Optimal coordinated operation of integrated energy hubs, considering regenerative braking utilization

Akbari, Saeed; Fazel, Seyed Saeed; Jadid, Shahram

doi:10.1049/els2.12032

Cited by 13 publications

(2 citation statements)

References 40 publications

(66 reference statements)

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“…The smart grid structure will achieve important targets like operational cost reduction and environmental pollution [8]. Many studies on the electrical railway network have focused on regenerative braking energy (RBE), and various solutions have been reported to utilize this energy [9].…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

Akbari¹,

Fazel²,

Hashemi‐Dezaki³

2023

Energy Engineering

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The networking of microgrids has received significant attention in the form of a smart grid. In this paper, a set of smart railway stations, which is assumed as microgrids, is connected together. It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid. Also, the operational costs of stations under various conditions decrease by applying the proposed method. The smart railway stations are studied in the presence of photovoltaic (PV) units, energy storage systems (ESSs), and regenerative braking strategies. Studying regenerative braking is one of the essential contributions. Moreover, the stochastic behaviors of the ESS's initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method. The networked microgrid scheme of railway stations (based on coordinated operation and scheduling) and independent operation of railway stations are studied. The proposed method is applied to realistic case studies, including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company (TUSROC). The rolling stock is simulated in the MATLAB environment. Thus, the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming (MILP).

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Section: Introductionmentioning

confidence: 99%

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

Akbari¹,

Fazel²,

Hashemi‐Dezaki³

2023

Energy Engineering

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“…Therefore, how to mine user behaviour characteristics from the user side and build an effective charging guidance strategy to fully mobilise the enthusiasm of users to respond is the key to stabilising the charging load. The mainstream method in the current literature is to describe the user decision problem as a model of random utility theory [9] or expected utility theory [10] and guide users to choose the best charging scheme through price, incentive policies or other methods. This type of model belongs to the decision model in neoclassical economics [11], and one of its basic assumptions is that the decision‐maker is a “rational person” with complete rationality [12].…”

Section: Introductionmentioning

confidence: 99%

Bounded rational real‐time charging pricing strategy under the traffic‐grid coupling network

Wang

Cai

et al. 2022

IET Electrical Syst in Trans

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This study proposes a bounded rational charging guidance strategy based on mental account theory, which guides users to charge in an orderly manner by formulating real‐time charging prices. Firstly, an orderly guidance framework for fast‐charging EVs under the traffic‐grid coupling network is constructed, and the influencing factors of various dimensions when users make charging decisions are analysed. Secondly, considering the bounded rational behaviour of users when making charging decisions, a multifactor bounded rational charging model for EV users based on mental account theory is proposed so as to obtain different charging costs for users when selecting charging stations. On this basis, a real‐time charging price strategy based on the Stackelberg game model is constructed, with the goal of maximising the economic benefits of charging station operators while reducing the charging cost of EV users as much as possible. Finally, the particle swarm optimisation algorithm is used to solve the game model so as to solve the real‐time charging price under various constraints. The simulation of an example verifies the rationality of the proposed real‐time charging price formulation method and the superiority of the bounded rational charging guidance strategy.

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Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Ehteshami

Hashemi‐Dezaki

Javadi

2022

Energy Science & Engineering

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Several studies have been reported for optimal operation of electrical railway systems (ERSs). However, the stochastic energy management of ERSs, including renewable energy resources (RERs), has received less attention. The RERs’ uncertainties might affect the ERSs. On the other hand, the calculation time of the Monte Carlo simulation (MCS)‐based approaches is an essential challenge, which should be solved, particularly in real‐time decisions and recursive optimization problems. Thus, it is crucial to study the ERSs' stochastic behaviors and uncertainties, including RERs. This paper tries to overcome the discussed concerns and challenges by proposing a novel ERS’s optimal stochastic energy management using clustering algorithms. In this paper, the backward scenario reduction algorithm has been used. In addition, regenerative braking energy (RBE) and energy management systems (ESSs) have been studied. Studying the impacts of changes in the number of passengers on the optimized operation of ERSs and investigating the interaction between the utility grid and the ERS are other contributions of this research. The proposed method is applied to an actual test system of Tehran Urban and Suburban Railway Operation Company (TUSROC). Test results are validated by comparing with available MCS‐based methods. Simulation results illustrate the accuracy and computation time advantages of the proposed method. Simulation results illustrate that <0.6% inaccuracy appears in the proposed method, while it would be 500 times faster than MCS‐based ones. The comparative test results show the advantages of the introduced method.

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Optimal coordinated operation of integrated energy hubs, considering regenerative braking utilization

Cited by 13 publications

References 40 publications

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

Bounded rational real‐time charging pricing strategy under the traffic‐grid coupling network

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

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