505 research outputs found

    Fast and Reliable Primary Frequency Reserves From Refrigerators with Decentralized Stochastic Control

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    Due to increasing shares of renewable energy sources, more frequency reserves are required to maintain power system stability. In this paper, we present a decentralized control scheme that allows a large aggregation of refrigerators to provide Primary Frequency Control (PFC) reserves to the grid based on local frequency measurements and without communication. The control is based on stochastic switching of refrigerators depending on the frequency deviation. We develop methods to account for typical lockout constraints of compressors and increased power consumption during the startup phase. In addition, we propose a procedure to dynamically reset the thermostat temperature limits in order to provide reliable PFC reserves, as well as a corrective temperature feedback loop to build robustness to biased frequency deviations. Furthermore, we introduce an additional randomization layer in the controller to account for thermostat resolution limitations, and finally, we modify the control design to account for refrigerator door openings. Extensive simulations with actual frequency signal data and with different aggregation sizes, load characteristics, and control parameters, demonstrate that the proposed controller outperforms a relevant state-of-the-art controller.Comment: 44 pages, 17 figures, 9 Tables, submitted to IEEE Transactions on Power System

    Indirect control of flexible demand for power system applications.

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    Simulation modeling for energy consumption of residential consumers in response to demand side management.

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    Energy efficiency in the electricity distribution system continues to gain importance as demand for electricity keeps rising and resources keep diminishing. Achieving higher energy efficiency by implementing control strategies and demand response (DR) programs has always been a topic of interest in the electric utility industry. The advent of smart grids with enhanced data communication capabilities propels DR to be an essential part of the next generation power distribution system. Fundamentally, DR has the ability to charge a customer the true price of electricity at the time of use, and the general perception is that consumers would shift their load to a cheaper off-peak period. Consequently, when designing incentives most DR literature assumes consumers always minimize total electricity cost when facing energy consumption decisions. However, in practice, it has been shown that customers often override financial incentives if they feel strongly about the inconvenience of load-shifting arrangements. In this dissertation, an energy consumption model based on consumers‟ response to both cost and convenience/comfort is proposed in studying the effects of differential pricing mechanisms. We use multi-attribute utility functions and a model predictive control mechanism to simulate consumer behavior of using non-thermostatic loads vi (prototypical home appliances) and thermostatically controlled load (HVAC). The distributed behavior patterns caused by risk nature, thermal preferences, household size, etc. are all incorporated using an object-oriented simulation model to represent a typical residential population. The simulation based optimization platform thus developed is used to study various types of pricing mechanisms including static and dynamic variable pricing. There are many electric utilities that have applied differential pricing structures to influence consumer behavior. However, majority of current DR practices include static variable pricings, since consumer response to dynamic prices is very difficult to predict. We also study a novel pricing method using demand charge on coincident load. Such a pricing model is based on consumers‟ individual contribution to the monthly system peak, which is highly stochastic. We propose to use the conditional Markov chain to calculate the probability that the system will reach a peak, and subsequently simulate consumers‟ behavior in response to that peak. Sensitivity analysis and comparisons of various rate structures are done using simulation. Overall, this dissertation provides a simulation model to study electricity consumers‟ response to DR programs and various rate structures, and thus can be used to guide the design of optimal pricing mechanism in demand side management

    An integral control formulation of Mean-field game based large scale coordination of loads in smart grids

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    Pressure on ancillary reserves, i.e.frequency preserving, in power systems has significantly mounted due to the recent generalized increase of the fraction of (highly fluctuating) wind and solar energy sources in grid generation mixes. The energy storage associated with millions of individual customer electric thermal (heating-cooling) loads is considered as a tool for smoothing power demand/generation imbalances. The piecewise constant level tracking problem of their collective energy content is formulated as a linear quadratic mean field game problem with integral control in the cost coefficients. The introduction of integral control brings with it a robustness potential to mismodeling, but also the potential of cost coefficient unboundedness. A suitable Banach space is introduced to establish the existence of Nash equilibria for the corresponding infinite population game, and algorithms are proposed for reliably computing a class of desirable near Nash equilibria. Numerical simulations illustrate the flexibility and robustness of the approach

    Control of heat pumps with CO2 emission intensity forecasts

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    An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emission forecasts as input to a Model Predictive Control (MPC) - a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study the control was applied using weather and power grid conditions during a full year period in 2017-2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned on the basis of standards and building codes. The main results are measured as the CO2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, i.e. the energy flexibility, not the absolute savings. The results show that around 16% savings could have been achieved during the period in well insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, e.g. level of insulation and thermal capacity. Danish building codes from 1977 and forward was used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0-5% depending on the heating system and thermal mass.Comment: 16 pages, 12 figures. Submitted to Energie

    Demand Response of a TCL population using Switching-Rate Actuation

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    Forecast and control of heating loads in receding horizon

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    Control Methods for Energy Management of Refrigeration Systems

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