corresponding result with professor factor hpro

complying with rule: 1.19.0 proh .

5.1.3 Conclusion

Load forecast takes a dominant role in the economic optimization and secure operation of electric power systems.

There are many methods used for load forecast, differently for short term or long term forecast. Each method has its own advantages and disadvantages. Based on the typical features of power system, the forecaster needs to test to choose the most suitable method.

The assessment of related factors could bring important affects to forecasted load. With long term and short term forecast, the forecaster needs to take into account different factors (as mentioned above), choose suitable factors and its parameter to put in forecast model.

So far, Distribution Licensees forecast annually load energy output then provide it to SCC, SCC summarizes all for whole power system. To improve the forecasting, the Consultant has some proposals as follows:

SCC and Distribution Licensees need to be equipped with load forecast software.

With annual load forecast:

SCC takes the responsibility to forecast demand energy for the whole power system, one of the most important input is the load

7It depends on historical data and power system 8It depends on historical data and power system



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forecast from Distribution Licensees. Which we mean here is that SCC shall use forecasted demand energy of Distribution Licensees as input data then SCC forecasts demand energy for whole system instead of summary as present, so that SCC could manage effectively on the forecast error, instead of being passive as current.

The annual load needs to be forecasted with different scenarios, which proposed by the Consultant as 3 scenarios: high, medium and low scenario.

SCC is responsible for monthly, weekly, daily, hourly or 30-min load forecast.

With each forecasted load, it is required to take into account factors that have high influences to forecast result as an input for forecasting process.

The result of forecasting process includes:

Annual forecast: Annual energy, monthly energy, Pmin and Pmax of each month, monthly typical load curve.

Monthly forecast: Monthly energy, Pmin and Pmax of each month, monthly typical load curve.

Weekly forecast: Weekly energy, daily energy, Pmin and Pmax of each day, daily typical load curve.

Daily forecast: Daily energy, Pmin and Pmax of each day, daily typical load curve.

Hourly forecast: Load curve for the next hours.

5.2 Hydro management

5.2.1 Inflow forecast

The most important job in hydro power plant operation is inflow forecast. Normally, a reservoir has its own features or its own hydrological period, based on that, a suitable method shall be chosen for inflow forecast. Some popular inflow forecast methods as follows:

Linear Regression

Exponential Smoothing

Periodic Autoregressive Moving Average



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Neural Network

….

In additional, some reservoirs have a loop of hydrological period to be repeated after few years, e.g.: inflow will be higher after each 5 years, or inflow will be much lower than the multi-year average level after a period of 10 years... To find out this kind of period, it required a deeply study on historical data of each reservoir.

Inflow forecast is different with load forecast, the long term load forecast depends on many factors as social economic development status, the development strategy of a country, load component, demand side management... The short term load forecast depends on weather, weekday or weekend... However, inflow forecast mainly depends on weather. Currently, any country has its own professional weather forecast organization with modern equipment, data monitoring station to collect data from lakes, rivers... so the weather forecast and inflow forecast is quite accurate. SCC shall buy this data from these organizations to put into power system operational planning model.

5.2.2 Using Probability Density Functions in Excel tool

In year-ahead and month-ahead operational planning, a very useful tool used to calculate probability of water inflow is Excel tool that its calculation is based on historical hydrological data. A simple way to do this is to use PERCENTILE function in Microsoft Excel.

For example: (This is a sample to calculate inflow probability)

Step 1: Create table on monthly inflow data from 1956 since 2001 (from row 21 to row 66)

Step 2: Create table to calculate the frequency as exhibition below



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Therein:

Column A is the probability

Column R to be calculated from column A, e.g. R4 = 1-A4/100

Step 3: Use PERCENTILE function to calculate the monthly inflow based on probability

For example: Probability = 40% of February:

C7 = PERCENTILE (C21:C66, R7) = 87.7 m3/s

Notes:

Because of climate change, deforestation..., the hydrological change of the reservoirs in recent years may be different with 30-40 years ago. Therefore, it needs to assess



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and sample accordingly to illustrate the recent hydrological status. E.g. the historical hydrological data is for 15, 20 or 25 recent years.

The historical hydrological data is needed to be updated every year and recalculate upon probability method.

Typically calculations:

Probability = 70%-75%: Illustration of average inflow scenario

Probability = 50%: Illustration of high inflow scenario

Probability = 85%: Illustration of low inflow scenario

5.2.3 Scenarios of calculation

Year-ahead and month-ahead operational planning

To calculate total energy of hydro power plants, SCC used ARP module of SDDP software to forecast inflow and stochastic algorithm to run 100 scenarios, then summarize into high inflow, average inflow and low inflow scenarios. Doing so is appropriate at the current. However, if taking into account detail energy output of each hydro power plant, the result may be incorrect. Hence, the Consultant proposes to supplement some more scenarios and to compare with the current calculation as follows:

High inflow scenario: Probability = 50%

3 average inflow scenarios:

Probability = 70%-75%

Average inflow, it is average of multi-year inflow

Forecast inflow for each reservoir using forecasting methods or buy data from professional weather forecast organization.

Low inflow scenario: Probability = 85%

Week-ahead and day-ahead operational planning

Short term inflow forecast (≤ 1 week) could be done accurately by buying forecast data from professional weather forecaster or using mathematic model to forecast. Therefore, it requires only 1 scenario for daily operational planning.



Projected Assessment of System Adequacy (PASA)

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6 Projected Assessment of System Adequacy (PASA)

In order to make more transparency of maintenance outage, Consultant proposes that SCC should use PASA.

PASA is a process that is required for the co-ordination of generation and transmission maintenance outages, and for ensuring the establishment of sufficient supply and transmission reserves to allow for a subsequent contingency while maintaining system security.

PASA is a comprehensive process of information collection, analysis and distribution to put Licensees in a strong position to make their own decisions about the implications of the future supply/demand balance, and transmission outages.

PASA compares the expected generating plant availability with the forecast demand plus the reserve requirement over a forecast period of 24 months. Anticipated network limitations caused by planned transmission plant outages will also be taken into account.

The PASA process generally takes two forms, for different future time periods under review, with the analysis becoming more detailed for periods closer to the present time.

1. For the period from day 8 to 24 months into the future, a week by week forecast of the balance between supply and demand is published, based on the forecast peak demand, forecast energy demand and projected plant capacity and energy capabilities advised by Licensees. This Medium Term PASA (MTP) would contain one set of values for each week. The full report would be updated quarterly to a time horizon of 24 months, and the immediate 10 week period would be updated weekly.

2. For the time period from day 2 days to 7 days into the future, a Short Term PASA (STP) would be published to show the anticipated capacity and energy supply/demand balance over that period. It would have an hourly resolution for capacity requirements and a daily resolution for energy requirements. It would be updated daily.

It is an aim of the PASA process to make sufficient information available to Licensees to allow system security to be maintained.

6.1 Inputs and Outputs of the PASA process

The principal inputs to PASA are summarized as follows:




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Demand and Energy forecasts having a resolution of one week in the long term and one day in the short term;

A reserve capacity and energy criterion;

Expected high load period MW capability with the same time resolution, having regard for planned maintenance outages;

Expected energy capability with the same time resolution, having regard for primary energy constraints (water, gas, coal or diesel) having a high probability of being achieved for the medium term analysis;

Transmission maintenance schedules.

Transmission Capacity

The outputs that are publicly available are the aggregate totals of supply and demand for each of the weeks and days of the analysis, and an assessment of the degree to which the reserve criteria are satisfied.

6.2 Sample Medium Term PASA (MTP)

6.2.1 Inputs to the Medium Term PASA

All data inputs to the Medium Term PASA process are defined in this section. Responsibility for the provision of input data to the Medium Term PASA varies, and is indicated in braces as {responsible party}.

Forecast Peak Demand [D] and Energy [E] for each week. {SCC}

The most probable peak demand and energy for each day of a 24 month forecast period for whole system will be prepared by SCC. These forecasts will be prepared on the basis of past trends, day type, and knowledge of past and future special events.

After preparation of these daily forecasts, the weekly peak demand will be selected by finding the highest of the daily peaks in each week (defined as Monday to Sunday).

Weekly energy values will be calculated by summing the 7 forecast daily energies from Monday to Sunday. Once finalised, the weekly peak and energy forecasts will be entered into the PASA application.

Demand [D]: * [52 values / year] * [2 years]

Energy [E]: * [52 values / year] * [2 years]




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The identifying date is that for the Monday of the week, energy is in MWh and demand is in MW.

Power Contingency Reserve [Rp] and Energy Contingency Reserve [Re] {SCC}

This is a MW and GWh quantity for each month that applies to every day in the month. The quantities specify the amount of reserve that is required to meet supply reliability standards for the load. The definition of reserve in terms of time to realisation, and the quantity required to be available, will appear in SCC documentation.

Provision will be made to specify a different value of R to allow for seasonal variation of reserve criterion if required.

Power Contingency Reserve [Rp]: * [52 values / year] * [2 years]

Energy Contingency Reserve [Re]: * [52 values / year] * [2 years]

Load forecast Accuracy [A] {SCC}

A single percentage value for each month that specifies the additional contingency reserve to be allocated for errors in the demand forecast. Provision is made for a different value for each month because load forecast error generally varies seasonally.

Load forecast Accuracy [A]: * [52 values / year] * [2 years]

Generating Plant Availability [G] {Generation Licensees}

A forecast plant capability (in MW) will be entered manually for each item of plant. This will take account of planned unavailability for maintenance, testing and any other known reason. Note that each item of plant will have a rating, which defines the upper bound for the forecast plant capability.

Generating Plant Availability [G]: * [52 weeks] * [2 years]

Energy limits [Ge] {Generator Licensees}

Generating plant energy limits in GWh will be entered for some items of plant if they are considered to be energy constrained over the period of a week. These energy limits represent the maximum amount of energy that can be generated over a week by that particular plant. Where no energy limit is provided, then generating capability will be considered to be constrained only by the MW capability.

Energy Limits [Ge]: * [52 weeks] * [2 years]

Anticipated network constraints on generation [Gc] {SCC}

SCC is responsible for the derivation of weekly network limitations based on information provided by with regard to transmission plant modeling data and planned outages.




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Any other known constraints that are forecast to affect generation will also be reported in a free text area attached to the PASA output. The effect of these constraints on the PASA results will be implemented by providing an additional field against each generating entity for SCC to enter the constrained generating capability [Gc]. If a value is entered for Gc, the lesser of Gc and G will be used in calculating the aggregate generation Ga.

6.2.2 Calculated Quantities

The following quantities will be calculated on a weekly basis from the above PASA inputs:

Demand plus Reserve [Dr]

Calculated as the sum of the weekly peak demand, the weekly reserve requirement, and the load forecast allowance,

i.e. Dr = D + Rp + (A * D/100)

Dr: * [52 weeks] * [2 years]

Energy plus Reserve [Er]

Calculated as the sum of the weekly peak demand, the weekly reserve requirement, and the load forecast allowance,

e. Er = E + Re + (A * E/100)

Er: * [52 weeks] * [2 years]

Aggregate Generating Power Capability [Ga]

Calculated as follows:

sum ( min( G , Gc if specified ))

Ga: * [52 weeks] * [2 years]

Aggregate Generating Energy Capability [Gae]

Calculated as follows:

sum ( min( G * 7 * 24/1000, Gc * 7 * 24/1000 if specified, Ge if specified ))

Gae: * [52 weeks] * [2 years]

Supply Surplus [Sp]




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Sp is a signed quantity to indicate the adequacy of generating plant to meet the forecast power demand plus reserve requirement. Supply Surplus is calculated as follows:

[Sp] = [Ga] - [Dr]

Sp: * [52 weeks] * [2 years]

Supply Energy Surplus [Se]

A signed quantity is to indicate the adequacy of generating plant to meet the forecast energy demand plus reserve requirement. Supply Energy Surplus is calculated as follows:

[Se] = [Gae] - [Er]

Se: * [52 weeks] * [2 years]

6.2.3 Outputs from the PASA Process

The Long Term PASA will have a weekly resolution (i.e. one set of values for each week). To assist in the interpretation of results, a number of standard tabular output reports will be needed, as follows:

Tabular output for SCC

This tabular report is intended for use only by SCC as it contains a detailed breakdown of the PASA components, including individual plant contributions. Since it is not intended to distribute data of this detail to Licensees, a different report is needed for this purpose.

1. Generating plant individual availabilities - on a unit and station basis, showing generator capacity, energy contributions and station constraints in different rows/columns of the reports.

2. Aggregate generating plant availability [Ga] and [Gae] calculated as above (should be the sum of (i)).

3. Forecast aggregate demand energy and reserve requirements. D, E, Rp, Re, Dr, and Er.

4. Supply surplus and Supply Energy surplus (signed quantities as above). [Sp] and [Se]

5. Accuracy of forecast - A

Tabular Report for Distribution to Licensees

This report contains tabular and graphical output, and is intended for distribution to Licensees. Tabular Information:




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Items (ii) through (vi) from above, Item (i) is NOT included.

6.3 Sample Short Term PASA (STP)

6.3.1 Inputs to the Short Term PASA

All data inputs to the Short Term PASA process are defined in this section.

Responsibility for the provision of input data to the Short Term PASA varies, and is indicated in brackets as {responsible party}.

Forecast Demand [D] {SCC}

Hourly forecasts of system demand for each day in the assessment period for each region will be prepared by SCC. These forecasts will be prepared on the basis of latest available weather forecasts, past trends, day type, and knowledge of past and future special events.

Demand [D]: * [24 values per day]

Load forecast Accuracy Allowance [A] {SCC}

A single percentage value for each month specifies the additional reserve to allow for errors in the demand forecast. Provision is made for a different value for each month.

Load forecast Accuracy [A]: * [12 values / year]

Short Term Realisable Unit Coverage [Ur] {SCC}

The number of MW can be replaced by Short Term Realisable Reserve following a unit failure. This figure is to be enterable (once).

26 Hour Recall Unit Coverage [U26] {SCC}

The number of MW can be replaced by 26 Hour Recall Reserve. This figure is to be enterable (once).

Generating Plant Availability - Immediately Realisable [Gi] {Generation

Licensees}

This is the Immediately Available Capacity in for each unit in MW.

Valid items of plant are listed in a configuration. Note that each item of plant will have a reasonability limit, which defines the upper bound for the plant capability.

Immediately Realisable Capacity [Gi]: * [24 values per day]




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Generating Plant Availability - 26 Hour Recall [Gr] {Generator Licensees}

This is the Available Capacity of each unit in MW when given 26 hours notice.

26hr Recall Capacity [Gr]: * [24 values per day]

Energy Limits [Ge] {Generator Licensees}

Daily energy constraints (GWh) imposed on generating plant. Where no energy limits is provided, generating plant will be considered constrained only by Gr or the Station Constraint.

Energy Limits [Ge]: * [1 value/day]

Station constraints on generation [Gc] {SCC}

Station constraints will be entered by SCC for each hour of the assessment period on station by station basis. These are due usually to network constraints - no entry means no constraint

Constraint Data [Gc]: * [24 values per day]

6.3.2 Calculated Quantities

The following quantities will be calculated from the above PASA inputs hourly for requested seven day periods:

Short Term Realisable Reserve Requirement [RR]

[RR] = [Ur] + [A] * [D]

26 Hour Recall Reserve Requirement [RR26]

[RR26] = [U26] + [A] * [D]

Aggregate Realisable Generating Plant Capability [GA] and [GA26]

For each generating station, the aggregate station capacity will be the minimum of:

Sum of realisable availability of each unit [Gi] and [Gr] in each station, or;

Station constraint for that station

The aggregate Generating Plant Capacity is the sum of all of the aggregate station capacities within a region.

Expected Realisable Reserve [ER]

[ER] = [GA] - [D]




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Realisable Supply Surplus [SU]

[SU] = [ER] - [RR]

Expected 26hr Recall Reserve [ER26]

[ER26] = [GA26] - [D]

26hr Recall Supply Surplus [SU26]

[SU26] = [ER26] - [RR26]

The following quantities will be calculated from the above PASA inputs daily for requested seven day periods for each region:

Short Term Energy Reserve Requirement [ERR]

[ERR] = ([Ur] + [A] * [D]) * 24 /1000

Aggregate Generating Plant Energy Capability [GAE]

For each generating station, the aggregate station energy capacity will be the minimum of:

Sum of realisable availability of each unit [Ge] in each station, or

Sum of the aggregate station capacity *24/1000 for that station

The aggregate Generating Plant Energy Capacity is the sum of all of the aggregate station energy capacities within each region.

Expected Energy Reserve [EER] + [IRe]

[EER] = [GAE] - daily sum of ([D]) /1000

Realisable Energy Supply Surplus [ESU]

[ESU] = [EER] - [ERR]

6.3.3 Outputs from the STP PASA Process

Tabular output for SCC

This tabular report is intended for use only by SCC as it contains a detailed breakdown of the PASA components, including individual plant contributions. Since it is not intended to distribute data of this detail other Licensees, a different report is needed. The default report will provide information on reserves for three hour periods:

Time of minimum expected realisable reserve surplus [SU];




50

Time of maximum expected demand [D]

Time of minimum expected 26 hr recall reserve surplus [SU26].

The first report will provide the following - one quantity for each day of the assessment period.

Generating plant individual availabilities - on a unit and station basis, showing generator capacity, energy constrained plant contributions and station constraints in different rows of the reports. Plant that is out of service on 26 hour recall shall be indicated with ”26 hr”.

Aggregate generating plant availability [Ga] calculated as above.

Forecast aggregate demand and reserve requirement: [D], [RR] and [ER].

Supply surplus (signed quantity as above): [SU]

Short Term Energy Reserve Requirement: [ERR]

Aggregate Generating Plant Energy Capability: [GAE]

Expected Energy Reserve: [EER]

Realisable Energy Supply Surplus: [ESU]

The second part of the report (for time of maximum demand [D]) will include:

Peak forecast demand;

Aggregate generating plant availability;

Expected Realisable Reserve;

Required Realisable Reserve;

Expected Realisable Supply Surplus.

The third part of the report (for time of minimum 26 hr recall reserve will include:

26 hr Recall Plant Capacity [R26]

Aggregate 26hr Recall Plant Capacity [GA26]

Expected 26hr Recall Reserve [ER26]

Required 26hr Recall Reserve [RR26]

Expected 26hr Recall Supply Surplus [SU26]

Tabular Report for Licensees




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This report contains tabular output, and is intended for Licensees and will include all the above with the exception of individual unit generating capacities.

Other Reports

Any half hour may optionally be selected and printed or viewed. This would show the same detail as the report for the time of minimum reserve.




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7 Operational planning, dispatch and audit

7.1 Year-ahead

7.1.1 Operational planning assessment

Prior to preparing year-ahead operational planning, SCC should make assessments of current year operational planning and compares with the actual situation to learn from experiences and prepare for the next year. The main contents include:

Assess the differences between forecasted load and actual load, including energy, peak load (Pmax) and low load (Pmin) of the whole power system;

Assess the actual generation of power plants comparing with the planning;

Assess the hydrological situation of reservoir in forecast and actuality, including: inflow, outflow/discharge, upstream elevation at the beginning and at the end of each month;

Assess the fuel supply for thermal power plants;

Assess the interrupting and shedding load (if any), including number of events and reasons for interrupting and shedding; cut-off energy and capacity;

Record any abnormal situations in actuality which differ from the planning;

Assess reasons of the situations above.

7.1.2 Input data

Load forecast:

Distribution Licensees forecast the load energy of its own system and provide them to SCC.

SCC forecasts load energy of the whole system, one of input data is forecasted energy of Distribution Licensees.

SCC forecasts: A (energy), Pmax and Pmin, typical load curve of each month.




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It should be 3 scenarios for load forecast: high scenario, low scenario and medium scenario.

Generation:

Energizing schedule of new power plants;

Technical parameters of power plants;

Maintenance schedule of generators;

Fuel price;

Constraints and parameters upon Power Purchase Agreement;

Constraints and parameters upon fuel for thermal power plants.

Hydrological forecast: In additional to the present method with 100 stochastic scenarios, the Consultant proposes some more scenarios based on inflow forecast.

Forced outage: The present option is appropriate.

Quantity and generation of renewable energy sources.

Network:

Network development planning (new build and upgrading).

Network maintenance schedule.

Must run constraints (if any).

Spinning reserves: With months that have high probability running out of water (at the end of dry season and beginning of rainy season), it could set spinning reserves at lowest level as 2.5%.

Result of PASA analysis.

7.1.3 Process

Define scenarios:

Scenarios of Inflow High load Medium load Low load

100 stochastic scenarios x x x

Low inflow (85%) x

Medium inflow (70-75%) x x x

Multi-year average x x x




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Forecasted inflow for each reservoir

x x x

High inflow (50%) x

Note: The table above could be revised to be suitable with the operational condition of Sri Lanka power system.

Update input data to optimization model

Analyse and summarize result: Based on scenarios, summarize and analyse the upper limit, lower limit and average of the whole system and of each power plant.

7.1.4 Output

The result should be for the whole year and provides detail to each month, includes:

Load forecast for the whole system: Pmax, Pmin, A (energy).

Generators and transmission network maintenance schedule

Operation schedule of new constructions

Generation of each power plant in 3 scenarios: high, low and average

Generation cost in 3 scenarios: high, low and average

Fuel used of each power plant and of whole system in 3 scenarios: high, low and average

Upstream elevation of each reservoir by the last day of each month in 3 scenarios: high, low and average

Warning of power system security

Solutions to ensure the reliability, security and stability of power system operation.

7.2 Month-ahead operational planning


Prior to preparing month-ahead operational planning, SCC should make assessment on current month operational planning and compare with the real situation to learn from experiences and prepares for the next month. The main contents include:

Assess the differences between forecasted load and actual load, including A (energy), Pmax and Pmin of the whole power system;




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Assess the hydrological situation of reservoir in forecast and actuality, including: inflow, outflow/discharge, upstream elevation at the beginning and at the end of each month;


Assess the interrupting and shedding load (if any), including number of events and the reason for interrupting and shedding; cut-off energy and capacity;

Record any abnormal situations in actuality which difference with the planning;

Assess the reasons of the situations above.

7.2.2 Input data update

In general, the monthly input data is taken from annual operational planning. After assessing actual operational situation and comparing with scenarios in annual planning, the most likable scenario with the actual one shall be taken as the basic for monthly operational planning. The monthly operational planning will be analyzed for the next 12 months, so SCC should update all input data in annual operational planning, update the actual operational situation, forecast and update new data.

Load forecast:

SCC updates forecasted load based on actual situation and forecast for the last month of analysis period.

Update generation information (if any):

Operational schedule of new power plant;



Fuel price;



Hydrological forecast: In additional to the present method with 100 stochastic scenarios, the Consultant proposes one more scenario based on inflow forecast.




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Forced outage: Set as year-ahead operational planning.


Update network information (if any):






7.2.3 Process

Develop difference scenarios

100 stochastic scenarios

Inflow forecast for each reservoir


Analyse and summarize analysis result: Based on scenarios, summarize and analyse the upper limit, lower limit and average (1 synthesis scenario from 100 stochastic scenarios and 1 scenario based on inflow forecast of each reservoir) of whole system and of each power plant.

7.2.4 Output

The result should be for the whole analysis period and provides detail to each day, includes:


Generators and transmission network maintenance schedule.


Estimated generation of each power plant: high limit, low limit and average.

Generation cost: low limit, high limit and average.




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Fuel used of each power plant and of the whole system: low limit, high limit and 2 medium scenarios.

Upstream elevation of each reservoir by the last day of each month: low limit, high limit and 2 medium scenarios.



7.3 Week-ahead operational planning


Prior to preparing week-ahead operational planning, SCC should make assessment on the current week operational planning and compare with the real situation to learn from experiences and prepare for the next week. The main contents include:

Assess the differences between forecasted and actual load, including A (energy), Pmax and Pmin of the whole power system;


Assess the hydrological situation of reservoir in forecast and actuality, including: inflow, outflow/discharge, upstream elevation at the beginning and at the end of week;






In general, the monthly input data is taken from monthly operational planning data. After assessing actual operational situation and comparing with scenarios in monthly planning, the most likable scenario with the actual one will be taken as the basic for monthly operational planning. The weekly operational planning will be




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analyzed for the next 4-5 weeks, so SCC should update all input data in monthly operational planning and update the actual operational situation.

Load forecast:

SCC update forecasts input data based on actual situation.

Update generation information:




Fuel price;



Hydrological forecast: Update forecast hydrological data from monthly operational planning and revised if needed.

Forced outage: No need, because weekly planning is very close to real time operation.








7.3.3 Process


Analyze and summarize the result.




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7.3.4 Output

The result should be for the whole analysis period and provides detail to each day, includes:




Estimated generation of each power plant.

Estimated generation cost.

Fuel used of each power plant and of the whole system.

Upstream elevation of each reservoir by the last day of each week.



7.4 Day-ahead operational schedule

7.4.1 Operational schedule assessment

Before making day-ahead operational schedule, SCC shall assess operation of previous day, main contents include:

Assess the differences between forecasted and actual load, including: Load curve, A (energy), Pmax and Pmin of the whole power system;

Assess the actual generation of power plants comparing with the schedule;

Assess the hydrological situation of reservoir in forecast and actuality, including: inflow, outflow/discharge;







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The day-ahead input data is taken from weekly operational planning data. The day-ahead schedule will be analyzed for the next 3 days, so SCC should update all input data in weekly operational planning and revise it upon actual operational situation.

Load forecast:

SCC forecasts the day-ahead load curve and revises the forecast based on actual operational situation.

Update generation information (if any):





Hydrological forecast: Update forecast hydrological data from weekly operational planning and revised if needed.

Water value of hydro power plants: from weekly operational planning

Forced outage: No need because day-ahead schedule is very close to real time operation.











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7.4.3 Process


Analyze and summarize the analysis result.

7.4.4 Output

The day-ahead schedule shall be for the whole analysis period and provides detail to each 30 minutes, includes:

Load forecast for the whole system.



Generation of each power plant.

Estimated generation cost.

Fuel used of each power plant and of the whole system.

Warning of power system security (if any)


7.5 Real-time dispatch

So far, the day-ahead schedule provides detail to each 30 minutes, the real time dispatch is absolutely obey this schedule, if there is any revision that must comply with merit order. If some big events occurred on power system, SCC could analyze again the schedule to operate the power system in the following hours; which basically appropriate with the current infrastructure.

However, if the generation price was not a constant but in form of block price in corresponding with difference capacity level, or if the generation cost of power plants were calculated based on heat rate curve or function, what SCC does in current would not make sure the most economic result for power system operation. In this case, the Consultant proposes as follows:

SCC should make hour-ahead schedule for each hour

Load forecast: SCC forecasts hour-ahead load and the following hours upon method mention in point 0




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Update power system operation situation (if there were any changes)

Run optimization software to make hour-ahead schedule

Operate the power system based on the latest hour-ahead schedule.

7.6 Ex-post analysis

7.6.1 Aims of analysis

To learn from experience for doing the better job at operational planning and actual operating.

To find factors those have the greatest effect on actual operating and forecasting so that having appropriate adjustments.

To enhance effectiveness in system operation and power supply safety.

Operational planning and real-time dispatch monitoring

7.6.2 Daily operation

Evaluation of load forecast error

The way to evaluate load forecast error is as follow:

Comparing forecasted load and actual load, then assessing error (%) detailing for every period of 30 minutes.

00:00 00:30 01:00 01:30 … … 23:00 23:30 Total

P_forecast (MW)

P_operation (MW)

Deviation (%)

Analyzing causes of the error (for instant, due to weather vagaries, big sporting and cultural events…)

Hydro management

Evaluating operation of hydro power plants includes following contents:




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Comparing forecasted inflow/discharge/outflow and actual inflow/discharge/outflow.

Comparing scheduled water spill and actual spill (if any).

Comparing planned elevation and actual elevation of reservoirs.

Based on comparing the forecasted hydrological conditions and actual ones, analyzing of errors will be conducted (for instant, due to weather vagaries, hydrological conditions, sudden flood…).

Power plants generation evaluation

Basic contents are followings:

Comparing scheduled and actual generation of power plants.

Power plants

P (MW) 00:00 00:30 01:00 01:30 … … 23:00 23:30 Total Causes

Power plant 1

Scheduled

Actual

Deviation (%)

Power plant n

Scheduled

Actual

Deviation (%)

Based on comparing scheduled and actual energy and power, analyzing causes of errors, some underlying causes as below:

Forecasted load is different from actual load.

Forecasted hydrological conditions are different from actual conditions (due to flood, weather vagaries, uncertainty in forecasting inflow…).

The maintenance progress of power plants and power grids are not exactly complied with registered plans.

Sudden faults happened in either generator or transmission grid.

Evaluation of generation cost and average generation price

Based on operational planning and actual operation, generation cost and average generation price will be calculated for both planned and actual, and then comparing those values between planned and actual.




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Average generation price = Total generation cost/generation energy output.

Energy output Planned

Actual

Deviation (%)

Generation cost Planned

Actual

Deviation (%)

Average generation price

Planned

Actual

Deviation (%)

Evaluation of actual operation and optimization scheduling

After every operation day, following data and information shall be updated into the daily optimal operation software:

Actual load of power system.

Unit status (available or unavailable).

Actual constraints.

After that, the software will recalculate an optimal operation schedule of each generator for previous day. Generation cost and average generation price will be calculated for both scheduled and actual, and then comparing those values.

7.6.3 Weekly operation



Comparing forecasted load and actual load, then assessing error (%) and detailing for every day.

Monday Tuesday … … Saturday Sunday Total

A_forecast (MWh)

A_operation (MWh)

Deviation (%)




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Analyzing causes of the error (for instant, due to weather vagaries, big sporting and cultural events…)

Hydro management

Evaluating the operation of hydro power plants includes following contents:

Comparing forecasted inflow/discharge /outflow and actual inflow/discharge /outflow.

Comparing planned water spill and actual spill (if any).

Comparing planned week-end elevation and actual elevation of reservoirs.

Analyzing causes of error.

Evaluation of generation energy


Comparing planned energy output and actual generation of power plants.

Causes of error.

Unusual notes.

Power plants

Output (MWh)

Monday Tuesday … … Sunday Total Causes

Power plant 1

Planned

Actual

Deviation (%)

Power plant n

Planned

Actual

Deviation (%)


Based on operational planning and actual operation, generation cost and average generation price will be calculated for both planned and actual, and then comparing those values.





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Actual

Deviation (%)


Actual

Deviation (%)


Planned

Actual

Deviation (%)

7.6.4 Monthly operation



Comparing forecasted load and actual load, then assessing error (%), detailing for every day and analyzing causes of error.

1 2 … … 30 31 Total

A_forecast (MWh)

A_operation (MWh)

Deviation (%)

Hydro management


Comparing forecasted inflow/discharge/outflow and actual inflow/discharge /outflow.

Comparing planned spill and actual spill (if any).

Comparing planned month-end elevation and actual elevation of reservoirs.

Analyzing causes of error.




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Evaluation of generation energy output


Comparing planned week-based energy output and actual energy output of power plants.

Causes of error.

Unusual notes.

Power plants

Output (MWh)

1 2 … … 30 31 Total Causes

Power plant 1

Planned

Actual

Deviation (%)

Power plant n

Planned

Actual

Deviation (%)


Based on operational planning and actual operation, generation cost and average generation price will be calculated for both planned week-based and actual operation, and then comparing those values between planned week-based and actual operation.



Actual

Deviation (%)


Actual

Deviation (%)


Planned

Actual

Deviation (%)




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7.6.5 Annual operation



Comparing forecasted load and actual load, then assessing error (%), detailing for every month.

A (MWh) 1 2 … … 11 12 Total

High scenario

Low scenario

Medium scenario

Operation

Deviation comparing with medium scenario (%)

Hydro management


Comparing forecasted inflow/discharge/outflow and actual inflow/discharge /outflow.

Comparing planned spill and actual spill (if any).

Reservoir Comparison 1 2 … … 11 12 Total

No. 1 Inflow

(m3/sec)

Forecasted

Actual

Deviation (%)

Discharge

(m3/sec)

Planned

Actual

Deviation (%)

Outflow

(m3/sec)

Planned

Actual

Deviation (%)




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Spill

(m3/sec)

Planned

Actual

Deviation (%)

No. n Inflow

(m3/sec)

Forecasted

Actual

Deviation (%)

Discharge

(m3/sec)

Planned

Actual

Deviation (%)

Outflow

(m3/sec)

Planned

Actual

Deviation (%)

Spill

(m3/sec)

Planned

Actual

Deviation (%)

Comparing planned month-end elevation and actual elevation of reservoir.

Reservoir Elevation comparison 1 2 … … 11 12 Notes

No. 1 Upper limitation

Lower limitation

Actual operation

No. n Upper limitation

Lower limitation

Actual operation

Evaluation of generation


Comparing planned generation (medium scenario) and actual generation of power plants.

Causes of error.

Power plants

Output (MWh)

1 2 … … 11 12 Total Causes

Power Planned




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plant 1 Actual

Deviation (%)

Power plant n

Planned

Actual

Deviation (%)


Based on operational planning and actual operation, generation cost and average generation price will be calculated for both planned medium scenario and actual operation, and then comparing those values between planned and actual operation.


Unit Comparison High scenario Medium Scenario

Low scenario


Actual

Deviation (%)


Actual

Deviation (%)


Planned

Actual

Deviation (%)




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8 Infrastructure and software

8.1 Data management infrastructure

An overall design of information infrastructure is proposed as the below picture.

Figure 26 Conceptual Overall Information Architecture

8.1.1 Infrastructure system at SCC

SCC includes 4 main following systems:

SCADA/EMS system: Collecting real-time operational data of power plants and substations, then System Operator runs applications such as state estimation, power system analysis (load flow, contingency analysis), economic dispatch ... and then issue operational instruction to power plants and substations via SCADA system.

PUCSL

SCC

Power Plants

Metering System

Applications

Customers

Substations

PUCSL Database

Applications

Other Licensees

SCADA / EMS

Web Portal

Central Database




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Central Database: This Central Database system stores all operation data of SCADA system, metering data used for settlement, technical data of power plants and substations...

Applications: These are applications used in SCC for load forecast, inflow forecast, optimization software used for year-ahead, month-ahead, week-ahead, day-ahead, hour ahead operational planning and schedule... and other applications used for analyzing power system. These applications use data stored in Central Database, and calculated results are also stored in Central Database. Some of calculated results shall be published transparently on Web Portal.

Web Portal: Web Portal is used to publish information to customers and other Licensees. Web Portal is also the way to exchange data among the Licensees. Each of Licensees has one account and has decentralized as prescribed.

8.1.2 Infrastructure system at PUCSL

Data base: PUCSL should have its own database that is provided from Central Database of SCC.

Applications: PUCSL should be equipped applications and software to monitor operational planning, real-time dispatch and disputing (if any). Necessary calculated results and analysis of PUCSL shall be published on Web Portal.

8.2 Software

8.2.1 Forecasting software

SCC should be equipped forecasting software to predict load and inflow of reservoirs. Distribution Licensees should be equipped forecasting software to predict load. This task is very difficult, and forecasted results are depended on many characteristics of forecasted objects, historical data and also forecasting methods. Therefore, forecasting software should have ability to be flexibly customized, and can simulate many input data as well as has suitable forecasting methods. Before deciding to buy forecasting software, SCC and Distribution Licensees need to have a pilot test to evaluate its accuracy for ensuring that software is matched requirements.

8.2.2 Optimization software

Optimization software is used to make operational planning with the objective function is to minimize generation cost provided that all constraints of power




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system operation, power grid operation, power plants’ reservoir operation, and thermal power plants, etc, are satisfied.

Software’s requirements are as follows:

No. Content

Operational planning

Year-ahead

Month-ahead

Week-ahead

Day-ahead

1 Customization High High High High

2 Run mode

2.1 Time interval 1 hour 1 hour ≤ 1 hour ≤ 1 hour

2.2 Stochastic run mode

2.3 Determistic run mode

3 Power System

3.1 Spinning reserve

4 Load model: Whole system, region, node

5 Hydro power plants

5.1 Cascade hydro power plants

5.2 Forecasted inflow (m3/sec)

5.3 Historical inflow (m3/sec)

5.4 Reservoir modelled as a function V = f (Z) or multi break point

V: Volume (m3)

Z: Elevation (m)

> 20 points

> 20 points

> 20 points

> 20 points

5.5 Tuabin efficiency curve: multi break point

> 20 points

> 20 points

> 20 points

> 20 points

5.6 Min, max spill (m3/sec)

5.7 Min, max out flow (m3/sec)

5.8 Min, max discharge (m3/sec)

5.9 Min, max elevation at each period (m)

5.10 Tail water and out flow curve: Ztail = f (Qout)

Ztail: Tail water (m)

Qout: Out flow (m3/sec)

5.11 Forbidden zone of each generator

5.12 Capacity (MW)

5.13 Outage schedule




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No. Content

Operational planning

Year-ahead

Month-ahead

Week-ahead

Day-ahead

5.14 Forced outage

6 Thermal power plants

6.1 Max, min capacity (MW)

6.2 Forced outage

6.3 Heat rate (multi points)

6.4 Max and min number of start up per day

6.5 Max and min number of shut down per day

6.6 Min up time (h)

6.7 Min down time (h)

6.8 Up rate (MW/h)

6.9 Down rate (MW/h)

6.10 Must run

6.11 Outtage schedule

6.12 Muti fuels

6.13 Generation of Gas turbine and steam curve (for gas turbine)

6.14 Start up cost (Hot, warm, cold)

6.15 Shut down cost

6.16 O&M cost

6.17 Capacity Charge including Non-Escalable Component and Escalable Component

6.18 Contract price

6.19 Max, min energy

7 Fuel

7.1 Fuel price

7.2 Fuel limitation

8 Transmission system

8.1 Grid including: transmission limitation, loss

8.2 Import data of transmission system from PSS/E



Conclusions

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9 Conclusions

Optimising power system planning and power system operation processes is an extremely important activity in any electricity industry. Minimising generation costs is always the first priority of the power system operator as well as authorities in management and the regulator. This is because generation costs usually comprise the highest proportion of total electricity power costs. Given the life span of generators and the fact that most are required to generate most of the time, saving a small percentage in generation costs can result in large cost savings over time.

SCC should make operational planning to ensure following requirements:

Ensuring safety and continuity operation of power system.

Complying with requirements for flood control, irrigation and maintaining ecological water flow according to the procedure of hydropower’s reservoir operation.

Satisfying constraints related to fuel for thermal power plants.

Satisfying allowed technical conditions of generators and transmission grid.

Ensuring the minimum generation cost for whole power system.

For the purposes of transparency, clearly and standardization, PUCSL should require SCC to prepare and and promulgate standard procedures to perform its tasks, for example:

The procedure of load forecast

The procedure of inflow forecast and operation of hydro power plants

The procedure of PASA and maintenance planning

The procedure of year-ahead, month-ahead, week-ahead, day ahead operational planning

The procedure of power system operation and real-time dispatch

The procedure of ex-post analysis

In the long term, to increase the effectiveness of dispatching and monitoring, SCC and PUCSL should be equipped an infrastructure system of data collection, data exchange and data management. SCC also should be equipped new software or be upgraded existing software to ensure operating planning closely with actual operation and to achieve the most economical operation.



Conclusions

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Year-ahead, month-ahead, week-ahead and day-ahead operational planning made by SCC will be reported to PUCSL before operation. After operation, SCC makes reports of operation analysis and submits to PUCSL. Moreover, PUCSL should also be equipped data collection system, software and tools to be able to monitor and inspect if necessary.

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