Uncertainty Of Renewable Energy Research Articles

Hybrid CNN-GRU Forecasting and Improved Teaching–Learning-Based Optimization for Cost-Efficient Microgrid Energy Management

In this paper, a two-stage framework is proposed for the energy management of microgrids, which combines a hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) forecast model and the Improved Teaching–Learning-Based Optimization (ITLBO) algorithm. The CNN-GRU model captures spatiotemporal patterns in historical data for effective renewable energy and load demand uncertainty quantification, while the ITLBO algorithm improves generation scheduling performance through utilization of adaptive luminance coefficients, Latin Hypercube initialization, and hybrid genetic operations. The proposed framework is then compared with four different forecasting models: standalone CNN or MLANN, and three popular optimization algorithms (PSO, TLBO, CO) for four cases, including baseline (perfect foresight), CNN-GRU forecast, CNN forecast, and MLANN forecast. The results show that the hybrid framework outperforms dedicated, in-domain models for forecast and scheduling, with the state-of-the-art CNN-GRU sliding window model producing the best forecasting accuracy, which subsequently translates into near-optimal scheduling performance. Through many experiments, we show that the ITLBO algorithm is robust and outperforms the classical optimization methods on convergence speed and solution quality while significantly eliminating the forecast errors uncertainty. Demand response is also a feature of these models, which boosts operational efficiency by scaling down peak grid usage without sacrificing affordability through energy saving capabilities. According to the results, the hybrid framework exhibits significant cost-efficiency by reducing the RMSE of solar irradiance forecasting by 11.6% when compared to standalone CNN and achieving a 69.7% reduction in operational costs under ITLBO optimization. The comparative analysis emphasizes the robustness and versatility of the framework, reinforcing its feasibility across a range of forecasting and optimization scenarios for real-world microgrid deployment.

Evaluation method of tie-line transmission capacity considering uncertainty of renewable energy and system frequency constraints

Evaluation method of tie-line transmission capacity considering uncertainty of renewable energy and system frequency constraints

Data-driven distributionally robust optimization of low-carbon data center energy systems considering multi-task response and renewable energy uncertainty

Data-driven distributionally robust optimization of low-carbon data center energy systems considering multi-task response and renewable energy uncertainty

Optimal scheduling of electricity–hydrogen–ammonia coupled integrated energy system based on uncertain renewable generations

Power-to-ammonia (P2A) technology presents an effective strategy for facilitating the low-carbon transition of integrated energy system (IES), effectively addressing the stochastic and intermittent nature of renewable energy generation. In response to the challenges posed by the increased penetration of renewable energy, this paper develops an optimization scheduling model of IES based on chance constraints within an electricity–hydrogen–ammonia coupled architecture. The model integrates P2A technology, hydrogen blending in gas turbine (GT) and gas boiler (GB), and coal–ammonia co-combustion, while further modeling the dynamic characteristics of the electrolyzer. To account for renewable energy uncertainties, the probability reserve is formulated using chance-constrained programming (CCP). The model is solved using the sequence operation theory, which converts the CCP into a deterministic equivalent model to balance the expected and stochastic outputs of renewable generation. Case studies on four scenarios demonstrate that the proposed model achieves a 5.82% reduction in carbon emissions and a renewable energy utilization rate of 98.3%, outperforming traditional scheduling approaches. Additionally, the optimal system performance is achieved when hydrogen blending ratios reach 16% for GT and 18% for GB. These results underscore the model's effectiveness in enhancing low-carbon operation, maximizing renewable energy utilization, and improving the economic efficiency of IES.

Stochastic Programming-Based Annual Peak-Regulation Potential Assessing Method for Virtual Power Plants

The intervention of distributed loads, propelled by the swift advancement of distributed energy sources and the escalating demand for diverse load types encompassing electricity and cooling within virtual power plants (VPPs), has exerted an influence on the symmetry of the grid. Consequently, a quantitative assessment of the annual peak-shaving capability of a VPP is instrumental in mitigating the peak-to-valley difference in the grid, enhancing the operational safety of the grid, and reducing grid asymmetry. This paper presents a peak-shaving optimization method for VPPs, which takes into account renewable energy uncertainty and flexible load demand response. Firstly, wind power (WP), photovoltaic (PV) generation, and demand-side response (DR) are integrated into the VPP framework. Uncertainties related to WP and PV generation are incorporated through the scenario method within deterministic constraints. Secondly, a stochastic programming (SP) model is established for the VPP, with the objective of maximizing the peak-regulation effect and minimizing electricity loss for demand-side users. The case study results indicate that the proposed model effectively tackles peak-regulation optimization across diverse new energy output scenarios and accurately assesses the peak-regulation potential of the power system. Specifically, the proportion of load decrease during peak hours is 18.61%, while the proportion of load increase during off-peak hours is 17.92%. The electricity loss degrees for users are merely 0.209 in summer and 0.167 in winter, respectively.

Low-Carbon Optimization Operation of Rural Energy System Considering High-Level Water Tower and Diverse Load Characteristics

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system.

Multi-time scale customer directrix load-based demand response under renewable energy and customer uncertainties

Multi-time scale customer directrix load-based demand response under renewable energy and customer uncertainties

Energy management of microgrid coalitions considering renewable energy prediction based on machine learning algorithms

This paper proposes a new energy management framework for the optimum scheduling of distributed generation resources within a smart distribution system that contains multiple interconnected microgrids under normal and abnormal operating conditions. First, a systematic management framework is established by defining the functions of various management units throughout the multi-microgrid system. In the suggested two-step approach, each microgrid schedules its own resources based on the developed model, while in the second step, the distribution system operator makes a decision on power transfers among microgrids and allocates the remaining unserved loads from the first step. Due to the inherent variability of renewable resources, an extreme learning machine (ELM) model is applied in order to forecast solar irradiation and wind power. The significant contributions of the work include (1) a new approach to uncertainty modeling of renewable energy sources and consumer loads, using ELM for probabilistic modeling of the fluctuations of renewables and demand; (2) coalition strategy formation in local and global energy trading for microgrids with an objective of cost minimization and profitability improvement; and (3) incorporation of vehicle-to-grid (V2G) systems for better electric vehicle charging management and contribution to system stability due to renewable energy uncertainties. Besides, the proposed framework has been applied and validated on a test system for practical scenarios. The approach demonstrates considerable enhancements in efficiency, resilience, and cost-effectiveness upon multi-microgrid networks under various operating conditions.

A two-layer game optimization strategy for an integrated energy system considering multiple responses and renewable energy uncertainty

A two-layer game optimization strategy for an integrated energy system considering multiple responses and renewable energy uncertainty

Strategic bidding of virtual power plants in integrated electricity-carbon-green certificate market with renewable energy uncertainties

Strategic bidding of virtual power plants in integrated electricity-carbon-green certificate market with renewable energy uncertainties

Two-layer multi-timescale rolling optimization of electric-hydrogen hybrid energy storage systems considering renewable energy uncertainties

Two-layer multi-timescale rolling optimization of electric-hydrogen hybrid energy storage systems considering renewable energy uncertainties

The Multi-Objective Distributed Robust Optimization Scheduling of Integrated Energy Systems Considering Green Hydrogen Certificates and Low-Carbon Demand Response

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration, a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and low-carbon demand response. Firstly, to leverage the low-carbon and clean characteristics of hydrogen energy, an efficient hydrogen utilization model was constructed, consisting of electricity-based hydrogen production, waste heat recovery, multi-stage hydrogen use, hydrogen blending in gas, and hydrogen storage. This significantly enhanced the system’s renewable energy consumption and carbon reduction. Secondly, to improve the consumption of green hydrogen, a novel reward–punishment green hydrogen certificate trading mechanism was proposed. The impact of green hydrogen trading prices on system operation was discussed, promoting the synergistic operation of green hydrogen and green electricity. Based on the traditional demand-response model, a novel low-carbon demand-response strategy is proposed, with carbon emission factors serving as guiding signals. Finally, considering the uncertainty of renewable energy, an innovative optimal trade-off multi-objective distributed robust model was proposed, which simultaneously considered low-carbon, economic, and robustness aspects. The model was solved using an improved adaptive particle swarm optimization algorithm. Case study results show that, after introducing the reward–punishment green hydrogen trading mechanism and low-carbon demand response, the system’s total cost was reduced by approximately 5.16% and 4.37%, and carbon emissions were reduced by approximately 7.84% and 6.72%, respectively. Moreover, the proposed multi-objective distributed robust model not only considers the system’s economy, low-carbon, and robustness but also offers higher solving efficiency and optimization performance compared to multi-objective optimization methods.

Optimized Dispatch of Integrated Energy Systems in Parks Considering P2G-CCS-CHP Synergy Under Renewable Energy Uncertainty

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%.

Network and Energy Storage Joint Planning and Reconstruction Strategy for Improving Power Supply and Renewable Energy Acceptance Capacities

The integration of distributed generation (DG) into distribution networks has significantly increased the strong coupling between power supply capacity and renewable energy acceptance capacity. Addressing this strong coupling while enhancing both capacities presents a critical challenge in modern distribution network development. This study introduces an innovative joint planning and reconstruction strategy for network and energy storage, designed to simultaneously enhance power supply capacity and renewable energy acceptance capacity. The proposed approach employs a bi-level optimization model: the upper level focuses on minimizing economic costs by determining the optimal locations and capacities of energy storage systems and the layout of network lines, while the lower level aims to maximize power supply and renewable energy acceptance capacities by optimizing line switch states. Additionally, this research quantifies the coupling relationship between these two capacities under uncertainty, providing a deeper understanding of their dynamic interaction. Advanced computational techniques, including Monte Carlo simulations and particle swarm optimization (PSO), are utilized to solve the model efficiently. Case studies demonstrate that the proposed strategy effectively enhances both power supply and renewable energy acceptance capacities. Furthermore, exploring the strong coupling relationship between these two capacities under various conditions not only optimizes the utilization of renewable energy in the power system and prevents resource waste, but also helps avoid the volatility impacts of renewable energy uncertainty on the power system in actual planning. Additionally, the network and energy storage joint planning and reconstruction strategy proposed in this study achieves cost minimization under the constraint of limited resources and simultaneously enhanced both capacities. The strategy provides feasible solutions for power grid planning in actual applications.

Optimal sizing and operation of microgrid considering renewable energy uncertainty based on scenario generation

Optimal sizing and operation of microgrid considering renewable energy uncertainty based on scenario generation

Hybrid multi-objective optimization of µ-synthesis robust controller for frequency regulation in isolated microgrids

Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability—a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions. Comparative analyses demonstrate that the MOPSO-optimized controller achieves superior robustness and performance, tolerating up to 236% uncertainty compared to 171% for conventional µ-synthesis controllers. Additionally, it significantly reduces frequency deviation and enhances transient response. Nyquist stability analysis confirms robustness across renewable energy uncertainties. The results highlight the proposed controller’s effectiveness in isolated microgrid frequency regulation, with future work focused on discrete-time implementation for practical digital signal processing (DSP) applications.

A novel multi-task algorithm for operational optimization of coal mine integrated energy system under multiple uncertainties

Abstract The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system’s multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output; then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.

A two-stage flexible scheduling method for power systems with wind power considering the coordination of multiple resources

The intermittency and uncertainty of renewable energy generations, such as wind power, present great challenges to the secure and stable operation of power grids. To accommodate a high penetration of renewable energy, it is vital to coordinate multiple flexible resources to deal with the intermittency and uncertainty of renewable energy and ensure the network security. In this paper, we propose a two-stage stochastic flexible dispatching method for power systems with large-scale wind power, which considers the coordination of unit commitment, optimal transmission switching, and optimal control of phase-shifting transformers within a unified framework. On the grid side, flexibility is improved through phase-shifting transformer regulation and optimal transmission switching. On the source side, flexibility is fully exploited through two-stage stochastic unit commitment. In the day-ahead scheduling stage, transmission topology optimization and unit commitment schemes are determined based on the predicted load demand and renewable energy output. In the real-time dispatching stage, phase-shifting transformers and unit outputs are adjusted and dispatched based on the possible scenarios of load demand and renewable energy output. The effectiveness of the proposed method is verified through case studies on the IEEE RTS-24 system and IEEE 118-bus system.

Multi-Microgrid Optimization Operation Strategy Considering Nonlinear Conditions and Renewable Energy Uncertainty: A Data-Driven Method

Multi-Microgrid Optimization Operation Strategy Considering Nonlinear Conditions and Renewable Energy Uncertainty: A Data-Driven Method

Low carbon economic dispatch for virtual power plant considering energy storage

ABSTRACT Virtual power plant (VPP) amalgamates diverse distributed resources, thereby unlocking the full potential of distributed energy’s dispatch capabilities. Energy storage is an effective means to address the uncertainty of renewable energy and achieve energy complementarity. This study meticulously evaluates the finite nature of traditional energy within VPP, the unpredictability associated with renewable energy outputs, and the constraints faced by energy storage devices. It introduces strategic operational frameworks for VPP through cooperation between VPP operator and energy storage provider managing new type of electric energy storage systems. With the dual objectives of amplifying the economic gains for VPP operator and maximizing benefits for energy storage provider, this research formulates a VPP economic low-carbon dispatch model, guided by a carbon tax framework. The improved particle swarm optimization algorithm is used to solve the proposed dispatch model. The numerical results confirmed that the collaboration between the VPP operator and the energy storage provider mitigates the issues caused by the variability of renewable energy outputs on the VPP, resulting in a mutually beneficial scenario for both the operator and the storage provider. Additionally, carbon emissions have been drastically decreased in response to low carbon targets.