A multi-objective optimization based controller to enable economic benefits, battery management properties, and grid signal shaping features


Background Merging energy management system control with on-site battery storage and renewable energy sources can yield significant environmental and economic benefits via power flow control between a local microgrid and the external power grid. McMaster researchers have developed a robust rolling horizon controller using mixed-integer-linear-program optimization for electricity energy management control of grid-connected microgrids. A multi-objective optimization based controller was designed to enable both economic benefits, battery management properties, and grid signal shaping features. This design philosophy enables fast system control via a robust counterpart optimization formulation, variable time-step lengths in the prediction horizon, and relaxation of binary constraints. The control process can be embodied within a desktop computer or hardware based embedded system. Technology Overview The conceptual system model of the Adaptive Energy Management System (A-EMS) is depicted in Figure 1. A microgrid representing either an industrial, commercial, or residential building/complex is connected with on-site electricity storage elements, renewable energy devices, controllable loads, and connections with external power grid and communication networks. The A-EMS can control the power dispatch in the microgrid system and can do so using external information such as weather forecasts and market electricity prices. The goals of the A-EMS are multi-faceted, ranging from economic benefits for consumers, grid reliability power profile shaping for utilities, extending battery/storage life, to providing emergency backup power during islanded mode. The brain of the invented system is the A-EMS controller, which performs the following functions: • It collects data from external and internal source of information. The data can include user-specified commands/preferences, weather-related information including past and forecast for the specific geographical location of the microgrid, status of smart appliances, power meter readings, temperature measurements, battery charger status, and any other available sensory information in the micro-grid • It processes the collected information and system historical data in order to predict critical information for the operation of the micro-grid. In particular, it predicts the electricity demand of the micro-grid, renewable energy production capacity, and the connection and disconnection times of electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs), EVs/PHEVs charge levels, all within a finite time horizon, e.g. 24 hours • It uses the prediction data, cost of electricity data, information on operational costs of the microgrid elements, and user preferences in order to make “optimal” decisions with respect to the operation of the micro-grid. These include charging and discharging of the energy storage elements, utilization of renewable energy sources, scheduling of EV/PHEV charging, operation of heating and AC systems, and operation of smart appliances. Optimality is measured with respect to a set of user-defined objectives that can include the overall cost of electricity for the user, peak shaving, load shifting, power factor correction, voltage/frequency regulation, spinning/non-spinning reserves, usage costs of on-site storage, PHEV charging, and scheduling constraints of electric AC loads. The A-EMS controller makes the power dispatch and load scheduling decisions at some specified time-step (e.g. 5 min to 1h) by formulating and solving a mixed-integer linear programing (MILP) problem. The A-EMS transmits the computed optimal operational commands to the micro-grid elements through its wired and/or wireless communication links • The A-EMS provides the user with feedback of real-time and historical information about the system operation via its GUIs described above. It also give the user the opportunity to customize and enhance the decision making process by specifying priorities and providing information on major electricity usages in future times • The A-EMS is responsible for coordinating transitions between grid-connected mode and islanded mode to ensure an uninterrupted supply of power for the microgrid Stage of Development: • Prototype constructed and tested • Data is available upon request • Optimization continuing • Manufacturable prototype being developed

Key Benefits

Micro-grids and distributed energy generation systems will have a critical role in the future developments of power systems to address the issues of sustainability and reliability. Energy management is a key aspect of micro-grids. Virtually all energy management technologies in the market are essentially information displays systems; if any, they have very limited control capability involving ad hoc programming of some controllable loads. • A key of feature of the A-EMS is integration and control of on-site energy storage elements in the form of batteries, ultra-capacitors and flywheels • The technology seamlessly integrates energy storage elements, renewable energy sources, plugged-in electric vehicles, and smart appliances in a micro-grid • It employs advanced prediction and optimization algorithms to intelligently control the follow of power in the micro-grid in order to reduce the energy cost for the consumer and also shape, in a desirable way, the micro-grid power profile for the grid • Three different types of loads are considered, i.e. critical, schedulable, and curtailable; the latter two are managed by the system controller. Islanding, emergency backup power mode is another feature • The system provides frequency/voltage, power factor control, power generation and can sell these ancilliary services to the external grid utility companies • The invention can enable micro-grids to participate in the energy markets, and control their power connection with the external grid to reduce electricity costs and/or maximize profit • The invention can also help stabilize the external grid through the ancilliary services mentioned above • The technology is flexible and modular and can be deployed at the different scales depending on the user requirements


Application areas include electric power industry, electrified automotive industry, and renewable energy industry. The technology can adaptively manage energy consumption/production in micro-grids involving residential, commercial, and industrial units with or without renewable energy sources. It helps reduce energy costs and the peak demand from the power grid. Moreover, utilities can integrate this technology with local energy storage at their substations to reduce the peak load of distribution transformers and increase the reliability of energy delivery to their customers.

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