Optimal offering strategies for wind power producers considering uncertainty and risk

This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn

Operational planning and bidding for district heating systems with uncertain renewable energy production

In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system achieving a higher integration of renewable energy sources (RES). DH operators can not only provide flexibility to the power system by acting on the electricity market, but also profit from the situation to lower the overall system cost. However, the operational planning and bidding includes several uncertain components at the time of planning: electricity prices as well as heat and power production from RES. In this publication, we propose a planning method that supports DH operators by scheduling the production and creating bids for the day-ahead and balancing electricity markets. The method is based on stochastic programming and extends bidding strategies for virtual power plants to the DH application. The uncertain factors are considered explicitly through scenario generation. We apply our solution approach to a real case study in Denmark and perform an extensive analysis of the production and trading behaviour of the DH system. The analysis provides insights on how DH system can provide regulating power as well as the impact of uncertainties and renewable sources on the planning. Furthermore, the case study shows the benefit in terms of cost reductions from considering a portfolio of units and both markets to adapt to RES production and market states

Hybrid power plant bidding strategy including a commercial compressed air energy storage aggregator and a wind power producer

© 2017 IEEE. In this paper, a commercial compressed air energy storage (CAES) aggregator equipped with a simple cycle mode operation having the ability to work like a gas turbine is coordinated with a wind power aggregator (WPA) as a hybrid power plant to participate in electricity markets. In the proposed approach, the WPA uses the CAES to tackle its stochastic input and uncertainties related to different electricity market prices, and CAES can also use WPA to manage its charging/discharging and simple cycle modes more economically. A three-stage stochastic decision-making method is used to model the mentioned optimization problem which considers three electricity markets including day-ahead, intraday and balancing markets. The problem is formulated as a mixed integer linear programming which can be solved with available commercial solvers. Also, conditional value-at-risk is added to the problem to control the financial risk of the problem and offer different operation strategies for different financials risk levels. The proposed method can provide both bidding quantity and bidding curves to be submitted to the electricity markets which is tested on a realistic case study based on a wind farm and electricity market located in Spain. The results confirm that the proposed method can provide extra profit in joint operation, have more flexibility and reduce the financial risks

Peer-to-peer energy trading between wind power producer and demand response aggregators for scheduling joint energy and reserve

In this article, a stochastic decision-making framework is presented in which a wind power producer (WPP) provides some required reserve capacity from demand response aggregators (DRAs) in a peer-to-peer (P2P) structure. In this structure, each DRA is able to choose the most competitive WPP, and purchase energy and sell reserve capacity to that WPP under a bilateral contract-based P2P electricity trading mechanism. Based on this structure, the WPP can determine the optimal buying reserve from DRAs to offset part of wind power deviation. The proposed framework is formulated as a bilevel stochastic model in which the upper level maximizes the WPP's profit based on the optimal bidding in the day-ahead and balancing markets, whereas the lower level minimizes DRAs' costs. In order to incorporate the risk associated with the WPP's decisions and to assess the effect of scheduling reserves on the profit variability, conditional value at risk is employed.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed