Advances and applications of chance-constrained approaches to systems optimisation under uncertainty

Geletu, Abebe; Klöppel, Michael; Zhang, Hui; Li, Pu

doi:10.1080/00207721.2012.670310

Cited by 88 publications

(66 citation statements)

References 123 publications

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“…Compared with deterministic constraints which must always hold, probabilistic constraints allow for some violations with some usually very small pre-defined probability. Two reasons motivate the use of probabilistic constraints when considering uncertainties [5], [15]: 1) When considering rare events for the uncertainty (such as large load or wind power forecast errors in the context of power systems), it might be unavoidable that the constraints are violated. In the theoretical framework, this applies when considering unbounded probability distribution function (as is the case with the usual Gaussian distribution for loads), for which there will always be cases, albeit unlikely, for which the constraints are violated.…”

Section: Chance-constrained Optimal Power Flowsmentioning

confidence: 99%

The value of using chance-constrained optimal power flows for generation re-dispatch under uncertainty with detailed security constraints

Hamon

Perninge

Söder

2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Abstract-The uncertainty faced in the operation of power systems increases as larger amounts of intermittent sources, such as wind and solar power, are being installed. Traditionally, an optimal generation re-dispatch is obtained by solving securityconstrained optimal power flows (SCOPF). The resulting system operation is then optimal for given values of the uncertain parameters. New methods have been developed to consider the uncertainty directly in the generation re-dispatch optimization problem. Chance-constrained optimal power flows (CCOPF) are such methods.In this paper, SCOPF and CCOPF are compared and the benefits of using CCOPF for power systems operation under uncertainty are discussed. The discussion is illustrated by a case study in the IEEE 39 bus system, in which the generation redispatch obtained by CCOPF is shown to always be cheaper than that obtained by SCOPF.

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Section: Chance-constrained Optimal Power Flowsmentioning

confidence: 99%

The value of using chance-constrained optimal power flows for generation re-dispatch under uncertainty with detailed security constraints

Hamon

Perninge

Söder

2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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“…Considering the form of the state constraint set X , there are two types of chance constraints according to the definitions below. 8) where P denotes the probability operator, δ x ∈ (0, 1) is the risk acceptability level of constraint violation for the states, and G ( j) and g ( j) denote the jth row of G and g, respectively. This requires that all rows j have to be jointly fulfilled with the probability 1 − δ x .…”

Section: Chance-constrained Mpcmentioning

confidence: 99%

“…By setting this value properly, the operator/user can trade conservatism against performance. Relevant works that address the CC-MPC approach in water systems can be found in [8,22] and references therein. Therefore, this chapter is focused on the design and assessment of CC-MPC and TB-MPC controllers for the operational management of transport water networks, which may be described using only flow equations, discussing their advantages and weaknesses in the sense of applicability and performance.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control for Water Transport Networks with Demand Forecast Uncertainty

Grosso

Ocampo‐Martínez

Puig³

2017

Advances in Industrial Control

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One of the challenges in water system management is the existence of different sources of uncertainty. The availability of historical data allows to accurately predict the behaviour of the system disturbances over large horizons, but still a meaningful degree of uncertainty is present. In previous chapters, the use of MPC to tackle the complex multi-variable interactions and large-scale nature of drinking-water network control is proposed. There are several examples of MPC applied to water systems in the literature, see, e.g., [2,7,10,16, 29, 30] and references therein.In a DWN, the main management purpose is the achievement of the highest level of consumer satisfaction and service quality in line with the prevailing regulatory framework, while making best use of available resources. Hence, networks must be reliable and resilient while being subject to constraints and to continuously varying conditions with both deterministic and probabilistic nature. Customer behaviour determines the transport and storage operations within the network, and flow demands can vary in both the long and the short term, often presenting time-based patterns in some applications. Therefore, a better understanding and forecasting of demands will improve both modelling and control of DWNs.While Chaps. 12 and 13 deal with the uncertainty in the classical way of feedforward action, this chapter focuses on the way that uncertainty can be faced by using stochastic-based approaches. The simplest way to do this is by ignoring the explicit influence of disturbances or using their expected value as done in the previous chapters. However, dealing with the demand uncertainty explicitly in the control model is expected to produce more robust control strategies. In [12], a reliability-based

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“…The rationale of this approach is to replace hard constraints with probabilistic constraints and the nominal cost function with its expected value in the MPC formulation [18], leading to a stochastic optimization problem. CC-MPC offers advantages as robustness, flexibility, low computational requirements, and the possibility of including the level of reliability associated with the constraints [19,20].…”

Section: Introductionmentioning

confidence: 99%

Stock management in hospital pharmacy using chance-constrained model predictive control

Jurado

Maestre

Velarde

et al. 2016

Computers in Biology and Medicine

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One of the most important problems in the pharmacy department of a hospital is stock management. The clinical needs of drugs must be satisfied with limited work labor while minimizing the use of economical resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.

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Advances and applications of chance-constrained approaches to systems optimisation under uncertainty

Cited by 88 publications

References 123 publications

The value of using chance-constrained optimal power flows for generation re-dispatch under uncertainty with detailed security constraints

The value of using chance-constrained optimal power flows for generation re-dispatch under uncertainty with detailed security constraints

Stochastic Model Predictive Control for Water Transport Networks with Demand Forecast Uncertainty

Stock management in hospital pharmacy using chance-constrained model predictive control

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