Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs.Therefore,there is the need for a smart structure to handle the new situation.This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs.The upper-level problem is decomposed into a set of low-level market clearing problems.Both electricity and heat markets are simultaneously modeled in this paper.DERs,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity markets.In addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat market.Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.

Forecasting Global Solar Insolation Using the Ensemble Kalman Filter Based Clearness Index Model

This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane.In the proposed forecasting model,constraints,such as latitude and whole precipitable water content in vertical column of that location,are used.These parameters can be easily measurable with a global positioning system(GPS).The earlier model was developed by using the above datasets generated from different locations in India.The model has been verified by calculating theoretical global insolation for different sites covering east,west,north,south and the central region with the measured values from the same locations.The model has also been validated on a region,from which data was not used during the development of the model.In the model,clearness index coefficients(KT)are updated using the ensemble Kalman filter(EnKF)algorithm.The forecasting efficacies using the KT model and EnKF algorithm have also been verified by comparing two popular algorithms,namely the recursive least square(RLS)and Kalman filter(KF)algorithms.The minimum mean absolute percentage error(MAPE),mean square error(MSE)and correlation coefficient(R)value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%,0.0285 and 0.9866 respectively.