Demand Side Management in Micro Grid

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.
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.
4

Micro Grid Architecture
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.
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.
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
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
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
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.
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.
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.
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.
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.
15

Contd.,
16
PI temperature controller

Contd.,
• Thermal load for the whole building is obtained by
summing up all the AH,U
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.
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.
19

Thermal Management
System
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
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
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)
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
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.
25

Flowchart of SA control
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
27

Thank You
28

Demand Side Management in Micro Grid

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