Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid

Abstract

Distributed power resources (DPRs) offer a transformative opportunity to improve the efficiency, sustainability, and reliability of modern power infrastructures through their integration. This work presents a novel method based on a mix of renewable energy sources, energy storage technologies, and conventional generators for the optimization of DPR operations under dynamic market settings. Maximizing economic gains is the major objective while preserving system resilience and stability. To handle the complexity of DPR interactions, we offer a strong, hierarchical control architecture encompassing main, secondary, and tertiary levels. System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. Results indicate cost reductions of approximately 54.7%, which were achieved by effectively prioritizing renewable sources and optimizing energy storage usage. This research contributes both theoretically and practically to accelerating the transition toward sustainable, resilient, and economically viable power systems.

1. Introduction

The global energy sector stands at a pivotal juncture, compelled by the urgent need to forge sustainable, resilient power systems capable of meeting modern demands. Central to this transformation is the rise of distributed power resources (DPRs), a diverse array of technologies including renewable energy sources, energy storage systems, and flexible loads that steadily dismantle the long-standing dominance of centralized power generation [1]. These resources promise a future of enhanced grid reliability, operational efficiency, and environmental sustainability [2]. Nevertheless, their integration into existing systems is far from straightforward, introducing a tangle of technical, economic, and regulatory challenges that demand innovative solutions [3].

Managing DPRs within a market-driven framework is a multifaceted endeavor, requiring a delicate balance between optimizing individual resource performance and maintaining the stability of the broader grid [4]. As power systems shift toward decentralization, advanced control strategies have become indispensable [5]. Concurrently, market mechanisms must adapt to accommodate the unique characteristics of these resources, such as their intermittency, scalability, and geographical dispersion [6,7]. From a regulatory standpoint, policies must evolve to reconcile the interests of DPR owners, utilities, and consumers, fostering an environment that encourages investment and innovation [8,9]. Recent advancements highlight the transformative role of artificial intelligence (AI) and machine learning in optimizing DPR performance, enabling real-time adaptability to fluctuating grid conditions and market signals [10,11]. Despite these developments, critical gaps persist in achieving seamless DPR integration. Virtual power plants (VPPs) have gained traction as a means of aggregating diverse DPRs, yet their scalability and coordination across hierarchical control levels remain underexplored [12,13]. Expanding on this theme, a novel market structure that incentivizes DPR owners to provide ancillary services, thereby enhancing grid stability and creating new revenue streams [14].

Transactive energy has emerged as a promising approach for DPR market integration. A blockchain-based transactive energy platform that enables peer-to-peer energy trading among DPR owners demonstrates the potential for decentralized market mechanisms [15]. Further exploring this idea, a game–theoretic model for optimizing DPR participation in local energy markets highlights the complex interactions between technical constraints and economic incentives [16]. Energy storage and demand response, often hailed as cornerstones of flexibility, must be tightly coupled with DPRs to maximize system resilience, especially under high penetration scenarios [17]. As digital interconnectivity deepens, cybersecurity emerges as a paramount concern, requiring robust frameworks to protect DPR systems from evolving threats [18].

The role of government incentives in promoting DPR adoption has been critically examined, as well as the effectiveness of various subsidy schemes for distributed energy resources, which provide insights into optimal policy design [19]. Moreover, the environmental imperative driving DPR adoption demands a thorough assessment of their lifecycle impacts and their contributions to broader sustainability goals, including synergies with electric vehicles (EVs) and smart grid technologies [20].

Ensuring grid stability and reliability in the presence of high DPR penetration remains a significant challenge. Recent research has focused on developing innovative solutions to address this issue. A robust control strategy for maintaining voltage stability in distribution networks with high DPR penetration demonstrates improved system performance under various operating conditions [21]. Advanced metering infrastructure (AMI) and edge computing further promise to enhance DPR operation by providing high-resolution data and reducing latency; yet, their full potential remains untapped [22].

The concept of virtual power plants (VPPs) has gained traction as a means of aggregating and coordinating DPRs for improved grid integration. A VPP management system that optimizes the collective operation of diverse DPRs demonstrates enhanced reliability and market performance [23]. Further exploring this approach, a hierarchical control framework for VPPs that balances local autonomy with system-wide coordination addresses the scalability challenges associated with DPR integration [24].

The role of energy storage in supporting DPR integration has been a focal point of recent research. Various energy storage technologies for enhancing the flexibility and reliability of DPR-based systems provide insights into optimal storage sizing and placement [25]. Building on this work, a coordinated control strategy for DPRs and energy storage systems demonstrate improved economic performance and grid support capabilities [26].

The concept of demand response as a form of virtual energy storage has also received significant attention. A novel demand response program that leverages the flexibility of distributed loads to support DPR integration showcases the potential for consumer participation in grid management [27]. Complementing this research, a machine-learning approach for predicting and optimizing demand response potential in DPR-rich environments highlights the importance of advanced forecasting techniques in maximizing system flexibility [28].

As DPRs become increasingly interconnected and reliant on digital technologies, cybersecurity has emerged as a critical concern. Recent studies have focused on developing robust security measures for DPR systems. A blockchain-based security framework for protecting DPR communications and control systems demonstrates enhanced resilience against cyber-attacks [29]. Extending this work, a multi-layered security architecture for DPR networks emphasizes the importance of comprehensive protection strategies [30].

The concept of cyber-physical resilience in DPR-based systems has gained prominence. A resilient control algorithm that enables DPRs to maintain critical operations during cyber–physical disturbances showcases improved system reliability under adverse conditions [31]. Further exploring this theme, a risk assessment framework for identifying and mitigating vulnerabilities in DPR systems highlights the need for proactive security measures in an evolving threat landscape [32].

The environmental benefits of DPRs have been key drivers of their adoption, and recent research has focused on quantifying and optimizing these impacts. The life cycle assessment of various DPR technologies provides insights into DPR’s long-term sustainability and carbon footprint [33]. Building on this work, an optimization model for maximizing the environmental benefits of DPRs within specific regional contexts emphasizes the importance of tailored deployment strategies [34].

The role of DPRs in supporting broader sustainability goals has also been examined. The potential of DPRs contribute to urban sustainability initiatives highlights synergies with smart city technologies and green infrastructure [35]. Complementing this research, the social and economic co-benefits of DPR deployment in rural communities demonstrate the multifaceted impacts of distributed energy systems on sustainable development [36].

The synergies between DPRs, electric vehicles (EVs), and smart grid technologies have been a focus of recent studies. An integrated control strategy for coordinating DPRs and EV charging infrastructure showcases improved grid stability and renewable energy utilization [37]. Expanding on this concept, a vehicle-to-grid (V2G) system that leverages EVs as mobile energy storage units for supporting DPR integration demonstrates enhanced grid flexibility and reliability [38,39].

The development of effective market designs and pricing mechanisms for DPRs remains an active area of research. Recent studies have focused on creating more equitable and efficient market structures. The dynamic pricing model that reflects the real-time value of DPR-generated electricity incentivizes optimal resource allocation [40,41]. Building on this work, a multi-sided platform for DPR transactions facilitates direct interactions between producers, consumers, and grid operators [42,43].

Our contributions extend beyond theoretical insights, offering practical strategies for next-generation power systems. We refine market designs with locational marginal pricing and multi-sided platforms tailored to DPR-rich environments, which ensure efficient resource allocation. Complementing this research, a hybrid market model that combines centralized and decentralized elements balances system-wide efficiency with local autonomy in DPR operations [44,45].

The concept of edge computing for DPR control has emerged as a promising approach for reducing latency and enhancing system responsiveness. An edge-based control architecture for DPRs that enables real-time optimization and fault detection showcases improved system performance and reliability. Ultimately, this study lays a foundation for a grid that is not only reliable and efficient but also environmentally sound, leading to integrating insights from life cycle assessments and smart city initiatives for shaping a sustainable energy ecosystem [46,47,48].

Our research tackles these challenges by delivering a comprehensive analysis of DPR operation and control within market environments, resulting in synthesis of cutting-edge advancements in control algorithms, market structures, and regulatory approaches. Unlike prior studies that often isolate technical or economic dimensions, our work adopts an integrative lens, illuminating synergies and trade-offs across these domains. We propose a novel hierarchical control framework that optimizes DPR performance across primary, secondary, and tertiary levels, balancing local autonomy with system-wide coordination [25,26]. Additionally, we introduce an innovative market participation model that leverages real-time data and predictive analytics to enhance economic outcomes while upholding grid reliability [27,28]. Through practical case studies, such as the operation of a small island grid, we demonstrate the applicability of our approach under diverse conditions, incorporating insights from demand response and storage optimization [29,30].

The structure of this paper is as follows: Section 2 outlines our methodologies and instruments, including the system description, mathematical modeling, and execution techniques. Section 3 summarizes the results of our case study, comparing system performance before MILP optimization and after PSO. Section 4 analyses these results and their implications for DPR integration. Section 5 offers directions for future investigations.

2. Materials and Methods

This methodology outlines the steps for operating and controlling distributed power resources (DPRs) within a market environment. The goal is to optimize the use of DPRs while ensuring stability, reliability, and profitability.

2.1. System Description and Assumptions

The system under consideration integrates various distributed power resources, operates within a defined market environment, and employs a structured control framework to ensure efficient and reliable operation.

• Distributed power resources (DPRs): These include renewable energy sources (solar PV and wind turbines), energy storage systems (ESS), and conventional distributed generators.
• Market environment: Comprises day-ahead and real-time markets for energy and ancillary services.
• Control framework: A hierarchical control structure with primary, secondary, and tertiary control levels.

2.2. Mathematical Modeling

2.2.1. Objective Function

The primary objective is to maximize the profit of DPRs while maintaining system reliability.

$$\mathrm{M}\mathrm{a}\mathrm{x}\sum _{\mathrm{t}=1}^{\mathrm{T}}({\mathrm{P}}_{\mathrm{t}}·{\lambda }_{\mathrm{t}}-{\mathrm{C}}_{\mathrm{t}})$$

(1)

where ${\mathrm{P}}_{\mathrm{t}}=\sum _{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{P}}_{\mathrm{i},\mathrm{t}}$ is the power generated by DPRs at time t, ${\lambda }_{\mathrm{t}}$ is the market price of electricity at time t, ${\mathrm{C}}_{\mathrm{t}}$ is the cost of operation at time t, and N is the number of DPRs.

2.2.2. Power Balance Constraint

The power generated must meet the demand and losses.

$$\begin{gathered} \sum _{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathrm{D}}_{\mathrm{t}}+{\mathrm{L}}_{\mathrm{t}} \\ {\mathrm{C}}_{\mathrm{t}}=\sum _i(c_i·{\mathrm{P}}_{\mathrm{i},\mathrm{t}})+c_{ESS,t} \end{gathered}$$

(2)

where ${\mathrm{P}}_{\mathrm{i},\mathrm{t}}$ is the power output of DPR i at time t, ${\mathrm{D}}_{\mathrm{t}}$ is the demand at the time t, ${\mathrm{L}}_{\mathrm{t}}$ is the loss at time t, $c_i$ represents unit-specific costs, and $c_{ESS,t}$ represents ESS cycling costs.

2.2.3. Capacity Constraints

Each DPR has a maximum and minimum capacity.

$${\mathrm{P}}_{\mathrm{i}}^{\mathrm{m}\mathrm{i}\mathrm{n}}\le {\mathrm{P}}_{\mathrm{i},\mathrm{t}}\le {\mathrm{P}}_{\mathrm{i}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$$

(3)

where ${\mathrm{P}}_{\mathrm{i}}^{\mathrm{m}\mathrm{i}\mathrm{n}}$ is the minimum power output of DPR i, and ${\mathrm{P}}_{\mathrm{i}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$ is the maximum power output of DPR i.

2.2.4. Renewable Energy Constraints

For renewable sources, the power output depends on the availability of resources.

$$0\le {\mathrm{P}}_{\mathrm{r},\mathrm{t}}\le {\mathrm{P}}_{\mathrm{r}}^{\mathrm{m}\mathrm{a}\mathrm{x}}·{\mathrm{A}}_{\mathrm{t}}$$

(4)

where ${\mathrm{P}}_{\mathrm{r},\mathrm{t}}$ is the power output of renewable resource r, at time t, and ${\mathrm{A}}_{\mathrm{t}}$ is the availability factor at time t.

2.2.5. Storage Constraints

For energy storage systems, the state of charge (SoC) must be maintained within limits.

$${\mathrm{S}\mathrm{o}\mathrm{C}}_{\mathrm{m}\mathrm{i}\mathrm{n}}\le {\mathrm{S}\mathrm{o}\mathrm{C}}_{\mathrm{t}}\le {\mathrm{S}\mathrm{o}\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$$

(5)

The SoC dynamics are given by the following expression:

$${\mathrm{S}\mathrm{o}\mathrm{C}}_{\mathrm{t}+1}={\mathrm{S}\mathrm{o}\mathrm{C}}_{\mathrm{t}}+{\eta }_{\mathrm{c}\mathrm{h}}·{\mathrm{P}}_{\mathrm{c}\mathrm{h},\mathrm{t}}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{s},\mathrm{t}}}{{\eta }_{\mathrm{d}\mathrm{i}\mathrm{s}}}}$$

(6)

where ${\mathrm{S}\mathrm{o}\mathrm{C}}_{\mathrm{t}}$ is the state of charge at time t, ${\mathrm{P}}_{\mathrm{c}\mathrm{h},\mathrm{t}}$ is the charging power at time t, ${\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{s},\mathrm{t}}$ is the discharging power at time t, ${\eta }_{\mathrm{c}\mathrm{h}}$ is the charging efficiency, and ${\eta }_{\mathrm{d}\mathrm{i}\mathrm{s}}$ is the discharging efficiency.

2.2.6. Market Participation

DPRs participate in both day-ahead and real-time markets.

In the day-ahead market, DPRs submit bids based on forecasted generation and demand.

$${\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{D}\mathrm{A}}\le {\mathrm{P}}_{\mathrm{i}}^{\mathrm{m}\mathrm{a}\mathrm{x}}·{\mathrm{A}}_{\mathrm{t}}$$

(7)

where ${\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{D}\mathrm{A}}$ is the day-ahead scheduled power for DPR i at time t.

In the real-time market, DPRs adjust their output based on actual conditions.

$${\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{R}\mathrm{T}}={\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{D}\mathrm{A}}+∆{\mathrm{P}}_{\mathrm{i},\mathrm{t}}$$

(8)

where ${\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{R}\mathrm{T}}$ is the real-time power output for DPR i at time t, and $∆{\mathrm{P}}_{\mathrm{i},\mathrm{t}}$ is the adjustment for a day-ahead schedule based on real-time conditions.

2.2.7. Control Strategies

Primary control is responsible for maintaining system frequency within acceptable limits by adjusting the power output of DPRs.

$$∆{\mathrm{P}}_{\mathrm{i},\mathrm{t}}=-{\mathrm{K}}_{\mathrm{f}}·∆{\mathrm{f}}_{\mathrm{t}}$$

(9)

where ${\mathrm{K}}_{\mathrm{f}}$ is the frequency droop coefficient and $∆{\mathrm{f}}_{\mathrm{t}}$ is the frequency deviation at time t.

Secondary control restores the system frequency to its nominal value and ensures proper sharing of load among DPRs.

$$∆{\mathrm{P}}_{\mathrm{i},\mathrm{t}}={\mathrm{K}}_{\mathrm{p}}·({∆P}_{imb,t})+k_i·\int {∆P}_{imb,t}dt$$

(10)

where ${\mathrm{K}}_{\mathrm{p}}$ an $k_i$ are the proportional gains of PI, and ${∆P}_{imb,t}$ is the power imbalance at time t.

Equation (11) shows a tertiary control that optimizes the economic dispatch of DPRs over a longer time horizon, and considers market prices and system constraints.

$$\mathrm{M}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{z}\mathrm{e}\sum _{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{C}}_{\mathrm{t}}+\sum _{\mathrm{i}=1}^{\mathrm{N}}{\lambda }_{\mathrm{t}}·{({\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{R}\mathrm{T}}-{\mathrm{P}}_{\mathrm{i},\mathrm{t}}^{\mathrm{D}\mathrm{A}})}^2$$

(11)

2.3. Implementation Steps

The implementation process involves several key steps. First, historical data on demand, generation, and market prices must be collected, while the forecasting models should be used to predict future demand, renewable generation, and market prices. Next, the optimization problem is formulated and employed to incorporate the objective function, constraints, and appropriate optimization solvers, such as linear programming or mixed-integer programming. Following this, control algorithms for primary, secondary, and tertiary control levels are developed and implemented within a hierarchical control framework. The system is then simulated under various market scenarios to test these control algorithms, ensuring stability and performance. Finally, the control algorithms are deployed for real-time operation, where the system continuously monitors and adjusts the distributed power resources (DPRs) based on real-time data.

2.3.1. Case Study

This case study focuses on the operation and control of distributed power resources (DPRs) within a small island grid. The island has a mix of solar PV, wind turbines, a diesel generator, and an energy storage system. The objective is to maximize the economic benefit while ensuring a reliable power supply to the island’s residents. The analysis covers a full 24-hour period to capture the day-to-day variations in demand and generation.

2.3.2. Distributed Power Resources (DPRs)

The energy system comprises a 5 MW solar PV array, 3 MW wind turbines, and a 4 MW diesel generator, supported by an energy storage system (ESS) with a 2 MWh capacity and a 1 MW charge/discharge rate. The system operates in a market environment that includes day-ahead and real-time energy markets.

2.3.3. Data Input

Electricity demand fluctuates between 2 MW and 10 MW throughout the day, peaking at midday and evening hours. Solar (PV) generation starts contributing from the sixth hour, reaching a maximum of 5 MW by the eighth and ninth hours, while wind generation consistently provides power between 1 MW and 3 MW. Diesel generation compensates for the variability in renewable energy, with higher reliance during periods of lower renewable output, especially in the evening. Market prices for electricity range from EUR 20/MWh to EUR 55/MWh, generally increasing as demand rises, with the highest prices observed during periods of peak demand and lower renewable generation.

The diesel generator operates at EUR 150/MWh, while the energy storage system (ESS) has an efficiency of 90% for charging and discharging. The maximum capacities for the energy resources are as follows: 5 MW for solar PV, 3 MW for wind, 4 MW for the diesel generator, and 1 MW for both charging and discharging of the ESS. The ESS has a state of charge (SoC) that can range between 0.2 MWh and 2 MWh, with an initial SoC set at 1 MWh. The simulation used MATLAB R2024a on a Dell Inspiron 14 5430, a 64-bit PC running Microsoft Windows 11 Home. The machine is equipped with a 13th Gen Intel Core i7-1360P processor, operating at 2200 MHz, with 12 cores and 16 logical processors from Beijing, China.

3. Results

3.1. System Before Optimization

Figure 1 and Figure 2 illustrate the contribution of different energy sources, such as solar PV, wind turbines, diesel generators, and energy storage systems (ESS), in meeting the island’s energy needs over 24 h.

3.2. MILP Optimization

Figure 3 presents the optimized operation of the distributed power resources using mixed-integer linear programming (MILP). That optimization helps the system better match the demand curve, suggesting improved efficiency. Renewable sources are prioritized and diesel generator usage and operational costs are reduced. The ESS is effectively used to smooth out fluctuations, resulting in enhanced stability and reliability.

Figure 4 illustrates the market costs associated with operating the power system in its pre-optimization state.

Figure 5 illustrates the market costs after the implementation of MILP optimization strategies. There is a notable reduction in costs across the majority of hours, particularly during periods of peak demand. The optimization strategy reduces the reliance on expensive diesel generation by utilizing more cost-effective renewable resources and storage solutions.

• Initial total cost before MILP: EUR 12,150.
• Total cost after MILP: EUR 9383.
• Cost reduction: EUR 2767.

Figure 6 illustrates the state of charge (SoC) of the energy storage system (ESS) over a day. The ESS charges during low-demand times by utilizing excess renewable energy. It discharges during high-demand periods, reducing the need for diesel generation. The SoC remains within optimal limits and demonstrates effective management.

3.3. PSO

Figure 7 illustrates the operation of the distributed power resources after particle swarm optimization (PSO). This presents further improvements in resource allocation compared to MILP. It enhances the utilization of renewables and storage, resulting in minimized diesel usage. Also, the system closely follows demand patterns, resulting in a maximized efficiency.

Figure 8 shows the state of charge of the ESS following PSO implementation. The PSO presents more strategic charging and discharging compared to pre-optimization. The ESS is used effectively to minimize costs and emissions. The optimization of ESS maintains SoC within limits and ensures reliability in this way.

Figure 9 presents the market costs after the implementation of PSO control measures. Furthermore, there is a potential to achieve cost savings in comparison to the MILP approach, particularly during periods of elevated demand. This figure demonstrates the efficacy of PSO in optimizing market participation and reducing expenses. The results indicate a significant reduction in costs and an improvement in economic performance.

• Initial total cost before PSO: EUR 12,150.
• Total cost after PSO: EUR 5510.
• Cost reduction: EUR 6640.

4. Discussion

The above strategies aim to optimize resource allocation, reduce operational costs, and enhance system efficiency.

• System Before Optimization:

The initial energy system relies heavily on diesel generators supplemented by solar PV, wind turbines, and energy storage systems (ESS) to meet energy demands. This configuration results in high operational costs and inefficiencies, primarily due to the significant dependence on diesel generation. The initial system significantly relies on diesel generators, accounting for around 40% of total energy provision, with associated high costs (EUR 150/MWh) that contribute to the total daily operational cost of EUR 12,150.

• MILP Optimization:

MILP reduced the reliance on diesel generation from 4 MW to approximately 2.8 MW during peak hours, thus achieving operational cost savings of EUR 2767 (22.8%). Renewable resource utilization increased, with solar and wind collectively reaching around 75% of total demand at peak generation hours. The ESS SoC management became optimal, effectively maintaining power between 0.4 MWh and 1.8 MWh, leading to stabilization of fluctuations and enhancement of system reliability.

• PSO:

PSO provided further optimization, significantly reducing diesel generator usage down to approximately 2 MW during peak periods. It further enhanced renewable integration, with solar and wind energy consistently meeting approximately 60% of peak demand. ESS operation improved substantially, with a strategic state of charge (SoC) ranging between 0.3 MWh and 1.8 MWh and achieving additional cost reductions. The total daily operational cost dramatically reduced to EUR 5510, marking a further saving of EUR 3873 compared to MILP and EUR 6640 (54.7%) compared to the original scenario.

The research indicates that PSO is the more effective optimization algorithm, achieving a cost reduction of approximately 54.7% compared to the system before optimization. The strategic use of ESS and increased reliance on renewable resources are crucial factors in these achievements, underscoring the importance of advanced optimization techniques in energy management systems.

5. Conclusions

The primary aim of this study was to explore the operational strategies and control mechanisms for distributed power resources (DPRs) within a market environment. This objective was pursued through a combination of theoretical modeling, simulation, and empirical analyses, enabling a comprehensive understanding of the interaction between market dynamics and DPR operation.

Methodologically, this study employed advanced optimization techniques and control algorithms to model the behavior of DPRs. A robust simulation framework was established, integrating real-world data to validate the theoretical models. The approach included sensitivity analyses for assessing the performance of DPRs under varying market conditions and operational scenarios.

Key quantitative findings include the following:

• Total operational costs were reduced significantly by approximately 54.7%, from EUR 12,150 before optimization to EUR 5510 after PSO implementation, highlighting PSO’s superior efficiency.
• Renewable energy utilization notably improved, with renewables consistently meeting around 60% peak demand, thereby substantially reducing reliance on diesel generation.
• Energy storage system (ESS) efficiency and effectiveness improved considerably, resulting in the optimization of the state-of-charge (SoC) management within the range of 0.3 MWh to 1.8 MWh.

These findings underscore the potential of DPRs to transform modern power systems by enhancing efficiency, reliability, and sustainability. However, this study also identifies several areas for future research. Future research directions are articulated, emphasizing the exploration of advanced financial mechanisms, scalability testing of optimization frameworks on larger-scale grids, and enhancing predictive control strategies to further bolster the integration and management of renewable resources.

In conclusion, this research study provides a foundational framework for the operation and control of distributed power resources in a market environment, offering valuable insights for policymakers, industry stakeholders, and researchers. The advancements of DPR technologies and their integration into market structures represent pivotal steps toward achieving resilient and sustainable energy systems.