Optimal Stochastic Design of Wind Integrated Energy Hub

Dolatabadi, Amirhossein; Mohammadi‐Ivatloo, Behnam; Abapour, Mehdi; Tohidi, Sajjad

doi:10.1109/tii.2017.2664101

Cited by 170 publications

(79 citation statements)

References 38 publications

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“…The corresponding scenario tree has 100 scenarios, and each scenario can be considered as a path with a possibility of 1/100. A scenario reduction technique in [28] is further utilized to reduce the computational cost, and thus only 10 scenarios are retained for the rolling optimization at each time slot.…”

Section: A System Data and Configurationmentioning

confidence: 99%

“…The energy hub model was proposed in [18] to optimize the coupled power flows of multi-energy carriers, and further studies on the interconnected hubs, energy flow analysis, topological and structural designs of hub couplings were investigated in [19]- [24]. Furthermore, various renewable generations, such as wind, solar, and hydropower, have been integrated into energy hubs in [25]- [28] for economic and emission reductions, and the flexibility provided by mutual interconnections from multi-inputs to multi-outputs can be utilized to cope with the effects of variable RES penetration. Hybridization of multiple RES technologies in energy hubs can also improve system reliability and energy efficiency [29]- [31].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling of Biogas–Solar–Wind Renewable Portfolio for Multicarrier Energy Supplies

Zhou

et al. 2018

IEEE Trans. Power Syst.

158

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This paper proposes a multi-source multi-product framework for coupled multi-carrier energy supplies with a biogas-solar-wind hybrid renewable system. In this framework, the biogas-solar-wind complementarities are fully exploited based on digesting thermodynamic effects for the synergetic interactions of electricity, gas and heating energy flows, and a coupling matrix is formulated for the modeling of production, conversion, storage, and consumption of different energy carriers. The multi-energy complementarity of biogas-solar-wind renewable portfolio can be utilized to facilitate the mitigation of renewable intermittency and the efficient utilization of batteries, and a multi-carrier generation scheduling scheme is further presented to dynamically optimize dispatch factors in the coupling matrix for energy-efficient conversion and storage, while different energy demands of end-users are satisfied. The proposed methodology has been fully tested and benchmarked on a stand-alone Microgrid over a 24-hour scheduling horizon. Comparative results demonstrate that the proposed scheme can lower the battery charging/discharging actions as well as the degradation cost, and also confirm its capability to accommodate high penetration of variable renewables. Index Terms-Energy hub, energy storage, renewable energy, Microgrid, multi-energy systems. . His main fields of research include smart grid operation and planning, renewable energy generation, and energy efficiency. Da Xu received the B.Sc. degree in automation . His research interests include power system stability, control, planning and operation, computational intelligence applications, and electric vehicle charging. Yijia Cao (M'98-SM'13) received the B.Sc. degree in electrical engineering from Xi'an Jiaotong University, Xi'an, China, in 1988, and the Ph.D. degree His research interests include power system cascading failure, smart grid information technology, smart grid operation and optimization. Ka Wing Chan (M'98) received the B.Sc. (with First Class Honors) and Ph.D. degrees in electronic and electrical engineering from the University of Bath, Bath, U.K., in 1988 and 1992, respectively. He currently is an Associate Head and Associate Professor in the Department of Electrical Engineering of The Hong Kong Polytechnic University. His general research interests include smart grid and renewable energy, power system stability analysis and control, power system planning and optimization, real-time power system simulation. Qiuwei Wu (M'08-SM'15) received the B.Eng. and M.Eng. degrees from

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Section: A System Data and Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Biogas–Solar–Wind Renewable Portfolio for Multicarrier Energy Supplies

Zhou

et al. 2018

IEEE Trans. Power Syst.

158

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“…An excellent resource, containing software tools for energy hub modelling can be found in [6]. The energy hub modelling approach has been applied to a variety operational challenges associated with MES such as expansion planning [7] and [8], optimal design [9] and [10], resource optimisation [11] and [12] and reliability assessment [13]. The energy hub is capable of representing MES flexibility provided by energy storage and the increased degrees of freedom in the energy resource dispatch.…”

Section: A Related Literaturementioning

confidence: 99%

Generalised control-oriented modelling framework for multi-energy systems

2019

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A requirement for new sources of flexibility in meeting energy demand and the growing interest in the interaction between energy sectors have drawn attention to multienergy systems, which can offer numerous benefits, e.g. providing flexibility to counteract the intermittency of renewable generation and increase energy efficiency. In order to maximise these benefits, advanced control methods are required. The model predictive control methodology is a promising candidate for such applications as it is capable of incorporating economic and operational objectives whilst respecting various technical, regulatory and environmental constraints. In order to implement such a control strategy effectively, it is necessary to develop appropriate system models. This paper presents a novel generalised modelling framework for multi-energy systems that is particularly well suited, though not limited to, predictive control applications. The proposed approach is capable of representing energy converter arrangements of arbitrary complexity containing multiple energy vectors, as well as multi-directional energy flow, multi-generation and multi-mode devices, a wide range of controllable producers/consumers, energy storage and flexible loads. The effectiveness of the approach is demonstrated using a representative case study based on three buildings at the University of Manchester where the developed model is incorporated into a model predictive control scheme. The simulation results show how the controller minimises the cost of purchasing energy whilst satisfying operational constraints, and managing various types of flexible demand.

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“…A notable feature of the expansion planning method proposed in [21] was the consideration of CHP units and boilers in a district energy system to provide heat and electricity. Expansion planning methods were also proposed in [14,22] to determine the requisite size of energy hub facilities, including CHP units, boilers, and energy storage resources. However, these methods rarely consider resources other than those detailed above.…”

Section: Introductionmentioning

confidence: 99%

“…Linearization is realized by selecting typical days in project years instead of using an entire year of demand data [14,20,22], or by using a stepwise representation of a load duration curve (LDC) [23]. Although it is better to linearize the curve as closely as possible to the original data, the computational burden increases with the number of linearization segments [24].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Energy System Expansion Planning Method Using a Linearized Load-Energy Curve: A Case Study in South Korea

Park

Kim

et al. 2017

Energies

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Multi-energy systems can integrate heat and electrical energy efficiently, using resources such as cogeneration. In order to meet energy demand cost-effectively in a multi-energy system, adopting appropriate energy resources at the right time is of great importance. In this paper, we propose an expansion planning method for a multi-energy system that supplies heat and electrical energy. The proposed approach formulates expansion planning as a mixed integer linear programming (MILP) problem. The objective is to minimize the sum of the annualized cost of the multi-energy system. The candidate resources that constitute the cost of the multi-energy system are fuel-based power generators, heat-only boilers, a combined heat and power (CHP) unit, energy storage resources, and a renewable electrical power source. We use a load-energy curve, instead of a load-duration curve, for constructing the optimization model, which is subsequently linearized using a Douglas-Peucker algorithm. The residual load-energy curve, for utilizing the renewable electrical power source, is also linearized. This study demonstrates the effectiveness of the proposed method through a comparison with a conventional linearization method. In addition, we evaluate the cost and planning schedules of different case studies, according to the configuration of resources in the multi-energy system.

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Optimal Stochastic Design of Wind Integrated Energy Hub

Cited by 170 publications

References 38 publications

Optimal Scheduling of Biogas–Solar–Wind Renewable Portfolio for Multicarrier Energy Supplies

Optimal Scheduling of Biogas–Solar–Wind Renewable Portfolio for Multicarrier Energy Supplies

Generalised control-oriented modelling framework for multi-energy systems

A Multi-Energy System Expansion Planning Method Using a Linearized Load-Energy Curve: A Case Study in South Korea

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