A large amount of electric vehicles (EVs) charging load will bring significant impact to the power system. An appropriate resource allocation strategy is required for securing the power system safety and satisfying EVs charging demand. This paper proposed a power coordination allocation strategy of EVs’ in distribution systems. The strategy divides the allocation into two stages. The first stage is based on scores assigned to EVs through an entropy method, whereas the second stage allocates energy according to EV’s state of charge. The charging power is delivered in order to maximize EV users’ satisfaction and fairness without violation of grid constraints. Simulation on a typical power-limited residential distribution network proves the effectiveness of the strategy. The analysis re- sults indicate that compared with traditional methods, EVs, which have higher charging requirement and shorter available time will get more energy delivered than others. The root- mean-square-error (RMSE) and standard-deviation (SD) results prove the effectiveness of the methodology for improving the balance of power delivery.

The paper aims at the higher reactive power management complexity caused by the access of distributed power, and the problem such as large data exchange capacity, low accuracy of reactive power distribution, a slow convergence rate, and so on, may appear when the controlled objects are large. This paper proposes a reactive power and voltage control management strategy based on virtual reactance cloud control. The coupling between active power and reactive power in the system is effectively eliminated through the virtual reactance. At the same time, huge amounts of data are treated to parallel processing by using the cloud computing model parallel distributed processing, realize the uncertainty transformation between qualitative concept and quantitative value. The power distribution matrix is formed according to graph theory, and the accurate allocation of reactive power is realized by applying the cloud control model. Finally, the validity and rationality of this method are verified by testing a practical node system through simulation.

HVAC systems use a substantial part of the whole energy usage of buildings. The optimizing of their operation can greatly affect the power use of a building, making them an interesting subject when trying to save energy. However, this should not affect the comfort of the people inside. Many approaches aim to optimize the operation of the heating and cooling system; in this paper, we present an approach to steer the heat pumps to reduce energy usage while aiming to maintain a certain level of comfort. For this purpose, we employ a market-based distributed method for power-balancing. To maintain the comfort level, the market-based distributed system assigns each device a cost-curve, parametrized with the current temperature of the room. This allows the cost to reflect the urgency of the HVAC operation. This approach was tested in a real-world environment: we use 10 heat pumps responsible for temperature control in 10 comparable-sized rooms. The test was performed for 3 months in summer. We limited the total peak power, and the algorithm balanced the consumption of the heat pumps with the available supply. The experiments showed that the system successfully managed to operate within the limit (lowering peak usage), and - to a certain point - reduce the cost without significantly deteriorating the working conditions of the occupants of the rooms. This test allowed us to estimate the minimal peak power requirement for the tested set-up that will still keep the room temperatures in or close to comfortable levels. The experiments show that a fully distributed market-based approach with parametrized cost functions can be used to limit peak usage while maintaining temperatures.