Le Xie

Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment

The study of Cyber-Physical System (CPS) has been an active area of research. Internet Data Center (IDC) is an important emerging Cyber-Physical System. As the demand on Internet services drastically increases in recent years, the power used by IDCs has been skyrocketing. While most existing research focuses on reducing power consumptions of IDCs, the power management problem for minimizing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the current multi-electricity market, where the price of electricity may exhibit time and location diversities. Further, for these service providers, guaranteeing quality of service (i.e. service level objectives-SLO) such as service delay guarantees to the end users is of paramount importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing quality of service geared to the location diversity and time diversity of electricity price. We model the problem as a constrained mixed-integer programming and propose an efficient solution method. Extensive evaluations based on real-life electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach.

Wind Integration in Power Systems: Operational Challenges and Possible Solutions

This paper surveys major technical challenges for power system operations in support of large-scale wind energy integration. The fundamental difficulties of integrating wind power arise from its high inter-temporal variation and limited predictability. The impact of wind power integration is manifested in, but not limited to, scheduling, frequency regulations, and system stabilization requirements. Possible alternatives are suggested for a more reliable and cost-effective power system operation. New computationally efficient methods for improving system performances by using prediction and operational interdependencies over different time horizons remain critical open research problems.

False Data Injection Attacks in Electricity Markets

We present a potential class of cyber attack, named false data injection attack, against the state estimation in deregulated electricity markets. With the knowledge of the system configuration, we show that such attacks will circumvent the bad data measurement detection equipped in present SCADA systems, and lead to profitable financial misconduct such as virtual bidding the ex-post locational marginal price (LMP). We demonstrate the potential attacks on an IEEE 14-bus system.

Integrity Data Attacks in Power Market Operations

We study the economic impact of a potential class of integrity cyber attacks, named false data injection attacks, on electric power market operations. In particular, we show that with the knowledge of the transmission system topology, attackers may circumvent the bad data detection algorithms equipped in todays state estimator. This, in turn, may be leveraged by attackers for consistent financial arbitrage such as virtual bidding at selected pairs of nodes. This paper is a first attempt to formalize the economic impact of malicious data attacks on real-time market operations. We show how an attack could systematically construct a profitable attacking strategy, in the meantime being undetected by the system operator. Such a result is also valuable for the system operators to examine the potential economic loss due to such cyber attack. The potential impact of the false data injection attacks is illustrated on real-time market operations of the IEEE 14-bus system.

Modeling of Future Cyber–Physical Energy Systems for Distributed Sensing and Control

This paper proposes modeling the rapidly evolving energy systems as cyber-based physical systems. It introduces a novel cyber-based dynamical model whose mathematical description depends on the cyber technologies supporting the physical system. This paper discusses how such a model can be used to ensure full observability through a cooperative information exchange among its components; this is achieved without requiring local observability of the system components. This paper also shows how this cyber-physical model is used to develop interactive protocols between the controllers embedded within the system layers and the network operator. Our approach leads to a synergistic framework for model-based sensing and control of future energy systems. The newly introduced cyber-physical model has network structure-preserving properties that are key to effective distributed decision making. The aggregate load modeling that we develop using data mining techniques and novel sensing technologies facilitates operations of complex electric power systems.

Fully Distributed State Estimation for Wide-Area Monitoring Systems

This paper presents a fully distributed state estimation algorithm for wide-area monitoring in power systems. Through iterative information exchange with designated neighboring control areas, all the balancing authorities (control areas) can achieve an unbiased estimate of the entire power systems state. In comparison with existing hierarchical or distributed state estimation methods, the novelty of the proposed approach lies in that: 1) the assumption of local observability of all the control areas is no longer needed; 2) the communication topology can be different than the physical topology of the power interconnection; and 3) for DC state estimation, no coordinator is required for each local control area to achieve provable convergence of the entire power systems states to those of the centralized estimation. The performance of both DC and AC state estimation using the proposed algorithm is illustrated in the IEEE 14-bus and 118-bus systems.

Coupon Incentive-Based Demand Response: Theory and Case Study

This paper presents the formulation and critical assessment of a novel type of demand response (DR) program targeting retail customers (such as small/medium size commercial, industrial, and residential customers) who are equipped with smart meters yet still face a flat rate. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer retail customers coupon incentives via near-real-time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping and/or price spikes. This scheme is referred to as coupon incentive-based demand response (CIDR). In contrast to the real-time pricing or peak load pricing DR programs, CIDR continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response. Theoretical analysis shows the benefits of the proposed scheme in terms of social welfare, consumer surplus, LSE profit, the robustness of the retail electricity rate, and readiness for implementation. The pros and cons are discussed in comparison with existing DR programs. Numerical illustration is performed based on realistic supply and demand data obtained from the Electric Reliability Council of Texas (ERCOT).

Efficient Coordination of Wind Power and Price-Responsive Demand—Part I: Theoretical Foundations

In Part I of this two-part paper, we introduce several possible methods for integrating wind power, price-responsive demand and other distributed energy resources (DERs). These methods differ with respect to information exchange requirements, computational complexity, and physical implementability. A novel look-ahead interactive dispatch that internalizes inter-temporal constraints at the DERs level, and dispatches the results of distributed decisions subject to spatial security constraints, is proposed as a possible effective algorithm. This method requires only the use of todays static security-constrained economic dispatch (SCED) by the system operators. The optimization accounting for inter-temporal constraints, and ramping rates in particular, is done by the DERs while they create their own supply and demand functions. To implement this method, todays supervisory control and data acquisition (SCADA) needs to be transformed into a multi-directional, multi-layered information exchange system.

Model predictive economic/environmental dispatch of power systems with intermittent resources

This paper presents potential benefits of applying model predictive control (MPC) to solving the multi-objective economic/environmental dispatch problem in electric power systems with many intermittent resources. Based on the predictive model of the available output in the next short time period (e.g. 5 minutes) from the intermittent resources, this paper introduces a look-ahead optimal control algorithm for dispatching the available generation resources with the objective of minimizing objective function comprising both generation and environmental costs. This method is compared with (1) the static economic dispatch which treats intermittent resources as uncertain negative loads, and (2) the MPC dispatch with single objective function of minimizing the total generation cost. We show that the proposed MPC approach could lower the generation costs by directly dispatching the generator output from the renewable resources in order to compensate temporal load variations over pre-defined time horizon. Furthermore, the multi-objective economic/environmental cost function provides a formulation to study the tradeoff of efficiency and environmental impact in future energy systems. Simulation is implemented in a 12-bus power system comprising five generators to illustrate potential benefits from this look-ahead dispatch of both intermittent and more conventional power plants. The proposed method is directly applicable to managing power systems with large presence of wind and photovoltaic resources.

Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models.