pptx

Report
Residential Energy Consumption
Controlling Techniques to Enable
Autonomous Demand Side
Management in Future Smart Grid
Communications
by
Engr Naeem Malik
1
Abstract
• Increasing demand of consumers have affected the power system badly as
power generation system faces a number of challenges both in quality and
quantity.
• An overview of home appliances scheduling techniques has been discussed
to implement Demand Side Management (DSM) in smart grid.
• Optimal energy consumption scheduling minimizes the energy
consumption cost.
• Reduces the Peak-to-Average Ratio (PAR) as well as peak load demand to
shape the peak load curve.
2
Introduction (1/2)
• A system that implements communication and information technology in
electrical grid is known as smart grid.
• Smart grid improves the customers' load utilization by deploying the
communication based monitoring and controlling architectures.
• With the addition of different types of new loads e.g. Plug-in Hybrid
Electric Vehicles (PHEVs), the normal residential load has potentially
increased.
• Need to develop new methods for peak load reduction.
• Oil and coal fired power plants are used to meet the peak demands, as a
result a huge amount of CO2 and green house gases is emitted.
3
Introduction (2/2)
• Smart grid enables DSM to overcome these problems.
• DSM was proposed in the late 1970s.
• DSM programs are implemented to exploit better utilization of current
available generating power capacity without installing new power
generation infrastructure.
• DSM controls the residential loads by shifting the load from peak hours to
off-peak hours in order to reduce the peak load curve.
4
Related work
• Caron, Stphane, and George Kesidis proposed an incentive based energy
consumption controlling scheme for Direct Load Contol (DLC).
• Costanzo, Giuseppe T., Jan Kheir, and Guchuan Zhu discussed an energy
consumption scheduling technique to shape the peak load curve.
• Rossello Busquet, Ana, et al. elaborated a priority based scheduling scheme
for household appliances to control the load.
5
Different Scheduling Schemes for DSM
•
Efficiency of power consumption is an important factor.
•
Due to limited energy assets and expensive process of integrating new energy
resources, there is an important need to improve our system power utilization.
•
Utility companies need to reduce the peak load demand to achieve high
reliability in electric grid.
•
Smart grid applies DSM programs to control the peak load demand and energy
consumption cost.
•
Different energy consumption controlling techniques to minimize the peak load
and monetary cost are discussed in the following slides.
6
An Autonomous Three Layered
Structure Model for DSM (1/3)
Fig.1. Scheme architecture for demand side load management system
7
An Autonomous Three Layered
Structure Model for DSM (2/3)
• Present architecture controls the appliances using online scheduling
approach in the run-time manner.
• Main three modules for Admission control (AC), load balancer (LB) and
demand response manager (DRM) to control peak load demand.
• AC module schedules the appliances by using spring algorithm.
• AC accepts the requests based on priority, power request, available capacity
and rejects the rest.
• LB schedules the rejected requests and performs an optimal scheduling.
8
An Autonomous Three Layered
Structure Model for DSM (3/3)
• LB triggered by events such as request rejection, changes on available
capacity, energy price.
• LB minimizes the cost function analogous to energy price.
• AC and LB schedule the appliances on run time with respect to limited
capacity constraints and overall peak load and energy consumption cost is
minimized.
• DRM represent an interface b/w DSM system and smart grid.
• Load forecaster provide information of load forecast to DRM and LB.
9
Backtracking-based technique for load
control (1/3)
Fig.2. Power scheduler operation
10
Backtracking-based technique for load
control (2/3)
• Schedule home appliances to reduce the peak load and monetary cost.
• Backtracking algorithm is used for scheduling the home appliances
(tasks).
• Task Ti can be modeled with Fi , Ai , Di , Ui.
•
Ti is non-preemptive, Start time of appliance between Ai to (Di - Ui).
•
Ti is preemptive, ((Di - Ai)C Ui) vectors are used to map the profile
entry.
• Backtracking frame a search tree on the allocation table.
11
Backtracking-based technique for load
control (3/3)
• Scheduler copies the profile entry of different appliances one by one
according to task profile to the allocation table.
• Potential search tree consists of all feasible solutions including worthless
solutions.
• At each intervening node, which passes to a feasible solution, it checks
either the node can guide to a feasible solution if not remaining search tree
is pruned.
• Scheduler search the feasible time slots for the appliances schedule.
• Appliances (tasks) to be scheduled are less than 10 and this model reduces
peak load up to 23.1%.
12
Game-Theoretic based DSM (1/3)
Fig.3. Home scheduler model with ECS devices deployment
13
Game-Theoretic based DSM (2/3)
• Energy Consumption Scheduler (ECS) is deployed in smart meters for
scheduling the household appliances.
• Convex optimization based technique.
• Proposes an energy consumption scheduling game to reduce the Peak to
Average ratio (PAR) and energy consumption cost.
• Users are players and their daily schedule of using appliances are strategies.
• Energy cost minimization is achieved at Nash equilibrium of energy
scheduling game.
14
Game-Theoretic based DSM (3/3)
• Two types of appliances are considered in this scheme; shiftable and nonshiftable appliances.
• Scheduler manages and shifts the appliances energy consumption for
appropriate scheduling.
• Feasible energy consumption scheduling set for the appliances of user ‘n’ is
acquired as follows:
• Present technique reduces PAR up to 18\% and energy cost reduces to 17%.
15
ECS device based scheduling (1/3)
Fig.4. Smart grid system model with ‘N’ load subscribers
16
ECS device based scheduling (2/3)
• Energy Consumption Scheduling (ECS) devices are used for scheduling the
home appliances.
• ECS devices are connected with power grid and Local Area Network
(LAN) to communicate with the smart grid.
• ECS devices schedule the energy consumption of household appliances
according to individual energy needs of all subscribers.
• Convex optimization based technique.
17
ECS device based scheduling (3/3)
• ECS devices run an algorithm to find an optimal schedule for the energy
consumption of each subscriber home.
• Simulation results show that ECS devices efficiently schedule the
appliances energy consumption in the whole day.
• Present scheme reduces the cost up to 37%.
Fig.5. Daily cost $ 86.47 (ECS devices
are not used)
Fig.6. Daily cost $53.81 (ECS devices
are used)
18
An Optimal and autonomous
residential load control scheme (1/3)
Fig.7. Smart meter operation in residential load control scheme
19
An Optimal and autonomous
residential load control scheme (2/3)
• An optimal energy consumption scheduling scheme minimizes the PAR
and reduces the waiting time of each appliance operation in household.
• Residential load controller predict the prices in real time.
• Real-time pricing and inclining block rates are combined to balance the
load and minimize peak-to-average ratio.
• Deployed Energy Consumption Scheduling (ECS) device in residential
smart meters to control the load of household appliances.
• Price predictor estimates upcoming price rates.
20
An Optimal and autonomous
residential load control scheme (3/3)
• Price predictor and energy scheduler are two main units to control
the residential load.
• Price predictor estimates the upcoming prices and allows scheduler
to schedule the appliances according to user's need.
• Load demand high in smart grid, Grid send request to smart meters
to reduce the load.
•
In this case, scheduler increases upcoming prices of next 2 or 3
hours by optimization technique.
• Automatically suspends some portion of load and the total load
reduces.
21
Vickrey-Clarke-Groves (VCG)
Mechanism Based DSM (1/2)
• Vickrey-Clarke-Groves (VCG) mechanism maximizes the social welfare
i.e. the difference between aggregate utility function of all users and total
energy cost.
• Each user deployed Energy Consumption Controller (ECC) device in its
smart meter for scheduling the household appliances.
• Efficient pricing method is used to reduce the energy cost.
• VCG mechanism develops the DSM programs to enable efficient energy
consumption among all users.
• Each user provides its energy demand to the utility company.
22
Vickrey-Clarke-Groves (VCG)
Mechanism Based DSM (2/2)
• Energy provider estimates the optimal energy consumption level of each
user and declares particular electricity payment for each user.
• An optimization problem is evolved to reduce the total energy cost charged
on energy provider while maximize aggregate utility functions of all users.
• Optimization problem provide efficient energy consumption schedule for
user's energy consumption in order to reduce the cost:
Where
• Xn
Power consumption vector of user ‘n’.
• Un (.) Utility function of user ‘n’.
• Ck(Lk) Cost function of Lk energy units offered by utility in each time slot
k.
23
A Scheme for tackling load uncertainty
(1/2)
• Tackling the load irregularity to reduce energy cost in real-time.
• Schedule energy consumption under the combined implementation of Real
Time Pricing (RTP) and Inclining Block Rates (IBR).
• Each user's smart meter deployed Energy Consumption Control (ECC)
unit.
• ECC unit schedules and manages the household energy consumption.
• Appliances are divided into two categories must run loads and controllable
loads.
24
A Scheme for tackling load uncertainty
(2/2)
• Must-run loads start operation immediately at any time without
interruption of ECC unit e.g. Personal Computer (PC), TV.
• Controllable appliances operation can be interrupted or delayed.
• Operation cycle of appliance separate into T time slots.
• ECC unit implements a centralized algorithm and determines the
optimal appliances schedule in each time slot.
• Proposed mechanism formulated as an optimization problem and
energy cost can be minimized by solving optimization problem.
25
Comparison of different Energy
consumption controlling schemes
Table I
26
Conclusion
• Different residential load controlling techniques in smart grid.
• Residential load controlling techniques are employed for efficient
consumption of electricity in residential buildings like homes and offices.
• Energy consumption controlling techniques reduce the peak load by
shifting the heavy loads from peak-hours to off peak-hours to shape the
load curve and minimize the energy consumption cost.
• Consumer are also encouraged to schedule the appliances.
• Scheme 1 (an autonomous three layered structured model) is more efficient
reduces the peak load up to 66.66%.
• ECS device based scheme and VCG mechanism minimize the cost up to
37%.
27

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