Network Energy Research Articles

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

The optimal scheduling method of the power grid considering the node coupling degree and the duality of mutual inverse mapping is studied to effectively establish the random dynamic scene of the power grid and improve its optimal scheduling effect. Considering the duality of the mutual inverse mapping, a random dynamic scene of the power grid is generated. The optimization model of the grid random dynamic scene partition was established considering the coupling degree of the nodes in the region. The anti-prey particle swarm optimization algorithm was used to solve the optimization model, and the scene partition results were obtained. To construct an optimization scheduling model for the regional power grid with the objective functions of minimizing energy abandonment rate and grid loss and the constraints of power flow, output, and climbing power. A predator-prey particle swarm optimization algorithm is used to solve the optimization scheduling model, and the minimum energy abandonment rate, minimum grid loss, and corresponding optimal scheduling strategies are obtained. The experimental results show that this method can effectively generate random dynamic scenes of a power grid and has a better scene partitioning effect. Under different working conditions, this method can achieve optimal dispatching of the power grid and reduce the power grid energy abandonment rate and network loss.

Increasing the accuracy of electrical energy accounting at reduced load

The object of research is a three-phase electricity metering unit, which includes a digital meter and measuring current transformers. The reduction of non-technological energy losses is restrained due to the insufficient accuracy of the accounting of electric energy in distribution power networks under a reduced load current of the metering unit. The possibility of representing the dependence of the relative error of electricity measurement on current values by a fuzzy function at reduced load has been confirmed. The boundaries of such a function are approximated with sufficient accuracy by the sum of two exponents, which is explained by its significant nonlinearity in the range of reduced current. The proposed EMRL software allows to estimate the real consumption and the most possible level of underaccounting based on the array of electricity meter readings. The accuracy of estimating by the EMRL the amount of electricity consumed with a probability of 0.7 can be estimated with a relative error not exceeding 2 %. The probability of psychophysical assessments of the accuracy of EMRL «very good» and «good» is at least 0.833. The trend of a significant decrease in the relative value of underaccounting with an increase in the level of electricity consumption was revealed. With a daily consumption of up to 10 kW·h, the amount of underaccounting can reach 18 %, and with a consumption of more than 20 kW·h, it does not exceed 6 %. The adequacy of the results of estimating the amount of consumed electricity at reduced load using the EMRL was confirmed by experimental data at a significance level of 0.05. The software capabilities allow to increase the accuracy of the accounting of electrical energy in distribution networks with a reduced load current of the metering unit. The program can be used as part of automated systems of commercial electricity metering or advanced metering infrastructure to determine the most possible underaccounting due to the operation of metering units at a reduced load

Optimal investment of distribution energy resources via energy performance contracts: An evolutionary game approach

Investment in distributed energy resources (DERs) is crucial for the expansion of distribution networks. However, traditional methods often lack comprehensive project management throughout their entire lifecycle and precise matching between distribution network operator (DNO) and energy consumer (EC) at the investment level. To address these issues, this paper proposes a novel investment model based on energy performance contracts (EPCs). Three EPC mechanisms are introduced: energy shared savings model (ESSM), energy guaranteed saving model (EGSM), and energy cost trust model (ECTM). Additionally, DERs investment strategies under EPC mechanisms are proposed to satisfy full-process management of project and precise matching between EC and DNO. The investment and operation problem is solved by an evolutionary game theory that ensures bounded rational agent to achieve satisfactory expected returns through repeated dynamic games. Results from the case study of an actual urban 37-bus system demonstrate that different EPC mechanisms are suitable for various scenarios depending on the EC's profit demand and project scale. The proposed model provides a comprehensive framework for DERs investment, considering the interests of both DNO and EC, and promotes the optimal expansion of distribution networks in a more practical and effective manner.

Multi-Agent DRL-Based Resource Scheduling and Energy Management for Electric Vehicles

With the emergence of vehicular edge computing (VEC) and electric vehicles (EVs), integrating computation and charging tasks presents challenges due to limited resources and dynamic vehicular networks. This research focuses on the joint optimization of computation offloading and charging scheduling in VEC networks. Specifically, we optimize the offloading factor, charging association variable, and charging rates to minimize the system delay and energy consumption by leveraging the multi-attributes of EVs in both information and energy networks. Considering the dynamic environment, we model the problem as a Markov Decision Process, and use the Multi-Agent Reinforcement Learning (MARL) algorithm MADDPG, with its centralized training and distributed execution mechanisms. Simulation results demonstrate that this approach significantly improves utility while reducing energy consumption and latency.

Privacy protection of communication networks using fully homomorphic encryption based on network slicing and attributes

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

Optimizing energy efficiency and routing in wireless sensor networks through genetic algorithm-based cluster head selection in a grid-based topology

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design.

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Fabricating High Strength Bio-Based Dynamic Networks from Epoxidized Soybean Oil and Poly(Butylene Adipate-co-Terephthalate).

Amid the rapid development of modern society, the widespread use of plastic products has led to significant environmental issues, including the accumulation of non-degradable waste and extensive consumption of non-renewable resources. Developing healable, recyclable, bio-based materials from abundant renewable resources using diverse dynamic interactions attracts increasing global attention. However, achieving a good balance between the self-healing capacity and mechanical performance, such as strength and toughness, remains challenging. In our study, we address this challenge by developing a new type of dynamic network from epoxidized soybean oil (ESO) and poly(butylene adipate-co-terephthalate) (PBAT) with good strength and toughness. For the synthetic strategy, a thiol-epoxy click reaction was conducted to functionalize ESO with thiol and hydroxyl groups. Subsequently, a curing reaction with isocyanates generated dynamic thiourethane and urethane bonds with different bonding energies in the dynamic networks to reach a trade-off between dynamic features and mechanical properties; amongst these, the thiourethane bonds with a lower bonding energy provide good dynamic features, while the urethane bonds with a higher bonding energy ensure good mechanical properties. The incorporation of flexible PBAT segments to form the rational multi-phase structure with crystalline domains further enhanced the products. A typical sample, OTSO100-PBAT100, exhibited a tensile strength of 33.2 MPa and an elongation at break of 1238%, demonstrating good healing capacity and desirable mechanical performance. This study provides a promising solution to contemporary environmental and energy challenges by developing materials that combine mechanical and repair properties. It addresses the specific gap of achieving a trade-off between tensile strength and elongation at break in bio-based self-healing materials, promising a wide range of applications.

Cryogenic energy assisted power generation utilizing low flammability refrigerants

Cryogenic carbon-neutral fuels are potential alternatives as future marine fuels, releasing waste cryogenic energy during regasification and waste thermal energy during combustion. Organic Rankine Cycles (ORCs), using flammable hydrocarbon working fluids, are the preferred waste energy reutilization technology, prioritized over Brayton and Kaline cycles due to their compact system configuration. However, hydrocarbon flammability and explosiveness poses a huge safety risk. Therein lies the novelty of this study which presents an advanced dynamic model of a cryogenic enhanced ORC utilizing low flammability hydrofluorocarbons as working fluids for simultaneous reutilization of waste thermal and cryogenic energy from carbon-neutral cryogenic fuels. The evaporation temperature exhibits a direct correlation with energy and an inverse correlation with the exergy performance. System overcharging leads to a drastic performance decline, while undercharging can be tolerated to a certain liquid-to-volume ratio until critical failure. Marine classification societies’ recommendations-based scenarios were employed to gauge the emission reduction potential of low flammability working fluids for cryogenic ORCs, pitted against traditional combustion technologies. A maximum specific net-work, thermal efficiency, exergy efficiency, and cryogenic energy efficiency of 45.64 kJ/kg, 10.43 %, 12.75 %, and 11.8 % was achieved, respectively, with 85 % reduction in GHG emissions, using R452B as the working fluid.

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy.

With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein-ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein-ligand docking method. The advantage of this method, called GalaxyDock-DL, lies in its minimal overfitting to the training data compared to several existing deep learning-based protein-ligand docking methods. Unlike some recent deep learning methods, it does not use information about known binding pocket center positions. Instead, the results of this docking method show a systematic dependence on the physical properties of the target protein-ligand complexes such as atomic thermal fluctuations and binding affinity. GalaxyDock-DL utilizes the global optimization technique of the conventional protein-ligand docking method, GalaxyDock, and a neural network energy function trained to stabilize the native state compared to non-native states, just as physical free energy does. This physical principle-based approach suggests directions not only for future structure prediction involving structurally flexible biomolecular complexes but also for predicting binding affinity, thereby providing guidance for the effective design of biofunctional ligands.

Uncovering an emerging policy direction for Australian energy and future fuels using a “participatory decision-making” framework

IntroductionAn online deliberative engagement process was undertaken with members of the general public to understand what they value or would like to change about the energy system, within the broader context of decarbonizing Australia's energy networks, identifying a role for future fuels (hydrogen and biogas). Citizens developed a set of principles that could guide Australia's path toward a low-carbon energy future, reflecting on expectations they place upon energy transition. Next, citizens' principles were shared with policy-makers in government and policy-influencers from the energy industry using an online interactive workshop.MethodsThis study analyses policy-makers and -influencers response to citizens' guiding principles using the 'diamond of participatory decision-making' framework for analysis. Convergence and divergence in diverse, complex and rich views across cohorts and implications thereupon energy policy were identified.ResultsAlthough considerable alignment between multi-stakeholders' views was noted, key areas of divergence, or what is called the “groan zone” were easily identified in relation to social and environmental justice issues. This groan zone highlights the struggles that energy policy-makers face -the need to listen and respond to citizens' voices, vs. the need for practical and workable policies that also support overarching government or industry objectives.DiscussionPolicy making when the views of different stakeholders align is relatively straightforward. However, this is not the case where the expectations diverge. More creative measures will be needed to address divergent views and expectations whilst maintaining procedural fairness, in this case, using democratic deliberative engagement processes. While the use of deliberative processes is gaining momentum worldwide, particularly concerning climate change and energy transition policies, this paper also highlights the benefits of conducting a robust post facto analysis of the content of the processes. Areas of alignment, where policy can be made and implemented relatively easily without contention are identified. Other areas (such as making electrification mandatory) might be more complex or have unwanted negative social and environmental justice effects. Overall, this paper bridges an analytical gap between “expectation studies” and participatory research. By borrowing terminology from a participatory research framework we sharpen the concepts in “expectation studies” from a consensus, inclusion and diversity standpoint.

Super-Durable, Tough Shape-Memory Polymeric Materials Woven from Interlocking Rigid-Flexible Chains.

Developing advanced engineering polymers that combine high strength and toughness represents not only a necessary path to excellence but also a major technical challenge. Here for the first time a rigid-flexible interlocking polymer (RFIP) is reported featuring remarkable mechanical properties, consisting of flexible polyurethane (PU) and rigid polyimide (PI) chains cleverly woven together around the copper(I) ions center. By rationally weaving PI, PU chains, and copper(I) ions, RFIP exhibits ultra-high strength (twice that of unwoven polymers, 91.4±3.3MPa), toughness (448.0±14.2MJ m-3), fatigue resistance (recoverable after 10000 cyclic stretches), and shape memory properties. Simulation results and characterization analysis together support the correlation between microstructure and macroscopic features, confirming the greater cohesive energy of the interwoven network and providing insights into strengthening toughening mechanisms. The essence of weaving on the atomic and molecular levels is fused to obtain brilliant and valuable mechanical properties, opening new perspectives in designing robust and stable polymers.

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Analyzing Human-Centric Wireless Energy Harvesting for Sustainable and Resilient Energy Systems

Discussions around wireless communications and energy harvesting in the literature are elitist and techno-centric. There is a need for more studies on a human-centric approach that simplifies the concept and links energy harvesting with human empowerment and well-being. Also, studies so far on the implementation of wireless energy harvesting in rural settlements have been scanty despite the important role wireless communication plays in social interactions and financial transactions within rural communities. The challenge of little awareness of the human sustainability impact of WEH is impeding stakeholders’ advocacy and advancement of wireless energy harvesting (WEH) despite its real and perceived benefits. So far, research efforts as reported in the literature are less than proportionate to the potential of WEH. Apart from providing sustainable energy for wireless devices and networks, its generation of nonelectric renewable energy is a huge plus for ensuring access to affordable, reliable, sustainable, and modern energy for all as contained in the United Nations Sustainable Development Goal 7 (SDG 7). Given the astronomical growth in the use of wireless devices globally, more research studies are needed to create more awareness for massive WEH advocacy. This study uses a model-driven approach to simplify and highlight critical aspects of WEH such as wireless body area network, sources of non-electric renewable energy sources, green energy (environmental protection), intermittent nature of renewable wireless energy sources, and research directions. Also, the application of WEH for the sustenance of lives and livelihood in rural economies is examined using the rural community of Adum-Aiona in Nigeria as a case study. It is expected that the information provided will create more awareness and foster further discussions around the human-centric angle of WEH even as advances in technology continue to be made.

Design of an optimized energy-efficient routing protocol for reliable wireless body area networks

Energy limitation is one of the essential parameters in the design of a Wireless body area networks (WBANs) as it is important to improve the lifetime of the network. WBAN routing is an effective approach for establishing energy efficiency sets and assign time slots for the network. Many algorithms that deal with interference model treats the whole WBAN as a minimum interference unit and increase their lifetime cycle. In this research, we report an effective low-energy adaptive clustering hierarchy (LEACH) routing protocol using MATLAB simulation and related C++ simulation codes to enhance the overall performance of the network by improving the energy efficiency and network lifetime cycles. Furthermore, the study sheds light up on the comparison of the protocol and proposes a modified protocol for WBAN. Based on the results obtained from conducting different configurations in the proposed design, the base station should be situated near the network to insure high network performance.

Data-driven evaluation for quantifying energy resilience in distribution systems with microgrids and P2P energy trading

Electrical distribution systems (EDS) face the major issue of complete interruption of power when the supply end of the electrical grid encounters extreme events. The integration of distributed energy resources (DERs) at the residential level has opened the path to building energy resilience within these systems, enabling them to withstand such incidents at the load end. To further enhance energy resilience, peer-to-peer (P2P) energy trading networks could be established at the community level. While several researchers have developed models to implement energy trading in the P2P network with DERs, enabling prosumers to benefit from cost savings, the impact of both DERs and P2P energy trading on the system’s resilience has not been evaluated with a quantifiable measure. In this work, we propose a methodology introducing a quantifiable measure for evaluating energy resilience in electrical distribution systems with DERs and P2P energy trading by calculating the percolation threshold (PT) using complex networks for both univariate and multivariate data. We employ cooperative game theory to model P2P energy trading, ensuring that all prosumers participate rationally. We consider the formation of microgrids in a standard IEEE-123 node test feeder system integrated with renewable energy sources. The improvement in energy resilience with trading of energy in the P2P network is analyzed for the most resilient microgrid in the distribution system. The results demonstrate a 67.91% total cost benefit and a 25.07% improvement in the resilience of the microgrid due to the integration of DERs and P2P energy trading for a period of one year.