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