Innovative Renewable Energy - Load Management Technology via Controlled Weight Motion

Transcription

1 Proceedings of the 4th International Middle East Power Systems Conference (MEPCON ), Cairo University, Egypt, December 9-2, 2, Paper ID 22. Innovative Renewable Energy - Load Management Technology via Controlled Weight Motion M.A. El-Kady, M.S. Al-Saud and M. Alkhamis Saudi Electricity Company Chair in Power System Reliability and Security College of Engineering, King Saud University P.O. Box 8, Riyadh 42, Saudi Arabia melkady@ksu.edu.sa, mamdooh@ksu.edu.sa,, moh_alkhamis@hotmail.com Abstract - The paper presents an innovative renewable energy - load management technology, which is currently being developed as a prototype, in preparation for potential large-scale implementation in power systems. The new technology employs controlled motion of large weights, which are being maneuvered (up and down) through the system peak / off-peak demand pattern. The load management scheme is similar to the pumping storage systems, but uses large weights instead of water and, therefore, is more suited to the operating environment in Saudi Arabia, in which water is relatively scarce. A portion of the offpeak valley-filling energy is supplied from wind-driven generators, which adds to the green energy component of the scheme. The controlled motion aspect of the new technology is based on partial implementation of previously published ideas by the authors as well as a registered patent. The paper describes the theoretical and analytical features of the introduced renewable energy - load management technology, and dwells on its practical implementation features. Index Terms - Power systems, Renewable energy, Load management, Demand-supply balance. I. INTRODUCTION Electric power companies around the world have come to the realization that they can no longer afford the unlimited expansion for the generation and transmission facilities in response to the overwhelming, un-coordinated and illmanaged growth in electricity demand []. Over the past two decades, power system management have been shifting the focus of their prime attention from traditional technical system operation and planning matters to the more pressing issues of better utilization of available energy resources and improved quality of existing consumer electricity services. The results of such a shift in attention and redefined priorities on the part of electric companies have been realized in the form of new topics and subjects, which have emerged, such as Load Management, Demand-Side Management, Energy Conservation, Alternate Energy Sources, Consumer Service Quality Assurance, Customer Awareness and Education as well as Consumer Incentive Strategies. The most successful electric companies today are those who have managed and adopted an integrated approach, which combines both utility and consumer related aspects into one set of coherent and well-coordinated strategies. Load management and energy conservation are currently being considered and adopted by many power utilities around the world in order to cope with the increasingly common situation of limited capacity additions facing, however, a continuously growing demand [2-5]. On the other hand, renewable energy technologies have been steadily forthcoming in order to provide a clean alternative to fossil-fuel-derived electricity. In modern times electric power is regarded as one of the most essential elements in economical and social development in fast developing countries, especially the Gulf countries, in which the demand reached unexpected high level. In Kingdom of Saudi Arabia the increasing demand on power is related to several factors:. Use of new building materials and civilization systems obtained from atmospheric and social environment different from the local environment. 2. The rich continuous support from the government to electricity field presented in performing large electricity projects and interconnections between main networks. 3. Increase power consumption levels according to its availability with prices much lower than the actual production cost due to government subsidies. 4. The effect of high temperatures on the extensive usage of air-conditioning devices during summer. The increasing demand problem could be faced by different methods such as:. Adding generating units to face the highly increasing peak load, especially between one o'clock and five o'clock p.m. in summer. 2. Reduction of electric power consumption by guiding the customers to use less power according to the need only. This will decrease customer consumption bill, decrease operation and investment costs, save consumed fuel in generating power stations and decrease the environmental effects resulting from oil products burning for electricity production. 3. Electrical load management by shifting the present electrical loads in maximum consuming time out of this critical time as much as possible and benefit from storage and non-electric instruments. 4. Cut-off of some loads if necessary, even on a periodic basis with some rewards for consumers. 482

2 In the Saudi interconnected electricity system, there is a large continual increase in electric power consumption with the maximum load reached during the summer period. There is also a clear difference in consumption levels among seasons. Also, difference in demand levels appears daily according to atmospheric effects, social conditions and human activities. The increase and differences in consumption upon annual and daily level create a style hard to deal with, especially with the maximum load passes through 3-6 hours per day and the addition of new generating units to deal with this limited-period load is obviously an expensive solution and hard to achieve economically. This paper presents an innovative renewable energy - load management technology, which is currently being developed as a prototype at King Saud University (KSU), in preparation for potential large-scale implementation in power systems. The new technology employs controlled motion of large weights, which are being maneuvered (up and down) through the system peak / off-peak demand pattern. The load management scheme is similar to the pumping storage systems, but uses large weights instead of water and, therefore, is more suited to the operating environment in Saudi Arabia, in which water is relatively scarce. A portion of the off-peak valley-filling energy is supplied from wind-driven energy schemes [6-9], which adds to the green energy component of the scheme. The load management and controlled motion aspect of the new technology is based on partial implementation of previously published ideas by the authors [, ] as well as a registered patent [2]. The paper describes the theoretical and analytical features of the introduced technology setup, and dwells on its practical implementation features. II. OVERALL TECHNOLOGICAL FEATURES The renewable energy - load management technology introduced in the paper constitutes the following salient features:. An efficient design for controlled motion of large weights, which are being maneuvered, via successive weight elevation and dropping, to follow certain desired energy storage/release pattern. The controlled motion design is currently being developed in the form of a prototype, as shown in Figure, in preparation for envisaged largescale implementation in the power system. The prototype uses a. ton weight to be lifted during the off-peak period via a 3-phase 38 V motor with partial contribution from a wind-driven mechanism. 2. A load management scheme, as depicted in Figure 2, which aims at reducing the peak load by lifting the mass weights during the off-peak period and using the stored potential energy to generate electricity during the following peak period of the demand pattern and, therefore, replacing the actual generation that would otherwise have to be used from the power plants. The load management analysis employs a set of advanced simulation modules which maps the peak/off-peak demand pattern of the system over a period of time and produces a projected estimate of the benefits. 3. A renewable energy scheme, using wind power as depicted in Figure 3 in order to supplement a portion of the off-peak (valley filling) stored energy, which otherwise would be drawn from the power grid. A hybrid setup involving both wind and solar energy conversion is also being investigated. The wind energy setup employs 2.2 m long fans with a 5-25 rpm rotation speed and a directly applied gearbox. Fig. A Prototype of the controlled motion design at King Saud University MW HOURS Fig. 2 Illustration of load management via load flattening (shifting) Fig. 3 Wind-driven setup at King Saud University 483

3 III. LOAD MANAGEMENT FORMULATION Load management is undertaken by power utilities to alter the load shape in order to achieve a better balance (matching) between the customer s cycle demand and the utilities current and planned generating as well as transmission and distribution resources. In the present setup, load flattening is the prime objective of the load management scheme. Load flattening scheme transfers loads that would occur on-peak to off-peak periods, thus combining peak clipping and valley filling. This can be achieved by implementing two distinctive load management functions, namely peak clipping and valley filling. These two functions are described as follows: Peak Clipping: Peak clipping aims at reducing the peak demand on a utility system by decreasing the on-peak electricity consumption. Let P(t) denote the system load (MW) as a function of time t (hours), between O and T. The total saving in energy costs as a result of peak clipping, to a new clipped power (MW) of P c, is given by CE = C [ Q( t) d( t)] sav e T In the present application of load flattening, the efficiency of the energy reallocation (shift from/to peak/off-peak) process plays an important role in assessing the overall merits of the load management scheme employed. The main objectives of the application are as follows:. Simulate the load flattening technique for Riyadh average load curve during the next twenty years as a case study. 2. Prove that at the ideal case where the efficiency of energy shift during the load flattening process is unity, the optimal clipping point of the peak load at which a maximum revenue in SR is achieved (due to saving in future capacity additions) will be at the average load level. 3. Find this optimal clipping point in actual cases where the efficiency of energy shift process is less than one, which expected to be at load level between the average and the maximum load depending on the ratio of loosed energy due to flattening of the load curve (value of the energy conservation efficiency). Consider the load-flattening scheme illustrated in Figure 4 in which the peak energy E is to be reallocated to the two valley portions as E2a and E2b. The energy reallocation process is assumed to take place with certain efficiency. where C e is the average cost of energy in Saudi Riyals per MWh (SR/MWh), and the difference function Q(t) is given by for P() t PC Qt () = { Pt () PC} forpt () > PC Load (MW) P E In addition to the saving in energy costs as a result of peak clipping, there is also a saving associated with reducing system capacity requirement. The capacity-type is given by [ { ()} ] CP = C Max P t P sav P C P2 E 2 a Time (hour) E 2 b where C p is the cost capacity (SR/MW) Fig. 4 Illustration of load flattening Valley Filling: Valley filling is designed to increase load during off-peak periods. Such action is appropriate to undertake when the incremental cost of serving this load is lower than the average cost of electricity. Let P(t) denote the system load (MW) as a function of time t (hours), between O and T. The total energy (MWh) added to increase the valley portion of the load curve to a new power level of P v (MW) is given by E = [ R( t) dt] add where R(t) is given by T for PV P() t Rt () = { PV P() t } for PV > P() t The load flattening action can be simulated by a set of expressions and relationships. The amount of shifted energy is given by E E2a + E2b = ζ ( Utility model) or E2a + E2b = E ζ ( Customer model) In the above two expressions, distinction is made between the case where the utility picks up the losses in energy reallocation process (utility model) and the case where the customer bears these losses (customer model). In both cases, ζ denotes the efficiency of energy shift process. The capital costs of generating capacity according to the original (case ) and the shifted (case 2) curves are: Cost = Cp P Cost = C P 2 p 2 484

4 where Cp is the total cost of capacity (SR/MW). The revenue in each case (SR) can be written as: Re v = Ce[ E + E] (876 n) Re v = C [ E + E + E ] (876 n) 2 e o 2a 2b where Ce is the cost of the unit of energy in (SR/MWH) and n is the number of years during the period study. We now define the two functions F and F 2 as: F = Rev Cost F = Rev Cost where F and F 2 denote the net benefit (revenue cost) in each case (SR). Hence, the difference between the two cases could be written as: f = F2 F which, according to the simple model considered, is an indicative of the overall merit of carrying out the load management program. Our goal is to find the optimal clipping point (P C =P 2 ) at which the function f will reach its maximum positive value for different values of energy shift efficiency. At this point (P C ) the high economical clipping benefit from the schem will be achieved. III. IMPLEMENTATION SCENARIO A demonstrative implementation case scenario of the renewable energy - load management scheme has been analyzed for a typical daily demand pattern (load curve) of a portion of the Saudi electricity system. Controlled motion data from the prototype setup at KSU was used to obtain the projected range of efficiency as well as the time-scale for the up and down motions. Renewable energy sources in the form of wind power generators are envisaged to account for about 25% of the off-peak energy storage (valley filling) in this implementation scenario. A computerized simulation module was used to simulate the average daily load curve of the considered subsystem as shown in Figure 5. The simulation module was run at different values of energy shift efficiency and the benefit in MSR (million Saudi Riyals) due to load flattening of the load curve was found at certain points specified by the software module. The module scans the load curve starting from average load until the max load is reached, with closed increment factor of.5 MW. The relation between the benefit and clipping level was then plotted for each value of energy shift efficiency. Two business models were used in the present load management analysis depending on who (supplier of customer) bears the cost of losses. Figure 6 shows the relation between the benefit and clipping level at energy shift efficiency equal to one for the customer-bear-losses scenario. Demand (MW) Time (HOURS) 8 x Fig. 5 Load curve of the implementation case scenario Fig. 6 Relation between benefit and clipping point at unity shift efficiency (customer model) At this case the maximum benefit was achieved when the load was clipped at the average load level. We can therefore say that, at the ideal energy shift (eff =), P C optimal = P avg = MW. Figure 7 shows the relation between the benefit and clipping level at energy shift efficiency equal to.9. At this case the maximum benefit was achieved at P C = MW. Figure 8, on the other hand, shows the relation between the benefit and clipping level at energy shift efficiency equal to.8, where the maximum benefit was achieved at P C = MW. Both Figures 7 and 8 are drawn for the customer-bear-losses scenario. When the utility-bear-losses scenario is considered, Figures 9, and show the relation between the benefit and clipping level at energy shift efficiency equal to.9,.85 and.8, respectively. 485

5 3.5 x 6 2 x 6 Net Benefit due to Load Flattening Net Benefit due to Load Flattening Fig. 7 Relation between benefit and clipping point at.9 shift efficiency (customer model).5 x Fig. 8 Relation between benefit and clipping point at.8 shift efficiency (customer model) 3.5 x Figure 9 Relation between benefit and clipping level at.9 energy shift efficiency (utility model) Figure The relation between benefit and clipping level at.85 energy shift efficiency (utility model) x Figure The relation between benefit and clipping level at.8 energy shift efficiency (utility model) It is to be noted that the negative sign appearing in the benefit-clipping level diagrams is not due to load flattening per se, but rather due to the energy shift process itself. This simply indicates that the losses of benefits due to energy losses are greater than the revenue to load flattening. It starts appearing in Figure where the efficiency of energy shift process equal to.8, and the margin of negative benefit-clipping level increases as the efficiency decreases. Because of this reason, the margin of benefit due to the load flattening process is very narrow (positive margin). Hence, for the specific demand pattern analyzed, we can conclude that for energy shift process efficiency less than 6%, the load flattening process would not be beneficial. The maximum net benefit corresponding to the optimal clipping level for the chosen value of energy shift efficiency is summarized in Table I. It is to be noted that the benefits are calculated for the particular daily load curve under study. 486

6 TABLE I MAXIMUM NET BENEFIT, CLIPPING LEVEL ACCORDING TO Energy Shift Efficiency ef (%) ENERGY SHIFT EFFICIENCY Clipping Level P c (MW) Maximum Net Benefit f max (MSR) Furthermore, there are two important factors which can improve the net benefit achieved from the controlled weight motion technology scheme: a) Increasing the efficiency of the energy shift process. This can be achieved by improving the efficiency of the individual controlled motion components, including the gearbox, shaft-couplers, etc. b) Operating at or close to the optimal clipping level of the peak load at which the maximum benefit is attained from the controlled weight motion technology. ACKNOWLEDGMENT This work was supported by the Saudi Electricity Company (SEC) which funds the SEC Chair in Power System Reliability and Security. The same analysis can easily be repeated for all other days in the year using the associated daily demand patterns in order to obtain an estimate of the yearly savings. The clipping level P C is controlled in practice by the intensity of applying the controlled weight motion scheme during the up-motion (off-peak valley filling) and down-motion (peak clipping). It is also noted that the renewable energy portion of the off-peak valley filling if applied in the real-time frame would normally depend on the availability of the wind energy and, therefore, should be designed as an optional complementary power. In this regard, energy storage schemes could be examined for more flexible usage of the renewable energy component of the load management process. VII. DISCUSSION AND CONCLUSIONS This paper has presented an innovative renewable energy - load management technology, which is currently being developed as a prototype at King Saud University (KSU), in preparation for potential large-scale implementation in power systems. The new technology employs controlled motion of large weights, which are being maneuvered (up and down) through the system peak / off-peak demand pattern. The load management scheme is similar to the pumping storage systems, but uses large weights instead of water and, therefore, is more suited to the operating environment in Saudi Arabia, in which water is relatively scarce. A portion of the off-peak valley-filling energy is supplied from wind-driven generators, which adds to the green energy component of the overall load management scheme. The implementation case scenario presented in the paper for a typical daily demand pattern (load curve) of a portion of the Saudi electricity system has demonstrated that the controlled weight motion technology can be employed as an effective load management scheme in practical system operation. REFERENCES [] M.A. El-Kady and A.M. Shaalan, "Integrated utility-consumer strategies for demand management and energy conservation", Proceedings of The Joint Engineering Committee/KACST/IEE/IEEE Workshop on Energy Conservation and Load Management (Riyadh), 997. [2] C. Alvarez, R.P. Malhame and A. Gabaldon, A class of models for load management application and evaluation revisited, IEEE Transactions on Power Systems, vol. 7, no. 4, November 992, pp [3] M.A. El-Kady and A.M. Shaalan, "Impact of Load Management and Energy Conservation Strategies on the Environment and Operating Practices of Power Systems", Proc. The 33rd Intersociety Energy Conversion Engineering Conference (IECEC), (Colorado, USA), 998, Paper # 57-I34. [4] D. Ha, S. Ploix, E. Zamai and M. Jacomino, Realtimes dynamic optimization for demand-side load management, International Journal of Management Science and Engineering Management, vol. 3, no. 4, 28, pp [5] B. Thomas, Load management techniques, Proceedings of the IEEE, 2, pp [6] T. Burton, D. Sharpe, N. Jenkins and E. Bossanyi. Wind Energy Handbook, John Wiley & Sons, st edition (2), ISBN [7] E.I. Baring-Gould, L. Flowers, P. Lundsager, L. Mott, M. Shirazi and J. Zimmerman, Worldwide Status of Wind/Diesel Applications, Proc. of the 23 AWEA Conference, Austin, TX. June, 23. [8] R. Howk, Utility Plans Bird Point Wind Farm, Alaska Journal of Commerce, vol. 27, no. 22. June, 23. [9] J.F. Manwell, J.G. McGowan and A.L. Rogers, Wind Energy Explained: Theory, Design and Application, John Wiley & Sons, Ltd. 22. [] M.A. El-Kady, A.M. Shaalan and A.S. Almisnid, "Review and Assessment of Load Management and Energy Conservation Strategies in Power Systems", Proc. The Sixth Annual IEEE Technical Exchange Meeting, Dhahran, Saudi Arabia, April 2-2, 999. [] M.S. Al-Saud, M.A. El-Kady and A.M. Shaalan, "Design of Optimal Load Management Strategies in Electric Power Systems", Proc. The AMS 99 - IASTED International Conference on Applied Modelling and Simulation, Cairns, Queensland, Australia, September -3, 999, Paper # [2] M. Alkhamis, US Patent # , Feb