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Residential Energy Consumption Controlling Techniques to Enable

Autonomous Demand Side Management in Future Smart Grid

Communicationsby

Engr Naeem Malik

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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.

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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.

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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.

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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.

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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.

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An Autonomous Three Layered Structure Model for DSM (1/3)

Fig.1. Scheme architecture for demand side load management system

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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.

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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.

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Backtracking-based technique for load control (1/3)

Fig.2. Power scheduler operation

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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.

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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%.

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Game-Theoretic based DSM (1/3)

Fig.3. Home scheduler model with ECS devices deployment

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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.

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Game-Theoretic based DSM (3/3)

• Two types of appliances are considered in this scheme; shiftable and non-shiftable 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%.

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ECS device based scheduling (1/3)

Fig.4. Smart grid system model with ‘N’ load subscribers

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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.

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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)

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An Optimal and autonomous residential load control scheme (1/3)

Fig.7. Smart meter operation in residential load control scheme

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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.

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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.

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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.

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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.

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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.

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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.

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Comparison of different Energy consumption controlling schemes

Table I

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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%.