Data-driven cooperative trading framework for a risk-constrained wind integrated power system considering market uncertainties

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling. 

Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

Multiobjective home energy management systems in nearly-zero energy buildings under uncertainties considering vehicle-to-home: A novel lexicographic-based stochastic-information gap decision theory approach

This paper studies the risk management of a battery bidding in both day-ahead and intraday markets arising from the uncertain nature of electricity prices. To this end, a coherent risk measure, Second-order Stochastic Dominance (SSD), which is capable of expressing battery preferences in the form of a preset fixed benchmark (profit), is incorporated into the bidding model. The SSD serves the decision-maker as a risk-averse optimizer exploring for profit distribution members greater than a preset fixed benchmark. The most challenging facet of SSD-constrained methodologies is how to effectually define the preset fixed benchmark. In this regard, first, a generic approach is offered to find the feasible region for benchmark selection in SSD-constrained optimization problems. Then, a novel benchmark selection technique considering both the decision-maker's regret and out-of-sample profit, leverages the VIKOR method to get the ranking of different solutions and find the compromise benchmark in the risk-aware environment. Consequently, two decisive criteria from both ex- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ante</i> and ex- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">post</i> tests are involved in the benchmark selection procedure, making the bidding problem regret- and consequence-aware. The numerical results of the developed methodology against risk-neutral and deterministic approaches show the efficiency of the proposed model. 

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Risk-Aware Battery Bidding With a Novel Benchmark Selection Under Second-Order Stochastic Dominance

Fueled by the increasing penetration of renewable energy in the power system, it is a promising way to form renewable energy aggregators so as to improve their competitiveness in the electricity market. This paper focuses on aggregators integrating wind, solar, hydropower and storage, where hydro works to moderate fluctuations and storage helps improve the profitability of the aggregator. The paper establishes a two-phase stochastic information-gap-decision-theory (IGDT) based bidding framework for such aggregator. The interaction between the aggregator and the competitive market is modeled as a stochastic bi-level problem, in which the strategic behaviors of self-interested competitors are fully considered. A density-based K-means clustering algorithm combining seasonal factors is proposed to achieve predicted offering scenarios of rival generators based on their historical market data. The hybrid stochastic-IGDT handles competitors’ offer strategies through scenario-based stochastic programming, and manages uncertainties of wind and solar generation under risk-averse and risk-seeking strategies. The results from Elia’s data demonstrate that the proposed bidding model not only shows better adaptability to the competitors' strategic behaviors, but also generates more profit for the aggregator through collaborative bidding of multiple sources. 

Hooman Khaloie

Papers

Offering and Bidding for a Wind Producer Paired With Battery and CAES Units Considering Battery Degradation