Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach

Dadashi, Mojtaba; Haghifam, Sara; Zare, Kazem; Haghifam, Mahmoud‐Reza; Abapour, Mehdi

doi:10.1016/j.energy.2020.117926

Cited by 47 publications

(20 citation statements)

References 28 publications

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“…Furthermore, Rayati et al [33] have formalised a non-cooperative game-theoretic framework to adequately model the strategic interactions of DRAs, who constitute a balancing group within the context of European electricity markets, where the balancing group representative is responsible for the net imbalances of the associated portfolio of generation and/or demand. Moreover, Dadashi et al [34] have formulated a bi-level, stochastic game-theoretic model to solve the short-term decision-making problem on the optimal volumes of energy imports for each hour of the next day in the presence of a single DRA. Based on the numerical simulation results, they have demonstrated the effectiveness of their framework in yielding an equilibrium DR dispatch solution, where the profits of the utility and the DRA are increased by ~6% and ~70%, respectively, compared to a business-as-usual approach.…”

Section: Game-theoretic Models Applied To Demand-side Management Studiesmentioning

confidence: 99%

Modelling utility-aggregator-customer interactions in interruptible load programmes using non-cooperative game theory

Mohseni

Brent

Kelly

et al. 2021

International Journal of Electrical Power & Energy Systems

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Aggregator-activated demand response (DR) is widely recognised as a viable means for increasing the flexibility of renewable and sustainable energy systems (RSESs) necessary to accommodate a high penetration of variable renewables. To this end, by acting as DR aggregators and offering energy trading capabilities to smaller customers, energy retailers unlock additional sources of demand-side flexibility to ensure the cost-optimal operation of RSESs. Accordingly, a growing body of literature has highlighted the ways in which non-cooperative game theory could be used to reduce the gaps between modelled and real-world results for aggregator-mediated DR schemes. This paper aims to contribute to the trends of giving a realistic grounding to research on distributed DR-integrated energy scheduling by using insights from non-cooperative game theory to determine: (1) the optimal trade-off between importing electricity and utilising DR capacity in grid-tied RSESs, (2) the impact of the price elasticity of DR supply of different customer classes -especially, new sources of electricity demand, such as e-mobilityon the system-level dispatch of DR resources, and (3) the financial implications of harnessing the flexibility potential of a large number of end-consumers across different sectors. Accordingly, the principal goal of the paper is to develop an operational planning optimisation model that can be directly applied to real-world aggregatormediated, market-based demand-side flexibility provisioning domains. To this end, this paper presents the first DR elasticity-aware, non-cooperative game-theoretic DR scheduling model that: (1) yields the best compromise solution between imported power and dispatched DR resources from the utility's perspective, (2) characterises the utility-aggregator-customer interactions during the market-based DR trade process with several customer categories involved, and (3) disaggregates the total sectoral load on the system to individual end-consumers, which has potential implications for pre-feasibility and business case assessments. The application of the model to a conceptual micro-grid for the town of Ohakune, in New Zealand, demonstrates its effectiveness in reducing the daily system operational cost by ~66% and ~47% on a representative summer and winter day, respectively. Importantly, the paper provides statistically significant evidence supporting that activating the flexibility potential of small-to medium-scale end-consumers through specifically defined third-party aggregators in a market-based approach -that is aware of strategic interactions among instrumentally rational economic agents involved in the dispatch and delivery of DR resources -plays a significant role in the cost-optimal transition to 100%-renewable electricity generation systems within the smart grid paradigm.

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Section: Game-theoretic Models Applied To Demand-side Management Studiesmentioning

confidence: 99%

Modelling utility-aggregator-customer interactions in interruptible load programmes using non-cooperative game theory

Mohseni

Brent

Kelly

et al. 2021

International Journal of Electrical Power & Energy Systems

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“…The network needs to be upgraded with new investments that can reduce uncertainties and foster small producers' participation, increasing competition level ([23]). Literature on regulation is also focused on how to design tariffs ( [24]) and how to regulate old and new agents: this is particularly true for network operators ( [25]; [26]) and aggregators ( [27]; [28]; [29]). Medium and low voltage networks see the participation of many renewable energy producers, or prosumers, characterized by small size: in this context, the figure of the aggregator has taken hold as a subject capable of bringing into the market very diversified operators.…”

Section: Literature Backgroundmentioning

confidence: 99%

The participation of small-scale variable distributed renewable energy sources to the balancing services market

et al. 2021

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“…An overview of the state of the art in decision-making models for electricity retailers was provided in [6] and [7]. Most of the existing works focused on the integration of price-based [8]- [14] incentive-based [15]- [21], combined [22] and contract-based [23], [24] DR mechanisms. Price-based DR mechanisms assume that retail customers manage their energy consumption according to specific pricing arrangements such as time-of-use and real-time pricing.…”

Section: Randommentioning

confidence: 99%

A Decision Model for an Electricity Retailer With Energy Storage and Virtual Bidding Under Daily and Hourly CVaR Assessment

Prado

Chikezie

2021

IEEE Access

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This paper presents a short-term decision-making model for an electricity retailer with battery energy storage system (BESS) and virtual bidding through a two-stage stochastic optimization framework. In the first stage, the retailer determines the amount of power to be purchased in the day-ahead wholesale market and the optimal incremental and decremental virtual bidding strategies. In the second stage, the optimal energy storage decisions and the retailer's involvement in the real-time market are determined. The proposed model minimizes the retailer's expected procurement cost and generates the optimal power and virtual bidding curves in the day-ahead market. Two types of Conditional Value at Risk (CVaR) are integrated in the proposed model to manage the retailer's hourly and daily risks, respectively. Case studies with real-world data are performed to verify the retailer's cost reduction obtained with the integration of BESS and virtual bidding and to study how the hourly and daily risk-management strategies affect the retailer's procurement cost distribution for different risk-aversion levels.

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Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach

Cited by 47 publications

References 28 publications

Modelling utility-aggregator-customer interactions in interruptible load programmes using non-cooperative game theory

Modelling utility-aggregator-customer interactions in interruptible load programmes using non-cooperative game theory

The participation of small-scale variable distributed renewable energy sources to the balancing services market

A Decision Model for an Electricity Retailer With Energy Storage and Virtual Bidding Under Daily and Hourly CVaR Assessment

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