Portfolio Design of a Demand Response Aggregator With Satisficing Consumers

Salah, Florian; Henriquez, Rodrigo; Wenzel, George; Olivares, Daniel; Negrete-Pincetic, Matías; Weinhardt, Christof

doi:10.1109/tsg.2018.2799822

Cited by 33 publications

(23 citation statements)

References 32 publications

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“…The satisfaction of participating DR consumers was the focus of the research in [14]. The proposed model determined financial rewards to be offered to consumers while maximizing aggregator profit.…”

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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“…This is to mention that these constraints would avoid the new DR hours which may occur due to load during recovery hour. The load recovery quantities at (j+1) th event are given in (16), where the remaining fraction of the shifted load is recovered. X…”

Section: Load Recovery Modelingmentioning

confidence: 99%

“…In [13], this competition is quantified with the available stored energy between the aggregators using game theory. In [14][15][16], optimal consumer behavior is modeled to decide the optimal portfolio of DR contracts for a DRA participating in the electricity market. The works in [12][13][14][15][16] did not consider the RESs.…”

Section: Introductionmentioning

confidence: 99%

“…In [14][15][16], optimal consumer behavior is modeled to decide the optimal portfolio of DR contracts for a DRA participating in the electricity market. The works in [12][13][14][15][16] did not consider the RESs. In [17], the authors investigated the significance of automated DR for reducing the critical pressures on electricity supply industry, reducing the need for peaking plants and better utilization of RES.…”

Section: Introductionmentioning

confidence: 99%

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Risk assessment of microgrid aggregators considering demand response and uncertain renewable energy sources

Ghose

Pandey

Gadham

2019

J. Mod. Power Syst. Clean Energy

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In power market environment, the growing importance of demand response (DR) and renewable energy source (RES) attracts more for-profit DR and RES aggregators to compete with each other to maximize their profit. Meanwhile, the intermittent natures of these alternative sources along with the competition add to the probable financial risk of the aggregators. The objective of the paper is to highlight this financial risk of aggregators in such uncertain environment while estimating DR magnitude and power generated by RES. This work develops DR modeling incorporating the effect of estimating power at different confidence levels and uncertain participation of customers. In this paper, two well-known risk assessment techniques, value at risk and conditional value at risk, are applied to predict the power from RES and DR programs at a particular level of risk in different scenarios generated by Monte Carlo method. To establish the linkage between financial risk taking ability of individuals, the aggregators are classified into risk neutral aggregator, risk averse aggregator and risk taking aggregator. The paper uses data from Indian Energy Exchange to produce realistic results and refers certain policies of Indian Energy Exchange to frame mathematical expressions for benefit function considering uncertainties for each type of three aggregators. Extensive results show the importance of assessing the risks involved with two unpredictable variables and possible impacts on technical and financial attributes of the microgrid energy market.

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“…In addition to the scheduling of an aggregator's customers, it is fundamental to design the optimal set of customers for an aggregator. In this framework, authors in [28] study the impact of consumer behavior on the portfolio design of a DR aggregator, developing an optimization model. The present paper considers an aggregator (AGG) that has the knowledge of the power distribution grid because it is also a DSO or works in strict collaboration with the DSO.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Optimal Control of Multiple Microgrids and Buildings by an Aggregator

et al. 2020

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The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.

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Portfolio Design of a Demand Response Aggregator With Satisficing Consumers

Cited by 33 publications

References 32 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

Risk assessment of microgrid aggregators considering demand response and uncertain renewable energy sources

Optimal Control of Multiple Microgrids and Buildings by an Aggregator

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