Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability

Joung, Manho; Kim, Jin-Ho

doi:10.1016/j.apenergy.2012.05.009

Cited by 74 publications

(29 citation statements)

References 5 publications

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“…2 Interestingly, many ''energy market meltdowns'' in recent years can be attributed to a lack of priceresponsive load [31] and could have been alleviated by DR programs. Additionally, the work in [32] shows that the more price-responsive demand that is available the more competitive the long-term electricity market and the more reliable the system becomes.…”

Section: Energy Servicesmentioning

confidence: 97%

Challenges and barriers to demand response deployment and evaluation

2015

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Section: Energy Servicesmentioning

confidence: 97%

Challenges and barriers to demand response deployment and evaluation

2015

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“…Moreover determining the optimal incentive in incentive-based DRPs should be based on a feasible approach. It is because of the fact that if it is determined inappropriately, it may impose high additional cost at the supply side or it may create new peaks when DRP ends [8], and decrease the network reliability [9] which is not based on independent system operator's (ISO's) point of view.…”

Section: Introductionmentioning

confidence: 98%

Dynamic Economic Dispatch Problem Integrated With Demand Response (DEDDR) Considering Non-Linear Responsive Load Models

Abdi

Dehnavi

Mohammadi

2016

IEEE Trans. Smart Grid

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Intelligent implementation of demand response programs (DRPs) not only decreases electricity price in electricity markets, but also improves network reliability. In this paper, the dynamic economic dispatch (DED) problem has been optimally integrated with the incentive-based DRPs. Moreover, mathematical load modeling can be so effective in the load curve estimation with the lowest error. So, economic models of the linear and non-linear responsive loads (power, exponential, and logarithmic) have been developed for time-based and incentive-based DRPs and integrated with DED. Also, a procedure to select the most conservative responsive load model for the load estimation has been presented too. Also, determining the optimal incentive in the incentive-based DRPs is one of the independent system operator's challenges. In the proposed combined model, the fuel cost is minimized and the optimal incentive is determined simultaneously. Valve-point loading effect, prohibited operating zones, spinning reserve requirements, and the other non-linear practical constraints make the combined problem into a complicated, non-linear, non-smooth, and non-convex optimization problem, which has been solved with a population-based meta-heuristic algorithm namely random drift particle swarm optimization algorithm. The proposed combined model is applied on a ten units test system. Results indicate the practical benefits of the proposed model.Index Terms-Demand response, dynamic economic dispatch, DEDDR, incentive-based demand response programs (DRPs), non-linear responsive loads modelling, optimal incentive, random drift particle swarm optimization (RDPSO).

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“…In Refs. [9], the effects of DRPs and smart metering infrastructures such as AMIs on the system reliability and price fluctuation have been investigated. Actually, the optimal incorporation of DRPs and smart metering has been investigated.…”

Section: Introductionmentioning

confidence: 99%