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Category: Automated grid

A survey on industry impact and challenges thereof

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At its 2014 World Congress, IFAC launched a “Pilot” Industry Committee with the objective of increasing industry participation in and impact from IFAC activities. I chair this committee with the support of Roger Goodall (Loughborough University, UK) and Serge Boverie (Continental, France) as co-chairs. This committee was established as an outcome of an Industry Task Force led by Roger Goodall in the last triennium.

In 2015 the committee undertook a survey of its members to get their views on the impact of advanced control and challenges associated with enhancing the impact. The survey had two questions. 23 of our 27 members then (excluding the chair) responded. The majority of the membership is either currently with or has prior affiliation with industry; all others have had substantial industry involvement as well. Most of the members were nominated by IFAC National Member Organizations and Technical Committees.

Although limited in many ways, I thought the survey responses would be of interest to the controls community.

Survey Question 1: Impact of Specific Advanced Control Technologies

First, we asked for members’ perceptions about the industry success (or lack thereof) of a dozen advanced control technologies. PID control was also included in the list for calibration purposes. A glossary was included with the survey, listing topics covered under each technology. Members were asked to assess the impact of each of these technologies by selecting one of the following:

  • High multi-industry impact: Substantial benefits in each of several industry sectors; adoption by many companies in different sectors; standard practice in industry
  • High single-industry impact: Substantial benefits in one industry sector; adoption by many companies in the sector; standard practice in the industry
  • Medium impact: Significant benefits in one or more industry sectors; adoption by one or two companies; not standard practice
  • Low impact: A few successful applications in one or more companies/industries
  • No impact: Not aware of any successful deployed real-world application

The results: The control technologies are listed below, in order of industry impact as perceived by the committee members:

Rank and Technology High-impact ratings Low- or no-impact ratings
1. PID control 100% 0%
2. Model-predictive control 78% 9%
3. System identification 61% 9%
4. Process data analytics  61%  17%
5. Soft sensing 52% 22%
6. Fault detection and identification 50% 18%
7. Decentralized and/or coordinated control 48% 30%
8. Intelligent control 35% 30%
9. Discrete-event systems 23% 32%
10. Nonlinear control 22% 35%
11. Adaptive control 17% 43%
12. Robust control 13% 43%
13. Hybrid dynamical systems 13% 43%


On the face of it, these results are disappointing. No advanced control technology is unanimously acknowledged by industry-aware control experts as having had high industry impact—90 years after its invention (or discovery), we still have nothing that compares with PID! It’s also concerning that the “crown jewels” of control theory appear at the bottom of the list.

However, the fact that all the technologies had at least some positive assessments suggests that the impact could well be higher than indicated: Many control scientists and engineers are likely not aware of the impact of control technologies outside the application domains of their experience. Thus the problem may be as much the perception as the reality.

Survey Question 2: Issues and Challenges with Industry Impact

The second question listed a number of statements and asked respondents to indicate their level of agreement with each. Agreement could be indicated as strongly agree, agree, neutral, disagree, or strongly disagree.

The statements and the levels of agreement are tabulated below. I have also noted any significant differences of opinion between the industry and academic members of the committee.

Statement Agreement Disagreement Academia/Industry
Industry lacks staff with the technical competency in advanced control that is required for high-impact applications 83% 4%
Control researchers are much poorer than researchers in other fields at communicating their ideas and results to industry management 26% 30%
The maturity or readiness level of results of advanced control research is too low for attracting industry interest 57% 22% 42% of industry respondents but no academic respondent disagreed
Advanced control has limited relevance to problems facing industries and their customers 4% 65%
The conflict between industry deadlines and academic research timelines is worse in control than in related engineering fields 30% 35%
Control researchers place too much emphasis on applied mathematics or advanced algorithms whereas successful industry applications require deep domain knowledge 83% 13%
Control researchers place too little emphasis on plant/process modeling and model-development methodologies 57% 17% No one from industry disagrees 30% of academics disagree
Students in control (undergraduate and graduate) are not sufficiently exposed to problems in industry 70% 13% No one from industry disagrees 30% of academics disagree
The academic control community is not seriously interested in collaboration with industry 26% 39% 33% of industry respondents but only 11% of academic respondents agree
There is no problem—advanced control is successful and appreciated in relevant industries 13% 83%

 

A clear message is that domain understanding/modeling is crucially important but not adequately pursued and taught. Neither expertise nor experience in advanced control per se is sufficient to realize industry impact.

Conclusions

This survey wasn’t, and nor was it intended to be, scientific or comprehensive, but I and my fellow committee members have found the results thought- and discussion-provoking. We are continuing to explore the challenging problem of industry impact from control research. Among other outputs, we expect to recommend specific enhancements to IFAC events, publications, and volunteer groups. Your feedback is welcome and will be appreciated!

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Word document  with references can be downloaded here (400KB)

Article provided by:
Tariq Samad
Senior Fellow
Honeywell/W.R. Sweatt Chair in Technology Management
The University of Minnesota
Vice chair, IFAC Technical Commitee
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Consumer-driven automation for smartening the grid

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Consumers are expected to play a considerable greater role in smart grid deployment and it is crucial to boost their awareness of this more active role. Smart grid is a great opportunity for all consumers, whose involvement in demand side management will significantly speed up the development of a smart grid market. The way the energy is used has to be revolutionised and, to actualize that, consumers need to understand what benefits they will achieve and how to change their behaviour to gain those benefits. All the players in the electricity system need to learn how to engage and effectively educate consumers, and improve their trust. We do not know the best way to make this happen yet, but we do know the highly negative impact of inadequate consumer engagement on future deployment plans. Thus, control solutions and automation systems for demand side management necessitate taking consumers into account, their preferences, their needs and uncertainty in their behaviour.

The next-generation electric grid needs to be smart and sustainable to deal with the explosive growth of global energy demand and achieve environmental goals. To effectively smarten the grid we need to rethink the roles and responsibilities of all players in the electricity system. This smartening is a progressive and revolutionary process (Figure 1). However different settings will be around the world and deployed at different rates, the use of information and communications technology to monitor and actively control generation and demand in near real-time is indisputably a common feature. Therefore, control and automation are essential for enabling consumers to actively support the grid.

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Figure 1. Smarter electricity systems (source: IEA, 2011) [Click on image to view larger version]

The increased control over the network can enable a wider, more sophisticated range of smart methods and innovative schemes, such as demand response and smart energy management systems for buildings, to facilitate local management of demand and generation. Demand response includes both manual and automated consumer response, smart appliances and thermostats, which are able to respond to price signals, or carbon-based signals. These smart devices are connected to an energy management system or controlled directly by the utility or a system operator. Smart energy management systems for buildings need to incorporate the user into the design and thus be responsive to their occupants in order to improve their comfort and allow smart appliances and heating systems to be on the market and respond to price signals to help decreasing the electricity bills. The benefits for consumers can be diverse, e.g., reduction of the electricity bill, improving of living conditions, supporting a more environmentally friendly energy behaviour.

In particular, smart energy management systems are required to be able to:

  • respond to signals from the grid and take action on this basis (e.g., decreasing energy use when prices are high or automatically shifting consumption to times when prices are lower);
  • manage local generation facilities, such as solar panels, and fed back into the grid any energy
  • optimally schedule storage devices, which can be used to balance out the smart grid.

Those advanced and innovative energy management systems make buildings smart and we can claim that a smart grid cannot exist without smart buildings. Hence, there will be more and more active roles for consumers of different sizes to play in a smart grid, for instance:

  • Residential consumers can choose among different tariff schemes and optimally shift smart appliance demand away from peak times through smart meters and energy management systems;
  • Industrial and commercial consumers can participate in the energy market through
    a wide range of demand response schemes;
  • Generator owners can participate in demand response schemes and the market by supplying needed energy to the grid.

Novel control and automation systems are becoming quite widespread, although standardised solutions are still not available, which means that expensive tailored configuration are required. This clearly limits the engagement of consumers, in particular small-scale consumers. In addition to designing and deploying control and communication solutions more affordable to a wider range of consumers’ sizes, effective motivational factors must be explored and thoroughly examined (e.g., environmental concerns, better comfort, control over electricity bills). The risk here is that consumers who do not make the savings expected from their behavioural change might consider the whole experience disappointing and frustrating.

Accurate, systematic and methodical research and evaluation are still needed to identify the optimal methodology to understand better the interaction between consumers and energy market, as well as the effect of enabling technologies for smart grid deployment.

A persistent behavioural change is vital to effectively enable smart energy technology development. We still need an answer to the following questions:

  • Is there an optimal mix of behavioural change, consumer feedback and automation technologies?
  • How much customer education is required and what are the best approaches?
  • Which types of automated demand response schemes are most useful to different types of customers (residential, commercial, industrial)?

Research groups, along with industry and governments, need to design and test more consumer-focused control solutions that can foster large-scale consumer behaviour change.

 


Download the article

Word document  with references can be downloaded here (400KB)

Article provided by:
Alessandra Parisio
School of Electrical and Electronic Engineering
The University of Manchestervice
IFAC Technical Committee 9.3 (Control for Smart Cities)
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