Optimimal Scheduling Based On Real-time Price In Smart Grid

Due to the limited resources and the enhance of human consciousness inenvironmental protection, moreover, with the increasing power demand of consumers inquantity and quality, the current power system has been put into serious challenges, soconstruct the smart grid has become a common choice for the electric power industry. Tosave energy and reduce the growth rate of electricity demand by changing energyconsumption of consumers from their normal electric usage patterns, demand responsedevelop fast under this knowledge. Demand response (DR) is a mechanism for changingthe mode of electricity market participation behavior according to the market price signalsor incentives. Consumers adjust electricity demand, according to electric power marketincentives to achieve energy conservation and emissions reduction, and optimize theallocation of resources, promoting market stability and the reliability of the power grid,which is important to the development of the electricity market.Firstly, for the case that the energy provider can not meet the minimum powerconsumption of the consumers, a demand response scheme for smart grid is presented.The thesis proposes distributed power consumption scheduling algorithms for theconsumers and real-time pricing algorithm for the energy provider and designs twodifferent admission control algorithms make the consumers meet the minimum powerconsumption requirement. Simulation results demonstrate that the scheme realizes thebalance between supply and demand in smart grid, makes a higher utilization rate of theelectrical energy provided by the energy providers, meanwhile, ensures the power qualityof consumers.Secondly, this thesis concerns with demand response problem in smart grid, whereconsumers are served by multiple energy providers. A Real-Time Pricing (RTP)-basedenergy consumption scheduling scheme is proposed, which consists of energy consumptionallocation, energy provider selection and real-time pricing. Specifically, the energyscheduling problem is first cast into a combinatorial optimization problem. Then, adistributed algorithm is designed, where energy providers and consumers jointly computethe optimal energy consumption allocation, energy provider selection and price. The algorithm can maximize the aggregate utility of all consumers in the system in a fair andefficient fashion. Simulation results confirm that the proposed distributed algorithm can notonly make a higher utilization rate of the electrical energy provided by the energy providers,but also maximizes the total utility of all the consumers and achieve the balance of supplyand demand in the grid.Lastly, this thesis studies the power consumption scheduling problem for residentialconsumers in smart grid. There are two types of appliances in a household, and theelectricity price is announced by the service provider one day ahead, a power consumptionscheduling strategy for the consumer to achieve a desired trade-off between the paymentand the dissatisfaction is proposed. The power scheduling problem is formulated as anon-convex optimization problem including integer and continuous variables. An optimalscheduling strategy is obtained by solving the combinational optimization problem.Simulation results demonstrate that the scheduling strategy can not only reduce thepayment of the consumer but also lower the peak-to-average ratio (PAR) in a day.