1. System Control based Renewable Energy Resources in Smart Grid Consumer Dr. Saba Al-Rubaye Postdoctoral Research Fellow Stony Brook University June 19, 2014

2. Abstract 2 System control is a key component in the smart grid to effectively reduce power generation costs and user bills. The load control problem is addressed in a network of multiple utility companies and consumers where every unit is concerned about maximizing its own benefit. 1. Developed an optimization model to adjust the hourly load level of a particular consumer in response to hourly electricity charges with respect to renewable energy resources. 2. Studied the electricity pricing models to provide the price assessment competency. Home appliances are assigned dynamic priority according to their different energy consumption modes and their corresponding status. 3. Proposed a real-time household load priority scheduling algorithm based on renewable source availability forecasting in order to minimize the total cost of energy consumption overall.

3. Introduction All retail consumers are charged average price not reflecting the actual wholesale price. A smart grid solutions, various time-differentiated pricing models: Real-time pricing (RTP) Day-ahead pricing (DAP) Time-of-use-pricing (TOUP) Critical-peak pricing (CPP) RTP can reduce the peak hour load demand in the power system, which in turn lowers the requirement on system generation capacity. It can also reduce users’ Electricity bills by encouraging them to consume more power during hours with lower electricity prices. Due to unpredictable real-time prices and distributed energy resources, the smart grid poses great challenges for energy management and load scheduling with RTP and distributed generation 3

4. Contribution Propose load control scheme in a retail electricity market with Real-time pricing combined with Inclining block rate (IBR) Minimizing electricity payment by optimally scheduling the operation and energy consumption for each appliance, subject to the special needs indicated by the users. propose a real-time household load priority scheduling algorithm based on renewable sources availability prediction. Home appliances are assigned dynamic priorities according to their different energy consumption modes and their corresponding status. 4

5. System overview: 5 Each consumer equipped with a smart meter (Zigbee) Periodically receive the updated price information from the utility The energy scheduling unit include a price predictor unit, estimating the upcoming prices. Main idea: develop an algorithm to minimize the average expected cost of a utility company, which supplies power by renewable energy resource. Goal is: To maximize the benefits of renewable sources and minimize the total cost of consumption of grid import energy for given consumers’ comfort constraints. To effectively schedule appliances according to the real-time output of renewable sources and the electricity market price changes, which generally deviate from the corresponding forecasting,

6. Control System Architecture 5 Monitor: (wireless sensor-ZigBee) measures the current, power, voltage etc. using the smart meter. A wireless sensor network consists of a set of spatially distributed autonomous sensors. These sensor is monitor physical or environmental conditions Scheduler: is responsible for analyzing the data collected by the predictor and wireless sensor. Then, determines the optimal choice of energy consumption scheduling Predictor: is responsible for estimate the real-time pricing that is provided by the utility company via a local area network.

7. System Model For each appliance a, define an energy consumption scheduling vector: : scheduling horizon, indicating the number of hours ahead which are taken into account for decision making in energy consumption scheduling. : the corresponding 1- energy consumption that is scheduled for appliance a. 6

8. System Model : the total energy needed for the operation of appliance a Setting constraints on the beginning and end of a time interval in which the energy consumption for appliance a is valid to be scheduled Beginning of a time interval End of a time interval 7

9. System Model Each appliance has certain maximum power levels and minimum stand-by power levels. There is usually a limit on the total energy consumption at each residential unit at each hour. It can be set by the utility to impose the following set of constraints on energy scheduling: energy consumption for appliance a 8

10. System Model Using Linear Programming can achieve scheduling for all possible energy consumption vectors : E denotes the vector of energy consumption scheduling variables for all appliances. Power Level energy consumption for appliance a 9 Beginning of a time interval End of a time interval

11. Price Prediction 10 We consider dynamic pricing situation where the upcoming prices are announced only for is bigger than 1 and less than hours ahead of time, where is the price announcement horizon. Price prediction based on prior knowledge Wholesale market price Higher during the afternoon Higher on hot days in the summer, cold days in the winter Depending on working days or weekends Developing a price predictor can be implemented easily in housing smart meter

12. Dynamic Pricing Model- Real Time 11 Total Electricity Payment corresponding to all appliances within the upcoming scheduling horizon General hourly pricing function scheduling horizon energy consumption for appliance a

13. Payment and Cost of waiting 13

14. Problem Optimization 12 To control the importance of the waiting cost terms in the objective function of the proposed design optimization problem cost of waiting typical value =1 is an adjustable control parameter. The higher the value of this parameter will be the cost of waiting. energy consumption scheduling

15. 14 Online Appliance Scheduling

16. Conclusion 16 load control in real-time electricity pricing environments essentially requires some price prediction capabilities to enable planning ahead for the household energy consumption. A real-time household load priority scheduling algorithm based on prediction of renewable source availability is important in order to maximize the benefits of renewable sources and minimize the total cost of consumption of grid energy for the consumers.

17. Thank You