Residential Energy Consumption Controlling Techniques to Enable Autonomous Demand Side Management in Future Smart Grid

1. Residential Energy Consumption Controlling Techniques to Enable Autonomous Demand Side Management in Future Smart Grid CommunicationsbyEngrNaeemMalik

2. 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.

3. 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.

4. 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.

5. 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. • RosselloBusquet, Ana, et al. elaborated a priority based scheduling scheme for household appliances to control the load.

6. 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.

7. An Autonomous Three Layered Structure Model for DSM (1/3) Fig.1. Scheme architecture for demand side load management system

8. 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.

9. 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.

10. Backtracking-based technique for load control (1/3) Fig.2. Power scheduler operation

11. 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 Tican 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.

12. 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%.

13. Game-Theoretic based DSM (1/3) Fig.3. Home scheduler model with ECS devices deployment

14. 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.

15. 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%.

16. ECS device based scheduling (1/3) Fig.4. Smart grid system model with ‘N’ load subscribers

17. 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.

18. 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.6. Daily cost $53.81 (ECS devices are used) Fig.5. Daily cost $ 86.47 (ECS devices are not used)

19. An Optimal and autonomous residential load control scheme (1/3) Fig.7. Smart meter operation in residential load control scheme

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. Comparison of different Energy consumption controlling schemes Table I

27. 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%.