Energy storage as a service: Optimal pricing for transmission congestion relief
- Authors: Arteaga, Juan , Zareipour, Hamidreza , Amjady, Nima
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE open access journal of power and energy Vol. 7, no. (2020), p. 514-523
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- Description: This paper focuses on pricing Energy Storage as a Service (ESaaS) for Transmission congestion relief (TCR). We consider a merchant storage facility that competes in an electricity market to trade energy and ancillary services on a day-to-day basis. The facility also has the opportunity to provide a firm TCR service to a regional network operator under a long-term contract. Providing the additional TCR service would impose limitations on the facility's ability to fully harvest daily market trade opportunities. Thus, we model the opportunity costs associated with the TCR service and use it in a hybrid cost-value customized pricing technique to determine the risk-constrained optimal price of ESaaS for TCR. Given the long-term nature of the commitment to provide the TCR service, we use the Conditional Value at Risk (CVaR) metric to mitigate the long-term financial risks faced by the facility. The proposed pricing strategy enables the storage owner to estimate the additional financial gains and the associated risks that would likely result from adding the new service to its operation. Numerical simulations are provided to support the proposed methodology.
Optimal solar and energy storage system sizing for behind the meter applications
- Authors: Arteaga, Juan , Farrokhabadi, Mostafa , Amjady, Nima , Zareipour, Hamidreza
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Sustainable Energy Vol. 14, no. 1 (2023), p. 537-549
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- Description: In this paper, we propose an optimal sizing model for a solar plus energy storage (PV-ESS) system for behind the meter applications. A dynamic optimization algorithm is proposed that maximizes the net worth of a project; the method can account for decreasing technology costs in the future and defer some of the investment costs. Two kinds of uncertainties are considered and mitigated according to their frequency of occurrence and forecast accuracy. The proposed optimization model is decomposed and structured in such a way that it can be efficiently solved using parallel computation. The simulation results provide evidence of the algorithm's ability to optimally size and time the investment in a PV-ESS system so that the total project cost is minimized. © 2010-2012 IEEE.
Maximizing the utilization of existing grids for renewable energy integration
- Authors: Ranjbar, Hossein , Kazemi, Mostafa , Amjady, Nima , Zareipour, Hamidreza , Hosseini, Seyed
- Date: 2022
- Type: Text , Journal article
- Relation: Renewable energy Vol. 189, no. (2022), p. 618-629
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- Description: This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The Progressive Hedging Algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases. The proposed model is tested on the modified Garver 6-bus and IEEE 118-bus test systems, and its results are compared with the results of the conventional VRES integration model. These results and comparisons illustrate the effectiveness of the proposed approach in terms of maximizing VRES integration and enhancing computational performance.
A new multi-resolution closed-loop wind power forecasting method
- Authors: Nejati, Maryam , Amjady, Nima , Zareipour, Hamidreza
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Sustainable Energy Vol. 14, no. 4 (2023), p. 2079-2091
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- Description: By the increasing number and size of wind farms, wind generation forecasting has become a basic requirement for their connection to the power grid; otherwise, power system operators and electricity market participants cannot make the right decisions and may incur significant costs and penalties. In this paper, a new multi-resolution closed-loop wind power forecasting method with a difference signal feedback loop is proposed. Within the proposed method, wind power is initially predicted in two different resolutions (such as with hourly and sub-hourly time steps) by two low/high-resolution pre-predictors and then the inconsistency between their predictions is measured through the difference signal. The generated difference signal is used as a guide for the two low/high-resolution wind power post-predictors. If their wind power forecasts are inconsistent, the difference signal is updated and used as the feedback for the low/high-resolution post-predictors. This closed-loop forecasting-updating process is iterated until the post-predictors reach consistent results. To evaluate the performance of the proposed multi-resolution closed-loop method, it is tested on two different real-world wind farms and the results are compared with the results of several other widely used/recently published wind power forecast methods using various error metrics and different forecast horizons. © 2010-2012 IEEE.
A new feature selection technique for load and price forecast of electrical power systems
- Authors: Abedinia, Oveis , Amjady, Nima , Zareipour, Hamidreza
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE transactions on power systems Vol. 32, no. 1 (2017), p. 62-74
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- Description: Load and price forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and price forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter.
A new hybrid stochastic-robust optimization approach for self-scheduling of generation companies
- Authors: Dehghan, Shahab , Amjady, Nima , Vatani, Behdad , Zareipour, Hamidreza
- Date: 2016
- Type: Text , Journal article
- Relation: International transactions on electrical energy systems Vol. 26, no. 6 (2016), p. 1244-1259
- Full Text: false
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- Description: Summary This paper presents a new mixed‐integer linear programming model for day‐ahead self‐scheduling of generating companies integrating the underlying ideas of robust optimization (RO) and stochastic programming (SP) to cope with the uncertainties of electricity market prices and availability/unavailability of units. The proposed hybrid approach models the uncertainty of electricity market prices by bounded intervals instead of probability distributions, aiming to derive a more tractable optimization model. Conservatism against uncertain electricity market prices is adjusted by a certain parameter named budget of robustness. Also, a renovated Markov chain approach considering the chance of return (i.e., return rate) for failed units, in addition to forced outage rate of units, in each hour of the scheduling period is introduced in this paper to produce a set of scenarios modeling the availability/unavailability of units. Therefore, the proposed hybrid self‐scheduling approach benefits from both the tractability of RO and modeling accuracy of SP. The proposed approach is implemented on IEEE 118‐bus test system under different circumstances to illustrate its effectiveness compared to deterministic self‐scheduling model as well as the models only employing RO or SP. Copyright © 2015 John Wiley & Sons, Ltd.