scholarly journals A Real-Time Electricity Price Decision Model for Demand Side Management in Wind Power Heating Mode

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiang Li ◽  
Jian Li ◽  
Zhengyong Huang ◽  
Fulin Fan ◽  
Weijun Teng
Keyword(s):  
Real Time ◽  
Wind Power ◽  
Decision Model ◽  
Large Scale ◽  
Electric Heating ◽  
Demand Side Management ◽  
Electricity Price ◽  
Wind Generation ◽  
Demand Side ◽  
Time Price

The problem of wind power curtailment (WPC) during winter heating periods in China’s “Three-North regions” is becoming worse. Wind power heating, though being an effective way to increase wind power consumptions, is constrained by high electric heating costs under a peak-to-valley electricity price pattern. This study develops a real-time price (RTP) decision model which adjusts the time-varying RTPs within an acceptable range of heating users based on the WPC distribution over a particular dispatch day. The lower RTPs accompanying the higher WPC can guide the electric heating user side equipped with regenerative electric boilers (REBs) to actively increase REB imports to absorb additional wind generation. Then, the demand side response using REBs under the RTP scheme is optimized to minimize the total heating cost met by electric heating users while assisting in the large-scale wind generation accommodation. The total heating costs and WPC reductions under different heating scenarios are compared and discussed alongside the effectiveness of the RTP-based demand side management in terms of reducing the WPC and heating costs and increasing the feasibility of wind power heating during winter heating periods.

Priority and delay constrained demand side management in real-time price environment with renewable energy source

2016 ◽  
Vol 40 (14) ◽  
pp. 2002-2021 ◽  
Author(s):  
Muhammad Babar Rasheed ◽  
Nadeem Javaid ◽  
Ashfaq Ahmad ◽  
Muhammad Awais ◽  
Zahoor Ali Khan ◽  
...  
Keyword(s):  
Energy Source ◽  
Renewable Energy Source ◽  
Renewable Energy ◽  
Real Time ◽  
Demand Side Management ◽  
Demand Side ◽  
Time Price

Beneficial role of diurnal smoothing for grid integration of wind power

2021 ◽  
Author(s):  
Anasuya Gangopadhyay ◽  
Ashwin K Seshadri ◽  
Ralf Toumi
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Diurnal Cycle ◽  
Power Plants ◽  
Large Scale ◽  
Positive Impact ◽  
Forecast Error ◽  
Wind Generation ◽  
Grid Integration ◽  
Wind Maximum

<p>Smoothing of wind generation variability is important for grid integration of large-scale wind power plants. One approach to achieving smoothing is aggregating wind generation from plants that have uncorrelated or negatively correlated wind speed. It is well known that the wind speed correlation on average decays with increasing distance between plants, but the correlations may not be explained by distance alone. In India, the wind speed diurnal cycle plays a significant role in explaining the hourly correlation of wind speed between location pairs. This creates an opportunity of “diurnal smoothing”. At a given separation distance the hourly wind speeds correlation is reduced for those pairs that have a difference of +/- 12 hours in local time of wind maximum. This effect is more prominent for location pairs separated by 200 km or more and where the amplitude of the diurnal cycle is more than about  0.5 m/s. “Diurnal smoothing” also has a positive impact on the aggregate wind predictability and forecast error. “Diurnal smoothing” could also be important for other regions with diurnal wind speed cycles.</p>

scholarly journals Distributed Demand Side Management with Stochastic Wind Power Forecasting

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Sergio Grammatico ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Smart Grids ◽  
Demand Side Management ◽  
Expected Value ◽  
Noncooperative Game ◽  
Theory Approach ◽  
Demand Side ◽  
Wind Power Forecasting ◽  
Power Forecasting

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

scholarly journals A secondary option market design for reserve procurement by renewable energy sources.

2021 ◽  
Author(s):  
Reza Ghaffari
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Real Time ◽  
Wind Power ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Wind Power Generation ◽  
Reliable Operation ◽  
Actual Load ◽  
Balancing Services

Wind power generation is uncertain and intermittent accentuating variability. Currently in many power systems worldwide, the total generation-load unbalance caused by mismatch between forecast and actual wind power output is handled by automatic governor control and real-time 5-minute balancing markets, which are operated by the independent system operators for maintaining reliable operation of power systems. Mechanisms such as automatic governor control and real-time 5-minute balancing markets are in place to correct the mismatch between the load forecast and the actual load. They are not designed to address increased uncertainty and variability introduced by large-scale wind power or solar power generation expected in the future. Thus, large-scale wind power generation with increased uncertainty and intermittency causing variability poses a techno-economic challenge of sourcing least cost load balancing services (reserve).

scholarly journals Is real-time electricity pricing suitable for residential users without demand-side management?

Energy ◽  
2016 ◽  
Vol 109 ◽  
pp. 310-325 ◽  
Author(s):  
Javier Campillo ◽  
Erik Dahlquist ◽  
Fredrik Wallin ◽  
Iana Vassileva
Keyword(s):  
Real Time ◽  
Demand Side Management ◽  
Demand Side ◽  
Electricity Pricing

A Novel Approach of Battery Energy Storage for Improving Value of Wind Power in Deregulated Markets

2013 ◽  
Vol 14 (3) ◽  
pp. 255-264 ◽  
Author(s):  
Y Minh Nguyen ◽  
Yong Tae Yoon
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Wind Power ◽  
Frequency Control ◽  
Minimum Cost ◽  
Wind Generation ◽  
Frequency Regulation ◽  
Battery Energy Storage ◽  
Novel Approach ◽  
Battery Energy

Abstract Wind power producers face many regulation costs in deregulated environment, which remarkably lowers the value of wind power in comparison with the conventional sources. One of these costs is associated with the real-time variation of power output and being paid in frequency control market according to the variation band. In this regard, this paper presents a new approach to the scheduling and operation of battery energy storage installed in wind generation system. This approach depends on the statistic data of wind generation and the prediction of frequency control market prices to determine the optimal charging and discharging of batteries in real-time, which ultimately gives the minimum cost of frequency regulation for wind power producers. The optimization problem is formulated as the trade-off between the decrease in regulation payment and the increase in the cost of using battery energy storage. The approach is illustrated in the case study and the results of simulation show its effectiveness.

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