Optimal Time-of-use Pricing for Renewable Energy-powered Microgrids: A Multi-agent Evolutionary Game Theory-based Approach

While price schedules can help improve the economic efficiency of renewable energy-powered microgrids,timeof-use(TOU)pricing has been identified as an effective way for microgrid development,which is presently limited by its high costs.In this study,we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system,that comprises a government agent,local utility company agent,and different types of consumer agents.In the proposed model,we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction.Two pricing strategies,namely,the TOU seasonal pricing and TOU monthly pricing,are evaluated and compared with traditional fixed pricing.The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing.Additionally,TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.

Novel Planning Approach for Fast-charging Station in Integrated System

A promising way to boost popularity of electric vehicles(EVs)is to properly layout fast charging stations(FCSs)by jointly considering interactions among EV drivers,power systems and traffic network constraints.This paper proposes a novel sensitivity analysis-based FCS planning approach,which considers the voltage sensitivity of each sub-network in the distribution network and charging service availability for EV drivers in the transportation network.In addition,energy storage systems are optimally installed to provide voltage regulation service and enhance charging capacity.Simulation tests conducted on two distribution network and transportation network coupled systems validate the efficacy of the proposed approach.Moreover,comparison studies demonstrate the proposed approach outperforms a Voronoi graph and particle swarm optimization combined planning approach in terms of much higher computation efficiency.

Two-time-scale Congestion Management Scheme for Microgrid Integrated Distribution Networks

Microgrids(MGs)with high penetration of dis-tributed generators may cause congestion in the distribution net-work during operation.To address this issue,this paper proposes a two-time-scale congestion management scheme for multiple MGs integrated distribution networks.Day-ahead hourly-scale dynamic congestion management(DCM)is formulated as a con-strained optimization problem,which can be solved by utilizing the proposed alternating iterative method,with the privacy of both the distribution network and MGs being preserved.The sub-hourly-scale contract energy tracking aims at fully utilizing the controllable resources of the MGs to minimize the difference of the contract and actual exchanged energy between the MG and distribution network.Through coordination of the proposed two timescales of management schemes,the MGs integrated distribution networks can operate economically while avoiding the probable congestion predicament with high penetration of renewable energy.Simulation studies with a i3-bus system MGs integrated distribution network demonstrated this proposed approach is effective to manage the congestion problem in the distribution network,while the energy tracking approach can improve the welfare of the MGs engaged in energy contracts execution.IndexTerms-Alternating giterativemethod,congestion management,microgrids,renewable energy integration.

Attack-resilient Distributed Control for Islanded Single-/three-phase Microgrids Based on Distributed Adaptive Observers

This paper investigates the power sharing and voltage regulation issues of islanded single-/three-phase microgrids(S/T-MGs)where both sources and loads are unbalanced and the presence of adversarial cyber-attacks against sensors of distributed generator(DG)units is considered.Firstly,each DG unit is modeled as a heterogeneous linear dynamic agent with disturbances caused by sources and loads,then the problem is formulated as a distributed containment control problem.After that,to guarantee satisfactory power sharing and voltage control performance asymptotically achieved for the S/T-MGs,an attack-resilient distributed secondary control approach is developed by designing a distributed adaptive observer.With this approach,the effect of the cyber-attacks can be neutralized to ensure system stability and preserve bounded voltage synchronization.Simulation results are presented to demonstrate the effectiveness of the proposed control approach.

Convex Reformulation for Two-sided Distributionally Robust Chance Constraints with Inexact Moment Information

Constraints on each node and line in power systems generally have upper and lower bounds,denoted as twosided constraints.Most existing power system optimization methods with the distributionally robust(DR)chance-constrained program treat the two-sided DR chance constraint separately,which is an inexact approximation.This letter derives an equivalent reformulation for the generic two-sided DR chance constraint under the interval moment based ambiguity set,which does not require the exact moment information.The derived reformulation is a second-order cone program(SOCP)formulation and is then applied to the optimal power flow(OPF)problem under uncertainty.Numerical results on several IEEE systems demonstrate the effectiveness of the proposed SOCP formulation and show the differences with other DR chance-constrained OPF approaches.