The Experts below are selected from a list of 167880 Experts worldwide ranked by ideXlab platform
Baosen Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Efficient scenario generation of multiple Renewable Power plants considering spatial and temporal correlations
Applied Energy, 2018Co-Authors: Chenghui Tang, Yishen Wang, Yuanzhang Sun, Baosen ZhangAbstract:Abstract Consideration of the spatial and temporal correlations of multiple Renewable Power plants is critical to the efficient operation of Power systems with high amounts of Renewable Power integration. However, existing methods either assumes that each plant behaves independently or require high computational complexity to capture the joint behavior of the plants. We propose an efficient dynamic scenario generation method based on Gibbs sampling to overcome these challenges. Firstly, the generated Renewable Power scenarios are drawn from the jointly distribution that accurately captures statistical behaviors in the historical data of multiple Renewable Power plants. Secondly, the sampling complexity only grows linearly with the number of Renewable Power plants, making our approach applicable to large systems. Based on this sampling technique, we propose a distribution-based model and a scenario-based models for the economic dispatch problem and show when they should be used based on the desired accuracy and available computational resources. Through a comprehensive case study, we show that compared with existing methods, the proposed approaches are more consistent with actual Renewable Power generation observed in practice, and can lower the operation cost while maintaining appropriate risk levels.
-
Economic Dispatch Considering Spatial and Temporal Correlations of Multiple Renewable Power Plants
arXiv: Optimization and Control, 2017Co-Authors: Chenghui Tang, Yishen Wang, Yuanzhang Sun, Baosen ZhangAbstract:The correlations of multiple Renewable Power plants (RPPs) should be fully considered in the Power system with very high penetration Renewable Power integration. This paper models the uncertainties, spatial correlation of multiple RPPs based on Copula theory and actual probability historical histograms by one-dimension distributions for economic dispatch (ED) problem. An efficient dynamic Renewable Power scenario generation method based on Gibbs sampling is proposed to generate Renewable Power scenarios considering the uncertainties, spatial correlation and variability (temporal correlation) of multiple RPPs, in which the sampling space complexity do not increase with the number of RPPs. Distribution-based and scenario-based methods are proposed and compared to solve the real-time ED problem with multiple RPPs. Results show that the proposed dynamic scenario generation method is much more consist with the actual Renewable Power. The proposed ED methods show better understanding for the uncertainties, spatial and temporal correlations of Renewable Power and more economical compared with the traditional ones.
-
competition and coalition formation of Renewable Power producers
IEEE Transactions on Power Systems, 2015Co-Authors: Baosen Zhang, Ramesh Johari, Ram RajagopalAbstract:We investigate group formations and strategic behaviors of Renewable Power producers in electricity markets. These producers currently bid into the day-ahead market in a conservative fashion because of the real-time risk associated with not meeting their bid amount. It has been suggested in the literature that producers would bid less conservatively if they can form large groups to take advantages of spatial diversity to reduce the uncertainty in their aggregate output. We show that large groups of Renewable producers would act strategically to lower the aggregate output because of market Power. To maximize Renewable Power production, we characterize the trade-off between market Power and generation uncertainty as a function of the size of the groups. We show there is a sweet spot in the sense that there exists groups that are large enough to achieve the uncertainty reduction of the grand coalition, but are small enough such that they have no significant market Power. We consider both independent and correlated forecast errors under a fixed real-time penalty. We also consider a real-time market where both selling and buying of energy are allowed. We validate our claims using PJM and NREL data.
Mark Williams - One of the best experts on this subject based on the ideXlab platform.
-
Research Guides: LAW 9134 - Renewable Power Seminar: Federal Agencies
2012Co-Authors: Mark WilliamsAbstract:This guide is intended to assist students in Professor Rossi's Renewable Power Seminar in researching and writing their seminar papers.
-
Research Guides: LAW 9134 - Renewable Power Seminar: About this Guide
2012Co-Authors: Mark WilliamsAbstract:This guide is intended to assist students in Professor Rossi's Renewable Power Seminar in researching and writing their seminar papers.
-
Research Guides: LAW 9134 - Renewable Power Seminar: Public Utilities Research
2012Co-Authors: Mark WilliamsAbstract:This guide is intended to assist students in Professor Rossi's Renewable Power Seminar in researching and writing their seminar papers.
-
Research Guides: LAW 9134 - Renewable Power Seminar: Analyst/Trade Literature
2012Co-Authors: Mark WilliamsAbstract:This guide is intended to assist students in Professor Rossi's Renewable Power Seminar in researching and writing their seminar papers.
-
Research Guides: LAW 9134 - Renewable Power Seminar: Non-Vanderbilt Resources
2012Co-Authors: Mark WilliamsAbstract:This guide is intended to assist students in Professor Rossi's Renewable Power Seminar in researching and writing their seminar papers.
Ram Rajagopal - One of the best experts on this subject based on the ideXlab platform.
-
competition and coalition formation of Renewable Power producers
IEEE Transactions on Power Systems, 2015Co-Authors: Baosen Zhang, Ramesh Johari, Ram RajagopalAbstract:We investigate group formations and strategic behaviors of Renewable Power producers in electricity markets. These producers currently bid into the day-ahead market in a conservative fashion because of the real-time risk associated with not meeting their bid amount. It has been suggested in the literature that producers would bid less conservatively if they can form large groups to take advantages of spatial diversity to reduce the uncertainty in their aggregate output. We show that large groups of Renewable producers would act strategically to lower the aggregate output because of market Power. To maximize Renewable Power production, we characterize the trade-off between market Power and generation uncertainty as a function of the size of the groups. We show there is a sweet spot in the sense that there exists groups that are large enough to achieve the uncertainty reduction of the grand coalition, but are small enough such that they have no significant market Power. We consider both independent and correlated forecast errors under a fixed real-time penalty. We also consider a real-time market where both selling and buying of energy are allowed. We validate our claims using PJM and NREL data.
-
Risk-limiting dispatch for integrating Renewable Power
International Journal of Electrical Power & Energy Systems, 2013Co-Authors: Ram Rajagopal, Eilyan Bitar, Pravin VaraiyaAbstract:Abstract Risk-limiting dispatch or RLD is formulated as the optimal solution to a multi-stage, stochastic decision problem. At each stage, the system operator (SO) purchases forward energy and reserve capacity over a block or interval of time. The blocks get shorter as operations approach real time. Each decision is based on the most recent available information, including demand, Renewable Power, weather forecasts. The accumulated energy blocks must at each time t match the net demand D(t) = L(t) − W(t). The load L and Renewable Power W are both random processes. The expected cost of a dispatch is the sum of the costs of the energy and reserve capacity and the penalty or risk from mismatch between net demand and energy supply. The paper derives computable ‘closed-form’ formulas for RLD. Numerical examples demonstrate that the minimum expected cost can be substantially reduced by recognizing that risk from current decisions can be mitigated by future decisions; by additional intra-day energy and reserve capacity markets; and by better forecasts. These reductions are quantified and can be used to explore changes in the SO’s decision structure, forecasting technology, and Renewable penetration.
Chenghui Tang - One of the best experts on this subject based on the ideXlab platform.
-
Classification, principle and pricing manner of Renewable Power purchase agreement
IOP Conference Series: Earth and Environmental Science, 2019Co-Authors: Chenghui Tang, Fan ZhangAbstract:As a promising manner of Renewable energy consuming, Renewable Power purchase agreement has get more and more attentions from multiple countries. This paper introduces the financial Renewable Power purchase agreement, physical Renewable Power purchase agreement and their corresponding operations. The electricity price of Renewable Power purchase agreement is discussed. The introduction of Renewable Power purchase agreement in this paper provides a reference for the development of Renewable energy in China.
-
Day-ahead stochastic electricity market clearing considering Renewable Power uncertainty
IOP Conference Series: Earth and Environmental Science, 2019Co-Authors: Chenghui Tang, Fan ZhangAbstract:The electricity market development in China faces a new background of increasing proportion of Renewable Power. Different from conventional Power plants, the output of Renewable Power plants usually has strong uncertainty, which is a challenge for electricity market clearing strategy. This paper proposes a day-ahead electricity market clearing method that considers the uncertainty of Renewable Power sources, which is modelled based on the truncated versatile distribution. The scheduled Power of conventional Power plants and Renewable Power plants is determined by minimizing the social cost. Case studies show that the proposed method can greatly reduce the total social cost.
-
Efficient scenario generation of multiple Renewable Power plants considering spatial and temporal correlations
Applied Energy, 2018Co-Authors: Chenghui Tang, Yishen Wang, Yuanzhang Sun, Baosen ZhangAbstract:Abstract Consideration of the spatial and temporal correlations of multiple Renewable Power plants is critical to the efficient operation of Power systems with high amounts of Renewable Power integration. However, existing methods either assumes that each plant behaves independently or require high computational complexity to capture the joint behavior of the plants. We propose an efficient dynamic scenario generation method based on Gibbs sampling to overcome these challenges. Firstly, the generated Renewable Power scenarios are drawn from the jointly distribution that accurately captures statistical behaviors in the historical data of multiple Renewable Power plants. Secondly, the sampling complexity only grows linearly with the number of Renewable Power plants, making our approach applicable to large systems. Based on this sampling technique, we propose a distribution-based model and a scenario-based models for the economic dispatch problem and show when they should be used based on the desired accuracy and available computational resources. Through a comprehensive case study, we show that compared with existing methods, the proposed approaches are more consistent with actual Renewable Power generation observed in practice, and can lower the operation cost while maintaining appropriate risk levels.
-
A Reliable Quadratic Programming Algorithm for Convex Economic Dispatch with Renewable Power Uncertainty
2018 8th International Conference on Power and Energy Systems (ICPES), 2018Co-Authors: Chenghui Tang, Cai LiangAbstract:In the stochastic economic dispatch with Renewable Power, the Renewable Power uncertainty cost is usually considered based on the overestimation and underestimation penalty cost, which is formulated as an integral of Renewable Power distribution. However, although the model could be convex, iteration algorithms such as sequential linear programming are needed to solve the economic dispatch model due to the integral item. Iteration methods bring some new problems such as step size setup and sometimes lead to a bad convergence. To address this issue, this paper proposes a reliable algorithm for convex economic dispatch with Renewable Power uncertainty. The integral form of Renewable Power uncertainty cost is converted into a linear form, which can be reliably solved based on off-the-shell commercial solvers. Numerical studies in the IEEE 118-bus system are presented to demonstrate the merits of the proposed method. The results show that, compared with iteration algorithms, the proposed model is much more efficient.
-
Combined Electricity-Gas-Heat Energy Internet Scheduling with Power-To-Gas and Renewable Power Uncertainty
2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), 2018Co-Authors: Chenghui Tang, Fan ZhangAbstract:As a promising utilization for the future development of energy, the Energy Internet is receiving more attention. In the Energy Internet, electricity, gas, heat and other energy forms are utilized and coupled together. A combined electricity-gas-heat Energy Internet scheduling method is proposed in this paper. The Renewable Power uncertainty is modeled based on recently proposed truncated versatile distribution and involved in the scheduling model, in which the Renewable Power cost is considered by underestimation and overestimation penalty. The overall economy is achieved by fully considering the Power-to-gas devices, combined heat and Power generators and gas-fired boilers. Numerical studies for the day-ahead BES scheduling in an industrial peak are presented to demonstrate the merits of the proposed method. Results show that by capturing Renewable Power uncertainty, the system cost could be greatly reduced. The energy and gas storage could both greatly contribute to reduce the cost of Renewable Power uncertainty.
Mustafa Demirel - One of the best experts on this subject based on the ideXlab platform.
-
a real options evaluation model for the diffusion prospects of new Renewable Power generation technologies
Energy Economics, 2008Co-Authors: Gurkan Kumbaroglu, Reinhard Madlener, Mustafa DemirelAbstract:This study presents a policy planning model that integrates learning curve information on Renewable Power generation technologies into a dynamic programming formulation featuring real options analysis. The model recursively evaluates a set of investment alternatives on a year-by-year basis, thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of Renewable Power generation technologies. Price uncertainty is introduced through stochastic processes for the average wholesale price of electricity and for input fuel prices. Demand for electricity is assumed to be increasingly price-sensitive, as the electricity market deregulation proceeds, reflecting new options of consumers to react to electricity price changes (such as time-of-use pricing, unbundled electricity services, and choice of supplier). The empirical analysis is based on data for the Turkish electricity supply industry. Apart from general implications for policy-making, it provides some interesting insights about the impact of uncertainty and technical change on the diffusion of various emerging Renewable energy technologies.
