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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).

This paper presents an optimized logistic regression machine learning model that predicts the occupancy of an Electric Vehicle (EV) charging station given the occupancy of neighboring stations. The model was optimized for the time of day. Trained on data from 57 EV charging stations around the University of California San Diego campus, the model achieved an 88.43% average accuracy and 92.23% maximum accuracy in predicting occupancy, outperforming a persistence model benchmark., 5 pages, 3 figures

Optimal dispatch is a major concern in the optimization of hybrid energy systems (HESs). Efficient and effective dispatch models that satisfy the load demand at the minimum net present cost (NPC) are crucial because of the high capital costs of renewable energy technologies. The dispatch algorithms native to hybrid optimization of multiple energy resources (HOMER) software, cycle-charging (CC) and load-following (LF), are powerful for modeling and optimizing HESs. In these control strategies, the decision to use fuel cell systems (FCs) or battery energy storage systems (BESs) at each time step is made based on the lowest cost choice. In addition, the simultaneous operation of a FC with a BES reduces the operating efficiency of the FC. These deficiencies can affect the optimal design of HESs. This study introduces a dispatch algorithm specifically designed to minimize the NPC by maximizing the usage of FCs over other components of HESs. The framework resolves the dispatch deficiencies of native HOMER dispatch algorithms. The MATLAB Version 2021a, Mathworks Inc., Natick, MA, USA Link feature in HOMER software was used to implement the proposed dispatch (PD) algorithm. The results show that the PD achieved cost savings of 4% compared to the CC and LF control dispatch strategies. Furthermore, FCs contributed approximately 23.7% of the total electricity production in the HES, which is more than that of CC (18.2%) and LF (18.6%). The developed model can be beneficial to engineers and stakeholders when optimizing HESs to achieve the minimum NPC and efficient energy management.

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.

Large ramps and ramp rates in photovoltaic (PV) power output are of concern and sometimes even explicitly restricted by grid operators. Battery energy storage systems can smooth the power output and maintain ramp rates within permissible limits. To enable PV plant and energy storage system design and planning, a method to estimate the largest expected ramps for a given location is proposed. Because clouds are the dominant source of PV power output variability, an analytical relationship between the worst expected ramp rate, cloud motion vector, and the geometrical layout of the PV plant is developed. The ability of the proposed method to bracket actual ramp rates is assessed over 10 months under different meteorological conditions, demonstrating an average compliance rate of 98.9% for a 2 min evaluation time window. The largest observed ramp of 29.7% s(-1)is contained with the worst case estimate of 34.3% s(-1). This method provides a convenient yet economical approach to worst-case PV ramp rate modeling and is compatible with solar irradiance measured at coarse temporal resolution. Juan Bosch was financed in part by Project No. PID2019-108953RB-C21, funded by the Ministerio de Ciencia e Innovación and co-financed by the European Regional Development Fund. In addition, Iñigo de la Parra was partially supported by the Spanish State Research Agency (AEI) and FEDER-UE under Grant Nos. DPI2016-80641-R and DPI2016-80642-R.

In many jurisdictions, policy-makers are seeking to decentralize the electric power system while also promoting deep reductions in the emission of greenhouse gases (GHG). We examine the potential roles for residential energy storage (RES), a technology thought to be at the epicenter of these twin revolutions. We model the impact of grid-connected RES operation on electricity costs and GHG emissions for households in 16 of the largest U.S. utility service territories under 3 plausible operational modes. Regardless of operation mode, RES mostly increases emissions when users seek to minimize their electricity cost. When operated with the goal of minimizing emissions, RES can reduce average household emissions by 2.2-6.4%, implying a cost equivalent of $180 to $5160 per metric ton of carbon dioxide avoided. While RES is costly compared with many other emission-control measures, tariffs that internalize the social cost of carbon would reduce emissions by 0.1-5.9% relative to cost-minimizing operation. Policy-makers should be careful about assuming that decentralization will clean the electric power system, especially if it proceeds without carbon-mindful tariff reforms.

High solar Photovoltaic (PV) penetration on distribution systems can cause over-voltage problems. To this end, an Optimal Tap Control (OTC) method is proposed to regulate On-Load Tap Changers (OLTCs) by minimizing the maximum deviation of the voltage profile from 1 p.u. on the entire feeder. A secondary objective is to reduce the number of tap operations (TOs), which is implemented for the optimization horizon based on voltage forecasts derived from high resolution PV generation forecasts. A linearization technique is applied to make the optimization problem convex and able to be solved at operational timescales. Simulations on a PC show the solution time for one time step is only 1.1 s for a large feeder with 4 OLTCs and 1623 buses. OTC results are compared against existing methods through simulations on two feeders in the Californian network. OTC is firstly compared against an advanced rule-based Voltage Level Control (VLC) method. OTC and VLC achieve the same reduction of voltage violations, but unlike VLC, OTC is capable of coordinating multiple OLTCs. Scalability to multiple OLTCs is therefore demonstrated against a basic conventional rule-based control method called Autonomous Tap Control (ATC). Comparing to ATC, the test feeder under control of OTC can accommodate around 67% more PV without over-voltage issues. Though a side effect of OTC is an increase in tap operations, the secondary objective functionally balances operations between all OLTCs such that impacts on their lifetime and maintenance are minimized.

Variability of solar surface irradiances in the 1 minute range is of interest especially for solar energy applications. Eight variability classes are defined for the 1 minute resolved direct normal irradiance (DNI) variability inside an hour. They combine high, medium, and low irradiance conditions with small, medium, and large scale variations from one minute to the next minute.A reference data base of 333 individual hours with ground-based 1 minute DNI observations was created by expert review from one year of observations at the BSRN station in Carpentras, France. Each variability class is represented by 16 to 63 members.Variability indices as previously published or newly suggested are used as classifiers to detect the class members automatically. Up to 77 % of all class members are identified correctly by this automatic scheme. The variability classification method allows the comparison of different project sites in a statistical and automatic manner to quantify short-term variability impacts on solar power production.

International audience; Cloud height information is crucial for various applications. This includes solar nowcasting systems. Multiple methods to obtain the altitudes of clouds are available. In this paper, cloud base heights derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three low-cost and low-maintenance ground based systems are presented and compared against ceilometer measurements on 59 days with variable cloud conditions in southern Spain. All three ground based systems derive cloud speeds in absolute units of [m/s] from which cloud heights are determined using angular cloud speeds derived from an all-sky imager. The cloud speed in [m/s] is obtained from (1) a cloud shadow speed sensor (CSS), (2) a shadow camera (SC) or (3) derived from two all-sky imagers. Compared to 10-min median ceilometer measurements for cloud heights below 5000 m, the CSS-based system shows root-mean squared deviations (RMSD) of 996 m (45%), mean absolute deviations (MAD) of 626 m (29%) and a bias of −142 m (−6%). The SC-based system has an RMSD of 1193 m (54%), a MAD of 593 m (27%) and a bias of 238 m (11%). The two all-sky imagers based system show deviations of RMSD 826 m (38%), MAD of 432 m (20%) and a bias of 202 m (9%). The ECMWF derived cloud heights deviate from the ceilometer measurements with an RMSD 1206 m (55%), MAD of 814 m (37%) and a bias of −533 m (−24%). Due to the multi-layer nature of clouds and systematic differences between the considered approaches, benchmarking cloud heights is an extremely difficult task. The limitations of such comparisons are discussed. This study aims at determining the best approach to derive cloud heights for camera based solar nowcasting systems. The approach based on two all-sky imagers is found to be the most promising, having the overall best accuracy and the most obtained measurements.

Improved coastal stratocumulus (Sc) cloud forecasts are needed because traditional satellite cloud motion vectors (CMV) do not accurately predict how Sc clouds move or dissipate in time, which often results in an underprediction of irradiance in the morning hours. CMV forecasts assume clouds move in the direction of the average regional wind field, which is not necessarily the case for Sc clouds. Sc clouds over the land form at night and typically reach their maximum coverage before sunrise. During the day, heating from solar radiation at the surface and entrainment of dry and warm air from above causes Sc clouds to dissipate. A Sc cloud edge forecast using Geostationary Operational Environmental Satellite is proposed to improve Sc cloud dissipation forecasts during the day. The inland edge of the Sc clouds is tracked in time and extrapolated into the future. For coastal regions where land elevation increases away from the coast, such as coastal California, the Sc cloud inland boundary is correlated to the land elevation. Dissipation after sunrise often follows land elevation as the mass of air required to be heated to become cloud-free decreases with increasing elevation as cloud top height is fairly constant along the cloud edge. The correlation between land elevation and the Sc cloud eastern boundary is exploited by extrapolating the evolution of cloud edge elevation in time. This method is tested in central and northern California on 25 days and in southern California on 19 days. When compared to the CMV (persistence forecasts), the proposed Sc cloud edge forecasts show a reduction of 30 W m−2 (104 W m−2) in hourly mean absolute error (MAE) of global horizontal irradiance (GHI). Additionally, out of 11 stations the Sc cloud edge forecast results show a higher forecast skill than CMV (persistence) at 7 (9) stations.

Urban morphology and inter-building shadowing result in a non-uniform distribution of surface heating in urban areas, which can significantly modify the urban flow and thermal field. In Part I, we found that in an idealized three-dimensional urban array, the spatial distribution of the thermal field is correlated with the orientation of surface heating with respect to the wind direction (i.e. leeward or windward heating), while the dispersion field changes more strongly with the vertical temperature gradient in the street canyon. Here, we evaluate these results more closely and translate them into metrics of “city breathability,” with large-eddy simulations coupled with an urban energy-balance model employed for this purpose. First, we quantify breathability by, (i) calculating the pollutant concentration at the pedestrian level (horizontal plane at $$z\approx 1.5$$ –2 m) and averaged over the canopy, and (ii) examining the air exchange rate at the horizontal and vertical ventilating faces of the canyon, such that the in-canopy pollutant advection is distinguished from the vertical removal of pollution. Next, we quantify the change in breathability metrics as a function of previously defined buoyancy parameters, horizontal and vertical Richardson numbers ( $$Ri_\text {h}$$ and $$Ri_\text {v}$$ , respectively), which characterize realistic surface heating. We find that, unlike the analysis of airflow and thermal fields, consideration of the realistic heating distribution is not crucial in the analysis of city breathability, as the pollutant concentration is mainly correlated with the vertical temperature gradient ( $$Ri_\text {v}$$ ) as opposed to the horizontal ( $$Ri_\text {h}$$ ) or bulk ( $$Ri_\text {b}$$ ) thermal forcing. Additionally, we observe that, due to the formation of the primary vortex, the air exchange rate at the roof level (the horizontal ventilating faces of the building canyon) is dominated by the mean flow. Lastly, since $$Ri_\text {h}$$ and $$Ri_\text {v}$$ depend on the meteorological factors (ambient air temperature, wind speed, and wind direction) as well as urban design parameters (such as surface albedo), we propose a methodology for mapping overall outdoor ventilation and city breathability using this characterization method. This methodology helps identify the effects of design on urban microclimate, and ultimately informs urban designers and architects of the impact of their design on air quality, human health, and comfort.

The main contributor to spatio-temporal variability in the solar resource is clouds passing over photovoltaic (PV) modules. Cloud velocity is a principal input to many short-term forecast and variability models. In this paper spatio-temporal correlations of irradiance data are analyzed to estimate cloud motion. The analysis is performed using two spatially and temporally resolved simulated irradiance datasets generated from large eddy simulation. Cloud motion is estimated using two different methods; the cross-correlation method (CCM) applied to two or a few consecutive time steps and cross-spectral analysis (CSA) where the cloud speed and direction are estimated by cross-spectral analysis of a longer time series. CSA is modified to estimate the cloud motion direction as the case with least variation for all the velocities in the cloud motion direction. To ensure reliable cloud motion estimation, quality control (QC) is added to the CSA and CCM analyses. The results show 33% (52°) and 21% (6°) improvement in the cloud motion speed (direction) estimation using the modified CSA and CCM over the original methods (without QC), respectively. In general, CCM results are accurate for all the different cloud cover fractions with average relative mean bias error (rMBE) of cloud speed and mean absolute error of cloud direction equal to 3% and 3°, respectively. For low cloud cover fractions, CSA estimates the cloud motion speed and direction with rMBE and mean absolute error equal to 10% and 11°, respectively. However, for high cloud cover fractions and unsteady cloud speed, CSA results are not reliable for 3–4 h time series; however, splitting the whole time series into shorter time intervals reduces the rMBE and mean absolute error to 15% and 16° respectively.

The intra-hour intermittency of solar energy and demand introduce significant design challenges for microgrids. To avoid costly energy shortfalls and mitigate outage probability, islanded microgrids must be designed with sufficient distributed energy resources (DER) to meet demand and fulfill the energy and power balance. To avoid excessive runtime, current design tools typically only utilize hourly data. As such, the variable nature of solar and demand is often overlooked. Thus, DER designed based on hourly data may result in significant energy shortfalls when deployed in real-world conditions. This research introduces a new, fast method for optimizing DER investments and performing dispatch planning to consider intra-hour variability. A novel set of constraints which operate on intra-hour data are implemented in a mixed-integer-linear-program microgrid investment optimization. Variability is represented by the single worst-case intra-hour fluctuation. This allows for fast optimization times compared to other approaches tested. Applied to a residential microgrid case study with 5-minute intra-hour resolution, this new method is shown to maintain optimality within 2% and reduce runtime by 98.2% compared to full-scale-optimizations which consider every time-step explicitly. Applicable to a variety of technologies and demand types, this method provides a general framework for incorporating intra-hour variability into microgrid design.

Whole sky imagers are commonly used for forecasting irradiance available for solar energy production, but validation of the forecast models used is difficult due to sparse reference data. We document the use of Large Eddy Simulations (LES) and a 3D Radiative Transfer Model to produce virtual clouds, sky images, and radiation measurements, which permit comprehensive validation of the sky imager forecast. We then use this virtual testbed to investigate the primary sources of sky imager forecast error on a cumulus cloud scene. The largest source of nowcast (0-min-ahead forecast) errors is the converging-ray geometry implied by use of a camera, while longer-term forecasts suffer from overly-simplistic assumptions about cloud evolution. We expect to use these findings to focus future algorithm development, and the virtual testbed to evaluate our progress.

This work proposes forecast models for solar-integrated, utility-scale feeders in the San Diego Gas & Electric operating region. The models predict the net load for horizons ranging from 10 to 30 min. The forecasting methods implemented include hybrid methods based on Artificial Neural Network (ANN) and Support Vector Regression (SVR), which are both coupled with image processing methods for sky images. These methods are compared against reference persistence methods. Three enhancement methods are implemented to further decrease forecasting error: (1) decomposing the time series of the net load to remove low-frequency load variation due to daily human activities; (2) segregating the model training between daytime and nighttime; and (3) incorporating sky image features as exogenous inputs in the daytime forecasts. The ANN and SVR models are trained and validated using six-month measurements of the net load and assessed using common statistic metrics: MBE, MAPE, rRMSE, and forecast skill, which is defined as the reduction of RMSE over the RMSE of reference persistence model. Results for the independent testing set show that data-driven models, with the enhancement methods, significantly outperform the reference persistence model, achieving forecasting skills (improvement over reference persistence model) as large as 43% depending on location, solar penetration and forecast horizons.

Solar power variability is a concern to grid operators as unanticipated changes in PV plant power output can strain the electric grid. The main cause of solar variability is clouds passing over PV modules. However, geographic diversity across a region leads to a reduction in the cloud-induced variability. In this paper, spatiotemporal correlations of irradiance data are analyzed and spatial and spatiotemporal ordinary Kriging methods are applied to model irradiation at an arbitrary point based on the given time series of irradiation at some observed locations. The correlations among the irradiances at observed locations are modeled by general parametric covariance functions. Besides the isotropic covariance function (which is independent of direction), a new non-separable anisotropic parametric covariance function is proposed to model the transient clouds. Also, a new approach is proposed to estimate the spatial and temporal decorrelation distances analytically using the applied parametric covariance functions, which reduce the size of the computations without loss in accuracy (parameter shrinkage). The analysis has been performed and the Kriging method is first validated by using two spatially and temporally resolved artificial irradiance datasets generated from Large Eddy Simulation. Then, the spatiotemporal Kriging method is applied on real irradiance and output power data in California (Sacramento and San Diego areas) where the cloud motion had to be estimated during the process using cross-correlation method (CCM). Results confirm that the anisotropic model is most accurate with an average normalized root mean squared error (nRMSE) of 7.92% representing a 66% relative improvement over the persistence model.

We develop a new convex optimization (CO)-based charge/discharge scheduling algorithm for distributed energy storage systems (ESSs) co-located with solar photovoltaic (PV) systems. The CO-based scheduling algorithm minimizes the monthly electricity expenses of a customer who owns an ESS and incorporates both a time-of-use volumetric tariff and a demand charge tariff. Further, we propose the novel idea of a “supply charge” tariff that incentivizes ESS customers to store excess solar PV generation that may otherwise result in reverse power flow in the distribution grid. By means of a case study we observe the CO-based daily charge/discharge schedules reduce (1) peak net demand (that is, load minus PV generation) of the customer, (2) power fluctuations in the customer net demand profile, and (3) the reliance of the customer on the grid by way of promoting energy self-consumption of local solar PV generation. Two alternate methods for behind-the-meter ESS scheduling are considered as benchmarks for cost minimization, peak net demand reduction, and mitigation of net demand fluctuations. The algorithm is tested using real 30-min interval residential load and solar data of 53 customers over 2-years. Results show that the CO-based scheduling algorithm provides mean peak net demand reductions between 46% and 64%, reduces mean net demand fluctuations by 25–49%, and increases the mean solar PV self-consumption between 24% and 39% when compared to a customer without an ESS. Introduction of a supply charge reduces the maximum solar PV power supply to the grid by 19% on average and does not financially impact ESS owners.

As urbanization progresses, more realistic methods are required to analyze the urban microclimate. However, given the complexity and computational cost of numerical models, the effects of realistic representations should be evaluated to identify the level of detail required for an accurate analysis. We consider the realistic representation of surface heating in an idealized three-dimensional urban configuration, and evaluate the spatial variability of flow statistics (mean flow and turbulent fluxes) in urban streets. Large-eddy simulations coupled with an urban energy balance model are employed, and the heating distribution of urban surfaces is parametrized using sets of horizontal and vertical Richardson numbers, characterizing thermal stratification and heating orientation with respect to the wind direction. For all studied conditions, the thermal field is strongly affected by the orientation of heating with respect to the airflow. The modification of airflow by the horizontal heating is also pronounced for strongly unstable conditions. The formation of the canyon vortices is affected by the three-dimensional heating distribution in both spanwise and streamwise street canyons, such that the secondary vortex is seen adjacent to the windward wall. For the dispersion field, however, the overall heating of urban surfaces, and more importantly, the vertical temperature gradient, dominate the distribution of concentration and the removal of pollutants from the building canyon. Accordingly, the spatial variability of concentration is not significantly affected by the detailed heating distribution. The analysis is extended to assess the effects of three-dimensional surface heating on turbulent transfer. Quadrant analysis reveals that the differential heating also affects the dominance of ejection and sweep events and the efficiency of turbulent transfer (exuberance) within the street canyon and at the roof level, while the vertical variation of these parameters is less dependent on the detailed heating of urban facets.