Integration of DERs in the Aggregator Platform for the Optimal Participation in Wholesale and Local Electricity Markets

Aging and abnormal stresses cause insulation degradation in underground cables, reducing their in-service lifetime. Partial discharge (PD) monitoring is an effective tool to monitor the insulation condition. For growing networks, monitoring solutions need more efficient diagnostics, particularly to classify PD activity by source type. One bottleneck here is feature extraction, for which many computationally expensive techniques have been proposed. This paper presents a more efficient approach, enabling real-time PD classification at high classification performance. It is applied to phase resolved PD cycles, measured on a medium voltage cable in a laboratory environment, containing either internal, corona, or surface discharge activity.©2021 IET. This paper is a postprint of a paper submitted to and accepted for publication in CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Modeling a Local Electricity Market for Transactive Energy Trading of Multi-Aggregators

The present article aims at modeling a day-ahead local electricity market (DA LEM) for transactive energy trading at the distribution level. In this regard, a wide range of distributed energy resources (DERs) in the form of multiple aggregators (AGs) participates in the DA LEM in order to trade energy with the distribution system operator (DSO), the operator of the market. On the other hand, the DSO, as the owner of the system, has the responsibility to procure the required energy of its customers with respect to the technical constraints of the distribution network. To settle the designed local market, a Stackelberg game-based approach is exploited in this research work. In the raised Stackelberg scheme, the leader of the game, the DSO, seeks to maximize its expected profit, while followers of the game, DER AGs, tend to minimize their operating costs. Ultimately, to evaluate the proposed framework, a typical case study is implemented on a modified IEEE-33 bus test system.© Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Design and Evaluation of Crowd-sourcing Platforms Based on Users Confidence Judgments

Crowd-sourcing deals with solving problems by assigning them to a large number of non-experts called crowd using their spare time. In these systems, the final answer to the question is determined by summing up the votes obtained from the community. The popularity of using these systems has increased by facilitation of access to community members through mobile phones and the Internet. One of the issues raised in crowd-sourcing is how to choose people and how to collect answers. Usually, the separation of users is done based on their performance in a pre-test. Designing the pre-test for performance calculation is challenging; The pre-test questions should be chosen in a way that they test the characteristics in people related to the main questions. One of the ways to increase the accuracy of crowd-sourcing systems is to pay attention to people's cognitive characteristics and decision-making model to form a crowd and improve the estimation of the accuracy of their answers to questions. People can estimate the correctness of their responses while making a decision. The accuracy of this estimate is determined by a quantity called metacognition ability. Metacoginition is referred to the case where the confidence level is considered along with the answer to increase the accuracy of the solution. In this paper, by both mathematical and experimental analysis, we would answer the following question: Is it possible to improve the performance of the crowd-sourcing system by knowing the metacognition of individuals and recording and using the users' confidence in their answers

A two-stage stochastic bilevel programming approach for offering strategy of DER aggregators in local and wholesale electricity markets

A two-stage stochastic programming scheme is proposed in order to evaluate the offering strategy of a distributed energy resource aggregator in both wholesale and local electricity markets and appropriately cope with uncertainties associated with its decision-making problem. In this regard, the aggregator combines a broad range of virtual and real distributed energy resources to simultaneously participate in the local electricity market as a price-maker or strategic player and the wholesale electricity market as a price-taker or non-strategic player. To model the studied aggregator as a strategic entity in the local market, a bilevel programming approach is exploited in this work. Accordingly, at the upper level of the raised problem, the aggregator tends to promote its expected profit through taking part in the wholesale and local electricity markets, while at the lower level, the considered local market is cleared in a way to maximise the social welfare. In the end, the effectiveness of the proposed framework for the simultaneous participation of the distributed energy resource aggregator in these two markets has been explored utilising a case study.© 2022 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

A Market-Based Mechanism for Local Energy Trading in Integrated Electricity-Heat Networks

Due to the proliferation of power-to-X-to-power (P2X2P) conversion technologies across various energy systems, the sector-integration concept has gained considerable attention in recent years. In this context, to facilitate the integration of local energy systems at the distribution level and take advantage of their provided privileges, the development of an efficient and pragmatic market-based mechanism is required. Hence, the present chapter focuses on modeling a local energy market (LEM) framework that enables the integration of electrical distribution systems (EDSs) as well as district heating systems (DHSs) via power-to-heat (P2H) conversion technologies. The suggested LEM platform is based on a centralized one-sided auction-based energy trading process which is settled by the distribution system operator (DSO) with the objective of social welfare maximization. In the end, the raised LEM clearing model is applied on an integrated electricity-heat network (IEHN) in the presence of technical constraints of both EDS and DHS.©2023 Springer. This is a post-peer-review, pre-copyedit version of an article published in Trading in Local Energy Markets and Energy Communities: Concepts, Structures and Technologies. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-21402-8fi=vertaisarvioitu|en=peerReviewed

Stochastic bi-level coordination of active distribution network and renewable-based microgrid considering eco-friendly compressed air energy storage system and intelligent parking lot

The optimal operation of active distribution systems in the presence of private renewable-based entities is one of the primary challenges of future power networks. In this regard, developing a practical framework to deal with this kind of issue is essential. Hence, in this paper, a novel bi-level stochastic programming approach is presented for optimal energy and reserve scheduling of the active distribution system in the presence of different eco-efficient autonomous players. In the proposed model, the distribution system operator, as a leader, attempts to minimize its total operating costs. At the same time, the renewable-based microgrid owner, as an independent follower, tends to maximize its profit from exchanging energy and reserve with the distribution system operator. The suggested scheme is a non-linear bi-level problem which is transformed into a non-linear single-level problem through Karush–Kuhn–Tucker conditions. In order to find the global optima, the non-linear single-level problem is linearized by utilizing the Big-M method. Finally, to investigate the effectiveness of the provided model, it is tested on the modified IEEE 15-Bus active distribution system under different cases and scenarios. Obtained results indicate that the operation cost of the distribution system operator can be reduced up to 134.09$, from 10710.11$ to 10576.02$, and the profit of the microgrid owner can be increased significantly 906.93$, from 659.455$to 1566.39$, by considering both environmentally friendly units, IPL and CAES.©2020 Elsevier Ltd. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

A Revenue-Cost Sharing Methodology for the Peer-to-Peer Energy Trading in a Residential Community

Generally, the low selling price of energy to the utility has increased local prosumers’ tendency to exchange their surplus power with their neighbors at the distribution level. Nonetheless, to this end, providing an appropriate paradigm based on Peer-to-Peer (P2P) energy trading is highly required. Accordingly, this research work seeks to present a new revenue-cost sharing methodology for trading the generated energy as well as the storage capacity of several types of households with one another in a residential community. The proposed algorithm implements an energy management program in a way to not only optimize the performance of the residential community but also minimize its total operating costs. On the other hand, to determine the P2P electricity price and calculate the electricity cost of each household, one pricing mechanism according to traded powers is employed in this study. In the end, to assess the efficiency of the raised P2P framework, the optimal operation of a typical community in the presence of wide ranges of real as well as virtual resources in two case studies, without and with considering P2P energy sharing, is investigated and compared.©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed