A bi-level model for strategic bidding of a price-maker retailer with flexible demands in day-ahead electricity market

Sharifi, Reza; Anvari‐Moghaddam, Amjad; Fathi, S. H.; Vahidinasab, Vahid

doi:10.1016/j.ijepes.2020.106065

Cited by 54 publications

(10 citation statements)

References 35 publications

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“…In another instance, Sharifi et al [30] have developed a single-leader, multi-follower Stackelberg strategic bidding model to assist the associated DRA decision-making process in building the optimal aggregate demand bidding curve for submission to the day-ahead electricity market. Based on the numeric simulation results, the authors have shown the effectiveness of the model in increasing the profit of a representative DRA who participates in the day-ahead electricity market by as much as 6.4%.…”

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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“…Referred to Chen et al [25], they compared four dynamic pricing models with and without menu costs for deteriorating products and analyzing the impact of menu costs on deteriorating products. Dynamic pricing is used in electricity market for load management in the peak period [26]. Sharifi et al [27] studied optimal pricing strategies and demand response models for a pool-based electricity market based on the bilevel Stackelberg-based model in order to enhance retailer's profit and consumers' welfare during peak hours of power consumption.…”

Section: Dynamicmentioning

confidence: 99%

Dynamic Optimal Pricing of Ridesharing Platforms under Network Externalities with Stochastic Demand

Wang

Xie

2021

Complexity

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Ridesharing two-sided platforms link the stochastic demand side and the self-scheduling capacity supply side where there are network externalities. The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic demand with network externalities in order to maximize platforms’ revenue and social welfare. We propose dynamic pricing strategies under two demand scenarios that minimize order loss in the surge demand period and maximize social welfare in the declining demand period. The numerical simulation results show that dynamic pricing strategies could stimulate the supply to reduce delayed orders in the surge demand scenario and adjust the demand to maximize social welfare under declining demand scenario. Additionally, we further find that the direct network externalities positively influence the platforms’ revenue, and the indirect network externalities have a negative effect on social welfare in the declining demand scenario, and a higher wage ratio cannot enhance the platforms’ revenue.

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“…In Reference 23, the authors presented a multilevel optimization process, a bilevel Stackelberg‐based model between retailer and customers. The price‐maker retailer as the leader seeks the optimal prices to maximize its profit, and consumers as followers respond to these prices to minimize the cost of purchased electricity.…”

Section: Related Workmentioning

confidence: 99%

Improving demand‐response scheme in smart grids using reinforcement learning

Bagherpour

Mozayani

Badnava

2021

Intl J of Energy Research

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With efficient load management, in peak times, customers can reduce their consumption, shift the shiftable loads to proper times or even store the electrical energy in the low prices range. To achieve these effects, the dynamic pricing policy is one of the most effective ways to encourage users to change their consumption patterns. However, because of the uncertainty of electricity consumption, it is a complicated problem to determine an optimal pricing policy. For this purpose, in this study, a deep contextual bandit algorithm is considered to solve this problem, which uses a deep neural network to learns the context and the associated reward. Existing works on dynamic pricing policy used some limited datasets and assumptions, which may be contrary to the nature of the real-world markets. Due to the different demands of the consumers, which change throughout the year, and the existence of multi pricing agents in future markets, it is important to have a good market structure, method, and especially datasets which representative of all possible demands.The simulations were performed in different system models. Results show that the proposed algorithm can improve the system's reliability, reduce energy cost, and control the power system's ramp rate. Especially in multiagent markets with multiobjective costs, we can adjust the pricing model based on the market strategy to satisfy customers or reduce energy consumption.

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A bi-level model for strategic bidding of a price-maker retailer with flexible demands in day-ahead electricity market

Cited by 54 publications

References 35 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

Dynamic Optimal Pricing of Ridesharing Platforms under Network Externalities with Stochastic Demand

Improving demand‐response scheme in smart grids using reinforcement learning

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