Demand Side Management Program Evaluation Based on Industrial and Commercial Field Data

Transcription

1 Proceedings of the 14 th International Middle East Power Systems Conference (MEPCON 10), Cairo University, Egypt, December 19-21, 2010, Paper ID 105. Demand Side Management Program Evaluation Based on Industrial and Commercial Field Data M. M. Eissa (Senior Member, IEEE) Electrical Power Engineering Department, Faculty of Engineering, Helwan-Cairo-EGYPT ABSTRACT: Demand response is defined as changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. Different statistical studies on daily load curves for consumers connected to 22kV lines are classified. The study proves that Demand Response Program solution for the consumers is based on the shape of the load curve. The load curve criteria used for classification is based on peak ratio and night ratio. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the KSA) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas with larger commercial and industrial customers. Index Terms: DSM, Load Management, Field Data, Classification, Time of Use, Incentive programs. I. INTRODUCTION Load management is the process of scheduling the loads to reduce the electric energy consumption and or the maximum demand. It is basically optimizing the processes/loads to improve the system load factor [1]. Load management procedures involve changes to equipment and/or consumption patterns on the customer side. There are many methods of load management which can be followed by an industry or a utility, such as load shedding and restoring, load shifting, installing energy-efficient processes and equipment, energy storage devices, co-generation and non-conventional sources of energy, and reactive power-control. Load management mechanisms are those that provide customers with the option to avoid or curtail central station electricity use during peak hours. Typical load management mechanisms include the programs given above [2]-[3]. The important benefits of load management are reduction in maximum demand, reduction in power loss, better equipment utilization and saving through reduced maximum demand charges. Load shifting is to reduce customer demand during the peak period by shifting the use of appliances and equipment to partial peak and off-peak periods. Closer examination of how these costs arise shows that it can be possible to take advantage of incentives and favourable pricing offered by utilities in order to encourage consumers to use energy in such a way and at such times that it enables 15 the utility to manage load patterns. By making the best use of these incentives, it is possible to achieve significant savings in production costs, with no adverse effect on product quality or productivity [4]. Demand Response is increasingly viewed as an important tool for use by the electric utility industry in meeting the growing demand for electricity. Demand Response is a subset of the broader category of end-use customer energy solutions known as Demand-Side Management (DSM). In addition to Demand Response, DSM includes energy efficiency programs. Demand response may be defined as changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices [5]. The demand response program can be classified as: Price-based demand response such as real-time pricing (RTP), critical-peak pricing (CPP) and time-of-use (TOU) tariffs, give customers time-varying rates that reflect the value and cost of electricity in different time periods. With this information, customers tend to use less electricity at times when electricity prices are high. Incentive-based demand response programs pay participating customers to reduce their loads at times requested by the program sponsor, triggered either by a grid reliability problem or high electricity prices. There are two basic categories of demand response options: time varying retail tariffs and incentive demand response programs as given in Fig. 1. Time-varying retail tariffs, which include TOU, RTP and CPP rates can be characterized as price-based demand response. In these tariff options, the price of electricity fluctuates in accordance with variations in the underlying costs of electricity production. Customers on these rates can reduce their electricity bills if they respond by adjusting the timing of their electricity usage to take advantage of lowerpriced periods and/or avoid consuming when prices are higher. Customer response is typically driven by an internal economic decision making process and any load modifications are entirely voluntary. The incentive based demand response programs represent contractual arrangements designed by policymakers, grid operators, and utilities to elicit demand reductions from customers at critical times. These programs give participating

2 customers incentives to reduce load during the peak periods. The incentives may be in the form of explicit bill credits or payments for pre-contracted or measured load reductions. Customer enrollment and response are voluntary, although some demand response programs levy penalties on customers that enroll but fail to respond or fulfill contractual commitments when events are declared. Different statistical studies on daily load curves in KSA's industrials have lead to a classification of the consumers connected to medium voltage lines in many classes from uses with peak-hour or off-peak uses. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the KSA) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas, such as commercial and industrial zones and medium size industries sectors. An annual load curve is given by a numeric vector of dimension that representing the energy kw, kva, kvar and power factor during the period of winter and part of the summer months, see Figure 2. The data is recorded by 30 minutes interval meters. Figure 3 shows an example for an annual load curve for one of the KSA s customers. Figure 1: Demand Response Programs. To encourage reduction in peak demand, many utilities have already implemented time of use rates (TOU) or have plans for introducing such rates [6]-[8]. The main purpose of applying such program is to reduce the load curve during the peak periods. However, some utilities applied these programs mandatory during the peak periods. The programs can be applied successful in case of having the customers peak demands during the day. On the other hand, keeping the program voluntary for the customers during the peak period (economic decision making) give no commitment for the customers with the load reduction during the peak periods. In this case, the load factor of the utility cannot make scene for such programs while most customers have no peak during the peak periods. Figure 2: The field data recorded from the field for one of the customers. The examination of the daily load curves for the commercial and industrial customers is the first step towards the implementation of the suitable Demand Response Program. In this study, industrial and commercial field data will be reviewed and analyzed that support that incentive based programs are better than pricing tariffs. Different statistical studies on daily load curves in KSA's industrials have lead to a classification of the consumers connected to medium voltage lines in many classes from uses with peak-hour or off-peak uses. According to the data classified and analyzed here, the study will encourage the utility to apply the suitable solution of load management programs based on incentive response with penalty during the peak demands. II. FIELD DATA 16 Figure 3: The annual load curve for one of the customers. III. DATA CLASSIFICATION Different statistical studies on daily load curves in KSA's industrials have lead to a classification of the consumers connected to 22 kv medium voltage lines in many classes from uses with peak-hour or off-peak uses. To increase the total load factor of the utility, the suitable load management program should be applied that consists in decreasing the PR value and increasing the NR value at the customers. PR is the

3 ratio of the mean power absorbed during peak hours in certain period, to the mean power absorbed during off-peak day hours in same period. NR is the ratio of the mean power absorbed during night hours in certain period, to the mean power absorbed during off-peak day hours in same period [9]. According to the PR value, different study of LM systems helps to determine which the best Demand Response solution for the industrial is applied. The analyzed data given here can be grouped into two parts; the industrial and commercial data. The methodology is to classify customer segments on the basis of the electrical behavior of customers. As the power system has a commitment to supply the maximum demand, the power system will apportion a fraction of its generating capacity to the consumer in question. Naturally, from the system point of view the economic load demand by the consumer should be a flat one equal to the maximum demand over the day. This will ensure that the committed capacity is utilized uniformly throughout the day, the maximum possible energy will be produced by the committed capacity, and the cost per kwh of energy will be least. programs based on tariffs time. Such customers should reduce the peak uses and shift some loads to the off peak period. So, a great benefit for the customers if they participated in the tariff program (mandatory or voluntary). There are off peak disconnection load such as given in Figure 6, while the PR is roughly modulated and the NR is also very low. In such a case, the load management is going to decrease the PR and increasing the NR value. Such schemes can shift the loads using the ice storage methodology or using standby generators during peak period. So, the customers have not any problems during the peak period. The demand response program such as incentive ones is highly recommended in this case. As an example for the flat load is given in Figure 4. Load management consists in decreasing the PR value and increasing the NR value. The customers of this type are using the energy all the time. As shown from figure that the customers with flat consumption have not any problem during the peak period. Figure 5: Load consumption customers with modulated peak and off-peak uses. Figure 4: Flat load consumption curve. Figure 5 shows the customers with off peak and small peak period uses; in such a case the customers have a very high PR with a very high value of NR. Load management consists in decreasing the PR value and keeps the NR value. Such cases are recommended to apply one of the load management 17 Figure 6: Load consumption customers with off-peak disconnection. Figure 7 shows the load customers with peak hour disconnection. In such a case, the PR is very low and NR is very high. The customers with such type have any problem at the peak period and the responding to a mandatory program

4 or tariffs time is not suitable for them. The customers with such type are highly needed to a very strong incentive program to encourage them for reducing the load during the peak. Figure 8 shows the load consumption with average off-peak uses. In this type, the load management program can be applied strongly while the load consumption can be transferred to the off-peak period. This classification proves that the customers have not any problem during peak period. The incentive program is highly appreciated for this type of load performances. Time-Based Rate Programs such as Time-of-Use (TOU) rates are pre-set rates offered in a wide variety of time-periods: from seasons to time-of-day depending on the desired application. The pre-set rates reflect underlying costs for production in hopes that consumers will reduce/curtail their use during the higher priced time-periods [8]. Many utilities now require their larger customers to use TOU rates. Consumers can change their electricity use behavior if price differentials are substantial. There is a multifarious experience with TOU rates with variety levels of success, as the results can be hard to predict. The load reduction associated with TOU programs tends to be reflected in actual recorded loads and embedded in load forecasts. Applying the TOU programs as a voluntary program cannot guarantee for the utility the required improvement in the load factor curve, specifically during the peak periods. As a voluntary program and based on the customer response according to the tariff time may cause a problem for the utility in case of power exceeds during the peak period, see Figure 10. Also, applying the TOU program for customers as a mandatory program cannot guarantee for the utility the load reduction during the peak periods while most of the analyzed customers data have not any problem during the peak as explained above. The most prevalent timevarying program is suitable for residential electric consumers rather than industrial and commercial sectors. Figure 7: Load consumption customers with Peak disconnection. The different categories described above from figures 4 to 8 can be situated on a plane using two criteria (PR and NR). PR is the mean power absorbed during peak hours in a certain period to the mean power absorbed during off peak day hours in same period. NR is the ratio of the mean power absorbed during night hours in certain period to the mean power absorbed during off peak day hours in same period, see Figure 9. The load management solution aims to decreasing the PR and increasing the NR. By this way, you identify which class of customers you belong to. Consequently this will assist in identifying the suitable program for the customers. As shown in the figure, most customers (C1, C2, C5, C6, C7, C8 and C9) have small values of the PR except C3 and C4 customers that have larger values of PR. IV. VESUS INCETIVE PROGRAMS A. Time-of-Use Program Figure 8: Load consumption customers with average off- Peak period. B. Incentive-Based Demand Response Programs These programs include an inducement or incentive for customer participation and they provide an active tool for load-serving entities, electric utilities or grid operators to 18

5 NR manage their costs and maintain reliability. Customers commit to providing specific load reductions during events in return for payments and are penalized if they do not comply. The next step that follows this work is to introduce an incentive reliable program so called Summer Reduction Bidding Program (SRBP). It is a voluntary demand response program to incentive customers to reduce load in a delivery peak period through summer months. Payment is given for the participated customer thru the utility based on the level of load reduction for each month. Incentive Programs C6 & C7 C1 & C2 C8 & C9 Tariff Programs C3& C4 incentive programs. So, customers types such as (C1, C2, C5, C6, C7, C8 and C9) are recommended to participate in incentive Demand Programs to reduce their load curves in terms of a fixed level of demand, or to a pre-specified level of demand. In this case, the utility will guarantee an enhancement in the value of the load factor. CONCLUSIONS Medium voltage industrial and commercial field data are classified and reviewed using the NR and PR ratios. Most analyzed customers load curves are with small values of PR. The data classification here proved that the incentive based Demand Response Program is better solution than pricing tariffs for the KSA utility. The incentives may be in the form of explicit bill credits or payments for pre-contracted or measured load reductions. Customer enrollment and response are voluntary, although some demand response programs levy penalties on customers that enroll but fail to respond or fulfill contractual commitments when events are declared. The data classified and analyzed here encourages the KSA utility to apply the suitable solution of load management programs based on incentive response with penalty during the peak demands. This of course will enhance the total load factor of the utility. REFERENCES MW C5 PR Figure 9: The load curve situation using PR and NR Power Excess Time Peak Period Figure 10: The load curve performance for one of the customer. As shown from the above discussion that Time of Use programs is sensitive to the customers who have large values of PR. The customers with low values of PR should relate to 19 [1] Isaksen, L, Ma, FS, and Simons, NW, Bibliography and load management, IEEE Transactions on Power Apparatus and Systems 1981: PAS-100(5):1981 [2] S. Ashok, R. Banerjee, Load-management applications for the industrial sector, Applied Energy 66 (2000) [3] L. Effler, G.Schellsfede and H.Wagncr, "Optimization of Energy Requirement and Load Management", IEEE Trans. on Power Systems, vo1.7, no.1, 1992, pp [4] S.K. Nelson and B.F. Hobbs, "Screening DSM Programs with a Value Based Test" IEEE Trans. On Power Systems, vo1.7, no.3, August, 1992, pp [5] A report to the United States congress, benefits of demand response in electricity markets and recommendations for achieving 2d.pdf [6] I. Apolinario, N. Felizardo, A. Leite Garcia, P. Oliveira, A. Trindade, J. Vasconcelos and P. Verdelho, Application of Additive Tariffs in the electricity sector, WEC Regional Energy Forum- FOREN 2004, June [7] Saudi Electricity Company, [8] Sheen JN, TOU pricing of electricity for load management in Taiwan power company, IEEE Trans on Power Systems, 1994, vol (9), pp: [9] U.S. Department of Energy, Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them, A Report to the United States Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, February 2006 (February DOE EPAct Report).