Energy consumption and price reduction

mercredi 24 Juin 2020

We’ve been researching how to monitor individual appliances consumption in order to decrease peak and energy prices for end-user. That’s what we’ve found.

The first method to separate the appliance’s consumption is obvious and that is Intrusive Load Monitoring (ILM): use a metering device for each individual appliance that you need.

Opposite, Non-Intrusive Load Monitoring (NILM) is the energy disaggregation technique that provides a method to separate the individual consumption for certain appliances, respecting consumers’ privacy and often using already-deployed smart meters [1].

ILM is more precise than NILM but it is more expensive. NILM, on the other hand, is much cheaper, often it only needs smart meter. Let’s focus on NILM.

Main stages in NILM are [1]:

  1. Data collection: electrical data, including current, voltage, and power data, are obtained from smart meters, acquisition boards or by using specific hardware;
  2. Event detection: an event is any change in the state of an appliance over time. An event implies variations in power and current, which can be detected in the electrical data previously collected by means of thresholds;
  3. Feature extraction: appliances provide load signature information or features that can be used to distinguish one from another;
  4. Load identification: using the features previously identified, a classification procedure takes place to determine which appliances are operating at a specified time or period, and/or their states

So, the input of NILM is consumption characteristics, e.g. current, voltage, and the output is which appliances are turned on/off at a given time and/or their state.

There are appliances that have different load profiles at a different state, that’s what state in fourth stage means. It adds complexity to the algorithm. Another problem is that the house can have multiple appliances of the same type. Also, there are appliances with low consumption, for example, LED, which are hard to disaggregate, and appliances with consumption not varying in a periodic fashion.

Thus, NILM is not quite an easy task. Heuristic algorithms, such as Genetic Algorithms (GA) or Particle Swarm Algorithms (PSA), along with machine learning techniques are often used to solve that.

One of the important applications of NILM is the Home Energy Management System (HEMS). HEMS is responsible for scheduling appliances to reduce energy bills (Real-Time Pricing should be present) while saving a user’s comfort. It is also responsible for renewable energy system management if any. HEMS not only saves user’s money but also reduces consumption peak, which is good for energy producers because they can use fewer power generators.

Next, there are two examples of HEMS.

In [2] the following scheme was used:

Energy Management Controller (EMC) uses the Constrained Swarm Intelligence-based Consumer-Centric DSM module and database of historical records to disaggregate energy and schedule appliances. Also, HEMS is taking into consideration alternative energy (solar power on the image).

A phenomenal reduction in peak power consumption is achieved by 13.97% in that scheme.

In [3] the following scheme was used:

Data Acquisition Device is present. It is used to get consumption data. ZigBee-based Plug-load Control Relays are used to switch appliances on and off. Also, there is a computer that runs NILM algorithm (upper) and Home Gateway (lower) which has Database and communicates with ZigBee-based Plug-load Control Relays. Homeowners can get access to data using an internet browser.

NILM in that scheme is pretty complex:

.

It has three stages: off-line load learning & modeling, on-line load monitoring, and day-ahead in-home load scheduling. Such techniques as Hard c-Means clustering, sequential forward selection (SFS), hard c-means-based k-nearest neighbor classifier (k-NNC) are used. (See [3] for details).

Also, we’ve found a company called Smappee that offers appliances submeter, which can be integrated with HEMS: https://www.smappee.com/uk/homepage

That company has three ways to record appliances consumption (https://www.smappee.com/uk/blog/smappee-complete-submetering/):

The first and second ways use ILM, while the third way, which is cheaper, uses NILM. In a third way you should install a clamp to your power cable. The company claims that its patented NILM has a 70% average accuracy.

We came to the conclusion that for our project we could use NILM. NILM is pretty complex but real task. It’s already used by companies, such as Smappee, but may be not very accurate. We are not experts in field, so we can try ready solutions. For that purpose NILM toolkit [4] would be handy, which gives opportunity to evaluate different NILM algorithms on open datasets, such as REDD, BLUED, PLAID, REFIT, TRACEBASE, WHITED, UK-DALE, DRED [1]. Core idea of our project is similar to HEMS, maybe we should try to schedule appliances too.

Students:
Iolanda De Almeida Oliveira, Pavel Ivliev, Michael Kämpf, Igor Kirianov

Supervising Professor:
Zarina Charlesworth

References

  1. A Ruano, A Hernandez, J Ureña, M Ruano, J Garcia NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review. Energies 12, 2203 (2019)
  2. YH Lin, YC Hu Residential consumer-centric demand-side management based on energy disaggregation-piloting constrained swarm intelligence: Towards edge computing. Sensors 2018, 18, 1365.
  3. YH Lin, MS Tsai An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring With Automated Multiobjective Power Scheduling. IEEE Trans. Smart Grid 2015, 6, 1839-1851.
  4. Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava. NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring. In: 5th International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK. 2014. DOI:10.1145/2602044.2602051 arXiv:1404.3878

Energy Challenge Neuchâtel

mercredi 06 Mai 2020

Great News about Cleverly – the clever app for your home!

After an intensive week in February 2020 and the following weeks, we have submitted our project to the Energy Challenge 2020 of the Canton of Neuchâtel.

Cleverly is therefore competing with various other projects for the initial investment of CHF 3’000.–. This seed capital will help us to create a first prototype of our app and start our collaboration with partners such as local governments, communities and corporations.

We strongly believe that a community platform is needed to transform our future into a bright, electrifying one! We will keep you updated.

Your Cleverly Team

Students:
Iolanda De Almeida Oliveira, Pavel Ivliev, Michael Kämpf, Igor Kirianov

Supervising Professor:
Zarina Charlesworth

Environmental impact of smart grids

mercredi 22 Avr 2020

Most researchers agree that it is rather a question of when than of if the smart grid will be introduced (Tuballa & Abundo, 2016). To date, we have been writing and talking about the potential future business models and necessary steps in order to initiate the proclaimed transition process. However, we did not spend sufficient time on elaborating the potential environmental impact a smart grid may create.

An overwhelming part of the smart grid research has been focusing on the technological, legal and social aspects of a smart grid transformation (Pratt et al., 2010). The main measures of success stated are “improved reliability and cost-effective operation” (p. 5). However, as Pratt et al. argue, smart grids may create a potentially significant benefit for governmental climate change actions and therefore may propose an opportunity for accelerated, state-sponsored programs. To date, most empirical research regarding Co2 and energy savings in connection with smart grids are based on assumptions and represent solely estimates calculated by the respective studies (Hledik, 2009). Therefore, it is important to remember, that the actual environmental impact may be above or beyond the intervals provided by researchers.

In order to demonstrate a certain consensus between different studies, three specific researches will be briefly summarized. For further information on the different categories and mechanisms considered within each study as well as the underlying empirical data, please refer to the bibliography at the end of this blog post.

First, the study conducted by Pratt et al. (2010) for the United States Department of Energy focused on eight mechanisms impacting the energy consumption and the generation mix. One of the greatest impacts is created by information and feedback systems according to Pratt et al with close to three precent. The findings of the study suggest that the energy consumption and therewith linked Co2 emission may be reduced by 18% (p. 7), assuming a 100% smart grid implementation.

Second, the study conducted by Rohmund, Wikler, Faruqui, Siddiqui, & Tempchin (2009) is based on overall seven mechanisms and their respective influence. Similar to Pratt et al., the study attributes the greatest reduction potential for feedback systems on the energy usage. The authors of the study state the interval of the potential energy consumption and therewith linked Co2 emission reduction between 3.1% and 11.3% (p. 125)

Third, Hledik (2009) uses in his study five different mechanisms for measuring the potential energy consumption and Co2 emission reduction. In contrast to the research of Pratt et al. and Rohmund et al., Hledik argues that the major reduction potential is offered by load shifting and decentralized production and distribution. Overall, Hledik estimates the overall potential reduction to lay approximately between 5.1% and 15.7% of the total output (p. 38).

The aim of this brief comparison of the findings of current empirical research is, to demonstrate that governmental actors may consider the implementation of smart grids as a viable option for achieving their set climate goals (EU Commission Task Force for Smart Grids, 2016; Hledik, 2009). It is undisputable that smart grids create a positive externality regarding climate change and therefore propose an interesting additional measurement for climate policy making.

The difficulty for governments, however, is the non-existence of reliable research based on real-world data due to the lack of large-scale smart grid initiatives (EU Commission Task Force for Smart Grids, 2011). Nevertheless, we believe that smart grids have to be discussed on a national level and supported by sufficient funding in order to diversify the national climate actions.

Authors:
Iolanda De Almeida Oliveira, Pavel Ivliev, Michael Kämpf, Igor Kirianov

Supervising Professor:
Zarina Charlesworth

References

EU Commission Task Force for Smart Grids. (2011). Task Force Smart Grids Expert Group 2 : Regulatory Recommendations for Data Safety , Data Handling and Data Protection Report. Task Force for Smart Grids.

EU Commission Task Force for Smart Grids. (2016). Smart Electricity Grids.

Hledik, R. (2009). How Green Is the Smart Grid? Electricity Journal, 22(2), 29–41.

Pratt, R. G., Balducci, P., Gerkensmeyer, C., Katipamula, S., Kintner-Meyer, M. C. W., Sanquist, T. F., … Secrets, T. J. (2010). The smart grid: an estimation of the energy and CO2 benefits. United States Department of Energy.

Rohmund, I., Wikler, G., Faruqui, A., Siddiqui, O., & Tempchin, R. (2009). Assessment of Achievable Potential for Energy Efficiency and Demand Response in the U.S. (2010 – 2030). EPRI. Palo Alto.

Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews, 59, 710–725.

Consumer-centric networks

mardi 14 Avr 2020

The way we produce energy drastically changed during the last two decades (Pinson et al., 2017; Sousa et al., 2019). Since then, new technology and increasing consumer engagement initiated a far-reaching energy transition towards a more sustainable and self-sustaining energy future. Researchers agree that consumer engagement as well as technological advancements are at the core of a successful energy transition on a global scale (Katz et al., 2011; Lin & Hu, 2018a; Saad, Glass, Mandayam, & Poor, 2016; Sousa et al., 2019).

One major challenge of renewable energy is the rather volatile energy production throughout the day (Honebein, Cammarano, & Boice, 2011). Further, renewable energy production is no longer produced by large energy corporations with the ability to influence the energy output directly. That enlightens a major challenge of how we produce energy and how we ensure the grid stability.

One opportunity is, to move the power grid, like the production, closer to the community and the individuals (Sousa et al., 2019). That means, when the energy is produced locally, the ownership structure of the grid should be managed locally as well.

But it is not only about the way we produce energy that matters but also the way we use the produced energy (Farhangi, 2017). The behavior HOW we consume energy is crucial in the wake of the urbanization wave coming this very century. It is vital to understand how and where we use energy and how we can influence our consumption effectively.

Therefore, the need for a community-based platform which incentivizes the efficient and effective use of energy as well as the option to become an active part of the energy community is crucial for the aspired energy transition (Ipakchi & Albuyeh, 2009).

However, the enabler of such a transition are threefold.

First, the appropriate technology must be in place. With the right technology, the community and private business sector will be able to rethink the nature of electricity markets and introduce new business models of these markets into the economy (Lin & Hu, 2018b; Tuballa & Abundo, 2016). One major challenge technology must address is the question of framework. In a system with heterogeneous components, a framework including all possible components, disregarding their operational characteristics, is vital to the proper functioning (Abrahamse & Steg, 2013; Metzger & Rieger, 2009). There has been a tech push for more than a decade with diverse framework proposals such as for example the transactive energy framework. Further, researchers and practitioners alike consider blockchain technology as the true enabler for decentralized and more community-based energy systems (Mengelkamp, Notheisen, Beer, Dauer, & Weinhardt, 2018). Smart contracts, based on the ledger system of the blockchain, are at heart of their arguments that such a technology has the capability of boosting the energy transition. Furthermore, smart contracts offer a solution to different legal challenges regarding the purchasing and selling of self-produced electricity within the community. Moreover, platforms based on the blockchain technology may allow the system to be run without a third-party supervision and therefore more efficient and effective than current business models allow. The great technological barrier is the stability of the system and the capacity (Lin & Hu, 2018a). Energy systems are crucial to our daily life and without energy, the economic and social life may collapse (Abrahamse & Steg, 2009; Huijts, Molin, & Steg, 2012). Therefore, before implementing such technology on a larger scale, we must ensure the stability, ability and appropriateness of the technology for handling such processes. A great challenge hereby is the fast and ever-changing technological environment where we have to implement technology today which is suitable for future technological inventions. Therefore, potential implemented technologies must be open-sourced and allow the community to become an active part of future inventions.

Second, the consumer engagement has to be mobilized right from the beginning (Abrahamse & Steg, 2009, 2013). One major benefit for consumers in a consumer-centric approach is, that they can actively influence how they produce, share and source energy. The promising change hereby is, that this influence is possible throughout the system and includes the small consumers on a residential level as well (Saad et al., 2016). This possible influence increases, according to Saad et al., the awareness level and motivates small actors to actively participate in the energy transition. For consumer engagement to be successful, it needs a high degree of transparency and interaction between the energy providers and the consumers. Combining the consumer’s opportunity to interact with the production and sourcing of energy and an information platform providing crucial information on the personal energy consumption as well as effective measures to reduce such consumption, may prove to be at the heart of the energy transition itself – at least from the consumer-centric point of view (Pinson et al., 2017). At heart of consumer engagement is, to achieve enough momentum to attract sufficient members of the population and, therefore, reach the required scale of economies. Hereby, collaborations between projects and government may be one possible way to address such challenges efficiently (EU Commission Task Force for Smart Grids, 2011a).

Third, governments must provide the suitable legal environment (Agrell, Bogetoft, & Mikkers, 2013; EU Commission Task Force for Smart Grids, 2011b). The legal framework is a great challenge for the energy transition as the transition questions current energy models. Governments have to adapt their legal code and enable the community to drive the energy transition forward without being at risk of unnecessary legal prosecution (Honebein et al., 2011). One major question the government have to answer is the contractual basis of producing, selling and buying energy and how to prove such contracts in case of a legal dispute. Further, the government has to ensure that the grid stability is ensured at all time and provides a framework where governmental entities and communities can collaborate together.

Researchers are sure that the energy transition will happen and the energy grid will become more decentralized and localized (Allcott, 2011; Ipakchi & Albuyeh, 2009; Parag & Sovacool, 2016). The question is, however, about the way this transition will take place and what role individual producers will play. A consumer-centric energy grid is one possible solution to that question. After reviewing a lot of literature and having many discussions with experts, group 2 believes that a consumer-centric approach offers promising opportunities when the energy transition is analyzed from a community-centric point of view.

Nevertheless, important questions such as the role of energy corporations, the securing of the grid’s stability and bridging energy production in the case of not sufficient local production outputs remain unanswered.

Authors:
Iolanda De Almeida Oliveira, Pavel Ivliev, Michael Kämpf, Igor Kirianov

Supervising Professor:
Zarina Charlesworth

References

Abrahamse, W., & Steg, L. (2009). How do socio-demographic and psychological factors relate to households’ direct and indirect energy use and savings? Journal of Economic Psychology, 30(5), 711–720.

Abrahamse, W., & Steg, L. (2013). Social influence approaches to encourage resource conservation: A meta-analysis. Global Environmental Change, 23(6), 1773–1785.

Agrell, P. J., Bogetoft, P., & Mikkers, M. (2013). Smart-grid investments, regulation and organization. Energy Policy, 52, 656–666.

Allcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95(9–10), 1082–1095.

EU Commission Task Force for Smart Grids. (2011a). Roles and Responsibilities of Actors involved in the Smart Grids Deployment. Task Force for Smart Grids.

EU Commission Task Force for Smart Grids. (2011b). Task Force Smart Grids Expert Group 2 : Regulatory Recommendations for Data Safety , Data Handling and Data Protection Report. Task Force for Smart Grids.

Farhangi, H. (2017). Smart Grid. In Encyclopedia of Sustainable Technologies (pp. 195–203).

Honebein, P. C., Cammarano, R. F., & Boice, C. (2011). Building a Social Roadmap for the Smart Grid. The Electricity Journal, 24(4), 78–85.

Huijts, N. M. A., Molin, E. J. E., & Steg, L. (2012). Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. Renewable and Sustainable Energy Reviews, 16, 525–531. https://doi.org/10.1016/j.rser.2011.08.018

Ipakchi, A., & Albuyeh, F. (2009). Grid of the future. IEEE Power and Energy Magazine, 7(2), 52–62.

Katz, R. H., Culler, D. E., Sanders, S., Alspaugh, S., Chen, Y., Dawson-Haggerty, S., … Shankar, S. (2011). An information-centric energy infrastructure: The Berkeley view. Sustainable Computing: Informatics and Systems, 1, 7–22.

Lin, Y. H., & Hu, Y. C. (2018a). Residential consumer-centric demand-side management based on energy disaggregation-piloting constrained swarm intelligence: Towards edge computing. Sensors (Switzerland).

Lin, Y. H., & Hu, Y. C. (2018b). Residential consumer-centric demand-side management based on energy disaggregation-piloting constrained swarm intelligence: Towards edge computing. Sensors (Switzerland), 18(5), 1365.

Mengelkamp, E., Notheisen, B., Beer, C., Dauer, D., & Weinhardt, C. (2018). A blockchain-based smart grid: towards sustainable local energy markets. Computer Science – Research and Development, 33, 207–214.

Metzger, P., & Rieger, M. (2009). Equilibria in games with prospect theory preferences (598).

Parag, Y., & Sovacool, B. K. (2016). Electricity market design for the prosumer era. Nature Energy.

Pinson, P., Baroche, T., Moret, F., Sousa, T., Sorin, E., & You, S. (2017). The Emergence of Consumer-centric Electricity Markets. Distribution & Utilization, 34(12), 27–31.

Saad, W., Glass, A. L., Mandayam, N. B., & Poor, H. V. (2016). Toward a consumer-centric grid: A behavioral perspective. Proceedings of the IEEE, 104(4), 865–882.

Sousa, T., Soares, T., Pinson, P., Moret, F., Baroche, T., & Sorin, E. (2019). Peer-to-peer and community-based markets: A comprehensive review. Renewable and Sustainable Energy Reviews, 104, 367–378.

Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews, 59, 710–725.