Increasing electric vehicles (EV) charging efficiency and reducing charging cost are important issues of EVs power management and key factor that affect the spread of EVs. Researching the competitive relationship between power grids and EV users is the key to solving this problem. Therefore, the authors propose a charging control strategy that is based on a 1‐N‐type Stackelberg game. In the game, power grid, retailers, and users are all able to do power decision making. Thus, the charging strategy here can flexibly meet different demands of power grid, retailers, and users. The equilibrium of the game model is solved by the inverse induction method. The benefits are compared by simulation, by the disordered charging process, and by charging control methods based on static time‐sharing electricity prices. The influence of the parameters on the charging process in the game model is analysed, and the feasibility of the proposed method is verified. Results show that load peak is reduced by 34.9% while users' charging costs are reduced by 25.1%. The approaches they proposed can effectively solve the competition between the two.

This paper proposes a sliding mode control (SMC) strategy to solve the problem of independent control of temperature and humidity in air conditioners; the complexity increases due to the need to make full use of the wind side for cooling. First, dynamics of indoor temperature and humidity are determined based on mathematical models. Second, in order to reduce the chattering effect of SMC and coordinate indoor conditions to satisfy people’s needs, the independent control of temperature and humidity is realized via a novel fuzzy sliding mode control strategy based on reaching laws. Finally, simulation results are provided to verify the effectiveness of the proposed method.

Previous studies show that the indoor environment quality (IEQ) of buildings directly affects human health and comfort. This study aims to predict the change of indoor parameters at the next moment under the influences of the current indoor climate and outdoor climate and control the IEQ parameters based on the human thermal adaption in advance. We combine the simulation and the mathematical method to establish the office building model with air-conditioning and lighting systems and construct the bilinear model of the IEQ parameters and control variables. Unknown parameters are identified using the experimental method. Model predictive control (MPC) based on human thermal comfort is discussed by considering human thermal adaptation, and the neutral temperature is calculated through the dynamic relationship between outdoor and indoor temperatures. Results show that the temperature setpoint is adjusted in accordance with human adaptability, and the air-conditioning, fan, and lighting systems are controlled via MPC. The usage time of air-conditioning and light is reduced, and thus, energy is saved.

With the popularity of solar Photovoltaic (PV) power generation, the real-time interaction between distributed microgrids and large grids has become a new hotspot of concern. In distributed PV power trading, we aim to achieve a dynamic balance between PV users and the grid. This involves real-time power prediction for users, secure blockchain-based recording and protection of trading data, and efficient matching of microgrid and large-grid trading information, including pricing details. System simulations and experimental data analysis have demonstrated the benefits of this transaction model, including enhanced real-time interaction between microgrid and grid. For the buyer, the cost of purchasing electricity can be reduced by about 6%, and for the seller, the income from selling electricity is 1.5 times that of direct Internet access, which has positive significance in improving the income from selling electricity and reducing the cost of electricity for users.