Design And Reliability Analysis Of Cognitive Radio-based Smart Grid Communication Network

The electricity supply company has been facing significant challenges in terms of meeting the projected demand for energy,environmental issues,security,reliability and integration of renewable energy.Currently,most of the power grids are based on many decades old hierarchical centralized infrastructures.The electric industry is poised to make the transformation from a centralized,producer-controlled network to one that is less centralized and more consumer-interactive.The move to a smarter grid promises to change the industry’s entire business model and its relationship with all stakeholders,involving and affecting utilities,regulators,energy service providers,technology and automation vendors and all consumers of electric power.A smarter grid makes this transformation possible by bringing the philosophies,concepts and technologies that enabled the internet to the utility and the electric grid.The advancement in Information and Communication Technology(ICT)has motivated to convert the existing grid into the Smart Grid(SG).Recently,cognitive radio(CR)and smart grid are two areas which have received considerable research impetus.Cognitive radios are intelligent software defined radios(SDRs)that efficiently utilize the unused regions of the spectrum,to achieve higher data rates.Cognitive radio networks promise to resolve the bandwidth scarcity problem by allowing unlicensed devices to transmit in unused ’’spectrum holes" in licensed bands without causing harmful interference to authorized users.This thesis presents the Cognitive Radio framework for wireless networks in SG.The proposed Cognitive Radio framework is a complete model for CR that describes the decision and sharing procedures in smart grid communication network.This research is based on some of the latest communication technologies and standards,most of which are leading candidates for SG environment such as Cognitive radio,IEEE 802.22 using TVWS and 802.11 af standards.In order to develop a standard for CRs,the IEEE 802.22 working group was formed in November 2004.The corresponding IEEE 802.22 standard defines the physical(PHY)and medium access control(MAC)layers for a wireless regional area network(WRAN)that uses white spaces within the television bands between 54 and 862MHz,especially within rural areas where usage may be lower.This study reflects on following the points:1.Probability of sensing Primary User’s spectrum efficiently in real-time for optimum use of spectrum in Cognitive Radio;and2.Verifying the feasibility of Cognitive Radio to meet the timing constraints(latency)of data transmission for making it more reliable communication network.A critical and an important piece of the SG infrastructure is the AMI for data gathering,control and supervision capabilities that allowing two-way data flow e.g.,real-time energy pricing and real-time demand data back to the Utilities and Operators.AMI has hierarchical structure based on SMs,HAN gateway,Data concentrators and meter data management system(MDMS).An important smart grid function supported by AMI is Demand Response(DR),which is mainly used to automatically gather the metering information from the customer premises thereby reducing operational costs.Data concentrators work as bridge devices connect the customer premises and the utility companies,hence they carry plenty of data and their failure will cause data loss which leads to huge economic loss.Due to failure of data concentrator,link will be damaged between Smart meter and MDMS which will cause of demand-estimation error.Redundancy optimization is an efficient way to improve the system reliability while reducing the risk cost of unavailable(i.e.,economic loss caused by equipment failure).It has been used before in telecommunication and other sectors but not directly used for AMI communication infrastructure directly.To investigate the problem of redundancy optimization for AMI,firstly,combining with the demand response(DR)and probability theory to analyzation of the economic loss caused by a data concentrator failure(i.e.,risk cost of unavailable)to prove the significance of demand-estimation error cost with-respect to number of users in subareas.Then,establishing the redundancy optimization model,by considering the failure cost,redundant equipment investment cost,and network reliability.In the end,an improved Genetic Algorithm(GA)is used to solve the optimization problem for minimizing the whole network cost and increased reliability for less number of users per service area to check the efficiency of this method.The increasing complexity of power networks calls for new decentralized solutions for electrical power system control.Scalability refers to the capability of a system or network being expanded or upgraded easily to satisfy ever-increasing growing demand,the development of smart grid is anticipated to be highly desirable in realizing the scalability.The traditional AMI architecture uses a centralized operation center with a centralized MDMS,which makes this system non-scalable.The system needs to be scalable so that with increased demand,it can be expanded at minimal cost to check the scalability of proposed GA method.Firstly,we assume a subarea in which number of users are non-realistic to justify that this method is improved for reliability of SG or not.But in reality,when we scale it for large number of users then the economic loss per subarea is higher because of large number of users.When we check it for large scale dataset(i.e.large number of users)then the reliability becomes near to 0.99 with minimal cost of whole network with higher number of redundancy concentrator.