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Sayyad Nojavan - One of the best experts on this subject based on the ideXlab platform.
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optimal scheduling of electric vehicles aggregator under Market Price uncertainty using robust optimization technique
International Journal of Electrical Power & Energy Systems, 2020Co-Authors: Yan Cao, Liang Huang, Kittisak Jermsittiparsert, Hamed Ahmadinezamabad, Sayyad NojavanAbstract:Abstract Today, the uncertainty of upstream grid Price is one of the most important challenging topics for the electric vehicle (EV) aggregators. So, a robust optimization technique is applied in this work to investigate robust scheduling of EV aggregators considering Price uncertainty. The proposed EV aggregator participates in Market Price with the aim of maximizing the profit. In order to model the Market Price uncertainty with the mentioned technique, the upper and lower amounts of upstream grid Prices are used instead of the estimated Prices. The output of the proposed algorithm is used to build the various charging and discharging strategies which can be used by the operator to robust scheduling of EV aggregator under upstream grid Price uncertainty. With considering the obtained results, it can be shown that the total profit of EV aggregator in optimistic strategy is raised 69.78% in comparison with the deterministic strategy while it is decreased 54.94% in pessimistic cases. It should be noted that the applied technique is formulated as a MIP model which is implemented in GAMS and global optimal result is guaranteed.
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risk based scheduling of smart apartment building under Market Price uncertainty using robust optimization approach
Sustainable Cities and Society, 2019Co-Authors: Afshin Najafighalelou, Kazem Zare, Sayyad NojavanAbstract:Abstract Nowadays Market Price uncertainty is one of the important challenging issues in the optimal scheduling of smart apartment building (SAB). So, in this paper, a robust optimization approach (ROA) is proposed for robust scheduling of SAB in the presence of Price uncertainty. For modeling the Market Price uncertainty, upper and lower limits of the Market Price are considered instead of the forecasted Market Prices. The proposed sample system includes a SAB that contains ten smart homes (SHs) with different living habits and equipped with different equipment, i.e. combined heat and power (CHP), boiler, battery storage system (BSS), thermal storage system (TSS) and smart appliances. To assess the effectiveness of a home energy management system (HEMS) on the performance of the proposed problem, two different controlling scenarios are studied, namely normal and smart scenarios. It should be mentioned that the proposed model is formulated as mixed-integer linear programming (MILP) which guarantees global optimal solution and carried out with general algebraic modeling system (GAMS) software.
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heating and power hub models for robust performance of smart building using information gap decision theory
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: Afshin Najafighalelou, Sayyad Nojavan, Kazem ZareAbstract:Abstract One of the big challenging issues for the operators of smart home is optimal scheduling of these homes within various uncertainties that can lead to increase or decrease of the operation cost of smart home. In this paper, information gap decision theory (IGDT) is proposed for robust scheduling of apartment smart building in the presence of Price uncertainty. IGDT approach doesn’t depend on the size of model. So, the operators of apartment smart building which are known as small scale loads can use IGDT to make more informed decisions against the Price uncertainty. IGDT method contains two functions i.e. robustness function and opportunity functions. Robustness function is used to model the negative impacts of Market Price uncertainty while the opportunity function is used to model positive effects of Market Price uncertainty. By comparing the obtained results from robustness function of IGDT, it can be found that by taking risk-averse strategy and analyzing one of the obtained strategies, operation cost of apartment smart building is increased 26.18% while robustness of apartment smart building against increase of Market Price is increased up to 51.87% which means that the apartment smart building has become robust against increase of Market Price. On the other hand, according to the obtained results from opportunity function of IGDT, by taking risk-seeking strategy and analyzing one of the obtained strategies, due to 56.92% reduction of Market Price, the operation cost of smart home is reduced 3 £ which is 26.18% of total operation cost of apartment smart building. In fact, these strategies obtained from robustness and opportunity functions help home energy management system to take appropriate decisions to handle various possible outcomes of uncertainty. The proposed IGDT-based sample model is solved using General Algebraic Modeling System (GAMS).
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robust optimization based Price taker retailer bidding strategy under pool Market Price uncertainty
International Journal of Electrical Power & Energy Systems, 2015Co-Authors: Sayyad Nojavan, Behnam Mohammadiivatloo, Kazem ZareAbstract:Abstract In the restructured electricity Markets, retailers purchase the required demand of its consumers from different energy resources such as self-generating facilities, bilateral contracts and pool Market. In this process, the pool Market Price uncertainty modeling is important for obtaining the maximum profit. Therefore, in this paper, a robust optimization approach is proposed to obtain the optimal bidding strategy of retailer, which should be submitted to pool Market. By the proposed method, a collection of robust mixed-integer linear programming problem (RMILP) is solved to build optimal bidding strategy for retailer. For pool Market Price uncertainty modeling, upper and lower limits of pool Prices are considered instead of the forecasted Prices. The range of pool Prices are sequentially partitioned into a successive of nested subintervals, which permit formulating a collection of RMILP problems. The results of these problems give sufficient data to obtain optimal bidding strategy for submit to pool Market by retailer. A detailed analysis is utilized to delineate the proposed method.
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risk based optimal bidding strategy of generation company in day ahead electricity Market using information gap decision theory
International Journal of Electrical Power & Energy Systems, 2013Co-Authors: Sayyad Nojavan, Kazem ZareAbstract:This paper considers a Price-taker generation company to participate in day-ahead electricity energy Market. While making optimal bidding strategy for producer, factors such as the characteristics of generator and the Market Price uncertainty need to be considered because of having direct impact on the expected profit and bidding curve. The Market Price considered an uncertain variable and it is assumed that the generation company forecasted the Market Prices. In this study, the uncertainty model of Market Price is considered based on the concept of weighted average squared error using a variance–covariance matrix. Information gap decision theory is used to develop the bidding strategy of a generation company. It assesses the robustness/opportunity of optimal bidding strategy in the face of the Market Price uncertainty while producer considers whether a decision risk-averse or risk-taking. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity Market. A case study is used to illustrate the proposed approach.
Kazem Zare - One of the best experts on this subject based on the ideXlab platform.
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risk based scheduling of smart apartment building under Market Price uncertainty using robust optimization approach
Sustainable Cities and Society, 2019Co-Authors: Afshin Najafighalelou, Kazem Zare, Sayyad NojavanAbstract:Abstract Nowadays Market Price uncertainty is one of the important challenging issues in the optimal scheduling of smart apartment building (SAB). So, in this paper, a robust optimization approach (ROA) is proposed for robust scheduling of SAB in the presence of Price uncertainty. For modeling the Market Price uncertainty, upper and lower limits of the Market Price are considered instead of the forecasted Market Prices. The proposed sample system includes a SAB that contains ten smart homes (SHs) with different living habits and equipped with different equipment, i.e. combined heat and power (CHP), boiler, battery storage system (BSS), thermal storage system (TSS) and smart appliances. To assess the effectiveness of a home energy management system (HEMS) on the performance of the proposed problem, two different controlling scenarios are studied, namely normal and smart scenarios. It should be mentioned that the proposed model is formulated as mixed-integer linear programming (MILP) which guarantees global optimal solution and carried out with general algebraic modeling system (GAMS) software.
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heating and power hub models for robust performance of smart building using information gap decision theory
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: Afshin Najafighalelou, Sayyad Nojavan, Kazem ZareAbstract:Abstract One of the big challenging issues for the operators of smart home is optimal scheduling of these homes within various uncertainties that can lead to increase or decrease of the operation cost of smart home. In this paper, information gap decision theory (IGDT) is proposed for robust scheduling of apartment smart building in the presence of Price uncertainty. IGDT approach doesn’t depend on the size of model. So, the operators of apartment smart building which are known as small scale loads can use IGDT to make more informed decisions against the Price uncertainty. IGDT method contains two functions i.e. robustness function and opportunity functions. Robustness function is used to model the negative impacts of Market Price uncertainty while the opportunity function is used to model positive effects of Market Price uncertainty. By comparing the obtained results from robustness function of IGDT, it can be found that by taking risk-averse strategy and analyzing one of the obtained strategies, operation cost of apartment smart building is increased 26.18% while robustness of apartment smart building against increase of Market Price is increased up to 51.87% which means that the apartment smart building has become robust against increase of Market Price. On the other hand, according to the obtained results from opportunity function of IGDT, by taking risk-seeking strategy and analyzing one of the obtained strategies, due to 56.92% reduction of Market Price, the operation cost of smart home is reduced 3 £ which is 26.18% of total operation cost of apartment smart building. In fact, these strategies obtained from robustness and opportunity functions help home energy management system to take appropriate decisions to handle various possible outcomes of uncertainty. The proposed IGDT-based sample model is solved using General Algebraic Modeling System (GAMS).
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robust optimization based Price taker retailer bidding strategy under pool Market Price uncertainty
International Journal of Electrical Power & Energy Systems, 2015Co-Authors: Sayyad Nojavan, Behnam Mohammadiivatloo, Kazem ZareAbstract:Abstract In the restructured electricity Markets, retailers purchase the required demand of its consumers from different energy resources such as self-generating facilities, bilateral contracts and pool Market. In this process, the pool Market Price uncertainty modeling is important for obtaining the maximum profit. Therefore, in this paper, a robust optimization approach is proposed to obtain the optimal bidding strategy of retailer, which should be submitted to pool Market. By the proposed method, a collection of robust mixed-integer linear programming problem (RMILP) is solved to build optimal bidding strategy for retailer. For pool Market Price uncertainty modeling, upper and lower limits of pool Prices are considered instead of the forecasted Prices. The range of pool Prices are sequentially partitioned into a successive of nested subintervals, which permit formulating a collection of RMILP problems. The results of these problems give sufficient data to obtain optimal bidding strategy for submit to pool Market by retailer. A detailed analysis is utilized to delineate the proposed method.
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risk based optimal bidding strategy of generation company in day ahead electricity Market using information gap decision theory
International Journal of Electrical Power & Energy Systems, 2013Co-Authors: Sayyad Nojavan, Kazem ZareAbstract:This paper considers a Price-taker generation company to participate in day-ahead electricity energy Market. While making optimal bidding strategy for producer, factors such as the characteristics of generator and the Market Price uncertainty need to be considered because of having direct impact on the expected profit and bidding curve. The Market Price considered an uncertain variable and it is assumed that the generation company forecasted the Market Prices. In this study, the uncertainty model of Market Price is considered based on the concept of weighted average squared error using a variance–covariance matrix. Information gap decision theory is used to develop the bidding strategy of a generation company. It assesses the robustness/opportunity of optimal bidding strategy in the face of the Market Price uncertainty while producer considers whether a decision risk-averse or risk-taking. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity Market. A case study is used to illustrate the proposed approach.
Ehud I Ronn - One of the best experts on this subject based on the ideXlab platform.
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computing the Market Price of volatility risk in the energy commodity Markets
Journal of Banking and Finance, 2008Co-Authors: James S Doran, Ehud I RonnAbstract:In this paper, we demonstrate the need for a negative Market Price of volatility risk to recover the difference between Black-Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637-654]/Black [Black, F., 1976. Studies of stock Price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177-181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative Market Price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy Markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10Â year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative Market Price of volatility risk highlights the importance of volatility risk in understanding Priced volatility in these financial Markets.
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estimating the commodity Market Price of risk for energy Prices
Energy Economics, 2008Co-Authors: Sergey P Kolos, Ehud I RonnAbstract:The purpose of this paper is to estimate the “Market Price of risk” (MPR) for energy commodities, the ratio of expected return to standard deviation. The MPR sign determines whether energy forward Prices are upward- or downward-biased predictors of expected spot Prices. We estimate MPRs using spot and futures Prices, while accounting for the Samuelson effect. We find long-term MPRs generally positive and short-term negative, consistent with positive energy betas and hedging, respectively. In spot electricity Markets, MPRs in Day-Ahead Prices agree with short-dated futures. Our results relate risk premia to informed hedging decisions, and futures Prices to forecast/expected Prices.
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computing the Market Price of volatility risk in the energy commodity Markets
Social Science Research Network, 2004Co-Authors: James S Doran, Ehud I RonnAbstract:In this paper we demonstrate the need for a negative Market Price of volatility risk to recover the difference between Black-Scholes (1973)/Black (1976) implied volatility and realized term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative Market Price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy Markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a ten year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative Market Price of volatility risk highlights the importance of volatility risk in understanding Priced volatility in these financial Markets.
N Kumarappan - One of the best experts on this subject based on the ideXlab platform.
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day ahead deregulated electricity Market Price forecasting using recurrent neural network
IEEE Systems Journal, 2013Co-Authors: S Anbazhagan, N KumarappanAbstract:This paper proposes a recurrent neural network model for the day ahead deregulated electricity Market Price forecasting that could be realized using the Elman network. In a deregulated Market, electricity Price is influenced by many factors and exhibits a very complicated and irregular fluctuation. Both power producers and consumers need a single compact and robust Price forecasting tool for maximizing their profits and utilities. In order to validate the chaotic characteristic of electricity Price, an Elman network is modeled. The proposed Elman network is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been observed that a nearly state of the art Elman network forecasting accuracy can be achieved with less computation time. The proposed Elman network approach is compared with autoregressive integrated moving average (ARIMA), mixed model, neural network, wavelet ARIMA, weighted nearest neighbors, fuzzy neural network, hybrid intelligent system, adaptive wavelet neural network, neural networks with wavelet transform, wavelet transform and a hybrid of neural networks and fuzzy logic, wavelet-ARIMA radial basis function neural networks, cascaded neuro-evolutionary algorithm, and wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system approaches to forecast the electricity Market of mainland Spain. Finally, the accuracy of the Price forecasting is also applied to the electricity Market of New York in 2010, which shows the effectiveness of the proposed approach.
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day ahead deregulated electricity Market Price classification using neural network input featured by dct
International Journal of Electrical Power & Energy Systems, 2012Co-Authors: S Anbazhagan, N KumarappanAbstract:Abstract The optimal profit is determined by applying a perfect Price forecast. A Price forecast with a less prediction errors, yields maximum profits for Market players. The numerical electricity Price forecasting is high in forecasting errors of various approaches. In this paper, discrete cosine transforms (DCTs) based neural network (NN) approach (DCT–NN) is used to classify the electricity Markets of mainland Spain and New York are presented. These electricity Price classifications are important because all Market participants do not to know the exact value of future Prices in their decision-making process. In this paper, classifications of electricity Market Prices with respect to pre-specified electricity Price threshold are used. In this proposed approach, all time series (historical Price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using NN. The Price classification NN and the proposed DCT–NN were developed and compared to check the performance. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity Market Price classification.
Claudio A Canizares - One of the best experts on this subject based on the ideXlab platform.
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economic impact of electricity Market Price forecasting errors a demand side analysis
IEEE Transactions on Power Systems, 2010Co-Authors: Hamidreza Zareipour, Claudio A Canizares, Kankar BhattacharyaAbstract:Several techniques have been proposed in the literature to forecast electricity Market Prices and improve forecast accuracy. However, no studies have been reported examining the economic impact of Price forecast inaccuracies on forecast users. Therefore, in this paper, the application of electricity Market Price forecasts to short-term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed. Using electricity Market Price forecasts as the expected next-day electricity Prices, optimal operating schedules and the associated costs are determined for each load. These costs are compared with those of a ?perfect? Price forecast scenario in which actual Prices are used to determine the operating schedules. Numerical results and discussions are provided based on Price forecasts with different error characteristics.
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electricity Market Price volatility the case of ontario
Energy Policy, 2007Co-Authors: Hamidreza Zareipour, Kankar Bhattacharya, Claudio A CanizaresAbstract:Abstract Price volatility analysis has been reported in the literature for most competitive electricity Markets around the world. However, no studies have been published yet that quantify Price volatility in the Ontario electricity Market, which is the focus of the present paper. In this paper, a comparative volatility analysis is conducted for the Ontario Market and its neighboring electricity Markets. Volatility indices are developed based on historical volatility and Price velocity concepts, previously applied to other electricity Market Prices, and employed in the present work. The analysis is carried out in two scenarios: in the first scenario, the volatility indices are determined for the entire Price time series. In the second scenario, the Price time series are broken up into 24 time series for each of the 24 h and volatility indices are calculated for each specific hour separately. The volatility indices are also applied to the locational marginal Prices of several pricing points in the New England, New York, and PJM electricity Markets. The outcomes reveal that Price volatility is significantly higher in Ontario than the three studied neighboring electricity Markets. Furthermore, comparison of the results of this study with similar findings previously published for 15 other electricity Markets demonstrates that the Ontario electricity Market is one of the most volatile electricity Markets world-wide. This high volatility is argued to be associated with the fact that Ontario is a single-settlement, real-time Market.
