Monitoring and management of power transmission dynamics in an industrial smart grid

This article is a position paper whose purpose is to give the context for presentations in a special session at PowerTech 2013. The special session is being proposed by the EU FP7 Real-Smart Consortium, a Marie Curie Industry-Academic Pathways and Partnerships project. The paper gives an overview of topics on modeling, monitoring and management of power transmission dynamics with participation from large industrial loads. © 2013 IEEE

Frequency Restoration Reserve Control Scheme with Participation of Industrial Loads

In order to accommodate larger amounts of renewable energy resources, whose power output is inherently unpredictable, there is an increasing need for frequency control power reserves. Loads are already used to provide replacement reserves, i.e. the slowest kind of reserves, in several power systems. This paper proposes a control scheme for frequency restoration reserves with participation of industrial loads. Frequency restoration reserves are required to change their active power within a time frame of tens of seconds to tens of minutes in response to a regulation signal. Industrial loads in many cases already have the capacity and capability to participate in this service. A mapping of their process constraints to power and energy demand is proposed in order to integrate industrial loads in existing control schemes. The proposed control scheme has been implemented in a 74-bus test system. Dynamic simulations show that industrial loads can be successfully integrated into the power system as frequency restoration reserves. © 2013 IEEE

Model predictive control for power system frequency control taking into account imbalance uncertainty

© IFAC.Model predictive control (MPC) is investigated as a control method for frequency control of power systems which are exposed to increasing wind power penetration. For such power systems, the unpredicted power imbalance can be assumed to be dominated by the fluctuations in produced wind power. An MPC is designed for controlling the frequency of wind-penetrated power systems, which uses the knowledge of the estimated worst-case power imbalance to make the MPC more robust. This is done by considering three different disturbances in the MPC: one towards the positive worst-case, one towards the negative worst-case, and one neutral in the middle. The robustified MPC is designed so that it finds an input which makes sure that the constraints of the system are fulfilled in case of all three disturbances. Through simulations on a network with concentrated wind power, it is shown that in certain cases where the state-of-the-art frequency control (PI control) and nominal MPC violate the system constraints, the robustified MPC fulfills them due to the inclusion of the worst-case estimates of the power imbalance

Applying model predictive control to power system frequency control

Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) controller, and simulations show that the MPC improves frequency deviation and control performance. © 2013 IEEE

Derivation of diagnostic models based on formalized process knowledge

© IFAC.Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange

A change in the NICE guidelines on antibiotic prophylaxis

Since 2008, NICE clinical guidelines have stated: ‘Antibiotic prophylaxis against infective endocarditis is not recommended for people undergoing dental procedures’. This put UK guidance at odds with guidance in the rest of the world, where antibiotic prophylaxis is recommended for patients at high-risk of infective endocarditis undergoing invasive dental procedures. Many dentists also felt this wording prohibited the use of antibiotic prophylaxis, regardless of the wishes of the patient or their personal risk of infective endocarditis and made it difficult for them to use their clinical judgment to deliver individualised care in the best interests of their patients. NICE have now changed this guidance to ‘Antibiotic prophylaxis against infective endocarditis is not recommended routinely for people undergoing dental procedures.’ This article examines the implications of this small but important change

Energy Induced Separation Network Synthesis of an Olefin Compression Section: A Case Study

When latent heat is transferred in a heat exchanger network, the formation of the second phase creates an opportunity for separation. This network is known as a Heat Induced Separation Network (HISEN). HISENs have been extended to include pressure adjusting devices for improving the thermodynamic feasibility of the network. This extended network is termed an Energy Induced Separation Network (EISEN). Most examples of EISENs in the literature are environmental pollution treatment case studies which do not require liquid phase mass integration or shaft power integration. They assume a predetermined extent of separation and mostly are based on conceptual methods of design. This paper explains how the optimization framework must be developed in order to systematically address the general characteristics of EISENs. The framework is illustrated using a case study of the synthesis problem of an olefin compression section