Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators

Xydas, Erotokritos; Qadrdan, Meysam; Marmaras, Charalampos; Cipcigan, Liana; Jenkins, Nick; Ameli, Hossein

doi:10.1016/j.apenergy.2016.10.019

Cited by 51 publications

(20 citation statements)

References 25 publications

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“…The economic benefit of accurate solar forecasting in minimizing the generation cost, as well as managing power curtailment was investigated and illustrated by Martinez-Anido et al [32]. A detailed approach has been adopted for wind power forecast application in [35] and in [36] considering several power market scenarios.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management

et al. 2019

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The need for energy and environmental sustainability has spurred investments in renewable energy technologies worldwide. However, the flexibility needs of the power system have increased due to the intermittent nature of the energy sources. This paper investigates the prospects of interlinking short-term flexibility value into long-term capacity planning towards achieving a microgrid with a high renewable energy fraction. Demand Response Programs (DRP) based on critical peak and time-ahead dynamic pricing are compared for effective demand-side flexibility management. The system components include PV, wind, and energy storages (ESS), and several optimal component-sizing scenarios are evaluated and compared using two different ESSs without and with the inclusion of DRP. To achieve this, a multi-objective problem which involves the simultaneous minimization of the loss of power supply probability (LPSP) index and total life-cycle costs is solved under each scenario to investigate the most cost-effective microgrid planning approach. The time-ahead resource forecast for DRP was implemented using the scikit-learn package in Python, and the optimization problems are solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in MATLAB R . From the results, the inclusion of forecast-based DRP and PHES resulted in significant investment cost savings due to reduced system component sizing.Keywords: demand response program (DRP); photovoltaic system (PV); pumped heat energy storage (PHES); critical peak pricing (CPP) DRP; time-ahead dynamic pricing (TADP) DRP; loss of power supply probability (LPSP); energy storage system (ESS); Multi-Objective Particle Swarm Optimization (MOPSO)

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management

et al. 2019

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“…After the Climate Change Conference in Paris in 2015, in many countries, installing renewable energy sources (RES) to reduce their greenhouse gas emissions is projected . The integration of RES makes the operation of the power systems especially microgrids more challenging.…”

Section: Introductionmentioning

confidence: 99%

“…After the Climate Change Conference in Paris in 2015, in many countries, installing renewable energy sources (RES) to reduce their greenhouse gas emissions is projected. 1 The integration of RES makes the operation of the power systems especially microgrids more challenging. Coordinating the optimal active and reactive power of the distributed generation (DG) units, energy storage systems (ESSs), and loads to meet technical and economic objectives is the main tasks in operation of microgrids.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy-logic-based control methodology for secure operation of a microgrid in interconnected and isolated modes

Ameli

Abbasi

Ameli

et al. 2017

Int Trans Electr Energ Syst

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Summary Due to the global concerns regarding the climate change, integration of renewable energy sources is considered as a mitigation approach in electric power generation. This requires advanced frequency and voltage control methodologies to overcome the challenges especially in microgrids. This paper presents a 2‐step frequency and voltage control methodology for microgrids with high penetration of variable renewable energy sources. An optimized Proportional‐Integral controller is designed for a Superconductor Magnetic Energy Storage System to minimize the transient frequency deviations. In cases that the Superconductor Magnetic Energy Storage System cannot stabilize the microgrid frequency in the isolated mode, the microgrid controller activates the next level of the frequency control. In the second level, an intelligent fuzzy‐logic frequency controller is designed to adjust controllable loads, controllable generation units as well as perform load shedding. In the interconnected mode, the microgrid controller is able to activate the second level to contribute to the system frequency control. Finally, an intelligent fuzzy‐logic voltage controller, realized through distribution static synchronous compensator, is devised to control the voltage magnitude of the main feeders of the microgrid. In this work, a real‐time operation algorithm for frequency as well as voltage control is proposed and has been tested by set of simulations on a low voltage benchmark network.

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“…Neither of them proves that the generated scenarios follow a predefined joint distribution. Using historical time series data of wind power and the kernel density estimator, Xydas et al propose a generation method for forecast scenarios [21]. Alternatively, Morales et al adopt the autoregressive model to generate time series data for wind power scenarios [22].…”

Section: Introductionmentioning

confidence: 99%

A conditional model of wind power forecast errors and its application in scenario generation

2018

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A B S T R A C TIn power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.

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Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators

Cited by 51 publications

References 25 publications

Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management

Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management

A fuzzy-logic-based control methodology for secure operation of a microgrid in interconnected and isolated modes

A conditional model of wind power forecast errors and its application in scenario generation

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