Line loss rate plays an essential role in evaluating the economic operation of power systems. However, in a low voltage (LV) distribution network, calculating line loss rate has become more cumbersome due to poor configuration of the measuring and detecting device, the difficulty in collecting operational data, and the excessive number of components and nodes. Most previous studies mainly focused on the approaches to calculate or predict line loss rate, but rarely involve the evaluation of the prediction results. In this paper, we propose an approach based on a gradient boosting decision tree (GBDT), to predict line loss rate. GBDT inherits the advantages of both statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. An empirical study on a data set in a city demonstrates that our proposed approach performs well in predicting line loss rate, given a large number of unlabeled examples. Experiments and analysis also confirmed the effectiveness of our proposed approach in anomaly detection and practical project management.

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS.

The concept of “oil to electricity” is crucial for expanding the share of electricity in final energy consumption as well as for encouraging energy efficiency and emission reduction. Initially, a multidimensional strategy analysis is conducted for the government, ports, and ships concerned. From an economics perspective, a mathematical model of electricity substitution benefit analysis based on multiagent cooperative game theory under cap and trade and carbon tax policies is constructed, and the effect of carbon emissions caused by ships on the environment and society is converted into economic value. How several variables, such as transformation costs, ship electricity consumption, subsidy rates, carbon tax prices, and the ratio of shore power usage time to berthing time, affect the functioning of shore power is analyzed. The best electricity price under various circumstances is determined while considering the benefits of the three parties to maximize social welfare. The reduction in carbon dioxide and pollutant emissions is calculated. Meanwhile, the environmental advantages of the “replacement of oil with electricity” procedure are estimated. An example supports the claim that the suggested modeling approach can successfully resolve the economic benefits of each participant for the period that fosters the growth of electricity replacement projects and offers a sound scientific foundation for the formation of pertinent legislation.

As the increasing penetration of sustainable energy brings risks and opportunities for energy system reliability, at the same time, considering the multi-dimensional differentiation of users’ reliability demands can further explore the potential value of reliability resources in Integrated Energy Microgrid (IEM). To activate the reliability resources in a market-oriented perspective and flexibly optimize the operational reservation in dispatch, an optimal dispatching model in IEM considering reliability principal–agent contracts is proposed. We establish the reliability principal–agent mechanism and propose a cooperative gaming model of Integrated Energy Operator (IEO) and Integrated Energy User (IEU) based on the optimal dispatching model. At the upper level, the economic dispatching model of IEO is established to optimize the operation reservation, and the reliability principal–agent contract from users in the lower level would influence reliability improvement. Each IEU in the lower level maximizes its energy utilization and gives the corresponding reliability principal–agent incentives according to the reliability improvement degree and its actual demand. The bi-level model is solved by the KKT condition and strong duality theorem. A case study verifies the effectiveness of the proposed model in reducing the energy dispatch cost, improving the economic benefits of each participant, realizing the optimal allocation of reliability resources and optimizing the IEM energy structure, and the sensitivity analysis of dispatch cost with the user’s energy-using benefits is discussed.

The availability of hydrological data for small hydropower plants is an important prerequisite for reservoir scheduling, reservoir flood control and integrated water resources. To address the problem of a lack of hydrological data in small hydropower plants, this paper proposes a method to predict the power generation flow of small hydropower stations without hydrological data using the Soil and Water Assessment Tool model (SWAT) when the traditional data-driven methods cannot study the problem of power generation flow prediction in small hydropower stations well. The method can use gridded meteorological data as the input of the model to solve the problem of small hydropower stations without meteorological data. The problem that small hydropower plants without hydrological data cannot calibrate the hydrological model is solved by calculating the generation flow through the output of small hydropower station and by using the similarity analysis method to migrate the generation flow of similar small hydropower stations. The model was tested in a watershed in southwest China to demonstrate the effectiveness of the proposed method. The results show that the coefficient of determination between the predicted and measured values of small hydropower stations without information is about 0.84, which achieves a better prediction.

With the global low-carbon transition and increasing wind and photovoltaic penetration, virtual power plant (VPP) scheduling increasingly requires coordinated low-carbon operation, resource management, and long-term stability. Existing studies often treat demand response (DR) resources as passive adjustable capacity, but insufficiently consider how satisfaction variation, fatigue accumulation, and scheduling experience under continuous dispatch feed back into subsequent response behavior. Meanwhile, the coordination among electricity, gas, carbon, and green certificate markets and market incentives reflecting the low-carbon value of power-to-gas (P2G) remain underexplored. To address these issues, this paper introduces a response-willingness feedback mechanism that feeds the satisfaction and fatigue accumulation of the demand response aggregator (DRA) back into subsequent deliverable DR capacity and scheduling behavior. A virtual power plant operator (VPPO)–DRA bi-level optimal scheduling model is then formulated under a Carbon–Green Certificate Coordinated P2G Incentive Mechanism, characterizing the Stackelberg interaction between the VPPO and the DRA. A hierarchical solution framework integrating CMA-ES, dynamic programming, and Gurobi is developed. Deterministic comparisons showed that the model maintained VPP profitability while improving DR sustainability and renewable accommodation. Under ex post stress testing, the fixed nominal day-ahead VPPO strategy maintained feasibility in all 200 cases and positive VPP profit in 94.0%.