An algebraic approach for determination of DG parameters to support voltage profiles in radial distribution networks

Rapidly increasing electricity demands and capacity shortage of transmission and distribution facilities are the main driving forces for the growth of Distributed Generation (DG) integration in power grids. One of the reasons for choosing a DG is its ability to support voltage in a distribution system. Selection of effective DG characteristics and DG parameters is a significant concern of distribution system planners to obtain maximum potential benefits from the DG unit. This paper addresses the issue of improving the network voltage profile in distribution systems by installing a DG of the most suitable size, at a suitable location. An analytical approach is developed based on algebraic equations for uniformly distributed loads to determine the optimal operation, size and location of the DG in order to achieve required levels of network voltage. The developed method is simple to use for conceptual design and analysis of distribution system expansion with a DG and suitable for a quick estimation of DG parameters (such as optimal operating angle, size and location of a DG system) in a radial network. A practical network is used to verify the proposed technique and test results are presented.

Multi-agent systems for modeling high penetration photovoltaic system impacts in distribution networks

This paper reviews the use of multi-agent systems to model the impacts of high levels of photovoltaic (PV) system penetration in distribution networks and presents some preliminary data obtained from the Perth Solar City high penetration PV trial. The Perth Solar City trial consists of a low voltage distribution feeder supplying 75 customers where 29 consumers have roof top photovoltaic systems. Data is collected from smart meters at each consumer premises, from data loggers at the transformer low voltage (LV) side and from a nearby distribution network SCADA measurement point on the high voltage side (HV) side of the transformer. The data will be used to progressively develop MAS models.

Empirical modelling of the relationship between bus and car speeds on signalised urban networks

Vehicle speed is an important attribute for analysing the utility of a transport mode. The speed relationship between multiple modes of transport is of interest to traffic planners and operators. This paper quantifies the relationship between bus speed and average car speed by integrating Bluetooth data and Transit Signal Priority data from the urban network in Brisbane, Australia. The method proposed in this paper is the first of its kind to relate bus speed and average car speed by integrating multi-source traffic data in a corridor-based method. Three transferable regression models relating not-in-service bus, in-service bus during peak periods, and in-service bus during off-peak periods with average car speed are proposed. The models are cross-validated and the interrelationships are significant.

Smart applications for energy harvested WSNs

A Wireless Sensor Network (WSN) powered using harvested energies is limited in its operation by instantaneous power. Since energy availability can be different across nodes in the network, network setup and collaboration is a non trivial task. At the same time, in the event of excess energy, exciting node collaboration possibilities exist; often not feasible with battery driven sensor networks. Operations such as sensing, computation, storage and communication are required to achieve the common goal for any sensor network. In this paper, we design and implement a smart application that uses a Decision Engine, and morphs itself into an energy matched application. The results are based on measurements using IRIS motes running on solar energy. We have done away with batteries; instead used low leakage super capacitors to store harvested energy. The Decision Engine utilizes two pieces of data to provide its recommendations. Firstly, a history based energy prediction model assists the engine with information about in-coming energy. The second input is the energy cost database for operations. The energy driven Decision Engine calculates the energy budgets and recommends the best possible set of operations. Under excess energy condition, the Decision Engine, promiscuously sniffs the neighborhood looking for all possible data from neighbors. This data includes neighbor's energy level and sensor data. Equipped with this data, nodes establish detailed data correlation and thus enhance collaboration such as filling up data gaps on behalf of nodes hibernating under low energy conditions. The results are encouraging. Node and network life time of the sensor nodes running the smart application is found to be significantly higher compared to the base application.

A smart approach to precision attitude maneuvers of spacecrafts

A nonlinear adaptive approach is presented to achieve rest-to-rest attitude maneuvers for spacecrafts in the presence of parameter uncertainties and unknown disturbances. A nonlinear controller, designed on the principle of dynamic inversion achieves the goals for the nominal model but suffers performance degradation in the presence of off-nominal parameter values and unwanted inputs. To address this issue, a model-following neuro-adaptive control design is carried out by taking the help of neural networks. Due to the structured approach followed here, the adaptation is restricted to the momentum level equations.The adaptive technique presented is computationally nonintensive and hence can be implemented in real-time. Because of these features, this new approach is named as structured model-following adaptive real-time technique (SMART). From simulation studies, this SMART approach is found to be very effective in achieving precision attitude maneuvers in the presence of parameter uncertainties and unknown disturbances.

Wireless sensor networks forhuman intruder detection

In this paper we report on the outcomes of a research and demonstration project on human intrusion detection in a large secure space using an ad hoc wireless sensor network. This project has been a unique experience in collaborative research, involving ten investigators (with expertise in areas such as sensors, circuits, computer systems,communication and networking, signal processing and security) to execute a large funded project that spanned three to four years. In this paper we report on the specific engineering solution that was developed: the various architectural choices and the associated specific designs. In addition to developing a demonstrable system, the various problems that arose have given rise to a large amount of basic research in areas such as geographical packet routing, distributed statistical detection, sensors and associated circuits, a low power adaptive micro-radio, and power optimising embedded systems software. We provide an overview of the research results obtained.

Low-Cost Cloud-Based Design of Smart Rural Energy Device in Microgrid: Perspective India An Energy-On-Demand Service for Rural India

In this paper we present a combination of technologies to provide an Energy-on-Demand (EoD) service to enable low cost innovation suitable for microgrid networks. The system is designed around the low cost and simple Rural Energy Device (RED) Box which in combination with Short Message Service (SMS) communication methodology serves as an elementary proxy for Smart meters which are typically used in urban settings. Further, customer behavior and familiarity in using such devices based on mobile experience has been incorporated into the design philosophy. Customers are incentivized to interact with the system thus providing valuable behavioral and usage data to the Utility Service Provider (USP). Data that is collected over time can be used by the USP for analytics envisioned by using remote computing services known as cloud computing service. Cloud computing allows for a sharing of computational resources at the virtual level across several networks. The customer-system interaction is facilitated by a third party Telecom Service provider (TSP). The approximate cost of the RED Box is envisaged to be under USD 10 on production scale.

Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed.

Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Strategies for Power Line Communications Smart Metering Network Deployment

Smart Grids are becoming a reality all over the world. Nowadays, the research efforts for the introduction and deployment of these grids are mainly focused on the development of the field of Smart Metering. This emerging application requires the use of technologies to access the significant number of points of supply (PoS) existing in the grid, covering the Low Voltage (LV) segment with the lowest possible costs. Power Line Communications (PLC) have been extensively used in electricity grids for a variety of purposes and, of late, have been the focus of renewed interest. PLC are really well suited for quick and inexpensive pervasive deployments. However, no LV grid is the same in any electricity company (utility), and the particularities of each grid evolution, architecture, circumstances and materials, makes it a challenge to deploy Smart Metering networks with PLC technologies, with the Smart Grid as an ultimate goal. This paper covers the evolution of Smart Metering networks, together with the evolution of PLC technologies until both worlds have converged to project PLC-enabled Smart Metering networks towards Smart Grid. This paper develops guidelines over a set of strategic aspects of PLC Smart Metering network deployment based on the knowledge gathered on real field; and introduces the future challenges of these networks in their evolution towards the Smart Grid.

The s-mote: a versatile heterogeneous multi-radio platform for wireless sensor networks applications

This paper presents a novel architecture and its implementation for a versatile, miniaturised mote which can communicate concurrently using a variety of combinations of ISM bands, has increased processing capability, and interoperability with mainstream GSM technology. All these features are integrated in a small form factor platform. The platform can have many configurations which could satisfy a variety of applications’ constraints. To the best of our knowledge, it is the first integrated platform of this type reported in the literature. The proposed platform opens the way for enhanced levels of Quality of Service (QoS), with respect to reliability, availability and latency, in addition to facilitating interoperability and power reduction compared to existing platforms. The small form factor also allows potential of integration with other mobile platforms including smart phones.

Towards persistent structural health monitoring through sustainable wireless sensor networks

This paper documents the design, implementation and characterisation of a wireless sensor node (GENESI Node v1.0), applicable to long-term structural health monitoring. Presented is a three layer abstraction of the hardware platform; consisting of a Sensor Layer, a Main Layer and a Power Layer. Extended operational lifetime is one of the primary design goals, necessitating the inclusion of supplemental energy sources, energy awareness, and the implementation of optimal components (microcontroller(s), RF transceiver, etc.) to achieve lowest-possible power consumption, whilst ensuring that the functional requirements of the intended application area are satisfied. A novel Smart Power Unit has been developed; including intelligence, ambient available energy harvesting (EH), storage, electrochemical fuel cell integration, and recharging capability, which acts as the Power Layer for the node. The functional node has been prototyped, demonstrated and characterised in a variety of operational modes. It is demonstrable via simulation that, under normal operating conditions within a structural health monitoring application, the node may operate perpetually.

Hybrid energy storage for energy harvesting powered wireless sensor networks

Science Foundation Ireland (CSET - Centre for Science, Engineering and Technology, grant 07/CE/I1147); Scientific Foundation Ireland (ITOBO (398-CRP))

Optimisation of Smart Grid performance using centralised and distributed control techniques

A massive change is currently taking place in the manner in which power networks are operated. Traditionally, power networks consisted of large power stations which were controlled from centralised locations. The trend in modern power networks is for generated power to be produced by a diverse array of energy sources which are spread over a large geographical area. As a result, controlling these systems from a centralised controller is impractical. Thus, future power networks will be controlled by a large number of intelligent distributed controllers which must work together to coordinate their actions. The term Smart Grid is the umbrella term used to denote this combination of power systems, artificial intelligence, and communications engineering. This thesis focuses on the application of optimal control techniques to Smart Grids with a focus in particular on iterative distributed MPC. A novel convergence and stability proof for iterative distributed MPC based on the Alternating Direction Method of Multipliers is derived. Distributed and centralised MPC, and an optimised PID controllers' performance are then compared when applied to a highly interconnected, nonlinear, MIMO testbed based on a part of the Nordic power grid. Finally, a novel tuning algorithm is proposed for iterative distributed MPC which simultaneously optimises both the closed loop performance and the communication overhead associated with the desired control.