Assessing the Potential Benefits and Limits of Electric Storage

sustainability

Article Assessing the Potential Beneﬁts and Limits of Electric Storage Heaters for Wind Curtailment Mitigation: A Finnish Case Study

Mubbashir Ali *, Jussi Ekström and Matti Lehtonen

Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland; jussi.ekstrom@aalto.fi (J.E.); matti.lehtonen@aalto.fi (M.L.) * Correspondence: mubbashir.ali@aalto.fi; Tel.: +358-50-436-7307

Academic Editor: João P. S. Catalão Received: 11 April 2017; Accepted: 12 May 2017; Published: 16 May 2017

Abstract: Driven by policy changes and technological advancement, wind power installations are booming as compared to any other types of power generation. However, the increased penetration of renewable generation in the power systems has resulted in high level of curtailment. Advanced energy storage technologies have been increasingly scrutinized as a feasible mitigation option in smart grids. This paper investigates the potential of mitigating wind generation curtailment via aggregating the domestic electric storage heaters. The key ﬁndings show that aggregating domestic thermal storages is a viable option for curtailment mitigation, but with the indispensable caution that mitigation potential signiﬁcantly saturates as the share of wind generation escalates beyond a certain limit.

Keywords: curtailment mitigation; electric storage heaters; wind generation

1. Introduction In the EU, the integration of wind energy in power systems is expanding because of policy-driven targets with a total installed wind power capacity about 150 GW [1]. In 2016 alone, around 13 GW has been added to the system, accounting for about 50% of the new capacity installation [1]. Though this is a practical step towards a carbon-neutral power system, this must be treated carefully as the systems have already been experiencing elevated levels of curtailment [2]. It is projected that the curtailment level will jump from 0.4 TWh (2020) to 9.3 TWh (2030) [3]. Recently, curtailment mitigation solutions such as demand response [4–6], active network management [7,8], network reinforcement [9,10], market rules and support schemes [11], and storage technologies [12–17] have been investigated in literature. While each option has pros and cons, storage technologies would be imperative to ensure a tolerable level of curtailment. The objective of options in curtailment mitigation is to increase the system’s ﬂexibility to accommodate the variability and uncertainty of renewable power generation. In this context, in order to better cope with the curtailment mitigation challenge, domestic thermal energy storages (for space heating and domestic hot water usage) are a viable proposition as compared to the advanced energy storage technologies such as compressed air energy storage and battery technology. The reason is twofold; ﬁrstly, the cost of advanced energy storage systems is higher and secondly, the systems deteriorate with continual cyclic charging and discharging. However, electric storage heater installations are ubiquitous in the household sector in Finland. These installations have a large thermal storage tank (with typical storage units around 300–500 L) to store hot water during off-peak hours and could be employed to allow excess wind production. Aggregating the large population of these storage heaters and integrating additional distributed thermal storage can provide

Sustainability 2017, 9, 836; doi:10.3390/su9050836 www.mdpi.com/journal/sustainability Sustainability 2017, 9, 836 2 of 15 extra capacity (increased power and energy limits) to accommodate large-scale renewable generation; nonetheless, it is imperative to estimate the potential beneﬁts and limits of this solution before making any investment decision. The research scope of this article is the comprehensive evaluation of curtailment mitigation potential via aggregating customer-owned thermal storages. Using a Finnish case study, a mathematical formulation of the proposed framework is presented to minimize the wind energy curtailment given the total distributed thermal storage capacity in the system. To demonstrate the seasonal impact of thermal storage selection on wind curtailment, the simulations are performed covering winter and summer seasons. Furthermore, to account for the uncertainty and variability of wind energy, the simulations are conducted for probable scenarios of wind generation; the results are then showcased bounded by the conﬁdence interval. The remainder of this paper is organized as follows: Section2 describes the wind generation and space heating estimation methodologies; Section3 introduces the framework for investigating the curtailment mitigation potential through aggregating customer-owned thermal storages; case studies based on the Finnish system are presented in Section4; and Section5 presents the closing remarks.

2. Modeling Methodologies In this section, wind generation modeling and space heating estimation models are presented.

2.1. Wind Generation Model In order to determine the effects of aggregating customer-owned thermal storages on wind energy curtailment, representative wind generation time series are required to be simulated. In this paper, time series of wind generation with varying levels of penetration in the system are obtained using a well-known statistical approach as utilized in previous works [18,19]. This is centered on a statistical method combining probability integral transformations and time-series modeling, thereby simulating wind speed discrete-time data for a new group of wind turbines. In the next step, using a wind speed time series and a wind turbines model (specifications differ for each turbine type and wind farm), a wind generation time series is simulated. This wind generation model is modified from work in [18] as it accounts the real wind power installation scenarios in Finland until the beginning of 2016. The 2016 generation structure is used as a base for the simulated scenarios which consists of an aggregate of 1008 MW of generation capacity with 377 individually modeled wind turbines with real world specifications (turbine technical information including e.g., power curve parameters and nominal power). The model distinctly considers the geographical spread of multiple wind farms and different wind turbines. It is assumed that the average capacity factor for the aggregate wind generation is 0.282, which is in line with the current experience. The wind generation is scaled for each of the considered scenarios (100 years) based on the aggregate electricity generation in Finland during 2015, excluding imports and exports. Figure1 presents the simulated wind generation with varying levels of penetration (10%, 30%, 50% and 70% of total generation) in the system. Sustainability 2017, 9, 836 3 of 15

Sustainability 2017,, 99,, 836836 3 of 15

Figure 1. Time series of the simulated wind power generation for varying level of penetration in the system (From top to bottom: 10%, 30%, 50% and 70% penetration scenarios).

2.2. SpaceFigure Heating 1. Time Modeling series of the simulated wind power generationgeneration for varying level of penetration in the system (From top to bottom: 10%, 30%, 50% and 70% penetration scenarios). Thissystem subsection (From toppresents to bottom: a 2-capaci 10%, 30%,ty 50% building and 70% model, penetration as illustratscenarios).ed in Figure 2, to estimate the space2.2. heating Space Heating requirement Modeling [7]. It is a reasonably accurate model to derive the space heating requirement of a typical detached This subsection presents a 2-capacity2-capacity building model, as illustratedillustrated in Figure 22,, toto estimateestimate thethe house. As presented in Figure 2, there are two distinct specific heat capacities, C which denotes space heating requirement [[7].7]. a the heat Itcapacity is aa reasonablyreasonably of air, while accurateaccurate C modelmodelm represents to derivederive the thethe thermal space heating masses requirement of house ofstructures. a typicaltypical detacheddetached The thermal

house. As As presented presented in in Figure Figure 2,2, there ar aree two distinct specific specific heat heat capacities, capacities, CCaa whichwhich denotes capacitance, Cm is much larger than Ca and hence has a dominating role to provide thermal inertia the heat capacity of air, while CCm represents the thermal masses of house structures. The thermal and theit is heat lumped capacity in the of air, mass while node mpoint, represents and po thertrays thermal roughly masses the of meanhouse thermalstructures. mass The temperaturethermal capacitance, Cm is much larger than Ca and hence has a dominating role to provide thermal inertia of theandcapacitance, house. it is lumped InC them in is2-capacity much the mass larger nodemodel, than point, Ctherea and and are portrays hence different has roughly a dominatingtemperature the mean role nodes, thermal to provide which mass thermal temperature are coupled inertia by eitherofand theheat it house.is lumpedconductance In thein the 2-capacity massor by node model,a heatpoint, there capacity and are po differentrtrays flow. roughly temperatureInfiltration/exfiltration the mean nodes, thermal which mass (passage are temperature coupled of byair) is connectedeitherof the heat house.between conductance In the the 2-capacity external or by a heatmodel,and capacity indoor there flow. areambient different Infiltration/exfiltration temperatures. temperature nodes,Therefore, (passage which of it air)are is is safecoupled connected to assumeby therebetweeneither is no heat warming the conductance external of andthe or indoorinfiltration by a ambient heat aircapacity temperatures. in the flow. house Infiltration/exfiltration Therefore, structure it and is safe the to air(passage assume flow therehasof air) isthe nois same temperaturewarmingconnected ofas between thethe infiltration outside the external air. air inInthe andaddition, house indoor structure it ambient is supposed and temperatures. the airthat flow the has Therefore,windows the same itof temperatureis the safe houses to assume as have the an insignificantoutsidethere is air.no thermal Inwarming addition, mass of itthe iscompared supposedinfiltration with that air the inthe windowsthe rest house of of thestructure the building houses and have envelope,the an air insignificant flow whereas has the thermal thesame space masstemperature compared as the with outside the rest air. of theIn addition, building it envelope, is supposed whereas that thethe spacewindows heating of the system houses is assumed have an heating system is assumed to be convective. toinsignificant be convective. thermal mass compared with the rest of the building envelope, whereas the space heating system is assumed to be convective.

Figure 2. Thermal model of a detached house. FigureFigure 2. 2. Thermal Thermal modelmodel ofof a a detached detached house. house. Sustainability 2017, 9, 836 4 of 15

The representation of a 2-capacity building thermal model is related by ﬁrst-order differential Equations (1) and (2).

∂Ta C = H (Te − Ta) + H (Tm − Ta) + H (Tg − Ta) + H (Tx − Ta) + Qsh (1) a ∂t e m g x t ∂Tm C = H (Ta − Tm) + H (Te − Tm) (2) m ∂t m y The above Equations (1) and (2) can be transformed into an approximately equivalent discrete time model and can be solved for unknown indoor ambient and thermal mass temperatures Ta and Tm respectively. The exact solution for the above differential equation is given by (3) and (4).

a a12b2 − a22b1 Tt = (3) a12a21 − a11a22

m a21b1 − a11b2 Tt = (4) a12a21 − a11a22 where, ∆t a11 = 1 + He + Hm + Hg + Hx (5) Ca ∆t a12 = − Hm (6) Ca

a21 = −∆Hm (7) ∆t a22 = 1 + Hm + Hy (8) Cm

a ∆t h e g x shi b1 = Tt−1 + HeTt + HgTt + HxTt + Qt (9) Ca

m ∆t e b2 = Tt−1 + HeTt (10) Cm The thermal model parameters were identiﬁed by abating the difference between the reference step response obtained from IDA (Dynamic multi-zone simulation environment for the study of buildings thermal indoor climate. http://www.equa.se/en/ida-ice) and the 2-capacity building model step-response. The optimal combination of the above model parameters was obtained via the classical search technique.

3. Framework This section presents the framework enabling the quantiﬁcation of thermal energy storages and their aggregated potential for mitigating the wind curtailment. Mathematically, the objective function can be formulated as follows. minimize WEC conv wind crit heating WEC = ∑ [(Xt + Xt ) − (Dt + Dt )] t (11) ∃ WEC ≥ 0, ∀t ∈ T The ﬁrst term in the objective function represents the total generation in the system, wind, and non-renewable generation in the system, whereas the latter represents the system demand, critical and heating demand in a given time period. The objective function (11) is subjected to three set of constraints: thermal comfort constraints, energy storage characterization, and those due to system constraints. They are stated and discussed in the following. Sustainability 2017, 9, 836 5 of 15

a ∆t h m e g x shi T + HmT + HeT + HgT + HxT + Q a t−1 Ca t−1 t t t t Tt = (12) 1 + ∆t (H + H + H + H ) Ca m e g x Tm + ∆t (H Ta + H Te) m t−1 Cm m t−1 y t Tt = (13) 1 + ∆t (H + H ) Cm m y φ φ (Tset − n ) ≤ Ta ≤ (Tset + n ), ∀t ∈ T, ∀n ∈ N (14) n,t 2 n,t n,t 2 TS TS n TS sh dhw (SoCn,t+1 − SoCn,t )β = (Pn,t − Qn,t − Qn,t )∆t − ξn,t, ∀t ∈ T, ∀n ∈ N (15) TS TS,max 0 ≤ Pn,t ≤ Pn,t , ∀t ∈ T, ∀n ∈ N (16) TS,min TS TS,max SoCn,t ≤ SoCn,t ≤ SoCn,t (17) n TS ξn,t = η SoCn,t−1, ∀t ∈ T, ∀n ∈ N (18)

heating TS Dt = ∑ Pn,t , ∀t ∈ T (19) n crit crit Dt = ∑ Pn,t , ∀t ∈ T (20) n crit TS Pn,t + Pn,t ≤ υn, ∀n ∈ N, ∀t ∈ T (21) Equations (12) and (13) are approximated versions of (3) and (4) to estimate the space heating requirement with less computation; however, accuracy is not compromised. The constraint (14) establishes that indoor ambient temperature remains within the predefined upper and lower temperature limits, hence respecting the customers’ thermal comfort at all times. Constraint (15) determines the thermal storage charging behavior. The rated charging power of energy storage unit is bounded by (16), while (17) bounds the upper and lower limits of state of charge of energy storage, respectively. Expression (18) calculates the energy losses of thermal storage, which depends on efficiency of storage unit. The total demand of the flexible load group, namely, space heating and domestic hot water, and the critical load group is determined by (19) and (20). Constraint (21) ascertains that the total household demand remains under the demand limit dictated by the household main fuse rating. The above model is based on a linear programming format and can be solved using linear programming via CPLEX solver using Matlab/GAMS interface. Figure3 clarifies the process and modules graphically. Sustainability 2017, 9, 836 6 of 15 Sustainability 2017, 9, 836 6 of 15

Initialize, Select Season, Wind penetration, Storage capacity

Simulating the Demand time Generation time Modelling the population of series series wind generation heating load

Critical demand + Flexible Conventional generation + demand Wind generation

Storage characteristics Solve the optimization model to obtain wind Customers curtailment temperature (11)-(21) preferences

Results Accumulation

End

Figure 3. Flowchart illustra illustratingting the process.

4. Case Study The case study is basedbased onon thethe FinnishFinnish system.system. To comprehensively evaluate the potential benefitsbeneﬁts of aggregating domestic thermal storages for wind curtailment mitigation, simulations are conducted byby varying varying the the storage storage size size for twofor differenttwo different weather weather situations, situations, namely namely winter andwinter summer. and summer.In addition, In theaddition, problem the is problem solved for is solved four scenarios for four (10%scenarios wind, (10% 30% wind, wind, 30% 50% wind, and50% 70%wind, wind), and which70% wind), represents which the represents different the levels different of wind levels penetration of wind inpenetration the system. in the In thesystem. basic In simulation, the basic simulation,the purpose the is to purpose highlight is to the highlight impact of the seasonal impact variationof seasonal on variation thermal storage on thermal capacity storage potential capacity for potentialcurtailment for mitigationcurtailment given mitigation varying given wind varying penetration; wind therefore,penetration; a single therefore, run of a windsingle generation run of wind is generationconsidered. is Later, considered. we account Later, for we issues account of uncertainty for issues byof solvinguncertainty the problemby solving for the a number problem of windfor a numbersimulation of wind runs insimulation order to determineruns in order the to conﬁdence determine interval the confidence boundsof interval the results. bounds of the results.

4.1. Input Data Preparation The electrical demand time series is obtained from the transmission system operator for the year of 2015. Next, we construct the baseline of the domestic heating load by using the building model introduced in Section 2.22.2.. The thermal thermal parameters of the existing existing building stock in Finland Finland is obtained obtained from [[20]20] while outside temperature profiles proﬁles are obtainedobtained from [[21].21]. It is to be noted that the mean indoor ambient temperature is is set to be 21 ◦°C.C. The domestic hot water usage aggregated load is constructed based on the typical consumption of a Finnish household [22], [22], with the assumption that hot waterwater consumptionconsumption is is the the same same on aon daily a daily level andlevel average and average consumption consumption is the same is the per same customer. per customer.In the simulation, In the simulation, it is assumed it thatis assumed 300,000 storagethat 300,000 heating storage units areheating presented units inare the presented system that in canthe systembe used that for bothcan be space used heating for both and space domestic heating hot and water. domestic The parameters hot water. ofThe the parameters storage heating of the units storage are TS,max TS,max heatingset as follows: units are Power set ratingas follows:Pn Power= 8 kW rating (storage Pn capacity = 8 =kW 1 day)/16 (storage kW capacity (otherwise) = 1 day)/16 and storage kW (otherwise)loss coefﬁcient andη nstorage= 1%. Sinceloss coefficient the study is conductedn = 1%. Since considering the study a futureis conducted grids scenario, considering it is assumeda future grids scenario, it is assumed that there are two million EVs in the system while their charging is Sustainability 2017, 9, 836 7 of 15

SustainabilitySustainability 2017 2017, 9, 8369, 836 7 of7 of15 15 that there are two million EVs in the system while their charging is uncontrolled and follows a typical uncontrolledpatternuncontrolled as modelled and and follows follows in [23 a]. typicala Figure typical4 patterna,b pattern gives as theas modelled modelled time series in in [23]. of[23]. demand Figure Figure 4a,b data 4a,b gives in gives winter the the time and time summerseries series of of demandrespectively.demand data data in in winter winter and and summer summer respectively. respectively.

1500015000 CriticalCritical demand demandHeatingHeating load load

1000010000

50005000

0 0 DecDec Jan Jan Feb Feb Mar Mar TimeTime (hour) (hour) (a() a) 1500015000 CriticalCritical demand demandHeatingHeating load load

1000010000

50005000

0 0 JunJun Jul Jul Aug Aug Sep Sep TimeTime (hour) (hour) (b()b )

FigureFigureFigure 4. 4. 4.DemandDemand Demand data data data used used used in in inthe the the simulations simulations simulations ( aa )() awinter winter) winter ( (b ())b summer. summer.) summer.

TheThe generation generation time time series series is isconstructed constructed by by re replacingplacing the the fraction fraction of of conventional conventional generation generation The generation time series is constructed by replacing the fraction of conventional generation withwith wind wind generation generation penetration. penetration. The The resulting resulting system system level level production production scenarios scenarios for for winter winter and and with wind generation penetration. The resulting system level production scenarios for winter and summersummer are are shown shown in in Figure Figure 5a,b 5a,b respectively. respectively. summer are shown in Figure5a,b respectively.

4104 210 2 10% Wind 1.51.5 10% Wind 1 1 0.50.5 4104 210 2 30% Wind 1.51.5 30% Wind 1 1 0.50.5 4 10410 2 2 50%50% Wind Wind 1.51.5 1 1 0.50.5 4104 210 2 70% Wind 1.51.5 70% Wind 1 1 0.50.5 DecDec Jan Jan Feb Feb Mar Mar (Hour) (Hour) (a() a)

Figure 5. Cont. Sustainability 2017,, 9,, 836836 8 of 15

104 2 1.5 10% Wind 1 0.5

104 2 1.5 30% Wind 1 0.5

104 2 50% Wind 1.5 1 0.5 104 2 1.5 70% Wind 1 0.5 Jun Jul Aug Sep (Hour) (b)

Figure 5. Generation profiles proﬁles used in the simulations (a) winter (b) summer.

4.2. Simulation Results 4.2. Simulation Results In this section, the impact of aggregating domestic thermal storages for curtailment mitigation In this section, the impact of aggregating domestic thermal storages for curtailment mitigation is is discussed. The problem presented in the proposed framework is solved for a definite energy discussed. The problem presented in the proposed framework is solved for a definite energy storage storage capacity (i.e., 1 day, 2 days, and so forth) and respective energy curtailment levels are capacity (i.e., 1 day, 2 days, and so forth) and respective energy curtailment levels are obtained. It is to obtained. It is to be noted that the customer-owned thermal storage is expressed as a function of days. be noted that the customer-owned thermal storage is expressed as a function of days. (Where 1 day of (Where 1 day of storage implies that the customer can store the heating demand for one typical winter storage implies that the customer can store the heating demand for one typical winter day i.e., 60 kWh). day i.e., 60 kWh). The evolution of wind curtailment in the system as a function of storage capacity for different The evolution of wind curtailment in the system as a function of storage capacity for different wind penetration rates (10%, 30%, 50% and 70%) is shown in Figure6. As can be seen from the results, wind penetration rates (10%, 30%, 50% and 70%) is shown in Figure 6. As can be seen from the results, the curtailment mitigation potential is saturated, and benefits of adding more storage beyond a certain the curtailment mitigation potential is saturated, and benefits of adding more storage beyond a capacity is not useful. This trend becomes more dominant with the increase in wind penetration in the certain capacity is not useful. This trend becomes more dominant with the increase in wind system. For instance, in the 50% and 70% wind scenarios where wind penetration is high, the benefits penetration in the system. For instance, in the 50% and 70% wind scenarios where wind penetration saturate after a few days of storage capacity. In addition, this can also have a substantial influence is high, the benefits saturate after a few days of storage capacity. In addition, this can also have a when deciding the global optimal storage capacity. It is to be noted that the seasonal load variation substantial influence when deciding the global optimal storage capacity. It is to be noted that the influences the curtailment rate and will consequently influence the energy storage selection. As can be seasonal load variation influences the curtailment rate and will consequently influence the energy seen from Figure6, the potential saturated quickly in summer (dotted lines) as compared to winter storage selection. As can be seen from Figure 6, the potential saturated quickly in summer (dotted (solid lines). The primary reason for this behavior is that the flexible heating load represents only a lines) as compared to winter (solid lines). The primary reason for this behavior is that the flexible small fraction of total load during summer. heating load represents only a small fraction of total load during summer. The curtailments level obtained w.r.t thermal energy storage capacity are presented in Table1. The curtailments level obtained w.r.t thermal energy storage capacity are presented in Table 1. It can be seen that significant levels of curtailment were present in the system even with larger storages It can be seen that significant levels of curtailment were present in the system even with larger when a large amount of wind was hosted by the system. It is suggested that the storage capacity of storages when a large amount of wind was hosted by the system. It is suggested that the storage 1 week will achieve the 5% curtailment level with low-to-medium penetration of wind in the system. capacity of 1 week will achieve the 5% curtailment level with low-to-medium penetration of wind in However, with a higher penetration of wind generation in the system, even weekly levels of storage the system. However, with a higher penetration of wind generation in the system, even weekly levels are inadequate. For instance, a storage capacity of three weeks is unable to achieve an under-5% of storage are inadequate. For instance, a storage capacity of three weeks is unable to achieve an curtailment mark when the wind penetration represents 50% of total generation in the system. under-5% curtailment mark when the wind penetration represents 50% of total generation in the system. Sustainability 2017, 9, 836 9 of 15 Sustainability 2017, 9, 836 9 of 15

1 10% Wind 0.5 0 0123456789101112131415161718192021 1 30% Wind 0.5 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 0.5 50% Wind 0 0123456789101112131415161718192021 1

0.5 70% Wind 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Storage Capacity (function of days)

Figure 6. Changes in wind curtailment (expressed as p.u.) for different sizes of thermal storage. (Solid Figure 6. Changes in wind curtailment (expressed as p.u.) for different sizes of thermal storage. (Solid line: Winter; Dotted line: Summer). line: Winter; Dotted line: Summer). Table 1. Overview of the basic results. Table 1. Overview of the basic results.

WindWind Curtailment Curtailment (%) (%) StorageStorage Capacity 10% Wind 30% Wind 50% Wind 70% Wind (Days)Capacity 10% Wind 30% Wind 50% Wind 70% Wind Summer Winter Summer Winter Summer Winter Summer Winter (Days) Summer Winter Summer Winter Summer Winter Summer Winter 1 day 1.0% 1.1% 4.5% 4.6% 8.2% 8.5% 11.9% 12.4% 2 days1 day 1.0% 0.8% 1.1%0.8% 4.5%4.4% 4.6%3.9% 8.2%8.1% 8.5%7.6% 11.9%11.7% 12.4%11.4% 2 days 0.8% 0.8% 4.4% 3.9% 8.1% 7.6% 11.7% 11.4% 3 days 0.7% 0.6% 4.3% 3.6% 8.0% 7.1% 11.6% 10.9% 3 days 0.7% 0.6% 4.3% 3.6% 8.0% 7.1% 11.6% 10.9% 4 days 0.6% 0.5% 4.2% 3.2% 7.9% 6.8% 11.5% 10.6% 4 days 0.6% 0.5% 4.2% 3.2% 7.9% 6.8% 11.5% 10.6% 5 days5 days 0.5% 0.5% 0.4%0.4% 4.1%4.1% 2.9%2.9% 7.8%7.8% 6.5%6.5% 11.4%11.4% 10.3%10.3% 6 days6 days 0.4% 0.4% 0.2%0.2% 4.0%4.0% 2.8%2.8% 7.6%7.6% 6.3%6.3% 11.3%11.3% 10.1%10.1% 7 days7 days 0.3% 0.3% 0.1%0.1% 3.9%3.9% 2.6%2.6% 7.5%7.5% 6.1%6.1% 11.2%11.2% 9.9% 9.9% 8 days8 days 0.2% 0.2% 0.0%0.0% 3.8%3.8% 2.5%2.5% 7.4%7.4% 5.9%5.9% 11.1%11.1% 9.8% 9.8% 9 days9 days 0.1% 0.1% 0.0%0.0% 3.7%3.7% 2.4%2.4% 7.3%7.3% 5.8%5.8% 11.0%11.0% 9.6% 9.6% 1010 days days 0.0% 0.0% 0.0%0.0% 3.6%3.6% 2.3%2.3% 7.2%7.2% 5.7%5.7% 10.9%10.9% 9.6% 9.6% 1111 days days 0.0% 0.0% 0.0%0.0% 3.5%3.5% 2.2%2.2% 7.1%7.1% 5.6%5.6% 10.8%10.8% 9.5% 9.5% 1212 days days 0.0% 0.0% 0.0%0.0% 3.4%3.4% 2.1%2.1% 7.0%7.0% 5.5%5.5% 10.7%10.7% 9.4% 9.4% 1313 days days 0.0% 0.0% 0.0%0.0% 3.3%3.3% 2.1%2.1% 6.9%6.9% 5.5%5.5% 10.6%10.6% 9.3% 9.3% 14 days 0.0% 0.0% 3.2% 2.0% 6.8% 5.4% 10.5% 9.3% 14 days 0.0% 0.0% 3.2% 2.0% 6.8% 5.4% 10.5% 9.3% 15 days 0.0% 0.0% 3.1% 1.9% 6.7% 5.3% 10.4% 9.2% 15 days 0.0% 0.0% 3.1% 1.9% 6.7% 5.3% 10.4% 9.2% 16 days 0.0% 0.0% 2.9% 1.9% 6.6% 5.3% 10.3% 9.1% 1617 days days 0.0% 0.0% 0.0%0.0% 2.8%2.9% 1.8%1.9% 6.5%6.6% 5.2%5.3% 10.2%10.3% 9.1% 9.1% 1718 days days 0.0% 0.0% 0.0%0.0% 2.7%2.8% 1.7%1.8% 6.4%6.5% 5.1%5.2% 10.1%10.2% 9.0% 9.1% 1819 days days 0.0% 0.0% 0.0%0.0% 2.6%2.7% 1.6%1.7% 6.3%6.4% 5.0%5.1% 10.0%10.1% 8.9% 9.0% 1920 days days 0.0% 0.0% 0.0%0.0% 2.5%2.6% 1.6%1.6% 6.2%6.3% 5.0%5.0% 9.9%10.0% 8.8% 8.9% 2021 days days 0.0% 0.0% 0.0%0.0% 2.4%2.5% 1.5%1.6% 6.1%6.2% 4.9%5.0% 9.8%9.9% 8.8% 8.8% 21 days 0.0% 0.0% 2.4% 1.5% 6.1% 4.9% 9.8% 8.8%

In the previous analysis, ﬁxed rated power of storage was assumed, however it may vary from In the previous analysis, fixed rated power of storage was assumed, however it may vary from installation to installation and system to system. Here a sensitivity analysis is conducted to assess installation to installation and system to system. Here a sensitivity analysis is conducted to assess the the inﬂuence of rated power of thermal storage on results. Figure7 presents the impact of rated influence of rated power of thermal storage on results. Figure 7 presents the impact of rated input input power of thermal storage on the wind curtailment during winter season assuming 30% wind power of thermal storage on the wind curtailment during winter season assuming 30% wind penetration. As can be seen, in this particular case, reducing the rated power to half slightly decreases penetration. As can be seen, in this particular case, reducing the rated power to half slightly decreases the performance. This effect becomes steadily more pronounced with larger thermal storages. On the the performance. This effect becomes steadily more pronounced with larger thermal storages. On the other hand, doubling the rated power of thermal storage would have negligible impact on curtailment. other hand, doubling the rated power of thermal storage would have negligible impact on curtailment. SustainabilitySustainability2017, 20179, 836, 9, 836 10 of 1510 of 15 Sustainability 2017, 9, 836 10 of 15 (p.u.) (p.u.) Changes in wind energy Curtailment Changes in wind energy Curtailment

Figure 7. Impact of rated power of thermal storage on curtailment. Figure 7. Impact of rated power of thermal storage on curtailment. Figure 7. Impact of rated power of thermal storage on curtailment. 4.3. Economic4.3. Economic Impacts Impacts of theof the Increased Increased Storage Storage on on WindWind Penetration 4.3. Economic Impacts of the Increased Storage on Wind Penetration The windThe wind generation generation revenue revenue loss loss depends depends on the the co costst of of curtailed curtailed renewable renewable energy. energy. In Finland, In Finland, The wind generation revenue loss depends on the cost of curtailed renewable energy. In Finland, the wind generation curtailment cost is 83.5 €/MWh which is based on the current feed-in tariff, the windthe wind generation generation curtailment curtailment costcost isis 83.583.5 €/€/MWhMWh which which is based is based on th one thecurrent current feed-in feed-in tariff, tariff, whereas the cost of adding a customer-owned thermal storage is roughly estimated to be 2–3 €/L (i.e., whereaswhereas the the cost cost of of adding adding aa customer-owned therma thermall storage storage is roughly is roughly estimated estimated to be 2–3 to €/L be (i.e., 2–3 €/L approximately €1000 for adding one day of storage). Using the above cost estimates with a discount (i.e.,approximately approximately €1000€1000 for adding for adding one day one of daystorage). of storage). Using the Usingabove cost the estimates above cost with estimates a discount with a rate of 7%, and assuming the lifetime of distributed thermal storage unit to be 20 years, the equivalent discountrate of rate 7%, and of 7%, assuming and assuming the lifetime the of distributed lifetime of thermal distributed storage thermal unit to be storage 20 years, unit the equivalent to be 20 years, annual discounted marginal cost of adding one day of thermal storage (300,000 units) in the system the equivalentannual discounted annual marginal discounted cost marginalof adding costone day of adding of thermal one storage day of (300,000 thermal units) storage in the (300,000 system units) comes out to be €5.1 million and is assumed to increase linearly. On the other hand, the benefits comes out to be €5.1 million and is assumed to increase linearly. On the other hand, the benefits in the(reduction system comes in revenue out to loss be from€5.1 wind million curtailment) and is assumed as illustrated to increase by Figure linearly. 8 starts On to thesaturate other as hand, the the (reduction in revenue loss from wind curtailment) as illustrated by Figure 8 starts to saturate as the benefitsstorage (reduction capacity in increases revenue beyond loss from a few wind days. curtailment) It can be seen as that illustrated 2 days (when by Figure wind8 penetrationstarts to saturate is storage capacity increases beyond a few days. It can be seen that 2 days (when wind penetration is as the10%) storage and about capacity 6 days increases (when wind beyond penetration a few days. 50% Itor can more) be of seen storage that capacity 2 days (when will bring wind optimum penetration 10%) and about 6 days (when wind penetration 50% or more) of storage capacity will bring optimum is 10%)benefits. and about The results 6 days are (when based wind on penetrationthe example 50%study or and more) a comprehensive of storage capacity cost-benefit will bring analysis, optimum benefits. The results are based on the example study and a comprehensive cost-benefit analysis, which is beyond the scope of this work, and would determine cost-optimal thermal storage in a given benefits.which The is beyond results the are scope based of onthis the work, example and would study determine and a comprehensive cost-optimal thermal cost-benefit storage analysis, in a given which system. is beyondsystem. the scope of this work, and would determine cost-optimal thermal storage in a given system.

25 25 10% Wind Penetration 10% Wind Penetration 30% Wind Penetration 20 30% Wind Penetration 20 70% Wind Penetration 70% Wind Penetration Marginal Cost 15 Marginal Cost 15

10 million €) 10 million €) Marginal Benefits

Marginal Benefits 5 5 (Reduction in in (Reduction revenue losses in

(Reduction in in (Reduction revenue losses in 0 0 23456789101112131415161718192021 23456789101112131415161718192021 Storage Capacity (days) Storage Capacity (days)

Figure 8. Economic assessment of the increased thermal storage for curtailment mitigation. Figure 8. Economic assessment of the increased thermal storage for curtailment mitigation. Figure 8. Economic assessment of the increased thermal storage for curtailment mitigation. 4.4. Impact4.4. Impact of Wind of Wind Uncertainty Uncertaint andy and Variability Variability onon CurtailmentCurtailment 4.4. Impact of Wind Uncertainty and Variability on Curtailment The basic results presented in Figure 6 are based on a single run of the wind time series. TheThe basic basic results results presented presented in Figurein Figure6 are 6 basedare base ond a on single a single run ofrun the of wind the wind time series.time series. However, However, given the variability and uncertainty of wind, it is imperative to determine the confidence givenHowever, the variability given the and variability uncertainty and uncertainty of wind, it of is wind, imperative it is imperative to determine to determine the confidence the confidence interval of the observed trends. For doing so, we repeated the basic study for multiple runs and calculated the mean path and the lower and upper confidence bounds. The approach was to calculate the 5th and 95th percentiles for each storage capacity, such that 90% of the data should be within the calculated confidence bounds. In this context, Figure9a–d presents the statistical analysis of the results obtained Sustainability 2017, 9, 836 11 of 15

interval of the observed trends. For doing so, we repeated the basic study for multiple runs and calculated the mean path and the lower and upper confidence bounds. The approach was to calculate Sustainability 2017, 9, 836 11 of 15 the 5th and 95th percentiles for each storage capacity, such that 90% of the data should be within the calculated confidence bounds. In this context, Figure 9a–d presents the statistical analysis of the forresults 10%, obtained 30%, 50% for and 10%, 70% 30%, respectively. 50% and 70% As respective can be observed,ly. As can the be conﬁdence observed, the intervals confidence serve intervals as good estimatesserve as good of the estimates storage capacityof the storage potential capa tocity mitigate potential curtailment. to mitigate curtailment.

1 Winter

0.5

0 0 1 2 3 4 5 6 7 8 9 101112131415161718192021 1 Summer

0.5

0 0123456789101112131415161718192021 Storage Capacity (days) (a) 1 Winter

0.5

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 Summer

0.5

0 0 1 2 3 4 5 6 7 8 9 101112131415161718192021 Storage Capacity (days) (b)

(c)

Figure 9. Cont. Sustainability 2017, 9, 836 12 of 15 Sustainability 2017, 9, 836 12 of 15

0.5 Winter

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1

0.5 Summer

0 0 1 2 3 4 5 6 7 8 9 101112131415161718192021 Storage Capacity (days)

(d)

Figure 9. ImpactImpact of of aggregating aggregating customer-owned customer-owned thermal thermal storages storages on on curtailment curtailment (a) (10%a) 10% wind wind (b) (30%b) 30% wind wind (c) (50%c) 50% wind wind (d) ( d70%) 70% wind wind penetration penetration in in the the system. system. (Solid (Solid line line is is the the mean mean trend while dotted lineline showsshows 5th5th andand 95th95th boundsbounds onon conﬁdenceconfidence interval).interval).

4.5. Optimal Thermal Storage Sizing Based on Desired CurtailmentCurtailment The impactimpact ofof windwind penetrationpenetration in thethe systemsystem onon thethe optimaloptimal storagestorage sizesize toto achieveachieve preferredpreferred curtailmentcurtailment levellevel isis investigated.investigated. In this sensitivity analysis, we consider only the winterwinter seasonseason andand determine thethe optimaloptimal energyenergy capacitycapacity toto reducereduce thethe curtailmentcurtailment toto aa certaincertain level.level. The optimaloptimal thermal storage to reduce the curtailment to levels of 5%, 3% and 1% are givengiven inin TableTable2 2.. It It can can be be seen seen that that higherhigher storagestorage capacitycapacity isis requiredrequired ifif aa lowlow levellevel ofof curtailmentcurtailment isis desired.desired. ItIt isis to be noted that with a higher level of wind penetrationpenetration in the system, it is infeasible for domestic thermalthermal storagesstorages to to mitigate mitigate renewable renewable curtailment curtailment to theto the desired desired level. level. This This limitation limitation is because is because there isthere a demand is a demand limit atlimit customer at customer premises premises due to due the to network the network limits; limits; hence hence increasing increasing the storage the storage size withoutsize without increasing increasing the rated the rated charging charging power power is infeasible is infeasible in these in these cases. cases.

TableTable 2.2.Optimal Optimal thermal thermal storage storage sizing sizing to mitigate to mitigate curtailment curtailment w.r.t different w.r.t leveldifferent of wind level penetration of wind inpenetration the system. in the system. Optimal Storage Size Curtailment Below Optimal Storage Size Curtailment 10% Wind 30% Wind 50% Wind 70% Wind Below 5% 10% 1 day Wind 30% 2 days Wind 50%30 Wind days 70% Wind N/A 3% 5% 1 1 day 11 2 days days 30 days 55 days N/A N/A 1% 3% 3 1 days day 11 32 daysdays 55 days N/A N/A N/A 1% 3 days 32 days N/A N/A 5. Conclusions 5. Conclusions Increased integration of wind generation in a power system will create technical challenges for the systemIncreased operator integration including of wind curtailment. generation Energy in a storage power systemprovides will an createopportunity technical to harness challenges the forexcess the systemproduction operator and includingutilize th curtailment.e stored energy Energy during storage peak provides periods. an opportunityThis paper presents to harness a theframework excess production to evaluate and the utilizepotential the benefits stored energyof aggregating during peakdomestic periods. thermal This storages paper presents for wind a frameworkcurtailment tomitigation. evaluate theThe potential proposed benefits framework of aggregating is applied domestic to the Finnish thermal system storages considering for wind curtailmentmultiple future mitigation. scenarios The proposedof wind frameworkpenetration is in applied the system. to the Finnish The analysis system consideringdemonstrated multiple that futureaggregating scenarios customer-owned of wind penetration thermal storages in the system. presented The a good analysis opportunity demonstrated to mitigate that aggregatingcurtailment, customer-ownedhowever, the potential thermal to harness storages excess presented wind aprod gooduction opportunity becomes to saturated. mitigate The curtailment, saturation however, trend is themore potential dominant to harnessduring th excesse summer wind season production as the becomeslow, flexible saturated. demand The frequently saturation coincides trend is with more a dominanthigh generation during period. the summer Furthermore, season it is as observed the low, that flexible as the demandshare of wind frequently generation coincides in the withsystem a highsoars generationbeyond a certain period. level Furthermore, (30% in this it iscase), observed the flexibility that as theto reduce share ofcurtailment wind generation via customer- in the owned thermal storages diminishes rapidly. Therefore, the technological development of large-scale Sustainability 2017, 9, 836 13 of 15 system soars beyond a certain level (30% in this case), the flexibility to reduce curtailment via customer-owned thermal storages diminishes rapidly. Therefore, the technological development of large-scale centralized storage and collaboration with distributed energy resources is critical to mitigate renewable curtailment in future smart grids.

Acknowledgments: This work was supported by Aalto University’s Sustainable Transition of European Energy Markets (STEEM) Project. Author Contributions: Mubbashir Ali proposed the framework and performed the simulations. Jussi Ekström performed the wind generation time series modeling and statistical analysis on the results. Matti Lehtonen directed the work. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Nomenclature

Indices and Sets n, N Index and set of customers t, T Index and set of time e.g., hours Parameters and Constants ◦ Ca Heat capacity of the indoor air (MJ/ C) ◦ Cm Heat capacity of the building fabric (MJ/ C) ◦ He Summation of the infiltration heat capacity flow and house’s window heat conductance (W/ C) ◦ Hg Floor heat conductance (W/ C) ◦ Hm Thermal conductance which allows Cm to be lumped in the mass node point (W/ C) ◦ Hx Ventilation air heat conductance (W/ C) ◦ Hy Heat conductance in the solid walls and convection of surface (W/ C) TS,max Pn Power rating of thermal storage (kW) crit Pn,t Power of all critical appliances at time t of customer n (kW) dhw Qn,t Domestic hot water consumption of customer n at time t TS,max SoCn Maximum allowable state of charge of thermal storage (of customer n) TS,min SoCn Minimum allowable state of charge of thermal storage (of customer n) set ◦ Tn,t Set point temperature of dwelling at time t (of customer n)( C) e ◦ Tt Outside temperature at time t ( C) g ◦ Tt Ground temperature at time t ( C) x ◦ Tt Ventilation supply air temperature ( C) wind Xt Wind generation at time t (kW) conv Xt Conventional generation at time t (kW) βn Maximum thermal storage capacity (of customer n) (kWh) ∆t Duration of time slot (hours) ηn Storage loss coefficient (of customer n) υn Demand limit (kW) ◦ φn Internal temperature dead-band (of customer n)( C) Functions and Variables crit Dt Critical demand at time t (kW) heating Dt Heating demand at time t (kW) TS Pn,t Electrical power supplied to storage space heating unit at time t (of customer n) (kW) sh Qn,t HVAC thermal output power at time t (of customer n) (kW) SoCn,t State of charge of thermal storage at time t (of customer n) a ◦ Tn,t Indoor ambient temperature of dwelling at time t (of customer n)( C) m ◦ Tt Thermal mass temperature at time t ( C) ξn,t Storage thermal losses at time t (of customer n) (kWh) Sustainability 2017, 9, 836 14 of 15

References

1. WindEurope. Available online: https://windeurope.org/about-wind/statistics/european/wind-in-power- 2016/ (accessed on 3 March 2017). 2. Bird, L.; Lewb, D.; Milligana, M.; Carlinic, E.M.; Estanqueirod, A.; Flynne, D.; Gomez-Lazarof, E.; Holttineng, H.; Menemenlish, N.; Orthsi, A.; et al. Wind and solar energy curtailment: A review of international experience. Renew. Sustain. Energy Rev. 2016, 65, 577–586. [CrossRef] 3. TWENTIES. Report on the Economic Impact of Enhanced Network Flexibility and New Solutions for Ancillary Services (Indicative Title); WindEurope: Brussels, Belgium, 2013. 4. Ali, M.; Ekström, J.; Alahäivälä, A.; Lehtonen, M. Assessing the Upward Demand Response Potential for Mitigating the Wind Generation Curtailment: A Case Study. In Proceedings of the 14th International Conference on the European Energy Market (EEM), Dresden, Germany, 6–9 June 2017. 5. McKenna, E.; Grünewald, P.; Thomson, M. Going with the wind: Temporal characteristics of potential wind curtailment in Ireland in 2020 and opportunities for demand response. IET Renew. Power Gener. 2015, 9, 66–77. [CrossRef] 6. Zhang, C.; Xu, Y.; Dong, Z.Y.; Wong, K.P. Robust Coordination of Distributed Generation and Price-Based Demand Response in Microgrids. IEEE Trans. Smart Grid 2017.[CrossRef] 7. Ali, M.; Degefa, M.; Humayun, M.; Safdarian, A.; Lehtonen, M. Increased Utilization of Wind Generation by Coordinating the Demand Response and Real-time Thermal Rating. IEEE Trans. Power Syst. 2016, 31, 3737–3746. [CrossRef] 8. Ochoa, L.F.; Dent, C.J.; Harrison, G.P. Distribution Network Capacity Assessment: Variable DG and Active Networks. IEEE Trans. Power Syst. 2010, 25, 87–95. [CrossRef] 9. Orfanos, G.A.; Georgilakis, P.S.; Hatziargyriou, N.D. Transmission Expansion Planning of Systems with Increasing Wind Power Integration. IEEE Trans. Power Syst. 2013, 28, 1355–1362. [CrossRef] 10. Cruz, M.R.M.; Fitiwi, D.Z.; Santos, S.F.; Catalão, P.S. Inﬂuence of distributed storage systems and network switching/reinforcement on RES-based DG integration level. In Proceedings of the 13th International Conference on the European Energy Market (EEM), Porto, Portugal, 6–9 June 2016; pp. 1–5. 11. Chaves-Avila, J.P.; Banez-Chicharro, F.; Ramos, A. Impact of support schemes and market rules on renewable electricity generation and system operation: The Spanish case. IET Renew. Power Gener. 2017, 11, 238–244. [CrossRef] 12. Alnaser, S.W.; Ochoa, L.F. Optimal Sizing and Control of Energy Storage in Wind Power-Rich Distribution Networks. IEEE Trans. Power Syst. 2016, 31, 2004–2013. [CrossRef] 13. Ke, X.; Lu, N.; Jin, C. Control and size energy storage systems for managing energy imbalance of variable generation resources. IEEE Trans. Sustain. Energy 2015, 6, 70–78. [CrossRef] 14. Cleary, B.; Duffy, A.; OConnor, A.; Conlon, M.; Fthenakis, V. Assessing the economic beneﬁts of compressed air energy storage for mitigating wind curtailment. IEEE Trans. Sustain. Energy 2015, 6, 1021–1028. [CrossRef] 15. Makarov, Y.V.; Du, P.; Kintner-Meyer, M.C.W.; Jin, C.; Illian, H.F. Sizing Energy Storage to Accommodate High Penetration of Variable Energy Resources. IEEE Trans. Sustain. Energy 2012, 3, 34–40. [CrossRef] 16. Fernández-Blanco, R.; Dvorkin, Y.; Xu, B.; Wang, Y.; Kirschen, D.S. Optimal Energy Storage Siting and Sizing: A WECC Case Study. IEEE Trans. Sustain. Energy 2017, 8, 733–743. [CrossRef] 17. Albadi, M.H.; Al-Busaidi, A.S.; El-Saadany, E.F. Using PHES to facilitate wind power integration in isolated systems—Case study. In Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT), Toronto, ON, Canada, 22–25 March 2017; pp. 469–474. 18. Ekström, J.; Koivisto, M.; Mellin, I.; Millar, J.; Saarijärvi, E.; Haarla, L. Assessment of Large Scale Wind Power Generation with New Generation Locations without Measurement Data. Renew. Energy 2015, 83, 362–374. [CrossRef] 19. Koivisto, M.; Ekström, J.; Seppänen, J.; Mellin, I.; Millar, J.; Haarla, L. A statistical model for comparing future wind power scenarios with varying geographical distribution of installed generation capacity. Wind Energy 2016, 19, 665–679. [CrossRef] 20. Ministry of the Environment. C3: Finnish Code of Building Regulations (Thermal Insulation in a Building); Ministry of the Environment: Helsinki, Finland, 2010. (In Finnish) 21. Finnish Meteorological Institute. Available online: http://en.ilmatieteenlaitos.fi/ (accessed on 2 March 2017). Sustainability 2017, 9, 836 15 of 15

22. Ali, M.; Safdarian, A.; Lehtonen, M. Demand response potential of HVAC load considering user preferences. In Proceedings of the Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Istanbul, Turkey, 12–15 October 2014; pp. 1–6. 23. Alahaivala, A.; Saarijärvi, E.; Lehtonen, M. Modeling electric vehicle charging ﬂexibility for the maintaining of power balance. Int. Rev. Electr. Eng. 2013, 8, 1759–1769.

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).