Self-Scheduling of Demand Response Aggregators in Short-Term Markets Based on Information Gap Decision Theory

Vahid‐Ghavidel, Morteza; Mahmoudi, Nadali; Mohammadi‐Ivatloo, Behnam

doi:10.1109/tsg.2017.2788890

Cited by 75 publications

(38 citation statements)

References 31 publications

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“…So, to handle such mentioned problems, retailer attempts to model uncertainty of market price as well as local units output to have robust performance in the energy market and maximize his benefit [95]. Uncertainty-based participation of demand response aggregator in demand response programs has been investigated through an IGDT-based optimization framework in [96] in which the uncertainties of electricity price in market as well as participation level of consumers in demand response have been modeled via IGDT.…”

Section: • Retailermentioning

confidence: 99%

Application of information gap decision theory in practical energy problems: A comprehensive review

2019

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The uncertainty and risk modeling is a hot topic in operation and planning of energy systems. The system operators and planners are decision makers that need to handle the uncertainty of input data of their models. As an example, energy consumption has always been a critical problem for operators since the forecasted values, and the actual consumption is never expected to be the same. The penetration of renewable energy resources is continuously increasing in recent and upcoming years.These technologies are not dispatch-able and are highly dependent on natural resources. This would make the real-time energy balancing more complicated. Another source of uncertainty is related to energy market prices which are determined by the market participants' behaviors. To consider these issues, the uncertainty modeling should be performed. Various approaches have been previously utilized to model the uncertainty of these parameters such as probabilistic approaches, possibilistic approaches, hybrid possibilisticprobabilistic approach, information gap decision theory, robust and interval optimization techniques. This paper reviews the research works that used information gap decision theory for uncertainty modeling in energy and power systems.

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Section: • Retailermentioning

confidence: 99%

Application of information gap decision theory in practical energy problems: A comprehensive review

2019

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“…Elasticity can be calculated using (22) which shows the percentage of elasticity (PoE) at time t, where P 0;t and P f ;t are initial and final magnitudes of power and k 0;t and k f ;t are initial and final values of prices in kW/h at time t. PoE is the ratio of the changes in price to change in demand during recovery and DR event at time t.…”

Section: Price Based On Load Responsive Modelmentioning

confidence: 99%

“…In this work, the uncertainty of the customers is quantified with the participation factors. The uncertain nature of the enduser responses and market prices are the basis for finding the short-term self-scheduling for DRA using information gap decision theory (IGDT) [22]. In [17][18][19][20][21][22], a simplified approach for load curtailment and load recovery is considered without using proper constraints.…”

Section: Introductionmentioning

confidence: 99%

“…The uncertain nature of the enduser responses and market prices are the basis for finding the short-term self-scheduling for DRA using information gap decision theory (IGDT) [22]. In [17][18][19][20][21][22], a simplified approach for load curtailment and load recovery is considered without using proper constraints. The load recovery model requires constraints like the constraints of starting and stopping of load recovery hours and the magnitude of load that may be recovered in a specific recovery event considering the base load of customers.…”

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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“…Scenariobased scheduling was utilized to model uncertainty in this paper. Vahid-Ghavidel et al [24] applied IGDT to model the optimal framework for the self-scheduling of DRAs in the risk-averse environment. The uncertainty of electricity prices in the day-ahead and balancing markets was modeled by IGDT method.…”

Section: Literature Review 121 Demand Response Aggregatorsmentioning

confidence: 99%

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

2019

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This paper proposes a new cooperative scheduling framework for Demand Response Aggregators (DRAs) and Electric Vehicle Aggregators (EVAs) in a day-ahead market. The proposed model implements the Information-Gap Decision Theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees to obtain the predetermined pro t by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenariobased and bi-level IGDT-based methods, respectively. The DRA provides DR from two demand-side management programs including Time-Of-Use (TOU) and reward-based DR. Then, the obtained DR is o ered in day-ahead markets. Furthermore, the EVAs not only meet the EV owners' demand economically, but also participate in the day-ahead market while are willing to set DR contracts with the DRA. The objective function is to maximize the total pro t of DR and EV aggregators by pursuing two di erent strategies to deal with price uncertainty, i.e., risk-seeking strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated with respect to a case study with realistic data of electricity markets.

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Self-Scheduling of Demand Response Aggregators in Short-Term Markets Based on Information Gap Decision Theory

Cited by 75 publications

References 31 publications

Application of information gap decision theory in practical energy problems: A comprehensive review

Application of information gap decision theory in practical energy problems: A comprehensive review

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

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

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