The increasing penetration of distributed energy resources and diverse load characteristics in interconnected multi-microgrid systems creates significant challenges for coordinated energy management and optimal resource planning. This study proposes a multi-objective optimization framework for the simultaneous sizing of photovoltaic (PV) systems and battery energy storage systems (BESSs), combined with coordinated energy management and bidirectional power exchange among residential, commercial, and industrial microgrids connected to the IEEE 33-bus distribution network. The framework incorporates 24 h load profiles, photovoltaic generation, time-of-use electricity pricing, and distribution network operational constraints. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed to simultaneously minimize the total daily cost, network power losses, and grid dependency while satisfying the voltage, feeder loading, and battery state-of-charge constraints. The economic objective combines the equivalent daily investment costs of PV and BESSs with daily operating costs using a capital recovery factor (CRF)-based formulation to ensure dimensional consistency. The best compromise solution is selected using the minimum normalized Euclidean distance to the ideal Pareto solution. Simulation results demonstrate that the proposed framework achieves a total daily cost of $13,412.52/day, total network power losses of 4179.1 kW, and grid dependency of 3262.28 kWh while maintaining all operational constraints within acceptable limits. Furthermore, coordinated PV–BESS operation improves voltage regulation, reduces feeder loading and network power losses, enhances renewable energy utilization, and decreases the reliance on the utility grid. The results demonstrate that the proposed framework provides an effective techno-economic approach for the coordinated planning and operation of interconnected multi-microgrid systems with high renewable energy penetration.
This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.
Mohd Bilal, Arshad Mohammad, Imdadullah et al.· Scientific Reports· 1 citation
The inherent intermittency of renewable energy sources and the mismatch between generation and community load demand pose significant challenges to the reliability of microgrids. To address these issues, this paper proposes a robust multi-objective optimization framework for the capacity sizing of a Hybrid Renewable Energy System (HRES) integrating Wind, Photovoltaic (PV), Battery Energy Storage System (BESS), and a Power-to-Heat coupling subsystem. A rule-based Energy Management Strategy (EMS) is developed to coordinate the electrical and thermal power flows, prioritizing renewable utilization and storage efficiency. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is employed to solve the sizing problem, simultaneously minimizing the Net Present Cost (NPC) and the Loss of Energy Ratio (LER). A case study based on real-world hourly meteorological data and a large-scale community load profile demonstrates the proposed approach. The simulation results demonstrate that the MOEA/D algorithm effectively identifies a set of Pareto-optimal solutions, revealing the trade-off between economic investment and system reliability. The optimal compromise solution suggests a PV-dominated configuration (924 MW) supported by a hybrid storage system comprising 2478 MWh of battery and 585 MWh of thermal storage. This configuration achieves a high reliability with an LER of 1.92% and a lifecycle cost of $2.40 billion, proving the techno-economic feasibility of the proposed electric-thermal coupling scheme for community energy supply.
Pu-Zhuang Liu, Nor Azwan Bin Mohamed Kamari· IOP Conference Series: Earth...· 0 citations
The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied.
L. Grisales-Noreña, F. Machuca‐Martínez, O. D. Montoya· The Scientist· 0 citations
To solve the problems of low renewable energy utilization and high grid dependence in multi-park hybrid microgrids (MPHMs), this paper aims to develop a centralized optimization framework for the wind-solar-storage resource configuration to realize the coordinated operation of new energy suppliers, integrated energy service providers and end-users. An multi-objective economic dispatch model considering source-storage-load-grid coordination, time-of-use pricing and demand response incentive, as well as technical constraints of energy storage is established. An improved hybrid intelligent algorithm combining NSGA-III with enhanced solution selection based on clustering is designed to efficiently solve the optimization problem and obtain practical configurations. The proposed method is applied to a system including industrial, commercial and residential parks with different loads and renewable generation capacity. Simulation results indicate that the optimal energy storage configuration can eliminate the renewable curtailment in all parks and reduce both daily electricity purchasing cost from main grid and total daily power supply cost. These results demonstrate that coordinated planning can improve both sustainability and economy of MPHMs. Sensitivity analyses validate that the solution is robust to the variations of storage cost, incentive policy and renewable generation capacity. The proposed method performs better than non-storage scenario even in unfavorable cost conditions. The proposed scalable and practical method provides a valuable reference for optimizing multi-area microgrids and integrated energy systems.
Tsz-Sen Zhu· Mathematical Modeling and Al...· 0 citations
The increasing penetration of renewable energy sources and storage technologies is driving the transformation of conventional power distribution systems toward decentralized microgrid-based architectures. A major challenge lies in jointly determining the optimal segmentation of existing networks into microgrids and the strategic allocation of distributed energy resources, while balancing investment, operational efficiency, and reliability. This paper proposes a novel single-stage optimization methodology that simultaneously determines microgrid segmentation and the optimal placement of batteries, solar, and wind generators. The formulation minimizes annual operating costs by considering investment and operational expenses, energy losses, and the cost of energy not supplied due to service interruptions. Starting from a conventional radial topology, the optimization is performed using simulated annealing to explore a large combinatorial solution space efficiently. The methodology is applied to the IEEE 69-node distribution system, achieving a 15% reduction in total annual costs and enhanced reliability, with System Average Interruption Frequency Index and System Average Interruption Duration Index reduced by 35% and 47%, respectively. These results highlight the effectiveness and flexibility of the proposed approach as a medium-term planning tool for the design of self-sufficient and resilient distribution networks.
C. Bonetti, G. D. Puccini, J. Rodríguez-García et al.· Green Energy and Environment...· 0 citations
With the high-proportion integration of renewable energy, integrated energy systems face greater demands regarding renewable energy utilisation, power balancing, and operational efficiency. By aggregating distributed generation, energy storage and load resources, integrated energy systems can provide effective support for multi-energy coordinated scheduling. This paper proposes a 24 h day-ahead multi-objective optimal scheduling framework for an integrated hydro–wind–photovoltaic–storage energy system based on multi-objective particle swarm optimisation (MOPSO). Firstly, this paper establishes mathematical models for wind power, photovoltaic (PV), hydropower, and energy storage units. Subsequently, it incorporates the outputs of hydropower, wind power, PV, and storage, along with the charging and discharging of energy storage and the process of purchasing electricity from and selling electricity to the main grid, into a unified optimisation model. The objectives are to maximise economic benefit and variable renewable energy utilisation while minimising the peak-to-valley difference in residual load. To address the conflicts between these multiple objectives, a MOPSO algorithm combined with a normalised weighted scoring method is employed to select a compromise optimal solution. Results from case studies based on typical days of the four seasons and various operational strategies demonstrate that the proposed method can rationally allocate the outputs of different energy sources, reduce the system’s dependence on the main grid, and improve variable renewable energy utilisation, thereby providing a reference for the optimal scheduling of integrated energy systems.
Ruizhu Guo, Wei Song, Yingliang Bai et al.· Energies· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
