Optimal demand response based on utility maximization in power networks

Li, Na; Chen, Lijun; Low, Steven H.

doi:10.1109/pes.2011.6039082

Cited by 796 publications

(569 citation statements)

References 10 publications

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“…Thereby, the depreciation cost cannot be ignored, which varies with materials, technologies and capacities. It is usually defined as a quadratic function of charging/discharging power or a linear function of capacity [20,34]. In this paper, the depreciation cost is assumed to be a linear function of battery capacity.…”

Section: Energy Storage Systemmentioning

confidence: 99%

Game-theoretic energy management with storage capacity optimization in the smart grids

Gao

Liu

Wei

et al. 2018

J. Mod. Power Syst. Clean Energy

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With the development of smart grids, a renewable energy generation system has been introduced into a smart house. The generation system usually supplies a storage system with the capability to store the produced energy for satisfying a user's future demand. In this paper, the main objective is to determine the best strategies of energy consumption and optimal storage capacities for residential users, which are both closely related to the energy cost of the users. Energy management with storage capacity optimization is studied by considering the cost of renewable energy generation, depreciation cost of storage and bidirectional energy trading. To minimize the cost to residential users, the non-cooperative game-theoretic method is employed to formulate the model that combines energy consumption and storage capacity optimization. The distributed algorithm is presented to understand the Nash equilibrium which can guarantee Pareto optimality in terms of minimizing the energy cost. Simulation results show that the proposed game approach can significantly benefit residential users. Furthermore, it also contributes to reducing the peak-to-average ratio (PAR) of overall energy demand.

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Section: Energy Storage Systemmentioning

confidence: 99%

Game-theoretic energy management with storage capacity optimization in the smart grids

Gao

Liu

Wei

et al. 2018

J. Mod. Power Syst. Clean Energy

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“…Moreover, the reasonability of Assumptions 2-3 can be implied from previous works on demand response, where the utility functions are non-decreasing and concave, which means users are interested to consume more power if possible, and the level of satisfaction for users can gradually get saturated. For example, the utility functions often take the quadratic form and have linear marginal benefits [25] [26].…”

Section: B Load Control Optimization Problemmentioning

confidence: 99%

Frequency-based load control in power systems

Zhao

Topcu

Low

2012

2012 American Control Conference (ACC)

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Abstract-Maintaining demand-supply balance and regulating frequency are key issues in power system control. Conventional approaches focus on adjusting the generation so that it follows the load. However, relying on solely regulating generation is inefficient, especially for power systems where contingencies like a sudden loss in generation or a sudden change in load frequently occur. We present a frequency-based load control scheme for demand-supply balancing and frequency regulation. We formulate a load control optimization problem which aims to balance the change in load with the change in supply while minimizing the overall end-use disutility. By studying the power system model that characterizes the frequency response to real power imbalance between demand and supply, we design decentralized synchronous and asynchronous algorithms which take advantage of local frequency measurements to solve the load control problem. Case studies show that the proposed load control scheme is capable of relatively quickly balancing the power and restoring the frequency under generation-loss like contingencies, even when users only have the knowledge of a simplified system model instead of an accurate one.

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“…For example, Li, Chen and Low (Li, 2011) show that there exist time-varying prices that can align individual optimality with social optimality. In their model, the utility company collects forecasts of total demands from all customers, and then sets the prices to the marginal cost.…”

Section: Introductionmentioning

confidence: 99%

“…The cost function ( ) represents the cost for the energy provider to supply units of energy at time and is widely assumed to be increasing and strictly convex (see e.g. (Li, 2011) and(Mohsenian-Rad, 2010) As an example, the energy cost function for thermal generators is shown to be quadratic as follows (Mohsenian-Rad, 2010):…”

mentioning

confidence: 99%

Demand Management for Home Energy Networks using Cost-optimal Appliance Scheduling

Rakocevic

Jahromizadeh²,

Gruber³

et al. 2014

Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems

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Abstract:This paper uses problem decomposition to show that optimal dynamic home energy prices can be used to reduce the cost of supplying energy, while at the same time reducing the cost of energy for the home users. The paper makes no specific recommendations on the nature of energy pricing, but shows that energy prices can normally be found that not only result in optimal energy consumption schedules for the energy provider's problem and are economically viable for the energy provider, but also reduce total users energy costs. Following this, the paper presents a heuristic real-time algorithm for demand management using home appliance scheduling. The presented algorithm ensures users' privacy by requiring users to only communicate their aggregate energy consumption schedules to the energy provider at each iteration of the algorithm. The performance of the algorithm is evaluated using a comprehensive probabilistic user demand model which is based on real user data from energy provider E.ON. The simulation results show potential reduction of up to 17% of the mean peak-to-average power estimate, reducing the user daily energy cost for up to 14%.

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Optimal demand response based on utility maximization in power networks

Cited by 796 publications

References 10 publications

Game-theoretic energy management with storage capacity optimization in the smart grids

Game-theoretic energy management with storage capacity optimization in the smart grids

Frequency-based load control in power systems

Demand Management for Home Energy Networks using Cost-optimal Appliance Scheduling

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