نمایش همه 3 نتیجه
A cloud computing framework on demand side management game in smart energy hubs
The presence of enerرایگان!
The presence of energy hubs in the future vision of energy networks creates an opportunity for electrical engineers to move toward more efficient energy systems. At the same time, it is envisioned that smart grid can cover the natural gas network in the near future. This paper modifies the classic Energy Hub model to present an upgraded model in the smart environment entitling ‘‘Smart Energy Hub’’. Supporting real time, two-way communication between utility companies and smart energy hubs, and allowing intelligent infrastructures at both ends to manage power consumption necessitates large-scale real-time computing capabilities to handle the communication and the storage of huge transferable data. To manage communications to large numbers of endpoints in a secure, scalable and highly-available environment, in this paper we provide a cloud computing framework for a group of smart energy hubs. Then, we use game theory to model the demand side management among the smart energy hubs. Simulation results confirm that at the Nash equilibrium, peak to average ratio of the total electricity demand reduces significantly and at the same time the hubs will pay less considerably for their energy bill.
Microgrid operation and management using probabilistic reconfiguration and unit commitment
A stochastic model fرایگان!
A stochastic model for day-ahead Micro-Grid (MG) management is proposed in this paper. The presented model uses probabilistic reconfiguration and Unit Commitment (UC) simultaneously to achieve the optimal set points of the MG’s units besides the MG optimal topology for day-ahead power market. The proposed operation method is employed to maximize MG’s benefit considering load demand and wind power generation uncertainty. MG’s day-ahead benefit is considered as the Objective Function (OF) and Particle Swarm Optimization (PSO) algorithm is used to solve the problem. For modeling uncertainties, some scenarios are generated according to Monte Carlo Simulation (MCS), and MG optimal operation is analyzed under these scenarios. The case study is a typical 10-bus MG, including Wind Turbine (WT), battery, Micro-Turbines (MTs), vital and non-vital loads. This MG is connected to the upstream network in one bus. Finally, the optimal set points of dispatchable units and best topology of MG are determined by scenario aggregation, and these amounts are proposed for the day-ahead operation. In fact, the proposed model is able to minimize the undesirable impact of uncertainties on MG’s benefit by creating different scenarios.
Reactive Power Generation Management for the Improvement of Power System Voltage Stability Margin
Voltage stability maرایگان!
Voltage stability margin (VSM) of the power system relates to the reactive power reserves in the network. This paper presents a method to improve the VSM by generatorreactive power generation rescheduling. The management of the var generation formulated as an optimization problem and pseudo-gradient evolutionary programming (PGEP) was used to obtain the optimal solution. Modal analysis technique was used to guide the searching direction. Simulation results on the New England 39-bus system demonstrate that the proposed method is effective. Compared with the standard evolutionary programming (SEP), better solution can be obtained, and the convergence speed of the algorithm is improved also. The simulation results show that after the optimal reactive power rescheduling the reactive power reserves of the system is increased and the active/reactive power losses are decreased. The most important advantage is that, the voltage stability margin of power system can be improved without adding new var compensation equipment and changing the active power distribution.