Resilence Control of DC Microgrid Integrated with Multi-Nanogrids Considering Connected-Islanded States

In this paper, a DC microgrid (DCMG) integrated with a set of nano-grids (NG) is studied. DCMG exchanges predetermined active and reactive power with the upstream network. DCMG and NGs are coordinately controlled and managed in such a way the exchanged P-Q power with external grid are kept on scheduled level following all events and operating conditions. The proposed control system, in addition to the ability of mutual support between DCMG and NGs, makes NGs support each other in critical situations. On the other hand, in all operating conditions, DCMG not only feeds three-phase loads with time-varying active and reactive power on the grid side but also injects constant active power into the grid. During events, NGs support each other, NGs support DCMG, and DCMG supports NGs. Such control strategies are realized by the proposed control method to increase resilience of the system. For these purposes, all resources and loads in DCMG and NGs are equipped with individual controllers. Then, a central control unit analyzes, monitors, and regularizes performance of individual controllers in DCMG and NGs. Nonlinear simulations show the proposed model can effectively control DCMG and NGs under normal and critical conditions.

Stochastic Transmission Expansion Planning Incorporating Reliability Solved Using SFLA Meta-heuristic Optimization Technique

This paper addresses stochastic transmission expansion planning(TEP)under uncertain load conditions when reliability is taken into consideration.The main objective of the proposed TEP is to minimize the total planning cost by denoting the place,number,and type of new transmission lines subject to safe operation criteria.In this paper,the objective function consists of two terms,namely,investment cost(IC)of new lines and reliability cost.The reliability cost is incorporated as the loss of load cost(LOLC).Network uncertainties in the form of loads are molded as Gaussian probability distribution function(PDF).Monte-Carlo simulation is applied to tackle the uncertainties.The proposed stochastic TEP is expressed as constrained optimization planning and solved using shuffled frog leaping algorithm(SFLA)SFLA is compared to other optimization techniques such as particle swarm optimization(PSO)and genetic algorithms(GA).Finally,stochastic planning(planning including uncertainty)and deterministic planning(planning excluding uncertainty)are compared to demonstrate impacts of uncertainty on the results.Simulation results in different cases and scenarios verify the effectiveness and viability of the proposed stochastic TEP,including uncertainty and reliability.

Stochastic Linear Programming for Optimal Planning of Battery Storage Systems Under Unbalanced-uncertain Conditions

Battery energy storage system(BESS)has already been studied to deal with uncertain parameters of the electrical systems such as loads and renewable energies.However,the BESS have not been properly studied under unbalanced operation of power grids.This paper aims to study the modelling and operation of BESS under unbalanced-uncertain conditions in the power grids.The proposed model manages the BESS to optimize energy cost,deal with load uncertainties,and settle the unbalanced loading at the same time.The three-phase unbalanced-uncertain loads are modelled and the BESSs are utilized to produce separate charging/discharging pattern on each phase to remove the unbalanced condition.The IEEE 69-bus grid is considered as case study.The load uncertainty is developed by Gaussian probability function and the stochastic programming is adopted to tackle the uncertainties.The model is formulated as mixed-integer linear programming and solved by GAMS/CPLEX.The results demonstrate that the model is able to deal with the unbalanced-uncertain conditions at the same time.The model also minimizes the operation cost and satisfies all security constraints of power grid.

A multi-functional dynamic state estimator for error validation:measurement and parameter errors and sudden load changes

We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.