The impact of urban district composition on storage technology reliance: trade-offs between thermal storage, batteries, and power-to-hydrogen

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Research Collection

Journal Article

The impact of urban district composition on storage technology reliance: trade-offs between thermal storage, batteries, and power-to-hydrogen

Author(s):

Petkov, Ivalin; Gabrielli, Paolo; Spokaite, Marija Publication Date:

2021-06-01 Permanent Link:

https://doi.org/10.3929/ethz-b-000473364

Originally published in:

Energy 224, http://doi.org/10.1016/j.energy.2021.120102

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The impact of urban district composition on storage technology

reliance: trade-offs between thermal storage, batteries, and power-to- hydrogen

Ivalin Petkov

^a^,^*

, Paolo Gabrielli

^b

, Marija Spokaite

^a

aGroup for Sustainability and Technology, ETH Zurich, 8092, Zurich, Switzerland

bInstitute of Energy and Process Engineering, ETH Zurich, 8092, Zurich, Switzerland

a r t i c l e i n f o

Article history:

Received 14 October 2020 Received in revised form 15 January 2021 Accepted 9 February 2021 Available online 18 February 2021

Keywords:

District multi-energy systems Power-to-hydrogen Seasonal energy storage Optimization under uncertainty Energy system design Zero-emissions energy systems

a b s t r a c t

The composition of urban districts - residential, commercial, or‘mixed’topologies - can have drastically different energy load proﬁles and peak demands, leading to altered optimal District Multi-Energy System (D-MES) designs. Although demand-inﬂuenced differences in D-MES designs are generally understood, there is a research gap of how the interplay of various storage technologies differs between topologies. In this paper, we investigate the extent of D-MES reliance on storage technologies over various district topologies in central Europe. The core of this analysis relies on a multi-objective MILP optimization model utilized in an uncertainty analysis framework.

Optimal D-MES results show distributions of designs which vary depending on uncertain parameters.

For district energy planners, mainfindings show that mixed districts are the most promising case for D- MES planning due to a diversified demand type balance leading to lower costs and emissions. Residential districts typified by high peaks demonstrate larger storage requirements. All three storage technologies are key components of low emissions D-MES, enabling increased sector-coupling and renewable self- consumption. Such districts utilize power-to-hydrogen seasonally while batteries are relied upon for short-term load management. The most influential parameters for power-to-hydrogen installation are mainly attributed to competing technologies on the electric (batteries) and thermal (ASHPs) sectors.

1. Introduction

A signiﬁcant number of towns and cities around the world are transitioning towards renewable energy-based systems with goals to decarbonize all sectors by as early as 2030 [1]. At urban scales, District Multi-Energy Systems (D-MES) have demonstrated lower costs and CO2emissions than traditional energy systems which rely on the electric grid and utilize fossil fuel heating systems. D-MES integrate multiple conversion, storage, and network technologies, leading to the interaction of multiple energy carriers enabling increased self-consumption of renewable energy and ﬂexible sector-coupling [2]. Although, the cost-effectiveness and decarbonization potential of D-MES can be highly disparate depending on the composition of urban districts, as demonstrated in several studies [3e6]. Urban districts generally have compositions of

building energy load profile types such as residential, commercial, industrial, or‘mixed’encompassing electric, heating, and cooling demands. Defined as district topologies, each possess different load profile shapes and peak demand patterns at daily to seasonal scales.

To manage various district load topologies, a particular focus for the design of optimal D-MES must consider the necessity of various energy storage mediums for providing energy demand side management [7] and/or temporal shifting of demands [8,9]. While the inﬂuence of energy demands on D-MES design manifestations are generally understood for the majority of D-MES technologies [10], there is a research gap regarding the optimal interplay of short- and long-term storage technologies between district topologies [11,12].

General D-MES studies focus on the design of single- or multi- site conﬁgurations across different scales and settings. For instance, both Bracco et al. [13] and Orehounig et al. [14] presented mixed integer linear programming (MILP) models for the optimal design and operation of D-MES, in Northern Italy and for a rural alpine community in Switzerland, respectively. Particular focus in both papers was on achieving energy autonomy and self-

*Corresponding author.

E-mail address:epetkov@ethz.ch(I. Petkov).

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sufficiency with renewable energy. Other studies focused on other particular aspects and considerations of D-MES design and operation such as retrofitting, interconnection, and sector-coupling. Wu et al. [15] presented a MILP model considering the simultaneous optimization of D-MES and retrofitting for a range of building types in Switzerland. Wouters et al. [16] developed a multi-site MILP for a residential D-MES in South Australia considering interconnection.

Comodi et al. [17] developed a model for the optimal design of an energy system composed of multiple interconnected D-MES located inﬁve sites at a university campus in Singapore. Flores and Brouwer [18] also focused on a university campus, presenting a model for the optimal design of an energy system in California considering both building energy demands and transportation needs of campus ﬂeet vehicles. While all of the aforementioned papers demonstrated the importance of various energy storage technologies for decarbonization, the storage time horizon is generally of a short-term basis due to model computational constraints.

Focusing on similar key technologies, multiple studies explored the role of seasonal energy storage in optimal D-MES designs as a means to address increasing local utilization of renewable energy.

Both Ata et al. [19] and Murray et al. [20] explored sector-coupling integration in D-MES between the building and mobility sectors through optimization frameworks. McKenna et al. [21] investigated the role that seasonal thermal storage plays in the optimal design of a D-MES for a German residential district with high fractions of renewable heat supply. Nastasti et al. [22] explored the role of Power-to-Hydrogen (PtH2) to supply both mobility and thermal energy at an urban-scale. Giannakoudis et al. [23] presented a stochastic optimization under uncertainty framework including hydrogen storage, although without taking different demand scenarios into account. The results from Refs. [4,20e22] are in agree- ment that seasonal energy storage - no matter the energy carrier - is utilized solely in deep decarbonization (i.e. minimum emissions) scenarios due to signiﬁcant cost increases and necessity of a high renewable energy surplus.

While seasonal energy storage presents great value for decar- bonizing sectors such as buildings, mobility, and industry, efﬁ- ciently integrating its technical characteristics in optimization models presents challenges. Recent advances, particularly Gabrielli et al. [24] and Kotzur et al. [25], investigated optimal D-MES design with seasonal storage and introduced methods that can perform the design task without the need to consider a full year’s horizon in hourly time steps, hence, signiﬁcantly reducing the models’ computational burden. Particularly, Gabrielli et al. [24] focused on the role of seasonal storage with PtH2for D-MES for which this study relies on as a modeling basis.

Designing reliable D-MES must account for parameter

uncertainty to avoid suboptimal results. This necessitates capturing the stochastic nature of techno-economic and contextual parameters in a certain economic or geographic area. Comprehensive approaches to investigate the impacts of uncertainty in the context of optimal D-MES design are presented in Mavromatidis et al. [3] and Karmellos et al. [26]. Further, Shams et al. [27] presented a stochastic model for D-MES operative scheduling with electrical and thermal demand response programs for increased resilience. Wang et al. [28] developed an optimization and robustness analysis framework based on various load scenarios along with demand and environmental condition uncertainties. General analytical exam- ples for designing robust D-MES under multiple uncertainties are given in Refs. [29,30] with further detail into risk management in Refs. [31,32]. In summary, the aforementioned uncertainty analyses generally point to the importance of energy demand patterns, environmental conditions, along with parameter input data (interest rates, energy prices, feed-in-tariffs, etc.) for inﬂuencing D- MES design and operation.

Overall, there is a need to further understand the interplay of various energy storage technologies in various district topologies with uncertainty. In this paper, we investigate the role of short- term energy storage - Hot Water sensible Thermal Storage (HWTS) and electrical battery storage - along with seasonal energy storage - PtH₂ - for D-MES over various district topologies in a central European climate zone (Dfb temperate continental/humid continental on the K€oppen-Geiger system [33]). We accomplish this by relying on an optimization with uncertainty framework based on Monte Carlo (MC) simulations, followed by a global sensitivity analysis (GSA) using a two-sample Kolmogorov-Smirnov (KS) test. For the purpose of this work, we take a‘current to near- future’ European viewpoint towards all techno-economic and context parameters. We utilize the uncertainty characterization provided in the in-depth literature review presented in Petkov and Gabrielli [4]. We build on the aforementioned study to understand the importance and varied inﬂuence of uncertain parameters and district topology on storage technology inclusion in D-MES design and operation.

This paper is structured as follows. Section2describes the fea- tures of the investigated D-MES, formulates the optimization problem, and describes all relevant input data. Results and discussion are presented in Section 3, and ﬁnally conclusions are drawn in Section4.

2. District multi-energy system modeling

Here, weﬁrst describe the D-MES topology, followed by the optimization framework which were originally presented in Ref. [24]. Next, we present the district load proﬁle topologies, Nomenclature

A constraint matrix B constraint matrix

c cost vector

d cost vector

x continuous decision variables y binary decision variables T length of the time horizon

ASHP electricity-driven Air-Source Heat Pump COP Coefﬁcient of Performance

D-MES District Multi-Energy System EC Electrolyzer

FC Fuel Cell

GSA Global Sensitivity Analysis H2St Hydrogen Storage

HWTS Hot Water sensible Thermal Storage KS Kolmogorov-Smirnov

MC Monte Carlo

MILP Mixed Integer Linear Programming PEM Proton Exchange Membrane PtH2 Power-to-Hydrogen PV Photovoltaic UA Uncertainty Analysis

WT Wind Turbine

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followed by the uncertainty analysis, with further detail on both provided in Ref. [4].

2.1. System topology

The D-MES can be considered a centralized‘energy hub’where energy is converted and stored centrally to be delivered to end- users in a district. The optimization model returns the optimal selection and size of the considered conversion and storage technologies, as well as their hourly operation over the year. The D-MES has four energy carriers (electricity, heat, natural gas, and H2), and consists of the nine conversion and storage technologies - solar photovoltaic panels (PV), wind turbines (WT), electrically-driven air-source heat pumps (ASHP), natural gas boilers, HWTS, li-ion batteries, and namely, the PtH₂system, consisting of a proton exchange membrane electrolyzer (PEMEC), a PEM fuel cell (PEMFC) and a pressurized H2storage tank at 40 bar. The D-MES is assumed to be connected to both the NG and electricity grids.

2.2. Optimization framework

The optimization problem is formulated as a deterministic mixed integer linear program (MILP) that determines the optimal selection, size, and operation of the considered technologies to satisfy the energy demands while minimizing for total system cost and/or CO₂emissions over a one year time horizon. It is written generally as:

minx;y

c^Txþd^Ty

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s:t:AxþBy¼b

x02R^N^x;y2f0;1g^N^y

wherexandyrepresent the continuous and binary decision variables with the corresponding cost vectorscandd, and constraint matricesA,B;bis the constraint known-term;NxandNyindicate the dimension ofxandy, respectively. Both continuous and binary variables are optimized. Total annual costs are given by the sum of capital, operation, and maintenance costs. We utilize the augmented epsilon-constraint (ε-constraint) to not completely exclude the total cost in the minimum-emission optimization [34].

The optimization framework requires input data such as environmental conditions, energy demands, along with techno- economic and context parameters. Environmental conditions and energy demands, presented inFig. 1, are considered deterministic as they are addressed in Ref. [31], while uncertain technology (costs, efﬁciencies, etc.) and context (energy carrier prices, economic parameters, and carbon intensities of the electricity grid) parameters are captured in a comprehensive literature review demonstrated in the Uncertainty Characterization section of [4].

2.3. District topology

In this work, we compare various urban district load proﬁle topologies deﬁned as compositions of residential, commercial, and

‘mixed’energy load profile types. To output meaningful results, acquiring accurate energy demand profile data is pivotal. Unfor- tunately, it is difficult to access hourly energy demand profile data which is accurate enough to model the interactions between electrical, heating, and cooling demands. Ferrari et al. [35] presented a review of over 70 papers for methods of estimating building energy demands at a district level, highlighting the need

for reliable demand profiles at an hourly resolution for D-MES design. Few databases are freely available online to quickly extract demand profiles from reference buildings which are climate zone referenced. Of course, any reference building energy demand profile database will be an estimation for the use case of interest, and gathering real-world data is paramount. Nonetheless, if this is not possible, it is still important to have a profile of reference for various building types.

To generate district load profiles of various district topologies, we developed a methodology based on one of the few available freely accessible reference building energy demand databases which is maintained by the U.S. Department of Energy (DOE) Building Technologies program and the Office of Energy Efficiency and Renewable Energy (EERE) [36]. These reference residential and common commercial buildings (16 types) were based on the Building America House Simulation Protocols (residential) and DOE Commercial Reference Buildings in all typical meteorological year (TMY) 3 locations. The hourly profiles are based on TMY3 data from 1991 to 2005 conducted by the National Solar Radiation Database, which is the most recent version as of this writing. The open-source database contains residential and commercial energy demand data available on OpenEI [37]. Although it is climate-referenced for U.S.

IECC climate zones, this can easily be converted to the global K€oppen-Geiger climate zone classiﬁcation for wider applicability [33].

Thermal and electrical district load proﬁles are synthesized based on various proportions of residential and commercial building proﬁles based on the‘Dfb’central European climate zone for three district topologies - residential, commercial, and mixed.

Further details regarding district load synthesis are provided in Appendix B of Petkov and Gabrielli [4]. Although, in this work, we synthesize various district topologies in one climate zone to evaluate the impact of load types on general D-MES design and storage technology installation and operation. For the purpose of comparison for D-MES design, districts have equally sized annual electric demands (10 GWh/yr) with thermal loads scaled to the climate zone based on a thermal-to-electrical ratio. The Dfb climate zone has an average ratio of 2, and thus the districts have annual thermal demands of 20 GWh/yr. Nonetheless, energy demand profile seasonality is kept intact allowing for the basis of comparison for peak load profiles which strongly differ between district topologies, depicted inTable 1. The significant differences in seasonality of both commercial and residential district energy profiles can be easily seen in the heatmaps ofFig. 1. Commercial profiles are typified by

‘working-hours’electrical demands peaking in the summer, along with having the highest thermal peak of all topologies. Residential profiles are typified by larger seasonalities, possessing the highest electrical peak of all profiles, typically later in the evening and more accentuated in the summer months, while thermal peaks occur earlier in the morning compared to the commercial profile. The mixed district combines both commercial and residential profiles, allowing for smoother peaks and demand seasonalities.

2.4. Uncertainty analysis

We brieﬂy describe the uncertainty analysis (UA) framework originally reported in Ref. [4]. To attain a distribution of optimal designs, MC simulations are conducted to account for interactions between different input factors from the uncertainty characterization of techno-economic and context parameters, allowing for further sensitivity analysis of uncertain parameters in the D-MES.

While general optimization studies with deterministic data inputs present Pareto fronts between the multiple objectives of minimum- cost and minimum-emissions, in this work we only analyze the

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‘pure’objective results over the three district topologies. For each of these six cases, we run 300 MC simulations to adequately account for the techno-economic uncertainty.

Next, we perform a GSA to evaluate the most influential parameters on the system design. We utilize the two-sample KS test in order to statistically account for the differences between empirical results in the MC simulations. This involves setting a threshold value to ‘filter’ MC results between those that are behavioral or non-behavioral, allowing for the identification of the

most inﬂuential parameters. For example, we use the KS testﬁnd the set of MC results which present the most sensitive parameters for which high fractions (i.e. over quartile 3) of PtH2are installed.

3. Results and discussion

First, we explore the objective function results of both system costs and CO2 emissions. Next, the optimal D-MES design and operation are presented, followed by a focus on storage technologies and PtH2operation and the GSA.

3.1. Objective functions

Fig. 2 shows the total system costs (top) and CO2 emissions (bottom) for all the cases investigated. Similar objective function results of previous multi-objective optimizations studies [3e6,31]

are seen with decreasing emissions involving an increase in cost, and vice versa.

Fig. 1.District energy demands across topologies, with electricity demands shown in blue, and thermal demands shown in red. Average daily (top-left) and annual (top-right). Heat maps of electricity (bottom-left) and thermal (bottom-right). Note scales vary between district subplots. Environmental data (ambient temperature, solar irradance, and wind speed) presented on bottom-right. (For interpretation of the references to colour in thisﬁgure legend, the reader is referred to the Web version of this article.)

Table 1

District topology peak energy demands. Annual demands scaled equally for all districts - electric 10 GWh/yr, thermal 20 GWh/yr - based on the climate zone.

Peak demand [MW]

Electric Thermal

Commercial 2.67 19.26

Mixed 2.90 14.33

Residential 3.36 9.61

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As we do not consider life cycle analysis in this study, all district topologies are able to achieve zero operational CO2emissions in the minimum-emissions objective. System emissions at the median in the minimum-cost objective are nearly equal between the commercial and residential districts, although 4% lower in the mixed district due to a more favorable relationship between energy demands to renewable generation, leading to fewer storage requirements. The minimum-cost emissions quartile ranges are similar for all districts, while the commercial district shows the highest possible emissions up to nearly 490 gCO₂/kWh, corresponding with the highest thermal peak and thus boiler usage.

Regarding system costs in the minimum-emissions objective, the commercial and mixed district see similar total system costs at the median, while the residential district shows a 14% increase. This is likely due to the higher nighttime electric peak particularly in winter and thus higher storage requirements. As the minimum- emissions objective cost ranges are signiﬁcantly larger than for the minimum-cost objective, the inset ﬁgure (axis on right) is necessary to demonstrate the cost objective’s range. Nonetheless, a similar pattern is observed in the minimum-cost objective, with the residential district difference decreasing to 2%.

3.2. D-MES design and operation

Trends in objective functions can be better understood by looking at the D-MES design through technology selection and size.

Conversion and storage technology size results are presented in Fig. 3and inTable 2for median values. Further,Fig. 4demonstrates technology contributions to system costs along with relevant system metrics such as autarky, self-consumption, thermal electriﬁ- cation, and net demand increase.

Minimum-emissions optimizations achieve zero operational CO2 emissions with maximal sizing of renewable conversion technologies (solar PV and WTs), reliance on ASHP and PEMFC for electriﬁed heat, along with high utilization of batteries for short- term electrical energy balance. As the residential district has the highest electrical peak and system costs, technologies which input electricity as an energy carrier are sized higher relative to peak (ASHP and battery) while the opposite is true for the commercial district, which possesses the highest thermal peak and, accordingly, has higher sizing of HWTS and boilers. When going from the minimum-cost to minimum-emissions objectives, there is a direct trade-off of boilers for ASHPs, demonstrated by thermal electriﬁ- cation patterns inFig. 4.e. In the cost objective, districts mainly rely Fig. 2.Objective function outputs of total annual costs (top) and CO2emissions (bottom) for the three district topologies and two different objective functions (from left to right:

minimum CO2emissions to minimum cost). Boxes span from 25% to 75% quartile, with median reported in bold. Crosses represent outliers. Insetﬁgure for minimum-cost objective system costs has axis displayed on right.

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on boilers for heating and to a lesser extent ASHPs, while still relying on renewables, demonstrating the present-day attractive- ness of renewable-powered D-MES. Due to thermal electrification from ASHPs and PEMEC, total net demand increases (Fig. 4.c., f., and i.) by around 60% with little variation between district topologies in the emissions objective, while in the cost objective a large range is presented due to the varied ASHP installations. Although the cost objective districts are largely autarkic, self-consumption is low due to lack of sufficient energy storage, shown in the large ranges of autarkies (Fig. 4.a., d., and g.) and thermal electrification (Fig. 4.e.).

In both objectives, the commercial district has higher electric

autarky but similar self-consumption to other districts due to higher electricity exports. On the other hand, the residential district has the lowest electric autarky, self-consumption, and exports due to difﬁculties in meeting higher electrical peaks in winter. A similar but opposite pattern for thermal demand is observed for the commercial district, achieving the lowest gas autarky in the cost objective.

3.3. Storage technologies

Renewable energyﬂuctuations can be balanced both by utilizing Fig. 3.Conversion and storage technology size ranges across districts. To normalize technology sizes between district demands, sizes were scaled based on peak electric (Elecp) or thermal (Thermp) loads. Storage technologies were scaled based on annual demands. Note different scales for each subﬁgure, and PEMFC scale by electric peak. Boxes span from 25%

to 75% quartile, with median reported in bold. Crosses represent outliers. Median values for actual technology sizes and relevant metrics shown inTable 2.

Table 2

Median conversion and storage technology sizes and relevant metrics across districts to complement results inFig. 3. Technology sizes are reported in [kW] for conversion technologies and in [kWh] for storage technologies. Relevant metrics are reported as technology sizes scaled based on electric peak (Elecp), thermal peak (Thermp), or total energy demand (Demand).

District Median actual capacity [kW] or [kWh] Median relevant metric [scaling]

Commercial Mixed Residential Commercial Mixed Residential

Objective Emissions Cost Emissions Cost Emissions Cost [scaling] Emissions Cost Emissions Cost Emissions Cost

Technologies[unit] PV[kW] 4929 4929 4929 4929 4929 4028 Elecp 1.87 1.87 1.72 1.72 1.49 1.22

WT[kW] 22,500 e 22,500 e 22,500 e Elecp 8.44 e 7.77 e 6.70 e

PEMEC[kW] 2632 e 3252 e 2926 e Elecp 0.99 e 1.12 e 0.87 e

PEMFC[kW] 3244 e 4765 e 5083 e Elecp 1.22 e 1.65 e 1.51 e

Boiler[kW] e 11,006 e 8513 e 6755 Thermp e 0.57 e 0.59 e 0.70

ASHP[kW] 2858 353 2181 480 1836 606 Thermp 0.15 0.02 0.15 0.03 0.19 0.06

HWTS[kWh] 45,806 26,371 40,154 17,156 30,788 9107 Demand 0.0015 0.0009 0.0013 0.0006 0.0010 0.0003

Battery[kWh] 434,424 e 447,523 e 498,022 e Demand 0.015 e 0.015 e 0.017 e

H2St[kWh] 2,468,859 e 2,382,117 e 2,635,597 e Demand 0.083 e 0.080 e 0.088 e

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different energy storage technologies and interacting with the grid.

Although, it must be noted that in the emissions objective, the optimization chooses not to import electricity in order to avoid inherent grid emissions, based on the electric grid’s carbon in- tensity. As a function of total annual energy, the residential district has a higher reliance on batteries compared to the commercial district (median battery storage of total energy 2.5% vs. 2.2%, respectively), while the commercial district has a higher reliance on HWTS (median HWTS storage of total energy 0.15% vs. 0.23%, respectively). At the median, the mixed district sees the lowest reliance of H2St as a fraction of total energy (11.9%) due to the smoother peak demand proﬁles, followed by the commercial district (12.3%), and the residential district (13.2%) - to be explored further in the GSA. In general,Fig. 3shows that H₂St contributes a range of 7e15% of annual demands, while batteries and HWTS are used for short-term energy balancing to a much smaller degree.

Although batteries provide a fraction of the energy of H₂St in the emissions objective, batteries take the majority of total system costs, ranging from 70 to 80% of total costs, trending slightly higher to the residential district, shown inFig. 4. This leads to higher self- consumptionﬁgures by 5% at the median for the residential district.

This demonstrates the importance of batteries for short-term energy balancing to achieve low emissions D-MES. On the other hand, while H2St contribute to a larger fraction of energy demands, their contribution to total costs are much lower than batteries, with a range of 15e25% of total system costs. Here, we demonstrate the value of the offsetting the seasonal mismatch between renewable energy and energy demand.

Although PEM technologies are sized slightly higher in the mixed district emissions objective relative to electric peak,Fig. 5 shows the 2% higher contribution to both net electric and thermal loads in the residential district. The same pattern is seen in Fig. 4.i. with the PEMEC contribution to net electric load andFig. 3.i.

with the H2St. Since the FC has a higher ratio of electrical to thermal efﬁciency, it contributes more to net annual electric demand, demonstrating the importance of electrical peak in sizing PtH2

systems across D-MES topologies.

PtH₂is used to maximize self-consumption and thermal electrification to achieve zero operational CO2emissions, aligning with thefindings of previous studies [4,5,31]. The system operates ac- cording toFig. 6, with HWTS used for maximum weekly cycles, and batteries used for maximum 3 month cycles. H₂St follows a highly seasonal pattern of charging in the summer and discharging in winter, with peaks occurring around 7500 h and valleys around 1500e2500 h depending on district demands. Specifically, the commercial district has a zero-point at 1500 h while the residential does at 2500 h. This is attributed to the residential districts’ lingering heating demands into the springtime, shown in Fig. 1.

Further, the residential MC runs show a wider range of H2St peaks, implicating a trade-off of peak demand management depending on the sampled input data. The value of H₂St as a seasonal storage option is due to the lack of self-discharge in the PtH2 system, despite its low round-trip efﬁciency compared to the short-term storage options of batteries and HWTS, which possess higher efﬁ- ciencies and self-discharges.

Fig. 7 shows the PEM technology operation for all districts, Fig. 4.System-related post-process metric ranges across districts. Note different scales between top rows in subﬁgures (a), (d), and (i) Boxes span from 25% to 75% quartile, with median reported in bold. Crosses represent outliers.

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demonstrating a generally seasonal operation with rare‘outliers’of FC utilization in the summer months based on the input demands.

As the residential district is typiﬁed by a higher wintertime electrical peak compared to the available renewable balance, there is a higher reliance on PtH2. This is demonstrated by higher peaks of PEMFC output in accordance to the lack of renewables, whereas the commercial and mixed district see smoother PtH₂operation during a smaller time period in the winter.

3.4. Global sensitivity analysis

We now present the two-tailed KS test results for PtH2instal- lation and operation for the emissions objective, based on testing for the most sensitive parameters at quartile thresholds along with a subjective‘high’value for each PtH2 technology across district topologies.

Fig. 8illustrates this as an example for the PEMFC (top) and Fig. 5.PEMFC technology contributions to net electric (left) and thermal loads (right) between districts. Boxes span from 25% to 75% quartile, with median reported in bold.

Fig. 6.Stored energy in HWTS, batteries, and H2St for the minimum-emissions district topologies for all MC runs (median value shown as separate color).

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PEMEC (bottom) for the commercial district, taken at quartiles 3 and 2, respectively. These are found to represent the six most influential parameters across all district topologies, although in different orders depending on the quartile tested. Overall, the most influential parameters are, in order of significance: (1) ASHP efficiency (coefficient of performance - COP), (2) PEMEC efficiency, (3) Battery self-discharge, (4) capital cost, and (5) lifetime. Summa- rized, the most influential parameters for PtH₂ are the key parameters of competing electrical (batteries) and thermal (ASHPs) technologies. Competition exists between ASHPs and batteries with PtH₂for electrified thermal and electrical storage, respectively, in order to meet the minimum-emissions objective. This is comple- mentary to thefindings of Petkov and Gabrielli [4] adding to the significance of battery self-discharge as a dynamic consideration of round-trip efficiency for electrical storage competition. Further, at high values of PtH2installation, the PEM technologies’capital costs, lifetimes, and efficiencies also become significant.

The relationship between the important parameters of competing technologies to PtH2 - ASHP COP and battery self- discharge - are shown inFig. 9. Interestingly, there is direct trade- off (demonstrated by the diagonal separation) of high PtH₂installation between various levels of ASHP COP and battery self- discharge - at high (low) ASHP COP and low (high) battery self- discharge, we see low (high) PtH₂installation.

4. Conclusions

This work presents an analysis of the optimal interplay of short- and long-term storage technologies between urban district topologies deﬁned as compositions of residential, commercial, or‘mixed’ energy proﬁles. We utilize a MILP optimization coupled with an uncertainty analysis framework to explore the relationship between district energy topologies and storage technology installation and operation, with a particular focus on seasonal energy storage. After analyzing the various D-MES technology selection and sizing considerations between district topologies through Monte Carlo simulations, we determine key D-MES sensitivities of uncertain input parameters on PtH2installation. This complements the work of Petkov and Gabrielli [4] by providing depth on the impact of district topologies and parameter uncertainty on D-MES design within a central European climate zone.

In the emissions objective, all districts rely on PtH2seasonal storage to achieve zero operational CO₂ emissions to maximize renewable self-consumption and thermal electrification (sector- coupling). The residential district, typified by the highest electrical peak of all districts, demonstrated higher total system costs. This was mostly due the need for larger storage requirements to assure a zero-emissions energy source in times of low renewable availabil- ity. Further, we demonstrate the importance of mixed district profiles as the most promising case for D-MES planning.

Fig. 7.PtH2system operation across all districts topologies for all MC runs in the emissions objective. Negative (PEMEC) and positive (PEMFC) power demonstrates charging and discharging of the PtH2system. FC electricity and heat are overlayed (not stacked) to show higher contribution of electricity generation.

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For district energy system planners, this implicates an increased focus to utilize a diversified balance of energy demand types, leading to reduced system costs and CO₂emissions. Nevertheless, batteries are heavily relied on to smooth short-to medium-term (maximum 3 month) renewablefluctuations, while PtH2is used to offset the long-term mismatch between renewable energy and energy demand. The impact of uncertainty proves to be significant, with PtH2 deployment being highly sensitive to the sampled

parameters of competing technologies mainly attributed to the cost, efﬁciency, or lifetime of competing technologies on the electrical (batteries) and thermal (ASHPs) sectors.

Declaration of competing interest

The authors declare that they have no known competing ﬁnancial interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.

Acknowledgments

This research is supported in part by funds from the Swiss Competence Center for Energy Research (SCCER) Future Energy Efﬁcient Buildings and Districts (FEEB&D), and the Swiss National Science Foundation (SNF) under the National Research Program Energy Turnaround (NRP70). The authors would like to thank all members of the SusTec, sus.lab, and IMES project teams who assisted in the development of this work.

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