The energy management (EM) of smart multi-carrier energy hub (SMCEH) systems for cost and emission reduction remains a challenging problem due to the diversity of renewable energy resources (RERs), varying load demands, and the stochastic nature of these resources. This paper addresses the EM problem of SMCEHs to minimize operational costs and greenhouse gas (GHG) emissions using the particle swarm optimization (PSO) algorithm. The studied SMCEHs are designed to simultaneously supply electrical, cooling, and thermal demands. The hub system comprises wind turbines (WTs), photovoltaic (PV) panels, gas turbines (GT), electric chillers (EC), gas boilers (GBs), absorption chillers (AC), battery storage systems, and thermal storage units. To assess system performance and the impact of key technologies, three case studies are investigated: (i) EM of SMCEHs without RERs, (ii) EM of SMCEHs with RERs, and (iii) EM of SMCEHs with RERs and an integrated carbon capture unit (CCU). These scenarios enable a systematic evaluation of the role of renewable integration and carbon capture in enhancing system performance. The results demonstrate that incorporating RERs into SMCEHs leads to a substantial reduction in both operational costs and GHG emissions. Furthermore, the integration of a CCU provides additional emission reductions, underscoring its effectiveness in supporting the low-carbon operation of SMCEHs. The obtained results show that integrating RERs into SMCEH decreases the total cost and emissions by 64.12% and 7.95%, respectively, compared to the scenario without RERs. Furthermore, the integration of the CCU into SMCEHs provides a 39.36% reduction in total costs and a 72.57% decrease in CO2 emissions. The suggested energy management solution promotes a sustainable and low-carbon emission system by maximum utilization of the RERs and CCU.

Due to the intermittent feature of the highly capacities of the renewable energy sources (RESs) along with the fluctuating loads pose significant problems in the distribution network (DN) operations. Consequently, the DN is supported by storage units for alleviating such problems through storing the excessive RESs’ energy and supplying it during peak hours. In this context, this present work is a novel stochastic energy management system (EMS) for enhancing the techno-economic and environmental performance of the reconfigured IEEE 33-bus DN through the optimal allocation of the RESs including photovoltaic (PV) panels and wind turbine (WT) along with hydrogen storage system simultaneously depending on the Modified Golf Optimization Algorithm (MGOA). The proposed EMS employs the Monte Carlo Simulation (MCS) and Backward Reduction Method (BRM) for modelling the uncertainties in weather conditions, load demand, and electricity prices. The planned MGOA’s ability is verified through comparing its results with other optimization methods on benchmark functions and the demonstrated DN using statistical analysis and the Friedman test. The output finding indicates that the EMS, focusing on the optimal planning and operation of the RESs and hydrogen storage in the reconfigurable DN, can successfully achieve significant reductions of 63.90%, 52.56%, 52.49%, and 62.26% in power losses, voltage deviation, total annual cost, and emissions of the studied DN, and thereby significantly enhance the distribution grid's techno-economic and environmental performance.

The Combined Heat and Power Economic Dispatch (CHPED) problem represents a significant optimization challenge in modern power systems due to its inherent complexity arising from multiple operational constraints. This complexity is further exacerbated when considering the effect of power losses (PLs), valve-point loading effect (VPLE), and prohibited operating zones (POZs). Consequently, an efficient and robust optimization algorithm is essential for obtaining a globally optimal solution while satisfying all constraints. To address these challenges, this work evaluates the effectiveness of the Modified Dung Beetle Optimizer (MDBO) for solving the CHPED problem, considering PLs, VPLE, and POZs. The MDBO enhances the search process and mitigates the limitations of the conventional Dung Beetle Optimizer, particularly stagnation and premature convergence to local optima. The novelty in this paper is proposing a modified version of the traditional DBO (MDBO) by integrating three improvement strategies, including the fitness distance balance (FDB), Chaotic mutation (CM), and adaptive local search approach (ALSA), to solve the CHPED problem. The proposed MDBO has the ability to overcome the shortcomings of traditional DBO, such as its premature convergence and tendency to local optima. The effectiveness of the proposed MDBO has been evaluated on CHPED problems involving 4-unit, 7-unit, 24-unit, and 48-unit systems under various operating conditions. Moreover, MDBO performance has been rigorously assessed using standard benchmark test suites, including CEC-2019. The results demonstrate a significant reduction in operating costs, confirming the superior performance of the MDBO in comparison to existing optimization techniques like Sand Cat Swarm Optimizer (SCSO), African vultures optimization algorithm (AVOA), Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO), Grey Wolf Optimizer (GWO), Liver cancer algorithm (LCA), Zebra Optimizer Algorithm (ZOA), and Whale Optimization Algorithm (WOA). Furthermore, the proposed algorithm consistently outperforms alternative methods reported in the literature, offering a more efficient and reliable solution for CHPED optimization.

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs.

The extensive integration of renewable energy resources (RERs) into modern power grids (MPGs), which severely lowers system inertia, standing severe frequency variations. These grids are vulnerable to cyber- attacks and natural uncertainties, creating a crucial need for resilient control schemes. This article introduces a novel resilience-coordinated scheme for interconnected MPGs. The scheme integrates Accelerating Virtual Rotor Control (AVRC) with Load Frequency Control (LFC), both governed by a modified Active Disturbance Rejection Controller (ADRC-IR). To optimize this controller's performance, a modified Horned Lizard Optimization Algorithm (MHLOA) is developed, enhancing its global search capabilities and convergence. MHLOA effectiveness is confirmed using a standard benchmark function. The ADRC-IR controller significantly reduces frequency and tie-line power deviations in the LFC system by 87.07%, 83.82%, and 76.54% respectively, compared to the TID, ADRC, and ADRC-PR controllers under high RESs penetration. Furthermore, the proposed AVRC/I & LFC strategy relied on ADRC-IR, which improves MPGs performance by 62.37%, 35.86%, and 30.22% when compared to (i) MPGs without AVRC/I, (ii) MPGs with LFC & AVRC/I, and (iii) MPGs with LFC & AVRC/I relied on a PI controller under high RESs penetration and cyber-attacks. The proposed scheme effectively preserves frequency stability within acceptable boundaries, demonstrating its resilience against MPGs challenges.

Recently, microgrid (MG) structures include a mix of renewable energy sources (RES) and conventional sources. At high levels of RES penetration, reduced inertia and frequency stability have been confirmed in several studies. Properly designed and structured load frequency control (LFC) and virtual inertia control (VIC) are feasible solutions to these problems. In this paper, a new hybridized two-degree-of-freedom (2DOF) non-integer controller is proposed for multi-generation, multi-area MGs’ frequency regulation. The proposed new LFC is based on a 2DOF tilt-integral/tilt-derivative-double-derivative controller with a filter (TI-TD2F2). Meanwhile, the proposed design process considers coordinating capacitive energy storage (CES) to help regulate frequency deviation, as well as the high penetration of RESs (wind and PV). The incorporation of CES participation in frequency regulation helps provide fast VIC for the studied multi-MG system. Furthermore, an Enhanced Escape Algorithm (EESC) optimization algorithm is proposed to simultaneously optimize the control parameter set of the two-area MG system. The proposed EESC optimization algorithm identifies appropriate parameters for controller design, yielding better overall dynamic performance. An enhanced Escape Algorithm (EESC) is based on boosting the searching mechanism of the conventional Escape Algorithm by the integration of three modifications, including the Chaos map logistic mutation mechanism, the Fitness distance balance mechanism, and the Sorted Quasi-oppositional based learning (SQOBL). The proposed 2DOF TI-TD2F2 controller demonstrates improved frequency stability and sustainable operation under load changes, variation in RESs, and other uncertainties of system parameters. The obtained results showed that the proposed EESC optimization algorithm adjusts the parameters of the TI-TD2F2 controller, which significantly improves the dynamic performance in load frequency and tie-line power control. Compared to traditional TID and FOPID controllers, TI-TD2F2 achieves up to a 70–80% reduction in tie-line power deviation and up to 60% faster settling time in many scenarios, demonstrating better robustness, faster response, and better overall system stability.

To stay ahead of emerging assimilation of green transportation and distributed energy resources entails the advanced application of optimization strategies for stochastic short-term hydrothermal scheduling (STHS) framework. Contrary to existing studies related to this problem, which tends to focus on a finite load types ranges and lack a coherent framework for embedding assorted energy requirements such as hydrogen fuel cell electric vehicles (HFCEVs), plug-in electric vehicles (PEVs), and ammonia production units. This paper offers a comprehensive innovative optimization approach for operation and design of STHS framework with valve-point loading effect (vple) as well as handling of multiple connected loads to the system such as HFCEVs, PEVs, and ammonia production units considering system uncertainties. The proposed STHS framework with inclusion of distributed energy resources aims to maximum utilization of power from these resources in day-ahead hours to less relies on fossil generation units to mitigate the fuel cost. To deal with the system uncertainties such as uncertain behaviour of HFCEVs and PEVs, variations in solar irradiance, windspeed and load demand, backward reduction method (BRM) and Monte-Carlo simulation (MCS) are employed. To solve such non-convex intricated STHS framework, an advanced optimization approach named modified escape optimization (MESC) is proposed based on three novel modifications into the international structure of traditional ESC such as chaotic mutation, quasi-oppositional based learning and fractional order Grünwald–Letnikov. To validate the performance of the proposed network, two cases are considered in the paper without and with optimal inclusion of solar PV and Wind Turbines and compared their results with different state-of-the-art techniques. The case with inclusion of solar PV and Wind turbines, the proposed MESC algorithm is provided the least a day-ahead fuel cost with reduction of 16.05%.

Due to continually rising electrical demand, attention in renewable energy sources (RESs) integrated into radial electric distribution networks (EDNs) becomes prominent. Nonetheless, the excessive generation of the RESs associated with its stochastic output powers, along with load variations, creates various challenges. To address this problem, superconducting magnetic energy storages (SMESs) can be assigned to alleviate the RESs' effects and to assure stable performance. In this regard, the novelty of this paper is proposing a stochastic optimal planning (i.e., locations and capacities) as well as operation capabilities of RESs and SMESs in standard and real EDNs associated with time-varying voltage-dependent mixed loads and protective schemes for assessing optimal multi-benefit optimization framework focusing on the techno-economic-environmental performance for these networks in terms of power losses, voltage deviation, voltage stability, reliability, security, total net present cost, and carbon emission based on an Adaptive Mountain Gazelle Optimizer (AMGO). Monte Carlo simulation (MCS) and back reduction method (BRM) are conducted to predict the stochastic action of various uncertainty sources. The significant outcomes of this strategy can successfully enrich the techno-economic-environmental performance of the IEEE 69-bus EDN through improving the power losses, voltage deviation, voltage stability, load-oriented reliability, security, total net present cost, and carbon emission by 36.83 %, 66.98 %, 5.02 %, 2.1 %, 22.23 %, 67 %, and 90.24 %, respectively. Additionally, the results verified the robustness and superiority of the suggested AMGO against various optimizers.

The optimization of combined heat and power economic dispatch (CHPED) presents a critical, complex, nonlinear, and non-convex challenge vital for achieving optimal economic performance in modern power systems. The difficulty of CHPED increases further when factors such as valve-point loading effect (VPLE), prohibited zones, and system losses are considered. This study presents an enhanced weighted mean of vectors optimizer (EINFO) to address the CHPED in small-scale systems, including 4-unit, 7-unit, and 24-unit setups, and accommodates VPLE for large-scale systems. The EINFO improves the global search capability of the conventional INFO method by incorporating three strategies: fitness distance balance (FDB), a chaotic mechanism (CM), and quasi-oppositional based learning (QOBL). Evaluations using the CEC 2022 benchmark functions involve statistical comparisons, convergence analyses, and boxplot assessments against several established methods, including SCA, BDO, AVOA, GTO, MGO, ARO, BWO, FFA, and traditional INFO. The results show that EINFO achieves cost reductions ranging from 0.0000324 to 2.0643

Improvement performance of transmission systems is crucial task that can be boosted via optimal reactive power dispatch (ORPD). However, the continuous variations of load demand and the power produced by the renewable energy sources (RERs) increases the complicities of solving the stochastic optimal reactive power dispatch (SORPD) solution. In this regard, a modified Dandelion Optimizer (MDO) algorithm is introduced to optimize the SORPD solution with taking into consideration the stochastic fluctuations or the random variations of the load demand and the power produced by RERs. The suggested MDO depends upon developing the searching exploration and exploitation abilities by integration of three methodologies involving the Quasi-oppositional-based-learning (QOBL), the Weibull flight motion strategy (WFM) and the fitness distance balance (FDB). The SORPD is solved for IEEE 30-bus system to reduce summation of expected power losses (SEPL) and enhance the summation of expected voltage stability (SEVS) with and without integration RERs. The uncertainties of the load demand and the power produced by the RERs are represented using Monte Carlo simulations and scenario reduction approach in which 15 scenarios are generated to model the stochastic nature of the load demand and the power produced by RERs. The simulation results reveal to that application the proposed algorithm for SORPD can reduce the SEPL and improve SEVS considerably, especially with integration of the RERs. The Comparative results demonstrate that the MDO algorithm is the best for solution the SORPD against sand cat swarm optimization (SCSO), gorilla troop optimizer (GTO), harmony search (HS), and Beluga whale optimization (BWO).

Power networks witness a high penetration of distributed energy resources (DERs). These DERs produce a new generation of end-users known as prosumers, who can both consume and generate energy. The increasing number of prosumers creates opportunities for much more flexible energy markets. Peer-to-peer (P2P) energy trading has arisen as a novel trading paradigm to facilitate the exchange of surplus energy between prosumers and consumers in local markets. In this context, this paper proposes an optimization model for P2P energy trading among energy prosumers connected to the utility grid. Each prosumer is equipped with renewable energy resources. Demand side response (DSR) strategies are incorporated to reduce costs effectively. The optimization problem is solved using the genetic algorithm considering three different scenarios. The simulation results demonstrate that incorporation of DSR strategies and P2P trading mechanisms reduces significantly the overall prosumers’ operating costs compared to conventional grid-only operations.

The Combined Heat and Power Economic Dispatch (CHPED) problem seeks to optimize the outputs of power and heat from the generation units to meet the required power and heat demands. Ammonia is widely used as a fertilizer, fuel, and energy carrier, but its production represents a significant power load. Consequently, optimizing power generated by the generation units requires robust methods. This study examines the effectiveness of Quadratic Interpolation Optimization (QIO) in solving the CHPED problem, considering the power demand of ammonia production. To evaluate the QIO’s performance, its results are compared with the outcomes from several other optimization algorithms: African Vultures Optimization Algorithm (AVOA), Dandelion Optimizer (DO), Sand Cat Swarm Optimization (SCSO), Harris Hawk Optimization (HHO), Grey Wolf Optimizer (GWO), RIME Optimization Algorithm (RIME), and Zebra Optimization Algorithm (ZOA). The findings reveal that, under the same conditions, QIO consistently outperforms these algorithms.