A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Rashidizadeh‐Kermani, Homa; Vahedipour‐Dahraie, Mostafa; Anvari‐Moghaddam, Amjad

doi:10.1002/2050-7038.12109

Cited by 27 publications

(15 citation statements)

References 30 publications

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“…In [17], a decision-making model for a demand aggregator as a load-serving entity (LSE) was proposed. The LSE maximized profit in an uncertain day-ahead market by optimal scheduling of interruptible loads as well as efficient charging and discharging of plug-in electric vehicles (PEVs).…”

Section: Methodsmentioning

confidence: 99%

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Essiet

Sun

2020

Applied Sciences

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This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.

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Section: Methodsmentioning

confidence: 99%

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Essiet

Sun

2020

Applied Sciences

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“…The generated scenarios are combined to obtain a two-stage scenario tree as a vector of independent random variables. Due to the large size of this tree, an effective scenario reduction algorithm proposed in [19] is used to reduce the size of the scenarios. The generated scenarios for each variable are reduced by Roulette Wheel Mechanism.…”

Section: Framework Of Decision Making Problemmentioning

confidence: 99%

Joint Energy and Reserve Scheduling of a Wind Power Producer in a Peer-to-Peer Mechanism

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Shafie‐khah

et al. 2021

IEEE Systems Journal

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This paper proposes a risk constrained decision making problem for wind power producers (WPPs) in a competitive environment. In this problem, the WPP copts to maximize its likely profit whereas aggregators want to minimize their payments. So, this bi-level problem is converted to a single level one. Then, the WPP offers proper prices to the aggregators to attract them to supply their demand. Also, these aggregators can procure reserve for the WPP to compensate its uncertainties. Therefore, through a peer-to-peer (P2P) trading mechanism, the WPP requests the aggregators to allocate reserve to cover the uncertainties of the wind generation. Also, due to the presence of uncertain resources of the problem, a risk measurement tool is applied to the problem to control the uncertainties. The effectiveness of the model is assessed on realistic data from the Nordpool market and the results show that as the loads become responsive, more loads are allowed to choose their WPP to supply their load. Also, the reserve that is provided by these responsive loads to the WPP increases.

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“…However, the interaction between the aggregator and customers through a bi-level model has not been addressed in [19]. In [20], a stochastic bi-level scheduling model for decision-making of a load serving entity in DA and regulating markets with uncertainties is proposed. In this model, LSE as the main interacting player of the market sells electricity to end-use customers and plug-in EVs to maximize its expected profit.…”

Section: Ch D T E / mentioning

confidence: 99%

“…Moreover, the customers are concerned about the technical constraints of their loads and EVs that should be satisfied as investigated in [7]. Moreover, constraints ( 19)-( 20) impose limits on EVs' battery at each period that should be considered in the problem [20].…”

Section: Lower Level: Customers' Cost Minimizationmentioning

confidence: 99%

“…Equation (19) shows that the state of charge of each EV in period t depends on the state of charge in period t-1 and the charging power absorbed from the upstream grid during period t. Also, based on (20), the state of charge of each EV is restricted between certain levels at each hour and in each scenario and cannot exceed this bound. The dual variables are separated by a colon after equations ( 17) - (20).…”

Section: Lower Level: Customers' Cost Minimizationmentioning

confidence: 99%

See 1 more Smart Citation

A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Shafie‐khah

et al. 2020

IEEE Trans. Smart Grid

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A regret-based stochastic bi-level framework for optimal decision making of a demand response (DR) aggregator to purchase energy from short term electricity market and wind generation units is proposed. Based on this model, the aggregator offers selling prices to the customers, aiming to maximize its expected profit in a competitive market. The clients' reactions to the offering prices of aggregators and competition among rival aggregators are explicitly considered in the proposed model. Different sources of uncertainty impressing the decisions made by the aggregator are characterized via a set of scenarios and are accounted for by using stochastic programming. Conditional value-at-risk (CVaR) is used for minimizing the expected value of regret over a set of worst scenarios whose collective probability is lower than a limitation value. Simulations are carried out to compare CVaR-based approach with value-at-risk (VaR) concept and traditional scenario based stochastic programming (SBSP) strategy. The findings show that the proposed CVaR strategy outperforms the SBSP approach in terms of making more risk-averse energy biddings and attracting more customers in the competitive market. The results show that although the aggregator offers the same prices in both CVaR and VaR approaches, the average of regret is lower in the VaR approach.

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A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Cited by 27 publications

References 30 publications

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa

Joint Energy and Reserve Scheduling of a Wind Power Producer in a Peer-to-Peer Mechanism

A Regret-Based Stochastic Bi-Level Framework for Scheduling of DR Aggregator Under Uncertainties

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