Utility based power flexibility framework for office buildings-concept and field study evaluation

Aduda K. O.*, Zeiler W

Building Services Group, Department of Built Environment, Technische Universiteit Eindhoven, Den Dolech 2, 5612 AZ

Eindhoven, The Netherlands.

This paper proposes a utility based framework for leveraging power systems flexibility from office

buildings. Rationale lies in the need for consideration of user comfort, cost effectiveness and other

local details in office buildings when using them as sources for demand-side flexibility in power

systems network. Currently, existing similar frameworks are mostly biased towards supply-side

considerations. Consequently, demand-side performance characteristics such as thermal comfort,

indoor air quality, comfort recovery, availability and response time are overly simplified or altogether

ignored. The paper follows a two pronged approach to correct noted past trend. First, a review of past

studies and recent models in power systems flexibility is undertaken to develop critical performance

metrics for power systems flexibility that are applicable for buildings. These are then used to

formulate operational framework for power flexibility for office buildings. Next, case based field

study data for an average sized office building in Dutch setting is used to illustrate critical process

dynamics when using the suggested bottoms-framework as a demand side flexibility resource.

Keywords: office buildings, power flexibility, power systems network

*corresponding author- Email address: k.o.aduda@tue.nl, Tel: +31 (0)40 247 2039

1. Introduction

Due to impetus from the Brundtland Report[1], the last three decades has seen a tremendous

increase in decentralized energy production mostly in form of renewable energy sources (RES). As a

testimony to this, by 2014 installed global renewable power capacity stood at 1 829 GW and was 1

000 GW higher than in 2000[2]. Also noted during the same period were two key facts. First, the share

of hydropower in the renewable total reduced from 93% in 2000 to 64% in 2014 main due to rapid

growth of solar and wind energy production[2]. Second, developed and giant economies such as

Germany, China, United States, Japan, Denmark amongst others account for the greatest portion of

energy production from renewable energy sources[2]. Enormous further increase in RES production is

forecasted in the near future with countries like Denmark and Germany aiming for over 80% to 100%

of national total energy demand by 2050 and renewable electricity market share already accounting for

over half of new power plant investments in the EU[3]. This is expected to be further exacerbated by

resolutions of recent ‘21st Conference of the Parties’ (CP-21) accords which legally obligated

participating countries accented to among other issues curbing long-term increase in global average

temperature to less than 2°C above pre-industrial levels and thereafter limiting the increase in global

average temperature to 1.5°C[4]. Resolutions from CP-21 were meant as an agreed pathway towards

reducing the risks and the impacts of climate change[4]. It is subsequently expected that new

investments into low grade coal power plants will be curtailed and renewable energy resource

development will be more emphatic than before.

1.1. Distributed Generation and Impact on Power Systems Infrastructure

For electricity supply chain framework this has led to a paradigm shift from centralized generation

to distributed generation (DG) aided by the fact that investments in new power systems upgrades and

generation plant construction are often costly and the process highly protracted [5]. DG paradigm is

inclusive of: power generation from multiple distributed resources mostly renewable energy based

ones, use of optimal energy systems to service specific built environment needs, market accessibility

for different connected loads and active end user involvement in energy management. In the built

environment this has mainly come in form of adoption of ‘net zero energy building’ (NZEB) policy.

The European context of NZEB refers to energy efficient buildings that are connected to an energy

infrastructure and that have capabilities to generate electricity to balance the supplied amount per unit

timeframe (often annually) [6][7].Though well intentioned and arguably successful, NZEB has also

imposed a subsequent extra control burden on power infrastructure due to related uncertainties[7]

mainly as a result of their wide dispersal from each other, and highly variable and stochastic nature of

their production[3][8].

Additional power systems flexibility is proposed to solve the consequential control burden of RES

integration. Generally the idea of flexibility in systems engineering conceptually embodies the ability

of any system to be changed easily to cope with equally dynamic use environments [9]. For electrical

power systems, the enormous rise in RES production and associated need for their integration

necessitates ability to cope with resultant frequent mismatch in supply and demand whilst ensuring

non-disruption of services. Power systems flexibility is defined as the ability to cost effectively

balance electricity supply and demand on continually basis with negligible disruption to service for

connected loads[11][12]. In modern power systems, flexibility is inclusive of both supply-side and

demand-side resources. Supply side power flexibility refers to ability of conventional power plants

(often in form of fossil powered) and upstream located large electrical storage systems or curtailment

of RES feed in with capacity to balance electricity production and demand within power systems’

operations guidelines [3]. On the other hand, demand side flexibility refers to intelligent coordination

of connected loads to balance the mismatch between supply and demand within required power

systems’ operations guidelines[3]; this is often done under auspices of demand side management

(DSM) programs. In the context of this paper DSM describe collaborative undertakings between end-

users and power system utilities on the demand side of electricity distribution infrastructure in efforts

to modify load pattern and reduce operational costs[10].

1.2. Purpose and Contribution

Whereas DSM schemes offer the benefit of delayed expenditure on new power systems

infrastructure in addition to associated low direct carbon dioxide emission footprints as a result of their

use, they are also hampered by two main hiccups. First, as reported by Taylor and Mathieu[11], load

based flexibility which forms the backbone of DSM framework are themselves bedeviled with a series

of uncertainties; their use must as a result incorporate an adept risk management plan. These may

either be physical uncertainties’ (such as those related to unpredicted user and equipment behaviors)

and informational uncertainties whereby the load aggregator does not have full access to end user

information Informational uncertainties’ is mainly related to concerns for privacy by connected

consumers when using smart meters as reported in Molina-Markham et al. [12] and Sankar et al. [13].

Second, due to involvement of large number of small loads[14] with multiple response

characteristics [15]involved they also require innovative solutions for successful coordination and

aggregation towards successful delivery of power systems flexibility service within systems operator’s

specified guidelines. To solve this complexity, cluster based hierarchical aggregation framework has

been proposed [14]. In this framework networked loads would be referred to as resource clusters;

each resource cluster has a Cluster Manager (CM) tasked with market interactions and, resource

cluster coordination, organization and control[14]. However, it is worth noting that so far formulations

on cluster based hierarchical aggregation frameworks fail to answer questions with regards to level of

centralization or decentralization needed, effective information exchange details and economic

compensation for load flexibility services rendered . In addition, cluster based hierarchical aggregation

frameworks as suggested are largely biased to utilities supply side issues. Consequently, building side

related contexts and associated risks are ignored.

To address shortfalls highlighted here above, this paper proposes a context aware framework for

optimal coordination of power systems flexibility from buildings in the wake of increased requirement

for renewable energy sources (RES) integration in electricity supply chain infrastructure. Specific

contribution of the paper lies in the ability to of the framework to manage associated uncertainties in

harvesting demand side flexibility from buildings and ensuring cost effectiveness and user centricity

during periods of flexibility service offer. There are 4 remaining sections of the paper. Section 2

briefly outlines the methodology adopted by the study. Section 3 reviews literature on demand side

flexibility models and related performance implications. Section 4 presents results and discussions

from field studies’ data. Conclusions of the study are outlined in Section 5.

2. Methodological Approach

A two pronged approach was followed in the study: literature review and analysis of field study

data. The literature review was aimed at achieving the following 3 objectives:

1. Clarification of concepts and context of demand side use as power flexibility source.

2. Evaluation of existing demand flexibility models with an aim of developing a pragmatic

modification applicable for real life scenario.

3. Formulation of a building centred framework for leveraging demand side power flexibility

from buildings.

Literature review emphasized pragmatism in adapting existing models to reflect cost and comfort

effects of power flexibility. The study was limited to office buildings mainly due to the fact that office

buildings offer the best environment for implementation of sustainable principles due to their

organized facility management structure. In addition, past studies have mostly been concentrated on

residential type facilities as evident in [16][17][18] amongst many others.

Use of field study data enabled illustration of the efficacy of suggested framework. The field studies

data used were with regards to an existing office building connected directly to a 0.4kV power

transformer(Figure 1 below illustrate graphical load model as installed in the test facility with

connection to a redacted layout of the network). The case study building had an approximate floor area

of 1500m2 with an occupancy fluctuating between 20 to 45.

The field data collected relates to comfort and energy performance; it was specifically used to

illustrate performance implications and cascaded implications in 2 operational strategies for demand-

side flexibility. Specifically, the operational strategies involved use case scenarios for air supply fan

and cooling system. Respective details are discussed in section 5.

The next section presents a review of past studies and recent models in power systems flexibility;

the review section develops critical performance metrics for power systems flexibility that are

applicable for buildings. These are then used to set operational framework for flexibility based energy

interactions between actors in smart built environment.

Figure 1: Load model for the case study building

3. Aggregation framework and demand side flexibility models

Demand side flexibility models may be categorized into 2, namely: these are bottom-up and top-

down[19]. Top-down demand side flexibility models are defined in terms of overall elasticity of

demand with respect to price, examples are as reported in[20].Top-down model for demand side

flexibility harvesting are often prescriptive and biased towards grid side value systems; they ignore

building based details such as thermal comfort, indoor air quality, comfort recovery, availability and

response time. On the other hand, bottom-up demand side flexibility models are constructed from

generalized building models which are based on ability to contribute towards solving optimal

distributed control problem in maintaining power systems integrity[19]. Table 1 evaluates presence or

absence of performance metrics for some recent models applicable for demand-side flexibility.

Table 1: Performance metrics for top-down flexibility models applicable to demand side analysis

Model & Reference CI DC RP RR Ts ts BC CC AC

De Coninck & Helsen [19] √√ √√ √√ XX √√ √√ √√ √√ XX

Rosso et al. [20] √√ √√ √√ XX √√ √√ √√ XX XX

Venkatesan et al. [21] √√ √√ √√ XX √√ √√ √√ XX XX

Ulbig & Andersson [22] XX √√ √√ √√ √√ √√ √√ XX XX

Morales et al. [23] XX √√ √√ √√ √√ √√ √√ √√ √√

Bruninx et al. [24] √√ √√ √√ XX XX √√ √√ XX XX

Key:

√√: Indicates that the performance metrics is given critical consideration in the model

XX : Indicates that the performance metrics is not given much consideration in the model

Specific performance metrics considerations in Table1 are as follows:

Cost Implications (CI): This refers to possible cost implications of using connected loads as

power flexibility resources.

Demand Change (DC): This may be demand reduction or increase as a result of using

connected loads as power flexibility resources.

Ramp Potential (RP): This refers to the energy delivered (in terms of savings) or energy

consumed by connected loads as a result of being used as power flexibility resources.

Ramp Rate (RR): This refers to the energy delivered (in terms of savings) or energy consumed

by connected loads as a result of being used as power flexibility resources.

Time of Service (Ts): refers to the time of the day during which the building can be able to

participate in grid support activities.

Duration of Service (ts): describes the total period during which grid support activity can be

engaged in by the building.

Basic Comfort Consideration (BC): This describes the minimum quality of comfort as advised

by the design or health and safety codes.

Detailed Comfort Consideration (CC): This entails the details of indoor air quality and

prevailing thermal comfort values over and above the basic guidelines.

Acceptance (AC): this is the portion of load and comfort deterioration that is acceptable to

occupants or that is not disruptive.

Analysis in Table 1 demonstrate two key issues. First, addition of cost implications would make

Morales et al. (2014) model the most detailed in terms of consideration of local details. Second, power

and energy characteristics, basic comfort and total period for power flexibility service provision is

taken into account by all models. In view of this, it would seem logical to improve Morales et al.

(2014) model by addition of cost based function. Morales et al. (2014) model is summarized in

equations 1 to 3.

For load general case:

(1)

(2)

(3)

Where:

: Maximum load that can be consumed by flexible demand k during time t.

: Minimum load that can be consumed by flexible demand k during time t.

: Scheduled load for flexible demand k during period t.

: load increase of flexible demand during period t and scenario w for cases of down

regulation

: load reduction of flexible demand during period t and scenario w for cases of up

regulation

:Actual load of a flexible demand k during period t and scenario w

Transformation of this model to reflect facility side operation costs requires a two steps procedure.

First, non-disruption and acceptability consideration is made obvious by directly relating the

respective loads to the percent of persons dissatisfied (PPD) with the flexibility model. However, PPD

has been previously related to indoor air quality as shown in [25]. This approach is adopted herein in

effort towards a unitary indoor comfort and power characteristics model for demand side flexibility.

This is done by converting , , and to PPD equivalents; the resultant

equations for demand flexibility are outlined as (4), (5) and (6).

(4)

(5)

(6)

Where:

: Maximum possible PPD associated with flexible demand k during time t.

: Minimum possible PPD associated with flexible demand k during time t.

: PDD value as a result of scheduled flexible demand k during period t.

: Actual PPD value realized by flexible demand k during period t and scenario w.

: PPD value for equivalent load increase of flexible demand during period t and

scenario w for cases of down regulation

: PPD value for equivalent load reduction of flexible demand during period t and

scenario w for cases of up regulation

Next, PPD is related to the productivity of occupants. This can then be easily converted to cost in

euros (€). As observed in [26], for office buildings labour productivity is more costly than energy.

Also, past studies such as [25] and [27] respectively labour productivity for office buildings to PPD.

In [25], deterioration in labour performance as a result of reduction in indoor air quality is related to

PPD in an equation that can be reduced to (7).

(7a)

(7b)

Where:

ProdIAQ is productivity or labour performance deterioration as a result of equivalent

deterioration in indoor air quality, and

C is the perceived indoor air quality in decipo1l.

In [27], the relationship between deterioration in productivity or labour performance where thinking

related tasks are involved and increase in room temperature beyond 24°C is outlined can be reduced

to the equation (8).

(8)

Where:

ProdT is productivity or labour performance deterioration as a result of equivalent

deterioration in PMV.

The relationship between PMV and PPD is defined in equation (9).

(9)

Given the relationship between comfort and productivity expressed in equations (6) to (9), the

principal constraint to the above for any flexibility is expressed in equation 10. In equation 10, the

penalty is the allowable transactional cost in percent of labour productivity deterioration. It is directly

related to costs and defines the maximum penalty payable as a result of using office building as a

power flexibility resource.

(10)

Graphically, the improved model in terms of PPD and time based characteristics may be represented

as shown in Figure 2.

Figure 2: Demand side flexibility model for buildingsUsing Figure 3, the demand side power flexibility for buildings can then be defined in terms of

facility costs [€], acceptability[PPD], availability period [Minutes], comfort systems response time

1 One decipol is the pollution caused by one person ventilated by 10 1/s of unpolluted air[38]

User

Workplace

Building

Room

Built Environment

Presence, Comfort, Power & Energy footprint

Presence, Comfort, Power & Energy footprint

Dynamic Count, Comfort, Power & Energy footprint

Energy Flexibility Calculator

Overall Comfort Comfort -Energy Balancing

Market participation

Energy Flexibility Aggregation

Power network reliability

Cooperative Energy Flexibility

[Time of the Day], Comfort recovery Time [Minutes], Demand reduction or increase Power

[kW] and Energy delivered [kWh]. These parameters can be measured from the bottom up from the

workplace level upwards to the built environment. This makes the modified model a bottoms up type

with emphasis on building side performance. The proposal can be implemented easily within a

demand side management programme due to its modularity; such an implementation would easily

enable integration of the sources of flexibility from user level upwards through the built environment

level.

3. Real life scale implementation

Contextual details of office buildings’ based power flexibility across various architectural systems

levels are illustrated in Figure 3.

Figure 3: Contextual details of power flexibility leverage across various architectural systems level for office buildings

Information and power flexibility flows between these architectural levels emphasize aspects of

performance. For example at the lower end of the chain (e.g. at user level), comfort and presence are

the key consideration, conversely at the upper end of the network (e.g. at built environment) energy

based performance is emphasized. It is apparent that hierarchical informational flow across various

system levels may imply possible communication overload; consequently, aggregation of information

and distributed decision making is advised. Table 2 outline critical building flexibility parameters and

related information flow aggregation across the mentioned architectural system levels.

Table 2: Power flexibility parameters and related informational flow variable across architectural system levels

Architectural System Level

Flexibility parameter & units Informational flow variable

User Acceptability[PPD] Presence, comfort level & deviation from guidelines.

Workplace Acceptability[PPD] Occupancy count, comfort level & deviation from guidelines.

Room

Acceptability [PPD] Occupancy count, comfort level & deviation from guidelines.

Availability period[Minutes] & [Time of Day]

Time of flexibility service, Duration of flexibility service, Projected demand (-ve & +ve) & consumption(-ve & +ve).

Comfort systems response time [Time of the Day]

Delay period, Pre-set period & Re-set period.

Comfort recovery Time [Minutes] Delay period, Pre-set period & Re-set period.Demand[kW] Dynamic demand.

Building Costs [€] Rate, Price, Energy Use, Dynamic DemandAcceptability[PPD] Comfort level, Deviation from BoundariesAvailability period[Minutes] & [Time of Day]

Time of flexibility service, Duration of flexibility service, Projected demand (-ve & +ve) & consumption(-ve & +ve)

Comfort systems response time [Minutes] Delay period, Pre-set period & Re-set periodComfort recovery Time [Minutes] Delay period, Pre-set period & Re-set periodDemand reduction or increase Power [kW]

Peak reduction, Energy in peak, Increase in demand, Energy in valleys, Potential shifts,

Energy delivered [kWh] Peak reduction, Energy in peak, Increase in demand, Energy in valleys, Potential shifts,

Built Environment

Availability period[Minutes] & [Time of Day]

Time of flexibility service, Duration of flexibility service, Projected demand (-ve & +ve) & consumption(-ve & +ve)

Comfort systems response time [Time of the Day]

Delay period, Pre-set period & Re-set period

Comfort recovery Time [Minutes] Delay period, Pre-set period & Re-set periodDemand reduction or increase Power [kW]

Peak reduction, Energy in peak, Increase in demand, Energy in valleys

Energy delivered [kWh] Peak reduction, Energy in peak, Increase in demand, Energy in valleys

There are 2 main decision making centers at building level namely, the room and building.

3.1 Room decision center-tasks and decisions

Room level decision center is tasked with the following:

i. Demand control of services and appliances in the room with respect to prevailing comfort and

occupancy characteristics.

ii. Dynamic calculation of room based energy flexibility.

iii. Dynamically updating the building decision center on changes in room based energy

flexibility and any comfort related deviation from code based minimum and maximum

boundaries.

3.2. Building decision center-tasks and decisions

There are 5 main tasks for the building decision center, these are as follows:

i. Aggregation of room based energy flexibility.

ii. Evaluation and selection of appropriate building strategy for energy flexibility service.

iii. Calculation of whole building energy flexibility including that for centralized services based

on strategy selected.

iv. Continuous communication of whole building energy flexibility to a regional energy

aggregator or distribution service network operator.

v. Balancing energy and comfort optimization.

Decisions involving whole building flexibility and its communication to the aggregator are central

success of failure of power flexibility leverage from buildings. Even though interactions of the

building with the built environment emphasizes power network reliability, building based decisions

must also take into account occupants acceptability, cooperation and collaboration during power

flexibility leverage from offices[28]. Consequently , a decision tool that ably balances comfort

priorities and power flexibility service leverage from buildings is required. This article proposes the

use of a tool based on ‘Measuring Attractiveness By Categorical Evaluation TecHniques’, abbreviated

‘MACBETH’ framework[29][30]. MACBETH is a weighted aggregation method that assumes a

utility based decision at any point xi composed of 2 pathways: an attractive decision (with a value 1)

and a less attractive one (with a value 0). Subsequently at each point of decision making, a utility

decision is made such that[29]:

(11)

In this case the best decision therefore takes into account the balance between comfort and grid-

wide energy management based on the best utility value .

In more simplified terms for a case in which peak power reduction is required at building level,

is given by equation (12)[31].

(12)

Where:

is the utility function associated with electricity use reduction in the building at a

specific time.

is the reduction in active power consumption of the building at specific time k and

scenario w.

fi( ) represents the monetary equivalent of benefit or cost (negative benefit), associated

with ; it is the monetary penalty calculated from relationship outlined in equation (10)

and social welfare related benefits.

m is the monetary value for electricity reduction.

3.3. Algorithm for demand side power flexibility leverage from office buildings

The full algorithm defining power flexibility based interactions for office buildings is summarized in

Figure 4.

The main difference between the room and the building decision algorithms is emphasis on comfort in

the former and dedication to energy flexibility based interactions in the later. The building decision

algorithm uses utility function based equation introduced in (12) in attempt to balance indoor comfort

and participation in grid support activities using the relationship between productivity in office

buildings and indoor comfort conditions, the social welfare value, and net costs of electricity. The

social welfare value can be derived from cost equivalent of prevention of investment in new

infrastructure and overall reduction in energy related carbon emissions as a result of demand side

power flexibility. Details on modalities of choosing an applicable social welfare value is beyond the

scope of this paper. Net costs of electricity refers to the aggregated payments by the office building to

electrical utility organization for connectivity and the received compensation for participation in grid

support activities(compensatory offer for demand side flexibility resource to the power systems

network).

(a) Room based algorithm(b) Whole building based algorithm

Figure 4: Full algorithm for power flexibility leverage in office buildings

4. Evaluation with field study

Evaluation study was aimed at determining efficacy of the suggested framework for power

flexibility leverage from office buildings and associated grid-wide performance dynamics for a full

cycle of demand side flexibility episode. Two experiments conducted during field studies; these were

air supply fan setting reduction and fixed duty cycling for the cooling system. Further details on the

field studies are discussed in sections 5.1 and 5.2.

4.1 Air supply fan (ASF) setting reduction experiment

The air supply fan forms part of the air handling unit together with the air exhaust fan, ducting, air

control valves and filters. They are controlled by a Proportional-Integral-Derivative (PID) controller

which determines the output based on desired pressure (250Pa) and measured pressure. Air supply fan

is operational between 22:00 and 05:00 hours on normal weekdays and whenever night ventilation is

required. Night ventilation occurs anytime that the building is not occupied and, ambient temperature

is 3°C lower than the room temperature but greater than 12°C and room temperature is greater than

21°C. The case study building has 32 rooms; each of the rooms has unique characteristics. Sections

5.1.1 to 5.1.3 describe details of the ASF setting reduction experiment.

4.1.1 Protocol and Instrumentation for ASF setting reduction experiment

The air supply fan is critical in ensuring acceptable indoor air quality in the building. For indoor air

quality in offices, EN15251[32]and the ASHRAE 62.1 Standard [33] outline boundary conditions in

terms of carbon dioxide (CO2) concentration and ventilation requirements as follows:

CO2 concentration of 695 ppm above outside conditions, 10 l/s per occupant [32] and 2.5 l/s

per occupant[33] as premium, and

CO2 concentration of 695 ppm above outside conditions, 10 l/s per occupant [32] and 2.5 l/s

per occupant [33] as basic.

During the tests, CO2 measurement was obtained by direct measurement in respective rooms in the

building using sensor based instruments. Energy and power performance data was were extracted from

power meters installed at all load groups in the case study building.

The following protocol was followed in the ventilation based experiment.

1. The experiment involves operation of the air supply fan at 2 nominal settings; these are 80%

nominal setting (corresponding to 71% PID setting and 200Pa duct air flow pressure) and 60%

nominal setting (corresponding to 57% PID setting and 156Pa duct air flow pressure). Table3: Schedule of ASF setting on experiment day

Time(Hour: Minute)

7:00 to 9:00 9:00 to 11:00 11:00 to 13:00 13:00 to 15:00 15:00 to 17:00

ASF PID Setting 71% 57% 71% 57% 71%

2. This was done cyclically and alternately (between 80% and 60% nominal settings). Specific

schedule of settings during experiment were as outlined in Table 3.

3. During the same period power consumption details were retrieved from installed load group

meters.

4. Comfort and energy performance comparison was then done for the two ASF operational

settings. Comparison was based on PPD, energy delivered and demand reduction achieved.

07:12 08:24 09:36 10:48 12:00 13:12 14:24 15:36 16:48 07:12 08:24300

400

500

600

700

800

900

Time

Car

bon

Dio

xide

Con

cent

ratio

n-pp

m

Carbon Dioxide Concentration Profiles for Rooms in Building during Experiment Day

Room ARoom BRoom CRoom D

57%PID

71%PID

57%PID

Instrumentation used for comfort data were wireless sensor based with the following accuracy

limits:

Carbon dioxide (CO2) concentration: ±50 ppm

Temperature: ±0.4°C

Power consumption meter readings was available for all load categories; these were

accessed via the building management system (BMS). Accuracy of the measurements were

to the nearest kW unit.

The CO2 and temperature sensors were located in four rooms which were located at the end of

ventilation ducting in the building (these are designated A, B, C and D). The instrumentation sensor

location was deemed most optimal due to the fact that they presented the most critical nodes with

lowest positive duct air flow pressure; subsequently these nodes had the potential for worst

performance for indoor air quality and thermal comfort.

4.1.2. Experimental results-Air supply fan performance characteristics

Indoor air quality performance, during the experiment is varies for respective measurements. For

control purposes, the highest/worst performing profile(room D)is adopted.

Figure 5: Highest Carbon Dioxide Concentration Profile in Test Building during the Experiment

Figure 6 presents the CO2 concentration profiles of the rooms. Assumed outside ambient CO2

measurement at the test building site is 350 ppm, this makes maximum allowable total indoor

CO2concentrataion (if basic comfort considered) to be 1000 ppm. This value was never surpassed

although the readings during periods of 80% could be interpreted to be near this margin considering

error margin for the CO2 concentration sensors. Table 4 outline critical power characteristics that were

revealed in ASF setting reduction experiment.

Table 4: ASF power demand and power flexibility potential definition at various PID settingsNominal setting

PID Setting for ASF

Power Demand

Demand Reduction

Indoor Air Quality

Response Time

Continuous Flexibility Availability Period [Minutes]

for ASF Recovery Period[Minutes]

[Seconds] 06:30 Hours to 09:30 Hours

09:30 Hours to 17:30 Hours

80% 71% 4.0 kW 2.0 kW 605

0 18060% 57% 2.0 kW 4.0 kW 90 0 135

The following definitions applied for tabulated results:

Power Demand: This is the amount of power needed by connected load group.

Demand Reduction: This defines the power demand cut off as a result of reduced ASF

operational setting.

Indoor Air Quality Recovery Period: This is the time taken for indoor air quality to revert to

premium comfort conditions after an episode of demand flexibility service.

Response Time: Is the total time taken for comfort loads to be controlled for demand side

flexibility service activity.

Continuous Flexibility Availability Period: Defines the total time taken for demand side

flexibility service activity.

4.1.3. Discussion and Analysis-Air supply fan Experiment

4.1.3.1 Analysis and Discussion of Room level Decisions-ASF Experiment

Each respective room will push information to the Building Decision Centre with respect to prevailing

indoor air quality, deviation from maximum or minimum indoor air quality , (that is, labour

performance deterioration as a result of equivalent deterioration in indoor air quality) and power

flexibility. Evidently these information thread lines will be different for every room as illustrated by

results in Figure 6 . Thus depending on occupancy and fan control, various rooms will be able to

return greater flexibility than others. potential associated with each room. The implication is that with

innovative facility planning greater flexibility could be leveraged as compared to the status quo

whereby occupancy is not strategically planned. For the ASF in this case, control is centralized hence

associated power flexibility tasks are performed at Building Decision Centre Level. However, this is

not the same for any decentralized system; in such cases the dynamic power flexibility potential (in

terms of demand)would be calculated as follows:

(13)

where: is the specific room based power flexibility (room 1 one to room n)

is the fractional proportion of the total air flow allotted to any particular room,

is the power flexibility potential for the whole building ( in terms of demand ; in this

case it is the difference between demand at 71 and 57% PID settings i.e. 2 kW).

The results for room flexibility calculation is tabulated in Table 5.

Table 5: Room based power flexibility as a result of ASF setting reduction from 71% PID to 57% PID

Room Fan Airflow rate [m3/h]

Room power Flexibility [kW] Cumulative PowerFlexibility [kW]

1 47 0.005 0.01 0.012 200 0.022 0.044 0.0543 160 0.018 0.036 0.094 200 0.022 0.044 0.1345 160 0.018 0.036 0.176 200 0.011 0.022 0.1927 100 0.035 0.07 0.2628 320 0.018 0.036 0.2989 160 0.035 0.07 0.368

10 320 0.018 0.036 0.40411 160 0.018 0.036 0.4412 160 0.022 0.044 0.48413 200 0.011 0.022 0.50614 100 0.018 0.036 0.54215 200 0.088 0.176 0.71816 160 0.008 0.016 0.73417 200 0.018 0.036 0.7718 800 0.022 0.044 0.81419 70 0.018 0.036 0.8520 160 0.022 0.044 0.89421 200 0.011 0.022 0.91622 160 0.035 0.07 0.98623 200 0.018 0.036 1.02224 100 0.053 0.106 1.12825 320 0.022 0.044 1.17226 160 0.011 0.022 1.19427 480 0.044 0.088 1.28228 200 0.088 0.176 1.45829 100 0.018 0.036 1.49430 400 0.025 0.05 1.54431 800 0.099 0.198 1.74232 160 0.137 0.274 2.016

4.1.3.2 Analysis and Discussion of Building level Decisions-ASF Experiment

At building level, the decision on whether or not to participate in grid support activity is a 2 steps

process. First the building aggregates whole building power flexibility from the rooms; resultant

cumulative power flexibility is presented in Figure 7.

Figure 7: Specific Maximum Room Based Flexibility and Cumulative Power Flexibility in the test building as a result of ASF setting reduction from 71% PID to 57% PID(with all rooms contributing maximally)

Apart from aggregation of available flexibility, the building decision center also solves the utility

function equation (equation 12) as part of decision making for participation in grid support activity.

This entails solving equation 12 for every programme time unit of power systems support activity. The

programme time unit 96 programme time units and each of then lasts for 15 minutes. Thus the

building solves has to dynamically compute 96 solutions to equation 12 in 24 hours. Solutions form

the basis for continual availability of the building for grid support activity.

4.2 Fixed duty cycling experiment for cooling system

The cooling system at the test office is made up of a roof top unit with the energy flexibility fully

reliant on chiller operation strategies. Cooling is air based with the energy being transferred from the

chiller coils via air handling unit which blows cold air into 3 zones: ZW, NO and E. The chiller is

double stage type with single stage being operational whenever the outdoor ambient air temperature is

greater than 18°C for one-hour duration during weekdays between 7:00 hours to 18:00 hours; the

double stage operation of the chiller kicks in when outdoor ambient air temperature is above 26°C for

a period of 30 minutes. The cooling system is shutdown on all public holidays.

Experiment with the cooling system entailed operating it at fixed duty cycles of half an hour on and

one hour off. Details of the protocol, instrumentation, emanating results and their discussing for these

are outlined in section 5.2.1 to 5.2.3.

4.2.1. Protocol and Instrumentation for cooling system fixed duty cycling experiment

Cooling system fixed duty cycling entailed operating the main component (that is the chiller) at fixed

duration schedules of ‘ON’ and ‘OFF’. The following protocol was followed during the experiment:

1. The Chiller was first allowed to operate on normal thermostatic controlled on-off cycle till

9:30 am.

2. At 9:30 am, the Chiller was switched off for a period of one hour and thereafter switched off

for 30 minutes; this pattern of on-off operation cycling was repeatedly done for the whole day.

07:12 08:24 09:36 10:48 12:00 13:12 14:24 15:36 16:48 18:00 19:120

10

20

Time

Pow

er C

onsu

mpt

ion

kW

3. During the experiment period, care was taken to ensure that thermal comfort remained within

code defined boundaries. The following were adopted as boundary for thermal comfort

guidelines during the experiment (in terms of PMV2, PPD3 and operative temperature4) as

advised in [34] and [32]:

-0.2 <PMV>0.2 and PPD < 6% considered as most comfortable, this translates to an

operative temperature of 25.5°C during summer when the experiment was conducted;

-0.7 <PMV>0.7 and PPD < 15% as minimum basic, this translate to 27.0°C for

summer time.

4. During the experiment, indoor thermal comfort were measured and load group profile

measurements for the building retrieved.

Instrumentation used were similar to those described in section 5.1.1. (instruments used were sensor

based temperature and relative humidity measurement with accuracies of ±0.4°C and ±1%

respectively.

4.2.2. Results for cooling system fixed duty cycling experiment

The experiment was done for ambient outside temperature of between 25 °C to 28°C. Table 6 outline

resultant thermal comfort recovery[minutes], availability period [minutes] and power delivered by the

exercise in terms of demand reduction [kW].

Table 6: Power performance and comfort implications of Fixed Duty cycling load control strategy

Fixed duty cycling pattern Ambient outside air temperature (°C)

Availability period[minutes]

Power Delivered[kW]

Thermal comfort recovery [minutes]

1/2hour ON-1 hour OFF 23 to 27°C 60 minutes 7 27

The power characteristics of the chiller during the experiment is illustrated in Figure 7.

Figure 7: Chiller power characteristics during 1/2hour ON-1 hour OFF fixed duty cycling experiment for the cooling system

The following 2 issues are noted on Chiller power characteristics during the experiment:

i. Before the beginning of the experiment, the Chiller works on a thermostatic modulated

schedules of operation and non-operation at single stage mode. The frequency of operations of

2 PMV as calculable variable based on heat balance on an assumed average human being based on their thermal perception on the basis of hot, warm, slightly warm, neutral, slightly cool, cool and cold[34]3 PPD is defined as the percentage of the number of indoor population that are dissatisfied with the indoor climate[34]4 For buildings, operative temperature is given by the average value of air temperature and mean radiant temperature; it provides an easier way of assessing thermal comfort. In this building operative temperature and relative humidity was derived from sensor based measurement at respective rooms in the building.

07:12 08:24 09:36 10:48 12:00 13:12 14:24 15:36 16:48 07:12 08:2422

22.5

23

23.5

24

24.5

25

Time

Ope

rativ

e Te

mpe

ratu

re-d

eg. C

Indoor Thermal Comfort Profiles for Various Rooms in Building during Experiment Day(1/2 hour ON-1 hour OFF Cooling System Duty Cycling)

Room A1Room B1Room C1Room D1

OFF OFF OFF OFF OFF

Lowest operativetemperature

Highest operativetemperature

the Chiller from 7:00 am to 9:30 am during this period is 15; the off periods last for

approximately 5 minutes whilst the on-periods last between 5-9 minutes.

ii. last for approximately 5 minutes whilst the on-periods last between 5-9 minutes.

iii. After being off for an hour, the Chiller operates on double stage mode for the first 15-17

minutes when switched on before reverting back to single stage operation.

iv. Other comfort and power systems flexibility characteristics for the scenario are as follows:

Power Demand: Varies with highest double stage demand between 17-19 kW.

Demand Reduction: For the off session, demand is eliminated; this translates to wiping

out of approximately 8 number modulated single stage chiller demand sessions each of

which lasts for 5minutes.

Response Time: The response time of the Chiller is 5 minutes.

Figure 8 depicts indoor thermal comfort characteristics during the experiment.

Figure 8: Typical indoor thermal comfort profiles during 1/2hour ON-1 hour OFF fixed duty cycling experiment for the cooling system

Cyclic rise in operative temperature coinciding with periods when the Chiller is switched off are

observable; these alternate with reduction in operative temperature whenever the Chiller is on. Also,

differential thermal comfort performance for various rooms within the building is noted.

4.2.3. Analysis and Discussion for cooling system fixed duty cycling experiment

4.2.3.1. Room based DecisionsThere are 2 aspects of room decisions with respect to fixed duty cycling of cooling system, these are

ensuring that thermal comfort is within code advised boundaries and also updating the building level

decision centre on available room power flexibility potential. The later task also includes dynamically

updating on the deviation from code defined minimal thermal comfort level of the room. Due to

associated unique characteristics (in terms of air flow rate, occupancy, orientation, volume and floor

area), each room would therefore have equally unique value of power flexibility as a result of room

07:12 08:24 09:36 10:48 12:00 13:12 14:24 15:36 16:48-1.5

-1.4

-1.3

-1.2

-1.1

-1

Time

Loss

in P

rodu

ctiv

ity %

Room based Productivity Reduction due to Flexibility Offer on Test Day

M5M11M38M50

07:12 08:24 09:36 10:48 12:00 13:12 14:24 15:36 16:485.6

5.65

5.7

5.75

5.8

5.85

Time

Per

cent

of P

erso

ns D

issa

tisfie

d-P

PD

% Room based Flexibility Potential in terms of PPD on Test Day

M5M11M38M50

based power flexibility as a result of fixed duty cycling; this explains the observed variation in thermal

profiles across various rooms in the building. The above observation applies also for a centralized

cooling system as is the case study; however, for centralized cooling systems, room based power

flexibility is only hypothetical as gains are all centralized. Figure 9 below illustrate the room based

thermal power flexibility potential and associated productivity loss (penalty for building offer of

power flexibility).

Figure 9: Room based flexibility in terms of PPD and related loss in productivityIt is observed that productivity loss reduces from approximately 1.1% before the experiment to 1.5%

during maximum reduction in indoor thermal comfort as a result of the experiments. The calculations

on productivity loss are based on model by Seppänen et al.[27]. The margin of deviation noticeable is

however very minimal compared to actual feedback of occupants during the experiment. This may be

due to the fact that the occupants are used to premium thermal comfort conditions and any

deterioration becomes easily noticeable. At between 5.85% to 5.65%, the PPD remains on the border

between minimum guidelines for basic indoor thermal comfort and premium thermal comfort (that is

6%[32][33]) during the entire period of experiment. It is also noted that the room conditions do not

revert back to their initial thermal comfort level during the period of experiment. Using, the modified

Morales et al.[23] model depicted in Figure 2 implies that the system still has approximately 9%

available as a demand flexibility resource; however, this may not be much taken that the behaviour is

exponential and PPD increases rapidly as thermal comfort deteriorates.

The idea of devolving comfort decisions to room level is motivated by the need to consider local

details during grid support activities and to ensure that the function of the building remains centred on

comfort provision. However, room details consideration may enhance may complicate decision

building level decision making process. In particular, variation in thermal comfort performance across

the room poses a difficulty in deciding specific period at which building should deployed resources for

power grid support activity. For instance, use of sensors from best performing room as an indicator for

deployment for power grid support by building may greatly compromise comfort in worst performing

ones and vice versa.

4.3.3.2 Utility Decision at Building LevelBuilding level decisions as envisaged here concentrate on solving the main utility function equation

12. The utility function algorithm has two main parts: the negative and positive benefits. The positive

benefit includes social welfare and energy export (negative energy flow) costs payable to participating

buildings. Energy export costs are based on delivered energy in terms of peak reduction to the grid.

Negative benefits are the penalty payable by buildings in terms of loss in labour productivity as a

result of participation in power grid support activity; in this study they are referred to as power

flexibility penalty for the building. The following applied in analysis of utility decision at building

level:

Social welfare costs is equivalent to the level of CO2 emission subsidies; in the European

Union this is €7.2 per ton (USD to Euro conversion ratio used is 0.91)[35]. This value is

multiplied with CO2emission rate to In the Netherlands carbon dioxide emission rate per

kWh of electricity production is 0.054kg per kWh [36]. Based on these statistics, the cost of

carbon dioxide emission prevention in the Netherlands was assumed to be €/kWh 0.0004.

Energy export : It is assumed that the cost of power export to the grid is at current base

consumption tariff rate for offices, that is €/kWh 0.089[37]; on real time basis this price is

continually changing for every programme time unit of power network operations and not

static as used herein.

Power Flexibility Penalty: Calculation for power flexibility penalty considered labour costs

and occupancy. A flat hourly labour cost of € 15 was assumed to apply; Calculations for

productivity loss as a result of thermal discomfort were done for occupancy fractions of

10%, 20%...100% (100% occupancy for the building is 45). Equations (8) and (9) applied

with interpolation done for operative temperature use. Calculations were done for

productivity loss in worst case scenario (this is the highest returned productivity loss value

for the rooms) and for the best case scenario (this is the lowest returned productivity loss

value for the rooms).

Energy Flexibility Delivered: This was calculated based on constant demand reduction of 7

kW for 1 hour (the result was 7kWh of electricity delivered).

Case 1: €0.0004 per kWh of carbon dioxide emission social cost, €0.089 per kWh payment for negative energy flow to the power grid

Case 2: €4 per kWh of carbon dioxide emission social cost,€0.089 per kWh payment for negative energy flow to the power grid

Results for utility based decision at building level based on above static point is as illustrated in Figure

10; it is noted that real scale calculations are based on dynamic pricing unlike our case analysis.

Figure 10: Productivity loss/Penalty for various occupancy scenariosFigure 10 illustrate that cost effectiveness cannot be the sole basis for making utility based decision;

this is proven by the fact that even with a thousand folds increase in carbon emission reduction

compensation, the test building still ends up losing an equivalent of €955 to €715 per hour of ‘1/2hour

ON-1 hour OFF’ fixed duty cycling strategy. This is due to comparatively high labour costs (against

very cheap electricity cost for industrial and business sector consumers).

Also implied is that occupancy consideration is critical when using thermal comfort system as a

demand flexibility resource for office buildings. This is closely related to higher costs of labour as

compared to energy. It therefore makes sense that office buildings with multiple Chillers or zone

dedicated Cooling Systems would offer enormous advantages as demand flexibility resources due to

associated flexibility in controls.

5. Conclusions and recommendations

This article has presented an adaptation of demand side flexibility model by Morales et al.[23] for

office buildings. The adapted model is unique in its building centeredness and specific use of PPD as a

variable for power flexibility definition in office buildings; this enables the integration of cost

implications necessary for decision making before participation in power network support activities.

Also presented is a bottoms-up framework for demand side flexibility leverage from office buildings.

Using field experiments, the building side process dynamics and associated implications have been

illustrated some scenarios in office buildings.

The limitation of this paper is lack of extensive data for real scale illustration. Presently, only two

operational strategies for a single office building are evaluated. Organization of real scale studies

involving multiple office buildings present a logistical difficulty due to large number of stakeholders

involved. This makes the option emulation of participation of buildings in grid support activities the

easiest one in such studies. Consequently, the study was unable to decipher actual impact as a result of

aggregation of a number of office buildings for grid support activities using the suggested framework.

Also, the strategies used in illustration were minimal as compared to the full range available in office

buildings with similar installations. In addition, they were not implemented for the full range of

possible ambient weather conditions.

In view of the above it is recommended as follows:

1. Further tests to fully demonstrate the effectiveness of the suggested framework for multiple

buildings and actual scenarios of power network support activities.

2. Full investigation of all other strategies for demand side flexibility in office buildings at

various ambient conditions.

These recommendations are based on the fact that whereas preparation and successful real scale type

tests are fraught with difficulties in logistics, they provide the best avenues for suggesting sustainable

frameworks for building energy management in the changing power systems networks.

Acknowledgement

The support of Kropman Installatietechniek, Almende, CWI and Rijksdienst voor Ondernemend

Nederland is acknowledged in realizing this study.

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