Economic demand response model in liberalised electricity markets with respect to flexibility of consumers

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

doi:10.1049/iet-gtd.2017.0412

Cited by 53 publications

(44 citation statements)

References 29 publications

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“…As a result, the shift of demand from peak to off-peak periods is irrespective of time-of-use. Reference [53] considers an economic model that allows the consumer to take decisions based on their own maximum benefits while satisfying the budget constraints. This is an interesting approach from an economic point of view because it accounts for monetary constraints.…”

Section: Evaluation Of Pdr: An Economic Model To Evaluate the Size Ofmentioning

confidence: 99%

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

et al. 2018

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Abstract:The objectives of improving the efficiency, and integration, of renewable sources by are complex in practice and should be linked to an increase of demand-side flexibility. The main challenges to achieving this flexibility are the lack of incentives and an adequate framework. For instance, customers' revenue is usually low, the volatility of prices is high and there is not any practical feedback to customers from smart meters. The possibility of increasing customer revenue could reduce the uncertainty with respect to economic concerns, improving investments in efficiency, enabling technology and thus, engaging more customers in these policies. This objective could be achieved by the participation of customers in several markets. Moreover, Demand Response and Energy Efficiency can share ICT technologies but this participation needs to perform an aggregation of demand. The idea of this paper is to present some methodologies for facilitating the definition and evaluation of energy versus cost curves; and subsequently to estimate potential revenues due to Demand Response. This can be accomplished by models that estimate: demand and energy aggregation; economic opportunities and benefits; impacts on customer convenience; customer feedback and price analysis. By doing so, we would have comprehensive information that can help customers and aggregators to define energy packages and their monetary value with the objective of fostering their market participation.

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Section: Evaluation Of Pdr: An Economic Model To Evaluate the Size Ofmentioning

confidence: 99%

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

et al. 2018

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“…There is an appearing consensus that demand‐side management (DSM) can have an active duty in keeping balance between supply and demand in future smart grids . At demand side of a smart restructured power system, responsive loads can not only supply various types of demand response (DR) services such as peak load shifting and ancillary services, but also contribute heavily in reduction of operating cost and emission as well as improvement of system reliability . Moreover, plug‐in electric vehicles (PEVs) as highly elastic resources at the demand side could make a number of advantages to the future smart grids by their charging and discharging power.…”

Section: Introductionmentioning

confidence: 99%

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

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Anvari‐Moghaddam

2019

Int Trans Electr Energ Syst

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Summary This paper investigates a stochastic bi‐level scheduling model for decision‐making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting player of the market sells electricity to end‐use customers and plug‐in electric vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level decision‐making process with different objectives is considered to solve the problem. In one level, the objective is to maximize the LSE's profit by optimally scheduling of responsive loads and PEVs charging/discharging process, while in the other level, the payments of the customers and PEV owners should be minimized in a competitive market. In the proposed decision‐making process, to model the uncertainties, market prices, required energy of customers and PEVs, and the rival LSEs' prices are considered as random variables. The bi‐level stochastic problem is then converted into a linear single‐level stochastic model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.

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“…DR is defined as a set of actions taken to reduce customer's electricity usage in response to higher market prices or market incentives . In addition, DR offers other benefits such as enhanced system reliability and a lower chance for market power exertion by generating companies . Moreover, system operator may implement DR programs to reduce load temporarily in emergency conditions such as generating units' outages or unpredictable change in renewable generation .…”

Section: Introductionmentioning

confidence: 99%

Security-constrained unit commitment in AC microgrids considering stochastic price-based demand response and renewable generation

Vahedipour-Dahraei

Najafi

Anvari‐Moghaddam

2018

Int Trans Electr Energ Syst

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Summary In this paper, a stochastic model for scheduling of AC security‐constrained unit commitment associated with demand response (DR) actions is developed in an islanded residential microgrid. The proposed model maximizes the expected profit of microgrid operator and minimizes the total customers' payments for electricity consumption under load and renewable energy sources uncertainties. In this regard, a stochastic dispatch model for responsive load is developed in order to investigate the effect of price‐based DR programs in a microgrid environment. In this scheme, customers are able to participate in real‐time price program with shifting and curtailing their responsive loads. A multi‐segment AC‐optimal power flow, which allows the voltage and reactive power to be considered directly, is also used to study the effects of DR participation on microgrid operation. The proposed AC security‐constrained unit commitment and DR scheduling problem is modeled as a mixed‐integer programming problem and is solved using the CPLEX solver in the general algebraic modeling system.

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Economic demand response model in liberalised electricity markets with respect to flexibility of consumers

Cited by 53 publications

References 29 publications

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

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

Security-constrained unit commitment in AC microgrids considering stochastic price-based demand response and renewable generation

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