Showcase Aberdeen

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Case study benchmarking: Robert Gordon University
ABS 10_020

Robert Gordon University

The e-harbours project has uncovered a variety of very different showcases and strategies for capturing flexibility. But which ones are the best? And how does one define best? And can one even think about best when comparing an experimental smart home in Malmo with an electric vehicle recharging system in Zaanstad or the flexibility found in a large industrial process in Antwerp?  After all, these business cases are trying to achieve slightly different things and will help optimise harbour energy in different ways.  Ultimately, success or failure and ‘best’ are subjective terms.

Nevertheless – and taking full account of these qualifications – it is still a useful exercise to at least try to get some sense of those  kinds of strategies that capture a significant amount of flexibility, which are relatively inexpensive and which are more easily transferable to harbours in other regions and other countries. Robert Gordon University were charged with developing a methodology for consolidating and comparing the strategies identified from 21 business cases produced by 8 partners in five countries.

What we have done?

The approach developed by RGU is a combination of the simple, the confidential and the complex.

The simple part

The simple part was the development of a set of variables that describe concepts such as flexibility, cost and transferability and which enable partners to produce data in a standard form (from a range of very different harbours, showcases and individual business cases). This data, collected through a questionnaire from partners, is tabular form will allow an easy ‘high’ level’ comparison of the business cases.

But not all variables are equal. Some variables – such – as flexibility – are regarded by e-harbours partners as more important than others. Consequently, the complex bit involves weighting these variables in terms of relative importance and developing a mathematical model which allows us to compare – with some validity – the relative performance of very different business cases against a variety of quantitative and qualitative criteria.

Data on a range of variables were captured. To allow comparability, flexibility (defined technically by VITO as a variable combination of time cost, energy availability and Power) has been monetised as percentage of the total annual energy bill.

Hard numbers were also collected for variables such as the energy mix before and after the intervention (to establish how much renewable energy the intervention adds to the system), and economic data such as investment and running costs.

The RGU team also collected qualitative data. Partners were asked to score their business cases on how organisational, technical and legislative transferability and on other important business case factors such factors such as stakeholder interest and potential for local wealth creation.

In terms of the criteria, we have the following 9 set of criteria which are developed by the RGU team and the e-harbours International stearin group and approved by a panel of experts. These are:

  • Flexibility
  • additional investment cost
  • net running cost
  • CO2 savings
  • organizational transferability
  • legislative transferability
  • technical transferability
  • stakeholders’ interest
  • job and wealth creation.

The confidential bit
An added complication was the fact that some of the data, particularly around the area of annual energy cost is highly sensitive, commercially for some businesses.  We therefore have to be very careful about how we treat this.

As such, we can either show you the data collected and the mathematical model used for the benchmarking but not identify the organisations, or we can keep the data confidential and discuss the organisations or – more importantly – what they do. We think it adds more value to do the former. But if you want more information about how the model works, please contact the RGU team.

In some respects, the data from the individual business cases or harbour organisations are not important. It is wider approach or strategy for capturing flexibility that is important, whether it is the application of a smart grid, optimising a contract or trading on the wholesale market.

The complex part
After we collected the data, we used a multi-criteria decision making methodology (MCDM) to benchmark different business case.

Although the model used ranks the business cases, the aim here is not to classify eharbour Business cases from best to worse, becuase all busines cases  – by their definmition  – will enable stakehodlers to reduce their energy costs in theory (so they all work to some extent). Rather, the   aim is to find the best all round business cases, i.e. those which capture flexibility but whcih are also cost effective, transferable, easy to implement legislatively, etc .

Since there is a range of data captured, we have implemented a hierarchical benchmarking system. Promethee II (type of MCDM methodology) works best with 3 to 5 criteria. So we reduced 9 criteria into 3 criteria by benchmarking all the economic criteria into one. Then benchmarking all the qualitative criteria into one and finally, just benchmarking economic, environmental and qualitative data.

Variations on  MCDM which has been used in different subject areas with good success rate,  but selection of a particular tool depends on the problem definition. For this study we used a programme called Promethee II. Apart from being simple, flexible and user friendly Promethee II is suitable for ranked data and for dealing with conflicting criteria, both of which we have to deal with in e-harbours.

Findings

Table 1: Scenario 1 The best all round strateges[i]

 

Rank Strategy Phi
1 Contract optimisation, Antwerp 0,4000
2 Contract optimisation, Antwerp 0,2500
3 Introducing renewables to local grid, Scalloway 0,2389
4 Trading on wholesale market, Antwerp 0,1889
5 Building Smart apartments, Malmo 0,1778
6 Offering reserve capacity, Antwerp 0,1667
7 Trading on wholesale market, Antwerp 0,1167
8 Trading on wholesale market, Zaanstad 0,0722
9 Trading on wholesale market, Amsterdam 0,0389
10 Capturing flexibility in large chemical process, Hamburg -0,0056

 

  • It is clear from the benchmarking that the easiest path to optimising energy use and reducing expenditure on energy is not particulalrly smart. It is about ensuring that an organisations energy tarrifs are optimised. This is not surprising. It is easy (relatively)! It requires little time or investment, no capital costs, such good practice is highly transferable and involvees little bureaucarasy or legislative barriers and guarantees a quick return. Making sure that an organsiations energy bill is optimised is the lowest of the low hanging fruit.
  • Similarly, with a little more time and trouble, trading on the wholesale market is a lucrative way to reduce energy costs, but only for organisations or in countries where it is possible for harbour stakehodlers to do so. It is transferable, but not to everyone. Similarly , offereing reserve capactity scores well.
  • Some more technical strategies do score well, however. Introducing renewables to the local energy system can provide a good return and reduce stakehodler costs, particularlty in small harbpours. There are capital costs and legislative and organisation barriers, but a return can be made on investment in just a few years.
  • Putting smart energy at the heat of harbour regeneration and new house building is also a good all round way of capturing flexibility.  Again there are capital costs, and there might be organisational barriers in some places, but the potential energy savings are massive.
  • It is good to see some ’traditional’ smart energy making the top 10 in the form of the large industrial chemical process in Hamburg. This was the kind of flexibility the orginators of e-harbours had in mind when they esstablished the project. Yes, we have found some reasons why its does not make good business sense to invest in smart energy systems at the moment. But, here is a good exampel of a straegy which can reduce energy costs from industrial operations and which is realtively afforable and transferable.,

 Reports and more information

  • Please refer to questionnaire 4.6 for more explanation on criteria.
  • Also refer to the data sheet of questionnaire uploaded in the website.

 


Table 1 summarizes the rankings produced by the simple scenario with equal weighting scheme and true criteria. To be more specific, we assume each criterion is equally important, the weight vector is [0.11,0.11,0.11,0.11, 0.11,0.11,0.11,0.11,0.11]. Where a true criterion provides an absolute discriminating power; that is, any difference matters regardless of its magnitude. Scenario 1 suggests that Strategy 11 is the best while strategy 20 is the worst.

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Case study 2: Fish labelling: energy labels

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trafficlightsProject description:  This showcase involved research into the design of an energy label for fish that have been caught, processed, transported and sold using in the Fraserburgh area.  The work which was a collaboration between two of the University’s research institutes, the Institute for Innovation, Design & Sustainability (IDEAS) and the Institute for Management, Governance and Society Research (IMaGeS), incorporated energy life cycle analysis techniques as well as research into the design and use of food labels to explore the viability of an eco-label for fish products which displays the amount of energy used in catching, processing and transporting the product.  The showcase brought together two strands of research; life cycle analysis and modelling of the amount of energy consumed at different stages of the fish production process, as well as an investigation of the design and use of eco-labels.  The aim of this is to allow consumers to choose fish which has been produced using less energy than alternatives, and to encourage producers to improve their energy efficiency so that their products appear in a lower energy category.

What we have done

  • Identify possible methods of life cycle analysis
  • Identify appropriate case study species
  • Chart outline supply chain for selected species
  • Model the energy consumed along the ‘supply chain’ for fish landed in the Fraserburgh area
  • Undertake a literature review to establish whether any fish energy label already exists
  • Investigate the international standards for eco-labelling
  • Research types of labels and the response of consumers to those labels

 Findings

Figure 1

Figure 1

Life Cycle Analysis within fish production has been addressed by a number of studies however  while several studies have been carried out addressing farmed fish, fewer studies were directed at caught fish.

  • The main methods adopted are a combination of materials flow analysis (MFA), and Life Cycle Analysis (LCA), considering inputs and outputs such as fuel, electricity, water, packaging, chemicals associated with cooling and processing, greenhouse gasses, and waste.
  • Guidelines for LCA have been set out by the United Nations Environment Programme as well as the British Standards Institute (PAS 2050: 2011 and PAS 2050-2:2012 for seafood and aquatic food products); however while some studies focus on greenhouse gas emissions, there are no studies specifically targeted at energy consumption.
  • Mackerel (pelagic) and Haddock (whitefish) were found to be representative products of the Fraserbugh area.
  • Five key stages were identified as part of the supply chain for fish produce as depicted in Figure 1.
  • Preliminary findings indicate that catching whitefish is considerably more energy intensive (per tonne of fish caught) than catching pelagic fish.  While the total amount of energy used to catch a pelagic fish like mackerel is greater than that used to catch whitefish like haddock, the significantly greater tonnage of fish caught by pelagic vessels results in a considerably lower KWh/tonne figure.
  • No existing fish energy label was found, indeed, no embedded energy label for any food product was found. The closest categories of labels were those indicating such things as organic production or sustainable catch, and energy labels for items such as domestic electrical products.
  • The key standards for eco-labelling are the ISO 14020 series and their equivalent adoption in nation standards such as the UK’s BSI 14020 standards. The three standards investigated were 14024 (Type I labels), 14021 (Type II labels) and 14025 (Type III labels). Of these the 14025 standard is the most rigorous and would therefore demand the highest level of respect. Type III labels require a comprehensive Life Cycle Assessment to be undertaken.
  • Five different types of label were identified, although one of these is not appropriate for the display of fish energy. The other types are Stamp of Approval, Absolute Numbers, Traffic Lights (with sub-types linked to Stamp of Approval and Absolute Numbers) and Sliding Scale. These vary in the amount of data displayed, which in turn influences the minimum size of the label.

trafficlightsIt has been shown that consumers have a preference for Traffic Light labels, and the two styles of this type of label are shown here.

Recommendations

  • Further research needs to be carried out on modelling energy use in both pelagic and whitefish supply, utilising a larger and more representative sample.
  • More in-depth research needs to be carried out in order to model the entire life cycle beyond the distribution stage (from catching to use and disposal).
  • Obtain producer and retailer reaction to ensure that the selected design is acceptable, for instance in respect of label size and placement.
  • Test sample labels with consumer groups to gauge their reaction.
  • Use the information gained from the previous points to finalise a design for the label, which needs to include points such as the graphic design of the label, the text size and phraseology, and the data content that best satisfies the needs of all parties.

 Reports and more information

  • Reports
    • Burnett, S. and Grinnall, A.  (2012) E-harbours Fish Energy Labelling Report. Aberdeen: Robert Gordon University
    • Akinsete, E. (2013) Energy Label Project Update: 01/02/13. Aberdeen: Robert Gordon University
  • Articles
    • An overview of the fish energy label project appeared in Newsletter #5
    • An interview with Ebun Akinsete and Andy Grinnall was conducted by Sander Kooistra in December 2013
  • Contact
  • Map

Fraserburgh_map

Case study 3: Fraserburgh Harbour – Installing a wind turbine

Project description: This showcase focused on an energy use and management in Fraserburgh Harbour and looked at ways to reduce consumption and the potential benefits of feeding in renewable energy into the local grid.

 What we have done

We collected energy and weather data from Fraserburgh harbour. We looked at the exisiting harbour businesses, their existing use of energy and the operational constraints they face. We looked to  identify gaps and opportunities and arrive at  recommendations that will help local stakeholders reduce their energy bills. This work was subdivided into two parts:

Monitoring Energy Usage

  • We organised a site survey and various meetings with stakeholders to gain an understanding of:
    • the equipment and plant in use on site, what it’s used for (and when),
    •  the availability of records, bills or other available historical data,
    • the requirements for energy data acquisition.
  • We then installed energy data loggers in plant rooms to record the power consumed by different harbour processes. Instruments such as Pico-current monitoring kits(CM3) and power quality analysers(PQA) were installed and recorded the energy consumed at 10 minute intervals.
  •  We then plotted power consumption and analysed the data gathered to look at overall patterns of energy consumption and times of high peak and low peak demand.
  •  From the demand plotted within the harbour, and the understanding gained of the harbours stakehodlers and their industial processes, we looked at practical and ’implemetental’ ways of reducing energy use and bills for harbour stakeholders.
  • We also made recommendations on the potential cost of implementing each measure and possible savings that could result from it.
  • We are in the process of developing an analytical model that can be used to predict energy use in any small or medium harbour from limited data inputs.

Monitoring Meteorological Data

We also looked at the potential viability of local wind energy into the harbours energy mix:

  • We installed a weather station within the harbour to record meteorological data. A mast was installed to measure the wind speed from two anemometers placed at different heights: one at 10 meters above ground level, the other at 20 meters.
  • We also installed weatherlink software in the office to link the weather station to a computer direct to a network set up. This communicated with the mast and logged data such as wind speed, temperature, wind direction, dew point and humidity.
  • ·         We then recorded weather information over a 9 month period and analysed the data to determine whether a wind turbine might be viable in the harbour area.

Findings

  • The overall energy usage around the harbour was calculated to be an average of 1019421.4KWh/ year per bussiness. Power consumption profile was relatively constant across most businesses.
  • In terms of harbour functions, the key energy users are :

–       Harbour Administration  – 53674.17 KWh per month or around 16% of total consumption

–       Buying/ selling fish  –  19,500KWh per month per market for busy periods and 12,500 KWh per month  at less busy time; an average of 28500KWh per month for the two markets or around 8% of total consumption independant of busy period or not

–       Maintaining vessels  – 18465.55KWh per month or 5% of total consumption.

–       Conservation of fish (ice production) – 73253.07KWh per month or 22% of total consumption..

–       Fish processing – 165914.34KWh per month or 49% of total consumption

 

The potential for renewables?

  • Fraserburgh has  a mean wind speed of 5.6 ms-1 and a maximum recorded gust speed of 25.2 ms-1.  The specific wind power density was found to be 215Wm-2, making this a Class 4 site and thus suitable for  commercial wind power generation.
  • Turbulence is not an issue. The wind direction was consistent with equivalent data from Aberdeen over the same period, and showed that most of the stronger wind-speeds – from the prevailing SW – were not disturbed by having to pass over the town of Fraserburgh first.

What about the stakeholders?

  • Harbour stakehodlers are very intersted in reducing their energy bills.
  • Switching to wind to improve their environment performace is not a prioirity, but stakehodlers are interested in exploring wind if it reduces their energy costs.

 Recommendations

The following recommendations are proposed:

  • It is recommended to install a wind turbine of approximate 800KW which will generate more than 1,521 MWh of energy per year. This could cost up to £1.5 million with a feed-in-tariff generation rate (current, £/KWh) of £0.09-£0.19 and start to pay back after 7years.
  • This could potentially supply up to 37% of the energy requirements of the businesses and premises surveyed in Fraserburgh harbor.

 

Case study 4:  Fraserburgh Harbour – switching to LED Lighting

Project description: This showcase focused on an energy use and management in Fraserburgh Harbour and looked at ways to reduce consumption and the potential benefits of feeding in renewable energy into the local grid.

 What we have done

We collected energy and weather data from Fraserburgh harbour. We looked at the exisiting harbour businesses, their existing use of energy and the operational constraints they face. We looked to  identify gaps and opportunities and arrive at  recommendations that will help local stakeholders reduce their energy bills. This work was subdivided into two parts:

Monitoring Energy Usage

  • We organised a site survey and various meetings with stakeholders to gain an understanding of:
    • the equipment and plant in use on site, what it’s used for (and when),
    •  the availability of records, bills or other available historical data,
    • the requirements for energy data acquisition.
  • We then installed energy data loggers in plant rooms to record the power consumed by different harbour processes. Instruments such as Pico-current monitoring kits(CM3) and power quality analysers(PQA) were installed and recorded the energy consumed at 10 minute intervals.
  •  We then plotted power consumption and analysed the data gathered to look at overall patterns of energy consumption and times of high peak and low peak demand.
  •  From the demand plotted within the harbour, and the understanding gained of the harbours stakehodlers and their industial processes, we looked at practical and ’implemetental’ ways of reducing energy use and bills for harbour stakeholders.
  • We also made recommendations on the potential cost of implementing each measure and possible savings that could result from it.
  • We are in the process of developing an analytical model that can be used to predict energy use in any small or medium harbour from limited data inputs.

Monitoring Meteorological Data

We also looked at the potential viability of local wind energy into the harbours energy mix:

  • We installed a weather station within the harbour to record meteorological data. A mast was installed to measure the wind speed from two anemometers placed at different heights: one at 10 meters above ground level, the other at 20 meters.
  • We also installed weatherlink software in the office to link the weather station to a computer direct to a network set up. This communicated with the mast and logged data such as wind speed, temperature, wind direction, dew point and humidity.
  • ·         We then recorded weather information over a 9 month period and analysed the data to determine whether a wind turbine might be viable in the harbour area.

Findings

  • The overall energy usage around the harbour was calculated to be an average of 1019421.4KWh/ year per bussiness. Power consumption profile was relatively constant across most businesses.
  • ·         In terms of harbour functions, the key energy users are :

–       Harbour Administration  – 53674.17 KWh per month or around 16% of total consumption

–       Buying/ selling fish  –  19,500KWh per month per market for busy periods and 12,500 KWh per month  at less busy time; an average of 28500KWh per month for the two markets or around 8% of total consumption independant of busy period or not

–       Maintaining vessels  – 18465.55KWh per month or 5% of total consumption.

–       Conservation of fish (ice production) – 73253.07KWh per month or 22% of total consumption..

–       Fish processing – 165914.34KWh per month or 49% of total consumption.

 

Did we find flexibility?

  • Most harbour stakeholders already have peak and off peak meters installed, so some available flexibility is already being captured.
  • However, lighting represents a significant demand for energy within Fraserburgh harbours.
  • LED floodlights are not used by most local users. With investment, there is therefore potential to reduce long-term energy bills by switching from conventional lighting to LED.
  • For example, if LEDs replace CFL (compact florescent light) lighting – the majority of lights in the harbour – the savings – in terms of energy and cost – will be about 50% of current levels.
  • Where LEDs replace incandescent light bulbs (which are relatively few in number in Fraserburgh), the savings will be close to 90%
  • In addition to energy savings, the use of LED Flood lights also many advantages over conventional halogens lumens. These include:
    •  longer life( up to 50,000 hours),
    • Better operation at low temperatures and better resistance to inclement weather
    • brighter lights (so fewer lights are required)
    • absence of mercury, lead and carbon emissions,  

 

Recommendations

  • It is recommended that all lights in Fraserburgh harbour are replaced with LED units. This represents a sound long term strategy for substantially reducing energy and energy costs within Fraserburgh harbour.
  • Even though initial cost is expensive, and depending on the type, wattage and application required, the initial capital cost of the LEDs would be paid back from energy and other savings within 1-2 years.
 Case study 5: Fraserburgh Harbour – Heat Recovery from Ice factory

Project description: This showcase focused on an energy use and management in Fraserburgh Harbour and looked at ways to reduce consumption and the potential benefits of feeding in renewable energy into the local grid.

 What we have done

We collected energy and weather data from Fraserburgh harbour. We looked at four exisiting harbour businesses, their existing use of energy and the operational constraints they face. We looked to  identify gaps and opportunities and arrive at  recommendations that will help local stakeholders reduce their energy bills. This work was subdivided into two parts:

Monitoring Energy Usage

  • We organised a site survey and various meetings with stakeholders to gain an understanding of:
    • the equipment and plant in use on site, what it’s used for (and when),
    •  the availability of records, bills or other available historical data,
    • the requirements for energy data acquisition.
  • We then installed energy data loggers in plant rooms to record the power consumed by different harbour processes. Instruments such as Pico-current monitoring kits(CM3) and power quality analysers(PQA) were installed and recorded the energy consumed at 10 minute intervals.
  •  We then plotted power consumption and analysed the data gathered to look at overall patterns of energy consumption and times of high peak and low peak demand.
  •  From the demand plotted within the harbour, and the understanding gained of the harbours stakehodlers and their industial processes, we looked at practical and ’implemetental’ ways of reducing energy use and bills for harbour stakeholders.
  • We also made recommendations on the potential cost of implementing each measure and possible savings that could result from it.
  • We are in the process of developing an analytical model that can be used to predict energy use in any small or medium harbour from limited data inputs.

Monitoring Meteorological Data

We also looked at the potential viability of local wind energy into the harbours energy mix:

  • We installed a weather station within the harbour to record meteorological data. A mast was installed to measure the wind speed from two anemometers placed at different heights: one at 10 meters above ground level, the other at 20 meters.
  • We also installed weatherlink software in the office to link the weather station to a computer direct to a network set up. This communicated with the mast and logged data such as wind speed, temperature, wind direction, dew point and humidity.
  • ·         We then recorded weather information over a 9 month period and analysed the data to determine whether a wind turbine might be viable in the harbour area.

Findings

  • The overall energy usage around the harbour was calculated to be an average of 1019421.4KWh/ year per bussiness. Power consumption profile was relatively constant across most businesses.
  • ·         In terms of harbour functions, the key energy users are :

–       Harbour Administration  – 53674.17 KWh per month or around 16% of total consumption

–       Buying/ selling fish  –  19,500KWh per month per market for busy periods and 12,500 KWh per month  at less busy time; an average of 28500KWh per month for the two markets or around 8% of total consumption independant of busy period or not

–       Maintaining vessels  – 18465.55KWh per month or 5% of total consumption.

–       Conservation of fish (ice production) – 73253.07KWh per month or 22% of total consumption..

–       Fish processing – 165914.34KWh per month or 49% of total consumption

 

Did we find flexibility?

  • Most harbour stakeholders already have peak and off peak meters installed, so some available flexibility is already being captured.
  • Much of the plant and machines were old and energy inefficient.  Again – some potential for lower energy costs following significant investment.
  • One potential source of flexibility was the heat dissipated by refrigeration systems at the ice factory, which produces around 320 tonnes of ice a year.
  • The amount of heat dissipated by refrigeration systems was found to be an average of 13.5 GJ per year (based on a temperature change of 100C by refrigeration systems) and corresponding electricity use was estimated at 3.17GJ. 322.65 tonnes of ice is produced per year.
  • This heat is not recycled currently

Recommendations

  • The use of full heat recovery system is recommended and would lead to huge savings. If a full heat recovery system was to be implemented and used for space heating, this would be enough to supply approximately three times the amount of heating currently required by the ice factory (assuming a 70% efficiency performance of the system).
  • The ice factory could thus export excess heat to other local business reducing energy costs and improving energy efficiency at the harbour.
  • Based on the four harbour businesses studies as part of e-harbours, a virtual power plant could be created.  The heat captured at the ice factory equates to 64% of the current energy demand of these four businesses.
  • This represents a substantial opportunity for capturing energy efficiency and flexibility.

 

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