Han-I Su

Bio: Han-I Su is an academic researcher from Stanford University. The author has contributed to research in topics: Upper and lower bounds & Distortion. The author has an hindex of 7, co-authored 11 publications receiving 342 citations.

Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing

Abstract: The high variability of renewable energy is a major obstacle toward its increased penetration. Energy storage can help reduce the power imbalance due to the mismatch between the available renewable power and the load. How much can storage reduce this power imbalance? How much storage is needed to achieve this reduction? This paper presents a simple analytic model that leads to some answers to these questions. Considering the multitimescale grid operation, we formulate the power imbalance problem for each timescale as an infinite horizon stochastic control problem and show that a greedy policy minimizes the average magnitude of the residual power imbalance. Observing from the wind power data that in shorter timescales the power imbalance can be modeled as an iid zero-mean Laplace distributed process, we obtain closed form expressions for the minimum cost and the stationary distribution of the stored power. We show that most of the reduction in the power imbalance can be achieved with relatively small storage capacity. In longer timescales, the correlation in the power imbalance cannot be ignored. As such, we relax the iid assumption to a weakly dependent stationary process and quantify the limit on the minimum cost for arbitrarily large storage capacity.

Modeling and analysis of the role of fast-response energy storage in the smart grid

Abstract: The large short time-scale variability of renewable energy resources presents significant challenges to the reliable operation of power systems. This variability can be mitigated by deploying fast-ramping generators. However, these generators are costly to operate and produce environmentally harmful emissions. Fast-response energy storage devices, such as batteries and flywheels, provide an environmentally friendly alternative, but are expensive and have limited capacity. To study the environmental benefits of storage, we introduce a slotted-time dynamic residual dc power flow model with the prediction error of the difference between the generation (including renewables) and the load as input and the fast-ramping generation and the storage (charging/discharging) operation as the control variables used to ensure that the demand is satisfied (as much as possible) in each time slot. We assume the input prediction error sequence to be i.i.d. zero-mean random variables. The optimal power flow problem is then formulated as an infinite horizon average-cost dynamic program with the cost function taken as a weighted sum of the average fast-ramping generation and the loss of load probability. We find the optimal policies at the two extremes of the cost function weights and propose a two-threshold policy for the general case. We also obtain refined analytical results under the assumption of Laplace distributed prediction error and corroborate this assumption using simulated wind power generation data from NREL.

Cascade multiterminal source coding

Abstract: We investigate distributed source coding of two correlated sources X and Y where messages are passed to a decoder in a cascade fashion. The encoder of X sends a message at rate R1 to the encoder of Y. The encoder of Y then sends a message to the decoder at rate R 2 based both on Y and on the message it received about X. The decoder's task is to estimate a function of X and Y. For example, we consider the minimum mean squared-error distortion when encoding the sum of jointly Gaussian random variables under these constraints. We also characterize the rates needed to reconstruct a function of X and Y losslessly. Our general contribution toward understanding the limits of the cascade multiterminal source coding network is in the form of inner and outer bounds on the achievable rate region for satisfying a distortion constraint for an arbitrary distortion function d(x, y, z). The inner bound makes use of a balance between two encoding tactics—relaying the information about X and recompressing the information about X jointly with Y. In the Gaussian case, a threshold is discovered for identifying which of the two extreme strategies optimizes the inner bound. Relaying outperforms recompressing the sum at the relay for some rate pairs if the variance of X is greater than the variance of Y.

Distributed Lossy Averaging

Abstract: In this paper, an information theoretic formulation of the distributed averaging problem previously studied in computer science and control is presented. We assume a network with m nodes each observing a white Gaussian noise (WGN) source. The nodes communicate and perform local processing with the goal of computing the average of the sources to within a prescribed mean squared error distortion. The network rate distortion function R* (D) for a two-node network with correlated Gaussian sources is established. A general cutset lower bound on R*(D) is established and shown to be achievable to within a factor of 2 via a centralized protocol over a star network. A lower bound on the network rate distortion function for distributed weighted-sum protocols, which is larger in order than the cutset bound by a factor of log m, is established. An upper bound on the network rate distortion function for gossip-base weighted-sum protocols, which is only log log m larger in order than the lower bound for a complete graph network, is established. The results suggest that using distributed protocols results in a factor of log m increase in order relative to centralized protocols.

Limits on the Benefits of Energy Storage for Renewable Integration

Abstract: The high variability of renewable energy resources presents significant challenges to the operation of the electric power grid Conventional generators can be used to mitigate this variability but are costly to operate and produce carbon emissions Energy storage provides a more environmentally friendly alternative, but is costly to deploy in large amounts This paper studies the limits on the benefits of energy storage to renewable energy: How effective is storage at mitigating the adverse effects of renewable energy variability? How much storage is needed? What are the optimal control policies for operating storage? To provide answers to these questions, we first formulate the power flow in a single-bus power system with storage as an infinite horizon stochastic program We find the optimal policies for arbitrary net renewable generation process when the cost function is the average conventional generation (environmental cost) and when it is the average loss of load probability (reliability cost) We obtain more refined results by considering the multi-timescale operation of the power system We view the power flow in each timescale as the superposition of a predicted (deterministic) component and an prediction error (residual) component and formulate the residual power flow problem as an infinite horizon dynamic program Assuming that the net generation prediction error is an IID process, we quantify the asymptotic benefits of storage With the additional assumption of Laplace distributed prediction error, we obtain closed form expressions for the stationary distribution of storage and conventional generation Finally, we propose a two-threshold policy that trades off conventional generation saving with loss of load probability We illustrate our results and corroborate the IID and Laplace assumptions numerically using datasets from CAISO and NREL

Network Information Theory

Abstract: This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.

Gossip Algorithms for Distributed Signal Processing

Abstract: Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.

Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach

Abstract: Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the random and intermittent characteristics of renewable energy, new challenges arise for the reliable operation of microgrids. To address this issue, we study in this paper the real-time energy management for a single microgrid system that constitutes a renewable generation system, an energy storage system, and an aggregated load. We model the renewable energy offset by the load over time, termed net energy profile, to be practically predictable, but with finite errors that can be arbitrarily distributed. We aim to minimize the total energy cost (modeled as sum of time-varying strictly convex functions) of the conventional energy drawn from the main grid over a finite horizon by jointly optimizing the energy charged/discharged to/from the storage system over time subject to practical load and storage constraints. To solve this problem in real time, we propose a new off-line optimization approach to devise the online algorithm. In this approach, we first assume that the net energy profile is perfectly predicted or known ahead of time, under which we derive the optimal off-line energy scheduling solution in closed-form. Next, inspired by the optimal off-line solution, we propose a sliding-window based online algorithm for real-time energy management under the practical setup of noisy predicted net energy profile with arbitrary errors. Finally, we conduct simulations based on the real wind generation data of the Ireland power system to evaluate the performance of our proposed algorithm, as compared with other heuristically designed algorithms, as well as the conventional dynamic programming based solution.