Fundamentals and business model for resource aggregator of demand response in electricity markets

Lu, Xiaoxing; Li, Kangping; Xu, Hanchen; Wang, Fei; Zhou, Zhenyu; Zhang, Yagang

doi:10.1016/j.energy.2020.117885

Cited by 203 publications

(65 citation statements)

References 75 publications

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“…[51]- [55] Similar tariffs might lead to different outcomes locally A B D [33], [45], [46], [56] Forecasts of individuals are more error-prone than forecast of aggregates B [57], [58] Correlation of behavior and subsequent control issues due to wrong (price) signals B [59], [60] Different tariffs in different parts of the distribution grid can lead to transmission system issues…”

Section: A Cmentioning

confidence: 99%

“…As such, forecasts of individual demand sources can be error-prone, thus localized markets should allow for a certain degree of aggregation [57]. This is especially important considering that end-consumers can be vastly inhomogeneous, further amplifying this error [58].…”

Section: B Integration Of Demand Responsementioning

confidence: 99%

“…Ref. [58] illustrates this and shows, for example, how time of use pricing can support storable loads but curtailable loads require dynamic load capping.…”

Section: B Integration Of Demand Responsementioning

confidence: 99%

See 2 more Smart Citations

Developments and Challenges in Local Electricity Markets: A Comprehensive Review

et al. 2021

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In recent years, power systems have undergone changes in technology and definition of the associated stakeholders. With the increase in distributed renewable generation and small-to medium-sized consumers starting to actively participate on the supply side, a suitable incorporation of decentralized agents into the power system is required. A promising scheme to support this shift is given by local electricity markets. These provide an opportunity to extend the liberal wholesale markets for electrical power found in Europe and the United States to the communal level. Compared to these more established markets, local electricity markets, however, neither have few practical implementations nor standardized frameworks. In order to fill this research gap and classify the types of local electricity markets, the presented paper therefore starts with the challenges that these markets attempt to solve. This is then extended to an analysis of the theoretical and practical background with a focus on these derived challenges. The theoretical background is provided in the form of an introduction to state-of-the-art models and the associated literature, whereas the practical background is provided in form of a summary of ongoing and recent projects on local electricity markets. As a result, this paper presents a foundation for future research and projects attempting to approach the here presented challenges in distribution of generation, integration of demand response, decentralization of markets and legal and social issues via local electricity markets.

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Section: A Cmentioning

confidence: 99%

Section: B Integration Of Demand Responsementioning

confidence: 99%

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Developments and Challenges in Local Electricity Markets: A Comprehensive Review

et al. 2021

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“…The development of distributed energy resources (DERs), which include photovoltaics (PVs), wind turbines (WTs), energy storage systems (ESSs), and demand response (DR), is considered a potential solution and the penetration of DERs is likely to increase owing to environmental policies [1]. The DER aggregator, which aggregates a substantial number of various small-sized renewable sources, has been used to manage the task of fulfilling the expected local load through bidding in the day-ahead power market and scheduling various DERs [2]. As a profit-seeking entity, the DER aggregator is expected to design a day-ahead optimal bidding and scheduling strategy to maximize profits.…”

Section: A Motivationmentioning

confidence: 99%

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

2021

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Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit-enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Thenr, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed datadriven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

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“…Through an energy prosumer, various distributed and scattered power sources are efficiently managed and insufficient energy is covered through transactions, thereby providing various benefits for energy saving, such as reduced electricity bills [ 6 , 7 , 8 ]. The business of energy prosumers is related to changes in consumer behavior as a part of the new energy industry.…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource

Park

Cho

Kim

et al. 2021

Sensors

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Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

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Fundamentals and business model for resource aggregator of demand response in electricity markets

Cited by 203 publications

References 75 publications

Developments and Challenges in Local Electricity Markets: A Comprehensive Review

Developments and Challenges in Local Electricity Markets: A Comprehensive Review

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource

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