Microgrid Energy Management during High-Stress Operation

Abstract

We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements.

CZT-Based Harmonic Analysis in Smart Grid Using Low-Cost Electronic Measurement Boards

“…Therefore, the evolution of appropriate measurement systems supported by a reliable communication infrastructure is the basis for the development of smart grids to increase remote service monitoring, such as power quality analysis, anti-islanding detection, arc fault detection, and so on. In this context, the number of devices connected to the grid has increased dramatically, and the proliferation of distributed generation (DG) from renewable sources has encouraged users to become active by installing power plants that can independently power their utilities [1][2][3][4][5][6][7]. In this context, power systems have also become more complex, exhibiting time-varying harmonic behaviour due to the increasing non-linearity of electronic loads.…”