Integrating wind power using intelligent electric water heating

Energy 48 (2012) 135e143

Contents lists available at SciVerse ScienceDirect

Energy journal homepage: www.elsevier.com/locate/energy

Integrating wind power using intelligent electric water heating Niall Fitzgerald b, c, Aoife M. Foley a, b, c, *, Eamon McKeogh a, b a

School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Northern Ireland Dept. of Civil & Environmental Engineering, School of Engineering, University College Cork, College Rd., Cork, Ireland c Environmental Research Institute, University College Cork, Lee Rd. Cork, Ireland b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 December 2011 Received in revised form 4 March 2012 Accepted 6 March 2012 Available online 3 May 2012

Dwindling fossil fuel resources and pressures to reduce greenhouse gas emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is to instantaneously meet demand, to operate to standards and reduce greenhouse gas emissions. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating has been studied previously, particularly at the domestic level to provide load control, peak shave and to benefit end-users financially with lower bills, particularly in vertically integrated monopolies. In this paper a number of continuous direct load control demand response based electric water heating algorithms are modelled to test the effectiveness of wholesale electricity market signals to study the system benefits. The results are compared and contrasted to determine which control algorithm showed the best potential for energy savings, system marginal price savings and wind integration. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Demand side management Demand response Energy storage Energy efficiency Electricity markets Wind power

1. Introduction Environmental and economic issues demand the more efficient use of energy to reduce greenhouse gas emissions and conserve non-renewable natural resources. The mix of power sources in generation portfolios will also continue to diversify as fossil fuel resources decline and renewable energy sources grow. The European Union (EU) also set a target to cut energy consumption by 20% by 2020 [1]. A similar pattern of energy policy plans and targets is repeated internationally [2e4]. The EU has issued mandatory smart metering targets [5]. In the USA plans are also afoot to ensure the development of a smart grid [6]. It is recognised that information communication technology in the form of smart metering and the smart grid will play a major role in the transition to an energy efficiency low carbon economy [7]. In addition many countries have set high wind energy targets, although coal will still contribute [8]. Nuclear power was also expected to grow in most regions except Europe by 2030, but following the recent events in Japan. This seems doubtful. Ireland has a target of generating 40% electricity * Corresponding author. School of Mechanical and Aerospace Engineering, Queen's University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Northern Ireland. Tel.: þ44 28 9097 4492; fax: þ44 28 9097 4148. E-mail addresses: [email protected], [email protected] (A.M. Foley). 0360-5442/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2012.03.014

from renewable energy sources by 2020 [9]. This is expected to come mainly from variable wind power. Existing wind power prediction and forecasting is limited [10]. Next to wind power forecasting, energy storage, geographical dispersion of renewable power sources and interconnection with other grids, demand side management (DSM) can reduce the effects of wind power variability [11]. Therefore as the generation portfolio mix increases and electricity system balancing becomes more onerous, the use of all the tools available, including demand side management (DSM) will become even more important [12]. Thus the door will be opened to DSM programs such as price reactive demand response (DR). Appliances such as electric water heaters, heat pumps and airconditioners which have an associated thermal storage are prime candidates for DR especially in power systems with high variable renewable penetrations such as Ireland, as they can act as controllable loads for balancing purposes. Electric water heating has already been put forward as a possible solution for storing surplus wind power as thermal energy in addition to improving power demand [13]. It has been applied previously, particularly at the domestic level to control loads, peak shave and to benefit end-users financially with lower bills in vertically integrated monopoly systems, but with the drive to improve energy efficiency, the benefits of using other market signals to control electric water heating merits investigation

136

N. Fitzgerald et al. / Energy 48 (2012) 135e143

[14e16]. In terms of electric water heating systems there is potential. Fig. 1 shows the breakdown of residential end use electricity consumption in the EU-15 member states and the USA, for 2001 and 2003 respectively. In the EU alone it is estimated that up to six million electric water heaters were sold in 2006 [17]. In the USA it was estimated that 41 million homes used some form of electric water heating in 2001 [18]. Internationally electric water heating using storage tanks can account for 7%e30% of residential electricity consumption [19e21]. In South Africa usage has been reported as high as 40% [22]. In Ireland electric water heating can account for up to 23% of the domestic electricity usage annually [23]. This percentage includes electric showers and other forms of instant hot water. In fact in Ireland it is estimated to be installed in 48.6% (710,676) of dwellings as a primary (3.6%) or as a secondary (45%) source of hot water [24]. The demand trend in Fig. 1 suggests electric water heating is an ideal candidate for wind power integration because of the energy storage capability of hot water tanks. Finn, Fitzpatrick, Connolly, Leahy, Relihan (2011) examined the use of immersion heated hot water in Ireland’s electricity market to facilitate wind generated electricity using price optimised load scheduling in 2020 [25]. That study found a correlation between day-ahead predicted half-hourly electricity prices and real-time wind availability, reinforcing the suitability of electric water heating as tool to improve energy efficiency, integrate wind power and make financial savings. In this analysis an electric water heating model was developed to examine the potential to transform electric water heating, a passive load, into an intelligent responsive agent capable of reducing electricity consumption, reducing peak demand and possibly provide some balancing load to assist the integration of variable wind power. A direct load control algorithm in residential hot water storage tanks were used in the model to test the effectiveness of market signals to control heating patterns. The model simulated a 12 month period for the best potential energy savings, wholesale electricity price savings and wind power integration.

many DSM programs in operation in the world today [26e29]. One interesting feature of the review of this documentation is the lack of consistent terminology since the move to liberalised electricity markets. The development of an international standard lexicon of terms in DSM would be a worthwhile project. Demand side management techniques fall into five main categories, which include measures to reduce transmission and distribution losses, energy conservation programs, more energy efficient equipment such as the STAR program, load management (LM) measures to shift load to less ‘peaky’ periods of the day using some sort of pricing arrangement and strategic or planned load growth or building [30]. The success of LM is evident from the number of programs still in operation today. However, in monopoly markets it appears that LM is subsidised and a revenue loss for the utility companies in direct financial revenue returns. In liberalised markets LM has been generally replaced by DR and pricing structures depending on the technology in place. Load management techniques and terms include load shifting, peak clipping, energy efficiency (or conservation), planned load growth, valley filling and flexible load shaping. There is also a number of pricing type arrangements often referred to as rate scheduling. Again depending on the electricity market regime, terminology can vary, so caution is needed. Examples include the traditional time-of-use pricing more associated with monopoly type markets and price based DR and incentive based programs, which have been introduced in liberalised electricity markets. Demand response is also called demand side response. Demand response is used in preference to LM to imply participation by both electricity suppliers and end-users which is typically event driven and usually includes some element of time of use or dynamic pricing. For example the switching on and off of EWH units, air-conditioners and swimming pool pumps to a price signal in a smart meter in the home.

2. Demand side management: overview & techniques

A number of studies have investigated market effects of demand side programs [31e34]. In Chua-Liang and Kirschen (2009) and Kirschen (2003) some interesting aspects of electricity markets from the demand side are discussed and the importance of the

Demand side management in the electricity industry refers to numerous techniques used to manipulate electrical loads. There are

3. Demand side management: markets & wind power

Fig. 1. Electricity end use, EU-15 & USA e residential Sector.

N. Fitzgerald et al. / Energy 48 (2012) 135e143

short-run price elasticity of the demand for electricity is highlighted as a means to improving market operation as well as system security [35,36]. DR is a potential tool to enable more efficient electricity markets. Of course it should be emphasised that DSM and effective DR programs does not equate to less electricity consumed and lower electricity bills for consumers, but rather a better use of power. DR can potentially in a liberalised market environment, as LM in the pre-reform era, reduce the need for additional grid infrastructure and an oversupply of generating units. Obviously this would not include the initial upgrades and telecommunications infrastructure. It also appears that in liberalised markets grid infrastructure suffers because market participants do not want to support and maintain it. In light of future international smart grid plans the effects of market regimes on DR merit further research. In existing liberalised market regimes, transmission system operators (TSO) tend to depend on the more traditional system balancing services, which are referred to as ancillary services for system security. Ancillary service type arrangements are detailed in [37]. Raineri, Ríos and Schiele (2006) compare the technical and economic aspects of ancillary services markets internationally [38]. More recent studies have also been undertaken by various market participants and academics to quantify the additional system balancing costs, if any, of variable intermittent renewable power, particularly wind power [39e43]. However, the overall results are not comparable as no two power systems are the same and a clear pattern of dissimilarity is evident. System balancing is passed onto the electricity end-user in their bill usual in the form of standing charges in various guises [44]. The use of ancillary services for system balancing has not completely stopped DSM in liberalised markets. Fig. 2 presents the annual DSM spend in the USA up to 2007 [13]. A review of international literature has indicated that DSM programs in the electricity industry appear to be more prolific in the USA. Prior to the liberalisation of electricity industry in the USA in the early 1990’s, a survey of revealed that utilities were spending on average 1% of their revenue on DSM [45]. Is the continuation of DSM in the USA due in part to the fact that the incumbents have spun off wholly owned DSM companies? DSM companies in Europe and in the rest of the world appear to be less successful and state that lack of

137

knowledge and transparency, price signals, hourly and real-time pricing, market design and market barriers as reasons for the lack of take-up of DSM services [46]. Thus the market structure has a very important role to play in DSM. DR offers an alternative method of energy efficiency, wind power integration and system balancing, which is driven by the customer. Research indicates that there are potential system benefits for all participants if DR was implemented using a suitable program in the correct market environment. The EWH presented in this paper is an investigation of one such DR program using a DLC intelligent meter. 4. Electric water heating model & test system 4.1. Methodology The methodology used in this study involved the development a number of direct load control algorithms to investigate system benefits and match electric water heating loads with wind generation. The model ran for a simulation period of 12 months using real-time historical data from the 1st July 2008 to the 30th June 2009. Wind generation and system demand data from the transmission system operator, EirGrid and the ex-ante (1-day ahead forecast) system marginal price (SMP) and the ex-post (settled) SMP from the Single Electricity Market Operator (SEMO) in Ireland is used [47e49]. In the model the temperature of the volume of hot water in the tank is increased and decreased without incorporating thermal layers (the volume of the whole tank is assumed to have the same temperature at all times). Generally electric hot water tanks have two immersion elements installed, one that heats a small amount of water at the top of the tank and one that heats the full tank. For simplicity, this model only simulated the immersion, which heats the full tank. The test system is based on Equation (1), which is the basic heat transfer equation for an electric immersion heater, and the convection heat losses associated with the tank due to the temperature difference between the water in the tank and the ambient air temperature outside the tank, which was taken to be room temperature (18  C) [50,51].

M:SHw :


dT ¼ U:A Tamp  T þ Q dt

Fig. 2. Annual DSM spend in the USA, $Billions.

(1)

138

N. Fitzgerald et al. / Energy 48 (2012) 135e143

Equation (1) is rearranged to give Equation (2) to model electric water heating over a 12 month period [52].







TðtÞ ¼ Tamb þ Tðs0Þ þTamb :e

UA MSHw




ðtsÞ

þ

 U:A Q :RE  1  eM:SHw :ðtsÞ U:A (2)

where M ¼ mass of water in the tank (kg) Dw ¼ density of water (kg/m3) SHw ¼ Specific heat capacity of water (J/kg K) T ¼ Temperature of water in tank at time t ( C) t ¼ time (minutes) s ¼ time when change of state occurs (minutes) T(s0) ¼ new initial temperature when there is a change in state ( C) U ¼ U-value (W/m2/ C) A ¼ Surface area of the EWH storage tank (m2) Tamb ¼ Ambient air temperature ( C) Q ¼ Input power of the heater (0 when not heating) RE ¼ Assumed operating efficiency of 0.98. As hot water was drawn form the storage tank, it was replaced by cold water from the inlet supply. The temperature of the cold water inlet supply varied over the year because of changing air and soil temperatures. The mean monthly soil temperatures from 13 observatories around Ireland from the period 1961 to 1990 was selected as the best approximation for the monthly variations in cold water inlet supply temperature [53]. The new temperature of the water in the storage tank, after the hot water was replaced with inlet supply cold water, was governed by Equation (3).

Tnew ¼

ðTcurrent Mcurrent Þ þ ðTinlet Minlet Þ Mcurrent þ Minlet

(3)

where Tnew ¼ Temperature when water drawn ( C) Tcurrent ¼ Temperature in EHW tank after draw (before water from inlet supply added) ( C) Tinlet ¼ Temperature of water from inlet supply ( C) Minlet ¼ Mass of water from inlet supply (kg) Mcurrent ¼ Mass of water in EHW tank after water drawn (kg) 4.2. Control algorithms A standard water draw prediction was assumed for each 24 h period and remains constant for the simulation. The volume of hot water drawn by a household during a 24 h period was estimated to be 200 L (L) based on historical data collected by the Building Research Establishment (BRE) [54]. The following water draw prediction was based on estimated water use in a household in Ireland. A water draw at 7:00am of 30 L; at 8:00pm of 30 L; at 5:00pm of 40 L; at 6:00pm of 30 L; at 7:00pm of 40 L; at 10:00pm of 30 L. For the purposes of this model, each water draw was a single draw and hysteresis of 2  C is designed into each control algorithm. It was important to have a degree of hysteresis to prevent fast switching of the control unit due to small changes in temperature and limits due to changing market data. Fast switching can shorten the life of the immersion elements in the storage tank. Fig. 3 illustrates the general control algorithm concept functions. The model was continuous with discrete jumps describing the heat draws. The temperature limit varied between Tmax limit (70  C) and Tmin limit (45  C), Z varied between 0 and 1, depending on the

Fig. 3. General control algorithm concept.

market data used, as shown in Equation (4) [52]. Z was a variable which was calculated for each control algorithm. In the model the water was kept within 70  Ce45  C in order that customer comfort was maintained by the implementation of any of the control algorithms.

Temperature limit ¼ Tmax ðZ:Tmax :Tmin Þ

(4)

For each control algorithm the model is simulated for 12 months with identical input variables and market data. The control algorithms are based on a 24 h optimisation horizon because of the daily usage patterns of hot water and the limited storage provided. Each control algorithm was modelled for different U-values of the insulation of the storage tank and for different storage tank volumes. Two U-values were also selected to best represent the current installed stock of storage tanks in Ireland. The benefit of better insulated storage tanks was also investigated by varying the thickness. A U-value of 0.8W/m2/ C represented a retro-fitted lagging insulation with a thickness of 50 mm and a U-value of 0.44W/m2/ C represented factory pre-insulation with a thickness of 50 mm [55]. Two storage tank volumes were selected to test the potential benefits of having larger storage volumes. As a 150 L tank represented most of the currently installed stock a larger 300 L tank was used. In the model the baseline system specification (SS), SS-1, referred to a tank with a U-value of 0.8W/m2/ C and a storage volume of 150 L, SS-2 referred to a tank with a U-value of 0.8W/m2/  C and a storage volume of 300 L and SS-3 referred to a U-value of 0.44 W/m2/ C with a storage volume of 150 L and SS-4 referred to a U-value of 0.44 W/m2/ C and a storage volume of 300 L. The model was run for each system specification option and each storage tank within the aggregation follows the standard water draw prediction. The draw time was randomised within the defined hour to better simulate the water usage of consumers. 4.3. Standard control algorithm The control algorithm was designed to simulate a standard thermostat installed in existing storage tanks in Ireland. The existing control algorithm switch on when the temperature of the water drops below 68  C and switch off when the temperature of

N. Fitzgerald et al. / Energy 48 (2012) 135e143

Tsysdem

   Sysdem  Sysmin :ðTmax  Tmin Þ ¼ Tmax  Sysmax  Sysmin

(5)

Wind Generation Optimization EWH 80

2000

70 60

1500

50 40

1000

30 20

500

Wind Generation (MW)

Temperature of Water in Tank (ºC)

the water rises above 70  C, regardless of external market signals. The results of the standard thermostat control algorithm with SS-1 were used as the baseline comparison throughout the analysis. In this algorithm the temperature limits are controlled in response to the system demand. The system demand data for each 24 h optimisation period was used and the maximum system demand (Sysmax) and the minimum system demand (Sysmin) was obtained. The system demand (Sysdem) at each simulation interval and the Sysmax and Sysmin and the temperature limits, Tmax and Tmin are used to calculate the temperature (Tsysdem) the water was allowed to reach during each interval, during the 24 h optimisation period using Equation (5), as shown in Fig. 4.

139

10 0

0

2

4

6

8

10

12

14

16

18

20

22

0

Time (hrs) 4.3.1. Wind generation optimisation control algorithm In this algorithm the temperature limits are controlled in response to wind power generation. The wind power generation data for each 24 h optimisation period was used and the maximum wind generation (Gmax) and the minimum wind generation (Gmin) obtained. The wind penetration (G) at each simulation interval and the Gmax and Gmin and the temperature limits, Tmax and Tmin are then used to calculate the temperature (Twindgen) the water was allowed to reach during each interval, during the 24 h optimisation period using Equation (6), as shown in Fig. 5.

  Gmax  G :ðTmax  Tmin Þ Gmax Gmin



(6)

4.3.2. Wind penetration optimisation control algorithm Wind power penetration is the percentage of the total system demand that was supplied by wind power. In this algorithm the temperature limits are controlled in response to wind penetration. The wind penetration data for each 24 h optimisation period was used and the maximum wind penetration (Penmax) and minimum wind penetration (Penmin) obtained. The wind penetration (Pen) at each simulation interval and the Penmax and Penmin and the temperature limits, Tmax and Tmin are then used to calculate the

temperature (Twindpen) the water was allowed to reach during each interval, during the 24 h optimisation period using Equation (7), as shown in Fig. 6.

 Tsmp ¼ Tmax 

60

8000

50

7000 6000

40

5000

30

4000

20

3000 10 2000 6

8

10

12

14

16

18

20

22

Time (hrs) Fig. 4. System demand optimisation over a 24 h simulation.

Temperature of Water in Tank (ºC)

9000

80

System Demand (MW)

Temperature of Water in Tank (ºC)

70

4

  Price  Pricemin :ðTmax  Tmin Þ Pricemax  Pricemin

(8)

Wind Penetration Optimization EWH 10000

2

(7)

4.3.3. Ex-ante price optimisation control algorithm In this algorithm the temperature limits are controlled in response to the ex-ante price. The ex-ante price data for each 24 h optimisation period was used and the maximum ex-ante price (Pricemax) and minimum ex-ante price (Pricemin) obtained. The exante price (Price) at each simulation interval and the Pricemax and Pricemin and the temperature limits, Tmax and Tmin are then used to calculate the temperature (Tsmp) the water was allowed to reach during each interval, during the 24 h optimisation period using Equation (8), as shown in Fig. 7.

System Demand Optimization EWH 80

0 0

  Penmax  Pen :ðTmax  Tmin Þ Penmax  Penmin

 Twindpen ¼ Tmax

100

70 80 60 50

60

40 40

30 20

20 10 0

0 0

2

4

6

8

10

12

14

16

18

20

22

Time (hrs) Fig. 6. Wind penetration optimisation over a 24 h simulation.

Wind Penetration (%)

Twindgen ¼ Tmax 

Fig. 5. Wind generation optimisation over a 24 h simulation.

140

N. Fitzgerald et al. / Energy 48 (2012) 135e143

Electricity Consumed

Ex-Ante System Marginal Price Optimization EWH 300

70

9000

250

7000

60

40

150

30

100

20 50

10

6000

kWh

200

50

0

Standard EWH Baseline Comparison System Demand Optimisation EWH Wind Generation Optimisation EWH Wind Penetration Optimisation EWH Ex-Ante SMP Optimisation EWH

8000

Price ( /MWh)

Temperature of Water in Tank (ºC)

80

5000 4000 3000 2000 1000

0

2

4

6

8

10

12

14

16

18

20

22

0

Time (hrs) Fig. 7. Ex-ante price optimisation over a 24 h simulation.

0

SS-1

SS-2

SS-3

SS-4

System Specification Fig. 8. Electricity consumed over a 12 month period.

For the analysis of the model the ex-post price was used. This does not include transmission charges, distribution charges, capacity charges or government levies, but it quantifies the savings and the benefits of introducing a DR program for domestic customers if they were subject to a real-time pricing tariff. The results of each control algorithm, at each system specification are compared to the standard control algorithm at SS-1 to establish the system benefits. The baseline system specification SS-1 with the standard control algorithm is representative of the bulk of electric water heating systems that are installed in Ireland. The results of the analysis are represented as a series of plots of the standard electric water heater control algorithm optimised against the system demand algorithm, the wind penetration control algorithm and the ex-ante price algorithm to investigate the magnitude of peak shedding and the potential to provide energy storage as a system balancing tool for surplus wind power. Finally, an aggregated load approach is used in the analysis to study the potential of the control strategies implemented for 100,000 electric hot water units over a time period of 24 h.

algorithm showed a 20% reduction in electricity consumed for SS-1. When the tank volume is increased to 300 L the result is a lower reduction of 14% in electricity consumed. A 23% reduction in electricity consumption is recorded with SS-3. When the tank volume is increased to 300 L the result is a reduction of 20% in electricity consumed. For each tank volume the reduction due to the lower Uvalue of 0.44 W/m2/ C is between 3% and 6%. The ex-ante price optimisation algorithm recorded a 12% reduction in electricity consumed for SS-1. When the tank volume is increased to 300 L the result is a lower reduction of 6% in electricity consumed. A 16% reduction in electricity consumed is recorded with SS-3. When the tank volume is increased to 300 L the result is a reduction of 12% in electricity consumed. For each tank volume the reduction due to the lower U-value of 0.44 W/m2/ C is between 4% and 8%. The system demand optimisation showed the greatest reduction in electricity consumed, over the 12 month simulation period, of 25%. The results showed that increasing the specification of the storage tank insulation from 0.8W/m2/ C to 0.44 W/m2/ C results in a reduction of 3%e8% in electricity consumed. Increasing the volume of the storage tank, resulted in more electricity being consumed due to a larger volume of water being heated.

5.1. Electricity consumed

5.2. Ex-post price of electricity consumed

Fig. 8 shows the electricity consumed by each control algorithm over the 12 month period. It is clear from the results that all the control algorithms showed efficiencies in electricity consumed when compared to the standard control algorithm at the baseline system specification. The system demand optimisation algorithm showed a 22% reduction in electricity consumed for SS-1. When the tank volume is increased to 300 L, the result is a lower reduction of 15% in electricity consumed. A 25% reduction in electricity consumption is recorded with SS-3. When the tank volume is increased to 300 L the result is a reduction of 21% in electricity consumed. For each tank volume the reduction due to the lower Uvalue of 0.44 W/m2/ C is between 3% and 6%. The wind generation optimisation algorithm produced a 17% reduction in electricity consumed for SS-1. When the tank volume is increased to 300 L, the result is a lower reduction of 11% in electricity consumed. A 20% reduction in electricity consumption is recorded with SS-3. When the tank volume is increased to 300 L, the result is a reduction of 17% in electricity consumed. For each tank volume the reduction due to the lower U-value of 0.44 W/m2/  C is between 3% and 6%. The wind penetration optimisation

Fig. 9 shows the ex-post price of electricity consumed by each control algorithm over the 12 month period. It is clear from the results that all control algorithms showed improvements in electricity consumed when compared to the standard control algorithm for the baseline system specification of SS-1. The system demand algorithm resulted in a 31% reduction in the ex-post price of electricity consumed for SS-1. When the tank volume is increased to 300 L the result is a reduction of 34% in the ex-post price of electricity consumed. A 34% reduction in the ex-post SMP of electricity consumed is recorded for SS-3. When the tank volume is increased to 300 L the result is a reduction of 38% in the ex-post price of electricity consumed. For each tank volume the reduction due to the lower U-value of 0.44 W/m2/ C is between 3% and 4%. For the wind generation optimisation algorithm a 20% reduction in the expost price in electricity consumed is recorded for SS-1. When the tank volume is increased to 300 L the result is a reduction of 18% in the ex-post price of electricity consumed. A 23% reduction in the ex-post price of electricity consumed is recorded for SS-3. When the tank volume is increased to 300 L the result is a reduction of 23% in the ex-post price of electricity consumed. For each tank

5. Results & analysis

N. Fitzgerald et al. / Energy 48 (2012) 135e143

Wind Penetration of Electricity Consumed

Standard EWH Baseline Comparison System Demand Optimisation EWH Wind Generation Optimisation EWH Wind Penetration Optimisation EWH Ex-Ante SMP Optimisation EWH

Percenatge

Ex-Post SMP ( /MWh)

Ex-Post SMP of Electricity Consumed 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0

SS-1

SS-2

SS-3

SS-4

System Specification

141

16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

Standard EWH Baseline Comparison System Demand Optimisation EWH Wind Generation Optimisation EWH Wind Penetration Optimisation EWH Ex-Ante SMP Optimisation EWH

SS-1

SS-2

SS-3

SS-4

System Specification

Fig. 9. Ex-post price of electricity consumed over 12 month simulation.

Fig. 10. Wind penetration of electricity consumed over 12 month simulation.

volume the reduction due to the lower U-value of 0.44 W/m2/ C is between 3% and 5%. For the wind penetration optimisation algorithm a 25% reduction in the ex-post price of electricity consumed is recorded for SS-1. When the tank volume is increased to 300 L the result is a reduction of 26% in the ex-post price of electricity consumed. A 28% reduction in the ex-post price of electricity consumed is recorded for SS-3. When the tank volume is increased to 300 L the result is a reduction of 31% in the ex-post price of electricity consumed. For each tank volume the reduction due to the lower U-value of 0.44 W/m2/ C is between 3% and 5%. The exante price optimisation algorithm recorded a 21% reduction in the ex-post price of electricity consumed for SS-1. When the tank volume is increased to 300 L the result is a reduction of 21% in the ex-post price of electricity consumed. A 24% reduction in ex-post price of electricity consumed is recorded for SS-3. When the tank volume is increased to 300 L the result is a reduction of 26% in the ex-post price of electricity consumed. For each tank volume the reduction due to the lower U-value of 0.44 W/m2/ C is between 3% and 5%. In summary, increasing the volume of the storage tank, results in more electricity being consumed due to a larger volume of water being heated. However, the ex-post price in electricity consumed does not increase for the system demand optimisation algorithm, wind penetration optimisation algorithm and ex-ante price optimisation algorithm. There is actually a reduction in the price in electricity consumed, except for the ex-ante price optimisation algorithm where at a U-value of 0.8W/m2/ C, the reduction remains at 21% even as the volume increases. The intelligent control algorithms for the larger 300 L tank consumes and stores more off-peak electricity to offset peak electricity use, resulting in a reduction in the price of electricity consumed. The system demand optimisation algorithm shows the greatest reduction in electricity consumed at 38%.

water heating units could only be 39.4%. The moderate increases simulated using this model show that the electric water heater load can be optimised to forecast daily fluctuations in wind but not to inter-day fluctuations. This is due to the limited 150 L to 300 L storage tanks, daily usage patterns of hot water and the need to keep the temperature of hot water within fixed limits for customer comfort. 5.4. Aggregated load Fig. 11 presents 100,000 aggregated electric water heating units, which corresponds to an installed load of 300 MW, for the standard control algorithm results, the system demand optimisation algorithm results, the wind penetration optimisation algorithm results and the ex-ante price optimisation algorithm results simulated over a 24 h period for SS-1 with wind penetration plotted. The standard control algorithm results are plotted as a reference. Fig. 11 shows the best results, which occurred for a 24 h simulation on the 1st of October 2008, for electricity consumed, load shedding during the peak hours of 5:00pm to 8:00pm and for providing storage for wind power is the wind penetration optimisation algorithm. The wind penetration optimisation algorithm consumes 1045 MWh for

5.3. Wind penetration of electricity consumed Fig. 10 shows the average wind penetration of electricity consumed by each control algorithm over the 12 month period. Both the wind generation optimisation algorithm results and the wind penetration optimisation algorithm results show moderate increases compared to the standard control algorithm results. The maximum penetration of wind power on the Irish grid during the 12 month simulation period was 39.4%, which means that the maximum wind penetration of electricity consumed by electric

Fig. 11. Comparison of aggregate, standard control, wind penetration optimisation and ex-ante price optimisation for 24 h period.

142

N. Fitzgerald et al. / Energy 48 (2012) 135e143

100,000 units during the 24 h period. In comparison, the standard control algorithm consumed 1793 MWh, the system demand optimisation algorithm consumed 1299 MWh and the ex-ante price optimisation algorithm consumed 1573 MWh. Fig. 11 also illustrates that the wind penetration optimisation algorithm consumed approximately 125 MW less at each 15 min interval during the peak period of 5:00pm to 8:00pm than the standard control algorithm. The system demand optimisation algorithm consumed approximately 80 MW less at each 15 min interval during the peak period of 5:00pm to 8:00pm. The ex-ante price optimisation algorithm consumed approximately 40 MW less at each 15 min interval during the peak period of 5:00pm to 8:00pm than the standard control algorithm. Finally, Fig. 11 also demonstrated that during the highest wind penetration from 1:00am to 6:00am on that day the wind penetration optimisation algorithm performed the best and provided up to 175 MW load, the system demand optimisation algorithm provided up to 160 MW load, the ex-ante price optimisation algorithm provided 140 MW load and the standard control algorithm provides 0 MW load. 6. Discussion As expected the electricity consumed with the increase in storage tank volume, but this was offset by improving the insulation of the tank. The model showed that the application of intelligent control algorithms for the simulation of a single electric water heating unit results in electricity reductions of 25%, cost reductions in the price of electricity of 38%, which corresponds to V162.48 per annum over the 12 month simulation period and an opportunity to integrate wind power. Peak shedding of up to 160 MW was achieved with the system demand optimisation algorithm based on the aggregated 100,000 units on the 1st October 2008. The results of the model indicated moderate wind energy storage increases over the 12 month simulation period compared to the standard control algorithm because it appears that electric water heating is reactive to forecasted daily fluctuations in wind generation and not to inter-day fluctuations. Electric water heating showed benefits for storage during 24 h periods where there is a large variation between the maximum and minimum wind power generated. The model indicates that only when wind power penetration is over 30% can electric water heating provide more than 175 MW of load. The model also showed that electricity producers will not loose revenue, but rather electricity consumption is moved to less congested times on the system. Such a program also enables a more efficient and flexible operational and management regime for the system operator. In order to implement such a program the costs associated with increasing tank sizes, installing intelligent (smart) thermostats and additional insulation should be investigated. However, further modelling is required at a larger scale to investigate other system benefits such as effects on the ancillary services markets and customer interest. This could be carried out as a pilot study in follow-up research. The advantages for the consumer are both soft and hard. Potential advantages include a better quality of power, a larger hot water tank and a guaranteed supply of hot water. It is difficult to fully promise a reduced electricity bill. In addition there is the potential for increased renewable energy penetration. 7. Conclusion This paper examined the ability of DR to improve power system efficiency and to integrate variable wind power in the wholesale electricity market in Ireland. It was found that direct benefits from DR include potential electricity system stability by changing the

pattern of electricity consumed, shedding load during peak periods and integrating wind power. This paper also presented an overview of DSM and examined the influence of electricity markets structures on DSM and the ability of DR to improve power system efficiency and to integrate variable wind power. In conclusion, this analysis supported the findings of a number of other previous studies and reconfirmed that DR can improve energy utilisation. However, unlike the previous studies this study particularly examined using DR as a tool to integrate wind power in a wholesale electricity market. References [1] Commission of the European Communities. Action plan for energy efficiency: realising the potential; 2006. [2] United States Congress. Federal department, public law 109 e 58 e energy policy act of 2005; 2005. [3] Government of India Planning Commission. Integrated energy policy e report of the expert committee; 2006. [4] Australian Government. Energy task force. Department of the prime minister and cabinet, white paper e securing Australia’s energy future. [5] European Smart Metering Group (ESMIG). Press release, April 27th. In two votes the European parliament expresses its broad support for smart metering: every household in the EU to be fitted with smart meters by 2022; 2009. [6] Federal Energy Regulation Commission (FERC). Press release, December 17th. Item E-4: FERC approves first smart grid proposal using new policy; 2009. [7] Commission of the European Communities. Commission recommendation of 9.10.2009 on mobilizing information and communications technologies to facilitate the transition to an energy-efficient, low-carbon economy; 2009 [SEC(2009) 1315], [SEC(2009) 1316], Brussels, 9.10.2009, c(2009) 7604 final. [8] International Energy Agency (IEA). World energy outlook;, ISBN 9789264061309; 2009. [9] Gormley J. Minister for environment, heritage and local government, custom house, Dublin, Ireland. Press release, 15th October 2008; 2008. [10] Foley AM, Leahy PG, Marvuglia A, Mc Keogh EJ. Current methods and advances in forecasting of wind power generation. Renewable Energy 2012; 37(1):1e8. [11] Foley AM. 2011. The Challenges, barriers and Opportunities integrating wind power in the all Island grid using energy storage. PhD dissertation, University College Cork, Ireland. [12] Gellings CW, Samotyj M, Howe B. The future’s smart delivery system: meeting the demands for high security, high quality, reliability and availability, institute of electrical and electronic engineers. Power & Energy Magazine 2004;2(5):40e8. [13] Strahan D. Renewable energy: hot tub technology to the rescue? The guardian, 17th June. Available at: http://www.guardian.co.uk/technology/ 2009/jun/17/immersion-heater-renewable-energy; 2009. [14] Paull L, MacKay D, Li H, Chang LA. Water heater model for increased powers systems efficiency. In: Proceedings of the institute of electrical and electronic engineers 22nd Canadian conference for electrical and computer engineering, St. John’s, Newfoundland and Labrador, Canada; 2009. [15] Tiptipakorn S, Lee W-J. A residential consumer-centered load strategy in realtime electricity pricing environment. In: Proceedings of the institute of electrical and electronic engineers 39th north American power symposium (NAPS 2007), New Mexico, USA; 2007. [16] Sioshansi R. Evaluating the Impacts of real-time pricing on the cost and value of wind generation. Institute of Electrical and Electronic Engineers Transactions on Power Systems 2010;25(2):741e8. [17] Atanasiu B, Bertoldi P. Residential electricity consumption in new member states and candidate countries. Energy and Building; 2008:112e25. [18] United States Energy Information Administration. Independent statistics and analysis. Electric Power Industry 2007; 2009 [Year in Review]. [19] Orphelin M, Adnot J. Improvement of methods for reconstructing water heating aggregated load curves and evaluating demand-side control benefits. Institute of Electrical and Electronic Engineers Transactions on Power Systems 1999;14(4):1549e55. [20] Lane IE, Beute N. A model of the domestic hot water load. Institute of Electrical and Electronic Engineers Transactions on Power Systems 1996;11(4):1850e5. [21] Rankin R, Rousseau PG. Demand side management in south Africa at industrial residence water heating systems using in line water heating methodology. Energy Conversion & Management 2007;49(1):62e74. [22] Beute N. 1993. Domestic utilisation of electrical grid energy in South Africa. PhD Dissertation. Potchefstroom University, South Africa. [23] O’Leary F, Howley M, Ó Gallachóir BP. Energy in the residential sector 2008 report. Dublin, Ireland: Sustainable Energy Authority of Ireland; 2008. [24] Dennehy E. Private correspondence with the energy policy statistical support unit (EPSU). Estimate of number of installed electric water heaters in Ireland. Dublin, Ireland: Sustainable Energy Authority of Ireland; 2009. [25] Finn P, Fitzpatrick C, Connolly D, Leahy M, Relihan L. Facilitation of renewable electricity using price based appliance control in Ireland’s electricity market. Energy 2011;36(5):2952e60. [26] KEMA Ltd. in collaboration with the UK Department of Trade and Industry. A scoping study: demand side measures for small business and residential

N. Fitzgerald et al. / Energy 48 (2012) 135e143

[27]

[28]

[29] [30] [31] [32]

[33] [34]

[35]

[36] [37] [38]

[39]

[40] [41]

customers on Ireland’s electrical system. Dublin, Ireland: Sustainable Energy Authority Ireland; 2005. Karkkainen S, editor. Integration of demand side management, distributed generation, renewable energy sources and energy storages, state of the art. Main report, international energy agency (IEA), Task XVII: demand-side management programme, Vol. 1; 2008. Heffner GC. Configuring load as a resource for competitive electricity markets, review of demand response programs in the US and around the world. California, USA: Lawrence Berkeley National Laboratory; 2002. Nadel S. Utility demand-side management experience and potential e a critical review. Annual Review of Energy and the Environment 1992;17:507e35. Kreith F, Yogi Goswami D. Energy conservation handbook. Taylor and Francis Group; 2008. ISBN-13:978-1-4200-4429-4[Hardcover]. Spees K, Lave LB. Demand response and electricity markets. The Electricity Journal 2007;20(3):69e85. Popovic ZN, Popovic DS. Direct load control as a market-based program in deregulated power industries. In: Proceedings of the institute of electrical and electronic engineers powertech conference, Bologna, Italy; 2003. Herter K, Wayland S. Residential response to critical-peak pricing of electricity: California evidence. Energy 2009;35(4):1561e7. Spees K. 2008. Meeting electric peak on the demand side: wholesale and Retail market Impacts of real-time pricing and peak load management policy. PhD Dissertation. Carnegie Mellon University, USA. Chua-Liang S, Kirschen D. Quantify the effects of demand response on electricity markets. Institute of Electrical and Electronic Engineers Transactions on Power Systems 2009;24(3):1199e8950. Kirschen DS. Demand side view of electricity markets. Institute of Electrical and Electronic Engineers Transactions on Power Systems 2003;18(2):520e7. Rebours Y, Kirschen D, Trotignon M. Fundamental design issues in markets for ancillary services. The Electricity Journal 2007;20(6):26e34. Raineri R, Ríos S, Schiele D. Technical and economic aspects of ancillary services markets in the electric power industry: an international comparison. Energy Policy; 2006. Holttinen H, Lemström B, Meibom P, Bindner H, Orths A, Van Hulle F, et al. Design and operation of power systems with large amounts of wind power, state of the art report; 2007. VTT Working Papers 82, Espoo, Finland. ILEX and Strbac, G. Quantifying the system costs of additional renewable in 2020. London, UK: ILEX for DTI; 2002. Shiu H, Milligan M, Kirby B, Jackson K. California renewable portfolio standard, renewable generation integration cost analysis. PIER public interest energy research program. California Energy Commission; 2006.

143

[42] Sheffrin A. California independent system operator (CAISO), integration of wind resources in CAISO markets and grid operations. The association of power exchanges, APEx 2007 program; 2007. [43] Cox J. Impact of intermittency: how wind variability could change the shape of the British and Irish electricity markets. UK: Pöyry Energy (Oxford) Ltd.; 2009. Summary report. [44] Elsen R, Aouini B, Barócsi Z, Bonnet T, Bouzoualegh A, Compter HJ, et al. Ancillary services, unbundling electricity products e an emerging market. Thermal Working Group, Union of the Electricity Industry e EURELECTRIC; 2007. Ref: 2003-150-0007. [45] Eto J. The past, present and the future of U.S. utility demand-side management programs. Berkeley, USA: Environmental Energy Technologies Division, Ernest Orlando Lawrence Berkeley National Laboratory. University of California; 1996. [46] Didden MH, D’haeseleer WD. Demand side management in a competitive European market: who should be responsible for its implementation? Energy Policy 2003;31(13):1307e14. [47] EirGrid. Download centre. Available at: http://www.eirgrid.com/operations/ systemperformancedata/downloadcentre/; 2009 [Data accessed 12.12.10]. [48] Single Electricity Market Operator (SEMO). Available at: http://www. allislandmarket.com/; 2009 [Data accessed 12.12.10]. [49] Synergy Module. Available at: http://www.chipeservices.com/; 2009 [Data accessed 12.12.10]. [50] Das S. Process heat transfer. Harrow, UK: Alpha Science International Ltd.; 2005. [51] Nehrir MH, Runmin J, Pierre DA, Hammerstrom DJ. Power management of aggregate electric water heater loads by voltage control. In: Institute of electrical and electronic engineers power engineering society general meeting, Tampa, Florida, USA; 2007. [52] Goh CHK, Apt J. Consumer strategies for controlling electric water heaters under dynamic pricing. Carnegie Mellon University, USA: Carnegie Mellon Electricity Industry Centre; 2004. Working Paper 2004 CEIC-0402. [53] Met Éireann. Monthly data. Available at: http://www.met.ie/climate/monthlydata.asp%3fNum%3d80; 2009 [Data accessed 12.12.10]. [54] Building Research Establishment (BRE). Implications for the modeling of fuel poverty in England, estimates of hot water consumption from the 1998 EFUS produced by the BRE housing centre. Technical report. Department for environment, Food and Rural Affairs (DEFRA) and Department of Trade and Industry (DETI); 2005. [55] Sustainable Energy Authority of Ireland. Energy hotline, U-value of insulation on hot water cylinders in Ireland; 2009 [Data accessed 10.05.09].