The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.

Distributed Control Techniques in Microgrids

https://www.hse.ru/data/2011/09/14/1266804112/Jahangirian%20et%20al..pdf

Simulation in manufacturing and business: A review

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.

Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids

“Spreadsheet simulation” refers to the use of a spreadsheet as a platform for representing simulation models and performing simulation experiments. This tutorial explains the reasons for using this platform for simulation, discusses why this is frequently an efficient way to build simulation models and execute them, describes how to setup a spreadsheet simulation, and finally examines some limitations on the use of spreadsheets for simulation.

Proceedings of the 2004 winter simulation conference

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Voltage Stability Of Electric Power Systems

Brief paper: Freeway traffic estimation within particle filtering framework

This paper gives a precise meaning to the robustness of risk-sensitive filters for problems in which one is uncertain as to the exact value of the probability model. It is shown that risk-sensitive estimators (including filters) enjoy an error bound which is the sum of two terms, the first of which coincides with an upper bound on the error that one would obtain if one knew exactly the underlying probability model, while the second term is a measure of the distance between the true and design probability models. The paper includes a discussion of several approaches to estimation, including H/sub /spl infin// filtering.

Robustness and risk-sensitive filtering

Traffic flow on freeways is a non-linear, many-particle phenomenon, with complex interactions between vehicles. This paper presents a stochastic model of freeway traffic at a time scale and of a level of detail suitable for on-line estimation, routing and ramp metering control. The freeway is considered as a network of interconnected components, corresponding to one-way road links consisting of consecutively connected short sections (cells). The compositional model proposed here extends the Daganzo cell transmission model by defining sending and receiving functions explicitly as random variables, and by also specifying the dynamics of the average speed in each cell. Simple stochastic equations describing the macroscopic traffic behavior of each cell, as well as its interaction with neighboring cells are obtained. This will allow the simulation of quite large road networks by composing many links. The model is validated over synthetic data with abrupt changes in the number of lanes and over real traffic data sets collected from a Belgian freeway.

/pdf/a-compositional-stochastic-model-for-real-time-freeway-4ncuvyhcln.pdf

A compositional stochastic model for real time freeway traffic simulation

The paper proposes an abstract model for the problem of optimal control of systems subject to random perturbations, for which the principle of optimality takes on an appealing form. This model is specialized to the case where the state of the controlled system is realized as a jump process. The additional structure permits operationally useful optimality conditions. Some illustrative examples are solved.

Optimal Control of Jump Processes

Reliable supervisory control of engineering systems requires failure diagnosis algorithms for discrete-event systems. For large, modularly designed plants, such as communication networks, robustness considerations and limitations on the communication between local sensors lead to decentralized implementations of failure diagnosis algorithms. A trade-off has to be made between the speed of diagnosis and the cost of communication and computation. An algorithm is proposed for decentralized failure diagnosis with asymmetric communication in which Diagnoser 2 also estimates the observer state of Diagnoser 1 and sends only that subset of failure states which is relevant for the other diagnoser when this is useful for Diagnoser 1's control task of failure detection and diagnosis. This algorithm can help in suggesting practically implementable heuristic algorithms.

René Boel

Papers

Brief paper: Freeway traffic estimation within particle filtering framework

Robustness and risk-sensitive filtering

A compositional stochastic model for real time freeway traffic simulation

Optimal Control of Jump Processes

Decentralized failure diagnosis for discrete-event systems with costly communication between diagnosers