Load Patterns Of Customers Research Articles

Effects of time-of-use pricing for residential customers and wholesale market consequences in South Korea

Using a large-scale pilot test on time-of-use (TOU) pricing for residential customers, this study analyzes the effects of TOU pricing on the load patterns of residential customers and on producer surplus in South Korea. We estimate the difference in the electricity demand functions of residential customers across peak, intermediate, and off-peak periods before and after TOU pricing. In addition, we assess the supply curve in the wholesale electricity market and calculate the changes in electricity purchase costs by integrating the supply curve over the range of consumption changes across the periods. This study simulates the effects of TOU pricing on producer surplus that would be obtained if all residential customers in South Korea switch from increasing block pricing to TOU pricing. Our results show that TOU pricing can be an effective measure for load transfer and simultaneously increase producer surplus. We find that although the revenue of electric utility generally decreases, the cost savings are greater and thus producer surplus increases. The results can be a valuable reference for the South Korean government regarding the nationwide expansion of TOU for residential electricity in the future.

Graph convolutional network-based aggregated demand response baseline load estimation

Accurate aggregated baseline load (ABL) estimation is very important for the demand response (DR) compensation settlement between system operators and DR aggregators. Current ABL estimation methods totally ignore the spatial correlation between load patterns of different customers, which will lead to large errors when the load pattern on the DR event day fluctuates largely compared with that in historical days. To this end, this paper proposes a Graph Convolutional Network (GCN)-based ABL estimation method to improve the estimation accuracy. The basic idea is to enhance the estimator's ability to capture load uncertainty by sharing load fluctuation information between different customers. The proposed method contains three main steps: First, all customers are grouped into different clusters by the K-means algorithm according to their historical typical load patterns (TLPs). Second, these clusters are transformed into an undirected graph based on an adjacency matrix reflecting the spatial correlations, which are constructed according to the difference between the TLPs in different clusters. Third, the ABL estimation is transformed into a node regression problem of the graph. Case studies on a real load dataset verify the effectiveness and superiority of the proposed method.

Investigating distribution systems impacts with clustered technology penetration and customer load patterns

Electric vehicles (EVs), photovoltaics, heat pumps and energy storage are changing the demands placed on electricity systems and can pose significant challenges for system operators and distribution companies. Furthermore, clustering of behaviours and technologies throughout different areas of distribution systems can produce broad variation in load curves and impacts on the network. This paper investigates local clustering impacts in a utility service area as a case study to develop methods and gain insights which can be applied to other datasets. Through clustering the variation in technology penetration rates across distribution transformers is revealed, a level of granular variability which has not been well-quantified in past literature. A second clustering framework is the applied to transformer load profiles to identify a small but diversely representative set of novel archetypical local loads. These profiles provide a summary of the dataset variability, showing how simple modeling can begin to illustrate the impacts of future technology penetration across different regions of the system. The results of the case study demonstrate that home EV charging will significantly increase peak residential transformer loading (up to 19% with 25% EV penetration), potentially drastically decreasing their useful life. Results also produced insights into possible mitigation strategies. By taking advantage of alternate charging opportunities (like workplace) the load can be spread across transformers, reducing growth in local residential and aggregate peaks by 2–8%. Energy storage is found to be more effective on residential transformers than business ones, promoting deferral of capacity investment, while simultaneously matching local and regional grid requirements for demand smoothing. In contrast, photovoltaics are found most effective at lowering new and baseline peak demands when on commercial and industrial transformers, particularly for small businesses where moderate penetration scenarios for EVs and PVs showed peak demand actually declining by 1–9%. The data analysis and clustering techniques developed through this case study can provide valuable insight into large datasets for policy development and potentially revelatory illustration of the varying effects of new technology within evolving networks.

PV-Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer With Distributed PV System

Customer baseline load (CBL) estimation is very important in demand response (DR) program. Due to the increasing installation of distributed photovoltaic system (DPVS), the load patterns of residential customers become more complex and random. The actual load power of the customer is coupled with the DPVS output power, which makes it more difficult to estimate CBL. Since the electricity meter can only measure the net load data, this article proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVS. CBL is the difference between actual load power and DPVS output power, so the CBL estimate is converted into two sub-problems: the estimation of actual load power and the estimation of DPVS output power. First, the actual load power of DR customers is estimated based on the load power of the control group customers. Then, the DPVS output during DR period is obtained based on the DPVS output estimation model. Finally, CBL is estimated based on the actual load power and DPVS output power. In order to verify the effectiveness and feasibility of the approach, two real datasets from Sydney and Austin are used to simulate the CBL estimation. Compared with the net load directly estimating the CBL, the comparison results indicate that the proposed method shows better accuracy performance.

Modeling and analysis of residential flexibility: Timing of white good usage

Challenges that smart grids aim to address include the increasing fraction of supply by renewable energy sources, as well as plain rise of demand, e.g., by increased electrification of transportation. Part of the solution to these challenges lies in exploiting the opportunity to steer residential electricity consumption (e.g., for flattening the peak load or balancing the supply and demand in presence of the renewable energy production). To optimally exploit this opportunity, it is crucial to have insights on how flexible the residential demand is. Load flexibility is characterized by the amount of power, time of availability and duration of deferrable consumption. Residential flexibility however, is challenging to exploit due to the variation in types of customer loads and differences in appliance usage habits from one household to the other. Existing analyses of individual customer flexibility behavior in terms of timing are often based on inferences from surveys or customer load patterns (e.g., as observed through smart meter data): there is a high level of uncertainty about customer habits in offering the flexibility. Even though some of these studies rely on real world data, only few of them have quantitative data on actual flexible appliance usage, and none of them characterizes individual user behavior. In this paper, we address this gap and contribute with: (1) a new quantitative specification of flexibility, (2) two systematic methodologies for modeling individual customer behavior, (3) evaluation of the proposed models in terms of how accurately the data they generate corresponds with real world customer behavior, and (4) a basic analysis of factors influencing the flexibility behavior based on statistical tests. Experimental results for (2)–(4) are based on a unique data set from a real-life field trial.

Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns

Distribution system planners usually provide dedicated feeders to its different class of customers, each of whom has its own characteristic load pattern which varies hourly and seasonally. A more realistic modeling should be devised by considering the daily and seasonal variations in the aggregate load patterns of different class of customers. This paper addresses a new methodology to provide integrated solution for the optimal allocation of distributed generations and network reconfiguration considering load patterns of customers. The objectives considered are to maximize annual energy loss reduction and to maintain a better node voltage profile. Bat algorithm (BA) is a new bio-inspired search algorithm which has shown an advance capability to reach into the promising region, but its exploration is inadequate. The problem is solved by proposing the improved BA (IBA). The proposed method is investigated on the benchmark IEEE 33-bus test distribution system and the results are very promising.

Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers

To the extent that demand response represents an intentional electricity usage adjustment to price changes or incentive payments, consumers who exhibit more-variable load patterns on normal days may be capable of altering their loads more significantly in response to dynamic pricing plans. This study investigates the variation in the pre-enrollment load patterns of Korean commercial and industrial electricity customers and their impact on event-day loads during a critical peak pricing experiment in the winter of 2013. Contrary to conventional approaches to profiling electricity loads, this study proposes a new clustering technique based on variability indices that collectively represent the potential demand–response resource that these customers would supply. Our analysis reveals that variability in pre-enrollment load patterns does indeed have great predictive power for estimating their impact on demand–response loads. Customers in relatively low-variability clusters provided limited or no response, whereas customers in relatively high-variability clusters consistently presented large load impacts, accounting for most of the program-level peak reductions. This study suggests that dynamic pricing programs themselves may not offer adequate motivation for meaningful adjustments in load patterns, particularly for customers in low-variability clusters.

A New Index and Classification Approach for Load Pattern Analysis of Large Electricity Customers

Conducting load pattern analysis is an important task in obtaining typical load profiles (TLPs) of customers and grouping them into classes according to their load characteristics. When using clustering techniques to obtain the load patterns of electricity customers, choosing a suitable clustering algorithm and determining an appropriate cluster number are always important and difficult issues. Therefore, this paper proposes a stability index for choosing the most suitable clustering algorithm and a priority index (based on the stability index) for determining the priority rank of clusters. Based on three known clustering algorithms, an analysis approach is presented to demonstrate the use of these indices. In the approach, all load curves of customers are first clustered with the clustering algorithms under a serial given number of clusters. The two above-mentioned indices are then calculated. Following this, the most suitable clustering algorithm is chosen and the optimal number of clusters can be determined from the rank list for special application purposes. A case study with large electricity customers connected to a distribution network in Northern China illustrates the approach. The results prove the efficiency of the approach using the proposed indices in the classification and generation of the TLPs of large electricity customers.

Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers

This paper describes a two-stage methodology that was developed for the classification of electricity customers. It is based on pattern recognition methods, such as k-means, Kohonen adaptive vector quantization, fuzzy k-means, and hierarchical clustering, which are theoretically described and properly adapted. In the first stage, typical chronological load curves of various customers are estimated using pattern recognition methods, and their results are compared using six adequacy measures. In the second stage, classification of customers is performed by the same methods and measures, together with the representative load patterns of customers being obtained from the first stage. The results of the first stage can be used for load forecasting of customers and determination of tariffs. The results of the second stage provide valuable information for electricity suppliers in competitive energy markets. The developed methodology is applied on a set of medium voltage customers of the Greek power system, and the obtained results are presented and discussed.

Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns

Heuristic Rule-Based Phase Balancing of Distribution Systems by Considering Customer Load Patterns

In this paper, a heuristic backtracking search algorithm is proposed to adjust the phasing arrangement of primary feeders and laterals for phase balancing of distribution systems. The phase unbalance index of distribution feeders is calculated based on the phasing current magnitude of each line segment and branch, which has been solved by a three-phase load flow program. The database of an automated mapping/facility management (AM/FM) system is used to retrieve the component attributes, and the topology process is executed to determine the electrical network configuration and the customers served by each distribution transformer. By using the monthly energy consumption of customers in customer information system (CIS) and the typical daily load patterns of customer classes, the hourly loading profiles of distribution transformers and service zones can be derived to solve the individual phase loadings of each primary feeder and lateral. The phase balancing of distribution systems is enhanced by heuristic rule-based searching process to minimize the phase unbalance index. To demonstrate the effectiveness of proposed methodology, a practical distribution feeder with 2754 customers is selected for computer simulation to enhance three-phase balancing of distribution systems. It is concluded that three-phase balancing of distribution systems can be obtained by considering customer load characteristics.

Application of Petri nets to solve distribution system contingency by considering customer load patterns

For a distribution contingency such as feeder overloading or short circuit fault, the artificial intelligent Petri nets (PN) with best-first search approaches is applied in this paper to find the proper switching operation decision to solve the problem. A PN 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 represent the load behavior more accurately, the typical customer load patterns derived by load survey study are used to determine the daily load profiles of each section of distribution feeders. The current flows of line switches and distribution feeders are solved by load flow analysis over a daily period. To demonstrate the effectiveness of the proposed methodology, one of the distribution systems in Taiwan Power Company (Taipower), which serves a mixture of different types of customers, is selected to perform the computer simulation. It is found that the PN algorithm can enhance the feeder reconfiguration for system contingency and improve load balance by taking into account the load characteristics of the customers served.

Application of Petri-Net to Solve Distribution System Contingency by Considering Customer Load Patterns

For a distribution contingency such as feeder overloading or short circuit fault, the artificial intelligent Petri nets (PN) with best-first search approaches is applied in this paper to find the proper switching operation decision to solve the problem. A PN 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 represent the load behavior more accurately, the typical customer load patterns derived by load survey study are used to determine the daily load profiles of each section of distribution feeders. The current flows of line switches and distribution feeders are solved by load flow analysis over a daily period. To demonstrate the effectiveness of the proposed methodology, one of the distribution systems in Taiwan Power Company (Taipower), which serves a mixture of different types of customers, is selected to perform the computer simulation. It is found that the PN algorithm can enhance the feeder reconfiguration for system contingency and improve load balance by taking into account the load characteristics of the customers served.

The optimal loss reduction of distribution feeder based on transformer rearrangement

This paper presents an efficient strategy for transformer planning to reduce the system losses by means of transformer rearrangement. The load patterns of residential, commercial and industrial customers are derived by load survey and then used to calculate the transformer hourly loading. The three-phase load flow is used to solve the system losses and unbalance factor according to the load patterns and transformer hourly loading. With regard to transformer rearrangement, the Dynamic Programming (DP) technique is employed to find out the phase assignment of transformers. In the DP process, a backward procedure and a forward procedure are used to search for the best phase arrangement of transformers. The objective function including the cost of loss reduction and switching is formulated in this paper. To verify the efficiency of the proposed method, a practical feeder is selected for computer simulation. By comparison of the results between without transformer rearrangement and with transformer rearrangement, the proposed method is verified to be valid and available.

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.

Residential response to voluntary time-of-use electricity rates

The response of residential households to voluntary Time-of-Use (TOU) electricity rates is estimated using data from a recent experiment at Midwest Power Systems of Iowa. The study's design allows us to examine both the participation decision and the customer's load pattern changes once the TOU rate structure was in effect. Substitution elasticities between on-peak and off-peak electricity usage are estimated and compared to those obtained in earlier mandatory programs, indicating whether program volunteers are more responsive to TOU pricing than the typical household. Attitudinal questionnaires allow us to examine the role of usage perceptions in program participation.

Fixed/switched type shunt capacitor planning of distribution systems by considering customer load patterns and simplified feeder model

A shunt capacitor planning strategy to reduce system resistive losses and improve voltage profile for distribution feeders has been proposed. The objective function consists of peak load loss, energy loss and shunt capacitor cost, while operational constraints such as bus voltage profile and real shunt capacitor size are considered throughout the overall solution procedure. A three-phase load flow program, which accounts for the mutual coupling effect between conductors, unbalanced loading among phases, and feeders with multiple lateral branches, is applied to enhance the computer simulation. The simplified feeder model has been developed to derive the equivalent circuit of minor lateral branches so that the data processing work for the feeder configuration can be reduced efficiently. Besides, the customer load patterns as well as the feeder load curve for various seasons are derived so that the section load behaviour can be estimated more realistically. According to the reactive load duration curve of the feeders studied, the capacitor operation strategy by considering both fixed and switched shunt capacitors is developed to determine the proper size, location and switching time of capacitors to enhance the system operation efficiency. Two practical feeders in the Taipower (Taiwan Power) distribution system are selected for demonstration to show the effectiveness of the proposed method. Capacitors with optimal size are installed according to the schemes derived by computer analysis. Field tests are then performed and the energy loss reduction as well as the improvement of power quality of the test feeders can be justified.

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.