A Two-Stage Robust Optimization Method Based on the Expected Scenario for Islanded Microgrid Energy Management

Due to the strong coupling relationship between active power and reactive power in distribution network, the traditional method of optimizing the active and reactive power cannot make the distribution network operate in the optimal state of economical operation. And the uncertainties of load demand and power of distributed generation bring great challenges to the operation of distribution network. Based on this, a two-stage robust economical operation optimization method for distribution network considering multiple adjustment measures, including on-load tap changer, capacitor banks, static var compensator, electrical storage system and switch reconfiguration, is proposed on the basis of the branch power flow model. And the non-linear power flow constraint is relaxed by second order cone so that the economical operation optimization model is transformed into the mixed integer second order conic programming model to solve effectively. To solve the uncertainties of load demand and power of distributed generation, two-stage robust optimization method is adopted, and the optimal robust operation strategy of each adjustment measure is obtained through the column and constraint generation algorithm. Finally, the validity of the proposed model is verified by IEEE 33 bus system.

Two-stage robust optimization for space heating loads of buildings in integrated community energy systems

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

With the modernization and intelligent development of agriculture, the energy demand in rural areas continues to increase, which leads to an increased operational burden on the existing rural distribution network. Integrated energy stations (IESs), in rural areas, use renewable energy sources such as biogas, wind power, and photovoltaic as energy inputs, which can fully improve energy efficiency and help reduce the operating load and peak valley difference of rural distribution networks. In this paper, a multistage planning model is proposed for rural distribution networks with IESs based on the robust optimization method. Firstly, a rural distribution network operation framework with IESs is proposed, and a mathematical model of rural IESs is built based on the energy hub (EH). Then, the multistage robust planning model of rural distribution net-works with IESs is developed and typical scenarios of stochastic source and load are generated based on improved k-means. An iterative solution method for a two-stage robust optimization method is proposed based on the nested column constraint generation (NC&CG) algorithm. Finally, the effectiveness of the presented model and solution approach is assessed through case studies on a modified IEEE 33-node distribution network and a real 152-node distribution network.

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

The increasing integration of distributed renewable energy sources (RES), energy storage systems (ESS), electric vehicle (EV) charging stations, and demand response (DR) mechanisms has significantly enhanced microgrid deployment. However, the operational complexity and vulnerability of islanded microgrids to disruptions, especially during renewable energy fluctuations, pose critical challenges. Existing approaches primarily focus on minimizing operational costs or emissions but fail to simultaneously address load curtailment, voltage stability, and resilience under uncertain conditions. In this paper, we propose a novel resilience-oriented energy and load management framework for island microgrids, integrating a multi-objective optimization function that explicitly minimizes load curtailment, energy losses, voltage deviations, emissions, and energy procurement costs while maximizing the utilization of renewable energy sources. Unlike conventional models that separately optimize demand-side management (DSM), distributed generation (DG), EV charging/discharging, and ESS scheduling, our approach incorporates a coordinated control strategy that jointly optimizes these elements alongside reactive power compensation devices such as capacitors and shunt reactors. To effectively solve this high-dimensional, nonlinear problem, we employ the Multi-objective Moth Flame Algorithm (MOMFA), an enhanced metaheuristic evolutionary algorithm designed to handle complex trade-offs between cost, reliability, and resilience. The superiority of MOMFA over conventional optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) is validated through simulations on a realistic 33-node microgrid under various renewable energy outage scenarios. The results demonstrate that the proposed framework achieves a 60% reduction in voltage deviation, an 81% decrease in energy losses, and an 86% reduction in CO₂ emissions, while ensuring zero load curtailment, even under severe outage conditions. The proposed method offers a scalable, real-time implementable solution for microgrid operators seeking to enhance resilience against renewable energy intermittency and optimize energy utilization. This work significantly advances state-of-the-art microgrid energy management by providing a holistic, multi-objective, and resilience-driven optimization strategy.

In the real-time energy management of offshore islanded microgrids, determining the optimal operating points of storage systems, particularly in hydrogen-based storage, poses significant challenges. These arise from the stochastic nature of offshore Renewable Energy Sources (RES), variable power demand, and the volatility of hydrogen systems. To address these, a novel objective function has been developed that integrates electrolyzer system efficiency into the Energy Management Strategy (EMS) of a DC microgrid. Unlike most existing literature, which considers electrolyzer efficiency as a constant, this work treats efficiency as a dynamic variable that depends on operating current, temperature, and pressure. The behavior of the electrolyzer efficiency with respect to these parameters is modeled, verified, and subsequently incorporated into the EMS. A MINLP based EMS is developed to implement the proposed formulation, and its effectiveness is validated by demonstrating optimal microgrid performance under the above scenario. Notably, the impact of optimal temperature and pressure control is evidenced by electrolyzer efficiency improvements of 1.7% and 3.1% in the efficiency-focused and multi-objective scenarios, respectively. The proposed EMS is evaluated against a rule-based and heuristic (PSO) methods. In the cost-based case, the developed method shows 36.5% and 0.04% cost reductions relative to the rule-based approaches, and a 0.39% reduction relative to PSO. In the efficiency-based scheme, it attains 0.10% efficiency gain over PSO for the optimal temperature-pressure method, and in the multi-objective case, it delivers 7.40% efficiency gain versus PSO. • Novel optimization with electrolyzer efficiency for offshore islanded DC microgrids. • Electrolyzer efficiency modeled dynamically via current, temperature, and pressure. • Multi-objective framework balances cost and efficiency in flexible microgrid EMS. • Sensitivity to electrolyzer operating conditions is analyzed. • EMS validated against rule-based and metaheuristic approaches.

The power to gas (P2G) technology can realize the large-scale storage of electric energy in the form of natural gas, so that the power system and the natural gas system have the ability to flow energy, and the degree of coupling between the power system and the natural gas system is deepened. As the coupling between the power system and the natural gas system continues to increase, an integrated energy system (IES) with the power grid as the core has emerged. However, under uncertain conditions, the current planning methods still cannot guarantee the optimal interests of each manufacturer, so a collaborative planning method for gas-electric systems based on non-cooperative game theory is proposed. With each energy unit and electricity and gas network companies as independent interest groups, and with the maximum annual net income as the optimization goal, establishing a comprehensive non-combination of wind turbines, gas turbines, electric-to-gas turbines, transmission lines and natural gas pipelines in the gas-electric integrated system linear programming model. Aiming at the uncertainty of wind power and load, an improved two-stage robust optimization method is proposed. Finally, the modified IEEE 39-node power system and 20-node natural gas system gas-electric network verify the application value of the model and algorithm.

Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective

Intelligent decision support system performance depends on knowledge base quality. With the decision problem and knowledge base becoming more complicated, it is necessary to provide effective method to optimize the management of knowledge base. Based on detailed analyzing of running characteristics and potential detects of rule base in IDSS, a novel two-stage optimization method is proposed. Conventional optimization approach and genetic algorithm are combined to recognize and eliminate kinds of defects in rule base. Exact defect definitions and optimization operation details are given. The sample shows that the method is feasible in practice.

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting

In this article, we suggest a micro-grid methodology based on renewable energy sources (RES) in order to make more efficient use of renewable energy sources and use less energy from the traditional power grid. Off-grid, RES likes solar (generated by a photovoltaic panel) and wind can be stored in a battery and utilised to power loads. As an additional measure, a Fuzzy logic-based energy-saving method has been applied. Microgrids are used to integrate these systems in a decentralised fashion, and they provide a suite of technology solutions that facilitate communication between end users and dispersed power plants. This raises the question of how best to administer these systems. In order to guarantee that both the generating and distribution systems produce energy at low operating costs, energy management in microgrids is described as a data and monitoring network that allows the required functions. In this study, we discuss the difficulties associated with using RES and managing a microgrid. Voltage and frequency variations result from the dynamic nature of DG systems. Unpredictable dynamics result from the load's unknowability. As a result of this nonlinear dynamic, there are observable changes to the microgrid's operation. In this study, we test the efficiency of the microgrid in a variety of settings. We compare the PI controller's performance on the MATLAB/Simulink platform to that of a Fuzzy logic-based controller with various levels of complexity.

A two-stage multi-objective optimization method for envelope and energy generation systems of primary and secondary school teaching buildings in China

Energy Management (EM) in hybrid Microgrids (MGs) is essential for coordinating Renewable Energy Sources (RESs) and Hybrid Energy Storage Systems (HESSs) to ensure Power Quality (PQ), stable operation, and efficient power flow. Existing optimization–prediction approaches often address these issues in isolation or require high computational overhead, limiting their real-time applicability. To overcome these challenges, this paper proposes a novel dual-optimization framework combining the Artificial Lemming Algorithm (ALA) with the Temporal Kolmogorov–Arnold Network (TKAN), referred to as ALA-TKAN. Unlike conventional methods, ALA-TKAN integrates metaheuristic-based optimization of power flow and HESS scheduling with sequence-aware forecasting of load and renewable generation, enabling proactive and coordinated EM under dynamic conditions. Implemented in MATLAB, the proposed method demonstrates superior performance compared with state-of-the-art techniques such as PDO-MACNN, BWO, PSO, ANN, and MRA-FLC, achieving minimal power loss (2.9 MW), highest efficiency (99.2%), lowest energy cost (0.8 $/Wh), and reduced THD (1.4%). These results confirm the novelty and practical potential of ALA-TKAN as a unified, computationally efficient strategy for PQ enhancement and reliable operation of hybrid MGs.