A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids

Cooperating interconnected microgrids with the Distribution System Operation (DSO) can lead to an improvement in terms of operation and reliability. This paper investigates the optimal operation and scheduling of interconnected microgrids highly penetrated by renewable energy resources (DERs). Moreover, an efficient stochastic framework based on the Unscented Transform (UT) method is proposed to model uncertainties associated with the hourly market price, hourly load demand and DERs output power. Prior to the energy management, a newly developed linearization technique is employed to linearize nodal equations extracted from the AC power flow. The proposed stochastic problem is formulated as a single-objective optimization problem minimizing the interconnected AC MGs cost function. In order to validate the proposed technique, a modified IEEE 69 bus network is studied as the test case

Stochastic Coordinated Management of Electrical–Gas–Thermal Networks in Flexible Energy Hubs Considering Day-Ahead Energy and Ancillary Markets

This paper presents an optimal operation framework for electrical, gas, and thermal networks in the presence of energy hubs (EHs), so that EHs can benefit from day-ahead ancillary and energy markets. Therefore, to consider the goals of network operators (optimal operation of networks) and EHs (optimal operation in markets), the proposed model is developed in the form of a bi-level optimization. Its upper-level formulation minimizes the expected energy loss in the proposed networks based on the optimal power flow constraints and technical limits. At the lower-level problem, maximizing the expected profit of EHs in day-ahead energy and ancillary markets (including reactive and reserve regulation) is formulated based on the operational model of resources, storage devices, and responsive load in the EH framework, and the flexible constraints of EHs. This scheme includes the uncertainties of load, market price, renewable energy resources, and mobile storage energy demand, which uses the point estimation method to model them. Karush–Kuhn–Tucker is then used to extract the single-level model. Finally, by implementing the proposed scheme on a standard system, the obtained numerical results confirm the capability of the proposed model in improving the network’s operation and economic status of EHs. As a result, the proposed scheme is able to decrease operation indices such as energy losses, voltage drop, and temperature drop by approximately 28.5%, 39%, and 27.8%, respectively, compared to load flow analysis. This scheme can improve the flexibility of EHs, including non-controllable sources such as renewable resources, by nearly 100% and it obtains considerable profits for hubs.This research was funded by the “Basque Government (GISEL research group IT1522-22)

Two- stage stochastic operation framework for optimal management of the water- energy- hub

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166193/1/gtd2bf02716.pd

A novel comprehensive energy management model for multi-microgrids considering ancillary services

This article proposes a novel comprehensive multi-layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi-level optimization to address the participation of multi-microgrids (MMGs) in day-ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMG-connected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not-supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC-power flow, reliability, security, and flexibility of the MGs. To obtain a single-level model, the Karush–Kuhn–Tucker method is applied, and a hybrid stochastic-robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi-layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability).© 2022 The Authors. IET Generation, Transmission & Distribution 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-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.fi=vertaisarvioitu|en=peerReviewed

A Framework of Electricity Market based on Two-Layer Stochastic Power Management for Microgrids

This article develops a novel multi-microgrids (MMGs) participation framework in the day-ahead energy and ancillary services, i.e. services of reactive power and reserve regulation, markets incorporating the smart distribution network (SDN) objectives based on two-layer power management system (PMS). A bi-level optimization structure is introduced wherein the upper level models optimal scheduling of SDN in the presence of MMGs while considering the bilateral coordination between microgrids (MGs) and SDN’s operators, i.e. second layer’s PMS. This layer is responsible for minimizing energy loss, expected energy not-supplied, and voltage security as the sum of weighted functions. In addition, the proposed problem is subject to linearized AC optimal power flow (LAC-OPF), reliability and security constraints to make it more practical. Lower level addresses participation of MGs in the competitive market based on bilateral coordination among sources, active loads and MGs’ operator (first layer’s PMS). The problem formulation then tries to minimize the difference between MGs’ cost and revenue in markets while satisfying constraints of LAC-OPF equations, reliability, security, and flexibility of the MGs. Karush–Kuhn–Tucker method is exploited to achieve a single-level model. Moreover, a stochastic programming model is introduced to handle the uncertainties of load, renewable power, energy price, the energy demand of mobile storage, and availability of network equipment. The simulation results confirm the capabilities of the suggested stochastic two-layer scheme in simultaneous evaluation of the optimal status of different technical and economic indices of the SDN and© 2022 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

A Survey on Deep Learning Role in Distribution Automation System : A New Collaborative Learning-to-Learning (L2L) Concept

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.© 2022 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

A multi-agent privacy-preserving energy management framework for renewable networked microgrids

This paper proposes a fully distributed scheme to solve the day-ahead optimal power scheduling of networked microgrids in the presence of different renewable energy resources, such as photovoltaics and wind turbines, considering energy storage systems. The proposed method enables the optimization of the power scheduling problem through local computation of agents in the system and private communication between existing agents, without any centralized scheduling unit. In this paper, a cloud-fog-based framework is also introduced as a fast and economical infrastructure for the proposed distributed method. The suggested optimized energy framework proposes an area to regulate and update policies, detect misbehaving elements, and execute punishments centrally, while the general power scheduling problem is optimized in a distributed manner using the proposed method. The suggested cloud-fog-based method eliminates the need to invest in local databases and computing systems. The proposed scheme is examined on a small-scale microgrid and also a larger test networked microgrid, including 4 microgrids and 15 areas in a 24-h time period, to illustrate the scalability, convergence, and accuracy of the framework. The simulation results substantiate the fast and precise performance of the proposed framework for networked microgrids compared with other existing centralized and distributed methods.© 2023 The Authors. IET Generation, Transmission & Distribution 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