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ID: 091699

An agent-based analysis of the German electricity market with t / Veit, Daniel J; Weidlich, Anke; Krafft, Jacob A

Veit, Daniel J	Journal Article
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2009.

Summary/Abstract

While some agent-based models have been developed for analyzing the German electricity market, there has been little research done on the emerging issue of intra-German congestion and its effects on the bidding behavior of generator agents. Yet, studies of other markets have shown that transmission grid constraints considerably affect strategic behavior in electricity markets. In this paper, the implications of transmission constraints on power markets are analyzed for the case of Germany. Market splitting is applied in the case of congestion in the grid. For this purpose, the agent-based modeling of electricity systems (AMES) market package developed by Sun and Tesfatsion is modified to fit the German context, including a detailed representation of the German high-voltage grid and its interconnections. Implications of transmission constraints on prices and social welfare are analyzed for scenarios that include strategic behavior of market participants and high wind power generation. It can be shown that strategic behavior and transmission constraints are inter-related and may pose severe problems in the future German electricity market.

Key Words

Germany ; Electricity Market ; Agent-Based Simulation ; Power Markets ; Transmission Grid

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ID: 115156

Can a wind farm with CAES survive in the day-ahead market? / Mauch, Brandon; Carvalho, Pedro M S; Apt, Jay

Apt, Jay	Journal Article
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Publication

2012.

Summary/Abstract

We investigate the economic viability of coupling a wind farm with compressed air energy storage (CAES) to participate in the day-ahead electricity market at a time when renewable portfolio standards are not binding and wind competes freely in the marketplace. In our model, the CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Other sources of revenue (capacity markets, ancillary services, price arbitrage) are not included in the analysis. We present a model to calculate profit maximizing day-ahead dispatch schedules based on wind forecasts. Annual profits are determined with dispatch schedules and actual wind generation values. We find that annual income for the modeled wind-CAES system would not cover annualized capital costs using market prices from the years 2006 to 2009. We also estimate market prices with a carbon price of $20 and $50 per tonne CO2 and find that revenue would still not cover the capital costs. The implied cost per tonne of avoided CO2 to make a wind-CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices.

Key Words

Wind Energy ; Energy Storage ; Power Markets

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