A mixed integer programming model for optimal design of trigeneration in a hospital complex

Arcuri, Paola; Florio, Gaetano; Fragiacomo, Petronilla

doi:10.1016/j.energy.2006.10.023

Cited by 187 publications

(73 citation statements)

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“…Combined cooling, heating, and power (CCHP) systems are also referred to tri-generation systems. They have been widely used in small-medium scale power systems to solve energy-related problems, including increasing energy demands, higher energy costs, energy supply security, and environmental concerns [1]- [6]. A CCHP microgrid integrates power generation units (PGUs, e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Luo¹,

Gu²,

Gao³

et al. 2017

JOCET

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Abstract-The development of combined cooling, heating, and power (CCHP) microgrids using distributed cogeneration equipment and renewable energy sources has drawn considerable research attention. In this study, we propose an interval mixed-integer linear programming (IMILP) dispatching model for supporting the economic dispatch of a CCHP microgrid under uncertainties. Our model is based on the interval programming method, where uncertainties can be incorporated and communicated into the economic dispatch problem using interval values. The proposed IMILP dispatching model is decomposed into two sets of deterministic sub-models, f -and f + , which are solved sequentially. We use a case study of a hotel in China to demonstrate how this model can be applied to support economic dispatch decisions for a CCHP microgrid. Simulation results indicated that load and PV power uncertainties mainly affect the dispatching results of the power exchange, gas boiler and electricity chiller, while the power generation unit is consistent under different uncertainty levels. The obtained dispatching results can provide more flexibility for decision makers by generating alternative decisions for CCHP microgrid dispatching under uncertainties.Index Terms-CCHP, economic dispatch, interval programming, microgrid, uncertainty. I. INTRODUCTIONThe energy crisis and rising air pollution have led to greater worldwide focus on energy efficiency methods. Combined cooling, heating, and power (CCHP) systems are also referred to tri-generation systems. They have been widely used in small-medium scale power systems to solve energy-related problems, including increasing energy demands, higher energy costs, energy supply security, and environmental concerns [1]- [6]. A CCHP microgrid integrates power generation units (PGUs, e.g. Fuel cell or Micro turbine), auxiliary boilers, heat recovery systems, refrigeration equipment (electric chiller or absorption chiller), energy storage devices (heat storage tanks or storage batteries), and renewable energy sources (e.g. photovoltaic, wind, biomass) to implement the cascade utilization of energy. The main difference between CCHP microgrid and traditional CCHP systems is that the former can not only satisfy the cooling, Manuscript received August 28, 2015; revised February 25, 2016. This work was supported by the National High Technology Research and Development Program of China (863 Program, grant no. 2011AA05A107), the National Science Foundation of China (grant no. 51277027), the Natural Science Foundation of Jiangsu Province of China (grant no. SBK201122387), the State Grid Corporation of China.Zhao Luo, Wei Gu, Zhihe Wang, and Yiyuan Tang are with the School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China (e-mail: waiting.1986@live.com).Song Gao is with Jiangsu Electric Power Company, Nanjing, Jiangsu 210024, China.heating, and power demands of certain types of customers (such as office buildings, hotels, schools, commercial malls, and industrial loads), but also interacts ...

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Section: Introductionmentioning

confidence: 99%

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Luo¹,

Gu²,

Gao³

et al. 2017

JOCET

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“…A straightforward solution (see, e.g., [2], [3], [4], [5]) to this resource allocation problem is to model the decision making process (e.g., distributing the load in a virtual power plant (VPP) or scheduling energy-consuming domestic processes in a consumer coalition) as a mathematical optimisation problem such as a mixed integer program (MIP), a linear program (LP) or as a constraint satisfaction and optimisation problem (CSOP) as done by industrial distributed energy management tools such as Siemens DEMS [6] or PLEXOS Integrated Energy Model [7]. DEMS is used, e.g., by the municipal utility of the city of Munich for controlling a VPP [8].…”

Section: Constraint Optimisation Problems In Power Systemsmentioning

confidence: 99%

Synthesised Constraint Models for Distributed Energy Management

Schiendorfer

Steghöfer

Reif

2014

Annals of Computer Science and Information Systems

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Abstract-Resource allocation is a task frequently encountered in energy management systems such as the coordination of power generators in a virtual power plant (unit commitment). Standard solutions require fixed parametrised optimisation models that the participants have to stick to without leaving room for tailored behaviour or individual preferences. We present a modelling methodology that allows organisations to specify optimisation goals independently of concrete participants and participants to craft more detailed models and state individual preferences. While considerable efforts have been spent on devising efficient control algorithms and detailed physical models in power management systems, practical aspects of unifying several heterogeneous models for optimisation have been widely ignored -a gap we aim to close. As a by-product, we give a formulation of warm and cold start-up times for power plants that improves existing power plant models. The concepts are detailed with the loaddistribution problem faced in virtual power plants and evaluated on several random instances where we observe that a significant number of soft constraints of individual actors can be satisfied if considered. I. CONSTRAINT OPTIMISATION PROBLEMS IN POWER SYSTEMSR ESOURCE allocation and scheduling are difficult problems that occur frequently in energy systems, be it the coordination of power generation [1], demand-side management, or building control software. In a producer-based view, supply needs to meet the demand as accurately as possible in order to guarantee stability and avoid costs incurred by corrective measures. Similarly, consumers may try to find cost-minimising schedules for processes required throughout a day with respect to time-dependent energy prices. Current initiatives 1 are based on the assumption that groups of prosumers (i.e., energy producers and/or consumers) can form and team up to achieve better prices or production rates for their participants. We also adopt the notion of agents, indicating that the prosumers are in principle autonomous entities, even if they surrender the decision about their power output to the group.A straightforward solution (see, e.g., [2], [3], [4], [5]) to this resource allocation problem is to model the decision making process (e.g., distributing the load in a virtual power plant (VPP) or scheduling energy-consuming domestic processes in a consumer coalition) as a mathematical optimisation problem such as a mixed integer program (MIP), a linear program 1 cf. https://www.energiekosten-stop.at/ for consumer alliances or http: //www.swm.de/geschaeftskunden/effizienz-umwelt/virtuelles-kraftwerk.html for virtual power plants (LP) or as a constraint satisfaction and optimisation problem (CSOP) as done by industrial distributed energy management tools such as Siemens DEMS [6] or PLEXOS Integrated Energy Model [7]. DEMS is used, e.g., by the municipal utility of the city of Munich for controlling a VPP [8]. In essence, the problem is specified in terms of (decision) variables, their ass...

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“…E. Cardona [4] applied mono-objective linear programing with boundary constraints related to the secondary objectives for energy saving in airports. D.Ziher [5] also used the same approach for analyzing the tri-generation system in a hospital, while P.Arcuri et al [6] applied a mixed integer programming model with ✏ constraint. M.Casisi et al [7] proposed a mixed integer programming model to optimize a distributed cogeneration system with a district heating network.…”

Section: Introductionmentioning

confidence: 99%

Multi-objectives, multi-period optimization of district energy systems: III. Distribution networks

Fazlollahi

Becker

Maréchal

2014

Computers & Chemical Engineering

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A systematic procedure including process design and integration techniques for designing and operating energy distribution networks, and for transportation of resources is presented in this paper. In the developed model a simultaneous multi-objectives and multi-period optimization is principally investigated. In addition to optimize the transportation of resources/products, the proposed method helps decision makers to decide; which type and size of poly-generation technologies, centralized or decentralized, are best suited for the district, where in the district shall the equipment be located (geographically), how the services should be distributed, and what are the optimal flow, supply and return temperatures of the distribution networks. The design and the extension of distribution networks and transportation of resources, based on the geographical information system (GIS), are the novelties of the pressnt work.

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A mixed integer programming model for optimal design of trigeneration in a hospital complex

Cited by 187 publications

References 3 publications

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Synthesised Constraint Models for Distributed Energy Management

Multi-objectives, multi-period optimization of district energy systems: III. Distribution networks

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