AbstractThis paper presents a methodology to optimally design a multi-energy microgrid with thermal and electric loads considering $$N-1$$ N - 1 and probabilistic regulation reserves. This methodology consists of a chance-constrained optimization that determines the optimal sizing of the microgrid. Microgrid operations are rigorously considered by modelling hourly thermal and electric demand patterns as well as technology production schedules over a year. Such schedules include both electric and thermal power balances, ramp constraints, $$N-1$$ N - 1 and regulation reserves, among others. To ensure a reliable microgrid design and operation, reserve constraints have been proposed to deal with both $$N-1$$ N - 1 generation contingencies, and forecast errors. $$N-1$$ N - 1 reserves guarantee that a sudden outage of any of the electric generators is strategically covered by the remaining generators in order to avoid load shedding. Additionally, nonzero-mean random forecast errors of electric load and solar production are addressed by a set of chance constraints able to schedule asymmetric up and down regulation reserves. Their levels are high enough to cover hourly random forecast errors (or intermittencies) with a high threshold probability. The proposed methodology results in a mixed integer second-order cone program. Results of microgrid designs with and without reliability reserves are carefully analysed and compared. Neglecting reliability constraints leads to a lower-cost design at the expense of exposing the microgrid to unsafe operation. Finally, sensitivity analysis to study the optimal portfolio sizing with respect to electric BESS investment cost, solar production forecast mean error, and random intermittency threshold probability are performed.

AbstractSolar PV has experienced unprecedented growth in the last decade, with the most significant additions being utility-scale solar PV. The role of grid inverters is very critical in feeding power from distributed sources into the grid. With the increasing growth of grid-tied solar PV systems (both rooftop and large-scale), the awareness of power quality issues has risen with new regulations and standards to ensure the stability of the power grid. The power quality of microinverters has been investigated under steady solar irradiation and PV power source and also under real outdoor conditions in compliance with the accepted solar PV integration requirements. The current total harmonic distortion (THD) measured for the studied microinverter under outdoor conditions far exceeded the current THD for the study under steady indoor conditions and was beyond the accepted standard. However, the voltage THD outputs for the two studied cases were in good agreement with the grid codes. The voltage and current THD for the 400 Wm−2 (60 Wp) and the 1000 Wm−2 (146 Wp) scenarios under the steady solar irradiation (solar PV power) were 2.24%, 13%, and 2.27, 6.93%, respectively. The voltage and current THDs for the outdoor study were 2.03% and 14.28% for Solarex (pc-Si module), 1.94%, and 27.43% for Juta (mc-Si modules), and 1.97% and 33.6% for Dunasolar (a-Si glass module). Results showed a strong correlation between the intermittence of solar radiation and the current THD. 67%, 54%, and 37% of the recorded power factor for Dunasolar, Juta, and Solarex modules, respectively, exceeded the limits prescribed by the standards.

AbstractIn this paper, we study the optimal generation mix in power systems where only two technologies are available: variable renewable energy (VRE) and electric energy storage (EES). By using a net load duration curve approach, we formulate a least-cost optimization model in which EES is only limited by its power capacity. We solve this problem analytically and find least-cost and market equilibrium conditions that lead to the optimal capacities of VRE and EES. We show that, mathematically, an electricity price structure that depends on the period of the year (i.e. EES charging or discharging, VRE curtailment, load shedding) and on investments costs leads to cost recovery for VRE and EES. We show that when EES is the marginal technology (either charging or discharging) the price must be non-zero. More specifically, the equilibrium prices during EES charge or discharge are functions of the EES and VRE fixed costs. We confirm our analytical findings using a numerical model. We argue that, although the system we study is hypothetical and simplified, our findings provide insights and research directions for how to recover fixed costs in a future electricity market based on VRE and EES only.

AbstractThe imbalance between supply and demand is a critical factor in the operation of the power system, as it leads to a change in the system frequency. Therefore, it is essential to be able to predict its value from historical, measured and forecast data. Based on the assumption that system imbalance is correlated with measured values of system variables as well as predictions of exogenous variables, this work proposes a multi-step version of the autoregressive distributed lag model for the short-term forecast of system imbalance. The proposed forecasting model has been compared with a long short-term memory network-based procedure as well as with an extratree regression model using real data. The results show that the proposed multi-step autoregressive forecasting model outperforms the others in all three evaluation metrics. Since, in many cases, it is sufficient to specify the sign of the imbalance, this paper introduces the concept of sign accuracy as a function of the predicted imbalance and evaluates it for the investigated solutions.