Multi-agent system based active distribution networks

This thesis gives a particular vision of the future power delivery system with its main requirements. An investigation of suitable concepts and technologies which creates a road map forward the smart grid has been carried out. They should meet the requirements on sustainability, efficiency, flexibility and intelligence. The so called Active Distribution Network (ADN) is introduced as an important element of the future power delivery system. With an open architecture, the ADN is designed to integrate various types of networks, i.e., MicroGrid or Autonomous Network, and different forms of operation, i.e., islanding or interconnection. By enabling an additional local control layer, these so called cells are able to reconfigure, manage local faults, support voltage regulation, or manage power flow. Furthermore, the Multi-Agent System (MAS) concept is regarded as a potential technology to cope with the anticipated challenges of future grid operation. Analysis of benefits and challenges of implementing MAS shows that it is a suitable technology for a complex and highly dynamic operation and open architecture as the ADN. By taking advantages of the MAS technology, the AND is expected to fully enable distributed monitoring and control functions. This MAS-based ADN focuses mainly on control strategies and communication topologies for the distribution systems. The transition to the proposed concept does not require an intensive physical change to the existing infrastructure. The main point is that inside the MAS-based ADN, loads and generators interact with each other and the outside world. This infrastructure can be built up of several cells (local areas) that are able to operate autonomously by an additional agent-based control layer. The ADN adapts a MAS hierarchical control structure in which each agent handles three functional layers of management, coordination, and execution. In the operational structure, the ADN addresses two main function parts: Distributed State Estimation (DSE) to analyze the network topology, compute the state estimation, and detect bad data; and Local Control Scheduling (LCS) to establish the control set points for voltage coordination and power flow management. Under the distributed context of the controls, an appropriate method for DSE is proposed. The method takes advantage of the MAS technology to compute iteratively the local state variables through neighbor data measurements. Although using the classical Weighted Least Square (WLS) as a core, the proposed algorithm based on an agent environment distributes drastically computation burden to subtasks of state estimation with only two interactive buses and an interconnection line in between. The accuracy and complexity of the proposed estimation are investigated through both off-line and on-line simulations. Distributed and parallel working of processors improves significantly the computation time. This estimation is also suitable for a meshed configuration of the ADN, which includes more than one interconnection between each pair of the cells. Depending on the availability of a communication infrastructure, it is able to work locally inside the cells or globally for the whole ADN. As a part of the LCS, the voltage control function is investigated in both steady-state and dynamic environments. The autonomous voltage control within each network area (cell) can be deployed by a combination of active and reactive power support of distributed generation (DG). The coordinated voltage control defines the optimal tap setting of the on-load tap changer (OLTC) while comparing amounts of control actions in each area. Based on the sensitivity factors, these negotiations are thoroughly supported in the distributed environment of the MAS platform. To verify the proposed method, both steady-state and dynamic simulations are developed. Simulation results show that the proposed function helps to integrate more DG while mitigating voltage violation effectively. The optimal solution can be reached within a small number of calculation iterations. It opens a possibility to apply the proposed method as an on-line application. Furthermore, a distributed approach for the power flow management function is developed. By converting the power network to a represented graph, the optimal power flow is understood as the well-known minimum cost flow problem. Two fundamental solutions for the minimum cost flow, i.e., the Successive Shortest Path (SSP) algorithm and the Cost-Scaling Push-Relabel (CS-PR) algorithm, are introduced. The SSP algorithm is augmenting the power flow along the shortest path until reaching the capacity of at least one edge. After updating the flow, it finds another shortest path and augments the flow again. The CS-PR algorithm approaches the problem in a different way which is scaling cost and pushing as much flow as possible at each active node. Simulations of both meshed and radial test networks are developed to compare their performances in various network conditions. Simulation results show that the two methods can allow both generation and power flow controller devices to operate optimally. In the radial test network, the CS-PR needs less computation effort represented by a number of exchanged messages among the MAS platform than the SSP. Their performances in the meshed network are, however, almost the same. Last but not least, this novel concept of MAS-based AND is verified under a laboratory environment. The lab set-up separates some local network areas by using a three-inverter system. The MAS platform is created on different computers and is able to retrieve data from and to hardware components, i.e., the three-inverter system. In this set-up, a configuration of the power router is established in a combination of the three-inverter system with the MAS platform. Three control functions of the inverters, AC voltage control, DC bus voltage control, and PQ control, are developed in a Simulink diagram. By assigning suitable operation modes for the inverters, the set-up successfully experiments on synchronizing and disconnecting a cell to the rest of the grid. In the MAS platform, an obvious power routing strategy is executed to optimally manage power flow in the lab set-up. The results show that the proposed concept of the ADN with the power router interface works well and can be used to manage electrical networks with distributed generation and controllable loads, leading to active networks

Has Delaware Become the New Eastern District of Texas? The Unforeseen Consequences of the AIA

To stem the rising tide of patent suits brought by non-practicing entities (NPEs), Congress enacted the anti-joinder provisions of the Leahy-Smith America Invents Act (AIA) while, at nearly the same time, the Federal Circuit issued a series of decisions making it easier for defendants to transfer multi-defendant cases filed by NPEs away from the Eastern District of Texas. The unexpected result of these initiatives, however, has been that NPEs have selected the District of Delaware as their new “forum of choice,” making it the most popular forum for patent litigation in the country and displacing the Eastern District of Texas

Power flow management in active networks

This paper proposes a new method to manage the active power in the distribution systems, a function under the framework of the active network (AN) concept. An application of the graph theory is introduced to cope with the optimal power generation (DGs/Cells dispatch) and interarea power flows. The algorithm is implemented in a distributed way supported by the multi-agent system (MAS) technology. Simulations show how the method works in cases of optimal operation, congestion management, and power generation cost change

The interconnection in active distribution networks

The active distribution network (AN) has been mentioned recently to adapt with a large-scale implementation of distributed generators. One of its enhancements is increasing interconnections to provide more than one power flow path among local control areas. These parallel physical connections might cause several problems for the network such as congestion and loop flow. Considering the characteristics of the AN, this paper proposes a decentralized approach to control power flow which has some analogies to the telephone networks. The implementation of this control mechanism is based on a multi-agent system (MAS) technology. A simulation of the power system and MAS is created to illustrate the possibility of the proposed method

Has Delaware Become the New Eastern District of Texas? The Unforeseen Consequences of the AIA

To stem the rising tide of patent suits brought by non-practicing entities (NPEs), Congress enacted the anti-joinder provisions of the Leahy-Smith America Invents Act (AIA) while, at nearly the same time, the Federal Circuit issued a series of decisions making it easier for defendants to transfer multi-defendant cases filed by NPEs away from the Eastern District of Texas. The unexpected result of these initiatives, however, has been that NPEs have selected the District of Delaware as their new “forum of choice,” making it the most popular forum for patent litigation in the country and displacing the Eastern District of Texas

Reinforcement Learning With Simulated User For Automatic Dialog Strategy Optimization

In this paper, we propose a solution to the problem of formulating strategies for a spoken dialog system. Our approach is based on reinforcement learning with the help of a simulated user in order to identify an optimal dialog strategy. Our method considers the Markov decision process to be a framework for representation of speech dialog in which the states represent history and discourse context, the actions are dialog acts and the transition strategies are decisions on actions to take between states. We present our reinforcement learning architecture with a novel objective function that is based on dialog quality rather than its duration

Lab design and implementation of MAS-based active network

The introduction of distributed generation (DG) in ever increasing amounts into the existing electrical infrastructure challenges network operators in the way they manage the network. These DGs are often controllable but far from the present day control rooms. With the amount of generators increasing very fast, so will the number of sensors and actuators, growing to numbers way too large to handle in a single control room by human intervention. As the networks are changing from passive to active, more and more the need for automation arises. To accommodate this need the network can be divided into cells of which the borders are created naturally at places where the power flow over that border can be controlled. The cells are capable of managing tasks like protection, voltage and power control autonomously. If more power is needed they can exchange this with neighboring cells and in the worse case they can be completely decoupled from neighboring cells to ensure stability or to be operated in island mode

Deep learning methods for on-line flexibility prediction and optimal resource allocation in smart buildings

Unprecedented high volume of data is available with the upward growth of the advanced metering infrastructure. Because the built environment is the largest user of electricity, a deeper look at building energy consumption holds promise for helping to achieve overall optimization of the energy system. Yet, a knowledge transfer from the fusion of extensive data is under development. To overcome this limitation, in the big data era, more and more machine learning methods appear to be suitable to automatically extract, predict and optimized building electrical patterns by performing successive transformation of the data. More recently, there has been a revival of interest in deep learning methods as the most advance on-line solutions for large-scale and real databases. Enabling real-time applications from the high level of aggregation in the smart grid will put end-users in position to change their consumption patterns, offering useful benefits for the system as a whole.<br/

Voltage control coordination of distributed generators in cell-based active networks

This paper gives an introduction on the Cell-based Active Network (CBAN), a potential system concept for future distribution networks. One of its functions, to deal with limiting the network voltage changes, is focused on with a proposed control scheme. This is based on an appropriate dispatch of DG’s active and reactive power which is implemented autonomously within cells (feeders) of the CBAN. The test results show that the voltage regulation in the CBAN can help to control and mitigate voltage deviations effectively. The simulations are performed with a detailed model of a Doubly Fed Induction Generator (DFIG)

Deep learning methods for on-line flexibility prediction and optimal resource allocation in smart buildings

Unprecedented high volume of data is available with the upward growth of the advanced metering infrastructure. Because the built environment is the largest user of electricity, a deeper look at building energy consumption holds promise for helping to achieve overall optimization of the energy system. Yet, a knowledge transfer from the fusion of extensive data is under development. To overcome this limitation, in the big data era, more and more machine learning methods appear to be suitable to automatically extract, predict and optimized building electrical patterns by performing successive transformation of the data. More recently, there has been a revival of interest in deep learning methods as the most advance on-line solutions for large-scale and real databases. Enabling real-time applications from the high level of aggregation in the smart grid will put end-users in position to change their consumption patterns, offering useful benefits for the system as a whole.<br/