2.0 Project Title: Automated Energy Management Demand Response Software Tool for a Residential Buildings

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1 1.0 Name: Harikrishna Patadiya (SID: ) 2.0 Project Title: Automated Energy Management Demand Response Software Tool for a Residential Buildings 3.0 Background: The AB 1890 law in 1996 helped deregulate the electricity sector in California and it led to one of the most disastrous energy management crises in 2001 that the citizens of California had ever seen. Although many different factors led to the energy crisis of in California, its consequences were huge for the public. With many people losing access to electricity and skyrocketing prices, it became evident that better grid management was of absolute importance. One of the possible solutions as proposed by Christopher Weare that also happened to become a practical solution was to manage the electrical grid efficiently [1]. Strengthening the demandmanagement programs was a priority and that led to the program in California known as Demand Response. Demand Response programs allow Investor Operated Utilities (IOUs) to communicate with users the required demand reduction to balance the total load on the grid. Users can gain incentives for responding to the demand reduction needs [2]. As of spring 2013, enrollment into the Demand Response program was about 260 MW (megawatts) of commercial users. The average peak demand reduced has increased from 16.3MW in 2007 to 160MW in 2011 [4]. Such drastic reductions in demand during peak loads times can lead to a stable and a reliable electric grid. With such a useful tool at hand, it is of utmost important to have a reliable demand reduction strategy in the market and such is achieved through the use of Open Auto Demand Response communication link between the utility organizations and the users. The OpenADR protocol was initially researched by Lawrence Berkeley National Laboratory (LBNL) which yielded the first version of the protocol, OpenADR 1.0 in 2009 [3]. The research that took 6 years, starting 2002, yielded a method that can be incorporated through the internet to monitor any Demand Response Event information broadcasted by the utilities.

2 Figure 1 shows a sample method of performing Demand Response through the OpenADR architecture. Figure 1: OpenADR Architecture [5] Most of the Demand Response is done on a commercial scale so far and initial research yielded that there were no products for the residential market which can explain the lack of residential client participation in such program. The objective of this project, as described below, can help close that gap further which can lead to a better optimized and better managed electric grid, especially during a Demand Response Event. 4.0 Objectives: The objective of this project is to develop energy management model to achieve the desired reduction in usage during a Demand Response Event. 5.0 Methodology: 1. Research and define models for various subsystems as linear models. Subsystems include: - Air Conditioner (Continuous and ON/OFF control) - Refrigerator (ON/OFF control) - Television (ON/OFF control) - Non-critical Lights (ON/OFF control) Research on how to calculate actual energy usage of various systems to be done in the future to develop models for each. The simple model will be to use the rated power for each equipment as the power consumption model. 2. Model the total energy usage as a linear function of all subsystems P total = Σ(subsystems) 2

3 3. Define constraints for each of the subsystems that can be user defined based on usage needs Research will be conducted to learn and incorporate various constraints and how that affect the total power consumption of each device. The constraints will be based on three main criteria: power cycle limitations, user convenience, and safety. Power cycle limitation: Equipment may get damaged if the power is cycled multiple times and too frequently. Constraint on how many power cycles can be performed per hour can help eliminate device damage. User Convenience: User should have convenient environment to live in. During an event, the air conditioner can be turned off but it should be turned back on if keeping it off for over 2 hours will lead to uncomfortable environment. Another example would be to allow entertainment to be untouched during leisure hours. Safety Criteria: Some systems, such as fire alarm systems, gas detection systems, etc. should not be controlled to ensure that safety is held paramount. The constraints described above will be user inputs in addition to the subsystem information that is gathered from the user to perform analysis on and provide control strategy through the software tool being developed in this project. 4. Define energy reduction priority levels for the subsystems & simulate Demand Response events by creating virtualized VEN (Virtual End Node typically an end user) and VTN (Virtual Top Node typically an ISO) Electric Power Research Institute (EPRI) has created a simulated environment to help developers simulate a VEN and a VTN node to test communication for open source development [6]. This test environment will be set up to simulate a Demand Response event as well as other information between a simulated utility organization and a client. Here a contingency plan in case such research tool made available by EPRI seems to not work as a simulated environment is to manually write a Demand Response event in XML format based on the template that can be gathered from the OpenADR specifications. 5. Develop a minimization optimization algorithm for linear systems to manage energy usage by optimizing the reduction based on constrains and priority levels. min P total Further research will be conducted to learn how optimization can be performed on such systems. Needed resources for this will depend on resources that committee members can provide to help narrow the material that should be read to achieve the goal. Since optimization can have many implications, advice from the committee members in terms of narrowing down will be helpful. 3

4 Upon better definition of various optimization codes that can be used for such an application (linear optimization or quadratic optimization/ model predictive control/ etc.), further research will be done to develop a model that can work best for the given project. 6. Develop programs and functions in MATLAB or Python to automate the algorithm on a computer. Further comparison will be done between MATLAB and Python as the programming tool for use for this project. Initial research suggests that MATLAB can be the tool to use due to its applicability to the Engineering profession and its ease of use. Python is an open source programming language with very strong Engineering and Mathematical libraries too. Graphical User Interface (GUI) design capabilities will be researched further and compared to finalize the tool to be used. 7. Develop a Graphical User Interface to include into the software tool. 8. Optionally, if time permits, an effort will be made to encapsulate the software designed into a PLC (Programmable Logic Controller), a Raspberry Pi or Arduino to create a physical tool for use. Research for this part will be optional and will not be looked at until the project is nearly concluded. 6.0 Deliverables: A generalized linear model for subsystems for a residential building power consumption and a software tool to perform optimization control on the defined subsystems. The final software tool will be set up to successfully simulate and provide control strategy for a pre-defined system during a Demand Response Event. 7.0 Timeline: PLAN PLAN PERCENT PERIODS (Month divided into weeks) Plan Actual % Complete ACTIVITY START DURATION COMPLETE Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Weeks -> System Modeling 1 9 0% Background Research 1 7 0% Air Conditioner Subsystem 1 3 0% Refrigerator & Other Subsystem 4 3 0% Total energy usage model 6 4 0% Constraint & Priority Modeling 9 6 0% Background Research 9 5 0% Power cycle constraints 9 2 0% User comfort constraints % Safety constraints % Optimization Algorithm % Background Research % Algorithm development % Programming & GUI % MATLAB/Python % GUI Development % Testing and Troubleshooting % Debugging % Final Testing % Documentation and Report % Presentation % 4

5 8.0 References: [1] Weare, Christopher. The California Electricity Crisis: Causes and Policy Options. Public Policy Institute of California [2] Piette, Mary Ann, Ghatikar Rish. Automated Demand Response: Changes in Title 24. Lawrence Berkeley National Laboratory [3] OpenADR Alliance. Understanding OpenADR _11_sm.pdf. [4] Haaser, Barry. Enabling The Standard for Automated Demand Response. OpenADR Alliance [5] McParland, Charles. OpenADR Open Source Toolkit: Developing Open Source Software for the Smart Grid. Lawrence Berkeley National Laboratory [6] Electric Power Research Institute. OpenADR 2.0b Open Source Virtual End Node VEN v