Electricity cost management in mining 15% of all electrical energy consumed in 1993 in South .Africa was used by deep-level mines. It is of the utmost importance to have a rnine energy systems planning model to minimise the cost of the electrical energy consumption of deep-level mines.

by G. J. Delport and 1. E. Lane

Energy conversion model for end-user groups Energy conversion models for end-user groups such (3s a mineral processing plant, a mine winding system, an underground mining system, a compressed air system, an underground water pumping system, a fridge plant and a ventilation system have been developed. Each of these models can be used to calibrate the electrical energy consumption and quantification of energy norms of the end-user group, or in a simulation for inteqrated mine electrical

0

system and process limitations and constraints and maiiagement requirements. The rnodel must be able to play 'what if' games to quantify the possible saving on the electricity bill if an action or set of actions can be taken to reduce electricity costs without impinging on safety or prodiJctivity. Change of energy arriers, storage capacity or maschine capacity. The model must again quantify the possible savings of such changes.

energy planning to-accommodate the following factors:

External influences on the system, such as electricity tariffs. If the mine can respond to price signals from the electricity supplier, it can benefit by improving its cost effec.tiveness by optimising its electricity consumption. In this way the mine can help the electricity supplier to improve t.he system demand profile. The model must quantify these possibilities. Internal influences on the system, such as

The end-user group model methodology

A generic end-user group model was developed, by usirig a building block concept.' A building block, consisting of a storage buffer and a process, can be used to construct an end-user group model. The inputs and outputs of leach building block are given in Fig. 1 .

Inputs End-user group cclnfiguration: This is a

schematic diagram illustrating what the end-

1 Inputs and outputs for the end-user group model

POWER ENGINEERING JOURNAL AUGUST 1996 169

2 Machine control rules

user group consists of, including the buffers and the processes.

Electricity tariffs: The main objective of this research is to try to minimise electricity costs, therefore it is important to include the electricity tariff under which the end-user group is going to operate.’,’

Requiredproduction profile: What is the planned production, e.g. m3/s compressed air or litre/s of water pumped?

constraints: Are there certain guidelines that the end-user group must adhere to, e.g. if the underground water storage dam reaches its maximum level, the underground water pumps must start pumping to prevent a safety hazard (this is a ’has to happen’ situation, whatever the consequences to the electrical energy cost), or the maximum capacity of the compressors isx m’/s compressed air flow (this is a configuration fact)?

Mine management rules: These are management rules that can influence a possible reduction in electricity costs, e.g. mine management has a risk aversion culture and therefore wants the underground water pumps started if the underground water storage dam reaches 90% of its maximum level (this is a ’nice to have’ option and can be compared against a possible saving on electricity COS^).^,^

System and process limitations and

Buffersystems: What type and sizes of buffers are available and what are the sizes?

Actualsupply profile: Which of the other upstream end-user group’s flow of corn mod ities a re constraining the performance of this end-user group, e.g. the underground mining end-user group needs adequate inputs, from the compressed air system end-user group, the underground water pumping system end-user group and the ventilation system end-user group. If the compressed air system end-user group cannot supply the required flow of compressed air a t a certain pressure, it is seen as a supply-side constraint from the underground mining system end-user group’s point of view.

Load profile o f the mine without end-user group: What are the other end-user groups doing a t a specific point in time?

0 utp uts Proposed schedules: Determine the

schedule that will not lose production, but will result in the least expensive electricity account.

proposed schedules, what will the load profile of the end-user group look like?

Buffersystem levels: According to the proposed schedules, what will the buffer system levels of the end-user group look like?

Electricity load profiles: According to the

Table 1 Maximum demand reduction results of the separate simulations for tariff E

maximum demand 4.1 4 MW 5 94MW 10.08 MW

Table 2 Maximum demand reduction results of the combined simulation for tariff E

maximum demand 7 74 MW R 32,22/kW 249.38

170 POWER ENGINEERING JOURNAL AUGUST 1996

Actual production pro file : With a I I the inputs of this end-user group, the end-user group configuration, the electricity tariff, the required production profile, the system and process limitations and constraints, the mine ma nag em en t, the buffer systems, the act ua I supply profile(s) and the load profile of the rest of the miiie taken into account, what can this end-user group produce?

Required supply profiles: This end-user group needs’these upstream inputs to be able to produce the required flow of i ts commodity.

Rules used for developing the end-user group model are shown in Fig. 2. The model must first satisfy the system and process limitations and Constraints, e.g. if the underground hot water dam is going to exceed i t s maximum water level in the next time interval, the pumps must pump to prevent a safety hazard. If the pumps need not pump because of limitations and constraints, the model must c:omply with management requirements, e.g. does management have a risk aversion culture when deciding on the ’safe’ buffer levels, or can the buffers be used fully to minimise electricity cost? If both the previous rule:; do not make an action

compulsory, the model will take the electricity consumption of the rest of the mine (the total mine minus the end-user group), the available buffers and the process capacity into account in simulating the flow of the commodity in such a way that the electricity cost, according to the applicable tariff, is minimised.

If the simulation methodology and load modelling are in p l a ~ t ~ ’ , ~ ” and the mine’s configuration is programmed into the simulation, certain ’what-if’ simulations can be done. After determining the end-user group($ to target for load shifting,’ the possible load shifting per end-user group in the mine is simulated. For this article the mine winding system end-user group and the underground water pumping system end- user group are the targets for the simulation.

Two-part tariff (demand energy tariff, tariff E)

The possible maximum demand reduction results for the separa-te simulations of the mine winding system end-user group and the underground water pumping system end- user group are given in Table 1 . The results of the combined minevvinding and u n de rg rou n d water pu m pi n g systems

60 -7

-i _- 0 - I high demand hours low demand hours

total mine measured - mine winding simulation

60

50

40

30

20

-1 __ 0 - 1 high demand hours low demand IioiJrs

total mine measured - underground water pumping simulation

POWER ENGINEERING JOURNAL AUGUST 1996

3 Load duration curves after the mine winding system simulation for the high and low demand hours for tariff E (one week)

4 Load duration curves after the underground water pumping simulation for the high and low demand hours for tariff E (one week)

171

5 Load duration curves after the combined simulation for the high demand and low demand hours for tariff E

Table 3 and calculated data

Summary of the resulting maximum demands for the tariff E measured, simulated

measured maximum demand expected maximum demand reduction for the combined simulation expected maximum demand for the combined simulation actual maximum demand reduction for combined simulation actual maximum demand for the combined simulation calculated maximum demand for the combined simulation

Table 4 Summary of the total possible saving per month if the mine is on tariff E

maximum demand, MW 52 84 MW 1702 51 45 10 MW 145

total saving total saving per year

energy consumption 32 080 MWh 191 5 18 31 905 M 190

simulation are given in Table 2. It is important to note that the aim for a

demand charge tariff is to strive for a load factor of 1 during the high demand hours per day.

The load duration curves of the separate simulations are given in Figs. 3 and 4 and that of the combined simulation in Fig. 5. From these Figures the attempt made by the simulation to achieve a load factor of 1 during the 80 high demand hours is clearly visible. The simulation of the mine winding system end-user group is the least successful (f ig. 3) because the buffers and capacity of the existing mine winding system limits any further load shifting. The load profile for the underground water pumping system end- user group after simulation (Fig. 4) almost reaches a load factor of 1 during the 80 high demand hours of the week. Again, the capacities of the storage dams do not allow any further load shifting.

The combined simulation of the two

targeted end-user groups take much longer than the two separate simulations together. The main reason for this is the extra number of control rules that must be adhered to during the combined simulation. The minimum theoretical maximum demand possible, for the combined simulation, can be calculated (predicted) from the available results from the separate simulations of the mine winding system end-user group and the underground water pumping system end- user group. The lowest maximum demand for a load factor of 1 is calculated.8

The separate simulations are used to take the process limitations and constraints into account when calculating the possible load shifting from the 80 high demand hours to the 88 low demand hours in a week. The maximum demand calculated is 44.70MW, only 0.40 MW less than the combined simulation. A summary of the maximum demands for the tariff E case study is given in Table 3. Table 4 gives the total possible

172 POWER ENGINEERING JOURNAL AUGUST 1996

50

40

30

20

peak hours standard hours off-peak hours

total mine measured - mine winding simulation

saving per month if the mine is on tariff E and the two targi?ted end-user groups, mine winding and underground water pumping, are instrumented.

Time-differentiated tariff (tariff T I 1

tariff, offered by the electricity supply industry to large power users, is to provide an incentive to shift load from the peak hours to the standard and off-peak hours. The mines would not accept the new time- differentiated tariff if they are going to pay

The purpose of the time-differentiated

more for electricity. One way to find out if a mine is going to pay more for electricity is the use of simulation.

Tables 5 and 6 give the simulation results for the combined simulation if the mine is on a time-differentiated tariff for the low demand (summer) months and the high demand (winter) month, respectively.

The load duration curves of the separate mine winding simulation and the underground water pumping simulation are given in Fig.6 and Fig. 7, respectively. The load duration curves for the 85 high-demand

Table 5 Summary of the cost involved for the case study for tariff T I , low demand

measured simulated saving _____ (R x 1000)

value cost value cost (R x 1000) (R x 1000)

~ _ _ _ _ _ -

demand energy

consumption

consumption 12213MWh 12

consumption 14825 MWh

standard time energy

off-peak time energry

total per month total saving for 6 months

Table 6 Summary of the cost involved for the case study for tariff T I , low demand

maximum demand peak time energy

consumption standard time energy

consumption off-peak time energy

consumption total per month total saving for 6 months

measured simulated saving (R x 1000)

value cost value cost (R x 1000) (R x 1000)

52 84 MW 5

6 Load duration curves after the mine winding system simulation for the peak, standard and off-peak hours for tariff T I (summer week)

POWER ENCiINEERING JOURNAL AUGUST 1996 173

7 Load duration curves after the underground water pumping simulation for the peak, standard and off-peak hours for tariff T I (summer week)

174

hours of the total mine measured and the total mine after the combined simulation are given in Fig. 8. The noticeable reduction in the use of standard energy is due to the standard time on Saturday from 07:OO to 12:oo.

The minimum theoretical maximum demand possible, for the combined simulation from the available separate simulations of the mine winding system end- user group and the underground water pumping system end-user group is calculated as 43.82 MW. This is 2.72 MW less than the actual simulation. The reason for this is that the combined simulation under the time- differentiated tariff does not want to achieve a load factor of exactly 1 .

The simulation tries to shift load from the peak time to the standard time and the off- peak time, as well as from the standard time to the off-peak time. This is the first priority of the simulation. This can be seen in Fig. 8 where the load duration curve of the combined simulation does not stay constant a t 46.54 MW. No more load shifting is allowed by the simulation because saving in peak energy cost is less than sum of the cost

of the standard energy and the higher maximum demand cost.

Conclusions From Table 7 it can be concluded that if the

mine is on a two-part tariff (demand energy tariff) 95.98% of the possible saving is from the demand charge while the time-differentiated tariff demand charge contributes only 37.97% to the saving. In other words the diminishing return on investment has a bigger influence on the two-part tariff than on the time- d iff erentiated tariff.

If the mine decides to change to tariff T I an average of R 17 190 higher electricity account per month is possible, without any changes in the current configuration or operation of the mine. With the combined simulation, an average saving per month of R 171 384 can be achieved (Table 8), and therefore a total actual average saving of R 154 194. If the mine does not change its electricity tariff and keep the two-part tariff it can save a possible R 259 830 (Table 8).

The electricity supply industry does not have a tariff available that gives the mine enough incentive to change to that tariff and

Table 7 the case study

Summary of the maximum demands, energy consumptions and cost involved for

POWER ENGINEERING JOURNAL AUGUST 1996

70 1 60

50

40

30 z

20

0 1 peak hours standard hours off-peak hours

total mine measured - combined simulation

save on their electricity account with load management. If, for example, a time- differentiated tariff without a demand charge coulcl be made available to the mine, it would benefit both the mine and the electricity supply industry. From all the time- differentiated figures in this article (Figs. 6-8) it can be seen, before and after simulation, that the mine always peaks during the standard energy time. The simulation can therefore not move more load to the standard energy time, because the money saved on peak energy is less than the cost in standard energy time and the maximum demand component that is a result of the load shifting. The end-user group configuration may sti l l allow more load shifting, frorn peak to standard time, but it is not viable from an electricity cost point of view on tariff T I . If the mine can pay under a time-differentiated tariff with no demand charges in the standard period, more load shifting would be done and both the mine and the electricity supply industry would benefit from the lower overall resource costs.

This case study emphasises the diminishing return on investment that takes place, with the possible saving on maximum demand costs, if more than one end-user group is targeted for electrical energy cost optimisatiori. This was shown with an actual combined simulation of the two end-user groups, mine winding and underground water pumping, and confirmed with a mathematical calculation. Therefore, when the return on investment period for each additional end-user group is calculated, it is very importalnt to include the diminishing return on investment factor.

investment is necessary for the instrumentation of the two targeted end- user groups regardless of the applicable tariff structure. This provided a fair basis for comparison. It must be emphasised that none of the simulated scenarios would be possible in practice without an efficient

In this case study no other capital

energy measurement, control and information system.

Acknowledgments The authors thank the Department of

Mineral and Energy Affairs for supporting this research effort. We also thank the management of the mine for their participation in the load research described in this article.

References DELPORT, G. J.: 'Integrated electricity end-use planning in deep level mines', Thesis, University of Pretoria, Pretoria, 1994 CHANG, C. S.: 'A fast interactive optimal load management algorithm under time-varying tariffs', Electrical Power & Energy Systems, 1988, 4, (4) SCHWEPPE, F. C., MERRILL, H. M, and BURKE, W. J.: 'Least-cost planning: issues and methods', /E€€ Proceedings, 1989, 77, (6) COHEN, A. I., and WANG, C. C.: 'An optimization method for load management scheduling', / €€E Transactions on Power Systems, 1988,3, (2) SHAH, S., and SHAHIDEHPOUR, S. M.: 'A heuristic approach to load shedding scheme', /E€€ Transactions on Power Systems, 1988,4, (4) DELPORT, G. J. and LANE, I. E.: 'Load audits and simulations to develop DSM options in the mining sector', Paper presented a t DSM Participants Seminar, DMEA, 1993 DELPORT, G. J., and LANE, I.E.: 'Asimulation model for integrated mine energy systems planning', Paper presented at the 13th IASTED (International Association of Science and Technology for Development) International Conference on Modelling, Identification and Control, Grindelwald, Switzerland, 1994 DA-QIANG, M., and PING, J. : 'A novel approach to dynamic load modelling', /E€€ Transactions on Power Systems, 1989,4, (2)

0 IEE: 1996

8 Load duration curves for the combined simulation for the peak and standard and off- peak demand hours for T I (summer week)

~~

The authors are with Centre for New Electricity Studies, Department of Electrical and Electronic Engineering, University of Pretoria, Pretoria 0002, South Africa. E-mail: johan.delport@ee.up.ac.za

POWER ENGINEERING JOURNAL AUGUST 1996 175