چكيده به لاتين
The emergence of smart grids has created a good opportunity for micro-dispersed production and private sector VPP. With proper energy management, customers can buy the electricity they need for their home consumption in periods of low load and store it, or they can transfer the use of some electrical appliances to a suitable period cheaply, and then produce and store their energy. to supply the network in expensive hours to make money. The implementation of such a scenario creates a favorable effect on changing the shape of the load curve, which is one of the main goals of load response programs, the full realization of which requires the creation of a virtual energy market or exchange. Dynamic pricing is one of the most effective ways to encourage customers to change their consumption patterns. In these cases, the uncertainty of the amount of energy consumed by customers has made it difficult to determine an optimal pricing policy, which with the help of the energy exchange, the number of consumers' needs and producers' supply at least one day before consumption cannot be estimated, but accurately recognized.
Minimizing traffic impacts to retrieve similar content (dynamic electricity price) is the main challenge of applications. Over time, the demand for applications is increasing Mission-critical applications require assurance of quality, reliability, and timeliness Traffic to these applications must be carefully managed to maintain their integrity and completeness. Things such as delay and the percentage of packets being discarded are among the basic parameters of traffic management of these applications. The current Internet with TCP/IP does not provide the necessary time limits for such applications well Also, due to the high number of smart network subscribers, whether producers or consumers, we need congestion control policies in this field to ensure the quality of their service.
In this research, for dynamic pricing in the structure of the electricity market, the content-oriented network has been used to manage and store data in the intelligent electricity distribution network. And the content of the requests based on the consumption pattern in Iran's power grids is injected into the network completely randomly, and the behavior of the system in the first part was checked by data storage, which 53.2% improvement in service quality was observed. And in the second part, with traffic management, a 10% improvement in the service quality ceiling was seen, so that the loss was 22.7%, and the delay was also reduced by 75%. Meanwhile, the proposed framework of the desired system has been simulated with MATLAB.
