Conference Paper

Ancillary Services Dispatch using Linear Programming and Genetic Algorithm approaches

Knowledge Eng. & Decision-Support Res. Center, Polytech. of Porto, Porto, Portugal
DOI: 10.1109/MELCON.2010.5476000 Conference: MELECON 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference
Source: IEEE Xplore


Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

13 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper analyses solutions for optimal bidding for hydro units operating in simultaneous markets for energy and ancillary services and decision making process for plant refurbishment and generating capacity upgrade. Methodology based on the Decision Theory will be applied to identify the optimal solutions which minimize the expected costs and the related risks. The proposed methodology will be demonstrated for a real hydropower plant in Croatia (Varazdin HPP) which will be refurbished.
    No preview · Article · May 2011
  • [Show abstract] [Hide abstract]
    ABSTRACT: Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization.An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements.Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.
    No preview · Article · Aug 2013 · Applied Energy
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Hydro power planning problems are well known in academia. However, due to modeling and computational difficulties, not many suggested concepts are applied in practice. Additionally, the recent deregulation of electricity markets initiated different markets, which increased the need for better decision support tools for hydro operators. Therefore, as a main contribution of this thesis, a novel modeling frame- work is proposed, the multi-horizon modeling approach. This approach allows a very detailed and transparent modeling of many problems in hydro power planning by simultaneously being computationally very efficient. The models are applied in the thesis to pumped storage hy- dro power plants in a liberalized market environment in order to give decision support for the self-scheduling of them. In the thesis, first, the manyfold challenges in hydro power planning are discussed. Then, state-of-the-art modeling and solution methods to such problems are evaluated, focussing on problems with non-concave value functions and risk averse optimizations. Afterwards, multi-horizon models are analyzed, evaluated, and applied for different medium-term hydro power planning problems: • consideration of ancillary services, • risk-averse optimizations, • long-term evaluations, and • price-maker bidding in forward and electricity markets. It is shown how such models outperform traditional methodologies in different ways. Further, an extension of a solution method, dualized stochastic dual dynamic programming, with locally valid cutting planes is proposed. This approach allows to solve problems with non-concave value func- tions more appropriately. Furthermore, a measure of the severity of non-concavity is introduced in this context, which can lead to reduced computational requirements. In addition, the bidding into ancillary services markets is discussed and it is presented how delta-hedging can be used to mitigate bidding risk. Finally, short-term planning for hydro power plants is analyzed and decision support tools for the bidding in electricity markets and for strategic bidding in ancillary services markets are given. With the modeling, solution algorithm, and decision tools presented in this thesis, the planning problems in hydro power can be formulated in a more transparent and meaningful way. Further, the problems can be solved by less computational requirements. Therefore, using such tools, hydro power producers are able to operate their power plants in a more profitable and robust way taking into account multiple markets simultaneously.
    Full-text · Thesis · Aug 2015