Enhancing scalability of peer-to-peer energy markets using adaptive segmentation method

This paper proposes an adaptive segmentation method as a market clearing mechanism for peer-to-peer(P2P)energy trading scheme with large number of market players.In the proposed method,market players participate in the market by announcing their bids.In the first step,players are assigned to different segments based on their features,where the balanced k-means clustering method is implemented to form segments.These segments are formed based on the similarity between players,where the amount of energy for trade and its corresponding price are considered as features of players.In the next step,a distributed method is employed to clear the market in each segment without any need to private information of players.The novelty of this paper relies on developing an adaptive algorithm for dividing large number of market players into multiple segments to enhance scalability of the P2P trading by reducing data exchange and communication overheads.The proposed approach can be used along with any distributed method for market clearing.In this paper,two different structures including community-based market and decentralized bilateral trading market are used to demonstrate the efficacy of the proposed method.Simulation results show the beneficial properties of the proposed segmentation method.

The operating schedule for battery energy storage companies in electricity market

This paper presents a series of operating schedules for Battery Energy Storage Companies(BESC)to provide peak shaving and spinning reserve services in the electricity markets under increasing wind penetration.As individual market participants,BESC can bid in ancillary services markets in an Independent System Operator(ISO)and contribute towards frequency and voltage support in the grid.Recent development in batteries technologies and availability of the day-ahead spot market prices would make BESC economically feasible.Profit maximization of BESC is achieved by determining the optimum capacity of Energy Storage Systems(ESS)required for meeting spinning reserve requirements as well as peak shaving.Historic spot market prices and frequency deviations from Australia Energy Market Operator(AEMO)are used for numerical simulations and the economic benefits of BESC is considered reflecting various aspects in Australia’s National Electricity Markets(NEM).

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Distribution state estimation(DSE)is an essential part of an active distribution network with high level of distributed energy resources.The challenges of accurate DSE with limited measurement data is a well-known problem.In practice,the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance.This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter(ACKF).The Kalman filter method inherently provides state error covariance prediction.It can be utilized to accurately infer the error covariance of other parameters and provide a method to determine optimal measurement locations based on the sensitivity of error covariance to measurement noise covariance.This paper also proposes a generalized formulation of ACKF to allow scalar measurements to be incorporated into the complex-valued estimator.The proposed method is simulated by using modified IEEE 34-bus and IEEE 123-bus test feeders,and randomly generates the load data of complex-valued Wiener process.The ACKF method is compared with an equivalent formulation using the traditional weighted least squares(WLS)method and iterated extended Kalman filter(IEKF)method,which shows improved accuracy and computation performance.