Typical Load Patterns Research Articles

Temperature Adaptive Switching Operation for Distribution Systems

This purpose of this paper is to investigate the optimal switching problem of distribution systems by considering the customer load characteristics and the effect of temperature change on customer loading. The load survey is applied to derive the typical load patterns of residential, commercial, and industrial customer classes. The power consumption of sample test customers and weather information are used to solve the hourly temperature sensitivity of power consumption for each customer class. All the customers served by each distribution transformer are identified and their monthly energy consumption is retrieved from the customer information system in Taipower. The hourly loading of each service zone and its change due to temperature rise are obtained based on the customer load consumption and its temperature sensitivity. By performing the connectivity trace to find the feeder configuration and executing the three-phase load flow analysis, the hourly current loading of line switches, distribution feeders, and main transformers in substations is solved. The binary integer programming is then applied to determine the temperature adaptive optimal switching operation for noninterruptible load transfer among distribution feeders and main transformers.

Coloured Petri nets approach for solving distribution system contingency by considering customer load patterns

Typical customer load patterns, which have been derived by a load survey study, are used to determine the daily load profiles of each service section of distribution feeders. The hourly current flows of line switches and distribution feeders are solved by three-phase load flow analysis over a daily period. For a distribution contingency such as feeder overload or short circuit fault, the artificial intelligent coloured Petri nets with best-first search approaches are then applied to find the optimal switching operation decision. A coloured Petri nets model with inference mechanism is derived for load transfer among distribution feeders after the overloading feeders have been identified or the faulted section has been isolated. To determine the effectiveness of the proposed methodology, a Taipower sample distribution system, which serves a mixture of customers, is selected to perform the computer simulation. By comparison with the conventional Petri nets, the proposed coloured Petri nets can significantly reduce the size of network models, and a more effective switching operation plan can be designed by taking into account the load characteristics of the customers served.

Temperature adaptive switching operation for distribution systems

This paper investigates the optimal switching problem of distribution systems by considering the customer load characteristics and the effect of temperature change to the customer loading. The load survey is applied to derive the typical load patterns of residential, commercial, and industrial customer classes. The power consumption of sample test customers and weather information are used to solve the hourly temperature sensitivity of power consumption for each customer class. All the customers served by each distribution transformer are identified and their monthly energy consumption is retrieved from the customer information system in Taipower. The hourly loading of each service zone and its change due to temperature rise are obtained based on the customer load consumption and its temperature sensitivity. By performing the connectivity trace to find the feeder configuration and executing the three-phase load flow analysis, the hourly current loading of line switches, distribution feeders, and main transformers in substations are solved. The binary integer programming is then applied to determine the temperature adaptive optimal switching operation for noninterruptible load transfer among distribution feeders and main transformers.

Temperature effect to distribution system load profiles and feeder losses

A systematic procedure is proposed to study the effect of temperature change to the power system load demand by using the typical load patterns of customer classes. The billing data of all service customers are retrieved to derive the daily load profile of the selected Taipower district. To verify the accuracy of the estimated load composition, the simulation results are compared to the actual load profile collected by the SCADA system. The sensitivity analysis of load demand with respect to the temperature change for each customer class is performed by statistic regression according to the actual customer power consumption and temperature data. The load contribution by each customer class is updated by the corresponding temperature sensitivity and integrated together to form the new load profile of a service district with temperature change. To investigate the temperature effect to the distribution system operation, one of the Taipower distribution feeders is selected for computer simulation. The power demand at each load bus of the distribution feeder is calculated by applying the temperature sensitivity and the three-phase load flow analysis is then executed to find the new feeder loading and power loss with the temperature change.

Synthesis of power system load profiles by class load study

This paper proposes a methodology for the synthesis of power system load profiles by class load study. There are more than 900 customers over various customer classes are selected by the sampling design for the installation of intelligent meters over six districts in Taipower system. The load data of all the test customers have been collected and the typical load patterns of customer classes have been derived for each service district. The billing data of all the customers in the Taipower customer information system (CIS) are retrieved and the typical load patterns derived are applied to solve the hourly load composition of all customer classes. The actual power consumption in each study district, which has been recorded by the Taipower distribution dispatch control system (DDCS), is used to verify the accuracy of the power profiles derived. By the same way, the power profiles of Taipower system are then synthesized too. It is concluded that the system power profile can be estimated by the aggregation of class load patterns in a rather precise manner.

Optimal distribution transformer sizing by dynamic programming

The objective of this article is to propose an efficient strategy for transformer planning by taking into account the load characteristics so that the overall life cycle cost of distribution transformers can be reduced. The typical load patterns and load growth of residential, commercial and industrial customers are derived by a load survey system and then used to solve the transformer copper loss and core loss. The three-phase load flow analysis is performed to solve the yearly peak power loss and annual energy loss according to the customer typical load patterns. The transformer peak power loss and energy loss as well as the initial investment cost, installation cost and depreciation cost of distribution transformers are combined to form the overall cost function. The dynamic programming (DP) is then applied to find the optimal capacity and installation strategy of distribution transformers. To verify the accuracy of the proposed methodology, a practical Taipower distribution feeder has been selected for computer simulation. The conventional transformer sizing by considering the customer peak loading is also investigated for comparison. It is found that more than 5% of overall life cycle cost of distribution transformers can be saved by the proposed optimal transformer sizing strategy.

System Concept and Simulation Model for a ProposedClass of Delay-Tolerant Variably-Priced NetworkServices

We describe a system concept for the revenue-producing disposition of surplus capacity at off-peak times in real trunking networks. The idea is to approximate a competitive market for distribution of the networks' time-varying excess capacity with a pricing strategy controlled by the network. The scheme is intended to allow network operators to stimulate background traffic loads to gain new revenues from otherwise idle time on existing installed resources. The concept is suitable for low priority delay-tolerant or opportunistic applications such as remote backups, software distribution, dispatching batched faxes, disseminating newsgroup updates, updating web page caches or routing tables. Background service subscribers are notified of price reductions at off-peak times to elicit additional traffic for the network. Traffic aggregators act on behalf of subscribing organizations or groups of users. The background service is completely subordinate to the conventional tariff-priced on-demand calling services and the variable background pricing merges with the foreground under suitable total load. This paper focuses on the network problem of price setting to continually maximize the price-volume product in a time-varying price-sensitive traffic environment such as this concept implies. A price-stimulated offered traffic environment is simulated in which time of day, price, and hidden demand latency and demand curve characteristics all affect the offered traffic. An analytically optimum strategy is available for the particular traffic model used and the performance of a fuzzy logic price controller is tested against the revenue-optimal strategy. Depending on econometric assumptions for latent traffic demand and price-volume curves, increases in revenue from 4%-20% are obtained in simulation of a 30-trunk group having a typical daily load pattern.

Automated learning of load-balancing strategies in multiprogrammed distributed systems

Dynamic load-balancing strategies for distributed systems seek to improve average completion time of independent tasks by migrating each incoming task to the site where it is expected to finish the fastest: usually the site having the smallest load index. SMALL is an offline learning system for developing configuration-specific load-balancing strategies; it learns new load indices as well as tunes the parameters of given migration policies. Using a dynamic workload generator, a number of typical systemwide load patterns are first recorded; the completion times of several benchmark jobs are then measured at each site, under each of the recorded load patterns. These measurements are used to train comparator neural networks simultaneously, one per site. The comparators collectively model a set of perfect load indices in that they seek to rank, at arrival time, the possible destinations for an incoming task by their (not yet known) respective completion times. The numerous parameters of the decentralized dynamic load-balancing policy are then tuned using a genetic algorithm. We present experimental results for a mix of scientific and interactive workloads on Sun workstations connected by Ethernet. The policies tuned by SMALL are shown to exploit idle resources intelligently and effectively.

Application of load survey systems to proper tariff design

This paper proposes a proper rate making strategy for a public owned utility by taking into account the customer load characteristics. The load survey system has been well designed by sampling theory to find the customers for power consumption information collection. By this manner, the typical load patterns derived can effectively represent the load behavior of each customer class. The load patterns of each test customer during different seasons are solved by statistical analysis according to the load information collected. The seasonal typical load pattern of each customer class is then determined by integrating the load patterns of the same type customers. The power consumption of the customer class is then estimated by the typical class load pattern and the energy consumption of all customers in the same class, which can be retrieved from the customer information database. The typical load pattern of whole power system is then determined by aggregating the power consumption of all customer classes. The estimated system load consumption is then compared to the actual system load profile, it is found that a rather accurate system load profile can be predicted by the load survey system.

Derivation of class load pattern by field test for temperature sensitivity analysis

This paper proposes systematic procedures to derive the load pattern of various customer classes in a utility company. The questionnaires are adopted to find the power consumption of key electrical appliances. The customer load information is obtained through intelligent equipment which records customers' electricity demand on a 15-minute interval basis throughout the year. Five hundred meters are installed on statistically selected samples from the various customer classes. By the proposed sampling theory, the customer load characteristics will be derived with a sufficient confidence level. Statistical analysis is then performed to find the typical load pattern of each customer class based on the power measurements of field tests. The temperature effect on the power consumption of each customer class is then solved by investigating the relationship between customer power consumption and the ambient temperature. The proposed procedure has been adopted by Taipower Company to determine the customer load pattern to provide valuable information for better distribution planning and to design better load management programs to enhance system operating efficiency.

Development of simplified loss models for distribution system analysis

This paper proposes a methodology to derive a simplified model for distribution system loss analysis. According to the typical daily load patterns and the energy consumption of customers, the hourly loading of distribution transformers can be solved. A three-phase load flow program is applied to calculate the feeder power flow so that the hourly feeder loading will be the same as that obtained by the field test. The hourly primary conductor loss, secondary conductor loss, transformer copper loss and core loss are then solved according to the mathematical modeling of system components. The sensitivity analysis of system loss with respect to the feeder loading, power factor, feeder length, transformer capacity is then performed to derive the simplified loss model of distribution feeders. A distribution feeder of Taipower system is then selected for computer simulation to demonstrate the effectiveness of the proposed method. By applying the simplified loss model derived, the daily loss pattern of distribution feeders is solved according to the actual hourly feeder loading. It is concluded that the proposed methodology provides a useful tool for distribution engineers to estimate the operation efficiency of distribution systems in a very effective manner. >

Energy loss reduction by critical switches

A systematic method to derive an optimal switching plan to achieve energy loss minimization for short term and long term operation of distribution systems is presented. The short term optimal switching criterion is developed by binary integer programming with a branch and bound technique. An overall optimization problem is formulated for the optimal switching operation. It is further divided into several subproblems to find the largest energy loss reduction among all possible switching operations between two feeders. A quick method is applied to estimate the largest loss reduction for each feeder-pair in the distribution system during short term switching operation. The composite load profile of each feeder is derived by field tests over a one year period. After determining the hourly optimal network configuration by taking into the account the typical daily load patterns for all the distributed feeders, the long term optimal criterion for each season is derived and the corresponding critical switches are determined. >

Distribution system load estimation and service restoration using a fuzzy set approach

An approach based on fuzzy set theory is developed to estimate the loads in a distribution system and to devise a proper service restoration plan following a fault. To estimate the loads on branching points without real-time meters, typical hourly load patterns for several types of days are established for commercial, industrial, and residential customers. These load patterns are characterized by some linguistic variables using fuzzy set notations. The load of a branching point is estimated through fuzzy set operations. With the estimated loads at hand, a heuristic search method is proposed in order to reach a restoration plan with minimal number of switching operations in a short time. To demonstrate the effectiveness of the proposed fuzzy approach, load estimation and service restoration on a distribution system within the service area of Taipei West District Office of Taiwan Power Company are examined. It is found that, following a fault event, a proper restoration plan can be reached very efficiently. Therefore, the proposed approach can provide valuable information to distribution system operators in reaching a service restoration plan.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>