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EURECA 2013- Peak Demand Analysis for the Look-ahead Energy Management System: Peak Demand Analysis for the Look-ahead Energy Management System: A Case Study at Taylor’s University Nadarajan1, Aravind CV2* 1 Applied Electromagnetic and Mechanical cluster, Computer Intelligence Applied Group, Taylor’s University, Malaysia *[email protected] 1. Introduction The most common factor influence the energy management is the active energy consumption (KWh), the reactive energy consumption (KVARh) and the peak demand (KW). Conventionally the utility system put their effort on the reduction of KWh consumption and on addressing the reactive energy demand to improvise the power factor. However for the medium voltage and high voltage consumers’ proper KW demand management implies to reduce the use of contracted power, adjusting to the new requirement and avoiding the demand limit penalties [1]. Figure 1 shows the concept in the power management. As can be seen the power management is interlinked and the possible energy management between the KW and KVAr the net power consumption can be reduced. In order to find the demand requirement to propose new system architecture to address the demand requirement the peak demand analysis is to be investigated. Peak demand is the power consumed over a predetermined period of time, typically between 8 to 30 minutes. The power is calculated using a power demand meter, which records the highest KW value in the period of measurement, over a month’s time. The purpose of demand control is not to exceed the contracted maximum demand limit. The common way is to isolate the non-critical load during peak hours. A number power demand modeling and analysis, towards optimization of demand curve [2] as well as forecasting [1] are the subjects of interest in recent years. However, accuracy and resolution of the model are important [3]. We have utilized the data on energy management from the Taylor’s University, Malaysia laced at latitude of 3°07'51.99"N and longitude of 101°59'11.77"E. The demand analysis is based on the utilized power between Jan 2011 till April 2013. Our initial study is to derive the average peak demand requirement and suggest a KW management system for energy sustainability. From our study a detailed proposal on the energy management by suggesting a KW framework is to be presented towards the end of this research work. 107 Improvising Utilising Improvise KVAR Management Avoiding Adjusting KW Management Reducing Abstract— To derive an energy management for sustainable energy usage the peak demand analysis is highly critical. This paper presents the investigations on the peak demand analysis for the existing power system network at Taylor’s University. From the analysis it is inferred that the average peak demand of 3000kW could be managed with proper kilowatt management. The analysis pertaining to the computations of power analysis and a proposed framework to support the analysis is presented. Further to stabilising the load requirement, equally the economics of the system is improvised by about 7.33%. Keywords— peak demand, energy management, economics Architecture usage low power devices demand penalty power factor KWhr Managed Figure 1. KWhr Management Strategy 2. Methodology The methodology involved in this investigations is as shown in the Figure 2 and the power system architecture is as shown in Figure 3. The computation procedure for the demand analysis and the net KW demand computation is as below. Let the contracted power be (P C ), the maximum demand is (P D ) then the power used in excess (P E ) is computed as PE = PC - PD (1) where P Dm is the actual peak demand value from the maximum demand meter and K d is the demand factor P D = P Dm X K d (2) The penalty by the supplier to the utility is computed as P P = (P C - P D ) X K P (3) where K P is the penalty factor. Therefore the actual KW value (P A ) computed is given by P A = [(P C - P D ) X K P ] + [P Dm X K D ] (4) . Demand Meter Start KW merge Data Collection Unit Consumption Energy Data Base extract Data Analysis Power Demand Analysis Origin Mathematical Tool End Figure 2. Methodology Employed EURECA 2013- Peak Demand Analysis for the Look-ahead Energy Management System: From Subang Jaya Substation 11KV 50Hz Three phase Feeder 1 11KV 50Hz Three Phase Feeder 2 Contracted Power (Pc) 100VA, 11KV/110V Maximum Demand Meter (PD) KW Kf Pc -PD P+(Pc -PD)Kf KWhr Energy Meter (P) MSB 1 MSB 2 1600KVA, 11KV/433V (Pc -PD)Kf KW MSB 4 MSB 3 1600KVA, 11KV/433V 2500KVA, 11KV/433V 2500KVA, 11KV/433V Normally Open Lighting Load Power Load Lighting Load Power Load Figure 3. Power System Architecture for the Peak Demand Computations 3. Results and Discussions Table 1 shows the percentage share of the pay bill to the Tenaga nasional. As can be seen the average peak demand (PD) is about 20.87% and the KWh utility is about 78.77% for the three year period. If the average peak demand is catered through a energy management system the power system network ideally becomes sustainable. Figure 4 shows the unit consumption and during the second quarter the unit consumption is predominantly high and at the same time the peak demand (as in Figure 5) is critically very high. The peak demand is addressed through the design of a renewable structure and reconfigures the existing power system architecture in our further investigations. Peak Demand Limit Average Peak Demand 3600 Penalty 3400 3300 3200 3100 3000 2900 2800 Dec Oct Nov Sep Aug July June Month 2011 2012 Figure 5. Power Demand Analysis 4. Conclusions The initial investigations on the power demand curve analysis for the look-ahead energy management system is presented in this work. It is inferred that about 20.87% of the pay bill accounted to the peak demand requirement. A sustainable framework based on this analysis would be further investigated. 5. References 1000000 950000 900000 850000 800000 750000 2010 Month 2011 Dec Oct Nov Sep Aug July June May April Feb [1] Mar 700000 May 2010 1050000 Jan Unit Consumption, PU [KWhr] 1100000 April Jan Feb 2700 2600 1150000 Peak Demand Mar Maximum Demand, PD [KW] 3500 [2] 2012 [3] Figure 4. Unit Consumption of Energy García-Ascanio and C. Maté, “Electric power demand forecasting using interval time series: A comparison between VAR and iMLP,” Energy Policy, vol. 38, no. 2, pp. 715-725, Feb. 2010 N. Li, L. Chen, and S. H. Low, “Optimal demand response based on utility maximization in power networks,” in 2011 IEEE Power and Energy Society General Meeting, 2011, pp. 1-8 J. Widén and E. Wäckelgård, “A high-resolution stochastic model of domestic activity patterns and electricity demand,” Applied Energy, vol. 87, no. 6, pp. 1880-1892, Jun. 2010. Table 1. Percentage of Pay Bill Ratio at Taylor’s University Year 2010 2011 2012 KWh 79.38 78.58 78.37 PD 20.64 21.35 20.63 Renewable 0 0.07 0.09 108 Penalties 0.01 0 0