Optimal strategic coordination of distribution networks and interconnected energy hubs: A linear multi-follower bi-level optimization model

Mirzapour-Kamanaj, Amir; Majidi, Majid; Zare, Kazem; Kazemzadeh, Rasool

doi:10.1016/j.ijepes.2020.105925

Cited by 79 publications

(28 citation statements)

References 28 publications

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“…However, the impact of the electrical and thermal DR programs on the operation scheduling of the energy hubs was not investigated. Authors in [21,22] suggest the bi-level game theory to model the energy scheduling of the energy hubs.…”

Section: Introductionmentioning

confidence: 99%

“…Although the uncertainty of demand loads had been applied to the model, the role of the DR programs and ice storage on the control of uncertainty was not studied. In [22], the interaction among distribution company and energy hubs had been formulated as the leader multifollower optimization. At the upper-level, the distribution company tries to minimize its total cost, while the cost of the energy hubs had been considered at the lower-levels.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage

Bahmani

Karimi

Jadid

2021

International Journal of Electrical Power & Energy Systems

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Energy hub systems integrate various energy sources and interconnect different energy carriers in order to enhance the flexibility of the system. In this paper, a cooperative framework is proposed in which a network of energy hubs collaborate together and share their resources in order to reduce their costs. Each hub has several sources including CHP, boiler, renewable sources, electrical chiller, and absorption chiller. Moreover, energy storages are considered for electrical, heating, and cooling systems in order to increase the flexibility of energy hubs. Unlike the methods based on Nash-equilibrium points, which find the equilibrium point and have no guarantee for optimality of the solution, the employed cooperative method finds the optimal solution for the problem. We utilize the Shapley value to allocate the overall gain of the hub's coalition based on the contribution and efficiency of the energy hubs. The proposed method is modeled as a mixed integer linear programming problem, and the cost of network energy hubs are decreased in the cooperative operation, which shows the efficiency of this model. The results show 18.89, 10.23, and 8.72% improvement for hub1, hub2, and hub3, respectively, by using the fair revenue mechanism.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage

Bahmani

Karimi

Jadid

2021

International Journal of Electrical Power & Energy Systems

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“…In [33], a two-level optimization problem for the day ahead planning of active distribution systems equipped with renewable energy sources, distributed generation units, energy storage systems and electric vehicles has been presented. In [34], the authors have proposed an optimal bi-level program to study the economic interaction between energy hub systems and the electricity distribution network with the aim of minimizing the costs of the energy hub system and the electricity distribution network. In [35], the authors have presented a MPEC to investigate the strategic behavior of the energy hub system in integrated power and heating markets with the aim of increasing the profitability of the energy hub system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• In some works, e.g. [33][34][35][36], the authors have focused on the physical or economic interactions of hub energy systems with the distribution or transmission power network and ignore the constraints of the NG network. Given that NG energy is one of the main inputs for energy hub systems [23], ignoring the constraints of the NG network leads to inaccurate results.…”

Section: Contributionsmentioning

confidence: 99%

A bi-level market-clearing for coordinated regional-local multi-carrier systems in presence of energy storage technologies

Nasiri

Yazdankhah

Mirzaei

et al. 2020

Sustainable Cities and Society

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A multi-energy system (MES) provides greater flexibility for the operation of different energy carriers. It increases the reliability and efficiency of the networks in the presence of renewable energy sources (RESs). Various energy carriers such as power, gas, and heat can be interconnected by energy storage systems (ESSs) and combined heat and power units at different levels (e.g., within a region or a local).Non-coordinated optimization of energy systems at local and regional levels does not verify the whole optimal operation of systems since the systems operate without considering their interactions with each other. One of the most famous sources of flexibility is ESSs. Hence, this paper presents a stochastic decentralized approach to evaluate the impact of ESSs on regional-local MES market-clearing within a bi-level framework. On the regional level, the economic interaction between the electricity and NG systems is carried out by a centralized system operator (CSO). In addition, coordination between various energy carriers is implemented by the energy hub operator at the local level. To ameliorate the flexibility of the natural gas (NG) system in the regional MES, the linepack model of gas pipelines has been considered.

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“…The impact of the hub's configuration on the stability and security of the system is examined. In Reference 27, a bi‐level optimization framework is developed for optimal interaction of multi‐energy systems and distribution network in which total operation cost of the network is minimized subject to network's constraints in the upper‐level problem while the total cost of each system being connected to the network is minimized in the lower level problem. Each hub system is capable of exchanging power with other systems to supply the energy demand.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of multi‐energy microgrid with different energy storages and demand responsive loads utilizing a technical‐economic‐environmental programming

2020

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This article proposes a planning model for a multi-energy microgrid (MEM) that supplies the electricity, heating, and cooling loads. This controls flexible demands and provides continuous control in the presence of smart and comprehensive programming of electricity, heat, ice, compressed air, and hydrogen energy storage. The features of the MEM are considering the losses and amortization costs of electricity, heating and cooling energy storage, and the operating area of the combined heat and power (CHP) units in the planning procedure. Besides, the principle of convexity in the CHP unit operation area is attended. The proposed formulation is applicable in days of summer and winter seasons for a variety of planning studies. The outcomes represent that utilizing the proposed MEM planning results in significant advantages for the power network and the consumer. The cost savings are respectively obtained equal to 33% and 34% in the winter and summer seasons. The cost savings can be considered besides the investiture cost of MEM elements to specifying the MEM profits made during the designing step. Another valuable result is the cost-saving of PHEV smart charge about 1.38 times of PHEV smart discharge for summer and about 1.05 for winter. It also makes the curve of demand optimal utilization from the demand response program and energy storage plan. K E Y W O R D S combined heat and power (CHP), demand response, energy planning, energy storage, multienergy microgrid (MEM), plug-in hybrid electric vehicle (PHEV)

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Optimal strategic coordination of distribution networks and interconnected energy hubs: A linear multi-follower bi-level optimization model

Cited by 79 publications

References 28 publications

Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage

Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage

A bi-level market-clearing for coordinated regional-local multi-carrier systems in presence of energy storage technologies

Optimal planning of multi‐energy microgrid with different energy storages and demand responsive loads utilizing a technical‐economic‐environmental programming

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