Fengji Luo

Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCOs profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated.

Coordinated Operational Planning for Wind Farm With Battery Energy Storage System

This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.

A Review of False Data Injection Attacks Against Modern Power Systems

With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

The 2015 Ukraine Blackout: Implications for False Data Injection Attacks

In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event.

Wind power impact on system operations and planning

With the emission reduction scheme introduced, increasing number of wind farms have been planned and/or installed in many countries. In this paper, issues with wind power connection to the existing power grid are discussed, with particular emphasize on system operations and planning aspects. In addition to power system analysis, implications of wind power impacts on the electricity market operations are also discussed. From power system operations, wind power introduces stability and control challenges which are reflected in frequency control and voltage control ancillary services in a market environment. From system planning point of view, the increasing number of wind generation connection requests from generators requires a more systematic approach toward system studies on the connection impact on transmission system planning. The research presented in this paper focus on the wind power practice in Hong Kong and Australia. Case studies based on data from Australia and Hong Kong are given in this paper to highlight some of the issues and methodologies relevant to wind power impact studies. Issues on wind power impact on system operations and planning (general discussion paper, key words: wind power optimal dispatch with other generating resources, wind forecast utilization, network frequency control ancillary services, voltage control ancillary services, TAS example, HK example; key techniques: operational schemes, optimization methods, security constraints, impact on market - constraint impact; network connection options analysis;)

Power system fault diagnosis based on history driven differential evolution and stochastic time domain simulation

Abstract Fault diagnosis is an important task in power system analysis. In this paper, a hybrid method is proposed to perform online fault diagnosis of transmission lines. Stochastic time domain simulation (STDS) is firstly introduced to generate simulated fault and system data so as to improve the computational speed of fault diagnosis and handle the possible malfunction of protective relays and circuit breakers. The fault diagnosis problem is then formulated as an optimization problem, which can take into account the possible malfunction of protection devices and post-fault system trajectories. We propose a novel optimization algorithm, namely history driven differential evolution (HDDE) to solve the formulated optimization problem. The proposed methodology is finally tested using comprehensive case studies to demonstrate its effectiveness.

Preventive Dynamic Security Control of Power Systems Based on Pattern Discovery Technique

This paper presents a statistical learning-based method for preventive dynamic security control of power systems. Critical operating variables regarding system dynamic security are first selected via a distance-based feature estimation process. An unsupervised learning procedure called pattern discovery (PD) is then performed in the space of the critical variables to extract the subtle structure knowledge called patterns. The patterns are geometrically non-overlapped hyper-rectangles, representing the system dynamic secure/insecure regions and can be explicitly presented to provide decision support for real-time security monitoring and situational awareness. By formulating the secure patterns into a standard optimal power flow (OPF) model, the preventive control against dynamic insecurities can be efficiently and transparently attained. The proposed method is validated on the New England 39-bus system considering both single- and multi-contingency conditions.

Cloud-Based Information Infrastructure for Next-Generation Power Grid: Conception, Architecture, and Applications

This paper gives a comprehensive discussion on applying the cloud computing technology as the new information infrastructure for the next-generation power system. First, this paper analyzes the main requirements of the future power grid on the information infrastructure and the limitations of the current information infrastructure. Based on this, a layered cloud-based information infrastructure model for next-generation power grid is proposed. Thus, this paper discussed how different categories of the power applications can benefit from the cloud-based information infrastructure. For the demonstration purpose, this paper develops three specific cloud-enabled power applications. The first two applications demonstrate how to develop practical compute-intensive and data-intensive power applications by utilizing different layered services provided by the state-of-the-art public cloud computing platforms. In the third application, we propose a cloud-based collaborative direct load control framework in a smart grid and show the merits of the cloud-based information infrastructure on it. Some cybersecurity considerations and the challenges and limitations of the cloud-based information infrastructure are also discussed.

Hybrid cloud computing platform: The next generation IT backbone for smart grid

This paper discusses the prospective of applying cloud computing technologies in the development of smart grid. Firstly, the conceptions of cloud computing are introduced, and then a hybrid cloud computing platform for smart grid is designed. After that, the distinguished characteristics of the proposed platform are explained in detail, following with the introduction of some potential power system applications. Finally, some notable state-of-the-art products that can be used to build the proposed platform are introduced.

Demand response through smart home energy management using thermal inertia

In this paper, the value of thermal inertia in demand response to benefit customers is determined through a Mixed Integer Linear Programming (MILP) algorithm. Thermal models with different sophistications for a smart house are investigated. The energy consumption for cooling a smart house is optimized to minimize the expenditure of cooling load. One parameter and two-parameter thermal models are integrated into the optimization. The optimization of thermal load for maintaining the smart house within thermal comfort level is formulated as a MILP algorithm under the dynamic pricing policy. It is observed that the utilization of thermal inertia could potentially benefit both smart house owners and grid operators in the context of smart grid.