Yuan Wu

Optimal Energy Scheduling for Residential Smart Grid With Centralized Renewable Energy Source

Future smart grids will be featured by flexible supply-demand management and great penetration of renewable energy to enable efficient and economical grid operations. While the renewable energy offers a cheaper and cleaner energy supply, it introduces supply uncertainty due to the volatility of renewable source. It is therefore of practical importance to investigate the optimal exploitation of renewable energy based on the supply-demand framework of future smart grids, where the energy-providers (or the energy-users) adaptively adjust their energy-provisioning (or energy-demands) according to some system state information that takes into account the volatility of renewable energy. Specifically, we consider cost-efficient energy scheduling for residential smart grids equipped with a centralized renewable energy source. Our scheduling problem aims at: 1) quantifying the optimal utilization of renewable energy that achieves the tradeoff between the system-wide benefit from exploiting the renewable energy and the associated cost due to its volatility; and 2) evaluating how the volatility of renewable energy influences its optimal exploitation. We also propose computationally efficient and distributed algorithms to determine the optimal exploitation of renewable energy as well as the associated energy scheduling decisions.

Optimal Pricing and Energy Scheduling for Hybrid Energy Trading Market in Future Smart Grid

Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTCs pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTCs optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.

Cooperative Resource Sharing and Pricing for Proactive Dynamic Spectrum Access via Nash Bargaining Solution

In this paper, we investigate the cooperative resource sharing and pricing for the licensed Primary User (PU) and Cognitive Radio Networks (CRNs), where the PU jointly determines how to share its under-utilized radio resource with Secondary Users (SUs) and how to charge the SUs accordingly. Meanwhile, the SUs jointly determine how to utilize the shared radio resource from the PU and their preferred payments. Since both the PU and SUs expect to benefit from cooperation, we model their interactions as a Nash bargaining problem. Viewing the nonconvexity of bargaining problem, we first propose a two-step procedure to solve it efficiently. The two-step procedure explores the connection between the bargaining problem and its associated social optimization problem, and thus turns the original nonconvex bargaining problem into two consecutive convex optimization problems. We then propose two efficient algorithms, each with guaranteed convergence, to solve these two problems, respectively. Numerical results show that our proposed two-step procedure achieves the optimality of the bargaining problem with significantly reduced computational complexity. Also, our joint resource sharing and pricing scheme guarantees that each SU and PU can positively benefit from the cooperative bargaining, and the benefit is fairly allocated among them.

Optimal exploitation of renewable energy for residential smart grid with supply-demand model

Future smart grid has been widely conceived to be featured by its flexible supply-demand management and great exploitation of renewable energy. However, due to the volatility of renewable sources, exploitation of renewable energy introduces uncertainty in energy supply. Therefore, it is of practical importance to quantify the optimal exploitation of renewable energy based on the supply-demand framework for future smart grid, where the energy-providers (or the energy-users) adaptively adjust their energy-provisioning (or energy-demands) according to the system information that takes account of the volatility of renewable energy. In this work, we consider a residential smart grid equipped with a centralized renewable source as a supplement to the electricity acquired from the grid. Our model aims at: i) quantifying the optimal exploitation of renewable energy that trades off between the system-wide benefit from using the renewable energy and the associated cost due to its volatility, and ii) characterizing how the volatility of renewable energy influences its optimal exploitation. An efficient distributed algorithm is proposed to determine the optimal utilization of renewable energy as well as the associated energy scheduling decisions based on the supply-demand model.

Monitoring for Power-Line Change and Outage Detection in Smart Grid via the Alternating Direction Method of Multipliers

A novel distributed line outage detection algorithm was developed in this paper through convex relaxation and alternating direction method of multipliers (ADMM) method for smart grid system. The devised approach allows identification of multiple line outages using limited number of PMU measurements. The diagnosis procedure is performed close to the place where PMU measurements are collected and only partial variable estimates are exchanged among the neighbours of processors. It is shown that the proposed method outperforms the existing methods, which are either suffering from computational complexity or security and privacy issues. Numerical tests demonstrated the merits of the proposed schemes in co-ordinately figuring out multiple line outages in the system.

Fast distributed demand response algorithm in smart grid

This paper proposes a fast distributed demand response U+0028 DR U+0029 algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation U+0028 GaBP U+0029 solver. At the beginning of each time slot, each end-user U+002F energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption U+002F generation locally. The proposed demand response algorithm converges rapidly to a consumption U+002F generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user U+002F energysupplier estimates its energy consumption U+002F generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm.

Optimal management of local energy trading in future smart microgrid via pricing

In this paper, we investigate optimal management of local energy trading in future smart micro-grid (SMG) via pricing. In SMG, energy consumers and providers, in addition to trading with utility company, can also perform local energy trading controlled by a local trading manager (LTM) for reaping benefits. We first quantify the benefits achieved by the consumers and providers from local trading and then formulate a two-layered optimization framework to investigate i) how the energy consumers and providers maximize their benefits via appropriately adjusting their local trading decisions in response to the LTMs pricing, and ii) how the LTM adjusts its price in local market to benefit the consumers and providers as much as possible while guaranteeing a required gain for itself. We propose two algorithms to solve the layered optimization problem and perform numerical experiments with practical data set to validate the proposed local trading model and the algorithms.

Wavelet de-noising with improved threshold method for bridge health monitoring

In order to overcome the defects of traditional wavelet threshold method and improve the quality of bridge health monitoring signals, an improved wavelet threshold de-noising method based on the bridge deflection signals is proposed. First, the improved threshold can adaptively select different thresholds in different decomposition layers. Then, the wavelet coefficients of the bridge deflection signals are modeled to determine the statistical properties of deflection signals. Next, an improved wavelet shrinkage function is obtained based on these statistical properties. The bridge deflection signal de-noising experiments show that the actual de-noising effect is significant, and this proposed de-noising method is suitable for processing the deflection signal of the bridge monitoring system.