scholarly journals Optimal Allocation of Spinning Reserves in Interconnected Energy Systems with Demand Response Using a Bivariate Wind Prediction Model

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3816 ◽  
Author(s):  
Bapin ◽  
Bagheri ◽  
Zarikas
Keyword(s):  
Prediction Model ◽  
Demand Response ◽  
Optimal Allocation ◽  
Probabilistic Method ◽  
Energy Systems ◽  
Test System ◽  
Forecast Errors ◽  
Ancillary Service ◽  
Spinning Reserve ◽  
Spinning Reserves

The proposed study presents a novel probabilistic method for optimal allocation of spinning reserves taking into consideration load, wind and solar forecast errors, inter-zonal spinning reserve trading, and demand response provided by consumers as a single framework. The model considers the system contingencies due to random generator outages as well as the uncertainties caused by load and renewable energy forecast errors. The study utilizes a novel approach to model wind speed and its direction using the bivariate parametric model. The proposed model is applied to the IEEE two-area reliability test system (RTS) to analyze the influence of inter-zonal power generation and demand response (DR) on the adequacy and economic efficiency of energy systems. In addition, the study analyzed the effect of the bivariate wind prediction model on obtained results. The results demonstrate that the presence of inter-zonal capacity in ancillary service markets reduce the total expected energy not supplied (EENS) by 81.66%, while inclusion of a DR program results in an additional 1.76% reduction of EENS. Finally, the proposed bivariate wind prediction model showed a 0.27% reduction in spinning reserve requirements, compared to the univariate model.

Impact of Transmission Line Failures on the Cost/Risk-Based Spinning Reserve Allocation

2004 ◽  
Vol 1 (1) ◽  
Author(s):  
Lalit Goel ◽  
Z Song ◽  
P Wang
Keyword(s):  
Power Systems ◽  
Transmission Line ◽  
Service Providers ◽  
Test System ◽  
Ancillary Service ◽  
Spinning Reserve ◽  
The Cost ◽  
The Impact ◽  
Deregulated Power Systems ◽  
Cost Risk

In deregulated power systems spinning reserve (SR) can be allocated to different ancillary service providers at different locations based on supplier bids and customer choices on reliability. This paper investigates the impact of transmission line failures using a cost/risk-based spinning reserve allocation method (CRSRAM). The proposed method provides minimum spinning reserve cost, while securing the reliability risk at a minimum in the presence of not only generating unit outages but also transmission line failures. The implementation of this technique in a SR market to determine the SR schedules is presented in the paper. The IEEE Reliability Test System (RTS) has been used to illustrate the applications of the proposed method.

Ancillary Services Potential for Flexible Combined Cycles

2021 ◽  
Author(s):  
Alberto Vannoni ◽  
Jose Angel Garcia ◽  
Weimar Mantilla ◽  
Rafael Guedez ◽  
Alessandro Sorce
Keyword(s):  
Electricity Market ◽  
Gas Turbines ◽  
High Efficiency ◽  
Combined Cycle ◽  
Ancillary Services ◽  
Ancillary Service ◽  
Spinning Reserve ◽  
Service Market ◽  
Partial Load ◽  
Ancillary Services Market

Abstract Combined Cycle Gas Turbines, CCGTs, are often considered as the bridging technology to a decarbonized energy system thanks to their high exploitation rate of the fuel energetic potential. At present time in most European countries, however, revenues from the electricity market on their own are insufficient to operate existing CCGTs profitably, also discouraging new investments and compromising the future of the technology. In addition to their high efficiency, CCGTs offer ancillary services in support of the operation of the grid such as spinning reserve and frequency control, thus any potential risk of plant decommissioning or reduced investments could translate into a risk for the well-functioning of the network. To ensure the reliability of the electricity system in a transition towards a higher share of renewables, the economic sustainability of CCGTs must be preserved, for which it becomes relevant to monetize properly the ancillary services provided. In this paper, an accurate statistical analysis was performed on the day-ahead, intra-day, ancillary service, and balancing markets for the whole Italian power-oriented CCGT fleet. The profitability of 45 real production units, spread among 6 market zones, was assessed on an hourly basis considering local temperature, specific plant layouts, and off-design performance. The assessment revealed that net income from the ancillary service market doubled, on average, the one from the day-ahead energy market. It was observed that to be competitive in the ancillary services market CCGTs are required to be more flexible in terms of ramp rates, minimum environmental loads, and partial load efficiencies. This paper explores how integrating a Heat Pump and a Thermal Energy Storage within a CCGT could allow improving its competitiveness in the ancillary services market, and thus its profitability, by means of implementing a model of optimal dispatch operating on the ancillary services market.

Selling Power System Flexibility: Ancillary Service and Real-Time Energy Market Challenges for Storage

2014 ◽  
Author(s):  
Brendan Kirby
Keyword(s):  
Power System ◽  
Demand Response ◽  
New Technologies ◽  
Service Providers ◽  
Market Design ◽  
Ancillary Service ◽  
Market Prices ◽  
Regulatory Changes ◽  
System Access ◽  
Power System Flexibility

Power system operators obtain the flexibility required to reliably balance aggregate generation and load through ancillary service and five-minute energy markets. Market prices are based on the marginal opportunity costs of the generators. This market design works well for generators but inherently fails for storage and demand response, denying these new technologies a fair opportunity to compete and denying the power system access to potentially lower cost reliability resources. Market design or regulatory changes may be required for storage and demand response to be viable ancillary service providers.

scholarly journals Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

2019 ◽  
Vol 9 (20) ◽  
pp. 4417 ◽  
Author(s):  
Sana Mujeeb ◽  
Turki Ali Alghamdi ◽  
Sameeh Ullah ◽  
Aisha Fatima ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Fossil Fuel ◽  
Energy Systems ◽  
Accurate Estimation ◽  
Wind Power Forecasting ◽  
Power Forecasting

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

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