Hydrogen and renewable electricity-based microgrid is considered to be a promising way to reduce carbon emissions, promote the consumption of renewable energies and improve the sustainability of the energy system. In view of the fact that the existing day-ahead optimal operation model ignores the uncertainties and fluctuations of renewable energies and loads, a two-stage energy management model is proposed for the sustainable wind-PV-hydrogen-storage microgrid based on receding horizon optimization to eliminate the adverse effects of their uncertainties and fluctuations. In the first stage, the day-ahead optimization is performed based on the predicted outpower of WT and PV, the predicted demands of power and hydrogen loads. In the second stage, the intra-day optimization is performed based on the actual data to trace the day-ahead operation schemes. Since the intra-day optimization can update the operation scheme based on the latest data of renewable energies and loads, the proposed two-stage management model is effective in eliminating the uncertain factors and maintaining the stability of the whole system. Simulations show that the proposed two-stage energy management model is robust and effective in coordinating the operation of the wind-PV-hydrogen-storage microgrid and eliminating the uncertainties and fluctuations of WT, PV and loads. In addition, the battery storage can reduce the operation cost, alleviate the fluctuations of the exchanged power with the power grid and improve the performance of the energy management model.

In order to promote energy mutual aid among microgrids, expand the types of energy interaction, and improve the utilization of renewable energy, a two-layer sharing strategy for multi-microgrids (MMGs) based on the Nash game is proposed. Firstly, the low-carbon transformation of the micro-grid model is carried out, and the source side is transformed into a comprehensive and flexible operation mode for carbon capture thermal power plants. Then, the multi-microgrid subject electro-thermal double-layer sharing model based on the Nash game is constructed, which is decomposed into a revenue maximization sub-problem and a revenue redistribution sub-problem. In the sub-problem of revenue maximization, considering the lowest operation cost of carbon allowances and stepped carbon trading as the goal, the alternating direction multiplier method is used for a distributed solution. In the revenue redistribution sub-problem, the reasonable redistribution of income is realized by constructing the asymmetric energy mapping contribution function for different periods and energy types. Finally, the simulation results have verified the effectiveness of the proposed method. The results showed that the strategy of this paper can achieve the optimization of the economic objectives of the multi-microgrid (MMG) alliance and has the advantages of reasonable redistribution of benefits, promotion of wind and solar consumption, and reduction of carbon emissions.

Accurate power system load forecasting is the core prerequisite for guaranteeing the safe and stable operation of power grids and supporting the efficient scheduling of power systems. To improve the accuracy of load forecasting and portray the non-stationarity and multi-scale characteristics of the load sequence, this paper proposes a short-term load forecasting method based on ICEEMDAN decomposition-LSTM feature extraction-hybrid cosine attention mechanism iTransformer. Firstly, the original load sequence is decomposed using the Improved Complete Ensemble Empirical Modal Decomposition (ICEEMDAN) to extract the intrinsic modal function (IMF) and residuals (Res), and the multidimensional input feature set is constructed by combining exogenous variables such as meteorology. Secondly, multi-source features were extracted using the Long Short-Term Memory (LSTM) network to capture the complex nonlinear correlations and long-term dependencies. Finally, the extracted features are input into the iTransformer model that introduces the hybrid cosine attention mechanism. The hidden feature representation is obtained through the encoder layer modeling, and the linear mapping in the output layer generates the load forecast value. The results show that the prediction method proposed in this paper achieves better performance and can effectively improve the accuracy of short-term load prediction, which provides an effective technical support for the short-term scheduling and flexible operation of the power system.

The high integration of renewables like distributed photovoltaic (PV) into medium- and low-voltage distribution networks causes bidirectional power flows, increased voltage fluctuations, and operational uncertainty. Traditional power flow models struggle to balance efficiency and accuracy for multi-period optimization. This paper proposes a dual-objective voltage optimization method based on a Holomorphic Embedding time-series power flow model. First, a recursive relationship for nodal voltage power series expansion is derived, revealing the linear superposition of first-order coefficients with power injection changes and the rapid decay of higher-order terms. A linearized analytical model neglecting higher-order terms is built, improving the computational efficiency of time-series power flow calculations while maintaining accuracy. Then, integrating energy storage systems and static var compensators, a dual-objective optimization model minimizing voltage deviation and daily operational cost is formulated. Tests on a practical 91-node rural distribution system show that the proposed power flow model maintains a voltage error below 0.25% compared to the Newton–Raphson method across various PV integration scenarios, and the optimization reduces computation time by about 61.3% versus the Second-Order Cone Programming method, validating its advantages in precision and efficiency for balancing voltage quality and economy.

To address renewable generation and load uncertainty in multi-campus integrated energy systems, this paper proposes a distributionally robust day-ahead–real-time coordinated scheduling model under a cloud-edge collaborative architecture. The studied system consists of photovoltaic, wind power, and combined heat and power campuses, each equipped with energy storage and transferable load resources. The cloud layer determines the day-ahead baseline dispatch plan, while the edge layer performs scenario-dependent real-time corrections. To improve adaptability to adverse operating conditions, bounded forecast-error scenarios are constructed, and a conditional value-at-risk-based distributionally robust objective is formulated. Meanwhile, a soft day-ahead–real-time energy-binding mechanism is introduced to maintain plan-execution consistency while allowing necessary real-time adjustments. Case studies show that, compared with the cases without peer-to-peer energy exchange, demand response, and energy storage, the proposed model reduces the objective value by 5.22%, 10.96%, and 5.05%, respectively. Sensitivity analysis and stress tests verify its feasibility and robustness under increased uncertainty and reduced flexible-resource capacities.