Towards Integration of Energy Storage Systems
for Carbon Neutral Buildings
A Review of Multi-Criteria Decision Making Approaches
Xiaoshu Lü1,2(B), Tao Lu2, and Pekka Tervola2
1 Department of Electrical Engineering and Energy Technology, University of Vaasa,
P.O. Box 700, 65101 Vaasa, Finland
xiaoshu.lu@uwasa.fi
2 Department of Civil Engineering, Aalto University, P.O. Box. 11000, 02130 Espoo, Finland
Abstract. Building sector consumes about 40% of the global energy consumption
and emits over 30% of the global energy-related CO2 emissions. It is one of the
most resource-intensive sectors and the main contributor of the environmental
emissions. Additionally, the amount of greenhouse gas emissions is increasing
remarkably due to the rapid growth in urbanization. Distributed energy resources
(DERs) offer opportunities to support the deployment of large shares of renewable
energy sources (RES) in order to meet the sustainability goals of reducing carbon
emissions and increasing building resilience.
DERs consist primarily of energy generation and energy storage systems
(ESS) which are located near to the end-users of buildings, allowing easily integra-
tion of RES and realization of carbon neutrality. However, widespread adoption of
renewable energy is challenging because of its intermittent nature. Energy supply
does not satisfy with the demand, back-up supply from ESS is therefore required to
solve the problems. ESS are of great importance for balancing supply and demand
mismatches and offering the opportunity to replace fossil fuels with large shares
of renewable penetration on DERs to eventually achieve zero-carbon emissions
in buildings.
Due to numerous factors that influence ESS, selection of the suitable energy
storage technologies for specific building applications presents a challenge. In
the literature, different criteria have been suggested to contrast ESS’ strengths
and weaknesses for different applications. Methodologically, multi-criteria deci-
sion making (MCDM) has been widely employed in planning ESS. This paper
aims to provide a critical review of MCDM for the deployment solutions of RES
and ESS in carbon neutral building applications. A conceptual illustration is also
presented to synthesize the literature review and explain the key methodologies
of MCDM.
Keywords: Energy storage · buildings · multi-criteria decision making · review
© The Author(s) 2023
P. Schossig et al. (Eds.): IRES 2022, AHE 16, pp. 297–307, 2023.
https://doi.org/10.2991/978-94-6463-156-2_20
298 X. Lü et al.
1 Introduction
As building sector accounts for about 40% of energy consumption and over 30% of
greenhouse gas emissions in the world, decarbonization of buildings plays a key role in
achieving climate neutrality by 2050. It is anticipated that the energy infrastructure will
adopt distributed energy resources (DERs), especially renewable energy sources (RES)
such as solar photovoltaic (PV), wind, and geothermal which provide decarbonisation
and climate change mitigation benefits as well as building resilience against extreme
weather events.
DER consists primarily of energy generation and energy storage systems (ESS)
which are located near to the end-users of buildings. DER allows easily integration of
RES and realization of carbon neutrality, however, widespread adoption of renewable
energy is challenging because of its intermittent nature. Energys upply does not satisfy
with demand, backup supply of ESS, therefore, is the key to deal with the imbalance
problems.
Applications of EES to buildings have become widespread geographically depending
on many influential factors, for example, specific application needs, forms and materials
of energy stored, economic and environmental effects. All these influential factors will
serve to make comparisons and decisions on the most appropriate technique of ESS
for each type of building applications. Conflicting criteria are typical in evaluating deci-
sions. As a result, multi-criteria decision making (MCDM) has been widely employed in
planning energy storage systems. However, different MCDM approaches, criteria, goals
and results for decision support are difficult to compare or to reproduce. This paper aims
to provide a critical review of MCDM approaches to ESS decision support in carbon
neutral building applications. A conceptual illustration of carbon neutral campus is also
presented to explain the key methodologies of MCDM.
2 Overview of Energy Storage Systems (EES)
Energy storage systems (EES) are defined as equipment that can store different types of
energy and convert it back into energy at later time when needed. Energy can be stored
in five different forms in EES [1]: electrical, electrochemical, thermal, mechanical and
chemical through charge/discharge process. In building applications, thermal energy
storage (TES) is the most commonly used storage technology [1, 2] which will be
the focus of this paper. The basic design parameters for TES are energy density and
capacity, charging and discharging time, depth of discharge, round trip efficient etc.
At the material level, heat transfer between the material and the fluid, compatibility to
container material, reversible cycles, thermal losses, flexibility and modularity are the
important design criteria. Besides technical requirements, TES design should also meet
sustainability criteria regarding its life cycle impacts on the environment and economics
with low capital and operational costs and carbon emissions. Operational strategies in
life cycle of TES present essential design requirements.
A variety of TES technologies have been applied in buildings depending on the
locations, capacities and costs, etc. The indicators to measure TES and building integra-
tion system are the energy load and peak reduction and savings. A major challenge of
Towards Integration of Energy Storage Systems 299
applying TES technologies is how to find the best suitable materials and technology to
increase building’s ability to shift energy demand away from peak periods. The answers
depend on entirely on the particular building applications and the goals. In the following,
both material and building level applications of TES will be reviewed.
3 Applications of Thermal Energy storage (TES)
3.1 Material Level
TES can be stored using sensible, latent heat and thermochemical energy technologies
[3]. Sensible heat storage uses a high thermal mass to absorb and store heat without
phase change. The stored energy is proportional to the temperature difference and the
chosen materials can be solid materials (e.g. concrete and castable ceramics), liquid
materials (e.g. water, oil, and sodium), and their combinations. The chosen material
should have a high thermal inertia defined as the square root of the product of the thermal
conductivity and the volumetric heat capacity. Water is by far the most used liquid
material due to the best compromise between the cost, heat storage capacity, density and
environmental impact. Figure 1 lists some examples of solid materials with their thermal
inertia properties [4].
Latent heat storage systems are associated with phase change materials (PCMs)
which have wide applications in buildings due to their high storage capacities within
narrow temperature ranges. Building PCMs are generally categorised as organic com-
pounds (e.g. paraffins and fatty acids), inorganic compounds (e.g. salt hydrates and
metals) and eutectics [5] (Fig. 2).
Thermalchemical storage material (TCM) stores and releases heat through chemi-
cal reactions. While PCM storage systems have been beneficial for applications where
heating is the main purpose, TCM storage systems can be used for both heating and cool-
ing.. The application of PCM in cooling systems is increasing recently. Wall-integrated
PCMs, for example, help to reduce daytime temperatures or ice storage can also be used
very well in connection with cooling. TCM has a high storage density and is expected to
have high cycling stability because the process of charging and discharging, which corre-
sponds to desorption and adsorption of water vapor (adsorptive) on the TCM (adsorbent),
respectively, is fully reversible. A comparison [6] of working temperatures for PCM and
TCM applications is shown in Fig. 3.
Fig. 1. Important properties of EES materials [4]
300 X. Lü et al.
Fig. 2. Most common PCM applications in buildings and their property comparison [5]
Fig. 3. Comparison of working temperatures for PCM and TCM applications [6].
Most common TCM applications in buildings and their property comparison are
displayed in Fig. 4 [7].
3.2 Building Level
The current state of TES applied in buildings involves identifying opportunities for a
renewable and decarbonization purposes through TES integration technologies. Broadly
speaking, there are two general TES integration systems: passive and active technologies
[8–10]. Figure 5 summarizes the major TES technologies in building applications [8].
3.2.1 Passive Technology
In the passive method, TES is embedded into the building construction systems, such
as building envelops of walls, floors, and roofs, fenestration, and other systems in such
Towards Integration of Energy Storage Systems 301
Fig. 4. Most common TCM applications in buildings and their property comparison [7]
way that its operation does not need auxiliary equipment. Improving thermal inertia
in building envelope is an effective TES approach. PCMs are ideal materials for such a
purpose. Based on the integration locations, passive technology of PCM can be classified
as inside the building materials, new layers of building materials and others [9].
3.2.2 Active Technology
An active TES system, on the other hand, requires some additional energy for mechani-
cally assisted equipment to enable the operation of TES. There are many ways to integrate
TES with buildings, for example, in building envelops such as external solar facades,
the ventilation system, water tanks and ground [10]. Depending on the geological loca-
tions, water tank TES, pit TES, borehole TES and aquifer TES have been widely
deployed. Similar to the passive cooling, PCM is the common materials applied to active
technology to increase building thermal inertia and to improve the thermal performance
of TES.
3.3 Challenges and Outlooks
Since PCM usually has low thermal conductivity, PCM storage systems require com-
plicated and expensive heat exchangers. Different thermal conductivity enhancement
techniques have been proposed for PCM [11], however, this is still a challenging task.
The most critical disadvantage of TCM is related to the unstable chemical compound
obtained in charging period during the storing period. Although research on TCM has
302 X. Lü et al.
Fig. 5. Summary of the major TES technologies in building applications [8]
made some advancements, TCM remains a challenge in building applications due to its
high cost [7].
Every PCM material has its own properties and qualities that contribute to the ESS
overall performance. PCM used for building applications requires a melting temperature
between 25–30°C for indoor comfort. Hence material thermophysical properties and
costs are the key factors in determining its wide usability in the market. Since salt
hydrates have been produced on a large scale, they are much cheaper than the currently
available PCM in the required melting temperature range. Compared to paraffins, their
volume change undergoing phase change is smaller. This property can simplify the
construction of the overall storage system. Additionally, they have higher volumetric
storage density than organic PCM. Therefore, low-cost salt hydrate-based thermal energy
storage materials with high thermal conductivity, high storage capacity, and good thermal
cycling stability present a promising PCM option in the current and future market.
Towards Integration of Energy Storage Systems 303
4 Multi-Criteria Decision Making (MCDM)
4.1 Principles and Software
MCDM is particular useful for complex decision problems involving multiple and con-
flicting objectives and criteria. It provides means to compare options by assessing trade-
offs between different options. Due to wide diversity of technologies in TES, selection
and integration of TES requires simultaneous consideration of technical, economic,
social and environmental dimensions and optimize the conflicting objectives. Figure 6
summarizes the most influential factors in the literature [12].
There are several methods for performing MCDM. The simplest way is based on
a standard sweep algorithm for the constraints. This method can be computationally
expensive to generate a detailed Pareto front if a large number of constraints exist. To
compromise this method, a weighted sum of objectives is employed to replace con-
straints, unconstrained optimization can be used to generate an approximate Pareto
front. More advanced methods include Analytical Hierarchical Process (AHP), genetic
algorithm (GA) and other approaches to generating Pareto curve.
There are a few software tools that target at MCDM for RES [13]. HOMER (Hybrid
Optimization of Multiple Energy Resources, developed at National renewable energy
laboratory, USA) and iHOGA (Improved Hybrid Optimization by Genetic Algorithm,
developed at University of Zaragoza, Spain) are the two popular ones.
At building level, Vallati et al. (2015) [14] optimized RES generation and TES for
off-grid and then compared it with that for grid extension in terms of techno-economical
and environmental factors using HOMER. TES included batteries, flywheels, and com-
pressed air energy storage. Khan and Go (2021) [15] applied HOMER to conduct a
feasibility study of power system that included large-scale ESS based on technical, eco-
nomic and the demands of electricity generation in Malaysia. ESS included mixed TES
and batteries.
At building material level, Ijadi Maghsoodi et al. (2020) proposed an approach for
initial screening and selection of PCMs based on intervals of target values of materials’
thermophysical properties, costs, and risk criteria [16]. Mukhamet et al. (2021) applied
MCDM to select PCM for building envelopes based on climate factors, environmental
footprint and material properties, such as thermophysical (thermal conductivity, latent
Fig. 6. Summary of the most influential factors for integrated renewable, storage and building
energy systems [12]
304 X. Lü et al.
Fig. 7. Campus’ layout
Fig. 8. Schematic diagram showing the steps applying MCDM to size the storages
heat of fusion, phase change temperature, specific heat, density, cycling stability, super-
cooling), economic (initial cost), chemical (toxicity, flammability, corrosiveness), and
environmental (recyclability, embodied energy) [17].
4.2 Conceptual Illustration
The conceptual illustration aims to synthesize the literature review and to illustrate
MCDM applied to TES investigation for carbon neutral buildings. We focus on the
methodological considerations of MCDM using HOMER software. The illustrative
buildings belong to university campus. Here we investigate different implementation
scenarios for achieving zero carbon campus through upgrading campus’ infrastruc-
ture systems (see Fig. 7). The study groups of buildings are highlighted in Fig. 7. The
upgrade process takes the following steps (Fig. 8) of the analysis.
Because upgraded buildings must meet strict requirements in energy use, material
selection, indoor air quality and retrofit, green building standard RTS, Finnish certifica-
tion based on European standards (CEN TC 350 standards), is applied to assess the low
impact development philosophy and upgrade solutions that include potential RES and
ESS. As this paper focuses on ESS, we illustrate how to size ESS using MCDM.
4.2.1 Building Load Pattern
The load simulation for the buildings was performed using EnergyPlus. Collected build-
ing energy datasets on campus were used to calibrate and validate EnergyPlus model.
Figure 9 displays the load patten for one building.
Towards Integration of Energy Storage Systems 305
Fig. 9. Building energy consumption pattens
4.2.2 Energy System Balance Model
The RES and ESS provide energy balance to the estimated load profile (Fig. 8). Different
RES and ESS alternatives and their combination are considered and assessed by MCDM,
namely geothermal energy or ground source heat pump, PV radiation, recyclable waste,
borehole TES (BTES), water tank storage and supplementary from district networks.
Energy system models for the selected RES and ESS are constructed to balance the
load demand. For example, BTES model describes how the BTES parameters, such as
borehole spacing, depth, temperature and flow rate, affect the energy supply during heat
injection and storage. Based on the load and the models of other energy systems, sizing
of BTES can be conducted. Details of the models of energy systems are omitted due to
the space limitations. For different RES and ESS scenarios, optimum configuration of
TES can be selected in terms of various social, economic and environmental constraints
(see Fig. 8).
4.2.3 Multi-Criteria Decision Making (MCDM)
HOMER calculates the optimum TES configuration for different energy generation sce-
narios. The calculation evaluates technical, economic, environmental and social factors.
Currently the most common ranking technique is Technique for Order of Preference by
Similarity to the Ideal Solution (TOPSIS) method [18]. TOPSIS method consists of the
following steps: 1) Define the attributes; 2) Formulate the decision matrix; 3) Normalize
the decision matrix; 4) Perform calculation on the normalized matrix. The attributes are
based on the technical, economic, environmental and social factors. TOPSIS ranks the
attributes to determine the option closest to the ideal solution.
HOMER simulates energy system control and constraints based on the building
energy load, renewable generation, and attributes for various scenarios for a typical year
8760 h. The outputs from HOMER are optimal sizing of TES, net present cost, capital
cost, and carbon emissions, etc. (Fig. 8). Figure 10 shows an illustration.
306 X. Lü et al.
Fig. 10. Illustration of simulation result from HOMER (http://www.homerenergy.com)
5 Conclusions
This paper presents a literature review of MCDM for energy storage selection within
the building sector. MCDM is an interdisciplinary approach that has been increasingly
employed in applications of wide-scale RES deployment in buildings at different scales,
ranging from single buildings, districts to cities. The MCDM software HOMER is
introduced with a conceptual illustration.
Acknowledgements. This research was supported by the Academy of Finland (STARCLUB,
Grant No. 324023).
Authors’ Contributions. All the authors presented the idea and conducted literature review. X.
Lü wrote the manuscript with support from T. Lu and P. Tervola.
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