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.

Scalable Distributed Optimization Combining Conic Projection and Linear Programming for Energy Community Scheduling

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection(CP) and linear programming(LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization Programming

Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

Evaluating the Economic Impact of Users’Personality on Selection of Demand Response Programs

Demand response creates an opportunity for consumers to play an important role in the development of smart grids.With the advent of renewable energies and their uncertainties,demand response provides a possible solution to resolve these uncertainties.In addition to demand response schemes in the presence of renewable energy,the personality types of consumers can influence the choice of tariffs and change their electricity costs.In this paper,first,household residents with different types of personalities are considered as energy consumers.Secondly,the uncertainty of renewable energy sources is considered for the distributed generations scheduling by using a stochastic method called the Here-and-Now approach and considering three tariffs,time of use,real-time pricing,and direct load control in the residential sector to reduce total costs.Finally,the tariff choice is compared based on people preferences via various personality types,the Myers-Briggs Type Indicator test,and simulations results.Also,a probabilistic unit commitment methodology is used for distributed generations scheduling to minimize the total cost.The financial losses caused by non-optimal tariffs selection are determined through the comparison of tariffs.Simulation results show that time of use and direct load control tariffs are optimal ones in summer and winter seasons,respectively.

Integrated Optimization of Smart Home Appliances with Cost-effective Energy Management System

Smart grid enables consumers to control and sched-ule the consumption pattern of their appliances,minimize energy cost,peak-to-average ratio(PAR)and peak load demand.In this paper,a general architecture of home energy management system(HEMS)is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residen-tial customers.The optimization problem is developed under the time of use pricing(TOUP)scheme.To optimize the formulated problem,a powerful meta-heuristic algorithm called grey wolf optimizer(GWO)is utilized,which is compared with particle swarm optimization(PSO)algorithm to show its effectiveness.A rooftop photovoltaic(PV)system is integrated with the system to show the cost effectiveness of the appliances.For analysis,eight different cases are considered under various time scheduling algorithms.

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

Accurate regional wind power prediction plays an important role in the security and reliability of power systems.For the performance improvement of very short-term prediction intervals(PIs),a novel probabilistic prediction method based on composite conditional nonlinear quantile regression(CCNQR)is proposed.First,the hierarchical clustering method based on weighted multivariate time series motifs(WMTSM)is studied to consider the static difference,dynamic difference,and meteorological difference of wind power time series.Then,the correlations are used as sample weights for the conditional linear programming(CLP)of CCNQR.To optimize the performance of PIs,a composite evaluation including the accuracy of PI coverage probability(PICP),the average width(AW),and the offsets of points outside PIs(OPOPI)is used to quantify the appropriate upper and lower bounds.Moreover,the adaptive boundary quantiles(ABQs)are quantified for the optimal performance of PIs.Finally,based on the real wind farm data,the superiority of the proposed method is verified by adequate comparisons with the conventional methods.

Comprehensive Overview of Multi-agent Systems for Controlling Smart Grids

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power network.For controlling smart grids,various control systems with different architectures have already been developed.MAS-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant control.These systems provide an alternative for fast and accurate power network control.This paper provides a comprehensive overview of MASs used for the control of smart grids.The paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart grids.Use of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically reviewed.More than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

MILP Optimization Model for Assessing the Participation of Distributed Residential PV-battery Systems in Ancillary Services Market

A novel non-linear stochastic method based on a Mixed-Integer Linear Programming(MILP)optimization model is proposed to optimally manage a high number of photovoltaic(PV)-battery systems for the provision of up and down regulation in the ancillary services market.This method,considers both the technical constraints of the power system,and those of the equipment used by all the prosumers.This allows an aggregator of many residential prosumers endowed with photovoltaic(PV)-battery systems to evaluate the baseline of the aggregate by minimizing the costs related to the electrical energy absorbed from the grid and then to assess the up and down flexibility curves with relative offer prices.As confirmed by simulation results carried out considering different realistic case studies,the method can effectively be used by an aggregator to evaluate the economic impact of its participation in the ancillary services market,both for the aggregator and for its prosumers.

Robust Switch Selection in Radial Distribution Systems Using Combinatorial Optimization

Selecting the best type of equipment among available switches with different prices and reliability levels is a significant challenge in distribution system planning.In this paper,the optimal type of switches in a radial distribution system is selected by considering the total cost and reliability criterion and using the weighted augmented epsilon constraint method and combinatorial optimization.A new index is calculated to assess the robustness of each Pareto solution.Moreover,for each failure,repair time is considered based on historical data.Monte Carlo simulations are used to consider the switch failure uncertainty and fault repair time uncertainty in the model.The proposed framework is applied to an RTBS Bus-2 test system.Furthermore,the model is also applied to an industrial system to verify the proposed method’s excellent performance in larger practical engineering problems.

Economic Profit Enhancement of a Demand Response Aggregator Through Investment of Large-scale Energy Storage Systems

To provide flexibility for the operation of smart electricity networks,a large number of scattered demand response resources are managed by a demand response aggregator(DRA).Increasing the economic viability of this new entity,i.e.,DRA,has attracted a great deal of attention in recent years.Following this direction,this paper proposes stochastic model of multiple large-scale energy storage system(LESS)investments from the perspective of a DRA.A LESS directly connects to smart distribution networks and provides the possibility to save energy costs and thereafter increase the energy efficiency of the DRA.In this paper,a novel mixed-integer model is proposed to determine the optimal capacity and operation of a LESS in coordination with a DR scheme.The model,as a main contribution to literature,comprises novel managerial options,such as the number of allowed DR actions,the number of allowed charging and discharging.Moreover,the model is designed to be capable enough to exclude the hours in which the demand side is not allowed to participate in DR.The proposed model is tested through a numerical example with various case studies.The simulation results show the substantial economic impacts of considering the introduced managerial options in the coordination of a LESS operation with DR.

Flexible Stochastic Scheduling of Microgrids with Islanding Operations Complemented by Optimal Offering Strategies

This paper presents a stochastic framework for optimal scheduling of microgrids(MGs)considering unscheduled islanding events,initiated by disturbances in the main grid.This scheduling approach considers different uncertainties and determines the day-ahead schedule of the resources considering emergency operations.The proposed strategy attempts to effectively manage demand and supply side resources to mitigate the effects of uncertainties in both normal and emergency operations.The prevailing uncertainties associated with renewable power generations,demand and electricity prices as well as uncertainties of islanding duration are addressed in the presented framework.The objective is to maximize the expected profit of the operator over the scheduling horizon,while restricting the risk of mandatory load shedding imposed by uncertain parameters within an acceptable level.According to the proposed strategy,an efficient probabilistic index is obtained from generation reserve margin(GRM)in islanded mode,and applied to create a proper offering price signal to coordinate responsive loads with renewable generations providing more reliable operations.The effectiveness of the proposed strategy in terms of economy and reliability is investigated via a comparison with other methods.Extensive numerical results illustrate that the proposed offering price strategy can improve the MG’s operation from both reliability and economic aspects.

Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system.Local energy communities(LECs)are expected to play a vital role in this context.However,energy scheduling in LECs presents various challenges,including the preservation of customer privacy,adherence to distribution network constraints,and the management of computational burdens.This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method.The proposed approach uses the Limitedmemory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS)method,significantly reducing the computational effort required for solving the mixed integer programming(MIP)problem.It incorporates network constraints,evaluates energy losses,and enables community participants to provide ancillary services like a regulation reserve to the grid utility.To assess its robustness and efficiency,the proposed approach is tested on an 84-bus radial distribution network.Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.