Load-following reserves procurement considering flexible demand-side resources under high wind power penetration

Summary form only given. This paper proposes a methodology to determine the required level of spinning and nonspinning reserves in a power system with a high penetration of wind power. The computation of the required reserve levels and their costs is achieved through a stochastic programming market-clearing model spanning a daily time horizon. This model considers the network constraints and takes into account the cost of both the load shedding and the wind spillage. The methodology proposed is illustrated using an example and a realistic case study. Some conclusions are finally drawn.

Penghu Island is a small isolated power system with 70MW peak load and 30MW off-peak load. According to the operation records of the installed wind farms, Penghu has bettter wind conditions than any other site in Taiwan. The total capacity of wind farms in Penghu reaches up to 10.2MW, which means 35% penetration on wind power is possible during minimum load. The high level of wind power generation in the Penghu power system makes the decision of diesel unit commitment and control of the power system more difficult due to the high variability of wind power output in a time frame ranging from seconds to hours. This work investigates the significant factors that affect the system transient stability as high penetration of wind power is integrated. Additionally, the possible impact and coping strategies for the high wind power penetration are studied, and several specific suggestions are proposed to Taiwan Power Company. These strategies include revising the load shedding methods, unit commitment scheduling, wind turbine operation, the suitable energy storage system planning, and wind turbine protection.

In the last two decades, the wind power generation has been rapidly and widely developed in many regions and countries for tackling the problems of environmental pollution and sustainability of energy supply. However, the high share of intermittent and fluctuating wind power production has also increased the burden of system operator for securing power system reliability during the operational phase. Moreover, the power system restructuring and deregulation have not only introduced the competition for reducing cost but also changed the strategy of reliability evaluation and management of power systems. The conventional long-term reliability evaluation techniques have been well developed, which have been more focused on planning and expansion rather than operation of power systems. This paper proposes a new technique for evaluating operational reliabilities of restructured power systems with high wind power penetration. The proposed technique is based on the combination of the reliability network equivalent and time-sequential simulation approaches. The operational reliability network equivalents are developed to represent reliability models of wind farms, conventional generation and reserve provides, fast reserve providers and transmission network in restructured power systems. A contingency management schema for real time operation considering its coupling with the day-ahead market is proposed. The time-sequential Monte Carlo simulation is used to model the chronological characteristics of corresponding reliability network equivalents. A simplified method is also developed in the simulation procedures for improving the computational efficiency. The proposed technique can be used to evaluate customers’ reliabilities considering high penetration of wind power during the power system operation in the deregulated environment.

Geographic aggregation and wind power output variance in Denmark

With blossoming of wind power generation in the grid, many system operators recognize the requirement for additional flexibility to accommodate the variability and uncertainty from wind generation. Because of the high penetration of wind power, power systems are facing great operational challenges in maintaining generation balance. This study reviews the system flexibility requirement in a high wind power penetration system, and summarizes the indices to identify capabilities of new generators to satisfy the flexibility requirements. According to the review results, it presents that a system with more flexible units would accommodate much higher penetration of renewable energy and reduce the operational cost.

Power prediction of a wind farm cluster based on spatiotemporal correlations

Minimum energy storage (ES) and spinning reserve (SR) for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.

Under the “double carbon” goal, high wind power penetration will become an important feature of new power systems dominated by new energy in the future. Since wind power often has anti-peak shaving characteristics, the peak shaving problem of wind power high-penetration power systems is particularly prominent. Quantitative calculation of the system peak shaving cost of a high proportion of wind power grids is of great significance to the establishment and improvement of the peak shaving market in the northern part of our country, the promotion of rational system development and planning flexibility, and the improvement of system flexibility. Based on the analysis of existing wind power system peak shaving cost estimation methods, this paper proposes a comprehensive cost estimation method for system peak shaving under high wind power penetration rate, considering flexible resource investment and peak shaving market, and studies different wind power systems. The changing law of system peak shaving cost under the penetration rate; and the actual power system in a certain area of my country is used to simulate the peak shaving cost under different wind power penetration, which shows the effectiveness and rationality of the method.

Frequency response is a key concern for power system security. Usually, it is estimated in a deterministic approach. But in most cases, power systems behave stochastically. Moreover, presence of wind power imposes further variability on generation profile of a power system. Hence, it appears more pragmatic to address these uncertainties during an evaluation of frequency response. This paper develops an index to assess frequency response of a power system with high wind power penetration. A probabilistic method is used for this purpose. K-means algorithm, a well-known data mining technique is employed. The developed index is called a ‘probabilistic frequency response index’. A realistic interconnected power system is investigated for a case study. Impact of increased wind power penetration on probabilistic frequency response index is analysed.

As wind power costs have declined, capacity has grown quickly, often times in adjacent areas. Price and volatility risk from wind power’s intermittency can be mitigated through geographic diversification and transmission. But wind power generation has a fat-tailed and right-skewed distribution. In this article we investigate how geographic diversification of wind power and the effect of wind power on market prices varies across the distribution of production. In a case study from Denmark and Sweden, we show that during tail-end production periods, correlations between areas increase substantially as does congestion in the transmission network. This leads to highly non-linear price effects. The marginal effect of wind power on the local prices is shown to be substantially higher at the 10th decile of wind power production. This has implications for valuation models of wind power projects and for operations of electricity markets with high penetrations of wind power.

Decentralized computation method for robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power

This paper presents a comparison of static and dynamic continuation power flow techniques, time domain simulations and quasi-static time domain simulations for voltage stability analysis of networks with high wind power penetration. Three wind turbine models are considered, namely, constant-speed induction generator; doubly-fed induction generator; and direct- drive synchronous generator. Several simulations are solved in order to assess the behavior of wind turbine models and the reliability of voltage stability techniques. The case study is based on a 40-bus network that models an existing distribution system with one high voltage feeder.

Studies have demonstrated that Demand Response (DR) can significantly influence the effectiveness and reliability of future intelligent distribution networks. Despite the increasing deployment of wind energy, the issue of curtailment continues to pose a challenge for utilities that have a high penetration of wind power. This study aims to propose an effective approach for utilizing DR to improve the operating status of distribution networks while simultaneously reducing curtailed wind energy. Thus, considering the curtailed wind energy, this paper proposes a wind curtailment reduction DRP (WCR-DRP) to modify the demand pattern in response to critical times when wind curtailment may occur due to generator ramp limits or minimum output power. In this study, to implement power system operation, an optimal power flow (OPF) problem is utilized while considering the availability of demand response (DR) programs, and the power flow and operational constraints related to the system are considered . Formulating the optimization problem with two objective functions, including minimizing overall costs and reducing wind power curtailment, is achieved. The ?-constraint method resolves this optimization problem. Finally, the suggested model is employed over the enhanced IEEE 33-bus test system to evaluate its efficacy. Four scenarios are conducted, examining various operational parameters to show the practicality of the suggested approach. Outcomes show that suggested model significantly enhances the operation of the electrical distribution system while establishing optimal employment of wind power.

Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

Output power fluctuation of high penetration of wind power causes demand and supply imbalance in electric power systems and results in frequency deviation if the fluctuation is not fully compensated by other regulable power plants. In Japan, some electric utilities have started to accept only the wind farms which disconnect and give up generating power during light‐load periods with less adjustable reserve. Otherwise, wind farms are required to employ battery energy storage systems (BESSs) to charge the generated power during the light‐load periods. Instead of these uneconomical solutions, this paper proposes autonomous frequency regulation by controllable loads such as electric water heaters (EWHs). In the paper, the acceptable increase of wind power generation by the proposed load control has been evaluated quantitatively in the power system of the Hokkaido Island in Japan. The result indicates that the acceptable increase of wind power generation goes from 250 to 675 MW by applying the proposed autonomous frequency regulation on all EWHs, and the total cost to implement the autonomous frequency regulation on the EWHs is around 1/26 compared with a solution using BESSs. Copyright © 2009 John Wiley & Sons, Ltd.