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Demand Side
Management in
Micro-grids for Load
Control in Net Zero
Energy Buildings
By
Krishnakumar R V
M.E Power Engineering and Management
Anna University Chennai
College of Engineering, Guindy
Introduction
The energy spent in commercial, residential, and
institutional buildings constitute for 40% of the electricity
consumption in India. And it is expected to increase to 76%
by 2040. This can be reduced by promoting Nearly Zero
Energy Buildings
Hence this work is to present a smart micro grid
architecture which enables Demand Response Control.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
2
Demand Side Management
• It emphasis on energy management rather than energy
conservation.
• Where energy conservation is the reduction in the actual
energy consumption of the user compromising their
comfort.
• The change of user behaviour according to the dynamic
pricing signal.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
3
Net Zero Energy Buildings
The total amount of energy used by the building on an
annual basis is roughly equal to the amount of renewable
energy created on the site
•But the instantaneous power exchanged with the grid is
not zero.
•So the power flow is bidirectional which is coordinated by
BEMS.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
4
Micro Grid Architecture
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
5
Micro Grid contd.,
• The MG is owned either by the DSO or the building
itself.
• It is managed by a building manager with a perception of
the whole building rather than of a single independent
user.
• The BM buys energy and distributes the cost among
users with a regulation agreed.
• In order the manage the MG optimally with local
generation and Grid power the single units should be
aggregated to a single point of connection.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
6
Contd.,
HVAC and DHW systems
•In the suggested micro grid, heating and DHW are
organized with a common central ground source heat
pump (GSHP)
•The heating distribution is common, starting from a puffer
for the heating and a boiler for the DHW(Domestic Hot
Water).
•Each apartment is equipped with an under floor heating
system and a satellite center (SC) with metering systems.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
7
Micro Grid Contd.,
Electric power system
•Single point of coupling (POC) with the DSO in HV level
•Substation HV/LV and a common LV distribution
•Main LV Switch Board (MSB) to supply both the common
services (heating, elevators) and the single units
•Photovoltaic system (PV) connected to the MSB
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
8
Electric loads for the
apartments
Uncontrollable:
•It depend strongly on the habits of the occupants
•No control strategy can be implemented
Example: Lighting, refrigerator, TVs, PCs.
Plannable:
•It depend on the habits of the occupants which can be predicted.
•The ending time of the cycle is chosen by the occupants,
•But the actual starting time can be managed by the BEMS
•Once started, the cycle must end without interruption within the time set
by the user.
Example : dishwashers and washing machines
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
9
Contd.,
Controllable:
It may be operated and regulated by the BEMS without a
discomfort for the habits within certain limits.
Example: Heat pumps for Domestic Heating system, Boiler
of DHW system
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
10
Modelling of Power Demand
Steps:
1.Residents going out and the time spent away from home
are simulated with heuristic considerations through a
probability function. This provides a profile of
availability at home of each unit inhabitant.
2.For each load category, the daily number of activations is
determined through a Gauss or a Poisson distribution.
3.Monte Carlo simulation is used to sample start time of
each load. For each activation in each unit the Tstart is
defined using a probability curve from statistical data and
availability at home of each resident, for each load
category.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
11
Contd.,
4. Time of use is determined for each load category, after
each load activation, through Gauss or Uniform
distribution based on statistical user data.
5. Building power demand curve is obtained by
summing up the electrical load of each apartment in
the building.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
12
Monte Carlo simulation
• A day is divided into 96 time blocks with 15 min
granularity.
The two random variables associated with each Monte
Carlo trial are:
i. “X" between 1 and 96 which indicates the random time of
start of the electric load.
ii."Y" between zero and one, to be compared with the value
of the probability of activation at the same time X.
iii.If Y is greater than the probability, the appliance will be
activated, and another trial at another time X will be
done.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
13
Electric loads
• The electric behavior of the whole building is obtained by
summing load vectors of each unit.
• A day is divided into 96 time blocks with 15 min
granularity.
• The electric behavior of each kind of load is simulated by
a vector P of 96 elements.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
14
Thermal loads for a single
unit
Heating requirements of each apartment, modelled as a
single zone, are evaluated by energy balance equation with
implicit time discretization.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
15
Contd.,
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
16
PI temperature controller
Contd.,
• Thermal load for the whole building is obtained by
summing up all the AH,U
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
17
Building Energy Management
System for Demand Side
Management
• The general goal of a building management system
(BMS) is to manage the HVAC system
• A BMS becomes BEMS if it is able to control the energy
demand.
• It controls the electric demand at the point of interface
with the DSO instantaneously, considering local
generation.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
18
Objectives of Proposed BEMS
• Reducing the annual energy cost
• Maximizing Self Consumption Ratio
• Minimizing Peak Power to maintain flat load profile
• Minimizing the cost of the energy, in case of dynamic
costs.
• It operates by avoiding the users to change their
behavior of appliance utilization.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
19
Thermal Management
System
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
20
Contd.,
• TB  set point temperature of boilers and puffer
• TC  comfort set point temperature of each units
• TE  economy set point temperature of each
units
• TS  actual ambient set point temperature of each
unit
• HES  Home Electronic System
• BES  Building Electronic System
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
21
Operation of Proposed BMS
Control T1:
It is possible to reduce the electric peak load in case of high
global load or to store energy in case of low (or negative) global load by
forcing the set points TB of the central boilers.
•if p(t) > PM1 BEMS forces TB from TBN to TBL
•if p(t) < Pm1 BEMS forces TB from TBN to TBH
p(t) load demand of the building at POC;
PM1 threshold value of maximum power
Pm1 threshold value of minimum power
TBN normal operating boiler temperature
TBL lower value of the boiler temperature
TBH higher value of the boiler temperature
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
22
Contd.,
Control T2:
It is possible to reduce the electric peak load in case of high
global load or to store energy in case of low (or negative)
global load by forcing set points TC of local thermostats.
•if p(t) > PM2 BEMS forces TC from TCN to TCL
•if p(t) < Pm2 BEMS forces TC from TCN to TCH
TCL lower value of comfort mode set point
TCH higher value of comfort mode set point
TCN normal operating set point in case of presence of
inhabitants in the unit (comfort mode)
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
23
Contd.,
Control SA - Smart appliance management
•The suggested BEMS manages the global demand of the building by
scheduling the start times of smart appliance according to users' will.
• In this model, with smart appliances, users still decide operation cycle
timing, but they define end time rather than start-time.
•The user decides the best time according to the effective cost of the
cycle furnished by the BEMS through a scheduling timetable (ST).
•The prospected costs are evaluated by the BEMS considering the
dynamic cost of the kWh and the local generation
• Each time slot in the day is characterized by a color (for example:
red=more expensive than the base cost, yellow=base cost,
green=cheaper than base one
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
24
Contd.,
• The user considering his needs and the prospected cost,
decides the time (time x) at what the DW or the WM
must be completed.
• The BEMS improve the level of satisfaction of the
service, because the user decides the exact time of ending
of the cycle, according to the effective cost and his needs.
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
25
Flowchart of SA control
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
26
Conclusion
Thus the suggested BEMS considers the user behavior
that is fundamental in smart grid management.
It further improves the energy and power performances,
without forcing the habits, on the contrary improving the
users comfort
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
27
Thank You
06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai
28

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Demand Side Management in Micro Grid

  • 1. Demand Side Management in Micro-grids for Load Control in Net Zero Energy Buildings By Krishnakumar R V M.E Power Engineering and Management Anna University Chennai College of Engineering, Guindy
  • 2. Introduction The energy spent in commercial, residential, and institutional buildings constitute for 40% of the electricity consumption in India. And it is expected to increase to 76% by 2040. This can be reduced by promoting Nearly Zero Energy Buildings Hence this work is to present a smart micro grid architecture which enables Demand Response Control. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 2
  • 3. Demand Side Management • It emphasis on energy management rather than energy conservation. • Where energy conservation is the reduction in the actual energy consumption of the user compromising their comfort. • The change of user behaviour according to the dynamic pricing signal. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 3
  • 4. Net Zero Energy Buildings The total amount of energy used by the building on an annual basis is roughly equal to the amount of renewable energy created on the site •But the instantaneous power exchanged with the grid is not zero. •So the power flow is bidirectional which is coordinated by BEMS. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 4
  • 6. Micro Grid contd., • The MG is owned either by the DSO or the building itself. • It is managed by a building manager with a perception of the whole building rather than of a single independent user. • The BM buys energy and distributes the cost among users with a regulation agreed. • In order the manage the MG optimally with local generation and Grid power the single units should be aggregated to a single point of connection. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 6
  • 7. Contd., HVAC and DHW systems •In the suggested micro grid, heating and DHW are organized with a common central ground source heat pump (GSHP) •The heating distribution is common, starting from a puffer for the heating and a boiler for the DHW(Domestic Hot Water). •Each apartment is equipped with an under floor heating system and a satellite center (SC) with metering systems. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 7
  • 8. Micro Grid Contd., Electric power system •Single point of coupling (POC) with the DSO in HV level •Substation HV/LV and a common LV distribution •Main LV Switch Board (MSB) to supply both the common services (heating, elevators) and the single units •Photovoltaic system (PV) connected to the MSB 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 8
  • 9. Electric loads for the apartments Uncontrollable: •It depend strongly on the habits of the occupants •No control strategy can be implemented Example: Lighting, refrigerator, TVs, PCs. Plannable: •It depend on the habits of the occupants which can be predicted. •The ending time of the cycle is chosen by the occupants, •But the actual starting time can be managed by the BEMS •Once started, the cycle must end without interruption within the time set by the user. Example : dishwashers and washing machines 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 9
  • 10. Contd., Controllable: It may be operated and regulated by the BEMS without a discomfort for the habits within certain limits. Example: Heat pumps for Domestic Heating system, Boiler of DHW system 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 10
  • 11. Modelling of Power Demand Steps: 1.Residents going out and the time spent away from home are simulated with heuristic considerations through a probability function. This provides a profile of availability at home of each unit inhabitant. 2.For each load category, the daily number of activations is determined through a Gauss or a Poisson distribution. 3.Monte Carlo simulation is used to sample start time of each load. For each activation in each unit the Tstart is defined using a probability curve from statistical data and availability at home of each resident, for each load category. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 11
  • 12. Contd., 4. Time of use is determined for each load category, after each load activation, through Gauss or Uniform distribution based on statistical user data. 5. Building power demand curve is obtained by summing up the electrical load of each apartment in the building. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 12
  • 13. Monte Carlo simulation • A day is divided into 96 time blocks with 15 min granularity. The two random variables associated with each Monte Carlo trial are: i. “X" between 1 and 96 which indicates the random time of start of the electric load. ii."Y" between zero and one, to be compared with the value of the probability of activation at the same time X. iii.If Y is greater than the probability, the appliance will be activated, and another trial at another time X will be done. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 13
  • 14. Electric loads • The electric behavior of the whole building is obtained by summing load vectors of each unit. • A day is divided into 96 time blocks with 15 min granularity. • The electric behavior of each kind of load is simulated by a vector P of 96 elements. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 14
  • 15. Thermal loads for a single unit Heating requirements of each apartment, modelled as a single zone, are evaluated by energy balance equation with implicit time discretization. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 15
  • 17. Contd., • Thermal load for the whole building is obtained by summing up all the AH,U 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 17
  • 18. Building Energy Management System for Demand Side Management • The general goal of a building management system (BMS) is to manage the HVAC system • A BMS becomes BEMS if it is able to control the energy demand. • It controls the electric demand at the point of interface with the DSO instantaneously, considering local generation. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 18
  • 19. Objectives of Proposed BEMS • Reducing the annual energy cost • Maximizing Self Consumption Ratio • Minimizing Peak Power to maintain flat load profile • Minimizing the cost of the energy, in case of dynamic costs. • It operates by avoiding the users to change their behavior of appliance utilization. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 19
  • 21. Contd., • TB  set point temperature of boilers and puffer • TC  comfort set point temperature of each units • TE  economy set point temperature of each units • TS  actual ambient set point temperature of each unit • HES  Home Electronic System • BES  Building Electronic System 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 21
  • 22. Operation of Proposed BMS Control T1: It is possible to reduce the electric peak load in case of high global load or to store energy in case of low (or negative) global load by forcing the set points TB of the central boilers. •if p(t) > PM1 BEMS forces TB from TBN to TBL •if p(t) < Pm1 BEMS forces TB from TBN to TBH p(t) load demand of the building at POC; PM1 threshold value of maximum power Pm1 threshold value of minimum power TBN normal operating boiler temperature TBL lower value of the boiler temperature TBH higher value of the boiler temperature 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 22
  • 23. Contd., Control T2: It is possible to reduce the electric peak load in case of high global load or to store energy in case of low (or negative) global load by forcing set points TC of local thermostats. •if p(t) > PM2 BEMS forces TC from TCN to TCL •if p(t) < Pm2 BEMS forces TC from TCN to TCH TCL lower value of comfort mode set point TCH higher value of comfort mode set point TCN normal operating set point in case of presence of inhabitants in the unit (comfort mode) 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 23
  • 24. Contd., Control SA - Smart appliance management •The suggested BEMS manages the global demand of the building by scheduling the start times of smart appliance according to users' will. • In this model, with smart appliances, users still decide operation cycle timing, but they define end time rather than start-time. •The user decides the best time according to the effective cost of the cycle furnished by the BEMS through a scheduling timetable (ST). •The prospected costs are evaluated by the BEMS considering the dynamic cost of the kWh and the local generation • Each time slot in the day is characterized by a color (for example: red=more expensive than the base cost, yellow=base cost, green=cheaper than base one 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 24
  • 25. Contd., • The user considering his needs and the prospected cost, decides the time (time x) at what the DW or the WM must be completed. • The BEMS improve the level of satisfaction of the service, because the user decides the exact time of ending of the cycle, according to the effective cost and his needs. 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 25
  • 26. Flowchart of SA control 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 26
  • 27. Conclusion Thus the suggested BEMS considers the user behavior that is fundamental in smart grid management. It further improves the energy and power performances, without forcing the habits, on the contrary improving the users comfort 06/07/17DeptofPowerEngg&Mgmt,AnnaUniversityChennai 27

Editor's Notes

  1. Heuristic technique is a method which employs practical methods for problem solving but the solution is satisfactory but not optimal.
  2. SMS= smart metering system