MCDM Optimization-Based Development of a Plus-Energy Microgrid Architecture for University Buildings and Smart Parking

Abstract

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic feasibility assessed through a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The system is projected to generate approximately 1 GWh annually, with a 98% probability of exceeding 1076 MWh based on Gaussian estimation. Consumption is estimated at 460 MWh, while a 3.8 MWh battery ensures up to 72 h of autonomy. Rooftop panels and green parking arrays, fixed at 13.5° and 59°, minimize visual impact while contributing a surplus of +160% energy injection (or a net surplus of +60% energy after self-consumption). Assuming a battery cost of EUR 200/kWh, each hour of energy storage for the building requires 61 kWh of extra capacity with a cost of 12,200 (EUR/hr.storage). Recognizing environmental variability, these figures represent cross-validated probabilistic estimates derived from both PVsyst and Monte Carlo simulation using Python, reinforcing confidence in system feasibility. A holistic photovoltaic optimization strategy balances technical, economic, and architectural factors, demonstrating the potential of PEBs as a sustainable energy solution for academic institutions.

1. Introduction

The integration of photovoltaic (PV) systems within urban and landscape architecture plays a crucial role in enhancing sustainability, optimizing energy solutions, and promoting public health. Thoughtful panel orientation and placement not only maximize energy capture but also contribute to the harmonization of solar infrastructure with the built environment, mitigating visual disruption while preserving architectural aesthetics. By incorporating solar panels into green spaces, parking structures, and rooftops, cities can create energy-efficient environments that reduce carbon footprints, improve thermal comfort, and support clean air initiatives, ultimately fostering healthier urban living conditions. Additionally, optimized PV layouts minimize shading effects on surrounding vegetation and pedestrian areas, ensuring balanced light distribution that enhances biodiversity and public accessibility. This integrated approach transforms urban spaces into self-sustaining energy hubs, aligning environmental conservation with renewable energy strategies, and reinforcing the vital connection between architecture, sustainability, and human well-being.

Achieving sustainability within the built environment has become an essential objective in addressing the growing challenges of climate change, energy consumption, and resource optimization. Plus-Energy Buildings represent a significant step toward this goal. These buildings are designed to produce more energy than they consume over one year, making them not only energy-efficient but also key contributors to a decarbonized energy future. Central to the realization of PEBs is the integration of microgrid-localized energy networks that combine renewable energy generation, energy storage systems, demand management, and advanced control strategies.

This paper delves into the development of sustainable microgrids within a University of Coimbra (UC) campus and examines their evolution toward achieving PEB status. The focus is on enhancing microgrid flexibility to balance energy generation and consumption effectively. Flexibility measures, including demand-side management (DSM) and load shifting, are pivotal in ensuring that energy consumption aligns with periods of peak renewable energy generation. Such approaches optimize resource utilization, reduce dependency on the central grid, and enhance energy resilience. Incorporating flexible loads, particularly in heating, ventilation, and air conditioning (HVAC) systems and flexible electric vehicle charging, plays a crucial role in achieving cost-effective thermal comfort without compromising energy efficiency.

To support these efforts, advanced technologies such as battery energy storage systems, vehicle-to-grid (V2G) chargers, and real-time energy management systems are deployed. However, transitioning to PEBs requires addressing further challenges, including the integration of additional flexible loads, the incorporation of energy-efficient HVAC designs, and the implementation of predictive analytics for energy demand forecasting. By bridging the gap between energy sustainability and occupant comfort, these strategies pave the way for scalable solutions in building design.

Moreover, this study underscores the critical role of microgrids in decarbonization. By minimizing reliance on fossil fuels, integrating higher levels of renewable energy, and promoting grid interactivity, microgrids contribute to environmental sustainability. This university building project demonstrates a holistic approach to energy optimization, offering valuable insights for similar efforts in urban and campus environments worldwide. Ultimately, this exploration highlights how microgrids and PEBs collectively enable the transition toward a cleaner, smarter, and more sustainable energy future.

1.1. Research Motivation

University campuses are characterized by their large-scale buildings and intensive energy demands. As centers of education and innovation, they are uniquely positioned to address pressing energy challenges, including rising operational costs, increasing energy consumption, and the global imperative for decarbonization. These challenges underscore the importance of implementing advanced energy management strategies and integrating renewable energy technologies to enhance sustainability.

The University of Coimbra provides an exemplary case study in this regard. Over the past decade, it has adopted transformative measures to improve energy efficiency, such as conducting energy audits, retrofitting energy-efficient HVAC and lighting systems, as well as optimizing the operation of flexible electrical loads (HVAC and electric vehicle charging). In parallel, PV systems have been deployed across its buildings, demonstrating the potential of on-site renewable energy generation to compensate for consumption.

The Department of Electrical Engineering and Computers, serving as a living laboratory, has led the way in implementing and testing cutting-edge technologies. This approach has not only resulted in significant energy savings but also demonstrated the ability of PV systems to contribute a substantial share of electricity, progressively reducing reliance on external grids and becoming an Energy-Plus Building.

Motivated by the ongoing need for sustainable solutions, this paper focuses on advancing microgrid flexibility and energy efficiency to transition university campuses toward PEBs. By integrating demand-side management practices, enhancing HVAC systems with flexible load capabilities, and prioritizing occupant thermal comfort, university campuses can become pioneers in decarbonization and energy optimization. The insights gained here hold the potential to redefine energy practices in academic and urban environments alike.

1.2. Literature Review

In recent years, extensive research has been conducted on the development of microgrids for university campuses to optimize energy management and enhance sustainability. The work in [1] demonstrated the feasibility of ensuring energy supply during a two-day islanding event at Clemson University, USA, through precise microgrid component sizing. Similarly, the study in [2] presented comprehensive design guidelines and frameworks for microgrid development at the Malta College of Arts, Science, and Technology. The research presented in [3] achieved energy-efficient performance through the modeling and optimal operation of a microgrid at ETS Montreal, Canada, while the study in [4] illustrated the potential of integrating diverse energy sources such as PV, wind power, biogas, EVs, and battery storage at the University of Novi Sad in Serbia.

Several studies have addressed simulation platforms and testbeds, such as intelligent energy systems at the Chalmers University of Technology, Sweden [5], whereas in [6], a scalable microgrid testbed for Al Akhawayn University, Morocco, was developed. Notably, the work presented in [7] demonstrated a practical method to transition the distribution network of the Eindhoven University of Technology, Netherlands, into a smart grid. Additionally, another study reviewed existing campus microgrids and highlighted their main role as research-oriented testbeds rather than real-world implementations [8].

Recent advancements have focused on real-world applications and sustainability (

Table 1. University microgrid case study literature review.

University	Focus/Contribution	Key Findings/Results	Ref.
Clemson University (USA)	Microgrid component sizing is designed to provide sufficient energy for a two-day islanding event.	Demonstrated the feasibility of ensuring energy supply during a two-day isolation scenario.	[1,13]
Malta College of Arts, Science, and Technology	Design guidelines, main functionalities, key components, and control architecture for developing a microgrid.	Presented a comprehensive framework for microgrid development in an academic setting.	[2]
ETS Montreal (Canada)	System design, modeling, and optimal operation for the microgrid.	Achieved optimal operation strategies for energy-efficient microgrid performance.	[3]
University of Novi Sad Faculty of Technical Sciences (Serbia)	Microgrid incorporating PV, wind power, biogas, EVs, and battery storage.	Illustrated the potential of integrating diverse energy sources in a campus microgrid.	[4]
Chalmers University of Technology (Sweden)	Testbed for intelligent distribution grids, local energy systems, and energy-flexible buildings; modeling and simulation included.	Enabled simulation and testing of intelligent energy systems and flexible energy use.	[5]
Al Akhawayn University (Morocco)	General microgrid testbed development and simulation.	Provided a scalable and flexible microgrid testing environment for research and experimentation.	[6]
Eindhoven University of Technology (The Netherlands)	Transforming the current distribution network into a smart grid by simulation of future loads and mobile storage capabilities.	Demonstrated a practical method to transition existing grids to smart grids with advanced capabilities.	[7]
Various universities worldwide	A review of existing campus microgrids; highlights their primary use as testbeds and laboratories.	Concluded that most university microgrids are designed for research and testing purposes, not real-world implementation.	[8]
UNICAMP (Brazil)	Implementation of “Campus GRID” microgrid with PV, energy storage, and automated load control.	Demonstrated effective energy management and optimization of resources.	[9]
Oakland University (USA)	A comprehensive review of campus microgrids, focusing on renewable energy sources and hybrid configurations.	Identified optimal configurations for energy production and flexibility.	[10]
University of Puerto Rico at Mayagüez (USA)	Methodology for designing a microgrid using renewable resources and critical load analysis.	Validated reliability and cost-effectiveness of microgrid systems during outages.	[11]
Stellenbosch University (South Africa)	Solar-powered microgrid with battery storage for campus energy needs.	Reduced energy costs and promoted sustainability initiatives.	[12]
University of Coimbra	Design and management strategies for robust microgrids	Microgrid architecture enhances resilience by ensuring power supply during extreme events	[10,13]
University of Cambridge (UK)	Microgrid with solar PV, battery storage, and demand–response systems.	Improved energy efficiency and reduced carbon footprint.	[14]
National University of Singapore (Singapore)	Smart microgrid integrating solar, wind, and energy storage systems.	Enhanced energy efficiency and provided a testbed for smart grid technologies.	[15]
Kyoto University (Japan)	Microgrid with solar, wind, and hydrogen storage systems.	Demonstrated the feasibility of integrating hydrogen as an energy storage solution.	[16]
Technical University of Munich (Germany)	Microgrid testbed for renewable energy integration and smart grid research.	Enabled advanced research on energy systems and grid optimization.	[17]
Seoul National University (South Korea)	Assessment of the technical and financial feasibility of deploying a microgrid using a planning model.	Highlighted the financial viability and technical challenges of microgrid deployment in a campus setting.	[18]
University of Queensland (Australia)	Solar-powered microgrid with battery storage for research and energy independence.	Reduced reliance on the main grid and promoted renewable energy research.	[19]
Democritus University of Thrace (Greece)	Simulation platform for a smart microgrid configuration including PV installation, battery storage, and energy management system.	Developed a reliable simulation platform for evaluating smart microgrid performance.	[20]
University of California, San Diego (USA)	Advanced microgrid with solar, storage, and CHP systems.	Achieved significant energy cost savings and enhanced grid resilience.	[21]

). The “Campus GRID” microgrid at UNICAMP, Brazil [9], showcases effective energy management. The study presented in [10] identified optimal configurations for renewable energy systems at Oakland University, USA, while the research work in [11] validated the reliability and cost-effectiveness of microgrids at the University of Puerto Rico at Mayagüez. Furthermore, the case study in [12] demonstrated reduced energy costs and sustainability initiatives through the solar-powered microgrid at Stellenbosch University, South Africa. These studies collectively underline the growing interest in real implementations that ensure renewable generation, energy efficiency, environmental sustainability, and achieving a reliable power supply.

The current work advances the field by ensuring the real implementation of microgrid technologies at a building level, combining PV generation, lithium-ion battery storage, demand response (HVAC loads), and building-to-vehicle systems (B2V/V2B). This approach bridges the gap between theoretical design and practical impact, transforming the microgrid into both a functional system and a testbed for future innovations.

1.3. Research Novelty

This paper introduces an advanced optimization framework for microgrid design, integrating PV generation, energy storage, and flexible loads while accounting for architectural, economic, and technical constraints. Many existing buildings face the difficult goal of progressively becoming Zero-Energy or Energy-Plus. This paper addresses an ongoing pilot case study that illustrates how solar PV, battery storage, energy-efficient equipment (namely HVAC and lighting), as well as demand management (namely HVAC and EV charging), can be integrated to progressively achieve that goal. The core innovations of this study include the following:

• Advanced Fixed Tilt Optimization: This study proposes a dual fixed tilt approach (13.5° for rooftop and 59° for green parking), carefully selected to balance energy independence while minimizing grid injection in critical periods. This method enhances yield stability across seasonal variations and ensures high system efficiency.
• Optimized Row Spacing with Shading Loss Reduction: This research employs a geometric shading analysis combined with solar position algorithms to determine the most efficient row spacing between panels. This method reduces inter-row shading losses and improves energy capture, optimizing system performance compared to conventional setups.
• IAM-Based Energy Adjustment: The implementation of the Incidence Angle Modifier (IAM) correction refines energy estimation, accounting for angular losses in solar radiation. Unlike conventional models that assume a constant efficiency, IAM ensures a more accurate representation of received irradiance at varying tilt angles.
• Probabilistic Performance Assessment via Gaussian and Monte Carlo Modeling: To quantify uncertainties in solar generation, this study introduces a Gaussian-based probability analysis of irradiation and energy production validated by Monte Carlo simulation. This probabilistic approach improves confidence in system feasibility, providing an insightful P50 vs. P98 yield comparison for risk assessment.
• Trade-off Analysis Using Multi-Criteria Decision-Making (MCDM): Economic feasibility is evaluated through the TOPSIS decision-making framework, considering battery storage cost, surplus energy injection, and autonomous operation duration. This holistic assessment provides a data-driven financial optimization strategy, ensuring realistic scalability.

Each of these contributions addresses key gaps in conventional PV system design. The fixed tilt optimization strategy solves the issue of dynamic seasonal performance while minimizing operational disruptions. The IAM-based correction and row-spacing refinements overcome systematic losses that are often overlooked in standard solar array configurations. Moreover, the probabilistic modeling and MCDM analysis ensure robust decision-making, bridging the gap between technical optimization and financial sustainability.

This research lays the groundwork for more resilient, adaptable, and self-sufficient university microgrid systems, demonstrating how Plus-Energy Buildings (PEBs) can transition from theoretical concepts to practical implementations.

2. Theories and Design Fundamentals

2.1. Sustainable Microgrid

Sustainable microgrids are localized energy systems that generate, store, and distribute electricity either independently or in collaboration with the main power grid. These systems integrate renewable energy sources such as solar power coupled with energy storage, which significantly reduces greenhouse gas emissions and dependence on fossil fuels. By leveraging advanced energy management strategies and technologies, sustainable microgrids enhance energy efficiency, reliability, and resilience, making them a cornerstone of modern sustainable energy infrastructure [22].

2.2. Power Generation, Uncertainty, and Optimization

2.2.1. Gaussian Distribution and Probability

In photovoltaic systems, uncertainty in energy production arises from variations in weather conditions, system losses, and modeling inaccuracies [23]. To quantify this uncertainty, Gaussian distribution (normal distribution) is widely used in PV performance analysis. PVsyst incorporates this statistical approach in its P50–P90 evaluations, helping estimate the probability of achieving specific energy yields under variable conditions. The Gaussian probability density function (PDF) is expressed in Equation (1):

$$f\left(x\right)={\displaystyle \frac{1}{\sigma \sqrt{2\pi }}}{e}^{-{\displaystyle \frac{{\left(E-\mu \right)}^2}{2{\sigma }^2}}}$$

(1)

where

• $\mathrm{f}\left(\mathrm{x}\right)$ = probability density function (likelihood of a certain energy yield);
• E = energy yield value;
• μ = mean value (P50 energy production estimate);
• σ = standard deviation (uncertainty range);
• e = Euler’s number (~2.718).

In PVsyst, this formula is used to model the probability distribution of energy output, enabling users to assess the likelihood of different production levels. The P50 value represents the energy yield that has a 50% probability of being exceeded, whereas the P90 value ensures a 90% probability of meeting or surpassing the estimated yield. By applying this statistical approach, stakeholders can effectively manage financial risks and optimize system reliability.

2.2.2. Energy Output and Performance Ratio

First of all, it is required to estimate the solar power potential of the case geometry or area. It shows how to make more realistic decisions on the supply–demand balancing process and its performance ratio (PR), which is the ratio between actual and theoretical energy outputs of a PV plant. The total energy output of a PV system [24] is calculated using Equation (2):

$$E=A·{\mathsf{\eta }}_{pv}·H_{tilt}·PR$$

(2)

where

• E: annual energy output (in kilowatt-hours, kWh).
• A: total area of the PV system (m²).
• ${\mathsf{\eta }}_{\mathrm{p}\mathrm{v}}$: efficiency of the PV panels (e.g., 0.25 for 25%).
• ${\mathrm{H}}_{\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{t}}$: annual average solar radiation on tilted panels (kWh/m²/year).
• PR: performance ratio.

For grid-connected systems with storage, PR is adjusted to incorporate energy stored and losses during battery charge and discharge cycles [25] and defined by Equation (3), as follows:

$$PR={\displaystyle \frac{E_{Grid}+E_{Solar}}{GlobInc\times P_{nomPV}}}$$

(3)

where

• E_Grid is energy injected into the grid (kWh);
• E_Solar is energy used directly before grid export (kWh);
• GlobInc represents irradiation in the plane of the array (kWh/m²);
• P_nomPV addresses installed nominal power of the PV system (kWp).

The direct radiation angle of incidence of the sun on a sloped surface (solar incidence angle) is calculated based on the position of the sun and the angle of inclination of the panel or surface [26]. The general formula for the angle of incidence is defined in Equation (4), as follows:

$$\mathrm{Cos}\left({\mathsf{\theta }}_i\right)=\mathrm{Sin}\left(\mathsf{\beta }\right)\cdot \mathrm{Sin}\left(\mathsf{\alpha }\right)+\mathrm{Cos}\left(\mathsf{\beta }\right)\cdot \mathrm{Cos}\left(\mathsf{\alpha }\right)\cdot \mathrm{Cos}\left(\mathsf{\varphi }-\mathsf{\gamma }\right)$$

(4)

where

${\mathsf{\theta }}_i$: solar incidence angle.

$\beta$: tilt angle.

$\alpha$: solar elevation angle.

$\mathsf{\varphi }$: solar azimuth angle.

$\mathsf{\gamma }$: surface azimuth angle.

• If the panel is mounted at a 90-degree angle, this value will be exactly equal to the panel length.
• If the tilt angle is 0 degrees (i.e., the panel is completely horizontal to the ground), this value will be 0.
• This value is similar to the optimal dynamic height since both depend on the tilt angle, but it is not exactly equivalent, since the optimal dynamic height also depends on the effective height $\left(H_{effective}\right)$ of the panel.

2.2.3. Shadow Effect and Panel Optimization

Shadows impact the efficiency of solar panels by blocking direct sunlight, reducing the overall irradiance on the panel surface. The extent of this effect depends on the solar incidence angle.

The total panel area $\left(A_{\mathrm{total}}\right)$ is constant and calculated by multiplying the panel’s length and width, as in Equation (5):

$$A_{\mathit{total}}=L_P\times W_P$$

(5)

The shadow area depends on the shadow length $\left(L_{\mathrm{shadow}}\right)$ and the panel width ($W_P$), which is assumed to be n meters, as presented by Equations (6)–(8).

$$L_{\mathrm{shadow}}=H_{\mathrm{panel}}\times \mathit{tan}\left({\theta }_{\mathrm{shadow}}\right),$$

(6)

$$A_{\mathrm{shadow}}=L_{\mathrm{shadow}}\times W_P,$$

(7)

$$H_{\mathrm{panel}}=L_{\mathrm{panel}}\times \mathit{sin}\left({\theta }_{\mathrm{tilt}}\right)$$

(8)

where $H_{\mathrm{panel}}$ is the effective shadow height, and ${\theta }_{\mathrm{shadow}}$ is the solar incidence angle. The reduced power due to shadowing is calculated using Equation (9),

$$P_{\mathrm{reduced}}=P_{\max }\times \left(1-{\displaystyle \frac{A_{\mathrm{shadow}}}{A_{\mathrm{total}}}}\right),$$

(9)

where $P_{\max }$ is the maximum potential power generation.

2.3. Optimization Process and Constraints

The optimal tilt angle for maximum energy production is given by Equations (10) and (11),

$$\mathit{Optimal}\mathit{Tilt}={90}^{\circ }-\mathit{Mean}\mathit{Solar}\mathit{Elevation},$$

(10)

or by maximizing corrected energy production:

$${\mathsf{\theta }}_{\mathit{optimal}}=\mathit{arg}\underset{\theta }{\mathit{max}}\left(E_{\mathit{corrected}}\left(\theta \right)\right)$$

(11)

Row spacing (${\mathrm{D}}_{\mathrm{row}}$) is determined to minimize shadow effects while maintaining sufficient land utilization in Equations (12) and (13):

$$D_{row}:=\left(H_{effective}\right)\times \mathit{tan}\left({90}^{\circ }-\mathit{Tilt}\mathit{Angle}\right)\times \mathit{Row}\mathit{Spacing}\mathit{Factor},$$

(12)

$$H_{effective}=L_{panel}\times \mathit{Sin}\left(\mathit{Tilt}\mathit{Angle}\right)$$

(13)

The shadow’s effect on direct solar radiation must be considered separately from diffuse radiation, as the latter is less affected by shading. The maximum number of panel rows ($N_{rows}$) on the site is calculated using Equation (14).

$$N_{rows}=\max \left(1,⌊{\displaystyle \frac{L_{site}}{L_{panel}+D_{row}}}⌋\right)$$

(14)

The EnergyPlus Weather File (EPW) format was specifically designed for building energy simulations, providing detailed hourly climate data for a given location. It includes key meteorological factors such as temperature, humidity, wind speed, solar radiation, and other environmental conditions that influence energy modeling and efficiency calculations.

The optimal tilt angle and row spacing ensure maximum energy yield while minimizing land occupation. Solar incidence angles have been extracted from preprocessed data, such as the EPW file, for precise integration into the calculations. The goal is still to maximize winter energy production E, presented in Equation (15):

$$\mathrm{Maximize}E\left(\mathsf{\theta },n_r,n_m,d\right)$$

(15)

where

• The objective function is to maximize total energy output E(Array), considering the tilt angle, number of modules, and spacing.
• The decision variables are as follows:

  ○

  Tilt angle (θ): Monthly values to optimize energy capture.

  ○

  Number of rows ($n_r$): Based on site length, row spacing, and module height.

  ○

  Number of modules per row ($n_m$): Limited by site width.

  ○

  Spacing between sheds (d): To minimize shading losses.

• Constraints:

  $$n_r\cdot \left(L_{Pv}\cdot \mathit{cos}\left(\theta \right)+d\right)\le L_{parking}$$

  (16)

This constrain ensures that rows fit within a site length. $\mathrm{d}\ge \mathrm{e}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e}\mathrm{s} \mathrm{m}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{m} \mathrm{s}\mathrm{p}\mathrm{a}\mathrm{c}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{t}\mathrm{o} \mathrm{p}\mathrm{r}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{s}\mathrm{h}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}$.

The optimization framework is subject to several constraints to ensure practical feasibility and performance reliability. The tilt angle optimization is bounded by structural limitations and installation feasibility, preventing excessive angles that may lead to mechanical instability or inefficient land use in parking lots besides visual pollution regarding rooftops. Additionally, row spacing calculations account for both shading minimization and site length constraints, ensuring sufficient energy capture while avoiding unnecessary land occupation.

Furthermore, the maximum number of panel rows ($N_{rows}$) is also restricted by the available site dimensions, ensuring compliance with spatial limitations without compromising accessibility or maintenance considerations. The spacing constraint (d ≥ minimum spacing) guarantees minimal shading losses, maintaining optimal solar exposure throughout the year. These additions ensure that the simulation process reflects realistic operational constraints, improving the applicability and robustness of the optimization approach.

2.4. Microgrid Flexibility

Microgrid flexibility is a critical attribute that enables these systems to adapt dynamically to fluctuations in energy demand and supply, ensuring stable and efficient operation. This adaptability is particularly vital for integrating renewable energy sources, such as solar PV, which is inherently variable and intermittent. By employing advanced integration strategies and technologies, microgrid flexibility enhances energy reliability, optimizes resource utilization, and supports decarbonization efforts. By adopting DSM strategies, energy storage systems (ESSs), and flexible load management, microgrids can effectively address the challenges of modern energy systems and contribute to a low-carbon future.

2.4.1. Demand-Side Management

DSM plays a pivotal role in achieving microgrid flexibility. DSM strategies, such as load shifting and peak shaving, align energy consumption patterns with periods of high renewable energy generation. For instance, load shifting reschedules energy-intensive activities to times when solar or wind energy is abundant, thereby reducing reliance on fossil fuels and lowering operational costs [27]. If electricity is purchased from the grid, it is also important to minimize grid imports in peak periods. Real-time energy management systems further enhance flexibility by using predictive analytics and automation to balance supply and demand instantaneously [28].

Reviewing two case studies demonstrates that integrating appliance scheduling further reduces energy costs by 4.4%, decreases peak demand by up to 37.5%, and enhances economic viability with a payback period of 9–10 years [29]. The findings highlight the potential of demand response programs to improve microgrid efficiency and support sustainable energy planning in public buildings [29]. Another study examines demand response as a solution for balancing electricity demand, classifying strategies by control mechanisms and consumer incentives [30]. It also reviews optimization models for DR implementation, highlighting key system constraints and computational challenges [30].

Flexible loads, particularly heating, ventilation, and air conditioning systems, significantly contribute to microgrid adaptability. By adjusting HVAC operations based on real-time energy availability, microgrids can maintain occupant thermal comfort while minimizing energy consumption. Electric vehicle charging, a fast-growing load can also be used as a flexible load. Vehicle-to-grid availability will further enhance the potential of EVs to balance supply and demand. These approaches not only improve energy efficiency but also align with sustainability goals.

2.4.2. Energy Storage Systems and Degradation

ESS is another cornerstone of microgrid flexibility. Batteries store surplus energy generated during peak production periods and release it during times of high demand or low renewable generation. During winter, electricity can be purchased at off-peak rates to be used during peak periods. This capability not only stabilizes the energy supply but also mitigates the challenges posed by renewable energy intermittency. However, the integration of ESS requires careful management to address issues such as battery aging (new technology developments allow over for 8000 cycles and efficiency losses) [31].

To calculate the simplified battery storage capacity (BSC) required for a battery in a solar system [32], use the Equation (17) as follows:

$$BSC={\displaystyle \frac{E_{day}·N_{a.d}}{DoD·\mathsf{\eta }}}$$

(17)

where

• BSC is the battery storage capacity (Wh).
• ${\mathrm{E}}_{\mathrm{d}\mathrm{a}\mathrm{y}}$ represents the Daily Energy Usage (Wh).
• ${\mathrm{N}}_{\mathrm{a}.\mathrm{d}}$ is the Number of Autonomy Days.
• DoD is the Depth of Discharge.
• $\mathsf{\eta }$ is the system efficiency.

The battery depreciation is estimated using a combination of cycling aging and static aging [33], incorporating multiple variables that influence battery wear over time, using Equations (18)–(20) as follows:

$$SOW=\mathit{max}\left({SOW}_{Static},{SOW}_{Cycles}\right)$$

(18)

$$SOW={\displaystyle \frac{\mathrm{N}\times \mathrm{D}\mathrm{O}\mathrm{D}}{\mathrm{L}\mathrm{i}\mathrm{f}\mathrm{e} \mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e} \mathrm{a}\mathrm{t} \mathrm{G}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{n} \mathrm{D}\mathrm{O}\mathrm{D}}}$$

(19)

$${SOW}_{Static}=f\left(T,t\right)$$

(20)

where

${SOW}_{Static}$ is aging due to time and influenced by the following:

○

Temperature (T), where higher temperatures accelerate degradation.

○

Calendar aging (t), meaning the natural aging of the battery over time.

○

Self-discharge losses, imposing gradual energy loss when idle.

${SOW}_{Cycles}$ is aging due to charge/discharge cycles and depends on the following:

○

DOD, where a higher DOD increases wear.

○

Number of cycles (N), with more cycles leading to faster degradation.

○

Charge/discharge rate (C-rate), with faster charging/discharging affecting longevity.

○

Battery chemistry, where different chemistries degrade at different rates.

The battery’s overall State of Wear (SOW) is determined by the higher value between (${SOW}_{Static}$) and (${SOW}_{Cycles}$), which PVsyst software utilizes to predict when the battery may need replacement.

Table 2. Comparison of battery technologies: cost, performance, and life cycle.

Battery Type	Cost	Availability	Performance	Life Cycles	Energy Density	Mass Density
Lead–Acid [34]	Low	Widely available	Good at low discharge rates	Short (500–1500)	Low	Heavy and bulky
Lithium-Ion (Li-ion) [34]	Medium	Widely available	Good at fast charging, sensitive to high temperatures	Long (2000–5000)	High	Remarkable
Lithium Iron Phosphate (LiFePO4) [35]	Low	Widely available	Good for stationary storage, safe and stable	Long (5000–10,000)	Medium	Remarkable
Sodium-Ion [36]	Very Low	Very Limited	Environmentally friendly, more thermally stable	Long (2000–7000)	Low	Moderate
Flow Batteries [37]	High	Limited	Ideal for grid-scale storage	Varying over 10,000	Low	Heavy

presents a comparative analysis of different battery technologies based on key performance indicators such as cost, availability, life cycle, energy density, and mass density. Lead–acid batteries are widely available and cost-effective but have a shorter lifespan and lower energy density compared to lithium-based alternatives. Lithium-ion (Li-ion) batteries offer high energy densities and long life cycles, making them well-suited for fast charging applications; however, they are sensitive to high temperatures. Lithium iron phosphate (LiFePO₄) batteries are known for their stability and safety, making them an excellent choice for stationary energy storage, with an extended life cycle ranging between 4000 and 10,000 cycles. Sodium-ion batteries, while environmentally friendly and thermally stable, have moderate life cycles and energy densities, though their availability remains limited. Flow batteries, primarily used for grid-scale storage, boast an extremely long life cycle, but their lower energy density and heavier mass can limit their adaptability in certain applications. This comparative evaluation provides valuable insight into the suitability of different battery chemistries for various energy storage applications.

2.5. Integration of Renewable Energy Sources

Energy storage systems are employed to store surplus energy during peak solar generation and release it during periods of low production, ensuring reliability and continuity [38]. Advanced energy management systems further enhance the efficiency of solar energy integration. These systems utilize predictive algorithms to forecast energy generation and consumption patterns, enabling optimal scheduling and synchronization of solar PV systems with ESS and flexible loads [39,40]. Such strategies not only improve energy utilization but also contribute to cost savings by reducing reliance on grid electricity.

In the context of PEBs, solar PV systems enable energy self-sufficiency and the generation of surplus energy, which can be stored, and fed back into the grid or shared with neighboring buildings. This approach aligns with global decarbonization goals and supports the transition to a sustainable energy future [41,42].

From 2019 to 2030, global average battery prices decline significantly, starting at USD 170 per kWh and reaching USD 70 per kWh. After a slight increase in 2022, the trend continues downward, with the most notable projected drop between 2023 and 2026 (Figure 1). This indicates ongoing advancements in battery technology, namely the availability of sodium batteries, leading to increasing market competition.

2.6. Sustainable Development and TOPSIS

Sustainable development (SD) consists of three main dimensions: the economic, social, and environmental dimensions. The practical implementation of independently evaluating the different parameters and criteria of these dimensions highlights remarkable challenges [44]. Therefore, an integrated model of sustainable development is presented over its conventional form. Environmental criteria such as CO₂ emission are always related to grid dependency or energy consumption from carbon-based grids, the fully discharging period, and the economy (Figure 2).

Regarding MCDM, the TOPSIS technique has been used to make decisions about different alternatives among PV and battery types. In contrast to non-compensatory methods that use strict cut-offs to include or exclude alternatives, TOPSIS is an aggregative compensatory method. It compares different alternatives and allows for trade-offs between criteria, making it a more realistic approach [44]. A strong performance in one criterion can compensate for weaker performance in others, which is a notable advantage over non-compensatory methods [45,46]. The TOPSIS method follows seven steps:

• Define the estimation matrix;
• Normalize the decision matrix;

According to the Equation (21), the raw data $\left(X_{ij}\right)$ represents the performance score of each alternative (i) in a criterion (j). Since criteria may have different scales, normalization ensures a fair comparison by converting them into dimensionless values, as follows:

$$R_{ij}={\displaystyle \frac{X_{ij}}{\sqrt{{\sum }_{j=1}^n{X_{ij}}^2}}}$$

(21)

The normalization scales each value relative to the total sum of squared values within a criterion, ensuring uniform weighting.

III.

Estimate the normalized weighted decision matrix;

Weighted Normalization (Equation (22)): Once the values are normalized, they are weighted based on their importance $\left(W_j\right)$, assigned to each criterion. The weighted normalized value is calculated as follows:

$$V_{ij}=R_{ij}\times W_j$$

(22)

This adjusts the scores according to the predefined significance of each criterion in decision-making.

IV.

Identify the best and worst alternatives;

V.

Calculate the distance measures;

According to the distance measures (Equation (23)), each alternative is evaluated based on its proximity to the ideal solution. The Euclidean distance determines how close or far an alternative is from the best (+) and worst (−) criteria:

$$S_i^{\pm }={\left[{\sum }_{j=1}^m{\left(V_{ij}\pm V_j^{\pm }\right)}^2\right]}^{0.5}$$

(23)

This equation measures the deviation from the ideal and anti-ideal solutions, providing a basis for ranking.

IV.

Determine the closeness of each alternative to the ideal solution;

V.

Calculate a performance score and rank the alternatives.

According to the performance score (Equation (24)), the relative closeness index $\left(P_i\right)$ determines the final ranking, calculated as follows:

$$P_i={\displaystyle \frac{S_i^{-}}{S_i^{+}+S_i^{-}}}$$

(24)

where $R_{ij}$ implies the score of each parameter, which is not scaled, while $X_{ij}$ shows the parameters’ utility. $W_j$ is the main weight that is given to the indicator $V_j$. $S_i^{+}$ relates to the best criteria, while $S_i^{-}$ is associated with the worst criteria.

Non-compensatory methods refer to decision-making techniques that strictly filter alternatives based on predefined thresholds without allowing for trade-offs between criteria. Examples include the Elimination by Aspects (EBA) method, which systematically removes alternatives failing to meet a critical criterion, and the Lexicographic method, which ranks options solely by the most important attribute. These methods contrast with TOPSIS, an aggregative compensatory approach where trade-offs among multiple criteria, such as environmental, social, and economic factors enable more flexible decision-making.

It should be mentioned that the PV alternative and battery types refer to different technological variants rather than system deployment architectures. Specifically, PV panel alternatives represent variations in efficiency, price, and lifespan, while battery alternatives consider criteria such as grid independence, storage duration, and financial viability.

3. Methods and Materials

This section presents the methodological approach and technical tools utilized in the design and optimization of the solar energy system for the DEEC building at the University of Coimbra. This study aims to achieve energy independence while maintaining architectural integration and financial feasibility. To achieve this goal, a combination of computational modeling, MCDM techniques, and optimization algorithms were employed [47,48,49].

This research begins with an in-depth analysis of the DEEC building’s energy consumption, spatial constraints, and existing infrastructure. A comprehensive data collection process was conducted, assessing factors such as energy demand fluctuations, PV) generation potential, the state of the existing microgrid, and viable energy storage solutions. To optimize solar radiation utilization and panel orientation, advanced simulations were performed using Rhino-Grasshopper, enabling precise modeling of environmental conditions.

Optimization of the key parameters, such as panel tilt angles and arrangement, was carried out using Python-based algorithms to ensure maximum energy generation and seasonal adaptability. Additionally, the PV system’s performance was simulated and refined using PVsyst software, allowing for an accurate analysis of power output, efficiency, and integration with battery storage solutions. The financial feasibility of the system was assessed through cost modeling and an investment analysis, ensuring an economically viable approach to sustainable energy implementation.

The research framework illustrated in the following figure provides a structured overview of the interdisciplinary considerations guiding the systematic development of this energy solution (Figure 3).

3.1. DEEC Building Characteristics

The DEEC building, constructed in 1996, consists of 9 floors, with a total area of approximately 10,000 square meters and an annual electricity consumption of around 460 MWh. This building houses classrooms, offices, laboratories, administrative services, study rooms, a bar, a mechanical workshop, and a garage. Additionally, three research institutes affiliated with the University are located within this building.

3.2. Data Collection and Analysis

The department’s electricity consumption data was collected using smart meters. The data was then analyzed using Python software to determine the maximum and minimum consumption at different times of the day and year. This analysis was critical for understanding the energy demand patterns and implementing load-shifting algorithms to balance the supply and demand curve.

3.2.1. Electricity Demand

In the electrical engineering department, there is a strong emphasis on raising awareness and engaging students with sustainability issues. Therefore, the building has been used as a testing ground for new energy technologies. The lighting has been gradually replaced with high-efficiency LEDs, and the Building Management System (BMS) has been upgraded to improve the control of lighting and HVAC systems. Another significant upgrade involved replacing office equipment (such as computers, monitors, servers, and printers) with more energy-efficient options.

In 2018, a detailed survey assessment was conducted to characterize the building’s demand profile and gather all relevant energy data [50]. Data collection for lighting was based on building plans and class schedules for each room [50]. A walkthrough audit was also conducted to collect remaining information on existing systems, and the smart meter infrastructure provided raw data to evaluate the total electricity consumption [50]. Following this assessment, several energy conservation measures were implemented.

3.2.2. Photovoltaic Generation

The goal was to achieve net PEB whilst it was to transform the building into an NZEB by installing photovoltaic panels with sufficient capacity to cover a substantial portion of the annual electricity consumption. The first step involved installing a PV system in 2017 to significantly reduce the net demand.

Various strategies were employed in the available spaces. In one area, the PV panels were oriented southward, with a slope of 13° (less than the optimal slope of 34° to minimize visual impact), using the structure. In other areas, a structure oriented east–west was used to achieve a high density of PV panels and maximize energy generation during the early morning and late afternoon.

The existing system has a DC peak power capacity of 79 kWp and ensures an AC active power injection of 70 kW, resulting in an active power ratio of 88.8%. This system is capable of generating 115.6 MWh annually, with a specific energy yield of 1466 kWh/kWp and a performance ratio (the ratio between actual and theoretical energy outputs of the PV plant) of 88.2%. The annual generation of 115.6 MWh accounts for 22.3% of the building’s electricity demand.

According to

Table 3. Existing and proposed data.

Data	Existing	Proposed
Annual energy demand	518 MWh	460 MWh
Annual energy yield	116 MWh	>1 GWh
Grid feed-in	4.26 MWh	<460
Purchased electricity	407 MWh	0
Self-consumption	111 MWh	~460 MWh
Self-consumption (in % of PV)	96.3%	>50%
Self-sufficiency (in % of demand)	21.4%	≥99%

, the energy injected into the grid is 6.5 MWh, while self-consumption is 109 MWh, representing 94.4% of annual generation and 21% of annual demand. During winter months, nearly all generated energy is used for self-consumption, whereas in summer, a higher share must be injected into the grid due to increased solar radiation and decreased electricity demand.

Energy injection into the grid predominantly occurs during weekends when electricity demand is low. There is generally good alignment between generation and demand, but surplus generation during periods of lower occupancy leads to some energy being injected into the grid. Economically, this is not advantageous as the cost of consumed energy is much higher than the price received for injected energy. To achieve the goal of transforming the building into a ZEB, increasing the PV system capacity would lead to a larger generation surplus. Therefore, flexibility options have been adopted to better match generation with demand and to support an increase in the installed solar PV capacity. These flexibility options are integrated into a microgrid, utilizing energy storage, demand response with HVAC systems, and the control of electric vehicles through a vehicle-to-building and building-to-vehicle system. The system is designed to be fully independent of the grid in winter and to inject surplus energy into the grid. Table 3 illustrates the existing system performance and the estimation of the proposed performance based on data recorded by smart thermostats and sustainable development goals, transitioning from an NZEB toward a PEB.

3.2.3. Existing Microgrid

Economically, this situation is unfavorable, as the cost of consumed energy significantly exceeds the compensation received for energy supplied to the grid (ratio of 0.15 to 0.05). Consequently, the microgrid was designed to enhance the balance between energy production and consumption through flexibility measures, while also enabling potential expansion of the solar PV system’s capacity in the future. To improve flexibility, lithium battery storage systems, and vehicle-to-grid chargers were installed, but additional measures, such as incorporating flexible loads linked to the HVAC system, are still required.

3.2.4. Energy Storage

To assess the energy storage potential, various battery technologies were analyzed, focusing on their efficiency, cost, and environmental impact. The system architecture was evaluated under different scenarios, such as grid-connected with and without battery storage, to determine the optimal battery storage capacity. The most critical criteria for multi-criteria decision-making (MCDM) on the optimal capacity were maximizing grid independence and ensuring economic feasibility. Battery storage capacity was modeled using PVsyst software, and the optimal amount was selected using the TOPSIS method. This analysis included a comparison of different battery chemistries, such as lithium-ion and solid-state batteries, with attention to their respective advancements and market adoption rates.

Data on global average battery prices from 2019 to 2030 was utilized to project future trends and evaluate economic feasibility (Figure 1). This analysis included a comparison of different battery chemistries, such as lithium-ion and solid-state batteries, with attention to their respective advancements and market adoption rates. The integration of energy storage systems within the DEEC building was modeled to optimize energy utilization and sustainability.

3.3. Solar Radiation Modeling

Solar radiation modeling on the DEEC building’s geometry and environment was conducted using Rhino software with the Ladybug plugin. This modeling was essential for determining the solar potential of various surfaces and optimizing the placement of solar panels. The analysis revealed that due to architectural constraints, panels with a higher tilt angle for winter consumption could not be optimally placed on the building itself. Therefore, the parking lot behind the faculty building was considered as it had fewer limitations for panel placement.

The analysis reveals that horizontal surfaces receive higher solar radiation during the summer months, with July recording the peak value of 210 kWh/m². In contrast, vertical surfaces experience higher solar radiation in winter and spring, with January and March showing values of 100 kWh/m² and 130 kWh/m², respectively (Figure 4). These findings are crucial for optimizing solar energy systems and enhancing energy efficiency in buildings.

3.4. Effective Financing and Costs

The financial structure of the project incorporated multiple components, ensuring both economic feasibility and long-term sustainability. The financing aspect included a loan interest rate set at 2.5% per year, enabling accessible funding. Additionally, energy pricing mechanisms played a crucial role, with a feed-in tariff of EUR 0.05/kWh for energy supplied to the grid and a consumption tariff of EUR 0.13/kWh for electricity used. To accommodate market variations, a tariff evolution rate of +1% per year was applied, ensuring gradual adjustment over time.

Regarding costs, the implementation of PV panels was set at EUR 0.1/W, offering a cost-effective solution for solar energy generation. Meanwhile, battery costs ranged from EUR 110 to 120/kWh, reflecting the investment needed for energy storage and system efficiency. These elements collectively defined the project’s financial framework, balancing affordability and performance (

Table 4. Effective financing and costs of the project.

Financing	Loan Interest Rate		2.5%/year
	Feed-in-Tariff		EUR 0.05/kWh
	Consumption Tariff	Super Low	EUR 0.1052/kWh
		Low	EUR 0.1058/kWh
		Regular	EUR 0.1203/kWh
		Peak	EUR 0.213/kWh
	VAT		6%
Costs	PV Panel		EUR 0.12/W
	Battery		EUR 200/kWh
	Inverter		EUR 12/kW
	Supports for Modules		EUR 0.012/kWh
	Operating (OPEX)		EUR 0.0175/kWh/year

).

Figure 5 presents hourly electricity prices (EUR/kWh). During midnight to early morning hours (00:00–06:00), prices remained relatively stable at approximately EUR 0.1052–EUR 0.1058. A modest increase to EUR 0.1203 occurred at 07:00 AM, persisting until 08:00 AM. Winter prices experienced a sharp rise at 09:00 AM, reaching EUR 0.213, while summer prices remained unchanged. This peak continued in winter through 10:00 AM and 11:00 AM, contrasting with the summer values that increased only at 10:00 AM. A secondary evening price surge appeared in winter from 6:00 to 8:00 PM, again reaching EUR 0.213, whereas summer prices exhibited more stability, reaching EUR 0.213 at 10:00 AM, 8:00 PM, and 9:00 PM. Overall, winter prices exhibited higher volatility, with pronounced peaks during morning and evening hours, while summer prices remained more uniform, with fewer fluctuations.

3.5. MCDM for Criteria Weights for TOPSIS Based on SDPs

The weightage of important decisive criteria is regarded independently across different categories. The categories are related to the environmental, social, and economic dimensions of SD while the TOPSIS methodology is used as an MCDM tool to assess the preferences of the sustainable alternatives. This paper utilized SDPs to evaluate the weights of the TOPSIS criteria values, which are based on expert opinions gathered through library surveys.

Table 5. Criteria weights for TOPSIS evaluation based on SDPs.

SDPs	TOPSIS Criteria/Optimization Objectives	Weight of Optimized Alternatives
		Alt. 1	Alt. 2	Alt. 3	Alt. 4	Alt. 5
Environmental	Decarbonization	33.3%	33.3%	33.3%	33.3%	33.3%
Social	Independence from Grid	33.3%	33.3%	33.3%	33.3%	33.3%
Economic	Costs	33.3%	33.3%	33.3%	33.3%	33.3%
Total	-	100%	100%	100%	100%	100%

and

Table 6. TOPSIS criteria weights for storage evaluation based on SDPs.

SDPs	TOPSIS Criteria/Optimization Objectives	Weightage of Optimized Alternatives
		Alt. 1	Alt. 2	Alt. 3	Alt. 4	Alt. 5
Socio-economic 33%	NPV (M EUR)	10%	10%	10%	10%	10%
	ROI ($\times$100%)	5%	5%	5%	5%	5%
	Total installation cost (k EUR)	5%	5%	5%	5%	5%
	Payback (year)	8%	8%	8%	8%	8%
	LCOE (EUR/kWh)	5%	5%	5%	5%	5%
Socio-technical 33%	Grid independency	9%	9%	9%	9%	9%
	Inject to grid (B2V) %	5%	5%	5%	5%	5%
	Full discharge duration [h]	19%	19%	19%	19%	19%
Socio-environmental 33%	From grid (V2B) %	33%	33%	33%	33%	33%
Total		100%	100%	100%	100%	100%

illustrate the categorization of criteria weights concerning sustainable development, encompassing aspects such as cost, independence, and decarbonization, as shown in Figure 2.

The TOPSIS rating addresses which alternative of the optimized models would be applied during the design process regarding SDP criteria. Also, the decision about the optimal battery storage capacity of the system requires different criteria such as electricity demand from the grid (dependency %), electricity injection into the grid (full discharge duration), emissions, and NPV (Table 6).

Based on the defined consumption pattern, the number of solar panels required to meet the energy demand was calculated. The decision-making process for determining the type and number of panels and batteries involved the use of TOPSIS, a multi-criteria decision-making technique. This process ensured that the selected solar panels and batteries would be both efficient and economically viable.

There are 10 famous PV panel and battery brands and models known for their durability, safety, and affordability, which are also listed in PVsyst 7.4.8 (

Table 7. PV panel alternatives (6% VAT included).

Alternatives	Criteria					Ref.
	Price		EE (%)	Panel Area (m2)	Life Span (Years)
	EUR/pcs	EUR/Wp
Alt. 1	135	0.4	19.82	1.77	25	[51]
Alt. 2	211	0.66	18.71	1.71	25	[52]
Alt. 3	152	0.216	22.54	3.11	25	[53]
Alt. 4	84	0.12	22.54	3.11	25	[54]
Alt. 5	160	0.45	18.2	1.98	25	[55]
Alt. 6	145	0.207	21.54	3.11	25	[56]
Alt. 7	202	0.46	17.45	2.52	25	[57]
Alt. 8	105	0.15	22.5	3.1	25	[58]
Alt. 9	77	0.12	23.1	2.58	25	[59]
Alt. 10	60	0.12	22.5	3.1	30	[60]

).

4. Results of the Analysis

4.1. Results of PV Tilt and Row Spacing Optimization

The optimization of photovoltaic panel tilt and row spacing plays a crucial role in maximizing energy capture throughout the year. The analysis reveals monthly variations in optimal tilt angles, which are fixed at 13.5° and 59° to supply the most demanding months in December and avoid visual constraints (Figure 6).

Additionally, the impact of row spacing (L) on energy yield is examined across different configurations, ranging from L = 1 to L = 6 m. As row spacing increases, energy generation potential generally follows an upward trend, with January and December exhibiting the highest values due to lower sun angles. Conversely, mid-year months such as May and June demonstrate minimal energy gains at wider spacings, highlighting the reduced effectiveness of steep tilt angles during peak solar exposure periods (Figure 7).

The average optimal tilt angle for the year stands at 34°, providing a balanced configuration that accommodates seasonal fluctuations. Furthermore, the row spacing energy yields indicate that larger spacings contribute to higher energy capture, particularly in months with lower sun angles. These findings emphasize the significance of optimization in PV system design, ensuring efficient energy utilization with fixed angles across diverse climatic conditions.

In addition to the optimized row spacings mentioned above, initially designed for two-panel rows, adjustments were made to further increase energy production. The row spacing was reduced, and the number of rows was increased to compensate for shadow-induced losses. This strategic modification ensures higher overall energy generation despite partial shading (Figure 8).

Figure 9 reflects the monthly direct solar irradiance values for Coimbra across various tilt angles, highlighting two specific angles: tilt = 60° and tilt = 15°. The data shown in Figure 9 does not account for the diffused radiation or shading effect. However direct irradiance is crucial for a single array and a general conception of supply balance. A detailed analysis is classified as follows:

• Tilt = 60°:

  ○

  The highest irradiance values occur during the winter months (January and February), with 184 W/m² and 197 W/m², respectively.

  ○

  The irradiance values decrease significantly during the summer months (June: 122 W/m²; July: 140 W/m²), making it clear that tilt = 60° favors winter energy collection over summer efficiency.

  ○

  This trend confirms its suitability for applications like green parking PVs, where winter irradiance optimization is critical.

• Tilt = 15°:

  ○

  Irradiance values are balanced across the year, with consistent performance from winter (January: 121 W/m²) to summer (June: 196 W/m²; July: 214 W/m²).

  ○

  The data supports its use in rooftop PV systems, as tilt = 15° minimizes seasonal variations, creating a balanced output between summer and winter months.

A comparison of tilt = 60° vs. tilt = 15° highlights the winter optimization and year-round balance as follows:

• Winter Optimization: Tilt = 60° is better suited for winter, ensuring higher energy capture during lower solar angles.
• Year-Round Balance: Tilt = 15° provides a steadier performance across seasons, balancing energy output effectively between winter and summer.

The combination of tilt = 59° for green parking PVs and tilt = 13.5° for rooftop PVs strikes a strategic balance:

• Winter Curve Alignment: Tilt = 59° optimizes winter irradiance, supporting high-energy needs during colder months.
• Summer Curve Balance: Tilt = 13.5° ensures a balanced energy output, preventing oversaturation in summer while maintaining adequate winter performance.

Regarding seasonal efficiency, tilt = 60° heavily favors winter and tilt = 15° balances output, suggesting they complement each other depending on application priorities.

In terms of practical validation, the data decisively supports the proposed tilt angles, optimizing for specific use cases without diffuse energy and shading interference.

By implementing an optimized tilt angle of 59° for winter months and maintaining an E_array at pitch ≤ 2, the refined model in PV-Syst software achieved an impressive annual energy yield exceeding 1.2 GWh, without accounting for any system losses. This highlights the substantial improvement attained through spacing and tilt adjustments, enhancing overall system performance and energy output.

This optimization specifically focused on the green parking area located in the northern section of the department, utilizing panel lengths of 4.2 m for optimal layout and efficiency. Meanwhile, for roof-mounted panels and installations in the southern section of the department, which are more visually exposed, the design adhered to the existing system’s 13-degree tilt angle to maintain aesthetic consistency and structural integration (Figure 10).

Additionally, due to limited parking space and the need to reduce the horizontal spacing between panels, the rear rows of panels were elevated by approximately 0.5 m above the front rows. This adjustment mitigates shading effects while optimizing energy production, ensuring that the system operates at peak efficiency despite spatial constraints (Figure 10).

A monthly analysis was conducted on the solar energy potential around the DEEC geometry and environment caused by assuming vertical or higher tilted surfaces to maintain the flexibility of the microgrid. However, the architectural constraints prevent radical modifications on building envelopes. Therefore, the parking plot behind the building is proposed to be a green parking space supplying winter demands, with an optimal tilt of 59° facing south directly (Figure 11).

Table 8. Optimized PV system specifications: tilt, orientation, and module distribution.

No. Fixed Orientation	Tilt (◦)	Azimuth (◦)	Row Distance (m)	Row ∆h (m)	Sub-Array No.		Module Area (m2)		PV Module No.		Location in the DEEC Site
					PV System	3D Scene	PV System	3D Scene	PV System	3D Scene
rientation #1	59	0	4.2	0.2–0.5	1	21 + 2	2004	2004	742	743	Parking
Orientation #2	59	270	-	0	1	3	72.9	72.9	27	27	Awning
Orientation #3	13.5	30	1.25	0	1	13	1621	1648	600	610	Rooftop
Orientation #4	13.5	0	1.25	0	1	4	492	494	182	183	Rooftop

presents detailed characteristics of various monofacial PV panel installations at the DEEC site, highlighting key specifications and their respective locations. The installations vary in tilt, azimuth, row spacing, and module surface area, influencing their overall energy generation efficiency. Orientation #1, positioned over a parking area, features a 59° tilt with a south-facing orientation (0° azimuth) and a substantial module surface area of 2004 m², accommodating 742 PV modules. This winter-optimized fix setup is designed for maximum solar exposure, benefiting from optimized row spacing and height differences between 0.2 and 0.5 m. Orientation #2, mounted as an awning, shares the 59° tilt but faces west (270° azimuth). With only three sub-arrays and a modest module area of 72.9 m², it houses 27 PV modules, making it more compact and ideal for shaded energy production. Meanwhile, orientations #3 and #4 are rooftop systems with a shallow tilt of 13.5°, differing in azimuth angles (30° vs. 0°). The rooftop configurations with orientation #3 span 1621 m² and support 600 modules, whereas orientation #4 covers 492 m² with 182 modules. Both setups maintain a 1.25 m row spacing and lack row height variation, ensuring a consistent layout.

These optimized distinctions demonstrate how location, tilt angle, and module orientation contribute to energy capture efficiency, with parking installations maximizing exposure, fix-mounted panels leveraging partial shading, and rooftop arrays offering practical integration for building architecture.

4.2. Results of MCDM

To determine the ranking of the PV alternatives based on multiple criteria, the TOPSIS method was employed. The ranking considers factors such as price per watt, energy efficiency, panel area, and power capacity, with adjusted weightings of ±25% applied to evaluate the impact of variations. Among the analyzed alternatives, Alternative 1 achieved the highest ranking, with a 78.3% performance score, indicating the most favorable balance between efficiency and cost-effectiveness. Alternative 2 followed closely, with a 77.0% score, while Alternative 3 demonstrated a strong position, at 75.3%. As the ranking progressed, Alternative 10 displayed the lowest performance, at 24.2%, primarily due to lower efficiency values and higher costs. The results highlight the importance of optimizing panel efficiency and area usage while considering cost constraints. The top-ranked alternatives present promising options for maximizing energy output in solar installations (

Table 9. TOPSIS ranking for MCDM on PV alternatives.

Rank	−25%	+25%	−25%	+25%	TOPSIS
Alternative	Price (EUR/Wp)	EE (%)	Panel Area	Power (wp)	Pi
1	0.14	23.01	2.58	620	78.3%
2	0.12	21.67	2.8	605	77.0%
3	0.12	22.54	3.11	700	75.3%
4	0.15	22.54	3.106	700	73.7%
5	0.2	22.54	3.11	700	69.8%
6	0.2	21.54	3.11	700	69.6%
7	0.4	19.82	1.77	350	41.9%
8	0.45	18.2	1.98	360	33.6%
9	0.46	17.45	2.52	440	29.6%
10	0.6	18.71	1.71	320	24.2%

).

Table 10. TOPSIS ranking for MCDM on battery alternatives.

TOPSIS	Weight		No Storage	S = 0.87 MWh	S = 1.9 MWh	S = 2.5 MWh	S = 3.75 MWh
Socio-economic 33%	10%	NPV (M EUR)	1.07	1.13	0.9	0.765	0.469
	5%	ROI (×100%)	4.4	2.6	1.34	0.95	0.431
	5%	Total installation cost (k EUR)	246.3	441	667.5	807.9	1088.725
	8%	Payback (year)	4.5	6.3	9.4	11.1	14.6
	5%	LCOE (EUR/kWh)	0.0281	0.0381	0.0487	0.0554	0.0685
Socio-technical 33%	9%	Grid independency	76%	94%	98%	99%	99%
	5%	Inject to grid (B2V) %	84%	62%	60%	60%	60%
	19%	Full discharge duration [h]	0	17	36	48	72
Socio-environmental 33%	33%	From grid (V2B) %	0.2354	0.059	0.02	0.013	0.008
TOPSIS rank of sustainability	99%	Pi	50%	51%	58%	63%	69%

presents a comparative analysis of different battery storage capacities using the TOPSIS method for MCDM. The evaluation considers socio-economic, socio-technical, and socio-environmental factors, each contributing 33% to the ranking weight.

A robust energy storage system is the backbone of sustainable energy solutions, ensuring resilience, grid independence, and energy security. As battery storage capacity expands, the ability to operate without external power significantly increases. In this study, the full discharge duration improves from 0 h (no storage) to 72 h with S = 3.75 MWh, highlighting the critical role of storage in maintaining system reliability (Table 10).

The recent electricity shortages in Portugal and Spain, marked by severe grid disruptions and unexpected blackouts on 28 April 2025, have highlighted the critical role of energy storage in ensuring grid stability and resilience. Such crises emphasize the necessity of integrated storage solutions that enable prolonged self-sufficiency during power outages, reducing dependence on external grid infrastructure and enhancing energy security.

Meanwhile, the cost of photovoltaic (PV) generation is expected to decline significantly, with global installations exceeding 600 GW last year and projected to reach 1 TW by 2030. As PV deployment accelerates, energy storage will become increasingly vital, not only for maintaining system reliability and supporting critical loads but also for providing essential grid services such as arbitrage and peak demand management. The growing integration of storage technologies will play a pivotal role in stabilizing renewable energy generation and ensuring a resilient electricity supply in future energy systems.

According to the socio-economic analysis, initially, moderate battery storage (S = 0.87 MWh) enhances economic feasibility, yielding the highest Net Present Value (NPV) of M EUR 1.13. However, as storage capacity increases, installation costs rise, leading to a decline in NPV to M EUR 0.469 at S = 3.75 MWh.

A similar trend is observed in the payback period, which extends from 4.5 years (no storage) to 14.6 years at higher capacities, emphasizing the financial commitment required for larger battery investments. Additionally, the Levelized Cost of Energy (LCOE) steadily increases from EUR 0.0281/kWh to EUR 0.0685/kWh, reflecting the long-term economic trade-offs of expanding storage (Table 10 and Figure 10).

The socio-technical analysis of the storage capacity shows that grid independency improves significantly with increased storage capacity, moving from 76% without storage to 99% at S = 3.75 MWh. The percentage of energy injected into the grid (B2V) decreases as battery storage expands, with the highest injection rate of 84% for no storage and stabilizing at 60% for all battery configurations above S = 1.9 MWh. A full discharge duration increases substantially with larger batteries, extending from 0 h (no storage) to 72 h for S = 3.75 MWh, demonstrating improved self-sufficiency (Table 10 and Figure 11).

The socio-environmental analysis of storage capacity highlights that the dependence on grid-sourced electricity drops dramatically as storage grows, with 0.2354 for no storage, decreasing to 0.008 for S = 3.75 MWh. This reduction signifies enhanced energy autonomy and lower reliance on external sources.

The TOPSIS ranking for sustainability (Pi) reflects a progressive improvement in sustainability performance with increased battery storage. The score rises from 50% (no storage) to 69% for S = 3.75 MWh, demonstrating the overall benefit of integrating larger storage capacity in terms of sustainability (Table 10 and Figure 11).

The key observations of TOPSIS have been noted as follows:

• Economic trade-offs: While larger battery capacities increase grid independence and sustainability, they lead to higher installation costs, lower NPV, extended payback periods, and increased LCOE.
• Technical improvements: Greater storage enhances autonomy, ensuring longer full discharge durations and reducing dependency on grid-sourced electricity.
• Environmental benefits: Higher storage reduces grid consumption, supporting energy sustainability and improving system efficiency.
• Final sustainability ranking: Larger battery systems score higher on sustainability metrics, but their feasibility depends on economic constraints.

4.3. Results of Energy Storage on Power Dispatching

Figure 11 illustrates the effect of varying energy storage capacities on power distribution and system performance. The analysis compares scenarios from no storage to 3.75 MWh of storage, assessing how energy is sourced, stored, and utilized. These results highlight the critical role of energy storage in minimizing grid dependence while maximizing the efficiency of on-site power utilization.

The percentage of directly used solar energy remains constant at 15.8% across all storage configurations, with only a slight drop to 15.7% at S = 3.75 MWh. This suggests that storage integration does not significantly impact direct solar energy consumption.

As battery capacity increases, the percentage of energy retrieved from storage rises initially, but beyond 1.9 MWh, the rate of improvement slows, reflecting diminishing returns in energy contribution. While the share of total energy supply from stored battery energy rises slightly from 23.8% (S = 1.9 MWh) to 24.6% (S = 3.75 MWh), the full discharge duration doubles, extending from 36 h to 72 h, marking a substantial enhancement in energy autonomy. This highlights that while efficiency gains in stored energy utilization become marginal, the ability to sustain operations without grid reliance continues to improve significantly with larger battery capacities.

Grid reliance decreases as battery storage expands. Without storage, 23.5% of energy comes from the grid, but this drops progressively to only 0.8% at S = 3.75 MWh. Larger battery capacities minimize dependency on external grid power, enhancing self-sufficiency.

With no battery storage, 84.2% of generated energy is injected back into the grid. As storage capacity grows, this percentage steadily declines, reaching 59.7% at S = 3.75 MWh. This trend indicates that more generated energy is retained for self-consumption rather than being exported.

Battery storage capacity directly affects how long stored energy can be used without additional charging. With no storage, full discharging lasts only 0.1 h. For S = 0.87 MWh, stored energy can last 16.6 h, increasing to 71.9 h at S = 3.75 MWh. This extended duration highlights the improved resilience of systems with higher storage capacity.

The key notes of dispatching with different storage capacities are listed as follows:

• Minimal impact on direct solar consumption: Storage does not significantly influence immediate solar energy usage.
• Higher battery utilization: Larger storage capacities allow for more energy retrieval from batteries instead of direct consumption.
• Reduced grid dependence: Expanding storage minimizes the need for external power sources.
• Lower grid injection: More energy is retained locally rather than exported.
• Enhanced backup capability: Larger batteries provide prolonged energy availability during periods without generation.

4.4. Results of Financial Assessment

The financial feasibility of different systems was analyzed through key economic indicators, including payback period, NPV, return on investment, total financing, and reserve cost per hour. The results demonstrate the economic trade-offs associated with increased storage capacity (Figure 12).

The payback period reflects the time required to recover the initial investment. Without storage, the shortest payback period is 4.5 years, indicating a quicker return on investment. As battery capacity increases, the payback duration extends, reaching 14.6 years for S = 3.75 MWh, demonstrating a longer recovery time associated with larger storage investments.

NPV measures the total profitability of the system over its lifetime. The highest NPV is observed for S = 0.87 MWh, reaching M EUR 1.365, while the lowest NPV is M EUR 0.469 at S = 3.75 MWh. As storage capacity increases, NPV declines, suggesting that larger battery installations yield lower financial returns.

ROI quantifies the percentage return on capital investment. The best ROI is achieved with no storage, at 4.4, reflecting the most favorable economic performance. As battery storage grows, ROI declines, with the lowest value of 0.431 at S = 3.75 MWh. This decline indicates reduced financial efficiency with increasing storage capacity.

The key notes on the financial results with different storage capacities are listed as follows:

• Shorter payback for no storage: Investment rapidly recovers in just 4.5 years.
• Declining profitability with larger storage: Higher battery capacities result in lower NPV and extended payback periods.
• Reduced ROI with increased capacity: Larger batteries diminish return percentages, impacting financial viability.

4.5. Results of Probability-Based Production Analysis

The uncertainty simulation has been performed using a Gaussian distribution. The simulation incorporates the Typical Meteorological Year (TMY) dataset and multi-year variability trends, reflecting the uncertainties in solar irradiation and climate conditions. The annual variability of 1% accounts for fluctuations due to temperature changes, cloud cover inconsistencies, and seasonal anomalies. The probability distribution results provide key insights into expected power generation:

• P50 Value (Most Probable Yield): approximately 1137 MWh, representing the median expected production, meaning there is a 50% probability that the actual yield exceeds this value.
• P98 Value (Highly Conservative Estimate): approximately 1089 MWh, ensuring only a 2% probability of experiencing lower yields than this threshold.

These values are also confirmed by the analytical Monte Carlo simulation based on a Gaussian uncertainty model applied to all PV sub-arrays with tilt and orientation corrections. The E_Grid simulation value of 1099 MWh confirms that under standard operational conditions (P50), the system achieves the most probable expected net surplus energy production of approximately 58–60%, assuming nominal losses (Table 8). Ensuring the production of 1076 MWh with a probability of 98%, the system will remain a net Plus-Energy Building, as reflected in Figure 13.

4.6. Results of Monthly Supply–Demand and Injection Curve

This section presents the monthly distribution of energy production, consumption, and grid injection to analyze seasonal variations and system efficiency. The total annual energy production amounts to 1.2 GWh, while demand stands at 460 MWh, with 703 MWh injected into the grid and 24 MWh purchased from the grid (<1%), highlighting surplus energy availability (>99% (Figure 14)).

These results illustrate the seasonal dynamics of solar energy availability, consumption patterns, and surplus energy contributions to the grid. The findings emphasize the importance of energy storage and optimized system design to balance seasonal fluctuations effectively.

The production–demand trend illustrates that peak production recorded the highest electricity production in August (147.3 MWh), closely followed by July (147.1 MWh) while December recorded the lowest output, at 53.3 MWh, reflecting seasonal fluctuations. The demand pattern remained relatively stable throughout the year, fluctuating between 28.41 MWh (August) and 47.89 MWh (December).

The injection analysis shows that the most significant surplus occurred in August (105.91 MWh), highlighting excess energy generation. In December, injection was at its minimum (3.964 MWh), suggesting minimal surplus during this period. The injection curve follows a general increasing trend from January to August, after which it gradually declined.

According to the 2024 demand curve recorded by the department’s contour, the electricity purchase requirement was at its highest in January (10.1 MWh) and December (8.072 MWh), indicating a period where local production was insufficient to meet the demand. The lowest purchase requirement was recorded in August (0.028 MWh), where self-generated energy largely covered the demand.

Total production throughout the year reaches 1270.6 MWh, while total demand stands at 458.39 MWh. The total energy injection accounts for 703.34 MWh, which significantly contributes to external supply.

Purchases remain minimal (24.48 MWh over the year), suggesting a strong self-sufficiency in energy generation.

These results illustrate a well-balanced energy system where local production efficiently covers demand, with a significant surplus available for injection into the grid. However, seasonal fluctuations—especially low production in winter—impact purchase requirements.

4.6.1. Results of PR and Loss Analysis

Figure 15 presents the performance ratio (PR) over a 20-year period, along with its associated percentage losses. The data highlights the gradual efficiency decline in the system as time progresses.

At the beginning of the analysis period, the PR started at 68.1%, reflecting acceptable operational efficiency. However, over the years, this value gradually decreased, reaching 60.7% by Year 20. The average PR over the entire period stands at 64.4%, which provides insight into the overall long-term performance of the system.

The PR loss follows a progressively increasing pattern. In the first year, the efficiency decline was minor, at only 0.2%, but the rate of loss accelerated as the system aged. By Year 10, the PR loss reached 5.6%, and by the final year, it culminated in a 10.9% reduction from the initial performance. These results demonstrate a consistent and expected degradation of efficiency due to aging effects, environmental conditions, and system wear.

The decline in PR over time indicates the need for performance optimization strategies. Natural efficiency losses are common in long-term operations, but proactive maintenance, component replacements, and environmental management can help slow the decline. Long-term degradation reinforces the importance of monitoring performance trends closely and implementing measures to sustain efficiency.

While the system remains functional over two decades, the growing losses highlight the necessity of interventions to counteract performance reductions. Proper maintenance and technological advancements could mitigate the impact of efficiency losses, ensuring that the system remains as productive as possible throughout its operational lifespan.

4.6.2. Winter Demanding Week

This section presents a detailed hourly breakdown of energy production, demand, battery charging and discharging, and injection to the grid over multiple days. The dataset highlights energy flow dynamics, showcasing variations in solar power availability and consumption patterns throughout the day and night cycles (Figure 16).

Energy production follows a distinct pattern, increasing after 08:00. The highest production recorded was 487.902 kW at 12:00 on 18 January, while the lowest remained at 0 kW throughout all nighttime hours. Demand varies significantly throughout the day. The highest demand was observed at 16:00 on 22 January, reaching 127 kW. Purchased energy is required predominantly during low-production periods. The peak value reached 68.959 kW at 16:00 on 22 January, while several hours experienced 0 kW purchase, particularly during high-production periods between 09:00 and 15:00. Storage discharge is critical in maintaining energy supply when production is unavailable. The maximum discharge occurred at 08:00 on 16 January, where 65.75 kW was released. Surplus energy is injected into the grid when production exceeds the local demand. The highest injection occurred at 13:00 on 19 January, where 346.992 kW was added. There were several periods with 0 kW injection, particularly when production was insufficient to generate excess energy. The direct use of generated power peaked at 14:00 on 19 January, reaching 415.742 kW, while the minimum was 0 kW during night hours when production remained absent.

The system effectively balances energy production with demand through storage discharging and purchases. Production peaks midday, reducing dependency on external energy sources, while nighttime periods rely heavily on stored reserves. Injection occurs efficiently when surplus energy is available, ensuring optimal system performance.

4.6.3. Summer Demanding Week

This section presents the hourly variations in energy production, consumption, and grid injection over a series of days in June, highlighting seasonal dynamics in power generation and utilization (Figure 17).

During nighttime hours, production remained at 0 kW, with energy supply relying entirely on storage discharge and purchased energy. The first production increase appeared at 05:00, where 0.763 kW was recorded, gradually increasing through the morning hours. Peak production was observed at 12:00 on 20 June, reaching 647.448 kW, while the lowest daytime production occurred at 05:00 on 24 June, at 0.169 kW.

Summer energy demand remained relatively stable throughout the week, with variations reflecting daily consumption patterns. The highest demand was recorded at 16:00 on 20 June, reaching 70.25 kW, whereas the lowest demand occurred at 12:00 on 22 June, at 2 kW.

External energy purchases are required primarily during nighttime and early morning hours when local production is absent. The highest purchase requirement was 8.45 kW at 21:00 on 24 June, while there were multiple instances with 0 kW purchase when production sufficiently met the demand.

Stored energy plays a significant role in supplying demand during non-production hours. The maximum discharge reached 70.25 kW at 17:00 on 20 June, while periods of zero discharge occur when demand is fulfilled by direct production.

Surplus energy is injected into the grid when production exceeds local demand. The highest injection occurred at 12:00 on 22 June, reaching 567.984 kW, whereas some hours saw 0 kW injection, particularly when the production levels matched the demand.

The direct use of generated power is maximized when production is sufficient. The peak direct usage reached 647.448 kW at 12:00 on 20 June, while the minimum was 0 kW, recorded at nighttime when no production was available.

The system effectively shifts between stored reserves, purchased electricity, and direct production to maintain stability in supply. Daytime production peaks significantly reduce reliance on external energy sources, while nighttime demand is managed through storage discharge. Injection patterns highlight surplus generation, contributing to energy grid efficiency.

5. Discussions of the Results

The analysis presented in this study highlights the interplay between energy production, consumption, battery storage, and financial viability in optimizing a solar-based energy system. Through a comprehensive MCDM approach, combined with hourly and seasonal performance evaluations, several key insights emerge regarding system efficiency, economic feasibility, and the role of storage in power dispatching.

5.1. Energy Production and Dispatching Efficiency

The results demonstrate significant seasonal and hourly variations in solar energy production. Peak generation occurs during summer months, particularly between 10:00 and 14:00, when production surpasses 678 MWh at midday, leading to substantial grid injection. Conversely, winter months exhibit lower generation, necessitating increased reliance on stored energy and, in some cases, grid dependency for power continuity.

Battery storage plays a pivotal role in mitigating the impact of seasonal fluctuations. The optimal tilt angle adjustments ensure higher seasonal efficiency, yet energy dispatch strategies must account for seasonal demand variations. The implementation of 59° tilt for winter months and row spacing optimization demonstrates improved energy capture while reducing shadow losses.

5.2. Impact of Battery Storage on System Performance

The findings reveal that expanding battery storage significantly reduces grid dependence, shifting the system toward to almost self-sufficiency. The battery utilization rate increases from 0% (no storage) to 99.2% (3.76 MWh storage), proving its effectiveness in stabilizing energy availability during low-production hours. Additionally, the full discharge duration improves significantly, reaching 72 h with 3.76 MWh storage with a DOD of 85%, ensuring greater system resilience.

Despite these benefits, storage expansion is not without trade-offs. The financial analysis shows that larger battery capacities lead to extended payback periods, diminishing the overall economic return. While low-storage configurations achieve a higher ROI, high-capacity systems ensure greater energy independence but require higher upfront investments.

5.3. Economic Trade-Offs in System Optimization

Financial feasibility remains a critical factor in solar system optimization. The MCDM analysis highlights how PV and battery selection influence the return on investment, with Alternative 1 ranking highest among PV options and Alternative 1 among battery alternatives, both exhibiting superior efficiency-to-cost ratios.

The payback period increases significantly with larger storage capacities, from 4.5 years with no storage to 14.6 years with 3.75 MWh storage. Decreasing storage costs with emerging cheaper sodium batteries projected to enter the market will sharply reduce the payback time. Similarly, Net Present Value (NPV) declines as storage capacity grows, with the highest NPV recorded at M EUR 1.3 for 0.8 MWh storage, whereas 3.75 MWh storage results in a smaller financial return (M EUR 0.7). These findings underscore the delicate balance between financial sustainability and energy autonomy, necessitating strategic decision-making to optimize both economic viability and energy resilience.

5.4. System Adaptations for Improved Performance

Given the constraints identified, several structural adaptations were made to improve efficiency:

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Panel configurations in the northern department section were optimized with 4.2 m panel lengths, ensuring spatial efficiency.

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Roof-mounted panels in the southern department followed a 13° tilt, balancing energy capture with visual aesthetics.

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Row spacing was reduced to increase power density, while rear panel rows were elevated by 0.5 m to reduce shading losses.

These adjustments contributed to optimized energy dispatch, as validated by simulation results from PV-Syst software, where annual energy yield exceeded 1.2 GWh under ideal conditions.

6. Conclusions

This paper presents a comprehensive approach to designing and optimizing a microgrid system to achieve Plus-Energy performance, as well as full energy independence in an average year (99.2% probability) for the Electrical Engineering Department building at the University of Coimbra. By integrating environmental, architectural, and economic considerations, this research develops an optimized panel geometry, orientation, and row spacing strategy, ensuring high efficiency while maintaining aesthetic harmony.

A key innovation introduced is fixed tilt optimization, particularly the implementation of a 59° fixed tilt angle for green parking and a 13.5° fixed tilt angle for rooftop tilt, allowing the system to maintain full energy independence while ensuring reduced injection into the grid during this period. The optimized fixed row spacing strategy improves energy yield, minimizes shading losses, and maximizes system efficiency structural refinements often overlooked in conventional photovoltaic designs.

The economic analysis using the TOPSIS method highlights the trade-offs between battery storage expansion and financial feasibility. While a 3.76 MWh battery system with a DOD of 85% ensures 72 h of uninterrupted energy supply, sufficient in most cases for resilient operation during winter months, the findings suggest that storage investment requires careful cost evaluation. Assuming EUR 200/kWh, expanding energy storage by one hour for the building would require an extra investment of approximately EUR 12,255/hour of storage.

Computational modeling using Python, Rhino, and PVsyst validates the feasibility of surplus energy generation, with annual production exceeding 1 GWh, while energy-efficient local consumption is reduced to 460 MWh, ensuring an energy surplus of 160% injected into the grid under regular conditions. This reinforces the system’s potential as a sustainable energy solution for academic institutions, providing a framework for future large-scale implementations.

However, the results also highlight inherent uncertainties in energy generation variability, particularly in seasonal variations and external influencing factors such as weather conditions, system degradation, and operational fluctuations. The probability analysis accounts for these uncertainties, estimating a P50 yield of 1137 MWh after losses, with a 2% likelihood of falling below 1089 MWh (P98), ensuring a conservative risk assessment.

6.1. Contributions and Future Directions

The novelty of this research lies in the use of a comprehensive multi-criteria decision-making (MCDM) approach for optimizing a microgrid system integrating PV generation, energy storage, and flexible loads (HVAC and EV charging) to achieve Plus-Energy Building (PEB) performance. A holistic approach is used for photovoltaic optimization, integrating technical, economic, and architectural constraints while ensuring aesthetic compatibility.

6.2. Future Research

• Dynamic energy dispatch models can be integrated into real-time consumption forecasts based on weather and activity schedules for further optimization.
• Hybrid storage solutions, such as thermal storage, can be used to take advantage of the building’s thermal mass to optimize HVAC operation and to reduce dependence on high-cost battery investments.
• As the V2G technology becomes commercially available, progressive integration of this technology may be used to ensure reliable supply even during extreme weather events.
• Smart grid interaction strategies, including demand–response techniques and feed-in algorithms for HVAC load management, can be used to maximize surplus energy utilization.
• The DEEC microgrid can be integrated into an energy community with other university buildings.

The proposed design successfully balances energy independence, architectural harmony, and financial sustainability, demonstrating how academic buildings can transition into self-sufficient energy hubs through meticulous optimization and strategic financial planning.