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A Medium and Long Term Contract Trading Model for New Energy Units Based on Incomplete Information Game Theory
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Under the dual carbon target, with the gradual increase of the scale of new energy grid connection, the randomness and volatility of new energy output will further affect the safe and stable operation of the system. In view of the frequent fluctuation of the section load rate of the county external liaison line, this paper proposes a self balancing transaction scheduling strategy of new energy power within the county considering the section load rate, and establishes a day ahead real-time two-tier transaction joint optimization model considering the section load rate. The simulation calculation shows that this strategy can significantly improve the local consumption rate of new energy compared with the simple physical method using only energy storage technology, Reduce the power flow fluctuation of tie line, optimize the section load rate and maintain the stability of power grid.
Electricity markets are nowadays flooded with uncertainties that rise from renewable energy applications, technological development, and fossil fuel prices fluctuation, among others. These aspects result in a lumpy electricity prices for consumers, making it necessary to come up with risk management tools to help them hedge this associated risk. In this work a portfolio optimization applied to electricity sector, is proposed. A mixed integer programming model is presented to characterize the electricity portfolio of large consumers. The energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation. The study novelty highlights the energy portfolio characterization for players denoted as large consumers, which has been overlooked by the scientific community and, focuses on the Iberian electricity market as a real case study. A multi-objective methodology is explored, using a weighted-sum approach. The expected cost and the conditional value-at-risk (CVaR) minimization are used as objective function. Three case studies illustrate the model applicability through the characterization of how the portfolio evolves with different demand profiles and how to take advantage from seasonality characteristic in the spot market. A scenario analysis is explored to reflect the uncertainty on the price of the spot market. The expected cost and CVaR are optimized for each case study and the portfolio analysis for each risk posture is characterized. The results illustrate the advantage to reduce costs and risk if the prices seasonality is considered, triggering to an adaptive seasonal behavior, which support the decision-maker decision towards its goals.
Uncertainties in wind power forecast, day-ahead and imbalance prices for the next day possess a great deal of risk for the profit of generation companies participating in a day-ahead electricity market. Generation companies are exposed to imbalance penalties in the balancing market for unordered mismatches between associated day-ahead power schedule and real-time generation. Coordination of wind and thermal power plants alleviates the risks raised from wind uncertainties. This paper proposes a novel optimal coordination strategy by balancing wind power forecast deviations with thermal units in the Turkish day-ahead electricity market. The main focus of this study is to provide an optimal trade-off between the expected profit and the risk under wind uncertainty through conditional value at risk (CVaR) methodology. Coordination problem is formulated as a two-stage mixed-integer stochastic programming problem, where scenario-based wind power approach is used to handle the stochasticity of the wind power. Dynamic programming approach is utilized to attain the commitment status of thermal units. Profitability of the coordination with different day-ahead bidding strategies and trade-off between expected profit and CVaR are examined with comparative scenario studies.
The coupling of electrical batteries with variable renewable power generation can increase the production flexibility and revenue of power plant operators. This study focuses on developing an optimisation model to manage the operational revenue of a renewable power unit comprising a wind farm, solar photovoltaic (PV) power plant, and electrical battery. The power system integration was conducted by formulating a mixed-integer linear programme to schedule the day-ahead operation of the renewable power unit in two liberalised power markets: the Italian and Iberian day-ahead power markets. Several scenarios and case studies have been analysed to assess the value of storage for revenue maximisation. The proposed methodology results reveal that the average yearly net revenue of the hybrid PV-wind-storage power plant can increase by 4% compared to the standalone operation of the wind and solar PV power plants. Additionally, the results indicate that in the markets analysed the coupling of storage to wind power generates a higher revenue than coupling storage systems with solar power. The study demonstrated a correlation between the increase in revenue and the capacity installed in the battery and concluded that the use of hybrid VRE storage systems would be feasible in the Iberian Peninsula and Italy for battery installation costs ranging from €14,804/MW to €38,267/MW.
With the in-depth development of the direct power purchase by large consumers, the contradiction between the marketization of medium-long term bilateral transactions and the non-marketization of spot transactions has become increasingly prominent, thus the construction of the day-ahead electricity market is imminent. A bilateral game model is established with the incomplete information between power supply and demand sides in the day-ahead electricity market, which is aimed to provide a practical game scheme for both sides in the power transactions, so that both sides can obtain the greatest benefits in the power market. The model takes the direct power purchase price and amount as the key points of the game. For the part of the direct power purchase price, the generation cost factor is introduced and the co-integration theory is used to construct the relationship model between the direct power purchase price and the generation cost considering the error correction. The part of the direct electricity purchase amount is predicted by similar day method. Finally, the Nash equilibrium is used to find the solution of the bilateral game and the effectiveness of the proposed model is proved by an example.
This letter proposes a novel medium-long term energy transaction method, in which the energies are traded in block and their duration and shiftable ability can be distinguished and reflected. In the bilateral auction mechanism, the users are motivated to accommodate consuming modes by scheduling the shiftable loads. The case studies have shown that the method helps to promote the integration of renewable power and improve the social benefits.
Pouresmaeil Edris: Optimal Bidding Strategy for Offshore Wind Farms Equipped with Energy Storage in the Electricity Markets, 10th IEEE PES Innovative Smart Grid Technologies Europe
- Pourakbari-Kasmaei Kordkheili Ramin Ahmadi
- Lehtonen Mahdi
- Matti
Kordkheili Ramin Ahmadi, Pourakbari-Kasmaei Mahdi, Lehtonen Matti, Pouresmaeil Edris:
Optimal Bidding Strategy for Offshore Wind Farms Equipped with Energy Storage in the Electricity
Markets, 10th IEEE PES Innovative Smart Grid Technologies Europe,pp. 859-863(2020).