Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Identification Method of Urban Road Traffic Conditions in the Internet of Vehicles Environment

In accordance with the specific deployment way of infrastructure and data exchanging technology in the Internet of vehicles(IoV),the acquiring and calculating method for three basic traffic flow parameters in IoV scenarios,including traffic flow,speed and density,was researched.Considering the complexity of traffic flow and fuzziness of human thinking,fuzzy c-means clustering algorithm based on the genetic algorithm(GA-FCM) was adopted in soft classification of urban road traffic conditions.Genetic algorithm(GA) introduced into fuzzy clustering could avoid fuzzy c-means(FCM) algorithm converging to the local infinitesimal point,which made the cluster result more precise.By means of computer simulation,data exchanging environment in IoV was imitated,and then test data set was divided into four parts.The simulation indicates that the identification method is feasible and effective for urban road traffic conditions in IoV scenarios.

Optimal planning of stand-alone microgrids incorporating reliability

This paper proposes a new method for the planning of stand-alone microgrids.By means of clustering techniques,possible operating scenarios are obtained considering the daily patterns of wind and load profiles.Then,an approximate analytical model for reliability evaluation of battery energy storage system is developed in terms of the diverse scenarios,along with multistate models for wind energy system and diesel generating system.An optimal planning model is further illustrated based on the scenarios and the reliability models,with the objective of minimizing the present values of the costs occurring within the project lifetime,and with the constraints of system operation and reliability.Finally,a typical stand-alone microgrid is studied to verify the efficiency of the proposed method.