Risk-constrained optimal bidding and scheduling for load aggregators jointly considering customer responsiveness and PV output uncertainty

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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Economics of Recommendations Systems: The Role of Customer Responsiveness to Prices and Quality

SSRN Electronic Journal ◽

10.2139/ssrn.2468790 ◽

2014 ◽

Author(s):

Sumathi Chakravarthy ◽

G. Badri Narayanan

Keyword(s):

Customer Responsiveness

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Efficient Simple Policy Rules and the Implications of Potential Output Uncertainty

SSRN Electronic Journal ◽

10.2139/ssrn.321386 ◽

2003 ◽

Author(s):

Benjamin Hunt ◽

Aaron Drew

Keyword(s):

Policy Rules ◽

Potential Output ◽

Output Uncertainty

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Preliminary uncertainty analysis – a prerequisite for assessing the predictive uncertainty of hydrologic models

Water Science & Technology ◽

10.2166/wst.1996.0039 ◽

1996 ◽

Vol 33 (2) ◽

pp. 79-90 ◽

Cited By ~ 6

Author(s):

Jian Hua Lei ◽

Wolfgang Schilling

Keyword(s):

Uncertainty Analysis ◽

Model Parameters ◽

Observation Data ◽

Calibration Data ◽

Model Output ◽

Predictive Uncertainty ◽

Runoff Volume ◽

Output Uncertainty ◽

Sensitive Parameters ◽

Calculated Model

Physically-based urban rainfall-runoff models are mostly applied without parameter calibration. Given some preliminary estimates of the uncertainty of the model parameters the associated model output uncertainty can be calculated. Monte-Carlo simulation followed by multi-linear regression is used for this analysis. The calculated model output uncertainty can be compared to the uncertainty estimated by comparing model output and observed data. Based on this comparison systematic or spurious errors can be detected in the observation data, the validity of the model structure can be confirmed, and the most sensitive parameters can be identified. If the calculated model output uncertainty is unacceptably large the most sensitive parameters should be calibrated to reduce the uncertainty. Observation data for which systematic and/or spurious errors have been detected should be discarded from the calibration data. This procedure is referred to as preliminary uncertainty analysis; it is illustrated with an example. The HYSTEM program is applied to predict the runoff volume from an experimental catchment with a total area of 68 ha and an impervious area of 20 ha. Based on the preliminary uncertainty analysis, for 7 of 10 events the measured runoff volume is within the calculated uncertainty range, i.e. less than or equal to the calculated model predictive uncertainty. The remaining 3 events include most likely systematic or spurious errors in the observation data (either in the rainfall or the runoff measurements). These events are then discarded from further analysis. After calibrating the model the predictive uncertainty of the model is estimated.

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Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources

Energy ◽

10.1016/j.energy.2021.120476 ◽

2021 ◽

Vol 227 ◽

pp. 120476

Author(s):

Ahmad Nikpour ◽

Abolfazl Nateghi ◽

Miadreza Shafie-khah ◽

João P.S. Catalão

Keyword(s):

Renewable Energy ◽

Energy Resources ◽

Renewable Energy Resources ◽

Optimal Bidding

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Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

Energy & Environment ◽

10.1177/0958305x211014875 ◽

2021 ◽

pp. 0958305X2110148

Author(s):

Mojtaba Shivaie ◽

Mohammad Kiani-Moghaddam ◽

Philip D Weinsier

Keyword(s):

Electricity Market ◽

Large Scale ◽

Search Algorithm ◽

Power Grid ◽

Mathematical Formulation ◽

Test System ◽

Bidding Strategy ◽

Level Problem ◽

Optimal Bidding ◽

Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

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A Stochastic MPC Based Energy Management System for Simultaneous Participation in Continuous and Discrete Prosumer-to-Prosumer Energy Markets

Energies ◽

10.3390/en13143751 ◽

2020 ◽

Vol 13 (14) ◽

pp. 3751

Author(s):

Pablo Baez-Gonzalez ◽

Felix Garcia-Torres ◽

Miguel A. Ridao ◽

Carlos Bordons

Keyword(s):

Energy Management ◽

Management System ◽

Storage System ◽

Energy Storage System ◽

Energy Management System ◽

Bidding Strategies ◽

Production And Consumption ◽

Optimal Bidding ◽

Continuous Trading ◽

Stochastic Model Predictive Control

This article studies the exchange of self-produced renewable energy between prosumers (and with pure end consumers), through the discrete trading of energy packages and proposes a framework for optimizing this exchange. In order to mitigate the imbalances derived from discrepancies between production and consumption and their respective forecasts, the simultaneous continuous trading of instantaneous power quotas is proposed, giving rise to a time-ahead market running in parallel with a real-time one. An energy management system (EMS) based on stochastic model predictive control (SMPC) simultaneously determines the optimal bidding strategies for both markets, as well as the optimal utilisation of any energy storage system (ESS). Simulations carried out for a heterogeneous group of agents show that those with SMPC-EMS achieve savings of between 3% and 15% in their energy operation economic result. The proposed structures allows the peer-to-peer (P2P) energy trading between end users without ESS and constitute a viable alternative to avoid deviation penalties in secondary regulation markets.

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