Design, Control and Energy Management of Remote Off-Grid Power Systems in Northern Canada
Grant: NSERC Engage, Research Manitoba
Industry Partner: Solar Solutions Inc., Winnipeg, Canada.
Student Researchers: Roshani Kaluthanthrige, Mo’ath Farraj
Background and Objectives
The remote communities in Canada mostly rely on diesel as their primary energy resource. But owing to the growth in fuel prices, the adverse effects of fuel transportation and storage, and increased carbon footprint, energy policies have started to take a more environmentally healthy approach. In this context, Hybrid Renewable Energy Systems (HRES) incorporating locally available renewable energy sources and energy storage systems have emerged as a viable solution. Among the available renewable energy technologies solar photovoltaics have come under close scrutiny to be deployed in the Northern Canadian territories being a simple, maintenance free and inexhaustible energy resource. The optimum design, control, and energy management of HRES must be thoroughly analysed before actual deployment; specially when integrating large shares of solar energy into smaller off-grid power systems.
- Assessed the financial and technical viability of renewable energy projects proposed subjecting the remote communities in Northern Manitoba, Canada.
- Optimally sized a hybrid renewable energy system as a renewable retrofit for an existing primarily diesel-based remote off-grid power system in Northern Canada.
- Developed a generic energy storage selection technique.
- Formulated a day-ahead operational optimization technique for the subjected power system.
- Contributed to modelling a complete switched model of the proposed hybrid renewable energy system in PSCAD™/EMTDC™.
- Identified control requirements of remote off-grid power systems for both discrete and continuous state operations through simulation studies and industry expertise.
- Formulated a hierarchical control framework for the subjected power system configuration.
- Modelled and verified the proposed local and unit-level control functions in both PSCAD™/EMTDC™ and in RTDS™ .
Proposed Future Work
Model Based Engineering (MBE) integrated with Real-time hardware-in-the-loop (HIL) simulations have emerged as an appropriate platform to validate hardware controllers in power systems. In HIL simulations, protection, control, and communication devices are interfaced with a power system modeled in a real-time simulator. These physical devices interact with the simulated power system allowing a systematic and realistic evaluation of their performance. Therefore, it is intended to design and implement an enhanced grid controller for a HRES consisting of diesel, PV, and battery energy storage, and test its performance using a real-time controller hardware-in-the-loop testing platform.
Probabilistic Evaluation of Distribution Networks Containing Distributed Energy Sources, Energy Storage and Electric Vehicles
Grant: Mitacs Accelerate Program
Industry Partner: Manitoba Hydro, Winnipeg, Manitoba, Canada
Student Researcher: Anand Maniyam Pariyarath
Background and Objectives
The electric power industry is undergoing considerable changes with respect to structure, operation, regulation and modernization. One of the significant changes is the increased utilization of distributed energy sources (DES) such as wind and solar and other immerging technologies for example energy storage (ES) and electric vehicles (EV) particularly in electric distribution systems. The development of DES and the utilization of new technologies can have significant positive impacts on electric power industry by improving the operating capabilities of the grid, lowering cost, enhancing system reliability and deferring/reducing infrastructure investments.
However, the changes also bring new challenges to the industry particularly in planning, operation and design of such systems. The main objectives of the proposed research are, therefore, to develop appropriate models and methodologies for accurate and realistic evaluation of the reliability of distribution networks considering the role of DES, energy storage and electric vehicles.
1. Developed a computational model to estimate the stochastic power output of photovoltaic energy (PV) systems that is usable for Monte Carlo simulation (MCS) based long-term planning studies.
2. Developed a two-layer stochastic model for generating the EV charging demand patterns considering the spatial-temporal distribution of EVs while incorporating the parameters pertaining to individual EVs such as driving range, charging locations, driving distances, and driver’s charging habits.
3. Developed a complete methodology to examine the impact of EV penetration on distribution network reliability.
4. Developed a comprehensive methodology for distribution system reliability evaluation considering the complex interactions among EVs, PV systems, and energy storage.
5. Developed an economic evaluation model to calculate the total cost pertained to EV charging stations equipped with hybrid energy resources considering the capital cost, operation cost, emission cost, and cost of unreliability.
6. Presented the application of earlier developed stochastic models and MCS framework for optimization of the resource sizes of EVCSs considering the life-cycle costs, reliability and emissions.
Proposed Future Work
The current work utilized the concept of geographical zones to develop the EV charging demand pattern generation model. It is necessary to develop methodologies to identify zones using geographical information systems (GIS) of both municipal traffic and utility network. The application of machine learning-based clustering techniques may be considered for this purpose. Presently it is assumed that the distribution system components are 100% reliable. Some of the future work could be extended to consider a failure of power system components.