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Diseno de programas para la gestion eficiente de la demanda de energia electrica en usuarios industriales.

Design of demand management programs for the efficient use of electricity by industrial users

1. Introduction

The demand management programs (DMP) for electricity are a portfolio of strategies to improve the electrical power system on the consumer side (Palensky & Dietrich, 2011). The difference between the strategies implemented to make efficient use of the electrical energy depends on the degree of awareness of the end user that energy is a limited resource (Nguyen & Aiello, 2013) and the degree of modernization of the electricity network, enabling the implementation of next generation devices which can be manipulated by users remotely through applications via the web (Amin et al., 2015). Figure1 shows the main strategies applied to carry out DMP in the world (Law et al., 2012).

In Figure1 three global strategies are shown which can be implemented simultaneously; however they depend on the conditions of the electrical distribution network for their implementation.

The first strategy that is shown in figure 1 is based on the generation of a passive control of demand, to promote the efficient consumption of electrical energy from the awareness and education of people about the use of this type of energy. The second strategy is linked to a more active control of demand where the user has differential rates depending on the time of day or DMP that will allow the user to control their energy expenditure (Law et al., 2012). The third strategy intends to migrate to sophisticated monitoring and control systems in real time via an intelligent metering system that allows the user to have a real knowledge of their electric power consumption (Martinez & Rudnick, 2012).

The design of a DMP is a convenient cyclical process of continuous feedback through strategies initially covering the education and training of people with knowledge of eneigy efficiency and the importance of this issue, not only in the reduction of electricity consumption, but also in the relationship to environmental aspects, political and social rights. In this case it is important to know the needs and desires that customers have according to the characteristics of the demand of electrical eneigy within the production processes (Aalami et al., 2010).

Continuing with the training of staff to optimize energy consumption within the industrial processes, it is desirable to have the participation of the industrial users in the deregulated market, this means making direct contracts with the energy marketer, which would be monthly or annual contracts where they agree the quantities of energy for such periods of time. These users who participate within the deregulated market have measurement devices capable of exchanging information between the different agents of marketing companies, as well as being able to access the consumption data which can be managed (CREG, 2010).

The technology used in measuring devices in the deregulated market is the automatic meter reading (AMR), which allows for unidirectional communication, measurement of electric energy consumption in a given period of time, remote control of the meter and partial management of electric energy (Sioshansi, 2011). To perform an active demand management it is necessary to count with smart metering devices, therefore it is intended that the industrial users migrate from the AMR technology to the AMI (Advanced Meter Infrastructure), which can provide additional benefits such as the measurement of the energy quality, bidirectional communication and total management of the energy consumed (McLaughlin et al., 2009).

This article identifies the activities which characterize demand, manage the energy consumed by industrial users through the integration of activities by the agents who par-ticipate in the exercise for the delivery of energy supply on the basis of technical, economic, social and environmental aspects to be subsequently analyzed through the data analysis strategies called ALL DATA (Doukas et al., 2009), for the design of DMP which are firmly established and stand the test of time.

2. Methodology

From the original taxonomy known as All Data, which classifies in big, small, linked and open data, and whose initial aim was integrated into a system of analysis and production of information, a methodological approach that leads to decision making oriented derived by data (DDD by its initials in English data driven decision). The validity of the conclusions and decisions is guided by the interaction of two elements: the presence of a theoretical framework and the feedback that can be achieved with the context. The first provides the conceptual bases with which to go beyond information to knowledge, reading statistics to the categories of analysis inherent to the field of study being addressed. The second captures the dynamics and evolution of the phenomenon, and incorporates the new paths of the variables and their values. Both provide the feedback for the model.

In the specific case for the design strategies for the development, implementation and evaluation of DMPs, part of the consideration is the large volumes of data or Big data generated by the remote measurement systems (linked data) with which companies today as a consequence of the opportunities of discounts in the electrical energy tariff because of that industrial users are declared generally unregulated users or large customers (CREG, 1998).

The initial questions formulated with regard to this type of industrial consumption of electrical energy make reference to the description and the construction of a demand curve to provide the first elements of the diagnosis; however, these massive volumes of data are insufficient to give a deep understanding of the demands, needs, requirements and limitations for the formulation of a DMP to achieve participation in collective of demand and an efficient use of electrical energy.

The resource of small data, through personal interviews and surveys with decision-makers is an essential component in clarifying and sizing the large volumes that only reflect one of the aspects of the problem. The interaction big - small not only gives information product of the confrontation but also allows for the development of a new working hypothesis, to clarify and refine the information contained in the bulk data and above all to collect the perspectives and interests of the user and result in a DMP specific to the company and not only general-purpose.

The consideration of the industrial activity as social in nature makes it imperative to its contextualization in broader areas ranging from local to national and for this purpose the open data constitutes another of the key pieces, to identify the broader dimensions of efficiency, their relationships and potential contributions. In particular it is to create performance scenarios for industrial firms in the areas which in some way has incidence, so there is a need for the energy system to be a system which gives the product of its activity.

From the social perspective, there appear new dimensions which are not originally provided for by the methodological cycle of the DMP design, such as the environmental, economic and social agents and elements that invigorate the market, among several others.

The systemic methodology for the DMP design incorporates information as an articulating axis of the different dimensions, put in place by the demands of interaction between the theoretical framework and the context. The decisions guided by the data pose a sequence that begins with the description and diagnosis and conclude with predictions and prescriptive guidance, translated into a means for promotion and prevention.

3. Results and discussion

The technical part of the DMP seeks to optimize the electrical infrastructure of each user, to be efficient through the energy audits, as well as examine the measurement devices and communication for the user to acquire an active participation by knowing their consumption and other conditions of the state of their electrical system (Muttaqi et al., 2015).

In the economic aspects, the DMP is involved with improving the consumption patterns of the user industry, as well as the coordination of the processes involved in the negotiations of the energy market, as for example the remuneration of incentives for reductions in consumption or the collection of penalties for excess consumption of electrical energy (Rahmani-Andebili, 2016).

In the environmental aspect the DMP contribute to the reduction of Carbon dioxide emissions and the delay of the expansion of infrastructure for the generation of electrical power, because these programs aim to make efficient use through the reduction in consumption and the inclusion of another type of technology that allow you to consume from renewable sources or the environment (Zhou et al., 2016).

From a social aspect it aims to carry out the massive deployment of DMP to provide sustainability by the efficient use of energy, which may provide social construction for the country through the search for a more balanced society in obtaining this resource, essential for the quality of human life, and a collective progress for the country (Miara et al., 2014).

Figure 2 presents an overview of the dimensions that are involved in the design of DMP and is observed in the core strategies ALL DATA, followed by the agents that are directly involved with the design, implementation and refinement of the DMP, and on the outside are the technical, economic, environmental and social aspects which harbour the development of these types of proposals to make them sustainable and long lasting.

In the context of ALL DATA are terms such as BIG DATA, these are directly related to volume, variety and speed of data that are stored, there have also been considered definitions as value, viewing and veracity of the data categorized as SQL, and more recently the NoSQL (Liao et al., 2016), and which take into account both structured and unstructured data, therefore it is necessary to advance the development of the storage of information (Mauro et al., 2015). Followed by small data which relate to the timely data captured for example with technical surveys, sampling schemes, manual collection of data, among others. Open data is free access data, for example data that can be extracted from the statistics that are handled by the government, as well as the regulatory data among others, and finally linked data, these are those that at present it is essential to perform more work, as they relate to data sets that are transmitted via the internet from its origin to the basis of storage and can be produced in small fractions of time, are in the category IoT (Internet of Things) (Martinez-Prieto et al., 2015). In table 1 the various technical, economic, social and environmental aspects are combined with the ALL DATA strategies. The table 1 presents the sources of information for each of the strategies, this information is aiming to have a holistic view of the industrial user and from all points of view to identify the variables that affect, positively and negatively a DMP design.

Another important part of DMP design are the actors involved in the implementation, these agents are divided into client, regulator, operator and aggregator. Customers are the users to which it provides the service, in this case the industrial users, followed by the regulator in charge of regulating the electrical power system from generation until retail. The network operator is responsible for performing the technical coordination to provide a service with quality and customer specifications (XM, 2014), and finally proposes to implement in Colombia the figure of a new agent, which is called the aggregator of load which has as function to carry out the negotiations by a small group of members inside of the wholesale energy market (Rodriguez et al., 2013).

Figure 3 presents involving agents within the DMP design with regard to the activities developed for each of the strategies for obtaining data and their relationship with the different technical, economic, environmental and social aspects.

The client is looking to have an active participation and a clear and detailed knowledge about their consumption of electricity, the behaviour of their teams, the evolution and participation of the industrial activity in the country and in the world as well as to make use of that knowledge to optimize their processes and save on the consumption of electrical energy.

The regulator will have focused activities to stimulate the participation of users within the DMP retaining the technical, economic, social and environmental aspects involved in its implementation, the operator has activities linked with the technical part, which ensures the welfare and the correct operation of the electrical power system.

This section will discuss the progress that has been made in the country for the implementation of DMP in the industrial sector, taking into account the relationships of Table 1.

3.1 Technical aspects

For the technical aspects, progress has been made in the process of identifying the behaviour of industrial users through tools such as energy audits, which allow you to have a detailed knowledge of the company and identify the areas where you should pay greater attention concerning energy loss.

In 2011 the ISO 50001standard was introduced, providing the public and private sector organizations management strategies to increase energy efficiency. The standard is intended to provide organizations with a recognized framework for the integration of energy efficiency in their management practices (International Organization for Standardization, 2011).

To apply ISO 50001, companies in the electric sector propose a service called Energy Audits (EA). These are the tools of the management process for energy efficiency, through which it is possible to assess the performance of the equipment that energy systems consumers have in an electrical installation. It is a multidisciplinary activity, which in addition to involving various fields of engineering such as electricity, mechanical, hydraulic, pneumatic, control and informatics, also involves environmental aspects and the administrative and economic evaluation of projects (CNE (National Council of Energy), 2011).

Improving the energy performance can provide rapid benefits to industrial users, maximizing the use of its energy sources and assets related to energy measurement and monitoring, operational control of variables and acquisition of energy services, which reducing both energy cost and consumption. Users in turn contribute positively in reducing depletion of energy resources and mitigating the effects of energy use worldwide (Valencia et al., 2015).

ISO 50001 standard does not set targets to improve energy efficiency. This depends on the user organization or regulatory authorities. This means that any organization, regardless of its current domain of energy management, can implement ISO 50001 to establish a baseline and then improve at a pace appropriate to their context and capabilities. For this reason it is proposed that all the guidelines that are made there happen to be free to use and become a requirement from the regulator agent.

Through the implementation ofan energy audit, you can gain valuable information from the company given that it will allow you to know in detail the state of its business and also take decisions with regard to the increase of energy efficiency since these take into account the parameters of operation for all the elements, for example, transformers, machines, lighting and heating, among others.

For the design of DMP it is very important to take into account the data provided by the energy audit, given that these also provide a clear vision of the major reforms that can be performed on the technical aspect, in addition to benefitting the environmental and social aspects, to reducing greenhouse gas emissions and to building more efficient industries (Li et al., 2016).

3.2 Economic aspect

This new interest in the compilation of information brings about the development of a new methodological path for the analysis of the events associated with the energy market. It begins with a description of the market and its past behaviour to push forward the construction of a diagnosis in which you identify the variables and relevant dimensions, its influence in determining the current situation and to serve as a basis for the elaboration of hypotheses about its evolution. A key element in the new decisional scenario is that it constitutes the option to perform simulations, through which it is possible to examine the behaviour of a wide range of solutions and implement them before they reach the market.

These preliminary stages lead to the construction of predictive models (Valencia et al., 2015) capable of building alternative scenarios that reflect the possible routes of the market dynamics, from which determinations are derived in some of the directions in conjunction with actions to prevent negative and undesirable effects.

From the methodological proposals by intelligence business analysis to the Support Vector Machines, including Data mining, Neural Networks, Time Series, and Data Analysis is possible for the challenge of providing reliable bases in the development of responses to the market (Lopez, 2007).

For this study we are taking into account the time series as a methodology to define the behaviour of the industrial users on the basis of the hourly energy consumption, which is essential to the economic aspect as the analysis allows agents to modify the energy market to optimize usage (Shumway & Stoffer, 2010). This has led to the definition of the model which will be applied to the All Data of each industrial user.

The study of the Time Series aims to explain their variations across the prediction of future values and are based on the principle that the current and future values of the series is dependent to some extent on the values that were taken in the past so that the series have an inertia which prevents or makes unlikely overly abrupt changes in short periods of time (Gonzalez et al., 2013) .

This approach corresponds to what Breiman (2001) defined as one of the two possible cultures in the analysis of the temporary series and by extension to the analysis of data, in which the fundamental interest is to identify, assess and validate the mechanism of data generation, a stochastic process responsible for the series in study and a key concept in the construction of the model (Pena, 1993).

Due to its power and effectiveness the ARIMA model is used, formulated by Box and Jenkins in 1976 (Box et al., 1976). It considered the following components, the Autoregressive, the Moving Average and the integration that contribute in the design of the model both in the regular part and the seasonal part. The notation for each one is as follows: the order of the polynomial Autoregressive of the regular part is denoted by P and the order of the polynomial Autoregressive seasonal part with P, the order of the polynomial of Moving Average in the regular part with q and the polynomial of Moving Average in the seasonal part with Q; the d denotes the order of the differentiation not seasonal, D the order of the seasonality and s is the seasonal period.

The general notation is as follows:

[[fi].sub.p](B)[[FI].sub.p]([B.sup.s])[[gradacion].sup.d][[gradacion].sup.D.sub.s][y.sub.t] = [[theta].sub.q](B)[[THETA].sub.Q]([B.sup.s])[[epsilon].sub.t] (1)

Where st is the usual Gaussian Process of white noise, the general model is denoted as ARIMA(p,d, q) x [(P, D, Q).sub.s] the regular components autoregressive and moving average are represented by polynomials [fi](B) and [[theta].sub.q](B) order p and q, and the seasonal components autoregressive and moving average by [[FI].sub.p]([B.sub.s]) and [[THETA].sub.Q] ([B.sup.s]), order P and Q, and regular components and seasonal difference by [[gradacion].sup.d] = [(1-B).sup.d] and [V.sup.D.sub.s] = [(1-[B.sup.s]).sup.D]; [y.sup.t] it constitutes the series observed.

3.3 Social and environmental aspects

For the social and environmental aspects, we have designed regulations by the responsible entities social wise, promoting the efficient use of energy by strategies in response to the demand for the end user and in the environmental part we have identified strategies for consumption from renewable energies, among other types of generation, so that greenhouse gases are reduced. In Table 2 we can see the positive regulatory developments in Colombia related to the considerations that directly involve the participation and delivery for the supply of electrical energy to the demand.

Law 143 of1994 sets electric power as a public good and stipulates the regulators of the service. Then 16 years went by before the CREG (Colombian Regulator) re-defined a resolution for the control of demand, this was through a program of voluntary breakaway demand which is defined in resolution CREG 063 of 2010 (CREG, 2010). This program was created by the CREG to encourage users to reduce their consumption when the system had a state of emergency or critical conditions; these are situations that affect the wholesale energy market when the price of stock exchange of energy is higher than the price of shortages.

Law 1715 of 2014 (Congress of Colombia, 2014) which proposes the implementation of renewable energy and the efficient use of energy, within this same year appears decree 2492 (Ministry of Mines and Energy, 2014) , which called for the implementation of strategies responding to demand. From this decree, CREG published Resolution 011 of 2015 (CREG, 2015) which regulates the response to the demand in the daily market in critical conditions, as is the current case for shortages of the generation capacity due to the presence of the climatic phenomenon known as El Nino. This resolution aims to give reliability to the National Interconnected System, support the obligations of energy, and reduce prices on the stock exchange of energy and costs of the constraints.

Due to the energy crisis that the country is having because of the phenomenon of El Nino that has been recorded since before August 2015, due to the strong hydro-dependence that the country presents, the CREG proposes Resolution 029 of 2016 (CREG, 2016a) which is a proposal for a voluntary energy saving for regulated users, to encourage energy saving by providing a discount to the users who submit consumption below the savings target and generating penalties for users that consume above this saving goal, billing almost double the fee per additional kWh.

Additionally the regulation proposes that non regulated users who have emergency electric plants can receive incentives for disconnecting from the system and consuming from their own plants. The proposed incentives in the last two resolutions have generated energy saving initiatives. Resolution 0295 of 2016 (CREG, 2016) promotes use practices demand response programs voluntary disconnections, and in turn the resolution CREG 029 of 2016 provides a scheme of differential rates, so that they can be used on a permanent basis, they must be articulated with data analysis tools such as the time series that allow the construction of models for the prediction of consumption based on history (Pineda et al., 2015) which allow for better substantiated programs.

The permanence of the DMP in the Colombian energy sector cannot be an isolated exercise to collect and process large volumes of data to move closer to a reality with higher levels of complexity and therefore demand the integration of the dimensions and variables that affect in a significant way, as seen in Figure 2.

4. Conclusions

The DMP in the context of data analysis shows a great potential and a promising future, mainly because it allows you to generate global benefits to society, related to reducing negative impacts to allow for an increase in the reliability of supply through a reduction in the chargeability of the electrical structure and to postpone the need to increase the generation capacity.

In spite of the fact that Colombia has made progress in the development of strategies for the efficient use of energy, as well as for the control of demand in periods of shortages, it is necessary to progress with the whole and periodic implementation of DMP that not only benefit the industrial user, but also other agents in the sector such as the network operator, the aggregators of the market and the entire electrical system in general.

The impact techniques ALL DATA collections mainly focus on capturing data to be analysed can characterize the behaviour of demand. These techniques provide information based on technical, economic, social and environmental aspects, which are used to generate strategic DMP based on models with a systemic structure making them successful.

Systemic design of strategic DMP should take into account the needs and perspectives of users, also include the participation of other actors such as operators, regulators and aggregators, in order to obtain a successful implementation, monitoring and feedback such programs.

5. Acknowledgments

Thanks to the research projects "Characterization of electric energy demand in Manizales for the integration of users in programs for the efficient management of demand" and "Analysis of the implementation of active networks of distribution in existing distribution systems", winners of the convening of the National Program of projects for the strengthening of research, creation and innovation in Postgraduate Studies of the Universidad Nacional de Colombia 2013-2015, in the form of new research projects, creation and innovation.

6. Bibliographical references

Aalami, H. A., Moghaddam, M. P., & Yousefi, G. R. (2010). Demand response modeling considering interruptible/curtailable loads and capacity market programs. Applied Energy 87 (1), 243-250.

Amin, S. a. a., Ali-Eldin, A., & Ali, H. A. (2015). A context-aware dispatcher for the Internet of Things: The case of electric power distribution systems. http ://www. sciencedirect.com/science/ article/pii/S0045790615001792

Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time series analysis: Forecastingand control. San Francisco: Holdenday.

Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science 16 (3), 199-231.

CNE (Consejo Nacional de Energia). (2011). Metodologia de Eficiencia Energetica en la Industria (No. ATN/OC-11265-E). El Salvador. Retrieved from: http://www.cne.gob.sv/index. php?option=com_phocadownload&view=category &download=215:mind&id=31:metee&Itemid=63

Congreso de Colombia (2014). Ley 1715. Retrieved from:http://www.upme.gov.co/Normatividad/ Nacional/2014/LEY_1715_2014.pdf

CREG (1998). Resolucion 131, Colombia. Retrieved from: http://apolo.creg.gov.co/Publicac. nsf/1c09d18d2d5ffb5b05256eee00709c02/c2d01 cc1ae3da04a0525785a007a5fa5?OpenDocument

CREG (2010). Resolucion 063, Colombia. Retrieved from: http://apolo.creg.gov.co/Publicac.nsf/1c09d18 d2d5ffb5b05256eee00709c02/1b8ad1b4ea9d21660 525785a007a72b3/$FILE/Creg063-2010.pdf

CREG (2015). Resolucion 011. Retrieved from: http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2 d5ffb5b05256eee00709c02/84e16439657b002b0 5257e52005011b5/$FILE/Creg011-2015.pdf

CREG (2016). Resolucion 025. Retrieved from: http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2 d5ffb5b05256eee00709c02/5d1df8fc0d8ebbb305 257f70007ac82b?OpenDocument

CREG (2016a). Resolucion 029. Retrieved from: http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2 d5ffb5b05256eee00709c02/11d218ae3d892c160 5257f70004ed535?OpenDocument&Highlight=0 ,NoResolucionCREG029-2016

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings 1644 (1), 97-104. Retrieved from: http: //scitation. aip.org/content/aip/proceeding/ aipcp/10.1063/1.4907823

Doukas, H., Papadopoulou, A. G., Nychtis, C., Psarras, J., & van Beeck, N. (2009). Energy research and technology development data collection strategies: The case of Greece. Renewable and Sustainable Energy Reviews 13 (3), 682-688. http://www.sciencedirect.com/science/article/pii/ S1364032108000026

Gonzalez, C. G., Lise, A. V., & Felpeto, A. B. (2013). Tratamiento de datos con R, Statistica y SPSS (1st ed.). Espana: Ediciones Diaz de Santos.

Law, Y. W., Alpcan, T., Lee, V. C. S., Lo, A., Marusic, S., & Palaniswami, M. (2012). Demand response architectures and load management algorithms for energy-efficient power grids: A survey. In Proceedings - 2012 7th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2012, Melbourne, VIC p. 134-141. http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=6405519

Li, J., Zhang, Y., Shao, S., Zhang, S., & Ma, S. (2016). Application of cleaner production in a Chinese magnesia refractory material plant.

Journal of Cleaner Production 113, 1015-1023. http://www.sciencedirect.com/science/article/pii/ S0959652615017059

Liao, Y.-T., Zhou, J., Lu, C.-H., Chen, S.-C., Hsu, CH., Chen, W., & Chung, Y.-C. (2016). Data adapter for querying and transformation between SQL and NoSQL database. Future Generation Computer Systems. Retrieved from: http://www.sciencedirect. com/science/article/pii/S0167739X16300085

Lopez, C. P. (2007) Mineria de datos: tecnicas y herramientas (1st ed.). Madrid: Editorial Paraninfo.

Martinez, V. J. & Rudnick, H. (2012). Design of Demand Response programs in emerging countries. In 2012 IEEE International Conference on Power System Technology, POWERCON 2012, New Zealand. http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=6401387

Martinez-Prieto, M. A., Cuesta, C. E., Arias, M., & Fernandez, J. D. (2015). The solid architecture for real-time management of big semantic data. Future Generation Computer Systems 47, 62-79.

McLaughlin, S., Podkuiko, D., & McDaniel, P. (2009). Energy theft in the advanced metering infrastructure. In E. Rome & R. Bloomfield (Eds.), Critical Information Infrastructures Security. Bonn: Springer. (Chapter 14) http://link.springer. com/chapter/10.1007%2F978-3-642-14379-3_15

Miara, A., Tarr, C., Spellman, R., Vorosmarty, C. J., & Macknick, J. E. (2014). The power of efficiency: Optimizing environmental and social benefits through demand-side-management. Energy 76, 502-512.

Ministerio de Minas y Energia (2014). Decreto 2492. Retrieved from: https://www.minminas. gov.co/documents/10180//23517//36863-Decreto-2492-03Dic2014.pdf

Muttaqi, K. M., Aghaei, J., Ganapathy, V., & Nezhad, A. E. (2015). Technical challenges for electric power industries with implementation of distribution system automation in smart grids. Renewable and Sustainable Energy Reviews 46, 129-142.

Nguyen, T. A., & Aiello, M. (2013). Energy intelligent buildings based on user activity: A survey. Energy and Buildings 56, 244-257. http://www.sciencedirect.com/science/article/pii/ S0378778812004537

Organizacion Internacional de Normalizacion (2011). ISO 50001, Suiza. Retrieved from: http:// www.iso.org/iso/iso_50001_energy-es.pdf

Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Informatics 7 (3), 381-388. http://ieeexplore. ieee.org/xpl/articleDetails.jsp?arnumber=5930335

Pena, D. (1993). Estadistica, modelos y metodos: Fundamentos 2. Modelos lineales y series temporales (1st ed.). Madrid: Alianza Editorial.

Pineda, J., Carvajal, S. X., & Valencia, D. (2015). Big Data for Demand Management Programs Designing for Colombia's Industrial Sector. In ALLDATA 2015 : The First International Conference on Big Data, Small Data, Linked Data and Open Data, Barcelona, Spain, p. 5-10. Retrieved from: https:// www.thinkmind.org/index.php?view=article&articl eid=alldata_2015_1_20_90026

Rahmani-Andebili, M. (2016). Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets. Electric Power Systems Research 132, 115-124.

Rodriguez, M. V., Marin, P. F., Guillen, J. R., & Sotres, L. G. (2013). Gestion Activa de la Demanda para una Europa mas eficiente. Anales de Mecanica Y Electricidad 90 (4), 55-61.

Shumway, R. H., & Stoffer, D. S. (2010). Time series analysis and its applications: with R examples (3rd ed.). New York: Springer Science & Business Media. http://link.springer.com/book/10. 1007%2F978-1-4419-7865-3

Sioshansi, F. P. (2011). Smart grid: integrating renewable, distributed & efficient energy. Oxford, USA: Academic Press.

Valencia, D., Carvajal, S. X., & Pineda, J. (2015). Contribution of the All Data Methodologies for Design of Demand Management Programs in the Industrial Sector. In International Symposium on Power Quality, Valparaiso, Chile, p. 163-169. Retrieved from: http://www.revistas.unal.edu.co/ index.php/SICEL/article/view/52072/53250

XM (filial de isa). (2014) Informe de operacion del SIN y administracion del Mercado. Retrieved from : http : //informe sanuales .xm .com. co/2014/ SitePages/operacion/1-4-Agentes-del-mercado. aspx

Zhou, M., Pan, Y., Chen, Z., & Chen, X. (2016). Environmental resource planning under cap-andtrade: models for optimization. Journal of Cleaner Production 112 (part 2), 1582-1590.

Daniela Valencia-Lopez *, Sandra X. Carvajal Quintero *([seccion]), Jairo Pineda-Agudelo **

* Departamento de Electrica Electronica y Computacion, Universidad Nacional de Colombia. Manizales, Colombia.

** Departamento de Matematicas y Estadistica, Universidad Nacional de Colombia. Manizales, Colombia. davalencialo@unal.edu.co, s.sxcarvajalq@unal.edu.co, jpinedaa@unal.edu.co

(Recibido: Marco 31 de 2016 - Aceptado: Septiembre 23 de 2016)

Caption: Figure 1. Strategies vs. evolution of the DMP. Source: Own data.

Caption: Figure 2. System for the design of DMP. Source: Own data.

Caption: Figure 3. Activities to develop agents for the design of DMP. Source: Own data.
Table 1. Summary of data-oriented strategies with key design aspects
of DMP for industrial users.

                                       Strategies

Aspects                 BIG DATA               SMALL DATA

TECHNICIANS      Power consumption        Technological
                   of the industrial      changes and
                   user.                    updating
                                            electrical
                                            equipment.
ECONOMICS        Economic evaluation      Market behaviour
                   of all beneficiaries     with respect to
                   (company, System,        the benefits
                   Operator,                for all agents.
                   aggregators).

SOCIALS          Measurement of           The reflexion
                   incentives for the       about of the
                   efficient use            efficient use
                   of energy.               of energy.

                 Growth of the            Waste management
ENVIRONMENTALS     industry in relation     projects developed
                   to the increase of       for the use of
                   CO2 and other types      clean energies.
                   of waste.

                                      Strategies

Aspects                OPEN DATA              LINKED DATA

TECHNICIANS      Information from the    Design of virtual
                   network operator,       platforms for
                   with respect to the     real time
                   variables of the        information.
                   energy service.
ECONOMICS        Behaviour of the        Investments in
                   industrial activity     technological
                   at the national and     changes in terms
                   international level     of measurement
                   and development         and automation.
                   factors.
SOCIALS          Regulation of the       Friendly customer
                   efficient use of        relation regarding
                   energy as well as       power consumption
                   the incentives for      information
                   controlling demand.     through web
                                           applications.
                 Laws and decrees that   Reducing environmental
ENVIRONMENTALS     stimulate the           impacts through
                   implementation of       reductions in energy
                   renewable energies.     consumption.

Table 2. Regulatory framework.

Rule                 Comments

Law 143 of 1994      Through this the electrical energy for the
                       entire supply chain in the national territory
                       is set, in turn authorizing entities that
                       control this service.
Resolution CREG      Regulates the commercial aspects of the
  024, 1995            wholesale power market in the national grid,
                       which are part of the Rules of Procedure of
                       operation.
Resolution CREG      Adopts the methodology for the remuneration of
  071, 2006            the reliability charge in the wholesale
                       energy market.
Resolution CREG      Regulates the safety ring reliability charge
  063, 2010            called voluntarily breakaway demand.
Resolution CREG      Modification of resolutions CREG 063 of 2010
  116 and 20, 2013     and 071 of2006, in connection with the
                       verification and liquidation of the voluntary
                       Breakaway Demand and the calculation of the
                       commercial availability within the actual
                       remuneration of the Individual daily charge
                       for reliability.
Law 1715, 2014       Regulates the integration of non-conventional
                       renewable energies to the national energy
                       system.
Resolution CREG      Regulates the response to the demand of the
  098, 2014            daily market in conditions of scarcity.
Decree 2492, 2014    Adopts measures in the field of implementation
                       of response mechanisms to demand.
Resolution CREG      Regulates the program in response to demand for
  011, 2015            the daily market in critical conditions.
Resolution           Adopts the procedure that will be used by the
  CREG 025, 2016       National Center Office to enable the program
                       to respond to the demand in the ideal
                       predispatching, established in Resolution
                       CREG 011, 2015.
Resolution           Defines a scheme of differential rates for
  CREG 029, 2016       set-up costs for the provision of the
                       electric service to regulated users in the
                       National Interconnected System to promote
                       voluntary savings of energy.
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Title Annotation:INGENIERIA ELECTRICA
Author:Valencia-Lopez, Daniela; Carvajal Quintero, Sandra X.; Pineda-Agudelo, Jairo
Publication:Ingenieria y Competividad
Article Type:Ensayo
Date:Jun 1, 2017
Words:5796
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