Sell or Store?   An ADP Approach to Marketing Renewable Energy

Gönsch, Jochen; Hassler, Michael

doi:10.2139/ssrn.2480573

Cited by 3 publications

(8 citation statements)

References 24 publications

(33 reference statements)

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“…In most countries, multiple electricity markets coexist which are used sequentially by the market participants. Since the integrated or coordinated trading in these markets can dramatically increase the complexity of the decision problem, most authors focus on a single market setting, while other markets are (if at all) only included implicitly (e.g., Jiang and Powell 2015;Gönsch and Hassler 2016;Zhou et al 2016;Franz et al 2020;Ghavidel et al 2020;Finnah and Gönsch 2021). Like in sequential auction markets, a product can be traded multiple times in a continuous intraday market.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal bidding functions for renewable energies in sequential electricity markets

Finnah

2021

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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%

“…The contribution function describes the producer's one-stage revenue. Since we use the common price-taker assumption (e.g., Jiang and Powell (2015); Gönsch and Hassler (2016) 2020)), the power producer does not influence the market price, and the contribution function is relatively obvious.…”

Section: Contribution Functionmentioning

confidence: 99%

Optimal bidding functions for renewable energies in sequential electricity markets

Finnah

2021

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“…The literature on energy management decisions optimizes grid interactions using storage by treating supply levels as deterministic (Dufo-L ópez, 2015;Zhang et al, 2017;Pelzer et al, 2016) or by optimizing the buying, selling, or storage decisions in which the uncertainty of the generated renewable energy is taken into account (Jiang and Powell, 2015b,a;Shin et al, 2017;Grillo et al, 2015;Hannah and Dunson, 2011;Hassler, 2017;G önsch and Hassler, 2016;Keerthisinghe et al, 2019). Literature on energy procurement decisions without storage include Wang and Deng (2019) and Woo et al (2006).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Literature on energy procurement decisions without storage include Wang and Deng (2019) and Woo et al (2006). For example, Wang and Deng (2019) Studies that jointly optimize the use of storage and the decision to buy from or sell to the grid mostly focus on detailed intra-day decisions (Jiang and Powell, 2015b,a;Shin et al, 2017;Grillo et al, 2015;Hannah and Dunson, 2011;Hassler, 2017;G önsch and Hassler, 2016;Keerthisinghe et al, 2019). For example, Jiang and Powell (2015b) address the arbitrage problem with energy storage to place bids in an hour-ahead spot market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Grillo et al (2015) optimally schedule batteries with renewable energy. To the best of our knowledge, Zhou et al (2019) and G önsch and Hassler (2016) are the only papers that have included transmission constraints in focusing on the operational decision of when to buy, sell or store. They have addressed wind-based electricity with co-located storage.…”

Section: Literature Reviewmentioning

confidence: 99%

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Supply chain decisions for an adaptive, decentralized renewable energy system

Fokkema¹

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This thesis is based on the project entitled "ADAPNER" (Adaptive logistics in a circular economy) which is financed by the Netherlands Organization for Scientific Research (NWO). The main objective of ADAPNER is "Determining optimized adaptable and sustainable configurations for different distribution alternatives regarding biomass and biogas in a circular economy".The objective of this thesis is to determine these configurations for different decentralized renewable energy production, storage, and distribution alternatives.The scope of the alternatives considered in this thesis encompasses different renewable energy sources, storage types, and the connection to the electricity grid infrastructure. In particular, we focus on electricity generated by wind and solar photovoltaic (PV) energy. Biogas is both seen as a source to fulfill natural gas demand and also as a source for fulfilling electricity demand employing a CHP engine. We also include LNG as a potentially important fuel in the transportation sector. Furthermore, this thesis includes hydrogen storage as a way to bridge seasonal mismatches between renewable energy production and electricity demand, in which the hydrogen can be converted to electricity. Finally, we include the • Chapter 4:

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Seasonal hydrogen storage decisions under constrained electricity distribution capacity

Fokkema,

Broek,

Schrotenboer

et al. 2020

Preprint

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The transition to renewable energy systems causes increased decentralization of the energy supply. Solar parks are built to increase renewable energy penetration and to supply local communities that become increasingly self-sufficient. These parks are generally installed in rural areas where electricity grid distribution capacity is limited. This causes the produced energy to create grid congestion. Temporary storage can be a solution. In addition to batteries, which are most suitable for intraday storage, hydrogen provides a long-term storage option and can be used to overcome seasonal mismatches in supply and demand. In this paper, we examine the operational decisions related to storing energy using hydrogen, and buying from or selling to the grid considering grid capacity limitations. We model the problem as a Markov decision process taking into account seasonal production and demand patterns, uncertain solar energy generation, and local electricity prices. We show that ignoring seasonal demand and production patterns is suboptimal. In addition, we show that the introduction of a hydrogen storage facility for a solar farm in rural areas may lead to positive profits, whereas this is loss-making without storage facilities. In a sensitivity analysis, we show that only if distribution capacity is too small, hydrogen storage does not lead to profits and reduced congestion at the cable connection. When the distribution capacity is constrained, a higher storage capacity leads to more buying-related actions from the electricity grid to prevent future shortages and to exploit price differences. This leads to more congestion at the connected cable and is an important insight for policy-makers and net-operators.

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Sell or Store? An ADP Approach to Marketing Renewable Energy

Cited by 3 publications

References 24 publications

Optimal bidding functions for renewable energies in sequential electricity markets

Optimal bidding functions for renewable energies in sequential electricity markets

Supply chain decisions for an adaptive, decentralized renewable energy system

Seasonal hydrogen storage decisions under constrained electricity distribution capacity

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