Demand Response Management via Real-Time Electricity Price Control in Smart Grids

Qian, Li Ping; Zhang, Ying Jun; Huang, Jianwei; Wu, Yuan

doi:10.1109/jsac.2013.130710

Cited by 257 publications

(86 citation statements)

References 14 publications

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“…In the literature, customers' preferences on price are generally modeled [24], household's utility function is modeled [25], and time-utility model is introduced to simulate price elastic behaviors of aggregated load [26]. Optimization of price settlement based on iterative information exchange is also proposed [27] and a special effect called rebound-peak is also considered [28]. The modeling is usually more complex in this type of system, because it simultaneously contains price and its response.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Kang

Lee

2017

Energies

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Abstract:Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider's perspective.

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Section: Related Workmentioning

confidence: 99%

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Kang

Lee

2017

Energies

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“…Literature offers a number of solutions to the problem of demand reduction strategies for a wide range of application cases. Some interesting approaches include agent-based strategies [8], Model Predictive Control (MPC) of building climate control [9], linear programming approaches for intra-day demand optimization of small businesses [10], or simulated annealing approaches [11]. According to Deng et al [12], most DR approaches share that they are usually formulated as an optimization problem and the user behaviour is mathematically modelled.…”

Section: Introductionmentioning

confidence: 99%

Simulation-based Strategies for Smart Demand Response

Leobner¹,

Smolek²,

Heinzl³

et al. 2017

J. sustain. dev. energy water environ. syst.

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Demand Response can be seen as one effective way to harmonize demand and supply in order to achieve high self-coverage of energy consumption by means of renewable energy sources. This paper presents two different simulation-based concepts to integrate demand-response strategies into energy management systems in the customer domain of the Smart Grid. The first approach is a Model Predictive Control of the heating and cooling system of a low-energy office building. The second concept aims at industrial Demand Side Management by integrating energy use optimization into industrial automation systems. Both approaches are targeted at day-ahead planning. Furthermore, insights gained into the implications of the concepts onto the design of the model, simulation and optimization will be discussed. While both approaches share a similar architecture, different modelling and simulation approaches were required by the use cases.

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“…Under TOU pricing schemes, CPP mechanisms set a higher rate than the peak rate during periods when it is more difficult to meet customer load [9,10]. The electricity price in real-time pricing (RTP) varies with time according to a signal that reflects electricity system conditions [11][12][13]. TP is a mechanism for DSM to help customers adjust electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Virtual electricity retailer for residents under single electricity pricing environment

Wang

Shen

et al. 2017

J. Mod. Power Syst. Clean Energy

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Various residential electricity pricing strategies provide diverse methods for calculating consumption costs. Due to the existence of electricity company monopolies and single residential electricity pricing systems, residents of certain areas have no option but to accept the electricity pricing offered to them. Based on local residential electricity pricing strategies, a virtual electricity retailer (VER) mechanism is put forward. The proposed VER mechanism includes a pricing package plan (PPP), a consumption-based plan, an add-on plan, and an exclusive plan. A PPP optimization pricing model was established to maximize VER profits when taking into account income, allowances from sponsors, expenditures and customer savings. Finally, payment processes were designed under a fixed pricing system and a time-of-use pricing environment. This case study shows the impact of PPPs and the allowance and demonstrates that the model helps customers save electricity while maximizing VER profits.

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Demand Response Management via Real-Time Electricity Price Control in Smart Grids

Cited by 257 publications

References 14 publications

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Simulation-based Strategies for Smart Demand Response

Virtual electricity retailer for residents under single electricity pricing environment

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