Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity

Wozabal, David; Rameseder, Gunther

doi:10.1016/j.ejor.2019.07.022

Cited by 93 publications

(49 citation statements)

References 32 publications

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“…Therefore, the Spanish electricity market, which consists of seven sequential markets, cannot be handled in full complexity using this approach. The first to handle all seven Spanish markets is Wozabal and Rameseder (2020). In contrast with the rest of the literature, the authors model the trading problem for the Spanish electricity market as a dynamic program, in which each decision stage corresponds to one market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In contrast with the rest of the literature, the authors model the trading problem for the Spanish electricity market as a dynamic program, in which each decision stage corresponds to one market. Since the computational burden of dynamic programs increases only linearly in the number of stages/markets, Wozabal and Rameseder (2020) can solve the trading problem efficiently but heuristically with an approximate dual dynamic programming approach. Wozabal and Rameseder (2020) propose two model variants: one without updating the power production forecast and one with updates.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the computational burden of dynamic programs increases only linearly in the number of stages/markets, Wozabal and Rameseder (2020) can solve the trading problem efficiently but heuristically with an approximate dual dynamic programming approach. Wozabal and Rameseder (2020) propose two model variants: one without updating the power production forecast and one with updates. We refer to the more interesting model with production forecast updates.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal bidding functions for renewable energies in sequential electricity markets

Finnah

2021

OR Spectrum

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In most modern energy markets, electricity is traded in pay-as-clear auctions. Usually, multiple sequential markets with daily auctions, in which each hourly product is traded separately, coexist. In each market and for each traded hour, each power producer and consumer submits multiple price and volume combinations, called bids. After all bids are submitted by the market participants, the market-clearing price for each hour is published, and the market participants must fulfill their accepted commitments. The corresponding decision problem is particularly difficult to solve for market participants with stochastic supply or demand. We formulate the energy trading problem as a dynamic program and derive the optimal bidding functions analytically via backward recursion. We demonstrate that, for each hour and market, the optimal bidding function is completely defined by two bids. While we focus on power producers with stochastic supply (e.g., wind or solar), our model is applicable to power consumers with stochastic demand, as well. The optimal policy is applicable in most liberalized energy markets, virtually independent of the structure of the underlying electricity price process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Optimal bidding functions for renewable energies in sequential electricity markets

Finnah

2021

OR Spectrum

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“…Two approaches to cope with this in SDDP have been proposed in [25]; the first one models the data using autoregressive time series, while the other uses Markov chains to discretize the random data processes. In [26], an optimal bidding strategy for a VPP, comprising wind power parks, in the Spanish DA and six discrete auctions in ID market is solved with a variant of SDDP. However, none of the aforementioned works on SDDP has focused on modeling the trading of a VPP in the CID market.…”

Section: A Literature Reviewmentioning

confidence: 99%

Multistage Stochastic Programming for VPP Trading in Continuous Intraday Electricity Markets

Shinde¹,

Kouveliotis-Lysikatos²,

Amelin³

2021

Preprint

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<div>The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intraday markets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.</div>

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“…From the point of view of the direct participation of the VPP in different electricity markets to maximize its profit, several works propose different methodologies to decide the optimal bidding strategy of the VPP and reduce prediction errors and, thus, avoid the costs of deviations. References [36][37][38], among others, use stochastic optimization methods, while studies such as Reference [39] use robust programming models. The authors in References [40,41] propose the combination of robust and stochastic optimization for the resolution of this type of VPP problem.…”

Section: Introductionmentioning

confidence: 99%

Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

Naval¹,

Yusta²

2020

Energies

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The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs.

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Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity

Cited by 93 publications

References 32 publications

Optimal bidding functions for renewable energies in sequential electricity markets

Optimal bidding functions for renewable energies in sequential electricity markets

Multistage Stochastic Programming for VPP Trading in Continuous Intraday Electricity Markets

Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

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