Changes in electricity demand reflect the vibrancy of economic operations. Electricity data have advantages such as high accuracy, strong real-time capabilities, and predictability. This study calculates an economic-prosperity index based on electricity data to capture cyclical, economic changes from an electricity perspective. First, two indicators—the time-lagged correlation coefficient and the K-L information quantity—are used to identify leading, coincident, and lagging economic indicators. Indicator weight-division methods based on correlation coefficients and electricity consumption were used to construct a calculation model for the electricity data-based economic-prosperity index. We conducted empirical research on the electricity economic-prosperity index based on the economic-electricity data of a certain province. Finally, we predicted the future electricity-demand data of the province, calculated its future economic-prosperity index, and analyzed its future economic-development trend, based on the seasonal autoregressive integrated moving average (SARIMA) model. The results indicate that the economic prosperity-index calculation model based on electricity data can effectively reflect the trends of economic indicators through electricity data and can be used for future economic-development trend analysis.

The “fishery-solar hybrid project” effectively improves land use efficiency and reduces water evaporation. However, photovoltaic panels arranged on the water surface are affected by many factors, such as aging and power decline, making the prediction of water surface photovoltaic output challenging. The existing short-term photovoltaic output prediction technology extensively applies similar day selection to improve the prediction accuracy. However, it lacks consideration of aging phenomena, resulting in insufficient prediction accuracy for aquatic photovoltaic. Therefore, this paper proposes a short-term output prediction method for complementary fishing and solar power that decouples the aging characteristics of photovoltaic modules. First, an integrated learning model based on stacked recursive autoencoder-based learners was constructed to decouple the aging phenomenon of the dataset. Subsequently, the grey correlation analysis method was used to select similar days to train the deep learning predictor for the predicted time, and an improved Honey Badger algorithm was proposed to optimize the parameters of the base learners. Finally, the effectiveness and superiority of the proposed method were verified through a case study of the Fishery Light Complementary Project in Lufeng City, Yunnan Province.

Driven by the “dual-carbon” policy environment, China is committed to building an efficient and low-carbon energy supply system. However, the intermittency and volatility of renewable energy sources, such as wind and solar, pose challenges to the stable operation of the microenergy grid, with large amounts of biomass waste resources, such as straw, garbage, and biological sewage, not being fully developed and utilized. Therefore, this study proposes a micro-energy grid architecture based on a rural biomass waste energy conversion system, mathematically modeling its components. Subsequently, a two-stage scheduling optimization framework is designed, whereby in the day-ahead phase, a multi-objective scheduling optimization model is established with the objectives of minimizing operating costs and maximizing ecological and environmental benefits, and in the intraday phase, robust optimization is introduced to describe the uncertainty of wind and solar sources, thereby developing an intraday scrolling scheduling optimization model with the objective of minimizing deviation adjustment costs. Finally, an example analysis was conducted on a micro-energy network in northern China. The results are as follows 1) The multi-timescale scheduling model can take advantage of the regulating capability of each unit, connect the day-ahead scheduling plan with the intraday scheduling strategy, and formulate an optimal scheduling strategy. 2) Day-ahead multi-objective scheduling optimization considers the impossible triangle and successfully achieves a significant increase of 14.82% in ecological benefits at the expense of a moderate economic cost increase of 5.02%. Overall, the proposed optimization model can promote the energy use of rural biomass waste resources and is useful for achieving a clean and low-carbon transition of the energy structure.

As China is gradually transitioning towards a low-carbon energy structure, the proportion of new grid-connected energy sources, such as wind and solar power, continues to increase. To ensure the safe and reliable operation of the power system and meet the capacity planning for future new energy installations, flexible resources are required with corresponding adjustment capabilities in the power system. To this end, this study presents an optimization model for the allocation of multiple types of flexible resources, considering spatiotemporal response characteristics. First, a flexibility assessment model was developed based on the spatial and temporal response characteristics, constructing flexibility evaluation indices such as flexibility average deficit and flexibility coverage index. These indices are used to screen nodes with inadequate flexibility in the power system and analyze the flexibility adequacy at various nodes. Next, the adjustment characteristics of multiple types of flexible resources were analyzed, and a model was established to determine adjustment capabilities. Finally, by considering constraints based on time flexibility evaluation indices, a two-stage optimization model for flexible resource allocation was constructed. This model leverages the multiscale matching characteristics between flexible resources and fluctuation patterns of new energy sources, to guide the allocation of flexible resources to nodes with insufficient flexibility. The effectiveness and applicability of the proposed flexible resource allocation method were validated using the IEEE 9-node system.

The safe and stable operation of new power systems has become challenging, with the increasing proportion of new energy sources in power systems. Configuring new, flexible-resource capacities has become urgent. This study considers the uncertainty in the new energy output and constructs an optimal, two-stage, robust, configuration model that considers flexibility-demand constraints. The flexibility demand of the power system is modelled with respect to the uncertainty characteristics of new energy sources, such as wind power and photovoltaic (PV) power; the regulation characteristics of new flexibility resources are analyzed to model their regulation capacity; uncertainty parameters were introduced to establish a two-stage, robust, capacity optimization model considering the balance of flexible supply and demand, and the corresponding optimal planning results of the system were solved based on the CCG algorithm and the KKT theorem. The power system of a certain location in China was selected as the simulation object for empirical analysis to verify the validity of the constructed model. The results show that the optimal, two-stage, robust, configuration model constructed in this study can effectively cope both with the uncertainty of the power system and the flexibility demand brought about by new energy sources. A balance between the security and economy of the new power system can be planned.

To promote low-carbon operations and clean energy consumption in integrated energy systems, this study proposes a zero-carbon optimization model for an integrated energy system, considering dynamic energy prices and carbon certificate trading. First, we introduce the equipment for carbon capture and storage, the operational structure design of the integrated energy system, and the output model of various internal equipment in the system. Second, a comprehensive factor-based energy price and demand response model for price correction are proposed. Subsequently, considering the synergy between the carbon and green certificate trading markets, a zero-carbon operation optimization model is constructed with an objective function that minimizes the total operating cost of the integrated energy system. A case study was conducted on an integrated energy system in a certain region, and the results showed that the proposed model can improve the cleanliness, low-carbon emissions, and regulatory performance of the system.