Applying Risk Management to Combined Heat and Power Plants

Zafra-Cabeza, Ascensión; Ridao, Miguel A.; Alvarado, I.; Camacho, Eduardo F.

doi:10.1109/tpwrs.2008.922255

Cited by 28 publications

(7 citation statements)

References 17 publications

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“…In the OHPF model, Equation 18stands for the optimization objective function with heat losses and active power losses included; Equation (19) represents the heat losses and active power losses; Equation 20represents active power balance and reactive power balance; Equation 21represents pressure losses; Equation (22) denotes the mass flow balance; Equations (23) and 24, respectively, denote the upper and lower output limits of D l , Φ l and p i ; Equation 25represents the active power balance and reactive power balance; Equation (26) represents the constraints of P i , Q i , and V i ; Equation (27) represents voltage losses; Equation 28represents the constraints of injection active and reactive power; Equation 29represents the models of CHP plants [34], CPs [27], and EBs [35], sequentially; Equation (30) includes the upper and lower output limits of CHP plants, CPs and EBs.…”

Section: The Constraints Of Coupling Devicesmentioning

confidence: 99%

Modeling of a District Heating System and Optimal Heat-Power Flow

et al. 2018

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With ever-growing interconnections of various kinds of energy sources, the coupling between a power distribution system (PDS) and a district heating system (DHS) has been progressively intensified. Thus, it is becoming more and more important to take the PDS and the DHS as a whole in energy flow analysis. Given this background, a steady state model of DHS is first presented with hydraulic and thermal sub-models included. Structurally, the presented DHS model is composed of three major parts, i.e., the straight pipe, four kinds of local pipes, and the radiator. The impacts of pipeline parameters and the environment temperature on heat losses and pressure losses are then examined. The term "heat-power flow" is next defined, and the optimal heat-power flow (OHPF) model formulated as a quadratic planning problem, in which the objective is to minimize energy losses, including the heat losses and active power losses, and both the operational constraints of PDS and DHS are respected. The developed OHPF model is solved by the well-established IPOPT (Interior Point OPTimizer) commercial solver, which is based on the YALMIP/MATLAB toolbox. Finally, two sample systems are served for demonstrating the characteristics of the proposed models.

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Section: The Constraints Of Coupling Devicesmentioning

confidence: 99%

Modeling of a District Heating System and Optimal Heat-Power Flow

et al. 2018

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“…For this purpose, the AVPP must know the physical and economic limitations of the prosumers and combine them in a model. This model can then be solved by centralized optimization methods (see, e.g., Heo et al [2006] and Zafra-Cabeza et al [2008]). We call this approach regio-central since it combines regionalized information within the AVPP with a centralized scheduling algorithm.…”

Section: Running Example: Prosumer Scheduling In Autonomous Power Manmentioning

confidence: 99%

Cooperative Resource Allocation in Open Systems of Systems

Anders

Schiendorfer

Siefert

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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Resource allocation is a common problem in many technical systems. In multi-agent systems, the decentralized or regionalized solution of this problem usually requires the agents to cooperate due to their limited resources and knowledge. At the same time, if these systems are of large scale, scalability issues can be addressed by a self-organizing hierarchical system structure that enables problem decomposition and compartmentalization. In open systems, various uncertainties-introduced by the environment as well as the agents' possibly self-interested or even malicious behavior-have to be taken into account to be able to allocate the resources according to the actual demand.In this article, we present a trust-and cooperation-based algorithm that solves a dynamic resource allocation problem in open systems of systems. To measure and deal with uncertainties imposed by the environment and the agents at runtime, the algorithm uses the social concept of trust. In a hierarchical setting, we additionally show how agents create constraint models by learning the capabilities of subordinate agents if these are not able or willing to disclose this information. Throughout the article, the creation of power plant schedules in decentralized autonomous power management systems serves as a running example.

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“…Heat energy efficiency of combined heat and energy systems reaches to 90% [9]. Because of cogeneration systems decreases to energy input, energy costs are minimized [10]. Cogeneration installations start at the beginning of 20 th century.…”

Section: Literature Reviewmentioning

confidence: 99%

Interactive energy demand, production and usage optimization in manufacturing

Kunt

Kayakutlu

Selcuk

2015

2015 Portland International Conference on Management of Engineering and Technology (PICMET)

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Renewable energy resources and improvements in energy generation and usage are critical policies of energy dependent countries. 2012 BP report states that 48 % of both electricity and natural gas consumptions are realized by industrial usage. In developing countries manufacturing sites are increasing at least 4% a year. Cogeneration is a good solution for the efficiency improvement since it reduces costs about 30%-50% compared to the individual usages. In the whole Globe, only 9% of the energy demand is responded by cogeneration systems. One of the barriers of cogeneration implementation is the visionary difference among the production groups and the energy management groups. Researches state that, defining requirements per turbine and planning the load capacity provides incremental benefits. Energy generation plans for trading energy with the grid allow further economic advantages. This paper is one of the rare studies on integrating the energy load plans and the production plans. The combined plans can be updated interactively either by the energy management or by the manufacturing sites. The proposed mixed integer-programming (MIP) model allows using scenarios, which would allow considering energy costs per unit of product. The objective of the study is to reduce energy costs in parallel with the production costs. The proposed system is constructed for the stock-based production type where energy costs have an important share in the operational costs. The system can be used both with a single fuel resource or hybrid renewable resources.

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Applying Risk Management to Combined Heat and Power Plants

Cited by 28 publications

References 17 publications

Modeling of a District Heating System and Optimal Heat-Power Flow

Modeling of a District Heating System and Optimal Heat-Power Flow

Cooperative Resource Allocation in Open Systems of Systems

Interactive energy demand, production and usage optimization in manufacturing

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