- Email: [email protected]
Typification of load curves for DSM in Brazil for a smart grid environment
Electrical Power and Energy Systems 67 (2015) 216–221
Contents lists available at ScienceDirect
Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes
Typification of load curves for DSM in Brazil for a smart grid environment Maria N.Q. Macedo ⇑, Joaquim J.M. Galo, Luiz A.L. Almeida, Antonio C.C. Lima Department of Industrial Engineering, Federal University of Bahia, Federal Institute of Bahia, Salvador, Brazil
a r t i c l e
i n f o
Article history: Received 7 September 2014 Received in revised form 17 November 2014 Accepted 26 November 2014
Keywords: Smart grid Demand side management Load curve
a b s t r a c t The deployment of a smart grid environment is a worldwide trend and generates of a large volume of data. The load curve for each consumer in real time is an example of this. The challenge is the transformation of these data into useful information that may help to improve efficiency in the management, planning and operation of the power grid. The implementation of demand side management (DSM) requires an analysis of the data generated in a smart grid environment to determine which policies are most appropriate for each type of consumer. Because of the large number of customers, the application of these policies involves the selection of patterns for the load curve. This study discusses the use of DSM in a smart grid environment in Brazil and presents the simulation for creating load curve patterns using the k-means technique from the consumer data of a concessionaire for the Brazilian electric system. The result obtained in this research is the creation of the load curve patterns for selecting the policies of DSM. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction The increased complexity of electric power systems in recent years has contributed significantly to the search for greater efficiency in their management. This implies the need for a deeper knowledge of the behaviour of the load for their networks and their customers. The use of digital technology associated with telecommunications has provided major breakthroughs by providing systems that can supply information to improve the management of electrical systems [1–3]. A smart grid is based on the integrated use of information technology, automation, telecommunications and control of the power grid, which involves smart metres, sensors and digital network management devices that are bi-directional and allow the implementation of strategies to control and optimize the electric network with real-time data processing [4]. This convergence of technologies offers a volume of data with high reliability, encompassing the values at the points of consumption and scheduling the evaluations of voltage, current and power losses. Thus, the power grid can be controlled with more autonomy for the consumer units, and energy management can be implemented in a more decentralised manner, requiring the development
⇑ Corresponding author. E-mail address: [email protected] (M.N.Q. Macedo). http://dx.doi.org/10.1016/j.ijepes.2014.11.029 0142-0615/Ó 2014 Elsevier Ltd. All rights reserved.
of new methods of control and optimization for the operation of the electric system [5]. In addition, these new devices may exhibit multiple features, such as differentiated charging, dynamic pricing and direct control load, enabling the use of techniques for demand side management (DSM) to optimize the planning and management of the electrical system [6,7]. The challenge is the transformation of data into information that is relevant to the management of the electrical system, in addition to resolving the issues of sustainability and energy conservation. Processing these data with the use of tools that include statistical methods or artificial intelligence allows a greater insight into the consumer’s habits of consumption, and it contributes to the deployment of power management policies that are best suited to each case, such as DSM programs. This study discusses the use of DSM techniques in a smart grid environment and presents the simulation for creating load curve patterns that will be used to select which DSM techniques are best suited to each consumer. Section ‘Aspects of demand side management in Brazil’ of the article presents the characteristics and the policies associated with the DSM programs. Section ‘Load characterization in Brazil’ presents aspects of the characterization of the load. Section ‘Typification of the load curve in Brazil’ presents techniques for creating the patterns of the load curve. Section ‘Simulation and results’ presents the simulation and results, and Section ‘Conclusion’ presents the conclusion.
M.N.Q. Macedo et al. / Electrical Power and Energy Systems 67 (2015) 216–221
Aspects of demand side management in Brazil Currently, in Brazil’s electricity sector, the generation and transmission systems of energy companies already have systems of automation, supervision and control that use digital technology to monitor the processes in virtually all of the major centres. These systems offer several features such as supervision, remote control and remote measurement using the system of control and data acquisition (SCADA) implemented in the centres of operation that indicate the operating conditions of all systems automated in real time. Demand side management (DSM) is a set of techniques and policies that can help to provide greater efficiency in the planning and operation of the power grid as well as resolve the issues of sustainability and energy conservation. The problem is that the full deployment of DSM for the totality of consumers requires the availability of the load curve data and devices that can operate bi-directionally in real time on the network for each consumer individually. In the case of the distribution system in Brazil (voltage less than 34.5 kV), the reality is very different. Because of its complexity and the large number of consumers (approximately 66 million), the automated deployment of these systems is just beginning, and its operation is still performed conventionally. The power measurements for billing are performed manually for approximately 95% of the consumer units using the electromechanical metre reading, which causes poor monitoring of the workloads [8]. One of the most important steps in the deployment of DSM is the characterization of the load, which is the process that aims to identify and analyse the behaviour of the consumer load and the electrical system, enabling the calculation of costs for the use of the distribution system [9–11]. The load behaviour analysis of a system must comply with the following characteristics: The daily load curve (12:00 am tracking). Geographic location (urban, suburban, rural). Supply voltage (high, medium or low voltage). Purpose of the charging energy provided (residential, industrial, commercial). Disturbance of the load on the system.
Knowledge of the load profile also provides for the growth of demand in the existing system (lines and substations) and in the new networks, improving the calculation of the sizing system and improving the physical and financial planning studies for the expansion of the networks with greater precision. The characterization of the load is intended to identify and analyse the behaviour of the consumer load and electric system to identify the factors that contribute to their evolution for the prediction of the demand growth in the existing system and the new networks, enabling the optimization of system planning and management from the selection of actions most appropriate for each type of load. The knowledge of the behaviour of the consumer load also provides the dealership with the development of studies and actions on the tariff structure, energy conservation programs, sizing of the electrical system, stock trading and market studies. The techniques of the DSM program [10–14] most used are shown in Fig. 1. Peak clipping is a program of load cutting, demand reduction in time for a heavy load. Cutting or reducing the duration of the peak can be reached by direct load control, by shutdown of consumer equipment or by distributed generation. Conservation strategic is a program for seasonal energy consumption reduction mainly through efficient consumption and
217
combating energy waste. This program is quite comprehensive and includes incentives for technological change. Load building is a program to control the seasonal energy consumption increase in building. The dealership employs intelligent systems, processes, more efficient equipment and more competitive energy sources to achieve energetic efficiency. Valley filling is a program which encourages the off peak consumption. It builds non-peak consumption periods which is particularly desirable as the cost of production is lower, which causes a decrease in the average price and improves the efficiency of the system. Load shifting is a program, with the workload transfer period of greatest consumption (peak period to period of lower consumption), move tip out loads, without changing the total consumption. This is also possible with distributed generation. Flexible load shape is a set of actions and integrated planning between the concessionary and the consumer, subject to the needs of the moment. It is a partnership in order to model consumer loads, without affecting the actual conditions of security, limiting the power and energy that the individual consumer can use at certain times, through the installation of load limiting devices. All of these techniques are intended to equalize the demand and avoid spikes in consumption for certain schedules that may contribute to the anticipation of investments. Because of the large number of customers, the application of these techniques or DSM policies involves the selection of patterns for the load curve. The selection of the number of standards depends on the purpose of the concessionaire with respect to the classification of the curves: whether it is for the calculation of tariffs for the DSM policies or simply for the planning or operation of the system. For any one of these objectives, typification is required for selecting the most appropriate policies for each set of loads with the same characteristics [15,16]. It is important to note that the monitoring of the consumer load curves is an activity of great importance for the purposes of DSM policies. However, the register consumer analysis and the monitoring of feeders that provide electricity to consumers are also required, as shown in Fig. 2 so that one can have a complete diagnosis of the system and provide more effective decisions regarding the most appropriate policies to be implemented for each case. This study normally works with typical curves, which represents a reduction in this universe of networks and clients to more incidents and distinct behaviours. It should be noted that in the definition of typical curves where there are insufficient statistical calculations, user intervention is needed and must consider the significance of the types (very small or very large participation in the market) if the type is parsed by grouping different forms, especially for the differences or similarities in the tip schedule. The lack of knowledge of the profile of the load for each consumer group makes it considerably more difficult to assess the benefits of energy conservation programs and the technical calculation of the losses in the distribution system. Furthermore, in the short term, the management of the demand for the consumers and networks can mean large cost savings. Load characterization in Brazil Data acquisition is the first step in a study of the characterization of a charge. The quality of the data collected is a key point because it will be the entire basis of the studies for planning. Obtaining the data of all consumers in a region without the use of digital technology makes it necessary to employ statistical techniques based on the installation of metres in a small sample
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(a) Peak Clipping
(b) Valley filling
(c) Strategic Conservation
(d) Strategic Load Growth
(e) Load Shifting
(f) Flexible Load Shape
Fig. 1. Techniques of the DSM program.
The feeders load curve
Consumer registration data
Analysis Tools
DSM policies
Consumer Load curve
Fig. 2. Proposed schema for the choice of DSM policies.
of the population in which the variables of interest will be analysed. Thus, the knowledge obtained is then transferred to the entire population, which is modelled by that sample [17–20]. Load curve data can be obtained in two ways: from the recovery of measures when a digital metre is already installed or from the measures’ campaign. The recovery of the load curve measures is accompanied by areas of metering and billing for the concessionaires. In this case, the points of interest for the load studies already installed measuring equipment on site, allowing the lifting of the load curve with the values already stored previously [21–26]. When the points of interest for the studies do not have equipment that allow the lifting of the load curve, which is the case in Brazil for the overwhelming majority of consumers (95%), it is necessary to use a campaign of measures, which covers a set of activities and procedures for collecting data for the load curves in a given sample of consumers or for components of the electric system. This measure typically occurs every 4 years for the calculation of the tariffs for energy [8].
As these metres are placed to accompany the load curve of consumers temporarily, an important issue that arises is the absence of continuous monitoring of these loads in detail and in real time. This prevents the performance of more immediate measures or the most appropriate policies for each consumer profile to optimize the system dynamically. A smart grid environment offers consumers digital metres that are already installed permanently, which must accompany the 12:00 am behaviour of the load in real time and from specific programs that can contribute to a detailed knowledge of the load and can select the appropriate policies for each type of load. After collecting and archiving data from the load curve, the next step is to apply the data treatment. This treatment is comprised of the set of inference in the database, identifying the characteristic parameters of a given population and characterizing them in terms of their types. The idea is to create the patterns or typify the load curve, which may characterize the type of consumption. Additionally, consumers apply and verify which consumers are closest to the chosen standards.
M.N.Q. Macedo et al. / Electrical Power and Energy Systems 67 (2015) 216–221
Analysing the load curve involves a large volume of data that must be processed to have a base of knowledge regarding the various features of the system. Currently, information technology has presented the efficacy of the acquisition and storage of information, providing a large growth in the volumes of stored data and making it difficult to extract knowledge without the help of tools for the analysis of data from charts, tables or statistical methods. The great challenge is to process the data and to extract and make available the relevant information for decision-making. The on-line analytic processing (OLAP) allows a multidimensional analysis of the data and the use of techniques such as data mining (data mining), which will be described in the next section and consists of a knowledge extraction process based on the classification and grouping of data by applying algorithms, including the use of artificial intelligence tools [26,27]. The process of data classification is usually performed in stages, as shown in Fig. 3: Data collection and selection of variables for analysis. Choice of classification algorithm. Creation of array: similarity matrix generation between the elements that are classified. Example: load curves. Creation of standard: choice of patterns to be used. Identification of classes: assignment of each object to a class. Typification of the load curve in Brazil Because of the large number of consumers for the electrical system, current technology is unenforceable for the study of the individual behaviour for each consumer. The strategy then is to define the market in the study for a suitable number of consumption patterns that will represent the totality. The variables that will define these patterns are your load curves, which are called typical curves. In the studies that have as the objective the definition of the behaviour for a given voltage level, it is necessary to determine the consumer typology for this level. In Brazil, these studies are usually performed for the calculation of rates, and the method most used to define this room type is a method called dynamic clouds. This method emphasizes the minimization of the internal variance for grouping, maximizing your distance in relation to other groups. The method of dynamic clouds presents the classification around points called nuclei that belong to a given set of data. In this process, the clusters are not characterized by a centre of
Data collection
gravity, but by a certain number of objects called patterns that constitute a core. The problem with this method is the long processing time. The literature offers several methods for sorting data [28–33]. In addition, the application of classic techniques for the group also apply artificial intelligence techniques and analyse mass consumer data to typify patterns from the best possible typology of consumers. A method that is used frequently in the classification of load curves is the k-means algorithm, which was selected because of its effectiveness, low processing time, simple programming, and low memory requirement. The k-means algorithm is based on the minimization of the internal distance between the patterns of a group or cluster. Minimizing the error warrants finding a local minimum of the function, which will depend on the starting point of the algorithm. The algorithm provides an automatic sorting without the need for any human supervision, i.e., without any pre-sorting. Because of this characteristic, the k-means clustering is considered as an algorithm of unsupervised data mining. The k-means algorithm groups data around centres termed centroids, creating partitions with new classes. These partitions, in turn, have new centres that cause at the discretion of the closer new partitions. All of this occurs in a cycle that ends only when the partitions cannot be improved, or until they reach a predetermined level of precision. The centroids are at the heart of each class or standard adopted, as shown in Fig. 4. The determination of the gravity centres of the new classes can be calculated from Eq. (1)
E¼
k X X dðx; lðC i ÞÞ
ð1Þ
i¼1 x2C i
where E is the error function, l (Ci) is the centroid of the cluster Ci and d (x, l (Ci)) is the Euclidean distance between x and the centroid l (Ci). Main steps of the k-means algorithm: Step 1. Adopt the partition of departure, taking into account the available information or from elements of and drawn, around which form its classes. Typically, each class starting partition consists of a single object, chosen randomly or in a defined order. Step 2. Determine the gravity centres of their classes and the inertia of the partition. Step 3. Build a new partition crowding around each of the elements of gravity centre and closer to the centre than the other. If some of the elements of E are equidistant from various
Choice of Algorithm
Creation of the array
Identification of classes
Creation of standards
Fig. 3. Steps for data classification.
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Fig. 4. Centroids of clusters.
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Type 0
Type 1 Normalized load
Normalized load
1,2 1 0,8 0,6 0,4 0,2 0
0
5
10
15
20
25
1,2 1 0,8 0,6 0,4 0,2 0
0
5
Hours
Normalized load
Normalized load
0,8 0,6 0,4 0,2 0
5
10
15
20
25
Type 3
1,2
1
0
15
Hours
Type 2
1,2
10
20
25
1,0 0,8 0,6 0,4 0,2 0,0
0
5
10
15
20
25
Hours
Hours Fig. 5. Proposed patterns of load curves.
centres of gravity, arbitrarily shall be charged to any of the classes that contain these centres. Step 4. Determine the gravity centres of the new classes
Ik ¼
k X X jxj lj j2
ð2Þ
j¼1 xj 2C
Step 5. Compare the quality of your new partition, the previous one with the current-keeping the best. Step 6. If the process can be optimizing, restart the process in step 3. Otherwise the partition obtained will be great on. The problem with this method is its sensitivity in the selection of the initial clusters that can converge to a local minimum. Thus, variations of the k-means clustering occur with several strategies for start-up with the goal of finding a global minimum. Simulation and results Defining the typology requires a data file load curve for a representative sample of the relevant market and on the basis of this process, the file analysis of these data. The input data were supplied by the local dealership and were obtained from the measures that occur every 4 years for the calculation of tariffs. The 96 measures were provided daily for 2000 consumer measures of low voltage for a month and weekends. These data have been processed, have analysed the average curve for five working days for each consumer and were normalized to allow comparisons of only the curves, regardless of the absolute values of consumption. Based on the data obtained in the previous step, an array of standardized data and the MATLAB software were used to simulate the creation of the standard curves with different features using the k-means method. It was created and initially tested various patterns of the load curves for low voltage. However, from the analysis of the patterns created, the objective of the creation of these standards is to select the most suitable policy of DSM for each type of consumption. It was observed that four patterns of the load curve is sufficient to
define which DSM techniques should be used (for peak displacement control, load control, energy efficiency or to encourage distributed generation). Fig. 5 shows the four selected load curve patterns. Type 0 has a fairly constant consumption throughout the day with a slightly elevated peak-hour consumption. Management actions for this case can be based on energy efficiency and conservation policies. Type 1 has two more pronounced peaks in consumption, which implies the need for action to control the peak through tariff incentives and direct control load or peak displacement, power generation by the consumer, or accumulation negotiated with the consumer through tariff incentives, depending on the knowledge of the cadastral data of consumers. Type 2 has a sharp consumption peak, implying the need for policies to reduce the peak and fill the valleys for which direct control and/or distributed generation can be used. Type 3 has peaks only during the night. This load type is characteristic of LED streetlights. In this case, the actions may involve the replacement of lamps with higher-efficiency lamps. The selection of the number of standards depends on the company’s objective with respect to the classification of curves, e.g., for pricing policies for DSM or simply for the planning or operation of the system. In a smart grid environment, curves obtained using this method can be employed as a standard for conducting an automatic classification of consumers in real time and for selecting which DSM policies are most appropriate for each consumer. Conclusion The deployment of a smart grid environment enables the generation of a large volume of data. The challenge is the transformation of these data into useful information that may help to improve efficiency in the management, planning and operation of the power grid as well in resolving the issues of sustainability and energy conservation. In this new environment, the implementation of DSM techniques assumes an important role in the quest for efficiency
M.N.Q. Macedo et al. / Electrical Power and Energy Systems 67 (2015) 216–221
in the electricity sector. The knowledge of the profile of consumers from the real-time monitoring of load curves opens a window of opportunities for new applications in Brazil, as shown in this article. The full deployment of DSM for the totality of consumers requires the availability of the load curve data and devices that can operate bi-directionally in real time on the network for each consumer. Creating load curve patterns for DSM can definitely contribute to the DSM policies that are deployed appropriately to each consumer after its classification. The next work using the methodology proposed in this paper is the use of artificial intelligence techniques, such as artificial neural networks, to perform the automatic classification of load curves using standard curves are described in this paper.
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Contents lists available at ScienceDirect
Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes
Typification of load curves for DSM in Brazil for a smart grid environment Maria N.Q. Macedo ⇑, Joaquim J.M. Galo, Luiz A.L. Almeida, Antonio C.C. Lima Department of Industrial Engineering, Federal University of Bahia, Federal Institute of Bahia, Salvador, Brazil
a r t i c l e
i n f o
Article history: Received 7 September 2014 Received in revised form 17 November 2014 Accepted 26 November 2014
Keywords: Smart grid Demand side management Load curve
a b s t r a c t The deployment of a smart grid environment is a worldwide trend and generates of a large volume of data. The load curve for each consumer in real time is an example of this. The challenge is the transformation of these data into useful information that may help to improve efficiency in the management, planning and operation of the power grid. The implementation of demand side management (DSM) requires an analysis of the data generated in a smart grid environment to determine which policies are most appropriate for each type of consumer. Because of the large number of customers, the application of these policies involves the selection of patterns for the load curve. This study discusses the use of DSM in a smart grid environment in Brazil and presents the simulation for creating load curve patterns using the k-means technique from the consumer data of a concessionaire for the Brazilian electric system. The result obtained in this research is the creation of the load curve patterns for selecting the policies of DSM. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction The increased complexity of electric power systems in recent years has contributed significantly to the search for greater efficiency in their management. This implies the need for a deeper knowledge of the behaviour of the load for their networks and their customers. The use of digital technology associated with telecommunications has provided major breakthroughs by providing systems that can supply information to improve the management of electrical systems [1–3]. A smart grid is based on the integrated use of information technology, automation, telecommunications and control of the power grid, which involves smart metres, sensors and digital network management devices that are bi-directional and allow the implementation of strategies to control and optimize the electric network with real-time data processing [4]. This convergence of technologies offers a volume of data with high reliability, encompassing the values at the points of consumption and scheduling the evaluations of voltage, current and power losses. Thus, the power grid can be controlled with more autonomy for the consumer units, and energy management can be implemented in a more decentralised manner, requiring the development
⇑ Corresponding author. E-mail address: [email protected] (M.N.Q. Macedo). http://dx.doi.org/10.1016/j.ijepes.2014.11.029 0142-0615/Ó 2014 Elsevier Ltd. All rights reserved.
of new methods of control and optimization for the operation of the electric system [5]. In addition, these new devices may exhibit multiple features, such as differentiated charging, dynamic pricing and direct control load, enabling the use of techniques for demand side management (DSM) to optimize the planning and management of the electrical system [6,7]. The challenge is the transformation of data into information that is relevant to the management of the electrical system, in addition to resolving the issues of sustainability and energy conservation. Processing these data with the use of tools that include statistical methods or artificial intelligence allows a greater insight into the consumer’s habits of consumption, and it contributes to the deployment of power management policies that are best suited to each case, such as DSM programs. This study discusses the use of DSM techniques in a smart grid environment and presents the simulation for creating load curve patterns that will be used to select which DSM techniques are best suited to each consumer. Section ‘Aspects of demand side management in Brazil’ of the article presents the characteristics and the policies associated with the DSM programs. Section ‘Load characterization in Brazil’ presents aspects of the characterization of the load. Section ‘Typification of the load curve in Brazil’ presents techniques for creating the patterns of the load curve. Section ‘Simulation and results’ presents the simulation and results, and Section ‘Conclusion’ presents the conclusion.
M.N.Q. Macedo et al. / Electrical Power and Energy Systems 67 (2015) 216–221
Aspects of demand side management in Brazil Currently, in Brazil’s electricity sector, the generation and transmission systems of energy companies already have systems of automation, supervision and control that use digital technology to monitor the processes in virtually all of the major centres. These systems offer several features such as supervision, remote control and remote measurement using the system of control and data acquisition (SCADA) implemented in the centres of operation that indicate the operating conditions of all systems automated in real time. Demand side management (DSM) is a set of techniques and policies that can help to provide greater efficiency in the planning and operation of the power grid as well as resolve the issues of sustainability and energy conservation. The problem is that the full deployment of DSM for the totality of consumers requires the availability of the load curve data and devices that can operate bi-directionally in real time on the network for each consumer individually. In the case of the distribution system in Brazil (voltage less than 34.5 kV), the reality is very different. Because of its complexity and the large number of consumers (approximately 66 million), the automated deployment of these systems is just beginning, and its operation is still performed conventionally. The power measurements for billing are performed manually for approximately 95% of the consumer units using the electromechanical metre reading, which causes poor monitoring of the workloads [8]. One of the most important steps in the deployment of DSM is the characterization of the load, which is the process that aims to identify and analyse the behaviour of the consumer load and the electrical system, enabling the calculation of costs for the use of the distribution system [9–11]. The load behaviour analysis of a system must comply with the following characteristics: The daily load curve (12:00 am tracking). Geographic location (urban, suburban, rural). Supply voltage (high, medium or low voltage). Purpose of the charging energy provided (residential, industrial, commercial). Disturbance of the load on the system.
Knowledge of the load profile also provides for the growth of demand in the existing system (lines and substations) and in the new networks, improving the calculation of the sizing system and improving the physical and financial planning studies for the expansion of the networks with greater precision. The characterization of the load is intended to identify and analyse the behaviour of the consumer load and electric system to identify the factors that contribute to their evolution for the prediction of the demand growth in the existing system and the new networks, enabling the optimization of system planning and management from the selection of actions most appropriate for each type of load. The knowledge of the behaviour of the consumer load also provides the dealership with the development of studies and actions on the tariff structure, energy conservation programs, sizing of the electrical system, stock trading and market studies. The techniques of the DSM program [10–14] most used are shown in Fig. 1. Peak clipping is a program of load cutting, demand reduction in time for a heavy load. Cutting or reducing the duration of the peak can be reached by direct load control, by shutdown of consumer equipment or by distributed generation. Conservation strategic is a program for seasonal energy consumption reduction mainly through efficient consumption and
217
combating energy waste. This program is quite comprehensive and includes incentives for technological change. Load building is a program to control the seasonal energy consumption increase in building. The dealership employs intelligent systems, processes, more efficient equipment and more competitive energy sources to achieve energetic efficiency. Valley filling is a program which encourages the off peak consumption. It builds non-peak consumption periods which is particularly desirable as the cost of production is lower, which causes a decrease in the average price and improves the efficiency of the system. Load shifting is a program, with the workload transfer period of greatest consumption (peak period to period of lower consumption), move tip out loads, without changing the total consumption. This is also possible with distributed generation. Flexible load shape is a set of actions and integrated planning between the concessionary and the consumer, subject to the needs of the moment. It is a partnership in order to model consumer loads, without affecting the actual conditions of security, limiting the power and energy that the individual consumer can use at certain times, through the installation of load limiting devices. All of these techniques are intended to equalize the demand and avoid spikes in consumption for certain schedules that may contribute to the anticipation of investments. Because of the large number of customers, the application of these techniques or DSM policies involves the selection of patterns for the load curve. The selection of the number of standards depends on the purpose of the concessionaire with respect to the classification of the curves: whether it is for the calculation of tariffs for the DSM policies or simply for the planning or operation of the system. For any one of these objectives, typification is required for selecting the most appropriate policies for each set of loads with the same characteristics [15,16]. It is important to note that the monitoring of the consumer load curves is an activity of great importance for the purposes of DSM policies. However, the register consumer analysis and the monitoring of feeders that provide electricity to consumers are also required, as shown in Fig. 2 so that one can have a complete diagnosis of the system and provide more effective decisions regarding the most appropriate policies to be implemented for each case. This study normally works with typical curves, which represents a reduction in this universe of networks and clients to more incidents and distinct behaviours. It should be noted that in the definition of typical curves where there are insufficient statistical calculations, user intervention is needed and must consider the significance of the types (very small or very large participation in the market) if the type is parsed by grouping different forms, especially for the differences or similarities in the tip schedule. The lack of knowledge of the profile of the load for each consumer group makes it considerably more difficult to assess the benefits of energy conservation programs and the technical calculation of the losses in the distribution system. Furthermore, in the short term, the management of the demand for the consumers and networks can mean large cost savings. Load characterization in Brazil Data acquisition is the first step in a study of the characterization of a charge. The quality of the data collected is a key point because it will be the entire basis of the studies for planning. Obtaining the data of all consumers in a region without the use of digital technology makes it necessary to employ statistical techniques based on the installation of metres in a small sample
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(a) Peak Clipping
(b) Valley filling
(c) Strategic Conservation
(d) Strategic Load Growth
(e) Load Shifting
(f) Flexible Load Shape
Fig. 1. Techniques of the DSM program.
The feeders load curve
Consumer registration data
Analysis Tools
DSM policies
Consumer Load curve
Fig. 2. Proposed schema for the choice of DSM policies.
of the population in which the variables of interest will be analysed. Thus, the knowledge obtained is then transferred to the entire population, which is modelled by that sample [17–20]. Load curve data can be obtained in two ways: from the recovery of measures when a digital metre is already installed or from the measures’ campaign. The recovery of the load curve measures is accompanied by areas of metering and billing for the concessionaires. In this case, the points of interest for the load studies already installed measuring equipment on site, allowing the lifting of the load curve with the values already stored previously [21–26]. When the points of interest for the studies do not have equipment that allow the lifting of the load curve, which is the case in Brazil for the overwhelming majority of consumers (95%), it is necessary to use a campaign of measures, which covers a set of activities and procedures for collecting data for the load curves in a given sample of consumers or for components of the electric system. This measure typically occurs every 4 years for the calculation of the tariffs for energy [8].
As these metres are placed to accompany the load curve of consumers temporarily, an important issue that arises is the absence of continuous monitoring of these loads in detail and in real time. This prevents the performance of more immediate measures or the most appropriate policies for each consumer profile to optimize the system dynamically. A smart grid environment offers consumers digital metres that are already installed permanently, which must accompany the 12:00 am behaviour of the load in real time and from specific programs that can contribute to a detailed knowledge of the load and can select the appropriate policies for each type of load. After collecting and archiving data from the load curve, the next step is to apply the data treatment. This treatment is comprised of the set of inference in the database, identifying the characteristic parameters of a given population and characterizing them in terms of their types. The idea is to create the patterns or typify the load curve, which may characterize the type of consumption. Additionally, consumers apply and verify which consumers are closest to the chosen standards.
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Analysing the load curve involves a large volume of data that must be processed to have a base of knowledge regarding the various features of the system. Currently, information technology has presented the efficacy of the acquisition and storage of information, providing a large growth in the volumes of stored data and making it difficult to extract knowledge without the help of tools for the analysis of data from charts, tables or statistical methods. The great challenge is to process the data and to extract and make available the relevant information for decision-making. The on-line analytic processing (OLAP) allows a multidimensional analysis of the data and the use of techniques such as data mining (data mining), which will be described in the next section and consists of a knowledge extraction process based on the classification and grouping of data by applying algorithms, including the use of artificial intelligence tools [26,27]. The process of data classification is usually performed in stages, as shown in Fig. 3: Data collection and selection of variables for analysis. Choice of classification algorithm. Creation of array: similarity matrix generation between the elements that are classified. Example: load curves. Creation of standard: choice of patterns to be used. Identification of classes: assignment of each object to a class. Typification of the load curve in Brazil Because of the large number of consumers for the electrical system, current technology is unenforceable for the study of the individual behaviour for each consumer. The strategy then is to define the market in the study for a suitable number of consumption patterns that will represent the totality. The variables that will define these patterns are your load curves, which are called typical curves. In the studies that have as the objective the definition of the behaviour for a given voltage level, it is necessary to determine the consumer typology for this level. In Brazil, these studies are usually performed for the calculation of rates, and the method most used to define this room type is a method called dynamic clouds. This method emphasizes the minimization of the internal variance for grouping, maximizing your distance in relation to other groups. The method of dynamic clouds presents the classification around points called nuclei that belong to a given set of data. In this process, the clusters are not characterized by a centre of
Data collection
gravity, but by a certain number of objects called patterns that constitute a core. The problem with this method is the long processing time. The literature offers several methods for sorting data [28–33]. In addition, the application of classic techniques for the group also apply artificial intelligence techniques and analyse mass consumer data to typify patterns from the best possible typology of consumers. A method that is used frequently in the classification of load curves is the k-means algorithm, which was selected because of its effectiveness, low processing time, simple programming, and low memory requirement. The k-means algorithm is based on the minimization of the internal distance between the patterns of a group or cluster. Minimizing the error warrants finding a local minimum of the function, which will depend on the starting point of the algorithm. The algorithm provides an automatic sorting without the need for any human supervision, i.e., without any pre-sorting. Because of this characteristic, the k-means clustering is considered as an algorithm of unsupervised data mining. The k-means algorithm groups data around centres termed centroids, creating partitions with new classes. These partitions, in turn, have new centres that cause at the discretion of the closer new partitions. All of this occurs in a cycle that ends only when the partitions cannot be improved, or until they reach a predetermined level of precision. The centroids are at the heart of each class or standard adopted, as shown in Fig. 4. The determination of the gravity centres of the new classes can be calculated from Eq. (1)
E¼
k X X dðx; lðC i ÞÞ
ð1Þ
i¼1 x2C i
where E is the error function, l (Ci) is the centroid of the cluster Ci and d (x, l (Ci)) is the Euclidean distance between x and the centroid l (Ci). Main steps of the k-means algorithm: Step 1. Adopt the partition of departure, taking into account the available information or from elements of and drawn, around which form its classes. Typically, each class starting partition consists of a single object, chosen randomly or in a defined order. Step 2. Determine the gravity centres of their classes and the inertia of the partition. Step 3. Build a new partition crowding around each of the elements of gravity centre and closer to the centre than the other. If some of the elements of E are equidistant from various
Choice of Algorithm
Creation of the array
Identification of classes
Creation of standards
Fig. 3. Steps for data classification.
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Fig. 4. Centroids of clusters.
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Type 0
Type 1 Normalized load
Normalized load
1,2 1 0,8 0,6 0,4 0,2 0
0
5
10
15
20
25
1,2 1 0,8 0,6 0,4 0,2 0
0
5
Hours
Normalized load
Normalized load
0,8 0,6 0,4 0,2 0
5
10
15
20
25
Type 3
1,2
1
0
15
Hours
Type 2
1,2
10
20
25
1,0 0,8 0,6 0,4 0,2 0,0
0
5
10
15
20
25
Hours
Hours Fig. 5. Proposed patterns of load curves.
centres of gravity, arbitrarily shall be charged to any of the classes that contain these centres. Step 4. Determine the gravity centres of the new classes
Ik ¼
k X X jxj lj j2
ð2Þ
j¼1 xj 2C
Step 5. Compare the quality of your new partition, the previous one with the current-keeping the best. Step 6. If the process can be optimizing, restart the process in step 3. Otherwise the partition obtained will be great on. The problem with this method is its sensitivity in the selection of the initial clusters that can converge to a local minimum. Thus, variations of the k-means clustering occur with several strategies for start-up with the goal of finding a global minimum. Simulation and results Defining the typology requires a data file load curve for a representative sample of the relevant market and on the basis of this process, the file analysis of these data. The input data were supplied by the local dealership and were obtained from the measures that occur every 4 years for the calculation of tariffs. The 96 measures were provided daily for 2000 consumer measures of low voltage for a month and weekends. These data have been processed, have analysed the average curve for five working days for each consumer and were normalized to allow comparisons of only the curves, regardless of the absolute values of consumption. Based on the data obtained in the previous step, an array of standardized data and the MATLAB software were used to simulate the creation of the standard curves with different features using the k-means method. It was created and initially tested various patterns of the load curves for low voltage. However, from the analysis of the patterns created, the objective of the creation of these standards is to select the most suitable policy of DSM for each type of consumption. It was observed that four patterns of the load curve is sufficient to
define which DSM techniques should be used (for peak displacement control, load control, energy efficiency or to encourage distributed generation). Fig. 5 shows the four selected load curve patterns. Type 0 has a fairly constant consumption throughout the day with a slightly elevated peak-hour consumption. Management actions for this case can be based on energy efficiency and conservation policies. Type 1 has two more pronounced peaks in consumption, which implies the need for action to control the peak through tariff incentives and direct control load or peak displacement, power generation by the consumer, or accumulation negotiated with the consumer through tariff incentives, depending on the knowledge of the cadastral data of consumers. Type 2 has a sharp consumption peak, implying the need for policies to reduce the peak and fill the valleys for which direct control and/or distributed generation can be used. Type 3 has peaks only during the night. This load type is characteristic of LED streetlights. In this case, the actions may involve the replacement of lamps with higher-efficiency lamps. The selection of the number of standards depends on the company’s objective with respect to the classification of curves, e.g., for pricing policies for DSM or simply for the planning or operation of the system. In a smart grid environment, curves obtained using this method can be employed as a standard for conducting an automatic classification of consumers in real time and for selecting which DSM policies are most appropriate for each consumer. Conclusion The deployment of a smart grid environment enables the generation of a large volume of data. The challenge is the transformation of these data into useful information that may help to improve efficiency in the management, planning and operation of the power grid as well in resolving the issues of sustainability and energy conservation. In this new environment, the implementation of DSM techniques assumes an important role in the quest for efficiency
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in the electricity sector. The knowledge of the profile of consumers from the real-time monitoring of load curves opens a window of opportunities for new applications in Brazil, as shown in this article. The full deployment of DSM for the totality of consumers requires the availability of the load curve data and devices that can operate bi-directionally in real time on the network for each consumer. Creating load curve patterns for DSM can definitely contribute to the DSM policies that are deployed appropriately to each consumer after its classification. The next work using the methodology proposed in this paper is the use of artificial intelligence techniques, such as artificial neural networks, to perform the automatic classification of load curves using standard curves are described in this paper.
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