Research On Information Access And Load Scheduling In Smart Grid

The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. During the past decades, although the information and control technologies have changed a lot, the aging traditional grid still lagged. As widely considered to be the next generation of the power grid, the smart grid uses information and communications technology in an automated fashion to improve the agility, reliability, efficiency, security, economy and environ-mental friendliness.Information access and load scheduling in the smart distribution grid are at the core of the future smart grid. The meter data from smart meters will be up to tens of thousands of terabytes in near future, which poses a significant challenge for smart grid communication networks to collect, transmit and store such large-scale data. Novel wireless communication technologies, such as cognitive radio, are expected to ensure reliable and real-time data transmission. On the other hand, the significant growth is expected in electricity consumption in the coming decades. Besides, the widespread adoption of electric vehicles will potentially double the energy demand. The smart distribution grid aims to address the ever-increasing load through appropriate scheduling. Based on the state-of-the-art study, this dissertation researches on communication and load scheduling in the smart distribution grid. The main work and contributions are summarized as follows:1. A brief review of the background, overview, and related works on smart grid communication and load scheduling is provided.2. Research on cognitive radio enabled smart grid communication. To tackle the conflict be-tween spectrum scarcity and under-utilization, cognitive radio is leveraged to improve the communication quality. Due to the energy constraint of battery-powered sensors, energy-efficiency arises as a critical issue in sensor-aided cognitive radio networks. An optimal scheduling of each sensor active time can effectively extend the network lifetime. The prob-lem of energy-efficient cooperative spectrum sensing is formulated as a scheduling problem, which is proved to be NP-complete. Greedy Degradation is employed to degrade it into a linear integer programming problem, and three approaches namely Implicit Enumeration, General Greedy and λ-Greedy are proposed to solve the subproblem. Simulation results are presented to verify the performance of our approaches, as well as to study the effect of adjustable parameters on the performance.3. Research on the impact of communication quality on control performance in smart distri-bution grid. Cognitive radio is introduced into smart grid to improve the communication quality. By means of spectrum sensing and channel switching, smart meters can decide to transmit data on either an original unlicensed channel or an additional licensed channel, so as to reduce the communication outage. Considering the energy cost taxed by spectrum sensing together with the control performance degradation incurred by imperfect communications, the sensing-performance tradeoff problem is formulated between better control performance and lower communication cost, paving the way towards green smart grid. The impact of communication quality on control performance is also analyzed, which reduces the profit of power provider and the social welfare, although it may not always decrease the profit of power consumer. By employing the energy detector, it is proved that there exists a unique optimal sensing time which yields the maximum tradeoff revenue, under the constraint that the licensed channel is sufficiently protected. Numerical results are provided to validate theoretical analysis.4. Research on load scheduling in a coupled-constraint game approach. The residential ener-gy consumption scheduling problem is formulated as a coupled-constraint game by taking the interaction among users and the temporally-coupled constraint into consideration. The proposed solution consists of two parts. Firstly, dual decomposition is applied to transform the original coupled-constraint game into a decoupled one. Then, Nash equilibrium of the decoupled game is proven to be achievable via best response, which is computed by gradient projection. The proposed solution is also extended to an online version, which is able to alleviate the impact of the price prediction error. Numerical results demonstrate that the pro-posed approach can effectively shift the peak-hour demand to off-peak hours, enhance the welfare of each user, and minimize the peak-to-average ratio. The scalability of the approach and the impact of the user number are also investigated.5. Research on load scheduling with future price uncertainty. Under the real-time pricing en-vironment, due to the uncertainty of future prices, load scheduling is formulated as an opti- mization problem with expectation and temporally-coupled constraints. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, dual decomposition and stochastic gradient are proposed to solve the problem. That is, the pri-mal problem is firstly dually decomposed into a series of separable subproblems, and then the price uncertainty in each subproblem is addressed by stochastic gradient based on the statistical knowledge of future prices. In addition, an online approach is proposed to fur-ther alleviate the impact of price prediction error. Numerical results are provided to validate theoretical analysis.The conclusions are drawn with future work at the end of the dissertation.