The efficient use of the incorporation of photovoltaic generation (PV) and an electric vehicle (EV) with the home energy management system (HEMS) can play a significant role in improving grid stability in the residential area and bringing economic benefit to the homeowner. Therefore, this paper presents an energy management strategy in a smart home that integrates an electric vehicle with/without PV generation. The proposed strategy seeks to reduce the household electricity costs and flatten the load curve based on time-of-use pricing, time-varying household power demand, PV generation profile, and EV parameters (arrival and departure times, minimum and maximum limit of the state-of-charge, and initial state-of-charge). The proposed control strategy is divided into two stages: Stage A, which operates in three operating modes according to the unavailability of PV power generation, and Stage B, which operates in five operating modes according to the availability of PV generation. In this study, the proposed strategy enables controlling the amount of energy absorbed by the EV from the grid and/or PV and the amount of energy injected from the EV to the load to ensure that the household electricity costs are minimized, and the household power load profile is flattened. The findings show that both household electricity costs reduction and flattening of the power load profile are achieved. Moreover, the corresponding simulation results exhibit that the proposed strategy for the smart home with EV and PV provides better results than the smart home with EV and without PV in terms of electricity costs reduction and power load profile flattening.

With the emerging of the smart grid, it has become easier for consumers to control their consumption. The efficient use of the integration of renewable energy sources with electric vehicle (EV) and energy storage systems (ESSs) in the smart home is a popular choice to reduce electricity costs and improve the stability of the grid. Therefore, this study presents optimal energy management based on the Jaya algorithm for controlling energy flow in the smart home that contains photovoltaic generation (PV), integrated with ESS and EV. The objective of the proposed energy management is to reduce electricity cost while meeting the household load demand and energy requirement for the EV trip distance. By using the Jaya algorithm, the modes of home-to-vehicle (H2V) and vehicle-to-home (V2H) are controlled, in addition to controlling the purchase of energy from the grid and sale of the energy to the grid from surplus PV generation and ESS. Before EV participation in the V2H process, the amount of energy stored in the electric vehicle battery will be verified to be more than the energy amount required for the remaining EV trip to ensure that the required energy for the remaining EV trip is satisfied. Simulation results highlight the performance of the optimal energy scheduling to achieve the reduction of the daily electricity cost and meeting of load demand and EV energy required. The simulation results prove that optimal energy management solutions can be found with significant electricity cost savings. In addition, Jaya is compared with the particle swarm optimization (PSO) algorithm in order to evaluate its performance. Jaya outperforms PSO in terms of achieving optimal energy management objectives.

This paper designs the integrated charging station of PV and hydrogen storage based on the charging station. The energy storage system includes hydrogen energy storage for hydrogen production, and the charging station can provide services for electric vehicles and hydrogen vehicles at the same time. To improve the independent energy supply capacity of the hybrid charging station and reduce the cost, the components are reasonably configured. To minimize the configuration cost of the integrated charging station and the proportion of power purchase to the demand of the charging station, the energy flow strategy of the integrated charging station is designed, and the optimal configuration model of optical storage capacity is constructed. The NSGA-II algorithm optimizes the non-inferior Pareto solution set, and a fuzzy comprehensive evaluation evaluates the optimal configuration.

The stochastic characteristics of wind power and photovoltaic (PV) make the resource allocation of power system difficult. Therefore, it is necessary to consider the correlation between the power generation of wind and PV power stations to avoid resource waste and guarantee system power supply, while the traditional correlation analysis method cannot accurately describe the multiscale and time-varying characteristics of the correlation. In this paper, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the time series of the wind and PV power generation was decomposed into multiscale components. Moreover, the time-dependent intrinsic correlation (TDIC) is introduced to excavate the local correlation of the power generation time series under the framework of a multi-time scale, the dynamic change of a correlation is captured by analyzing the TDIC plots. The analysis shows that the strength and nature of the association between wind and PV vary with time scales and time spells, reflecting rich, dynamic characteristics. The correlation variation of different scale components in local time is of great significance to power system operation, planning, and resource optimal allocation.

As the final stage of power system restoration, the critical task of load restoration is to restore the remaining load as quickly as possible. With the continuous increase of the temperature-controlled load and the proportion of electric vehicle load in the urban power grid, the complexity of the load side in the restoration process gradually increases. Therefore, based on the existing grid environment, this paper considers the sudden increase in load recovery caused by cold load pick-up and the auxiliary effect of electric vehicle discharge on load recovery during the load recovery process. From the perspective of economy, safety, and speed, this paper establishes a multi-objective function that includes the amount of load, improved weighted power flow entropy, and the number of recovered lines. The multi-objective evolutionary algorithm based on decomposition is used to optimize the constructed multi-objective load recovery model. Through the IEEE30 node system, it is verified that the method proposed in this paper can effectively establish a fast and safe load recovery plan that meets the actual grid environment.

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.