A reconfigurable distributed multiagent system optimized for scalability

This thesis proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topology-independent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead. The proposed scalable multiagent system has been coupled with a scalable reconfiguration algorithm for an electric power system attempting to minimize the number of switch combination explored for reconfiguration. The reconfiguration algorithm reconfigures a power system while maintaining bus voltages within limits specified by constraints

Modelling and Simulation of Electrical Energy Systems through a Complex Systems Approach using Agent-Based Models

Complexity science aims to better understand the processes of both natural and man-made systems which are composed of many interacting entities at different scales. A disaggregated approach is proposed for simulating electricity systems, by using agent-based models coupled to continuous ones. The approach can help in acquiring a better understanding of the operation of the system itself, e.g. on emergent phenomena or scale effects; as well as in the improvement and design of future smart grids

Energy-efficient control of a smart grid with sustainable homes based on distributing risk

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 135-145).The goal of this thesis is to develop a distributed control system for a smart grid with sustainable homes. A central challenge is how to enhance energy efficiency in the presence of uncertainty. A major source of uncertainty in a smart grid is intermittent energy production by renewable energy sources. In the face of global climate change, it is crucial to reduce dependence on fossil fuels and shift to renewable energy sources, such as wind and solar. However, a large-scale introduction of wind and solar generation to an electrical grid poses a significant risk of blackouts since the energy supplied by the renewables is unpredictable and intermittent. The uncertain behavior of renewable energy sources increases the risk of blackouts. Therefore, an important challenge is to develop an intelligent control mechanism for the electrical grid that is both reliable and efficient. Uncertain weather conditions and human behavior pose challenges for a smart home. For example, autonomous room temperature control of a residential building may occasionally make the room environment uncomfortable for residents. Autonomous controllers must be able to take residents' preferences as an input, and to control the indoor environment in an energy-efficient manner while limiting the risk of failure to meet the residents' requirements in the presence of uncertainties. In order to overcome these challenges, we propose a distributed robust control method for a smart grid that includes smart homes as its building components. The proposed method consists of three algorithms: 1) market-based contingent energy dispatcher for an electrical grid, 2) a risk-sensitive plan executive for temperature control of a residential building, and 3) a chance-constrained model-predictive controller with a probabilistic guarantee of constraint satisfaction, which can control continuously operating systems such as an electrical grid and a building. We build the three algorithms upon the chance-constrained programming framework: minimization of a given cost function with chance constraints, which bound the probability of failure to satisfy given state constraints. Although these technologies provide promising capabilities, they cannot contribute to sustainability unless they are accepted by the society. In this thesis we specify policy challenges for a smart grid and a smart home, and discuss policy options that gives economical and regulatory incentives for the society to introduce these technologies on a large scale.by Masahiro Ono.S.M.in Technology and Polic

Robust, goal-directed plan execution with bounded risk

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 273-283).There is an increasing need for robust optimal plan execution for multi-agent systems in uncertain environments, while guaranteeing an acceptable probability of success. For example, a fleet of unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) are required to operate autonomously for an extensive mission duration in an uncertain environment. Previous work introduced the concept of a model-based executive, which increases the level of autonomy, elevating the level at which systems are commanded. This thesis develops model-based executives that reason explicitly from a stochastic plant model to find the optimal course of action, while ensuring that the probability of failure is within a user-specified risk bound. This thesis presents two robust mode-based executives: probabilistic Sulu or p-Sulu, and distributed probabilistic Sulu or dp-Sulu. The objective for p-Sulu and dp-Sulu is to allow users to command continuous, stochastic multi-agent systems in a manner that is both intuitive and safe. The user specifies the desired evolution of the plant state, as well as the acceptable probabilities of failure, as a temporal plan on states called a chance-constrained qualitative state plan (CCQSP). An example of a CCQSP statement is "go to A through B within 30 minutes, with less than 0.001% probability of failure." p-Sulu and dp-Sulu take a CCQSP, a continuous plant model with stochastic uncertainty, and an objective function as inputs, and outputs an optimal continuous control sequence, as well as an optimal discrete schedule. The difference between p-Sulu and dp-Sulu is that p-Sulu plans in a centralized manner while dp-Sulu plans in a distributed manner. dp-Sulu enables robust CCQSP execution for multi-agent systems. We solve the problem based on the key concept of risk allocation, which achieves tractability by allocating the specified risk to individual constraints and mapping the result into an equivalent deterministic constrained optimization problem. Risk allocation also enables a distributed plan execution for multi-agent systems by distributing the risk among agents to decompose the optimization problem. Building upon the risk allocation approach, we develop our first CCQSP executive, p-Sulu, in four spirals. First, we develop the Convex Risk Allocation (CRA) algorithm, which can solve a CCQSP planning problem with a convex state space and a fixed schedule, highlighting the capability of optimally allocating risk to individual constraints. Second, we develop the Non-convex Iterative Risk Allocation (NIRA) algorithm, which can handle non-convex state space. Third, we build upon NIRA a full-horizon CCQSP planner, p-Sulu FH, which can optimize not only the control sequence but also the schedule. Fourth, we develop p-Sulu, which enables the real-time execution of CCQSPs by employing the receding horizon approach. Our second CCQSP executive, dp-Sulu, is developed in two spirals. First, we develop the Market-based Iterative Risk Allocation (MIRA) algorithm, which can control a multiagent system in a distributed manner by optimally distributing risk among agents through the market-based method called tatonnement. Second and finally, we integrate the capability of MIRA into p-Sulu to build the robust model-based executive, dp-Sulu, which can execute CCQSPs on multi-agent systems in a distributed manner. Our simulation results demonstrate that our executives can efficiently execute CCQSP planning problems with significantly reduced suboptimality compared to prior art.by Masahiro Ono.Ph.D

An agent-based electrical power market

This demonstration shows an agent-based model for the electricity power market, in which the optimal power flow is determined in a bottom-up fashion. Here, each agent controls a single electrical node consisting of several power generators, loads (consumer demand), and is connected to neighbouring nodes through transmission lines. Furthermore, each of the components has associated physical constraints, such as the line and generators' capacities. Through a process resembling tâtonnement in markets, the optimal system solution which maximises social welfare is reached within a few iterations. The demonstrator visualises this process and also shows how the various constraints affect the system behaviour and how this changes with different settings.</p