Implementation of Advanced Demand Side Management for Microgrid Incorporating Demand Response and Home Energy Management System
- The proposed DSM framework comprised with advanced communication mediums and HEMSs is tested on a realistic microgrid environment which consists of solar PV, wind, and back-up diesel generators. The developed DSM strategy can maximize the renewable energy usages, maintain supply and demand balance, while reducing the usage of diesel generators in the microgrid.
- The developed smart HEMS in DSM is a fuzzy, logic-based load controller which optimizes household appliances based on the available renewable generation in the microgrid, local voltage measurements from the smart meter, weather conditions (temperature), and consumer consumption preferences, as well as TOU prices and DLC signals from utility.
- The smart HEMS can significantly reduce the consumer’s energy consumption, standby power loss, and energy costs, while maintaining the consumer’s comfort levels.
- Modeling of Microgrid: A model of a microgrid integrating the solar PV, a wind turbine, and a diesel generator supplying several typical households that participate in DR incorporated with a smart load controller system has been developed.
- Selection of Communication Medium: The most suitable communication infrastructure for DR and HEMS implementation is selected based on the literature review and geographic position of the community.
- Modeling of the Smart HEMS: A smart HEMS is developed based on realistic load profiles and appliances’ consumption data obtained from a smart load monitoring device installed in the end-users’ premises. The developed HEMS provides consumption decisions based on predefined fuzzy logic rules. The fuzzy rules are defined according to consumer consumption priorities, local voltage levels, total renewable generation in the microgrid, DR signals (dynamic pricing and DLC), and weather conditions.
- Case Study Analysis: The effectiveness of the proposed framework is evaluated through a case study analysis. The renewable energy sources and domestic loads have been designed based on realistic data of a region in Western Australia, which helps in determining the potential DR opportunity and standby power consumption of major appliances, including refrigerators, air conditioners (AC), dish washers, washing machines and dryers.
- Microgrid Analysis with HEMS: DR opportunities are investigated and identify energy savings for a microgrid system with HEMSs.
3. Modeling of the Microgrid
3.1. Modeling of Solar PV
3.2. Modeling of Wind Turbine
3.3. Modeling of Diesel Generator
4. Selection of the Communication Medium
5. Modeling of the HEMS
5.1. The Proposed Smart Load Monitoring System
5.2. Intelligent Load Controller
5.3. Fuzzy Load Controller
- Input and Output of the fuzzy load controller
- Fuzzy Membership Functions
- Fuzzy Rules
5.3.1. Input and Output of the Fuzzy Load Controller
5.3.2. Fuzzy Membership Functions
5.3.3. Fuzzy Rules
6. Case Study Analysis
6.1. Load Profiles of a Typical House
6.2. Energy Cost Saving Analysis with HEMS
7. Microgrid Analysis with HEMS
7.1. Residential Loads and Demand Analysis in Microgrid
7.2. Demand Response Opportunity in Microgrid
- Scenario A: only the solar farm and the diesel generator are supplying the residential loads.
- Scenario B: the solar farm and wind farm are active.
- Scenario C: the wind farm and the diesel generator are available, but the diesel generator is partially ON only in the high demand period.
- Scenario D: all the sources are simultaneously active, except for the diesel generator, which only turns ON when it is needed.
Conflicts of Interest
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|Base Loads||Schedulable/Non-Priority Loads|
|Input Conditions||Optimized Outputs|
|Fuzzy Rules||Time||Comfort Preference (°C)||Temperature Deviation (°C)||Forecasted Load (KW)||Consumption Time (mints)||Local Voltage Level (%)||Network RES Penetration (%)||Load Control (kW)||Perform Load Schedule (KW)||Allow Load Consumption (KW)|
|5||peak (pm)||16.5||14.5||5.4||16>||−7%||50%||Dec. = 4.0||4.86||1.11|
|Load Type||Peak Shaving Capacity (kW)||Shiftable Energy (kWh)||Standby Energy Loss (kWh)/day||Total Energy Cost without HEMS ($/day)||Total Energy Cost with HEMS ($/day)||Cost Saving (%/day)|
|Base loads||N/A||N/A||0.02||$3.49 |
(total daily energy 9.40 kWh)
(total daily energy 9.16 kWh)
(total daily energy saving 3%)
|Grid Condition (Scenario)||Total RES Penetration (kW)||Duration of Demand Surpassing (h)||Average Load Consumption Allowance||Load Shifting Frequency||Consumers’ Comfort Level||Diesel Gen. Fuel Consumption|
|Scenario A||150~300||>4.5||Very Low||Very High||Interrupted |
|Scenario B||200~350||>4||Low||High||Interrupted |
|Scenario C||200~350||>3||Medium||Medium||Not Interrupted||Medium|
|Scenario D (All sources available)||350~500||>2.5||More||Low||Not Interrupted||Low|
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Zunnurain, I.; Maruf, M.N.I.; Rahman, M.M.; Shafiullah, G. Implementation of Advanced Demand Side Management for Microgrid Incorporating Demand Response and Home Energy Management System. Infrastructures 2018, 3, 50. https://doi.org/10.3390/infrastructures3040050
Zunnurain I, Maruf MNI, Rahman MM, Shafiullah G. Implementation of Advanced Demand Side Management for Microgrid Incorporating Demand Response and Home Energy Management System. Infrastructures. 2018; 3(4):50. https://doi.org/10.3390/infrastructures3040050Chicago/Turabian Style
Zunnurain, Izaz, Md. Nasimul Islam Maruf, Md. Moktadir Rahman, and GM Shafiullah. 2018. "Implementation of Advanced Demand Side Management for Microgrid Incorporating Demand Response and Home Energy Management System" Infrastructures 3, no. 4: 50. https://doi.org/10.3390/infrastructures3040050