Dipti Srinivasan

Demand Side Management in Smart Grid Using Heuristic Optimization

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Neural Networks for Real-Time Traffic Signal Control

Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner

Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator

This paper presents a multiagent system (MAS) for real-time operation of a microgrid. The proposed operational strategy is mainly focused on generation scheduling and demand side management. In generation scheduling, schedule coordinator agent executes a two-stage scheduling: day-ahead and real-time scheduling. The day-ahead scheduling finds out hourly power settings of distributed energy resources (DERs) from a day-ahead energy market. The real-time scheduling updates the power settings of the distributed energy resources by considering the results of the day-ahead scheduling and feedback from real-time operation of the microgrid in real-time digital simulator (RTDS). A demand side management agent performs load shifting before the day-ahead scheduling, and does load curtailing in real-time whenever it is necessary and possible. The distributed multiagent model proposed in this paper provides a common communication interface for all components of the microgrid to interact with one another for autonomous intelligent control actions. Furthermore, the multiagent system maximizes the power production of local distributed generators, minimizes the operational cost of the microgrid, and optimizes the power exchange between the main power grid and the microgrid subject to system constraints and constraints of distributed energy resources. Outcome of simulation studies demonstrates the effectiveness of the proposed multiagent approach for real-time operation of a microgrid.

Evolutionary computation in power systems

This paper provides an overview and a list of references on the use of Evolutionary Algorithms (EA) in Power Systems and related fields. As didactic examples, the paper presents two applications of EA for two different problems in Power Systems.

Mobile agents based routing protocol for mobile ad hoc networks

A novel routing scheme for mobile ad hoc networks (MANETs), which combines the on-demand routing capability of Ad Hoc On-Demand Distance Vector (AODV) routing protocol with a distributed topology discovery mechanism using ant-like mobile agents is proposed in this paper. The proposed hybrid protocol reduces route discovery latency and the end-to-end delay by providing high connectivity without requiring much of the scarce network capacity. On the one side the proactive routing protocols in MANETs like Destination Sequenced Distance Vector (DSDV) require to know, the topology of the entire network. Hence they are not suitable for highly dynamic networks such as MANETs, since the topology update information needs to be propagated frequently throughout the network. These frequent broadcasts limit the available network capacity for actual data communication. On the other hand, on-demand, reactive routing schemes like AODV and Dynamic Source Routing (DSR), require the actual transmission of the data to be delayed until the route is discovered. Due to this long delay a pure reactive routing protocol may not be applicable for real-time data and multimedia communication. Through extensive simulations in this paper it is proved that the proposed Ant-AODV hybrid routing technique, is able to achieve reduced end-to-end delay compared to conventional ant-based and AODV routing protocols.

Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals

Electrical power systems are evolving from todays centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Cooperative, hybrid agent architecture for real-time traffic signal control

This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time traffic signal control of a complex traffic network. The large-scale traffic signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of signal plans used by the current real-time adaptive traffic control system. The multiagent architecture produces significant improvements in the conditions of the traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.

Multi-agent coordination for DER in MicroGrid

Multi-agent system (MAS) is one of the most exciting and the fastest growing domains in agent oriented technology which deals with modeling of autonomous decision making entities. This paper presents an application of MAS for distributed energy resource (DER) management in a MicroGrid. MicroGrid can be defined as low voltage distributed power networks comprising various distributed generators (DG), storage and controllable loads, which can be operated as interconnected or as islands from the main power grid. By representing each element in MicroGrid as an autonomous intelligent agent, multi agent modeling of a MicroGrid is designed and implemented. JADE framework is proposed for the modeling and reliability of the MicroGrid is confirmed with PowerWorld Simulator. Further, the FIPA contract net coordination between the agents is demonstrated through software simulation. As a result, this paper provides a MicroGrid modeling which has the necessary communication and coordination structure to create a scalable system. The optimized MicroGrid management and operations can be developed on it in future.

Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting

This paper describes the implementation and forecasting results of a hybrid fuzzy neural technique, which combines neural network modeling, and techniques from fuzzy logic and fuzzy set theory for electric load forecasting. The strengths of this powerful technique lie in its ability to forecast accurately on weekdays, as well as, on weekends, public holidays, and days before and after public holidays. Furthermore, use of fuzzy logic effectively handles the load variations due to special events. The fuzzy-neural network (FNN) has been extensively tested on actual data obtained from a power system for 24-hour ahead prediction based on forecast weather information. Very impressive results, with an average error of 0.62% on weekdays, 0.83% on Saturdays and 1.17% on Sundays and public holidays have been obtained. This approach avoids complex mathematical calculations and training on many years of data, and is simple to implement on a personal computer.

Benchmarking a reduced multivariate polynomial pattern classifier

A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. The basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.