Energy generation cost in multi-energy systems; an application to a non-merchant energy hub in supplying price responsive loads

Aghamohamadi, Mehrdad; Samadi, Mahdi; Rahmati, Iman

doi:10.1016/j.energy.2018.07.144

Cited by 27 publications

(6 citation statements)

References 21 publications

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“…Te outcomes demonstrate that the technique is applicable for efcient energy management in an integrated energy system. A new EH operation optimization model is proposed [35], which determines the optimal response of responsive loads to the associated hub energy generation costs to minimize the total EH operation cost as well as customer payments. Various research studies have been conducted to date on the concept of integrated DR. Te changes in energy consumption are examined through smart energy hubs combining electricity and natural gas [36].…”

Section: Motivationmentioning

confidence: 99%

Energy Hub Management considering Demand Response, Distributed Generation, and Electric Vehicle Charging Station

Nikkhah

Lotfi

Samadi

et al. 2023

International Transactions on Electrical Energy Systems

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Multicarrier energy systems have systematic flexibility for energy and load management. The integration of different types of energy under the title “energy hub” (EH) in multiple energy infrastructures such as electricity, natural gas, and heat in an integrated manner will meet demand at a lower cost. On the other hand, the desire for distributed generation (DG) systems has increased due to their many economic and technical advantages, as well as the technical development of these units and the reduction of environmental pollution. In this paper, a proposed EH system is optimized in terms of cost using linear programming, considering transformer converters, combined heat and power (CHP), heat exchangers, and energy storage, including electric vehicle charging stations (EVCSs) and heat storage. In this regard, four proposed scenarios are defined and evaluated from the aspect of cost in order to mitigate load shedding while considering uncertainty in order to bring the problem closer to reality. The results show that the total cost in scenarios 1, 2, and 3 (the best case) is reduced by 15.47%, 26.29%, and 27.93% compared to Scenario 0 (the base case), and Scenario 3 is the most economical possible case. This method can be a suitable tool for energy hub management, especially for solving possible load shedding.

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Section: Motivationmentioning

confidence: 99%

Energy Hub Management considering Demand Response, Distributed Generation, and Electric Vehicle Charging Station

Nikkhah

Lotfi

Samadi

et al. 2023

International Transactions on Electrical Energy Systems

Self Cite

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“…Distribution system planning-operation is an increasingly complex and information-intensive procedure. Thus, seeking for perceptibility across the continuum of power supply has expanded over intermittencies, update technology expenses, price signals, response sensitivities, and socioeconomic impact [159]. However, deployment and allocation of novel breakthrough technologies suffer from many issues that differ by means of technology and critical function in the power delivery system.…”

Section: Resilience-based Ads Planning-operationmentioning

confidence: 99%

A review on resilience studies in active distribution systems

Mishra

Ghadi

Azizivahed

et al. 2021

Renewable and Sustainable Energy Reviews

134

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“…Load elasticity allows both customers and upstream utility companies to achieve mutual benefits in terms of energy cost reduction. An elastic load is able to shift or curtail during peak hours or system contingencies, which results in additional payoffs for energy consumers . It also provides the upstream network a more reliable operation status at a lower expense.…”

Section: Responsive Load Modelmentioning

confidence: 99%

“…An elastic load is able to shift or curtail during peak hours or system contingencies, which results in additional payoffs for energy consumers. 24 It also provides the upstream network a more reliable operation status at a lower expense. In PBDR programs such as TOU schemes, an elastic load can be curtailed or shifted from one period to another.…”

Section: Load Elasticitymentioning

confidence: 99%

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Samadi

Aghamohamadi

Mahmoudi

2019

Int Trans Electr Energ Syst

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Summary In time‐of‐use (TOU) schemes, most similar clusters to load demand can properly map the consumption pattern. However, the performance of such scheme is affected by the load responsiveness to the obtained clusters. This paper presents a time period clustering approach for TOU schemes to find the optimal clusters, using not only the load patterns but also its modified version obtained by responsiveness of elastic loads. A responsive load model is employed to simulate the load pattern modifications considering the hourly prices, load elasticities, and customers' benefit function. An imperialist competitive algorithm (ICA) has been designed to iteratively solve the problem. The proposed approach maximizes the load factors pertaining to the modified seasonal load patterns that are obtained on the basis of load responsiveness to the allocated clusters in each iteration of ICA. A comprehensive seasonal case study has been conducted to evaluate the performance of the proposed model. Results and comparisons show that the proposed clustering technique can significantly enhance the performance of the existing TOU schemes in terms of load factor improvement and peak reduction as it considers both load patterns and the actual load responsiveness to the obtained clusters.

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Energy generation cost in multi-energy systems; an application to a non-merchant energy hub in supplying price responsive loads

Cited by 27 publications

References 21 publications

Energy Hub Management considering Demand Response, Distributed Generation, and Electric Vehicle Charging Station

Energy Hub Management considering Demand Response, Distributed Generation, and Electric Vehicle Charging Station

A review on resilience studies in active distribution systems

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

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