Evaluating Microgrid Investments: Introducing the MPIR Index for Economic and Environmental Synergy

Abstract

In view of the increasing environmental challenges and the growing demand for sustainable energy solutions, the optimization of microgrid systems with regard to economic efficiency and environmental compatibility is becoming ever more important. This paper presents the Microgrid Performance and Investment Rating (MPIR) index, a novel assessment framework developed to link economic and environmental objectives within microgrid configurations. The MPIR index evaluates microgrid configurations based on five critical dimensions: financial viability, sustainability, regional renewable integration readiness, energy demand, and community engagement, facilitating comprehensive and balanced decision making. The current cases focus on the area of Greece; however, the model can have a wider application. Developed using a two-target optimization model, this index integrates various energy sources—including photovoltaics, micro-wind turbines, and different types of batteries—with advanced energy management strategies to assess and improve microgrid performance. This paper presents case studies in which the MPIR index is applied to different microgrid scenarios. It demonstrates its effectiveness in identifying optimal configurations that reduce the carbon footprint while maximizing economic returns. The MPIR index provides a quantifiable, scalable tool for stakeholders, not only advancing the field of microgrid optimization, but also aligning with global sustainability goals and promoting the transition to a more resilient and sustainable energy future.

1. Introduction

The strategic role of renewable energy in the European Union is underlined by several key developments and policies aimed at addressing the interlinked challenges of energy security, economic stability, and environmental sustainability. The EU’s commitment to improving energy efficiency and expanding the use of renewable energy is reflected in its comprehensive policy frameworks and recent legislative adjustments in response to global and regional crises.

In 2022, during record-high energy costs and growing concerns about energy security, countries around the world, including EU Member States, prioritized energy efficiency measures. These initiatives are known for their dual benefits: reducing energy costs for consumers and improving national energy security while contributing to climate goals. The European Union in particular has seen a significant shift towards more investment in energy efficiency, regaining the momentum that was lost during the COVID-19 pandemic [1].

The State of the Energy Union report [2] underlines the central role of renewable energy in tackling climate and environmental problems within the EU. The report is in line with the objectives of the European Green Deal, which ambitiously aims to reduce greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels, paving the way for Europe to become the first climate-neutral continent by 2050 [3].

An important legislative response to these goals was the proposal to amend Directive (EU) 2018/2001 on the promotion of the use of energy from renewable sources (RED II). The amendment proposed in July 2021 aims to increase the target for the share of renewable energy in gross final energy consumption to at least 40% in 2030, which represents a significant increase on the previous 32% target of RED II [4].

Driven by geopolitical tensions, in particular Russia’s military actions against Ukraine, the EU has formulated the REPowerEU plan. This strategy aims to reduce the EU’s dependence on Russian fossil fuels through a series of measures that emphasize energy savings, the diversification of supply, and an accelerated transition to clean energy. The plan includes a proposal to increase the 2030 target for renewable energy to at least 45%, with measures to simplify and speed up approval procedures to enable faster deployment of renewable energy projects [5].

In addition, the relationship between the use of renewable energy, energy efficiency, and financial mechanisms such as green bonds is crucial. Green bonds have proven to be effective instruments for financing renewable energy projects and contribute significantly to reducing CO₂ emissions. Green bonds are financial instruments specifically designed to finance projects with environmental benefits and play a central role in promoting sustainable energy solutions. These bonds provide essential capital for the development and deployment of microgrid systems, particularly in scenarios where traditional financing mechanisms may fail. The MPIR index, our proposed assessment framework, emphasizes not only the technical and economic dimensions of microgrid investments, but also their environmental sustainability. In addition, the success of microgrid projects financed with green bonds can serve as an example of the MPIR index’s effectiveness. By evaluating these projects, the MPIR index can provide quantitative data on how green-financed microgrids perform compared to those financed in other ways, offering valuable insights into the impact of targeted environmental financing. However, research suggests that while these financial instruments promote green energy initiatives, there is no direct short-term causal relationship between CO₂ emissions, energy efficiency, and green financing. Therefore, to promote sustainable economic growth and address environmental challenges, long-term supportive policies that encourage private investment in green energy projects are crucial, especially in post-COVID-19 scenarios where green projects may face greater financial challenges [6]. These facets illustrate the EU’s integrated approach to promoting a resilient, sustainable energy landscape that adapts to new challenges through policy innovation and strategic investment in renewable energy and efficiency.

The increasing reliance on renewable energy sources and microgrids heralds a major shift in global energy systems. However, the challenge remains to optimally integrate these systems into the various environmental, economic, and social frameworks. Existing assessment tools for microgrid investments focus predominantly on economic and technical parameters, often overlooking the wider environmental impacts and community involvement that are critical to sustainable development. This omission represents a significant research gap, as comprehensive decision-making tools that can dynamically adapt to the complex interplay of these factors are still lacking in this area. To address this gap, we propose the Microgrid Performance and Investment Rating (MPIR) index, a novel framework developed to holistically evaluate microgrid systems. The MPIR index integrates multiple assessment dimensions—financial viability, environmental sustainability, and community impact—into a single, coherent framework. This approach allows for a balanced consideration of different factors and promotes more sustainable and community-focused energy solutions. This paper makes several important contributions to the existing body of knowledge. First, it presents a multi-dimensional assessment tool that goes beyond traditional cost and efficiency metrics to incorporate environmental and social impacts, filling a critical gap in microgrid assessment methodologies. Secondly, by applying the MPIR index to different case studies, this study demonstrates the practical applicability and benefits of our approach in the real world and provides empirical evidence to support the theoretical model. Finally, the results of this study help shape policy and investment strategies in the energy sector and promote a shift towards more sustainable and equitable energy systems.

2. Materials and Methods

2.1. State-of-the-Art and Novelty of the Research

The increasing use of renewable energy and the shift towards decentralized energy resources have highlighted the importance of innovative decision-making tools for microgrids. Despite significant progress, existing tools often lack comprehensive assessment frameworks that can dynamically adapt to the complex interplay of technical, economic, and environmental factors in microgrid systems.

Research on hierarchical control structures has attempted to optimize microgrid operations, but often requires complex coordination that may not directly account for return on investment [7,8].

Efforts to integrate EMSs into microgrids focus on optimizing energy flows and reducing costs. However, these systems are primarily reactive rather than proactive and lack comprehensive risk assessment capabilities that are critical for investors, as they do not offer sensitivity analysis of the dimensioning of the systems for the investors to more easily be able to choose the needed system [9,10,11,12,13].

While socio-economic factors are increasingly considered, existing research often separates economic feasibility from operational and environmental sustainability, which can lead to a misalignment of stakeholder objectives and applicability in practice [14].

Furthermore, the use of wireless communications in control strategies has been explored to improve the resilience and efficiency of microgrids. However, the focus remains on technical feasibility rather than performance and investment evaluation [15,16].

In a study regarding deep learning for microgrid energy management a deep neural network model was developed for the planning of microgrids. It emphasized the need for advanced decision support due to the increasing integration of renewable energy and its inherent unpredictability [17,18].

A study of stochastic models for microgrid optimization highlighted the use of stochastic programming to account for uncertainties in microgrid operation and pointed out the challenges faced by traditional deterministic models with fluctuating renewable energy [19].

A microgrid optimization using reinforcement learning paper explored the use of reinforcement learning to dynamically optimize the operation of microgrids and demonstrated the potential of AI in automating energy management decisions [18].

A blockchain for decentralized management research paper explored blockchain technology as a means to improve the transparency and efficiency of microgrid transactions, illustrating the shift towards decentralized energy systems. A hybrid systems for improved energy efficiency paper proposed a hybrid system combining photovoltaics and wind energy, highlighting the need for integrated solutions to improve the performance and sustainability of microgrids [18].

Gap in the literature and novelty of MPIR

The review of the literature shows that while significant progress has been made in microgrid decision making, there remains a gap in tools that holistically integrate multiple dimensions—economic viability, community impact, environmental sustainability, and technical performance—into a single, understandable framework. The MPIR model fills this gap as follows.

Integrating multiple metrics: Unlike most existing models, MPIR combines economic, environmental, and technical parameters into a unified rating system, simplifying decision making for investors and operators.

Improved stakeholder engagement: By considering community acceptance and regional readiness, MPIR ensures that the microgrid design is not only technically sound and economically viable, but also socially acceptable.

Facilitating adaptive planning: MPIR’s flexibility in weighing different performance criteria allows it to adapt to different policy frameworks and stakeholder priorities, making it a robust tool in both developed and emerging markets.

The MPIR framework is designed to be adaptable to the priorities of different stakeholders and regional policies, ensuring its applicability in different geographical and operational contexts. This adaptability is critical in the diverse landscape of microgrid projects, where one-size-fits-all solutions are often ineffective.

By incorporating community engagement and regional readiness, MPIR ensures that technology-driven decisions are based on socio-economic realities. This holistic approach increases the likelihood of successful implementation and sustainable operation, and closes the critical gap between technical feasibility and practical applicability.

MPIR bridges the gap between economic feasibility and environmental sustainability by integrating these aspects into a single framework. This ensures that microgrid projects not only generate a financial return, but also contribute to wider environmental goals and community development.

The integration of MPIR into microgrid decision-making processes represents a significant advance in the field. By providing a comprehensive, adaptable, and thorough assessment framework, MPIR addresses the multi-layered challenges of modern microgrids. It not only fills existing gaps, but also sets a new standard for future developments in microgrid assessment and optimization.

The MPIR represents a novel solution in the landscape of microgrid decision-making tools that is uniquely capable of addressing the critical gaps identified in the literature. By providing a multi-dimensional and adaptive approach, MPIR can significantly contribute to the targeted and sustainable development of microgrid systems.

The MPIR index offers a transformative approach to microgrid evaluation by integrating economic, environmental, and social dimensions, addressing gaps left by existing tools. This integration is pivotal, especially in the context of increasing demand for sustainable and resilient energy solutions. Unlike conventional tools, as it being shown in

Table 1. Comparative analysis of MPIR against established microgrid evaluation tools.

Evaluation Tool	Key Features	Limitations	MPIR Advantages
HOMER Pro v3.14.4	Optimizes for cost and system feasibility	Primarily economic focus; lacks environmental/social integration	MPIR integrates economic, environmental, and social metrics, offering a holistic performance assessment.
RETScreen Expert Version 8.0	Project viability and performance analysis	Focuses mainly on financial outputs; limited sustainability integration	MPIR embeds sustainability directly into its evaluation framework, allowing for broader regulatory compliance.
Traditional Simulation Tools, MATLAB Simulink (R2023a) or PSS®E (Siemens Power System Simulator Version 35.2)	Detailed technical simulations	Typically omits broader economic or social considerations	MPIR utilizes predictive analytics to improve decision making across multiple dimensions.

, such as HOMER, which primarily focus on cost and feasibility, MPIR incorporates a broader spectrum of metrics, making it indispensable for comprehensive microgrid assessment.

Contextual Integration of Case Studies

One of the key validations of MPIR’s superiority comes from its application in diverse settings. For example, in a case study conducted in Greece, MPIR was instrumental in evaluating the performance of renewable energy systems integrated into local microgrids. This study revealed that MPIR could enhance operational efficiencies by optimizing the balance between energy production, cost efficiency, and socio-environmental benefits.

Technical Scope and Flexibility [20,21]

HOMER is primarily used for the optimization of configurations for cost and efficiency but often underrepresents the operational dynamics and social impacts of microgrid systems. It focuses on economic and technical optimization without fully integrating broader social or environmental impacts.

RETScreen allows for performance analysis but lacks depth in real-time operational adaptability and integration with diverse energy sources under varying load demands.

DER-CAM excels in economic optimization of microgrids but often limits environmental and social parameters, focusing mainly on cost-effectiveness from an energy production standpoint.

Integration of Social and Environmental Factors

Unlike the aforementioned tools, MPIR integrates not just economic and technical metrics, but also includes comprehensive environmental and social impact assessments. This integration aligns with modern energy policy demands for sustainability and community involvement, making it more comprehensive for policy implementation and stakeholder engagement.

Adaptability and Real-Time Performance

MPIR also introduces superior adaptability in real-time performance adjustments, which is critical in environments with fluctuating energy demands and diverse energy resource inputs. This feature is particularly beneficial for dynamic environments compared to the more static modeling approaches like those seen in HOMER and DER-CAM.

Policy and Regulatory Alignment

Finally, the design of MPIR considers evolving regulatory frameworks and policies, making it highly relevant for regions with stringent environmental standards and social goals. This forward-looking approach ensures that MPIR remains relevant as policy landscapes evolve. MPIR offers a multi-dimensional and dynamic approach that not only meets the technical and economic optimization needs of microgrid systems. but also aligns with modern energy policies emphasizing sustainability and social responsibility. This holistic approach addresses the gaps left by traditional tools and positions MPIR as a critical tool for future-oriented energy system planning and implementation.

Microgrid optimization is a well-researched area, especially in the context of battery storage technologies, energy management, and cost efficiency. However, as can be read in the literature, existing tools and methods often focus on technical and economic aspects without fully considering social and environmental factors. This gap is particularly evident in existing optimization models, which tend to focus on either cost efficiency or technical performance, but not the holistic assessment required for sustainable microgrid systems. Several studies emphasize the dominance of lithium-ion batteries in energy storage solutions for microgrids, mainly due to their efficiency, long lifetime and ability to integrate with renewable energy systems. However, the challenge of cost efficiency remains. Studies show that cost reductions of 10% in Greece and 30% in Denmark are necessary for lithium-ion batteries to become a viable option for widespread adoption [22]. These findings highlight the need for tools that can help reconcile economic and environmental goals by assessing the broader implications of battery technology choices in microgrid configurations. Other research on energy storage optimization, such as swarm optimization for fuzzy logic control, demonstrates how technological advances can improve convenience and reliability for consumers [23]. Similarly, the implementation of maximum power point tracking (MPPT) techniques has led to remarkable improvements in microgrid performance, especially in self-sufficient communities that rely on renewable energy sources [24]. These studies illustrate the technical advances in optimizing power generation, but they fall short when it comes to evaluating the socio-economic and environmental trade-offs that investors and policy makers must consider in real-world microgrid projects. For example, studies on area reduction algorithms to minimize energy costs show that energy efficiency can be improved by more than 21% in certain applications [25], but these results often overlook the long-term sustainability of the system or its acceptance by the local community. In addition, simulations of battery storage show that low-energy homes can achieve significant savings, but the social and environmental aspects, such as community engagement and grid integration readiness, are often not considered [26]. The research landscape also highlights the need for comprehensive assessment frameworks. Conventional tools such as HOMER and RETScreen offer strong capabilities in economic and technical modeling, but their scope is limited when it comes to incorporating environmental and social metrics [27,28]. This limitation is critical as studies show that despite their high performance, lithium-ion batteries are associated with significant environmental impacts during production and their long-term sustainability remains an issue without deeper integration of life cycle assessments and stakeholder involvement in the decision-making process [28,29,30]. Another critical gap is the focus on the cost efficiency of battery storage solutions without fully considering the long-term financial sustainability and social acceptance of such technologies. For example, cost reductions of 60–70% are required for batteries to be a sustainable solution for energy storage in microgrids [31], yet few studies have considered how such cost reductions can be reconciled with community needs and environmental goals. Similarly, environmental assessments of energy systems show that the production of batteries, particularly lithium-ion batteries, contributes significantly to the overall carbon footprint [32,33] and yet conventional tools do not adequately account for these environmental impacts in their models. The development of optimization models, such as rolling two-stage optimization, has led to a 13.3% increase in energy efficiency and a 14.55% reduction in system costs [34], representing a significant advance in microgrid performance. However, these models often ignore important aspects, such as community involvement, environmental suitability, and alignment with regional policies, which are essential for the practical deployment of microgrids in different geographical and socio-economic contexts.

While previous research has made considerable progress in optimizing the technical and economic aspects of microgrids, it has largely neglected the broader socio-ecological dimensions. This research aims to address this gap through the development of the Microgrid Performance and Investment Rating (MPIR) index, which integrates economic viability, environmental sustainability, community engagement, and technical performance into a single, adaptable framework. This will fill the critical gaps in the existing literature and provide a comprehensive tool for stakeholders to evaluate microgrid systems in a way that is consistent with broader sustainability goals.

2.2. Development of the MPIR Index

An important step of this research was the design of the Microgrid Performance and Investment Rating (MPIR) index which represents an advance in the evaluation of microgrid systems. This multi-dimensional index is designed to capture the complex interaction of factors that determine the overall performance of a microgrid and attract investors and stakeholders. Encompassing dimensions such as financial sustainability, sustainability, credibility, and community involvement, MPIR offers a holistic assessment tool that goes beyond traditional metrics.

The MPIR (Microgrid Performance and Investment Rating) index is a multi-dimensional tool designed to evaluate microgrid systems holistically. It integrates technical, economic, social, and environmental factors, enabling a comprehensive performance assessment. The development process of MPIR is structured around various metrics, each selected based on its relevance to the success of microgrid projects. Below, we outline the steps involved in creating this index, ensuring clarity regarding data collection, the rationale for each metric, and the method for calculating the final score.

• Data Collection and Analysis

The MPIR index begins with extensive data collection, drawing from both quantitative and qualitative sources. These include energy production/consumption metrics, financial records, sustainability reports, and community feedback surveys.

The statistical methods applied, such as regression analysis, help in identifying relationships between variables. For instance, the link between investments in renewable energy technologies and the reduction of carbon footprints was analyzed. This ensures the methodology is data-driven and objective, which is essential for identifying patterns and trends that inform decision making [35,36,37].

2.

Financial Viability (FV) Assessment

Financial viability (FV) is a key metric that measures the economic feasibility of a microgrid project. This dimension focuses on the return on investment (ROI), considering both total earnings and the investment costs. Additionally, the economic assessment is performed relative to local conditions, for example, comparing project costs to the average annual salary in Greece. A detailed cost–benefit analysis ensures that all potential revenues (e.g., energy sales) and cost reductions (e.g., decreased reliance on external energy sources) are accounted for, offering a realistic view of the project’s financial health [38,39].

3.

Sustainability Assessment (SUI)

Sustainability is evaluated through the carbon footprint reduction achieved by incorporating renewable energy into the microgrid. This is calculated by comparing total emissions from microgrid energy production to those from conventional sources. The proportion of renewable energy utilization (from solar, wind, and biomass) directly impacts the sustainability score, as higher renewable penetration leads to higher sustainability scores. Studies have consistently shown that this is a critical factor for aligning with global climate goals [39,40,41].

4.

Community Acceptance

Community acceptance is crucial for the long-term viability of microgrid projects. The Community Usage Index (CUI) measures the extent of local community involvement, capturing data through surveys and other participatory methods. The focus is on quantifiable benefits such as job creation, energy cost savings, and improved energy security. The rationale for this dimension lies in studies demonstrating the direct correlation between community engagement and microgrid success [39,42].

5.

Energy Needs and Isolation (ENI)

This dimension assesses the necessity of microgrid solutions in geographically isolated or energy-underserved regions. By analyzing geographic and demographic data, we evaluate the adequacy of existing energy infrastructure, distance from the main grid, and energy reliability. The ENI score reflects the level of energy isolation and the need for alternative solutions [43,44]. Regions with a higher degree of energy isolation, where traditional grids struggle to reach, will score higher in this category.

6.

Grid Infrastructure and Stability (GIS)

GIS evaluates the capacity and stability of the existing electricity grid to support the integration of renewable energy-based microgrids. The focus is on the grid’s stability, carrying capacity, and adaptability to new microgrid elements. This metric ensures the system’s ability to handle fluctuations in energy demand and generation, contributing to the overall system efficiency [45,46].

7.

Climate and Renewable Potential (CRP)

The Climate and Renewable Potential (CRP) dimension evaluates the region’s ability to harness renewable energy based on its climate conditions. An in-depth analysis of solar radiation, wind patterns, precipitation, and temperature profiles determines the region’s renewable energy potential. Regions with higher renewable energy production capacity compared to their energy demands score favorably in this component [47,48].

8.

Landscape and Environmental Suitability (LES)

LES assesses the feasibility and environmental impact of installing and operating a microgrid in a particular location. This involves evaluating geographic, topographical, and environmental characteristics, such as land availability and the impact on local ecosystems. Regions with minimal environmental disruption during microgrid installation and operation will receive higher scores [49].

Multi-Criteria Scoring Algorithm and Weighting

The MPIR index uses a multi-criteria decision-making approach to combine the aforementioned dimensions into a single evaluative framework. The weighting of each dimension is flexible and adjustable based on project priorities or stakeholder preferences. For example, community-driven projects may prioritize CUI and SUI, while financially oriented projects may place more emphasis on FV and GIS.

This calculation ensures a balanced and comprehensive assessment, reflecting the microgrid’s performance across economic, social, environmental, and technical dimensions. By incorporating these metrics, the MPIR index facilitates informed decision making, aligning with broader goals such as sustainability, community development, and energy security [50,51].

The MPIR index, with its multifaceted approach, offers an innovative and thorough tool for evaluating microgrid systems. By integrating diverse dimensions, it provides a balanced assessment that supports decision making for investors, developers, and policymakers. The methodology ensures that technical, economic, social, and environmental factors are considered equally, guiding the sustainable development and implementation of microgrids in various contexts.

3. Results

3.1. Evaluation Approach and Explanation of MPIR

The evaluation model for microgrid systems considers both economic and environmental goals using an integrated, multi-dimensional approach. The key function is to balance these aspects while ensuring efficient microgrid operation. The modeling approach was based on the optimization approaches of [52,53,54]. The final objective function and optimization approach of the current research are illustrated in detail as follows.

Objective Function (OBF): The Objective Function is a weighted sum of the economic cost (Life-Cycle Cost, LCC) and environmental impact (Total Emissions, TE) of the microgrid system.

OBF = econopt × LCC + envopt × TE

(1)

where econopt and envopt are weighting factors that prioritize either economic efficiency or environmental sustainability, depending on policy goals or project requirements.

Economic Optimization:

Life-Cycle Cost: This was calculated by summing the Total Initial Cost (TIC), Total Maintenance Cost (TMC), Total Replacement Cost (TRC), and Total Energy Cost (TEC). Each component includes various operational costs, such as battery maintenance, energy transactions, and infrastructure costs.

Total Initial Cost (TIC):

This includes all upfront capital expenditures, such as the cost of purchasing and installing renewable energy systems (solar panels, wind turbines), battery storage, and grid connection infrastructure. For example, a microgrid that includes both photovoltaic and wind energy systems will have higher initial costs than a system that uses only one energy source.

Total Maintenance Cost (TMC):

This includes ongoing operational costs, such as battery maintenance, cleaning and maintenance of renewable energy components, and regular inspections of the system. The complexity of the microgrid configuration, such as the integration of multiple energy sources (e.g., solar, wind, batteries), will influence the required frequency and cost of maintenance.

Total Renewal Cost (TRC):

Over the life of the microgrid, certain components will need to be replaced. The TRC includes the cost of replacing major equipment such as inverters, batteries, or wind turbines. Lead-acid batteries, for example, have a shorter lifespan than lithium-ion batteries, resulting in a higher TRC in configurations with lead-acid batteries. Different configurations with different battery types will therefore have a significant impact on the TRC.

Total Energy Cost (TEC):

The TEC refers to the costs associated with energy transactions, such as buying electricity from the main grid during periods of low renewable generation or selling excess electricity to the grid. TECs depend heavily on the self-sufficiency of the microgrid.

Impact of microgrid configuration on the LCC:

The configuration of the microgrid has a direct impact on the LCC. A more complex configuration with multiple energy sources (e.g., the combination of solar, wind, and batteries) tends to increase the TIC due to higher capital investment, but can lower the TEC by improving energy self-sufficiency. Similarly, configurations with high-performance components, such as lithium-ion batteries, and advanced energy management systems can increase the initial costs but reduce the TMC and the TRC over time as the efficiency is improved and component lifetimes are extended. The geographic location of the microgrid also affects the LCC, as it influences the complexity of installation, frequency of maintenance, and the potential for energy generation from renewable sources.

LCC = TIC + TMC + TRC + TEC

(2)

Environmental Optimization:

Total Emissions: This factor calculates the total emissions over the system’s lifecycle, including both production and operational phases. It accounts for emissions from conventional heating systems, the use of network energy, and energy produced using renewable resources.

TE = TEproduction + TEoperation

(3)

Detailed Explanation of MPIR

Once the optimization has been made based on the objective function and the results have been obtained, the system is calculated using the Microgrid Performance and Investment Rating (MPIR), which is a comprehensive index that evaluates the performance of microgrid systems across various dimensions. It is calculated as follows:

MPIR = (FV + CUI + ENI + RR + SEI)/5

(4)

Each component of the MPIR reflects a different aspect of the microgrid’s operation and its impact.

The financial viability assesses the economic returns and cost-effectiveness of the microgrid investment against traditional energy solutions and relative to local economic standards.

FV = 0.5 × SR + 0.5 × ASR

(5)

where the SR (Savings Ratio) assesses the economic benefits in terms of cost savings against traditional energy costs and the ASR (Affordability/Sustainability Ratio) measures how the cost of the microgrid compares to the average income in the region to assess affordability.

ASR = (Total System Cost per Household/Average Salary) × 100

(6)

The Community Utilization Index (CUI) reflects the level of community engagement and acceptance of the microgrid system. This index includes surveys and participatory metrics.

The Environmental Impact Index (ENI) calculates the environmental benefits of the microgrid by comparing emissions and resource use with traditional energy sources.

ENI = (1 − (Microgrid Emissions/Traditional Grid Emissions)) × 100

(7)

The Regional Readiness (RR) assesses the readiness of the region or community to implement and support the microgrid based on infrastructure, regulatory, and logistical considerations.

RR = (CPR + GIS + LES)/3

(8)

where the CPR (Community Preparedness Rating), GIS (Grid Infrastructure Stability), and LES (Local Environmental Suitability) are included.

The Sustainability and Emissions Index (SEI) combines metrics related to renewable energy utilization and emission reductions. This index highlights the sustainability aspects of the microgrid.

SEI = (REU + EM)/2

(9)

where REU (Renewable Energy Utilization) measures the percentage of total energy produced from renewable sources and the EM (Emissions Metric) evaluates emissions reduction compared to the conventional grid.

These equations provide a framework to quantitatively assess and compare the performance of microgrid systems across different dimensions, ensuring that the evaluation is comprehensive and covers economic, environmental, community, and regional readiness aspects. This holistic approach helps in identifying the strengths and weaknesses of a microgrid project, facilitating targeted improvements and informed decision making.

In practice, the MPIR framework can guide decision making by highlighting strengths and weaknesses in specific areas, such as economic feasibility or community engagement, and suggesting targeted improvements. It provides a quantitative basis to compare different microgrid projects and strategies, aiding stakeholders from policymakers to developers in making informed choices that align with broader energy, economic, and environmental goals.

The optimization of the microgrid system in this study employs a multi-objective optimization approach using a Non-Linear Programming (NLP) framework. The chosen optimization algorithm is based on the weighted-sum method, which allows for the simultaneous consideration of both economic and environmental objectives. The optimization method balances the Life-Cycle Cost (LCC) and Total Emissions (TE), where the goal is to minimize both variables while evaluating the MPIR.

Constraints: the optimization is subject to multiple constraints related to energy production, storage, and consumption:

Energy balance constraint: ensuring that the total energy generated and stored meets the demand.

Storage limits: battery capacity constraints based on the technology used.

Cost limits: ensuring that the total cost does not exceed a predefined budget.

Environmental limits: ensuring that emissions stay within acceptable levels defined by sustainability goals.

Algorithm: The optimization algorithm follows these steps:

Step 1: Initialization—define the system parameters, including the types of renewable energy sources (solar, wind) and storage systems (batteries), as well as financial and environmental factors.

Step 2: Set objective weights—stakeholders assign weights to economic and environmental objectives (econopt and envopt) based on project priorities.

Step 3: Formulation of the Objective Function—the OBF is set up using the weighted-sum method.

Step 4: Solve the NLP problem—the optimization problem is solved using a gradient-based NLP solver (CPLEX) which is integrated within the AIMMS optimization platform (Version 4.91).

Step 5: Check feasibility—the solution is checked against constraints (energy balance, cost limits, etc.).

Step 6: Output results—the optimal configuration is identified, yielding minimized LCC and TE while fulfilling the constraints and evaluating the MPIR.

3.2. Case Studies Results

The analysis of the Microgrid Performance and Investment Rating (MPIR) for the seven scenarios A1 to A7, which is explained in detail below, provides a detailed understanding of the different approaches to implementing microgrids within the same region. As the regional context remains constant in all scenarios, the different MPIR ratings primarily reflect the internal configuration and decision-making strategies of the individual microgrid systems rather than external regional factors.

In the evolving landscape of energy management, microgrids are playing an increasingly important role in the integration of renewable energy sources. Recent investigations of battery storage systems have shown varying results in different scenarios, focusing on the intricate balance between sustainability, economic viability, and community impact. The case studies presented here include scenarios 1 to 6, each using a different type of battery technology, and scenario 7, which examines a conventional system without battery storage. These scenarios were used to assess the effectiveness of different battery types in real-world applications within microgrids and critically evaluate the practical benefits and limitations of each technology.

Scenarios 1 to 6 test different battery technologies in a microgrid to understand their performance, cost, and environmental impact under different operating conditions. Each scenario provides insights into the specific benefits and limitations of the battery types used. Scenario 7, unlike the other scenarios, is operated without battery storage and serves as a baseline to illustrate the impact and necessity of integrating storage technologies for effective microgrid management. The types of batteries used in the optimization were lead–acid batteries (Scenario 1), nickel cobalt aluminum (NCA) batteries (Scenario 2), lithium iron phosphate batteries (LFP) (Scenario 3), lithium nickel manganese cobalt oxide (NMC) batteries (Scenario 4), lithium–air (Scenario 5), and flow batteries (Scenario 6). The conventional scenario included no battery (Scenario 7) and served as a control scenario to illustrate the efficiency and sustainability of the energy system without the integration of storage solutions and to provide a basis for comparison for scenarios with advanced battery technologies. The analysis began with setting some default data; however, the optimization determines the size, type, and energy flow of the systems.

This study focused on the densely populated urban center of Thessaloniki, Greece. Urban centers like Thessaloniki are ideal for microgrid applications due to the large, concentrated energy demand that is often disconnected from primary energy production sites. Additionally, the vast unused rooftops in such urban areas present significant opportunities for the installation of photovoltaic systems. The dense housing makes these areas perfect candidates for community-based energy generation units, where residents can collectively share and manage green energy production and its associated costs.

Microgrid installed capacity: rooftop PV, 325 Wp per panel (REC N-Peak Black series, REC Group, Singapore); ground PV→400 Wp per panel (REC TwinPeak 2S Mono, REC Group, Singapore); lead–acid battery→varies based on scenario (Rolls 4000 series, Battery Engineering, Nova Scotia, Canada); lithium-ion battery→higher efficiency per scenario (LG Chem RESU 10H, LG Chem, Seoul, South Korea). The following economic parameters were included. Interest rate: for accurate system cost calculation, all values were discounted to their present value using the expected interest rate over the coming years. The average 10-year risk-free interest rate was used, estimated at approximately 2.6%. Inflation Rate: the average inflation rate for 2019, 0.18%, was applied for this study. Electricity pricing: the Public Power Corporation (PPC) pricing model for residential customers (C1 tariff) was used as the basis for energy pricing. Energy charges: as of September 2019, the price was €0.11058/kWh for consumption of up to 2000 kWh per four months, and €0.11936/kWh for consumption exceeding 2000 kWh. Additional charges: regulated charges, including those for the use of the National Electricity System, applied. Excess energy management: The project opted to sell surplus energy back to the grid. The sale price depended on the photovoltaic system’s connection date to the network, with a fixed price of €0.080/kWh for systems connected after 1 August 2019.

Figure 1 indicates the input data, the model calculations, and the outputs, illustrating the different scenarios that can occur.

Table 2. MPIR optimization indicator results by case study scenario.

Case Study	SUI (Sustainability)	FV (Financial Viability)	RRI (Regional Readiness)	ENI (Energy Needs)	CUI (Community Usage)	MPIR (Total)
A1	3.5	5.5	6.0	3.0	5.0	4.6
A2	5.5	5.5	6.0	3.0	5.0	5.0
A3	4.5	5.5	6.0	3.0	5.0	4.8
A4	4.0	4.0	6.0	3.0	5.0	4.4
A5	1.0	1.0	6.0	3.0	5.0	3.2
A6	1.0	1.0	6.0	3.0	5.0	3.2
A7	1.0	1.0	6.0	3.0	5.0	3.2

indicates the results of the case studies.

It is worth mentioning that case studies A1 to A7 were based on the main optimization results per battery type. For example, case study A1 includes several optimization results and sensitive analyses. A brief explanation is shown in

Table 3. Baseline scenarios of battery type 1.

Case Study	PV Roof	PV Ground	Small Wind Turbine	Conventional Burner	Heat Pump	Micro-CHP	Energy Bought	Energy Sold
A.1.a	x			x			x
A.1.b	x				x		x
A.1.c	x					x	x
A.1.d	x			x			x	x
A.1.e	x				x		x	x
A.1.f	x					x	x	x
A.1.g	x	x	x	x			x
A.1.h	x	x	x		x		x
A.1.i	x	x	x			x	x
A.1.j	x	x	x	x			x	x
A.1.k	x	x	x		x		x	x
A.1.l	x	x	x			x	x	x

and

Table 4. Optimization Results of Baseline scenarios of battery type 1.

Case Study	Battery Cap. (KWh)	Total Emissions (kgCO2eq)	Battery Prod. Emissions (kgCO2eq)	Grid Purchase Emissions (kgCO2eq)	Micro-CHP Emissions (kgCO2eq)	Total Cost (Euro)
A.1.a	0.000	16,268,561.28	0.00	1,026,919.17	0.00	6,373,409
A.1.b	16.857	2,021,002.03	421.14	2,021,002.03	0.00	10,977,022
A.1.c	0.000	1,011,809.59	0.00	1,004,749.04	7060.55	5,472,434
A.1.d	0.000	16,268,561.28	0.00	1,026,919.17	0.00	6,373,409
A.1.e	0.000	2,021,002.03	0.00	2,021,002.03	0.00	10,977,022
A.1.f	0.000	1,011,809.59	0.00	1,004,749.04	7060.55	5,472,434
A.1.g	0.729	16,215,070.09	18.20	973,427.99	0.00	6,084,056
A.1.h	30.102	1,969,138.83	752.03	1,969,138.83	0.00	10,789,737
A.1.i	0.000	973,940.26	0.00	966,879.71	7060.55	5,271,947
A.1.j	0.730	16,208,676.92	18.25	967,034.81	0.00	6,050,638
A.1.k	27.694	1,007,411.11	691.87	1,007,411.11	0.00	5,634,508
A.1.l	87.763	926,532.78	2192.58	919,472.23	7060.55	6,166,452

.

The energy cost in each scenario reflects market prices and includes the cost of buying electricity from the grid and the potential revenue from selling energy back to the grid. For example, scenarios A1.b and A1.h, which used significant battery capacity (16.857 kWh and 30.102 kWh, respectively), showed a clear relationship between the installed battery capacity and the reduction in energy purchased from the grid, reducing the total cost of ownership despite the higher battery costs.

The environmental impact was clearly highlighted by the emissions data. For example, A1.a had the highest emissions at 16,268,561.28 kgCO₂eq, indicating a high dependence on grid electricity without a storage system. A1.b drastically reduced emissions to 2,021,002.03 kgCO₂eq, a clear demonstration of how effective battery storage can be in reducing the carbon footprint of microgrid operations.

The scenarios with medium battery capacities, such as A1.g and A1.j, had lower overall emissions and costs due to optimized energy management, further underpinning the environmental and economic benefits of integrating battery storage into microgrid systems.

Figure 2: Energy Exchange for Case Study A1.b.

Technology impact: lead–acid batteries (used in scenario A1.b) demonstrated limited storage capacity, especially in the winter when battery storage dropped, leading to higher grid dependency. The high fluctuation in energy storage between seasons reflects the limited lifespan and depth of discharge of lead–acid batteries. During peak summer months, the system stored up to 30% of generated energy, reducing grid reliance by over 50%, but during winter months, the limited capacity of the lead-acid battery resulted in increased grid consumption.

The NCA battery used in A1.g outperformed the lead–acid technology in terms of both energy efficiency and capacity, significantly reducing grid imports by storing a greater amount of renewable energy during high-generation months. The improved energy efficiency of the NCA battery reduced grid imports by 40% in summer and increased energy sales by 15%, showing the advantage of high-capacity battery systems in urban microgrids (Figure 3).

The combination of rooftop and ground PV systems paired with the NMC battery resulted in an optimized energy storage and a reduced grid reliance. This scenario highlights the long-term economic viability of NMC batteries in urban microgrid applications. By combining rooftop and ground PV systems, A1.j demonstrated nearly zero grid reliance during summer, with energy storage capacity sufficient to export 25% of the generated power to the grid, making this setup ideal for reducing operational costs and increasing energy self-sufficiency (Figure 4).

The metrics for load and energy generation provide detailed insights into the efficiency of energy use within the microgrid.

Rooftop PV generation varied from scenario to scenario, with A1.j, for example, generating 779 kWh from rooftop and ground PV, significantly reducing the need for imported energy. Export and import dynamics are key, with scenarios with higher battery capacities and efficient PV systems (such as A1.h and A1.l) showing lower import demand and potential for energy export, indicating greater self-sufficiency and economic viability.

Scenario A7, which operated without battery storage, serves as a benchmark for the effectiveness of the battery technologies used. The comparative analysis shows that a lack of storage leads to higher operating costs and emissions, as the dependence on the less stable and more expensive grid energy is greater.

The optimization models used in these scenarios weigh energy costs, efficiency, and environmental impact. They incorporate real-time data such as market prices, detailed weather forecasts, and dynamic demand profiles to simulate different operating strategies and their outcomes. This detailed modeling underscores the potential of advanced battery storage technologies to improve microgrid performance in multiple dimensions—economic, environmental, and operational.

This approach not only illustrates the direct benefits of specific battery technologies, but also provides a framework for evaluating broader energy strategies within microgrid applications. By analyzing detailed input data and results, such scenarios provide invaluable insights for optimizing the performance of microgrids in real-world environments.

4. Discussion

Based on the above results, the following general comments can be made.

In terms of sustainability, scenarios A2 and A3 showed a stronger commitment to sustainable practices due to the inclusion of more renewable energy sources or efficient energy management systems. Scenarios A5, A6, and A7 performed worse. They had configurations that relied more on conventional energy sources or less efficient energy management practices. The analysis underscored the positive environmental impact of using advanced batteries such as NCA (Type 4) and LFP (Type 3) batteries, which substantially lower emissions compared to conventional energy systems.

In terms of financial viability (FV), scenarios A1 and A2 had higher financial viability scores, possibly indicating more cost-effective solutions or better financial planning. They included technologies or strategies that resulted in higher initial costs but offered long-term savings. Conversely, scenarios A5, A6, and A7 had lower economic viability, which may be due to higher operating costs or less favorable investment returns. Scenarios A1 and A2 scored higher in financial viability, which suggests that while they may involve higher initial costs (due to more advanced technologies and larger infrastructure investments), they offer significant long-term cost savings. This was particularly visible in scenario A2, where the integration of NCA batteries led to favorable returns on investment. On the other hand, scenarios A5, A6, and A7 faced economic challenges, likely due to the higher operational costs and less favorable returns from their reliance on less efficient systems. These results indicate the necessity for careful financial planning and technology selection to maximize the financial benefits of microgrid investments.

In terms of the regional readiness for the integration of renewable energy sources, it was noteworthy that all scenarios scored consistently high for regional readiness, reflecting the region’s potential infrastructure and policy support for integrating renewable energy systems into microgrids. This indicates that the region is ready to adopt a variety of renewable energy technologies and that external barriers to renewable energy integration are minimal. This suggests that external factors, such as policy support and infrastructure development, are conducive to renewable energy deployment, which should encourage stakeholders to invest further in such systems.

The Energy Needs Index (ENI) was consistently rated as a 3 in all scenarios, indicating that each microgrid configuration was designed to adequately meet the region’s energy needs. This consistency demonstrates a standardized approach to calculating and meeting the region’s energy needs.

In terms of the Community Utilization Index (CUI), all scenarios received a score of 5 for community use, indicating a moderate level of community engagement and benefits gained from the microgrid, regardless of the configuration of the system. This reflects policies or cultural practices in the region that mandate or encourage community participation in energy projects. Moving forward, increasing community engagement could further enhance the social acceptance and long-term viability of microgrid projects.

The lead–acid (Type 1), NCM (Type 2), LFP (Type 3), NCA (Type 4), lithium–air (Type 5), and flow battery (Type 6) scenarios showed different capacity utilization, emission levels, costs and energy sales. Each type had its own advantages and disadvantages depending on costs, performance, mining rates, and strategic targets for utilization or energy sales. The LFP (Type 3) and NCA (Type 4) battery scenarios represented a balanced and robust choice for different configurations, while flow batteries (Type 6) proved to be a potentially cost-effective option for long-term scenarios and high-revenue strategies. Lead–acid batteries (Type 1) were less favored due to the higher emissions and lower performance, while lithium–air batteries (Type 5) were limited by high degradation rates, which negatively impacted their long-term economic and environmental performance. This detailed analysis based on actual results shows that while batteries such as LFP and NCA offer strong performance and balance for different scenarios, flow batteries are particularly valuable as they have the potential for long-term, cost-effective energy-storage strategies. The insights from these analyses are invaluable for stakeholders looking to refine their strategies, optimize system configurations, and align microgrid development with regional goals and societal values.

The conclusions drawn from scenario A7 highlight the transformative potential of battery technology. Battery storage systems significantly reduce operating costs and carbon emissions, offering both immediate and long-term benefits. There is a clear economic incentive to invest in battery storage, as evidenced by the significant cost savings between different battery types. The environmental benefits are equally exciting, with significant emission reductions highlighting the role of battery storage in achieving sustainability goals. Data show that a strategic shift towards battery storage could lead to a more resilient, efficient, and sustainable energy landscape. This confirms that battery storage is not only crucial for reducing grid dependency but also for achieving sustainability targets. The financial and environmental benefits highlighted in the data emphasize the strategic value of adopting battery storage technologies in future energy systems.

An important finding from the results is the evolving role of battery technology in microgrid performance. The detailed comparisons between different battery types (lead–acid, NCA, LFP, NMC, lithium–air, and flow) show how advances in storage technologies can directly influence both economic and environmental outcomes. For example, scenarios A3 and A4, which used LFP and NCA batteries, offered the best balance between cost-effectiveness and sustainability. These advanced batteries not only reduced dependence on the grid, but also had a higher energy density and longer lifespan, making them ideal for modern microgrid systems. In contrast, older technologies such as lead–acid batteries (scenario A1) cannot keep up due to their lower efficiency and higher emissions, highlighting the importance of using modern solutions. This underscores the urgent need for continued investment in research and development for battery innovations to further optimize the benefits of microgrid applications.

An important aspect that emerged from the data was the resilience of microgrid systems equipped with battery storage. The integration of advanced storage technologies provides a buffer against fluctuations in energy demand and renewable energy generation. Scenarios A1 to A6 clearly showed how the inclusion of batteries mitigates the risks associated with unpredictable energy generation from solar or wind power plants and leads to a more reliable energy supply. Scenario A7, the conventional system without battery storage, showed the vulnerability of microgrids that rely solely on the grid. These results highlight the value of flexibility and resilience in energy systems, particularly in regions where energy demand is highly volatile or where renewable energy sources can be intermittent.

From a policy perspective, the consistently high score for regional readiness for renewable energy integration (RRI) in all scenarios suggests that the region is well prepared for large-scale microgrid integration. However, scenario A7 highlighted the environmental and financial pitfalls of not investing in grid modernization and renewable energy integration. In order to exploit the potential of microgrids, the political framework must support the widespread use of battery storage systems, offer financial incentives for the modernization of outdated infrastructure, and simplify approval procedures for renewable energy projects. Focusing on Thessaloniki, Greece, this study provides valuable lessons for similar urban regions around the world and emphasizes the role of governance and strategic planning in facilitating the transition to greener, more sustainable energy systems.

One area for further discussion is the economic scalability of microgrid systems. While our results suggest that advanced batteries such as LFP and NCA offer significant long-term savings, the high initial costs associated with these technologies can be a barrier to initial investment. Scenario A2, for example, had a favorable ROI, but the initial costs may deter smaller communities or businesses from adopting such solutions. This raises the question of how financing models for microgrids can evolve to encourage wider adoption. Options such as public–private partnerships, community-owned energy systems, or government subsidies for renewable energy projects could help alleviate the financial burden of transitioning to advanced battery technologies. This is an important consideration to ensure that microgrids remain economically viable at different scales.

Another dimension of the results that merits further investigation is the social and environmental impact of the different microgrid configurations. While scenario A5 (lithium-air) offered potential cost benefits, the high degradation rates and associated environmental impacts may outweigh the benefits, especially when considering long-term sustainability. In addition, the Community Utilization Index (CUI) showed that community engagement was moderate in all scenarios, but could still be improved. Greater community involvement in microgrid projects could lead to greater acceptance, long-term viability, and wider adoption of microgrids. This could include educating communities about the benefits of local energy generation, incentivizing participation, and designing systems that directly benefit the local population, both economically and socially.

Critical analysis of the effectiveness of the methodology

Improved multi-dimensional assessment: The results of the MPIR framework show the importance of a multi-dimensional approach that balances economic, environmental, and social factors. In particular, scenario A1 showed strong performance in financial viability (FV) and sustainability, where the balance between reducing energy costs and carbon footprint was optimized through the integration of battery storage and renewable energy sources. Compared to traditional frameworks such as HOMER, which focus on cost optimization, the MPIR framework offers a broader perspective by also considering community engagement and regional readiness (RR). This is crucial for areas where community acceptance and local political support are critical to project success. Scenarios A2 and A3, for example, showed how greater community involvement leads to greater acceptance and engagement, which was not a focus in previous frameworks.

Incorporation of real-time performance adjustments: As can be seen in scenario A1, the inclusion of dynamic energy flows between renewables, storage, and the grid resulted in a more responsive and resilient system. For example, the case studies showed that the battery integration scenarios (A1–A6) resulted in less reliance on the grid, reducing costs and emissions in a way that is not captured by models without real-time adjustments. Sustainability and environmental aspects: While models such as HOMER or RETScreen focus on economic viability, they fall short when considering the full scope of environmental impacts. The results clearly showed that in MPIR, the use of advanced batteries, such as lithium–air (scenario A5) and flow batteries (scenario A6), leads to a significant reduction in greenhouse gas emissions compared to grid-dependent scenarios (A7). In addition, the holistic environmental analysis, including landscape and environmental suitability (LES), provided insights into long-term sustainability that were previously overlooked. Implications for long-term energy planning: The MPIR framework has been shown to lead to better decision outcomes in long-term energy planning by balancing multiple dimensions (financial, environmental, social). A comparison with the state-of-the-art in microgrid tools, such as hierarchical control systems, shows that the MPIR framework significantly improves decision-making processes by focusing on both stakeholder needs and regional policy. This is in contrast to tools that lack these socio-economic considerations, which can lead to misplaced priorities and limited success in implementation.

5. Conclusions

In conclusion, this study presented the Microgrid Performance and Investment Rating (MPIR) index as a ground-breaking tool developed to holistically assess the performance of microgrid systems by integrating economic, environmental, and social dimensions. The analysis underscored the importance of comprehensive assessment frameworks that can dynamically adapt to the complex interplay of technical, economic, and environmental factors inherent in microgrid operations. The MPIR index is characterized by its ability to consolidate multiple critical metrics—financial viability (FV), sustainability, regional renewable integration readiness (RRI), the energy demand index (ENI), and the Community Utilization Index (CUI)—into a single, quantifiable framework. This comprehensive approach enables stakeholders to make informed decisions that balance economic returns, environmental sustainability, and community engagement. By incorporating advanced optimization techniques, the MPIR provides a robust decision-making tool that takes into account renewable energy sources and energy demand. This ensures that microgrid configurations are not only technically sound, but also economically viable and environmentally sustainable. The case studies analyzed in this study demonstrated the practical application of the MPIR index in different scenarios and showed the performance of different battery technologies under real-world conditions. For example, scenarios with LFP and NCA batteries showed balanced performance in terms of sustainability and financial viability, while flow batteries proved to be a promising option for long-term, high-yield strategies despite their high initial cost. Conversely, scenarios without battery storage (A7) illustrated the limitations and higher operating costs associated with conventional energy systems.

The MPIR index represents a significant advance in the field of microgrid assessment and optimization. It provides a comprehensive, scalable, and adaptable tool that addresses the multiple challenges of modern microgrids and paves the way for more resilient, efficient, and sustainable energy systems. This study not only contributes to the academic literature, but also provides practical insights for policy makers, investors, and community stakeholders, supporting the transition to a more sustainable energy future.

Future research could focus on refining the MPIR index by integrating dynamic weighting calculation methods tailored to stakeholder preferences and using techniques such as the Analytic Hierarchy Process (AHP) or the Delphi method to improve adaptability. Furthermore, extending the application of the MPIR framework to different geographical and socio-economic contexts, particularly in emerging or developing countries, would provide valuable insights into its global relevance. Research could also investigate the long-term performance of microgrids under changing environmental and economic conditions to further validate the robustness of the framework for real-world applications. These avenues could contribute significantly to the holistic optimization of microgrid systems and their role in achieving global sustainability goals.