Meeting Load Demand at Least Cost in De-centralised Electricity Environment
|
|
- Chester Cooper
- 7 years ago
- Views:
Transcription
1 International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:1 No:6 21 Meeting Load Demand at Least Cost in De-centralised Electricity Environment Hafiz T. Hassan, Kashif Imran, Muhammad F. Aslam and Intesar Ahmad Abstract This paper presents a least cost method of operating a power plant in view of variable spot price of electricity and load demand greater than plant capacity. Manager of an industrial power plant has to operate its generators at appropriate operating levels. Furthermore, manager has to put up a demand bid to a power exchange in order to cover the remaining load demand such that overall cost remains minimal. Fuel consumption data of three generators has been used in this paper to determine generators operating levels and a demand bid over a range of spot prices. Our results demonstrate that electricity trading through a power exchange leads to considerable savings when spot price drops below fixed price. Index Term De-centralised Electricity Environment, Demand Bid, Least Cost Operation, Spot Price. I. INTRODUCTION IF cheap electricity is available in a de-centralised electricity environment (DEE) then industry may shut down or run its generators at their lower limits and buy bulk of its power from market. With increasing spot price it becomes profitable to meet more and more of load demand by own power generation. If load demand is less than installed capacity then surplus power can be sold in case electricity fetches a high spot price. Industrial setups can maximize their profits by ensuring maximum saving in their electricity utilization. It is possible to meet electricity requirements in a most cost efficient manner by determining own production level and amount of power bought and / or sold in view of variable spot price. The rest of this paper is organized as follows: In Section II, several topics relevant to DEE are discussed. These include Unit Commitment, Spot Price, Price Forecasting and Plant Information Systems. Mathematical model is outlined in detail in Section III. It includes the function to be minimized as well as constraints and bounds. Solution Methodology is presented in Section IV and it involves determination of generator coefficients and transmission pricing. Section V displays results of simulations carried out. Section VI concludes this paper. II. DE-CENTRALIZED ELECTRICITY ENVIRONMENT A. Unit Commitment Unit Commitment Problem (UCP) is solved to determine which generating units must be on and which should be off before optimization problem determines the level of generation by each unit. This problem takes a new twist when electricity is traded at power exchange and a possible solution is suggested in [1]. If a generating unit is committed to run in a certain interval of time as a solution of UCP then it can not be shut down. In such a case, the generator must operate at or above its minimum capacity even if its operation is uneconomical. Such decisions are due to a variety of constraints imposed on generators and power system as a whole. There is a minimum spinning reserve that has to be maintained in view of system stability and reliability [2]. Moreover, thermal unit constraints limit minimum up and down times to allow gradual temperature changes in generators [3]. B. Spot Price Some industrial customers, for example metal industry, can bear with a reduction in their power supply [4]. This may give rise to numerous interruptible contracts individually negotiated with various industrial customers [5]. Power Exchange (PX) develops a demand curve from aggregated demand bids for each hour, on a day-ahead basis, starting with the highest priced bids and ending at the lowest ones. This gives rise to a set of hourly demand curves for next day each resembling a descending staircase - see Fig. 1. Demand curve starts with highest price for un-interruptible power supply. It is followed by different reduced prices for different levels of acceptable interruptions. Interruptible Contracts H. T. Hassan is with University of Engineering and Technology, Lahore, Pakistan ( tehzibulhasan@gmail.com). K. Imran is with the COMSATS Institute of Information Technology, Lahore, Pakistan (phone: ; fax: ; kashifimran@ciitlahore.edu.pk). M. F. Aslam is with Superior University, Lahore, Pakistan ( mfaslam@gmail.com). I. Ahmad is with the COMSATS Institute of Information Technology, Lahore, Pakistan ( drintesarahmad@ciitlahore.edu.pk). Fig. 1. A typical demand curve showing interruptible contracts
2 International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:1 No:6 22 to existing methods [14]. Fig. 2. A supply curve showing change in price due to production method Similarly a supply curve is established for each hour of the next day by aggregating supply bids in opposite order to the demand bids i.e. starting with the lowest priced bids from plants such as hydro or nuclear and ending at the highest ones from plants operating on gas and oil [6]. It leads to a supply curve, for every hour of the next day, as an ascending staircase pattern - see Fig. 2. Hourly spot price of electricity is determined on a day-ahead basis by intersection of the respective demand and supply curves as shown in Fig. 3. The point of intersection determines the hourly spot price and required power generation. C. Price Forecasting Forecasting electricity prices on a day-ahead basis is a crucial activity that enables decision making on part of market participants including managers of industrial power plants. Various price forecasting methods have proved useful including non-linear heuristic, linear regression and data-mining based techniques. Non linear heuristic methods can be further classified into fuzzy models, artificial neural networks (ANN), chaotic models and evolutionary computation. Heuristic methods have been extensively applied to price forecasting [7]-[1]. Linear regression techniques include auto-regressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH). Regression techniques are used for modelling the changing price and its volatility. [11]-[12]. Data mining techniques have been utilized for price forecasting in recent years [13] and a latest data mining-based D. Plant Information Systems Due to the deregulation of the electricity market, power systems have become more dynamic and offer better opportunities for all market participants. Role of plant information systems in DEE has increased manifolds and internet has found a variety of applications in power system monitoring and electricity trading [15]. Intelligent Agents can share information through intranet for economical operation of thermal power plants in a distributed DEE [16-17]. In order to perform as a dominant market participant, it is crucial to have greater understanding regarding the real time plant operation and constantly changing spot price of electricity. Since corporate offices and plants are usually resided at geographically distant locations, it has become very important to access process data for e-monitoring from a central expertise centre [18]. A web-based real time power system dynamic performance monitoring system is highly suitable for today s increasingly dynamic power systems [19]. More importantly, access to power plant data enables trading activities such as bidding for supply and demand of electricity and hence ensures operational optimisation of an industrial power plant. III. A. Minimization Function MATHEMATICAL MODEL A mathematical function incorporating hourly spot price and cost of generation and transmission can be developed for a power plant operating in a DEE [2]. Cost function in (1) must be minimised in order to maximize profits of an industry. Cost C P C P C P C P I P where P p Tb b Ts s B b S s 2 n C P C P * P * P P p i 1 Pi pi i 1 i pi i pi i and n (1) (2) C p : Hourly cost of total production of the power plant, P p. C pi : Hourly cost of production of the i-th generator, P pi. C Tb : Hourly cost of transmission of the power bought, P b. C Ts : Hourly cost of transmission of the power sold, P s. CB : Hourly cost of Pb. IS : Hourly income from Ps. ρ : Hourly spot price of electricity. α, β, γ : Generator coefficients. Fig. 3. Determination of Spot Price and required Power Production approach has been shown to be highly effective when compared
3 International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:1 No:6 23 B. Connstraints and Bounds Operation of industrial power plant is subject to certain nonlinear, linear, equality and inequality constraints and bounds as outlined below in (3), (4), (5), (6) and (7). P p + P b P s = P d (3) P b * P s = (4) P pi P max (5) P pi P min (6) P pi (7) Last two constraints put a limit on minimum level of generator operation. If power plant generators can not be shut down and must operate at or above its minimum capacity then only first of the two equations is considered. However, it is crucial to check both equations one at a time and choose optimum result in case generators can be shut down. A. Generator Data IV. METHODOLOGY Generator coefficients (α, β and γ) are used to model the hourly cost function of operating a generator in terms of its power production [3]. We took real Heavy Fuel Oil consumption data of three diesel generators in a power plant see Table I. Cost of Heavy Fuel Oil was taken as 5 Rs./ltr and a density of 93 gms./ltr was assumed [21]. Generator coefficients presented in Table II were calculated and used in our simulation. TABLE I Gen No. FUEL CONSUMPTION DATA FOR GENERATORS Max Capacity (MW) Min Operation (MW) TABLE II Power (MW) Fuel Usage (gms/kwh) GENERATOR COEFFICIENTS Coefficients Generator 1 Generator 2 Generator 3 α (Rs/MW 2 h) β Rs/MWh 5,956 9,9 8,343 γ Rs/h 22,84 11,933 6,886 TABLE III COST OF TRANSMISSION Cost of Transmission for Cost of Transmission for Bought Power, C Tb Sold Power, C Ts 4 Rs/MW 2 Rs/MW B. Transmission Pricing Two philosophies are commonly used for transmission pricing in the de-centralized markets: Point-to-point tariff and the point-of-connection (POC) tariff [22]. The point-to-point tariff is also called transaction based tariff because it is specific to a particular sale of power from a designated seller to a designated buyer. The basic principle of POC tariff is that payment at the point of connection gives access to the whole transmission network, and thus the whole electricity market. POC tariff is universal as it is applicable to both PX trades and bilateral transactions. One of the desired features of a pricing scheme is that it should provide appropriate price signals [23]. This means that the generators and the loads should pay different rates depending upon the surplus or deficit status of a Distribution Company (DISCO). Locational Transmission Price (LTP) for each node is decided by the results of real power tracing. LTP of a node reflects usage of various transmission lines and elements by load or generator on that node. LTP can be calculated by equations given in [24] and results can directly be used as POC tariffs for all nodes in the system. However, for practical implementation the LTP in a DISCO must be aggregated to get a single price for that DISCO. In our case, generators are located in the region of Lahore Electricity Supply Company (LESCO) where power is deficit. Hence, POC tariff for generators should be less than that for loads. POC tariff for generators and loads in four western states of India has been determined and it is in the range of few hundred rupees per megawatt of power transmitted. POC tariffs assumed for our simulation are given in Table III [25]. V. RESULTS The optimization problem has been solved for both cases of load demand being more and less than plant capacity. Both scenarios in which generators can and can not be shut down are simulated in Matlab. Furthermore, results for the scenario of load demand smaller than plant capacity whether generators have to be kept on or can be shut down are presented in [25] for two generators. That work has now been extended to three generators and results are presented here for the case of load demand larger than plant capacity whether generators can be shut down or have to be kept on. A program was developed that used Matlab s inbuilt fmincon function to find values of power produced and bought for minimal cost. Underlying technique of fmincon is Sequential Quadratic Programming and this function is used for constrained nonlinear problems. The program stored results of fmincon and plotted the results presented below by using standard Matlab functions.
4 International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:1 No:6 24 There is a great uncertainty in hourly spot prices which are highly volatile and as a result sudden spikes and dips occur in these prices. Historical and real time data of the Australian National Electricity Market (ANEM) is publicly made available by National Electricity Market Management Company (NEMMCO) through its website. It shows that electricity price experiences changes in the approximate range of half to double of the normal price. In our case, normal price is taken as Rs 11, per MWh so our range of interest is approximately Rs 5, per MWh to Rs 2, per MWh [25]. A. Generators can be shut down Power production graphs of generators vary between upper limit and zero level showing generators shut down status. There is no supply bid in this case because total plant capacity of 58.9 MW is smaller than 8 MW load demand. Below a spot price of Rs 8,1 per MWh it becomes economical to shut down all three generators as shown in Fig. 4. As soon as Rs 8,1 per MWh limit is exceeded, all three generators come on and generation level of generator 1 starts increasing simultaneously. Between the range of Rs 8,1 and Rs 8,6 per MWh generators 2 and 3 are run at their lower limits of operation. At a spot price of Rs 8,6 per MWh generator 3 starts taking increasing load but generator 2 keeps operating at its lower limit. When spot price rises to Rs 9,4 per MWh then generator 2 also starts to take more load than its lower limit see Fig. 4. Generator 3 reaches its upper limit before the other two generators at a spot price of Rs 1, per MWh. Generators 1 and 2 continue to take increasing load between spot price range of Rs 1, per MWh and Rs 1,7 per MWh. Then at a spot price of Rs 1,7 per MWh, generators 1 and 2 simultaneously reach their upper limits. Maximum power demanded from PX is total 8 MW load demand and minimum power demanded is MW see Fig. 5. Between spot price range of Rs 8 per MWh and Rs 81 per MWh power demand drops from 8 MW to 58.9 MW as all three generators turn on. Then power demand gradually drops to MW as all three generators reach their upper limits as shown in Fig Generator 1 Generator 2 Generator Fig. 4. Power production of generators when these can be shut down Fig. 5. Demand Bid when generators can be shut down Cost of electricity per hour increases as hourly spot price rises. Over a spot price range of Rs 5, per MWh to Rs 2, per MWh cost changes from Rs. 573,6 to Rs. 1,86, see Fig. 6. Savings in cost of electricity are calculated with reference to the power purchased at a fixed rate of Rs. 11, per MWh in a regulated environment in addition to own power production. However, as spot price keeps on rising power purchase becomes overly expensive. When compared to a fixed cost scenario of regulated environment, there is an additional cost (leading to negative portions in graph of saving). Reference cost of operation is calculated to be Rs 837,587. At a spot price of Rs. 5, per MWh there is a considerable saving of as much as nearly Rs. 364, per hour.to a negative portion in graph of saving) of about Rs. 246, per hour at a spot rate of Rs. 2, per MWh see Fig. 7. It is interesting to compare the results with those in [25] which show that electricity trading in a power exchange always leads to savings if load demand is less than plant capacity because in that case a power plant starts saving as soon as spot price varies from fixed price. B. Generators have to be kept on Power production graphs of generators vary between upper and lower permissible limits of generators. There is no supply bid in this case as well because total plant capacity of 58.9 MW is smaller than 8 MW load demand. Below a spot price of Rs 77 per MWh it only remains economical to run all three generators at their lower limits as shown in Fig. 8. As soon as Rs 77 per MWh limit is exceeded, generation level of genereator 1 starts increasing. Between the range of Rs 77 and Rs 86 per MWh generators 2 and 3 are run at their lower limits of operation. After that power production graph exactly resembles that case in which generators can be shut down see Fig. 8. Between spot price range of Rs 77 per MWh and Rs 86 per MWh power demand drops from 58.9 MW to MW as generator 1 starts taking more load than its lower limit and generators 2 and 3 are run at their lower limits. Then power demand gradually drops to MW as all three generators reach their upper limits as shown in Fig. 9. Cost of electricity per hour increases as hourly spot price rises. Over a spot price range of Rs 5, per MWh to Rs 2, per MWh cost changes from Rs. 536,7 to Rs.
5 International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:1 No:6 25 1,86, as shown in Fig. 1. It is important to note that at a spot price of Rs 2, per MWh cost remains the same whether generators can be shut down or have to be kept on. At a spot price of Rs. 5, per MWh there is a considerable saving of as much as nearly Rs. 3,8 per hour. However, as spot price keeps on rising power purchase becomes overly expensive. When compared to a fixed cost scenario of regulated environment, there is an additional cost (leading to a negative portion in graph of saving) of about Rs. 248,7 per hour at a spot rate of Rs. 2, per MWh see Fig Cost (Million Rs/h) Fig. 1. Cost when generators have to be kept on Cost (Million Rs/h) Fig. 6. Cost when generators can be shut down Saving (Million Rs/h) Fig. 11. Saving when generators have to be kept on Saving (Million Rs/h) Fig. 7. Saving when generators can be shut down Generator 1 Generator 2 Generator 3 4 Fig. 8. Power production of generators when these have to be kept on Fig. 9. Demand Bid when generators have to be kept on It is possible to save Rs. 67, per hour at a spot price of Rs. 5, per MWh and saving increases to Rs. 12, per hour at a spot rate of Rs. 2, per MWh see Fig. 12. VI. CONCLUSION Our results show that profits of industrial plants are maximized by participation in a de-centralised electricity market through a power exchange. At high spot price savings become negative meaning that additional cost is incurred. However, at low spot price savings take place that increase with a fall in spot price because it becomes economical to reduce own production and buy cheaper electricity from power exchange. When spot price drops to Rs 5, per MWh and generators have to be kept on then savings of Rs. 3,8 per hour are ensured with reference to a total operational cost of Rs 837,587 that means a saving of 35.91%. Furthermore, if generators can be shut down then at the spot price of Rs 5, per MWh savings increase to Rs. 364, per hour that corresponds to a saving of as much as 43.46%. Decentralization ensures that not only industry can maximize its profit but surplus power of industrial setups can be fed to national grid thus having potential of alleviating acute power shortage in Pakistan today. ACKNOWLEDGMENT Authors would like to acknowledge the support provided by their respective universities to conduct this research work. REFERENCES [1] J. Valenzuela, M. Mazumdar, Making Unit Commitment Decisions when Electricity is traded at Spot Market Prices, IEEE Power
6 International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:1 No:6 26 Engineering Society Winter Meeting, 21, Vol. 3, pp , 28 Jan.-1 Feb. 21. [2] Ponpranod Didsayabutra, Wei-Jen Lee, and Bundhit Eua-Arporn, Defining the Must-Run and Must-Take Units in a Deregulated Market, IEEE Trans. Industry Applications, vol. 38, no. 2, March/April 22. [3] A.J.Wood, B.F.Wollenberg, Power Generation, Operation and Control, John Wiley & sons, Inc., [4] A. Boogert and D. Dupont, When supply meets demand: the case of hourly spot electricity prices, IEEE Trans. Power Sys., vol. 23, no. 2, May 28. [5] C. Harris, Electricity Markets: Pricing, Structures and Economics, John Wiley & Sons, Ltd., 26. [6] R. Weron, Modelling and forecasting electricity loads and prices, John Wiley & Sons, Ltd., 26. [7] H. Liu, X. Wang, W. Zhang, and G. Xu, "Market clearing price forecasting based on dynamic fuzzy system, in Proc. IEEE 22 International Conference on Power System Technology (PowerCon 22), Vol. 2, pp , Oct. 22. [8] Li Zhang, P. B. Luh, and K. Kasiviswanathan, "Energy clearing price prediction and confidence interval estimation with cascaded neural networks," IEEE Trans. Power Sys., Vol. 18, No. 1, pp , Feb. 23. [9] W. Wu, J. Zhou, J. Yu, C.-J. Zhu, J.-J. Yang, Prediction of spot market prices of electricity using chaotic time series," Proc 24 International Conference on Machine Learning and Cybernetics, Vol. 2, pp , Aug. 24. [1] L. Xu, Z. Y. Dong, and A. Tay,, K. C. Tan, M. H. Lim, X. Yao, and L. P. Wang, Eds., Time series forecast with Elman neural networks and genetic algorithms, in Recent Advances in Simulated Evolution and Learning. Singapore: World Scientific, Sep. 24. [11] A. J. Conejo, M. A. Plazas, R. Espinola and A. B. Molina, Day-ahead electricity price forecasting using the wavelet transform and ARIMA models," IEEE Trans. Power Syst., Vol. 2, No. 2, pp , May 25. [12] R. C. Garcia, J. Contreras, M. van Akk and J. B. C. Garcia, A GARCH forecasting model to predict day-ahead electricity prices," IEEE Trans. Power Syst., Vol. 2, No. 2, pp , May 25. [13] X. Lu, Z. Y. Dong, and X. Li, Electricity market price spike forecast with data mining techniques, Electric Power Systems Research, Vol. 73, No. 1, pp January 25. [14] J. H. Zhao, Z. Y. Dong, Z. Xu, and K. P. Wong, A Statistical Approach for Interval Forecasting of the Electricity Price, IEEE Trans. Power Syst., Vol. 23, No. 2, pp , May. 28. [15] Loi Lei Lai, Power system restructuring and deregulation: Trading, performance and information technology, John Wiley & sons, Inc., 21. [16] M. F. Aslam, T. Hassan, D. Lidgate, An Intranet Based Distributed Economic Dispatch System for Interconnected Thermal Plants, 4 th International Universities Power Engineering Conference, UPEC-25. [17] M. F. Aslam, T. Hassan, T. A. Shami, Reengineering of Systems Operation: A solution to the challenges in the Deregulated Market Environment - I 5 th WSEAS International Conference on Power Systems and Electromagnetic Compatibility, PSE 5. [18] T. Dang, Integration of Power Plant information system with Business information system in the open electricity market: challenges and solutions, 5th IEEE International Conference on Industrial Informatics 27, Vol. 2, pp , June 27 [19] S. H. Haung, W. J. Lee, S. P. Wang, J. H. Chen, C. H. Hsu, Web-Based Real Time Power System Dynamic Performance Monitoring System, Industry Applications Conference, 25. Fourtieth IAS Annual Meeting. Conference Record of the 25, Vol. 4, pp , 2-6 Oct. 25 [2] F. Rossi, A. Russo, Optimal Management of an Industrial Power Plant in a Deregulated Market, 39 th International Universities Power Engineering Conference, UPEC-24, Vol. 3, pp , 6-8 Sept. 24 [21] [Online] Available: [22] G. Shuttleworth, Electricity transmission pricing: The European perspective, National Economic Research Associates, Tech. Rep., July1999. [23] R. Green, Electricity transmission pricing: an international comparisons, Utilities Policy, vol. 6, pp , [24] Anjan Roy, A. R. Abhyankar, P. Pentayya, and S. A. Khaparde, Electricity Transmission Pricing: Tracing Based Point-of-Connection Tariff for Indian Power System, IEEE Power Engineering Society, General Meeting 26, June 26. [25] T. Hassan, K. Imran, M. F. Aslam, Operational optimization of an industrial power plant for electricity trading in a power exchange, IEEE International Conference on Electrical Engineering (ICEE), 9-11 April, 29. Kashif Imran received his elementary education from England where he obtained GCSEs and GCEs. He did B.Sc. and M.Sc. in Electrical Engineering from University of Engineering and Technology (UET), Lahore, Pakistan. His area of specialization during both degrees was electrical power. He started his career as a Lecturer at UET Lahore. Then he moved to SIEMENS where he worked as Engineer on project coordination of 132kV grid stations. Later he joined NESPAK, leading engineering consultancy firm of Pakistan, as a Design Engineer in Power Distribution Section. In NESPAK, his professional experience includes design of overhead and underground power distribution systems for a variety of buildings and installations. Currently, he is Lecturer at COMSATS Institute of IT, Lahore, Pakistan where he teaches Electric Machines. He is an accomplished researcher with a high Impact Factor research paper titled Simulation Analysis of Emissions Trading Impact on a Non-Utility Power Plant that was published in Elsevier International Journal of Energy Policy in 29. His book titled Power Exchange as a Deregulated Electricity Market is in press to be published by Lambert Academic Publishing, Germany. His research interests include Power System Economics, Restructured Power Systems Simulation, Energy Management Systems and Power System Protection. Mr. Imran is a member of Pakistan Engineering Council. Hafiz Tehzeebul Hassan did B.Sc. and M.Sc. in Electrical Engineering from UET, Lahore. He later joined UET as a faculty member. His professional experience involves supervision of HV equipment tests as In-Charge of HV Lab in UET. He is an Associate Professor and a PhD student at UET Lahore. His research interests include Power System Analysis, Restructured Power Systems and Multi-Agent Systems. Mr. Hassan is a member of Pakistan Engineering Council. Muhammad Farooq Aslam obtained PhD in Electrical Engineering from UET, Lahore. He has served UET Lahore as a faculty member for over thirty years. He is a Professor and Chairman of the Department of Electrical Engineering at UMT Lahore. His research interests include Deregulated Electricity Markets, Artificial Intelligence and Multi-Agent Systems. Mr. Aslam is a member of Pakistan Engineering Council. Intesar Ahmad did B.Sc. and M.Sc. in Electrical Engineering from UET, Lahore and University of New South Wales, Australia respectively. In 28, he obtained PhD in Electrical Engineering from University of Adelaide, Australia. He has served as a faculty member in NUST Pakistan. Currently, he is Assistant Professor at COMSATS Institute of IT, Lahore, Pakistan. His research interests include Online Condition Monitoring of Electric Machines and Power Market Reforms. Dr. Ahmad is a member of Pakistan Engineering Council.
Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained Neural Networks
Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained eural etworks Pavlos S. Georgilakis Technical University of Crete, University Campus, Kounoupidiana, Chania,
More informationElectricity market price spike forecast with data mining techniques
Electric Power Systems Research 73 (2005) 19 29 Electricity market price spike forecast with data mining techniques Xin Lu a,1, Zhao Yang Dong b,, Xue Li c a School of Computer, University of Electronic
More informationIntroduction to Ontario's Physical Markets
Introduction to Ontario's Physical Markets Introduction to Ontario s Physical Markets AN IESO MARKETPLACE TRAINING PUBLICATION This document has been prepared to assist in the IESO training of market
More informationShort Term Electricity Price Forecasting Using ANN and Fuzzy Logic under Deregulated Environment
Short Term Electricity Price Forecasting Using ANN and Fuzzy Logic under Deregulated Environment Aarti Gupta 1, Pankaj Chawla 2, Sparsh Chawla 3 Assistant Professor, Dept. of EE, Hindu College of Engineering,
More informationAssessment of Price Risk of Power under Indian Electricity Market
Assessment of Price Risk of Power under Indian Electricity Market Sandeep Chawda Assistant Profesor JSPM s BSIOTR (W) Wagholi, Pune (INDIA) S. Deshmukh, PhD. Associate Professor PVG s COET Parvati, Pune
More informationInternational Journal of Software and Web Sciences (IJSWS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International
More informationEmail: mod_modaber@yahoo.com. 2Azerbaijan Shahid Madani University. This paper is extracted from the M.Sc. Thesis
Introduce an Optimal Pricing Strategy Using the Parameter of "Contingency Analysis" Neplan Software in the Power Market Case Study (Azerbaijan Electricity Network) ABSTRACT Jalil Modabe 1, Navid Taghizadegan
More informationUSBR PLEXOS Demo November 8, 2012
PLEXOS for Power Systems Electricity Market Simulation USBR PLEXOS Demo November 8, 2012 Who We Are PLEXOS Solutions is founded in 2005 Acquired by Energy Exemplar in 2011 Goal People To solve the challenge
More informationCHAPTER 1 INTRODUCTION
CHAPTER 1 INTRODUCTION Power systems form the largest man made complex system. It basically consists of generating sources, transmission network and distribution centers. Secure and economic operation
More informationIS ENERGY IN ESTONIA CHEAP OR EXPENSIVE?
IS ENERGY IN ESTONIA CHEAP OR EXPENSIVE? Rita Raudjärv, Ljudmilla Kuskova Energy is a resource without which it is hard to imagine life in today's world. People seem to take it for granted that energy
More informationGeneration Expansion Planning under Wide-Scale RES Energy Penetration
CENTRE FOR RENEWABLE ENERGY SOURCES AND SAVING Generation Expansion Planning under Wide-Scale RES Energy Penetration K. Tigas, J. Mantzaris, G. Giannakidis, C. Nakos, N. Sakellaridis Energy Systems Analysis
More informationA Hybrid Load Balancing Policy underlying Cloud Computing Environment
A Hybrid Load Balancing Policy underlying Cloud Computing Environment S.C. WANG, S.C. TSENG, S.S. WANG*, K.Q. YAN* Chaoyang University of Technology 168, Jifeng E. Rd., Wufeng District, Taichung 41349
More informationDroop Control Forhybrid Micro grids With Wind Energy Source
Droop Control Forhybrid Micro grids With Wind Energy Source [1] Dinesh Kesaboina [2] K.Vaisakh [1][2] Department of Electrical & Electronics Engineering Andhra University College of Engineering Visakhapatnam,
More informationAdaptive model for thermal demand forecast in residential buildings
Adaptive model for thermal demand forecast in residential buildings Harb, Hassan 1 ; Schütz, Thomas 2 ; Streblow, Rita 3 ; Müller, Dirk 4 1 RWTH Aachen University, E.ON Energy Research Center, Institute
More informationThe Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network
, pp.67-76 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and
More informationSummary of specified general model for CHP system
Fakulteta za Elektrotehniko Eva Thorin, Heike Brand, Christoph Weber Summary of specified general model for CHP system OSCOGEN Deliverable D1.4 Contract No. ENK5-CT-2000-00094 Project co-funded by the
More informationPrediction Model for Crude Oil Price Using Artificial Neural Networks
Applied Mathematical Sciences, Vol. 8, 2014, no. 80, 3953-3965 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.43193 Prediction Model for Crude Oil Price Using Artificial Neural Networks
More information6545(Print), ISSN 0976 6553(Online) Volume 4, Issue 2, March April (2013), IAEME & TECHNOLOGY (IJEET)
INTERNATIONAL International Journal of JOURNAL Electrical Engineering OF ELECTRICAL and Technology (IJEET), ENGINEERING ISSN 0976 & TECHNOLOGY (IJEET) ISSN 0976 6545(Print) ISSN 0976 6553(Online) Volume
More informationOpus: University of Bath Online Publication Store http://opus.bath.ac.uk/
Sharma, K. C., Bhakar, R. and Padhy, N. P. (214) Stochastic cournot model for wind power trading in electricity markets. In: PES General Meeting/Conference & Exposition, 214 IEEE. IEEE. Link to official
More informationLP-based Mathematical Model for Optimal Microgrid Operation Considering Heat Trade with District Heat System
LP-based Mathematical Model for Optimal Microgrid Operation Considering Heat Trade with District Heat System Ji-Hye Lee and Hak-Man Kim Incheon National University hmkim@incheon.ac.kr Abstract Since Combined
More informationSimulation of the Central European Market for Electrical Energy
Simulation of the Central European Market for Electrical Energy T. Mirbach and H.-J. Haubrich RWTH Aachen University Institute of Power Systems and Power Economics Schinkelstr. 6, D-52056 Aachen, Germany
More informationADVANCED LOCAL PREDICTORS FOR SHORT TERM ELECTRIC LOAD
RESUME Ehab E. Elattar Assistant Professor Department of Electrical Engineering, college of Engineering Taif University, Taif, Kingdom of Saudi Arabia Phone: (+966) 549394587 (Mobile) E-mail: dr.elattar10@yahoo.com
More informationSales Forecast for Pickup Truck Parts:
Sales Forecast for Pickup Truck Parts: A Case Study on Brake Rubber Mojtaba Kamranfard University of Semnan Semnan, Iran mojtabakamranfard@gmail.com Kourosh Kiani Amirkabir University of Technology Tehran,
More informationRenewable Energy Management System (REMS): Using optimisation to plan renewable energy infrastructure investment in the Pacific
Renewable Energy Management System (REMS): Using optimisation to plan renewable energy infrastructure investment in the Pacific Abstract: Faisal Wahid PhD Student at the Department of Engineering Science,
More informationECONOMIC GENERATION AND SCHEDULING OF POWER BY GENETIC ALGORITHM
ECONOMIC GENERATION AND SCHEDULING OF POWER BY GENETIC ALGORITHM RAHUL GARG, 2 A.K.SHARMA READER, DEPARTMENT OF ELECTRICAL ENGINEERING, SBCET, JAIPUR (RAJ.) 2 ASSOCIATE PROF, DEPARTMENT OF ELECTRICAL ENGINEERING,
More informationForecasting Next-Day Electricity Prices by Time Series Models
342 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 2, MAY 2002 Forecasting Next-Day Electricity Prices by Time Series Models Francisco J. Nogales, Javier Contreras, Member, IEEE, Antonio J. Conejo, Senior
More informationINTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr.
INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. Meisenbach M. Hable G. Winkler P. Meier Technology, Laboratory
More informationConstrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm Martin Hlosta, Rostislav Stríž, Jan Kupčík, Jaroslav Zendulka, and Tomáš Hruška A. Imbalanced Data Classification
More informationMethodology for Merit Order Dispatch. Version 1.0
Methodology for Merit Order Dispatch Version 1.0 25 th April 2011 TABLE OF CONTENTS 1. OBJECTIVES... 1 2. ROADMAP FOR IMPLEMENTATION... 1 3. DEFINITIONS... 3 4. OPERATIONS PLANNING... 3 4.1. General Considerations...
More informationCALIFORNIA ISO. Pre-dispatch and Scheduling of RMR Energy in the Day Ahead Market
CALIFORNIA ISO Pre-dispatch and Scheduling of RMR Energy in the Day Ahead Market Prepared by the Department of Market Analysis California Independent System Operator September 1999 Table of Contents Executive
More informationWireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm
, pp. 99-108 http://dx.doi.org/10.1457/ijfgcn.015.8.1.11 Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm Wang DaWei and Wang Changliang Zhejiang Industry Polytechnic College
More informationMemo INTRODUCTION UNDER-SCHEDULING
California Independent System Operator Memo To: ISO Board of Governors From: Greg Cook, Senior Policy Analyst/Market Surveillance Committee Liaison CC: ISO Officers Date: September 28, 2000 Re: Management
More informationMultiobjective Optimization for Pricing System Security in Electricity Markets
596 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 18, NO. 2, MAY 2003 Multiobjective Optimization for Pricing System Security in Electricity Markets Federico Milano, Student Member, IEEE, Claudio A. Cañizares,
More informationREAL-TIME PRICE FORECAST WITH BIG DATA
REAL-TIME PRICE FORECAST WITH BIG DATA A STATE SPACE APPROACH Lang Tong (PI), Robert J. Thomas, Yuting Ji, and Jinsub Kim School of Electrical and Computer Engineering, Cornell University Jie Mei, Georgia
More informationA Sarsa based Autonomous Stock Trading Agent
A Sarsa based Autonomous Stock Trading Agent Achal Augustine The University of Texas at Austin Department of Computer Science Austin, TX 78712 USA achal@cs.utexas.edu Abstract This paper describes an autonomous
More informationA Forecasting Decision Support System
A Forecasting Decision Support System Hanaa E.Sayed a, *, Hossam A.Gabbar b, Soheir A. Fouad c, Khalil M. Ahmed c, Shigeji Miyazaki a a Department of Systems Engineering, Division of Industrial Innovation
More informationPower market integration. Geir-Arne Mo Team Lead Nordic Spot Trading Bergen Energi AS
Power market integration Geir-Arne Mo Team Lead Nordic Spot Trading Bergen Energi AS 1 Geir-Arne Mo Some background information: Working for Bergen Energi since 2015 Team Lead Nordic Spot Trading I work
More informationOperating Hydroelectric and Pumped Storage Units In A Competitive Environment
Operating electric and Pumped Storage Units In A Competitive Environment By Rajat Deb, PhD 1 LCG Consulting In recent years as restructuring has gained momentum, both new generation investment and efficient
More informationA Robust Method for Solving Transcendental Equations
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. Al-Amin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,
More informationA Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem
A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical
More informationTransient analysis of integrated solar/diesel hybrid power system using MATLAB Simulink
Transient analysis of integrated solar/diesel hybrid power system using ATLAB Simulink Takyin Taky Chan School of Electrical Engineering Victoria University PO Box 14428 C, elbourne 81, Australia. Taky.Chan@vu.edu.au
More informationStudy to Determine the Limit of Integrating Intermittent Renewable (wind and solar) Resources onto Pakistan's National Grid
Pakistan Study to Determine the Limit of Integrating Intermittent Renewable (wind and solar) Resources onto Pakistan's National Grid Final Report: Executive Summary - November 2015 for USAID Energy Policy
More informationAchieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services
Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services Ms. M. Subha #1, Mr. K. Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional
More informationA Survey on Carbon Emission Management and Intelligent System using Cloud
A Survey on Carbon Emission Management and Intelligent System using Cloud Dr.P EZHILARASU 1 (Associate Professor, Department of Computer Science and Engineering prof.p.ezhilarasu@gmail.com) S SARANYA 2
More informationPrice Responsive Demand for Operating Reserves in Co-Optimized Electricity Markets with Wind Power
Price Responsive Demand for Operating Reserves in Co-Optimized Electricity Markets with Wind Power Zhi Zhou, Audun Botterud Decision and Information Sciences Division Argonne National Laboratory zzhou@anl.gov,
More informationStudy of hybrid wind-hydro power plants operation and performance in the autonomous electricity system of Crete Island
Study of hybrid wind-hydro power plants operation and performance in the autonomous electricity system of Crete Island J. S. ANAGNOSTOPOULOS and D. E. PAPANTONIS School of Mechanical Engineering National
More informationOptimal Power Flow Analysis of Energy Storage for Congestion Relief, Emissions Reduction, and Cost Savings
1 Optimal Power Flow Analysis of Energy Storage for Congestion Relief, Emissions Reduction, and Cost Savings Zhouxing Hu, Student Member, IEEE, and Ward T. Jewell, Fellow, IEEE Abstract AC optimal power
More informationForecasting methods applied to engineering management
Forecasting methods applied to engineering management Áron Szász-Gábor Abstract. This paper presents arguments for the usefulness of a simple forecasting application package for sustaining operational
More informationReview of the activities of Electricity System Commercial Operator. Tbilisi, January 2014
Review of the activities of Electricity System Commercial Operator Tbilisi, January 2014 SECTOR PLAYERS Policy Implementation Ministry of Energy and Natural Resources Independent Regulator Georgian National
More informationArtificial Neural Network-based Electricity Price Forecasting for Smart Grid Deployment
Artificial Neural Network-based Electricity Price Forecasting for Smart Grid Deployment Bijay Neupane, Kasun S. Perera, Zeyar Aung, and Wei Lee Woon Masdar Institute of Science and Technology Abu Dhabi,
More informationENERGY-EFFICIENT TASK SCHEDULING ALGORITHMS FOR CLOUD DATA CENTERS
ENERGY-EFFICIENT TASK SCHEDULING ALGORITHMS FOR CLOUD DATA CENTERS T. Jenifer Nirubah 1, Rose Rani John 2 1 Post-Graduate Student, Department of Computer Science and Engineering, Karunya University, Tamil
More informationEffect of Remote Back-Up Protection System Failure on the Optimum Routine Test Time Interval of Power System Protection
Effect of Remote Back-Up Protection System Failure on the Optimum Routine Test Time Interval of Power System Protection Y. Damchi* and J. Sadeh* (C.A.) Abstract: Appropriate operation of protection system
More informationEffect of Remote Back-Up Protection System Failure on the Optimum Routine Test Time Interval of Power System Protection
Effect of Remote Back-Up Protection System Failure on the Optimum Routine Test Time Interval of Power System Protection Y. Damchi* and J. Sadeh* (C.A.) Abstract: Appropriate operation of protection system
More informationPrice Prediction of Share Market using Artificial Neural Network (ANN)
Prediction of Share Market using Artificial Neural Network (ANN) Zabir Haider Khan Department of CSE, SUST, Sylhet, Bangladesh Tasnim Sharmin Alin Department of CSE, SUST, Sylhet, Bangladesh Md. Akter
More informationjoint Resource Optimization and Scheduler
www.siemens.com/spectrum-power joint Resource Optimization and Scheduler All forecasting and planning applications in one component. Answers for infrastructure and cities. joint Resource Optimization and
More informationDifferent types of electricity markets modelled using PLEXOS Integrated Energy Model The UK Balancing Market example
Different types of electricity markets modelled using PLEXOS Integrated Energy Model The UK Balancing Market example Peny Panagiotakopoulou, Senior Power Systems Consultant, Energy Exemplar Europe Overview
More informationCopula model estimation and test of inventory portfolio pledge rate
International Journal of Business and Economics Research 2014; 3(4): 150-154 Published online August 10, 2014 (http://www.sciencepublishinggroup.com/j/ijber) doi: 10.11648/j.ijber.20140304.12 ISS: 2328-7543
More information2016 ERCOT System Planning Long-Term Hourly Peak Demand and Energy Forecast December 31, 2015
2016 ERCOT System Planning Long-Term Hourly Peak Demand and Energy Forecast December 31, 2015 2015 Electric Reliability Council of Texas, Inc. All rights reserved. Long-Term Hourly Peak Demand and Energy
More informationA Multi-level Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study
211 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (211) (211) IACSIT Press, Singapore A Multi-level Artificial Neural Network for Residential and Commercial Energy
More informationA Neural Network and Web-Based Decision Support System for Forex Forecasting and Trading
A Neural Network and Web-Based Decision Support System for Forex Forecasting and Trading K.K. Lai 1, Lean Yu 2,3, and Shouyang Wang 2,4 1 Department of Management Sciences, City University of Hong Kong,
More informationElectricity Exchanges in South Asia The Indian Energy Exchange Model
Electricity Exchanges in South Asia The Indian Energy Exchange Model 18 Mar 14 Rajesh K Mediratta Director (BD) rajesh.mediratta@iexindia.com www.iexindia.com In this presentation Overview - Indian Market
More informationAnalyses of delivery reliability in electrical power systems
Risk, Reliability and Societal Safety Aven & Vinnem (eds) 2007 Taylor & Francis Group, London, ISBN 978-0-415-44786-7 Analyses of delivery reliability in electrical power systems T. Digernes MathConsult,
More informationECCO International, Inc. 268 Bush Street, Suite 3633 San Francisco, CA 94104
PROMAX SHORT-TERM ENERGY & TRANSMISSION MARKET SIMULATION SOFTWARE PACKAGE ECCO International, Inc. 268 Bush Street, Suite 3633 San Francisco, CA 94104 ECCO International, Inc. Copyright 2009 EXECUTIVE
More informationUltrasonic Detection Algorithm Research on the Damage Depth of Concrete after Fire Jiangtao Yu 1,a, Yuan Liu 1,b, Zhoudao Lu 1,c, Peng Zhao 2,d
Advanced Materials Research Vols. 368-373 (2012) pp 2229-2234 Online available since 2011/Oct/24 at www.scientific.net (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/amr.368-373.2229
More informationSimulating the Multiple Time-Period Arrival in Yield Management
Simulating the Multiple Time-Period Arrival in Yield Management P.K.Suri #1, Rakesh Kumar #2, Pardeep Kumar Mittal #3 #1 Dean(R&D), Chairman & Professor(CSE/IT/MCA), H.C.T.M., Kaithal(Haryana), India #2
More informationTraffic Prediction in Wireless Mesh Networks Using Process Mining Algorithms
Traffic Prediction in Wireless Mesh Networks Using Process Mining Algorithms Kirill Krinkin Open Source and Linux lab Saint Petersburg, Russia kirill.krinkin@fruct.org Eugene Kalishenko Saint Petersburg
More informationDemand Response Management System ABB Smart Grid solution for demand response programs, distributed energy management and commercial operations
Demand Response Management System ABB Smart Grid solution for demand response programs, distributed energy management and commercial operations Utility Smart Grid programs seek to increase operational
More informationOPTIMAL DISTRIBUTION PLANNING INCREASING CAPACITY AND IMPROVING EFFICIENCY AND RELIABILITY WITH MINIMAL-COST ROBUST INVESTMENT
OPTIMAL DISTRIBUTION PLANNING INCREASING CAPACITY AND IMPROVING EFFICIENCY AND RELIABILITY WITH MINIMAL-COST ROBUST INVESTMENT L.A.F.M. Ferreira, P.M.S. Carvalho IST S.N.C. Grave L.M.F. Barruncho L.A.
More informationAn improved on-line algorithm for scheduling on two unrestrictive parallel batch processing machines
This is the Pre-Published Version. An improved on-line algorithm for scheduling on two unrestrictive parallel batch processing machines Q.Q. Nong, T.C.E. Cheng, C.T. Ng Department of Mathematics, Ocean
More informationThe Artificial Prediction Market
The Artificial Prediction Market Adrian Barbu Department of Statistics Florida State University Joint work with Nathan Lay, Siemens Corporate Research 1 Overview Main Contributions A mathematical theory
More informationA New Quantitative Behavioral Model for Financial Prediction
2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore A New Quantitative Behavioral Model for Financial Prediction Thimmaraya Ramesh
More informationTime Series Analysis of Household Electric Consumption with ARIMA and ARMA Models
, March 13-15, 2013, Hong Kong Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models Pasapitch Chujai*, Nittaya Kerdprasop, and Kittisak Kerdprasop Abstract The purposes of
More informationManagerial Economics Prof. Trupti Mishra S.J.M. School of Management Indian Institute of Technology, Bombay. Lecture - 13 Consumer Behaviour (Contd )
(Refer Slide Time: 00:28) Managerial Economics Prof. Trupti Mishra S.J.M. School of Management Indian Institute of Technology, Bombay Lecture - 13 Consumer Behaviour (Contd ) We will continue our discussion
More informationA New Method for Traffic Forecasting Based on the Data Mining Technology with Artificial Intelligent Algorithms
Research Journal of Applied Sciences, Engineering and Technology 5(12): 3417-3422, 213 ISSN: 24-7459; e-issn: 24-7467 Maxwell Scientific Organization, 213 Submitted: October 17, 212 Accepted: November
More informationCHAPTER 3 SECURITY CONSTRAINED OPTIMAL SHORT-TERM HYDROTHERMAL SCHEDULING
60 CHAPTER 3 SECURITY CONSTRAINED OPTIMAL SHORT-TERM HYDROTHERMAL SCHEDULING 3.1 INTRODUCTION Optimal short-term hydrothermal scheduling of power systems aims at determining optimal hydro and thermal generations
More informationWhen supply meets demand: the case of hourly spot electricity prices
When supply meets demand: the case of hourly spot electricity prices Alexander Boogert Commodities 2007 London, 17-18/01/2007 Essent Energy Trading, the Netherlands Birkbeck College, University of London,
More informationCoverage Related Issues in Networks
Coverage Related Issues in Networks Marida Dossena* 1 1 Department of Information Sciences, University of Naples Federico II, Napoli, Italy Email: marida.dossena@libero.it Abstract- Wireless sensor networks
More informationDemand Response Market Overview. Glossary of Demand Response Services
Demand Response Market Overview Glossary of Demand Response Services Open Energi has partnered with Tarmac to provide Demand Response What s inside... Market Overview Balancing Electricity Supply and Demand
More informationUNIT COMMITMENT AND ECONOMIC DISPATCH SOFTWARE TO OPTIMISE THE SHORT-TERM SCHEDULING OF ELECTRICAL POWER GENERATION
UNIT COMMITMENT AND ECONOMIC DISPATCH SOFTWARE TO OPTIMISE THE SHORT-TERM SCHEDULING OF ELECTRICAL POWER GENERATION INTRODUCTION Electricity generating companies and power systems have the problem of deciding
More informationDynamic Pricing for Usage of Cloud Resource
Dynamic Pricing for Usage of Cloud Resource K.Sangeetha, K.Ravikumar Graduate Student, Department of CSE, Rrase College of Engineering, Chennai, India. Professor, Department of CSE, Rrase College of Engineering,
More informationCapacity planning for fossil fuel and renewable energy resources power plants
Capacity planning for fossil fuel and renewable energy resources power plants S. F. Ghaderi *,Reza Tanha ** Ahmad Karimi *** *,** Research Institute of Energy Management and Planning and Department of
More informationProcurement Category: Energy. Energy Market Forces: Friend or Foe?
Procurement Category: Energy Energy Market Forces: Friend or Foe? As dynamic energy pricing becomes more prevalent in the industry, multi-site organizations are presented with new challenges, as well as
More informationelectricity restructuring compromise between competition and stability
the business scene Mohammad S. Ghazizadeh, Mohammad K. Sheikh-El-Eslami, and Hossein Seifi electricity restructuring compromise between competition and stability THE ISLAMIC REPUBLIC OF IRAN currently
More informationForecasting Of Indian Stock Market Index Using Artificial Neural Network
Forecasting Of Indian Stock Market Index Using Artificial Neural Network Proposal Page 1 of 8 ABSTRACT The objective of the study is to present the use of artificial neural network as a forecasting tool
More informationSensitivity Analysis 3.1 AN EXAMPLE FOR ANALYSIS
Sensitivity Analysis 3 We have already been introduced to sensitivity analysis in Chapter via the geometry of a simple example. We saw that the values of the decision variables and those of the slack and
More informationCurriculum Vitae July 2011 Shu Fan
Curriculum Vitae July 2011 Shu Fan General Information Current Position: Senior Research Fellow (Level C) Affiliation: Office: Telephone: +61 3 9905 5843 Fax: +61 3 9905 5474 Email: WWW: Research Interests
More informationAnalysis of China Motor Vehicle Insurance Business Trends
Analysis of China Motor Vehicle Insurance Business Trends 1 Xiaohui WU, 2 Zheng Zhang, 3 Lei Liu, 4 Lanlan Zhang 1, First Autho University of International Business and Economic, Beijing, wuxiaohui@iachina.cn
More informationA Comparison of Two Techniques for Next-Day Electricity Price Forecasting
A Comparison of Two Techniques for Next-Day Electricity Price Forecasting Alicia Troncoso Lora, Jesús Riquelme Santos, José Riquelme Santos, Antonio Gómez Expósito, and José Luís Martínez Ramos Department
More informationA COMPARATIVE STUDY OF MARKET BEHAVIORS IN A FUTURE SOUTH AFRICAN ELECTRICITY MARKET
A COMPARATIVE STUDY OF MARKET BEHAVIORS IN A FUTURE SOUTH AFRICAN ELECTRICITY MARKET J. Yan* J. Sousa**, and J. Lagarto** * Electrical Engineering Department, University of Cape Town, Private bag, Rondebosch
More informationSupply Chain Forecasting Model Using Computational Intelligence Techniques
CMU.J.Nat.Sci Special Issue on Manufacturing Technology (2011) Vol.10(1) 19 Supply Chain Forecasting Model Using Computational Intelligence Techniques Wimalin S. Laosiritaworn Department of Industrial
More informationThe problem with waiting time
The problem with waiting time Why the only way to real optimization of any process requires discrete event simulation Bill Nordgren, MS CIM, FlexSim Software Products Over the years there have been many
More informationWeb-Based Economic Optimization Tools for Reducing Operating Costs
Web-Based Economic Tools for Reducing Operating Costs Authors: Keywords: Abstract: Jeffery Williams Power & Water Solutions, Inc. David Egelston Power & Water Solutions, Inc. Browsers, Economics, Linear
More informationForecasting aggregate and disaggregate energy consumption using arima models: A literature survey
Journal of Statistical and Econometric Methods, vol.1, no.2, 2012, 71-79 ISSN: 2241-0384 (print), 2241-0376 (online) Scienpress Ltd, 2012 Forecasting aggregate and disaggregate energy consumption using
More informationA Robustness Simulation Method of Project Schedule based on the Monte Carlo Method
Send Orders for Reprints to reprints@benthamscience.ae 254 The Open Cybernetics & Systemics Journal, 2014, 8, 254-258 Open Access A Robustness Simulation Method of Project Schedule based on the Monte Carlo
More informationLinear Programming Supplement E
Linear Programming Supplement E Linear Programming Linear programming: A technique that is useful for allocating scarce resources among competing demands. Objective function: An expression in linear programming
More informationIntegration of Price Cap and Yardstick Competition Schemes in Electrical Distribution Regulation
1428 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 15, NO. 4, NOVEMBER 2000 Integration of Price Cap and Yardstick Competition Schemes in Electrical Distribution Regulation Hugh Rudnick and Jorge A. Donoso
More informationInternational Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
DOI: 10.15662/ijareeie.2014.0307061 Economic Dispatch of Power System Optimization with Power Generation Schedule Using Evolutionary Technique Girish Kumar 1, Rameshwar singh 2 PG Student [Control system],
More informationTOURISM DEMAND FORECASTING USING A NOVEL HIGH-PRECISION FUZZY TIME SERIES MODEL. Ruey-Chyn Tsaur and Ting-Chun Kuo
International Journal of Innovative Computing, Information and Control ICIC International c 2014 ISSN 1349-4198 Volume 10, Number 2, April 2014 pp. 695 701 OURISM DEMAND FORECASING USING A NOVEL HIGH-PRECISION
More informationOptimal PID Controller Design for AVR System
Tamkang Journal of Science and Engineering, Vol. 2, No. 3, pp. 259 270 (2009) 259 Optimal PID Controller Design for AVR System Ching-Chang Wong*, Shih-An Li and Hou-Yi Wang Department of Electrical Engineering,
More informationPowerPoint Energy Simulation For Clean Diesel
2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation Long-Term Electricity Supply-Demand Planning Simulation Using TEEP Model Yusak Tanoto, IEEE Member Ekadewi
More information