Unit Commitment

Kerr, R.; Scheidt, Joshua; Fontanna, A.; Wiley, J.

doi:10.1109/tpas.1966.291678

Cited by 99 publications

(30 citation statements)

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“…bUC is addressed in Kerr et al (1966) and Hara et al (1966), while in Hara et al (1966) the cost function considers penalties for loss of load and overproduction. In Kerr et al (1966) a set of 12 thermal units on a 2 h basis is scheduled. In Hara et al (1966) a problem with two groups, each of which has five thermal units is analyzed.…”

Section: Early Use: Exhaustive Enumerationmentioning

confidence: 99%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

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Section: Early Use: Exhaustive Enumerationmentioning

confidence: 99%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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“…However, such a method can only be used on small search spaces. Priority list [8], [9] commits the units according to previously given priorities, such as production costs, production capacity, etc. Priorities are then used to create a schedule.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary scheduling of flexible offers for balancing electricity supply and demand

Tušar

Dovgan

Filipič

2012

2012 IEEE Congress on Evolutionary Computation

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To address the needs of rapidly changing energy markets, an energy data management system capable of supporting higher utilization of renewable energy sources is being developed. The system receives flexible offers from producers and consumers of energy, aggregates them on a regional level and schedules the aggregated flexible offers to balance forecast energy supply and demand. This paper focuses on formulating and solving the optimization problem of scheduling aggregated flexible offers within such a system. Three metaheuristic scheduling algorithms (a randomized greedy search, an evolutionary algorithm and a hybrid between the two) tailored to this problem are introduced and their performance is assessed on a benchmark test problem and two realistic problems. The best results are achieved by the evolutionary algorithms, which can efficiently handle thousands of aggregated flex-offers. I. IntroductionRapidly changing electrical energy markets, which are faced with deregulation, increased smart metering and requirements for higher utilization of renewable energy sources, seek new solutions to support their flexibility, ensure reliable supply, and balance the costs and benefits of the involved parties. A system to serve the needs of a deregulated electricity market and enable the integration of a higher rate of energy from distributed and renewable sources is being developed in the European Seventh Framework Programme project MIRABEL (Micro-Request-Based Aggregation, Forecasting and Scheduling of Energy Demand, Supply and Distribution) [1]. The project proposes a conceptual and infrastructural approach to supply and demand side management where electricity producers and consumers issue flexible offers (termed flexoffers), indicating flexibilities in start time and energy amount. These flex-offers are then processed by the MIRABEL system to balance electricity supply and demand.As electricity market regulations vary across the countries, the Harmonized Electricity Market Role Model [2] was chosen as a common platform to build upon in MIRABEL. This model defines the roles of producers and consumers, sometimes denoted by a common term prosumers. A collection of metering points for imbalance settlement is called the balance group and the role providing balance responsibility and financial security for a balance group is the balance responsible party (BRP).Balance group is the basic domain where the MIRABEL system will be applied. To assist the BRP in equalizing

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“…• Optimal power flow analysis to determine cost of energy subject to the cost of generation, the transmission line thermal limits, the bus voltage upper/lower limits [10]. • Unit commitment to commit the plants for next day's generation respecting costs [37]. • Contingency analysis to determine the power system state (i.e., possible voltage or flow violations) of the network for a preselected set of contingencies [18].…”

Section: Making the Case For Blind Processingmentioning

confidence: 99%

Blind processing: Securing data against system administrators

Güneş

Evrenosoğlu

2010

2010 IEEE/IFIP Network Operations and Management Symposium Workshops

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Multi-owner systems such as power grid need information from all parties to operate efficiently. However, in general, information sharing is limited by market and other constraints. In addition, the emerging problem of demand side management in distribution systems as a part of "smarter grid" efforts, secure communication and execution between the utilities and the customers is required to ensure the privacy. In this paper, we propose blind processing, a novel communication and execution approach for entities that compete with each other but need to cooperate for the overall good of the system. Our goal is to allow information exchange between system components with protection mechanisms against everyone including system administrators. Shielding information will prevent gaining access to the sensitive data while providing a complete picture of the whole system in computations. Such a security mechanism can be provided by employing the functionality of Trusted Computing, a security technology that utilizes hardware and software modules to improve the trustworthiness of a system. Sensing and Control SubsystemsOffline communication Operation Center A Operation Center B Communication and Control LinesOperation Operation Center C Operation Center D

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Unit Commitment

Cited by 99 publications

References 0 publications

Large-scale Unit Commitment under uncertainty

Large-scale Unit Commitment under uncertainty

Evolutionary scheduling of flexible offers for balancing electricity supply and demand

Blind processing: Securing data against system administrators

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