505 research outputs found
Fast and Reliable Primary Frequency Reserves From Refrigerators with Decentralized Stochastic Control
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
Simulation modeling for energy consumption of residential consumers in response to demand side management.
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
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
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
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