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This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. A series of tests involved up to 69 heterogeneous active distributed energy resources consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive distributed energy resources consisting of building loads and solar photovoltaic systems. The distributed control setup consists of a set of Raspberry Pi end-points exchanging messages via an Ethernet switch. Actuation commands for the distributed energy resources are obtained by solving a power allocation problem at every regulation instant using distributed ratio-consensus, primal-dual, and Newton-like algorithms. The problem formulation minimizes the sum of distributed energy resource costs while tracking the aggregate setpoint provided by the system operator. We demonstrate accurate and fast real-time distributed computation of the optimization solution and effective tracking of the regulation signal over 40 min time horizons. An economic benefit analysis confirms eligibility to participate in an ancillary services market and demonstrates up to $\$ $ 53k of potential annual revenue for the selected population of distributed energy resources.

Concentrated solar power (CSP) technology can not only match peak demand in power systems but also play an important role in the carbon neutrality pathway worldwide. Actions in China is decisive. Few previous studies have estimated CSP technology’s power generation and CO2 emission reduction potentials in China. To address this knowledge gap, the geographical, technical, and CO2 emission reduction potential of CSP in China was evaluated by province based on a high resolution geographical information system with up-to-date data. A comprehensive framework including geographic and technical constrains was proposed. Exclusion criteria including solar radiation, slope, land-use type, natural reserve, and water resources were adopted to determine the suitability of CSP plant construction. Then, based on the power conversion efficiency difference from various CSP technologies, the technical potential was calculated on suitable land. The results show that approximately 1.02 × 106 km2 of land is available to support CSP development in China. Based on the available solar resource on the suitable land, the geographical potential is 2.13 × 1015 kWh. The potential installed capacity is 2.45 × 107–5.40 × 107 MW, considering four CSP technologies. The corresponding annual energy generation potential is 6.46 × 1013–1.85 × 1014 kWh. Considering the scenario of using the potential of CSP to replace the current power supply to the maximum extent, CO2 emission would have been reduced by 5.19 × 108, 5.61 × 108, and 6.24 × 108 t in 2017, 2018, and 2019, respectively. At the provincial level, more than 99% of China’s technical potential is concentrated in five western provinces, including Xinjiang, Inner Mongolia, Qinghai, Gansu, and Tibet. These results provide policy guidance and serve as a reference for the future development of CSP and site selection for CSP plant construction both in China and worldwide.

Operating distribution grids is increasingly challenging due to the increasing penetration of photovoltaic systems. To address these challenges, modern photovoltaic inverters include features for local control, which sometimes lead to suboptimal results. Improved communication infrastructure and photovoltaic inverters favour global control strategies, which receive information from all the systems in the grid. An estimation of distribution algorithm is used to optimize a global control strategy that minimizes active power curtailment and use of reactive power of the photovoltaic inverters, while maintaining voltage stability. Optimized global control outperforms every other local control evaluated in terms of apparent energy used for control (9.9% less usage compared to the second best alternative in all scenarios studied) and ranks second in terms of voltage stability (with a 0.14% of total time outside the voltage limits). Two new indicators to compare control strategies are proposed, and optimized global control strategy ranks best for both efficiency index (0.98) and average apparent power use (0.48 kVA).

Author(s): Zapata, Monica Zamora; Norris, Joel R; Kleissl, Jan | Abstract: Abstract The impact of initial states and meteorological variables on stratocumulus cloud dissipation time over coastal land is investigated using a mixed-layer model. A large set of realistic initial conditions and forcing parameters are derived from radiosonde observations and numerical weather prediction model outputs, including total water mixing ratio and liquid water potential temperature profiles (within the boundary layer, across the capping inversion, and at 3 km), inversion-base height and cloud thickness, large-scale divergence, wind speed, Bowen ratio, sea surface fluxes, sky effective radiative temperature, shortwave irradiance above the cloud, and sea level pressure. We study the sensitivity of predicted dissipation time using two analyses. In the first, we simulate 195 cloudy days (all variables covary as observed in nature). We caution that simulated predictions correlate only weakly to observations of dissipation time, but the simulation approach is robust and facilitates covariability testing. In the second, a single variable is varied around an idealized reference case. While both analyses agree in that initial conditions influence dissipation time more than forcing parameters, some results with covariability differ greatly from the more traditional sensitivity analysis and with previous studies: opposing trends are observed for boundary layer total water mixing ratio and Bowen ratio, and covariability diminishes the sensitivity to cloud thickness and inversion height by a factor of 5. With covariability, the most important features extending predicted cloud lifetime are (i) initially thicker clouds, higher inversion height, and stronger temperature inversion jumps, and (ii) boundary forcings of lower sky effective radiative temperature.

The role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.

The urban overheating, driven by the increasing expansion of our cities and the global climate change, is becoming one of the main environmental challenges of today. Consequently, cooling technologies are emerging as mitigation and adaptation strategies. Reflective roof and pavement surfaces have been widely studied for their potential benefits, but detailed evaluations of the effect of wall albedo on the urban microclimate are limited. This study addresses this gap by evaluating the effects of reflective walls on urban energy use and outdoor climate. The energy balance of an idealized neighborhood is represented using a 3D numerical model, Temperature of Urban Facets Indoor-Outdoor Building Energy Simulator (TUF-IOBES), which determines the cooling loads and outdoor air temperature. The study focuses on the tropical climate of Singapore, addressing the urban climate in highly-populated cities in low latitudes that are significantly affected by the UHI. Simulations are conducted for two neighborhoods representative of low-rise residential and high-rise commercial urban areas, spanning a range of urban density, canyon geometry, building construction, and occupant schedules. The building thermal load and outdoor temperature are then calculated for these two idealized neighborhoods, analyzing the effectiveness of cool walls while also considering the role of other design factors such as window-to-wall ratio (WWR) and glazing solar heat gain coefficient (SHGC) in modulating the impact. Unlike the analysis of cool roofs, we find that a universal conclusion regarding the impact of cool walls cannot be drawn. The role of wall albedo significantly depends on the collective design of urban areas as well as the use and occupancy of buildings. We find that urban density (in other words the local climate zone) followed by window properties are important factors in determining the impact of wall albedo on thermal loads and UHI, as they determine the radiative exchange between and into the buildings. Accordingly, contrary to the general expectation, for a high urban density (commercial neighborhood LCZ6) and high WWR and SHGC, we observe that cool (reflective) walls can increase the building energy use. Regarding UHI, increasing the reflectivity of walls decreases the canopy air temperature but the impact is marginal (∼ 0.1 °C) compared to other urban design parameters. Keywords: Cool walls; Reflective surfaces; Building energy use; Urban heat island; Urban design

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution.

Quasi-steady state time-series (QSTS) simulation of distribution feeders can become computationally burdensome due to many buses and devices, long simulation horizons, and/or high temporal resolution. To reduce this burden, network reduction removes buses and shifts loads/generation to the remaining buses of the circuit to produce a smaller equivalent. However, voltage-controlled devices have traditionally limited network reduction, since their operation depends on the measurement of voltage at their local bus. This work includes the reduction of buses with voltage-controlled devices by replacing the local voltage measurement with an estimate from a fast voltage sensitivity approach, which is integrated directly into a modified QSTS simulation. Comprehensive tests on an unbalanced feeder with real operating data and volt-var controlled inverters show agreement in cumulative reactive power output between the reduced and the original feeder circuits. The maximum voltage error is 0.005 Vp.u., which is nearly identical to the error in a benchmark reduction without smart inverter voltage control. The algorithm convergences for every time step, even when reducing the frequency of which the voltage estimation was updated. While the reduction methodology is demonstrated for inverter volt-var control, since it represents a frequent use case, it can be extended to other voltage-controlled devices.

Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs on commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimized in 14 commercial buildings with real load profiles, demand, and energy charges. The scientific contributions of this study are the incorporation of demand charges, quantification of EV, and smart charging electricity costs and benefits using several representative long-term datasets, and the derivation of approximate equations that simplify the estimation of EV economic impacts. Our analysis is primarily based on an idealized uniform EV commuter fleet case study. The V1G and V2B charging electricity costs as a function of the number of EVs initially diverge with increasing charging demand and then become parallel to one another with the V2B electricity costs being lower than V1G costs. A longer EV layover time leads to higher numbers of V2B charging stations that can be installed such that original (pre-EV) electricity costs are not exceeded as compared to a shorter layover time. Sensitivity analyses based on changing the final state of charge (SOC) of EVs between 90% and 80% and initial SOC between 50% and 40% (thereby keeping charging energy demand constant) show that the total electricity costs are the same for V0G and V1G charging, while for V2B charging, the total electricity costs decrease as final SOC decreases.

Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. An application of the methodology is presented using real event, location, and asset data.

Solar variability corresponds to strong variations of the solar irradiance, caused mainly by the presence of clouds. Practical uses of solar resource data, such as the design of photovoltaic solar plants, usually employ several years of hourly data, neglecting subhourly features. The effect of clouds on short-time variability can differ by cloud type, suggesting that some cloud effects could be ignored when working with hourly data. In this work, we compare statistics of solar variability calculated at different time intervals and separate the analysis by cloud categories. We use 1 min solar data and cloud radar products from the Atmospheric Radiation Measurement (ARM) cloud, aerosol, and complex terrain interactions campaign in Córdoba, Argentina, where a wide variety of clouds exist. We classify the clouds based on their vertical position and observe solar variability using the mean and standard deviation of the clear sky index for varying time intervals of 5, 15, 30, and 60 min. Time intervals affect the mean and standard deviation of the clear sky index differently for each cloud type: longer time intervals neglect small variability and overestimate the mean clear sky index of low and mid-clouds, while high clouds do not change as much. The effect is also palpable when measuring ramps: the percentile 95 of the ramps obtained for 1 min is 21 times greater compared to 1 h. This ratio varies per cloud type with the strongest differences occurring for mid-clouds, having ramps that are 73 times stronger.

Countries with some of the best solar resources suffer disproportionately from soiling and abrasion, which reduces system conversion efficiencies and decreases equipment lifetime. This Special Collection covers climatological analyses, soiling metrology, best installation practices to reduce soiling and abrasion, and improvements to equipment and materials to mitigate soiling and abrasion.

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods. Refereed/Peer-reviewed

Photovoltaic (PV) smart inverters can improve the voltage profile of distribution networks. A multi-objective optimization framework for coordination of reactive power injection of smart inverters and tap operations of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems is proposed. The optimization objective is to minimize voltage deviations and the number of tap operations simultaneously. A novel linearization method is proposed to linearize power flow equations and to convexify the problem, which guarantees convergence of the optimization and less computation costs. The optimization is modeled and solved using mixed-integer linear programming (MILP). The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced modified IEEE 37 bus test system and a large California utility feeder. Simulation results show that the proposed method accurately estimates feeder voltage, significantly reduces voltage deviations, mitigates over-voltage problems, and reduces voltage unbalance while eliminating unnecessary tap operations. The robustness of the method is validated against various levels of forecast error. The computational efficiency and scalability of the proposed approach are also demonstrated through the simulations on the large utility feeder., Comment: Accepted for Electric Power Systems Research. arXiv admin note: text overlap with arXiv:1901.09509

This paper presents a comparative analysis of the different forecasting techniques (Statistical method (ARIMA)), Machine Learning method (Multivariate Linear Regression) and Deep Learning method (LSTM)) for short term load forecasting. The forecasting model for each of these techniques takes into account the historical load (annual), site temperature data and US calendar data as input features. The model is trained on the first nine months of data and then tested for accuracy on the remaining three months. A case study using the load profile of a commercial building (amusement park) in San Diego, California is presented, where the performance of the above forecasting techniques is compared. The error metric used for comparison is the Root Mean Squared Error (RMSE) value. The results indicate that LSTM model offers the best performance in terms of forecasting accuracy. The designed LSTM model can be deployed as part of Energy Management System (EMS) for smart grids. The robustness of the LSTM model is further explored by comparing the above LSTM encoder-decoder architecture with a standard LSTM architecture. In addition to the above dataset, both architectures were tested on two more datasets- one for an office building and another for an operations center building located in San Diego. Both architectures were trained on first 9 months of load data and tested on the remaining 3 months. The encoder-decoder architecture performed better than the standard architecture across all the datasets.