A system dynamics approach to study the long-term interaction of the natural gas market and electricity market comprising high penetration of renewable energy resources

Due to the gas consumption of some power plants for electricity generation and providing an acceptable level of flexibility, the interaction of natural gas markets and electricity markets is inevitable. One of the main challenges of policymakers in the energy sector coupling is the investigation of such interactions. Our main goal is to analyze the effect of the penetration of renewable energy resources on the behavior of gas markets and vice versa from the policymaker’s viewpoint. Moreover, we tend to study the effect of an external shock on the behavior of the whole system and the role of renewable resources in mitigating these side effects. Therefore, we used System Dynamic Approach to model the long-term behavior of the natural gas markets to extend the existed models of the electricity markets behavior and couple these markets. The Net Present Value method was used for the economic assessment of the investment in the development of gas reserves, and new stock and flow variables were defined to simulate this development. The simulations are performed for four scenarios by using a valid case study. Considering the results of simulations and sensitivity analysis, as the wind capacity incentive rose, the gas and electricity prices declined and their fluctuation increased during the time horizon. Although the effect of the gas market shock on the system depends on the time of occurrence, as the penetration of renewable units increased, the severity of its side effects decreased and the price jumps in the markets were mitigated.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

An artificial neural network approach for revealing market competitors' behavior

For an electricity market player, obtaining a holistic viewpoint from the behavior of competitors is essential to determine its optimal bidding strategy. This paper proposes a novel approach for modeling and revealing the competitor's behavior from perspective of an intended player (IP). To this end, from perspective of IP, we define an Equivalent Rival (ER) whose behavior in the electricity market reflects the aggregation of behaviors of all individual competitors. It is assumed that IP and its ER participate in an equivalent market which its outcomes are approximately equal to those of the real market. The revealing procedure is designed as a two-stage Artificial Neural Network-based approach to estimate and predict the bids of ER after each run of the real market. Predicted bids of ER are used for the bidding strategy of IP. The proposed approach has been examined on two different case studies. In the first case study the aggregate supply curve of a market with 12 players has been obtained using the proposed approach and the result has been compared with a Bayesian inference approach. In the second case study a six-player electricity market is considered. The competitors' behavior has been revealed from perspective of an intended player using proposed approach and an optimal bidding strategy based on the proposed approach has been constructed. The results have been compared with those of a fuzzy Q-learning based optimal bidding strategy. The superiority of the proposed method in both case studies has been shown.fi=vertaisarvioitu|en=peerReviewed

Enhancing DC microgrid performance through machine learning-optimized droop control

A machine learning-based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication-less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.© 2024 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-NoDerivsLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.fi=vertaisarvioitu|en=peerReviewed

A microgrid formation-based restoration model for resilient distribution systems using distributed energy resources and demand response programs

In recent years, resilience enhancement of electricity distribution systems has attracted much attention due to the significant rise in high-impact rare (HR) natural event outages. The performance of the post-event restoration after an HR event is an effective measure for a resilient distribution network. In this paper, a multi-objective restoration model is presented for improving the resilience of an electricity distribution network. In the first objective function, the load shedding in the restoration process is minimized. As the second objective function, the restoration cost is minimized which contradicts the first objective function. Microgrid (MG) formation, distributed energy resources (DERs), and demand response (DR) programs are employed to create the necessary flexibility in distribution network restoration. In the proposed model, DERs include fossil-fueled generators, renewable wind-based and PV units, and energy storage system while demand response programs include transferable, curtailable, and shiftable loads. The proposed multi-objective model is solved using ɛ-constraint method and the optimal solution is selected using the fuzzy satisfying method. Finally, the proposed model was successfully examined on 37-bus and 118-bus distribution networks. Numerical results verified the efficacy of the proposed method as well.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Factores que afectan la ubicación de los centros médicos en la ciudad de Teherán Estudio de caso: Distrito 1 del municipio de Teherán

The historical growth of cities due to the rapid increase of population led to the increased land price in cities and caused problems to theappropriate distribution system as well as the allocation of suitable spaces to some fundamental services like medical centers. Supplyinghealth for all people in every society is one of the basic human rights which should be regarded by governments. The imbalancein fundamental services such as medical care and its imbalanced distribution system cause major challenges expressing themselvesin different economic and skeletal dimensions. In this study which aimed at determining the factors affecting the location of medicalcenters, first the main variables and indicators affecting the location of such centers were extracted and then ranked using the hierarchicalquestionnaire by some relevant experts. After ranking the obtained indicators using the hierarchical method to examine the medicalcenters at district 1 of Tehran and investigating the balance and proportion of medical centers, the location of medical centersin district 1 were examined using the geographical information system by hierarchical model. The obtained results indicated thatthe northeast and east areas of district 1 in Tehran had faced major challenges in terms of the access to medical centers and a specifiedarea in the geographical information system indicated this challenge in the north and northeast areas. The selected point by geographicalinformation system were the areas in this district

Markov-Based Reliability Assessment for Distribution Systems Considering Failure Rates

Switches (SWs) and renewable distributed generations (RDG) are subject to failure as well as other components of a system that are usually presumed fully reliable in the literature. It can lead to an overestimation of their contribution to reliability enhancement disregarding their dysfunction. This study considers the failure rate of SWs and RDGs to assess their impact on the reliability cost of the system and their effect on the optimal placement using a Markov-based approach. The optimal placement of SWs and RDGs in radial distribution systems is carried out simultaneously aim at minimizing the overall cost including investment, maintenance of SWs, and RDGs alongside the interruption cost. The performance of the model is verified through different scenarios and tested on RBTS bus 2. The results show that high values of the failure rates lead to fewer allocations to prevent plummeting the reliability of the system and increasing the capacity of the RDG results in fewer SWs allocation.©2023 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 Stochastic Planning Model for Improving Resilience of Distribution System Considering Master-Slave Distributed Generators and Network Reconfiguration

The recent experiences of extreme weather events highlight the significance of boosting the resilience of distribution systems. In this situation, the resilience of distribution systems planning leads to an efficient solution for protecting the system from these events via line hardening and the installation of distributed generators (DGs). For this aim, this study presents a new two-stage stochastic mixed-integer linear programming model (SMILP) to hedge against natural disaster uncertainty. The first stage involves making investment decisions about line hardening and DG installation. Then, in the second stage, the dynamic microgrids are created according to a master-slave concept with the ability of integrating distributed generators to minimize the cost of loss of load in each uncertain outage scenario. In particular, this paper presents an approach to select the line damage scenarios for the SMILP. In addition, the operational strategies such as load control capability, microgrid formation and network reconfiguration are integrated into the distribution system plans for resilience improvement in both planning and emergency response steps. The simulation results for an IEEE 33-bus test system demonstrate the effectiveness of the proposed model in improving disaster-induced the resilience of distribution systems.© 2021 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

Smart household management systems with renewable generation to increase the operation profit of a microgrid

During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.fi=vertaisarvioitu|en=peerReviewed

An Overview of Demand Response : From its Origins to the Smart Energy Community

The need to improve power system performance, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants’ economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select an appropriate plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.© 2021 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

Adaptive Optimal Control of Faulty Nonlinear DC Microgrids with Constant Power Loads: Dual-Extended Kalman Filter Approach

This article investigates the problem of estimating actuator fault and states and controlling the bus voltage in direct current microgrids (DC MGs) with linear and nonlinear constant power loads (CPLs). It is considered that the DC MG states are not fully measurable and the utilized sensors are not ideal and noisy. Additionally, the actuator fault occurs and it is modeled as an additive term in the power system dynamics. These issues, including nonlinearities, un-measurable states, noisy measures, and actuator fault indispensably degrade the operation of the DC MG. To solve this issue, initially, a dual-extended Kalman filter (dual-EKF) is suggested for the fault and state estimation. It decomposes the process of estimating the state and actuator fault to reduce the online computational burden. For the control purpose, a linear parameter varying (LPV) model predictive control (MPC) is suggested to regulate the current and voltage of the DC MG. It benefits the nonlinear system modeling of LPV representation and constrained-based design procedure of the MPC to result in an accurate and low online computational burden dealing with system constraints. By deploying the overall robust adaptive dual-EKF estimation-based LPV-MPC, there is no need to have any prior knowledge of all system states and actuator faults in prior. The theoretical analysis and controller design are validated by numerical simulations on a typical islanded DC MG and comparisons are done with state-of-the-art estimation and control strategies.©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