Demand-side participation in system reserve provision in a stochastic market model with high wind penetration

Kladnik, B.; Artač, Gašper; Stokelj, T.; Golob, R.; Gubina, Andrej F.

doi:10.1109/powercon.2012.6401372

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Since it is fossil-fueled power plants that typically provide BP, it is fair to assume that the DSS contributes largely to eco-efficiency. If prices for reserve and BP rise significantly in the future as suggested by research (Kladnik et al 2012;Madrigal and Porter 2013) the application of the DSS could be financially even more rewarding. The same holds for increased volatility of imbalance prices.…”

Section: Discussionmentioning

confidence: 99%

Make or buy: IT-based decision support for grid imbalance settlement in smarter electricity networks

Wederhake

Schlephorst²,

Zyprian³

2022

Energy Inform

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Decision (support) systems are a particularly important type of information system to energy informatics. A key challenge in energy informatics is that electricity supply must be in balance with demand at all times. More volatile renewable energy sources increase the relevance of electricity network balancing, i.e., imbalance settlement. Typically, electricity distribution network operators bought balancing power from external service providers (Buy option). Interestingly, however, more local energy resources help smarter electricity networks develop a Make option, as in our real-world evaluation. Choosing the better decision alternative within the relevant timeframes challenges human decision-making capabilities. Therefore, this research proposes a model-based decision system to improve the operators’ decisions concerning Make or Buy under various levels of data quality represented by availability, granularity, and timeliness. The study reports savings up to 40% of costs for imbalance settlement supporting ambitious development efforts by the municipality we study in our real-world evaluation.

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Section: Discussionmentioning

confidence: 99%

Make or buy: IT-based decision support for grid imbalance settlement in smarter electricity networks

Wederhake

Schlephorst²,

Zyprian³

2022

Energy Inform

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“…First, its unpredictability leads to greater fluctuation in the reserve requirements (Jacobsen and Zvingilaite, 2010) and, in addition to that, additional balancing reserves will be needed (Gross et al, 2006). Second, prices will also increase sharply (Kladnik et al, 2012).…”

Section: Scenario B: Market Design Of Control Reserve Exchangesmentioning

confidence: 99%

“…However, this only represents a snapshot. Prices for reserve and balancing energy are assumed to be rising significantly in the near future (Kladnik et al, 2012;Madrigal and Porter, 2012), which consequently leads to increased financial benefits for the retailer and could hence make these scenarios financially rewarding.…”

Section: Policy Implicationsmentioning

confidence: 99%

Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications

Feuerriegel

Neumann

2016

Energy Policy

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Demand Response allows for the management of demand side resources in real-time; i. e. shifting electricity demand according to fluctuating supply.When integrated into electricity markets, Demand Response can be used for load shifting and as a replacement for both control reserve and balancing energy. These three usage scenarios are compared based on historic German data from 2011 to determine that load shifting provides the highest benefit: its annual financial savings accumulate to e3.110 M for both households and the service sector. This equals to relative savings of 2.83 % compared to a scenario without load shifting. To improve Demand Response integration, the proposed model suggests policy implications: reducing bid sizes, delivery periods and the time-lag between market transactions and delivery dates in electricity markets.

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“…The aggregators are market actors, operating the aggregation platforms where many distributed DR units are scheduled. By aggregating flexible resources in his portfolio across several MV and LV networks, the aggregator can offer aggregated flexible energy products on the wholesale electricity and reserve markets, or provide ancillary services to the network operators (Artač, Flynn, Kladnik, Hajdnijak, & Gubina, ; Artač, Flynn, Kladnik, Pantoš, & Gubina, ; Kladnik, Artač, Štokelj, Golob, & Gubina, ). Examples of these flexible energy products are 15‐min energy product on the balancing market or 1‐hr product in the intraday wholesale electricity market, while the examples of the ancillary services include provision of tertiary and secondary reserve, voltage, or line congestion control for the DSO and voltage phase balance control (with advanced inverters).…”

Section: Introductionmentioning

confidence: 99%

The use of intelligent aggregator agents for advanced control of demand response

Medved

Artač

Gubina

2018

WIREs Energy & Environment

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With the integration of distributed renewable energy sources (DRES) and active demand (e.g., units providing demand response, DR) in the distribution grid, the importance of monitoring the network conditions, managing the line congestions and observing the voltage levels is increasing. The distribution system operator (DSO) needs a mechanism, such as the traffic light system, to screen and approve the proposed operation schedules of the flexible active resources in the distribution grid. Their aggregated control will require the aggregators to employ advanced scheduling algorithms. The DR scheduling algorithms can be set to pursue various goals, for example, maximization of profit or cost reduction, grid support, or provision of the ancillary services. In the paper, we present a new DR scheduling approach suitable for the aggregation agent using approximate Q‐learning (AQL) algorithm scheduling. We present the AQL algorithm and the associated assumptions used in simulations on a real‐world low‐voltage (LV) grid model, comparing the AQL approach results to those of the economic scheduling and the energy scheduling approaches. Our assumption was that the AQL approach could outperform the energy or economic approaches as the AQL agent would be able to learn to avoid the scheduling penalties. The results of our research show that the aggregator agent using the economic approach shows the best economic performance, but causes the most schedule violations. The energy scheduling approach improves the network voltage profile but lowers the aggregator's profit. The AQL approach results in the agent's economic performance between the former two approaches with minimal schedule violations, confirming our research hypothesis. This article is categorized under: Concentrating Solar Power > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Efficiency > Economics and Policy

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Demand-side participation in system reserve provision in a stochastic market model with high wind penetration

Cited by 8 publications

References 18 publications

Make or buy: IT-based decision support for grid imbalance settlement in smarter electricity networks

Make or buy: IT-based decision support for grid imbalance settlement in smarter electricity networks

Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications

The use of intelligent aggregator agents for advanced control of demand response

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