Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

A game theoretical approach for distributed resource allocation with uncertainty

Purpose-The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty.It proposes modeling the distributed resource allocation problem by Bayesian game.During this paper,three basic kinds of uncertainties are discussed.Therefore,the purpose of this paper is to build the connections between game theory and the resource allocation problem with general uncertainty.Design/methodology/approach-In this paper,the Bayesian game is proposed for modeling the resource allocation problem with uncertainty.The basic game theoretical model contains three parts:agents,utility function,and decision-making process.Therefore,the probabilistic weighted Shapley value(WSV)is applied to design the utility function of the agents.For achieving the Bayesian Nash equilibrium point,the rational learning method is introduced for optimizing the decision-making process of the agents.Findings-The paper provides empirical insights about how the game theoretical model deals with the resource allocation problem uncertainty.A probabilistic WSV function was proposed to design the utility function of agents.Moreover,the rational learning was used to optimize the decision-making process of agents for achieving Bayesian Nash equilibrium point.By comparing with the models with full information,the simulation results illustrated the effectiveness of the Bayesian game theoretical methods for the resource allocation problem under uncertainty.Originality/value-This paper designs a Bayesian theoretical model for the resource allocation problem under uncertainty.The relationships between the Bayesian game and the resource allocation problem are discussed.