Optimization of Self-Sufficient Microgrid Design with Integration of Renewable Energy Resources

Multi-Objective Optimal Sizing and Coordinated Energy Management of PV–BESS-Based Multiple Microgrids in Distribution Systems

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.

Optimal Scheduling of Microgrids Considering Hydrogen Energy Storage and Building Thermal Inertia

Against the backdrop of accelerating transitions to sustainable energy systems, the optimal operation of microgrids and the high-efficiency integration of renewable energy face growing technical challenges, which highlight the necessity of tapping into flexible multi-energy resources to the fullest extent. Aiming at low-carbon microgrid systems with electro-thermal demands, this paper proposes a sustainable dispatch strategy that actively integrates waste heat recovery and building thermal inertia. First, a refined mathematical model of a Hybrid Energy Storage System (HESS) is developed, considering waste heat recovery processes from the electrolyzer and the fuel cell. Second, an optimal dispatch model considering the HESS and building thermal inertia (BTI) is constructed, the PMV index is adopted to quantify the adjustable margin of user thermal demand, and the objective function accounts for multiple economic and environmental indices, including energy procurement costs, equipment maintenance costs, and carbon emission trading costs. Case studies show that this strategy can effectively enhance the regulation flexibility of the system, significantly reducing comprehensive operational costs by up to 51.6% and improving local renewable energy accommodation while ensuring environmental sustainability and low-carbon operation.

Economic-Driven Optimal Sizing of Energy Storage System in Microgrid with Multi-Stakeholders

Multiobjective Optimization for Microgrids Design

This paper addresses the multi-objective optimization problem for microgrid design, integrating various renewable energy sources and energy storage systems. The proposed framework aims to optimize the sizing of generation plants (solar, onshore wind, offshore wind) and battery storage, while simultaneously minimizing investment costs and managing the probability of power excess or deficit within the microgrid. To handle the inherent variability of wind energy, a symbolic regression method is employed for wind speed forecasting. The optimization task is tackled using two prominent metaheuristic algorithms: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-objective Particle Swarm Optimization (MOPSO) algorithm. The study details the mathematical models for all microgrid components, including their associated investment, maintenance, and replacement costs. A comparative analysis of NSGA-II and MOPSO is presented, evaluating their performance in achieving optimal Pareto fronts regarding investment costs and power balance, highlighting the trade-offs between initial investment and operational efficiency for sustainable microgrid solutions.

Coordinated Planning of Sectionalizing Switches and Mobile Energy Storage for Distribution System Reliability

With the growing integration of distributed energy resources and the increasing operational complexity of modern distribution networks, ensuring high reliability and maintaining voltage stability during fault recovery have become critical challenges for utilities. To address these challenges, this paper proposes a novel optimization framework for the coordinated planning of sectionalizing switches, tie lines, and mobile energy storage systems (MESSs). The framework employs a bilevel optimization model to achieve coordinated asset placement and operational scheduling. Specifically, the upper-level model aims to achieve a balanced trade-off between economic investment and reliability performance by minimizing both the installation costs and the outage penalties quantified by the expected energy not supplied (EENS). To efficiently solve the upper-level multiobjective problem, an improved multiobjective gold rush optimizer is employed, generating the Pareto-optimal planning solutions. During the iterative optimization process, an enhanced fault incidence matrix–based reliability assessment method is integrated, in which the contribution of MESSs is quantified through a sparse matrix R dis derived from the lower-level operational model, enabling more accurate analytical evaluation. Complementarily, the lower-level model focuses on operational quality during fault recovery by optimizing MESS dispatch using a mixed-integer second-order cone programming formulation. This model minimizes voltage deviations at isolated load nodes, thereby ensuring stable and effective power supply restoration. Additionally, the ZIP load model is also embedded to precisely capture load reduction behavior under fault conditions, enabling a more realistic evaluation of energy curtailment. Case studies conducted on a 41-node distribution network demonstrate that the proposed bilevel optimization approach effectively reduces EENS by up to 21% and reduces the number of unqualified voltage nodes with a moderate investment increase, highlighting the importance of the proposed method.

Multi-objective optimization framework for dynamic energy management in hybrid microgrids using NSGA-III.

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.