Yingzhong Gu

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.

Fast MPC-Based Coordination of Wind Power and Battery Energy Storage Systems

In this paper, a model predictive control (MPC)-based coordinated scheduling framework for variable wind generation and battery energy storage systems (BESSs) is presented. On the basis of the short-term forecast of available wind generation and price information, a joint look-ahead optimization is performed by the wind farm and storage system to determine their net power injection to the electric power grid. In conjunction with moderate battery capacity, the excess unpredictable wind generation can be used to charge the battery storage and vice versa. The benefits of the proposed scheduling approach are that (1) the combined profit of wind generation and BESS is increased; (2) the net power injection from the wind farm into the power grid is smoothed out; and (3) the look-ahead optimization updates the price prediction in a moving horizon, which leads to more robust profit for wind farm and BESS against price uncertainties. By formulating the MPC-based coordinated scheduling as a quadratic programming problem, several numerically efficient algorithms to compute the optimal control strategy for wind generation and BESS are proposed. The effectiveness of the proposed algorithm in a modified IEEE 24-bus reli- ability test system with aggregated plug-in hybrid electric vehicles is demonstrated. It is shown that the proposed algorithm can increase the joint profit of wind farm and BESS while smoothing out the net power injection to the electricity grid. The proposed MPC-based scheduling problem can be solved in approximately 400 ms, which makes the framework implementable in realtime electricity market operations. DOI: 10.1061/(ASCE)EY.1943-7897.0000071.

Fast Sensitivity Analysis Approach to Assessing Congestion Induced Wind Curtailment

Although the installed wind generation capacity has grown remarkably over the past decades, percentage of wind energy in electricity supply portfolio is still relatively low. Due to the technical limitations of power system operations, considerable wind generation cannot integrate into the grid but gets curtailed. Among various factors, transmission congestion accounts for a significant portion of wind curtailment. Derived from DC power network, an analytical approach is proposed to efficiently assess the congestion induced wind curtailment sensitivity without iterative simulation. Compared to empirical simulation-based wind curtailment studies, the proposed approach offers the following advantages: 1) computational efficiency, 2) low input information requirement, and 3) robustness against uncertainties. This approach could benefit system operators, wind farm owners as well as wind power investors to better understand the interactions between wind curtailment and power system operations and can further help for curtailment alleviation. Numerical experiments of a modified IEEE 24-bus Reliability Test System (RTS) as well as a practical 5889-bus system are conducted to verify the effectiveness and robustness of the proposed approach.

Early Detection and Optimal Corrective Measures of Power System Insecurity in Enhanced Look-Ahead Dispatch

This paper presents a novel algorithm for the early detection and optimal corrective measures of power system insecurity in an enhanced look-ahead dispatch framework. By introducing short-term dispatchable capacity (STDC) into the proposed look-ahead security management (LSM) scheme, the algorithm is capable of predicting and identifying future infeasibilities that pose security risks to the system under both normal conditions and assumed contingency conditions. An optimal recovery plan can be computed to prevent system insecurity at a minimal cost. Early awareness of such information is of vital importance to system operators for taking timely actions with more flexible and cost-effective measures. This, in addition to the economic benefits studied in the literature, demonstrates the advantage of security improvement of the look-ahead dispatch framework. The performance of the proposed algorithms is illustrated in a revised 24-bus IEEE Reliability Test System as well as in a practical 5889-bus system.

Stochastic Look-Ahead Economic Dispatch With Variable Generation Resources

With the continuing growth of renewable penetration in power systems, it becomes increasingly challenging to manage the operational uncertainty at near-real-time stage via deterministic scheduling approaches. This paper explores the necessity, benefits and implementability of applying stochastic programming to security constrained economic dispatch (SCED). We formulate a stochastic look-ahead economic dispatch (LAED-S) model for near-real-time power system operation. A concept of uncertainty responses is introduced to assess the power system economic risk with respect to net load uncertainties. This concept offers the system operator a simple yet effective gauge to decide whether a stochastic approach is more desirable than a deterministic one. For an efficient stochastic dispatch algorithm, an innovative hybrid computing architecture is proposed. It leverages the progressive hedging algorithm and the L-shaped method. Numerical experiments are conducted on a practical 5889-bus system to illustrate the effectiveness of the proposed approach.

Congestion-induced wind curtailment: Sensitivity analysis and case studies

During the past decade, there has been tremendous development in wind energy all over the world. However, due to the power system operational constraints, reliability requirement as well as other technical limitations (e.g. wind prediction errors), significant portions of the wind generation resources (WGR) are curtailed in real-time operations. Among various power system operating constraints, transmission congestion plays a significant role. In this paper, we analyze the problem of the wind curtailment due to transmission congestion. Furthermore, we propose a sensitivity index to assess the impact of transmission line limit on wind generation curtailment. An analytical expression of the sensitivity index is derived based on the network theory of power system. The modified IEEE RTS-24 Bus system is used to verify the proposed theory.

Look-ahead dispatch with forecast uncertainty and infeasibility management

We propose a framework of look-ahead dispatch which considers forecast uncertainty and infeasibility management. Two major advantages of the framework are demonstrated: 1) economic performance improvement under the presence of forecast uncertainty; and, 2)feasibility enhancement of the dispatch problem. In the look-ahead dispatch framework, a weighted predictive scheduling (WPS) technique is proposed to relieve the negative impacts on dispatch due to wind forecast uncertainty in future steps. Look-ahead security management (LSM) is introduced, which is a technique to identify and quantify potential infeasibility of the dispatch in advance. The LSM technique is shown to be capable of working out an optimal recovery plan for future violation of system security. Finally, the proposed techniques are illustrated in a modified 24 Bus Reliability Test System.

Horizontal decomposition-based stochastic day-ahead reliability unit commitment

This paper presents a progressive hedging (PH) based decomposition algorithm to improve the computational efficiency of stochastic day-ahead reliability unit commitment (RUC). Stochastic programming is introduced into RUC to facilitate a better decision making for market operation efficiency and energy supply reliability. However, the computational burden of stochastic RUC using todays computing power is still significant. We propose to apply progressive hedging (PH) algorithm to decompose the stochastic RUC problem. It is shown that a PH-based algorithm allows parallel computation of stochastic RUC, which can be much faster than conventional stochastic programming methods. A practical power system is used to verify the effectiveness of the proposed computation framework.

Reliability Assessment at Day-Ahead Operating Stage in Power Systems with Wind Generation

This paper proposes to evaluate reliability performances at operating stage for power systems with variable wind generation. This is a first step towards recognizing the stronger coupling of power system decision making across different timescales. In the day-ahead energy-reserve co-optimization unit commitment model, the validity of using operating reserve as an approximation of long-term reliability requirement is tested with realistic wind generation profile. Reliability indices such as Loss of Load Probability (LOLP) and Loss of Expected Energy (LOEE) are computed at each hour of the day at the conclusion of the day-ahead unit commitment decision. Numerical experiments conducted in the IEEE Reliability Test System suggest that while operating reserve requirement does not change within a day, the actual reliability performance of the system varies significantly depending on (i) system loading conditions , (ii) wind power variation, and (iii) operating rules. Also, the operating reserve requirement is shown to either over-approximate or under-approximate the reliability requirement of power system. It indicates that operating reserve could be a very coarse deterministic approximation of system reliability requirement especially with high wind penetration. A much more integrated approach for modeling, analysis, and decision making is envisaged for provision of reliable and cost-effective electricity services.

Early detection and optimal corrective measures of power system insecurity in enhanced look-ahead dispatch

This paper presents a novel algorithm for the early detection and optimal corrective measures of power system insecurity in an enhanced look-ahead dispatch framework. By introducing short-term dispatchable capacity (STDC) into the proposed look-ahead security management (LSM) scheme, the algorithm is capable of predicting and identifying future infeasibilities that pose security risks to the system under both normal conditions and assumed contingency conditions. An optimal recovery plan can be computed to prevent system insecurity at a minimal cost. Early awareness of such information is of vital importance to the system operators for taking timely actions with more flexible and cost-effective measures. This, in addition to the economic benefits studied in the literature, demonstrate the advantage of security improvement of the look-ahead dispatch framework. The performance of the proposed algorithms is illustrated in a revised 24 bus IEEE Reliability Test System as well as in a practical 5889 bus system.