Incentives-Based Mechanism for Efficient Demand Response Programs

In this work we investigate the inefficiency of the electricity system with strategic agents. Specifically, we prove that without a proper control the total demand of an inefficient system is at most twice the total demand of the optimal outcome. We propose an incentives scheme that promotes optimal outcomes in the inefficient electricity market. The economic incentives can be seen as an indirect revelation mechanism that allocates resources using a one-dimensional message space per resource to be allocated. The mechanism does not request private information from users and is valid for any concave customer's valuation function. We propose a distributed implementation of the mechanism using population games and evaluate the performance of four popular dynamics methods in terms of the cost to implement the mechanism. We find that the achievement of efficiency in strategic environments might be achieved at a cost, which is dependent on both the users' preferences and the dynamic evolution of the system. Some simulation results illustrate the ideas presented throughout the paper.Comment: 38 pages, 9 figures, submitted to journa

Autonomous Multi-Stage Flexible OPF for Active Distribution Systems with DERs

The variability of renewable resources creates challenges in the operation and control of power systems. One way to cope with this issue is to use the flexibility of customer resources in addition to utility resources to mitigate this variability. We present an approach that autonomously optimizes the available distributed energy resources (DERs) of the system to optimally balance generation and load and/or levelize the voltage profile. The method uses a dynamic state estimator which is continuously running on the system providing the real-time dynamic model of the system and operating condition. At user selected time intervals, the real-time model and operating condition is used to autonomously assemble a multi-stage optimal power flow in which customer energy resources are represented with their controls, allowing the use of customer flexibility to be part of the solution. Customer DERs may include photovoltaic rooftops with controllable inverters, batteries, thermostatically controlled loads, smart appliances, etc. The paper describes the autonomous formation of the Multi-Stage Flexible Optimal Power Flow and the solution of the problem, and presents sample results

Event-triggered near optimal adaptive control of interconnected systems

Increased interest in complex interconnected systems like smart-grid, cyber manufacturing have attracted researchers to develop optimal adaptive control schemes to elicit a desired performance when the complex system dynamics are uncertain. In this dissertation, motivated by the fact that aperiodic event sampling saves network resources while ensuring system stability, a suite of novel event-sampled distributed near-optimal adaptive control schemes are introduced for uncertain linear and affine nonlinear interconnected systems in a forward-in-time and online manner. First, a novel stochastic hybrid Q-learning scheme is proposed to generate optimal adaptive control law and to accelerate the learning process in the presence of random delays and packet losses resulting from the communication network for an uncertain linear interconnected system. Subsequently, a novel online reinforcement learning (RL) approach is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation by using neural networks (NNs) for generating distributed optimal control of nonlinear interconnected systems using state and output feedback. To relax the state vector measurements, distributed observers are introduced. Next, using RL, an improved NN learning rule is derived to solve the HJB equation for uncertain nonlinear interconnected systems with event-triggered feedback. Distributed NN identifiers are introduced both for approximating the uncertain nonlinear dynamics and to serve as a model for online exploration. Next, the control policy and the event-sampling errors are considered as non-cooperative players and a min-max optimization problem is formulated for linear and affine nonlinear systems by using zero-sum game approach for simultaneous optimization of both the control policy and the event based sampling instants. The net result is the development of optimal adaptive event-triggered control of uncertain dynamic systems --Abstract, page iv

A parallel controller implementation for dynamic resource allocation in virtualized computing environment

The ability to dynamically allocate system resources in a large scale distributed system is highly desirable. Dynamically allocating system resources can significantly reduce under-utilization of system resources and reduce the power consumed by the servers. Since typical enterprise computing systems consist of hundreds of servers, it is almost impossible to manually reconfigure each system parameter for optimal performance. Prior work has shown that by posing the dynamic resource provisioning problem as one of sequential optimization, we can dynamically allocate system resources for optimal performance in a dynamic operating environment. However, a single threaded implementation of this control technique does not scale well with increasing system size. Therefore, this thesis develops a parallel controller implementation for dynamic resource allocation using the OpenMP interface. We analyze the performance of this controller in a virtualized computing environment, and show that dynamic resource allocation can lead to an average of 30% savings in energy consumption, over an uncontrolled system. Parallelizing the controller also significantly reduces its execution time overhead, by as much as 263%, a compared to single threaded implementation.M.S., Computer Engineering -- Drexel University, 200

A generalized optimal power flow program for distribution system analysis and operation with distributed energy resources and solid state transformers

The present distribution system is gradually trending towards a smart grid paradigm with massive development of distributed energy resources (DER), advanced power electronics interfaces, and a digitalized communication platform. Such profound changes bring challenges as well as opportunities for an entity like the distribution network operator (DNO) to optimally operate DERs and other controllable elements to achieve higher levels of energy efficiency, economic benefits, supply reliability and power quality. The major contribution of this dissertation is in the development of a generalized three-phase optimal power flow (OPF) program in a novel control scheme for future distribution system optimization and economic operation. It is developed based on primal-dual interior point method (PDIPM). The program is general enough to model comprehensive system components and topologies. The program can also be customized by user-defined cost functions, system constraints, and new device, such as solid state transformers (SST). An energy storage optimal control using dynamic programming is also proposed to coordinate with the OPF based on a pricing signal called the distribution locational marginal price (DLMP). The proposed OPF program can be used by the DNO in an open access competitive control scheme to optimally aggregate the energy mix by combining the profitability of each resource while satisfying system security constraints --Abstract, page iv

Dynamically reconfigurable management of energy, performance, and accuracy applied to digital signal, image, and video Processing Applications

There is strong interest in the development of dynamically reconfigurable systems that can meet real-time constraints in energy/power-performance-accuracy (EPA/PPA). In this dissertation, I introduce a framework for implementing dynamically reconfigurable digital signal, image, and video processing systems. The basic idea is to first generate a collection of Pareto-optimal realizations in the EPA/PPA space. Dynamic EPA/PPA management is then achieved by selecting the Pareto-optimal implementations that can meet the real-time constraints. The systems are then demonstrated using Dynamic Partial Reconfiguration (DPR) and dynamic frequency control on FPGAs. The framework is demonstrated on: i) a dynamic pixel processor, ii) a dynamically reconfigurable 1-D digital filtering architecture, and iii) a dynamically reconfigurable 2-D separable digital filtering system. Efficient implementations of the pixel processor are based on the use of look-up tables and local-multiplexes to minimize FPGA resources. For the pixel-processor, different realizations are generated based on the number of input bits, the number of cores, the number of output bits, and the frequency of operation. For each parameters combination, there is a different pixel-processor realization. Pareto-optimal realizations are selected based on measurements of energy per frame, PSNR accuracy, and performance in terms of frames per second. Dynamic EPA/PPA management is demonstrated for a sequential list of real-time constraints by selecting optimal realizations and implementing using DPR and dynamic frequency control. Efficient FPGA implementations for the 1-D and 2-D FIR filters are based on the use a distributed arithmetic technique. Different realizations are generated by varying the number of coefficients, coefficient bitwidth, and output bitwidth. Pareto-optimal realizations are selected in the EPA space. Dynamic EPA management is demonstrated on the application of real-time EPA constraints on a digital video. The results suggest that the general framework can be applied to a variety of digital signal, image, and video processing systems. It is based on the use of offline-processing that is used to determine the Pareto-optimal realizations. Real-time constraints are met by selecting Pareto-optimal realizations pre-loaded in memory that are then implemented efficiently using DPR and/or dynamic frequency control

Effect of social distancing for office landscape on the ergonomic illumination

In office buildings valuable energy is wasted if not properly regulated as a function of presence of humans and active demands for illumination levels. Effective and clever usage of the sunlight is essential for optimal use of energy resources in large office buildings. Additionally, productivity of the employees can be improved by maintaining a constant light intensity. In context of social distancing enforced onto landscape area structure and occupancy, they have effects in the illumination pattern and ergonomics. This paper presents the practical setup to mimic the illumination regulatory problem in landscape offices and the dynamic properties of such a system in the context of social distancing regulations. The light level control is performed with distributed predictive control, whereas a comparison is made among various situations. The original contribution of the paper is a fast, adaptive control algorithm, which can deal with changing context parameters; e.g. varying landscape office structures. Copyright (C) 2020 The Authors

A Practical Integration of Automatic Generation Control and Demand Response

For a power grid to operate properly, electrical frequency must be continuously maintained close to its nominal value. Increasing penetration of distributed generation, such as solar and wind generation, introduces fluctuations in active power while also reducing the natural inertial response of the electricity grid, creating reliability concerns. While frequency regulation has traditionally been achieved by controlling generators, the control of Demand Response resources has been recognized in recent smart grid literature as an efficient means for providing additional regulation capability. To this end, several control methodologies have been proposed recently, but various features of these proposals make their practical implementations difficult. In this paper, we propose a new control algorithm that facilitates optimal frequency regulation through direct control of both generators and Demand Response, while addressing several issues that prevent practical implementation of other proposals. In particular, i) our algorithm is ideal for control over a large, low-bandwidth network as communication and measurement is only required every 2 seconds, ii) it enables Demand Response resources to recover energy lost during system transients, and iii) it accommodates both measured disturbances and unmeasured disturbances. We demonstrate the viability of our approach through dynamic simulations on a 118-bus grid model.NSF initiative, Award no. EFRI-144130