Evaluating the robustness of an active network management function in an operational environment

This paper presents the integration process of a distribution network Active Network Management (ANM) function within an operational environment in the form of a Micro-Grid Laboratory. This enables emulation of a real power network and enables investigation into the effects of data uncertainty on an online and automatic ANM algorithm's control decisions. The algorithm implemented within the operational environment is a Power Flow Management (PFM) approach based around the Constraint Satisfaction Problem (CSP). This paper show the impact of increasing uncertainty, in the input data available for an ANM scheme in terms of the variation in control actions. The inclusion of a State Estimator (SE), with known tolerances is shown to improve the ANM performance

Active distribution networks planning with high penetration of wind power

YesIn this paper, a stochastic method for active distribution networks planning within a distribution market environment considering multi-configuration of wind turbines is proposed. Multi-configuration multi-scenario market-based optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand and different operational status of wind turbines (multiple-wind turbine configurations). Scenario-based approach is used to model the abovementioned uncertainties. The method evaluates the impact of multiple-wind turbine configurations and active network management schemes on the amount of wind power that can be injected into the grid, the distribution locational marginal prices throughout the network and on the social welfare. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system. It was shown that multi-wind turbine configurations under active network management schemes, including coordinated voltage control and adaptive power factor control, can increase the amount of wind power that can be injected into the grid; therefore, the distribution locational marginal prices reduce throughout the network significantly

A multi-site real-time co-simulation platform for the testing of control strategies of distributed storage and V2G in distribution networks

© 2016 IEEE and EPE Association. This paper presents a real-time co-simulation platform aimed to test control strategies for the management of the interaction between a smart grid and active prosumers. The main feature of the proposed framework relies on the multi-site approach that allows the decoupling between the network model and the system under test. This allows separate testing with the exchange of a limited amount of information between the two systems, helping to preserve the confidentiality of data belonging to different parties. As an example the paper addresses the development and testing of a distributed storage and vehicle-to-grid management system connected to a real distribution network model

Optimal network congestion management using wind farms

With the increased use of wind energy for the power generation several TSO have increasing difficulties for congestion forecasting due to the unpredictable nature of the energy source. An actual method used to deal with days-ahead congestion planning is based on an order of disconnection of the generation of the type “last generation installed, first generation limited”. This paper proposes to enhance the congestion management using a real time supervisor. This supervisor is developed to perform automatic and dynamic re-dispatching using both wind and conventional generators. In order to reduce the production constraints to the minimum, the real time congestion management is based on an indicator of the efficiency of a re-dispatching on the power flowing in the overloaded line. This approach leads to reduced re-dispatching costs and increased network reliability. Actual and proposed methods are compared in the paper using Matlab/Simulink simulations of a realistic test grid. It is shown that the real-time supervisor allows maximization of renewable production during congestions while ensuring network reliability.Congestion management; Wind farm; Power Transfer Distribution Factors (PTDF); Power system control; Active power dispatch; Variable speed wind turbines.

Voltage Rise Problem in Distribution Networks with Distributed Generation: A Review of Technologies, Impact and Mitigation Approaches

Energy demand has constantly been on the rise due to aggressive industrialization and civilization. This rise in energy demand results in the massive penetration of distributed generation (DG) in the distribution network (DN) which has been a holistic approach to enhance the capacity of distribution networks. However, this has led to a number of issues in the low voltage network, one of which is the voltage rise problem. This happens when generation exceeds demand thereby causing reverse power flow and consequently leading to overvoltage. A number of methods have been discussed in the literature to overcome this challenge ranging from network augmentation to active management of the distribution networks. This paper discusses the issue of voltage rise problem and its impact on distribution networks with high amounts of distributed energy resources (DERs). It presents different DG technologies such as those based on conventional and unconventional resources and other DERs such as battery storage systems and fuel cells. The study provides a comprehensive overview of approaches employed to curtail the issue of voltage increase at the point of common coupling (PCC), which includes strategies based on the network reinforcement methodology and the active distribution network management. A techno-economic comparison is then introduced in the paper to ascertain the similarities and dissimilarities of different mitigation approaches based on the technology involved, ease of deployment, cost implication, and their pros and cons. The paper provides insights into directions for future research in mitigating the impact of voltage rise presented by grid-connected DGs without limiting their increased penetration in the existing power grid

Market clearing model for energy communities

Energy communities will be a key element within the future of the electric system and the smart grid environment. They will help pave the way for a cleaner energy transition with the citizens as the main force driving it. This figure will provide the necessary legal structure so that consumers, prosumers and small distributed generators could group together to actively participate into the electrical system. Based on this, the proposed master s thesis presents a new approach where distributed generators and consumers could be organized under a common energy community and implement a market clearing model to exchange their energy requirements in it. Thus, an active and reactive market clearing model for a network constrained multiperiod auction is proposed. This approach offers a new concept for exchanging market offers in its complex form (with active and reactive terms) and to also integrate the procedure for allocating network losses, congestion management and system stability within the solution

Assessment of novel distributed control techniques to address network constraints with demand side management

The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid.The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid

Management of Islanded Operation of Microgirds

Distributed generations with continuously growing penetration levels offer potential solutions to energy security and reliability with minimum environmental impacts. Distributed Generations when connected to the area electric power systems provide numerous advantages. However, grid integration of distributed generations presents several technical challenges which has forced the systems planners and operators to account for the repercussions on the distribution feeders which are no longer passive in the presence of distributed generations. Grid integration of distributed generations requires accurate and reliable islanding detection methodology for secure system operation. Two distributed generation islanding detection methodologies are proposed in this dissertation. First, a passive islanding detection technique for grid-connected distributed generations based on parallel decision trees is proposed. The proposed approach relies on capturing the underlying signature of a wide variety of system events on a set of critical system parameters and utilizes multiple optimal decision tress in a parallel network for classification of system events. Second, a hybrid islanding detection method for grid-connected inverter based distributed generations combining decision trees and Sandia frequency shift method is also proposed. The proposed method combines passive and active islanding detection techniques to aggregate their individual advantages and reduce or eliminate their drawbacks. In smart grid paradigm, microgrids are the enabling engine for systematic integration of distributed generations with the utility grid. A systematic approach for controlled islanding of grid-connected microgrids is also proposed in this dissertation. The objective of the proposed approach is to develop an adaptive controlled islanding methodology to be implemented as a preventive control component in emergency control strategy for microgrid operations. An emergency power management strategy for microgrid autonomous operation subsequent to inadvertent islanding events is also proposed in this dissertation. The proposed approach integrates microgrid resources such as energy storage systems, demand response resources, and controllable micro-sources to layout a comprehensive power management strategy for ensuring secure and stable microgrid operation following an unplanned islanding event. In this dissertation, various case studies are presented to validate the proposed methods. The simulation results demonstrate the effectiveness of the proposed methodologies

A Framework for Profit Maximization in a Grid-Connected Microgrid with Hybrid Resources Using a Novel Rule Base-BAT Algorithm

In this paper, an energy management system (EMS) is proposed for optimal operation of a microgrid (MG). Dispersed photovoltaic arrays (PVs) and wind turbine generators (WTs) as renewable energy sources (RES) supply a major part of the network demanded energy. Also, an energy storage system (ESS), a micro-turbine unit (MT), and a fuel cell unit (FC) are integrated. The uncertainty and stochastic nature of the network load and RES data are treated via probabilistic modeling and scenario-selection approach. The predicted day-ahead data of the most diverse hourly scenarios are entered into the proposed EMS to determine the active and reactive power (P-Q) participations of local distributed resources. Likewise, it specifies the discharging/charging power and state of the ESS in addition to the exchanged active/reactive power amounts with the main network. The main goal is to maximize the profit of the secondary grid while satisfying all technical constraints. In the proposed EMS, the day-ahead energy management is developed as a comprehensive optimization problem. Moreover, the paper proposes novel modifications to improve the BAT optimization technique. The optimization problem of the energy management in the microgrid is implemented using a new integrated rule base-improved BAT method. Furthermore, the proposed EMS competence is proven by comparing its performance to recent literature. © 2013 IEEE.Ministry of Higher Education, Egypt, MHEThis work was supported by the Ministry of Higher Education, Egypt

Fostering active network management through SMEs’practises

Managing the electricity network through ‘smart grid’ systems is a key strategy to address challenges of energy security, low carbon transitions and the replacement of ageing infrastructure networks in the UK. Small and medium enterprises (SMEs) have a significant role in shaping patterns of energy consumption. Understanding how their activities interrelate with changes in electricity systems is critical for active network management. A significant challenge for the transformation of electricity systems involves comprehending the complexity that stems from the variety of commercial activities and diversity of social and organizational practises among SMEs that interact with material infrastructures. We engage with SMEs to consider how smart grid interventions ‘fit’ into everyday operational activities. Drawing on analysis of empirical data on electricity use, smart metre data, surveys, interviews and ‘energy tours’ with SMEs to understand lighting, space heating and cooling, refrigeration and IT use, this paper argues for experimenting with the use of practise theory as a framework for bringing together technical and social aspects of energy use in SMEs. This approach reveals that material circumstances and temporal factors shape current energy demand among SMEs, with ‘connectedness’ an emergent factor