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Real-Time Operation Strategy for Energy Storage Considering Power Generation Rights Transfer
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Wind power producers are getting ready to participate in electricity markets as well as conventional units. This poses challenges to power system operators. Wind speed forecasting error increases power imbalance at real time operation, and hence, profits of wind power producers decrease due to balancing costs. A recently proposed scheme for reducing wind power plants power imbalance and increasing their profits is to team up each wind power producer with a non-wind generating firm. The joint firm participates in the market by bidding the joint supply function as a single unit. The objectives of this paper are 1) improving the efficiency of this scheme by considering both benefits and losses of positive and negative balancing prices, 2) determining the optimal generation capacity for the joined firm for maximum profitability of the scheme, and 3) performing sensitivity analysis on different parameters to determine the range of profitability of the scheme in different conditions. In order to evaluate the efficiency of the model, behavior of other generating firms should be known. To this end, supply function equilibrium model is used to determine the optimal behavior of generating firms considering their interactions. Performance of the improved scheme is discussed using a test system.
Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner’s arbitrage profit maximization problem under uncertainty in day-ahead (DA) and real-time (RT) market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.
Wind farms and energy storage systems are playing increasingly more important roles in power systems, which makes their offering non-negligible in some markets. From the perspective of wind farm-energy storage systems (WF-ESS), this paper proposes an integrated strategy of day-ahead offering and real-time operation policies to maximize their overall profit. As participants with large capacity in electricity markets can influence cleared prices by strategic offering, a large scaled WFESS is assumed to be a price maker in day-ahead markets. Correspondingly, the strategy considers influence of offering quantity on cleared day-ahead prices, and adopts linear decision rules as the real time control strategy. These allow enhancing overall profits from both day-ahead and balancing markets. The integrated price-maker strategy is formulated as a stochastic programming problem, where uncertainty of wind power generation and balancing prices are taken into account in the form of scenario sets, permitting to reformulate the optimization problem as a linear program. Case studies validate the effectiveness of the proposed strategy by highlighting and quantifying benefits comparing with the price-taker strategy, and also show the profit enhancement brought to the distributed resources.
Given the significant amount of installed generation-capacity based on wind power, and also due to current economic downturn, the subsidies and incentives that have been widely used by wind-power producers to recover their investment costs have decreased and are even expected to disappear in the near future. In these conditions, wind-power producers need to develop offering strategies to make their investments profitable counting solely on the market. This paper proposes a multi-stage risk-constrained stochastic complementarity model to derive the optimal offering strategy of a wind-power producer that participates in both the day-ahead and the balancing markets. Uncertainties concerning wind-power productions, market prices, demands' bids, and rivals' offers are efficiently modeled using a set of scenarios. The conditional-value-at-risk metric is used to model the profit risk associated with the offering decisions. The proposed model is recast as a tractable mixed-integer linear programming program solvable using available branch-and-cut algorithms. Results of a case study are reported and discussed to show the effectiveness and applicability of the proposed approach.