Residential Demand Response for Renewable Energy Resources in Smart Grid Systems

Park, Laihyuk; Jang, Yongwoon; Cho, Sungrae; Kim, Joongheon

doi:10.1109/tii.2017.2704282

Cited by 108 publications

(51 citation statements)

References 13 publications

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“…Another interesting contribution is found in [56], which presents a setting similar to the references above, and in addition proposes a soft peak power-limiting strategy consisting in the integration into the MILP problem of a critical peak pricing scheme, something which is generalized by the present paper. Additional and similar recent MILP formulations of a residential EMS are presented in [57], which uses it to assess the DR-driven load pattern elasticity of smart households, in [58], which minimizes the response fatigue of the controlled devices and considers uncertainties of PEV availability and small-scale renewable energy generation, and in [59], which aims at minimizing costs and maximizing user convenience in the context of real-time and capacity-based pricing schemes (for the capacity-based tariff case, Park et al [59] considers a quadratic tariff function and proposes an approximate technique).…”

Section: Related Workmentioning

confidence: 99%

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Liberati¹,

Giorgio

2017

Energies

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This paper presents a two-level control scheme for the energy management of an electricity prosumer node equipped with controllable loads, local generation, and storage devices. The main control objective is to optimize the prosumer's energy bill by means of intelligent load shifting and storage control. A generalized tariff model including both volumetric and capacity components is considered, and user preferences as well as all technical constraints are respected. Simulations based on real household consumption data acquired with a sampling period of 1 s are discussed. The proposed control scheme bestows the prosumer node with the flexibility needed to support smart grid use cases such as bill optimization (i.e., local energy trading), control of the profile at the point of connection with the grid, demand response, and reaction to main supply faults (e.g., islanding operation), etc.

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Section: Related Workmentioning

confidence: 99%

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Liberati¹,

Giorgio

2017

Energies

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“…On the other hand, a television may not be controllable because it usually turns on unpredictably. Therefore, the appliances are categorized according to their controllability as follows [20].…”

Section: System Architecturementioning

confidence: 99%

“…However, the user will likely be more dissatisfied in case (c). For this reason, divide ∑ t∈T w t a · I t a by the operating time of the appliance and formulate a dissatisfaction function D(·) as Equation (8) [20].…”

Section: Demand Response Modelmentioning

confidence: 99%

“…However, previous studies [11,15,16,20] are limited in that they assume that the user sets the preferred operation time for each appliance (the preferred time is referred to as user convenience in this paper). In this paper, a demand response scheme is proposed that automatically estimates a user's convenience without configuring the convenience for fully-automated energy scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Automated Energy Scheduling Algorithms for Residential Demand Response Systems

et al. 2017

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Abstract:Demand response technology is a key technology for distributing electricity tasks in response to electricity prices in a smart grid system. In the current demand response research, there has been much demand for an automated energy scheduling scheme that uses smart devices for residential customers in the smart grid. In this paper, two automated energy scheduling schemes are proposed for residential smart grid demand response systems: semi-automated scheduling and fully-automated scheduling. If it is possible to set the appliance preference, semi-automated scheduling will be conducted, and if it is impossible, fully-automated scheduling will be operated. The formulated optimization problems consider the electricity bill along with the user convenience. For the fully-automated scheduling, the appliance preference can automatically be found according to appliance type from the electricity consumption statistics. A performance evaluation validates that the proposed scheme shifts operation to avoid peak load, that the electricity bill is significantly reduced, and that user convenience is satisfied.

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“…These trends create both a need and an opportunity for dynamic pricing and demand response to help balance the power system. In a smart grid environment, price-responsive customers and devices can reschedule electricity loads from the times when electricity supply is scarce and production costs are high to times when supply is abundant and costs are low, thereby reducing bills and also improving the supply-demand balance for the power system as a whole [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

Izawa¹,

Fripp²

2018

Preprint

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A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants' willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a "brick wall" preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3% reduction in costs vs. a "brick-wall" MPC approach with similar comfort and 13% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3% vs. the "brick-wall" strategy and 15% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% vs. the "brick-wall" strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. Keywords: HVAC model predictive control, demand response, EnergyPlus, particle swarm optimization (PSO), renewable energy, smart grids MSC: 49M37, 65K05, 90-04, 90B35, 90B50, 90C29, 90C56, 90C90, 91B08, 91B10, 91B26, 91B42, 91B74

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Residential Demand Response for Renewable Energy Resources in Smart Grid Systems

Cited by 108 publications

References 13 publications

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Automated Energy Scheduling Algorithms for Residential Demand Response Systems

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

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